A framework for species distribution modelling with improved pseudo-absence generation

Iturbide, Maialen; Bedia, Joaquín; Herrera, Sixto; Hierro, Ó. del; Pinto, Míriam Plaza; Gutiérrez, José Manuel

doi:10.1016/j.ecolmodel.2015.05.018

Cited by 129 publications

(106 citation statements)

References 50 publications

(76 reference statements)

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“…Therefore we identified those locations which are both climatically and geographically distant from the observed presence locations with a three step approach according to Senay et al (2013) assuming such locations as unsuitable for Douglas-fir. Traditionally when reliable absences or no absence locations are available pseudoabsences are selected randomly or based on geographic or climate alone profiling (Barbet-Massin et al 2012, Iturbide et al 2015. In general, the random selection of pseudoabsence was found to be the most error prone strategy (Barbet-Massin et al 2012, Senay et al 2013, Iturbide et al 2015.…”

Section: Discussionmentioning

confidence: 99%

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

et al. 2018

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Climate change is likely to result in novel conditions with no analogy to current climate. Therefore, the application of species distribution models (SDMs) based on the correlation between observed species’ occurrence and their environment is questionable and calls for a better understanding of the traits that determine species occurrence. Here, we compared two intraspecific, trait‐based SDMs with occurrence‐based SDMs, all developed from European data, and analyzed their transferability to the native range of Douglas‐fir in North America. With data from 50 provenance trials of Douglas‐fir in central Europe multivariate universal response functions (URFs) were developed for two functional traits (dominant tree height and basal area) which are good indicators of growth and vitality under given environmental conditions. These trials included 290 North American provenances of Douglas‐fir. The URFs combine genetic effects i.e. the climate of provenance origin and environmental effects, i.e. the climate of planting locations into an integrated model to predict the respective functional trait from climate data. The URFs were applied as SDMs (URF‐SDMs) by converting growth performances into occurrence. For comparison, an ensemble occurrence‐based SDM was developed and block cross validated with the BIOMOD2 modeling platform utilizing the observed occurrence of Douglas‐fir in Europe. The two trait based SDMs and the occurrence‐based SDM, all calibrated with data from Europe, were applied to predict the known distribution of Douglas‐fir in its introduced and native range in Europe and North America. Both models performed well within their calibration range in Europe, but model transfer to its native range in North America was superior in case of the URF‐SDMs showing similar predictive power as SDMs developed in North America itself. The high transferability of the URF‐SDMs is a testimony of their applicability under novel climatic conditions highlighting the role of intraspecific trait variation for adaptation planning in climate change.

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Section: Discussionmentioning

confidence: 99%

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

et al. 2018

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“…To overcome this obstacle, we generated pseudo‐absence data (a.k.a. background points) by adapting the methodology in Iturbide et al () to our specific data. In the first step, we used the function OCSVMprofiling from the R package MOPA (Iturbide et al, ) to limit the geographic region for pseudo‐absence data generation using environmental profiling based on presence data.…”

Section: Methodsmentioning

confidence: 99%

Climate‐induced changes in the suitable habitat of cold‐water corals and commercially important deep‐sea fishes in the North Atlantic

Morato

González-Irusta

Domínguez-Carrió

et al. 2020

Global Change Biology

145

139

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The deep sea plays a critical role in global climate regulation through uptake and storage of heat and carbon dioxide. However, this regulating service causes warming, acidification and deoxygenation of deep waters, leading to decreased food availability at the seafloor. These changes and their projections are likely to affect productivity, biodiversity and distributions of deep‐sea fauna, thereby compromising key ecosystem services. Understanding how climate change can lead to shifts in deep‐sea species distributions is critically important in developing management measures. We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold‐water coral and commercially important deep‐sea fish species under present‐day (1951–2000) environmental conditions and to project changes under severe, high emissions future (2081–2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean. Our models projected a decrease of 28%–100% in suitable habitat for cold‐water corals and a shift in suitable habitat for deep‐sea fishes of 2.0°–9.9° towards higher latitudes. The largest reductions in suitable habitat were projected for the scleractinian coral Lophelia pertusa and the octocoral Paragorgia arborea, with declines of at least 79% and 99% respectively. We projected the expansion of suitable habitat by 2100 only for the fishes Helicolenus dactylopterus and Sebastes mentella (20%–30%), mostly through northern latitudinal range expansion. Our results projected limited climate refugia locations in the North Atlantic by 2100 for scleractinian corals (30%–42% of present‐day suitable habitat), even smaller refugia locations for the octocorals Acanella arbuscula and Acanthogorgia armata (6%–14%), and almost no refugia for P. arborea. Our results emphasize the need to understand how anticipated climate change will affect the distribution of deep‐sea species including commercially important fishes and foundation species, and highlight the importance of identifying and preserving climate refugia for a range of area‐based planning and management tools.

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“…Concurrently, many methodological studies have aimed to optimize model performance. Studies have explored the effect of presence sample size (Hernandez, Graham, Master, & Albert, 2006;Jiménez-Valverde, Lobo, & Hortal, 2009), spatial and/or environmental occurrence bias (Boria, Olson, Goodman, & Anderson, 2014;Park & Davis, 2017;Varela, Anderson, García-Valdés, & Fernández-González, 2014), various procedures of selecting pseudo-absences (Barbet-Massin, Jiguet, Albert, & Thuiller, 2012;Iturbide et al, 2015;Phillips et al, 2009), and designing a model training area that is ecologically valid (Anderson & Raza, 2010;Saupe et al, 2012). Additionally, studies have explored the selection of predictor variables using statistical approaches (correlation analysis, jackknifing, or contribution to model fit), as well as using knowledge about the species' ecology (Bucklin et al, 2015;Pliscoff, Luebert, Hilger, & Guisan, 2014;Synes & Osborne, 2011;Zeng, Low, & Yeo, 2016).…”

mentioning

confidence: 99%

Collinearity in ecological niche modeling: Confusions and challenges

Feng

Park

Liang

et al. 2019

Ecology and Evolution

281

155

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Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.

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A framework for species distribution modelling with improved pseudo-absence generation

Cited by 129 publications

References 50 publications

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

Climate‐induced changes in the suitable habitat of cold‐water corals and commercially important deep‐sea fishes in the North Atlantic

Collinearity in ecological niche modeling: Confusions and challenges

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