An approach to consider behavioral plasticity as a source of uncertainty when forecasting species' response to climate change

Abstract

The rapid ecological shifts that are occurring due to climate change present major challenges for managers and policymakers and, therefore, are one of the main concerns for environmental modelers and evolutionary biologists. Species distribution models (SDM) are appropriate tools for assessing the relationship between species distribution and environmental conditions, so being customarily used to forecast the biogeographical response of species to climate change. A serious limitation of species distribution models when forecasting the effects of climate change is that they normally assume that species behavior and climatic tolerances will remain constant through time. In this study, we propose a new methodology, based on fuzzy logic, useful for incorporating the potential capacity of species to adapt to new conditions into species distribution models. Our results demonstrate that it is possible to include different behavioral responses of species when predicting the effects of climate change on species distribution. Favorability models offered in this study show two extremes: one considering that the species will not modify its present behavior, and another assuming that the species will take full advantage of the possibilities offered by an increase in environmental favorability. This methodology may mean a more realistic approach to the assessment of the consequences of global change on species' distribution and conservation. Overlooking the potential of species' phenotypical plasticity may under- or overestimate the predicted response of species to changes in environmental drivers and its effects on species distribution. Using this approach, we could reinforce the science behind conservation planning in the current situation of rapid climate change.

Fire history influences large‐herbivore behavior at circadian, seasonal, and successional scales

“…Many species have some degree of behavioral plasticity that allows them to respond to changes in environmental conditions, but modeling these capacities remains challenging (Muñoz et al. ). This behavioral plasticity often leads to behavioral cycles that have important implications for coexistence of competitors (Monterroso et al.…”

“…Many species have some degree of behavioral plasticity that allows them to respond to changes in environmental conditions, but modeling these capacities remains challenging (Muñoz et al 2015). This behavioral plasticity often leads to behavioral cycles that have important implications for coexistence of competitors (Monterroso et al 2014), coexistence of predators and prey (Lone et al 2017), and adaptation under climate change (Muñoz et al 2015). For example, Monterroso et al (2014) found that most members of a diverse carnivore community exhibited substantial plasticity in diel activity patterns and that pairwise temporal overlap in species activity declined as the number of predator species present increased.…”

Satellite- versus temperature-derived green wave indices for predicting the timing of spring migration of avian herbivores

“…the date of 50% GWI from the Baltic Sea to the White Sea. This is because of the fact that species can adjust their behavior to climate change through phenotypic plasticity (Muñoz et al, 2015).…”

Decreasing brown bear (Ursus arctos) habitat due to climate change in Central Asia and the Asian Highlands

“…Around the world, climate change has had significant direct and indirect impacts on terrestrial species, by being a major cause of speciation and species extirpation (Pound & Salzmann, 2017). Many studies have recently focused on the ecological (Etterson & Mazer, 2016;Wikelski & Tertitski, 2016), ethological (Munoz, Marquez, & Real, 2015) and biological changes (Torres-Diaz et al, 2016;Hulme, 2016) in relation to climatic change. For example, various ecosystems are vulnerable to climate change which may induce a broad array of adverse effects such as disturbances of phenological events, food web disruptions, pathogens and disease spread and ultimately, in worst case scenarios, may include extinction risks (Wu, Lu, Zhou, Chen, & Xu, 2016).…”