@book{rubino_mckerrow_tarr_williams_2022, title={Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data}, url={https://doi.org/10.3133/tm2A19}, DOI={10.3133/tm2A19}, abstractNote={First posted August 29, 2022 For additional information, contact: Director, Core Science Analytics and SynthesisU.S. Geological SurveyBox 25046, Mail Stop 302Denver, CO 80225 The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random.In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.}, author={Rubino, Matthew J. and McKerrow, Alexa J. and Tarr, Nathan M. and Williams, Steven G.}, year={2022} }
 @article{davidson_dunn_gergely_mckerrow_williams_case_2021, title={Refining the coarse filter approach: Using habitat-based species models to identify rarity and vulnerabilities in the protection of US biodiversity}, volume={28}, ISSN={["2351-9894"]}, DOI={10.1016/j.gecco.2021.e01598}, abstractNote={Preserving biodiversity and its many components is a priority of conservation science and how to efficiently allocate resources to preserve healthy populations of as many species, habitats, and ecosystems as possible. We used the U.S. Geological Survey (USGS) Gap Analysis Project (GAP) species models released in 2018, which identify predicted habitats for terrestrial vertebrates in the conterminous United States, to illustrate hotspots of biodiversity for the major taxonomic groups. This collection represents the first complete compilation of terrestrial vertebrate species models for the conterminous United States (U.S. Geological Survey (USGS), 2018a). We used the species models but not the available subspecies models; this resulted in the inclusion of 282 amphibian models, 621 bird models, 365 mammal models, and 322 reptiles in our analysis. We also used population trend information and made spatial queries to characterize species in three dimensions: geographic range (small or large), habitat breadth (narrow or wide), and population trend (decreasing vs stable or increasing). This characterization allowed us to divide the species into eight groups (A-H) with similar characteristics. Group A species (large geographic range, wide habitat breadth, and stable or increasing population trend) are species that are common now with no indication of becoming rare. Species B-H have theoretical or known characteristics that could lead them to become rare with the H species exhibiting small geographic range, narrow habitat breadth, and decreasing population trend. Finally, we evaluated the prevalence of mapped habitat on protected lands for each species, exploring the patterns of representation in the rare species groups by ecoregion. The species we identified with population and habitat use characteristics that potentially predispose them to being or becoming rare represented a large percentage of each taxon. Potentially rare species were widely distributed among ecoregions. Of the 20 ecoregions in the country, 14 have a greater number of rare species than the national average for at least one taxon. Protection of the habitat for the majority of these rare species is below that recommended (17% of available habitat) by the Convention on Biological Diversity (CBD). The Everglades ecoregion was the only ecoregion that protected more than half of its rare or potentially rare species.}, journal={GLOBAL ECOLOGY AND CONSERVATION}, author={Davidson, Anne and Dunn, Leah and Gergely, Kevin and McKerrow, Alexa and Williams, Steven and Case, Mackenzie}, year={2021}, month={Aug} }
 @book{gergely_boykin_mckerrow_rubino_tarr_williams_2019, title={Gap Analysis Project (GAP) Terrestrial Vertebrate Species Richness Maps for the Conterminous U.S.}, url={https://doi.org/10.3133/sir20195034}, DOI={10.3133/sir20195034}, abstractNote={..........................................................................................................................................................}, journal={Scientific Investigations Report}, institution={US Geological Survey}, author={Gergely, Kevin J. and Boykin, Kenneth G. and McKerrow, Alexa J. and Rubino, Matthew J. and Tarr, Nathan M. and Williams, Steven G.}, year={2019} }
 @article{clement_nichols_collazo_terando_hines_williams_2019, title={Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution}, volume={9}, ISBN={2045-7758}, url={https://doi.org/10.1002/ece3.4890}, DOI={10.1002/ece3.4890}, abstractNote={Abstract}, number={4}, journal={ECOLOGY AND EVOLUTION}, publisher={Wiley}, author={Clement, Matthew J. and Nichols, James D. and Collazo, Jaime A. and Terando, Adam J. and Hines, James E. and Williams, Steven G.}, year={2019}, month={Feb}, pages={1985–2003} }
 @article{mckerrow_tarr_rubino_williams_2018, title={Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution}, volume={24}, ISSN={["1472-4642"]}, url={http://dx.doi.org/10.1111/ddi.12779}, DOI={10.1111/ddi.12779}, abstractNote={Abstract}, number={10}, journal={DIVERSITY AND DISTRIBUTIONS}, author={McKerrow, Alexa J. and Tarr, Nathan M. and Rubino, Matthew J. and Williams, Steven G.}, year={2018}, month={Oct}, pages={1464–1477} }
 @article{yirka_collazo_williams_cobb_2018, title={Persistence-Based Area Prioritization for Conservation: Applying Occupancy and Habitat Threats and Risks Analyses}, volume={9}, ISSN={["1944-687X"]}, DOI={10.3996/112017-JFWM-089}, abstractNote={Abstract}, number={2}, journal={JOURNAL OF FISH AND WILDLIFE MANAGEMENT}, author={Yirka, Liani M. and Collazo, Jaime A. and Williams, Steven G. and Cobb, David T.}, year={2018}, month={Dec}, pages={543–553} }
 @article{martinuzzi_withey_pidgeon_plantinga_mckerrow_williams_helmers_radeloff_2015, title={Future land-use scenarios and the loss of wildlife habitats in the southeastern United States}, volume={25}, ISSN={["1939-5582"]}, DOI={10.1890/13-2078.1}, abstractNote={Land‐use change is a major cause of wildlife habitat loss. Understanding how changes in land‐use policies and economic factors can impact future trends in land use and wildlife habitat loss is therefore critical for conservation efforts. Our goal here was to evaluate the consequences of future land‐use changes under different conservation policies and crop market conditions on habitat loss for wildlife species in the southeastern United States. We predicted the rates of habitat loss for 336 terrestrial vertebrate species by 2051. We focused on habitat loss due to the expansion of urban, crop, and pasture. Future land‐use changes following business‐as‐usual conditions resulted in relatively low rates of wildlife habitat loss across the entire Southeast, but some ecoregions and species groups experienced much higher habitat loss than others. Increased crop commodity prices exacerbated wildlife habitat loss in most ecoregions, while the implementation of conservation policies (reduced urban sprawl, and payments for land conservation) reduced the projected habitat loss in some regions, to a certain degree. Overall, urban and crop expansion were the main drivers of habitat loss. Reptiles and wildlife species associated with open vegetation (grasslands, open woodlands) were the species groups most vulnerable to future land‐use change. Effective conservation of wildlife habitat in the Southeast should give special consideration to future land‐use changes, regional variations, and the forces that could shape land‐use decisions.}, number={1}, journal={ECOLOGICAL APPLICATIONS}, author={Martinuzzi, Sebastian and Withey, John C. and Pidgeon, Anna M. and Plantinga, Andrew J. and Mckerrow, Alexa J. and Williams, Steven G. and Helmers, David P. and Radeloff, Volker C.}, year={2015}, month={Jan}, pages={160–171} }