Biology subjects

Dee, L. E.

Publications and source records attributed to Dee, L. E..

9 recordsLinked to original sources

bioRxiv2026

Maximum trophic level predicts food webs susceptibility to coextinctions

As ecosystems face a growing number of threats, coextinctions (resultant extinctions following a primary extinction) are expected to proliferate. However, less is known about the conditions under which coextinctions could outpace primary extinctions. Because coextinctions often occur through lost species interactions, we posit that aspects of food web structure and complexity can help predict differences in vulnerability to coextinction across ecosystems. To test this, we leverage Bayesian network models to assess the extent to which variation in ecosystem vulnerability to coextinction varies with food web structure. We find that food webs with high maximum trophic level are most vulnerable to coextinction, and that maximum trophic level is a better predictor than other aspects of food web structure, such as species richness or trophic connectance. Extending this approach, we also find that maximum trophic level uncovers the relative vulnerability of ecosystem services to species coextinction across 12 empirical food webs.

ecology↗

bioRxiv2026

Primary productivity declines when species composition and climate are mismatched

Climate change drives shifts in species composition, but turnover in many communities lags behind the current pace of change. Anticipating the impact of the resulting community-climate disequilibria on ecosystem functioning is critical. Present-day communities may already be out of equilibrium with climate, providing an opportunity to estimate the effects of disequilibrium before they become more widespread. We analyzed plant community composition and function data from [~]60,000 rangeland monitoring sites across the western US to measure how community-climate disequilibrium contributes to spatial and temporal variation in net primary productivity (NPP) - a key ecosystem function. We found that communities were already substantially out of equilibrium with climate and accounting for this disequilibrium helped explain patterns of NPP. Communities farthest from equilibrium were less productive than those that were closely matched with climate. Our findings suggest that future increases in community-climate disequilibrium may further impair ecosystem functioning.

ecology↗

bioRxiv2026

Understanding and addressing under-sampling in plant-pollinator networks using stacked models for missing link prediction

Pollinator diversity and pollination are under threat from anthropogenic disturbances. Plant-pollinator networks are useful tools to study the consequences of these disturbances, but their links are often under-sampled, potentially biasing conclusions. We introduce a computational method that can be used to predict which unobserved links in a plant-pollinator network dataset are missing. This method extends a state-of-the-art machine learning approach to missing link prediction based on stacked generalization, which has been promising in other contexts, to the setting of bipartite plant-pollinator networks with species traits treated as node attributes. This approach predicts missing links using an ensemble of predictors based on both observed network topology and species traits. We demonstrate this approach on synthetic and empirical plant-pollinator networks. We take advantage of a unique empirical plant-pollinator dataset which samples from three sites using (a) records of observed visits of pollinators to plant species and (b) pollen analysis with detailed species trait annotations for both plant and pollinator nodes. Visit-based sampling under-samples the links in these networks, whereas pollen-based sampling identifies additional links, allowing us to investigate missing link prediction from realistic patterns of field under-sampling. We show that full plant-pollinator networks can be partially reconstructed from the under-sampled networks using our approach. Further, we show that this method can be used to identify which features are the most important for predicting missing links, which allows for investigation into mechanisms driving plant-pollinator link formation and observation. This method is broadly applicable to under-sampled plant-pollinator networks.

ecology↗

bioRxiv2025

Expert-led priorities for a response diversity research agenda in ecology

Response diversity aims to capture and explain the variation in ecological responses to environmental change. Response diversity is expected to drive ecological stability since a wider variety of responses to one or more environmental factors should stabilise fluctuations of ecosystem functions. However, uptake of empirical response diversity research has been slow. Here we assess current thinking around response diversity by conducting a targeted expert survey of response diversity researchers. Our survey revealed that one barrier to a unified research agenda on response diversity is the lack of agreement among respondents on the definition of response diversity, and to which dimension(s) of ecological stability response diversity might relate. When asked to select the temporal, spatial, and biological scales at which response diversity may be most relevant for ecological stability, respondents chose a wide range of scales indicating differences in how experts view response diversitys stabilising effect. Respondents considered studies incorporating both biotic interactions and abiotic environmental responses to be especially challenging, as were those thinking about responses to multiple environmental changes simultaneously. Moreover, respondents thought inconsistencies in the definitions of, and methods for measuring, response diversity were a major challenge facing the field. Despite these barriers, the survey revealed a desire for globally coordinated research efforts on response diversity in the form of syntheses, workshops, and distributed experiments, but that a standardised response diversity metric across diverse use-cases could be too restrictive. Our findings suggest we can shift response diversity from a loose collection of conceptual studies and inconsistent empirical applications towards a quantitative and coordinated research programme mechanistically linking biodiversity and ecological stability. As such, we are launching the Response Diversity Network--a research community interested in the science and application of response diversity-- whose activities we hope will benefit both individual studies of response diversity and globally coordinated research efforts.

