31

Center For Advanced Spatial Technologies (CAST)THE ARKANSAS GAP ANALYSIS PROJECT

FINAL REPORT

PREDICTED ANIMAL DISTRIBUTIONS AND SPECIESRICHNESS

TABLE OF CONTENTS1. INTRODUCTION2. LANDCOVER CLASSIFICATION AND MAPPING3. PREDICTED ANIMAL DISTRIBUTIONS AND SPECIES RICHNESS3.1. Introduction3.2. Methods3.2.1. Mammals3.2.2. Birds3.2.3. Reptiles3.2.4. Amphibians3.3. Results3.4. Accuracy Assessment3.5. Limitations and Discussion4. LAND STEWARDSHIP5. ANALYSIS BASED ON STEWARDSHIP AND MANAGEMENT STATUS6. CONCLUSIONS AND MANAGEMENT IMPLICATIONS7. DATA USE AND AVAILABILITY8. LITERATURE CITED9. GLOSSARY10. GLOSSARY OF ACRONYMS11. APPENDICES AND MAPS

3.1. Introduction

All species range maps predict occurrence within a particular area (Csuti 1994). Traditionally,predicted occurrences of most species begin with samples from collections made at point locations.Most species range maps are small-scale (e.g., >1:10,000,000) and derived primarily from point datafor field guides. The purpose of Gap Analysis vertebrate species maps are to provide more preciseinformation about current distribution of individual native species within their general ranges. Withthese data, better estimates can be made about configuration and actual amounts of habitat.

Gap Analysis maps are produced at a scale of 1:100,000 and are intended for applications atlandscape or �beta� scale (homogeneous areas generally covering 1,000 to 1,000,000 hectares (ha)and made up of more than one kind of natural community). Applications of these data to site orstand-level analyses (site - a microhabitat, generally 10 to 100 m2; stand - a single habitat type,generally 0.1 to 1,000 ha; Whittaker 1977, also see Stoms and Estes 1993) are likely to becompromised by finer-grained patterns of environmental heterogeneity that are resolved at thoselevels.

32

Gap Analysis uses predicted distributions of native vertebrate species to evaluate their conservationstatus relative to existing land management (Scott et al. 1993). However, maps of vertebrate speciesdistributions may be used to answer a wide variety of management, planning, and research questionsrelating to individual species or groups of species. In addition to maps, great utility may be found inconsolidating specimen collection records and literature into databases used to produce maps.

Previous to this effort no maps were available, digital or otherwise, showing likely present-daydistribution of species by habitat type across their ranges. Because of this, ordinary species (i.e.,those not threatened with extinction or managed as game animals) are generally not given sufficientconsideration in land-use decisions in context with large geographic regions. Decline of thesespecies due to incremental habitat loss can, and does, result in one threatened or endangered�surprise� after another. Frequently, records that do exist for these �ordinary� species are truncatedby state boundaries. Simply creating a consistent spatial framework for storing, retrieving,manipulating, analyzing, and updating status of each vertebrate species is a necessary and basicelement for preventing further erosion of biological resources.

3.2. Methods

U.S.G.S. Biological Resources Division�s Gap Analysis Program (BRD-GAP) has standards forgenerating predicted terrestrial distribution. Breeding habitat patches outside breeding distribution(but contiguous with habitats within breeding distribution) are included while all habitats outside ofgeographic range are excluded (Figure 3.1.). Tomlin (1990) refers to this procedure as a zonaloperator which can be performed by a Geographic Information Systems (GIS). Geographic ResourceAnalysis Support System (GRASS4.1) does not include a module to perform zonal operations.Generating models by combining existing GRASS4.1 modules with custom programming proved tobe time consuming (each model can run 16-30 hours on a SUN 690 server with 320 megabytes ofRAM) and created maps that required large amounts of hard disk space. Therefore, topologiesbetween all contiguous vegetation polygons (units for breeding habitat) and counties of Arkansas(units for breeding distribution) were obtained by running GRASS4.1 module r.stats and output wasloaded into INFORMIX where model generation occurred. In this manner, GRASS4.1 determinedspatial relationships between vegetation polygons and counties, while INFORMIX performedlinkages between terrestrial vertebrate species, appropriate vegetation types, county of occurrenceand vegetation polygons. Terrestrial vertebrate models were generated by INFORMIX StructuredQuery Language (SQL) queries that obtained all appropriate vegetation totally within a county ofoccurrence or contiguous with a county of occurrence. Maps were generated quickly (all 322 modelsran in 23 hours) by this methodology because topological relationships were only determined once.Disk storage requirements of resultant files were minimal as they were only database tables. All otheranalysis, including accuracy assessment, was performed using this two step methodology oftopological determination by GRASS4.1 and database linkage by INFORMIX. Queries were used togenerate maps by unloading SQL outputs to files and were used as inputs for GRASS4.1 moduler.reclass. These reclass maps were viewed on a digital display and later directed to a printer forhardcopy production (Catanzaro and Smith 1995).

