Abstract
Ecological niche modeling (ENM) is a technique widely used in many disciplines of science. Recently, the extent of using the environmental background for ENM calibration has been pointed out as playing a crucial role in determining model outcomes. However, when modeling freshwater species, the need for a background refinement has been ignored and its consideration possesses important implications not taken into account before. Here, using Maxent algorithm and global occurrence data characterizing the distribution of the invasive freshwater turtle, Trachemys scripta, we performed ENM transfer and compared native and invasive niche estimates for the species in the environmental space. We used two environmental backgrounds: (a) a traditional area, based on the current distribution and dispersal capacity of the species, and (b) a more restricted area, which corresponds exclusively to freshwater bodies. Our analysis revealed strong differences between the traditional and the restricted backgrounds in niche transferability, with differences in Maxent probability values ranging from − 0.59 to 0.41. Also, during comparisons between native and invasive niches, the niches were more similar when the traditional approach was used, compared to the restricted approach. Our results highlight the importance of considering the biological restriction of the species when establishing the extent of the background in ecological niche modeling; in this case, a more restricted area represented by freshwater environments.
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References
Alarcos G, del Cueto F, Rodríguez-Pereira A, Avia M (2010) Distribution records of non-native terrapins in Castilla and León region (Central Spain). Aquat Invasions 5:303–308. https://doi.org/10.3391/ai.2010.5.3.08
Alvarado-Serrano DF, Knowles LL (2014) Ecological niche models in phylogeographic studies: applications, advances and precautions. Mol Ecol Resour 14:233–248. https://doi.org/10.1111/1755-0998.12184
Barve N (2008) Tool for Partial ROC ver 1.0
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 Modell 222:1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011
Broennimann O, Fitzpatrick MC, Pearman PB, Petitpierre B, Pellissier L, Yoccoz NG, Thuiller W, Fortin M, Randin C, Zimmermann NE, Graham CH, Guisan A (2012) Measuring ecological niche overlap from occurrence and spatial environmental data. Glob Ecol Biogeogr 21:481–497. https://doi.org/10.1111/j.1466-8238.2011.00698.x
Brown JH, Lomolino MV (1998) Biogeography, 2nd edn. Sinauer Associates, Sunderland
Buckley LB, Hurlbert AH, Jetz W (2012) Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob Ecol Biogeogr 21:873–885. https://doi.org/10.1111/j.1466-8238.2011.00737.x
Bugter RJF, Ottburg I, Roessink I, Van Der Grift EA, Griffioen AJ (2011) Invasion of the turtles ? Exotic turtles in the Netherlands: a risk assessment. Alterra, The Netherlands
Busby JR (1991) BIOCLIM—a bioclimate analysis and prediction system. Nat Conserv cost Eff Biol Surv data Anal 6:64–68
Chen P, Wiley EO, Mcnyset KM (2007) Ecological niche modeling as a predictive tool: silver and bighead carps in North America. Biol Invasions 9:43–51. https://doi.org/10.1007/s10530-006-9004-x
Cordellier M, Pfenninger M (2009) Inferring the past to predict the future: climate modelling predictions and phylogeography for the freshwater gastropod Radix balthica (Pulmonata, Basommatophora). Mol Ecol 18:534–544. https://doi.org/10.1111/j.1365-294X.2008.04042.x
De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance. Chemom Intell Lab Syst 50:1–18. https://doi.org/10.1016/S0169-7439(99)00047-7
Di Cola V, Broennimann O, Petitpierre B, Breiner FT, D’Amen M, Randin C, Engler R, Pottier J, Pio D, Dubuis A, Pellissier L, Mateo RG, Hordijk W, Salamin N, Guisan A (2017) ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography (Cop) 40:774–787. https://doi.org/10.1111/ecog.02671
Domínguez-Domíngues O, Martínez-Meyer E, Zambrano L, Perez-Ponce de León G (2006) Using ecological-niche modeling as a conservation tool for freshwater species: live-bearing fishes in central Mexico. Conserv Biol 20:1730–1739. https://doi.org/10.1111/j.1523-1739.2006.00588.x
Domisch S, Wilson AM, Jetz W (2016) Model-based integration of observed and expert-based information for assessing the geographic and environmental distribution of freshwater species. Ecography (Cop) 39:1078–1088. https://doi.org/10.1111/ecog.01925
