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Distribution and habitat characterization of the Florida burrowing owl in non-urban areas


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Distribution and habitat characterization of the Florida burrowing owl in non-urban areas
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Mueller, Mark S
University of South Florida
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Tampa, Fla
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Subjects / Keywords:
Athene cunicularia floridana
Geographic information systems
Species of special concern
Avian ecology
Resource selection
Landscape ecology
Dissertations, Academic -- Environmental Science & Policy -- Masters -- USF
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )


The full geographic distribution and habitat use of the Florida Burrowing Owl, a state "Species of Special Concern," is not well-understood, particularly in remote, non-urban areas. This thesis aimed to expand and improve knowledge about non-urban burrowing owls. We first compiled databases of historic sighting observations. Fieldwork verified and updated existing breeding observation point records and also yielded new breeding locations. Using a GIS, we characterized observed land use, landcover, relevant soil attributes, projected future land use and managed area status for selected points. We quantified landcover within biologically-determined buffer distances around burrows from our own field-verified records. Using standard resource selection methods, we compared observed and available proportions, calculated selection indices, and determined selection/avoidance for each landcover class. These empirical results were used in combination with expert opinion and literature review to finalize criteria for and map "suitable" landcover. Suitability of relevant soil attributes were also empirically-determined and used to further reduce the overall "suitable" area. The final suitable habitat maps appear to relate well to the overall distribution of known non-urban burrowing owl records and demonstrate that a great deal of potentially-suitable breeding habitat exists throughout Florida's central interior. Improved pasture, the most prevalent landcover class, also appears to be the most strongly selected in this study and may be of high importance to non-urban, breeding burrowing owls. Our results could be useful to wildlife officials managing this species. Recommendations include improving surveys and conservation efforts in non-urban areas and enhancing cooperation with landowners, particularly ranchers, as success on private lands seems critical to the long-term persistence of this species.
Thesis (M.A.)--University of South Florida, 2006.
Includes bibliographical references.
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by Mark S. Mueller.
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Document formatted into pages; contains 130 pages.

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Distribution and habitat characterization of the Florida burrowing owl in non-urban areas
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by Mark S. Mueller.
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The full geographic distribution and habitat use of the Florida Burrowing Owl, a state "Species of Special Concern," is not well-understood, particularly in remote, non-urban areas. This thesis aimed to expand and improve knowledge about non-urban burrowing owls. We first compiled databases of historic sighting observations. Fieldwork verified and updated existing breeding observation point records and also yielded new breeding locations. Using a GIS, we characterized observed land use, landcover, relevant soil attributes, projected future land use and managed area status for selected points. We quantified landcover within biologically-determined buffer distances around burrows from our own field-verified records. Using standard resource selection methods, we compared observed and available proportions, calculated selection indices, and determined selection/avoidance for each landcover class. These empirical results were used in combination with expert opinion and literature review to finalize criteria for and map "suitable" landcover. Suitability of relevant soil attributes were also empirically-determined and used to further reduce the overall "suitable" area. The final suitable habitat maps appear to relate well to the overall distribution of known non-urban burrowing owl records and demonstrate that a great deal of potentially-suitable breeding habitat exists throughout Florida's central interior. Improved pasture, the most prevalent landcover class, also appears to be the most strongly selected in this study and may be of high importance to non-urban, breeding burrowing owls. Our results could be useful to wildlife officials managing this species. Recommendations include improving surveys and conservation efforts in non-urban areas and enhancing cooperation with landowners, particularly ranchers, as success on private lands seems critical to the long-term persistence of this species.
Thesis (M.A.)--University of South Florida, 2006.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
System requirements: World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
Title from PDF of title page.
Document formatted into pages; contains 130 pages.
Adviser: Melissa Grigione, Ph.D.
Athene cunicularia floridana.
Geographic information systems.
Species of special concern.
Avian ecology.
Resource selection.
Landscape ecology.
Dissertations, Academic
x Environmental Science & Policy
t USF Electronic Theses and Dissertations.
4 856


Distribution and Habitat Char acterization of the Florida Burrowing Owl in Non-Urban Areas by Mark S. Mueller A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Environmental Science and Policy College of Arts and Sciences University of South Florida Major Professor: Meliss a M. Grigione, Ph.D. Paul A. Zandbergen, Ph.D. Ronald J. Sarno, Ph.D. Date of Approval: June 23, 2006 Keywords: athene cunicularia fl oridana, GIS, geographic information systems, species of special concern, avian ecology, reso urce selection, landscape ecology Copyright 2006 Mark S. Mueller


Acknowledgments There are many people who have assisted me in some way in completing this thesistoo many to thank properly, but you know who you are and I thank you. That said, some individuals deserve special mention here. I wish to thank my advisor, Melissa Gr igione, and my other committee members, Ron Sarno and Paul Zandbergen, for providi ng interdisciplinary insights and content suggestions. Melissa supplied guidance as well as research funding that enabled me to travel all around Florida, incl uding remote and beautiful place s that most residents never experience first-hand. Pauls rigorous GIS courses prepared me well for this work. Ron always had interesting ideas to explore; pl us he just made the whole thing fun. Other faculty and staff at USFs Environmental Science & Policy Department also deserve thanks, particularly Dr. Rick Oches and Dr. Don Duke for their support. My fellow USF lab partners also de serve a huge thank-you: Per Nixon, whose unwavering enthusiasm was contagious; and Bob Mrykalo, who provided seemingly endless advice on a variety of topics and whos e tireless dedication was truly inspiring. My new colleagues at the Fish and Wildlife Research Institute al so supplied valuable input and technical assistance, especia lly Cherie Keller and Julie Mikolajczyk. All of the agencies and i ndividuals that provided data deserve thanks, especially Pamela Bowen for granting me full access to her survey database, without which this study would not have been possible. The Florida Fish and Wildlife Conservation Commission and the Florida Natural Ar eas Inventory also supplied data. Mr. Jim Strickland provided invaluable assistance in working with other ranchers and in publishing our article through th e Florida Cattlemens Association. Finally, I could not have ma de it through without plenty of help and support from all of my friends and family, especially my parents, Melissa Wilson, and Lindsey Kraatz.


i Table of Contents List of Tables iv List of Figures vi Abstract ix Chapter One Statewide Distribution, Database Compilation and Field Verification of Historic and New Populations 1 Introduction 1 Definition and Importance of Non-Urban Populations 3 Objectives 5 Methods 5 Data Sources 5 Study Areas 6 Database Format 6 Compilation Process 6 Location Verification and Field Survey Protocol 7 Education & Data Solicitation 9 Results 10 Historic Locations 10 New Locations 14 Education & Data Solicitation 16 Discussion 17 Database Compilation and Evaluation 17 Field Verification 18 Observed Land Use and Trends 21 Management Status and Imp lications for Non-Urban Populations 22 Conclusions 24 Chapter Two Geospatial Analysis of the Florida Burrowing Owl in Non-Urban Areas 25


ii Introduction 25 Purpose and Population of Interest 26 Landcover, Suitable Landcover, and Selection 26 Soils 28 Managed Areas and Future Land Use 28 Methods 29 Data Processing and Preparation 29 Study Areas and Florida Burro wing Owl Occurrence Data 30 Extraction of Attributes at Points 35 Landcover at Point 35 Soil attributes, Managed Area and Future Land Use Status at Point 36 Landcover Data 37 Total Available Landcover 37 Landcover (All) Extraction 38 Buffer Creation 39 Selection Indices, Selecti on/Avoidance, and Overall Chi-Square Test 39 Statistical Methods 40 Suitable Landcover 41 Soil Data and Suitable Soils 42 Point Extraction and Very Suitable Soils 42 Suitable Soils definition and Selection 43 Suitable Landcover Within Moderately Suitable Soils 43 Managed Areas and Future Land Use 44 Results 45 Occurrence Records Used 45 Landcover at Point 46 Landcover (All) by Study Area 49 Landcover Extraction by Buffer Polygons 51 Observed vs. Expected Proportions 53 Selection Indices, Selection/Avoidance, and Chi-Square Test 55 Suitable Landcover in Study Areas 59 Soil Data and Suitable Soil 65 Suitable Landcover/Suitable Soils Combination 71 Occurrence Records and Su itable Landcover/Suitable Soils Combination 77 Managed Areas and Future Land Use 80 Discussion 89 Occurrence Records 89 Landcover (All) 90


iii At Individual Points 90 Extraction by Buffers 92 Selection Indices, Selection/Avoidance, and Chi-Square Test 93 Potential Problems/Limitations 93 Statistical Outcomes 94 Suitable Landcover 96 Random Sampling 98 Soil Data and Suitable Soils 98 Possible Limitations 98 Empirical Results and Suitable Criteria 99 Suitable Landcover Within M oderately Suitable Soils 101 Occurrence Records Distribution and Suitable Landcover/Suitable Soils Combination 103 Future Land Use 104 Managed Areas 104 Conclusions 106 GIS Analysis and Testing 106 Overall Conclusions 107 Literature Cited 108 Appendices 113 Appendix A: Cox et al. (1994) Burrowing Owl Entry 114 Appendix B: Random Buffer Test Results 117 Appendix C: Full FWC 2003 Landc over Class Descriptions 121 Appendix D: SSURGO Soils Selected Metadata 128


iv List of Tables Table 1 Primary data sources, with nu mber of non-replicated point records (by type) 11 Table 2 All records fi eld visited by Mueller 12 Table 3 Landcover value and class descriptions as well as observed land use and grazing status during field visit for 30 selected non-urban records from Mueller 47 Table 4 Landcover value and class descrip tions as well as observed land use description given in attribute data for 45 selected non-urban records from Bowen 48 Table 5 Observed landcover counts/pr oportions in Muellers buffers vs. available proportion and expected co unts for full 10-county study area 54 Table 6 Selection Indices & Selecti on/Avoidance decisions for Muellers observed buffers in the 10-county study area 57 Table 7 Suitable landcover in 38 county study area 61 Table 8 Suitable landcover in 18 county study area 63 Table 9 Suitable landcover in the 10-county study area 64 Table 10 Suitable landcover in Breeding Bird Atlas polygons with recorded burrowing owl presence 65 Table 11 Muellers 19 Very Unique point records with selected soil attributes 66 Table 12 Bowens 28 Very Unique point re cords with selected soil attributes. 66 Table 13 Suitable landcove r/suitable soil combination grid proportions in 38-county study area 76 Table 14 Suitable landcove r/suitable soil combination grid proportions in 18county study area 76


v Table 15 Suitable landcove r/suitable soil combination grid proportions in 10county study area 76 Table 16 Authority over Managed Areas breakdown (statewide) 80 Table 17 Projected Future Land Use (Statewide; Categories Pooled) 81 Table 18 MA and FLU Status at Poin t. 19 selected Mueller records 82 Table 19 MA and FLU Status at Po int. 28 selected Bowen records 83 Table 20 MA and FLU Status at Po int. 8 selected FWC records. 85 Table 21 MA and FLU Status at Po int. 26 selected FNAI records 85 Table 22 Suitable Landcover within shoreline-clipped Managed Areas 86 Table B-1 Selection/Avoidance decisions and values for Selection Indices for each LC class 118 Table B-2 Random buffers Unstandard ized (w^) and Standardized (B) Selection Indices 119 Table B-3 Actual vs. Random buffers Standardized Sele ction Index (B) comparison 120


vi List of Figures Figure 1 Distribution map of historic non-urban records from all major databases 11 Figure 2 All non-urban site records field-verified by Mueller in 2005 14 Figure 3 Map of largest, previously unr eported non-urban burrowing owl colony 15 Figure 4 Labeled 38-counties with available soils and BBA presence 32 Figure 5 38-county study area with FW C/FNAI occurrence point records and Breeding Bird Atlas polygon records 33 Figure 6 18-county study area with Muellers 62 breeding occurrence point records 34 Figure 7 10-county study area with Muellers 62 breeding occurrence point records 35 Figure 8 FWCs 2003 statewide landcover map 38 Figure 9 Projected Future Land Use for all of Florida 45 Figure 10 Summary of Table 3s landcover at point values 47 Figure 11 Summary of Table 4s landcover at point values 49 Figure 12 All landcover classes in the 18-county study area 50 Figure 13 All landcover classes in the 10-county study area 51 Figure 14 Example of landcover extracted by selected buffers (step 1) 52 Figure 15 Example of landcover extracted by selected buffers (step 2) 53 Figure 16 Observed vs. expected proportions (Mueller Buffers) 55 Figure 17 Unstandardized Select ion Index (w^) comparison 58 Figure 18 Standardized Select ion Index (B) comparison 58


vii Figure 19 Suitable landcover statewide (67 Counties) 60 Figure 20 Suitable landcover in Hillsborough County 61 Figure 21 Suitable landcover in the 38-county study area 62 Figure 22 Suitable landcover in 18-county study area 63 Figure 23 Suitable landcover in 10-county study 64 Figure 24 All available soils in the 38-county study area 67 Figure 25 Moderately Suitable soils in the 38-county study area. 68 Figure 26 Highly Suitable soils in the 38-county study area 69 Figure 27 Example of varying levels of soil suitability. 70 Figure 28 Example of varying levels of soil suitability, with one unique-MUID polygon selected 71 Figure 29 Example of moderately suitabl e soils (contours) overlain on suitable landcover/suitable soils grid 72 Figure 30 Example portion of final su itable landcover/suitable soils combination grid 73 Figure 31 Example of final suitable landcove r/suitable soils combination grid at a county-level scale 74 Figure 32 Suitable landcover/suitable soil combination grid in 38-county study area 75 Figure 33 All occurrence records overlain on suitable landcover within suitable soils 78 Figure 34 All occurrence records overla in on improved pasture cells within suitable soils 79 Figure 35 Managed Area type statewide breakdown 80 Figure 36 Projected Future Landuse breakdown 81 Figure 37 Future Land Use at select ed points (Mueller and Bowen) 82


viii Figure 38 Demonstration of questiona ble accuracy of FNAI/FWC point coordinate 86 Figure 39 Landcover (all) in all s horeline-clipped Managed Areas 87 Figure 40 Suitable landcover within shoreline-clipped Managed Areas. 88 Figure A-1 Habitat distribution map a nd occurrence records for the Florida burrowing owl (Figure 60 in Cox et al. 1994) 114 Figure A-2 Zoomed section of suitability model map from Cox et al. (1994) 115 Figure B-1 Observed vs. expect ed proportions (random buffers) 117 Figure B-2 Actual versus Random buffers Standardized Selection Index (B) comparison 120


ix Distribution and Habitat Characterization of the Florida Burrowing Owl in NonUrban Areas Mark Mueller ABSTRACT The full geographic distribution and hab itat use of the Florida Burrowing Owl, a state Species of Special Concern, is not well-understood, particularly in remote, nonurban areas. This thesis aimed to expand and improve knowledge about non-urban burrowing owls. We first compiled databases of historic sighting observations. Fieldwork verified and updated existing breeding observation point records and also yielded new breeding locations. Using a GIS, we characterized observed land use, landcover, relevant soil attributes, projected future land use and ma naged area status for selected points. We quantified landcover within biologically-determined buffe r distances around burrows from our own field-verified records. Using standard resource selection methods, we compared observed and available proportions, calculated selection indices, and determined selection/avoidance for each land cover class. These empirical results were used in combination with expert opinion and l iterature review to finalize criteria for and map suitable landcover. Suitabi lity of relevant soil attributes were also empiricallydetermined and used to further reduce the overall suitable area. The final suitable habitat maps appear to relate well to the overall distribution of known non-urban burrowing owl records and demons trate that a great deal of potentiallysuitable breeding habitat exists throughout Florida s central interior. Improved pasture, the most prevalent landcover class, also appears to be the most strong ly selected in this study and may be of high importance to non-urban, breeding burrowing owls.


x Our results could be useful to w ildlife officials managing this species. Recommendations include improving surveys an d conservation efforts in non-urban areas and enhancing cooperation with landowners, pa rticularly ranchers, as success on private lands seems critical to the long-te rm persistence of this species.


1 Chapter 1: Statewide Distribution, Database Compilation and Field Verification of Historic and New Populations Introduction The Florida Burrowing Owl ( Athene cunicularia floridana ) has been studied less than its broader-rangi ng Western relative ( A.c. hypugaea ). In Florida, relatively little information is available on critical ecologica l characteristics of the burrowing owl. For example, accurate estimates of population size and statewide distri bution of both owls and suitable breeding and pos t-breeding habitat are lacki ng. Also poorly understood are some behavioral traits incl uding dispersal distance, immigr ation and emigration, and gene flow between subpopulations. Particularly impor tant to researchers and managers are the apparent behavioral diffe rences between Florida Burrowing Owls residing in urban/suburban settings and those found in mo re rural environments where significantly less research has been performed (USFWS 2003). Due to the many scientific uncertainties as well as various thr eats to populations (Haug et al. 1993, Millsap 1996) and the extirpation of seve ral urban subpopulations (e.g. Courser 1976), the Florida Burrowing Owl was designated a “Species of Special Concern” in 1979 by the Florida Fish and W ildlife Conservation Commission (Millsap and Bear 1997), which indicates a high vulne rability to becoming a “threatened” species “in the absence of appropriate pr otection or management” (FWC 2004). The majority of currently known populations of Florida Burrowing Owls are distributed among the state’s southwest and s outheast coastal regions with particularly large subpopulations found in urban and subur ban sections of Lee, Collier, Dade and Broward counties (Bowen 2000, USFWS 2003). Sm aller populations ar e scattered along the interior portions of the state from Hendry County in th e south to Madison County in the north (FNAI 2001), and are described as “spotty and local” (USFWS 2003), with one substantial but isolated p opulation on Eglin Air Force Base in Okaloosa County. In


2 contrast, historical evidence suggests that the species was once predominantly found in the extensive dry prairie ecosystems north of Lake Okeechobee and other interior portions of the state, and that emigration to co astal areas may represent a relatively recent range expansion (Hoxie 1889, Nic holson 1954, Ligon 1963, Courser 1979). Historically, the owl’s prime breeding hab itat consisted of dry prairie ecosystems, typified by open, treeless, well-drained, sandy soils and/or elevated areas with grasses and short herbaceous ground cover. It is be lieved that these conditions allowed good horizontal visibility to keep watch for predat ors and soils suitable for burrow construction (Palmer 1896, Courser 1979, Millsap 1996, Bowen 2000, Uhmann 2001, Mrykalo 2005a). In a laudable statewide burrowing ow l population census undertaken in the summer of 1999, Bowen (2000) counted 2,509 in dividual owls on 946 records. However, only fifty of these statewide records—represen ting just 5.3% of her recorded total—were classified as “agriculture,” w ith lack of data and restrict ed access to private land being cited as primary reasons for the low number observed in such areas (Bowen 2000). The particular criteria for classifying records as either urban or “a griculture” were not specified. Roadside surveying techniques may neglect the majority of rural land (Conway and Simon 2003), particularly large expanses of natural and improved pastures in remote areas, despite the potential suitability of such land for burrowing owls and other dryprairie natives (Morrison and Humphrey 2001). Bowen’s census represents the only complete effort to survey the sp ecies throughout the entire state. The increased difficulty and cost research ers face in accessing these remote and privately-owned sites, coupled with a gene ral lack of resources for owl monitoring efforts, has allowed for considerable uncertain ty about the true stat us of owl populations in such non-urban areas (Mrykalo 2005a), whic h at one time contained the majority of utilized breeding habitats (Nicholson 1954, Cour ser 1979). It is possible that the limited surveys of non-urban areas have led to a s ubstantial underestimation of overall burrowing owl density—for example, a University of South Florida research team observed approximately 70 owls over just 30 square kilometers of rural lands (Mrykalo 2005a).


3 While considerable effort has been spen t studying localized, urban populations of Florida Burrowing Owls (Courser 1976, Mill sap and Bear 1997; 2000, USFWS 2003), similar long-term monitoring efforts for non-urban populations have been lacking, despite calls to expand the monitoring of popul ations and conduct furt her inventories of breeding populations (Owre 1978, Millsap 1996, USFWS 2003). Such efforts seem particularly needed in non-urban areas, wher e access restrictions must first be addressed by working collaboratively with privat e landowners (USFWS 2003, Mueller et al. 2005a). To address this disparity, some resear ch has recently been undertaken to better understand non-urban populations, including work focusing on diet, dispersal and behavior (Mrykalo 2005a, Nixon in prep ). Definition and Importance of Non-Urban Populations It is difficult to give an exact defini tion for “non-urban”, except by defining what it is not. Both parts of this study consider “urban” populations of owls to be those found nesting in highly human-disturbed environm ents, such as: housing lots (occupied or vacant); airports; active golf courses; and similar areas. “Suburban” areas typified by densely residential zoning with large proporti ons of impervious surfaces, heavy vehicle traffic and likely direct harassment from humans and domestic pets would also be included in the “urban” category. Prominent examples of “non-urban” areas include grazed pastures, fire-maintained prairies, hay and sod farms, and any other vegetated area away from direct human disturbances. De pending on field judgments, we might also consider “non-urban” to include some relativ ely undisturbed areas of natural grasses in very low-density residential areas (e.g. horse grazed pastures on fair ly small tracts of improved pasture, even if in sight of scattered housing). There appear to be potentially importa nt differences between Florida Burrowing Owl populations utilizing urban/suburban habita ts and those nesting in more historicallynatural prairie remnants and in structurally -similar agricultural lands such as cattle pastures (Millsap and Bear 1997, Mrykalo 2005a ). For example, populations established in urban lands often reside year-round whereas at least portions of populations in other habitat types seem to undertake post-breedi ng dispersals to undetermined locations


4 (Stevenson and Anderson 1994, Mrykalo 2005a). Such behavioral differences suggest that research undertaken on owls in urban environments may not be directly applicable to non-urban populations. Burrowing owls in urban settings face a variety of threats. While the short vegetation height that owls require for burrows is provided by ruderal habitats such as golf-courses, airports, and vacant and occupi ed residential housing lots (Millsap 1996), such artificial, human-disturbed areas may be relatively unstable and unfavorable for long-term persistence (Millsap 1996, Bowen 20 00). In addition to increased mortality from such threats as vehicl e collisions (25% of deaths in Millsap and Bear 1988), pesticide pollution, domestic animal predat ion, and human harassment (Haug et al. 1993, Millsap and Bear 2000, USFWS 2003), the vast majority of vacant lots in suburban settings in Florida are destined for developm ent. Such vacant lots formed roughly 51% of Bowen’s censused urban territories, while onl y 9.8% of urban terr itories were found on occupied (developed) reside ntial lots (2000). Moreover, nesting success of owls on occupied residential lots seems to be lower than on vacant lots (Millsap and Bear 2000). Although there are suggestions for conservati on measures in urban areas (Millsap 1996, Millsap and Bear 2000), the trend toward rapi d and complete development of remaining vacant lots in breeding areas such as Marco Island (Ritchie, pers. comm.) may jeopardize the long-term security of the Florida Burrowing Owl in such urban/suburban environments. According to one estimate, over 75% of land in Florida is privately owned (Blanchard et al. 1998) and agri cultural and forest lands repr esent about 73% of Florida's total land area (IFAS 2000). One particular type of private land use may provide suitable conditions for breeding habitat. Livestock gr azing, either on improved pasture or native grasslands, has the positive e ffect of maintaining fairly short vegetation height—an important habitat characteristic for breedi ng burrowing owls (Morrison and Humphrey 2001, Mueller et al. 2005a). However, survey efforts on private lands have not been extensive, particularly on large tracts in remote locations, and private landowners seem unlikely to actively report such populations on their own behest. This situation suggests the potential for numerous, previously undi scovered burrowing owl populations in such


5 areas, particularly considering that much of the land in the state’s south-central interior— where burrowing owls were once most common (Hoxie 1889, Nicholson 1954, Ligon 1963, Courser 1979)—is now privately owned. Objectives This study attempted to improve the st ate of knowledge rega rding the overall distribution of the Florida Burrowing Owl, with special emphasis given to non-urban areas. In order to do so, I compiled a spatial database of previously-recorded locations from disparate sources. In order to pr ovide updated census information, we conducted field-verification of all non-ur ban territories reported in th e most recent comprehensive survey (Bowen 2000). In addition, selected lo cations from other databases were visited, primarily to determine the accur acy and utility of older survey data. Finally, efforts were made to educate and improve cooperation with private landown ers, and to solicit information on new burrowing owl locations for this study and for future research. Methods Data Sources The spatial database compiled for this study includes non-urban areas in which historical or new spatial da ta have been collected. Primary sources for historical nonurban data include: Bowen’s (2004) complete 1999 survey database; the Florida Natural Areas Inventory’s (FNAI) database of rare animals (NeSmith 2005); and the Florida Fish and Wildlife Conservation Co mmission’s (FWC) Wildlife Observations database (FWC 2005). Burrowing owl distribution information was also obtained from the Breeding Bird Atlas (FWC 2003), however, positional informati on in this database was provided only at the quadrangle level and was not directly useful for field veri fication purposes. New sources of non-urban data were colle cted by the primary author and other University of South Florida researchers (D rs. Grigione and Sarno and M.S. students Mrykalo and Nixon), as well as from pr ivate landowners and a phosphate company.


