Up the ante on bioeconomic submodels of marine food webs: A data assimilation-based approach

Abstract

SNF SAMFUNNS-OG NAERINGSLIVSFORSKNING AS-er et selskap i NHH-miljøet med oppgave å initiere, organisere og utføre eksternfinansiert forskning. Norges Handelshøyskole og Stiftelsen SNF er aksjonaerer. Virksomheten drives med basis i egen stab og fagmiljøene ved NHH.SNF er ett av Norges ledende forsk ningsmiljø innen anvendt økonomisk-administrativ forskning, og har gode samarbeidsrelasjoner til andre forskningsmiljøer i Norge og utlandet. SNF utfører forskning og forsknings baserte utredninger for sentrale beslutningstakere i privat og offentlig sektor. Forskningen organiseres i programmer og prosjekter av langsiktig og mer kortsiktig karakter. Alle publikasjoner er offentlig tilgjengelig. SNF CENTRE FOR APPLIED RESEARCH AT NHH-is a company within the NHH group. Its objective is to initiate, organize and conduct externally financed research. The company shareholders are the Norwegian School of Economics (NHH) and the SNF Foundation. Research is carried out by SNF´s own staff as well as faculty members at NHH. SNF is one of Norway´s leading research environment within applied economic administrative research. It has excellent working relations with other research environments in Norway as well as abroad. SNF conducts research and prepares research-based reports for major decision-makers both in the private and the public sector. Research is organized in programmes and projects on a long-term as well as a short-term basis. All our publications are publicly available. While economists have discussed ecosystem-based management and similar concepts, little attention has been devoted to the art of modeling. Models of ecosystems or foodwebs that make economic analysis viable should capture as much as possible of system structure and dynamics while balancing biological and ecological detail against dimensionality and model complexity. Relevant models need a strong, empirical content, but data availability may inhibit modeling e orts. Models are bound to be nonlinear, and model and observational uncertainty should be observed. We suggest the data assimilation method ensemble Kalman ltering to improve modeling of ecosystems or foodwebs. To illustrate the method, we model the dynamics of the main, pelagic species in the Norwegian Sea. In order to reduce the parameter dimensionality, the species are modeled to rely on a common carrying capacity.We also take further methodological steps to deal with a still high number of parameters.Our best model captures much of the observed dynamics in the sh stocks, but the estimated model error is moderate.

The Ensemble Kalman Filter for Multidimensional Bioeconomic Models

“…A few comments on these statistics are in order. The BIC‐scores, both here and later, are evaluated with a bandwidth of 200,000 (tons; see Ekerhovd and Kvamsdal [] for details). The innovation is the distance between the observations and the estimated state variables.…”

“…(Our main metrics of appropriateness are whether the state estimates resemble the stock assessment data and to what degree the spread of the ensemble in the parameter dimensions contracts over time. In addition, we have used the Bayesian Information Criterion [BIC], but carefully, since the criterion is not unique because of the Monte Carlo element of the filter; Ekerhovd and Kvamsdal []. We have also considered the distribution of the Kalman gain over time and stability of parameter estimates.…”

Multidisciplinary perspectives on living marine resources in the Arctic

“…In a simplified model of the Norwegian Sea pelagic complex, where mackerel, herring and blue whiting all share a common zooplankton food source (Ekerhovd & Kvamsdal 2017), a recent study compared outcomes under both cooperative and competitive (non-cooperative) fishing ). In the first-best scenario, where all parties cooperate with regard to management and allocation of quotas between fleets, all comparative advantages are exploited, the most valuable fishery (mackerel) is promoted, and fisheries outcomes are orders of magnitudes higher than under the fully non-cooperative scenario.…”

“…ccKvamsdal (2016).dd Ekerhovd & Gordon (2020). eeVestergaard (2018).ff Koenigstein et al (2016).gg Eikeset et al (2013).hh Poudel & Sandal (2015).ii Kvamsdal & Sandal (2015).jj Ekerhovd & Kvamsdal (2017).kk Ekerhovd & Steinshamn (2016). llBirkenbach et al (2020) Kvamsdal, Maroto et al (2020)…”

Using the best of two worlds: A bio‐economic stock assessment (BESA) method using catch and price data

“…Anderson et al, 2017; Branch et al, 2011; Costello et al, 2012; Froese et al, 2017; Martell & Froese, 2013; Thorson et al, 2013), selectivity (Winker et al, 2020) or standardized catch per unit effort (CPUE) (Pedersen & Berg, 2017; Winker et al, 2018). Standardized stock indices, often based on commercial CPUE, also complements catch data in bio‐economic state space models (Aeberhard et al, 2018; Auger‐Méthé et al, 2021; Ekerhovd & Kvamsdal, 2017; Froese et al, 2017; ICES, 2020; Kvamsdal & Sandal, 2015; Meyer & Millar, 1999; Millar & Meyer, 2000; Winker et al, 2018 provide overviews of these methods). These data‐limited assessment methods are valuable avenues to uncover latent information about stock size and parameters, although their performance varies and may depend on the specific stock (Bouch et al, 2020; Pons et al, 2020).…”