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The specificity of marine ecological indicators to fishing in the face of environmental change: a multi-model evaluation Shin, Y.-J.; Houle, J.E.; Akoglu, E.; Blanchard, J.L.; Bundy, A.; Coll, M.; Demarcq, H.; Fu, C.; Fulton, E.A.; Heymans, J.J.; Salihoglu, B.; Shannon, L.; Sporcic, M.; Velez, L. (2018). The specificity of marine ecological indicators to fishing in the face of environmental change: a multi-model evaluation. Ecol. Indic. 89: 317-326. https://dx.doi.org/10.1016/j.ecolind.2018.01.010 In: Ecological Indicators. Elsevier: Shannon. ISSN 1470-160X; e-ISSN 1872-7034

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Beschikbaar in	Auteurs
VLIZ: Non-open access 379833 [ aanvragen ]

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Trefwoord

Marien/Kust

Author keywords

Ecosystem approach to fisheries; Indicator performance; Marine ecosystem models; Scenarios; Multi-model evaluation; Signal-to-noise ratio

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Auteurs		Top
Shin, Y.-J. Houle, J.E. Akoglu, E. Blanchard, J.L. Bundy, A.	Coll, M. Demarcq, H. Fu, C. Fulton, E.A. Heymans, J.J.	Salihoglu, B. Shannon, L. Sporcic, M. Velez, L.

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Abstract

Ecological indicators are widely used to characterise ecosystem health. In the marine environment, indicators have been developed to assess the ecosystem effects of fishing to support an ecosystem approach to fisheries. However, very little work on the performance and robustness of ecological indicators has been carried out. An important aspect of robustness is that indicators should respond specifically to changes in the pressures they are designed to detect (e.g. fishing) rather than changes in other drivers (e.g. environment). We adopted a multi-model approach to compare and test the specificity of commonly used ecological indicators to capture fishing effects in the presence of environmental change and under different fishing strategies. We tested specificity in the presence of two types of environmental change: “random”, representing interannual climate variability and “directional”, representing climate change. We used phytoplankton biomass as a proxy of the environmental conditions, as this driver was comparable across all ecosystem models, then applied a signal-to-noise ratio analysis to test the specificity of indicators with random environmental change. For directional change, we used mean gradients to apportion the quantity of change in the indicators due to fishing and the environment. We found that depending on the fishing strategy and environmental change, ecological indicators could range from high to low specificity to fishing. As expected, the specificity of indicators to fishing almost always decreased as environmental variability increased. In 55–76% of the scenarios run with directional change in phytoplankton biomass across fishing strategies and ecosystem models, indicators were significantly more responsive to changes in fishing than to changes in phytoplankton biomass. This important result makes the tested ecological indicators good candidates to support fisheries management in a changing environment. Among the indicators, the catch over biomass ratio was most often the most specific indicator to fishing, whereas mean length was most often the most sensitive to change in phytoplankton biomass. However, the responses of indicators were highly variable depending on the ecosystem and fishing strategy under consideration. We therefore recommend that indicators should be tested in the particular ecosystem before they are used for monitoring and management purposes.

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