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Interactions between global and local stressors of ecosystems determine management effectiveness in cumulative impact mapping

Authors

  • Christopher J. Brown,

    Corresponding author
    1. School of Biological Sciences, The University of Queensland, St Lucia, Qld, Australia
    2. The Global Change Institute, The University of Queensland, St Lucia, Qld, Australia
    • Correspondence: Christopher J. Brown, The Global Change Institute Building, The University of Queensland, St Lucia, Qld 4072, Australia. E-mail: c.brown5@uq.edu.au

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  • Megan I. Saunders,

    1. The Global Change Institute, The University of Queensland, St Lucia, Qld, Australia
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  • Hugh P. Possingham,

    1. School of Biological Sciences, The University of Queensland, St Lucia, Qld, Australia
    2. Department of Life Sciences, Imperial College London, Berkshire, UK
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  • Anthony J. Richardson

    1. Climate Adaptation Flagship, CSIRO Marine and Atmospheric Research, Ecosciences Precinct, Dutton Park, Qld, Australia
    2. Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, St Lucia, Qld, Australia
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Abstract

Aim

Cumulative impact maps are used to identify the spatial distribution of multiple human impacts to species and ecosystems. Impacts can be caused by local stressors which can be managed, such as eutrophication, and by global stressors that cannot be managed, such as climate change. Cumulative impact maps typically assume that there are no interactive effects between stressors on biodiversity. However, the benefits of managing the ecosystem are affected by interactions between stressors. Our aim was to determine whether the assumption of no interactions in impact maps leads to incorrect identification of sites for management.

Location

General, Australasia.

Methods

We used the additive effects model to incorporate the effects of interactions into an interactive impact map. Seagrass meadows in Australasia threatened by a local stressor, nutrient inputs, and a global stressor, warming, were used as a case study. The reduction in the impact index was quantified for reductions in the nutrient stressor. We examined the outcomes for three scenarios: no interactions, antagonistic interactions or synergistic interactions.

Results

Cumulative impact maps imply that reducing a local stressor will give equivalent reductions in the impact index everywhere, regardless of spatial variability in a global stressor. We show that reductions in the impact index were greatest in refuges from warming if there was an antagonistic interaction between stressors, and greatest in areas of high warming stress if there was a synergistic interaction. Reducing the nutrient stressor in refuges from warming always reduced the impact index, regardless of the interaction.

Main conclusions

Interactions between local and global stressors should be considered when using cumulative impact maps to identify sites where management of a local stressor will provide the greatest impact reduction. If the interaction type is unknown, impact maps can be used to identify refuges from global stressors, as sites for management.

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