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The relationship between scale and predictor variables in species distribution models applied to conservation

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Abstract

Species distribution models have been used to assist decision-making in many different aspects of conservation, restoration, and environmental management. However, to apply species distribution models effectively, we need to discriminate between suitable and unsuitable environments and the models need to be developed at fine scales (i.e. covering small areas at a fine resolution). These characteristics allow more precise decision-making for heterogeneous environments in smaller areas, such as biomes. We also need to understand the potential limiting factors in relation to these models better, including the effects of sample bias in species occurrence records and the potential mismatch between the scale at which the models were built and the scale at which the predictor variables interact with species occurrence. Here we evaluate the effects of two methods used to reduce bias (geographic vs. environmental filters) and three predictor variable types (climactic, local and biotic) on model predictions. We explore these issues for the hyacinth macaw (Anodorhynchus hyacinthinus), a globally vulnerable species in the Pantanal biome of central South America. We consider broad-scale variables, local-scale habitat associations, and the interactions of the macaw with two plant species that provide its food and nesting location. Our results show that using broad-scale climate variables for local-scale models (i.e., models with a fine resolution with a small extent) can generate predictive distribution models that underpredict suitability. Using local and biotic variables generates more accurate models with predictions consistent with the known distribution of the bird species. Although not commonly used, local-scale variables strongly affect model performance by increasing accuracy, reducing omission error, and leading to more conservative predictions. On the other hand, these methods lead to variable results in relation to bias reduction, with their efficiency depending on the amount of sampling bias in the occurrence records. In conclusion, local variables and the method of bias reduction play an important role in species distribution models. Fine resolution models constructed at the local scale for small areas show the greatest skill in predicting species distribution.

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References

  • Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541–545. https://doi.org/10.1111/ecog.01132

    Article  Google Scholar 

  • Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skillstatistic (TSS). J Appl Ecol 43(6):1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x

    Article  Google Scholar 

  • Anderson RP, Raza A (2010) The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J Biogeogr 37:1378–1393. https://doi.org/10.1111/j.1365-2699.2010.02290.x

    Article  Google Scholar 

  • Antas PT, Carrara L, Yabe R de S, Ubaid F, Júnior S de O, Vasques E, Ferreira L (2010) A arara-azul na Reserva Particular de Patrimônio Natural SESC Pantanal. Rio de Janeiro

  • Atauchi PJ, Peterson AT, Flanagan J (2018) Species distribution models for Peruvian plantcutter improve with consideration of biotic interactions. J Avian Biol 49:01617. https://doi.org/10.1111/jav.01617

    Article  Google Scholar 

  • Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38(1):1–8

    Article  Google Scholar 

  • BirdLife International (2020) Species factsheet: Anodorhynchus hyacinthinus. http://www.birdlife.org on 03/06/2020.

  • Boria RA, Olson LE, Goodman SM, Anderson RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275:73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012

    Article  Google Scholar 

  • Bradie J, Leung B (2017) A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J Biogeogr 44:1344–1361. https://doi.org/10.1111/jbi.12894

    Article  Google Scholar 

  • Cardoso MRD, Marcuzzo FFN (2010) Mapeamento de três decênios da precipitação pluviométrica total e sazonal do bioma Pantanal (No. 3). Cáceres, MT

  • Castellanos AA, Huntley JW, Voelker G, Lawing AM (2019) Environmental filtering improves ecological niche models across multiple scales. Methods Ecol Evol 10:481–492. https://doi.org/10.1111/2041-210X.13142

    Article  Google Scholar 

  • Cobos ME, Peterson AT, Barve N, Osorio-Olvera L (2019) kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7:e6281. https://doi.org/10.7717/peerj.6281

    Article  PubMed  PubMed Central  Google Scholar 

  • Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci Model Dev 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015

    Article  Google Scholar 

  • Costanza R, D’Arge R, De Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O’Neill RV, Paruelo J, Raskin RG, Sutton P, Van Den Belt M (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260. https://doi.org/10.1038/387253a0

