A Spatial Signal of Niche Differentiation in Tropical Forests.

AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

Processes governing species richness in communities exposed to temporal environmental stochasticity: A review and synthesis of modelling approaches.

Research into the processes governing species richness has often assumed that the environment is fixed, whereas realistic environments are often characterised by random fluctuations over time. This temporal environmental stochasticity (TES) changes the demographic rates of species populations, with cascading effects on community dynamics and species richness. Theoretical and applied studies have used process-based mathematical models to determine how TES affects species richness, but under a variety of frameworks. Here, we critically review such studies to synthesise their findings and draw general conclusions. We first provide a broad mathematical framework encompassing the different ways in which TES has been modelled. We then review studies that have analysed models with TES under the assumption of negligible interspecific interactions, such that a community is conceptualised as the sum of independent species populations. These analyses have highlighted how TES can reduce species richness by increasing the frequency at which a species becomes rare and therefore prone to extinction. Next, we review studies that have relaxed the assumption of negligible interspecific interactions. To simplify the corresponding models and make them analytically tractable, such studies have used mean-field theory to derive fixed parameters representing the typical strength of interspecific interactions under TES. The resulting analyses have highlighted community-level effects that determine how TES affects species richness, for species that compete for a common limiting resource. With short temporal correlations of environmental conditions, a non-linear averaging effect of interspecific competition strength over time gives an increase in species richness. In contrast, with long temporal correlations of environmental conditions, strong selection favouring the fittest species between changes in environmental conditions results in a decrease in species richness. We compare such results with those from invasion analysis, which examines invasion growth rates (IGRs) instead of species richness directly. Qualitative differences sometimes arise because the IGR is the expected growth rate of a species when it is rare, which does not capture the variation around this mean or the probability of the species becoming rare. Our review elucidates key processes that have been found to mediate the negative and positive effects of TES on species richness, and by doing so highlights key areas for future research.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Life history complementarity and the maintenance of biodiversity.

Life history, the schedule of when and how fast organisms grow, die and reproduce, is a critical axis along which species differ from each other1-4. In parallel, competition is a fundamental mechanism that determines the potential for species coexistence5-8. Previous models of stochastic competition have demonstrated that large numbers of species can persist over long timescales, even when competing for a single common resource9-12, but how life history differences between species increase or decrease the possibility of coexistence and, conversely, whether competition constrains what combinations of life history strategies complement each other remain open questions. Here we show that specific combinations of life history strategy optimize the persistence times of species competing for a single resource before one species overtakes its competitors. This suggests that co-occurring species would tend to have such complementary life history strategies, which we demonstrate using empirical data for perennial plants.

Stability criteria for the consumption and exchange of essential resources.

Models of consumer effects on a shared resource environment have helped clarify how the interplay of consumer traits and resource supply impact stable coexistence. Recent models generalize this picture to include the exchange of resources alongside resource competition. These models exemplify the fact that although consumers shape the resource environment, the outcome of consumer interactions is context-dependent: such models can have either stable or unstable equilibria, depending on the resource supply. However, these recent models focus on a simplified version of microbial metabolism where the depletion of resources always leads to consumer growth. Here, we model an arbitrarily large system of consumers governed by Liebig's law, where species require and deplete multiple resources, but each consumer's growth rate is only limited by a single one of these resources. Resources that are taken up but not incorporated into new biomass are leaked back into the environment, possibly transformed by intracellular reactions, thereby tying the mismatch between depletion and growth to cross-feeding. For this set of dynamics, we show that feasible equilibria can be either stable or unstable, again depending on the resource environment. We identify special consumption and production networks which protect the community from instability when resources are scarce. Using simulations, we demonstrate that the qualitative stability patterns derived analytically apply to a broader class of network structures and resource inflow profiles, including cases where multiple species coexist on only one externally supplied resource. Our stability criteria bear some resemblance to classic stability results for pairwise interactions, but also demonstrate how environmental context can shape coexistence patterns when resource limitation and exchange are modeled directly.

Emergent neutrality in consumer-resource dynamics.

