Intransitive Stability Collapses Under the Influence of Dominant Competitors.

AbstractIntransitive competition has received much attention over the past decade. Indeed, these cyclic arrangements of species interactions have the potential to promote and stabilize species coexistence. However, the importance of intransitive interactions in real-world species-rich communities containing a mixture of hierarchic and intransitive interactions remains unknown. Here, using simulations, we explore the behavior of intransitive loops when they interact with outer competitors, as would be expected in real-world communities. Our results show that dominant competitors often cancel the beneficial effects of intransitive loops of inferior competitors. These results call for caution when inferring beneficial effects of intransitivity on species coexistence. Although intransitive loops are a frequent motif in competition networks, their positive effects on species coexistence may be less important than previously thought. The specific properties of a subnetwork-such as stabilization by intransitive loops-should thus not be interpreted independently of the global network.

Can plants build their niche through modulation of soil microbial activities linked with nitrogen cycling? A test with Arabidopsis thaliana.

In natural systems, different plant species have been shown to modulate specific nitrogen (N) cycling processes so as to meet their N demand, thereby potentially influencing their own niche. This phenomenon might go beyond plant interactions with symbiotic microorganisms and affect the much less explored plant interactions with free-living microorganisms involved in soil N cycling, such as nitrifiers and denitrifiers. Here, we investigated variability in the modulation of soil nitrifying and denitrifying enzyme activities (NEA and DEA, respectively), and their ratio (NEA : DEA), across 193 Arabidopsis thaliana accessions. We studied the genetic and environmental determinants of such plant-soil interactions, and effects on plant biomass production in the next generation. We found that NEA, DEA, and NEA : DEA varied c. 30-, 15- and 60-fold, respectively, among A. thaliana genotypes and were related to genes linked with stress response, flowering, and nitrate nutrition, as well as to soil parameters at the geographic origin of the analysed genotypes. Moreover, plant-mediated N cycling activities correlated with the aboveground biomass of next-generation plants in home vs away nonautoclaved soil, suggesting a transgenerational impact of soil biotic conditioning on plant performance. Altogether, these findings suggest that nutrient-based plant niche construction may be much more widespread than previously thought.

Assessing mutualistic metacommunity capacity by integrating spatial and interaction networks.

We develop a spatially realistic model of mutualistic metacommunities that exploits the joint structure of spatial and interaction networks. Assuming that all species have the same colonisation and extinction parameters, this model exhibits a sharp transition between stable non-null equilibrium states and a global extinction state. This behaviour allows defining a threshold on colonisation/extinction parameters for the long-term metacommunity persistence. This threshold, the 'metacommunity capacity', extends the metapopulation capacity concept and can be calculated from the spatial and interaction networks without needing to simulate the whole dynamics. In several applications we illustrate how the joint structure of the spatial and the interaction networks affects metacommunity capacity. It results that a weakly modular spatial network and a power-law degree distribution of the interaction network provide the most favourable configuration for the long-term persistence of a mutualistic metacommunity. Our model that encodes several explicit ecological assumptions should pave the way for a larger exploration of spatially realistic metacommunity models involving multiple interaction types.

Links to rare climates do not translate into distinct traits for island endemics.

Current models of island biogeography treat endemic and non-endemic species as if they were functionally equivalent, focussing primarily on species richness. Thus, the functional composition of island biotas in relation to island biogeographical variables remains largely unknown. Using plant trait data (plant height, leaf area and flower length) for 895 native species in the Canary Islands, we related functional trait distinctiveness and climate rarity for endemic and non-endemic species and island ages. Endemics showed a link to climatically rare conditions that is consistent with island geological change through time. However, functional trait distinctiveness did not differ between endemics and non-endemics and remained constant with island age. Thus, there is no obvious link between trait distinctiveness and occupancy of rare climates, at least for the traits measured here, suggesting that treating endemic and non-endemic species as functionally equivalent in island biogeography is not fundamentally wrong.

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.

Plant community impact on productivity: Trait diversity or key(stone) species effects?

