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Impact du réchauffement climatique sur la distribution spatiale des ressources halieutiques le long du littoral français: observations et scénarios

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par Sylvain Lenoir
Université Lille 1 Science - Doctorat 2011
  

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2.5. Discussion

To predict how the spatial distribution of marine species may change with climate, it is essential to understand the factors that limit their distributions. One way to achieve this is to use ecological niche models. However, only a limited number of models can deal with presence-only data. The models BIOCLIM and HABITAT use a rectilinear volume and a convex envelope (i.e. a closed polygonal chain), respectively (Carpenter et al. 1993). The drawbacks to these simple models are the imposed shapes, which can be the cause of a non-justified exclusion or inclusion of a geographical point from the predicted distribution (Carpenter et al. 1993). The model DOMAIN can solve this problem by the use of a point-to-point similarity metric. However, the metrics used (e.g. the Gower metric; Carpenter et al. 1993, Legendre & Legendre 1998) does not take into account the correlation between descriptors (Legendre & Legendre, 1998). Furthermore, a threshold is used to map the modelled distribution of the species. The RES model (Kaschner et al. 2006) uses a trapezoid shape that constitutes a good compromise between species with a shorter or a unimodal ecological niche and migratory species with larger or a bimodal niche (Kaschner et al. 2006). However, the bounds of this trapezoid need to be precisely defined, implicating often arbitrary choices and thus it requires a good knowledge of the species. While the Ecological Niche Factor Analysis (ENFA; Hirzel et al. 2002) could be adapted for our study, this technique requires the multinormality of the ecogeographical variables to extract the eigenvectors to calculate the marginality and the specialization factors and thereby a transformation of the ecogeographical variables is often needed (e.g. Box-Cox) prior to the analysis. Finally, the statistical technique MAXENT (Philips et al. 2006), which is based on the maximum-entropy principle, also requires accurate threshold definition and showed some application restrictions.

NPPEN offers a number of advantages on the above existing methods. Firstly, contrary to models such as RES (Kaschner et al. 2006) or the mixed model of Cheung and colleagues (Cheung et al. 2008a), our simple model does not need an a priori knowledge of the species biology. Our technique is also based on a non-parametric test that does not require the multinormality of the ecogeographical variables. Although the use of the Generalised Mahalanobis distance is not new in this kind of model (Farber & Kadmon 2003, Cayuela 2004, Etherington et al. 2009), this is the first time that this distance metric is embedded into a non-parametric test. For example, Cayuela (Cayuela 2004) rescaled the Mahalanobis distance into quantiles to produce a map of probability and Nogués-Bravo et al. (Nogués-Bravo et al. 2008) converted the distance into quartiles. Ibañez (Ibañez 1981) or Farber & Kadmon (Farber & Kadmon 2003) tested this distance by approximating this measure by a ÷² distribution with n-1 degrees of freedom. Legendre & Legendre (Legendre & Legendre 1998) also described the conversion of the D² by the Hotelling T² (Hotelling 1931) statistic and its test by the F statistic. These tests require the distribution to be multinormal. Although these authors stated that the test can tolerate some degrees of deviation from this assumption, it can be seen from histograms that the bathymetry data (Fig. II.5) were very far from the normality. Finally, the procedure does not need the selection of arbitrary thresholds and is fully statistical. The technique simply tests whether an observation belongs to a group of observations called here a training set or a reference matrix. NPPEN therefore can be used very quickly as an exploratory analysis to give a first approximation of the spatial distribution of a species. The test is also appropriate for many species for which no information on the physiology exists. The only caveat is that our model, as others, does not fully resolve the problem of autocorrelation (SAR). The spatial autocorrelation can inflate significantly the probabilities inferred from ENMs (Bahn & McGill 2007). We think that presence-only technique of ENMs are much less subject to this problem than other types of ENMs (e.g. GLM). The problem is that only few studies considered local functions of autocorrelation (Beaugrand & Ibañez 2002, Dormann et al. 2007). Most corrections applied are based on global function of autocorrelation with underlying assumption of isotropy (e.g. the Moran' index, global semivariograms), which is rarely the case in biogeography (Beaugrand & Ibañez 2002).

