It’s Not the Destination, It’s the Journey: Multispecies Model Ensembles for Ecosystem Approaches to Fisheries Management

Reum, Jonathan C. P.; Townsend, Howard; Gaichas, Sarah; Sagarese, Skyler; Kaplan, Isaac C.; Grüss, Arnaud

doi:10.3389/fmars.2021.631839

POLICY AND PRACTICE REVIEWS article

Front. Mar. Sci., 26 February 2021

Sec. Marine Fisheries, Aquaculture and Living Resources

Volume 8 - 2021 | https://doi.org/10.3389/fmars.2021.631839

This article is part of the Research Topic Using Ecological Models to Support and Shape Environmental Policy Decisions View all 28 articles

It’s Not the Destination, It’s the Journey: Multispecies Model Ensembles for Ecosystem Approaches to Fisheries Management

$\r\nJonathan C. P. Reum,*$ Jonathan C. P. Reum^1,2*

¹NOAA Fisheries Service – Alaska Fisheries Science Center, Seattle, WA, United States
²Institute for Marine and Antarctic Studies and Centre for Marine Socioecology, University of Tasmania, Hobart, TAS, Australia
³NOAA Fisheries Service – Office of Science and Technology, Silver Spring, MD, United States
⁴NOAA Fisheries Service – Northeast Fisheries Science Center, Woods Hole, MA, United States
⁵NOAA Fisheries Service – Southeast Fisheries Science Center, Miami, FL, United States
⁶NOAA Fisheries Service – Northwest Fisheries Science Center, Seattle, WA, United States
⁷National Institute of Water and Atmospheric Research, Wellington, New Zealand

As ecosystem-based fisheries management becomes more ingrained into the way fisheries agencies do business, a need for ecosystem and multispecies models arises. Yet ecosystems are complex, and model uncertainty can be large. Model ensembles have historically been used in other disciplines to address model uncertainty. To understand the benefits and limitations of multispecies model ensembles (MMEs), cases where they have been used in the United States to address fisheries management issues are reviewed. The cases include: (1) development of ecological reference points for Atlantic Menhaden, (2) the creation of time series to relate harmful algal blooms to grouper mortality in the Gulf of Mexico, and (3) fostering understanding of the role of forage fish in the California Current. Each case study briefly reviews the management issue, the models used and model synthesis approach taken, and the outcomes and lessons learned from the application of MMEs. Major conclusions drawn from these studies highlight how the act of developing an ensemble model suite can improve the credibility of multispecies models, how qualitative synthesis of projections can advance system understanding and build confidence in the absence of quantitative treatments, and how involving a diverse set of stakeholders early is useful for ensuring the utility of the models and ensemble. Procedures for review and uptake of information from single-species stock assessment models are well established, but the absence of well-defined procedures for MMEs in many fishery management decision-making bodies poses a major obstacle. The benefits and issues identified here should help accelerate the design, implementation, and utility of MMEs in applied fisheries contexts.

Introduction

Ecosystem-based fisheries management (EBFM) and an ecosystem approach to fisheries (EAF)—hereafter collectively referred to as EBFM—are now firmly embedded in modern fisheries science (Link, 2010; Dolan et al., 2016). At its core, EBFM emphasizes the need to consider the broader ecological and social contexts of fisheries to better inform policy and decision-making. In practice, this requires conceptualizing the larger systems fisheries operate within and the use of models to formalize and represent important processes. The complexity of the models used to support EBFM reflects the different scales of its implementation, ranging widely from inclusion of environmental forcings in simple population models to community, food web, and coupled social–ecological models for evaluating system-level tradeoffs (O’Farrell et al., 2017; Geary et al., 2020). However, ecological systems are often only partially understood, and multiple models differing in structure or parameterization may provide plausible alternative representations (Gardmark et al., 2013; Geary et al., 2020). As the relevance of multispecies models has increased, so have calls for explicit consideration of model uncertainty (Hill et al., 2007; Addison et al., 2013; Geary et al., 2020), but efforts to develop sets of multispecies models to inform EBFM problems remain limited.