6 Study Areas Field verification of histor ic locations focused primarily on central and southcentral Florida where the majority of Bo wen’s non-urban populations were recorded (2004). Counties in which sites were invest igated included (fro m north to south): Madison, Suwannee, Lafayette, Gilchrist, Alachua, Marion, Sumter, Lake, Orange, Hernando, Pasco, Hillsborough, Polk, Brev ard, Osceola, Manatee, Highlands, Okeechobee, Hendry, and Collier. Data collected from all sources, includi ng urban and non-urban locations not field verified by the author, span most other Fl orida counties except those in the Florida Panhandle, where only Okaloosa Count y had any reported owl locations. Database Format In addition to positional attributes (i.e. la titude/longitude coordinates) of each owl location, basic descriptive information was co mpiled and organized in a usable format. Fields were created for: date ; observer/source; number of active/inactive burrows and/or young and adult owls; directions to site; landow ner contact information; and information on attributes such as site description and observed landuse. Sometimes such information was already included in some sources, but for other pre-existing and new sources many or all of these attributes had to be created. Compilation Process E-mail and phone contacts were made w ith existing database proprietors to acquire as much data as they were willing and able to share. Bowen’s 1999 database was obtained (Bowen 2004) as well as the more limited and outdated location databases from FWC (FWC 2005) and FNAI (NeSmith 2005). To solicit new location data, communications were made with private landow ners directly and th rough intermediaries such as the Secretary of the Florida Cattlemen’s Association. An article was published in the journal of the Florida Cattlemen’s Associat ion (Mueller et al. 2005a), and customized informational letters and observation sheets were developed to solicit new and historic burrow locations from private landowners. C ontacts were also made to other potential


7 new data sources, such as state Wildlif e Management Areas, Water Management Districts, Audubon societies, state parks, c ounty and city governments, as well as other researchers likely to have field data on burrowing owl breeding locations. Obtained databases were compiled in Microsoft Excel and imported to ESRI’s ArcGIS 9.0 and 9.1 for analysis and visualization. Location Verification a nd Field Survey Protocol Verification efforts focused on the most recent and comprehensive database— Bowen’s (2000) survey of breeding loca tions undertaken in the summer of 1999, although a limited number of additional nonurban locations from the other primary sources were also visited. Site visits were made to these locations in 2005 between May and August—the same months covered in Bowe n’s census. Field veri fication of historic locations was conducted in the 20 Flor ida counties mentioned previously. Prior to visits, attempts were made to identify name and contact information for the landowner in order to explain our purpos e and secure permission to enter private property. To do so, records of county property appraisers’ offices, when available, were searched extensively. Numerous phone calls, emails, and mailings were made to track down contact information and to make queries a bout the current and historic status of any known burrowing owls, and to arrange permission for site visits on private property. Online trip-planning services were utilized in combination with detailed GIS road data as well as aerial and satellite photography in orde r to produce customized site maps and to pinpoint the exact location of historic sightings as accurately as possi ble. When available, historic Global Positioning System (GPS) c oordinates were downloaded into a handheld GPS unit (a Garmin 76) for field use. Thes e planning and navigational aides allowed for efficient and accurate site visits. Whenever possible, the landowner or aut horized associate would be met in the field to provide escort to the historic site or to other burrow or owl sighting locations of which landowners were aware. When la ndowner escort was not possible but access permission was granted, a combination of GPS coordinates and the site maps were used


8 to navigate as close to the recorded site as possible. This often revealed the precise location of the historic burrow(s). Radiating outward from the vicinity of th e historic point, a th orough visual search for burrows and signs of owls was made in all directions using 8x32 or stronger magnification binoculars and a Bushnell sp otting scope set to 20x magnification. Particular attention was given to likely perches, such as fenc e lines, and to indications of possible burrows, such as sand piles in appa rently-suitable breedi ng habitat. A minimum time of 20 minutes was spent surveying on foot around each point location. Additionally, while traveling to and from study areas, effort was made to locate additional owls by visually scanning likely perche s. Vehicle speed was reduced to about 20 miles per hour or lower in and around historic areas to in crease the probability of spotting burrowing owls in previously unreported locations. When owls were observed, attempts were made to distinguis h between juveniles and adults using size, feather pattern and appearance of down as indicators. The status of any burrows was determined to be active or inactive based on size, ev idence of feathers, droppings, insect parts or pellets, and by th e amount of debris, such as cobwebs or vegetative litter cove ring the tunnel entrance. Tunne l shape and size was used to distinguish between ow l and gopher tortoise ( gopherus polyphemus) burrows. Apparent gopher tortoise burrows were not counted unless there was some additional evidence suggesting actual use by burrowing owls, as occasionally occurs (Owre 1978). Positional coordinates of both active and inactive burrows and other features of interest were recorded using a handhel d, WAAS-enabled GPS receiver (Garmin 76), positioned at the burrow entrance, with care taken not to accidentally collapse burrows. If no burrow was found, the point from whic h observations were made was taken. Estimated positional error calculated and reported by the handheld unit averaged approximately 5 meters; very rarely would this estimate exceed 10 meters, as signal interference from tall vegetati on and/or buildings was uncommon at almost all sites. The Wide Area Augmentation System (WAAS) real -time correction feature was used for all GPS recordings. This primary GPS unit did not have differential correction capability.


9 In addition to position, notes were take n on apparent land use, flood status, and general habitat description. When possible, he lpful remarks such as nearby addresses or distinctive landmarks were recorded to assi st future researchers in relocating burrows. Digital photographs were taken at most site s and archived to help document vegetative conditions and identifying landmarks. Whenever express permission could not be granted, either through outright refusal or more often failure to make contact, privat e land was not entered. In such cases, survey efforts were made from the closest possible public property, often road side right-of-ways. When access allowed, small soil samples were taken from the middle of the burrow apron for storage and possible future analysis. When owls were observed but access to burrows was restricted, a soil sample was taken from the nearest possible point. Samples were gathered at selected sites w ith no owls for possible comparative analyses. Education & Data Solicitation Efforts were made to disseminate information about burrowing owls to the public, particularly private landowners, in orde r to encourage conservation measures and improve participation in survey efforts. There were numerous opportunities where informal educational efforts were undertaken ; for example, during phone and face-to-face discussions with landowners, basic ecological information and conservation tips were provided and landowners’ questions were answer ed. However, formal educational efforts were made in print. First, an article was written for the jour nal of the Florida Ca ttlemen’s Association at the invitation of its Secretar y (Mueller et al. 2005a). This article emphasized the desire of the authors to establish a cooperative wo rking relationship with Florida ranchers. It provided basic information on burrowing owl ecology and invited landowners to assist with survey efforts by contacting the au thors with any information about known burrowing owl locations. In addition, several types of informationa l packets were created and appropriately customized for various recipients in both urban and non-urban areas, including individual ranchers and farmers, as well as larger corpor ations such as agricultural and real estate


10 companies. The informational packets were distributed either by postal mail or directly to landowners in the field. During field work, packets were distributed to landowners with observed burrowing owl activity and often to th e immediate neighbors of such properties, as well as to properties with historic sightings. All packets included a letter of introducti on along with a two pa ge explanation of basic information on burrowing owl status and ecology. This included photographs and identifying characteristics to help distinguish adult owls from juve niles and active from inactive burrowing owl burrows. Accompanying th is were single page field observation forms developed by the author with fields for requested in formation, including: observer, date, location observed, number of owls and burrows, landuse status, vegetation height and type, bands and eye color, as we ll as space for other comments. A copy of a Florida Natural Areas Inventory field report form for “Occurrences of special animals” was also included in selected packets, although dist ribution of these was limited to landowners where friendly contact and willingness to participate was already established. Finally, photocopies of the Cattl emen’s Association article were included with most packets, particularly thos e intended for ranchers and farmers. Results Historic Locations Bowen’s (2004) database contained the larg est number of record s, with 946 total records. Of these, only 50 records were cl assified as “agricultural” with the rest considered “urban.” Each of Bowen’s site records consisted of a separate GPS point marking a distinct burrow or group of burrows shared by a single family group. Some of these points, particularly those in urban area s, were located in close proximity to each other and might be considered parts of a larger colony. Relatively few records from the FNAI and FWC databases could be classified as non-urban. The FWC Wildlife Observations da tabase listed 76 observations, of which only about 10 could be classi fied as non-urban. The FNAI da tabase had 122 records, of


11 which 79 were already replicated in eith er the FWC or Bowen databases. Of the remaining 43 FNAI records, 19 were urban and 24 were non-urban. Table 1. Primary data sources, with number of n on-replicated point records (by type). Bowen FWC FNAI Urban Records 896 66 19 Non-Urban Records 50 10 24 % Non-Urban 5.3%13.2% 55.8% Figure 1. Distribution map of historic non-ur ban records from all major databases. 42 of 50 site records classified by Bowe n as “agricultural” were visited, with access completely precluded for the remaini ng records (e.g. access roads into an area were now closed, airboats were required, etc.). Additionally, 20 of Bowen’s “urban” records—chosen because of poten tial misclassification—were visited.


12 At both the historic sites where Bowe n (2000) reported 1999 presence and at the newly discovered breeding locations near by, approximately 70 owls were observed by Mueller in 2005. Of those, about 31 were judge d to be adults, 28 juveniles, and 11 could not be confidently classified. Median group size of owls observed at the same general location (large colonies in some cases) wa s 4.7. At these historic Bowen and new nearby sites, a total of 41 active burro ws were recorded by Mueller, with another 32 judged to be inactive at the time of observation. These num bers represent only a portion of the totals of Table 2, which also includes other known site s such as the colonies at R. Mrykalo’s Manatee County and P. Nixon’s Hillsborough County study areas. Table 2. All records field visi ted by Mueller. Listed by county, with number of owls and burrows observed in each county (incl udes 17 visited FWC/FNAI records). County Records Visited Owls Observed (all authors) Active Burrows Inactive/Probable Burrows Alachua 2 001 Brevard 1 000 Collier 2 112 Gilchrist 5 641 Hendry 4 23122 Hernando 3 024 Highlands 12 420 Lafayette 4 000 Lake 5 014 Madison 1 000 Manatee 14 292015 Hillsborough 6 57396 Okeechobee 1 000 Orange 10 064 Osceola 4 000 Pasco 5 944 Polk 7 425 Sumter 7 244 Suwannee 1 001 Total: 94 1359653 A site was considered to be active if e ither owls or clearl y active owl burrows were present. Of Bowen’s vi sited “agricultural” records, 14.3% were found to be active in 2005. This number increases to 26.6% if ne wly discovered breeding locations within 2


13 kilometers of historic coordinates are included. Of the “urban” sites visited, approximately 35% were still active. Restrictions on access to private proper ty limited navigation to the precise GPS point indicated by Bowen in many cases. In a few such cases, landowners were reached but directly refused access (approximately 10% of all records). For the vast majority of inaccessible sites, however, mail, email and/ or repeated phone messages simply did not elicit any response, or la ndowner contact informati on could not be obtained. Consequently, about 30% of Bowen’s sites ha d to be observed from the closest possible location for which access was legally allowed. Thorough investigation for owls and burrows at many of Bowen’s historic sites was thus sharply lim ited. Including records where close inspection was not possible, a bout 56% had no visible signs of owls or burrows. Seventeen selected locations from the F NAI and FWC databases not replicated in Bowen (2000) were surveyed by Mueller. No owls or active burrows were observed in 2005, although one or two potential inactive burrows were found near these locations. Bowen may have surveyed some of these sites, but did not report active breeding. Directions to most of these sites were either incomplete or not included in the databases. GPS coordinates were often truncated, degrading both precision and accuracy. Additionally, some of these re cords dated back to 1975, with relatively few as recent as the 1990s. About half of these sites were obser ved to be either developed or overgrown with vegetation likely unsuitabl e for burrowing owl breeding. At 11 of Bowen’s sites, soil samples were gathered. When owls were observed but access to burrows was restricted, a soil sa mple was taken from the nearest possible point and marked as such. About 10 soil sample s were gathered at historic sites with no current owls.


14 Figure 2. All non-urban site records field-verified by Mueller in 2005. New locations Six previously unreported breeding ar eas were discovered while conducting fieldwork. Two of these sites were found in Manatee County (owl group sizes of 12 and 1), one in Highlands County (group of 4 owls), one in Polk County (4 owls), and two in Hendry County (owl groups of 20 and 2). A ll of these new sites were found on grazed pasture, except the Polk county site, which occurred along a roadside in a semiresidential area with numerous horse pastures. All of these sites were no farther than 2 kilometers from historic locations provide d by Bowen (2004). One colony of at least 20


15 owls—the largest observed in this study—was found about 400m inland from the public road (where Bowen’s observation of 2 owls was made) and could only be accessed with the assistance of the rancher landowner (Figure 3). Figure 3. Map of largest, previously unreported non-urban burrowing owl colony. Located on grazed pasture (previously an ir rigated cropfield) in south-central Hendry County. Points represent individual active burrows. Information about other point locations was also collected from fellow USF researchers and either field verified by Mueller or the ot her researchers. Field data collected since March of 2004 by Dr. Grigione and Dr. Sarno span 10 counties, although only two of these counties contain any ve rified non-urban poin ts (Hillsborough and Manatee). One non-urban colony in Manate e County with approximately 15 active


16 burrows, 10 adults and 7 fledged juvenile s was provided by R. Mrykalo, with GPS coordinates taken by the author in the 2004 breeding season. P. Nixon provided escort to two separate colonies located in southe rn Hillsborough County on cattle-grazed lands owned by a phosphate company. These two colonies contained a total of 37 owls (adults and juveniles) utilizing about 27 active burrows. Both the Manatee and Hillsborough locations are not included in the non-urban records reported by Bowen, although they are included in Table 2 as they were field-verified by Mueller. Information from a rancher led directly to the sighting of one additional new owl and burrow location, in Manatee County. Repor ted sightings of mu ltiple owls on nearby property belonging to another landowner could not be field verified due to denial of access permission. Soil samples were gathered from 4 of the new locations, and from every active burrow at the Hillsborough County sites visited with P. Nixon. Education & Data Solicitation Three landowners made contact in re sponse to the Florida Cattlemen’s Association article. Upon follow-up, however, none of these sources actually had sighted burrowing owls breeding on their property th at season, and could not provide precise directions to any br eeding locations. Information packet s with observation forms were mailed to these landowners to encourage future observations to be submitted. To learn of additional n on-urban populations, a large number of public agencies and managers of state-owned land were cont acted via phone and email, including several Wildlife Management Areas, Water Manage ment Districts, and city and county governments. Response rate from these entiti es was moderate, with several potential owl locations reported, but either not in time or with too little information to warrant field verification as part of this study. Dozens of contacts were made to individua l and incorporated owners of private land thought likely to have current or histor ic populations of owls. Approximately 50 informational packets were distributed to private landowners in the field or by postal mail, with a few landowners receiving multiple packets. None of these packets have yet


17 resulted in submitted reports; however, the ma jority of packets were distributed “cold,” without additional contact beyond the introducto ry letter, and in some cases packet submission was unnecessary as field visits and in-person discussions were made. Discussion Database Compilation and Evaluation The availability of information about Florida Burrowing Owls is limited, particularly data on distribution and a bundance in non-urban areas. Most existing research has focused on relatively large and dense urban and suburban populations in the vicinities of Cape Coral, Ma rco Island, and the municipalitie s of the southeastern coast with a large number of known breeding locations, as reflecte d in Bowen’s survey (2000). In contrast, relatively few collected lo cations came from non-urban areas. The FNAI database provided 24 non-ur ban points not repli cated elsewhere; however, most of these records were outdated by more than a decade. The FWC database provided only 10 non-urban records, although they were slight ly less outdated, with points taken between 1988 and 1993. Field visits to 17 non-urban records from these two databases produced no verifiable burrowing owl activ ity, despite an attempt to select the most up-to-date records with the best site directions. Serious weaknesses in these databases may have made detection of any owls unlikely, even if there was actual burrowing owl presence somewhere nearby. In addition to being outda ted, positional accuracy varied and seemed unreliable in both databases, with latit ude/longitude coordinates often appearing truncated or rounded. It is likel y that some of these coordina tes were estimated at time of creation because of the unavailability a nd/or poor accuracy of GPS receivers. The presence of selective ava ilability imposed on GPS signals prior to 1999 likely had a negative effect on positional accuracy even when GPS units were employed. Because no owls and only a couple of inactive burrows we re found at these visi ted sites, it appears that the usefulness of these historic databases is not as great as that of Bowen’s. Further evaluation of the remainder of th ese historic records is advisabl e, particularly if they are to be used in any analyses.


18 In comparison, Pamela Bowen’s database representing her 1 999 statewide census was neatly organized and comprehensive. Accurate positional coordinates and fairly detailed directions to most locations allowe d precise relocation of sites, including finding specific burrows when site access permitted—a critical feature lacking in the FWC and FNAI databases. Bowen’s GPS coordinates proved reliable and seemingly accurate within a matter of meters, although she too wa s hindered by site acce ss restrictions and often had to record coordinates from the closest publicly-acce ssible location (2000). Bowen’s database also provided numbers of adults and juveniles observed, which allowed for comparison to numbers observe d in this 2005 study and suggested an observational trend toward fairly small family groups in non-urban areas. Bowen’s survey also offered much more recent info rmation than the FWC and FNAI databases, which may have increased the probability of re-detecting owls sti ll inhabiting a general area, assuming some degree of site fidelity over time. Bowen characterized habitat as either “u rban” or “agricultural,” with the latter category further broken down into either “pasture” or “cropl and.” However, it should be noted that a handful of site s classified as “urban” by Bo wen may actually have been misclassified, depending on the exact criteri a used to define “urban”—criteria which were not specified in Bowen (2000). Observatio ns from this study suggest that some of these debatable records may have been classifi ed as “urban” due to relative proximity to residential housing, and not on the basis of actual landuse at burrows. Although only a very small percentage of Bowen’s “urban” records were visited (about 2%), some of these were actually situated in small parc els of natural or improved pasture or on two large nature preserves where habitat wa s managed specifically for burrowing owls. Regardless, Bowen’s database provided more recent demographical data and the ability to re-visit specific locations with both precision and accuracy. Therefore, field verification of historic breeding sites focused on this database. Field Verification Field verification allowed us to: obtain updated survey information for historic sites; informally evaluate the utility of the databases; weigh the strengths and weaknesses


19 of our survey methodology; and make reco mmendations for future survey efforts. Of Bowen’s recorded sites, 14.3% of those visited were found to still be active in 2005. If new sites within 2 km of historic ones are included, this number increases to 26.2%. This change could suggest that shortdistance relocations of owl groups may have occurred during the 6 year inte rval, with owls moving to find the most suitable breeding and/or foraging habitat in an area. The mean group size of non-urban owl records, calculated as about 4.7 individuals in this study and as 2.6 in Bowen (2004), shows that most sightings in both studies were of indi viduals, pairs, and small family groups, with only a few large colonies. This study found 2005 activity at about 35% of sites classified as “urban” in 1999, although the low sample si ze of only 20 “urban” records visited (out of Bowen’s 896 “urban”) should be noted befo re drawing any conclusions about presence at urban sites, which are not the focus of this study. Many of these 20 selected “urban” sites were chosen for field verification base d on their proximity to “agriculture” records and thus do not represent a random samp le of the total 896 “urban” records. There were both strengths and weakne sses in the survey methodology employed here. Strengths included usi ng GPS coordinates, aerial phot ography and GIS road layers to plot historic locations with high preci sion. Combining this information with tripplanning services allowed effi cient navigation to the site or closest publicly-accessible location. Additionally, a majority of Florid a’s county property appraisers’ records are now available online and were useful in obtaining landowner contact information. Establishing contact with landow ners prior to and during fiel d visits provided valuable information to both parties. Finally, vehicl e-based monitoring of likely perches while traveling throughout historic areas reveal ed previously undocumented populations. However, there were several methodologica l weaknesses that may have prevented observation of some non-urban owls or underrepresented the number actually present. During observation, owls may have been away foraging or hidden inside burrows. About 90% of the site records we re only field surveyed one time (although for those visited twice, there were no differences observed be tween successive visits). Some owls may have undergone post-breeding disp ersals prior to visits. For example, the sites in North Florida could not be surveyed until later in the summer (as was also the case in Bowen’s


20 survey) near the end of the br eeding season. At one North Florid a historic site in Madison County, two separate landowners reported seeing 4-6 owls earlie r in the season, but suggested those owls had dispersed just pr ior to Mueller’s midAugust field visit. Additionally, the inability to access such a large numb er of privately-owned properties, either due to direct refusal or failure to make contact, limited the accuracy of surveying at many sites. This problem is related to the underlying, fundamental weakness in both this and Bowen’s survey—relianc e on surveying conducted primarily from roadsides. Although both surveys made efforts to secure permission to enter and survey private property, restricted access prevents the majority of land in remote, non-urban areas from being observed at all. While almo st all of the observed active locations were found within sight of a road, there is no a pparent biological reas on that burrowing owls would be more abundant near roads, and even short distance moves inland from roads could cause such owls to go unseen. This s uggests a probable underrepo rting bias in nonurban areas surveyed from the roadside (Conway and Simon 2003). Finally, this survey would not have observ ed burrowing owls at historic sites if they had been eliminated by predation or had simply moved out of the area in search of mates or because of unfavorable changes in habitat conditions since 1999. Elimination by predation seems possible for the small family groups of 2-5 owls that made up the vast majority of Bowen’s non-urban “territories.” Such changes may have been relatively recent: presence within the last year was su spected for about 7 ot her sites statewide because of information from landowners and/or other evidence, such as insect and small mammal remnants on otherwise inactive burro ws. However, only sites with current, observed owl use were tallied as active. Surveys would need to be conducted more frequently than every 6 years to minimize these problems. There are also several factor s which could have aided pe rsistence of fairly small groups of owls over a 6 year period. Because our 2005 survey found many small family groups extant at their 1999 locations, it may be important to consider such factors. For example, the extent to which immigra tion occurs in non-urban burrowing owl populations is not known. Breeding interactions could be occurring between fairly small family groups located within a couple of kilo meters of each other. Regular interactions


21 between such groups would encourage main tenance of an otherwise small population over the six year interval. Roadside su rveys in non-urban areas would likely underestimate the overall popula tion size of such spread-out groups. Alternatively, some degree of inbreeding may have occurred to a llow a single family group to persist in the same location over multiple generations. W ithout genetic study, it is impossible to determine the level of relatedness between ow ls seen in 1999 and those observed in 2005, although it would be impossible to gather su ch data without first possessing precise knowledge of the whereabouts of such populations. Observed Land Use and Trends The most common land use observed at vi sited sites was improved pasture, with active cattle grazing frequently observed or inferred. About 70% of Bowen’s “agricultural” sites that still had owl activity in 2005 appeared to be on grazed pasture. Although a systematic survey of plant clas sifications was not conducted, bahia grass ( Paspalum notatum ) and structurally similar grasse s intended for cattle grazing (IFAS 2000) appeared to dominate. While there is need for further resear ch on interactions between cattle and burrowing owls, including the risk of burro w collapse from densely-stocked cattle (Nicholson 1954, Nixon in prep ), the vast majority of r ecorded non-urban locations in both this study and Bowen (2000) are found on grazed land. Although grazing has generally been viewed as unfavorable for wildlife habitat (Noss 1994, Fleischner 1994), it may be preferable to more intensive types of development and in certain circumstances has been shown to have a positive influence. For example, the Crested Caracara, a threatened raptor occurring in Florida with similar habitat needs to the burrowing owl, has shown a preference for and increased reproductive success on cattle-grazed lands over more natural public lands (Morrison and Humphrey 2001). Many of Florida’s public lands managed as natural areas are allowed to progress to later successional stages. These later su ccessional stages likely benefit many other types of flora and fauna, including wetland-dep endent native Florida species. Such areas may not offer suitable burrowing owl breedi ng habitat, however. Wildlife Management


22 Areas, which often contain large proportions of cattle-grazed leased land, may offer habitat more likely to be utilized for br eeding. Notable exceptions include parks and preserves specifically intended to preserve th e dry prairie ecosystem, such as Kissimmee Prairie State Park and military lands such as Avon Park and Eglin Air Force Base where frequent, low-intensity fires are allowed to burn or are actively pr escribed by managers. Management Status and Implications for Non-Urban Populations The Florida Burrowing Owl’s status as a Florida “Species of Special Concern” and as a federal “Bird of Conservation Concern” (USFWS 20 03) indicates a high vulnerability to becoming a “threatened” species “in the abse nce of appropriate protection or management” (FWC 2004). Thes e designations are in tended to encourage further research on the species but provide rela tively little legal protection; specifically, the state requires only that a permit be granted prior to de struction of burrows found in “urban areas” (FWC 2004), with permits only issued during the non-breeding seasons. Interestingly, the Commission “has no guide lines for management of burrowing owls in other than urban/suburban areas” (FWC 2004), desp ite the fact that ag ricultural and forest lands represent about 73% of Florida's to tal land area (IFAS 2000). According to the official guidelines, protections for these non-urban owls can be developed on a case-bycase basis in “situations where numerous bu rrows will be impacted” (FWC 2004). There are many possible reasons for this policy. These likely include: the increased difficulty of accessing and monitoring populations in remote areas; lack of adequate enforcement resources; poor cooperation with private landowners stemming from wariness toward government restrictions; and a lack of existing data regard ing specific locations of nonurban owls. An important question for managers to c onsider is the relative importance of nonurban populations of Florida Burrowing Owls fo r the overall conservation of the species. Research on non-urban populations has until recently been limite d. One possible reason could be that the population viability analyses (PVA) conducted by Bowen (2000) and cited in official species status assessmen ts (USFWS 2003) showed >50% probabilities of extinction over 100 years of small “island” populations containing f our or fewer adults.


