Cumulative biomass curves describe past and present conditions of Large Marine Ecosystems.

Ecosystem Approach cumulative biomass ecosystem indicators emergent properties trophic level

Journal

Global change biology
ISSN: 1365-2486
Titre abrégé: Glob Chang Biol
Pays: England
ID NLM: 9888746

Informations de publication

Date de publication:
02 2020
Historique:
received: 26 06 2019
revised: 26 08 2019
accepted: 26 08 2019
pubmed: 9 9 2019
medline: 17 3 2020
entrez: 9 9 2019
Statut: ppublish

Résumé

Implementing the Ecosystem Approach in marine ecosystems is moving from preliminary steps-dedicated to defining the optimal features for indicators and developing efficient indicator frameworks-towards an operational phase where multisector marine management decisions are executed using this information. Within this operational context, emergent ecosystem properties are becoming quite promising as they have been demonstrated to be globally widespread and repeatable, and to be quite effective in detecting significant state variations of complex systems. Biomass accumulation across TLs (CumB-TL) combines two important emergent properties of an ecosystem (energy flow, in terms of transfer efficiency, and storage, expressed as biomass), both amenable to detecting rapid ecosystem change. However, for further application, it is crucial to understand which types of drivers an indicator is sensitive to and how robust it is in relation to modifications of the external conditions and/or the system state. Here we address some outstanding questions of these CumB-TL curves related to their sensitivity to various drivers by carrying out a global scale assessment (using data from 62 LMEs) over six decades (1950-2010). We confirm the consistency of the S-pattern across all the LMEs, independent from latitude, ecosystem, environmental conditions, and stress level. The dynamics of the curve shape showed a tendency to stretch (i.e. decrease of steepness), in the presence of external disturbance and conversely to increase in steepness and shift towards higher TL in the case of recovery from stressed conditions. Our results suggest the presence of three main types of ecosystem dynamics, those showing an almost continuous increase in ecological state over time, those showing a continuous decrease in ecological state over time, and finally those showing a mixed behaviour flipping between recovering and degrading phases. These robust patterns suggest that the CumB-TL curve approach has some useful properties for use in further advancing the implementation of the Ecosystem Approach, allowing us to detect the state of a given marine ecosystem based on the dynamics of its curve shape, by using readily available time series data. The value of being able to identify conditions that might require management actions is quite high and, in many respects, represents the main objective in the context of an Ecosystem Approach, with large applications for detecting and responding to global changes in marine ecosystems.

Identifiants

pubmed: 31495042
doi: 10.1111/gcb.14827
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

786-797

Informations de copyright

© 2019 John Wiley & Sons Ltd.