A set of distinct, plausible models may permit multimodel inference and be treated as an ensemble. Model ensembles are used for analysis and operational forecasting in many fields including weather (Tracton and Kalnay, 1993; Zhou and Du, 2010) and long-term climate prediction (Tebaldi and Knutti, 2007; Semenov and Stratonovitch, 2010), and ecological applications of multispecies model ensembles (MMEs) include projection of impacts due to climate change (Gardmark et al., 2013; Cheung et al., 2016; Reum et al., 2020), fishing (Spence et al., 2018), and species eradications and invasions (Baker et al., 2017). Methods for combining quantitative ensemble projections are diverse: from unweighted methods (e.g., “democracy of models”) to more complex approaches that weight models based on various criteria including level of data support, e.g., Bayesian posterior model probabilities (Burnham and Anderson, 2002; King et al., 2009; Ianelli et al., 2016; Spence et al., 2018). In general, model ensembles address structural uncertainty with the added benefit that ensemble forecast quantities of interest can be more accurate than estimates from individual ensemble members (Hagedorn et al., 2005; Zhou and Du, 2010).

However, the benefits of adopting multi-model approaches in an EBFM context may extend beyond statistical and predictive advantages. With regard to policy making, a set of models explicitly acknowledges model uncertainty, which promotes transparency (Addison et al., 2013), and inclusion of distinct models increases avenues for representing diverse hypotheses, incorporating different knowledge sources, and engaging with stakeholders, all of which may help to legitimize EBFM policies and decisions (Fulton et al., 2015; Francis et al., 2018). From a research perspective, assembling a diverse candidate model set often also means recruiting researchers with different perspectives and areas of technical expertise onto a modeling team. Doing so may foster an environment favorable to knowledge exchange and the cross-pollination of ideas. Moreover, qualitative syntheses, rigorous comparisons of model behavior, and evaluation of the role key assumptions have on predictions can yield deeper insight into a system and guide future data collection and modeling efforts (Gardmark et al., 2013; Cheung et al., 2016; Hollowed et al., 2020; Reum et al., 2020). These benefits are valuable in their own right and can be attainable even when quantitative, statistical treatment of ensemble outputs remains out of reach.

As noted, some examples of multispecies model ensembles (MMEs) for fisheries research have been published (Gardmark et al., 2013; Cheung et al., 2016; Reum et al., 2020). However, MMEs for fisheries management applications are limited. The few instances of management application can partly be attributed to the novelty of MMEs in fisheries modeling (Townsend et al., 2014), but other challenges may exist. Understanding the benefits and limitations of MMEs is important so that the approach can be more fully leveraged in fisheries management. Here, we sought to identify how ensemble modeling and multispecies applications, in particular, are applied in practice to address objectives and issues related to EBFM.

We focus our review on three representative case studies in the United States that have developed MMEs in response to specific management objectives and goals, and presented outcomes to decision-makers, managers, or stakeholders. For each case study, we ask the general question: what do we get from considering an ensemble of multispecies models within a management context? In particular, we evaluated how multispecies model suites (1) were synthesized and utilized, (2) facilitated engagement with stakeholders, management plan teams, and researchers, and (3) influenced the credibility of the output or advice derived from the modeling exercises. We highlight lessons from the case studies that should accelerate adoption and implementation of multispecies model ensembles in support of EBFM.

Case Studies

Atlantic Menhaden

To address the potential effects of fishing mortality on a forage fish and its predators, the Atlantic States Marine Fisheries Commission used an MME approach. A lead model from the MME was used with a stock assessment model to help set ecologically based reference points for fishing mortality. Synthesis of the model outputs from the set was qualitative because time constraints prevented quantitative synthesis. Open, transparent development of the models in the MME enabled stakeholder engagement and benefited the decision-making process.

Management Issue

Atlantic menhaden (Brevoortia tyrannus) is a small-bodied forage fish found in estuarine and nearshore habitats along the eastern coastline of the United States and Canada. Since the development of the first fishery management plan for Atlantic menhaden (herein “menhaden”) in 1981, fisheries managers have acknowledged the potentially significant role of menhaden as a prey base for other fish stocks managed by the Atlantic States Marine Fisheries Commission (ASMFC,, 1998). To better resolve predation on menhaden, an ecosystem model—Multispecies Virtual Population Analysis—Extended, MSVPA-X (Garrison et al., 2010), was developed in the early 2000s to generate mortality rates that were then used in a statistical catch-at-age stock assessment model. The paired modeling approach was routinely used for the two subsequent stock assessments, but its use was discontinued.