Neutral theory assumes all species and individuals in a community are ecologically equivalent. This controversial hypothesis has been tested across many taxonomic groups and environmental contexts, and successfully predicts species abundance distributions across multiple high-diversity communities. However, it has been critiqued for its failure to predict a broader range of community properties, particularly regarding community dynamics from generational to geological timescales. Moreover, it is unclear whether neutrality can ever be a true description of a community given the ubiquity of interspecific differences, which presumably lead to ecological inequivalences. Here we derive analytical predictions for when and why non-neutral communities of consumers and resources may present neutral-like outcomes, which we verify using numerical simulations. Our results, which span both static and dynamical community properties, demonstrate the limitations of summarizing distributions to detect non-neutrality, and provide a potential explanation for the successes of neutral theory as a description of macroecological pattern.

Counting niches: Abundance-by-trait patterns reveal niche partitioning in a Neotropical forest.

Tropical forests challenge us to understand biodiversity, as numerous seemingly similar species persist on only a handful of shared resources. Recent ecological theory posits that biodiversity is sustained by a combination of species differences reducing interspecific competition and species similarities increasing time to competitive exclusion. Together, these mechanisms counterintuitively predict that competing species should cluster by traits, in contrast with traditional expectations of trait overdispersion. Here, we show for the first time that trees in a tropical forest exhibit a clustering pattern. In a 50-ha plot on Barro Colorado Island in Panama, species abundances exhibit clusters in two traits connected to light capture strategy, suggesting that competition for light structures community composition. Notably, we find four clusters by maximum height, quantitatively supporting the classical grouping of Neotropical woody plants into shrubs, understory, midstory, and canopy layers.

Ecophylogenetics Clarifies the Evolutionary Association between Mammals and Their Gut Microbiota.

Our knowledge of how the gut microbiome relates to mammalian evolution benefits from the identification of gut microbial taxa that are unexpectedly prevalent or unexpectedly conserved across mammals. Such taxa enable experimental determination of the traits needed for such microbes to succeed as gut generalists, as well as those traits that impact mammalian fitness. However, the punctuated resolution of microbial taxonomy may limit our ability to detect conserved gut microbes, especially in cases in which broadly related microbial lineages possess shared traits that drive their apparent ubiquity across mammals. To advance the discovery of conserved mammalian gut microbes, we developed a novel ecophylogenetic approach to taxonomy that groups microbes into taxonomic units based on their shared ancestry and their common distribution across mammals. Applying this approach to previously generated gut microbiome data uncovered monophyletic clades of gut bacteria that are conserved across mammals. It also resolved microbial clades exclusive to and conserved among particular mammalian lineages. Conserved clades often manifest phylogenetic patterns, such as cophylogeny with their host, that indicate that they are subject to selective processes, such as host filtering. Moreover, this analysis identified variation in the rate at which mammals acquire or lose conserved microbial clades and resolved a human-accelerated loss of conserved clades. Collectively, the data from this study reveal mammalian gut microbiota that possess traits linked to mammalian phylogeny, point to the existence of a core set of microbes that comprise the mammalian gut microbiome, and clarify potential evolutionary or ecologic mechanisms driving the gut microbiome's diversification throughout mammalian evolution.IMPORTANCE Our understanding of mammalian evolution has become microbiome-aware. While emerging research links mammalian biodiversity and the gut microbiome, we lack insight into which microbes potentially impact mammalian evolution. Microbes common to diverse mammalian species may be strong candidates, as their absence in the gut may affect how the microbiome functionally contributes to mammalian physiology to adversely affect fitness. Identifying such conserved gut microbes is thus important to ultimately assessing the microbiome's potential role in mammalian evolution. To advance their discovery, we developed an approach that identifies ancestrally related groups of microbes that distribute across mammals in a way that indicates their collective conservation. These conserved clades are presumed to have evolved a trait in their ancestor that matters to their distribution across mammals and which has been retained among clade members. We found not only that such clades do exist among mammals but also that they appear to be subject to natural selection and characterize human evolution.

