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Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change

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Abstract

An accurate estimation of the expected consequences of climate change requires the proper quantification of the effect of climate on current species distributions. Several interrelated sources of uncertainty may affect the likelihood of species distribution models (SDMs) to determine the relative importance of climate. Our aim was to assess the relationship between the influence of geographical background (GB) delimitation and that of subtracting the non-climate effects from the weight of climatic predictors to estimate the combined influence of these two factors on predictions in climate change scenarios. The distribution of 40 endemic mammals in Western Europe have been modeled by (i) using the whole territory of Western Europe as the GB and also specifically delimiting the GB with a geographical criterion and (ii) considering climatic predictors in addition to other non-climatic variables in order to extract the pure effect of climate. The models were used to quantify species’ sensitivity to new climate scenarios. Results showed discrepancies among the analytical approaches. Changes in distribution obtained by considering the pure effect of climate were lower than those obtained by considering the apparent effect, and GB-delimited models yielded higher changes than those trained in Western Europe. We evidence that climate weighting and GB delimitation have dramatic influences on the projections of models when transferred to new scenarios. We emphasize that scientific studies and derived adaptation policies based on SDMs without an explicit consideration of the GB and the weighting of the climate-related variables may be misleading and in need of revision.

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References

  • Acevedo P, Real R (2012) Favourability: concept, distinctive characteristics and potential usefulness. Naturwissenschaften 99:515–522

    Article  Google Scholar 

  • Acevedo P, Jiménez-Valverde A, Lobo JM, Real R (2012a) Delimiting the geographical background in species distribution modelling. J Biogeogr 39:1383–1390

    Article  Google Scholar 

  • Acevedo P, Jiménez-Valverde A, Melo-Ferreira J, Real R, Alves PC (2012b) Parapatric species and the implications for climate change studies: a case study on hares in Europe. Glob Chang Biol 18:1509–1519

    Article  Google Scholar 

  • Acevedo P, Melo-Ferreira J, Farelo L, Beltran-Beck B, Real R, Campos R, Alves PC (2015) Range dynamics driven by Quaternary climate oscillations explain the distribution of introgressed mtDNA of Lepus timidus origin in hares from the Iberian Peninsula. J Biogeogr 42:1727–1735

    Article  Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  • Alzaga V, Tizzani P, Acevedo P, Ruiz-Fons F, Vicente J, Gortázar C (2009) Deviance partitioning of host factors affecting parasitization in the European brown hare (Lepus europaeus). Naturwissenschaften 96:1157–1168

    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

    Article  Google Scholar 

  • Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19

    Article  Google Scholar 

  • Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222:1810–1819

    Article  Google Scholar 

  • Bates D, Maechler M, Bolker B (2012) lme4: linear mixed-effects models using S4 classes. R package version 0.999999–0. http://CRAN.R-project.org/package=lme4

  • Beaumont LJ, Pitman AJ, Poulsen M, Hughes L (2007) Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Glob Chang Biol 13:1368–1385

    Article  Google Scholar 

  • Braunisch V, Coppes J, Arlettaz R, Suchant R, Schmid H, Bollmann K (2013) Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36:001–013

    Article  Google Scholar 

  • Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G (2010) Uncertainty in ensemble forecasting of species distribution. Glob Chang Biol 16:1145–1157

    Article  Google Scholar 

  • Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García Marquéz JR, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:027–046

    Article  Google Scholar 

  • Eskildsen A, le Roux PC, Heikkinen RK, Høye TT, Kissling WD, Pöyry J, Wisz MS, Luoto M (2013) Testing species distribution models across space and time: high latitude butterflies and recent warming. Glob Ecol Biogeogr 22:1293–1303

    Article  Google Scholar 

  • Fordham DA, Akçakaya HR, Brook BW, Rodríguez A, Alves PC, Civantos E, Triviño M, Watts MJ, Araújo MB (2013) Adapted conservation measures are required to save the Iberian lynx in a changing climate. Nat Clim Chang 3:899–903

    Article  Google Scholar 

  • Fox J (1997) Applied regression analysis, linear models, and related methods. Sage Publications, Thousand Oaks

    Google Scholar 

  • Gaston KJ (2003) The structure and dynamics of geographic ranges, 1st edn. Oxford University Press, Oxford

    Google Scholar 

  • Gotelli NJ, Ellison AM (2004) A primer of ecological statistics. Sinauer Associates, Inc., Massachussetts

    Google Scholar 

  • Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009

    Article  Google Scholar 

  • Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, Regan TJ, Brotons L, McDonald-Madden E, 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 16:1424–1435

    Article  Google Scholar 

  • Halvorsen R (2012) A gradient analytic perspective on distribution modelling. Sommerfeltia 35:1–165