    Article  CAS  Google Scholar 

  • De Araújo CB, Marcondes-Machado LO, Costa GC (2014) The importance of biotic interactions in species distribution models: A test of the Eltonian Noise Hypothesis using parrots. J Biogeogr 41:513–523. https://doi.org/10.1111/jbi.12234

    Article  Google Scholar 

  • Dvorak WS, Urueña H, Moreno LA, Goforth J (1998) Provenance and family variation in Sterculia apetala in Colombia. For Ecol Manage 111:127–135. https://doi.org/10.1016/S0378-1127(98)00316-8

    Article  Google Scholar 

  • El-Gabbas A, Dormann CF (2018) Wrong, but useful: Regional species distribution models may not be improved by range-wide data under biased sampling. Ecol Evol 8:2196–2206. https://doi.org/10.1002/ece3.3834

    Article  PubMed  PubMed Central  Google Scholar 

  • Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x

    Article  Google Scholar 

  • Fernandes F, Fernandes A, Soares M, Pellegrin L, Lima I de (2007) Atualização do Mapa de Solos da Planície Pantaneira para o Sistema Brasileiro de Classificação de Solos (No. 61), Comunicado Técnico. Corumbá, MS. https://doi.org/10.1017/CBO9781107415324.004

  • Fick SE, Hijmans RJ (2017) WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086

    Article  Google Scholar 

  • Fourcade Y, Besnard AG, Secondi J (2017) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob Ecol Biogeogr 27:245–256. https://doi.org/10.1111/geb.12684

    Article  Google Scholar 

  • Fourcade Y, Engler JO, Rodder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122

    Article  CAS  Google Scholar 

  • Gogol-Prokurat M (2011) Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol Appl 21:33–47. https://doi.org/10.1890/09-1190.1

    Article  PubMed  Google Scholar 

  • Guedes NMR (1995) Competition and losses of Hyacinth macaws nests in the Pantanal, Brazil. In: Congreso de ornitologia neotropical V, Resumos, Asunción, Paraguay, p. 70

  • Guedes NMR (2002) The Hyacinth Macaw (Anodorhynchus hyacinthinus) Project in the Pantanal South, Brazil. In: Congresso Mundial sobre Papagayos. Conservando Los Loros y Sus Habitats, V, Ed. Loro Parque, Tenerife, España, 18–21/09/2002, pp. 163–174

  • Guedes NMR, Bianchi CA, Barros Y (2008) Anodorhynchus hyacinthinus. In: Machado ÂBM, Drummond GM, Paglia AP (eds) Livro vermelho da fauna Brasileira ameaçada de extinção, 1st edn. Ministério do Meio Ambiente, Brasilia, pp 467–468

    Google Scholar 

  • Guedes N, Carvalho A, Toledo MCB (2006) Uso do Sistema de Informação Geográfica (SIG) em trabalhos de conservação das araras-azuis e vermelhas no Pantanal sul-mato-grossense. Ensaios e Ciência Uniderp- Ciências Biológicas. Campo Grande. Ed: Uniderp, 10(1):167–179

  • Guerra A, de Roque FO, Garcia LC, Ochao-Quintero JMO, Oliveira PTS, Guariento RD, Rosa IMD (2020) Drivers and projections of vegetation loss in the Pantanal and surrounding ecosystems. Land Use Policy 91:1–10. https://doi.org/10.1016/j.landusepol.2019.104388

    Article  Google Scholar 

  • Guisan A, Graham CH, Elith J, Huettmann F, Dudik M, Ferrier S, Hijmans R, Lehmann A, Li J, Lohmann LG, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMC, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Williams SE, Wisz MS, Zimmermann NE (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340. https://doi.org/10.1111/j.1472-4642.2007.00342.x

    Article  Google Scholar 

  • Guisan A, Thuiller W, Zimmermann NE (2017) Habtat suitability and distribution models. Cambridge University Press, Cambridge, p 462

    Book  Google Scholar 

  • Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AI, Regan TJ, Lluis Brotons L, McDonald ME, Mantyka-Pringle C, Martin TG, Rhodes JR, Maggini R, Setterfield SA, Elith J, Schwartz MW, Wintle BA, Broennimann O, Austin M, Ferrier S, Kearney MR, Possingham HP, Buckley YM (2013) Predicting species distributions for conservation decisions. Ecol Lett 16(12):1424–1435