In order to keep Arkansas Gap Analysis Program (AR-GAP) models simple, most individual speciesreceived little special attention. However, species requiring special habitats, such as caves, riparianareas, or water bodies, received additional modeling. Locations of caves within Arkansas were

33

Figure 3.1. Schematic representation of the steps necessary to model terrestrial vertebrates for NBS GAP.

identified using U.S.G.S. Geographic Names Information System (GNIS). Non-flowing aquaticfeatures were generated by patching water defined by TM sensor into doubly-lined drainages mappedin 1992 U.S.G.S. Topographically Integrated Geographic Encoding and Reference (TIGER) digital

34

dataset. Standing water was obtained from 30 m vegetation map and waterbodies were separatedinto large bodies (greater than 100 ha) and small bodies (less than 100 ha). Perennial streams andannual streams, defined by 1992 TIGER files, were rasterized at 30 m. Flowing water layers werepatched into standing water layers to produce a final water layer map. Methodology for modelgeneration of species requiring water habitats duplicated procedures used for terrestrial species.

3.2.1. Mammals:

Sixty-seven mammals were included in AR-GAP (Appendix 11.10.) following taxonomy of Banks etal. (1987). County of occurrence range maps were generated by combining museum specimenlocations obtained from Dr. Rick McDaniel (Arkansas State University, Jonesboro) and Dr. BobWiley (University of Arkansas, Monticello) with distributions shown in Sealander and Heidt (1990).Natural history information contained in Sealander and Heidt (1990) was used for habitat associationmatrix data.

3.2.2. Birds:

A complete list of AR-GAP birds was developed with following criterion: species must have at least5 sightings in Arkansas, or more than 1 sighting or breeding record within Arkansas since 1980.Species clearly out of their range were excluded. These criteria reduced a total of 381 avian speciesthat have been reported in Arkansas (James and Neal 1986; James et al. 1994) to 144 species(Appendix 11.10.). Avian range maps were compiled using three datasets. County level source datafrom Arkansas Birds (James and Neal 1986), checklists developed by Neal and Mlodinow (1988)and Hyatt and Moren (1990) were stored in INFORMIX. A matrix of bird species and counties wasreviewed by the Gap Avian Committee (Appendix 11.11.) and any appropriate additions or changeswere made. The Gap Avian Sub-Committee (Appendix 11.11.) reviewed this augmented dataset andchanges made were stored in INFORMIX. Avian habitat association matrix follows Hamel (1992)and was loaded into INFORMIX after vegetation types were cross-walked into Arkansas�s NaturalVegetation Classification (Figure 3.2.). Modifications were made for those species occurring inagricultural and/or urban areas, species inhabiting water bodies or riparian areas, and species notincluded by Hamel (1992). A dynamic linkage between INFORMIX and GRASS 4.1 was createdand maps were generated for each species, reviewed by the Gap Avian Sub-Committee and correctedas needed.

3.2.3. Reptiles & Amphibians:

A complete list of reptiles and amphibians was generated with assistance from Dr. Stan E. Trauth(Arkansas State University, Jonesboro) and followed Banks et al. (1987) taxonomy. One-hundredand ten reptiles and amphibians were included in AR-GAP (Appendix 11.10.). County of occurrencerange maps and habitat association matrix data were obtained from Conant and Collins (1991).