23 Significantly, these analyses di d not include the possibility of immigration or emigration. Since the majority of non-urban populations observed in Bowen’s census were small and distant from other observed populations, rese archers and managers may have concluded that significant conserva tion effort in such areas is not as critical as in larger, urban subpopulations. Although it is true that genetically isol ated small populations often do not persist (Lande 1988), a critical a ssumption was made in these analyses—that no immigration into these small populations would occur. Th is assumption may not be true, given the capability of owls for long-distance dispersal (e.g. Sykes 1974, Courser 1979). Allowing for immigration—even at minor levels—can si gnificantly alter PVA results (e.g. Stacey and Taper 1992). The analyses also assume th at all breeding individuals in a colony were observed—which may not be the case in non-ur ban areas, due to acc ess restrictions and limited visibility from roadside surveys. Thus, any conclusions reached about discounting these types of small, remote populations ma y be counterproductive. Indeed, as urban development progresses and passes thresholds thought to have ne gative reproductive consequences for burrowing owls (i.e. 60% lot development noted in Wesemann 1986 and 1987), the relative conservation importa nce of non-urban areas may increase. Implementing effective management pl ans for non-urban burrowing owls will necessitate obtaining more accurate inform ation on distribution in non-urban areas as well as improving cooperation with private la ndowners. A logical st ep—which could be easily implemented by the FWC—would be to regularly inquire about burrowing owl presence with public land-managing agen cies—state and county parks, Wildlife Management Areas, Water Management Districts, etc. Our experience suggests that some individual biologists and rangers responsible for such lands (for example, one overseeing the Dinner Island Wildlife Management Ar ea in Hendry County) knew of burrowing owl locations but had not been asked to share that information with the wildlife agency. A more difficult—but necessary—step woul d be to expand surveys of private lands, particularly of the large expanses of grazed land throughout the interior of the state with historical presence dating to the 1800s (Hoxie 1889, Palmer 1896, Ligon 1963).


24 Such lands may possess considerable numbe rs of undocumented burrowing owls due to consistent maintenance of breeding habitat conditions. Technological tools and methods such as GIS-based habitat characterization and suitability modeling (see Ch. 2) could help limit the amount of probable habitat to be surveyed. Of course, the basic question of what constitutes suitable burrowing owl breeding habitat and where this habitat is dist ributed needs to be further researched and refined (see Ch. 2; Mueller et al. 2005b). However, even if such methods are successfully implemented, the need for improved coope ration with landowners remains clear. To achieve this, several hurdl es need to be addressed, including a lack of amiable communication, fear of potentially restrictive regulations, and a lack of knowledge about the Florida Burrowing Owl’s ecology and lega l status. For example, many landowners incorrectly believed that the species was clas sified as “endangered.” To address these issues, managers must develop strategies to encourage private landowners to conserve burrowing owls and their habitat. Offering pos itive incentives and/or basic assurances against additional restrictions might help. Th e benefits of improving cooperation could be substantial. For example, eas ing access restrictions on priv ate property would allow for more extensive and productive survey effo rts. Improved cooperation with private landowners (particularly cattle ranchers) is likely to be of increased importance in future conservation efforts. Conclusions It is difficult to draw definite conclusi ons from this distribution research. Rather, this work primarily serves to suggest needs for future research and management efforts. These include: expanding the scope and im proving the effectiveness of surveys and management efforts in non-urban areas; refining knowledge about suitable habitat; updating and expanding the FWC and/or FNAI databases of known non-urban owl locations; and enhancing coope ration with private landowners particularly ranchers. While this work has attempted to further these aims, it should be continued by other researchers and the state wildlife agency in order to promote long-term conservation of the Florida Burrowing Owl.


25 Chapter 2: Geospatial Analysis of the Florida Burrowing Owl ( Athene cunicularia floridana ) in Non-Urban Areas Introduction Geospatial technologies such as Geogra phic Information Systems (GIS) have emerged as valuable tools in wildlife and natural resource conservation as they can quickly yield useful information while effici ently minimizing cost-i ntensive field work. Various data sources can be combined in a dynamic GIS database to display, describe and even predict complex interactions between multiple factors in a given ecosystem, and can be employed to help focus and direct field research and conservation management efforts. In order to better unde rstand its spatial ecology and to help inform such future management decisions, this study uses geospa tial analysis techni ques to characterize habitat of the Florida Burrowing Owl ( athene cunicularia floridana ). For this subspecies, relatively little is known about important ec ological characteristics such as current statewide distribution an d the habitat preferences that ma y influence this distribution. There have been calls to expand mon itoring of populations and conduct further inventories of breeding populations stat ewide (Owre 1978, Millsap 1996, USFWS 2003). However, little research or monitoring has been performed on populations in much of Florida’s non-urban interior, which at one time contained the majority of utilized breeding habitats (Courser 1979). The diffi culty in locating and legally accessing burrowing owl nesting sites on vast, remote tr acts of private land, c oupled with a general lack of resources for regional owl monitori ng efforts, has allowed for considerable uncertainty about the true status of owl populations in such areas (Mrykalo 2005a). Current knowledge about dist ribution and habitat use is limited, with Bowen (2000) reporting just 50 non-urban records (just 5.3% of her total) on “pas ture” and “cropland.” Other historic databases ar e outdated and/or imprecise. Novel methods for reducing


26 intensive field costs—such as GIS m odeling (Shaw and Atkinson 1990)—could help address these problems, and shoul d be developed and applied. Purpose and Population of Interest We are interested in evaluating habitat selection and availabi lity as well as the potential for conservation of burrowing owls in non-urban areas, for several reasons. In addition to the general lack of research be ing performed in non-urban areas, urban and non-urban populations seem to demonstrate some behavioral differen ces such as differing dispersal tendencies (Mrykalo 2005a). This could minimize the rele vance of urban-based research for non-urban populations. Moreove r, urban owls may encounter reduced reproductive success on occupied lots as compar ed to vacant lots (Millsap 2000) and such vacant lots are rapidly disappearing in the popul ar, coastal urban areas with existing large burrowing owl populations. The population of interest here is limite d to non-urban nesting burrowing owls in the defined study areas, with an emphasis on adult males when considering foraging habitat. The definition for “non-urban” given in the previous chapter is again used here. Landcover, “Suitable” Landcover, and Selection Among others, Green and Anthony (1989) suggest that burrowing owls were “selecting habitats of relatively short ve getation for nesting.” Uhmann et al. (2001) created a local-scale, non-GIS Habitat Suitability Index for the Western Burrowing Owl ( Athene cunicularia hypugaea) which ranked various habi tat variables according to expert opinion and used an iteratively-formed model to compare predicted suitability values with historical presence of breeding ow ls. They concluded that habitat suitability was reduced by the presence of tall vegeta tion at burrows. However, this study was limited to micro-habitat evaluation with inte nsive field work required to characterize habitat. It did not consider any wide-scale ha bitat factors or quantif y suitable habitat at broader scales. This leads to the question of whether a nd how “suitable” breeding habitat can be successfully determined using remote, br oad-scale methods. Although several other


27 studies have evaluated local-level land us e and cover types for the Western Burrowing owl (e.g. T. Rich 1986, Haug and Oliphant 1990, Gervais et al. 2003) a nd at least one has considered regional-level landcover for th e Western species (Buchanan 1997), there has as yet been only one study of landcover at non-local scales for the Florida Burrowing Owl—Cox et al. (1994). (One study, Mryaklo 2005, considers local habitat composition in home-ranges for one colony). In their 1994 “Closing the Gaps” study, Cox et al. used just one of 22 available landcover classes—Dry Prairie—or iginally classified by Kautz et al. (1993). Contrary to this model’s methods, non-urban Florida Burro wing Owls have been frequently observed using landcover types other than “natural” dr y prairies, including extensive sightings on grazed pasture (Bowen 2000, Mrykalo 2005a, Mue ller et al. 2005a). Nonetheless, only this class was used to identify “suitable” landcover cells within the fairly large extents of all Breeding Bird Atlas (FWC 2003) polygons with any confirmed or “probable” burrowing owl presence, including urban owls (Figures and full text entry in Appendix A). This rather limited analysis yielded a ve ry small number of “su itable” cells, and the authors admitted that “habitat is much more common than depicted…” in the map. There appeared to be great potentia l to improve upon this somewhat outdated analysis for the Florida subspecies, particularly consideri ng the improved 2003 landcover data available. Our study uses recorded field observations of used sites’ land use and cover as well as the Florida Fish a nd Wildlife Conservation Co mmission’s updated “Florida Vegetation and Land Cover” 2003 dataset (Stys et al. 2004). We evaluate landcover at individual point records (local scale) as well as within a set “buffer” distance to quantify potential foraging habitat within the imme diate surrounding landscape. The 600-m radius buffer value is based on the best available em pirical data for foraging distances traveled by adult male burrowing owls nesting in non-ur ban environments that could be found in the literature (e.g. Haug and Oliphant 1990, Gervais et al. 2003). Unfortunately, the available studies all involve the Western Burrowing Owl. With the exception of Mrykalo (2005a), no other research appears in the l iterature about the ho me-range of non-urban burrowing owls in Florida.


28 In order to test whether and to what extent the habitat su rrounding burrowing owl nesting locations was different than that available in th e entire study area, several statistical procedures were performed to te st for resource sel ection (Neu et al. 1974, Manly et al. 1993) Soils Soil characteristics are likely important to burrowing owls, as they influence construction and maintenance of their nesting burrows. Soil characteristics that lead to frequent or persistent fl ooding and burrow submergence c ould potentially cause early abandonment of burrows or even de ath of unfledged chicks/juveniles. GIS use of soil data in evaluating plan t and animal habitat suitability is not uncommon, although the less detailed STATSG O is often used (e.g. Cox et al. 1994, Mann 1999). GIS evaluation of soil data can be “useful in long-term planning for conservation management and restoration, esp ecially where intensive ground surveys are expensive and/or impractical” (Mann 1999). This study is primarily interested in using the soil data simply to remove otherwise suitable-appearing la ndcover cells that seem lik ely to be flooded during the breeding season, based on obtained soil attributes. Managed Areas and Future Land Use The statewide future land use map was developed over a decade ago and depended on combining data from multiple regional planning councils and local governments (SWFRPC 1994). The scale of this da taset is not intended for local analyses but rather for broad, regional considerations. Th erefore, this study uses these data only to consider potential overall trends for future land use. The definition of “managed area” is im portant to understand for this study. The data source, the Florida Natural Areas Inventor y defines it as “public (and some private) lands that the FNAI has identified as havi ng natural resource valu e and that are being managed at least partially for conservation purposes” (FNAI ).


29 It should also be noted that “managed area” does not necessarily imply that a parcel is dedicated solely to sustaining natural wildlife. Consider multi-use “Wildlife Management Areas,” which allow hunting as well as regulated grazing, or the state forests, which allow timber extraction. Als o, the managing authority for a given managed area is not necessarily the permanent owner of that la nd. For example, some types of conservation easements allow private landowners to “lease” land to or from the state, and may allow limited livestock grazing. In the case of the Florida Burrowing Owl, this practice could be more beneficial than de trimental as grazing can maintain the low vegetation height burrowing owls seem to prefer for breeding. Cox et al. (1994) identified several ex isting conservation-managed areas with apparent concentrations of nearby occurrence records but did not quantify the percentage of records occurring in managed areas. This study will use the major point record databases to assess the actual percentage of records in ma naged areas, as well as the overall status of Florida’s managed areas and the amount of “suitable” landcover occurring therein. Methods Data Processing and Preparation Environmental Systems Research Institu te’s ArcGIS 9.1 and Spatial Analyst extension (ESRI 2005), “Hawth’s Analysis Tools for ArcGIS 3.23” (Beyer 2004) and “XToolsPro 3.1” (DataEast 2006) extensions were used for all spatia l analysis and some database processing, along with Microsoft Excel. Shapefiles were either created by the author, provided via data requests to individuals and agencies or downloaded from the Florid a Geographic Data Library (FGDL 2006). Projections and da tums were determined and defined for all layers, and all layers were reprojected to a common format pr ior to any GIS analysis. This format is a customized Albers Conical Equal-Area projection with a NAD83-HARN datum utilized by the Florida Geographic Data Library (FGDL 2006) and the Florida Fish and Wildlife Conservation Commission’s (FWC) Fish and Wildlife Research Institute (FWRI).


30 Study Areas and Florida Burrowing Owl Occurrence Data Point data used in this GIS analysis form ed three separate databases: 1) Mueller’s 2005 field visits to historic a nd new breeding sites; 2) Pame la Bowen’s provided database of breeding locations from her Spring/S ummer 1999 statewide census (Bowen 2005); and 3) the FWC’s “Wildlife Observations Database” (FWC 2005) and the Florida Natural Area Inventory’s “Element Occurrence” re cords (NeSmith 2005). Note that the FWC/FNAI coordinates are not necessarily breeding locations, just observations of owls. The FNAI database already contained many of the records from Bowen’s and the FWC databases so it was thoroughly inspected and only FNAI records not repeated in Bowen or FWC were utilized. The remaining FNAI unique records were merged with the FWC points using the “Merge” tool in ArcGIS. Mueller records used in this GIS analys is are slightly different from those reported in Chapter 1. Only verified non-urba n locations visited by Mueller directly are included so that firsthand knowledge of landcove r at site could be considered. However, the actual GPS coordinates used for seve ral active burrows at one large colony in Manatee County (Rutland Ranch) were recorded by fellow researcher R. Mrykalo in July 2004 using a Garmin 76 as Mrykalo had supe rior knowledge of which burrows were actively used at this colony. These points were considered part of th e Mueller records as the site was visited and verified by Muelle r. Non-urban points used from Mueller’s records consisted of 62 indi vidual point records spanning 10 counties (Figure 7). Each point record represents a field-visited locat ion at which either: 1) burrowing owls were observed; 2) an “active” burrow (unobstructed circular tunnel entrance, pellets, scat and/or prey remains) was observe d; or 3) an apparent burrow th at appeared likely to have been active in the last year (via presence of at least one of the above “active” criteria) and had ancillary information such as intervie ws with landowners that suggested recent burrowing owl presence. GPS coordinates were recorded as close to the burrow as possible. In both Mueller’s and Bowen’s data set, when direct access to a burrow was not possible due to access restricti ons, a GPS reading was taken at the closest possible public location. For Mueller’s points, this was usuall y alongside the fence line on the roadside.


31 Bowen’s database contained 50 individual “agriculture” point records covering 18 counties (Figure 6). All of Bowen’s point records were associated with observed burrowing owls. 8 point coordinates were used from th e FWC’s database and 26 points from the FNAI’s provided database (NeSmith 2005) for a total of 34 combined records used in GIS analysis (Figure 5). Site descriptions with in the attributes of each source’s shapefile were used to classify each r ecord as urban or non-urban. In addition to the point locations, 29 1 quadrangle-shaped polygons from the 19861991 Breeding Bird Atlas (FWC 2003) spanni ng 35 counties were utilized in certain analyses. All of the polygons were queried and those with any burrowing owl presence were exported (Figure 5). Presence informati on in this dataset was only available at the quadrangle level. The 38-county study area s hown (Figures 4 and 5) derives primarily from the counties with historic burrowing owl presence in the Atlas. However, it excludes Duval and Taylor counties, each with one Atlas presence polygon, as those counties lacked available soil data. This st udy area also includes three non-BBA counties (Pinellas, Sarasota and Hamilton) due to th eir close proximity to counties with non-urban presence and/or because of historic presence either in Bowen’s 1999 survey or other literature (e.g. Ligon 1963 and Courser 1979). The county study area boundaries used th roughout this study were obtained from FGDL, although the original source was the Fl orida Dept. of Environmental Protection’s (FDEP) 1997 “Counties with shorelines” (FGDL 2006). The reported scale is a fairly detailed 1:24,000. To form single polygons fo r each county, the “D issolve” function was performed, using county name as the disso lve field. This resulted in 67 polygons representing all of Florida’s counties, although for each study area, selected counties were exported into new shapefiles (e.g. Figure 4).




33 Figure 5. 38-county study area with FWC/FNAI occurrence point records and Breeding Bird Atlas polygon records. FWC / FNAI NonUrban Records (34) BBA "BUOW" Records (291) Counties w/ BBA Presence & Available Soils (38) All Other Counties FWC & FNAI Non-Urban & Breeding Bird Atlas Records 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Duval Taylor


34 Figure 6. 18-county study area with Mueller’s 62 breeding occurrence point records. PB All Agricultural Records (50) Counties w/ PB Agriculture Records Counties w/ BBA Presence & Available Soils (38) 18 County Study Area (PB "Agriculture" Records) 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN


35 Figure 7. 10-county study area with Mueller’s 62 breeding occurrence point records. MM Active/Prob. NonUrban All (62) Counties w/ MM Active/Probable Records Counties w/ BBA Presence & Available Soils (38) 10 County Study Area (MM NonUrban Active & Probable Records) 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Extraction of Attributes at Points Landcover at Point: To reduce the overall number of points us ed in the landcover at point extraction, points from Mueller and Bowen were interact ively and systematically selected using temporary buffers and visual inspection with the Measure tool. Points that fell within 120 meters (4 full landcover grid cells) of another non-urban point in the same database were selected and considered “unique” for landcove r extraction purposes. For both the Mueller


36 and Bowen-collected records, notes about each point location were reviewed to compare the abundance of burrowing owls and for the Mueller records, the status of burrows (clearly active or probably active in the last year). Records with the greatest total number of owls or the most clearly active burrows were given preference over those with fewer owls or “probable” burrows. Fo r Mueller’s records it was also determined if any of the selected points were taken from the nearest possible public location in the case of denied property access. Of records within 120-m, this did not occur, but had it, the point with the closest actual distance to bu rrow would have been chosen. Such a decision could not be made with the Bowen points. These selection criteria ensured that the most productive and/or active point records were chosen in the case of multiple records within a 120-m area. In the case of tied records, th e top record from each <120-m grouping was systematically kept while any ot hers records were discarded. This method was applied to prevent the po ssibility of the same landcover grid cell being counted more than once and also to reduce biasing the landc over at point results towards the landcover classes found in large colonies with multiple burrows. Applying this method proved unnecessary for the 34 FWC and FNAI non-urban points as the closest distance between two points in that database was about 2 km. After reducing the number of non-urban r ecords in this fash ion, landcover class for the cell in which each point coordinate fe ll was extracted via ES RI’s Spatial Analyst “Extract Values to Points” tool using the full Florida Landcover 2003 grid. The 1-43 numeric value and a “Class” text descripti on was appended to each point’s attributes (Tables 3, 4, 20, and 21). Soil Attributes, Managed Area and Futu re Land Use Status at Point: Extraction of attributes for the soil, Ma naged Area and Future Land Use layers was conducted for each point dataset using the “Intersect Point” tool in the “Hawth’s Analysis Tools for ArcGIS” extension (Beyer 2004), which added sele cted attributes of interest from each layer to each point record’s attributes.


37 Landcover Data Total Available Landcover: The FWC’s “Florida Vegetation and Land C over” raster grid file was based on 14 Landsat ETM+ scenes. Stys et al. (2004) classified landcover categories using both unsupervised and supervised classification guided by the use of ancillary data such as previous landuse/landcover data, aerial photography, and ground-truthing. Each 30x30 meter raster pixel (“cell” he reafter) is assigned a single num eric “value” corresponding to one of 43 distinct landcover cl asses (Figure 8). Each 30x30 m pi xel represents an area of 900 square meters.


38 Figure 8. FWC’s 2003 statewide landcover map. Florida 2003 Landcover Classes (All 43) Coastal Strand Sand/beach Xeric oak scrub Sand pine scrub Sandhill Dry Prairie Mixed hardwood-pine forest Hardwood hammock & forest Pinelands Cabbage Palm-Live Oak Hammock Tropical Hardwood Hammock Freshwater Marsh/Wet Prairie Sawgrass Marsh Cattail Marsh Shrub swamp Bay swamp Cypress swamp Cypress/Pine/Cabbage palm Mixed wetland forest Hardwood swamp Hydric Hammock Bottomland Hardwood Forest Salt marsh Mangrove swamp Scrub Mangrove Tidal flats Open Water Shrub and brushland Grassland Bare Soil/Clearcut Improved Pasture Unimproved Pasture Sugar cane Citrus Row/Field Crops Other agriculture Exotic plants Australian Pine Melaleuca Brazillian Pepper High impact urban Low impact urban Extractive Landcover 2003 (Statewide) 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Landcover (All) Extraction: Total available landcover was extracted se parately for each of the study areas, the Breeding Bird Atlas polygons and created bu ffers using the “Extract by Mask” tool (Figures 12 and 13). Cell totals and corres ponding area units were determined for each study area for use in determining proportions of “suitable” landcover classes within each study area.


39 Buffer Creation: ArcGIS’s “Buffer” tool was used to generate circular bu ffers of radius 600 meters around the 30 previously selected non-urban point records from Mueller. The “Dissolve All” option was selected so that buffers cl oser than 600-m would merge into a single, irregularly-shaped polygon. This al so caused attribute data to be lost and all buffers to be treated as a single polygon shapefile. 600 meters represents about 20 landcove r cells. This buffer size was based on empirical data that stated that 95% of all movements during breeding, both diurnal and nocturnal, occurred within 600 m of the nest (Haug and Oliphant 1990). The authors obtained this estimate from hundreds of radiotelemetry relocations for six adult male Western Burrowing Owls breedi ng in a non-urban setting. Gerv ais et al. (2003) echo this result with 80% of foraging observations o ccurring within 600 m of the burrow. While Mrykalo (2005) obtained 95% home-range kern els for the Florida subspecies in a nonurban environment, these were based on juve niles and obtained only during the daytime. Selection Indices, Selection/Avoidance, and Overall Chi-Square Test For these selection-related analyses, Muelle r’s records were chosen as they best matched the source time period of the landcover data and had all been field-verified by the author. Therefore, the pooled buffers forming the above-described shapefile composed the “used” landcover, while th e total landcover in th e 10-county study area shown in Figure 13 composed the “available” landcover. In this format, our data falls into “Desi gn I” of Thomas and Taylor’s (1990) three main study designs for comparing resource use and availability, as usage and availability are measured for the whole study area and all individuals (pooled, not individuallyrecognized). The “Extract by Mask” function was used to extract all landcover within the buffer polygon shapefile and yielded cell counts of all landcover cla sses occurring within it (field “MM Obs Cells” within Table 5). Us ing the observed cell counts in each class, the relative proportion of that class out of the total of observed cells in all of the buffers


40 was calculated (e.g. Dry Prairie had 1522 cells in buffers out of a total of 27030 cells in buffers, for .0563 or 5.63% of total “used” cells ). Additionally, the relative proportion of each landcover class present in the full 10-county study area was calculated (e.g. Dry Prairie had 1,457,400 cells in the study ar ea, out of 28,896,512 total cells, for .0504 or 5.04% of total “available” cells). All proporti ons are presented in Table 5 and Figure 16. Statistical Methods: In all below statistica l testing, the methodology for animal resource selection described by Manly et al. ( 1993) and Fielding (2006) was fo llowed exactly to test for significant overall difference, to calcul ate selection indice s and to determine selection/avoidance status for each landcover class. A detailed step by step example can be found at (Fielding 2006). Prior to testing, an “expected count” field was created by multiplying the proportion of a class available in the full study area times the total number of observed cells in the buffer polygons (U+ i ). Landcover classes with e xpected counts less than 5 were pooled into an “Other, Rare” category as per Manly et al. (1993) to meet the assumptions of the test. First, as originally described by Neu et al (1974), a Chi-Square Goodness of Fit test was conducted using the observed versus expected cell counts for the 10-county study area. Then, selection indices were calculated for each landcover class using the observed (“Oi”) and available (“ i”) proportions for each landcover class present in the overall 10county study area. The equation is simply Oi / i (observed proportion divided by available proportion). The larger the calcu lated value, the st ronger the apparent preference. This index can be standardized so that all values sum to one, using the equation: k i i i i i i iO O B1) / ( / ) / ( where “k” equals the number of classes considered. This is referred to as the “Che sson-Manly” index and can be interpreted as “the relative expected use of a habitat had al l types been equally available” (Garshelis 2000). Standardizing allows for more direct co mparison within the context of all possible landcover categories.


