Abstract
Identifying and quantifying the effects of climate change that alter the habitat overlap of marine predators and their prey population distributions is of great importance for the sustainable management of populations. This study uses Bayesian joint models with integrated nested Laplace approximation (INLA) to predict future spatial density distributions in the form of common spatial trends of predator–prey overlap in 2050 under the “business‐as‐usual, worst‐case” climate change scenario. This was done for combinations of six mobile marine predator species (gray seal, harbor seal, harbor porpoise, common guillemot, black‐legged kittiwake, and northern gannet) and two of their common prey species (herring and sandeels). A range of five explanatory variables that cover both physical and biological aspects of critical marine habitat were used as follows: bottom temperature, stratification, depth‐averaged speed, net primary production, and maximum subsurface chlorophyll. Four different methods were explored to quantify relative ecological cost/benefits of climate change to the common spatial trends of predator–prey density distributions. All but one future joint model showed significant decreases in overall spatial percentage change. The most dramatic loss in predator–prey population overlap was shown by harbor seals with large declines in the common spatial trend for both prey species. On the positive side, both gannets and guillemots are projected to have localized regions with increased overlap with sandeels. Most joint predator–prey models showed large changes in centroid location, however the direction of change in centroids was not simply northwards, but mostly ranged from northwest to northeast. This approach can be very useful in informing the design of spatial management policies under climate change by using the potential differences in ecological costs to weigh up the trade‐offs in decisions involving issues of large‐scale spatial use of our oceans, such as marine protected areas, commercial fishing, and large‐scale marine renewable developments.
Highlights
We need to understand more explicitly the spatial functioning of our marine systems and especially critical marine habitats: those limited areas that are more likely to be the foraging habitats of mobile species such as seabirds and mammals (Cox, Embling, Hosegood, Votier, & Ingram, 2018; Sharples, Scott, & Inall, 2013)
In order to proceed with some certainty that we are protecting and maintaining our important top-predator populations, we need to be able to predict, quantify, and separate the possible “ecological costs” of changes due to climate change from those of large-scale renewable developments as well as benefits from marine protected areas (MPAs)
We explore spatial joint model outputs to compare the efficiency of methods that estimate the ecological costs of trophic interactions between predator and prey and competing species for similar prey, in present versus future “business-as-usual” greenhouse-gas emissions scenario (De Dominicis et al, 2018; Stocker et al, 2013), projected to mean climate change conditions centered on 2050
Summary
We need to understand more explicitly the spatial functioning of our marine systems and especially critical marine habitats: those limited areas that are more likely to be the foraging habitats of mobile species such as seabirds and mammals (Cox, Embling, Hosegood, Votier, & Ingram, 2018; Sharples, Scott, & Inall, 2013). These variables were chosen as they cover the main physical and biological parameters that can affect pelagic habitats and primary production (Holt, Butenschon, Wakelin, Artioli, & Allen, 2012; Holt, Hughes, et al, 2012; Holt & Proctor, 2008; Holt et al, 2016) under both climate change and, the biggest change to our shallow seas, very large extraction of energy from offshore renewable developments (Boon et al, 2018; De Dominics et al, 2018; van der Molen et al, 2014) These variables are important habitat variables as they capture the range of features: fronts, other areas of high production, and mixing characteristics of shallow seas including density differences due to regions of freshwater influence (Cox et al, 2018).
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