3.3. Results

A map of each terrestrial vertebrate in Arkansas was generated by AR-GAP (e.g. Figure 3.3.; see Listof Maps). Each map followed a generalized format with breeding distribution shown alongsidebreeding habitat and final predictive model was displayed next to a legend for all three maps. Inorder to simplify explanation of data produced by AR-GAP, examples in both this chapter and

35

Figure 3.2. A graphical representation of the hierarchical relationships between those vegetation units that were able tobe discriminated by remote sensing techniques followed by the Arkansas Gap Analysis project (hierarchy from Foti et al.(1994)). The bold groupings are the vegetation units used to create the Wildlife-Habitat Relationship models (groupings

based on Hamel (1992)).

chapter 5 will be focused on Ovenbirds (Seiurus aurocapillus), a common neotropical migrant foundthroughout much of Arkansas. Ovenbirds in Arkansas were limited to Interior Highlands (upper left,Figure 3.3.) as shown by James & Neal (1986), Neal & Mlodinow (1988) and Hyatt & Moran(1990).

Throughout AR-GAP data collection, it rapidly became evident that researching gaps in our

Temperate lowland andsubmontane broad-leavedcold-deciduous forest withconical crowns

Temperate evergreen needle-leavedupland forest with conical crowns

Temperate evergreen needle-leavedforest with rounded crowns

Bare

Cold-deciduous alluvial forest

Cold-deciduous swamp forest

Cold-deciduous broad-leaved upland forestwith evergreen needle-leaved trees

Evergreen needle-leavedwoodland with conical crownsMixed upland woodland evergreenwith rounded crowns

Cold-deciduous uplanddeciduous woodlandTemperate deciduous shrubland

Tall dense upland grassland

Forest

Tall grass

Tall grass consisting mainlyof bunch grasses

T.1.B.2.b.III; Pinus taeda - Pinus echinata -Quercus spp.

R.6.A.1.a.I; BareR.1.B.3.c.II; Betula - Platanus - Acer

R.1.B.3.c.I; Salix - Populus

P.5.A.4.b.III; Arundinaria gigantea

P.5.A.4.a.I; Tall grass

P.1.B.3.d.II; Nyssa

P.1.B.3.d.I.1.a.; Taxodium distichum

P.1.B.3.d.I; Taxodium distichum - mixed hardwood

P.1.B.3.c.VIII; Liquidambar styraciflua

P.1.B.3.c.VII; Quercus phellos

P.1.B.3.c.V; Quercus nuttallii

P.1.B.3.c.IV; Celtis laevigata

P.1.B.3.c.III; Quercus falcata var. Pagodifolia

P.1.B.3.c.II; Carya aquatica

P.1.B.3.c.I; Quercus lyrata

T.5.A.1.a.I; Mesic Prairie

T.4.B.3.a.II; Mixed shrub species

T.2.B.4.a.I; Quercus spp. - Carya texana

T.2.B.3.a.II; Juniperus ashei -Quercus spp.

T.2.B.3.a.I; Pinus echinata Quercus spp.

T.2.A.2.b.I; Juniperus virginiana - Quercus spp.

T.1.B.3.a.III; Quercus rubrus - Quercus spp.

T.1.B.3.a.IV; Quercus falcata - Quercus spp.

T.1.B.3.a.I; Fagus grandifolia

T.1.B.3.a.V; Quercus stellata

T.1.B.2.b.IV; Juniperus virginiana

T.1.B.3.a.II; Quercus alba - mixed hardwoods

T.1.B.2.b.II; Quercus spp. - Pinus echinata - Carya spp.

T.1.A.9.c.I; Juniperus virginiana

T.1.A.9.b.II; Pinus taeda

T.1.A.9.b.I; Pinus echinata

RIVERINE

Formation (level 4) Alliance (level 5)

TERRESTRIAL

PALUSTRINE

36

Figure 3.3. Example of three datasets used to generate predicted distributions of breeding terrestrial vertebrates.

protective network of managed lands uncovered significant gaps in knowledge about distribution ofterrestrial vertebrates, as well as their natural history (Smith and Catanzaro 1996). Two categories,Predicted by Gap Avian Committee and Predicted by Gap Avian Sub-Committee, were added in anattempt to rectify informational gaps (upper left, Figure 3.3.). Ovenbirds were predicted to occur in