Our technique is currently restricted to presence-only data. Although some adjustments could be made to apply the method (e.g. calculate the test for different category of abundance), it is probably preferable in such a case to use other techniques such as Generalised Linear Model (GLM; McCullagh & Nelder 1983) or Generalised Additive Models (GAM; Hastie & Tibshirani 1990). Guisan & Zimmermann (Guisan & Zimmermann 2000) provided an extended review on the different techniques used to assess the spatial distribution of a species. Perhaps, another limitation of the technique lies in the fact that it should only be employed with a limited number of ecogeographical variables. If a high number of variables are used it would be preferable to use a principal component analysis prior to the application of the test, or use the Mahalanobis distance factor analysis (MEDIFA; (Calenge et al. 2008) to better understand the contribution of the ecogeographical variables; this can also be done a posteriori by calculating the correlation (here, the rank correlation coefficient of Spearman or Kendall (Legendre & Legendre 1998) between the modeled probability and each environmental factor. NPPEN might also be subjected to what could be described as a «border effect». Indeed, the modelling of the niche of Atlantic cod (see Fig. II.6) showed a reduction in the probability of cod occurrence towards shallow regions, which is unexpected based upon our knowledge of the species (Sundby 2000). Indeed, the technique works in such a way that maximum probability is concentrated towards the middle of the niche. Therefore some borders of the multidimensional niche might be underestimated. Although this problem is difficult to circumvent, it could be overcome partially by modelling the absence of the species (i.e. estimate the probability of the absence of the species). Probabilities issued from such a modelling approach would be less sensitive to the border effect discussed above and would be complementary, i.e. by assessing the fundamental niche whereas the ENMs applied on presence data estimate the realized niche (Pulliam 2000, Helaouët & Beaugrand 2009).

Modelling the absence of a species has never be done, as far as we know, however, this could be as interesting as modelling the presence of a species, especially in the case of an exploited species such as Atlantic cod. Indeed, modelling the probability of absence is very informative for policymakers and fisheries scientists. It should not be assessed from map of probability of presence however, but instead should be based on physiological evidence (Bigg et al. 2008, Helaouët & Beaugrand 2009). When presence-only data are available to model the spatial distribution of cod, several known scientific facts and physiological evidence exist that could be used in NPPEN. First, the species is generally found where the bathymetry is shallower than 800 m (see Fig. II.5). While some authors found a sharp decrease in the frequency of occurrence of this species at 400 m and an absence from 600 m (Bigg et al. 2008), this not a sharp constraint and so we can be conservative. From field and experimental studies, we know that cod are unable to reproduce at salinities below 11 because their sperm become immobile and their eggs sink (Brander, Personal Communication), therefore we can predict confidently that cod will cease to reproduce in areas where salinity falls below these levels. For temperature, different thresholds could be used. Beaugrand et al. (Beaugrand et al. 2008) found a pronounced increase in the variance of the Atlantic cod when the thermal regime was between 9 and 12°C with maximum variance between 9 and 10°C. Brander (2005) in a synthesis report on this species found maximum spawning temperatures of 12.7°C in Georges Bank. Pepin et al. (1997) found a sustained decrease in the percentage of egg survival in laboratory between 10° and 12°C. Here also, it would be logical to select the threshold of 12.7°C (as monthly SST) in order to remain conservative.

Our model explains in part the pronounced decrease observed in the abundance of cod in the North Sea (Brander et al. 2006) although the decline modelled from our study seems less pronounced. Two main phenomena could explain this result. First, our model does not incorporate information on plankton. Recent studies have shown however that plankton amplifies the effect of temperature change (Beaugrand & Kirby 2010b, a). If plankton exacerbates the effect of climate, our model could be too conservative. Second, overfishing has exerted a sustained pressure on the stock that has probably increased its sensitivity to climate change (Hsieh et al. 2006). It is also expected that these two phenomena act in synergy to reduce the size of the stock (Kirby & Beaugrand 2009). Our model only explains in part the collapse of cod observed in eastern part of North America (e.g. The Georges Bank, the Eastern Scotian Shelf and Newfoundland). This is mainly observed when our model is based on modelled SST data (Fig. II.8). Recently, Beaugrand & Kirby (Beaugrand & Kirby 2010a) also show that some plankton indicators decreased at the same time than the observed collapse of cod stocks in these regions. However, the region is complex and some other plankton variables were unable to explain completely this collapse. Here also, overfishing has had a well-documented effect (Myers et al. 1996, Hsieh et al. 2006). The stock may resist sustained pressure up to a point when environmental conditions become less favorable and trigger the collapse of the stock. While it is impossible to compensate directly for both the direct and indirect effects of global warming on the ocean, the consideration of change in the carrying capacity of the ecosystem in the management of the species should be made more explicit in ecosystem fisheries based management (Pikitch et al. 2004).

Finally, projections suggest substantial changes in the spatial distribution of cod at the scale of the North Atlantic Ocean. Our projections indicate that cod should decrease to the level of commercial extinction in the North Sea and in regions on the eastern side of North America (e.g. Georges Bank, the Scotian Shelf and Newfoundland) where collapses have already been detected. These results tend to suggest that the rebuilding of cod stocks in the North Sea might be difficult. Instead, our effort should perhaps be made on what resource is likely to present or to develop over the next decades to enable fishermen to anticipate changes in the resources should climate continue to warm.

Acknowledgements

We thank Dr Richard R Kirby for helpful comments on an early version of the manuscript. We are grateful to past and present members and supporters of the FishBase website

(http://www.FishBase.org), whose continuous efforts have allowed the establishment of the fish data set. We thank Dr Keith Brander for helpful comments on an early version of the manuscript. The research was supported by the French Agency of Research and Technology (ANRT, grant CIFRE 862/2007).

CHAPITRE III

Distribution spatiale modélisée des poissons marins et projections des changements dans l'océan Atlantique Nord

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