@article{mckerrow_davidson_rubino_faber-langendoen_dockter_2021, title={Quantifying the Representation of Plant Communities in the Protected Areas of the US: An Analysis Based on the US National Vegetation Classification Groups}, volume={12}, ISSN={["1999-4907"]}, DOI={10.3390/f12070864}, abstractNote={Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.}, number={7}, journal={FORESTS}, author={McKerrow, Alexa and Davidson, Anne and Rubino, Matthew and Faber-Langendoen, Don and Dockter, Daryn}, year={2021}, month={Jul} }
 @article{gergely_boykin_mckerrow_rubino_tarr_williams_2019, title={Gap Analysis Project (GAP) Terrestrial Vertebrate Species Richness Maps for the Conterminous U.S.}, url={http://dx.doi.org/10.3133/sir20195034}, DOI={10.3133/sir20195034}, abstractNote={..........................................................................................................................................................}, journal={Scientific Investigations Report}, publisher={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} }
 @misc{u.s. geological survey - gap analysis project species habitat richness_2019, DOI={10.5066/p9yw3zq2}, journal={U.S. Geological Survey}, year={2019} }
 @misc{blue-spotted salamander (ambystoma laterale) abupsx_conus_2001v1 range map_2018, DOI={10.5066/F73N22CM}, journal={U.S. Geological Survey}, year={2018} }
 @misc{cassin's vireo (vireo cassinii) bcavix_conus_2001v1 range map_2018, DOI={10.5066/F7XW4HS0}, journal={U.S. Geological Survey}, year={2018} }
 @misc{cave swallow (petrochelidon fulva) bcaswx_conus_2001v1 range map_2018, DOI={10.5066/F75T3JGG}, journal={U.S. Geological Survey}, year={2018} }
 @misc{chestnut-backed chickadee (poecile rufescens) bcbchx_conus_2001v1 range map_2018, DOI={10.5066/F7T43S3F}, journal={U.S. Geological Survey}, year={2018} }
 @misc{chuck-will's-widow (caprimulgus carolinensis) bcwwix_conus_2001v1 range map_2018, DOI={10.5066/F7V123S1}, journal={U.S. Geological Survey}, year={2018} }
 @misc{couch's kingbird (tyrannus couchii) bcokix_conus_2001v1 range map_2018, DOI={10.5066/F7N878R3}, journal={U.S. Geological Survey}, year={2018} }
 @article{impact of increased wood pellet demand on biodiversity in the south-eastern united states_2018, url={http://dx.doi.org/10.1111/gcbb.12554}, DOI={10.1111/gcbb.12554}, abstractNote={Abstract}, journal={GCB Bioenergy}, year={2018}, month={Nov} }
 @misc{masked booby (sula dactylatra) bmabox_conus_2001v1 range map_2018, DOI={10.5066/F7KH0M9J}, journal={U.S. Geological Survey}, year={2018} }
 @misc{osprey (pandion haliaetus) bosprx_conus_2001v1 range map_2018, DOI={10.5066/F7R78D70}, journal={U.S. Geological Survey}, year={2018} }
 @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} }
 @misc{pied-billed grebe (podilymbus podiceps) bpbgrx_conus_2001v1 range map_2018, DOI={10.5066/F7833R1T}, journal={U.S. Geological Survey}, year={2018} }
 @misc{prairie falcon (falco mexicanus) bprfax_conus_2001v1 range map_2018, DOI={10.5066/F7MG7NH8}, journal={U.S. Geological Survey}, year={2018} }
 @misc{rose-breasted grosbeak (pheucticus ludovicianus) brbgrx_conus_2001v1 range map_2018, DOI={10.5066/F7CV4GQC}, journal={U.S. Geological Survey}, year={2018} }
 @misc{yellow-bellied flycatcher (empidonax flaviventris) bybflx_conus_2001v1 range map_2018, DOI={10.5066/F76T0KMV}, journal={U.S. Geological Survey}, year={2018} }
 @book{costanza_beck_pyne_terando_rubino_white_collazo_2016, title={Assessing climate-sensitive ecosystems in the southeastern United States}, ISSN={2331-1258}, url={http://dx.doi.org/10.3133/ofr20161073}, DOI={10.3133/ofr20161073}, abstractNote={First posted August 11, 2016 For additional information, contact: Director, South Atlantic Water Science Center U.S. Geological Survey 3916 Sunset Ridge Rd Raleigh, N.C. 27607 http://nc.water.usgs.gov/ Climate change impacts ecosystems in many ways, from effects on species to phenology to wildfire dynamics. Assessing the potential vulnerability of ecosystems to future changes in climate is an important first step in prioritizing and planning for conservation. Although assessments of climate change vulnerability commonly are done for species, fewer have been done for ecosystems. To aid regional conservation planning efforts, we assessed climate change vulnerability for ecosystems in the Southeastern United States