FN Archimer Export Format PT J TI Understanding what controls the spatial distribution of fish populations using a multi-model approach BT AF PLANQUE, Benjamin LOOTS, CHRISTOPHE PETITGAS, Pierre LINDSTROM, Ulf VAZ, Sandrine AS 1:1;2:2;3:3;4:1;5:2; FF 1:PDG-DOP-DCN-EMH;2:PDG-DOP-DCMMN-HMMN-RHBL;3:PDG-DOP-DCN-EMH;4:;5:PDG-DOP-DCMMN-HMMN-RHBL; C1 Inst Marine Res, N-9294 Tromso, Norway. Inst Francais Rech Exploitat Mer, Lab Ressources Halieut, F-62321 Boulogne, France. Inst Francais Rech Exploitat Mer, Dept Ecol & Modeles Halieut, F-44311 Nantes 03, France. C2 INST MAR RES, NORWAY IFREMER, FRANCE IFREMER, FRANCE SI NANTES BOULOGNE SE PDG-DOP-DCN-EMH PDG-DOP-DCMMN-HMMN-RHBL IN WOS Ifremer jusqu'en 2018 copubli-europe IF 2.044 TC 134 UR https://archimer.ifremer.fr/doc/00022/13309/10360.pdf LA English DT Article CR IBTS 2000 IBTS 2001 IBTS 2002 IBTS 2003 IBTS 2004 IBTS 2005 IBTS 2006 IBTS 2007 IBTS 2008 IBTS 2009 IBTS 2010 IBTS 92/2 IBTS 93/1 IBTS 93/2 IBTS 94/1 IBTS 94/2 IBTS 95/1 IBTS 95/2 IBTS 96/1 IBTS 96/2 IBTS 97 IBTS 98 IBTS 99 IYFS 92 - IBTS 92/1 BO Thalassa DE ;demographic structure;density-dependent habitat selection;environmental control;fish spatial distribution models;multi-model inference;population memory;spatial dependency AB 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. PY 2011 SO Fisheries Oceanography SN 1054-6006 PU Wiley-blackwell Publishing, Inc VL 20 IS 1 UT 000285205300001 BP 1 EP 17 DI 10.1111/j.1365-2419.2010.00546.x ID 13309 ER EF