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.

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.

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.

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.

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.

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.

Very High Resolution Species Distribution Modeling Based on Remote Sensing Imagery: How to Capture Fine-Grained and Large-Scale Vegetation Ecology With Convolutional Neural Networks?

Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and spatial accuracy of an SDM depend very strongly on the type of model used and the environmental data used as explanatory variables. In this article, we study a country-wide species distribution model based on very high resolution (VHR) (1 m) remote sensing images processed by a convolutional neural network. We demonstrate that this model can capture landscape and habitat information at very fine spatial scales while providing overall better predictive performance than conventional models. Moreover, to demonstrate the ecological significance of the model, we propose an original analysis based on the t-distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction technique. It allows visualizing the relation between input data and species traits or environment learned by the model as well as conducting some statistical tests verifying them. We also analyze the spatial mapping of the t-SNE dimensions at both national and local levels, showing the model benefit of automatically learning environmental variation at multiple scales.

Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family.

Species distribution models (SDMs) are widely used numerical tools that rely on correlations between geolocated presences (and possibly absences) and environmental predictors to model the ecological preferences of species. Recently, SDMs exploiting deep learning and remote sensing images have emerged and have demonstrated high predictive performance. In particular, it has been shown that one of the key advantages of these models (called deep-SDMs) is their ability to capture the spatial structure of the landscape, unlike prior models. In this paper, we examine whether the temporal dimension of remote sensing images can also be exploited by deep-SDMs. Indeed, satellites such as Sentinel-2 are now providing data with a high temporal revisit, and it is likely that the resulting time-series of images contain relevant information about the seasonal variations of the environment and vegetation. To confirm this hypothesis, we built a substantial and original dataset (called DeepOrchidSeries) aimed at modeling the distribution of orchids on a global scale based on Sentinel-2 image time series. It includes around 1 million occurrences of orchids worldwide, each being paired with a 12-month-long time series of high-resolution images (640 x 640 m RGB+IR patches centered on the geolocated observations). This ambitious dataset enabled us to train several deep-SDMs based on convolutional neural networks (CNNs) whose input was extended to include the temporal dimension. To quantify the contribution of the temporal dimension, we designed a novel interpretability methodology based on temporal permutation tests, temporal sampling, and temporal averaging. We show that the predictive performance of the model is greatly increased by the seasonality information contained in the temporal series. In particular, occurrence-poor species and diversity-rich regions are the ones that benefit the most from this improvement, revealing the importance of habitat's temporal dynamics to characterize species distribution.

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.

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.

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.

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.

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.

Loss of pollinator specialization revealed by historical opportunistic data: Insights from network-based analysis.

We are currently facing a large decline in bee populations worldwide. Who are the winners and losers? Generalist bee species, notably those able to shift their diet to new or alternative floral resources, are expected to be among the least vulnerable to environmental change. However, studies of interactions between bees and plants over large temporal and geographical scales are limited by a lack of historical records. Here, we used a unique opportunistic century-old countrywide database of bee specimens collected on plants to track changes in the plant-bee interaction network over time. In each historical period considered, and using a network-based modularity analysis, we identified some major groups of species interacting more with each other than with other species (i.e. modules). These modules were related to coherent functional groups thanks to an a posteriory trait-based analysis. We then compared over time the ecological specialization of bees in the network by computing their degree of interaction within and between modules. "True" specialist species (or peripheral species) are involved in few interactions both inside and between modules. We found a global loss of specialist species and specialist strategies. This means that bee species observed in each period tended to use more diverse floral resources from different ecological groups over time, highly specialist species tending to enter/leave the network. Considering the role and functional traits of species in the network, combined with a long-term time series, provides a new perspective for the study of species specialization.

Ecological Specialization and Rarity of Arable Weeds: Insights from a Comprehensive Survey in France.

: The definition of "arable weeds" remains contentious. Although much attention has been devoted to specialized, segetal weeds, many taxa found in arable fields also commonly occur in other habitats. The extent to which adjacent habitats are favorable to the weed flora and act as potential sources of colonizers in arable fields remains unclear. In addition, weeds form assemblages with large spatiotemporal variability, so that many taxa in weed flora are rarely observed in plot-based surveys. We thus addressed the following questions: How often do weeds occur in other habitats than arable fields? How does including field edges extend the taxonomic and ecological diversity of weeds? How does the weed flora vary across surveys at different spatial and temporal scales? We built a comprehensive dataset of weed taxa in France by compiling weed flora, lists of specialized segetal weeds, and plot-based surveys in agricultural fields, with different spatial and temporal coverages. We informed life forms, biogeographical origins and conservation status of these weeds. We also defined a broader dataset of plants occupying open habitats in France and assessed habitat specialization of weeds and of other plant species absent from arable fields. Our results show that many arable weeds are frequently recorded in both arable fields and non-cultivated open habitats and are, on average, more generalist than species absent from arable fields. Surveys encompassing field edges included species also occurring in mesic grasslands and nitrophilous fringes, suggesting spill-over from surrounding habitats. A total of 71.5% of the French weed flora was not captured in plot-based surveys at regional and national scales, and many rare and declining taxa were of Mediterranean origin. This result underlines the importance of implementing conservation measures for specialist plant species that are particularly reliant on arable fields as a habitat, while also pointing out biotic homogenization of agricultural landscapes as a factor in the declining plant diversity of farmed landscapes. Our dataset provides a reference species pool for France, with associated ecological and biogeographical information.

Weeds: Against the Rules?

Establishing laws of plant and ecosystems functioning has been an overarching objective of functional and evolutionary ecology. However, most theories neglect the role of human activities in creating novel ecosystems characterized by species assemblages and environmental factors that are not observed in natural systems. We argue that agricultural weeds, as an emblematic case of such an 'ecological novelty', constitute an original and underutilized model for challenging current concepts in ecology and evolution. We highlight key aspects of weed ecology and evolutionary biology that can help to test and recast ecological and evolutionary laws in a changing world. We invite ecologists to seize upon weeds as a model system to improve our understanding of the short-term and long-term dynamics of ecological systems in the Anthropocene.

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.

Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection.

The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.