Biology subjects

Stouffer, D. B.

Publications and source records attributed to Stouffer, D. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Shifting resource limitation explains multiphasic patterns of density dependence

Several recent studies have presented sublinear density dependence as a universal phenomenon across species, driven by factors unrelated to resource limitation (e.g. predation or non-resource based inhibition). Through a combination of bacterial growth experiments and mathematical modelling, we instead show that regimes of sublinear density dependence readily emerge under sequential shifts in resource limitation, without the need to invoke other processes. We nevertheless predict that superlinear density dependence, driven by standard resource limitation, will still predominate at both high and low densities.

ecology↗

bioRxiv2026

Partner fidelity with root symbionts impacts plant performance in the face of changing above- and belowground community context

Global environmental changes generate novel communities. Some species adapt to novel community contexts by demonstrating interaction fidelity, or consistently interacting with the same species, while others demonstrate interaction flexibility, or interacting with whichever partners are available (i.e., rewiring). However, the drivers and benefits of fidelity are unclear. Here, we use data from a large-scale mesocosm experiment to determine whether plant characteristics (e.g., provenance, functional group) and community contexts impact plant interaction fidelity to belowground mutualist and antagonist partners (i.e., root fungi and bacteria), and how this fidelity relates to plant performance. We found that the plant-antagonist relationships exhibited higher fidelity than plant-mutualist relationships with the strength of fidelity depending on a combination of plant characteristics and community context. Conversely, plant-mutualist fidelity impacted plant performance with competing effects depending on plant provenance and aboveground herbivore presence. Our study advances understanding of how species interactions influence plant performance in a changing world. Statement of authorshipAMM, JDT, JMT and DBS designed the concept. All authors contributed to refining the ideas. AM coded the analysis to produce the results with contributions from JDT, DBS, and BBM. HRL and DBS helped AMM with the statistical analysis. LPW, WJA, IAD, and JMT designed the experiment while LPW and WJA conducted the experiment. AMM wrote the first draft of the manuscript with assistance from all authors. IAD, JMT, and JDT obtained funding. All authors contributed to further editing of the manuscript.

ecology↗

bioRxiv2025

Simplifying species interaction matrices by grouping: optimal groupings differ between effects and responses

Most ecological models of species interactions require many parameters, making them expensive to fit to experimental or observational data. To reduce the number of parameters, species are often divided into groups a priori, for example on the basis of functional or phylogenetic similarity, and species within these groups are assumed to behave identically. Here, we assess the validity of such grouping by deriving the optimal groupings a posteriori based on the fit of the Beverton-Holt model to experimental data from six species of Drosophila competing in laboratory conditions. We find evidence that grouping can indeed improve the performance (as judged by AICc) of species-interaction models, but we also observe that there is no single optimal grouping. Further, those groupings that did prove beneficial could not have been easily predicted from available species attributes, calling into question the prevailing method of a priori species grouping. Lastly, we find that species did not group equivalently in their competitive effect and in their response to competition, invalidating a key assumption often made implicitly when defining species groupings. Our study suggests the need for a different, more data-informed, approach to grouping in species-interaction models.

ecology↗

bioRxiv2024

Integrating intraspecific variation into population dynamics reveals how interacting species persist in mutualistic communities

Mutualistic interactions among organisms are fundamental to the origin and maintenance of bio-diversity. Yet, the study of community dynamics often relies on species-level values, ignoring the effects of intraspecific variation. We propose a theoretical framework for evaluating the extent to which variation within populations can influence species persistence in mutualistic systems. Next, drawing from detailed empirical data on plant-pollinator interactions and plant fitness, we quantify intraspecific variation in the mutualistic benefits received by plants and incorporate this variation into estimations of structural stability, a robust theoretical measure of species likelihood of persistence. Through explicit consideration of intraspecific variation, we demonstrate that, in the absence of heterospecific plant competitors, plant populations composed of specialized individuals promote the persistence of the plant species they belong to and their associated pollinator community. However, these positive effects do not hold when plant species compete with a broader plant community. In this case, having more heterogeneous mixtures of plant individuals (i.e., both specialized and generalized) is more beneficial for a populations persistence. By integrating the proposed framework with empirical data, we are able to explicitly account for individual-level variation, opening the door to a better understanding of the mechanisms promoting biodiversity in mutualistic communities.