37

three Arkansas counties. Potential habitat used by breeding Ovenbirds was distributed evenly acrossArkansas (upper right, Figure 3.3.). Habitats of higher value to Ovenbirds (mixed pine-hardwoodsand oak-hickory) were limited principally to Boston Mountains (see Figure 5.2 for a map ofecoregions). Significant amounts of marginal Ovenbird habitat (elm-ash-cottonwood, loblolly pine-shortleaf pine, and oak-gum-cypress) were found throughout Arkansas. Ovenbird predictive model(lower left, Figure 3.3.) was a combination of breeding distribution and breeding habitats. Habitatswithin counties occupied by Ovenbirds were highlighted, while habitats outside their breedingdistribution were not. Exceptions occur only when contiguous vegetation polygons crossed over intocounties where Ovenbirds were not thought to breed (southern Arkansas).

Breeding distribution maps were translated from counties to Environmental Monitoring AssessmentProgram (EMAP) 635 km2 hexagons (White et. al 1992) for further analysis. Range of biodiversitywas quite narrow when displayed in this manner. Only 70 species separated the most specioushexagon from the least (Figure 3.4.). Least specious hexagon included 200 species and mostspecious hexagon included 270 species (62% and 83% of Arkansas�s terrestrial vertebrate diversityrespectively). Less specious hexagons were located in Mississippi Alluvial Basin where landscapeshave been highly modified for agricultural purposes. Hexagons with high diversity of terrestrialvertebrates were at the confluence of Arkansas Valley, Ouachita Mountain, and Mississippi AlluvialBasin ecoregions. Other hexagons with high biodiversity were in western Arkansas and parts ofOuachita Mountains. Avian hexagon biodiversity ranged from 80 to 126 species (56% to 88% ofArkansas avian diversity) (Figure 3.5.). Hexagons with high avian biodiversity were in central,northwest, and southwest Arkansas. Hexagons with high avian biodiversity were found alongborders of two or more ecoregions. Mammalian biodiversity per hexagon ranged from 48 species to62 species (72% to 93% of Arkansas mammalian diversity) (Figure 3.5.). Hexagons with highconcentrations of mammalian biodiversity were found in northwest Arkansas while relatively lowconcentrations of mammals were found in southern regions. Amphibian hexagon biodiversity rangedfrom 18 species to 33 species (40% to 73% of Arkansas amphibian diversity) (Figure 3.5.).Hexagons with high concentrations of amphibian biodiversity were scattered throughout InteriorHighlands while low concentrations of amphibian biodiversity were found in Mississippi AlluvialBasin. Reptilian hexagon biodiversity ranged from 41 species to 60 species (64% to 94% Arkansasreptilian diversity) (Figure 3.5.). A concentration of reptiles was found in central Arkansas.Mississippi Alluvial Basin was not depauperated of reptiles as it was for other taxonomic groups.

Terrestrial vertebrate diversity map (Figure 3.6.) totals all 322 individual predictive models ofbreeding birds, mammals, reptiles and amphibians. Vegetation polygon with highest biodiversity inArkansas was oak-gum-cypress and provided habitat for 214 species (64% Arkansas biodiversity).Other areas of high terrestrial biodiversity were bottomland hardwood regions in central andsouthwest. Low areas of biodiversity were agricultural areas. Major centers of avian biodiversitywere upland oak-hickory forests (Figure 3.7.). Vegetation polygon with highest avian diversityprovided habitat for 107 bird species (74% of Arkansas avian biodiversity). There was no majorcenter of mammalian diversity (Figure 3.7.). Vegetation polygon with highest mammalianbiodiversity in Arkansas provided habitat for 39 species (58% of Arkansas mammalian biodiversity).Major centers of amphibian biodiversity were oak-gum-cypress areas (Figure 3.7.). The polygonwith highest amphibian biodiversity provided habitat for 38 species (84% of Arkansas amphibianbiodiversity). Most areas in Arkansas had similar numerical biodiversity of amphibians, due to evenspatial distributions of water bodies. Major centers of reptilian biodiversity were oak-gum-cypress(Figure 3.7.). The polygon with highest reptile biodiversity in Arkansas provided habitat for 57

38

Figure 3.4. Distribution of terrestrial vertebrates.

species (90% of Arkansas reptilian biodiversity).