Références

Blanchard, J. L., Coll, M., Trenkel, V. M., Vergnon, R., Yemane, D., Jouffre, D., … Shin, Y.-J. (2010). Trend analysis of indicators: A comparison of recent changes in the status of marine ecosystems around the world. ICES Journal of Marine Science, 67, 732-744. https://doi.org/10.1093/icesjms/fsp282
Boyce, D. G., Lewis, M., & Worm, B. (2012). Integrating global chlorophyll data from 1890 to 2010. Limnology and Oceanography: Methods, 10, 840-852. https://doi.org/10.4319/lom.2012.10.840b
Bundy, A., Shannon, L. J., Rochet, M. J., Neira, S., Shin, Y. J., Hill, L., & Aydin, K. (2010). The good(ish), the bad and the ugly: A tripartite classification of ecosystem trends. ICES Journal of Marine Science, 67, 745-768. https://doi.org/10.1093/icesjms/fsp283
Costanza, R., de Groot, R., Sutton, P., Van der Ploeg, S., Anderson, S. J., Kubiszewski, I., … Turner, R. K. (2014). Changes in the global value of ecosystem services. Global Environmental Change, 26, 152-158. https://doi.org/10.1016/j.gloenvcha.2014.04.002
Dickey-Collas, M. (2014). Why the complex nature of integrated ecosystem assessments requires a flexible and adaptive approach. ICES Journal of Marine Science, 71, 1174-1182. https://doi.org/10.1093/icesjms/fsu027
Fu, C., Xu, Y. I., Bundy, A., Grüss, A., Coll, M., Heymans, J. J., … Shin, Y.-J. (2019). Making ecological indicators management ready: Assessing the specificity, sensitivity, and threshold response of ecological indicators. Ecological Indicators, 105, 16-28. https://doi.org/10.1016/j.ecolind.2019.05.055
Gislason, H. (1999). Single and multispecies reference points for Baltic fish stocks. ICES Journal of Marine Science, 56, 571-583. https://doi.org/10.1006/jmsc.1999.0492
Golden, C., Allison, E. H., Cheung, W. W., Dey, M. M., Halpern, B. S., McCauley, D. J., … Myers, S. S. (2016). Fall in fish catch threatens human health. Nature, 534, 317-320. https://doi.org/10.1038/534317a
Gunderson, L. H., & Holling, C. S. (Eds.). (2002). Panarchy: Understanding transformations in human and natural systems. Washington, DC: Island Press.
Hewitt, J. E., Ellis, J. I., & Thrush, S. F. (2016). Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems. Global Change Biology, 22, 2665-2675. https://doi.org/10.1111/gcb.13176
Jennings, S. (2005). Indicators to support an ecosystem approach to fisheries. Fish and Fishery, 6, 212-232. https://doi.org/10.1111/j.1467-2979.2005.00189.x
Jennings, S., & Dulvy, N. K. (2005). Reference points and reference directions for size-based indicators of community structure. ICES Journal of Marine Science, 62, 397-404. https://doi.org/10.1016/j.icesjms.2004.07.030
Libralato, S., Pranovi, F., Zucchetta, M., Anelli, M. M., & Link, J. S. (2019). Global thresholds in properties emerging from cumulative curves of marine ecosystems. Ecological Indicators, 103, 554-562. https://doi.org/10.1016/j.ecolind.2019.03.053
Link, J. S. (2005). Translating ecosystem indicators into decision criteria. ICES Journal of Marine Science, 62, 569-576. https://doi.org/10.1016/j.icesjms.2004.12.015
Link, J. S. (2018). System level optimal yield: Increased value, less risk, improved stability, and better fisheries. Canadian Journal of Fisheries and Aquatic Sciences, 75, 1-16. https://doi.org/10.1139/cjfas-2017-0250
Link, J. S., Pranovi, F., Libralato, S., Coll, M., Christensen, V., Solidoro, C., & Fulton, E. (2015). Emergent properties delineate marine ecosystem perturbation and recovery. Trend in Ecology & Evolution, 30, 649-661. https://doi.org/10.1016/j.tree.2015.08.011
Longhurst, A., Sathyendranath, S., Platt, T., & Caverhill, C. (1995). An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research, 17, 1245-1271.
Murray, A. R., Dickey-Collas, M., Ferretti, J., Johannesen, E., Macdonald, N. M., McLaughlin, R., … Link, J. S. (2018). Ocean ecosystem-based management mandates and implementation in the North Atlantic. Frontiers in Marine Science, 5. https://doi.org/10.3389/fmars.2018.00485
O'Leary, J. K., Micheli, F., Airoldi, L., Boch, C., De Leo, G., Elahi, R., … Wong, J. (2017). The resilience of marine ecosystems to climatic disturbances. BioScience, 67, 208-220. https://doi.org/10.1093/biosci/biw161
Pauly, D., & Zeller, D. (2016). Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nature Communications, 7, 10244. https://doi.org/10.1038/ncomms10244
Pranovi, F., Libralato, S., Zucchetta, M., & Link, J. S. (2014). Biomass accumulation across trophic level: Analysis of landings for the Mediterranean Sea. Marine Ecology Progress Series, 512, 201-216. https://doi.org/10.3354/meps10881
Pranovi, F., & Link, J. (2009). Ecosystem exploitation and trophodynamic indicators: A comparison between the Northern Adriatic Sea and Southern New England. Progress in Oceanography, 81, 149-164. https://doi.org/10.1016/j.pocean.2009.04.008
Pranovi, F., Link, J., Fu, C., Cook, A., Hui, L. H., Gaichas, S., … Benoit, H. (2012). Trophic level determinants of biomass accumulation in marine ecosystems. Marine Ecology Progress Series, 459, 185-201. https://doi.org/10.3354/meps09738
