Search for: All records

Identifying overarching processes that maintain biodiversity in natural communities remains a challenge in ecology. Although functional traits help explain regional species distributions, they often fall short at the local community level. We investigate whether traits can offer mechanism-based insights into local diversity maintenance due to associations with the sign and strength of plant interactions. We examine the effect of 12 plant functional traits on the sign and strength of pairwise species interactions across two Mediterranean annual plant communities. Results show that traits mediate a spectrum from facilitative to competitive interactions and are influenced by neighbor density and identity. At low densities, species with conservative resource-use traits are consistently stronger facilitators than acquisitive species. The traits of the focal species, however, explain facilitation at low densities better than the traits of neighbors or the pairwise differences in traits. When neighbor density increases, facilitation switches to competition, a pattern we hypothesize reflects a density-mediated reduction in how much neighbors’ traits drive interactions. Moreover, species frequently receiving heterospecific facilitation also display traits associated with self-competition and low intrinsic population growth rate. This study of community-level trait sorting provides insights into the persistence of co-occurring populations, showing trait-based generalizable insights into the local context dependency of species interactions. 

ABSTRACT Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion, restoration and synthetic ecology. However, predicting and prioritising assembly outcomes remains difficult. We address this challenge via a mechanism‐free approach useful when little data or knowledge exist (LOVE; Learning Outcomes Via Experiments). We carry out assembly experiments (‘actions’, here, random combinations of species additions) potentially in multiple environments, wait, and measure abundance outcomes. We then train a model to predict outcomes of novel actions or prioritise actions that would yield the most desirable outcomes. Across 10 single‐ and multi‐environment datasets, when trained on 89 randomly selected actions,LOVEpredicts outcomes with 0.5%–3.4% mean error, and prioritises actions for maximising richness, maximising abundance, or removing unwanted species, with 94%–99% mean true positive rate and 10%–84% mean true negative rate across tasks.LOVEcomplements existing mechanism‐first approaches for community ecology and may help address numerous applied challenges. 

ABSTRACT With many species interacting in nature, determining which interactions describe community dynamics is nontrivial. By applying a computational modeling approach to an extensive field survey, we assessed the importance of interactions from plants (both inter‐ and intra‐specific), pollinators and insect herbivores on plant performance (i.e., viable seed production). We compared the inclusion of interaction effects as aggregate guild‐level terms versus terms specific to taxonomic groups. We found that a continuum from positive to negative interactions, containing mostly guild‐level effects and a few strong taxonomic‐specific effects, was sufficient to describe plant performance. While interactions with herbivores and intraspecific plants varied from weakly negative to weakly positive, heterospecific plants mainly promoted competition and pollinators facilitated plants. The consistency of these empirical findings over 3 years suggests that including the guild‐level effects and a few taxonomic‐specific groups rather than all pairwise and high‐order interactions, can be sufficient for accurately describing species variation in plant performance across natural communities. 

Abstract The ability of species to form diverse communities is not fully understood. Species are known to interact in various ways with their neighborhood. Despite this, common phenomenological models of species coexistence assume that per capita interactions are constant and competitive, even as the environment changes. In this study, we investigate how neighbor density‐dependent variation in the strength and sign of species interactions changes species and community dynamics. We demonstrate that incorporating these sources of variation significantly improves predictions of ecological dynamics compared to the outcomes of typical models, which hold interaction strengths constant. We compared the performance of models based on different functions of neighbor density and identity in describing population trajectories (i.e., persistence over time) and community dynamics (i.e., temporal stability, synchrony, and degree of oscillation) in simulated two‐species communities and a real, diverse annual plant system. In our simulated communities, we observed the highest level of coexistence between species pairs when species interactions varied from competitive to facilitative, depending on neighbor density (i.e., following a sigmoid function). Introducing within‐guild facilitation through a nonlinear bounded function allowed populations, both simulated and empirical, to avoid extinction or runaway growth. In fact, nonlinear bounded functions (i.e., exponential and sigmoid functions) accurately predicted population trends over time within the range of abundances observed over the last 10 years. With the sigmoid function, the simulated communities of two species exhibited a higher probability of synchrony and oscillation compared to other functional forms. These simulated communities did not always show temporal stability, even when they were predicted to coexist. Overall, varying species interactions lead to realistic ecological trajectories and community dynamics when bounded by asymptotes based on neighbor density. These findings are crucial for advancing our understanding of how diverse communities are sustained and for applying ecological theory to real‐world studies. 

In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting—but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity. 

Pollination plays a central role in both crop production and maintaining biodiversity. However, habitat loss, pesticides, invasive species and larger environmental fluctuations are contributing to a dramatic decline of pollinators worldwide. Different management solutions require knowledge of how ecological communities will respond following interventions. Yet, anticipating the response of these systems to interventions remains extremely challenging due to the unpredictable nature of ecological communities, whose nonlinear behaviour depends on the specific details of species interactions and the various unknown or unmeasured confounding factors. Here, we propose that this knowledge can be derived by following a probabilistic systems analysis rooted on non-parametric causal inference. The main outcome of this analysis is to estimate the extent to which a hypothesized cause can increase or decrease the probability that a given effect happens without making assumptions about the form of the cause–effect relationship. We discuss a road map for how this analysis can be accomplished with the aim of increasing our system-level causative knowledge of natural communities. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’. 

Ecological theory predicts that species interactions embedded in multitrophic networks shape the opportunities for species to persist. However, the lack of experimental support of this prediction has limited our understanding of how species interactions occurring within and across trophic levels simultaneously regulate the maintenance of biodiversity. Here, we integrate a mathematical approach and detailed experiments in plant–pollinator communities to demonstrate the need to jointly account for species interactions within and across trophic levels when estimating the ability of species to persist. Within the plant trophic level, we show that the persistence probability of plant species increases when introducing the effects of plant–pollinator interactions. Across trophic levels, we show that the persistence probabilities of both plants and pollinators exhibit idiosyncratic changes when experimentally manipulating the multitrophic structure. Importantly, these idiosyncratic effects are not recovered by traditional simulations. Our work provides tractable experimental and theoretical platforms upon which it is possible to investigate the multitrophic factors affecting species persistence in ecological communities. 

Summary Maintaining ecological stability is essential for sustaining ecosystem functions and the benefits they provide to society. Ecological theory predicts that plant diversity destabilizes local populations, yet empirical studies report variable effects.We hypothesize that this discrepancy arises at least in part from differences captured by different diversity (average vs cumulative richness, i.e. the mean annual richness vs the cumulative richness across years) and stability metrics (abundance‐unweighted vs weighted mean population stability). To test this, we analyzed data from > 8000 permanent vegetation plots across biomes on five continents.We found a negative (i.e. destabilizing) diversity–stability relationship when using abundance‐weighted rather than unweighted measures of population stability, which are more influenced by dominant species. Similarly, cumulative richness – capturing total species occurrence over time and long‐term turnover – reveals a stronger destabilizing effect compared to average annual richness.Our findings reveal that, when specific metrics of diversity and stability are considered, more species and potentially the associated increase in interspecific competition tend to destabilize populations across natural ecosystems world‐wide – particularly those of dominant species.