Biology subjects

Fagan, W. F.

Publications and source records attributed to Fagan, W. F..

At least 19 recordsLinked to original sources

bioRxiv2025

Phylogeny, ecology, and allometry: hierarchical controls of insect elemental composition

Elemental composition links organismal physiology to biogeochemical cycles, yet the relative importance of evolutionary and ecological drivers remains unresolved for insects. Integrating a global stoichiometric database with a comprehensive phylogenetic backbone, we demonstrate hierarchical control of insect stoichiometry, with phylogeny accounting for [~]50% of variation in C:N:P ratios, followed by temperature, body mass, trophic group and habitat. We also identify nutrient enrichment in terrestrial lineages relative to aquatic ones. Our work offers a general framework for partitioning evolutionary, ecological, and physiological influences on organismal stoichiometry, highlighting new avenues to link macroevolutionary patterns to underlying processes.

ecology↗

bioRxiv2025

Dynamic Lake Ice Conditions Shape Caribou Water-Crossing Behavior in the Arctic

Successful animal migration hinges on navigation and decision-making in dynamic environments. Yet, how individuals navigate transient, fine-scale landscape barriers, such as seasonally ice-covered water bodies, remains poorly understood. Understanding these responses is critical for forecasting migration routes and connectivity under global change. In the Arctic, rising temperatures are causing earlier ice melt and later freeze-up, reshaping landscape permeability and potentially disrupting migration routes for overland migrants, such as barren-ground caribou (Rangifer tarandus), a keystone Arctic species, which relies on frozen lakes and rivers for efficient spring travel to calving grounds. While caribou generally prefer ice to open water, behavioral responses to changing ice conditions have not been quantitatively assessed. We analyzed 20 years (2001-2021) of GPS data for 406 adult caribou and daily MODIS land surface albedo to examine lake-crossing decisions at Contwoyto Lake, a long (>100 km) glacial lake in northern Canada. We classified transit events as crossing or circumnavigation based on GPS trajectories relative to lake boundaries and linked behavioral decisions to spatially and temporally resolved ice conditions. Our models revealed distinct seasonal drivers. Spring crossing decisions were shaped by intermediate-scale ice conditions, with a behavioral threshold at a path-averaged annual albedo percentile rank of 0.56, corresponding to intermediate late-spring melt conditions when lake ice transitions from continuous cover toward fragmented surfaces. In fall, when the lake was ice-free, movement-related factors such as relative speeds along alternative routes better explained behavior. Our findings show how ice acts as a seasonal behavior filter, shaping functional connectivity through perceptual and energetic constraints. Although developed for caribou, this framework is transferable across species and systems. By linking high-resolution, spatiotemporal remote sensing to individual behavior, our framework identifies quantitative behavioral thresholds in response to dynamic, climate-sensitive landscape features, supporting predictive monitoring of climate-driven shifts in migratory behavior and emerging constraints on movement. Open Research StatementThe MODIS datasets used in this study are publicly available from NASAs Land Processes Distributed Active Archive Center (LP DAAC). Daily shortwave black-sky albedo values were obtained from MCD43A3.061, and land cover types from MCD12Q1.061. Caribou GPS collar data were provided by the Government of the Northwest Territories, Department of Environment and Climate Change (GNWT-ECC), and are not publicly available due to data-sharing restrictions. Qualified researchers may request access from GNWT-ECC (https://www.gov.nt.ca/ecc/en). The code used for data processing and analysis will be archived in GitHub upon manuscript acceptance.

ecology↗

bioRxiv2025

Birds that don't exist: niche pre-emption as a constraint on morphological evolution in the Passeroidea

Understanding why some viable body forms never evolve can reveal how ecological and evolutionary forces shape biodiversity. We investigate this question in the Passeroidea, a large group of songbirds, by analyzing their morphological trait space using topological data analysis and ancestral state reconstruction. We identify a long-standing morphological gap densely surrounded by extant species but unoccupied throughout passeroid diversification. The gap patterns deviate from neutral expectations and show no evidence of past occupation, rendering neutral evolution and extinction unlikely. Similar morphologies exist in other bird lineages, ruling out intrinsic constraints or niche absence. Geographic distributions and traits of passeroids versus non-passeroid gap occupants point to competitive exclusion as the plausible explanation: early-colonizing territorial specialists outside the Passeroidea may have preemptively occupied key habitats, limiting evolutionary opportunities for later-arriving lineages. We demonstrate how historical contingency can shape macroevolutionary outcomes, and introduce a generalizable framework for investigating structural gaps in trait evolution.

