Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition.

Although metacommunity ecology has been a major field of research in the last decades, with both conceptual and empirical outputs, the analysis of the temporal dynamics of metacommunities has only emerged recently and consists mostly of repeated static analyses. Here we propose a novel analytical framework to assess metacommunity processes using path analyses of spatial and temporal diversity turnovers. We detail the principles and practical aspects of this framework and apply it to simulated datasets to illustrate its ability to decipher the respective contributions of entangled drivers of metacommunity dynamics. We then apply it to four empirical datasets. Empirical results support the view that metacommunity dynamics may be generally shaped by multiple ecological processes acting in concert, with environmental filtering being variable across both space and time. These results reinforce our call to go beyond static analyses of metacommunities that are blind to the temporal part of environmental variability.

Predicting stochastic community dynamics in grasslands under the assumption of competitive symmetry.

Community dynamics is influenced by multiple ecological processes such as environmental spatiotemporal variation, competition between individuals and demographic stochasticity. Quantifying the respective influence of these various processes and making predictions on community dynamics require the use of a dynamical framework encompassing these various components. We here demonstrate how to adapt the framework of stochastic community dynamics to the peculiarities of herbaceous communities, by using a short temporal resolution adapted to the time scale of competition between herbaceous plants, and by taking into account the seasonal drops in plant aerial biomass following winter, harvesting or consumption by herbivores. We develop a hybrid inference method for this novel modelling framework that both uses numerical simulations and likelihood computations. Applying this methodology to empirical data from the Jena biodiversity experiment, we find that environmental stochasticity has a larger effect on community dynamics than demographic stochasticity, and that both effects are generally smaller than observation errors at the plot scale. We further evidence that plant intrinsic growth rates and carrying capacities are moderately predictable from plant vegetative height, specific leaf area and leaf dry matter content. We do not find any trade-off between demographical components, since species with larger intrinsic growth rates tend to also have lower demographic and environmental variances. Finally, we find that our model is able to make relatively good predictions of multi-specific community dynamics based on the assumption of competitive symmetry.

Why preferring parametric forecasting to nonparametric methods?

A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting.

Bitrophic interactions shape biodiversity in space.

Ecologists and conservation biologists often study particular trophic groups in isolation, which precludes an explicit assessment of the impact of multitrophic interactions on community structure and dynamics. Network ecology helps to fill this gap by focusing on species interactions, but it often ignores spatial processes. Here, we are taking a step forward in the integration of metacommunity and network approaches by studying a model of bitrophic interactions in a spatial context. We quantify the effect of bitrophic interactions on the diversity of plants and their animal interactors, and we show their complex dependence on the structure of the interaction network, the strength of interactions, and the dispersal rate. We then develop a method to parameterize our model with real-world networks and apply it to 54 datasets describing three types of interactions: pollination, fungal association, and insect herbivory. In all three network types, bitrophic interactions generally lead to an increase of plant and animal spatial heterogeneity by decreasing local species richness while increasing ß-diversity.

Explaining ontogenetic shifts in root-shoot scaling with transient dynamics.

BACKGROUND AND AIMS: Simple models of herbaceous plant growth based on optimal partitioning theory predict, at steady state, an isometric relationship between shoot and root biomass during plant ontogeny, i.e. a constant root-shoot ratio. This prediction has received mixed empirical support, suggesting either that optimal partitioning is too coarse an assumption to model plant biomass allocation, or that additional processes need to be modelled to account for empirical findings within the optimal partitioning framework. In this study, simulations are used to compare quantitatively two potential explanations for observed non-isometric relationships, namely nutrient limitation during the experiments and initial developmental constraints. METHODS: A simple plant growth model was built to simulate the growth of herbaceous species, based on optimal partitioning theory combined with empirically measured plant functional traits. Its ability to reproduce plant relative growth rate and final root weight ratio was assessed against previously published data. Predicted root-shoot ratios during plant ontogeny were compared with experimental observations. The effects of nutrient limitation and initial developmental constraints on root-shoot ratios were then tested. KEY RESULTS: The model was found to reproduce overall plant growth patterns accurately, but failed, in its simplest form, at explaining non-isometric growth trajectories. Both nutrient limitation and ontogenetic developmental constraints were further shown to cause transient dynamics resulting in a deviation from isometry. Nitrogen limitation alone was not sufficient to explain the observed trajectories of most plant species. The inclusion of initial developmental constraints (fixed non-optimal initial root-shoot ratios) enabled the reproduction of the observed trajectories and were consistent with observed initial root-shoot ratios. CONCLUSIONS: This study highlights the fact that considering transient dynamics enables theoretical predictions based on optimal partitioning to be reconciled with empirically measured ontogenetic root-shoot allometries. The transient dynamics cannot be solely explained by nutrient limitation during the experiments, pointing to a likely role for initial developmental constraints in the observed non-isometric growth trajectories.