41 Finally, to determine selection or avoidance for each landcover class, the available proportion in the full study area was evaluated against 95% confidence intervals around the used proportion. Avoidance (“A”) occurs when the value for available proportion falls above the C.I. range, whereas Se lection (“S”) occurs wh en this value falls below the C.I. range (Table 6). To account for the large number of non-independent tests, a Bonferroni inequality adjustment is fi rst performed. This conservative procedure expands the confidence interval around the observed proportion, making it more difficult for the value of the available proportion to fa ll outside that interval and be considered different (“Out” in field “In/Out C.I.” in Tabl e 6). For the number of classes used in our test, 34, the adjusted z value for an of .05 changes from Z /2 = 1.96 to Z /34 = 3.180. (The p level of significance changes from the standard .05 to a more conservative .001471). “Suitable” Landcover Initially, the landcover classes deemed “s uitable” habitat for breeding burrowing owl were to be decided based on literature re view, discussions with experts and a careful reading of the landcover descrip tions given in Stys et al. (2004) (see Appendix C). These classes would have been Dry Prairie, Grassland, Bare Soil /Clearcut, Improved Pasture and Unimproved Pasture. However, to maintain scientific objectivity, reduce the chance of misunderstanding the provided class descriptions, and to account for the possibility of inaccurate classifications in the landcover data it was decided that the primary basis for suitability determination would be actual empirical results. The Selection/Avoidance classifications determined above seemed to provide the most appropriate empirical results, although one of th e “selected” classes, “Extra ctive,” was removed from consideration as explained in the Discussion section. Once the “suitable” landcover classes were determined, Spatial Analyst’s “Extract By Attributes” function was used to extract th ese classes from the full landcover grid into a new grid file (Figure 19). Then, the “Extra ct by Mask” tool extracted the suitable cells by each study area of interest (Tables 7, 8, and 9, and Figures 21, 22, and 23) and by the


42 Breeding Bird Atlas polygons (Table 10). Area calculations were made within ArcGIS by adding fields and calculating valu es within the .dbf files. Soil Data and “Suitable” Soils The Soil survey geographic database of detailed soils (SSURGO) was obtained from the United States Department of Ag riculture’s Natural Resources Conservation Service via the FGDL (2006). These data vary in time source and resolution, but for our study area appear to have been created around 1990 and at an average 1:24,000 scale (FGDL 2006). Certain counties that would have been relevant to this study did not have any available data, including Duval and Taylor counties, which each had a single Breeding Bird Atlas polygon with burrowing ow l presence. Additionally, eight relevant counties in the 38-county study area had inva lid or missing data for one of the soil attributes of interest, thus preventing use of that va riable in some analyses. A thorough examination of the NRCS’s guide to using SSURGO data (NRCS 1995) was undertaken in order to properly utilize these comp lex data, including recognition of its limitations. Each of dozens of attribute fields pr esent in the various “relate tables” was examined for data consiste ncy and relevance to this study, specifically for the potential to influence flooding of burrows (Appendix D contains complete descriptions for each of the used attribute fields). The “Comp.dbf” file, containing soil component attribute information, was the only table chosen for use. For each individual county, this table was joined to the “ssoils#” shapefile c ontaining spatial topology using the “Join by Attributes” function with the map unit identification (“MUID”) as the common field. Finally, all 38 counties’ soils shap efiles, with joined component attributes, were combined into a single sh apefile using the “Merge” tool. Point Extraction and “Very Suitable” Points: Due to the relatively broad scale of the soils data and to prevent repetition of the same map unit polygon from biasing results, another “filter” was designed to further reduce the number of points eligible for soil data use. In addition to being one of the previously “unique” records described above, these “very unique” points also could not


43 occur in the same soils polygon. Because each soils polygon has a unique MUID, this number was used to further filter Muelle r’s 30 and Bowen’s 45 “unique” records. When multiple points shared a single MUID, a selec tion process identical to that used in the landcover “filter” was employed. First, we inte ractively examined the records and gave preference to those with the greatest total number of owls or the most clearly active burrows. If multiple point records still exis ted, the top record from each grouping was systematically kept while ot her records were discarded. For each of Mueller’s 19 and Bowen’s 29 remaining “very unique” records, the “Intersect Point” tool extracted the so il component attributes for each unique-MUID polygon, and these were inspected for trends. Ta bles 11 and 12 show some of the most relevant attributes. “Suitable” Soils Definition and Selection: To determine which criteria to use in de fining “suitable” soils, we considered the empirical results obtained from the two point databases, th e descriptions of variables provided in the NRCS’ guide, and the actual av ailability and consistency of data present in the actual tables. One of the most promis ing attribute fields, “H ydric,” was unavailable for 8 of the 38 counties. In the end, a “modera tely suitable” set of criteria were chosen. These included probability of annual flooding and classified hydrologic group. The following attribute query was performed on the merged, 38-county polygon shapefile: ("HYDGRP" = 'A' OR "HYDGRP" = 'B' OR "HYDGRP" = 'B/D') AND ("ANFLOOD" = 'NONE' OR "ANFLOOD" = 'RARE' ). An experimental “highly suitable” set of criteria which added ( AND "HYDRIC" = 'N' ) to the previous query was also tested, although it could not validly be used with the full 38-county dataset. “Suitable” Landcover Within “Moderately Suitable” Soils Cells from the previously-created “suita ble” landcover grid occurring within the boundaries of this new “moderately suitabl e” soils polygon shapefile were extracted using “Extract By Mask.” Then, additional “Extract by Mask” func tions were performed for each of the study area boundaries (using the same county boundaries as before).


44 Considering only cells that are of both a “su itable” landcover class and also occur in a polygon with moderately suitable soils reduces the total number of “suitable” landcover cells present. Maps and tables were created for each study area and selected examples (Tables 13, 14, and 15 and Figures 29, 30, 31, and 32). Managed Areas and Future Land Use: Shapefiles for conservation-managed areas and projected future land use were obtained from the Florida Fish and Wild life Conservation Commission’s Fish and Wildlife Research Institute. FWRI had corrected minor topological er rors present in the original Managed Areas shapefile created by the Florida Natural Ar eas Inventory and split the file into four categories representing Federal, State, Local and Private managed areas. These files were recombined using the “Merge” tool. The Future Land Use data was actually split into two geographical regions representing north and south Florida. These two shapefiles were joined using the “Merge” tool (Figure 9). For each of the merged datasets, XTools Pr o 3.1’s “Calculate Area” tool was used to calculate the area in square kilometers of each polygon. Using queries and Microsoft Excel, relative area and relative percent totals were calculated for each managed area and future land use type (Tables 16 and 17 and Figures 35 and 36). Available landcover and suitable landcover were extracted within the boundaries of Managed Areas using Spatial Analyst’s “E xtract by Mask” tool (Table 22; Figures 39 and 40). Prior to doing so, a copy of the merg ed managed area shapefile was created and “Clipped” using the Florida Dept. of Envi ronmental Protection’s detailed shoreline shapefile (FGDL 2006) to excl ude marine-only portions of managed areas, which would have been substantial in Florida’s many marine reserves but are irre levant in the case of burrowing owl breeding habitat.


45 Figure 9. Projected Future Land Use for all of Florida. Results Occurrence Records Used Of the three non-urban point databases, Mueller’s visited locations contained 62 individual active or “probable” point r ecords, Bowen’s contained 50 individual


46 “agriculture” point records, and the merged FWC/FNAI database contained 34 point records deemed “non-urban” base d on attribute descriptions. After the number of point records was interactively and systematically reduced using the 120-m distance “filter” method, Muel ler’s database contained 30 records and Bowen’s had 45. The FWC/FNAI database was not affected by this “filter.” For landcover extraction analysis, a ll 291 polygons with recorded burrowing owl presence from the Breeding Bird Atlas record s were utilized. Note that these polygons are not all of uniform area/perimeter as some are irregularly-shaped. Landcover At Point Tables 3 and 4 and Figures 10 and 11 gi ve the extracted landcover class for each of the reduced selection of 30 Mueller and 45 Bowen records. For the FWC/FNAI point records, this information is included in tables 20 and 21, although the accuracy of landcover at the given coordina tes in those databases is qu estionable (e.g. Figure 38).


47 Table 3. Landcover value and class descriptio ns as well as observed land use and grazing status during field visit for 30 selected non-urban records from Mueller. Each point is at least 120 m apart; however, very large co lonies still have multiple records. COUNTY MM_OBS_LU GRAZED LC03 LC_CLASS COLLIERgrazed pastures; partly floodedy31Improved Pasture ALACHUAgrazed pastures throughout areay31Improved Pasture HENDRYWMA grazed pasturey31Improved Pasture HENDRYgrazed pasture; irrigated canal systemy30Bare Soil/Clearcut HENDRYgrazed pasture; irrigated canal systemy31Improved Pasture HENDRYgrazed pasture; irrigated canal systemy30Bare Soil/Clearcut HENDRYgrazed pasture; irrigated canal systemy30Bare Soil/Clearcut HENDRYgrazed pasture; irrigated canal systemy30Bare Soil/Clearcut HERNANDOmowed; wild grasses; near timber plotn31Improved Pasture HIGHLANDSPasture, road shouldern30Bare Soil/Clearcut HILLSBOROUGH"natural" fire maintained prairien31Improved Pasture MANATEEgrazed pastures near tomato landsy31Improved Pasture MANATEELightly grazed pasture near roady31Improved Pasture MANATEEgrazed pasture; roadsidey31Improved Pasture MANATEELightly grazed pasture near roady31Improved Pasture ORANGEhorse grazed & mowed imp. pasturey35Row/Field Crops PASCOovergrown wild various herbaceousn31Improved Pasture SUWANNEEsemi-rural pasture in loose residentialy35Row/Field Crops HILLSBOROUGHgrazed pasture on phosphate landy43Extractive HILLSBOROUGHgrazed pasture on phosphate landy31Improved Pasture HILLSBOROUGHgrazed pasture on phosphate landy31Improved Pasture HILLSBOROUGHgrazed pasture on phosphate landy31Improved Pasture HILLSBOROUGHgrazed pasture on phosphate landy31Improved Pasture HILLSBOROUGHgrazed pasture on phosphate landy31Improved Pasture PASCOsemi-rural pasture in loose residentialn31Improved Pasture MANATEEfire-maintained preserve pasturen35Row/Field Crops MANATEEfire-maintained preserve pasturen35Row/Field Crops MANATEEfire-maintained preserve pasturen35Row/Field Crops MANATEEfire-maintained preserve pasturen35Row/Field Crops MANATEEfire-maintained preserve pasturen35Row/Field Crops Figure 10. Summary of Table 3’ s landcover at point values. Landcover at GPS Point; M. Mueller Non-Urban Records (30 Unique Points) 0 5 10 15 20 Bare Soil/ClearcutRow/Field CropsImproved Pasture LC Class# of Occurrences


48 Table 4. Landcover value and class descriptio ns as well as observed land use description given in attribute data for 45 selected non-urban records from Bowen. Each point is at least 120 m apart; however very large colonies still have multiple records. COUNTY PB_LU LC03 LC_CLASS BREVARDPasture35Row/Field Crops BREVARDCropland31Improved Pasture BREVARDPasture35Row/Field Crops COLLIERPasture31Improved Pasture COLLIERPasture31Improved Pasture ALACHUAPasture31Improved Pasture GILCHRISTPasture42Low Impact Urban HENDRYPasture30Bare Soil/Clearcut HENDRYPasture31Improved Pasture HENDRYPasture31Improved Pasture HERNANDOCropland31Improved Pasture PASCOPasture41High Impact Urban HIGHLANDSPasture41High Impact Urban HIGHLANDSPasture31Improved Pasture HIGHLANDSPasture31Improved Pasture HIGHLANDSPasture41High Impact Urban HIGHLANDSPasture31Improved Pasture HIGHLANDSPasture31Improved Pasture HIGHLANDSPasture41High Impact Urban HIGHLANDSPasture41High Impact Urban HILLSBOROUGHPasture (ungrazed)31Improved Pasture HILLSBOROUGHPasture (ungrazed)31Improved Pasture HILLSBOROUGHPasture (ungrazed)31Improved Pasture LAFAYETTEPasture31Improved Pasture MADISONCropland31Improved Pasture MANATEEPasture41High Impact Urban MANATEEPasture31Improved Pasture MANATEEPasture41High Impact Urban MANATEEPasture41High Impact Urban MARTINPasture31Improved Pasture MARTINPasture31Improved Pasture OKEECHOBEEPasture31Improved Pasture OKEECHOBEEPasture31Improved Pasture OKEECHOBEEPasture31Improved Pasture ORANGEPasture35Row/Field Crops ORANGEPasture36Other Agriculture ORANGEPasture31Improved Pasture OSCEOLAPasture41High Impact Urban OSCEOLAPasture41High Impact Urban OSCEOLAPasture41High Impact Urban PASCOPasture41High Impact Urban PASCOPasture31Improved Pasture PASCOPasture31Improved Pasture POLKPasture31Improved Pasture SUWANNEEPasture31Improved Pasture


49 Figure 11. Summary of Table 4’ s landcover at point values. Landcover at GPS Point; P. Bowen Agricultural Records (45 Unique Point Records) 0 4 8 12 16 20 24 28Low Impact Urban High Impact Urban Bare Soil/Clearcut Row/Field Crops Improved Pasture Other AgricultureLC Class# of Occurrences Landcover (All) by Study Area Comprehensive breakdowns of data for a ll available landcove r classes for every study area are too large to present in table fo rm here. However, all classes present in the 10-county study area are shown in table 5. Fi gures 12 and 13 show all landcover within two of the study areas in map form.


50 Figure 12. All landcover classe s in the 18-county study area. LC Classes in S.A. (40) Coastal Strand Sand/beach Xeric oak scrub Sand pine scrub Sandhill Dry Prairie Mixed hardwood-pine forest Hardwood hammock & forest Pinelands Cabbage Palm-Live Oak Hammock Tropical Hardwood Hammock Freshwater Marsh/Wet Prairie Sawgrass Marsh Cattail Marsh Shrub swamp Bay swamp Cypress swamp Cypress/Pine/Cabbage palm Mixed wetland forest Hardwood swamp Hydric Hammock Salt marsh Mangrove swamp Tidal flats Open Water Shrub and brushland Grassland Bare Soil/Clearcut Improved Pasture Unimproved Pasture Sugar cane Citrus Row/Field Crops Other agriculture Exotic plants Australian Pine Brazillian Pepper High impact urban Low impact urban Extractive Landcover in 18 County Study Area 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN


51 Figure 13. All landcover classe s in the 10-county study area. LC Classes in S.A. Sand/beach Xeric oak scrub Sand pine scrub Sandhill Dry Prairie Mixed hardwood-pine forest Hardwood hammock & forest Pinelands Cabbage Palm-Live Oak Hammock Tropical Hardwood Hammock Freshwater Marsh/Wet Prairie Sawgrass Marsh Cattail Marsh Shrub swamp Bay swamp Cypress swamp Cypress/Pine/Cabbage palm Mixed wetland forest Hardwood swamp Hydric Hammock Salt marsh Mangrove swamp Tidal flats Open Water Shrub and brushland Grassland Bare Soil/Clearcut Improved Pasture Unimproved Pasture Sugar cane Citrus Row/Field Crops Other agriculture Exotic plants Australian Pine Brazillian Pepper High impact urban Low impact urban Extractive Landcover in 10 County Study Area 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Landcover Extraction by Buffer Polygons: Figures 14 and 15 demonstrate how landcove r is extracted for individual buffer polygons, with an illustration of two point records occurring within 600 m of each other.


52 Figure 14. Example of landcover extracted by selected buffers (step 1). HIGHLANDS OKEECHOBEE 01234 0.5 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN PB "Agriculture" Points 600m Buffers around PB Points Counties w/ PB "Agriculture" Pts All Landcover and Selected Buffers at County Level Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops


53 Figure 15. Example of landcover extracted by selected buffers (step 2). 06001,2001,8002,400 300 MetersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN PB "Agriculture" Points Dry Prairie Mixed hardwood-pine forest Pinelands Freshwater Marsh/Wet Prairie Shrub swamp Cypress swamp Mixed wetland forest Open Water Grassland Bare Soil/Clearcut Improved Pasture Citrus Row/Field Crops High impact urban Low impact urban Landcover (All) Extracted by 600m-Radii Buffers Observed vs. Expected Proportions: Table 5 and Figure 16 summarize the proportions of landcover observed in Mueller’s point buffers versus the overall available proportion in the full study area.


54 Table 5. Observed landcover counts/proporti ons in Mueller’s buffers vs. available proportion and expected counts for full 10county study area. Landcover classes w ith no observed cells are italicized. Neu_SymniPUi Used Samp PropPAi Avail ProportionPAi U+ Manly_SymUiOi i U+ i LC_Val Class MM_Obs_Cells MM_Rel_Total MM_%_Rel FullStudyArea_Cells FSA_Rel_Total FSA_Exp_Cells FSA_%_Total 3Xeric Oak Scrub440.0016280.16281399140.0048421310.4842 4Sand Pine Scrub40.0001480.0148408740.001414380.14145Sandhill00.0000000.00004115480.0142423851.42426Dry Prairie15220.0563085.630814574000.050435 1363 5.0435 7Mixed Pine-Hardwood Forest2280.0084350.84356757430.0233856322.3385 8Hardwood Hammocks and Forest2160.0079910.799112182770.04216011404.2160 9Pinelands7610.0281542.815427815220.09625826029.625810Cabbage Palm-Live Oak Hammock00.0000000.0000210430.000728200.072812Freshwater Marsh and Wet Prairi e 7440.0275252.752521168200.07325519807.325513Sawgrass Marsh00.0000000.0000281960.000976260.0976 14Cattail Marsh00.0000000.0000302090.001045280.104515Shrub Swamp2490.0092120.92126163470.0213295772.1329 16Bay Swamp110.0004070.0407354450.001227330.1227 17Cypress Swamp640.0023680.236819823780.06860318546.8603 18Cypress/Pine/Cabbage Palm60.0002220.02221272420.0044031190.4403 19Mixed Wetland Forest1010.0037370.373711771180.04073611014.0736 20Hardwood Swamp3500.0129491.294912414000.04296011614.296023Salt Marsh00.0000000.00001759330.0060881650.6088 24Mangrove Swamp00.0000000.00003733110.0129193491.291927Open Water1000.0037000.370012436940.04304011634.3040 28Shrub and Brushland6590.0243802.43808920560.0308718343.0871 29Grassland1280.0047350.4735800010.002769750.2769 30Bare Soil/Clearcut18870.0698116.98119243990.031990 865 3.1990 31Improved Pasture127890.47314147.314139005540.134984364913.4984 32Unimproved Pasture850.0031450.31452266280.0078432120.784333Sugar cane00.0000000.00003676900.0127243441.272434Citrus7880.0291532.915313417790.04643412554.6434 35Row/Field Crops34790.12870912.870912323980.042649 1153 4.2649 36Other Agriculture1980.0073250.73251989070.0068831860.688337Exotic Plants00.0000000.000053540.00018550.018541High Impact Urban13890.0513875.138727283510.09441825529.4418 42Low Impact Urban9600.0355163.55169733210.0336839103.3683 43Extractive2680.0099150.99151193110.0041291120.412999"Other, Rare"00.0000000.0000113490.000393110.039334 27030 1.000000000100.0000 28896512 1.00000000027030100.0000 U+ =27030


55 Figure 16. Observed vs. expected proportions (Mueller buffers). Observed vs. Expected Proportions (%) (Actual Buffers vs Full Study Area) 0 5 10 15 20 25 30 35 40 45 503456789101213141516171819202324272829303132333435363741424399Landcover Class% of Total Observed Proportion Expected Proportion Selection Indices, Selection/Avoidance, and Chi-Square Test The Chi-Square Goodness of Fit test wa s conducted using the observed versus expected cell counts for the 10-county study ar ea. Because multiple landcover categories had observed counts substantially differen t from expected counts, the observed 2 sum of 38,882 far exceeded the critical value of 47.4, leading to an extremely small p value (< 0.0001) and a rejection of the null hypothesis that the two distributions are equivalent. However, the meaningfulness of this test is limited in this case. Given that the two distributions differed si gnificantly overall, determinations were made regarding selection or avoidance of i ndividual landcover classes. To do so, the available proportion was evalua ted against the Bonferroni-a djusted confidence intervals (Manly et al. 1993, Fielding 2006). All but two of 25 considered landcover classes’ available proportions fell out side the Bonferroni-adjusted 95% confidence intervals (Table 6). This indicates that those 23 landcover classes’ obser ved proportions were significantly different than what would be expected given the available proportions.


56 According to this method, “selected” landcover classes include: Dry Prairie, Grassland, Bare Soil/Clearcut, Improved Pa sture, Row/Field Crops and Extractive while 17 classes were avoided (Table 6). Table 6 shows the results for each class’ Selection Index (w^) and Standard ized Selection Index (Bi). The above selected classes also had the highest values for the selection indices, with Improved Pasture, Row/Field Crops and Bare Soil/Clearcut having th e highest values for each. Neither Selection/Avoidance nor Selecti on Index values could be calculated for classes with no observed (used) occurrence in the buffers, su ch as “Sawgrass Marsh,” and these categories are not shown in Figures 17 a nd 18. Several other landcover classes with small index values are also omitted from the Figures for practical reasons. Refer to Table 6 for the complete list of categories and thei r results (classes available in the overall study area but not observed in the sample are italicized).


57 Table 6. Selection Indices & Selection/ Avoidance decisions for Mu eller’s observed buffers in the 10-county study area. “Selected” classes shown in bold. Cla sses without observed counts italicized. LC Val Landcover Class Bonf. Adjust. Low. Limit Bonf. Adjust. Upp. Limit A vail. Proportion In/Out C.I. A void. Select Sel. Index (w^) Standardized S.I. (B) Manly et al. (1993) Symbology:Oi-Z /k*Sqrt[{Oi*(1-Oi) / U+}]Oi+Z /k*Sqrt[{Oi*(1-Oi) / U+}] iOi / i(Oi / i) / (Oi / i) 3Xeric Oak Scrub0.000848070.002407570.004842OUTA0.336194710.01570859 4Sand Pine Scrub-0.000087290.000383260.001414OUTA0.104619400.00488831 5Sandhill0.000000000.000000000.014242n/a 0.000000000.000000006Dry Prairie0.05184920.06076650.050435OUTS1.11643970.05216537Mixed Pine-Hardwood Forest0.006666150.010204000.023385OUTA0.360705420.01685385 8Hardwood Hammocks and Forest0.006268990.009713250.042160OUTA0.189542710.00885632 9Pinelands0.024954480.031353330.096258OUTA0.292483610.01366620 10Cabbage Palm-Live Oak Hammock0.000000000.000000000.000728n/a 0.000000000.00000000 12Freshwater Marsh and Wet Prairie0.024360460.030689480.073255OUTA0.375740820.01755637 13Sawgrass Marsh0.000000000.000000000.000976n/a 0.000000000.00000000 14Cattail Marsh0.000000000.000000000.001045n/a 0.000000000.00000000 15Shrub Swamp0.007364120.011059860.021329OUTA0.431890290.02017994 16Bay Swamp0.000016840.000797070.001227OUTA0.331769980.01550185 17Cypress Swamp0.001427680.003307800.068603OUTA0.034513810.00161265 18Cypress/Pine/Cabbage Palm-0.000066170.000510120.004403OUTA0.050410400.00235541 19Mixed Wetland Forest0.002556460.004916720.040736OUTA0.091727750.00428595 20Hardwood Swamp0.010761900.015135250.042960OUTA0.301408630.01408322 23Salt Marsh0.000000000.000000000.006088n/a 0.000000000.00000000 24Mangrove Swamp0.000000000.000000000.012919n/a 0.000000000.00000000 27Open Water0.002525300.004873890.043040OUTA0.085957910.00401636 28Shrub and Brushland0.021397240.027363390.030871OUTA0.789755530.0369010829Grassland0.00340760.00606330.002769OUTS1.71046400.079920930Bare Soil/Clearcut0.06488240.07474020.031990OUTS2.18228670.101966731Improved Pasture0.46348390.48279810.134984OUTS3.50517470.163778232Unimproved Pasture0.002061710.004227600.007843OUTA0.400963410.01873489 33Sugar cane0.000000000.000000000.012724n/a 0.000000000.00000000 34Citrus0.025898780.032406810.046434OUTA0.627833670.0293353335Row/Field Crops0.12223160.13518610.042649OUTS3.01788590.141009836Other Agriculture0.005675830.008974560.006883IN 1.064178550.04972341 37Exotic Plants0.000000000.000000000.000185n/a 0.000000000.00000000 41High Impact Urban0.047116870.055657830.094418OUTA0.544253690.02543008 42Low Impact Urban0.031936250.039095940.033683IN1.054422140.0492675443Extractive0.00799850.01183130.004129OUTS2.40134020.112201999"Other, Rare"0.000000000.00000000.000393n/a 0.000000000.00000000 34<-Count of LC types; Totals: -->1.0000000017621.40196371.0000000


58 Figure 17. Unstandardized Selection Index (w ^) Comparison. <.5 = red; >1.0 = green Selection Index Comparison of 19 Landcover Classes0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Sand Pine Scrub Dry Prairie Hardwood Hammocks & Forest Pinelands Bay Swamp Cypress Swamp Cypress/Pine/Cabbage Palm Mixed Wetland Forest Hardwood Swamp Open Water Shrub and Brushland Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Other Agriculture High Impact Urban Low Impact Urban ExtractiveSel. Ind. (w^) Figure 18. Standardized Select ion Index (B) Comparison. Va lues <.02 =red; >.05 =green Standardized Selection Index Comparison of 19 Landcover Classes0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 0.16 0.17Sand Pine Scrub Dry Prairie Hardwood Hammocks & Forest Pinelands Bay Swamp Cypress Swamp Cypress/Pine/Cabbage Palm Mixed Wetland Forest Hardwood Swamp Open Water Shrub and Brushland Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Other Agriculture High Impact Urban Low Impact Urban ExtractiveStandardized S.I. (B)


59 Selection indices for randomly-generated buffers are presented for comparison in Appendix B. “Suitable” Landcover in Study Areas “Suitable” landcover classes were empi rically determined using the above selection indices and selection/avoidance deci sions in combination w ith expert discussion and an analysis of the descriptions of each la ndcover class given in Stys et al. (2004). The “Extractive” landcover class was not included as one of the five chosen “Suitable” classes. Results were generated for each of the st udy areas as well as within the extent of the Breeding Bird Atlas polygons (below tables and figures ). For the study areas, the “Open Water” class was included. The propor tions/percentages of the total available landcover made up by the suitable classes woul d be slightly higher if “Open Water” was excluded (e.g. Improved Pasture in the 18-c ounty study area would be 16.0% instead of the reported 15.0%). Figures 19 and 20 demonstrate why some caution should be taken when viewing some of these printed study area maps, whic h tend to overemphasize the appearance of “suitable” landcover cells at br oad scales—actual cell distribu tions in the study areas are smaller than they appear. For example, compare Figures 19 (statewide) and 21 (38counties). The statewide map considerably ov eremphasizes the appearance of the suitable classes, while the 38-county map more closely represents reality.