    Article  Google Scholar 

  • Hamilton SK, Sippel SJ, Melack JM (1996) Inundation patterns in the Pantanal wetland of South America determined from passive microwave remote sensing. Hydrobiologie 137:1–23

    Article  Google Scholar 

  • Harris MB, Tomas W, Mourão G, Silva CJ, Guimarães E, Sonoda F, Fachim E (2005) Safeguarding the Pantanal wetlands: threats and conservation initiatives. Conserv Biol 19:714–720. https://doi.org/10.1111/j.1523-1739.2005.00708.x

    Article  Google Scholar 

  • Heikkinen RK, Luoto M, Virkkala R, Pearson RG, Körber JH (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Glob Ecol Biogeogr 16:754–763. https://doi.org/10.1111/j.1466-8238.2007.00345.x

    Article  Google Scholar 

  • Hengl T, De Jesus JM, Heuvelink GBM, Gonzalez MR, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE. https://doi.org/10.1371/journal.pone.0169748

    Article  PubMed  PubMed Central  Google Scholar 

  • Hortal J, Roura-Pascual N, Sanders NJ, Rahbek C (2010) Understanding (insect) species distributions across spatial scales. Ecography 33:51–53. https://doi.org/10.1111/j.1600-0587.2009.06428.x

    Article  Google Scholar 

  • ICMBIO - Instituto chico mendes de conservação da biodiversidade (2018) Livro Vermelho da Fauna Brasileira Ameaçada de Extinção: Volume III—Aves. Brasília, ed. 1. Disponível em https://www.icmbio.gov.br/portal/images/stories/comunicacao/publicacoes/publicacoes-diversas/livro_vermelho_2018_vol3.pdf, acessado 27 Jun 2020

  • Johnson M, Tomas W, Guedes N (1997) On the Hyacinth macaw’s nesting tree: density of young manduvis around adult trees under three different management conditions in the Pantanal wetland, Brasil. Ararajuba 5:185–188

    Google Scholar 

  • Júnior AS, Tomas WM, Ishii IH, Guedes NMR, Hay JD (2007) Occurrence of Hyacinth Macaw nesting sites in Sterculia apetala in the Pantanal Wetland Brazil. Gaia Sci 1:127–130. https://doi.org/10.21707/gs.v1i2.2268

    Article  Google Scholar 

  • Júnior AS (2010) Análise de populações de Sterculia apetala em diferentes cenários de manejo da paisagem e sua influência no oferecimento futuro de habitat reprodutivo para Anodorhynchus hyacintinus no Pantanal. (doctoral teses) Universidade de Brasília

  • Junk WJ, Bayley PB, Sparks RE (1989) The flood pulse concept. Int Large River Symp 106:110–127

    Google Scholar 

  • Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096

    Article  Google Scholar 

  • Leroy B, Delsol R, Hugueny B, Meynard CN, Barhoumi C, Barbet-Massin M, Bellard C (2018) Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance. J Biogeogr 45(9):1994–2002

    Article  Google Scholar 

  • Moraes EC, Pereira G, Cardozo FS (2013) Evaluation of Reduction of Pantanal Wetlands in 2012 81–93. Geografia 38:91–93

    Google Scholar 

  • Negrelle RRB (2015) Attalea phalerata Mart. Ex Spreng.: Aspectos botânicos, ecológicos, etnobotânicos e agronômicos. Ciência Florest 25:1061–1066. https://doi.org/10.1007/978-3-319-05509-1_14

    Article  Google Scholar 

  • Newbold T (2010) Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Prog Phys Geogr 34:3–22. https://doi.org/10.1177/0309133309355630

    Article  Google Scholar 

  • Padovani C (2010) Dinâmica Espaço-Temporal das inundações do Pantanal. (doctoral thesis) Escola superior de Agricultura “Luiz de Queiroz.”, Piracicaba

  • Perillo LN, Neves FDS, Antonini Y, Martins RP (2017) Compositional changes in bee and wasp communities along Neotropical mountain altitudinal gradient. PLoS ONE 12:1–14. https://doi.org/10.1371/journal.pone.0182054

