Understanding the consequences of species loss in complex ecological communities is one of the great challenges in current biodiversity research. For a long time, this topic has been addressed by traditional biodiversity experiments. Most of these approaches treat species as trait-free, taxonomic units characterizing communities only by species number without accounting for species traits. However, extinctions do not occur at random as there is a clear correlation between extinction risk and species traits. In this review, we assume that large species will be most threatened by extinction and use novel allometric and size-spectrum concepts that include body mass as a primary species trait at the levels of populations and individuals, respectively, to re-assess three classic debates on the relationships between biodiversity and (i) food-web structural complexity, (ii) community dynamic stability, and (iii) ecosystem functioning. Contrasting current expectations, size-structured approaches suggest that the loss of large species, that typically exploit most resource species, may lead to future food webs that are less interwoven and more structured by chains of interactions and compartments. The disruption of natural body-mass distributions maintaining food-web stability may trigger avalanches of secondary extinctions and strong trophic cascades with expected knock-on effects on the functionality of the ecosystems. Therefore, we argue that it is crucial to take into account body size as a species trait when analysing the consequences of biodiversity loss for natural ecosystems. Applying size-structured approaches provides an integrative ecological concept that enables a better understanding of each species' unique role across communities and the causes and consequences of biodiversity loss.

Despite the exceptional complexity formed byspecies and their interactions in ecological networks, such asfood webs, regularities in the network structures are repeat-edly demonstrated. The interactions are determined by thecharacteristics of a species. The characteristics are in turndetermined by the species’ phylogenetic relationships, butalso by factors not related to evolutionary history. Here, wetest whether species’ phylogenetic relationships provides asignificant proxy for food web intervality. We thereafterquantify the degree to which different species traits remainvaluable predictors of food web structure after the base-line effect of species’ relatedness has been removed. Wefind that the phylogenetic relationships provide a significantbackground from which to estimate food web intervalityand thereby structure. However, we also find that thereis an important, non-negligible part of some traits, e.g.,body size, in food webs that is not accounted for by thephylogenetic relationships. Additionally, both these rela-tionships differ depending if a predator or a prey perspectiveis adopted. Clearly, species’ evolutionary history as well astraits not determined by phylogenetic relationships shapes predator-prey interactions in food webs, and the underly-ing evolutionary processes take place on slightly differenttime scales depending on the direction of predator-preyadaptations.

How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species species interactions to come into play. Our results give new insights into species response to environmental variation. They especially highlight the importance of considering both species interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment.

The non-specific lipid transfer proteins (nsLTP) are unique to land plants. The nsLTPs are characterized by a compact structure with a central hydrophobic cavity and can be classified to different types based on sequence similarity, intron position or spacing between the cysteine residues. The type G nsLTPs (LTPGs) have a GPI-anchor in the C-terminal region which attaches the protein to the exterior side of the plasma membrane. The function of these proteins, which are encoded by large gene families, has not been systematically investigated so far. In this study we have explored microarray data to investigate the expression pattern of the LTPGs in Arabidopsis and rice. We identified that the LTPG genes in each plant can be arranged in three expression modules with significant coexpression within the modules. According to expression patterns and module sizes, the Arabidopsis module AtI is functionally equivalent to the rice module OsI, AtII corresponds to OsII and AtIII is functionally comparable to OsIII. Starting from modules AtI, AtII and AtIII we generated extended networks with Arabidopsis genes coexpressed with the modules. Gene ontology analyses of the obtained networks suggest roles for LTPGs in the synthesis or deposition of cuticular waxes, suberin and sporopollenin. The AtI-module is primarily involved with cuticular wax, the AtII-module with suberin and the AtIII-module with sporopollenin. Further transcript analysis revealed that several transcript forms exist for several of the LTPG genes in both Arabidopsis and rice. The data suggests that the GPI-anchor attachment and localization of LTPGs may be controlled to some extent by alternative splicing.

