ARDC Research Link Australia

ORCID Profile
Orcid icon. 0000-0002-5172-0361

Current Organisation
Stanford University

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Research Topics

In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.

ANZSRC Field of Research (FoR)

Dynamical Systems in Applications | Applied Mathematics | Stochastic Analysis and Modelling | Geophysical Fluid Dynamics

ANZSRC Socio-Economic Objective (SEO)

Climate and Climate Change not elsewhere classified | Atmosphere and Weather not elsewhere classified | Expanding Knowledge in the Mathematical Sciences |

Publications

Publication

Synergistic interactions among growing stressors increase risk to an Arctic ecosystem

Publisher: Springer Science and Business Media LLC

Date: 07-12-2020

DOI: 10.1038/S41467-020-19899-Z

Abstract: Oceans provide critical ecosystem services, but are subject to a growing number of external pressures, including overfishing, pollution, habitat destruction, and climate change. Current models typically treat stressors on species and ecosystems independently, though in reality, stressors often interact in ways that are not well understood. Here, we use a network interaction model (OSIRIS) to explicitly study stressor interactions in the Chukchi Sea (Arctic Ocean) due to its extensive climate-driven loss of sea ice and accelerated growth of other stressors, including shipping and oil exploration. The model includes numerous trophic levels ranging from phytoplankton to polar bears. We find that climate-related stressors have a larger impact on animal populations than do acute stressors like increased shipping and subsistence harvesting. In particular, organisms with a strong temperature-growth rate relationship show the greatest changes in biomass as interaction strength increased, but also exhibit the greatest variability. Neglecting interactions between stressors vastly underestimates the risk of population crashes. Our results indicate that models must account for stressor interactions to enable responsible management and decision-making.