and Caribbean.First, we solicited input from experts to create a list of candidate ecosystems for assessment. From that list, 12 ecosystems were selected for a vulnerability assessment that was based on a synthesis of available geographic information system (GIS) data and literature related to 3 components of vulnerability—sensitivity, exposure, and adaptive capacity. This literature and data synthesis comprised “Phase I” of the assessment. Sensitivity is the degree to which the species or processes in the ecosystem are affected by climate. Exposure is the likely future change in important climate and sea level variables. Adaptive capacity is the degree to which ecosystems can adjust to changing conditions. Where available, GIS data relevant to each of these components were used. For example, we summarized observed and projected climate, protected areas existing in 2011, projected sea-level rise, and projected urbanization across each ecosystem’s distribution. These summaries were supplemented with information in the literature, and a short narrative assessment was compiled for each ecosystem. We also summarized all information into a qualitative vulnerability rating for each ecosystem.Next, for 2 of the 12 ecosystems (East Gulf Coastal Plain Near-Coast Pine Flatwoods and Nashville Basin Limestone Glade and Woodland), the NatureServe Habitat Climate Change Vulnerability Index (HCCVI) framework was used as an alternative approach for assessing vulnerability. Use of the HCCVI approach comprised “Phase II” of the assessment. This approach uses summaries of GIS data and models to develop a series of numeric indices for components of vulnerability. We incorporated many of the data sources used in Phase I, but added the results of several other data sources, including climate envelope modeling and vegetation dynamics modeling. The results of Phase II were high and low numeric vulnerability ratings for mid-century and the end of century for each ecosystem. The high and low ratings represented the potential range of vulnerability scores owing to uncertainties in future climate conditions and ecosystem effects.Of the 12 ecosystems assessed in the first approach, five were rated as having high vulnerability (Caribbean Coastal Mangrove, Caribbean Montane Wet Elfin Forest, East Gulf Coastal Plain Southern Loess Bluff Forest, Edwards Plateau Limestone Shrubland, and Nashville Basin Limestone Glade and Woodland). Six ecosystems had medium vulnerability, and one ecosystem had low vulnerability. For the two ecosystems assessed with both approaches, vulnerability ratings generally agreed. The assessment concluded by comparing the two approaches, identifying critical research needs, and making suggestions for future ecosystem vulnerability assessments in the Southeast and beyond. Research needs include reducing uncertainty in the degree of climate exposure likely in the future, as well as acquiring more information on how climate might affect biotic interactions and hydrologic processes. Ideally, a comprehensive vulnerability assessment would include both the narrative summaries that resulted from the synthesis in Phase I, as well as a numeric index that incorporates uncertainty as in Phase II.}, number={2016–10732016–1073}, journal={Open-File Report}, institution={US Geological Survey}, author={Costanza, Jennifer and Beck, Scott and Pyne, Milo and Terando, Adam and Rubino, Matthew J. and White, Rickie and Collazo, Jaime}, year={2016} }
 @article{tarr_rubino_costanza_mckerrow_collazo_abt_2016, title={Projected gains and losses of wildlife habitat from bioenergy-induced landscape change}, volume={9}, ISSN={1757-1693}, url={http://dx.doi.org/10.1111/gcbb.12383}, DOI={10.1111/gcbb.12383}, abstractNote={Abstract}, number={5}, journal={GCB Bioenergy}, publisher={Wiley}, author={Tarr, Nathan M. and Rubino, Matthew J. and Costanza, Jennifer K. and McKerrow, Alexa J. and Collazo, Jaime A. and Abt, Robert C.}, year={2016}, month={Aug}, pages={909–923} }
 @article{sackett_pow_rubino_aday_cope_kullman_rice_kwak_law_2015, title={Sources of endocrine-disrupting compounds in North Carolina waterways: A geographic information systems approach}, volume={34}, ISSN={0730-7268}, url={http://dx.doi.org/10.1002/ETC.2797}, DOI={10.1002/etc.2797}, abstractNote={Abstract}, number={2}, journal={Environmental Toxicology and Chemistry}, publisher={Wiley}, author={Sackett, Dana K. and Pow, Crystal Lee and Rubino, Matthew J. and Aday, D. Derek and Cope, W. Gregory and Kullman, Seth and Rice, James A. and Kwak, Thomas J. and Law, Mac}, year={2015}, month={Jan}, pages={437–445} }
