Abstract
Maps of potential biodiversity are prominent tools for regional conservation planning because they allow to quantify the diversity of species that potentially inhabit different habitats. These maps are constructed from modeling species ecological niches based on the association between the geographical localities where species were detected and the environmental variables at those localities. Previous researches addressed the development of MPB for administrative regions using regional species distributions data to modeling ecological niches, which may lead to flawed predictions on potential biodiversity. Our aim is to assess the consequences of failing to include all available records on full species distribution to develop MPB. As a study case, we produced two MPBs for an administrative region of Argentina using RSD and FSD data sets of 14 species of insects and performed Environmental Niche Factor Analysis to model their ecological niches. Our results evidenced that both MPB were not spatially congruent in their predictions on potential biodiversity, because the ecological niches represented by RSD and FSD data were different in their position and volume. We found that the MPB based on RSD data may underpredict the potential biodiversity of distinct habitats at the landscape level, and that the use of this map may underestimate areas with different conservation potential within an administrative region. These results suggest that the choice of the extent of geographic records used to construct the MPB strongly conditions the quality and credibility of these maps.
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References
Abraham EM (2000) Recursos y problemas ambientales de la Provincia de Mendoza. In: Abraham EM, Rodríguez Martínez F (eds) Argentina: Recursos y problemas ambientales de la zona árida. Universidad de Granada, Andalucía, pp 15–23
Adler D, Murdoch D, README) and others (see (2020) rgl: 3D Visualization Using OpenGL. Version 0.100.54URL https://CRAN.R-project.org/package=rgl
Barve N, Barve V, Jiménez-Valverde A et al (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
Breiner FT, Guisan A, Bergamini A, Nobis MP (2015) Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol Evol 6:1210–1218. https://doi.org/10.1111/2041-210X.12403
Calenge C (2006) The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol Modell 197:516–519. https://doi.org/10.1016/j.ecolmodel.2006.03.017
Cao M, He T, Zhou W (2018) HDtest: high dimensional hypothesis testing for mean vectors, covariance matrices, and white noise of vector time series. Version 2.1URL https://CRAN.R-project.org/package=HDtest
Cola VD, Broennimann O, Petitpierre B et al (2017) ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40:774–787. https://doi.org/10.1111/ecog.02671
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. https://doi.org/10.1002/ece3.3834
Elith J, Graham CH (2009) Do they? how do they? why do they differ? on finding reasons for differing performances of species distribution models. Ecography 32:66–77. https://doi.org/10.1111/j.1600-0587.2008.05505.x
Feeley KJ, Silman MR (2011) Keep collecting: accurate species distribution modelling requires more collections than previously thought: temporal autocorrelated biases necessitate more collections. Diversity Distrib 17:1132–1140. https://doi.org/10.1111/j.1472-4642.2011.00813.x
Gimona A, van der Horst D (2007) Mapping hotspots of multiple landscape functions: a case study on farmland afforestation in Scotland. Landscape Ecol 22:1255–1264. https://doi.org/10.1007/s10980-007-9105-7
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Gullan PJ, Cranston PS (2010) The insects: an outline of entomology. Blackwell Publishing, Hoboken, NJ
Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
Hijmans RJ, Etten J van, Sumner M, et al (2020) raster: Geographic Data Analysis and Modeling. Version 3.1–5URL https://CRAN.R-project.org/package=raster
Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036
IUCN Standards and Petitions Committee (2019) Guidelines for Using the IUCN Red List Categories and Criteria. Version 14. Prepared by the Standards and Petitions Committee.
Kang B-H, Lee J-H, Park J-K (2012) Carabid beetle species as a biological indicator for different habitat types of agricultural landscapes in Korea. J Ecol Environ 35:35–39. https://doi.org/10.5141/JEFB.2012.006
Kery M (2002) Inferring the absence of a species – a case study of snakes. J Wildlife Manag 66:9
Lahoz-Monfort JJ, Guillera-Arroita G, Wintle BA (2014) Imperfect detection impacts the performance of species distribution models: imperfect detection impacts species distribution models. Global Ecol Biogeography 23:504–515. https://doi.org/10.1111/geb.12138
Loiselle BA, Howell CA, Graham CH et al (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600. https://doi.org/10.1111/j.1523-1739.2003.00233.x
Manderbach R, Hering D (2001) Typology of riparian ground beetle communities (Coleoptera, Carabidae, Bembidion spec.) in Central Europe and adjacent areas. Archiv für Hydrobiologie 152:583–608
Martínez Pastur G, Peri PL, Soler RM et al (2016) Biodiversity potential of Nothofagus forests in Tierra del Fuego (Argentina): tool proposal for regional conservation planning. Biodivers Conserv 25:1843–1862. https://doi.org/10.1007/s10531-016-1162-2
Martinez-Meyer E, Diaz-Porras D, Peterson AT, Yanez-Arenas C (2012) Ecological niche structure and rangewide abundance patterns of species. Biol Lett 9:20120637–20120637. https://doi.org/10.1098/rsbl.2012.0637
Merow C, Smith MJ, Edwards TC et al (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281. https://doi.org/10.1111/ecog.00845
Monk J (2014) How long should we ignore imperfect detection of species in the marine environment when modelling their distribution? Fish Fisheries 15:352–358. https://doi.org/10.1111/faf.12039
Norte F (2000) Mapa climático de Mendoza. In: Abraham EM, Rodríguez Martínez F (eds) Argentina: recursos y problemas ambientales de la zona árida. Universidad de Granada, Andalucía, pp 25–27
Osorio-Olvera L, Soberón J, Barve V, et al (2018) ntbox: from getting biodiversity data to evaluating species species distribution models in a friendly GUI environment. R package version 0.4.1.5.
