Community science validates climate suitability projections from ecological niche modeling.

التفاصيل البيبلوغرافية
العنوان:	Community science validates climate suitability projections from ecological niche modeling.
المؤلفون:	Saunders SP; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., Michel NL; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., Bateman BL; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., Wilsey CB; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., Dale K; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., LeBaron GS; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA., Langham GM; National Audubon Society, 225 Varick Street, New York, New York, 10014, USA.
المصدر:	Ecological applications : a publication of the Ecological Society of America [Ecol Appl] 2020 Sep; Vol. 30 (6), pp. e02128. Date of Electronic Publication: 2020 Apr 23.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Ecological Society of America Country of Publication: United States NLM ID: 9889808 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1051-0761 (Print) Linking ISSN: 10510761 NLM ISO Abbreviation: Ecol Appl Subsets: MEDLINE
أسماء مطبوعة:	Publication: Washington, D.C. : Ecological Society of America Original Publication: Tempe, AZ : The Society, 1991-
مواضيع طبية MeSH:	Climate Change* , Ecosystem*, Animals ; Birds ; Models, Theoretical ; Reproducibility of Results ; United States
مستخلص:	Climate change poses an intensifying threat to many bird species and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated in situ and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate. (© 2020 by the Ecological Society of America.)
References:	Anderson, R. P. 2013. A framework for using niche models to estimate impacts of climate change on species distributions: niche models and climate change. Annals of the New York Academy of Sciences 1297:8-28. Araújo, M. B., and A. T. Peterson. 2012. Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527-1539. Araújo, M. B., R. G. Pearson, W. Thuiller, and M. Erhard. 2005. Validation of species-climate impact models under climate change. Global Change Biology 11:1504-1513. Baker, D. J., A. J. Hartley, S. H. M. Butchart, and S. G. Willis. 2016. Choice of baseline climate data impacts projected species’ responses to climate change. Global Change Biology 22:2392-2404. Barbet-Massin, M., Q. Rome, C. Villemant, and F. Courchamp. 2018. Can species distribution models really predict the expansion of invasive species? PLoS ONE 13:e0193085. Bateman, B. L., A. M. Pidgeon, V. C. Radeloff, J. Vanderwal, W. E. Thogmartin, S. J. Vavrus, and P. J. Heglund. 2015. The pace of past climate change vs. potential bird distributions and land use in the United States. Global Change Biology 22:1130-1144. Bradsworth, N., J. G. White, B. Isaac, and R. Cooke. 2017. Species distribution models derived from citizen science data predict the fine scale movements of owls in an urbanizing landscape. Biological Conservation 213:27-35. Brown, J. H., D. W. Mehlman, and G. C. Stevens. 1995. Spatial variation in abundance. Ecology 76:2028-2043. Buckley, T. R., K. Marske, and D. Attanayake. 2010. Phylogeography and ecological niche modelling of the New Zealand stick insect Clitarchus hookeri (White) support survival in multiple coastal refugia. Journal of Biogeography 37:682-695. Chen, I.-C., J. K. Hill, R. Ohlemuller, D. B. Roy, and C. D. Thomas. 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333:1024-1026. Cooper, C. B., J. Shirk, and B. Zuckerberg. 2014. The invisible prevalence of citizen science in global research: migratory birds and climate change. PLoS ONE 9:e106508. Costa, G. C., C. Nogueira, R. B. Machado, and G. R. Colli. 2010. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biological Conservation 19:883-899. Coxen, C. L., J. K. Frey, S. A. Carleton, and D. P. Collins. 2017. Species distribution models for a migratory bird based on citizen science and satellite tracking data. Global Ecology and Conservation 11:298-311. Currie, D. J., and S. Venne. 2017. Climate change is not a major driver of shifts in the geographical distributions of North American birds: avian distributions and climate change. Global Ecology and Biogeography 26:333-346. Distler, T., J. G. Schuetz, J. Velásquez-Tibatá, and G. M. Langham. 2015. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. Journal of Biogeography 42:976-988. Doblas-Reyes, F. J., I. Andreu-Burillo, Y. Chikamoto, J. García-Serrano, V. Guemas, M. Kimoto, T. Mochizuki, L. R. L. Rodrigues, and G. J. van Oldenborgh. 2013. Initialized near-term regional climate change prediction. Nature Communications 4:1715. Dormann, C. F., et al. 2012. Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography 39:2119-2131. Elith, J., et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129-151. Elith, J., M. Kearney, and S. Phillips. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342. Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, M. Iliff, and S. Kelling. 2018. eBird status and trends. Version: November 2018. Cornell Lab of Ornithology, Ithaca, New York, USA. https://ebird.org/science/status-and-trends. Foden, W. B., et al. 2018. Climate change vulnerability assessment of species. WIREs Climate Change 10:e551. Fordham, D. A., et al. 2012. Plant extinction risk under climate change: Are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology 18:1357-1371. Fordham, D. A., C. Bertelsmeier, B. W. Brook, R. Early, D. Neto, S. C. Brown, S. Ollier, and M. B. Araújo. 2018. How complex should models be? Comparing correlative and mechanistic range dynamics models. Global Change Biology 24:1357-1370. Fournier, A. M. V., K. L. Drake, and D. C. Tozer. 2017. Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds. Journal of Avian Biology 48:1556-1562. Freeman, B. G., M. N. Scholer, V. Ruiz-Gutierrez, and J. W. Fitzpatrick. 2018. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proceedings of the National Academy of Sciences USA 115:11982-11987. Gelman, A., and J. Hill. 2006. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, UK. Gillings, S., D. E. Balmer, and R. J. Fuller. 2015. Directionality of recent bird distribution shifts and climate change in Great Britain. Global Change Biology 21:2155-2168. Grand, J., C. Wilsey, J. X. Wu, and N. L. Michel. 2019. The future of North American grassland birds: incorporating persistent and emergent threats into full annual cycle conservation priorities. Conservation Science and Practice 1:e20. Gu, W., and R. K. Swihart. 2004. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation 116:195-203. Guillera-Arroita, G., J. J. Lahoz-Monfort, J. Elith, A. Gordon, H. Kujala, P. E. Lentini, M. A. McCarthy, R. Tingley, and B. A. Wintle. 2015. Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography 24:276-292. Guisan, A., et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters 16:1424-1435. Hampe, A. 2004. Bioclimate envelope models: what they detect and what they hide. Global Ecology and Biogeography 13:469-471. Hartmann,D. L., A. M. G. Klein Tank, M. Rusticucci, L. V Alexander, S. Brönnimann, Y. A. R. Charabi, F. J. Dentener, E. J. Dlugokencky, D. R. Easterling, A. Kaplan, et al. 2013. Observations: atmosphere and surface. In Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Vol. 9781107057999). Pages 159-254. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781107415324.008. Heikkinen, R. K., M. Luoto, M. B. Araújo, R. Virkkala, W. Thuiller, and M. T. Sykes. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography: Earth and Environment 30:751-777. Hochachka, W. M., D. Fink, R. A. Hutchinson, D. Sheldon, W. K. Wong, and S. Kelling. 2012. Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution 27:130-137. Jenni, L., and M. Kéry. 2003. Timing of autumn bird migration under climate change: advances in long-distance migrants, delays in short-distance migrants. Proceedings of the Royal Society B 270:1467-1471. Kellner, K.2015. Package jagsUI: a wrapper around rjags to streamline JAGS analyses. https://cran.r-project.org/web/packages/jagsUI/index.html. Kéry, M. 2010. Introduction to WinBUGS for ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses. Academic Press, Cambridge, Massachusetts, USA. Kéry, M., and M. Schaub 2012. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Cambridge, Massachusetts, USA. La Sorte, F. A., and F. R. Thompson. 2007. Poleward shifts in winter ranges of North American birds. Ecology 88:1803-1812. La Sorte, F. A., S. H. M. Butchart, W. Jetz, and K. Böhning-Gaese. 2014. Range-wide latitudinal and elevational temperature gradients for the world’s terrestrial birds: implications under global climate change. PLoS ONE 9:e98361. La Sorte, F. A., C. A. Lepczyk, J. L. Burnett, A. H. Hurlbert, M. W. Tingley, and B. Zuckerberg. 2018. Opportunities and challenges for big data ornithology. Condor 120:414-426. Langham, G. M., J. G. Schuetz, T. Distler, C. U. Soykan, and C. Wilsey. 2015. Conservation status of North American birds in the face of future climate change. PLoS ONE 10:e0135350. Linden, D. W., and G. J. Roloff. 2015. Improving inferences from short-term ecological studies with Bayesian hierarchical modeling: white-headed woodpeckers in managed forests. Ecology and Evolution 5:3378-3388. Link, W. A., and R. J. Barker. 