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978

    Article  Google Scholar 

  • Hortal J, Lobo JM, Jiménez-Valverde A (2012) Basic questions in biogeography and the (lack of) simplicity of species distributions: putting species distribution models in the right place. Natureza Conservaçao 10:108–118

    Google Scholar 

  • Hosmer DW, Lemeshow S (1989) Applied logistic regression. John Wiley and Sons, Inc., New York

    Google Scholar 

  • Lavergne S, Thuiller W, Molina J, Debussche M (2005) Environmental and human factors influencing rare plant local occurrence, extinction and persistence: a 115-year study in the Mediterranean region. J Biogeogr 32:799–811

    Article  Google Scholar 

  • Legendre P, Legendre L (1998) Numerical ecology, 2nd English edn. Elsevier Science, Amsterdam

    Google Scholar 

  • Lobo JM (2016) The use of occurrence data to predict the effects of climate change on insects. Curr Opin Insect Sci 17:62–68

    Article  Google Scholar 

  • Lobo JM, Castro I, Moreno JC (2001) Spatial and environmental determinants of vascular plant species richness distribution in the Iberian Peninsula and Balearic Islands. Biol J Linn Soc 73:233–253

    Article  Google Scholar 

  • Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114

    Article  Google Scholar 

  • Maiorano L, Falcucci A, Zimmermann NE, Psomas A, Pottier J et al (2011) The future of terrestrial mammals in the Mediterranean basin under climate change. Philos Trans R Soc B 366:2681–2692

    Article  Google Scholar 

  • Márquez AL, Real R, Olivero J, Estrada A (2011) Combining climate with other influential factors for modelling climate change impact on species distribution. Clim Chang 108:135–157

    Article  Google Scholar 

  • Meyer CB, Thuiller W (2006) Accuracy of resource selection functions across spatial scales. Divers Distrib 12:288–297

    Article  Google Scholar 

  • Mitchell-Jones AJ, Amori G, Bogdanowicz W et al (1999) The atlas of European mammals. T & AD Poyser Ltd, London

    Google Scholar 

  • Nakicenovic N, Alcamo J, Davis G et al (2000) IPCC special report on emissions scenarios. Cambridge University Press, Cambridge

    Google Scholar 

  • Niamir A, Skidmore AK, Toxopeus AG, Real R (2016) Use of taxonomy to delineate spatial extent of atlas data for species distribution models. Glob Ecol Biogeogr 25:227–237

    Article  Google Scholar 

  • Nyström Sandman A, Wikström SA, Blomqvist M, Kautsky 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 

  • Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371

    Article  Google Scholar 

  • Péres-Neto PR, Legendre P (2010) Estimating and controlling for spatial structure in the study of ecological communities. Glob Ecol Biogeogr 19:174–184

    Article  Google Scholar 

  • Peterson AT, Soberón J, Pearson RG, Anderson RP, Nakamura M, Martinez-Meyer E, Araújo MB (2011) Ecological niches and geographical distributions. Princeton University Press, Princeton

    Google Scholar 

  • Qiao H, Soberón J, Peterson AT (2015) No silver bullets in correlative ecological niche modeling: insights from testing among many potential algorithms for niche estimation. Methods Ecol Evol 6:1126–1136

    Article  Google Scholar 

  • R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna ISBN 3-900051-07-0, URL http://www.R-project.org/

    Google Scholar 

  • Randin CF, Dirnböck T, Dullinger S, Zimmermann NE, Zappa M, Guisan A (2006) Are niche-based species distribution models transferable in space? J Biogeogr 33:1689–1703

    Article  Google Scholar 

  • Real R, Barbosa AM, Vargas JM (2006) Obtaining environmental favourability functions from logistic regression. Environ Ecol Stat 13:237–245

    Article  Google Scholar 

  • Real R, Márquez AL, Olivero J, Estrada A (2010) Species distribution models in climate change scenarios are still not useful for informing policy planning: an uncertainty assessment using fuzzy logic. Ecography 33:304–314

    Google Scholar 

  • Real R, Romero D, Olivero J, Estrada A, Márquez AL (2013) Estimating how inflated or obscured effects of climate affect forecasted species distribution. PLoS One 8(1):e53646. https://doi.org/10.1371/journal.pone.0053646

    Article  Google Scholar 

  • Real R, Barbosa AM, Bull J (2016) Species distributions, quantum theory, and the enhancement of biodiversity measures. Syst Biol. https://doi.org/10.1093/sysbio/syw072

  • Record S, Fitzpatrick MC, Finley AO, Veloz S, Ellison AM (2013) Should species distribution models account for spatial autocorrelation? A test of model projections across eight millennia of climate change. Glob Ecol Biogeogr 22:760–771

