Physiology in ecological niche modeling: using zebra mussel's upper thermal tolerance to refine model predictions through Bayesian analysis

Abstract

Climate change and human-mediated dispersal are increasingly influencing species' geographic distributions. Ecological niche models (ENMs) are widely used in forecasting species' distributions, but are weak in extrapolation to novel environments because they rely on available distributional data and do not incorporate mechanistic information, such as species' physiological response to abiotic conditions. To improve accuracy of ENMs, we incorporated physiological knowledge through Bayesian analysis. In a case study of the zebra mussel Dreissena polymorpha, we used native and global occurrences to obtain native and global models representing narrower and broader understanding of zebra mussel' response to temperature. We also obtained thermal limit and survival information for zebra mussel from peer-reviewed literature and used the two types of information separately and jointly to calibrate native models. We showed that, compared to global models, native models predicted lower relative probability of presence along zebra mussel's upper thermal limit, suggesting the shortcoming of native models in predicting zebra mussel's response to warm temperature. We also found that native models showed improved prediction of relative probability of presence when thermal limit was used alone, and best approximated global models when both thermal limit and survival data were used. Our result suggests that integration of physiological knowledge enhances extrapolation of ENM in novel environments. Our modeling framework can be generalized for other species or other physiological limits and may incorporate evolutionary information (e.g. evolved thermal tolerance), thus has the potential to improve predictions of species' invasive potential and distributional response to climate change.

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

“…Knowledge of species ecology and physiology can also be useful to delineate transferability areas (Feng and Papeş 2017) and improve distribution models, as recently shown for Southern Ocean species (Guillaumot et al 2018a, Guillaumot et al 2019. Feng et al (2020) developed a new modelling algorithm, called Plateau, which uses experimental data to define upper temperature conditions in distribution models. For temperature and salinity, physiological experiments and field observations can be used in models to determine species tolerance thresholds.…”

“…Among the broad array of analytical tools developed for marine ecology studies over the last two decades, Species Distribution Modelling (SDM) has been increasingly used (Peterson 2001, Elith et al 2006, Austin 2007, Gobeyn et al 2019) and applied to Southern Ocean pelagic (Pinkerton et al 2010, Freer et al 2019, benthic organisms (Loots et al 2007, Pierrat et al 2012, Basher and Costello 2016, Xavier et al 2016, Gallego et al 2017, Guillaumot et al 2018a, 2018b, Fabri-Ruiz et al 2019, Jerosch et al 2019) and even marine mammals (Nachtsheim et al 2017). SDM represents a complementary approach to individual-based modelling and eco-physiological experiments, quickly and synthetically identifying environmental correlates of species distribution (Brotons et al 2012, Feng and Papeş 2017, Feng et al 2020. SDM is also used to define species distribution spatial range (Nori et al 2011, Walsh andHudiburg 2018) and can be used as decision criteria for conservation purposes (Guisan et al 2013, Marshall et al 2014.…”

Challenges and opportunities of species distribution modelling of terrestrial arthropod predators

“…Whereas the performance of the approach has been tested in a handful of invertebrate species thus far (e.g. Feng et al, 2020;Zhou et al, under review), similar tools will become increasingly useful as the availability of traits and computation power increase, leading to more realistic and evolutionary-driven predictions of biodiversity change.…”

Systematic Conservation Planning at an Ocean Basin Scale: Identifying a Viable Network of Deep-Sea Protected Areas in the North Atlantic and the Mediterranean

“…Another data gap relates to knowledge of intraspecific diversity (i.e., genomic and physiological data) allowing an appraisal of the species capacity to adapt to forthcoming environmental changes (Buckley et al, 2011;Feng et al, 2019), either through phenotypic plasticity or through genetic adaptation, as this may confound predictions of future habitat shifts and the location of potential climate refugia. Equally, little is known about the reproductive cycle and larval biology of most VME indicator species (but see Larsson et al, 2014;Strömberg and Larsson, 2017), making it difficult to predict changes in connectivity patterns and population renewal capacity under climate change scenarios.…”