Stability criteria for complex microbial communities.

Competition and mutualism are inevitable processes in microbial ecology, and a central question is which and how many taxa will persist in the face of these interactions. Ecological theory has demonstrated that when direct, pairwise interactions among a group of species are too numerous, or too strong, then the coexistence of these species will be unstable to any slight perturbation. Here, we refine and to some extent overturn that understanding, by considering explicitly the resources that microbes consume and produce. In contrast to more complex organisms, microbial cells consume primarily abiotic resources, and mutualistic interactions are often mediated through the mechanism of crossfeeding. We show that if microbes consume, but do not produce resources, then any positive equilibrium will always be stable to small perturbations. We go on to show that in the presence of crossfeeding, stability is no longer guaranteed. However, positive equilibria remain stable whenever mutualistic interactions are either sufficiently weak, or when all pairs of taxa reciprocate each other's assistance.

Born to Run? Quantifying the Balance of Prior Bias and New Information in Prey Escape Decisions.

Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes's theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.

Cross-scale neutral ecology and the maintenance of biodiversity.

One of the first successes of neutral ecology was to predict realistically-broad distributions of rare and abundant species. However, it has remained an outstanding theoretical challenge to describe how this distribution of abundances changes with spatial scale, and this gap has hampered attempts to use observed species abundances as a way to quantify what non-neutral processes are needed to fully explain observed patterns. To address this, we introduce a new formulation of spatial neutral biodiversity theory and derive analytical predictions for the way abundance distributions change with scale. For tropical forest data where neutrality has been extensively tested before now, we apply this approach and identify an incompatibility between neutral fits at regional and local scales. We use this approach derive a sharp quantification of what remains to be explained by non-neutral processes at the local scale, setting a quantitative target for more general models for the maintenance of biodiversity.

Translucent windows: how uncertainty in competitive interactions impacts detection of community pattern.

Traits can provide a window into the mechanisms that maintain coexistence among competing species. Recent theory suggests that competitive interactions will lead to groups, or clusters, of species with similar traits. However, theoretical predictions typically assume complete knowledge of the map between competition and measured traits. These assumptions limit the plausible application of these patterns for inferring competitive interactions in nature. Here, we relax these restrictions and find that the clustering pattern is robust to contributions of unknown or unobserved niche axes. However, it may not be visible unless measured traits are close proxies for niche strategies. We conclude that patterns along single niche axes may reveal properties of interspecific competition in nature, but detecting these patterns requires natural history expertise firmly tying traits to niches.

Inferring processes of cultural transmission: the critical role of rare variants in distinguishing neutrality from novelty biases.

Neutral evolution assumes that there are no selective forces distinguishing different variants in a population. Despite this striking assumption, many recent studies have sought to assess whether neutrality can provide a good description of different episodes of cultural change. One approach has been to test whether neutral predictions are consistent with observed progeny distributions, recording the number of variants that have produced a given number of new instances within a specified time interval: a classic example is the distribution of baby names. Using an overlapping generations model, we show that these distributions consist of two phases: a power-law phase with a constant exponent of [Formula: see text], followed by an exponential cut-off for variants with very large numbers of progeny. Maximum-likelihood estimations of the model parameters provide a direct way to establish whether observed empirical patterns are consistent with neutral evolution. We apply our approach to a complete dataset of baby names from Australia. Crucially, we show that analyses based on only the most popular variants, as is often the case in studies of cultural evolution, can provide misleading evidence for underlying transmission hypotheses. While neutrality provides a plausible description of progeny distributions of abundant variants, rare variants deviate from neutrality. Further, we develop a simulation framework that allows the detection of alternative cultural transmission processes. We show that anti-novelty bias is able to replicate the complete progeny distribution of the Australian dataset.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.

Can editors save peer review from peer reviewers?