1. Ecological communities are composed of populations connected in tangled networks of ecological interactions. Therefore, the extinction of a species can reverberate through the network and cause other (possibly distantly connected) species to go extinct as well. The study of these secondary extinctions is a fertile area of research in ecological network theory.
2. However, to facilitate practical applications, several improvements to the current analytical approaches are needed. In particular, we need to consider that (i) species have different ‘a priori’ probabilities of extinction, (ii) disturbances can simultaneously affect several species, and (iii) extinction risk of consumers likely grows with resource loss. All these points can be included in dynamical models, which are, however, difficult to parameterize.
3. Here we advance the study of secondary extinctions with Bayesian networks. We show how this approach can account for different extinction responses using binary – where each resource has the same importance – and quantitative data – where resources are weighted by their importance. We simulate ecological networks using a popular dynamical model (the Allometric Trophic Network model) and use it to test our method.
4. We find that the Bayesian network model captures the majority of the secondary extinctions produced by the dynamical model and that consumers’ responses to species loss are best modelled using a nonlinear sigmoid function. We also show that an approach based exclusively on food web structure loses power when species at higher trophic levels are preferentially lost. Because the loss of apex predators is unfortunately widespread, the results highlight a serious limitation of studies on network robustness.

How many dimensions (trait-axes) are required to predict whether two species interact? This unansweredquestion originated with the idea of ecological niches, and yet bears relevance today for understanding whatdetermines network structure. Here, we analyse a set of 200 ecological networks, including food webs,antagonistic and mutualistic networks, and find that the number of dimensions needed to completelyexplain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-qualitywebs including several species traits, we identify which traits contribute the most to explaining networkstructure. We show that accounting for a few traits dramatically improves our understanding of the structureof ecological networks. Matching traits for resources and consumers, for example, fruit size and billgape, are the most successful combinations. These results link ecologically important species attributes tolarge-scale community structure.

Climate change is increasingly affecting the structure and dynamics of ecological communities bothat local and at regional scales, and this can be expected to have important consequences for theirrobustness and long-term persistence. The aim of the present work is to analyse how the spatialstructure of the landscape and dispersal patterns of species (dispersal rate and average dispersal distance)affects metacommunity response to two disturbances: (i) increased mortality during dispersaland (ii) local species extinction. We analyse the disturbances both in isolation and in combination.Using a spatially and dynamically explicit metacommunity model, we find that the effect of dispersalon metacommunity persistence is two-sided: on the one hand, high dispersal significantly reducesthe risk of bottom-up extinction cascades following the local removal of a species; on the otherhand, when dispersal imposes a risk to the dispersing individuals, high dispersal increases extinctionrisks, especially when dispersal is global. Large-bodied species with long generation times at thehighest trophic level are particularly vulnerable to extinction when dispersal involves a risk. Thissuggests that decreasing the mortality risk of dispersing individuals by improving the quality ofthe habitat matrix may greatly increase the robustness of metacommunities.

Explaining the structure of ecosystems is one of the great challenges of ecology. Simple models for foodweb structure aim at disentangling the complexity of ecological interaction networks and detect the main forces that are responsible for their shape. Trophic interactions are influenced by species traits, which in turn are largely determined by evolutionary history. Closely related species are more likely to share similar traits, such as body size, feeding mode and habitat preference than distant ones. Here, we present a theoretical framework for analysing whether evolutionary history—represented by taxonomic classification—provides valuable information on food web structure. In doing so, we measure which taxonomic ranks better explain species interactions. Our analysis is based on partitioning of the species into taxonomic units. For each partition, we compute the likelihood that a probabilistic model for food web structurere produces the data using this information. We find that taxonomic partitions produce significantly higher likelihoods than expected at random. Marginal likelihoods (Bayes factors) are used to perform model selection among taxonomic ranks. We show that food webs are best explained by the coarser taxonomic ranks (kingdom to class). Our methods provide a way to explicitly include evolutionary history in models for food web structure.

Global warming leads to increased intensity and frequency of weather extremes. Such increased environmental variability might in turn result in increased variation in the demographic rates of interacting species with potentially important consequences for the dynamics of food-webs. Using a theoretical approach we here explore the response of food-webs to a highly variable environment. We investigate how species richness and correlation in the responses of species to environmental fluctuations affect the risk of extinction cascades. We find that the risk of extinction cascades increases with increasing species richness, especially when correlation among species is low. Initial extinctions of primary producer species unleash bottom-up extinction cascades, especially in webs with specialist consumers. In this sense, species-rich ecosystems are less robust to increasing levels of environmental variability than species-poor ones. Our study thus suggests that highly species-rich ecosystems like coral reefs and tropical rainforests might be particularly vulnerable to increased climate variability.