60 Figure 19. Suitable Landcover Statewide (67 Counties). Embedded table shows each suitable classes’ proportion of total statewid e landcover available (excludes open water). SuitableLandcover Classes Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops County Boundaries "Suitable" Landcover (Statewide) 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN LC_Val Count Area_m2 Area_km2 %_Total_67Cnts 6552318049708620004970.8623.5655 29361414325272600325.27260.2333 30494739544526555004452.65553.1938 31133273641199462760011994.62768.6034 35630713156764179005676.41794.0715 Total:304664842741983560027419.8419.6674 Total 67 County Cells:154908318m^2139417486200 km^2139417.4862 Due to screen vs. data resolution artifacts, pixels' size & range appear overly large at the statewide scale. Local scale (inset) better shows the actual cell distribution.Important Note: Figure 20 shows suitable landcover in just one county as an example. Note that although the screen resolution at the county scale more clos ely approaches reality, the cells may still be slightly overemphasized at this scale. 010203040 5 Kilometers


61 Figure 20. Suitable landcover in Hillsborough County. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Suitable Landcover in Hillsborough County 05101520 2.5 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARNScreen resolution now closer to data resolutionNote: Table 7. Suitable landcover in 38 county study area. LC_Val Count Area_m2 A rea_km2 %_Total_38Cnts 6533254647992914004799.29145.2158 29359191323271900323.27190.3513 30305376727483903002748.39032.9869 31123285511109569590011095.69612.0585 35292713426344206002634.42062.8630 Total:240011892160107010021601.0723.4755 Total 38 County Cells:102239134m^292015220600 km^292015.2206


62 Figure 21. Suitable Landcover in the 38-county study area. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Suitable Landcover In 38 County Study Area 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Counties w/ BBA Presence & Available Soils (38) Due to screen vs. data resolution artifacts, pixels' size & range may appear overly large at this scaleImportant Note:


63 Table 8. Suitable landcover in 18 county study area. LC_Val Count Area_m2 A rea_km2 %_Total_18Cnts 6335053030154770003015.4776.627745 29307794277014600277.01460.608853 30149643213467888001346.78882.960120 31759714968374341006837.434115.028061 35175689915812091001581.20913.475354 Total:145088041305792360013057.9228.700133 Total 18 County Cells:50553090m^245497781000 km^245497.7810 Figure 22. Suitable landcover in 18-county study area. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Suitable Landcover in 18 County Study Area 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARNDue to screen vs. data resolution artifacts, pixels' size & range appear overly large at this scaleImportant Note: Counties w/ PB Agriculture Records


64 Table 9. Suitable landcover in the 10-county study area. LC_Val Count Area_m2 A rea_km2 %_Total_10Cnts 6145740013116600001311.665.043515 29800017200090072.00090.276853 30924399831959100831.95913.198999 31390055435104986003510.498613.498356 35123239811091582001109.15824.264868 Total:759475268352768006835.2826.28259078 Total 10 County Cells:28896512m^226006860800 km^226006.8608 Figure 23. Suitable landcover in 10-county study area. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Suitable Landcover in 10 County Study Area 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARNDue to screen vs. data resolution artifacts, pixels' size & range appear overly large at this scaleImportant Note: Counties w/ MM Active/Prob. Records


65 Table 10. Suitable landcover in Breeding Bi rd Atlas polygons with recorded burrowing owl presence. LC_Val Count Area_m2 A rea_km2 %_Total_BBAs 6490154441138600441.13865.312967 29175251577250015.77250.189960 30365399328859100328.85913.960700 31168547815169302001516.930218.269542 35428494385644600385.64464.644611 Total:32.37778 Total BBA "Buow" Cells:9225617m^28303055300 According to these results, “suitable” landcover composes about 23.5% of the 38county study area, 28.7% of the 18-county stud y area, 26.3% of the 10-county study area, and a full 32.4% of the available landcove r within the Breedi ng Bird Atlas polygons. Soils Data and “Suitable” Soils Selected soil attributes for each of Mueller’s 19 and Bowen’s 29 “very unique” records are presented in Tables 11 and 12. E ach record represents one soil polygon with a unique MUID. Appendix D contains complete descriptions for each of the used SSURGO variables. For both point datasets, “None” was the only value present in the annual flood field. Surface texture varied between sa nd (“S”) and fine sand (“FS”). Hydrological groups included “A,” “B/D,” and “C,” with “C” occurring only on ce in Bowen’s records and 4 times in Mueller’s. The hydric field, when available, was predominantly nonhydric (“N”) soils (e.g. 83% non-hydric of Mu eller’s 18 available). Data on water table beginning and ending month were not always av ailable, however all available beginning months were June or July while ending months varied throughout the fall to mid-winter months (Tables 11 and 12). Values corresponding to unique landcover cl asses are also incl uded in the “LC03” column for comparison.




67 The following figures visually demonstr ate the reduction in numbers of soil polygons as increasingly-stringe nt suitability crit eria are introduced. Note that the “Highly Suitable” soils polygons exclude c ounties where “Hydric” at tribute information is not available. A local-level demonstr ation is also shown (Figures 27 and 28). Figure 24. All available soils in the 38-county study area. The total available number of polygons in the 38-county study area is 378,627.


68 Figure 25. “Moderately Suitable” soils in the 38-county study area. The number of polygons in the 38-county study area is now “moderately” reduced to 183,215, a reduction of about 48.4%


69 Figure 26. “Highly Suitable” soils in the 38-county study area. The number of polygons in this 30-count y study area is only 90,320. However, this excludes 8 counties without hydric data (shown as all wh ite), so direct comparisons cannot be made for the full study area.


70 Figure 27. Example of varying levels of soil suitability.


71 Figure 28. Example of varying levels of soil suitability, with one unique-MUID polygon selected. Suitable Landcover/Suitable Soils Combination There are fewer cells that are of both a “suitable” landcover class and also occur in a polygon with moderately suitable soils. Th e difference is not substantial, however, as can be seen in Tables 13, 14, and 15 and in Figures 29 and 30 which visually demonstrate the reductions at a local scale. The over all percent of total cells now considered “suitable” decreased by 4.21% in the 38-count y area, 4.78% in the 18-county area, and 4.18% in the 10-county area.


72 Figure 29. Example of “moderately suitabl e” soils (contours) overlain on suitable landcover/suitable soils grid. Suitable landcove r cells in unsuitable soils shown in red. HIGHLANDS OKEECHOBEE 01234 0.5 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN PB "Agriculture" Points 600m Buffers around PB Points "Moderately" Suitable Soils Counties w/ PB "Agriculture" Pts Suit. Soils Overlain on Suit. LC/Suit. Soil grid and All Suit. LC grid Excluded LC Cells Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops


73 Figure 30. Example portion of final suitable la ndcover/suitable soil s combination grid. HIGHLANDS OKEECHOBEE 01234 0.5 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN PB "Agriculture" Points 600m Buffers around PB Points Counties w/ PB "Agriculture" Pts Suitable LC in Suit. Soils at County Level Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops


74 Figure 31. Example of final suitable landcove r/suitable soils combination grid at a county-level scale, with exampl e point records (red diamonds). POLK HIGHLANDS OSCEOLA OKEECHOBEE BREVARD MARTIN Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops 010203040 5 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN PB "Agriculture" Points (Not Buffers) Counties w/ PB "Agriculture" Pts Suitable LC in Suit. Soils at County Level


75 Figure 32. Suitable landcover/suitable soil combination grid in 38-county study area. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Suitable Landcover Cells In "Moderately" Suitable Soils Polygons (38 County Study Area) 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Counties w/ BBA Presence & Avail. Soils Due to screen vs. data resolution artifacts, pixels' size & range may appear overly large at this scaleImportant Note:


76 Table 13. Suitable landcover/ suitable soil combination gr id proportions in 38-county study area. LC_Val Count Area_m2 A rea_km2 %_Total_38Cnts 6448718740384683004038.46834.388913 29222389200150100200.15010.217518 30241730021755700002175.572.364359 311013571991221471009122.14719.913737 35244011221961008002196.10082.386671 Total:197027071773243630017732.4419.27119903 Total 38 County Cells/Area:102239134m^292015220600 km^292015.2206 Table 14. Suitable landcover/ suitable soil combination gr id proportions in 18-county study area. LC_Val Count Area_m2 A rea_km2 %_Total_18Cnts 6286529525787655002578.76555.667893 29183916165524400165.52440.363808 30119766810779012001077.90122.369129 31638079957427191005742.719112.621976 35146539313188537001318.85372.898721 Total:120930711088376390010883.7623.921527 Total 18 County Cells:50553090m^245497781000 km^245497.7810 Table 15. Suitable landcover/ suitable soil combination gi rd proportions in 10-county study area. LC_Val Count Area_m2 A rea_km2 %_Total_10Cnts 6122766511048985001104.89854.248489 29562315060790050.60790.194594 30805958725362200725.36222.789119 31325420629287854002928.785411.261588 351042672938404800938.40483.608297 Total:638673257480588005748.0622.102086 Total 10 County Cells:28896512m^226006860800 km^226006.8608


77Occurrence Records and Suitable Landcover/Suitable Soils Combination These two final maps of the 38-county study area show all used non-urban / “agriculture” point records as well as the Breeding Bird Atlas polygons (includes urban records) overlain on the final suitable landc over/suitable soils combination grid. Figure 33 shows all five suitable landcover types w ithin suitable soils while Figure 34 shows only “Improved Pasture.”


78 Figure 33. All occurrence records overlain on suitable landcover within suitable soils. # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # *[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ All NonUrban / "Agriculture" Points and Suitable LC within Suitable Soils 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN MM Active/Prob. NonUrban All (62)[ _PB All Agricultural Records (50)# *FWC / FNAI NonUrban Records (34) BBA "BUOW" Records (291) Counties w/ BBA Presence & Avail. Soils Suitable LC w/in Suitable Soils Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops Cape Coral Area Ft. Lauderdale Area


79 Figure 34. All occurrence records overlain on improved pasture cells within suitable soils. # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # *[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ All NonUrban/"Agriculture" Points & Improved Pasture w/in Suitable Soils 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN MM Active/Prob. NonUrban All (62)[ _PB All Agricultural Records (50)# *FWC / FNAI NonUrban Records (34) BBA "BUOW" Records (291) Counties w/ BBA Presence & Avail. Soils Suitable LC w/in Suitable Soils Improved Pasture Cape Coral Area Ft. Lauderdale Area


80Managed Areas and Future Land Use About 96% of Florida’s land area mana ged in some way for conservation purposes falls under federal or state jurisdic tion, with local and private land accounting for only about 4% (Table 16, Figure 35). Table 16. Authority over “Managed Areas” breakdown (statewide). MA_TYPE AREA_KM2 REL_TOTAL %_TOTAL Federal226900.485648.56 State219480.469746.97 Local14530.03113.11 Private6380.01371.37 Total:467291.000000100.00 Figure 35. Managed Area t ype statewide breakdown. Florida Managed Areas (Breakdown by Type) 0 10 20 30 40 50FederalStateLocalPrivate Management AuthorityPercent of Total Some of the Future Land Use categories were grouped here for simplification. “Commercial” and “Industrial” were combin ed, as were “Military” and “Mining.” “Estate,” “Single-Family” and “Multi-Family” were pooled into a “R esidential” category and the relatively rare categories of “Fed eral Land, “Water Bodies” (Inland) and “Undefined” were pooled into an “Other ” category (Table 17 and Figure 36).


81 According to these data, roughly 46.5% of Florida’s total la nd area (including inland open water) is projected to remain agri cultural, 20.2% as Pr eserve and only 33.4% as all other categories. Table 17. Projected Future Land Use (Statewide; categories pooled). FLU_TYPE AREA_KM2 %_TOTAL A g ricultural6874046.49 Preserv e 2981320.1 6 "Residential"3530923.88 Commercial/Industrial45043.05 Military/Mining44182.99 "Other"50743.43 Total:147858100.00 Figure 36. Projected Future Landuse brea kdown. (Statewide, categories pooled). Projected Future Land Use Breakdown (All of Florida) 0 5 10 15 20 25 30 35 40 45 50A g ri c ultu r al P r eser ve Resid e n t ial C ommerc ia l/Indu s tr ia l M il it ary/Mi nin g Other% of Total The status of individual point records in Managed Areas as well as each point’s Future Land Use was determined for the sel ected “very unique” records (those with both unique soil MUID’s and occurring farther th an 120 m apart) from Mueller’s and Bowen’s point databases (Tables 18 and 19 and Figure 37). Filtering the availa ble records in this way reduces repetition and is probably necessa ry at the scale of the Managed Area and Future Land Use data. Landcover and observe d landuse(s) at each poi nt are also shown


82 for comparison purposes. From Mueller’s 19 “u nique” records, 15 fall in “Agriculture” while only four points fall in “Residential” Future Land Use categories. Bowen’s records have a slightly higher proporti on of “Residential” with seve n and 21 “Agriculture.” Of the selected “unique” sites, four of Mueller’s 19 fell in conservation-managed areas, and four of Bowen’s 28 did so as well. Figure 37. Future Land Use at selected points (Mueller and Bowen). “Agriculture” abbreviated “AG” and “Residen tial” abbreviated “Res.” Future Land Use at Selected Points0 50 100% % of Total 79751425 MM_AGPB_AGMM_ResPB_Res Table 18. MA and FLU Status at Point. 19 Selected Mueller Non-Urban Records (Unique MUIDs). COUNTY MM_OBSERVED_LU LC_CLASS MANAME MATYPE FLU_PROJECT COLLIERgrazed pastures; partly floodedImproved Pasture AGRICULTURE ALACHUAgrazed pastures throughout areaImproved Pasture AGRICULTURE HENDRYgrazed pasture, WMAImproved PastureDinner Island Ranch WMAStateAGRICULTURE HENDRYgrazed pasture; irrigated canal systemBare Soil/Clearcut AGRICULTURE HENDRYgrazed pasture; irrigated canal systemImproved Pasture AGRICULTURE HENDRYgrazed pasture; irrigated canal systemBare Soil/Clearcut AGRICULTURE HERNANDOmowed; wild grasses; near timber plotImproved PastureWithlacoochee State ForestStateESTATE HIGHLAND S Pasture, road shoulderBare Soil/Clearcut AGRICULTURE HILLSBOR."natural" fire maintained prairieImproved PastureLittle Manatee River CorridorLocalAGRICULTURE MANATEEgrazed pastures near tomato landsImproved Pasture AGRICULTURE MANATEELightly grazed pasture near roadImproved Pasture AGRICULTURE ORANGEhorse grazed & mowed imp. pastureRow/Field Crops ESTATE PASCOovergrown wild various herbaceousImproved Pasture MULTI-FAMILY SUWANNEEsemi-rural pasture in loose re sidentialRow/Field Crops AGRICULTURE HILLSBOR.grazed pasture on phosphate landExtractive AGRICULTURE HILLSBOR.grazed pasture on phosphate landImproved Pasture AGRICULTURE HILLSBOR.grazed pasture on phosphate landImproved Pasture AGRICULTURE PASCOsemi-rural pasture in loose residentialImproved Pasture ESTATE MANATEEfire-maintained preserve pastureRow/Field CropsLake Manatee Low. WatrshdStateAGRICULTURE


83 Table 19. MA & FLU Status at Point. 28 Se lected Bowen “Agriculture” Records (Unique MUIDs). COUNTY PB_LU LC_CLASS MANAME MATYP E FLU_PROJECT BREVARDPastureRow/Field CropsRiver Lakes Conservation AreaStateAGRICULTURE BREVARDCroplandImproved Pasture AGRICULTURE COLLIERPastureImproved Pasture AGRICULTURE ALACHUAPastureImproved Pasture AGRICULTURE GILCHRISTPastureLow Impact Urban AGRICULTURE HENDRYPastureBare Soil/Clearcut AGRICULTURE HENDRYPastureImproved Pasture AGRICULTURE HERNANDOCroplandImproved PastureWithlacoochee State ForestStateESTATE PASCOPastureHigh Impact Urban AGRICULTURE HIGHLANDSPastureHigh Impact Urban AGRICULTURE HIGHLANDSPastureHigh Impact Urban AGRICULTURE HIGHLANDSPastureHigh Impact Urban AGRICULTURE HILLSBOR.PastureImproved PastureLittle Manatee River CorridorLocalAGRICULTURE LAFAYETTEPastureImproved Pasture AGRICULTURE MADISONCroplandImproved Pasture AGRICULTURE MANATEEPastureHigh Impact Urban AGRICULTURE MANATEEPastureImproved Pasture AGRICULTURE MARTINPastureImproved PastureAllapattah FlatsStateAGRICULTURE OKEECHOBEEPastureImproved Pasture AGRICULTURE ORANGEPastureRow/Field Crops ESTATE ORANGEPastureImproved Pasture ESTATE OSCEOLAPastureHigh Impact Urban AGRICULTURE OSCEOLAPastureHigh Impact Urban AGRICULTURE PASCOPastureHigh Impact Urban ESTATE PASCOPastureImproved Pasture SINGLE FAMILY PASCOPastureImproved Pasture MULTI-FAMILY POLKPastureImproved Pasture MULTI-FAMILY SUWANNEEPastureImproved Pasture AGRICULTURE Status in Managed Areas and Future Land Use was also determined for non-urban records from the FWC and FNAI point databa ses. Because of the questionable accuracy of these coordinates, MUIDs were not used to select “unique” records. However, upon inspection, no two point records appeared closer than about 2 km and were deemed sufficiently unique for Managed Area and Futu re Land Use evaluation. Note that unlike the other databases, these recorded locations do not necessarily reflect breeding sites, only owl observations. Of the eight selected FWC records, half fall in “Agriculture” and half in “Residential” (Table 20). Of the 26 select ed FNAI records, 17 are projected for “Agriculture” use, 6 for “Residential” and one point each fell in “Military,” “Preserve,” and “Industrial” (Table 21). Apparently, zero FWC records occur in managed areas,


84 while at least five FNAI records do. Four a dditional FNAI records likely also occur in managed areas based on their original attrib utes; however, due to poor accuracy and/or precision, these four points’ given coordinates fell outsid e of the actual boundaries of these managed areas (see Figure 38). Assuming th e attribute data is actually correct, nine of the 26 FNAI records (~35%) occur in conservation-managed areas. Extracted landcover at each point is again included fo r comparison, although its utility is more limited with these likely imprecise and demonstrably inaccurate point coordinates.


85 Table 20. MA and FLU Status at Poin t. 8 selected FWC non-urban records. COUNTY WILDOBS_ID DATE LC03 LC_CLASS (at point) MANAME MATYPE FLU MARION58331989/04/1 0 41High Impact Urban--AGRICULTURE BREVARD58301989/03/1 7 31Improved Pasture--AGRICULTURE HIGHLANDS56041989/02/2 5 35Row/Field Crops--AGRICULTURE HIGHLANDS149921993/03/2 4 31Improved Pasture--AGRICULTURE LAKE150221989/10/0 2 7Mixed Pine-Hardwood Forest--MULTI-FAMILY ORANGE58231989/01/2 8 34Citrus--ESTATE ORANGE58441989/06/0 5 7Mixed Pine-Hardwood Forest--ESTATE POLK58451989/06/218Hardwood Hammocks and Forest--MULTI-FAMILY Table 21. MA and FLU Status at Point. 26 selected FNAI non-urban records. COUNTY EO_ID LASTOBS LC_CLASS (at point) MANAME_FNAI_EO MANAME_BY_POLYGONS Type FLU LEVY235591987-01-31Improved Pasture AGRICULTURE CHARLOTTE113961985-Freshwater Marsh/Wet Prairie AGRICULTURE CITRUS157631975-Improved PastureWithlacoochee State Forest AGRICULTURE GLADES171741985-Improved PastureFisheating Creek Conservation Easement AGRICULTURE MARION281621990Improved Pasture AGRICULTURE ALACHUA96811986-04-30Other Agriculture AGRICULTURE ALACHUA88041999Improved Pasture AGRICULTURE ALACHUA56441975-High Impact Urban AGRICULTURE COLLIER12038ZZRow/Field Crops AGRICULTURE COLLIER13561ZZImproved Pasture ESTATE HERNANDO134071995-03-29Improved PastureWithlacoochee State ForestWithlacoochee State ForestStateESTATE HIGHLANDS54021995-06-10Dry PrairieAvon Park Air Force RangeAvon Park Air Force RangeFederalMILITARY LAFAYETTE275731991-05-28High Impact Urban AGRICULTURE LAFAYETTE153141990-03-07Improved Pasture AGRICULTURE LAFAYETTE235581975-Pinelands AGRICULTURE LAKE31911987-07-31Bare Soil/Clearcut ESTATE LAKE113651995-05-24Improved PastureRock Springs Run State Reserve, ESTATE MANATEE156351985-09Improved Pasture AGRICULTURE OSCEOLA1206ZZFreshwater Marsh/Wet Prairi e Three Lakes Wildlife Management AreaThree Lakes Wildlife Management AreaStatePRESERVE OSCEOLA175471987Improved Pasture AGRICULTURE OSCEOLA134051997-05-15Improved PastureEscape Ranch Conservation EasementEscape Ranch Conservation EasementStateESTATE OSCEOLA76201986-04Row/Field CropsThree Lakes Wildlife Management Area AGRICULTURE OSCEOLA260501984-Improved Pasture AGRICULTURE OSCEOLA211881985-Row/Field Crops ESTATE SUWANNEE178431987-02Bare Soil/Clearcut AGRICULTURE PALM BEAC H 260321990-03-28Xeric Oak ScrubFlorida Atlantic University Ecological SiteFlorida Atlantic University Ecological SiteStateINDUST RIAL


86 Figure 38. Demonstration of questionable ac curacy of FNAI/FWC point coordinate. "Rock Springs Run Reserve" given as Managed Area in FNAI Attributes, despite provided coordinates falling outside MA Projection: FL Albers (FGDL Standard) Datum: NAD83 HARN Florida Managed Areas, All Types County Boundaries FWC / FNAI NonUrban in MA Example of FWC/FNAI GPS Coordinate Inaccuracy 012 0.5 Kilometers Total available landcover and suitable landcover were extracted within the boundaries of all conservation-managed areas, after this shapefile was clipped by the detailed shoreline boundary (F igures 39 and 40; Table 22). Table 22. Suitable Landcover within shoreline-clipped Managed Areas. Landcover Class LC Count A rea_m2 A rea_km2 %_Rel_MAs Dry Prairie6162403814616342001461.63423.75 Grassland29490664415940044.15940.11 Bare Soil/Clearcut30814874733386600733.38661.88 Improved Pasture31871781784602900784.60292.02 Row/Field Crops35231889208700100208.70010.54 Total:359164832324832003232.48328.3037 Total MAs Cells:43253814 m^238928432600 km^238928.4326


87 Figure 39. Landcover (all) in all shoreline-clipped Managed Areas. Sand/beach Xeric oak scrub Sand pine scrub Sandhill Dry Prairie Mixed hardwood-pine forest Hardwood hammock & forest Pinelands Cabbage Palm-Live Oak Hammock Tropical Hardwood Hammock Freshwater Marsh/Wet Prairie Sawgrass Marsh Cattail Marsh Shrub swamp Bay swamp Cypress swamp Cypress/Pine/Cabbage palm Mixed wetland forest Hardwood swamp Hydric Hammock Salt marsh Mangrove swamp Tidal flats Open Water Shrub and brushland Grassland Bare Soil/Clearcut Improved Pasture Unimproved Pasture Sugar cane Citrus Row/Field Crops Other agriculture Exotic plants Australian Pine Brazillian Pepper High impact urban Low impact urban Extractive Landcover (All) in Managed Areas 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN

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88 Figure 40. Suitable Landcover within shoreline-clipped Managed Areas. Dry Prairie Grassland Bare Soil/Clearcut Improved Pasture Row/Field Crops County Boundaries Landcover (Suitable) in Managed Areas 050100150200 25 KilometersProjection: FL Albers (FGDL Standard) Datum: NAD83 HARN Suitable Landcover in Managed Areas0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0% of Total Available L C % of Total LC 3.750.111.882.020.54 Dry PrairieGrassland Bare Soil/Clearcut Improved Pasture Row/Field Crops

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89Discussion Occurrence Records Both Mueller and Bowen’s point coordina tes were deemed precise enough for use in these analyses. The positional accuracy erro r inherent in the used GPS units (usually 37 m in open areas) was minor compared to th e resolutions of the other data layers considered. Of possible concern, however, ar e burrows that were not publicly accessible and for which access permission could not be obtained. These coordinates, however precise, occurred some distance from the actua l burrow and extraction at that point could have provided a different la ndcover cell or soil MUID. Of Mueller’s “filtered” 30 records, this was the case for only two point s. Their coordinates were taken alongside a fence with visible burrows only about 20 m away. In each case, burrowing owl family groups were perching on the fence from which coordinates were reco rded. The number of “closest possible” points is unknown for the selected Bowen records. The fact that only 5 of Bowen’s reco rds were removed by the 120-m “filter” indicates that fewer of Bowen’s “agriculture” records occurred in large colonies, or, if they did, only one burrow from a spread-out colony was observed. For example, at one of Bowen’s historic points in Hendry County, a point record taken from the roadside indicated a total of 4 owls. However, with the assistance of th e rancher landowner, Mueller discovered approximately 10 other dis tinct burrows with a total of 20 observed burrowing owls spread throughout a large area not visible from Bowen’s roadside point coordinate. While the number and geographic range of Bowen’s “filtered” point records (45) exceeds that of Mueller’s (30), Bowen’s 1999 records are 6 years ol der than Mueller’s 2005 records, which more closely approxima te the time period of the 2003 landcover data. As seen from Figure 38, caution should be taken when utilizing the FNAI and FWC point coordinates in locallevel analyses as their both their accuracy and precision are relatively poor. These points are also considerably outdat ed, as noted in Chapter 1. There is no guarantee that any of the historic nesting coordinates are still active, and in