Outside controlled experimental plots, the impact of community attributes on primary productivity has rarely been compared to that of individual species. Here, we identified plant species of high importance for productivity (key species) in >29,000 diverse grassland communities in the European Alps, and compared their effects with those of community-level measures of functional composition (weighted means, variances, skewness and kurtosis). After accounting for the environment, the five most important key species jointly explained more deviance of productivity than any measure of functional composition alone. Key species were generally tall with high specific leaf areas. By dividing the observations according to distinct habitats, the explanatory power of key species and functional composition increased and key-species plant types and functional composition-productivity relationships varied systematically, presumably because of changing interactions and trade-offs between traits. Our results advocate for a careful consideration of species' individual effects on ecosystem functioning in complement to community-level measures.

Functionally distinct tree species support long-term productivity in extreme environments.

Despite evidence of a positive effect of functional diversity on ecosystem productivity, the importance of functionally distinct species (i.e. species that display an original combination of traits) is poorly understood. To investigate how distinct species affect ecosystem productivity, we used a forest-gap model to simulate realistic temperate forest successions along an environmental gradient and measured ecosystem productivity at the end of the successional trajectories. We performed 10 560 simulations with different sets and numbers of species, bearing either distinct or indistinct functional traits, and compared them to random assemblages, to mimic the consequences of a regional loss of species. Long-term ecosystem productivity dropped when distinct species were lost first from the regional pool of species, under the harshest environmental conditions. On the contrary, productivity was more dependent on ordinary species in milder environments. Our findings show that species functional distinctiveness, integrating multiple trait dimensions, can capture species-specific effects on ecosystem productivity. In a context of an environmentally changing world, they highlight the need to investigate the role of distinct species in sustaining ecosystem processes, particularly in extreme environmental conditions.

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment.

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted "punctual models"). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure ("ablation experiments"). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d'Ivoire.

Forest monitoring requires more automated systems to analyze high ecosystem heterogeneity. The traditional pixel-based detection method has proven to be less and less effective. A novel change detection method is therefore proposed to detect changes in forest cover using satellite images at very high spatial resolution. This is object-oriented classification, which groups pixels into interpreted objects, based on their spectral values, spatial, and textural properties. Using sentinel and Lansat images, we tested for the first time in the West African rainforest zone the effectiveness of this method for better detection, delineation, and analysis of land use and occupation types. The mean shift algorithm was used in both the segmentation and classification processes. Next, we compared the proposed object-oriented method with a pixel-based image classification detection method by implementing both methods under the same conditions. High detection accuracy (> 90%) and an overall Kappa greater than 0.90 were obtained by the object-oriented method, which is about 20% higher than the pixel-based method. The object-based method was free of salt and pepper effects and was less prone to image misregistration in terms of change detection accuracy and mapping results. This study demonstrates that the object-based classifier is a much better approach than the classical pixel-based classifier. In addition, it shows the problems of detecting heterogeneous landscapes and explains the observed confusions between the types of vegetation formations specific to tropical wetlands. The results obtained are encouraging and the contribution of high-resolution images and the object-based method to better discrimination of tropical wetland vegetation is discussed.

Unveiling ecological assembly rules from commonalities in trait distributions.

Deciphering the effect of neutral and deterministic processes on community assembly is critical to understand and predict diversity patterns. The information held in community trait distributions is commonly assumed as a signature of these processes, but empirical and modelling attempts have most often failed to untangle their confounding, sometimes opposing, impacts. Here, we simulated the assembly of trait distributions through stochastic (dispersal limitation) and/or deterministic scenarios (environmental filtering and niche differentiation). We characterized the shape of trait distributions using the skewness-kurtosis relationship. We identified commonalities in the co-variation between the skewness and the kurtosis of trait distributions with a unique signature for each simulated assembly scenario. Our findings were robust to variation in the composition of regional species pools, dispersal limitation and environmental conditions. While ecological communities can exhibit a high degree of idiosyncrasy, identification of commonalities across multiple communities can help to unveil ecological assembly rules in real-world ecosystems.

The dimensionality and structure of species trait spaces.

Trait-based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade-off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.

Rebound in functional distinctiveness following warming and reduced fishing in the North Sea.