Concurrently, the Atlantic Menhaden Management Board began to consider the larger ecological role of menhaden in their decision-making and requested additional information on how menhaden removals by the fishery might affect predator populations. The original MSVPA-X, while resolving predation impacts on menhaden, did not relate predator productivity to menhaden abundance, and could not directly address the issue. In 2015, the ASMFC convened an Ecosystem Management Objectives Workshop to explicitly delineate the desired objectives for the menhaden fishery and tasked a workgroup to develop ecological reference points based on alternative multispecies models identified by a technical committee. The broad objectives were to (1) sustain the menhaden stock and provide for the fishery, (2) sustain menhaden to provide for predators, (3) provide stability for a variety of fisheries, and (4) minimize risk to sustainable yield for menhaden management (and management of menhaden predators) attributable to a changing environment. Additional detail on the performance measures associated with these objectives is available in ASMFC, (2015). A newly developed Ecological Reference Point Workgroup (herein “workgroup”) was convened following the Ecosystem Management Objectives Workshop to identify a modeling framework that would begin to address these objectives and performance measures. We highlight the multiple model aspects of the larger modeling and management processes, and direct interested readers to Anstead et al. (2020) for additional details regarding the history of the menhaden fishery, its management, and the modeling used to inform its management.

Model Set and Synthesis Approach

The workgroup sought a model framework that would address as many of the Ecosystem Management Objectives as possible as well as closely replicate the population abundance and fishing mortality rate patterns produced by the menhaden single species stock assessment model (SEDAR, 2020). Considerable uncertainty in predator responses to changes in the menhaden stock existed. To partly account for this, the workgroup identified a set of multispecies food web models that ranged in taxonomic complexity. Food web interactions in the models reflected the degree to which predator groupings were taxonomically resolved. In more complex food web models, the ability to represent age structure for all populations becomes difficult, so the workgroup also wanted to address uncertainty in age structure. Finally, as understanding environmental variability was an important objective, the workgroup preferred models that could address uncertainty in natural processes. Overall, the selected models reflected an emphasis on addressing uncertainty attributable to structural complexity and, to a lesser extent, to natural variability and parameter uncertainty.

The initial set of candidate models was assembled based on the expertise of the workgroup and other known existing models or models in development. One workgroup member had worked previously with a Surplus Production Model with time-varying r (SPM TVr), which can be useful for dealing with uncertainty in natural processes that drive stocks in production or mortality (Nesslage and Wilberg, 2019). The Steele-Henderson Surplus Production Model (SPM S-H) can help determine the importance of predators on menhaden population dynamics based on relative fits to data (Uphoff and Sharov, 2018). A multispecies statistical catch-at-age model (VADER—Virtual Assessment for the Description of Ecosystem Responses) included the age structure of prey and key predator stocks, but unlike MSVPA-X, modeled predation based on the consumption demand of predators (McNamee, 2018). Two Ecopath with Ecosim (EwE) models were also included in the model set. The first model included relatively fine taxonomic resolution (NWACS-Full, Northwest Atlantic Coastal Shelf) and was developed and parameterized by an academic partner (Buchheister et al., 2017a,b). The second model consisted of a scaled-down version of the first model (NWACS-MICE, Northwest Atlantic Coastal Shelf—Model of Intermediate Complexity for Ecosystem Assessment) and was composed of coarser taxonomic groupings. The latter model was easier to update with new data on an operational basis and was more computationally efficient and therefore amenable to parameter sensitivity analyses (Chagaris et al., 2020).

Initially, the workgroup intended to consider the proposed candidate models, review their structures, and select a single model to fully develop for use in setting ecological reference points (ERPs). Additional meetings were convened, so lead modelers could present an interim version of each candidate model and allow time for workgroup members to have hands-on experience with the models. Ultimately, the workgroup elected to move forward with fully developing all models, which would then be presented for review and potential use in setting ERPs individually or as an ensemble average. This decision was made because the members found that each model was useful for informing some subset of the ecosystem management objectives and provided some useful insights about the ecosystem. The fully developed models were parameterized or tuned using standardized, current data sets of biomass indices, harvests, and environmental variables.

All models were presented for Management Review (SEDAR, 2020). However, because of limited time for preparation and review, the working group opted to forego calculation of ensemble averages and instead focused on selecting a single model, NWACS-MICE, as the preferred model for setting ERPs (Figure 1). Since model review was scheduled for fall of 2019, the availability of the final data in late summer complicated updating data inputs for model development. Translating outputs (abundance/biomass and fishing mortality/exploitation rate) between model types (surplus production/biomass pool and age-structured) was a complex task that needed more time and consideration to complete. This additional layer would have also complicated the model review and would require more time than had been allotted for reviewers.

