Marine Mussels. Elizabeth Gosling

ranges of species are determined by climatic conditions, and this forms the basis of a number of models that use associations between aspects of climate and species’ occurrences to estimate the conditions that are suitable for maintaining viable populations (Araújo & Peterson 2012). The most frequently used climate change model is species distribution modelling (SDM), also known as environmental (or ecological) niche modelling, habitat modelling, predictive habitat distribution modelling and range mapping. SDM uses computer algorithms to predict the distribution of a species across geographic space and time using environmental data (Elith & Leathwick 2009). It can explain how environmental conditions influence the occurrence or abundance of a species, and can provide models (ecological forecasting) of a species’ future distribution under climate change, a species’ past distribution (in order to assess evolutionary relationships) or the potential future distribution of an invasive species. There are two main types of SDM: correlative SDMs, also known as climate envelope models or bioclimatic models, which model the observed distribution of a species as a function of environmental conditions; and mechanistic SDMs, also known as process‐based models or biophysical models, which use independently derived information about a species’ physiology to develop a model of the environmental conditions under which it can exist (Kearney & Porter 2009). In empirical modelling, a number of climatic variables, such as maximum and minimum temperatures and precipitation, are measured for many different locations and statistically compared to the occurrence of the focal species at these locations (Jeschke & Strayer 2008). Most bioclimatic models do not explicitly consider biotic interactions (e.g. predation and competition between species) or limitations to dispersal, and assume that species lack sufficient plasticity to adapt to environments beyond those currently occupied (Jeschke & Strayer 2008; Fuller et al. 2010). The ecological effects of climate change can only be predicted if there is an understanding of the physiology of the species in its natural environment. Evidence based on temperature indicates that species’ distributions are driven both by short‐term exposure to lethal conditions and by repeated or longer‐term exposures leading to energetic failures. The relationship between physiological performance (e.g. scope for growth, SFG) and body temperature can be depicted as a thermal performance curve (Figure 3.17). Woodin et al. (2013) define the difference between lethal and sublethal exposure limits as the transient event margin (TEM), the range of environmental conditions below the short‐term lethal limit that a species may endure on a transient basis but which would lead to mortality over longer time spans. The magnitude of the disparity (TEM) between performance and tolerance temperature thresholds relative to environmental variance determines the likelihood of failure of bioclimatic model predictions (Woodin et al. 2013). To define TEM, both CT_max SFG and LT_max data are necessary, but in the majority of data sets only LT_max estimates are available. Jones et al. (2010) used a mechanistic biogeographic model for M. edulis on the Atlantic coast of N. America based on LT_max. Physiological limits were compared against environmental temperatures and the biogeographic distribution of the species was accurately predicted. The hindcast using the model also successfully predicted the historical changes in distribution over half a decade described earlier. However, when the same model applied to Europe, it failed to predict the distribution of M. edulis; in reality, its distribution in Europe was 50% less than that predicted. When an energetics model was applied to Europe, the predicted distribution was close to the actual distribution of the species on western European coasts. This highlights the need for caution in applying the same model across large geographic distances (see details in Woodin et al. 2013).

Figure 3.17 (a) Relation between performance as scope for growth (SFG) and temperature. Lethal limits are LTmin and LTmax, performance limits are critical minimum temperature (CTmin) and critical maximum temperature (CTmax), the body temperature at any point in space and time is Tbody and the optimal temperature is Topt. The upper transient event margin (TEM) = LTmax − CTmax. Thermal response curves vary from species to species but all typically display some optimum temperature (Topt) at which performance is maximised, as well as critical minima (CTmin) and critical maxima (CTmax) beyond which mortality and/or reproductive failure occur (Helmuth et al. 2010). (b) Temperature versus frequency for environments a, b and c. TEM from (a) is expressed on yearly environmental variance curves for three environments, all with the same mean environmental temperature but differing in variance.

      From Woodin et al. (2013). Source: From Woodin et al. (2013). Reprinted with permission from John Wiley & Sons Ltd. CC BY 3.0.

Closely related species with different physiological tolerances and distributions make ideal systems for documenting range shifts in response to a changing climate. The closely related species M. edulis, M. trossulus and M. galloprovincialis have distinct biogeographical ranges that are correlated with SST. Fly & Hilbish (2013) determined the SFG of these three species over a range of temperatures (5–30 °C) to determine whether energetics could predict their distributions. SFG represents energy available for growth and/or reproduction above that necessary for maintenance requirements. The results showed, as expected, reasonable correlation with the temperature regimes of habitats presently occupied, with M. trossulus, a boreal species, exhibiting positive SFG up to 17 °C, M. edulis, a cold temperate species, up to 23 °C, and M. galloprovincialis, a warm‐water species, up to 30 °C. In the spring, temperatures of peak performance (T_opt) for both M. trossulus and M. edulis occurred at ~15 °C, and both species exhibited negative SFG at 25 °C, which was the T_opt for M. galloprovincialis. Overall, SFG was lower in summer than in autumn and winter. M. trossulus only showed positive SFG at 10 °C, while M. edulis and M. galloprovincialis maintained positive SFG at all temperatures above 5 °C, with T_opts at 20 and 25 °C, respectively (see Figure 6.15). The warm end of each species’ range correlated positively with the seawater temperature at which that species’ SFG became negative (i.e. when metabolic expenditure exceeds energy acquisition) in summer and autumn. Energetics at cold temperatures did not predict the cold end of the species’ ranges, as there was no clear SFG advantage to explain the dominance of M. trossulus in cold habitats. As SST continues to warm due to climate change, the energetics of these three species provide a basis for developing mechanistic models⁸ predicting future distribution and productivity changes in mussel populations (Fly & Hilbish 2013).

      Montalto et al. (2016) used three interconnected models (climatic, biophysical and energetics) to estimate changes in growth, reproduction and mortality risk by 2050 for three commercially and ecologically important mussels (Brachidontes pharaonis, Mytilaster minimus and M. galloprovincialis) at 51 sites in the Mediterranean Sea. Helmuth et al. (2014) have cautioned that to focus solely on range limits may lead to failure to notice highly significant impacts that are both occurring now and will likely occur in the future. All three species inhabit intertidal habitats, and thus are affected by changes in both terrestrial and marine environments. They differ from one another in their thermal optima, performance breadth and lethal temperatures. Montalto et al. (2016) predicted highly variable responses (both positive and negative) in the timing of reproductive maturity and the risk of lethality among the species and sites that do not conform to simple latitudinal gradients, which would be undetectable by methods focused only on lethal limits and/or range boundaries. Also, results strongly suggest that the three species will likely experience changes in the timing of reproductive maturity, and that in intertidal zones thermal stress may cause changes to the risk of mortality. However, these consequences are highly species‐ and site‐specific in ways that do not always conform to simple latitudinal gradients, and in some cases reveal potential benefits for Mediterranean Sea ecosystems.

Challenges with managing bivalve culture may become more pronounced as the uncertainty associated with climate change makes it difficult to predict future production levels. Steeves et al. (2018)