A model-based approach to detect interspecific interactions during biofilm development.

A model-based approach was developed to detect interspecific interactions during biofilm development. This approach relied on the comparison of experimental data with a simple null model of biofilm growth dynamics where individual species grew independently of one another, except that they competed for space. Such a model was directly parameterized with a 4D confocal image series of biofilms and then used as a null model to detect interspecific interactions between pairs of bacterial species. This approach was tested in two bispecific competitive trials. In the first trial, the progressive exclusion of Pseudomonas fluorescens by Pseudomonas putida appeared to be due solely to the different intrinsic growth rates of the two strains. In contrast, modelling results suggested the presence of interference competition between Pseudomonas aeruginosa and P. putida in mixed biofilms. The authors' approach enables the detection of ecologically relevant interactions which constitute a prerequisite to building a comprehensive view of the dynamics and functioning of spatially structured bacterial communities.

Using phylogenies in conservation: new perspectives.

The 2011 meeting of the European Ecological Federation took place in Ávila, Spain, from 26th September to 29th September. The French Ecological Society (SFE) and the Foundation for Research on Biodiversity (FRB) sponsored a session entitled 'Evolutionary history, ecosystem function and conservation biology: new perspectives'. We report on the main insights obtained from this symposium.

Analyzing tropical forest tree species abundance distributions using a nonneutral model and through approximate Bayesian inference.

The neutral theory of biodiversity challenges the classical niche-based view of ecological communities, where species attributes and environmental conditions jointly determine community composition. Functional equivalence among species, as assumed by neutral ecological theory, has been recurrently falsified, yet many patterns of tropical tree communities appear consistent with neutral predictions. This may mean that neutral theory is a good first-approximation theory or that species abundance data sets contain too little information to reject neutrality. Here we present a simple test of neutrality based on species abundance distributions in ecological communities. Based on this test, we show that deviations from neutrality are more frequent than previously thought in tropical forest trees, especially at small spatial scales. We then develop a nonneutral model that generalizes Hubbell's dispersal-limited neutral model in a simple way by including one additional parameter of frequency dependence. We also develop a statistical method to infer the parameters of this model from empirical data by approximate Bayesian computation. In more than half of the permanent tree plots, we show that our new model fits the data better than does the neutral model. Finally, we discuss whether observed deviations from neutrality may be interpreted as the signature of environmental filtering on tropical tree species abundance distributions.

A stochastic dispersal-limited trait-based model of community dynamics.

I present a model of stochastic community dynamics in which death occurs randomly in the community, propagules disperse randomly from a regional pool, and recruitment of new individuals of a species is proportional to the species local abundance multiplied by its local competitive ability. The competitive ability of a species is assumed to be determined by a function of one trait of the species, and I call this function the environmental filtering function. I show that information on local species abundances in a network of plots, together with trait data for each species, enables the inference of both the immigration rate and the environmental filtering function in each plot. I further study how the diversity patterns produced by this model deviate from the neutral predictions, and how this deviation depends on the characteristics of the environmental filtering function. I show that this inference framework is more powerful at detecting trait-based environmental filtering than existing statistical approaches based on trait distributions, and discuss how the predictions of this model could be used to assess environmental heterogeneity in a plot, to detect functionally meaningful trade-offs among species traits, and to test the assumption that there exists a simple relationship between species traits and local competitive ability.

Inferring the parameters of the neutral theory of biodiversity using phylogenetic information and implications for tropical forests.

We develop a statistical method to infer the parameters of Hubbell's neutral model of biodiversity using data on local species abundances and their phylogenetic relatedness. This method uses the approximate Bayesian computation (ABC) approach, where the data are summarized into a small number of informative summary statistics. We used three statistics: the number of species in the sample, Shannon H index of evenness and Shao and Sokal's B(1) index of phylogenetic tree imbalance. Our approach was found to outperform previous methods, illustrating the potential of ABC methods in ecology. Applying it to four large tropical forest tree data sets, the best-fit immigration rates m were found to be two orders of magnitude smaller and regional diversities theta larger than previously reported for the same data. This implies that neutral-compatible regional pools of tropical trees should extend over continental scales, and that m measures, in this context, mostly the frequency of long-distance dispersal events.