 @article{russell_gale_muñoz_dorney_rubino_2014, title={A Spatially Explicit Model for Mapping Headwater Streams}, volume={51}, DOI={10.1111/jawr.12250}, abstractNote={Abstract}, number={1}, journal={JAWRA Journal of the American Water Resources Association}, publisher={Wiley-Blackwell}, author={Russell, Periann P. and Gale, Susan M. and Muñoz, Breda and Dorney, John R. and Rubino, Matthew J.}, year={2014}, month={Oct}, pages={226–239} }
 @article{hess_koch_rubino_eschelbach_drew_favreau_2006, title={Comparing the potential effectiveness of conservation planning approaches in central North Carolina, USA}, volume={128}, ISSN={["1873-2917"]}, DOI={10.1016/j.biocon.2005.10.003}, abstractNote={We compared four approaches to conservation site selection to protect forest biodiversity in the Triangle Region of North Carolina, USA. Using biological inventory data and an inventory-based conservation plan as benchmarks, we evaluated the potential effectiveness of a focal species plan and three “simple” plans (large forested patches, close to wetlands and riparian areas, diverse forest types). Effectiveness was measured in three ways: the number of inventory elements captured at least once by the plan (representation), the total number of inventory elements captured (completeness), and the proportion of land in the inventory-based plan included (overlap). We further examined the potential effectiveness of the simple plans by calculating their overlap with land identified by the focal species approach. The simple and focal species plans did not differ markedly in terms of representation, but diverged when completeness and overlap were considered. Although representation rates for all four plans were relatively high, lower rates for completeness and overlap raise concerns about long-term viability. The simple plans did not identify the same lands as the focal species plan, and are thus unlikely to provide appropriate habitat for the focal species. Each approach we tested failed to capture some subset of species and communities, highlighting the importance of explicit conservation targets and consideration of ecological processes. Forced to act quickly and with little data, our findings suggest using initially a set of complementary simple plans, each focused on a different habitat type. This should be considered a stopgap measure, however, while more sophisticated plans are constructed, defining explicit conservation targets and considering ecological processes.}, number={3}, journal={BIOLOGICAL CONSERVATION}, publisher={Elsevier BV}, author={Hess, GR and Koch, FH and Rubino, MJ and Eschelbach, KA and Drew, CA and Favreau, JM}, year={2006}, month={Mar}, pages={358–368} }
 @article{hess_bartel_leidner_rosenfeld_rubino_snider_ricketts_2006, title={Effectiveness of biodiversity indicators varies with extent, grain, and region}, volume={132}, ISSN={["1873-2917"]}, DOI={10.1016/j.biocon.2006.04.037}, abstractNote={Abstract The use of indicator taxa for conservation planning is common, despite inconsistent evidence regarding their effectiveness. These inconsistencies may be the result of differences among species and taxonomic groups studied, geographic location, or scale of analysis. The scale of analysis can be defined by grain and extent, which are often confounded. Grain is the size of each observational unit and extent is the size of the entire study area. Using species occurrence records compiled by NatureServe from survey data, range maps, and expert opinion, we examined correlations in species richness between each of seven taxa (amphibians, birds, butterflies, freshwater fish, mammals, freshwater mussels, and reptiles) and total richness of the remaining six taxa at varying grains and extents in two regions of the US (Mid-Atlantic and Pacific Northwest). We examined four different spatial units of interest: hexagon (∼649 km 2 ), subecoregion (3800–34,000 km 2 ), ecoregion (8300–79,000 km 2 ), and geographic region (315,000–426,000 km 2 ). We analyzed the correlations with varying extent of analysis (grain held constant at the hexagon) and varying grain (extent held constant at the region). The strength of correlation among taxa was context dependent, varying widely with grain, extent, region, and taxon. This suggests that (1) taxon, grain, extent, and study location explain, in part, inconsistent results of previous studies; (2) planning based on indicator relationships developed at other grains or extents should be undertaken cautiously; and (3) planning based on indicator relationships developed in other geographic locations is risky, even if planning occurs at an equivalent grain and extent.