    Article  CAS  Google Scholar 

  • Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008

    Article  Google Scholar 

  • Petitpierre B, Broennimann O, Kueffer C, Daehler C, Guisan A (2017) Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. Glob Ecol Biogeogr 26(3):275–287

    Article  Google Scholar 

  • Phillips SB, Aneja VP, Kang D, Arya SP (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 6:231–252. https://doi.org/10.1016/j.ecolmodel.2005.03.026

    Article  Google Scholar 

  • Pinho JB, Nogueira FM (2003) Hyacinth macaw (Anodorhynchus hyacinthinus) reproduction in the northern Pantanal, Mato Grosso, Brazil. Ornitol Neotrop 14(1):29–38

    Google Scholar 

  • Pott A, Pott V (1994) Plantas do Pantanal. Embrapa-SPI, Brasília

    Google Scholar 

  • Pott A, Pott VJ (2009) Vegetação do Pantanal: Fitogeografia e dinâmica (No. 2), Simpósio de Geotecnologias no Pantanal. Corumbá, MS.

  • Presti FT, Guedes NMR, Antas PTZ, Miyaki CY (2015) Population genetic structure in hyacinth macaws (Anodorhynchus hyacinthinus) and identification of the probable origin of confiscated individuals. J Hered 106:491–502. https://doi.org/10.1093/jhered/esv038

    Article  PubMed  CAS  Google Scholar 

  • R Core Team (2019) R: A language and environment for statistical computing. www.r-project.org

  • Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643. https://doi.org/10.1111/jbi.12227

    Article  Google Scholar 

  • Raes N (2012) Partial versus full species distribution models. Nat Conserv 10:127–138. https://doi.org/10.4322/natcon.2012.020

    Article  Google Scholar 

  • Regos A, Gagne L, Alcaraz-Segura D, Honrado JP, Domínguez J (2019) Effects of species traits and environmental predictors on performance and transferability of ecological niche models. Sci Rep. https://doi.org/10.1038/s41598-019-40766-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Roque FO, Ochoa-Quintero J, Ribeiro DB, Sugai LSM, Costa-Pereira R, Lourival R, Bino G (2016) Upland habitat loss as a threat to Pantanal wetlands. Conserv Biol 30:1131–1134. https://doi.org/10.1111/cobi.12713

    Article  PubMed  Google Scholar 

  • Samy G, Chavan V, Ariño AH, Otegui J, Hobern D, Sood R, Robles E (2013) Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. Biodivers Inform 8:94–172. https://doi.org/10.17161/bi.v8i2.4124

    Article  Google Scholar 

  • Sánchez-Fernández D, Lobo JM, Hernández-Manrique OL (2011) Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles. Divers Distrib 17:163–171. https://doi.org/10.1111/j.1472-4642.2010.00716.x

    Article  Google Scholar 

  • Sandman AN, Wikstrom SA, Blomqvist M, Kaut-sky H, Isaeus M (2013) Scale-dependent influence of environmental variables on species distribution: a case study on five coastal benthic species in the Baltic Sea. Ecography 36:354–363

    Article  Google Scholar 

  • Scherer-neto P, Maria N, Guedes R, Cecília M, Toledo B (2019) Long-term monitoring of a hyacinth macaw Anodorhynchus hyacinthinus (Psittacidae) roost in the Pantanal, Brazil. Endang Species Res 39:25–34. https://doi.org/10.3354/esr00954

    Article  Google Scholar 

  • Soberon J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inf. 2:1–10. https://doi.org/10.17161/bi.v2i0.4

    Article  Google Scholar 

  • SOS-Pantanal, WWF-Brasil, ECOA, C.-I., Fundacion-AVINA (2017) Monitoramento das alterações da cobertura vegetal e uso do solo na Bacia do Alto Paraguai Porção Brasileira-Período de análise: 2016 a 2017. WWF- Brasil. Brasília

  • Sousa-Baena MS, Garcia LC, Peterson AT (2014) Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. Divers Distrib 20:369–381. https://doi.org/10.1111/ddi.12136