Functionally distinct species (i.e. species with unique trait combinations in the community) can support important ecological roles and contribute disproportionately to ecosystem functioning. Yet, how functionally distinct species have responded to recent climate change and human exploitation has been widely overlooked. Here, using ecological traits and long-term fish data in the North Sea, we identified functionally distinct and functionally common species, and evaluated their spatial and temporal dynamics in relation to environmental variables and fishing pressure. Functionally distinct species were characterized by late sexual maturity, few, large offspring, and high parental care, many being sharks and skates that play critical roles in structuring food webs. Both functionally distinct and functionally common species increased in abundance as ocean temperatures warmed and fishing pressure decreased over the last three decades; however, functionally distinct species increased throughout the North Sea, but primarily in southern North Sea where fishing was historically most intense, indicating a rebound following fleet decommissioning and reduced harvesting. Yet, some of the most functionally distinct species are currently listed as threatened by the IUCN and considered highly vulnerable to fishing pressure. Alarmingly these species have not rebounded. This work highlights the relevance and potential of integrating functional distinctiveness into ecosystem management and conservation prioritization.

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.

What makes a weed a weed? A large-scale evaluation of arable weeds through a functional lens.

PREMISE OF THE STUDY: Despite long-term research efforts, a comprehensive perspective on the ecological and functional properties determining plant weediness is still lacking. We investigated here key functional attributes of arable weeds compared to non-weed plants, at large spatial scale. METHODS: We used an intensive survey of plant communities in cultivated and non-cultivated habitats to define a pool of plants occurring in arable fields (weeds) and one of plants occurring only in open non-arable habitats (non-weeds) in France. We compared the two pools based on nine functional traits and three functional spaces (LHS, reproductive and resource requirement hypervolumes). Within the weed pool, we quantified the trait variation of weeds along a continuum of specialization to arable fields. KEY RESULTS: Weeds were mostly therophytes and had higher specific leaf area, earlier and longer flowering, and higher affinity for nutrient-rich, sunny and dry environments compared to non-weeds, although functional spaces of weeds and non-weeds largely overlapped. When fidelity to arable fields increased, the spectrum of weed ecological strategies decreased as did the overlap with non-weeds, especially for the resource requirement hypervolume. CONCLUSIONS: Arable weeds constitute a delimited pool defined by a trait syndrome providing tolerance to the ecological filters of arable fields (notably, regular soil disturbances and fertilization). The identification of such a syndrome is of great interest to predict the weedy potential of newly established alien plants. An important reservoir of plants may also become weeds after changes in agricultural practices, considering the large overlap between weeds and non-weeds.

Ecological and biogeographical drivers of freshwater green algae biodiversity: from local communities to large-scale species pools of desmids.

Dispersal limitation, niche-based processes as well as historical legacies shape microbial biodiversity, but their respective influences remain unknown for many groups of microbes. We analysed metacommunity structure and functional trait variation in 148 communities of desmids, freshwater green algae, distributed throughout Europe. We delineated biogeographic modules for both taxa and sites using bipartite network analysis given that the taxa of a module co-occurred more often than expected by chance in sites of the same module. The network analysis distinguished two main acidic and neutral habitats, reflecting environmental filtering, and within each habitat separated species pools with distinct geographic locations, representing a plausible influence of historical biogeography. The geographic differentiation was consistent with a hypothesis of glacial refugia on Atlantic coast. Distance decay in community composition in addition to environmental influence further suggested a role of dispersal limitation. Next, we quantified the variation in cell volume and surface-to-volume of taxa within and among communities, to examine morphological and physiological adaptations of desmids in varying environments. Communities from continental climate contained larger desmids. Conversely, we found a functional convergence of smaller, fast-growing, desmids in oceanic regions. Overall, our findings suggest that niche-based processes, dispersal limitation, and historical legacy together drive the distribution and structure of desmid communities. Combining trait- and network-based analyses can resolve long-lasting questions in microbial ecology and biogeography, and could be successfully used in macrobial ecology too.

What it takes to invade grassland ecosystems: traits, introduction history and filtering processes.

