Mapping diversity indices: not a trivial issue

Abstract

Summary1. 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. 2. 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. 3. 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 b-diversity, which quantifies diversity variations between locations, by the mean of a b-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. 4. 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.

Modelling spatial biodiversity in the world’s largest mangrove ecosystem—The Bangladesh Sundarbans: A baseline for conservation

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“…Both habitat‐based and covariate‐free (direct interpolation methods such as Kriging) approaches have been used for mapping biodiversity indices. Although covariate‐free approaches have been criticized for low predictive ability (Granger et al, ), the relative performance of the approaches has rarely been tested using field data.…”

Time-lapsing biodiversity: An open source method for measuring diversity changes by remote sensing

“…Estimating values of diversity over an area given a sample is crucial for a number of dierent ecological tasks (Granger et al, 2015 In some cases, spatial non-stationarity has been advocated as one of the major problems when the variability of a certain variable is non-uniform in space (Osborne et al, 2007). In our case, we would promote our approach to also account for potential anomalies, or simply spots of diversity variation in time, when measuring beta-diversity from satellites.…”

How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

“…Information about communities, here defined as a taxonomic assemblage of distinct populations of species that co‐occur in a given space at a given time (Begon, Harper, & Townsend, ), is therefore essential to make informed decisions for conservation prioritisation (D'Amen et al., ; Guisan et al., ; Mateo, de la Estrella, Felicisimo, Munoz, & Guisan, ) and to create biodiversity indices (e.g. Essential Biodiversity Variables; Pereira et al., ) for policy decisions (Fleishman, Noss, & Noon, ; Granger et al., ).…”