ecology↗

bioRxiv2024

Introduced trout hinder the recovery of native fish following an extreme flood disturbance

O_LIIn rivers, we are seeing a shift away from natural flow regimes towards larger and more frequent extreme drought and flood events. However, it is unclear how increasing intensity and frequency of extreme flow disturbances will play out alongside existing biotic pressures, such as biological invasions, to impact aquatic biodiversity. In New Zealand, vulnerable native non-diadromous galaxiid fishes face pressure from introduced trout through interspecific competition and predation, which may influence the recovery of native galaxiids after flood disturbances. C_LIO_LIHere, we employed a capture-mark-recapture study across 12 sites, along a gradient of disturbance following a major flood event, to examine the impact of extreme flooding on the population structure of non-diadromous galaxiids (Galaxias vulgaris and G. paucispondylus), and the effect of trout presence on individual galaxiid growth rates recovering from this event. C_LIO_LIWe found a lower abundance of all non-diadromous galaxiid size classes under higher flood magnitudes, but smaller size classes (i.e., young-of-year and 1-2 year cohorts) were more impacted. C_LIO_LIFurthermore, the presence of trout, whether at low or high abundances, reduced the individual growth of native non-diadromous galaxiids, despite interspecific effects being a weaker regulator of individual growth compared to conspecific effects. Moreover, trout effects on galaxiids varied by both galaxiid size and density, such that growth of smaller individual galaxiids in low densities were most affected by the presence of trout regardless of trout density. C_LIO_LIIn summary, our results demonstrate that non-diadromous galaxiid population dynamics in future are likely to be affected by flood disturbance regimes and introduced trout presence, the outcome of which involves a complex balance between reduced population persistence and increased individual resistance of larger individuals. C_LIO_LIConservation efforts that focus on maintaining strategically placed trout-free source populations of adult galaxiids could therefore be an important tool to enable native dispersal into trout-affected habitat and maintain population resilience in the face of increasingly larger and more frequent extreme events, given that recruitment of non-diadromous galaxiids is higher in the absence of trout. C_LI

ecology↗

bioRxiv2024

Eltonian niche modelling: applying joint hierarchical niche models to ecological networks

There is currently a dichotomy in the modelling of Grinnellian and Eltonian niches. Despite similar underlying data, Grinnellian niches are modelled with species-distribution models (SDMs), whereas Eltonian niches are modelled with ecological-network analysis, mainly because the sparsity of species-interaction data prevents the application of SDMs to Eltonian-niche modelling. Here, we propose to adapt recently developed joint species distribution models (JSDMs) to data on ecological networks, functional traits, and phylogenies to model species Eltonian niches. JSDMs overcome sparsity and improve predictions for individual species by considering non-independent relationships among co-occurring species; this unique ability makes them particularly suited for sparse datasets such as ecological networks. Our Eltonian JSDMs reveal strong relationships between species Eltonian niches and their functional traits and phylogeny. Moreover, we demonstrate that JSDMs can accurately predict the interactions of species for which no empirical interaction data are available, based solely on their functional traits. This facilitates prediction of new interactions in communities with altered composition, e.g. following climate-change induced local extinctions or species introductions. The high interpretability of Eltonian JSDMs will provide unique insights into mechanisms underlying species interactions and the potential impacts of environmental changes and invasive species on species interactions in ecological communities.

ecology↗

bioRxiv2024

Variable responses of alpine-plant communities to warming and loss of dominant species