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90 fact attempted visits to the area surrounding 17 FNAI/FWC historic sites failed to yield any owls or active/probable burrows ( one possible inactive burrow was found). While the number of Breeding Bird Atlas records is substantial and indicate that burrowing owls (includes urban and non-urba n) were distribute d throughout a large portion of the state between 1986 and 1991 (Fig ure 5), the lack of detailed spatial resolution limits the usefulness of this datase t for any local analyses. In theory, just one burrowing owl could have been found in the extr eme corner of one block, and the entire 6-block quadrangle would have been reporte d as having presence. To address this weakness, future researchers could obtain all of the field cards used to create the Atlas and manually digitize presence for a species at the more detailed block level instead of the existing 6-block (quadrangl e) scale. However, even if the spatial limitation were reduced in this way, the data are already so mewhat outdated. In addition, some of the reported polygons overlap large amounts of open water (i.e. those in the Keys and near lakes) not usable for nesting. Despite these weaknesses, the BBA and FWC/FNAI datasets are useful when considering the general statew ide distribution and trend of burrowing owls over time (e.g. Figure 5). They also can be used to help focus future survey efforts on probable areas, particularly when used in combination with maps of suitable ha bitat (e.g. Figure 33). Landcover (All) At Individual Points: The 120-m filter used to reduce the numb er of point records considered was intended to preclude repetition of the sa me landcover cell, and did so. However, Mueller’s records contained three large col onies that each had multiple burrows farther than 120-m apart. Many of these points in e ach colony ended up having the same type of landcover class. This could be because cer tain landcover classes such as improved pasture often occur continuously over a la rge area (more than four full 30x30m cells). However, it should be noted that the imme diate landscape around each colony did contain various other types of landcover that were av ailable to these burrowi ng owls but were not utilized for nesting. Therefore, it was decide d to consider all records farther than 120-m

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91 apart separately for the landcover extraction at point. The influence of large colonies was diminished in results using the 600-m buffers as most of such points were closer together than 600-m and the dissolved buffers did not double-count shared landcover cells. While deriving landcover at point could prove useful in considering its effect on the precise placement of constructed burrows, quantification of landc over classes within the 600-m radii buffers is likely a better indi cator of habitat preferred for foraging, including nocturnal foraging. Using both types of data allo ws consideration of whether burrowing owls are choosing habitat at a mi cro-scale (immediate burrow surroundings) differently than the surrounding foraging habitat. The landcover classes extracted by Mue ller’s and Bowen’s selected points provide some interesting results (Tables 3 a nd 4 and Figures 10 and 11). All of Mueller’s points fall in one of three “s uitable” classes, as might be expected. More than half are improved pasture, as are roughly half of Bowen’s records. However, many of the extracted classes for Bowen’s records are “High Impact Urban,” despite an observed landuse of “pasture.” This is likely explai ned by the exact position from which Bowen took GPS coordinates, which was probably from the roadside when that was the closest publicly-accessible location (Bowen 2000). Muel ler may have stood closer to the fence line or used a slightly more accurate GPS receiver (Garmin 76 with WAAS-enabled vs. Garmin 12 without WAAS). It is also possible that some very narrow dirt roads, such as those in most of the rural areas visited by Muel ler, may not have been classified as “high impact urban,” either because the classifier did not consider it an impervious surface or possibly because large patches of identical land cover on either side of the road such as improved pasture overlapped such small roads at the 30x30-m cell resolution. It is interesting to compare other extract ed landcover class descriptions with the observed land use from field notes for both datasets. Bowen’s 3 “Cropland”-described records had an “Improved Pasture” landcover class, while some of Mueller’s observed land uses also differed. For example, the 4 points in Hendry Count y described as “Bare Soil/Clearcut” occurred on slightly elevated canal banks in moderately-grazed cattle pasture, while those in Manatee County de scribed as Row/Field Crops occurred in Rutland Ranch, a fire-maintained conservati on easement not used for any farming.

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92 Some of this difference may be due to the differing temporal resolution— Bowen’s points were recorded in 1999, Mu eller’s in 2005, while the landcover was determined based mostly on imagery from 2001-2003. Some changes are likely to have occurred during that time. However, it also illustrates that the landcover classifications are not always perfect at micro-habita t scales, given the 30x30 m pixel size. Extraction by Buffers: Ideally, any analysis of landcover classes within “used” points or buffers would have the same temporal resolution between landcover and point data. Because this wasn’t possible, the temporally-closer dataset (M ueller) was employed, despite having fewer available point records. A larger sample size would have increased confidence in the selection-related tests. The 600-m value used for creating the buffe rs was based on the best available, relevant empirical data that could be found in the literature. Haug and Oliphant’s (1990) finding that 95% of all movements of adu lt males occurred within 600-m was echoed by Gervais et al. (2003) who found that 80% of nocturnal foraging observations occurred within 600 meters. Unfortunately, both studi es involve the Wester n Burrowing Owl and not the Florida subspecies. While Mrykalo (2005) obtained 95% home-range kernels for juveniles of the Florida subsp ecies in a non-urban environmen t, these were based on just four juveniles and obtained only during the daytime. Mrykalo’s daytime 95% kernel home range of about 141m seems roughly si milar to the diurnal distances reported by Haug and Oliphant (1990). An attempted noc turnal telemetry session by Mrykalo (2005a) failed to yield relocations, although the juvenile owls were noted to be extremely active and may have exceeded the transmission range of 1.61 km. However, both Gervais et al. (2003) and Green and Anthony (198 9) suggested anecdotally th at the dispersing juveniles they observed may have utilized habitats diffe rently than adults. Therefore, this study’s buffer distance is most relevant for adult males in non-urban environments. No other research appears in the lite rature about the home-range of non-urban burrowing owls in Florida.

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93 Our selection analyses are in terested in the habitat su rrounding the burrows that is used for all foraging, including nocturnal foragi ng, because the nature of this habitat may influence burrowing owls’ nest-site selection. Therefore, the 600-m buffer distance seems to be the best choice. Future research mi ght consider applying a daytime-only buffer distance, which might use Myrkalo’s 141m distance, for comparison. Selection Indices, Selection/Avoidance and Chi-Square Tests Potential Problems/Limitations: Artificial political boundaries of county lines had to be used in defining the “available” landcover proportions. While doing so limited “available” habitat only to counties that had at least some evidence of recent burrowing owl nesting usage, in reality some of the observed buffers were quite near county boundaries and the mobile burrowing owls could conceivably use the excl uded land for foraging. Since there was no way of knowing whether an adjacent county had recent nesting, such “nearby” counties were not included in this analysis. Including additional counties as “available” could affect selection results. Fortunately, only in one instance did a por tion of a single buffer overlap a county without landcover data, a nd upon inspection, th e few missing values would have been predominantly “improved pa sture.” Therefore, our selection results should not have been affected by missing “available” landcover data. Another concern that had to be addressed for the various selection tests was the possibility of large colonies (with multip le points farther than 120-m apart) overlyinfluencing the results. This was addressed by “dissolving” overlapping buffers so that buffers from points closer than 600 would be merged prior to landcover extraction. Although these combined buffers yielded a sli ghtly to moderately larger number of landcover cells than a regular-s haped 600m-radii buffer, diss olving buffers prevented any double counting of landcover cells in large colonies. Therefor e, abundance at each site had only a minor influence on the selection results. These statistical measures assume that th e individual burrowing owl nests used to create the buffers approximate a random samp le of the entire popul ation of interest—in this case, the population being only non-urban nesting burrowing owls in the 10-county

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94 study area. Ideally, a random sampling of at least 30 points out of several hundred known points would be used. In pract ice, wildlife researches must use whatever animals are available from surveys and assume they ar e representative of th e population under study (Manly et al. 1993, Alldredge et al. 1998), as we did in our study due to the limited number of known non-urban burrowing owl nest s with reliable GPS coordinates. Manly et al. (1993) and A. Fielding (pers. comm.) suggest this practice is valid, so long as expected counts for each class are kept at 5 or higher (1993) e ither via dropping or pooling such data. This was the case in our anal ysis as we pooled the very rare-occurring landcover classes with expected cell counts less than 5 into an “Other, Rare” category to maintain a total available proportion of 1. 00 when determining selection/avoidance. Statistical Outcomes: The Chi-Square test for significant di fference originated by Neu (1974) and described in Manly et al. (1993) and Fielding (2006) is used in much of the literature evaluating habitat selection and preference (e.g. Stinnet and Klebenow 1986, Dasgupta and Alldredge 2000, Potvin et al 2003) and wa s found to be potentially more useful than other resource selection methods in Mclean et al.’s (1998) comparison. However, the meaningfulness of the Chi-Square test appe ars limited in our analysis. To test the accuracy of our initial result, experimental modification of cell counts was performed. This hypothetical manipulation of counts reve aled that relatively small differences between observed and expected cell counts would cause the Ch i-Square test to report a significant difference, even at extreme levels of This could be due partly to the fairly large number of classes considered (k=34). Ho wever, a more substantial cause seems to be the large quantities of cells—the observed cell counts ranged from 0 to 12,789 and the expected cell counts ranged from 5 to 3,648, with a mean of 795 cells (improved pasture has the highest count in each), whereas most studies employing this method seem to deal with much smaller areas. Transforming the obs erved and expected cell counts to hectares, a smaller yet proportionately-valid unit, reduced th e severity of this effect but still yielded an extremely significant overall test result.

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95 The more useful statistical measure in th is analysis is the use of Bonferroniadjusted confidence intervals around the obser ved (used) proporti on. Comparing these values to the available pr oportions allows estimation of whether individual landcover categories are “selected” or “avoided” (N eu et al. 1974, Fielding 2006) by nesting burrowing owls. While the selection/avoidance decisions obtained in this study should be taken with some measure of caution consider ing the moderate sample size, large and politically-defined “av ailable” study area, and the larg e number of landcover classes considered, the empirical results seem to co rrespond fairly well with what we initially hypothesized based on landcover descriptions and field observations. Landcover classes dominated by wetlands and hardwood canopy we re consistently avoided and had the lowest selection index values, whereas classes with short, grassy vegetation were consistently selected and had the highest se lection index values (T able 6 and Figures 17 and 18). Quantitative comparisons between individual classes can be made using the standardized selec tion index. For example, Dry Prairie (Bi=.0522) appears to be selected with a standardized selecti on index value about three times larger than Mixed PineHardwood Forest (Bi=.0169). Based on the empirical landcover resu lts, we found that Row/Field Crops (Bi=.1410) should be included as “suitabl e” landcover, while Unimproved Pasture (Bi=.0187) should not, contrary to our initial hypothesis. Th e former composes about 12.9% of the used buffers’ total area—the s econd highest proportion of any used class— while the latter formed only .31%. One problematic result from this analysis was that the “Extractive” landcover class was shown to be selected and had very high selection index values. This can be explained by a single point’s proximity to a large swath of “Extractive”-classified cells on land owned by a phosphate-mining company. As of May 2006, this area is undergoing preparations for active phosphate mining (P. Nixon pers. comm.); however, field visits by the author in the summer of 2005 confirmed that the actual land use in the immediate vicinity was grazed pasture and that the area was not being actively mined. Therefore, the landcover classification for this particular point is inaccurate. In this instance, the individual performing the supervised classi fication may have relied too heavily on the

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96 ancillary 1995 land use/land cover dataset (S tys et al. 2004) to make a judgment about landcover at this location. With this one exception, empirically determ ining what values to use in creating a region-wide map of suitable landcover proved useful and important, in that the underused unimproved pasture class was dropped fr om consideration while the heavily used “Row/Field Crops” was added. “Suitable” Landcover While analyzing the given landcover class descriptions (see Appendix C) using expert opinion and literature review was necessary and helpfu l, utilizing empirical results lends objective credibility to the final decisi on. The Selection/Avoidance classifications seemed to provide the most appropriate empi rical results, narrowi ng the possible choices to just six categories. This yielded slightly different choices than the original hypothesized classes by adding Row/Field Crops and rem oving Unimproved Pasture. The substantial proportion of Row/Field Cr ops was somewhat surprising at first, but upon review, the “Field Crops” section is defined to include “hay and grasses” (Appendix C) and several historic sites obs erved by Mueller occurred on hay and sod farms. Such areas may also provide fa vorable foraging opportunities, although the literature is conflicted on this matter (e.g. Haug and Oliphant 1990, Gervais et al. 2003). While “Unimproved Pasture” was described as native grasses on cleared lands, this class also contains “major stands of trees a nd brush” (Appendix C) and the height and composition of these grasses may not be maintained by natural or other means. Interestingly, the single landcover cla ss utilized by Cox et al. (1994)—Dry Prairie—was just barely selected using the confidence intervals a nd had only marginally high selection index values (e.g. S.I.=1.12, Ta ble 6) that indicate that it was used only slightly more than expected based on Muel ler’s observed locations in the 10-county study area. Because the sample size of data points is modest, the habitat value of this class should not be discounted. However, it should probably not be the only landcover class considered in analyses, as it was in Cox et al (1994). The importa nce placed on the Dry Prairie class in this original Florida GAP study (Cox et al. 1994) might be explained by

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97 the obvious relevance of its description, whic h states that “the largest areas of these treeless plains historically occurred just north of Lake Okeechob ee,” the area with the largest historic population of burrowing ow ls, where short, grassy conditions were maintained by “annual or frequent fires.” Signi ficantly, the description also notes that “many of these areas have been conver ted to improved pasture” (Appendix C). One of the six empirically “selected” cl asses, “Extractive,” was removed from consideration, leaving five classes compos ing “suitable” landcover. The “Extractive” class was not used in defining the final “suitable” landcover criteria because the description given for this class effectivel y precludes the possibility of burrowing owl nesting use and severely limits the likelihood of foraging use. It defines the class as “encompass[ing] surface and subsurface mini ng operations. Areas included are sand, gravel and clay pits, phosphate mines, a nd limestone quarries. Industrial complexes where the extracted material is refined, p ackaged or further processed may also be included in this category” (Appendix C). Of the selected suitable classes, Impr oved Pasture dominated. While this class already composes a substantia l portion of the total availa ble landcover statewide (~7.0% including open water and ~8.6% excluding it ), the observed percentages in the study areas are about double that percent, with 12-15% (including open water) in the study areas and an even higher percentage in the historic Breeding Bird Atlas polygons with owl presence (~18%). Because the three used study areas have such high proportions of Improved Pasture, the level required to stat e that the observed bu ffers show significant selection for this class is also very high. Yet it seems safe to do so, considering improved pasture composes about 47% of the cells in the observed buffers (Table 5) and has by far the highest selection index values (Table 6) It seems clear, base d on this study’s input data, that this particular landcover class is important to non-urban, breeding burrowing owls. While not statistically measured, Figure 34 visually shows an apparent correlation of most non-urban point reco rds (and many of the urban/ non-urban Breeding Bird Atlas polygons) with the general distribution of improved pasture throughout the state. As previously noted, some caution should be taken when viewing some of the suitability maps as they are shown in this document, as at broad scales the geographic

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98 size and range of actual cell distributions te nd to be overemphasized. Figures 19, 21, 22 and 23 are particularly overemphasized here. If these maps are to be used for management purposes or ground surveys, the raw shapefiles should be obtained first. Random Sampling While not presented above, we also cr eated statistically-valid random samples using Hawth’s Analysis Tools 3.23’s Sampling Tools to match the used input datasets (Mueller’s 30 selected point records as well as the 291 Breeding Bird Atlas polygons.) This was done for experimental comparison purposes only because the random samples were not needed for the statistical met hods employed, which considered available proportions within the full study area. Methodol ogy and selected resu lts for this random sampling are presented in Appendix B. With a couple exceptions, the observed proportions in the random buffers matched the available proportions far closer than the observed proportions in Mu eller’s real buffers. Soil Data and “Suitable” Soils Possible Limitations: The detailed SSURGO soils data, while cr eated at a higher resolution than the regional-level STATSGO soils data, may stil l be more meaningful for broad-scale analyses than highly-local analyses. However, these soils datasets have been successfully utilized in combination with landcover cla sses elsewhere. In the original Florida GAP study, Cox et al. (1994) used th e STATSGO soils in an ancillary fashion to assist with certain classifications. And while this dataset could also be consid ered somewhat outdated, with most of our counties’ surveys performed around 1990 (FGDL 2006), soil composition and distribution changes more slowly than that of vegetation and ther efore these data are likely still relevant in most areas. Another inherent limitation in this dataset is that the full complement of available tabular data cannot be joined to the spatial data. Each map unit polygon may in reality consist of up to three compone nts, each with potentially di fferent attributes. However,

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99 “there are no graphic delineations for the lo cations of the component s within a map unit” (NRCS 1995). Only the attri butes from the dominant component, or sequence, can be joined to the spatial data. Fortunately, most map units have only one sequence and in the case of multiple sequences, the dominant seque nce usually composes about 75% or more of the overall map unit polygon. For users, ther e is no practical solution to this inherent limitation—the data originators would have to re -create the spatial dataset to better reflect the more detailed field data. Although not a problem per se, the vector polygon format of the soils data also prevents use of the same methodology employed in determining the selection indices and selection/avoidance by burrowing owls. The pol ygons could be converted to raster cells, but with a substantial loss of accuracy at fine scales. Empirical Results and “Suitable” Criteria: The “very suitable” reduction of “elig ible” point records via MUIDs seemed necessary to avoid biasing results toward la rge colonies with closely-spaced colonies. However, should future research wish to consider the relationship between apparent abundance/productivity and soil type (as we ll as Managed Area and Future Land Use status), this conservative step could be omitted. Using MUID as a filter is not as uniform a criteria as the 120-m filter, because some map units can actually span great distances— we visually observed some map unit pol ygons that spanned several kilometers. The empirical results obtained from th e two reliable point record databases suggest that burrows tend to occur in map units where annual flooding is not a frequent or even occasional occurrence, according to the “ANFLOOD” attribute field. However, this author’s own field observations and those of Mrykalo (2005a) suggest that flooding does occur in areas used for breeding, at least dur ing the later portions of the summers of 2004 and 2005. The “WTBEG” and “WTEND” fields in the observed r ecords apparently suggest that a seasonal water ta ble is usually not expected until June or July—when at least some juveniles may have already fl edged, if not dispersed (Mrykalo 2005a). Despite not appearing in the somewhatlimited number of empirical results, the “Rare” value for “ANFLOOD” was included as “suitable” based on it s description. On

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100 the other hand, surface texture type (“SURFTE X”) was not included at this time because of inconsistency in the data—for example, some records were null and many others were reported as “VAR” for variable surface text ure. While this variable is likely of importance in burrow construction, We lacked su fficient expertise in soil science to rule out the potential suitability of many of th e other dozens of possi ble surface texture values, with so many subtle gradations of texture possible (e.g. “Sandy Loam,” “Loamy Sand,” “Silty Loam,” “Fine Sandy Loam,” etc.). While the 47 point records showed only three hydrologic gro ups (“A,” “B/D” and “C”), the “B” group was also included based on its similar description (Appendix D). Because the “B” group only composes about 3.7% of the total “moderately suitable” records, its rarity is a more likely ex planation for it not appearing than actual unsuitability. The “B/D” type is noteworthy because “B/D” can actua lly mean one of two types. In an “undrained” stat e, these soils actually are of type “D,” having slow infiltration rates and/or a high water table, etc. (Appendix D). When drained, however, such soils are of type “B,” which seem less flood-prone due to modera te infiltration rates and relatively well-draining soils. Thus, with the type of irrigation that often accompanies agricultural land uses, these soils appear more suitable for burrowing owls than those lacking some sort of drainage mechanism. One of the most promising attribute fi elds, “HYDRIC,” was unavailable for 8 of the 38 counties in the main study area. Hydric soils form under consistent “conditions of saturation, flooding or ponding” and thus seem ill-suited for burrow construction and maintenance. Although the empirical results ar e not overwhelming for this variable as a small number of Mueller’s records were actua lly in hydric-classified map units, utilizing this field to reduce the number of suitable so ils would have had a more dramatic effect than the “moderately suitable” criteria used (see Figure 26). Because of the missing data, however, we couldn’t introduce this criteri on for the full 38-county dataset. Doing so would improperly inflate the apparent proportions of the final suitable landcover/“moderately suitable” soils combinati on grid in those counties. Including the hydric criterion might be more appropri ate if only the 10-county study area were

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101 considered, because only Suwannee County l acks “Hydric” data in that study area and only one of Mueller’s point records occurs in Suwannee. Efforts to use other limiting attribute fields were similarly frustrated by the presence of inconsistent or ambiguous da ta. For example, the “DRAINAGE” variable seemed potentially useful, yet many records ha d more than one recorded drainage value in each, and/or were in an inconsistent form at. (Empirical results were also varied and inconclusive for this variable). Because of the limited number of usab le soil attribute variables, the used “moderately suitable” criteria are somewhat cons ervative in that they are more likely to include actually unsuitable soils than exclude actually suitable ones. Given the various limitations of the soils data described here using a conservative restriction method for soils may actually be desirable. This is especi ally true if we judge the landcover data to be relatively superior, as the final suitabil ity “filter” places greater emphasis on the “suitable” landcover results. Future research could experiment with further restricting the “suitable” soils criteria. For example, an apparently high co rrelation of the “Muck” value in the surface texture field with hydric soils could be used as a “workaround” for the missing hydric data and deserves exploration, although the “Muck” entry was inadequately described in the NRCS guide (1995). “Suitable” soils could also be determined separately for each study area, although this was unnecessary in this analysis since the 38-county study area already encompassed the smaller study areas, a nd we didn’t need to obt ain soils results in each area independently to create the final “ doubly suitable” grid. Instead, this study was primarily interested in using the soils data simply to remove landcover cells that seemed likely to be flooded during the breeding season. “Suitable” Landcover Within “Moderately Suitable” Soils We used the “moderately suitable” soils to further reduce the total number of “eligible” landcover ce lls beyond just extracting the five “suitable” classes. The conceptual purpose of doing so is to help acc ount for lands that occur in areas very likely to be flooded due to unfavorable soil char acteristics. While presenting the vegetative

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102 conditions that seemed to be empirically preferred by non-urban burrowing owls, “suitable” landcover cells with such soil characteristics are more likely to experience burrow flooding, possibly early in the breeding season prior to juveniles’ fledging. For example, grasslands or areas of bare soil w ith hydrological group clas sifications of “C” or “D” (that tend to lead to poor surface water in filtration and ponding) may be less likely to be used for burrow construction or may be abandoned with the onset of seasonal rains. There may be some degree of inherent correlation between “moderately suitable” soils and some of the landcover classes. The Dr y Prairie class in particular might consist mostly of plants that prefer fairly dry soil conditions. The extent of this correlation is difficult to quantify; however, the fact that re latively few “suitable” landcover cells were removed might be an indicat or of this in itself. The obtained suitable landcover/suitable so ils grid may be a better estimate of land that is actually usable for burrowing owl nesting than the standalone suitable landcover grid, which was created based on buffers extending 600-m out from nests. The latter considers apparent preference for cer tain types of foraging habitat more than preference for habitat at the nest. Fortunately the results of extr action of landcover at points (Tables 3 and 4) appear to be very si milar to the 600-m buffer results, so the two uses are likely not exclusive. Although this step did not dramatically re duce the number of “suitable” landcover cells, the moderate level of reduction may be more desirable than what would be achieved using a stricter defin ition of “suitable” soils (e.g. th e “highly suitable” criteria used when “hydric” data were available). This could be true for two reasons: 1) we have less confidence in the attribute accuracy and sp atial precision of the soils dataset than in the landcover data; and 2) we can reduce the likelihood of classifying cells as unsuitable when in fact they might be suitable. While we chose to error permissively, deciding which way to error—overly permissive or overly stringent—depends on the intended application. For example, if those conduc ting surveys for non-urban breeding burrowing owls wish to further reduce the amount of hab itat to census, they could add additional soil attribute criteria (e.g. restri cting by chosen surface textur es) or reduce the number of “suitable” landcover classes.

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103 Despite the relatively moderate reduction of cells achieved, this result is still potentially useful, as it reduces somewhat the scope of areas to be surveyed, for example. Occurrence Records Distribution and Suitable Landcover/Suitable Soils Combination It is difficult to make definitive conc lusions about the overall spatial distribution of non-urban burrowing owls, because these r ecords probably reflect only a small portion of the total non-urban populat ion statewide. Based on the available data, however, one apparent trend is that non-urba n records are concentrated in the interior portions of the state. Additionally, in the north ern section of the state, a cl ustered path of observations can be discerned (Figure 33). Additional survey data could help validate these initial conclusions. Although geostatistical methods could grant a more defensible statement, there appears, at least visually, to be a fa irly strong correlation between the mapped distribution of cells that are both of suitable landcover and oc cur within suitable soils and the current and historic occu rrence records (Figure 33). This apparent correlation seems to become more clearly defined when only th e “Improved Pasture” cells (within suitable soils) are mapped (Figure 34). This is encourag ing, as it appears to validate this study’s methodology to some degree. The main exceptio n appears to be some of the Breeding Bird Atlas polygons; however, no distincti on can be made between urban and non-urban presence with those polygons. For example, the lone Atlas polygon in Duval County likely represents an occurrence at an airport (Cou rser 1979), which this study would have defined as “urban.” Where there are known to be high concentrations of urban nesting burrowing owls (e.g. around Cape Coral and throughout Ft. Lauderdale), there are also dense concentrations of Atlas polygons, but very few selected suitability grid cells (Figures 33 and 34). Although not shown here the occurrence of improved pasture throughout the remainder of the state is subs tantially less than in the 38-county region where the known non-urban records occur.