}, number={4}, journal={BIOLOGICAL CONSERVATION}, publisher={Elsevier BV}, author={Hess, George R. and Bartel, Rebecca A. and Leidner, Allison K. and Rosenfeld, Kristen M. and Rubino, Matthew J. and Snider, Sunny B. and Ricketts, Taylor H.}, year={2006}, month={Oct}, pages={448–457} }
 @article{favreau_drew_hess_rubino_koch_eschelbach_2006, title={Recommendations for assessing the effectiveness of surrogate species approaches}, volume={15}, ISSN={["1572-9710"]}, DOI={10.1007/s10531-005-2631-1}, number={12}, journal={BIODIVERSITY AND CONSERVATION}, publisher={Springer Nature}, author={Favreau, Jorie M. and Drew, C. Ashton and Hess, George R. and Rubino, Matthew J. and Koch, Frank H. and Eschelbach, Katherine A.}, year={2006}, month={Nov}, pages={3949–3969} }
 @article{stefanski_rubino_hess_2003, title={Estimating patch occupancy when patches are incompletely surveyed}, volume={2543}, journal={Insect Biochemistry and Molecular Biology}, author={Stefanski, L. A. and Rubino, M. J. and Hess, G. R.}, year={2003}, pages={1–20} }
 @article{rubino_hess_2003, title={Planning open spaces for wildlife 2: modeling and verifying focal species habitat}, volume={64}, ISSN={["1872-6062"]}, DOI={10.1016/s0169-2046(02)00203-7}, abstractNote={In the face of human population growth that is transforming the Earth, scientists, land managers, and planners are working to prevent, mitigate, and reverse the consequent loss of species, ecosystems, and landscapes. Because of the need to act quickly with incomplete data, a number of shortcuts have been developed that rely on identifying key species for planning efforts. By developing conservation plans for a small set of carefully selected focal species, planners hope to create a protective umbrella for a wider array of species and functional landscapes. In an earlier paper, we described an approach for selecting a set of focal species. In this paper, we report a process for the rapid identification and verification of potential habitat for a focal species. Using the barred owl as an example, we present the process for a suburbanizing region of North Carolina, USA. The barred owl was selected to represent bottomland hardwood and forested wetland landscapes in the region. Using a geographic information system (GIS), we assembled data layers from readily available remotely sensed, conventional survey, and physiographic data to create a model of barred owl habitat. Barred owls occupy bottomland hardwood forests, which we identified using land cover, soils, and wetlands data. We eliminated from consideration bottomland forest habitat within 100 m of a road and within 60 m of open vegetative cover. Patches of the remaining bottomland forest larger than 86 ha in size were considered large enough to meet all barred owl habitat needs. Simple presence/absence surveys detected barred owls in approximately 65% of patches identified by our model as suitable habitat. We tested the barred owl’s suitability as an umbrella for bottomland forest species using an existing database of rare and outstanding elements of natural diversity. Umbrella coverage for barred owl habitat (bottomland forest patches≥86 ha) varied with taxa from 0% for invertebrate species to 75% for vertebrate species. However, umbrella coverage for all bottomland forest, including patches <86 ha, was at or near 100% for all taxa. The relatively simple modeling and verification processes we used can be carried out with a minimal amount of data and time, making it an attractive tool in situations where time and resources are in short supply.}, number={1-2}, journal={LANDSCAPE AND URBAN PLANNING}, publisher={Elsevier BV}, author={Rubino, MJ and Hess, GR}, year={2003}, month={Jun}, pages={89–104} }