Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species

Abstract

Species distribution models (SDMs) correlate species occurrences with environmental predictors, and can be used to forecast distributions under future climates. SDMs have been criticized for not explicitly including the physiological processes underlying the species response to the environment. Recently, new methods have been suggested to combine SDMs with physiological estimates of performance (physiology‐SDMs). In this study, we compare SDM and physiology‐SDM predictions for select marine species in the Mediterranean Sea, a region subjected to exceptionally rapid climate change. We focused on six species and created physiology‐SDMs that incorporate physiological thermal performance curves from experimental data with species occurrence records. We then contrasted projections of SDMs and physiology‐SDMs under future climate (year 2100) for the entire Mediterranean Sea, and particularly the ‘warm’ trailing edge in the Levant region. Across the Mediterranean, we found cross‐validation model performance to be similar for regular SDMs and physiology‐SDMs. However, we also show that for around half the species the physiology‐SDMs substantially outperform regular SDM in the warm Levant. Moreover, for all species the uncertainty associated with the coefficients estimated from the physiology‐SDMs were much lower than in the regular SDMs. Under future climate, we find that both SDMs and physiology‐SDMs showed similar patterns, with species predicted to shift their distribution north‐west in accordance with warming sea temperatures. However, for the physiology‐SDMs predicted distributional changes are more moderate than those predicted by regular SDMs. We conclude, that while physiology‐SDM predictions generally agree with the regular SDMs, incorporation of the physiological data led to less extreme range shift forecasts. The results suggest that climate‐induced range shifts may be less drastic than previously predicted, and thus most species are unlikely to completely disappear with warming climate. Taken together, the findings emphasize that physiological experimental data can provide valuable supplemental information to predict range shifts of marine species.

Projected Rapid Habitat Expansion of Tropical Seagrass Species in the Mediterranean Sea as Climate Change Progresses

“…However, there is less evidence of the invasive capacity of H. decipiens, as this species was just recently found for the first time in 2018 along the coast of Greece (38 • N) (Gerakaris et al, 2020). Our hindcast model was implemented using only SST, while our SDMs were implemented with only one additional environmental parameter (SST and surface salinity) (i.e., Chefaoui et al, 2018;Wilson et al, 2019;Gamliel et al, 2020). Temperature and salinity are some of the main factors defining the physiological thresholds and distribution of seagrasses (Lee et al, 2007;Marbà and Duarte, 2010), although, other factors (biochemical, physical and biological) may also contribute to determining seagrass habitat ranges.…”

“…The aims of the present study were to (i) improve our understanding of the trait distance and relatedness between H. stipulacea and H. decipiens populations from the Mediterranean Sea and other worldwide populations, (ii) assess whether increases in SST over the last 100 years in the Mediterranean Sea could partially explain the colonization of H. stipulacea in this basin, and finally, (iii) examine the potential habitat expansion of H. stipulacea and H. decipiens in the Mediterranean basin during this century by implementing Species Distribution Models (SDMs) in relation to SSTs and salinities. SDMs have been widely applied to investigate the potential biogeographical shifts in response to the effects of global environmental changes of native and invasive terrestrial and aquatic species, including seagrass (i.e., Valle et al, 2014 ; Chefaoui et al, 2018 ; Beca-Carretero et al, 2020b ; Gamliel et al, 2020 ). Here, we hypothesize that a wide potential expansions of the habitat suitability of H. stipulacea and to a lesser degree of H. decipiens are expected to occur during this century as the Mediterranean Sea becomes warmer and saltier.…”

The Tropical Seagrass Halophila stipulacea: Reviewing What We Know From Its Native and Invasive Habitats, Alongside Identifying Knowledge Gaps

“…Additionally, this study also showed that carbon-fixing enzymes were able to function in the presence of intra-cellular salt concentrations in vitro, which is an important adaptive mechanism to salinity variations. Detailed experiments exploring the thresholds of H. stipulacea's hyperand hypo-salinity tolerance need to be conducted and combined with niche models in order to predict if salinity is a limiting factor for the spread of this species (see for example Oscar et al, 2018;Gamliel et al, 2020).…”

“…These models are developed using the knowledge on the current distribution of the studied species (the realized niche) which can be potentially projected in space or time to forecast the species distributions within the invaded ranges within a time frame (Fitzpatrick and Hargrove, 2009;Gallien et al, 2010). Applying SDMs for H. stipulacea (Gamliel et al, 2020) using mean annual bottom temperature and net primary productivity as environmental predictors revealed some interesting patterns (Figure 8). The main result was the striking differences in the predicted suitability of the Mediterranean Sea to support H. stipulacea when the model is based on the native (Figure 8A) vs. invaded (Figure 8B) range occurrences.…”

“…Such models may provide more robust forecasts of species distributions in novel climates. Gamliel et al (2020) used a recently proposed a Bayesian approach that combines SDMs with physiological data (sensu Talluto et al, 2016) to forecast the distribution of H. stipulacea. The physiological data included H. stipulacea's change in leaf area at different temperatures (Georgiou et al, 2016), which was used to calculate a temperature response curve.…”