Species distribution models in climate change scenarios are still not useful for informing policy planning: an uncertainty assessment using fuzzy logic

Abstract

We compared the effect of general circulation models and greenhouse gas emission scenarios on the uncertainty associated with models predicting changes in areas favourable to animal species. Given that mountain species are particularly at risk due to climate warming, we selected one amphibian (Baetic midwife toad), one reptile (Lataste's viper), one bird (Bonelli's eagle), and one mammal (Iberian wild goat) present in Spanish mountains to model their distributional response to climate change during this century. Climate forecasts for the whole century were provided by the Agencia Estatal de Meteorología (AEMET; National Meteorological Agency) of Spain, which adapted the general circulation models CGCM2 and ECHAM4 and produced expected temperature and precipitation values for Spain according to the A2 and B2 emission scenarios. We constructed separate models of the species response to spatial, topographic, human, and climate variables using current values of the corresponding variables. We predicted future areas favourable to the species by replacing the current climate values with those expected according to each climate change scenario, while keeping spatial, topographic and human variables constant. Fuzzy logic was used to compute the coincidence between predictions for different emission scenarios in the same global circulation model, and the consistency between predictions for the same emission scenario applying different general circulation models. In general, coincidences were higher than consistencies and, thus, discrepancies between predictions were more attributable to uncertainty in global circulation models, i.e. our insufficient knowledge concerning the effect of the oceans and atmosphere on climate, than to the putative effect of different emission scenarios on future climates. Our conclusion is that species distribution models in climate warming scenarios are still not useful for informing emission policy planning, although they have great potential as tools once consistencies become higher than coincidences.

Potential distribution model of Pinaceae species under climate change scenarios in Michoacán

“…El primero se refiere a bienes como los alimentos, combustibles, reciclaje de nutrientes, filtración de agua, absorción de contaminación, y puede adquirir mayor valor económico si se consideran las posibilidades de la biotecnología en ingeniería genética en el futuro. En cuanto al valor intrínseco, la disminución de la diversidad biológica representa la pérdida de la viabilidad genética en especies y ecosistemas; por ejemplo, la pérdida de especies arbóreas y, a su vez, de hábitat de otras especies como Danaus plexippus L. (mariposa monarca) (Meléndez, 2010;Sáenz-Romero et al, 2012;Villaseñor, 2005). En México no existe un sistema adecuado para designar áreas de protección para especies de Pinus (56 especies) sensibles al cambio climático, pues menos de 10 % del área de distribución se destina como área natural protegida (Gutiérrez & Duivenvoorden, 2010).…”

“…Este tipo de modelado emplea climas futuros predichos con base en la combinación de modelos de circulación general atmósfera-océano y reportes de diferentes escenarios. Dichos modelos plantean hipótesis diferentes sobre el efecto de los océanos y la atmósfera sobre el clima; el efecto es caracterizado por diferentes supuestos futuros socioeconómicos y de desarrollo tecnológico (Real, Luz Márquez, Olivero, & Estrada, 2010).…”

“…The former refers to goods such as food, fuel, nutrient recycling, water filtration, and contamination adsorption, and it can acquire greater economic value if the possibilities of biotechnology in genetic engineering are considered in the future. With regard to the intrinsic value, the decrease in biological diversity represents the loss of genetic viability of the species and ecosystems, for example, the loss of tree species and, in turn, the loss of habitat for other species such as Danaus plexippus L. (monarch butterfly) (Meléndez, 2010;Sáenz-Romero et al, 2012;Villaseñor, 2005). In Mexico, there is no adequate system to designate protection areas for Pinus species (56 species) sensitive to climate change, given that less than 10 % of the distribution area is designated as a natural protected area (Gutiérrez & Duivenvoorden, 2010).…”

“…This type of modeling utilizes predicted future climates based on a combination of general atmosphere-ocean circulation models and reports of various scenarios. Such models pose different hypotheses on the effect of oceans and the atmosphere on climate; the effect is characterized by different supposed futures of socioeconomic and technological development (Real, Luz Márquez, Olivero, & Estrada, 2010). …”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…There is currently much debate about the basic assumptions underlying this approach (e.g. Pearson & Dawson 2003;Hampe 2004;Pearson & Dawson 2004;Heikkinen et al 2006;Dormann 2007a;Beale et al 2008;Jiménez-Valverde et al 2008;Thuiller et al 2008;Araújo et al 2009;Aspinall et al 2009;Beale et al 2009;Duncan et al 2009;Morin & Thuiller 2009;Peterson et al 2009;Willis & Bhagwat 2009;Chapman 2010;Mouton et al 2010;Real et al 2010;Willis et al 2010b), namely that present-day distributions are controlled by climate (Araújo & Luoto 2007;Beale et al 2009;Blach-Overgaard et al 2010;Chapman 2010), that the distributions are in equilibrium with climate today (Svenning & Skov 2004;Araújo & Pearson 2005;Svenning & Skov 2005de Marco et al 2008;Svenning et al 2008;Normand et al 2009), that the distributional data and the climate data are reliable (Rolland 2003;Kitricos & Leriche 2010) and, in the case of montane and alpine biota, that the modern climate data are from the same altitudes as where the species being modelled actually grow (Dahl 1951(Dahl , 1998Randin et al 2006;Lundquist & Cayan 2007;Pape et al 2009;…”