2006. Model weights and the foundations of multimodel inference. Ecology 87:2626-2635. Lobo, J. M., A. Jiménez-Valverde, and R. Real. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17:145-151. MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248-2255. MacKenzie, D. I., J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200-2207. Maclean, I. M. D., et al. 2008. Climate change causes rapid changes in the distribution and site abundances of birds in winter. Global Change Biology 14:2489-2500. MacNally, R. 2000. Regression and model-building in conservation biology, biogeography and ecology: the distinction between-and reconciliation of-‘predictive’ and ‘explanatory’ models. Biodiversity and Conservation 9:655-671. Madin, E. M. P., N. C. Ban, Z. A. Doubleday, T. H. Holmes, G. T. Pecl, and F. Smith. 2012. Socio-economic and management implications of range-shifting species in marine systems. Global Environmental Change 22:137-146. Matthews, S. N., L. R. Iverson, A. M. Prasad, and M. P. Peters. 2011. Changes in potential habitat of 147 North American breeding bird species in response to redistribution of trees and climate following predicted climate change. Ecography 34:933-945. Maurer, E. P. 2007. Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios. Climatic Change 82:309-325. Maxwell, S. L., R. A. Fuller, T. M. Brooks, and J. E. M. Watson. 2016. Biodiversity: the ravages of guns, nets and bulldozers. Nature News 536:143. Meehan, T. D., W. Jetz, and J. H. Brown. 2004. Energetic determinants of abundance in winter landbird communities. Ecology Letters 7:532-537. Meehan, T. D., S. Arthur, N. L. Michel, C. B. Wilsey, and G. M. Langham. 2019. Trends in tricolored blackbird colony size: 2008 through 2017. Journal of Wildlife Management 83:1237-1243. Mehlman, D. W. 1997. Change in avian abundance across the geographic range in response to environmental change. Ecological Applications 7:614-624. Meineri, E., A.-S. Deville, D. Grémillet, M. Gauthier-Clerc, and A. Béchet. 2015. Combining correlative and mechanistic habitat suitability models to improve ecological compensation. Biological Reviews 90:314-329. Møller, A. P., D. Rubolini, and E. Lehikoinen. 2008. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proceedings of the National Academy of Sciences USA 105:16195-16200. Møller, A. P., W. Fiedler, and P. Berthold. 2010. Effects of climate change on birds. Oxford University Press, Oxford, UK. Morán-Ordóñez, A., J. J. Lahoz-Monfort, J. Elith, and B. A. Wintle. 2017. Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: What factors influence the reliability of predictions? Global Ecology and Biogeography 26:371-384. Mota-Vargas, C., and O. R. Rojas-Soto. 2016. Taxonomy and ecological niche modeling: Implications for the conservation of wood partridges (genus Dendrortyx). Journal for Nature Conservation 29:1-13. Mutshinda, C. M., Z. V. Finkel, and A. J. Irwin. 2013. Which environmental factors control phytoplankton populations? A Bayesian variable selection approach. Ecological Modelling 269:1-8. Niven, D. K., and G. S. Butcher. 2009. Northward shifts in the abundance of North American birds in early winter: a response to warmer winter temperatures? National Audubon Society. http://www.audubon.org/sites/default/files/documents/report.pdf. Ntzoufras, I. 2002. Gibbs variable selection using BUGS. Journal of Statistical Software 7:1-19. Opdam, P., and D. Wascher. 2004. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biological Conservation 117:285-297. Parmesan, C., and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37. Payne, B. L., and J. Bro-Jørgensen. 2016. Disproportionate climate-induced range loss forecast for the most threatened African antelopes. Current Biology 26:1200-1205. Pearce-Higgins, J. W., R. E. Green, and R. Green. 2014. Birds and climate change: impacts and conservation responses. Cambridge University Press, Cambridge, UK. Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecology and Biogeography 12:361-371. Peterson, A. T., and C. R. Robins. 2003. Using ecological-niche modeling to predict barred owl invasions with implications for spotted owl conservation. Conservation Biology 17:1161-1165. Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martínez-Meyer, M. Nakamura, and M. Araújo. 2011. Ecological niches and geographic distributions. Pages 328. Princeton University Press, Princeton, New Jersey, USA. Peterson, A. T., A. G. Navarro-Sigüenza, E. Martínez-Meyer, A. P. Cuervo-Robayo, H. Berlanga, and J. Soberón. 2015. Twentieth century turnover of Mexican endemic avifaunas: landscape change versus climate drivers. Science Advances 1:e1400071. Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing. Volume 124.Pages 20-22 Vienna, Austria. Princé, K., and B. Zuckerberg. 2015. Climate change in our backyards: the reshuffling of North America’s winter bird communities. Global Change Biology 21:572-585. Qiao, H., J. Soberón, and A. T. Peterson. 2015. No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution 6:1126-1136. R Core Team. 2019. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.r-project.org. Ramirez-Villegas, J., F. Cuesta, C. Devenish, M. Peralvo, A. Jarvis, and C. A. Arnillas. 2014. Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change. Journal for Nature Conservation 22:391-404. Randin, C. F., T. Dirnböck, S. Dullinger, N. E. Zimmermann, M. Zappa, and A. Guisan. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography 33:1689-1703. Ridgeway, G.2010. gbm: generalized boosted regression models: R package version 1.6-3.1. http://CRAN.R-project.org/package=gbm. Roberts, D. R., et al. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40:913-929. Rougier, T., G. Lassalle, H. Drouineau, N. Dumoulin, T. Faure, G. Deffuant, E. Rochard, and P. Lambert. 2015. The combined use of correlative and mechanistic species distribution models benefits low conservation status species. PLoS ONE 10:e0139194. Roy, C., et al. 2019. Monitoring boreal avian populations: how can we estimate trends and trajectories from noisy data? Avian Conservation and Ecology 14:8. Royle, J. A., and R. M. Dorazio. 2008. Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press, Cambridge, Massachusetts, USA. Royle, J. A., and M. Kéry. 2007. A Bayesian state-space formulation of dynamic occupancy models. Ecology 88:1813-1823. Sauer, J. R., and W. A. Link. 2011. Analysis of the North American Breeding Bird Survey using hierarchical models. Auk 128:87-98. Sauer, J. R., J. E. Fallon, and R. Johnson. 2003. Use of North American Breeding Bird Survey data to estimate population change for bird conservation regions. Journal of Wildlife Management 67:372-389. Sauer, J. R., K. L. Pardieck, D. J. Ziolkowski Jr., A. C. Smith, M.-A. R. Hudson, V. Rodriguez, H. Berlanga, D. K. Niven, and W. A. Link. 2017. The first 50 years of the North American Breeding Bird Survey. Condor 119:576-593. Saunders, S. P., L. Ries, K. S. Oberhauser, W. E. Thogmartin, and E. F. Zipkin. 2018. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography 41:278-290. Schuetz, J. G., G. M. Langham, C. U. Soykan, C. B. Wilsey, T. Auer, and C. C. Sanchez. 2015. Making spatial prioritizations robust to climate change uncertainties: a case study with North American birds. Ecological Applications 25:1819-1831. Schwartz, M. W. 2012. Using niche models with climate projections to inform conservation management decisions. Biological Conservation 155:149-156. Searcy, C. A., and H. B. Shaffer. 2014. Field validation supports novel niche modeling strategies in a cryptic endangered amphibian. Ecography 37:983-992. Sirois-Delisle, C., and J. T. Kerr. 2018. Climate change-driven range losses among bumblebee species are poised to accelerate. Scientific Reports 8:14464. Sohl, T. L. 2014. The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075. PLoS ONE 9:e112251. Soykan, C. U., J. Sauer, J. G. Schuetz, G. S. LeBaron, K. Dale, and G. M. Langham. 2016. Population trends for North American winter birds based on hierarchical models. Ecosphere 7:e01351. Specht, H. M., and T. W. Arnold. 2018. Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator-prey dynamics. Journal of Applied Ecology 55:2854-2864. Srivathsa, A., M. Puri, N. S. Kumar, D. Jathanna, and K. U. Karanth. 2018. Substituting space for time: empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling. Journal of Applied Ecology 55:754-765. Stanton Jr., R. A., F. R. Thompson III, and D. C. Kesler. 2015. Site occupancy of Brown-headed Nuthatches varies with habitat restoration and range-limit context. Journal of Wildlife Management 79:917-926. Stephens, P. A., et al. 2016. Consistent response of bird populations to climate change on two continents. Science 352:84-87. Strong, C., B. Zuckerberg, J. L. Betancourt, and W. D. Koenig. 2015. Climatic dipoles drive two principal modes of North American boreal bird irruption. Proceedings of the National Academy of Sciences USA 112:E2795-E2802. Tenan, S., R. B. O’Hara, I. Hendriks, and G. Tavecchia. 2014. Bayesian model selection: the steepest mountain to climb. Ecological Modelling 283:62-69. Thomas, C. D., et al. 2004. Extinction risk from climate change. Nature 427:145-148. Thorne, J. H., C. Seo, A. Basabose, M. Gray, N. M. Belfiore, and R. J. Hijmans. 