R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Raffaelli, D. (2005). Tracing perturbation effects in food webs: The potential and limitation in experimental approaches. In P. C. de Ruiter, J. C. Moore, & V. Wolters (Eds.), Dynamic food webs: Multispecies assemblages, ecosystem development and environmental change (pp. 348-353). Boston, MA: Academic Press.
Rice, J. C., & Rochet, M. J. (2005). A framework for selecting a suite of indicators for fisheries management. ICES Journal of Marine Science, 62, 516-527. https://doi.org/10.1016/j.icesjms.2005.01.003
Richetts, J. H., & Head, G. A. (1999). A five-parameter logistic equation for investigating asymmetry of curvature in baroreflex studies. The American Journal of Physiology, 277, 441-454.
Ritz, C., Baty, F., Streibig, J., & Gerhard, D. (2015). Dose-response analysis using R. PLoS ONE, 10(12), e0146021. https://doi.org/10.1371/journal.pone.0146021
Rochet, M. J., & Trenkel, V. M. (2003). Which community indicators can measure the impact of fishing? A review and proposals. Canadian Journal of Fisheries and Aquatic Science, 60, 86-99. https://doi.org/10.1139/f02-164
Rova, S., Müller, F., Meire, P., & Pranovi, F. (2019). Sustainability perspectives and spatial patterns of multiple ecosystem services in the Venice lagoon: Possible roles in the implementation of the EU Water Framework Directive. Ecological Indicators, 98, 556-567. https://doi.org/10.1016/j.ecolind.2018.11.045
Rova, S., & Pranovi, F. (2017). Analysis and management of multiple ecosystem services within a social-ecological context. Ecological Indicators, 72, 436-443. https://doi.org/10.1016/j.ecolind.2016.07.050
Schlenger, A. J., Libralato, S., & Ballance, L. T. (2018). Temporal variability of primary production explains marine ecosystem structure and function. Ecosystems, 22, 1-15. https://doi.org/10.1007/s10021-018-0272-y
Schröter, M., Stumpf, K. H., Loos, J., van Oudenhoven, A. P. E., Böhnke-Henrichs, A., & Abson, D. J. (2017). Refocusing ecosystem services towards sustainability. Ecosystem Services, 25, 35-43. https://doi.org/10.1016/j.ecoser.2017.03.019
Shannon, L., Coll, M., Bundy, A., Gascuel, D., Heymans, J. J., Kleisner, K., … Shin, Y. (2014). Trophic level-based indicators to track fishing impacts across marine ecosystems. Marine Ecology Progress Series, 512, 115-140. https://doi.org/10.3354/meps10821
Shin, Y. J., Houle, J. E., Akoglu, E., Blanchard, J. L., Bundy, A., Coll, M., … Velez, L. (2018). The specificity of marine ecological indicators to fishing in the face of environmental change: A multi-model evaluation. Ecological Indicators, 89, 317-326. https://doi.org/10.1016/j.ecolind.2018.01.010
Shin, Y. J., Rochet, M. J., Jennings, S., Field, J. G., & Gislason, H. (2005). Using size-based indicators to evaluate the ecosystem effects of fishing. ICES Journal of Marine Science, 62, 384-396. https://doi.org/10.1016/j.icesjms.2005.01.004
Soliveres, S., van der Plas, F., Manning, P., Prati, D., Gossner, M. M., Renner, S. C., … Allan, E. (2016). Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature, 536, 456-459. https://doi.org/10.1038/nature19092
Stergiou, K. I., & Karpouzi, V. S. (2001). Feeding habits and trophic levels of Mediterranean fish. Reviews in Fish Biology and Fisheries, 11, 217-254.
Tam, J. C., Link, J. S., Large, S. I., Andrews, K., Friedland, K. D., Gove, J., … Zador, S. (2017). Comparing apples to oranges: Common trends and thresholds in anthropogenic and environmental pressures across multiple marine ecosystems. Frontiers in Marine Science, 4. https://doi.org/10.3389/fmars.2017.00282
Tam, J. C., Link, J. S., Rossberg, A. G., Rogers, S. I., Levin, P. S., Rochet, M.-J., … Rindorf, A. (2017). Towards ecosystem-based management: Identifying operational food-web indicators for marine ecosystems. ICES Journal of Marine Science, 74, 2040-2052. https://doi.org/10.1093/icesjms/fsw230
Walters, C. J., Christensen, V., Martell, S., & Kitchell, J. F. (2005). Single-species versus ecosystem harvest management: Ecosystem structure erosion under myopic management. ICES Journal of Marine Science, 62, 558-568.
Wood, S. N. (2003). Thin plate regression splines. J.R. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65, 95-114. https://doi.org/10.1111/1467-9868.00374
Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton, FL: CRC.
Wood, S. N., Pya, N., & Saefken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 111, 1548-1575. https://doi.org/10.1080/01621459.2016.1180986

Auteurs

Fabio Pranovi (F)

Environmental Sciences, Informatics and Statistic Department, University of Venice, Venice, Italy.

Simone Libralato (S)

Division of Oceanography, ECHO Group Ecology and Computational Hydrodynamics in Oceanography, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy.

Matteo Zucchetta (M)

Environmental Sciences, Informatics and Statistic Department, University of Venice, Venice, Italy.

Marco Anelli Monti (M)

Environmental Sciences, Informatics and Statistic Department, University of Venice, Venice, Italy.

Jason S Link (JS)

National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Woods Hole, MA, USA.

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