CHAPTER 3: Modelling the distribution of Epinephelus andersoni with future sea surface temperature changes
3.1 Introduction
Although global climate change is a topic of much current political controversy, it is not a new topic in terms of biological research (Parmesan 2006). The importance of climatic thresholds for restricting the geographic distributions of many species (Grinnell 1917) was recognised before climate change and species distribution models (SDMs) became popular research topics. Current research now predicts that future projected climatic changes will cause shifts in the distributions of a number of species globally (Thomas et al. 2006), including those of marine organisms (e.g. Fields et al. 1993;
Clark 2006; Harley et al. 2006; Brander 2007; Hoegh-Guldberg et al. 2007; Lasram et al. 2010).
Climate-induced changes in the marine environment are anticipated to have substantial effects on global fisheries, a number of them negative, especially when combined with the high levels of exploitation experienced by virtually all wild marine stocks (Perry et al. 2005; Harley et al. 2006;
Brander 2007).
In a global review on the numbers of modelling studies that have been conducted to date, Robinson et al. (2011) found that relatively few studies have used SDMs to determine the effect of climate change on marine species relative to terrestrial studies. Of the marine SDM studies that have been conducted, the majority focus on the effect of climate change on commercially exploited fishes, reflecting the commercial value attached to marine fish stocks (Robinson et al. 2011). These SDM studies have proven effective at mapping the distributions of important fisheries species world-wide and have provided useful information for resource managers (Kupschus 2003; Eastwood et al. 2003;
Hedger et al. 2004; Maxwell et al. 2009). Specifically in southern Africa, SDMs have been used to predict the effects of climate change on the distribution of terrestrial species (e.g. Erasmus et al.
2002; Pearson et al. 2006; Keith et al. 2009; Coetzee et al. 2009) but no known SDM studies have been published on coastal and marine species (but see Clark et al. 2000; Lutjeharms et al. 2001;
Bakun and Weeks 2004; Clark 2006; James et al. 2008 for suggested climate change impacts on southern African marine and coastal environments). Since SDMs model species’ distributions, it is useful to define such a distribution as: the complex expression of their ecology and evolutionary history (Brown 1995), which are determined by a multitude of factors, working with different intensities and at different spatial scales (Pearson and Dawson 2003). Factors affecting distributions include: abiotic conditions such as climatic and environmental conditions; biotic factors such as inter and intra-specific interactions; dispersal; and evolutionary capacity (Soberón and Peterson 2005).
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Although numerous environmental variables are needed to accurately predict terrestrial species occurrences as suggested by the definition of a species’ distribution, in the marine environment it has been shown that distributions can be determined by fewer variables, one of the most influential being water temperature (Sabatés et al. 2006; Hiddink and Hofstede 2008; Dulvy et al. 2008; Cheung et al. 2009; Lasram et al. 2010; Sunday et al. 2012). This can be supported by the fact that many marine fishes live closer to their thermal tolerance limits than terrestrial species (Harley et al. 2006;
Sunday et al. 2012). Bathymetry (especially bathymetric complexity) has also been shown to be influential in determining marine fish distributions (Pittman et al. 2007; Beger and Possingham 2008;
Pittman and Brown 2011).
There are two main modelling approaches which differ in their complexity (i.e. in the extent to which they include the variables that affect species’ distributions), namely correlative and mechanistic models (Pearson and Dawson 2003). Correlative models are relatively basic and correlate observed presence (and sometimes absence) data with background environmental variables, under the assumption that the best indicator of a species’ environmental requirements is its current
distribution (Pearson and Dawson 2003). Mechanistic models are physiologically-based models that make fewer assumptions than correlative models (Pearson and Dawson 2003) and can include physiological variables such as constraints to growth, regeneration (Sykes et al. 1996) and
reproduction (Kearney and Porter 2004). Although mechanistic models are generally more robust under climate change scenarios than correlative models (Pearson and Dawson 2003), data
limitations make correlative models a more popular approach (Pearson and Dawson 2003; Kearney and Porter 2004).
Many studies have shown that the type of correlative model used to predict species’ distributions can have profound effects on model results, and models’ performances have also been found to vary under different circumstances (Thuiller 2003, 2004; Araújo et al. 2005; Hijmans and Graham 2006;
Pearson et al. 2006; Pearson 2007). There are two key areas where models differ: firstly their data input requirements vary (e.g. presence-only versus presence-absence data); and secondly there are variable model extrapolation assumptions when potential species’ distributions are being projected into future “unknown” environmental conditions that were not present for the current projection (Thuiller 2003; Thuiller et al. 2004; Pearson et al. 2006). Generally, presence-absence data are preferred since they provide more information on the current distribution and the prevalence of the species than presence-only data (Phillips et al. 2009). However, obtaining detailed occurrence data for marine organisms is not always practical or possible. This is especially true when studying marine species that are mobile and difficult to detect (Kaschner et al. 2006) such as fishes (Maxwell et al.
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2009), necessitating the use of pseudo-absence data since all models require some form of absence data (Phillips et al. 2009).
Another aspect that affects the accuracy of model results is the geographical extent to which the environmental data are used to project the future distributions of a species; if the extent is excessively restricted it could result in skewed model results (Thuiller et al. 2004). Furthermore, model outputs are likely to represent only one of a number of possible future scenarios (Pearson and Dawson 2003). An increasingly popular approach is to combine a number of individual models into an ensemble model in an attempt to reduce the uncertainty implicit in correlative SDMs (e.g.
Lawler et al. 2006; Araújo and New 2007; Diniz-filho et al. 2009; Thuiller et al. 2009a; Lasram et al.
2010). Ensemble modelling approaches have been used to determine the potential effects of climate change on various species, providing useful information for conservation and the management of natural resources (Araújo et al. 2005; Diniz-filho et al. 2009; Marmion et al. 2009; Capinha and Anastácio 2011). Although results need to be interpreted with caution due to the uncertainty associated with correlative models (Araújo et al. 2005; Heikkinen et al. 2006; Pearson et al. 2006;
Diniz-filho et al. 2009; Maxwell et al. 2009), the use of models as a first approximation of future species’ distribution changes can play a role in helping resource managers prepare for future climate- induced changes (Pearson and Dawson 2003; Lawler et al. 2006; Elith and Leathwick 2009).
The catface rockcod Epinephelus andersoni is a range-restricted, commercially important species that is endemic to rocky reefs in the inshore zone along the southern African coast (Fennessy and Sadovy 2002). Since E. andersoni is a range-restricted endemic (Heemstra and Randall 1993;
Heemstra and Heemstra 2004; Craig et al. 2011) and it likely to be over-exploited (Fennessy and Mann 2013) it is probable that climatic changes will impact its distribution. It is clear that research is needed if this species is to be managed effectively, especially in light of future climatic changes. The aim of this chapter was to determine possible range shifts or changes in range size of E. andersoni with future changes in sea surface temperature (SST). The findings of this study may contribute to future management and conservation of E. andersoni. It is also hoped that this study will lead to further research on climate-related impacts on other range-restricted endemics and important fisheries species in southern Africa.
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