    Article  Google Scholar 

  • Rocchini D, Hortal J, Lobo JM, Jiménez-Valverde A, Ricotta C, Bacaro G, Chiarucci A (2011) Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Prog Phys Geogr 35:211–226

    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

    Article  Google Scholar 

  • Soberón J (2010) Niche and area of distribution modeling: a population ecology perspective. Ecography 33:159–167

    Article  Google Scholar 

  • Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148:1–13

    Article  Google Scholar 

  • Thomas CD (2010) Climate, climate change and range boundaries. Divers Distrib 16:488–495

    Article  Google Scholar 

  • Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC et al (2004) Extinction risk from climate change. Nature 427:145–148

    Article  Google Scholar 

  • Thuiller W, Brotons L, Araújo MB, Lavorel S (2004) Effects of restricting environmental range of data to project current and future species distributions. Ecography 27:165–172

    Article  Google Scholar 

  • Vale CG, Tarroso P, Brito JC (2013) Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone. Divers Distrib 20:20–33

    Article  Google Scholar 

  • VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model 220:589–594

    Article  Google Scholar 

  • Wang Z, Rahbek C, Fang J (2012) Effects of geographical extent on the determinants of woody plant diversity. Ecography 35:1160–1167

    Article  Google Scholar 

  • Wenger SJ, Som NA, Dauwalter DC, Isaak DJ, Neville HM, Luce CH, Dunham JB, Young MK, Fausch KD, Rieman BE (2013) Probabilistic accounting of uncertainty in forecasts of species distributions under climate change. Glob Ecol Biogeogr 19:3343–3354

    Google Scholar 

  • Werkowska W, Márquez AL, Real R, Acevedo P (2017) A practical overview of transferability in species distribution modeling. Environ Rev 25:127–133

    Article  Google Scholar 

  • Williams KJ, Belbin L, Austin MP, Stein JL, Ferrier S (2012) Which environmental variables should I use in my biodiversity model? Int J Geogr Inf Sci 26:1–39

    Article  Google Scholar 

  • Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York

    Book  Google Scholar 

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Acknowledgements

We would like to thank the Societas Europaea Mammalogica and Tony Mitchell-Jones for providing the distribution data used to prepare The Atlas of European Mammals. We are grateful to A. L. Márquez for her useful advice as regards the study of species sensitivity to climate change. Sally Newton kindly reviewed the manuscript for grammar.

Funding

P.A. is currently supported by the Spanish Ministerio de Economía y Competitividad (MINECO) and Universidad de Castilla-La Mancha (UCLM) through a Ramón y Cajal contract (RYC-2012-11970) and partly by the AGL2016-76358- R grant (MINECO-FEDER, UE). A. J.-V. was supported by the MINECO Ramón y Cajal Program (RYC-2013-14441). This work has been partially supported by the Spanish Ministry of Agriculture, Food and Environment, Spanish National Park’s Network, project 1098/2014.

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Authors and Affiliations

  1. Instituto de Investigación en Recursos Cinegéticos (IREC), UCLM-CSIC-JCCM, E-13071, Ciudad Real, Spain

    Pelayo Acevedo

  2. Grupo de Investigación de Biología del Suelo y de los Ecosistemas Subterráneos, Departamento de Ciencias de la Vida, Universidad de Alcalá. A.P. 20 Campus Universitario, Alcalá de Henares, E-28805, Madrid, Spain

    Alberto Jiménez-Valverde

  3. Departamento de Biogeografía y Cambio Global, Museo Nacional de Ciencias Naturales (CSIC), E-28006, Madrid, Spain

    Jorge M. Lobo

  4. Departamento de Biología Animal, Facultad de Ciencias, Universidad de Málaga, E-29071, Málaga, Spain

    Raimundo Real

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  1. Pelayo Acevedo
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  2. Alberto Jiménez-Valverde
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  3. Jorge M. Lobo
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  4. Raimundo Real
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Contributions

P.A. and R.R. conceived the initial idea; P.A. carried out the statistical analyses; P.A., A. J.-V., J.M.L., and R.R. participated in the discussion of the results and wrote the manuscript.

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Correspondence to Pelayo Acevedo.

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BIOSKETCH

Pelayo Acevedo is a researcher at the Instituto de Investigación en Recursos Cinegéticos (IREC), of the Universidad de Castilla-La Mancha. His interests include the study of factors affecting the distribution and abundance of pathogens, and their hosts and vectors, through fragmented habitats.

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Appendix S1 Map of the study area. Appendix S2 List of the study species and statistical parameters of the models. Appendix S3 Example of geographical background delimitation and predictions from species distribution models from the different analytical approaches. (DOCX 667 kb)

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Acevedo, P., Jiménez-Valverde, A., Lobo, J.M. et al. Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change. Climatic Change 145, 131–143 (2017). https://doi.org/10.1007/s10584-017-2082-1

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