Résumé: A size-structured, bioenergetics model was implemented to examine the effects of short-term environmental changes on European anchovy, Engraulis encrasicolus, in the North-western Mediterranean Sea. The model approach was based on Dynamic Energy Budget (DEB) theory and details the acquisition and allocation of energy (J d(-1)) during an organisms' full life-cycle. Model calibration was achieved using biometric data collected from the Gulf of Lions between 2002 and 2011. Bioenergetics simulations successfully captured ontogenetic and seasonal growth patterns, including active growth in spring/summer, loss of mass in autumn/winter and the timing and amplitude of multi-batch spawning events. Scenario analysis determined that vital rates (growth and fecundity) were highly sensitive to short-term environmental changes. The DEB model provided a robust foundation for the implementation of an individual-based population model (IBM) in which we used to test the responses of intrinsic and density-independent population growth rates (r) to observed and projected environmental variability. IBM projections estimate that r could be reduced by as much as 15% (relative to that estimated under mean conditions) due to either a 5% (0.8 degrees C) drop in temperature (due to a reduced spawning duration), a 18% (25 mg zooplankton m(-3)) depletion in food supply, a 30% increase in egg mortality rates, or with the phytoplankton bloom peaking 5 weeks earlier (in late-February/Winter). The sensitivity of r to short-term (1 year) and long-term (4-10 year) environmental changes were similar, highlighting the importance of first-year spawners. In its current form, the models presented here could be incorporated into spatially-explicit, higher-trophic (predator-prey and end-to-end ecosystem), larval-dispersal and toxicokinetic models or adapted to other short-lived foraging fish (clupeid) species. (C) 2012 Elsevier B.V. All rights reserved.

Résumé: The distribution of marine organisms is strongly influenced by climatic gradients worldwide. The ecological niche (sensu Hutchinson) of a species, i.e. the combination of environmental tolerances and resources required by an organism, interacts with the environment to determine its geographical range. This duality between niche and distribution allows climate change biologists to model potential species’ distributions from past to future conditions. While species distribution models (SDMs) have been intensively used over the last years, no consensual framework to parametrise, calibrate and evaluate models has emerged. Here, to model the contemporary (1990–2017) spatial distribution of seven highly harvested European small pelagic fish species, we implemented a comprehensive and replicable numerical procedure based on 8 SDMs (7 from the Biomod2 framework plus the NPPEN model). This procedure considers critical issues in species distribution modelling such as sampling bias, pseudo-absence selection, model evaluation and uncertainty quantification respectively through (i) an environmental filtration of observation data, (ii) a convex hull based pseudo-absence selection, (iii) a multi-criteria evaluation of model outputs and (iv) an ensemble modelling approach. By mitigating environmental sampling bias in observation data and by identifying the most ecologically relevant predictors, our framework helps to improve the modelling of fish species’ environmental suitability. Not only average temperature, but also temperature variability appears as major factors driving small pelagic fish distribution, and areas of highest environmental suitability were found along the north-western Mediterranean coasts, the Bay of Biscay and the North Sea. We demonstrate in this study that the use of appropriate data pre-processing techniques, an often-overlooked step in modelling, increase model predictive performance, strengthening our confidence in the reliability of predictions.

Résumé: In the context of the expansion of animal tracking and bio-logging, state-space models have been developed with the objective to characterise animals' trajectories and to understand the factors controlling their behaviour. In the fisheries community, the electronic tagging of vessels commonly designated by Vessel Monitoring Systems (VMS) is developing and provides a new insight for the understanding, the analysis and the modelling of the trajectories of vessels and their prospecting behaviour. VMS data are thus a clue for the proper definition of fishing effort which remains a fundamental parameter of tuna stock assessments. In this context, we used the VMS (recording of hourly positions) of the French tropical tuna purse-seiners operating in the Indian Ocean to characterise three types of movement (states) on the VMS trajectories (stillness, tracking, and cruising). Based on empirical evidences, and on the regular frequency of VMS acquisition, this was achieved by the development of a Bayesian Hidden Markov model for the speeds and turning angles derived from the hourly steps of the trajectories. In a second phase, states were related to activities disentangling stillness into fishing or stop at sea. Finally the quality of the model performances was rigorously quantified thanks to observers' data. Confronting model prediction and true activities allowed estimating that 10% of the hourly steps were misclassified. The assumptions and model' choices are discussed, highlighting the fact that VMS data and observers' data having different time resolutions, the effective use of validating data was troublesome. However, without validation, these analyses remain speculative. The validation part of this work represents an important step for the operational use of state-space models in ecology in the broad sense (predators' tracking data, e.g. birds or mammals trajectories).