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104Future Land Use This study uses the future land use data only to evaluate potential overall trends for future land use. At the state level, agri culture use alone is pr ojected to account for nearly half of the state’s land, with another 20% projected as some type of preserved land (Table 17). Although data accuracy is questionabl e at the local level, a clear majority of known non-urban breeding sites are on land projected to remain in agricultural use (Table 18 and 19 and Figure 37). Of the four points in Mueller’s selected 19 projected to be “Residential,” three occurred on fairly small patc hes of land characterized on s ite by Mueller as non-urban; however, these sites were in relatively close proximity to larger residential areas. The broad scale at which the future land use pol ygons were created likely ignored such small areas with divergent land use. Before discount ing this dataset’s util ity entirely, however, it should be noted that the ar ea surrounding one point record with active burrows on thenundeveloped land between Withlacoochee State Forest and the Sherman Hills golf course appeared to be undergoing resi dential development, exactly as projected by the Future Land Use dataset. Managed Areas The vast majority of conservation-mana ged land in Florida is managed by the state or federal government (Table 16). This makes sense considering the large acreages of state and federal parks and wildlife management areas. Suitable landcover classes compose only about 8.3% of the total landcover in all conservation-managed areas in Florida. Th is percentage would be even lower if considerable quantities of water in marine and coastal preserves (such as those in the Florida Keys) hadn’t been excluded. It appe ars that most managed areas predominantly favor wetland and forested classes. This likely benefits the majority of Florida’s wildlife, which are dependent on those types of landc over, but not breeding burrowing owls. There are some notable exceptions in the central interior of the state where some managed areas intentionally maintain prairie-like habitat via prescribed burns and mechanical means (e.g. Kissimmee Prairie Preserve State Park, Avon Park Air Force Base).

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105 A fairly small proportion of both Mue ller’s and Bowen’s “very unique” records occur in conservation-managed areas. The sections of the “Little Manatee River Corridor” (J. Layman, pers. comm.) and th e “Lake Manatee Lower Watershed” where these owls occur has been managed via pr escribed burning with the burrowing owls habitat needs’ specifically considered (M rykalo pers. comm.). However, the other managed areas are likely not managed with burrowing owls in mind. For example, the Withlacoochee State Forest site is manage d for timber extraction and the owls there actually occur along the fence line in an area now undergoing development. The location at Dinner Island Wildlife Management Area is on a cattle-grazed section. A larger proportion of records from the FNAI database (between 19% 35% depending on calculation method) occur in mana ged areas. This may be due to the higher likelihood of burrowing owl occurrences being reported to FNAI by biologists working in those managed areas, although none of th e FWC’s non-urban records occurred in managed areas. As demonstrated by Table 21 and Figure 38, caution should be taken when utilizing the FNAI and FWC point coordi nates in local-level analyses due to their relatively poor positional precision and accuracy. What is apparent from the point records is that most manage d areas do not appear to be heavily favored by nesting burrowing ow ls. While this may simply reflect the fact that there are a limited number of conservati on-managed areas, it could also be due to the relative lack of suitable habitat present in most of Florida’s conservation-managed lands (with noted exceptions). If efforts are to be made to preserve or restore habitat appropriate for prairie-depe ndent species, focusing on trac ts of existing conservationmanaged with apparently suitable habitat coul d be a practical place to start. However, given the limited amount of land already managed for conservation purposes, efforts must also extend to suitable habitat on non-managed lands, including improved pastures, which compose a dominating 12% of the tota l landcover in our 38-county study area. An example of such efforts would be negotiating multi-use conservation easements with ranchers to provide burrowing owls with need ed habitat without overl y-restricting private landowners’ property rights and potentially anta gonizing them.

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106Conclusions GIS Analysis and Testing The final map (Figures 33) appears to demonstrate relatively good adherence between our final combined suitability grid and the current/historic occurrence records of breeding, non-urban burrowing owls. This may indicate that the methodology employed herein is basically sound, despite some inhere nt weaknesses. For example, these analyses assume a fair degree of accuracy in the input datasets, which may not always be the case. In particular, there is an issue of scale when eval uating precisely-defined GPS coordinates within broader-scale input datase ts (e.g. ~1:24,000 for the soils data). The Future Land Use data is especially questiona ble in this regard and results from its use should probably be treated with extra cauti on, although the overall proportion of land projected to remain Agriculture or Preserve is noteworthy. Given the limitations of some input data, every attempt was made to minimize scale-related errors and to qualify potentially inac curate results, such as the possibility of errors when using the imprecise FWC and F NAI point coordinates. Unfortunately, image and screen/map resolution problems also hinder effective interpretation of a few of the maps presented here (i.e. Figur es 19, 22, and 23). If these distribution maps of suitable landcover cells, suitable soils polygons, and the combination grid thereof are to be used by managers or other researchers, they should be requested in original shapefile format for the most accurate use. Our methodology uses statistically-teste d empirical results to inform our suitability criteria. The empirical results he lped to refine our original hypothesized definitions, which were based on expert opin ion, literature review and casual field observations. Given the moderate sample size of used points and possible flaws in the input datasets, using a combination of empi rical results and informed judgment seems most appropriate when making final suitability decisions. Our results suggest that a great deal of potentially-suitable bree ding habitat exists throughout the 38-county study area Improved Pasture, the most prevalent class, also

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107 appears to be the most highly selected a nd may be of high importance to non-urban, breeding burrowing owls. Overall Conclusions The results of this study, including the potentially-suitable habitat distribution maps, may be useful to stat e wildlife managers consideri ng how best to manage the Florida Burrowing Owl, a “Species of Special Concern.” The full geographic distribution of the Florida subspecies is not well understood, particularly in remote, non-urban areas, as most research has been conducted in easily -accessible urban locat ions and there are a limited number of available research scientists and state wildlife biologists. Given this situation, this study aimed to create a product that would encourage further survey efforts by narrowing their scope to the most pr obable areas, thereby enhancing their effectiveness. As a practical matter, increased surveys and conservation efforts might start with existing managed areas, which appear to co ntain at least a modest proportion of known burrowing owls, although these are not alwa ys reported or documented by resident biologists. Soliciting survey help from lo cal and regional Audubon Societies might also be useful. However, expanded and improve d cooperation with private landowners is required to effectively locate and conserve bur rowing owls in the majority of identified areas with potentially-suitable habitat (and th e most occurrence records), most of which occur on large tracts of privately-owned land. Given Florida’s high population growth a nd the ever-decreasing availability of vacant (undeveloped) lots and other usable ha bitat in increasingly urbanized areas, an emphasis on the potential importance of othe r, non-urban areas seems critical for the long-term persistence of the Florida Burrowing Owl.

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108 Literature Cited Alldredge, J. D. Thomas and L. McDonald. 1998. Survey and comparison of methods for study of resource selection J. of Agricultural, Biological, and Environmental Statistics 3(3): 237-253. Beyer, H. 2004. Hawth's Analysis Tools for ArcGIS. Available: Blanchard, J., S. Jue, and A. Crook. 1998. Fl orida conservation lands. Florida Natural Areas Inventory. Tallahassee, FL. Available: Bowen, P. 2000. Demographic, distributi on, and metapopulation analyses of the burrowing owl (Athene cunicularia ) in Florida. M.S. Thesis, Univ. of Central Florida. Bowen, P. 2004. Environmental Scientist I II. St. Johns River Water Management District. Personal communication. Buchanan, J. 1997. A spatial analysis of the burrowing owl ( speotyto cunicularia ) population in Santa Clara County, California, using a geographic information system. pp. 90-96. In J.R. Duncan, D.H. Johnson and T.H. Nicholls, eds. Biology and conservation of owls of the northern hemisphere; second in ternational symposium. February 5-9, 1997. Winnipeg, Canada. Byers, C., R. Steinhorst, and P. Krausman. 1984. Clarification of a technique for analysis of utilization-availability data. J. of Wildlife Management 48: 1050-1053. Conway, C. and J. Simon. 2003. Comparison of detection probability associated with burrowing owl survey methods. J. W ildlife Management 67(3): 501-511. Courser, W. 1976. A population study of the bur rowing owl near Tampa, Florida. M.S. Thesis, Univ. of South Florida. Courser, W. 1979. Continued breed ing range expansion of the burrowing owl in Florida. American Birds 33: 143-144. Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert. 1994. Closing the gaps in Floridas wildlife habitat conservation system. Repor t. Florida Game and Fresh Water Fish Commission. Tallahassee, FL.

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109 Dasgupta, N. and J. Alldredge. 2000. A Ch i-Square Goodness-of-Fit analysis of dependent resource selection data. Biometrics 56: 402-408. DataEast LLC. 2006. XTools Pro 3.1. Available: Environmental Systems Research Institu te (ESRI). 2005. ArcGIS 9.1. Redlands, CA. Fielding, A. 2006. Habitat utilizatio n studies. Division of Biology, Manchester Metropolitan University. Updated 3 January, 2006. Available: /analysis/utilize.htm#Preference Fleischner, T. 1994. Ecological costs of liv estock grazing. Conser vation Biology 8: 629 644. Florida Fish and Wildlife Conservation Commission (FWC). 2003. Florida's breeding bird atlas: A collaborative study of Florida's birdlife. Available: Florida Fish and Wildlife Conservation Commission (FWC). 2004. Burrowing owl nest protection guidelines and procedur es in urban areas. Available: Florida Fish and Wildlife Conservation Co mmission (FWC). 2005. Wildlife Observations Database. Office of Environmental Services. Available: itat_sec/GIS/spp_locs.htm Florida Fish and Wildlife Conservation Commission (FWC). 2 006. Florida Vegetation and Landcover 2003. Office of Environmental Services Available: abitat_sec/gis/fl_veg03.htm Florida Natural Areas Inventory (FNAI). 2001. Florida Burrowing Owl : Athene cunicularia floridana. Field Guide to the Rare Animals of Florida. Florida State University. Tallahassee, FL. Garshelis, D. 2000. Delusions in habitat evaluation: measuring use, selection, and importance: Pp. 111 in Research Techniques in Anim al Ecology; Controversies and Consequences. L. Boitani and T. Fuller, eds. Columbia University Press, New York. Gervais, J., D. Rosenberg, and R. Anthony. 2003. Space use and pesticide exposure risk of male burrowing owls in an agricultural landscape. J. Wildlife Management 67(1): 155164. Green, G. and R. Anthony. 1989. Nesting succe ss and habitat relationships of burrowing owls in the Columbia Basin, Oregon. The Condor 91: 347-354.

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110 Haug, E. and L.Oliphant. 1990. Movements, activity patterns, and habitat use of burrowing owls in Saskatchewan. J. Wildlife Management 54(1): 27-35. Haug. E., B. Millsap, and M. Martell. 1993. Burrowing owl ( speotyto cunicularia ). In The Birds of North America, No. 61. A. Poole and F. Gill, eds. The Academy of Natural Sciences; Washington, D.C.: The Am erican Ornithologists Union. Hoxie, W. 1889. Nesting of the Florida Burro wing Owl. Ornithologist and Oologist 14: 33-34. Institute of Food and Agricultural Sciences (IFAS). 2000. University of Florida, IFAS Extension. Available: Kautz, R. T. Gilbert, and G. Mauldin. 1993. Vegetative cover in Florida based on 19851989 Landsat Thematic Mapper imagery. Florida Scientist 56: 135-154. Layman, J. Environmental Scientist. 2005. Hillsborough County Resource Conservation Services. Personal communication. Ligon, J. 1963. Breeding range expansion of the burrowing owl in Florida. The Auk 80(3): 367-368. Manly, B., L. McDonald, and D. Thomas. 1993. Resource selection by an imals: statistical design and analysis for field studies. 2 nd ed. Chapman and Hall, London, U.K. McClean, S., M. Rumble, R. King, and W. Baker. 1998. Evaluation of resource selection methods with different definitions of availability. J. Wildlife Management 62: 793. Mann, L. 1999. The role of soil classification in geographic information system modeling of habitat pattern: threatened calcare ous ecosystems. Ecosystems 2: 524-538. Millsap, B. 1996. Florida Burrowing Owl. Pages 579-587 in Rodgers Jr., James A., Kale II, Herbert W., and Henry T. Smith, eds., Rare and Endangered Biota of Florida: Volume V: Birds. University Presses of Florida. Gainesville, Florida. Millsap, B., and C. Bear. 1988. Cape Co ral burrowing owl population monitoring. Annual performance report, FL Game & Fr eshwater Fish Commission. Tallahassee, FL. Millsap, B. and C. Bear. 1997. Territory fidelit y, mate fidelity, and dispersal in an urban nesting population of Florid a burrowing owls. J. Raptor Research, Report. 9: 91-98. Millsap, B. and C. Bear. 2000. Density and reproduction of burrowing owls along an urban development gradient. J. Wildlife Management 64(1): 33-41.

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111 Morrison, J. and S. Humphrey. 2001. Conserva tion value of private lands for Crested Caracaras in Florida. Conservation Biology 15: 675-684. Mrykalo, R. 2005a. The Florida Burrowing Owl in a rural environment: breeding habitat, dispersal, post-breeding habita t, behavior, and diet. M.S. Thesis, Univ. South Florida, Tampa. Mrykalo, R. 2005b. Environmental Scie ntist. Personal communication. Mueller, M. M. Grigione, R. Sarno. 2005a Florida Burrowing Owls and cattle could benefit each other. The Florida Cattlema n and Livestock Journal. 69(5): 70-71. Mueller, M. M. Grigione, R. Sarno, R. Mrykalo. 2005b. Habitat suitability modeling for the Florida Burrowing Owl. Poster presentation and abstract proceedings. Association of American Geographers 2005 Annual Mee ting, April 5-9 2005, Denver, Colorado. Natural Resources Conservation Service (NRCS). 1995. Soil survey geographic (SSURGO) database. U.S. Dept. of Agriculture. Available: NeSmith, K. Florida Natural Areas Inve ntory (FNAI). 2005. Florida Burrowing Owl location database. Email to the author. Neu, C., C. Byers and J. Peek. 1974. A technique for analysis of utilization-availability data. J. Wildlife Management 38: 541-545. Nicholson, D. 1954. The Florida bu rrowing owl: a vanishing speci es. FL Naturalist 27(1): 3-4. Nixon, P. ( in prep ). Effects of translocation on Flor ida Burrowing Owl be havior and diet. M.S. Thesis, Univ. South Florida, Tampa. Nixon, P. 2006. Graduate Research Assistant. Univ. South Florida, Tampa, FL. Personal communication. Noss, R. 1994. Cows and conservati on biology. Conservation Biology 8: 613. Owre, O. 1978. Species of Special C oncern: Florida Burrowing Owl. Pp. 97-99 in H.W. Kale, III ed., Rare and endangered biota of Florida. Vol. II. Birds. Univ. Presses of Florida, Gainesville, FL. Palmer, W. 1896. On the Florida ground owl ( speotyto floridana ). The Auk 13: 99-108. Potvin, F., B. Boots and A. Dempster. 2003. Comparison among three approaches to evaluate winter habitat selection by wh ite-tailed deer on Anticosti Island using

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112 occurrences from an aerial survey and forest vegetation maps. Canadian J. of Zoology 81: 1662-1670. Rich, T. 1986. Habitat and nest-s ite selection by burrowing owls in the sagebrush steppe of Idaho. J. Wildlife Management 50(4): 548-555. Ritchie, N. 2004. Environmental Specia list: City of Marco Island. Personal communication. Shaw, D. and S. Atkinson. 1990. An introduction to the use of geographic information systems for ornithological re search. The Condor 92: 564-570 Sodhi, N. and L. Oliphant. 1992. Hunting ranges and habitat use and selection of urbanbreeding merlins. The Condor 94(3): 743-749. Southwest Florida Regional Planning C ouncil (SWFRPC). 1994. Preparation of the Statewide Future Land Use Map. Available: Stacey, P. and M. Taper. 1992. Environmental variation and the persistence of small populations. Ecological App lications 2(1): 18-29. Stevenson, H. and B. Anderson. 1994. The Bird life of Florida. University Press of Florida, Gainesville, Florida. p. 907. Stys, B., R. Kautz, D. Reed, M. Kertis, R. Kawula, C. Keller, and A. Davis. 2004. Florida vegetation and land cover data derived from 2003 Landsat ETM+ imagery. Florida Fish and Wildlife Conservation Commission. Tallahassee, FL. Sykes, P. 1974. Florida Burrowing Owl collect ed in North Carolina. The Auk 91: 636637. Thomas, D. and E. Taylor. 1990. Study designs and tests for comparing resource use and availability. J. Wildlife Management 54: 322-330. Uhmann, T.V., Kenkel, N.C., and Baydack, R.K. 2001. Development of a habitat suitability index model for burrowing owls in the eastern Canadian prairies. J. Raptor Research. 35(4): 378-384. United States Fish and Wildlife Servi ce (USFWS). 2003. Status Assessment and Conservation Plan for the Western Burrowing Owl in the U.S. Dept. of the Interior. Biological Technical Publication FWS/BTP-R6001-2003. Wesemann, T. 1986. Factors influencing the distribution and abundance of burrowing owls ( Athene cunicularia ) in Cape Coral, Florida. M.S. Thesis, Appalachian State Univ., Boone, North Carolina.

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113 Appendices

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Appendix A: Cox et al. (1994) Burrowing Owl Entry The below figures and text represent th e complete Florida Burrowing Owl entry from Closing the Gaps in Floridas Wildlife Habitat Conservation System by Cox et al. (1994). Figure A-1. Habitat distribution map and occu rrence records for the Florida burrowing owl (Figure 60 in Cox et al. 1994). 114

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Appendix A (continued) Figure A-2. Zoomed section of suitability model map from Cox et al. (1994). Shows central Florida and the Kissimmee Prairie re gion (Area 1). Red ar eas are suitable habitat. (Area 1 : Kissimmee Prairie regionincludes Avon Park Air Force Range, Audubon Kissimmee Prairie Preserve, Arbuckle State Forest, and Three Lakes Wildlife Management Area). >> Section 6.2.12. Florida Burrowing Owl The map of potential burrowing owl hab itat was created by establishing a smallradius circle (250 m) around occurrence reco rds stored in the Florida Natural Areas Inventory database. Breeding bird atlas blocks where burrowi ng owls were reported as probable or confirmed breeders (Kale et al. 1992) were also us ed. We isolated the dry prairie land cover within these atlas bl ocks. The map of potential burrowing owl habitat (Figure 60) shows small patches of poten tial habitat in very few areas of the state. Burrowing owl habitat is much more common than depicted here because ruderal areas that sustain burrowing owls cannot be identi fied from the land-cover map. The largest remaining patches of natural burrowing ow l habitat occur along the Kissimmee River. The greatest apparent concentration of nat ural burrowing owl hab itat on conservation 115

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116 Appendix A (continued) areas occurs along the Kissimmee Prairie region and includes Avon Park Air Force Range, Audubon Kissimmee Prairie Preserve, Arbuckle State Forest and Three Lakes Wildlife Management Area. Outlining additional protection options for this species is problematic due to the difficulty in identifying appropriate habitat conditions, a lack of information on dispersal capabilities and population demographics, and a lack of knowledge on the density of territories in various habitat c onditions. However, by combining breeding bird atlas and Florida Natural Areas Inventory data onto a single map (Figure 60), some potentially important areas outside the current system of conservation areas stand out. The concentration of occurrence record s surrounding the Avon Park Air Force Range (Area 1, Figure 60) implies a sizeable population in this region, yet there are few records shown specifically w ithin this conservation area. The area between Avon Park Air Force Range and Lake Kissimmee shows se veral atlas records and contains several patches of native dry prairie, while the area between Avon Park Air Force Range and Three Lakes Wildlife Management Area also sh ows a concentration of breeding bird atlas records and Florida Natural Areas Inventory records. If bu rrowing owl dispersal distances are on the order of 5-15 km, this region c ould be considered one large population. A concentration of occurrence records in sout heast Florida along th e Miami Ridge (Area 2, Figure 60) implies a sizeable owl population on agricultural lands in this area. This population is confronted by a burgeoning urban environment, and more specific conservation plans must await better informati on on habitat use and distributions in this area. There are also concentrations of record s of burrowing owls on agricultural lands to the west, northwest, and southwest of Lake Okeechobee (Area 3, Figure 60). Many remnant patches of prairie habitat in these areas warrant consideration for conservation. Conservation of rangeland within this general area would also benefit burrowing owls An apparently large, unprotected population of owls also inhabits west central Lee County and Charlotte County (A rea 4, Figure 60). The population in Lee County occurs largely on Ca pe Coral and has been the subject of an ongoing survey program (Millsap and Bear 1989). No specific habitat conservation recommendations were developed for burrowing owls because of the difficulty of identifying appropriate ruderal habitat areas. We believe the conservation recommendati ons developed for other species (e.g., Audubons crested caracara, sandhill crane, a nd Florida grasshopper sparrow) will, to a large extent, also benefit burrowing owls. <<

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117 Appendix B: Random Buffer Test Results (Proportions, Selection Indices, Selection/Avoidance Decisions) The below section was not included in the body of the thesis as it did not prove necessary for use in any of the used statisti cal procedures. It was created in case it would be useful for any future stat istical testing. The created random samples presented here are meant for use in comparing results to those of the real points (Muellers observed 30 point records). Methodology : The Generate Random Points tool was used to create 30 random points (with a minimum distance of 120 meters between points enforced) within the same 10-county study area boundaries. A reclassified total landcover grid with open water and high impact urban set to no data was employed to prevent any random point placement in those classes. Selection indi ces were calculated and selec tion/avoidance decisions made using identical methodology. Results : For the most part, the random buffers landcover proportions roughly approximated the available proportion within the full 10-county st udy area. Because the sample size was only moderate, there were some exceptions. In particular, note the apparent heavy selection of tw o classes: Grassland and Extractive. This result is likely due to two random points being placed at points in the landscape heavily surrounded by these two otherwise-uncommon classes. The proportion of improved pasture also was somewhat higher than would be expected, a lthough the apparent selection is not too strong (S.I.= 1.5). Figure B-1. Observed vs. Expected Proportions (Random Buffers) 0 5 10 15 20 25345678910121314151617181920212324272829303132333435363741424399Landcover Class% of Total Observed vs. Expected Proportions (%) (Random Buffers vs Full Study Area) Expected Proportion Observed Proportion

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118 Appendix B (continued) Table B-1. Selection/Avoidance decisions and valu es for Selection Indices for each LC class. LC Val Landcover Class Bonf. Adj. Lower Limit Bonf. Adj. Upper Limit A vail. Proportion In/Out C.I. A void. Select Sel. Index (w^) Standardized S.I. (B) Manly et al. Symbology:Oi Z /k *Sqrt[{Oi*(1-Oi) / U+}]Oi + Z /k *Sqrt[{Oi*(1-Oi) / U+}] i PUi / PAi(PUi / Pai) / (PUi / PAi) 3Xeric Oak Scrub 0 00.00484190n/a 0 0 4Sand Pine Scrub 0 00.00141450n/a 0 0 5Sandhill 0.01039267 0.013996930.01424213 A 0.85624798 0.02687566 6 Dry Prairie 0.02627227 0.031785570.05043515OU T A 0.57556925 0.01806580 7Mixed Pine-Hardwood Forest 0.01941936 0.024216810.02338493IN 0.93299756 0.02928465 8Hardwood Hammocks and Forest 0.05011102 0.057521340.04216000OUT S1.27647472 0.04006561 9Pinelands 0.10181684 0.111963290.09625805OUT S1.11045318 0.03485457 10Cabbage Palm-Live Oak Hammoc k 0 00.00072822n/a 0 0 12Freshwater Marsh and Wet Prairie 0.08771185 0.097224920.07325521OUT S1.26227730 0.03961998 13Sawgrass Marsh -0.00006654 0.000172580.00097576 A 0.05433813 0.00170555 14Cattail Marsh 0 00.00104542n/a 0 0 15Shrub Swamp 0.01819131 0.022846840.02132946IN 0.96200627 0.03019517 16Bay Swamp 0 00.00122662n/a 0 0 17Cypress Swamp 0.07116366 0.079839760.06860267OUT S1.10056511 0.03454421 18Cypress/Pine/Cabbage Palm 0.00479543 0.007346350.00440337OUT S1.37869189 0.04327397 19Mixed Wetland Forest 0.03548709 0.041817330.04073564IN 0.94885478 0.02978237 20Hardwood Swamp 0.02236030 0.027479310.04296020OUTA 0.58006724 0.01820698 21Hydric Hammock 0 00.00016826n/a 0 0 23Salt Marsh 0 00.00608838n/a 0 0 24Mangrove Swamp 0 00.01291889n/a 0 0 27Open Water 0.02601941 0.031508230.04303959OUTA 0.66831072 0.02097674 28Shrub and Brushland 0.03477413 0.041045710.03087072OUT S1.22802199 0.03854479 2 9 Grassland 0.01224154 0.0161246 2 0.00276853OU T S 5.12295561 0.16079780 30Bare Soil/Clearcut 0.01992125 0.024775340.03198999OU T A 0.69860282 0.02192754 31Improved Pasture 0.20191285 0.215255310.1349835 6 OU T S 1.54525545 0.0485020 2 32Unimproved Pasture 0.01283536 0.016803300.00784275OUT S1.88955905 0.05930892 33Sugar cane 0 00.01272437n/a 0 0 34Citrus 0.02163030 0.026671710.04643394OUTA 0.52011528 0.01632522 35Row/Field Crops 0.04872630 0.056042930.0426486 8 OU T S 1.22828227 0.0385529 6 36Other Agriculture 0.00534306 0.008018200.00688343IN 0.97053835 0.03046297 37Exotic Plants 0 00.00018528n/a 0 0 41High Impact Urban 0.04396275 0.050944600.09441800OUTA 0.50259136 0.01577519 42Low Impact Urban 0.03576727 0.042120380.03368299OUT S1.15618659 0.03629004 43Extractive 0.01944444 0.024244750.00412891OU T S 5.29064903 0.16606131 99"Other, Rare" Pooled 0 00.00022449n/a 0 0 35<-Count of LC types; Totals: --> 1.00000000 81231.85961190 1.00000000

