Abstract
The concerns related to climate changes have been gaining attention in the last few years due to the negative impacts on the environment, economy, and society. To better understand and anticipate the effects of climate changes in the distribution of species, several techniques have been adopted comprising models of different complexity. In general, these models apply algorithms and statistical methods capable of predicting in a particular study area, the locations considered suitable for a species to survive and reproduce, given a set of eco-geographical variables that influence species behavior. Logistic regression algorithm and Probability density function are two common methods that can be used to model the species suitability. The former is a representative of a class of models that requires the availability (or imputation) of presence-absence data whereas the latter represents the models that only require presence data. Both approaches are compared regarding the capability to accurately predict the environmental suitability for species. On a different way, the behaviour of the species in the projected environments are analysed by simulating its potential distribution in the projected environment. A case study reporting results from two types of species with economical interest is presented: the strawberry tree (Arbutus unedo) in mainland Portugal, and the Apis mellifera (African Lineage) in the Iberian Peninsula.
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References
Barbet-Massin, M., Jiguet, F., Albert, C.H., Thuiller, W.: Selecting pseudo-absences for species distribution models: how, where and how many? Meth. Ecol. Evol. 3(2), 327–338 (2012)
Beaumont, L.J., et al.: Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecol. Model. 342, 135–146 (2016)
Bioco, J., Prata, P., Canovas, F., Fazendeiro, P.: \(SDSim\): a generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenarios. Environ. Model. Softw. 147, 105234 (2022)
Burrough, P.A., McDonnell, R.A.: Principles of Geographical Information Systems, p. 190. Oxford University Press, New York (1998)
Cánovas, F., De la Rúa, P., Serrano, J., Galián, J.: Analysis of a contact area between two distinct evolutionary honeybee units: an ecological perspective. J. Insect Conserv. 18(5), 927–937 (2014). https://doi.org/10.1007/s10841-014-9701-1
Danielson, J.J., Gesch, D.B.: Global multi-resolution terrain elevation data 2010 (GMTED2010). US Department of the Interior, US Geological Survey (2011)
Elith, J., et al.: Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2), 129–151 (2006). https://doi.org/10.1111/j.2006.0906-7590.04596.x
Engler, R., Guisan, A., Rechsteiner, L.: An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41(2), 263–274 (2004)
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25(15), 1965–1978 (2005)
Liu, C., Newell, G., White, M.: The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites. Ecography 42(3), 535–548 (2019)
Pearce, J., Ferrier, S.: An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecol. Model. 128(2–3), 127–147 (2000)
Phillips, S.J., Anderson, R.P., Schapire, R.E.: Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006)
Ribeiro, M.M., et al.: Bioclimatic modeling in the Last Glacial Maximum, Mid-Holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.). PLoS ONE 14(1), e0210062 (2019)
Robertson, M.P., Caithness, N., Villet, M.H.: A PCA-based modelling technique for predicting environmental suitability for organisms from presence records. Divers. Distrib. 7(1–2), 15–27 (2001)
VanDerWal, J., Shoo, L.P., Graham, C., Williams, S.E.: Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol. Model. 220(4), 589–594 (2009)
Wisz, M.S., Guisan, A.: Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecol. 9(1), 1–13 (2009)
Zaniewski, A.E., Lehmann, A., McC Overton, J.: Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol. Model. 157(2–3), 261–280 (2002)
Acknowledgments
This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, cofinanced by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. This work was also funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.
We thank all the authors of the paper [13] for providing the occurrence data used in the first case study.
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Bioco, J., Prata, P., Canovas, F., Fazendeiro, P. (2022). On the Modelling of Species Distribution: Logistic Regression Versus Density Probability Function. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_25
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