Abstract:Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology. The spatial distribution of fish populations observed in the wild results from the complex interactions of multiple controls both external or internal to the fish populations. Whilst species distribution models (SDMs) have been mostly concerned with static description of species distribution as a function of environmental constraints, models of animal movements (MAMs) have focussed on the dynamic nature of spatial distribution of groups of individuals under a number of constraints external and internal to the population. Besides SDMs and MAMs, modelling the spatial distribution of fish populations can be achieved by models that are fundamentally static (like SDMs) but can also incorporate many hypotheses on the control of fish spatial distribution (like MAMs). The hypotheses underlying these models need to make sense at the population level -rather than at the individual or species level -we term these 'population distribution models' (PDMs). PDMs are statistical models that rely on several hypotheses, which include: (i) control through geographical attachment, (ii) environmental conditions, (iii) density-dependent habitat selection, (iv) spatial dependency, (v) population demographic structure, (vi) species interactions and (vii) population memory. We review the basis behind each of these conceptual models and we examine corresponding numerical applications. We argue that the conceptual models are complementary rather than competing, that existing numerical applications are still rarely compared and combined, and that PDMs can offer a statistical framework to achieve this. We recommend that the numerical models associated with different hypotheses be constructed within such a common general framework. This will permit evaluation, comparison and combination of the multiple hypotheses on fish spatial distribution. It will ultimately lead to a more comprehensive understanding of the factors controlling the spatial distribution of fish populations and to more accurate predictions in which model uncertainty is accounted for.

Loots, C., Vaz, S., Planque, B., and Koubbi, P. 2010. What controls the spatial distribution of the North Sea plaice spawning population? Confronting ecological hypotheses through a model selection framework. – ICES Journal of Marine Science, 67: 244–257. The spatial dynamics of spawning fish are crucial because they influence the survival rates of eggs and larvae and ultimately impact the reproductive success of populations. The factors that control these dynamics are complex and potentially many, and they interact. A model-selection-based approach was developed to confront various hypotheses of control of the spatial distribution of spawning population of North Sea plaice (Pleuronectes platessa). For each hypothesis or combination thereof, statistical models were constructed. These were then ranked and selected based on their ability to adjust and predict observed spatial distributions. The North Sea plaice population seems to have developed strong attachment to specific spawning sites, where geographic location and population memory are important controlling factors. Temporal changes in spatial distribution patterns appear to be influenced primarily by population size and demography. Variations in hydrographic conditions such as temperature and salinity do not appear to control interannual fluctuations in spatial distribution. This means that, for reproduction, applying conventional habitat models may falsely attribute major controlling effects to environmental conditions. It is concluded that a multiple-hypothesis approach is essential to understanding and predicting the present and future distribution of the North Sea plaice population during its spawning season.

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Planque, B., Bellier, E., and Loots, C. 2011. Uncertainties in projecting spatial distributions of marine populations. – ICES Journal of Marine Science, 68: 1045–1050. Projection of future spatial distributions of marine populations is a central issue for ecologists and managers. The measure of projection uncertainty is particularly important, because projections can only be useful if they are given with a known and sufficiently high level of confidence. Uncertainties can arise for the observation process, conceptual and numerical model formulations, parameter estimates, model evaluation, appropriate consideration of spatial and temporal scales, and finally the potential of adaptation of living systems. Comprehensive analyses of these multiple sources of uncertainty have not been carried out so far, and how these uncertainties are considered in current studies has not yet been described. To analyse how these different sources of uncertainty are currently considered in marine research, we did a survey of published literature during the period 2005–2009. From the 75 publications selected, we calculated how frequently each type of uncertainty was considered. We found that little attention is given to most sources of uncertainty, except for uncertainty in parameter estimates. As a result, most current projections are expected to be far less reliable than usually assumed. The conclusion is that, unless uncertainty can be better accounted for, such projections may be of limited use, or even risky to use for management purposes.