Modelling the impacts of climate change on thermal habitat suitability for shallow-water marine fish at a global scale

Abstract

Understanding and predicting the response of marine communities to climate change at large spatial scales, and distilling this information for policymakers, are prerequisites for ecosystem-based management. Changes in thermal habitat suitability across species’ distributions are especially concerning because of their implications for abundance, affecting species’ conservation, trophic interactions and fisheries. However, most predictive studies of the effects of climate change have tended to be sub-global in scale and focused on shifts in species’ range edges or commercially exploited species. Here, we develop a widely applicable methodology based on climate response curves to predict global-scale changes in thermal habitat suitability. We apply the approach across the distributions of 2,293 shallow-water fish species under Representative Concentration Pathways 4.5 and 8.5 by 2050–2100. We find a clear pattern of predicted declines in thermal habitat suitability in the tropics versus general increases at higher latitudes. The Indo-Pacific, the Caribbean and western Africa emerge as the areas of most concern, where high species richness and the strongest declines in thermal habitat suitability coincide. This reflects a pattern of consistently narrow thermal ranges, with most species in these regions already exposed to temperatures above inferred thermal optima. In contrast, in temperate regions, such as northern Europe, where most species live below thermal optima and thermal ranges are wider, positive changes in thermal habitat suitability suggest that these areas are likely to emerge as the greatest beneficiaries of climate change, despite strong predicted temperature increases.

Habitat preference of common minke whale (Balaenoptera acutorostrata) in Skjálfandi Bay, Iceland

“…Prey species require certain SST ranges for survival and reproduction, but minke whale diet is highly variable and opportunistic. SST preference is unique for each prey species and does not follow a common pattern, as different species have different thermal affinities (Lavender et al, 2021). On the contrary, depth, sediment type and chlorophyll seem to influence prey distribution in a similar fashion regardless of prey species.…”

Reduced Hypoxia Tolerance and Altered Gill Morphology at Elevated Temperatures May Limit the Survival of Tilapia (GIFT, Oreochromis niloticus) under Global Warming

“…This was observed with respect to Pdhb, Cs, Aco2, Idhg, Mdh2, and Sucla1 (t (4) = 3.211, p = 0.033; t (4) = 7.64, p = 1.58 × 10 −3 ; t (4) = 10.235, p = 5.14 × 10 −4 ; t (4) = 5.80, p = 4.39 × 10 −3 ; t (4) = 17.88, p = 5.75 × 10 −5 ; t (4) = 4.08, p = 0.0151; t-test, Figure 4B). For aquatic animals, temperature is one of the most important environmental factors, and it directly affects habitat suitability and the geographical range of a species [31,32]. Exposure to temperatures above the optimum for long periods of time reduces aerobic activity potential, weakens physiological and ecological adaptability, and increases mortality of aquatic organisms [8,13,14,33,34].…”

Modelling distribution and fate of coralligenous habitat in the Northern Adriatic Sea under a severe climate change scenario

“…Developed to predict the distribution of habitat in response to a given set of environmental factors, Habitat Suitability Models (hereafter HSMs) have been applied in an increasingly number of contexts, becoming important tools to support biodiversity management (Lawler et al, 2006;Degraer et al, 2008;Glockzin et al, 2009;Gogina and Zettler, 2010;Bates et al, 2013;Freeman et al, 2013;Guisan et al, 2013). In particular, HSMs can have an important role in ecosystem-based management planning (Guisan and Thuiller, 2005;Heikkinen et al, 2006;Pompe et al, 2008;Elith and Leathwick, 2009;Kharouba et al, 2009;Lavender et al, 2021), by helping in i) identifying priority areas of conservation (Cañadas et al, 2005), ii) assessing the spatial distribution of suitable habitats for a species or a community to live within protected areas (Monk et al, 2010), iii) predicting sites at risk of invasion by exotic species (Compton et al, 2010) and iv) investigating the distribution of possible diseases (Williams et al, 2010). These models can be grouped into two categories (Meineri et al, 2015): mechanistic methods aiming at reproducing the ecological dynamics by explicitly describing (and formalizing into equations to be solved by mean of numerical techniques) their driving processes (e.g.…”