Bastien Mérigot

Large-Scale Diversity of Slope Fishes: Pattern Inconsistency between Multiple Diversity Indices

Large-scale studies focused on the diversity of continental slope ecosystems are still rare, usually restricted to a limited number of diversity indices and mainly based on the empirical comparison of heterogeneous local data sets. In contrast, we investigate large-scale fish diversity on the basis of multiple diversity indices and using 1454 standardized trawl hauls collected throughout the upper and middle slope of the whole northern Mediterranean Sea (36°3′- 45°7′ N; 5°3′W - 28°E). We have analyzed (1) the empirical relationships between a set of 11 diversity indices in order to assess their degree of complementarity/redundancy and (2) the consistency of spatial patterns exhibited by each of the complementary groups of indices. Regarding species richness, our results contrasted both the traditional view based on the hump-shaped theory for bathymetric pattern and the commonly-admitted hypothesis of a large-scale decreasing trend correlated with a similar gradient of primary production in the Mediterranean Sea. More generally, we found that the components of slope fish diversity we analyzed did not always show a consistent pattern of distribution according either to depth or to spatial areas, suggesting that they are not driven by the same factors. These results, which stress the need to extend the number of indices traditionally considered in diversity monitoring networks, could provide a basis for rethinking not only the methodological approach used in monitoring systems, but also the definition of priority zones for protection. Finally, our results call into question the feasibility of properly investigating large-scale diversity patterns using a widespread approach in ecology, which is based on the compilation of pre-existing heterogeneous and disparate data sets, in particular when focusing on indices that are very sensitive to sampling design standardization, such as species richness.

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Mapping diversity indices: not a trivial issue

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...

Incorporation of phylogeny in biological diversity measurement: Drawbacks of extensively used indices, and advantages of quadratic entropy

Phylogeny, involving molecular and genetic information, is increasingly being used in organismal and systems studies such as in evolution, ecology and conservation biology [1–5]. Phylogeny is notably occupying a growing place in biodiversity studies through the assessment of phylogenetic diversity (PD) [6]. PD, which is defined as the differences in the evolutionary history of species within a community, integrates both distances between species along a phylogenetic tree and species presence/ absence, or abundances (see [1] and references therein). Given the increasing rate of change in biological diversity, mediated by ever increasing direct human pressures and global environmental change [3, 7–10], there is a growing need to complement the most traditional diversity measures, such as species richness and evenness. Indeed, while both of these traditional measures quantify the number of species in a community and how evenly individuals are distributed among the species, respectively, they also both consider species as equivalent and do not take into account differences between species in their evolutionary history [1, 2]. In this context, the assessment of PD is of considerable interest for several key research areas sustaining biological conservation. Firstly, it provides a basis for assessment of the evolutionary potential of a community, i.e. studying either its capacity to generate new evolutionary solutions in the face of change or to persist despite those changes [3, 7]. Secondly, partitioning PD into different components/scales (i.e. a, b and g components [1, 11], see last section of the essay) can contribute to bridging evolutionary (trait evolution, speciation) and ecological (dispersal ability, biotic interactions such as competition or predation, environmental gradient and filtering) factors. This approach allows a better understanding of how these factors generate species distributions and its turnover across space and time [3, 4, 10, 12, 13]. Thirdly, King [1] has recently drawn special attention to considering PD in ecosystem functioning research, while it is still poorly investigated in spite of its importance in ecosystem processes and for human well being [1, 3, 14]. For instance, in a North American grassland experiment, Cadotte et al. [14] demonstrated that PD can predict biomass production better than other diversity components such as species richness or functional diversity (quantified as the diversity of some biological traits characteristic of species in a community, see [14]). Their work provided support for the hypothesis that phylogenetically diverse communities can maximize resource partitioning and thus improve the efficiency of ecosystem functioning (through a more extensive use of total available resources). To support each of the abovementioned key research areas, the preliminary (but essential) step is to adequately assess the PD of communities. However, PD assessment can vary widely according to the properties of the diversity indices used (e.g. [11, 15, 16]). To quantify PD, King [1] promoted the use of some very popular indices introduced by Warwick and Clarke [17, 18] which for years have been widely computed in studies dealing with taxonomy (e.g. [19]), and are also increasingly applied to phylogeny (e.g. [20–22]) or even functional diversity (e.g. [23]). However, we show here that some DOI 10.1002/bies.201100103

Fitting the truncated negative binomial distribution to count data. A comparison of estimators, with an application to groundfishesfrom the Mauritanian Exclusive Economic Zone

Modeling empirical distributions of repeated counts with parametric probability distributions is a frequent problem when studying species abundance. One must choose a family of distributions which is flexible enough to take into account very diverse patterns and possess parameters with clear biological/ecological interpretations. The negative binomial distribution fulfills these criteria and was selected for modeling counts of marine fish and invertebrates. This distribution depends on a vector

Consequences of fishing moratoria on catch and bycatch: the case of tropical tuna purse-seiners and whale and whale shark associated sets

\left( K,\mathfrak {P}\right)

Mortality of marine megafauna induced by fisheries: Insights from the whale shark, the world’s largest fish

K,P of parameters, and ranges from the Poisson distribution (when