The economic value of environmental data: a notional insurance scheme for the European anchovy

Anchovy population dynamics in the Gulf of Cádiz are governed by environmental processes. Sea surface temperature, intense easterly winds, and discharges from the Guadalquivir River have been identified as key factors determining early life stage mortality in this anchovy stock. We have constructed an environment-based recruitment model that simulates the abundance of juveniles under alternative parameters representing plausible biological hypotheses. We are able to evaluate how modelling environment-based recruitment can affect stock assessment and how responding to environmental information can benefit fishery management to allow greater average catch levels through the application of harvest control rules (HCRs) based on environmental conditions. While the environment-based rules generally increase allowable catch levels the variance in catch levels also increases, detracting from the improved value based only on average yield. In addition to changes in revenue, the probability of stock collapse is also reduced by using environmental factors in HCRs. To assess the value of these management systems we simulate a notional insurance scheme, which applies a value to both average yields and uncertainty. The value of the information-driven rules can be determined by comparing the relevant premiums payable for equal levels of insurance cover on revenue within each specific management regime. We demonstrate the net value of incorporating environmental factors in the management of anchovies in the Gulf of Cádiz despite the increased variability in revenue. This could be an effective method to describe outcomes for both commercial fisheries and ecosystem management policies, and as a guide to management of other species whose dynamics are predictable based on in-season observations

The economic value of environmental data: a notional insurance scheme for the European anchovy

Anchovy population dynamics in the Gulf of Cádiz are governed by environmental processes. Sea surface temperature, intense easterly winds, and discharges from the Guadalquivir River have been identified as key factors determining early life stage mortality in this anchovy stock. We have constructed an environment-based recruitment model that simulates the abundance of juveniles under alternative parameters representing plausible biological hypotheses. We are able to evaluate how modelling environment-based recruitment can affect stock assessment and how responding to environmental information can benefit fishery management to allow greater average catch levels through the application of harvest control rules (HCRs) based on environmental conditions. While the environment-based rules generally increase allowable catch levels the variance in catch levels also increases, detracting from the improved value based only on average yield. In addition to changes in revenue, the probability of stock collapse is also reduced by using environmental factors in HCRs. To assess the value of these management systems we simulate a notional insurance scheme, which applies a value to both average yields and uncertainty. The value of the information-driven rules can be determined by comparing the relevant premiums payable for equal levels of insurance cover on revenue within each specific management regime. We demonstrate the net value of incorporating environmental factors in the management of anchovies in the Gulf of Cádiz despite the increased variability in revenue. This could be an effective method to describe outcomes for both commercial fisheries and ecosystem management policies, and as a guide to management of other species whose dynamics are predictable based on in-season observations.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 244706/ECOKNOWS project. However, the paper does not necessarily reflect EC views and in no way anticipates the Commission's future policy in the area. M.M.R. was funded by P09-RNM-5358 of the Junta de Andalucía and the European Union Seventh Framework Programme (FP7-KBBE-2013) under grant agreement 613571/MAREFRAME project.Peer reviewe

The economic value of environmental data: A notional insurance scheme for the European anchovy

Anchovy population dynamics in the Gulf of Cádiz are governed by environmental processes. Sea surface temperature, intense easterly winds, and discharges from the Guadalquivir River have been identified as key factors determining early life stage mortality in this anchovy stock. We have constructed an environment-based recruitment model that simulates the abundance of juveniles under alternative parameters representing plausible biological hypotheses. We are able to evaluate how modelling environment-based recruitment can affect stock assessment and how responding to environmental information can benefit fishery management to allow greater average catch levels through the application of harvest control rules (HCRs) based on environmental conditions. While the environment-based rules generally increase allowable catch levels the variance in catch levels also increases, detracting from the improved value based only on average yield. In addition to changes in revenue, the probability of stock collapse is also reduced by using environmental factors in HCRs. To assess the value of these management systems we simulate a notional insurance scheme, which applies a value to both average yields and uncertainty. The value of the information-driven rules can be determined by comparing the relevant premiums payable for equal levels of insurance cover on revenue within each specific management regime. We demonstrate the net value of incorporating environmental factors in the management of anchovies in the Gulf of Cádiz despite the increased variability in revenue. This could be an effective method to describe outcomes for both commercial fisheries and ecosystem management policies, and as a guide to management of other species whose dynamics are predictable based on in-season observations