Drake JM, Bossenbroek JM (2004) The potential distribution of zebra mussels in the United States. Bioscience 54:931. https://doi.org/10.1641/0006-3568(2004)054%5b0931:TPDOZM%5d2.0.CO;2
Elith JM (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
Ficetola GF, Padoa-Schioppa E, Monti A, Massa R, De Bernardi F, Bottoni L (2004) The importance of aquatic and terrestrial habitat for the European pond turtle (Emys orbicularis): implications for conservation planning and management. Can J Zool 82:1704–1712. https://doi.org/10.1139/z04-170
Gama M, Crespo D, Dolbeth M, Anastácio PM (2017) Ensemble forecasting of Corbicula fluminea worldwide distribution: Projections of the impact of climate change. Aquat Conserv Mar Freshw Ecosyst. https://doi.org/10.1002/aqc.2767
GBIF (2007) Global Biodiversity Information Facility. Free and open access to biodiversity data. https://www.gbif.org/. Accessed 6 June 2018
Gutiérrez-Velázquez A, Rojas-Soto O, Reyes-Castillo P, Halffter G (2013) The classic theory of Mexican Transition Zone revisited: the distributional congruence patterns of Passalidae (Coleoptera). Invertebr Syst 27:282. https://doi.org/10.1071/IS12056
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. https://doi.org/10.1002/joc.1276
Hutchinson GE (1957) Concludings remarks. Cold Spring Harb Symp Quant Biol 22:415–427
IUCN (2015) The IUCN Red List of Threatened Species Version 2015.4. http://www.iucnredlist.org/. Accessed 23 Feb 2015
Kikillus H, Hare KM, Hartley S (2010) Minimizing false-negatives when predicting the potential distribution of an invasive species: a bioclimatic envelope for the red-eared slider at global and regional scales. Anim Conserv 13:5–15. https://doi.org/10.1111/j.1469-1795.2008.00299.x
Kulhanek SA, Leung B, Ricciardi A (2011) Using ecological niche models to predict the abundance and impact of invasive species: application to the common carp. Ecol Appl 21:203–213. https://doi.org/10.1890/09-1639.1
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Loo SE, Mac Nally R, Lake PS (2007) Forecasting New Zealand Mudsnail invasion range: model comparisons using native and invaded ranges. Ecol Appl 17:181–189. https://doi.org/10.1890/1051-0761(2007)017%5b0181:FNZMIR%5d2.0.CO;2
Lowe S, Browne M, Boudjelas S, De Porter M (2004) 100 of the world’s worst invasive alien species, A selection from. The Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN)
Maldonado C, Molina CI, Zizka A, Persson C, Taylor CM, Albán J, Chilquillo E, Rønsted N, Antonelli A (2015) Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Glob Ecol Biogeogr 24:973–984. https://doi.org/10.1111/geb.12326
Masin S, Bonardi A, Padoa-Schioppa E, Bottoni L, Ficetola GF (2014) Risk of invasion by frequently traded freshwater turtles. Biol Invasions 16:217–231. https://doi.org/10.1007/s10530-013-0515-y
McGarvey DJ, Menon M, Woods T, Tassone S, Reese J, Vergamini M, Kellogg E (2017) On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out? Ecography (Cop) 41:695–712. https://doi.org/10.1111/ecog.03134
Mcnyset KM (2009) Ecological niche conservatism in North American freshwater fishes. Biol J Lin Soc 96:282–295. https://doi.org/10.1111/j.1095-8312.2008.01121.x
Mendoza-González G, Martínez ML, Rojas-Soto OR, Vázquez G, Gallego-Fernández JB (2013) Ecological niche modeling of coastal dune plants and future potential distribution in response to climate change and sea level rise. Glob Chang Biol 19:2524–2535. https://doi.org/10.1111/gcb.12236
Nori J, Moreno-Azócar DL, Cruz FB, Bonino MF, Leynaud GC (2016) Translating niche features: modelling differential exposure of Argentine reptiles to global climate change. Austral Ecol 41:367–375. https://doi.org/10.1111/aec.12321
Nori J, Tessarolo G, Ficetola GF, Loyola R, Di Cola V, Leynaud G (2017) Buying environmental problems: the invasive potential of imported freshwater turtles in Argentina. Aquat Conserv Mar Freshw Ecosyst 27:685–691. https://doi.org/10.1002/aqc.2715
Owens HL, Campbell LP, Dornak LL, Saupe EE, Barve N, Soberón J, Ingenloff K, Lira-Noriega A, Hensz CM, Myers CE, Peterson AT (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol Modell 263:10–18. https://doi.org/10.1016/j.ecolmodel.2013.04.011
