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Genovesi, B., Mouillot, D., Laugier, T., Fiandrino, A., Laabir, M., Vaquer, A., et al. (2013). Influences of sedimentation and hydrodynamics on the spatial distribution of Alexandrium catenella/tamarense resting cysts in a shellfish farming lagoon impacted by toxic blooms. Harmful Algae, 25, 15–25.
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Gerlotto, F., Jones, E., Bez, N., & Reid, D. G. (2010). When good neighbours become good friends: observing small scale structures in fish aggregations using multibeam sonar. Aquatic Living Resources, 23, 143–151.
Résumé: Converging results in different scientific fields (behavioural ecology, fisheries biology, acoustic tagging, fisheries acoustics, behavioural modelling) suggest the existence of “micro-groups” inside fish schools. These would comprise a few (5-10) fish maintaining contact during a period long enough to allow individuals to recognise each other. It is hypothesised that they would prefer to share the space with familiar rather than anonymous conspecifics. To evaluate whether acoustic methods could be used to recognise “micro-structures” inside fish schools and help test the “micro-group” hypothesis we analysed acoustic data from anchovy schools off Peru, and gadoids in the North Sea. Data collection used a multibeam sonar (Reson SeaBat 6012). In the Peruvian case study, the sonar was mounted set horizontally on a drifting research vessel and the internal structure of the schools of anchovies was analysed, although individual fish could not be discriminated. In the North Sea case study, the sonar was orientated vertically above a demersal trawl to allow observation of individual fish entering the trawl. Geostatistical analyses were used to evaluate the existence of small spatial structures in anchovy schools. In these schools, “micro-structures” with a scale as small as 0.5 m were observed acoustically. For the gadoids nearest neighbour distance (NDD) measurements were carried out, suggesting that the fish aggregated in small groups (2 to 25 individuals, with an average of 3.7 fish per group) in the trawl catches. The perspectives and limitations of these results are discussed.
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Granger, V., Bez, N., Fromentin, J. - M., Meynard, C., Jadaud, A., & Mérigot, B. (2015). Mapping diversity indices: not a trivial issue. Methods Ecol Evol, 6(6), 688–696.
Résumé: * Mapping diversity indices, that is estimating values in all locations of a given area from some sampled locations, is central to numerous research and applied fields in ecology. * Two approaches are used to map diversity indices without including abiotic or biotic variables: (i) the indirect approach, which consists in estimating each individual species distribution over the area, then stacking the distributions of all species to estimate and map a posteriori the diversity index, (ii) the direct approach, which relies on computing a diversity index in each sampled locations and then to interpolate these values to all locations of the studied area for mapping. * For both approaches, we document drawbacks from theoretical and practical viewpoints and argue about the need for adequate interpolation methods. First, we point out that the indirect approach is problematic because of the high proportion of rare species in natural communities. This leads to zero-inflated distributions, which cannot be interpolated using standard statistical approaches. Secondly, the direct approach is inaccurate because diversity indices are not spatially additive, that is the diversity of a studied area (e.g. region) is not the sum of the local diversities. Therefore, the arithmetic variance and some of its derivatives, such as the variogram, are not appropriate to ecologically measure variation in diversity indices. For the direct approach, we propose to consider the β-diversity, which quantifies diversity variations between locations, by the mean of a β-gram within the interpolation procedure. We applied this method, as well as the traditional interpolation methods for comparison purposes on different faunistic and floristic data sets collected from scientific surveys. We considered two common diversity indices, the species richness and the Rao's quadratic entropy, knowing that the above issues are true for complementary species diversity indices as well as those dealing with other biodiversity levels such as genetic diversity. * We conclude that none of the approaches provided an accurate mapping of diversity indices and that further methodological developments are still needed. We finally discuss lines of research that may resolve this key issue, dealing with conditional simulations and models taking into account biotic and abiotic explanatory variables.
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Gruss, A., Yemane, D., & Fairweather, T. P. (2016). Exploring the spatial distribution patterns of South African Cape hakes using generalised additive models. Afr. J. Mar. Sci., 38(3), 395–409.
Résumé: We developed delta generalised additive models (GAMs) to predict the spatial distribution of different size classes of South African hakes, Merluccius capensis and M. paradoxus, using demersal trawl survey data and geographical (latitude and longitude) and environmental features (depth, temperature, bottom dissolved oxygen and sediment type). Our approach consists of fitting, for each hake size class, two independent models, a binomial GAM and a quasi-Poisson GAM, whose predictions are then combined using the delta method. Delta GAMs were validated using an iterative cross-validation procedure, and their predictions were then employed to produce distribution maps for the southern Benguela. Delta GAM predictions confirmed existing knowledge about the spatial distribution patterns of South African hakes, and brought new insights into the factors influencing the presence/absence and abundance of these species. Our GAM approach can be used to produce distribution maps for spatially explicit ecosystem models of the southern Benguela in a rigorous and objective way. Ecosystem models are critical features of the ecosystem approach to fisheries, and distribution maps constructed using our GAM approach will enable a reliable allocation of species biomasses in spatially explicit ecosystem models, which will increase trust in the spatial overlaps and, therefore, the trophic interactions predicted by these models.
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Hattab, T., Albouy, C., Lasram, F. B. R., Somot, S., Le Loc'h, F., & Leprieur, F. (2014). Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach. Global Ecology and Biogeography, 23(12), 1417–1429.
Résumé: Aim In this paper, we applied the concept of ‘hierarchical filters’ in community ecology to model marine species distribution at nested spatial scales. Location Global, Mediterranean Sea and the Gulf of Gabes (Tunisia). Methods We combined the predictions of bioclimatic envelope models (BEMs) and habitat models to assess the current distribution of 20 exploited marine species in the Gulf of Gabes. BEMs were first built at a global extent to account for the full range of climatic conditions encountered by a given species. Habitat models were then built using fine-grained habitat variables at the scale of the Gulf of Gabes. We also used this hierarchical filtering approach to project the future distribution of these species under both climate change (the A2 scenario implemented with the Mediterranean climatic model NEMOMED8) and habitat loss (the loss of Posidonia oceanica meadows) scenarios. Results The hierarchical filtering approach predicted current species geographical ranges to be on average 56% smaller than those predicted using the BEMs alone. This pattern was also observed under the climate change scenario. Combining the habitat loss and climate change scenarios indicated that the magnitude of range shifts due to climate change was larger than from the loss of P. oceanica meadows. Main conclusions Our findings emphasize that BEMs may overestimate current and future ranges of marine species if species–habitat relationships are not also considered. A hierarchical filtering approach that accounts for fine-grained habitat variables limits the uncertainty associated with model-based recommendations, thus ensuring their outputs remain applicable within the context of marine resource management.
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