    Article  Google Scholar 

  • Syphard AD, Franklin J (2009) Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography 32(6):907–918

    Article  Google Scholar 

  • Thuiller W, Brotons L, Arau MB, Lavorel S (2004) Effects of restricting environmental range of data to project current and future species distributions. Ecography 27:165–172. https://doi.org/10.1111/j.0906-7590.2004.03673.x

    Article  Google Scholar 

  • Titeux N, Maes D, Van Daele T, Onkelinx T, Heikkinen RK, Romo H, García-Barros E, Munguira ML, Thuiller W, van Swaay CAM, Schweiger O, Settele J, Harpke A, Wiemers M, Brotons L, Luoto M (2017) The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Divers Distrib 23:1393–1407. https://doi.org/10.1111/ddi.12634

    Article  Google Scholar 

  • Tomas WM et al (2019) Sustainability agenda for the Pantanal wetland: perspectives on a collaborative interface for science, policy, and decision-making. Trop Conserv Sci 12:194008291987263. https://doi.org/10.1177/1940082919872634

    Article  Google Scholar 

  • Treglia ML, Fisher RN, Fitzgerald LA (2015) Integrating multiple distribution models to guide conservation efforts of an endangered toad. PLoS ONE 10:1–18. https://doi.org/10.1371/journal.pone.0131628

    Article  CAS  Google Scholar 

  • Tulloch A, Szabo JK (2012) A behavioural ecology approach to understand volunteer surveying for citizen science data sets. Emu 112:313–325

    Article  Google Scholar 

  • USGS—United States Geological Survey (2020) Sentinel-2 Digital Object Identifier. Disponible in https://www.usgs.gov. Accessed 21 Jun 06 2020. https://doi.org/10.5066/F76W992G

  • Varela S, Anderson RP, García-Valdés R, Fernández-González F (2014) Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37:1084–1091. https://doi.org/10.1111/j.1600-0587.2013.00441.x

    Article  Google Scholar 

  • Wang HH, Wonkka CL, Treglia ML, Grant WE, Smeins FE, Rogers WE (2018) Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies. Biodivers Conserv 28:171–182. https://doi.org/10.1007/s10531-018-1645-4

    Article  Google Scholar 

  • Wang HH, Wonkka CL, Treglia ML, Grant WE, Smeins FE, Rogers WE (2015) Species distribution modelling for conservation of an endangered endemic orchid. AoB Plants 7:1–12. https://doi.org/10.1093/aobpla/plv039

    Article  Google Scholar 

  • Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342. https://doi.org/10.1890/10-1171.1

    Article  PubMed  Google Scholar 

  • Warren DL, Wright AN, Seifert SN, Shaffer HB (2014) Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Divers Distrib 20:334–343. https://doi.org/10.1111/ddi.12160

    Article  Google Scholar 

  • Warton DI, Renner IW, Ramp D (2013) Model-based control of observer bias for the analysis of presence-only data in ecology. PLoS ONE 8:e79168. https://doi.org/10.1371/journal.pone.0079168

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Authors and Affiliations

  1. Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil

    M. R. Oliveira & L. C. Garcia

  2. Instituto Federal de Educação Ciência e Tecnlogia de Rôndonia, Porto Velho, Brazil

    A. Santos Júnior

  3. Universidade Anhanguera-Uniderp e Instituto Arara-Azul, Campo Grande, Brazil

    N. M. R. Guedes

  4. Empresa Brasileira de Pesquisa Agropecuária - Embrapa Pantanal, Corumbá, Brazil

    W. M. Tomas & C. R. Padovani

  5. KU Biodiversity Institute - University of Kansas, Lawrence, USA

    A.T. Peterson

  6. Universidade Federal da Bahia, Salvador, Brazil

    J. K. Szabo

  7. Smithsonian Conservation Biology Institute, Front Royal, USA

    A. R. Camilo

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  1. M. R. Oliveira
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Oliveira, M.R., Tomas, W.M., Guedes, N.M.R. et al. The relationship between scale and predictor variables in species distribution models applied to conservation. Biodivers Conserv 30, 1971–1990 (2021). https://doi.org/10.1007/s10531-021-02176-w

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