• Energy Research

Ecosystem regime shifts can alter ecosystem services, affect human well-being, and trigger policy conflicts due to economic losses and reductions in societal and environmental benefits. Intensive anthropogenic activities make the Sea of Marmara ecosystem suffer from nearly all existing available types of ecosystem pressures such as biological degradation, exposure to hydrological processes, nutrient and organic matter enrichment, plastic pollution, ocean warming, resulting in deterioration of habitats. In this study, using an integrated ecosystem assessment, we investigated for the first time the historical development and ecosystem state of the Sea of Marmara. Multivariate analyses were applied to the most comprehensive and unique long-term data sets of 9 biotic and 15 abiotic variables for ecosystem state and drivers respectively, from 1986 to 2020. Observed changes were confirmed by detecting shifts in the datasets. The Sea of Marmara ecosystem was classified into three regimes: i) an early initial state regime under the top-down control of predatory medium pelagic fish and fisheries exploitation until mid-1990s, ii) a transitional regime between mid-1990s and mid-2010s as from ecosystem restructuring, and iii) an alternate state late regime with prevailing impacts of climate change from mid-2010s until 2020. During the 20 years transitional regime, three different phases were also characterized; i) the 1st phase between mid-1990s and early 2000s with its gradual change in ecosystem state from a decrease in predators and significant shift in physical drivers of the ecosystem, ii) the 2nd phase between 2000 and mid-2000s with a strong shift in ecosystem state, an ongoing increase in climate indices and fishing mortality, and a gradual decrease in water quality; and iii) the 3rd phase between mid-2000s and mid-2010s with the reorganization of the ecosystem dominated by small pelagic fish and ameliorated water quality. During late regime, we observed that most of the biotic variables, mainly fish biomass, and climate variables did not return to their initial state despite the improvement in some abiotic variables such as water quality. We identify these observed changes in the SoM ecosystem as a non-linear regime shift. Finally, we also developed concrete suggestions for improved regional management.

Ecosystem regime shifts can alter ecosystem services, affect human well-being, and trigger policy conflicts due to economic losses and reductions in societal and environmental benefits. Intensive anthropogenic activities make the Sea of Marmara ecosystem suffer from nearly all existing available types of ecosystem pressures such as biological degradation, exposure to hydrological processes, nutrient and organic matter enrichment, plastic pollution, ocean warming, resulting in deterioration of habitats. In this study, using an integrated ecosystem assessment, we investigated for the first time the historical development and ecosystem state of the Sea of Marmara. Multivariate analyses were applied to the most comprehensive and unique long-term data sets of 9 biotic and 15 abiotic variables for ecosystem state and drivers respectively, from 1986 to 2020. Observed changes were confirmed by detecting shifts in the datasets. The Sea of Marmara ecosystem was classified into three regimes: i) an early initial state regime under the top-down control of predatory medium pelagic fish and fisheries exploitation until mid-1990s, ii) a transitional regime between mid-1990s and mid-2010s as from ecosystem restructuring, and iii) an alternate state late regime with prevailing impacts of climate change from mid-2010s until 2020. During the 20 years transitional regime, three different phases were also characterized; i) the 1st phase between mid-1990s and early 2000s with its gradual change in ecosystem state from a decrease in predators and significant shift in physical drivers of the ecosystem, ii) the 2nd phase between 2000 and mid-2000s with a strong shift in ecosystem state, an ongoing increase in climate indices and fishing mortality, and a gradual decrease in water quality; and iii) the 3rd phase between mid-2000s and mid-2010s with the reorganization of the ecosystem dominated by small pelagic fish and ameliorated water quality. During late regime, we observed that most of the biotic variables, mainly fish biomass, and climate variables did not return to their initial state despite the improvement in some abiotic variables such as water quality. We identify these observed changes in the SoM ecosystem as a non-linear regime shift. Finally, we also developed concrete suggestions for improved regional management.

Shin, Yunne-Jai ... et al.-- This is a contribution to the IndiSeas Working Group, co-funded by IOC-UNESCO and the Euromarine Consortium in 2015.-- 10 pages, 4 figures, 2 tables, supplementary data https://doi.org/10.1016/j.ecolind.2018.01.010 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 L.J.S was supported through the South African Research Chair Initiative, funded through the South African Department of Science and Technology and administered by the South African National Research Foundation. J.E.H., L.V., and Y.J.S were funded by the EMIBIOS project (FRB Fondation pour la Recherche sur la Biodiversité, contract n°APP-SCEN-2010-II). J.E.H. was supported by a Beaufort Marine Research Award carried out under the Sea Change Strategy and the Strategy for Science Technology and Innovation (2006–2013), with the support of the Marine Institute, funded under the Marine Research Sub-Programme of the Irish National Development Plan 2007–2013. L.J.S and Y.J.S. were funded by the European project MEECE (FP7, contract n°212085). M.C. was supported by a Marie Curie CIG grant to BIOWEB project and the Spanish Research Program Ramon y Cajal. Funding from CSIRO and the Australian Fisheries Research and Development Corporation on behalf of the Australian Government supported the development of Atlantis-SE. J.J.H was supported by the UK Natural Environment Research Council and Department for Environment, Food and Rural Affairs under the project MERP: Grant No. NE/L003279/1, Marine Ecosystems Research Programme Peer Reviewed

Shin, Yunne-Jai ... et al.-- This is a contribution to the IndiSeas Working Group, co-funded by IOC-UNESCO and the Euromarine Consortium in 2015.-- 10 pages, 4 figures, 2 tables, supplementary data https://doi.org/10.1016/j.ecolind.2018.01.010 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 L.J.S was supported through the South African Research Chair Initiative, funded through the South African Department of Science and Technology and administered by the South African National Research Foundation. J.E.H., L.V., and Y.J.S were funded by the EMIBIOS project (FRB Fondation pour la Recherche sur la Biodiversité, contract n°APP-SCEN-2010-II). J.E.H. was supported by a Beaufort Marine Research Award carried out under the Sea Change Strategy and the Strategy for Science Technology and Innovation (2006–2013), with the support of the Marine Institute, funded under the Marine Research Sub-Programme of the Irish National Development Plan 2007–2013. L.J.S and Y.J.S. were funded by the European project MEECE (FP7, contract n°212085). M.C. was supported by a Marie Curie CIG grant to BIOWEB project and the Spanish Research Program Ramon y Cajal. Funding from CSIRO and the Australian Fisheries Research and Development Corporation on behalf of the Australian Government supported the development of Atlantis-SE. J.J.H was supported by the UK Natural Environment Research Council and Department for Environment, Food and Rural Affairs under the project MERP: Grant No. NE/L003279/1, Marine Ecosystems Research Programme Peer Reviewed