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Appendix B (continued) Table B-2. Random buffers Unstandardized (w^) and Standardized (B) Selection Indices for all classes availa ble in the 10-county study area. LC Value Landcover Class Selection Index (w^) Standardized S.I. (B) PUi / PAi(PUi / Pai) / (PUi / PAi) 3Xeric Oak Scrub 0 4Sand Pine Scrub 0 0 5Sandhill 0.85624798 0.02687566 6Dry Prairie 0.57556925 0.01806580 7Mixed Pine-Hardwood Forest 0.93299756 0.02928465 8Hardwood Hammocks and Forest 1.27647472 0.04006561 9Pinelands 1.11045318 0.03485457 10Cabbage Palm-Live Oak Hammock 0 0 12Freshwater Marsh and Wet Prairie 1.26227730 0.03961998 13Sawgrass Marsh 0.05433813 0.00170555 14Cattail Marsh 0.00000000 0.00000000 15Shrub Swamp 0.96200627 0.03019517 16Bay Swamp 0 0 17Cypress Swamp 1.10056511 0.03454421 18Cypress/Pine/Cabbage Palm 1.37869189 0.04327397 19Mixed Wetland Forest 0.94885478 0.02978237 20Hardwood Swamp 0.58006724 0.01820698 21Hydric Hammock 0 0 23Salt Marsh 0 0 24Mangrove Swamp 0 0 27Open Water 0.66831072 0.02097674 28Shrub and Brushland 1.22802199 0.03854479 29Grassland 5.12295561 0.16079780 30Bare Soil/Clearcut 0.69860282 0.02192754 31Improved Pasture 1.54525545 0.04850202 32Unimproved Pasture 1.88955905 0.05930892 33Sugar cane 0 0 34Citrus 0.52011528 0.01632522 35Row/Field Crops 1.22828227 0.03855296 36Other Agriculture 0.97053835 0.03046297 37Exotic Plants 0 0 41High Impact Urban 0.50259136 0.01577519 42Low Impact Urban 1.15618659 0.03629004 43Extractive 5.29064903 0.16606131 99"Other, Rare" 0 0 35 31.85961190 1.00000000 0 119

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Appendix B (continued) Figure B-2. Actual versus Random buffers Standardized Selection Index (B) comparison: Standardized Selection Index Comparison of Selected Landcover Classes (Real vs. Random Buffers)0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18Dr y Prairie Gr a ss lan d Ba r e So i l /C le arc ut Im p r ov ed Past ure Row /Fi eld C ro p s Extractiv eStandardized S.I. (B) Real Random Table B-3. Actual versus Ra ndom buffers Standardized Selection Index (B) comparison of selected landcover classes: (Real Buffers)(Real Buffers)(Random Buffers)(Random Buffers) LC Val Landcover Class Sel. Index (w^) Standardized S.I. (B) Sel. Index (w^) Standardized S.I. (B) PUi / PAi(PUi / Pai) / (PUi / PAi)PUi / PAi(PUi / Pai) / (PUi / PAi) 6Dry Prairie 1.11643968 0.052165290.57556925 0.01806580 29Grassland 1.71046398 0.079920895.12295561 0.16079780 30Bare Soil/Clearcut 2.18228673 0.101966660.69860282 0.02192754 31Improved Pasture 3.50517472 0.163778181.54525545 0.04850202 35Row/Field Crops 3.01788594 0.141009771.22828227 0.03855296 43Extractive 2.40134017 0.112201865.29064903 0.16606131 6 13.93359122 0. 6510426514.4613144 1 0.45390743 120

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121 Appendix C: Full FWC 2003 Landcover Class Descriptions The following can be found at: See also the main download page ( habitat_sec/gis/fl_veg03.htm ) with a link to Documentation for the full documentation of methods. Descriptions of Vegetation and Land Cover Types Mapped Using Landsat Imagery Terry Gilbert and Beth Stys Florida Fish and Wildlife Conservation Commission Office of Environmental Services 620 South Meridian Street Tallahassee, Florida 32399-1600 March 17, 2004 >> A. Upland Plant Communities Coastal Uplands 1. Coastal Strand: Coastal strand occurs on well-drained sandy soils and typically includes the zoned vegetation of the upper beach, nearby dunes, or on coastal rock formations. This community generally occurs in a long, narrow band parallel to the open waters of the Atlantic Ocean or Gulf of Mexico, and along th e shores of some saline bays or sounds in both north and south Florida. This community occupies areas formed along high-energy shorelines, and is strongly affected by wind, waves, and salt spray. Vegetation within this community typically co nsists of low growing vines, grasses, and herbaceous plants with very few small trees or large shrubs. Pioneer or early successional herbaceous vegetation characterizes the foredune and upper beach, while a gradual change to woody plant species occurs in more prot ected areas landward. Typical plant species include beach morning glory, railroad vine, sea oats, saw palmetto, Spanish bayonet, yaupon holly, wax myrtle, along with sea grape, cocoplum, and other tropicals in southern Florida. The coastal strand community only includes the zone of early successional vegetation that lies between the upper beach, and more highly developed communities landward. Adjacent or contiguous community types such as xeric oak scrubs, pinelands, or hardwood forests would therefore be classified and mapped accordingly. 2. Beach/Sand: This land cover class consists of ba rren land with little or no vegetation. Coastal areas that are constantly affected by wave and tidal action and areas of dune sands and other areas of bare sands along the coast, are included in this class. Xeric Uplands 3. Xeric Oak Scrub: Xeric oak scrub is a xeric hardwood community typically consisting of clumped patches of low growing oaks interspersed with bare areas of white sand. This community occurs on areas of deep, well-washed, sterile sands, and it is the

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122 Appendix C (continued) same understory complex of scrubby oaks and other ground cover species that occurs in the sand pine scrub community. This condition frequently occurs when the short time periods between severe fires results in the complete removal of sand pine as an overstory species. Also included in this category are sites within the Ocala National Forest which have been clear-cut, and are sometimes dominated during the first one to five years by the xeric oak scrub association. The xeric oak scrub community is dominated by myrtle oak, Chapman's oak, sand-live oak, scrub holly, scrub plum, scrub hickory, rosemary, and saw palmetto. Fire is important in setting back plant succession and maintaining viable oak scrubs. 4. Sand Pine Scrub: Sand pine scrub occurs on extremely well drained, sorted, sterile sands deposited along former shorelines and islands of ancient seas. This xeric plant community is dominated by an overstory of sand pine and has an understory of myrtle oak, Chapman's oak, sand-live oak, and scrub holly. Ground cover is usually sparse to absent, especially in mature stands, and rosemary and lichens occur in some open areas. Sites within the Ocala National Forest that have an overstory of direct seeded sand pine, and an intact understory of characteristic xe ric scrub oaks, are also included in this category. Fire is an important ecological management tool, and commonly results in even-aged stands within regenerated sites. The distribution of this community type is almost entirely restricted to within the state of Florida. 5. Sandhill: Sandhill communities occur in areas of rolling terrain on deep, welldrained, white to yellow, sterile sands. This xeric community is dominated by an overstory of scattered longleaf pine, along with an understory of turkey oak and bluejack oak. The park-like ground cover consists of various grasses and herbs, including wiregrass, partridge pea, beggars tick, milk pea, queen's delight, and others. Fire is an important factor in controlling hardwood competition and other aspects of sandhill ecology. Although many of these sites throughout the state have been modified through the selective or severe cutting of longleaf pine, these areas are still included in the sandhill category. Mesic Uplands 6. Dry Prairies: Dry prairies are large native grass and shrublands occurring on very flat terrain interspersed with scattered c ypress domes and strands, bayheads, isolated freshwater marshes, and hardwood hammocks. This community is characterized by many species of grasses, sedges, herbs, and shrubs, including saw palmetto, fetterbush, staggerbush, tar flower, gallberry, blueberry, wiregrass, carpet grasses, and various bluestems. The largest areas of these treeless plains historically o ccurred just north of Lake Okeechobee, and they were subject to an nual or frequent fires. Many of these areas have been converted to improved pasture. In central and south Florida, palmetto prairies, which consist of former pine flatwoods where the overstory trees have been thinned or removed, are also included in this category. These sites contain highly scattered pines that cover less than 10 to 15 percent of an area. 7. Mixed Hardwood-Pine Forests: This community is the southern extension of the Piedmont southern mixed hardwoods, and occurs mainly on the clay soils on the northern Pandhandle. Younger stands may be predominantly pines, while a complex of various hardwoods become co-dominants as the system matures over time through plant succession. The overstory consists of shor tleaf and loblolly pine, American beech, mockernut hickory, southern red oak, water oak, American holly, and dogwood. Also included in this category are other upla nd forests that occur statewide and contain a mixture of conifers and hardwoods as th e co-dominant overstor y component. These communities contain longleaf pine, slash pine, and loblolly pine in mixed association with live oak, laurel oak, and water oak, together with other hardwood species characteristic of the upland hard wood hammocks and forests class.

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123 Appendix C (continued) 8. Hardwood Hammocks and Forests: This class includes the major upland hardwood associations that oc cur statewide on fairly rich sandy soils. Variations in species composition, and the local or spatial distributions of these communities are due in part to differences in soil moisture regimes, soil type, and geographic location within the state. Mesic and xeric variations ar e included within this association. The mesic hammock community represents the climax vegetation type within many areas of northern and central Florida. Character istic species in the extreme north include American beech, southern magnolia, Shumard oak, white oak, mockernut hickory, pignut hickory, sourgum, basswood, white ash, mulberry, and spruce pine. Mesic hammocks of the peninsula are less diverse due to the absen ce of hardwood species that are adapted to more northerly climates, and are characterized by laurel oak, hop hornbeam, blue beech, sweetgum, cabbage palm, American holly, and southern magnolia. Xeric hammocks occur on deep, well-drained, sandy soils where fire has been absent for long periods of time. These open, dry hammocks contain live oak, sand-live oak, bluejack oak, blackjack oak, southern red oak, sand-post oak, and pignut hickory. 9. Pinelands: The pinelands category includes north and south Florida pine flatwoods, south Florida Pine rocklands, and commercial pine plantations. Pine flatwoods occur on flat sandy terrain where the overstory is char acterized by longleaf pine, slash pine, or pond pine. Generally, flatwoods dominated by longleaf pine occur on well-drained sites, while pond pine is found in poorly drained areas, and slash pine occupies intermediate or moderately moist areas. The understory and ground cover within these three communities are somewhat similar and include several common species such as saw palmetto, gallberry, wax myrtle, and a wide variety of grasses and herbs. Generally wiregrass and runner oak dominate longleaf pine sites, fetterbush and bay trees are found in pond pine areas, while saw palmetto, gallberry, and rusty lyonia occupy slash pine flatwoods sites. Cypress domes, bayheads, titi swamps, and freshwater marshes are commonly interspersed in isolated depressions throughout this community type, and fire is a major disturbance factor. An additional pine flatwoods forest type occurs in extreme south Florida on rocklands where the overstory is the south Florida variety of slash pine, and tropical hardwood species occur in the understory. Scrubby flatwoods is another pineland type that occurs on drier ridges, and on or near old coastal dunes. Longleaf pine or slash pine dominates the overstory, while the ground cover is similar to the xeric oak scrub community. Commercial pine plantations are also reluctantly included in the pinelands association. This class includes sites predominately planted to slash pine, although longleaf pine and loblolly pine tracts also occur. Sand pine plantations, which have been planted on severely site-prepared sandhill sites in the north Florida pandhandle, are also included in this cat egory. An acceptable accurate separation of areas of densely stocked native flatwoods and older planted pine stands with a closed canopy was not consistently possible. 10. Cabbage Palm-Live Oak Hammock : This plant community is characterized by cabbage palms and live oaks occurring in small clumps within prairie communities. These hammocks typically have an open understory which may include such species as wax myrtle, water oak, and saw palmetto. Cabbage palm-live oak hammocks are often found bordering large lakes and rivers, and are distributed throughout the prairie region of south central Florida and extend northward in the St. John's River basin. Cabbage palms often form a fringe around hardwood islands located within improved pastures. 11. Tropical Hardwood Hammock: These upland hardwood forests occur in extreme south Florida and are characterized by tree an d shrub species on the northern edge of a range that extends southward into the Caribbean. These communities are sparsely distributed along coastal uplands south of a line from about Vero Beach on the Atlantic coast to Sarasota on the Gulf coast. They occur on many tree islands in the Everglades and on uplands throughout the Florida Keys. This cold-intolerant tropical community has

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124 Appendix C (continued) very high plant species diversity, sometimes containing over 35 species of trees and about 65 species of shrubs. Characteristic tropical plants include strangler fig, gumbo-limbo, mastic, bustic, lancewood, ironwoods, poisonwood, pigeon plum, Jamaica dogwood, and Bahama lysiloma. Live oak and cabbage palm are also sometimes found within this community. Tropical hammocks in the Florida Keys may also contain several plants, including lignum vitae, mahogany, thatch palms, and manchineel, which are extremely rare within the United States. B. Wetland Plant Communities Palustrine (Freshwater Wetlands) 12. Freshwater Marsh and Wet Prairie: These wetland communities are dominated by a wide assortment of herbaceous plant species growing on sand, clay, marl, and organic soils in areas of variable water depths and inundation regimes. Generally, freshwater marshes occur in deeper, more strongly inundated situations and are characterized by tall emergents and floatingleaved species. Freshwater marshes occur within flatwoods depressions, along broad, shallow lake and river shorelines, and scattered in open areas within hardwood and cypress swamps. Also, other portions of freshwater lakes, rivers, and canals that are dominated by floating-leaved plants such as lotus, spatterdock, duck weed, and water hyancinths are included in this category. Wet prairies commonly occur in shallow, periodically inundated areas and are usually dominated by aquatic grasses, sedges, and their associates. Wet prairies occur as scattered, shallow depressions within dry prai rie areas and on marl prairie areas in south Florida. Also included in th is category are areas in Sout hwest Florida with scattered dwarf cypress having less than 20 percent canopy coverage, and a dense ground cover of freshwater marsh plants. Various combinations of pickerel weed, sawgrass, maidencane, arrowhead, fire flag, cattail, spike rush, bulrush, white water lily, water shield, and various sedges dominate freshwater marshes and wet prairies. Many marsh or wet prairie types, such as sawgrass marsh or maidencane prairie, have been described and so-named based on their dominant plant species. 13 Sawgrass Marsh : Freshwater marshes dominated by sawgrass. 14. Cattail Marsh : Freshwater marsh areas dominated by cattails. 15. Shrub Swamp: Shrub swamps are wetland communities dominated by dense, lowgrowing, woody shrubs or small trees. Shrub swam ps are usually characteristic of wetland areas that are experiencing environmental ch ange, and are early to midsuccessional in species complement and structure. These changes are a result of natural or man-induced perturbations due to increased or decreased hydroperiod, fire, clear-cutti ng or land clearing, and siltation. Shrub swamps statewide may be dominated by one species, such as willow, or an array of opportunistic plants may form a dense, low canopy. Common species include willow, wax myrtle, primrose willow, buttonbush, and saplings of red maple, sweetbay, black gum, and other hydric tree species indicative of wooded wetlands. In northern Florida, some shrub swamps are a fire-maintained subclimax of bay swamps. These dense shrubby areas are dominated by black titi, swamp cyrilla, fetterbush, sweet pepperbush, doghobble, large gallberry, and myrtle-leaf holly. 16. Bay Swamp: These hardwood swamps contain broadleaf evergreen trees that occur in shallow, stagnant drainages or depressions often found within pine flatwoods, or at the base of sandy ridges where seepage maintain s constantly wet soils. The soils, which are usually covered by an abundant layer of leaf litter, are mostly acidic peat or muck that remains saturated for long periods but over which little water level fluctuation occurs. Overstory trees within bayheads are dominat ed by sweetbay, swamp bay, and loblolly bay. Depending on the location within the state, other species including pond pine, slash

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125 Appendix C (continued) pine, blackgum, cypress, and Atlantic white cedar can occur as scattered individuals, but bay trees dominate the canopy and characterize the community. Understory and gound cover species may include dahoon holly, wax myrtle, fetterbush, greenbriar, royal fern, cinnamon fern, and sphagnum moss. 17. Cypress Swamp: These regularly inundated wetlands form a forested border along large rivers, creeks, and lakes, or occur in de pressions as circular domes or linear strands. These communities are strongly dominated by eith er bald cypress or pond cypress, with very low numbers of scattered black gum, red maple, and sweetbay. Understory and ground cover are usually sparse due to frequent flooding but sometimes include such species as buttonbush, lizard's-tail, and various ferns. 18 Cypress/Pine/Cabbage Palm: This community includes cypress, pine and/or cabbage palm in combinations in which none of the species achiev es dominance. This assemblage forms a transition between moist upland and hydric sites. 19. Mixed Wetland Forest: This category includes mixed wetland forest communities in which neither hardwoods nor conifers achieve dominance. The mix can include hardwoods with pine or cypress and can repr esent a mixed hydric site or a transition between hardwoods and conifers on hydric/mesic sites. 20. Hardwood Swamp: These wooded wetland communities are composed of either pure stands of hardwoods, or occur as a mixture of hardwoods and cypress where hardwoods achieve dominance. This associ ation of wetland-adapted trees occurs throughout the state on organic soils and forms the forested floodplains of non-alluvial rivers, creeks, and broad lake basins. Tree sp ecies include a mixed overstory containing black gum, water tupelo, bald cypress, dahoon holly, red maple, swamp ash, cabbage palm, and sweetbay. 21. Hydric Hammock: Hydric hammocks occur on soils that are poorly drained or have high water tables. This association is a still-water wetland, flooded less frequently and for shorter periods of time than mixed hardwood and cypress swamps. Outcrops of limestone are common in the gulf coastal area. Typical plant species include laurel oak, live oak, cabbage palm, southern red cedar, and sweetgum. Canopy closure is typically 75-90%. The sub-canopy layer and ground layer vegetation is highly variable between sites. Wax myrtle is the most frequent shrub in hydric hammock. Other shrubs include yaupon, dahoon, and swamp dogwood. Ground cover may be absent or consist of a dense growth of ferns, sedges, grasses, an d greenbriars. Sites are usually between mesic hammocks or pine flatwoods and river swamp, wet prairie, or marsh. This hammock type is found in a narrow band along parts of the Gulf coast and along the St. Johns river where they often extend to the edge of coastal salt marshes. 22. Bottomland Hardwood Forest: These wetland forests are composed of a diverse assortment of hydric hardwoods which occur on the rich alluvial soils of silt and clay deposited along several Pandhandle rivers in cluding the Apalachicola, Choctawhatchee, and Escambia. These communities are characterized by an overstory that includes water hickory, overcup oak, swamp chestnut oak, river birch, American sycamore, red maple, Florida elm, bald cypress, blue beech, and swamp ash. Marine and Estuarine 23. Salt Marsh: These herbaceous and shrubby wetland communities occur statewide in brackish waters along protected low energy estuarine shorelines of the Atlantic and Gulf coasts. The largest continuous areas of salt marsh occur north of the range of mangroves, and border tidal creeks, bays and sounds. Salt marshes are sometimes interspersed within mangrove areas, and also occur as a transition zone between freshwater marshes and mangrove forests such as in the Ten Thousand Islands area along the southwest Florida coast. Plant distribution within salt marshes is largely dependent on the degree of tidal inundation, and many large areas are comple tely dominated by one species. Generally,

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126 Appendix C (continued) smooth cordgrass typically occupi es the lowest elevations im mediately adjacent to tidal creeks and pools, while black needlerush dominates less frequently inundated zones. The highest elevations form transitional areas char acterized by glasswort, saltwort, saltgrass, sea oxeye daisy, marsh elder, and saltbush. For the purposes of this project, cordgrass, needlerush, and transitional or high salt ma rshes are collectively mapped as this single category. 24. Mangrove Swamp: These dense, brackish water swamps occur along low-energy shorelines and in protected, tidally influenced bays of southern Florida. This community is composed of freeze-intolerant tree species that are distributed south of a line from Cedar Key on the Gulf coast to St. Augustine on the Atlantic coast. These swamp communities are usually dominated by red, black, and white mangroves that progress in a sere from seaward to landward areas, respectively, while buttonwood trees occur in areas above high tide. Openings and transitional areas in mangrove swamps sometimes contain glasswort, saltwort, and other salt marsh species. All three major species of mangroves are mapped as a single class with no effort made to differentiate these species into separate zones. 25. Scrub Mangrove : Areas sparsely vegetated with small, stunted mangroves (Keys only). 26. Tidal Flats : Areas composed of that portion of the shore environmen t protected from wave action and primarily composed of muds transported by tidal channels. C. Aquatic 27. Open Water: This class is comprised of the open water areas of inland freshwater lakes, ponds, rivers and creeks, and the bracki sh and saline waters of estuaries, bays, tidal creeks, the Gulf of Mexico and the Atlantic Ocean. D. Disturbed Communities Transitional 28. Shrub and Brushland: This association includes a variety of situations where natural upland community types have been recently disturbed through clear-cutting commercial pinelands, land clearing, or fire, and are recovering through natural successional processes. This type could be char acterized as an early condition of oldfield succession, and various shrubs, tree saplings, and lesser amounts of grasses and herbs dominate the community. Common species include wax myrtle, saltbush, sumac, elderberry, saw palmetto, blackberry, gallberry, fetterbush, staggerbush, broomsedge, dog fennel, together with oak, pine and other tree seedlings or saplings. 29. Grassland: These are upland communities where the predominant vegetative cover is very low growing grasses and forbs. This very early successional category includes all sites with herbaceous vegeta tion during the time period be tween bare ground, and the shrub and brush stage. It also includes areas th at may be maintained in this stage through periodic mowing, such as along dikes or levees. 30 Bare soil/Clearcut: Areas of bare soil representing recent timber cutting operations, areas devoid of vegetation as a consequence of recent fires, natural areas of exposed bare soil (e.g., sandy areas within xeric communitie s), or bare soil exposed due to vegetation removal for unknown reasons. Agriculture 31. Improved Pasture: Land that has been cleared, tilled, reseeded with specific grass types, and periodically improved with brush control and fertilizer application.

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127 Appendix C (continued) 32. Unimproved/Woodland Pasture : Cleared land with major stands of trees and brush where native grasses have been allowed to develop. Normally, unimproved pastures are not managed with brush control or fertilizer application. 33. Sugarcane : Agricultural lands planted to sugar cane. 34. Citrus : Agricultural lands planted to groves of citrus (e.g., oranges, grapefruit, lemons). 35. Row/Field Crops : Row crops are agricultural fiel ds in which rows remain well defined even after crops have been harvested. Typical row crops in Florida include corn, tomatoes, potatoes, cotton, and beans. Field crops are agricultural croplands not planted in rows. Typical field crops in Florida include hay and grasses. 36. Other Agriculture : Agricultural lands other than pasture land, sugar cane fields, citrus groves, and croplands. Types of agricultural lands included in this category are peach orchards, pecan and avocado groves, nu rseries and vineyards, specialty farms, aquaculture, fallow cropland, and unidentified agricultural uses. Exotic Plants 37. Exotic Plants: Upland and wetland areas dominated by non-native trees that were planted or have escaped and invaded native plant communities. These exotics include melaleuca, Australian pine, Brazilian pepper, and eucalyptus. This class includes sites known to be vegetated by non-native but for which the actual species composition could not be determined. 38. Australian Pine: Sites known to be dominated by Australian pine through field inspection. 39. Melaleuca: Sites known to be dominated by melaleuca through field inspection. 39. Brazilian Pepper: Sites known to be dominated by Brazilian pepper through field inspection. Urban 41. High Impact Urban : Unvegetated areas such as ro ads, residential and commercial buildings, parking lots, etc. 42. Low Impact Urban : Disturbed areas within urbanized areas that may or may not be vegetated. Examples of land uses included in this category are lawns, golf courses, road shoulders, grassy areas surrounding places such as airports park facilities, etc. Many secondary roads, such as forest ro ads, are included in this category. Mining 43. Extractive : These areas encompass surface and subsurface mining operations. Areas included are sand, gravel and clay pits, phosphate mines, and limestone quarries. Industrial complexes were the extracted material is refined, packaged or further processed may also be included in this category.

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Appendix D: SSURGO Soils Selected Metadata Below are selected metadata for the used attribute fields from the soils data. The full metadata and guide for use can be found at: Field name Table(s) Full name Description Hydric soil definition: The definition of a hydric soil is a soil that formed under conditions of saturation, flooding or ponding long enough during the growing season to develop anaerobic conditions in the upper part. The concept of hydric soils includes soils developed under sufficie ntly wet conditions to support the growth and regeneration of hydrophytic vegetation. Soils that are sufficiently wet because of artificial measures are included in the co ncept of hydric soils. Also, soils in which the hydrology has been artificially modified are hydric if the soil, in an unaltered state, was hydric. Some series, designated as hydric, have phases that are not hydric depending on water table, flooding, and ponding characteristics. (USDA, Available: ). 128

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Appendix D (continued) Hydrologic group classifications: Map unit ID and all Relate T ables (some not available): 129

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Appendix D (continued) Possible attribute fields for the utilized relate table, comp.dbf, which stores soil component information for each unique map unit (MUID): 130