Peer review is the gold standard for scientific communication, but its ability to guarantee the quality of published research remains difficult to verify. Recent modeling studies suggest that peer review is sensitive to reviewer misbehavior, and it has been claimed that referees who sabotage work they perceive as competition may severely undermine the quality of publications. Here we examine which aspects of suboptimal reviewing practices most strongly impact quality, and test different mitigating strategies that editors may employ to counter them. We find that the biggest hazard to the quality of published literature is not selfish rejection of high-quality manuscripts but indifferent acceptance of low-quality ones. Bypassing or blacklisting bad reviewers and consulting additional reviewers to settle disagreements can reduce but not eliminate the impact. The other editorial strategies we tested do not significantly improve quality, but pairing manuscripts to reviewers unlikely to selfishly reject them and allowing revision of rejected manuscripts minimize rejection of above-average manuscripts. In its current form, peer review offers few incentives for impartial reviewing efforts. Editors can help, but structural changes are more likely to have a stronger impact.

Reinterpreting maximum entropy in ecology: a null hypothesis constrained by ecological mechanism.

Simplified mechanistic models in ecology have been criticised for the fact that a good fit to data does not imply the mechanism is true: pattern does not equal process. In parallel, the maximum entropy principle (MaxEnt) has been applied in ecology to make predictions constrained by just a handful of state variables, like total abundance or species richness. But an outstanding question remains: what principle tells us which state variables to constrain? Here we attempt to solve both problems simultaneously, by translating a given set of mechanisms into the state variables to be used in MaxEnt, and then using this MaxEnt theory as a null model against which to compare mechanistic predictions. In particular, we identify the sufficient statistics needed to parametrise a given mechanistic model from data and use them as MaxEnt constraints. Our approach isolates exactly what mechanism is telling us over and above the state variables alone.

Species-abundance distributions under colored environmental noise.

Natural communities at all spatiotemporal scales are subjected to a wide variety of environmental pressures, resulting in random changes in the demographic rates of species populations. Previous analyses have examined the effects of such environmental variance on the long-term growth rate and time to extinction of single populations, but studies of its effects on the diversity of communities remain scarce. In this study, we construct a new master-equation model incorporating demographic and environmental variance and use it to examine how statistical patterns of diversity, as encapsulated by species-abundance distributions (SADs), are altered by environmental variance. Unlike previous diffusion models with environmental variance uncorrelated in time (white noise), our model allows environmental variance to be correlated at different timescales (colored noise), thus facilitating representation of phenomena such as yearly and decadal changes in climate. We derive an exact analytical expression for SADs predicted by our model together with a close approximation, and use them to show that the main effect of adding environmental variance is to increase the proportion of abundant species, thus flattening the SAD relative to the log-series form found in the neutral case. This flattening effect becomes more prominent when environmental variance is more correlated in time and has greater effects on species' demographic rates, holding all other factors constant. Furthermore, we show how our model SADs are consistent with those from diffusion models near the white noise limit. The mathematical techniques we develop are catalysts for further theoretical work exploring the consequences of environmental variance for biodiversity.

Reproducing static and dynamic biodiversity patterns in tropical forests: the critical role of environmental variance.

Ecological communities are subjected to stochasticity in the form of demographic and environmental variance. Stochastic models that contain only demographic variance (neutral models) provide close quantitative fits to observed species-abundance distributions (SADs) but substantially underestimate observed temporal species-abundance fluctuations. To provide a holistic assessment of whether models with demographic and environmental variance perform better than neutral models, the fit of both to SADs and temporal species-abundance fluctuations at the same time has to be tested quantitatively. In this study, we quantitatively test how closely a model with demographic and environmental variance reproduces total numbers of species, total abundances, SADs and temporal species-abundance fluctuations for two tropical forest tree communities, using decadal data from long-term monitoring plots and considering individuals larger than two size thresholds for each community. We find that the model can indeed closely reproduce these static and dynamic patterns of biodiversity in the two communities for the two size thresholds, with better overall fits than corresponding neutral models. Therefore, our results provide evidence that stochastic models incorporating demographic and environmental variance can simultaneously capture important static and dynamic biodiversity patterns arising in tropical forest communities.

Comparing process-based and constraint-based approaches for modeling macroecological patterns.

Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.