Detecting ecosystem-relevant crossings of thresholds

With ongoing climate change, multiple stressors including ocean warming, deoxygenation, ocean acidification and limited nutrient availability are expected to lead to considerable regime shifts within marine ecosystems [1]. However, distinguishing such abrupt shifts from long-term trends in physical and biogeochemical ocean variables may not only be obscured by the natural variability of the system, but also the complexity of the ecosystem itself. Moreover, species-dependent physiological tolerances are likely going to limit the detectability of crossing of thresholds or tipping points of the whole ecosystem. The metabolic index describes temperature-dependent hypoxic tolerances with respect to the oxygen supply [2]. Critical values of the metabolic index indicate the geographical limits of marine species, therefore it is a useful metric to describe the extent of a potential habitat. Here, we assess the spatio-temporal detectability of abrupt changes in such a potential habitat for selected marine species using an environmental time series changepoint detection routine developed by [3]. We compare the number and timing of these abrupt changes in different Shared Socioeconomic Pathways (SSPs) run with the fully coupled Norwegian Earth System Model version 2 (NorESM2), i.e., analysing the SSP1-26, SSP-5-34-OS, and SSP5-85 scenarios. Preliminary results reveal global, regional and local abrupt changes of lost metabolically viable potential habitat in relation to environmental stressors under different evolving climates.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT). The work reflects only the authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.

 

[1] Heinze et al., 2020, The quiet crossing of tipping points, PNAS, 118(9)

[2] Deutsch et al., 2020, Metabolic trait diversity shapes marine biogeography, Nature, 585, 557-562

[3] Beaulieu and Killick, 2018, Distinguishing trends and shifts from memory in climate data, Journal of Climate, 31(23), 9519-9543