The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract

The ecological causes of functional distinctiveness in communities

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages

fundiversity: a modular R package to compute functional diversity indices

International audienceFunctional diversity is widely used and widespread. However, the main packages used to compute functional diversity indices are not flexible and not adapted to the volume of data used in modern ecological analyses. We here present fundiversity, an R package that eases the computation of classical functional diversity indices. It leverages parallelization and memoization (caching results in memory) to maximize efficiency with data with thousands of columns and rows. We also did a performance comparison with packages that provide analog functions. In addition to being more flexible, fundiversity was always an order of magnitude quicker than alternatives. fundiversity aims to be a lightweight, efficient tool to compute functional diversity indices, which can be used in a variety of contexts. Because it has been designed following clear principles, it is easy to extend. We hope the wider community will adopt it and we welcome all contributions

fundiversity: Easy Computation of Functional Diversity Indices

Computes six functional diversity indices: Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), Rao's entropy (Q) (reviewed in Villéger et al. 2008 ). Provides efficient, modular, and parallel functions to compute functional diversity indices. (preprint:

Is prediction of species richness from stacked species distribution models biased by habitat saturation?

International audienc

frmunoz/lottery: lottery v1.0.3

This release has been submitted to CRAN

Bisaloo/fundiversity: fundiversity

fundiversity provides a lightweight package to compute common functional diversity indices. To a get a glimpse of what fundiversity can do refer to the introductory vignette. The package is built using clear, public design principles inspired from our own experience and user feedback

Rekyt/funrar

funrar is a package to compute functional rarity indices, it quantifies how species are rare both from a functional and an extent point of view. Following the different facets of rarity proposed by Rabinowitz (1981)

orchid-pollinator data

Interactions between orchid and pollinator species. Data from the literature. Full_database: database with 243 orchid species and 773 pollinator species. Reduced_database: database with 153 orchid species (i.e. orchid species for which phylogenetic data were available) and 726 pollinator specie