3.4. Accuracy Assessment

Other GAP projects have obtained species lists from well studied management areas and comparedthese lists to GAP predicted lists. Very few areas in Arkansas have been well studied, therefore datafrom North American Breeding Bird Surveys (BBS) were used to test predicted avian distributions.

Terrestrial hexagon distribution

218 235 270200 253

39

Figure 3.5. Hexagon diversity for birds, mammals, reptiles, amphibians.

Mammalian Hexagon Distribution

Reptilian Hexagon Distribution

92 103 12680 115

Avian Hexagon Distribution

52 5548 58

22 2618 30

Amphibian Hexagon Distribution

45 50 6041 5533

62

40

Figure 3.6. Terrestrial vertebrate diversity (clump).

BBS data were collected during an annual late spring/early summer survey which started in 1966.Fifty census points were located 0.8 km (0.5 miles) apart on a 39 km (24.5 mile) roads. Each pointwas sampled by volunteers for three minutes and all species and number of birds seen and heardwithin 0.402 km (0.25 miles) of census points were recorded (see Erskine 1978; Robbins et al. 1986for full methodology). Effective geographic resolution BBS was a section, an aggregation of ten

Terrestrial Vertebrate Diversity (terrestrial and water)

54 107 21410 160

41

Figure 3.7. Diversity for birds, mammals, reptiles and amphibians.

26 53 1070 80

Avian Diversity (terrestrial and water) Mammalian Diversity (terrestrial and water)

10 20 390 30

10 200 30

Amphibian Diversity (terrestrial and water) Reptilian Diversity (terrestrial and water)

14 29 570 4338

42

BBS census points, thus each BBS routes had five sections. There were 32 Arkansas BBS routes(160 sections) run during 1980-1992. While this dataset was obtained in digital format, it was notreferenced to a digital map. A digital base map was generated to render this dataset as a GIS(Catanzaro and Smith 1996). Base map generation for BBS data involved isolating roadsrepresenting BBS routes on 1:100,000 DLGs. Each section was uniquely attributed and a 0.8 kmbuffer was applied to represent area sampled by each section. Final base map was related back toBBS data by linking each section to its respective BBS data stored in INFORMIX.

Topological relationships between BBS and completed vegetation map was determined usingGRASS4.1 module r.stats. A series of SQLs determined relationships between species predictedpresence or absence and BBS data. Errors of commission and omissions were generated and resultswere classified into four categories: PPV - species predicted to occur within a BBS section andconfirmed by BBS data (positive predictive value); NPV - species predicted to be absent from allvegetation polygons within a BBS section and not found by BBS data (negative predictive value);Omission error - species predicted to be absent from all vegetation polygons within a BBS sectionand found by BBS data; Commission error - species predicted to occur within a BBS section and notfound to by BBS data (Appendix 11.12.).

Predicted distributions of mammalian, reptilian and amphibian species were not tested. While digitaldatasets of museum specimens for these taxonomic groups were obtained, a lack of funds and timeprecluded such analysis.

Upon inspection of accuracy assessment data, it was evident that there was a significant differencebetween errors of omission and commission. For example, Ovenbird commission error was 38%and omission error was 3% while Louisiana Waterthrush commission error was 24% and omissionerror was 1% (Appendix 11.12.). Avian models tended to overpredict rather than underpredict(Smith and Catanzaro 1996). There could be two reasons for this phenomenon. First, there were atotal of 2,596 predictive records in breeding distribution database (i.e. counties attributed asPredicted by Gap Avian Committee or Predicted by Gap Avian Sub-Committee). Using predictivedata to build a predictive model could have introduced bias. These predictive records accounted for15% of 16,750 county distributional records and it was quite possible that commission errors may behigher in those counties a species was originally predicted to occur in. It should be noted however,that 86% of 2,596 predictive records were for species that were thought to be statewide breeders.These predictions filled information gaps associated with common species, as opposed to rangeextensions. Secondly, there were several large polygons within Arkansas. The largest naturalvegetation polygon, shortleaf pine, had an area of 411,874 ha and touched thirteen counties. Thesecond largest natural vegetation polygon, white oak, northern red oak - shortleaf pine � hickory,had an area of 272,397 ha and touched nine counties. If a species was found to occur in only one ofthese counties, GAP methodology dictated that the entire vegetation polygon must be included in thepredictive model. It is hypothesized that large polygons will cause an overprediction to occur (seelandcover section for further analysis).