Whether the success of alien species can be explained by their functional or phylogenetic characteristics remains unresolved because of data limitations, scale issues and weak quantifications of success. Using permanent grasslands across France (50 000 vegetation plots, 2000 species, 130 aliens) and building on the Rabinowitz's classification to quantify spread, we showed that phylogenetic and functional similarities to natives were the most important correlates of invasion success compared to intrinsic functional characteristics and introduction history. Results contrasted between spatial scales and components of invasion success. Widespread and common aliens were similar to co-occurring natives at coarse scales (indicating environmental filtering), but dissimilar at finer scales (indicating local competition). In contrast, regionally widespread but locally rare aliens showed patterns of competitive exclusion already at coarse scale. Quantifying trait differences between aliens and natives and distinguishing the components of invasion success improved our ability to understand and potentially predict alien spread at multiple scales.

Long-Distance Rescue and Slow Extinction Dynamics Govern Multiscale Metapopulations.

Rare long-distance dispersal is known to be critical for species dynamics, but how the interplay between short- and long-distance colonization influences regional persistence in a fragmented habitat remains poorly understood. We propose a metapopulation model that combines local colonization within habitat islands and long-distance colonization between islands. We study how regional occupancy dynamics are affected by the multiscale colonization process. We find that the island size distribution (ISD) is a key driver of the long-term occupancy dynamics. When the ISD is heterogeneous-that is, when the size of islands is variable-we show that extinction dynamics become very slow. We demonstrate that this behavior is unrelated to the well-known extinction debt near the critical extinction threshold. Hence, this finding questions the equivalence between extinction debt and critical transitions in the context of metapopulation collapse. Furthermore, we show that long-distance colonization can rescue small islands from extinction and sustain a steady regional occupancy. These results provide novel theoretical and practical insights into extinction dynamics and persistence in fragmented habitats and are thus relevant for the design of conservation strategies.

How do habitat filtering and niche conservatism affect community composition at different taxonomic resolutions?

Understanding how local species assembly depends on the regional biogeographic and environmental context is a challenging task in community ecology. In spatially implicit neutral models, a single immigration parameter, I(k), represents the flux of immigrants from a regional pool that compete with local offspring for establishment in communities. This flux counterbalances the effect of local stochastic extinctions to maintain local species diversity. If some species within the regional pool are not adapted to the local environment (habitat filtering), the migrant flux is reduced beyond that of the neutral model, such that habitat filtering influences the value of I(k) in non-neutral situations. Here, we propose a novel model in which immigrants from the regional pool are filtered according to their habitat preferences and the local environment, while taxa potentially retain habitat preferences from their ancestors (niche conservatism). Using both analytical reasoning and simulations, we demonstrate that I(k) is expected to be constant when estimated based on the community composition at several taxonomic levels, not only under neutral assumptions, but also when habitat filtering occurs, unless there is substantial niche conservatism. In the latter case, I(k) is expected to decrease when estimated based on the composition at species to genus and family levels, thus allowing a signature of niche conservatism to be detected by simply comparing I(k) estimates across taxonomic levels. We applied this approach to three rain forest data sets from South India and Central America and found no significant signature of niche conservatism when I(k) was compared across taxonomic levels, except at the family level in South India. We further observed more limited immigration in South Indian forests, supporting the hypothesis of a greater impact of habitat filtering and heterogeneity there than in Central America. Our results highlight the relevance of studying variations of I(k) in space and across taxonomic levels to test hypotheses about the ecological and evolutionary drivers of biodiversity patterns.

Statistical ecology comes of age.

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Why incorporate plant architecture into trait-based ecology?

Trait-based ecology has improved our understanding of the functioning of organisms, communities, ecosystems, and beyond. However, its predictive ability remains limited as long as phenotypic integration and temporal dynamics are not considered. We highlight how the morphogenetic processes that shape the 3D development of a plant during its lifetime affect its performance. We show that the diversity of architectural traits allows us to go beyond organ-level traits in capturing the temporal and spatial dimensions of ecological niches and informing community assembly processes. Overall, we argue that consideration of multilevel topological, geometrical, and ontogenetic features provides a dynamic view of the whole-plant phenotype and a relevant framework for investigating phenotypic integration, plant adaptation and performance, and community structure and dynamics.