ecology↗

bioRxiv2024

Predicting missing links in food webs using stacked models and species traits

Networks are a powerful way to represent the complexity of complex ecological systems. However, most ecological networks are incompletely observed, e.g., food webs typically contain only partial lists of species interactions. Computational methods for inferring such missing links from observed networks can facilitate field work and investigations of the ecological processes that shape food webs. Here, we describe a stacked generalization approach to predicting missing links in food webs that can learn to optimally combine both structural and trait-based predictions, while accounting for link direction and ecological assumptions. Tests of this method on synthetic food webs show that it performs very well on networks with strong group structure, strong trait structure, and various combinations thereof. Applied to a global database of 290 food webs, the method often achieves near-perfect performance for missing link prediction, and performs better when it can exploit both species traits and patterns in connectivity. Furthermore, we find that link predictability varies with ecosystem type, correlates with certain network characteristics like size, and is principally driven by a subset of ecologically-interpretable predictors. These results indicate broad applicability of stacked generalization for studying ecological interactions and understanding the processes that drive link formation in food webs.

ecology↗

bioRxiv2024

Causal inference with observational data and unobserved confounding variables

Experiments have long been the gold standard for causal inference in Ecology. Observational data has been primarily used to validate experimental results or to find patterns that inspire experiments - not for causal inference. As ecology tackles progressively larger problems, we are moving beyond the scales at which randomized controlled experiments are feasible. Using observational data for causal inference raises the problem of confounding variables, those affecting both a causal variable and response of interest. Unmeasured confounders lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this Omitted Variable Bias, other disciplines have developed rigorous approaches for causal inference from observational data addressing the problems of confounders. We show how Ecologists can harness some of these methods: identifying confounders via causal diagrams, using nested sampling designs, and statistical designs that address omitted variable bias for causal inference. Using a motivating example of warming effects on intertidal snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences, and how methods presented here outperform them, reducing bias and increasing statistical power. Our goal is to enable the widespread use of observational data as tool for causal inference for the next generation of Ecological studies.

ecology↗

bioRxiv2022

Trends in forest carbon offset markets in United States

Natural climate solutions are gaining international policy attention - with forests highlighted as a primary pathway for storing carbon. However, evaluations of additional carbon benefits and the permanence of forest carbon offsets projects remain scarce. In response, we compiled a novel database to analyze trends in existing forest management projects from the two largest offset project registries in the only carbon market in United States. We find that improved forest management projects represent 96% of all credits from forestry projects and 58% of all credits and span diverse practices with different potential for carbon storage. Our results also show that 26% of existing forest C offsets in the US are at risk from wildfire. From a policy perspective, our results underscore the need for more sophisticated insurance mechanisms for forest carbon offset reversals, and for a framework to monitor and evaluate cumulative and future carbon benefits of forest-based offset projects. SignificanceWe assess trends in ownership, forest management practices and disturbance risks in existing forest carbon offset projects in the US.

ecology↗

bioRxiv2020

Scaling up biodiversity ecosystem functioning relationships: the role of environmental variability in space and time

The biodiversity and ecosystem functioning (BEF) relationship is expected to be scale-dependent. The autocorrelation of environmental heterogeneity is hypothesized to explain this scale dependence because it influences how quickly biodiversity accumulates over space or time. However, this link has yet to be demonstrated in a formal model. Here we use a Lotka-Volterra competition model to simulate community dynamics when environmental conditions vary across either space or time. Species differ in their optimal environmental conditions, which results in turnover in community composition. We vary biodiversity by modelling communities with different sized regional species pools and ask how the amount of biomass per unit area depends on the number of species present, and the spatial or temporal scale at which it is measured. We find that more biodiversity is required to maintain functioning at larger temporal and spatial scales. The number of species required increases quickly when environmental autocorrelation is low, and slowly when autocorrelation is high. Both spatial and temporal environmental heterogeneity led to scale dependence in BEF, but autocorrelation had larger impacts when environmental change was temporal. These findings show how the biodiversity required to maintain functioning is expected to increase over space and time.

ecology↗

bioRxiv2020

Temperature variability alters the stability and thresholds for collapse of interacting species

Temperature variability and extremes can have profound impacts on populations and ecological communities. Predicting impacts of thermal variability poses a challenge because it has both direct physiological effects and indirect effects through species interactions. In addition, differences in thermal performance between predators and prey and non-linear averaging of temperature-dependent performance can result in complex and counterintuitive population dynamics in response to climate change. Yet the combined consequences of these effects remain underexplored. Here, modeling temperature-dependent predator-prey dynamics, we study how changes in temperature variability affect population size, collapse, and stable coexistence of both predator and prey, relative to under constant environments or warming alone. We find that the effects of temperature variation on interacting species can lead to a diversity of outcomes, from predator collapse to stable coexistence, depending on interaction strengths and differences in species thermal performance. Temperature variability also alters predictions about population collapse - in some cases allowing predators to persist for longer than predicted when considering warming alone, and in others accelerating collapse. To inform management responses that are robust to future climates with increasing temperature variability and extremes, we need to incorporate the consequences of temperature variation in complex ecosystems.

ecology↗