[Strengths, weaknesses, and opportunities of French research in trophic ecology]. / Forces, failles et opportunités de la recherche française en écologie trophique.

The French National Institute of Ecology and Environment (INEE) aims at fostering pluridisciplinarity in Environmental Science and, for that purpose, funds ex muros research groups (GDR) on thematic topics. Trophic ecology has been identified as a scientific field in ecology that would greatly benefit from such networking activity, as being profoundly scattered. This has motivated the seeding of a GDR, entitled "GRET". The contours of the GRET's action, and its ability to fill these gaps within trophic ecology at the French national scale, will depend on the causes of this relative scattering. This study relied on a nationally broadcasted poll aiming at characterizing the field of trophic ecology in France. Amongst all the unique individuals that fulfilled the poll, over 300 belonged at least partly to the field of trophic ecology. The sample included all French public research institutes and career stages. Three main disruptions within the community of scientist in trophic ecology were identified. The first highlighted the lack of interfaces between microbial and trophic ecology. The second evidenced that research questions were strongly linked to single study fields or ecosystem type. Last, research activities are still quite restricted to the ecosystem boundaries. All three rupture points limit the conceptual and applied progression in the field of trophic ecology. Here we show that most of the disruptions within French Trophic Ecology are culturally inherited, rather than motivated by scientific reasons or justified by socio-economic stakes. Comparison with the current literature confirms that these disruptions are not necessarily typical of the French research landscape, but instead echo the general weaknesses of the international research in ecology. Thereby, communication and networking actions within and toward the community of trophic ecologists, as planned within the GRET's objectives, should contribute to fill these gaps, by reintegrating microbes within trophic concepts and setting the seeds for trans- and meta-ecosystemic research opportunities. Once the community of trophic ecologists is aware of the scientific benefit in pushing its boundaries forwards, turning words and good intentions into concrete research projects will depend on the opportunities to obtain research funding.

Ecophylogenetics: advances and perspectives.

Ecophylogenetics can be viewed as an emerging fusion of ecology, biogeography and macroevolution. This new and fast-growing field is promoting the incorporation of evolution and historical contingencies into the ecological research agenda through the widespread use of phylogenetic data. Including phylogeny into ecological thinking represents an opportunity for biologists from different fields to collaborate and has provided promising avenues of research in both theoretical and empirical ecology, towards a better understanding of the assembly of communities, the functioning of ecosystems and their responses to environmental changes. The time is ripe to assess critically the extent to which the integration of phylogeny into these different fields of ecology has delivered on its promise. Here we review how phylogenetic information has been used to identify better the key components of species interactions with their biotic and abiotic environments, to determine the relationships between diversity and ecosystem functioning and ultimately to establish good management practices to protect overall biodiversity in the face of global change. We evaluate the relevance of information provided by phylogenies to ecologists, highlighting current potential weaknesses and needs for future developments. We suggest that despite the strong progress that has been made, a consistent unified framework is still missing to link local ecological dynamics to macroevolution. This is a necessary step in order to interpret observed phylogenetic patterns in a wider ecological context. Beyond the fundamental question of how evolutionary history contributes to shape communities, ecophylogenetics will help ecology to become a better integrative and predictive science.

Measurement of biological information with applications from genes to landscapes.

Biological diversity is quantified for reasons ranging from primer design, to bioprospecting, and community ecology. As a common index for all levels, we suggest Shannon's (S)H, already used in information theory and biodiversity of ecological communities. Since Lewontin's first use of this index to describe human genetic variation, it has been used for variation of viruses, splice-junctions, and informativeness of pedigrees. However, until now there has been no theory to predict expected values of this index under given genetic and demographic conditions. We present a new null theory for (S)H at the genetic level, and show that this index has advantages including (i) independence of measures at each hierarchical level of organization; (ii) robust estimation of genetic exchange over a wide range of conditions; (iii) ability to incorporate information on population size; and (iv) explicit relationship to standard statistical tests. Utilization of this index in conjunction with other existing indices offers powerful insights into genetic processes. Our genetic theory is also extendible to the ecological community level, and thus can aid the comparison and integration of diversity at the genetic and community levels, including the need for measures of community diversity that incorporate the genetic differentiation between species.