Projected distributions of Southern Ocean albatrosses, petrels and fisheries as a consequence of climatic change

Abstract

Given the major ongoing influence of environmental change on the oceans, there is a need to understand and predict the future distributions of marine species in order to plan appropriate mitigation to conserve vulnerable species and ecosystems. In this study we use tracking data from seven large seabird species of the Southern Ocean (black-browed albatross Thalassarche melanophris, grey-headed albatross T. chrysostoma, northern giant petrel Macronectes halli, southern giant petrel M. giganteus, Tristan albatross Diomedea dabbenena, wandering albatross D. exulans and white-chinned petrel Procellaria aequinoctialis, and on fishing effort in two types of fisheries (characterised by low or high-bycatch rates), to model the associations with environmental variables (bathymetry, chlorophyll-a concentration, sea surface temperature and wind speed) through ensemble species distribution models. We then projected these distributions according to four climate change scenarios built by the Intergovernmental Panel for Climate Change for 2050 and 2100. The resulting projections were consistent across scenarios, indicating that there is a strong likelihood of poleward shifts in distribution of seabirds, and several range contractions (resulting from a shift in the northern, but no change in the southern limit of the range in four species). Current trends for southerly shifts in fisheries distributions are also set to continue under these climate change scenarios at least until 2100; some of these may reflect habitat loss for target species that are already over-fished. It is of particular concern that a shift in the distribution of several highly threatened seabird species would increase their overlap with fisheries where there is a high-bycatch risk. Under such scenarios, the associated shifts in distribution of seabirds and increases in bycatch risk will require much-improved fisheries management in these sensitive areas to minimise impacts on populations in decline.

Year-round movements of white-chinned petrels from Marion Island, south-western Indian Ocean

“…Another potential threat is the effect of climate change and how increasing temperatures might affect seabird species distributions. Krüger et al (2018) predicted that climate change will cause white-chinned petrels to shift their distributions by almost 10°to the south, although surprisingly Péron et al (2010b) predicted white-chinned petrels would shift their distributions northwards in response to climate change. White-chinned petrels are also impacted on their breeding islands by introduced predators, which occur on breeding islands supporting c. 42% of the worldwide population (see review of Southern Ocean islands with extant alien predators by Angel et al 2009).…”

Avian SDMs: current state, challenges, and opportunities

“…potential foraging habitat, although other factors such as changing wind regimes or changing conditions at breeding sites also need to be incorporated (Hazen et al 2013, Cimino et al 2016. The recent addition of future layers to the Bio-ORACLE data set (Jueterbock et al 2013) now makes data for the end of the 21st and the 22nd centuries more accessible to researchers (Krüger et al 2017). …”

Biologically informed ecological niche models for an example pelagic, highly mobile species

“…Researchers have used these methods to address specific aspects of seabird distributional ecology such as habitat suitability (Ceia et al 2012;Oppel et al 2012;Catry et al 2013;Louzao et al 2013;McGowan et al 2013;Scales et al 2016), identification of hotspots in the present and past (Louzao et al 2013), selection of potential conservation areas and potential climate change impacts (Krüger 2017). But, many of the more recent applications use ensemble modelling (Scales et al 2016;Krüger 2017) or incorporate seabird movement data (Clay 2016;Quillfeldt 2017) that, whilst increasing in quantity and availability, is nowhere near as prevalent or accessible as point observation data.…”