Responses of ecological communities to perturbations are inherently variable because responses of their constituent populations also vary. Species within a single community may show combinations of no response, positive responses, and negative responses to any given perturbation often canceling each other out resulting in small or no signal that the community level. Here we explore the impacts of warming and loss of the dominant species on alpine ecosystems in a global study. We investigate warming and species-loss treatments on population- and community-level dynamics across alpine-plant communities at two elevations in five globally-distributed mountain locations. Communities showed varied responses to treatments; no community showed strong responses to a single treatment. Rather, most sites were influenced by both perturbations. Populations within these communities responded idiosyncratically, suggesting that constituent species are not all equally robust to perturbations even when community-level effects appear weak. Our results highlight the challenge of making general predictions about population- and community-level responses of alpine ecosystems in the face of present and future perturbations.

ecology↗

bioRxiv2023

Environmental context, parameter sensitivity and structural sensitivity impact predictions of annual-plant coexistence

Predicting the outcome of interactions between species is central to our current understanding of diversity maintenance. However, we have limited information about the robustness of many model-based predictions of species coexistence. This limitation is partly because several sources of uncertainty are often ignored when making predictions. Here, we introduce a framework to simultaneously explore how different mathematical models, different environmental contexts, and parameter uncertainty impact the probability of predicting species coexistence. Using a set of pairwise competition experiments on annual plants, we provide direct evidence that subtle differences between models lead to contrasting predictions of both coexistence and competitive exclusion. We also show that the effects of environmental context-dependency and parameter uncertainty on predictions of species coexistence are not independent of the model used to describe population dynamics. Our work suggests that predictions of species coexistence and extrapolations thereof may be particularly vulnerable to these underappreciated founts of uncertainty.

ecology↗

bioRxiv2022

Estimating interaction matrices from performance data for diverse systems

O_LINetwork theory allows us to understand complex systems by evaluating how their constituent elements interact with one another. Such networks are built from matrices which describe the effect of each element on all others. Quantifying the strength of these interactions from empirical data can be difficult, however, because the number of potential interactions increases non-linearly as more elements are included in the system, and not all interactions may be empirically observable when some elements are rare. C_LIO_LIWe present a novel modelling framework which estimates the strength of pairwise interactions in diverse horizontal systems, using measures of species performance in the presence of varying densities of their potential interaction partners. C_LIO_LIOur method allows us to directly estimate pairwise effects when they are statistically identifiable and approximate pairwise effects when they would otherwise be statistically unidentifiable. The resulting interaction matrices can include positive and negative effects, the effect of a species on itself, and are non-symmetrical. C_LIO_LIThe advantages of these features are illustrated with a case study on an annual wildflower community of 22 focal and 52 neighbouring species, and a discussion of potential applications of this framework extending well beyond plant community ecology. C_LI

ecology↗

bioRxiv2021

The dimensionality of plant-plant competition

To avoid extinction, every species must be able to exploit available resources at least as well as the other species in its community. All else being equal, theory predicts that the more distinct the niches of such co-occurring and competing species, the more species that can persist in the long run. However, both theoretical and experimental studies define a priori the nature and number of resources over which species compete. It therefore remains unclear whether or not species in empirically realistic contexts are actually exploiting all or some of the niches available to them. Here we provide a mathematical solution to this long-standing problem. Specifically, we show how to use the interactions between sets of co-occurring plant species to quantify their implied "niche dimensionality": the effective number of resources over which those species appear to be competing. We then apply this approach to quantify the niche dimensionality of 12 plant assemblages distributed across the globe. Contrary to conventional wisdom, we found that the niche dimensionality in these systems was much lower than the number of competing species. However, two high-resolution experiments also show that changes in the local environment induce a reshuffling of plants competitive roles and hence act to increase the assemblages effective niche dimensionality. Our results therefore indicate that homogeneous environments are unlikely to be able to maintain high diversity and also shows how environmental variation impacts species niches and hence their opportunities for long-term survival.

ecology↗

bioRxiv2021

Global and regional ecological boundaries drive abrupt changes in avian frugivory interactions

