Better Together: The Uses of Ecological and Socio-Economic Indicators With End-to-End Models in Marine Ecosystem Based Management

Tam, Jamie C.; Fay, Gavin; Link, Jason S.

doi:10.3389/fmars.2019.00560

Cited by 11 publications

(5 citation statements)

References 91 publications

(123 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alongside this, while ecological and environmental indicators support understanding of overarching patterns occurring within ecosystems, socioeconomic indicators are needed to help track patterns of human vulnerability and well-being (Tam et al 2019). This can aid the understanding of the complexity of the human dimensions of an EAF (Bowen and Riley 2003, Pollnac et al 2015, Colburn et al 2016, Auad et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…A suite of socioeconomic indicators could be encompassed into this framework, helping capture the cumulative influences of fishing, environmental variability, and coastal communities on ecosystems when informing management scenarios. This will enable fisheries managers and stakeholders not only to avoid ecological regime shifts, but also undesirable shifts to human coastal communities (Tam et al 2019), providing a more holistic approach to decision making. Following some further improvements and adjustments, it is possible that this framework could be used to synthesize large amounts of information about the states and trends of marine ecosystems, in a globally comparative way.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative framework to support an ecosystem approach to fisheries in a global context

Lockerbie¹,

Shannon²,

Lynam³

et al. 2020

E&S

View full text Add to dashboard Cite

Although an ecosystem approach to fisheries has been recognized as a means of progressing toward sustainable fishing, successful implementation of this approach has been limited. However, one way in which progress has been made is through the use of suites of indicators. Decision tree frameworks can be used to incorporate trends in ecological, fishing, and environmental indicators into ecosystem assessments. A relatively generic decision tree framework has been developed and successfully applied to multiple ecosystems. This framework incorporates trends in indicators, as well as the impacts of fishing pressure and environmental variability on ecological indicators in order to assess the state of each ecosystem. The inclusion of ecosystem expert knowledge from the outset ensures trends are correctly interpreted and allows analyses to contribute to global comparisons in a robust and meaningful manner. Although ecological and environmental indicators are well developed, those addressing the human dimensions of marine ecosystems are less so. This framework holds the potential to incorporate such indicators in order to fully assess marine ecosystems in a comparative context. Such assessments could help ensure food security from marine resources into the future as well as ensuring the well-being of coastal communities. Here critical review of the potential value of this framework has been conducted, with its usefulness emphasized in the similarities it holds, and in the contribution it could make, to current global methods of ecosystem assessments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A comparative framework to support an ecosystem approach to fisheries in a global context

Lockerbie¹,

Shannon²,

Lynam³

et al. 2020

E&S

View full text Add to dashboard Cite

show abstract

“…As demonstrated in the case study below involving swordfish fishing closures, MSE with spatial operating models can be used to assess performance of different spatial management strategies, including those that are dynamic and environmentally informed. Ecosystem models can also test the utility of ecosystem indicators in novel decision criteria and harvest rules (Fay et al, 2015;Fulton et al, 2019;Tam et al, 2019) that extend beyond classical single species management.…”

Section: Mse Is Expanding Beyond Single Species Approaches Into Ebfm Applicationsmentioning

confidence: 99%

Management Strategy Evaluation: Allowing the Light on the Hill to Illuminate More Than One Species

et al. 2021

View full text Add to dashboard Cite

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” (Smith, 1994) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches.

show abstract

“…Here, we apply the LIM approach to explore the predictability of a set of fisheries time series describing the temporal changes of specific stocks. These time series can be viewed as proxies that simplify complicated biological and socioeconomic conditions over time [Blanchard et al, 2010;Tam et al, 2019]. The three fisheries databases considered in this study are (1) stock biomass anomalies from scientific stock assessments performed for a limited number of stocks in different regions (RAM database, [Ricard et al, 2012]), (2) landings of stocks as reported by the country targeting the species (LME database, [Pauly et al, 2020]), and (3) the catches of species that are estimated from data reported to the United Nations (FAO database [Pauly et al, 1998]).…”

Section: Introductionmentioning

confidence: 99%

Predictability and empirical dynamics of fisheries time series in the North Pacific

et al. 2022

View full text Add to dashboard Cite

Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.

show abstract

Better Together: The Uses of Ecological and Socio-Economic Indicators With End-to-End Models in Marine Ecosystem Based Management

Cited by 11 publications

References 91 publications

A comparative framework to support an ecosystem approach to fisheries in a global context

A comparative framework to support an ecosystem approach to fisheries in a global context

Management Strategy Evaluation: Allowing the Light on the Hill to Illuminate More Than One Species

Predictability and empirical dynamics of fisheries time series in the North Pacific

Contact Info

Product

Resources

About