2013. Alternative biological assumptions strongly influence models of climate change effects on mountain gorillas. Ecosphere 4:108. Tingley, M. W., W. B. Monahan, S. R. Beissinger, and C. Moritz. 2009. Birds track their Grinnellian niche through a century of climate change. Proceedings of the National Academy of Sciences USA 106:19637-19643. Tingley, M. W., M. S. Koo, C. Moritz, A. C. Rush, and S. R. Beissinger. 2012. The push and pull of climate change causes heterogeneous shifts in avian elevational ranges. Global Change Biology 18:3279-3290. Tomotani, B. M., H. Jeugd, P. Gienapp, I. Hera, J. Pilzecker, C. Teichmann, and M. E. Visser. 2018. Climate change leads to differential shifts in the timing of annual cycle stages in a migratory bird. Global Change Biology 24:823-835. Tulloch, A. I. T., H. P. Possingham, L. N. Joseph, J. Szabo, and T. G. Martin. 2013. Realising the full potential of citizen science monitoring programs. Biological Conservation 165:128-138. U.S. Geological Survey. 2014. NLCD 2011 Land Cover (2011 Edition, amended 2014) - National Geospatial Data Asset (NGDA) Land Use Land Cover. U.S. Geological Survey, Reston, Virginia, USA. VanDerWal, J., L. P. Shoo, C. N. Johnson, and S. E. Williams. 2009. Abundance and the environmental niche: environmental suitability estimated from ecological niche models predicts the upper limit of local abundance. American Naturalist 174:282-291. Virkkala, R. 2016. Long-term decline of southern boreal forest birds: consequence of habitat alteration or climate change? Biodiversity Conservation 25:151-167. Wang, Y., N. Casajus, C. Buddle, D. Berteaux, and M. Larriv. 2018. Predicting the distribution of poorly-documented species, northern black widow (Latrodectus variolus) and black purse-web spider (Sphodros niger), using museum specimens and citizen science data. PLoS ONE 13:e0201094. Warren, D. L., and S. N. Seifert. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications 21:335-342. Weber, M. M., R. D. Stevens, J. A. F. Diniz-Filho, and C. E. V. Grelle. 2017. Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta-analysis. Ecography 40:817-828. Wenger, S. J., and J. D. Olden. 2012. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution 3:260-267. Wessely, J., K. Hülber, A. Gattringer, M. Kuttner, D. Moser, W. Rabitsch, S. Schindler, S. Dullinger, and F. Essl. 2017. Habitat-based conservation strategies cannot compensate for climate-change-induced range loss. Nature Climate Change 7:823-827. Wiens, J. J. 2016. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biology 14:e2001104. Wiens, J. J., D. Stralberg, D. Jongsomijit, C. A. Howell, and M. A. Snyder. 2009. Niches, models, and climate change: Assessing the assumptions and uncertainties. Proceedings of the National Academy of Sciences USA 106:19729-19736. Wilsey, C., L. Taylor, B. Bateman, C. Jensen, N. L. Michel, A. Panjabi, and G. Langham. 2019. Climate policy action needed to reduce vulnerability of conservation-reliant grassland birds in North America. Conservation Science and Practice 1:e21. Wright, A. N., R. J. Hijmans, M. W. Schwartz, and H. B. Shaffer. 2015. Multiple sources of uncertainty affect metrics for ranking conservation risk under climate change. Diversity and Distributions 21:111-122. Wu, J. X., C. B. Wilsey, L. Taylor, and G. W. Schuurman. 2018. Projected avifaunal responses to climate change across the U.S. National Park System. PLoS ONE 13:e0190557. Yackulic, C. B., R. Chandler, E. F. Zipkin, J. A. Royle, J. D. Nichols, E. H. C. Grant, and S. Veran. 2013. Presence-only modelling using MAXENT: When can we trust the inferences? Methods in Ecology and Evolution 4:236-243. Zurell, D., C. H. Graham, L. Gallien, W. Thuiller, and N. E. Zimmermann. 2018. Long-distance migratory birds threatened by multiple independent risks from global change. Nature Climate Change 8:992-996. Zwiener, V. P., A. P. Padial, M. C. M. Marques, F. V. Faleiro, R. Loyla, and A. T. Peterson. 2017. Planning for conservation and restoration under climate and land use change in the Brazilian Atlantic Forest. Diversity and Distributions 23:955-966.
فهرسة مساهمة:	Keywords: bioclimatic envelope model; bluebirds; citizen science; climate change; dynamic occupancy model; nuthatches; species distribution model; validation
سلسلة جزيئية:	Dryad 10.5061/dryad.x3ffbg7fp
تواريخ الأحداث:	Date Created: 20200331 Date Completed: 20210106 Latest Revision: 20210106
رمز التحديث:	20240628
DOI:	10.1002/eap.2128
PMID:	32223029
قاعدة البيانات:	MEDLINE

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تدمد:	1051-0761
DOI:	10.1002/eap.2128