FIGURE 1

Figure 1. A visual summary of the model sets and qualitative synthesis approach used in each case study. (A) Atlantic menhaden, (B) Gulf of Mexico gag and red grouper, and (C) sardine and anchovy in the California Current. In all case studies, data sets used to calibrate and parameterize ensemble models were shared across models depending on applicability, and Information derived from models within the set were shared between models. Solid black arrows represent unidirectional information flow of outputs and towards management uptake. Dashed lines information sharing between models in the ensemble set (e.g., predation rates in one model inform natural mortality rates in another).

Ultimately, the NWACS-MICE model was chosen because it addressed most of the management objectives (and with further development could address virtually all). The NWACS-Full model had a similar benefit, but because of its increased structural complexity, future updates of that model may have been onerous and difficult to perform on an operational management timeline. VADER had a similar structural complexity and was able to address similar objectives as NWACS-MICE, but effects of prey on predators had not been developed at the time of the review. Both surplus production models were useful for understanding potential factors influencing Atlantic menhaden mortality and were amenable to rapid updating, but they did not address as many ecosystem management objectives as the other models.

In the future, the workgroup plans to continue using this set of models (or other models with a similar range of complexity). During the model development process, the members found that insight gained from one model helped inform other models and the overall ERP development (Figure 1). For example, the NWACS-Full model informed the structure and development of NWACS-MICE. The SPM-TVr model pointed to the potential of environmental processes that should be considered in future model/ERP development. Often multiple data sources were available for model parameterization and validation. Testing multiple models with multiple data sets led to discussions about the data and ecosystem dynamics. Ultimately, the models produced similar patterns in key indicators (e.g., menhaden biomass and fishing mortality rates), which led the workgroup to greater confidence that key ecological processes were captured in the models. More details on the models and model selection process for this example case are in Cieri et al. (2020).

Outcomes and Lessons Learned

Overall, the objective of the ERP workgroup was achieved. The NWACS-MICE model was accepted for use by the Menhaden Management Board to be used in combination with the single species stock assessment model for setting ERPs. The use of multiple models helped reassure the reviewers that key components of the ecosystem had been considered and that the NWACS-MICE model captured their dynamics sufficiently for providing management advice (SEDAR, 2020).

In this example, several benefits can be noted. Clear management objectives are helpful for understanding the principal ecosystem components that need to be included in a model ensemble. Developing these objectives helped managers and modelers focus on the key management issues. Using multiple models was helpful for understanding the level of model complexity needed to capture key components of an ecosystem and key management issues. Thorough and open discussion and presentation of models among modelers and stakeholders was beneficial. The open process enabled the ensemble team to address concerns about the models and guide stakeholders through ensemble development, with the end goal being stakeholder buy-in. In addition, this builds a collaborative and cooperative atmosphere among team members, which is important because an ensemble team requires many people who have to work together intensely and often.

The limitations to this MME approach were largely based on time and the existing model review process. Initial development and review of multiple models (and eventually model-averaged ensembles) take considerably more time than using a single model. However, after the initial development of the model set, efficiencies can be identified to enable more rapid model production and operationalization. Reviewing multiple models (and eventually model-averaged ensembles) for fisheries management requires considerably more time (and expertise covering a range of disciplines) than a typical single model review. This additional effort should be taken into account when establishing review timelines.

In addition, the clear and open process for developing Ecosystem Management Objectives, developing the ecosystem models, and establishing the process for setting ERPs helped to ensure that this approach could be used for setting ERPs. Moreover, the open process provided a higher level of satisfaction among stakeholders. Previous Atlantic Menhaden Board meetings (in the 2000s) had been contentious. After the August 2020 Atlantic Menhaden Board meeting where the process for setting ERPs was approved, multiple stakeholders sent out press releases indicating their satisfaction with the process.

Gulf of Mexico Gag and Red Grouper

Severe red tides in this region frequently cause mass mortality events for fisheries. To aid in quantifying the effects of these events on reef fish population dynamics, multiple ecosystem models were developed. Outputs from these models were used to inform the variability in natural mortality for stock assessment models. In the process of developing the MME, stakeholders learned more about multispecies model approaches and the need for more data/research on red tides. Additional stakeholder involvement in model review and development should help build support for more robust use of the MME approach for this issue.