Préau C, Trochet A, Bertrand R, Isselin-Nondedeu F (2018) Modeling potential distributions of three European amphibian species comparing ENFA and MaxEnt. Amphibia-Reptilia 13:91–104
R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Rahbek C, Gotelli NJ, Colwell RK et al (2007) Predicting continental-scale patterns of bird species richness with spatially explicit models. Proc R Soc B 274:165–174. https://doi.org/10.1098/rspb.2006.3700
Roig F, Martínez Carretero E, Méndez E (2000) Vegetación de la provincia de Mendoza. En Recursos y Problemas Ambientales de la Zona árida. Provincias de Mendoza, San Juan y La Rioja, Argentina I:63–70
Rosas YM, Peri PL, Martínez Pastur G (2018) Potential biodiversity map of lizard species in Southern Patagonia: environmental characterization, desertification influence and analyses of protection areas. Amphib-Reptilia 39:289–301. https://doi.org/10.1163/15685381-20181001
Rosas YM, Peri PL, Carrara R et al (2019) Potential biodiversity map of darkling beetles (Tenebrionidae): environmental characterization, land-uses and analyses of protection areas in Southern Patagonia. J Insect Conserv 23:885–897. https://doi.org/10.1007/s10841-019-00170-w
Rosas YM, Peri PL, Lencinas MV, Martínez Pastur G (2019) Potential biodiversity map of understory plants for Nothofagus forests in Southern Patagonia: analyses of landscape, ecological niche and conservation values. Sci Tot Environ 682:301–309. https://doi.org/10.1016/j.scitotenv.2019.05.179
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: Regional data misrepresent potential distributions. Diversity Distrib 17:163–171. https://doi.org/10.1111/j.1472-4642.2010.00716.x
Sexton JP, Montiel J, Shay JE et al (2017) Evolution of ecological niche breadth. Annu Rev Ecol Evol Syst 48:183–206. https://doi.org/10.1146/annurev-ecolsys-110316-023003
Soberón JM (2010) Niche and area of distribution modeling: a population ecology perspective. Ecography 33:159–167. https://doi.org/10.1111/j.1600-0587.2009.06074.x
Soberon J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Nat Acad Sci 106:19644–19650. https://doi.org/10.1073/pnas.0901637106
Thiele HU (1977) Carabid beetles in their environments: a study on habitat selection by adaptations in physiology and behaviour. Springer, Berlin Heidelberg
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. https://doi.org/10.1111/j.0906-7590.2004.03673.x
Thuiller W, Lavorel S, Araujo MB (2005) Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecol Biogeography 14:347–357. https://doi.org/10.1111/j.1466-822X.2005.00162.x
United Nations Environment Programme (2018) Law and National Biodiversity Strategies and Action Plans, Nairobi, Kenya
van der Horst D, Gimona A (2005) Where new farm woodlands support biodiversity action plans: a spatial multi-criteria analysis. Biol Conserv 123:421–432. https://doi.org/10.1016/j.biocon.2004.11.020
Vandermeer JH (1972) Niche theory. Ann Rev Ecol Syst 3:107–132
Funding
This research was supported by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) by providing continued research support to the authors, and through the project: IADIZA-PUE. The Agencia Nacional de Promoción Científica y Técnica, Argentina (ANPCyT) additionally contributed to completing this work through the following projects: PICT#2013-3128, PICT#2013-1539.
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Carrara, R., Roig-Juñent, S.A. Maps of potential biodiversity: when the tools for regional conservation planning clash with species ecological niches. Biodivers Conserv 31, 651–665 (2022). https://doi.org/10.1007/s10531-022-02355-3
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DOI: https://doi.org/10.1007/s10531-022-02355-3