Navigating the integration of biotic interactions in biogeography

Biotic interactions are widely recognised as the backbone of ecological communities, but how best to study them is a subject of intense debate, especially at macro-ecological scales. While some researchers claim that biotic interactions need to be observed directly, others use proxies and statistical approaches to infer them. Despite this ambiguity, studying and predicting the influence of biotic interactions on biogeographic patterns is a thriving area of research with crucial implications for conservation. Three distinct approaches are currently being explored. The first approach involves empirical observation and measurement of biotic interactions' effects on species demography in laboratory or field settings. While these findings contribute to theory and to understanding species' demographies, they can be challenging to generalise on a larger scale. The second approach centers on inferring biotic associations from observed co-occurrences in space and time. The goal is to distinguish the environmental and biotic effects on species distributions. The third approach constructs extensive potential interaction networks, known as metanetworks, by leveraging existing knowledge about species ecology and interactions. This approach analyses local realisations of these networks using occurrence data and allows understanding large distributions of multi-taxa assemblages. In this piece, we appraise these three approaches, highlighting their respective strengths and limitations. Instead of seeing them as conflicting, we advocate for their integration to enhance our understanding and expand applications in the emerging field of interaction biogeography. This integration shows promise for ecosystem understanding and management in the Anthropocene era

Generating species assemblages for restoration and experimentation: A new method that can simultaneously converge on average trait values and maximize functional diversity

1. Restoring resilient ecosystems in an era of rapid environmental change requires a flexible framework for selecting assemblages of species based on functional traits. However, current trait‐based models have been limited to algorithms that select species assemblages that only converge on specified average trait values, and could not accommodate the common desire among restoration ecologists to generate functionally diverse assemblages. 2. We have solved this problem by applying a nonlinear optimization algorithm to solve for the species relative abundances that maximize Rao's quadratic entropy (Q) subject to other linear constraints. Rao's Q is a closed‐form algebraic expression of functional diversity that is maximized when the most abundant species are functionally dissimilar. 3. Previous models have maximized species evenness subject to the linear constraints by maximizing the entropy function (H’). Maximizing Q alone produces an undesirable species abundance distribution because species that exhibit extreme trait values have the highest abundances. We demonstrate that the maximization of an objective function that additively combines Q and H’ produces a more even relative abundance distribution across the trait dimension. 4. Some ecological restoration projects aim to restore communities that converge on one set of traits while diverging across another. The selectSpecies r function can derive assemblages for any size species pool that maximizes the diversity of any set of traits, while simultaneously converging on average values of any other set of traits. We demonstrate how the function works through examples using uniformly spaced trait distributions and data with a known structure. We also demonstrate the utility of the function using real trait data collected on dozens of species from three separate ecosystems: serpentine grasslands, ponderosa pine forests, and subtropical rainforests. 5. The quantitative selection of species based on their functional traits for ecological restoration and experimentation must be both rigorous and accessible to practitioners. The selectSpecies function provides ecologists with an easy‐to‐use open‐source solution to objectively derive species assemblages based on their functional traits

Determinants of change in subtropical tree diameter growth with ontogenetic stage

We evaluated the degree to which relative growth rate (RGR) of saplings and large trees is related to seven functional traits that describe physiological behavior and soil environmental factors related to topography and fertility for 57 subtropical tree species in Dinghushan, China. The mean values of functional traits and soil environmental factors for each species that were related to RGR varied with ontogenetic stage. Sapling RGR showed greater relationships with functional traits than large-tree RGR, whereas large-tree RGR was more associated with soil environment than was sapling RGR. The strongest single predictors of RGR were wood density for saplings and slope aspect for large trees. The stepwise regression model for large trees accounted for a larger proportion of variability (R 2 = 0.95) in RGR than the model for saplings (R 2 = 0.55). Functional diversity analysis revealed that the process of habitat filtering likely contributes to the substantial changes in regulation of RGR as communities transition from saplings to large trees. © 2014 Springer-Verlag Berlin Heidelberg

Plant functional and taxonomic diversity in European grasslands along climatic gradients

Aim: European grassland communities are highly diverse, but patterns and drivers of their continental-scale diversity remain elusive. This study analyses taxonomic and functional richness in European grasslands along continental-scale temperature and precipitation gradients. Location: Europe. Methods: We quantified functional and taxonomic richness of 55,748 vegetation plots. Six plant traits, related to resource acquisition and conservation, were analysed to describe plant community functional composition. Using a null-model approach we derived functional richness effect sizes that indicate higher or lower diversity than expected given the taxonomic richness. We assessed the variation in absolute functional and taxonomic richness and in functional richness effect sizes along gradients of minimum temperature, temperature range, annual precipitation, and precipitation seasonality using a multiple general additive modelling approach. Results: Functional and taxonomic richness was high at intermediate minimum temperatures and wide temperature ranges. Functional and taxonomic richness was low in correspondence with low minimum temperatures or narrow temperature ranges. Functional richness increased and taxonomic richness decreased at higher minimum temperatures and wide annual temperature ranges. Both functional and taxonomic richness decreased with increasing precipitation seasonality and showed a small increase at intermediate annual precipitation. Overall, effect sizes of functional richness were small. However, effect sizes indicated trait divergence at extremely low minimum temperatures and at low annual precipitation with extreme precipitation seasonality. Conclusions: Functional and taxonomic richness of European grassland communities vary considerably over temperature and precipitation gradients. Overall, they follow similar patterns over the climate gradients, except at high minimum temperatures and wide temperature ranges, where functional richness increases and taxonomic richness decreases. This contrasting pattern may trigger new ideas for studies that target specific hypotheses focused on community assembly processes. And though effect sizes were small, they indicate that it may be important to consider climate seasonality in plant diversity studies

Effects of species' similarity and dominance on the functional and phylogenetic structure of a plant meta-community

International audienc

Appendix A. Detailed information about the trait database and the phylogeny.

Detailed information about the trait database and the phylogeny

Appendix B. Results using Leinster and Cobbold's index and comparisons with Chao's diversity index.

Results using Leinster and Cobbold's index and comparisons with Chao's diversity index

Supplement 1. R scripts for performing the α, β, γ decomposition.

<h2>File List</h2><div> <p><a href="abgFunctions.R">abgFunctions.R</a> (MD5: cb64e90444f8ecfa9bd42d7e63325440)   Functions necessary to perform the α, β, γ-diversity decomposition </p> <p><a href="example.R">example.R</a> (MD5: 8e4fa85f9edfa1005b42200e2d6a14f1)   Example of analysis with the phylocom dataset (R-package <i>picante</i>)</p> </div><h2>Description</h2><div> <p>The R-scripts are designed to assist the reader to use the methods of the article. The example shows how to use them to calculate the α, β, γ-diversity of a meta-community either through Chao's diversity index or Leinster & Cobbold. We also provide the functions to calculate inter-community pairwise distance matrices. </p> </div