Species interactions can propagate disturbances across space, though ecological and biogeographic boundaries may limit this spread. We tested whether large-scale ecological boundaries (ecoregions and biomes) and human disturbance gradients increase dissimilarity among ecological networks, while accounting for background spatial and elevational effects and differences in network sampling. We assessed network dissimilarity patterns over a broad spatial scale, using 196 quantitative avian frugivory networks (encompassing 1,496 plant and 1,003 bird species) distributed across 67 ecoregions and 11 biomes. Dissimilarity in species and interactions, but not in network structure, increased significantly across ecoregion and biome boundaries and along human disturbance gradients. Our findings suggest that ecological boundaries contribute to maintaining the worlds biodiversity of interactions and mitigating the propagation of disturbances at large spatial scales. One-Sentence SummaryEcoregions and biomes delineate the large-scale distribution of plant-frugivore interactions.

ecology↗

bioRxiv2021

Geometric complexity and the information-theoretic comparison of functional-response models

The assessment of relative model performance using information criteria like AIC and BIC has become routine among functional-response studies, reflecting trends in the broader ecological literature. Such information criteria allow comparison across diverse models because they penalize each models fit by its parametric complexity -- in terms of their number of free parameters -- which allows simpler models to outperform similarly fitting models of higher parametric complexity. However, criteria like AIC and BIC do not consider an additional form of model complexity, referred to as geometric complexity, which relates specifically to the mathematical form of the model. Models of equivalent parametric complexity can differ in their geometric complexity and thereby in their ability to flexibly fit data. Here we use the Fisher Information Approximation to compare, explain, and contextualize how geometric complexity varies across a large compilation of single-prey functional-response models -- including prey-, ratio-, and predator-dependent formulations -- reflecting varying apparent degrees and forms of non-linearity. Because a models geometric complexity varies with the datas underlying experimental design, we also sought to determine which designs are best at leveling the playing field among functional-response models. Our analyses illustrate (1) the large differences in geometric complexity that exist among functional-response models, (2) there is no experimental design that can minimize these differences across all models, and (3) even the qualitative nature by which some models are more or less flexible than others is reversed by changes in experimental design. Failure to appreciate model flexibility in the empirical evaluation of functional-response models may therefore lead to biased inferences for predator-prey ecology, particularly at low experimental sample sizes where its impact is strongest. We conclude by discussing the statistical and epistemological challenges that model flexibility poses for the study of functional responses as it relates to the attainment of biological truth and predictive ability. Contribution to Field StatementThe use of criteria like AIC and BIC for selecting among functional-response models is now standard, well-accepted practice, just as it is in the ecological literature as a whole. The generic desire underlying the use of these criteria is to make the comparison of model performance an unbiased and equitable process by penalizing each models fit to data by its parametric complexity (relating to its number of free parameters). Here we introduce the Fisher Information Approximation to the ecological literature and use it to understand how the geometric complexity of models -- a form of model complexity relating to a models functional flexibility that is not considered by criteria like AIC and BIC -- varies across a large compilation of 40 different single-prey functional-response models. Our results add caution against the simplistic use and interpretation of information-theoretic model comparisons for functional-response experiments, showing just how large an effect that model flexibility can have on inferences of model performance. We therefore use our work to help clarify the challenges that ecologists studying functional responses must face in the attainment of biological truth and predictive ability.

ecology↗

bioRxiv2021

Predictions of biodiversity are improved by integrating trait-based competition with abiotic filtering

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

ecology↗

bioRxiv2021

Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes metadata. Graph embedding methods project the nodes of a network onto a--low dimensional--latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes metadata to their position in a latent feature space corresponds to a classic--and low dimensional--machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes meta-data. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

ecology↗

bioRxiv2021

Sahul's megafauna were vulnerable to extinction due to their position in the trophic network

Extinctions stemming from environmental change often trigger trophic cascades and coextinctions. However, it remains unclear whether trophic cascades were a large contributor to the megafauna extinctions that swept across several continents in the Late Pleistocene. The pathways to megafauna extinctions are particularly unclear for Sahul (landmass comprising Australia and New Guinea), where extinctions happened earlier than on other continents. We investigated the role of bottom-up trophic cascades in Late Pleistocene Sahul by constructing pre-extinction (~ 80 ka) trophic network models of the vertebrate community of Naracoorte, south-eastern Australia. These models allowed us to predict vertebrate species vulnerability to cascading extinctions based on their position in the network. We tested whether the observed extinctions could be explained by bottom-up cascades, or if they should be attributed to other external causes. Species that disappeared from the community were more vulnerable, overall, to bottom-up cascades than were species that survived. The position of extinct species in the network - having few or no predators - also suggests they might have been particularly vulnerable to a new predator. These results provide quantitative evidence that trophic cascades and naivety to predators could have contributed to the megafauna extinction event in Sahul.

