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Climate-induced changes in the physical, chemical, and biological environment are expected to increasingly stress marine ecosystems, with important consequences for fisheries exploitation. Here, we use the APECOSM-E numerical model (Apex Predator ECOSystem Model - Estimation) to evaluate the future impacts of climate change on the physiology, spatial distribution, and abundance of skipjack tuna, the worldwide most fished species of tropical tuna. The main novelties of our approach lie in the mechanistic link between environmental factors, metabolic rates, and behavioral responses and in the fully three dimensional representation of habitat and population abundance. Physical and biogeochemical fields used to force the model are provided by the last generation of the IPSL- CM5 Earth System Model run from 1990 to 2100 under a 'business-as-usual' scenario ( RCP8.5). Our simulations show significant changes in the spatial distribution of skipjack tuna suitable habitat, as well as in their population abundance. The model projects deterioration of skipjack habitat in most tropical waters and an improvement of habitat at higher latitudes. The primary driver of habitat changes is ocean warming, followed by food density changes. Our projections show an increase of global skipjack biomass between 2010 and 2050 followed by a marked decrease between 2050 and 2095. Spawning rates are consistent with population trends, showing that spawning depends primarily on the adult biomass. On the other hand, growth rates display very smooth temporal changes, suggesting that the ability of skipjack to keep high metabolic rates in the changing environment is generally effective. Uncertainties related to our model spatial resolution, to the lack or simplification of key processes and to the climate forcings are discussed. [ABSTRACT FROM AUTHOR]

The benefits of implementing no-take Marine Protected Areas (MPAs) for the conservation of highly migratory species are not easy to assess. They depend on several factors, such as the fish mobility, fisher behaviour and the area covered by the MPA with respect to the distribution area of the species to protect. In this study, we explore the simultaneous effects of MPAs and fishing scenarios on skipjack tuna population dynamics, using the spatially-explicit APECOSM-E model. The model represents the size-structured population dynamics of skipjack tuna in the Indian Ocean and their dependence on climatic variability and exploitation by fisheries. Numerical experiments were run from the beginning of industrial fisheries in the early 1980s to the year 2030, considering different scenarios for the future development of fisheries. These scenarios combined different trends in fishing effort and technological development, either assuming a continuous increase following historical trends or a stabilization of these factors at present values. The simulations were designed to explore the effects of two MPAs of different size and location: the recently established Chagos MPA, and a hypothetical MPA covering a large part of the Western Indian Ocean, where most of the skipjack catches are presently made. We modelled the redistribution of fishing effort around the MPAs assuming that the fishers had partial knowledge of the spatial distribution of the skipjack population. The effects of the two MPAs on the population dynamics, catch and fishing mortality are shown. Our results revealed a very minor effect of the Chagos MPA on the skipjack tuna population, while the Western Indian Ocean MPA had an important impact on the fishing mortality and succeeded in stabilizing the spawning population. The simulations also showed that the effect of an MPA depends on the evolution of fisheries and it is therefore important to explore different fishery scenarios to assess the future benefits of an MPA. [ABSTRACT FROM AUTHOR]

Abstract: APECOSM-E (Apex-Predator-Ecosystem-Model-Estimation) is a deterministic model that represents the 3D distribution and population dynamics of tropical tuna under the joint effect of environmental conditions and exploitation by fisheries. It is a simplified version of the top predator component of the APECOSM framework, based on a single partial differential equation. The model is structured in 3D space and fish size and considers size dependent reproduction, growth, predation, natural mortality and fishing mortality. Processes are time, space and size-dependent and linked to the environment through mechanistic bioenergetic or behavioral parameterizations. Physiological rates such as growth, reproduction and ageing mortality are derived from the Dynamic Energy Budget (DEB) theory, while horizontal movements and vertical distribution obey a mechanistically derived advection–diffusion formulation driven by habitat gradients and oceanic currents. The effect of fishing is accounted for through the use of fleet-specific size and depth selectivity functions and time-dependent catchability coefficients which relate observed fishing effort to catches and size-frequencies. In this paper we present the mathematical formulations of the physiological and behavioral components of the model, and an application to the skipjack tuna population in the Indian Ocean. The model is run with a daily time step on a 1°×1° horizontal grid and considers 20 vertical layers, reaching a maximal depth of 500m. Results show the effects of spatial and temporal variability of environmental conditions on tuna physiology in terms of growth, reproduction and survival. Moreover, our results suggest that observed trends in reported catches are connected to environmental conditions by means of recruitment dynamics. In addition, the model allows representing the horizontal and vertical distribution of skipjack tuna and assessing the effect of accessibility of the resource to fisheries. The ability of the model to represent the distribution of biomass in accordance with the pattern given by the observed fishing activity was evaluated by comparing the spatial distribution of the simulated biomass with the observed distribution of commercial purse seiners and bait boats catches in the Indian Ocean. The likelihood based method used for estimating the model parameters as well as an analysis of its sensitivity to their values is provided in a companion paper (Dueri et al., 2012). [Copyright &y& Elsevier]