Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept

Last modified: 
July 21, 2020 - 2:16pm
Type: Journal Article
Year of publication: 2020
Date published: 07/2020
Authors: Cristina Mangano, Nova Mieszkowska, Brian Helmuth, Tiago Domingos, Tania Sousa, Giuseppe Baiamonte, Giuseppe Bazan, Angela Cuttitta, Fabio Fiorentino, Antonio Giacoletti, Magnus Johnson, Giuseppe Lucido, Marco Marcelli, Riccardo Martellucci, Simone Mirto, Bernardo Patti, Fabio Pranovi, Gray Williams, Gianluca Sarà
Journal title: Frontiers in Marine Science
Volume: 7

Realistic predictions of climate change effects on natural resources are central to adaptation policies that try to reduce these impacts. However, most current forecasting approaches do not incorporate species-specific, process-based biological information, which limits their ability to inform actionable strategies. Mechanistic approaches, incorporating quantitative information on functional traits, can potentially predict species- and population-specific responses that result from the cumulative impacts of small-scale processes acting at the organismal level, and can be used to infer population-level dynamics and inform natural resources management. Here we present a proof-of-concept study using the European anchovy as a model species that shows how a trait-based, mechanistic species distribution model can be used to explore the vulnerability of marine species to environmental changes, producing quantitative outputs useful for informing fisheries management. We crossed scenarios of temperature and food to generate quantitative maps of selected mechanistic model outcomes (e.g., Maximum Length and Total Reproductive Output). These results highlight changing patterns of source and sink spawning areas as well as the incidence of reproductive failure. This study demonstrates that model predictions based on functional traits can reduce the degree of uncertainty when forecasting future trends of fish stocks. However, to be effective they must be based on high spatial- and temporal resolution environmental data. Such a sensitive and spatially explicit predictive approach may be used to inform more effective adaptive management strategies of resources in novel climatic conditions.

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