ecology↗

bioRxiv2020

The role of higher-order biotic interactions on tropical tree growth

Growth in individual size or biomass is a key demographic component in population models, with wide-ranging applications from quantifying species performance across abiotic or biotic conditions to assessing landscape-level dynamics under global change. In forest ecology, the responses of tree growth to biotic interactions are widely held to be crucial for understanding forest diversity, function, and structure. To date, most studies on plant-plant interactions only examine the additive competitive or facilitative interactions between species pairs; however, there is increasing evidence of non-additive, higher-order interactions (HOIs) impacting species demographic rates. When HOIs are present, the dynamics of a multi-species community cannot be fully understood or accurately predicted solely from pairwise outcomes because of how additional species interfere with the direct, pairwise interactions. Such HOIs should be particularly prevalent where species show nonlinear functional responses to resource availability and resource-acquisition traits themselves are density dependent. With this in mind, we used data from a tropical secondary forest--a system that fulfills both of these conditions--to build a ontogenetic diameter-growth model for individuals across ten woody-plant species. We allowed both direct and indirect interactions within communities to influence the species-specific growth parameters in a generalized Lotka-Volterra model. Specifically, indirect interactions entered the model as higher-order quadratic terms, i.e. non-additive effects of conspecific and heterospecific neighbour size on the focal individuals growth. For the whole community and for four out of ten focal species, the model that included HOIs had more statistical support than the model that included only direct interactions, despite the former containing a far greater number of parameters. HOIs had comparable effect sizes to direct interactions, and tended to further reduce the diameter growth rates of most species beyond what direct interactions had already reduced. In a simulation of successional stand dynamics, the inclusion of HOIs lead to rank swaps in species diameter hierarchies, even when community-level size distributions remained qualitatively similar. Our study highlights the implications, and discusses possible mechanisms, of non-additive density dependence in highly diverse and light-competitive tropical forests.

ecology↗

bioRxiv2020

Hidden layers of density dependence in consumer feeding rates

Functional responses relate a consumers feeding rates to variation in its abiotic and biotic environment, providing insight into consumer behavior and fitness, and underpinning population and food-web dynamics. Despite their broad relevance and long-standing history, we show here that the types of density dependence found in classic resource- and consumer-dependent functional-response models equate to strong and often untenable assumptions about the independence of processes underlying feeding rates. We first demonstrate mathematically how to quantify non-independence between feeding and consumer interference and between feeding on multiple resources. We then analyze two large collections of functional-response datasets to show that non-independence is pervasive and borne out in previously-hidden forms of density dependence. Our results provide a new lens through which to view variation in consumer feeding rates and disentangle the biological underpinnings of species interactions in multi-species contexts.

ecology↗

bioRxiv2020

Systematic bias in studies of consumer functional responses

Functional responses are a cornerstone to our understanding of consumer-resource interactions, so how to best describe them using models has been actively debated. Here we focus on the consumer dependence of functional responses to evidence systematic bias in the statistical comparison of functional-response models and the estimation of their parameters. Both forms of bias are universal to nonlinear models (irrespective of consumer dependence) and are rooted in a lack of sufficient replication. Using a large compilation of published datasets, we show that - due to the prevalence of low sample size studies - neither the overall frequency by which alternative models achieve top rank nor the frequency distribution of parameter point estimates should be treated as providing insight into the general form or central tendency of consumer interference. We call for renewed clarity in the varied purposes that motivate the study of functional responses, purposes that can compete with each other in dictating the design, analysis, and interpretation of functional-response experiments.

ecology↗