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Advisor(s)
Abstract(s)
Aim: With climate change challenging marine biodiversity and resource management,
it is crucial to anticipate future latitudinal and depth shifts under contrasting global
change scenarios to support policy-relevant
biodiversity impact assessments [e.g.,
Intergovernmental Panel on Climate Change (IPCC)]. We aim to demonstrate the benefits
of complying with the Paris Agreement (United Nations Framework Convention
on Climate Change) and limiting environmental changes, by assessing future distributional
shifts of 10 commercially important demersal fish species.
Location: Northern Atlantic Ocean.
Time period: Analyses of distributional shifts compared near present-day
conditions
(2000–2017)
with two Representative Concentration Pathway (RCP) scenarios of future
climate changes (2090–2100):
one following the Paris Agreement climate forcing
(RCP2.6) and another without stringent mitigation measures (RCP8.5).
Major taxa studied: Demersal fish.
Methods: We use machine learning distribution models coupled with biologically
meaningful predictors to project future latitudinal and depth shifts. Structuring projections
with information beyond temperature-based
predictors allowed us to encompass
the physiological limitations of species better.
Results: Our models highlighted the additional roles of temperature, primary productivity
and dissolved oxygen in shaping fish distributions (average relative contribution
to the models of 32.12 ± 10.24, 15.6 ± 7.5 and 12.1 ± 6.1%, respectively). We anticipated
a generalized trend of poleward shifts in both future scenarios, with aggravated
changes in suitable area with RCP8.5 (average area loss with RCP2.6 = 13.3 ± 4.1%;
RCP8.5 = 40.9 ± 13.3%). Shifts to deeper waters were also predicted to be of greater
magnitude with RCP8.5 (average depth gain = 25.4 ± 21.5 m) than with RCP2.6
(average depth gain = 10.4 ± 7.9 m). Habitat losses were projected mostly in the
Mediterranean, Celtic and Irish Seas, the southern areas of the North Sea and along
the NE coast of North America.
Main conclusions: Inclusion of biologically meaningful predictors beyond temperature
in species distribution modelling can improve predictive performances. Limiting
future climate changes by complying with the Paris Agreement can translate into reduced distributional shifts, supporting biodiversity conservation and resource
management.
Description
Keywords
Biologically meaningful predictors Climate change Demersal fish North Atlantic fisheries Paris agreement Species distribution modelling
Citation
Publisher
Wiley