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Latitudinal distributions of many organisms are limited by temperature. One major response is a shift in distribution, usually poleward (Root et al. 2003). Physiological processes that set thermal tolerance limits are thought to determine, or at least contribute to, some of the shifts that have been observed (Tomanek 2008 and references therein). As already mentioned, seasonal air and water temperatures since 1960 have increased along the eastern US seaboard, and south of Lewes, Delaware (38.8 °N) summer SST increases have exceeded the upper lethal limits (32 °C) of M. edulis (Jones et al. 2010), resulting in geographic contraction of its southern, equatorward range edge approximately 350 km north, or ~7.5 km per year (Somero 2012). At the southern part of the range, high water and air temperatures cause mass mortality events, while along the more northerly portion, mortality is caused by high temperatures during aerial exposure. Ultimately, water temperatures in excess of thermal tolerances have caused contraction of the mussel’s biogeographic range (Jones et al. 2010).

Range shifts vary greatly between species, and the distributions of Mytilus populations all over the world are responding differently to climate change. For example, Harley et al. (2011) compared distributions of M. californianus from 2009 to 2010 to a historical data set from 1957–1958. Sampling sites were believed to be within ~30 m of the original survey sites. The 52 years separating the two sampling intervals span a period of climatic warming. During the latter half of the 20th century, maximum air temperatures near the eastern and western ends of the Strait of Juan de Fuca near Washington state increased by ~0.2 and ~0.13 °C per decade, respectively, and mean annual water temperatures along the southern and western Vancouver Island coast warmed by ~0.08–0.11 °C per decade. Average daily maximum air temperatures during the summer, which are particularly relevant to thermal stress experienced in the intertidal zone, have warmed even more rapidly. At Victoria, on the southern end of Vancouver Island, summer average daily maxima have risen approximately linearly at a rate of 0.654 °C per decade since 1950, corresponding to an increase of 3.48 °C over 52 years, which has caused a 51% decrease in the vertical distribution of M. californianus, providing strong evidence that this change is linked to global warming. Associated with this decrease are local extinctions of M. californianus at 13% of the sites resurveyed between 2009 and 2010. Historical data for M. trossulus were only available for one site (former vertical range = 49 cm). By 2010, M. trossulus had been completely eliminated from that site, with the exception of three small juveniles found under a single rock.

From 1995 to 1999, the poleward movement of M. galloprovincialis showed a reversal concomitant with a cooling phase of the PDO (Hilbish et al. 2010). M. galloprovincialis has declined in abundance over the northern third of its geographic range (~540 km) and has become rare or absent across the northern 200 km of the range it previously colonised during its initial invasion. The distribution of the native species M. trossulus has, however, remained unchanged over the same time period. The difference in SST between warm and cold phases of the PDO is small (~1 °C), but Hilbish et al. (2010) deduced that even this minor decrease in temperature during the cold phase of the PDO may be enough to retard larval development in M. galloprovincialis, such that recruitment is handicapped in northern waters.

Geographic 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) coupled a previously established high‐resolution climate model with two dynamic energy budget (DEB) models (see Chapter 6) to explore the future growth and distribution of two economically and ecologically important species, the oyster Crassostrea virginica and the mussel M. edulis, along the Atlantic coast of Canada. SST data were extracted from the climate model and used as a forcing variable in the bioenergetic models. This approach was applied across three distinct time periods: the past (1986–1990), the present (2016–2020) and the future (2046–2050), thus permitting a comparison of bivalve performance under different temporal scenarios. Results showed that growth in the future is variable both spatially and interspecifically. Modelling outcomes suggested that warming ocean temperatures will cause an increase in growth rates of both species as a result of their ectothermic nature. Since the thermal tolerance of C. virginica is higher than that of M. edulis, oysters will generally out‐perform mussels (Gosling 2015). The predicted effects of temperature on bivalve physiology also provided insight into vulnerabilities, such as mortality, under future SST scenarios. This information is useful for adapting future management strategies for both farmed and wild shellfish. Although the study focused on a geographically specific area, the approach of coupling bioenergetic and climate models is valid for species and environments across the globe.

Apart from the impact on mussel biogeographic distribution and physiological traits already discussed, there is evidence that global warming can impact on: interspecific (Petes et al. 2007) and predator–prey (Freitas et al. 2007; Broitman et al. 2009; Menge et al. 2008; Harley 2011; Kordas et al. 2011; Monaco et al. 2016; Torossian et al. 2020) interactions; reproductive timing and larval dispersal (Carson et al. 2010); the immune response (Matozzo & Marin 2011; Beaudry et al. 2016; Hernroth & Baden 2018); byssal thread strength (Newcomb et al. 2019); developmental instability (Nishizaki et al. 2015); species invasion success (US EPA 2008; Occhipinti‐Ambrogi & Galil 2010; Firth et al. 2011; Somero 2010, 2011; Thyrring et al. 2017); mussel culture (Guyondet et al. 2015; Steeves et al. 2018; Silva et al. 2017; Filgueira et al. 2016; Callaway et al. 2012); and radiation‐induced damage (Dallas et al. 2016).