title: Dynamic ocean management increases the efficiency and efficacy of fisheries management.
abstract: In response to the inherent dynamic nature of the oceans and continuing difficulty in managing ecosystem impacts of fisheries, interest in the concept of dynamic ocean management, or real-time management of ocean resources, has accelerated in the last several years. However, scientists have yet to quantitatively assess the efficiency of dynamic management over static management. Of particular interest is how scale influences effectiveness, both in terms of how it reflects underlying ecological processes and how this relates to potential efficiency gains. Here, we address the empirical evidence gap and further the ecological theory underpinning dynamic management. We illustrate, through the simulation of closures across a range of spatiotemporal scales, that dynamic ocean management can address previously intractable problems at scales associated with coactive and social patterns (e.g., competition, predation, niche partitioning, parasitism, and social aggregations). Furthermore, it can significantly improve the efficiency of management: as the resolution of the closures used increases (i.e., as the closures become more targeted), the percentage of target catch forgone or displaced decreases, the reduction ratio (bycatch/catch) increases, and the total time-area required to achieve the desired bycatch reduction decreases. In the scenario examined, coarser scale management measures (annual time-area closures and monthly full-fishery closures) would displace up to four to five times the target catch and require 100-200 times more square kilometer-days of closure than dynamic measures (grid-based closures and move-on rules). To achieve similar reductions in juvenile bycatch, the fishery would forgo or displace between USD 15-52 million in landings using a static approach over a dynamic management approach.
authors.affiliation: Marine Geospatial Ecology Lab, Nicholas School of the Environment, Duke University, Durham, NC 27708; daniel.dunn@duke.edu.
authors.forename: Daniel C
authors.identifier: http://orcid.org/0000-0001-8932-0681
authors.initials: DC
authors.lastname: Dunn
authors.affiliation: Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529.
authors.forename: Sara M
authors.initials: SM
authors.lastname: Maxwell
authors.affiliation: Marine Geospatial Ecology Lab, Nicholas School of the Environment, Duke University, Durham, NC 27708;
authors.forename: Andre M
authors.identifier: http://orcid.org/0000-0001-5501-7190
authors.initials: AM
authors.lastname: Boustany
authors.affiliation: Marine Geospatial Ecology Lab, Nicholas School of the Environment, Duke University, Durham, NC 27708;
authors.forename: Patrick N
authors.initials: PN
authors.lastname: Halpin
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D017753:Ecosystem; D005398:Fisheries; D008962:Models, Theoretical; D009792:Oceans and Seas; D062211:Spatio-Temporal Analysis
keywords: bycatch; dynamic ocean management; ecosystem-based fisheries management; real-time management; spatiotemporal