Realizing the potential of trait‐based approaches to advance fisheries science. Issue 5 (30th July 2019)

• Record Type: Journal Article
• Authors: Barnett, Lewis A. K.; Jacobsen, Nis S.; Thorson, James T.; Cope, Jason M.
• Is Part Of: Fish and fisheries. Volume 20:Issue 5(2019)
• Page Start: 1034
• Page End: 1050
• Publication Date: 2019-07-30
• Subjects: community ecology -- ecosystem‐based fishery management -- functional trait -- population dynamics -- size‐spectrum -- stock assessment; Fisheries -- Periodicals; Fishes -- Periodicals; 639.2
• DOI: 10.1111/faf.12395
• Languages: English
• ISSNs: 1467-2960

Abstract

Abstract: Analysing how fish populations and their ecological communities respond to perturbations such as fishing and environmental variation is crucial to fisheries science. Researchers often predict fish population dynamics using species‐level life‐history parameters that are treated as fixed over time, while ignoring the impact of intraspecific variation on ecosystem dynamics. However, there is increasing recognition of the need to include processes operating at ecosystem levels (changes in drivers of productivity) while also accounting for variation over space, time and among individuals. To address similar challenges, community ecologists studying plants, insects and other taxa increasingly measure phenotypic characteristics of individual animals that affect fitness or ecological function (termed "functional traits"). Here, we review the history of trait‐based methods in fish and other taxa, and argue that fisheries science could see benefits by integrating trait‐based approaches within existing fisheries analyses. We argue that measuring and modelling functional traits can improve estimates of population and community dynamics, and rapidly detect responses to fishing and environmental drivers. We support this claim using three concrete examples: how trait‐based approaches could account for time‐varying parameters in population models; improve fisheries management and harvest control rules; and inform size‐based models of marine communities. We then present a step‐by‐step primer for how trait‐based methods could be adapted to complement existing models and analyses in fisheries science. Finally, we call for the creation and expansion of publicly available trait databases to facilitate adapting trait‐based methods in fisheries science, to complement existing public databases of life‐history parameters for marine organisms.