3.5. Limitations and Discussion

All maps, including AR-GAP products are predictions of ground based features. AR-GAP vertebratemodeling used simple models and their performance tested. These simple models should be viewedas a starting points for discussion of Arkansas terrestrial vertebrate distribution.

43

Vegetation structure (as opposed to composition) is an important factor determining avianbiodiversity (Cody 1985), temperature and moisture regimes are important predictors of reptile andamphibian distribution (Gans 1976, 1977, 1982; Duellman and Trueb 1986), soil types predictfossorial rodents (Churchfield 1990), and caves are necessary to most bats (Gaisler 1979: Kunz1982). General variables are difficult to correctly identify from orbiting commercial satellite sensorsand using such imagery to discern structural differences among vegetation types, sub-canopycomposition or structure is exceedingly difficult (Gonzales 1994; Hepinstall et. al 1996). While TMsensor does have a thermal component, spatial resolution of this spectral band is currently 120 m, asubstantial decrease from 30 m limits its� usefulness. Crist & Cicone (1984) developed an indexrelated to vegetation wetness through a mathematical transformation of TM data. This index doesnot identify soil moisture, and for forested areas is highly dependent upon season and vegetationmixes. Identification of soil types by aerial photographs is available (Soil Survey Division Staff1993), but satellite identification has met with only limited success (Sabins 1978) and depend uponquality data inputs. Recognizing limitations of spaceborne platforms, AR-GAP attempts to predictdistributions of terrestrial vertebrates solely on occurrence of appropriate vegetation types.

Some species of reptiles, amphibians, mammals, and birds are microhabitat specialists, living inhabitats which can not be identified by small scale maps (e.g. > 1:100,000). While mappablesurrogate habitats may be used, results can be unsatisfactory due to oversimplification of a speciesrequirements. Contrastingly, some species are habitat generalists and their models may beuninformative. Raccoons (Procyon lotor) are widely distributed in Arkansas (all 75 counties) andare found in a variety of habitats including forested, urban and agricultural areas. As a result, AR-GAP methodology predicted raccoons to occur in 97% of Arkansas. Uninformative results will alsobe generated when information gaps occur. While some taxa (e.g. game, endangered and/orthreatened, or scientifically interesting species) have been thoroughly studied, allowing developmentof complicated models, most biodiversity has not been studied sufficiently to generate evengeneralized models. A combination of these inherent difficulties interacting with the strength ofrelationships defined in the wildlife-habitat matrix will ultimately determine model performance.

Irrespective of these difficulties in general model techniques, programmatic restraints in modeling322 terrestrial vertebrates have given rise two specific issues particular to AR-GAP and need to beaddressed. In order to conclude AR-GAP in a timely matter, terrestrial vertebrate modeling wascompleted long before a smoothed 100 ha vegetation map was finalized. Effects of this choice areunclear, this unsmoothed map was used as the input for a GRASS4.1 module to find contiguousareas (r.clump). Results of r.clump may be biased for map categories that are one cell wide or mapcategory features that are diagonal in nature which are not considered contiguous. Unsmoothed dataas an input layer in r.clump more than likely improperly inflated numbers of contiguous polygonsand may have affected model performance.

Use of BBS data to test AR-GAP vertebrate models had several limitations, but free data, largesample size (160 BBS sections) with each section of equal area are considerable strengths. BBS dataonly cover breeding birds (a little over half of terrestrial vertebrates included in AR-GAP). Otherlimitations include: BBS was not designed to sample biodiversity per se, it was designed to samplepopulation trends; not every bird species included in AR-GAP had been recorded by BBS; and BBSmay be biased against species that breed before or after survey times, species that not active duringtimes surveyed or species difficult to locate. Model accuracies must be interpreted cautiously and

44

used more as a relative measure than an absolute guarantee. Overall average of all bird species was69.35% (+/- 3.4 for 95% confidence interval) but this could be inflated, deflated or biased; this maybe especially true of species with high negative predictive accuracy. It has been suggested that BBSwas not a representative sample of landscapes and this could affect accuracies.