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Arrizabalaga, H., Dufour, F., Kell, L., Merino, G., Ibaibarriaga, L., Chust, G., et al. (2015). Global habitat preferences of commercially valuable tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 102–112.
Résumé: In spite of its pivotal role in future implementations of the Ecosystem Approach to Fisheries Management, current knowledge about tuna habitat preferences remains fragmented and heterogeneous, because it relies mainly on regional or local studies that have used a variety of approaches making them difficult to combine. Therefore in this study we analyse data from six tuna species in the Pacific, Atlantic and Indian Oceans in order to provide a global, comparative perspective of habitat preferences. These data are longline catch per unit effort from 1958 to2007 for albacore, Atlantic bluefin, southern bluefin, bigeye, yellowfin and skipjack tunas. Both quotient analysis and Generalized Additive Models were used to determine habitat preference with respect to eight biotic and abiotic variables. Results confirmed that, compared to temperate tunas, tropical tunas prefer warm, anoxic, stratified waters. Atlantic and southern bluefin tuna prefer higher concentrations of chlorophyll than the rest. The two species also tolerate most extreme sea surface height anomalies and highest mixed layer depths. In general, Atlantic bluefin tuna tolerates the widest range of environmental conditions. An assessment of the most important variables determining fish habitat is also provided.
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Escalle, L., Pennino, M. G., Gaertner, D., Chavance, P., Delgado de Molina, A., Demarcq, H., et al. (2016). Environmental factors and megafauna spatio-temporal co-occurrence with purse-seine fisheries. Fish. Oceanogr., 25(4), 433–447.
Résumé: Tropical tuna purse-seine fisheries spatially co-occur with various megafauna species, such as whale sharks, dolphins and baleen whales in all oceans of the world. Here, we analyzed a 10-year (2002–2011) dataset from logbooks of European tropical tuna purse-seine vessels operating in the tropical Eastern Atlantic and Western Indian Oceans, with the aim of identifying the principle environmental variables under which such co-occurrence appear. We applied a Delta-model approach using Generalized Additive Models (GAM) and Boosted Regression Trees (BRT) models, accounting for spatial autocorrelation using a contiguity matrix based on a residuals autocovariate (RAC) approach. The variables that contributed most in the models were chlorophyll-a concentration in the Atlantic Ocean, as well as depth and monsoon in the Indian Ocean. High co-occurrence between whale sharks, baleen whales and tuna purse-seine fisheries were mostly observed in productive areas during particular seasons. In light of the lack of a full coverage scientific observer on board program, the large, long-term dataset obtained from logbooks of tuna purse-seine vessels is highly important for identifying seasonal and spatial co-occurrence between the distribution of fisheries and megafauna, and the underlying environmental variables. This study can help to design conservation management measures for megafauna species within the framework of spatial fishery management strategies.
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Husson, B., Certain, G., Filin, A., & Planque, B. (2020). Suitable habitats of fish species in the Barents Sea. Fish Oceanogr., 29(6), 526–540.
Résumé: Many marine species exhibit poleward migrations following climate change. The Barents Sea, a doorstep to the fast-warming Arctic, is experiencing large scale changes in its environment and its communities. Tracking and anticipating changes for management and conservation purposes at the scale of the ecosystem necessitate quantitative knowledge on individual species distribution drivers. This paper aims at identifying the factors controlling demersal habitats in the Barents Sea, investigating for which species we can predict current and future habitats and inferring those most likely to respond to climate change. We used non-linear quantile regressions (QGAM) to model the upper quantile of the biomass response of 33 fish species to 10 environmental gradients and revealed three environmental niche typologies. Four main predictors seem to be limiting species habitat: bottom and surface temperature, salinity, and depth. We highlighted three cases of present and future habitat predictability: (a) Habitats of widespread species are not likely to be limited by the existing conditions within the Barents Sea. (b) Habitats limited by a single factor are predictable and could shift if impacted by climate change. If the factor is depth, the habitat may stagnate or shrink if the environment becomes unsuitable. (c) Habitats limited by several factors are also predictable but need to be predicted from QGAM applied on projected environmental maps. These modeled suitable habitats can serve as input to species distribution forecasts and end-to-end models, and inform fisheries and conservation management.
Mots-Clés: climate change; climate-change; demersal fish; distribution models; distributions; ecology; environmental gradients; environmental niche; generalized additive models; habitat suitability models; limiting factors; marine fish; movement; quantile regression; spatial-distribution; species distribution
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