Parreira MR, Nabout JC, Tessarolo G, Lima-Ribeiro MS, Teresa FB (2019) Disentangling uncertainties from niche modeling in freshwater ecosystems. Ecol Model 391:1–8. https://doi.org/10.1016/j.ecolmodel.2018.10.024
Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, McClean C, Miles L, Segurado P, Dawson TP, Lees DC (2006) Model-based uncertainty in species range prediction. J Biogeogr 33:1704–1711. https://doi.org/10.1111/j.1365-2699.2006.01460.x
Peterson AT, Soberón J (2012) Species distribution modeling and ecological niche modeling: getting the concepts right. Nat Conserv 10:102–107. https://doi.org/10.4322/natcon.2012.019
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Modell 213:63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological Niches and Geographic Distributions (MPB-49). Princeton University Press, Princeton. https://doi.org/10.1515/9781400840670
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197. https://doi.org/10.1890/07-2153.1
Prieto-Torres DA, Nori J, Rojas-Soto O (2018) Identifying priority conservation areas for birds associated to endangered Neotropical dry forests. Biol Conserv 228:205–214. https://doi.org/10.1016/j.biocon.2018.10.025
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. https://doi.org/10.1111/j.1365-2699.2006.01466.x
Rangel TF, Loyola RD (2012) Labeling ecological niche models. Nat Conserv 10:119–126. https://doi.org/10.4322/natcon.2012.030
Rödder D, Schmidtlein S, Veith M, Lötters S (2009) Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS One 4:e7843. https://doi.org/10.1371/journal.pone.0007843
Rodrigues JFM, Coelho MTP, Varela S, Diniz-Filho JAF (2016) Invasion risk of the pond slider turtle is underestimated when niche expansion occurs. Freshw Biol 61:1119–1127. https://doi.org/10.1111/fwb.12772
Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM, Peterson AT, Owens HL (2012) Variation in niche and distribution model performance : the need for a priori assessment of key causal factors. Ecol Modell 237:11–22. https://doi.org/10.1016/j.ecolmodel.2012.04.001
Soberón J, Peterson TA (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inf 2:1–10
USGS (2001) HYDRO1k Elevation Derivative Database. https://lta.cr.usgs.gov/HYDRO1K. Accessed 01 Aug 2011
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
Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution (NY). 62:2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x
Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Front Ecol Environ 5:475–482. https://doi.org/10.1890/1540-9295(2007)5%5b475:NCNCAE%5d2.0.CO;2
Zurell D, Elith J, Schröder B (2012) Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions. Divers Distrib 18:628–634. https://doi.org/10.1111/j.1472-4642.2012.00887.x
Acknowledgements
A.T. Peterson provided useful comments to the MS. This MS also benefitted from discussions with Rosario Landgrave, David Prieto-Torres, Claudio Mota-Vargas, Mauricio Ortega-Andrade, Fabricio Villalobos and Andrés Lira. Special thanks to the Instituto de Ecología, A. C., for providing permission and support for O. Rojas-Soto´s sabbatical year at the Centro de Zoología Aplicada, and logistic support for this research. Javier Nori is a staff researcher at CONICET and Universidad Nacional de Córdoba; his work was funded by FONCYT and SECYT- UNC.
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Multivariate environmental similarity surface (MESS) analysis. This test was used to compare the environmental variables used for projection (Argentina) to those used for training the model (its native range in North America). Negative values (shown in red) indicate novel climate; these represent one or more environmental variables outside the range present in the training data; so predictions in those areas should be treated with strong caution. Positive values (shown in blue) represent points that are not novel at different degrees (e.g., a score of 100 meaning that a point is not at all novel; Elith et al., 2010) (PDF 436 kb)
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Nori, J., Rojas-Soto, O. On the environmental background of aquatic organisms for ecological niche modeling: a call for caution. Aquat Ecol 53, 595–605 (2019). https://doi.org/10.1007/s10452-019-09711-6
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DOI: https://doi.org/10.1007/s10452-019-09711-6