ecology↗

bioRxiv2025

Estimating population range distributions from animal tracking data

O_LIQuantifying the space requirements of a population is a fundamental problem in spatial ecology, particularly as it relates to the identification of important utilization areas and the designation of protected areas for conservation and wildlife management. C_LIO_LITraditionally, population space use estimation techniques scale up from the individual to the population level by aggregating individual animal tracks and then using a single pooled distribution estimator like minimum convex polygons (MCP) or kernel density estimation (KDE). These techniques fail to account for the high levels of temporal autocorrelation in modern tracking datasets, and estimates are often sensitive to the number of individuals sampled. C_LIO_LIWe introduce a new population kernel density estimator (PKDE) that accounts for temporal autocorrelation in tracking data, propagates uncertainty from the individual to the population level, accounts for inter-individual variation when scaling up to the population level, and is not highly sensitive to the number of individuals tracked. Through a combination of simulated data and empirical GPS tracking datasets from three species: (a) grizzly bear (Ursus arctos horribilis); (b) lowland tapir (Tapirus terrestris); and (c) bobcat (Lynx rufus), we demonstrate that PKDE produces minimally biased estimates of population-level space use compared to conventional methods like MCP and KDE. C_LIO_LIThe use of conventional estimators can lead to substantial underestimation of population space usage, making them unsuitable for area-based conservation planning. The statistically efficient PKDE estimator provides relatively unbiased estimates of space use with fewer individuals sampled. This method has been made available as a function in the ctmm R package. C_LI

ecology↗

bioRxiv2025

Using Random Forests to Infer Nonlinear Step Selection Effects

O_LIStep selection functions (SSFs) examine the factors that motivate animal movement by pairing observed steps with unobserved "comparison steps" and quantifying the effects of covariates corresponding to the values of the factors at each observed step. Traditionally, SSFs are fit with conditional logistic regression (CLR) models, which explicitly reflect how an animal must decide between an observed step and other available steps. These models, however, cannot infer nonlinear effects unless the specific parametric form (e.g., quadratic) of the nonlinear effect is specified. C_LIO_LITo enable more general inference of nonlinear effects, we propose replacing CLR with random forests (RFs). Using a fitted model, we also describe how to plot the relationship between changes in the value of a covariate upon the likelihood that the animal will select a given step with those covariate values (an "effect curve"). C_LIO_LITo evaluate our models and their corresponding effect curves, we simulated tracks with various nonlinear effects. RFs yield effect curves that closely resemble the specified nonlinear effect curves, achieving mean R2 = 0.953 and outperforming a CLR model with quadratic terms and spline-based generalized additive models. Additionally, we applied our best-performing RF to real wolf data (Barry et al., 2020). The resulting effect curves confirm the authors assumptions that their covariates affect wolf movement according to a step function. C_LIO_LIThese results suggest that RFs may be used either to discover unknown nonlinear step selection effects or to confirm previously hypothesized nonlinear effects. Going forward, we envision the use of RFs as an approach that complements existing SSF approaches, providing insights about nonlinear relationships between covariate values and the effects that they have on animal movement decisions. C_LI

ecology↗

bioRxiv2025

The range-resident logistic model: a new framework to formalize the population-dynamics consequences of range residency

Individual movement is critical in shaping population dynamics. However, existing frameworks linking these two processes often rely on unrealistic assumptions or numerical simulations. To address this gap, we introduce the range-resident logistic model, an easy-to-simulate and mathematically tractable extension of the spatial logistic model that incorporates empirically supported range-resident movement. Our framework unifies non-spatial and (sessile) spatial formulations of the logistic model as limiting cases. Between these regimes, the long-term population size depends nonlinearly on home-range size and spatial distribution. Neglecting range residency can hence lead to under- or overestimating population carrying capacity. To better understand these results, we also introduce a novel crowding index that depends on movement parameters and can be estimated from tracking data. This index captures the influence of spatial structure on population size, and serves as a robust predictor of abundance. The range-resident logistic model is thus a unifying framework bridging movement and population ecology.

ecology↗

bioRxiv2024

Home range spillover in habitats with impassable boundaries: Causes, biases, and corrections using autocorrelated kernel density estimation

O_LIAn animals home range plays a fundamental role in determining its resource use and overlap with conspecifics, competitors and predators, and is therefore a common focus of movement ecology studies. Autocorrelated kernel density estimation addresses many of the shortcomings of traditional home range estimators when animal tracking data is autocorrelated, but other challenges in home range estimation remain. C_LIO_LIOne such issue is known as spillover bias, in which home range estimates do not respect impassable movement boundaries (e.g., shorelines, fences), and occurs in all forms of kernel density estimation. While several approaches to addressing spillover bias are used when estimating home ranges, these approaches introduce bias throughout the remaining home range area, depending on the amount of spillover removed, or are otherwise inaccessible to most ecologists. Here, we introduce local corrections to home range kernels to mitigate spillover bias in (autocorrelated) kernel density estimation in the continuous time movement model (ctmm) package, and demonstrate their performance using simulations with known home range extents and distributions, and a real world case study. C_LIO_LISimulation results showed that local corrections minimised bias in bounded home range area estimates, and resulted in more accurate distributions when compared to commonly used post-hoc corrections, particularly at small-intermediate sample sizes. C_LIO_LIComparison of the impacts of local vs post-hoc corrections to bounded home ranges estimated from lake trout (Salvelinus namaycush) demonstrated that local corrections constrained bias within the remaining home range area, resulting in proportionally smaller home range areas compared to when post-hoc corrections are used. C_LI

ecology↗

bioRxiv2023

Intraspecific encounters can induce home-range shifts

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smiths bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct encounters to alter individuals space use. Open Research StatementMovement data for the coyotes and grizzly bears are posted on Movebank.org as datasets 1614661371 and 1044288582, respectively. Statistical tools for estimating, manipulating, and comparing home ranges from movement data are implemented in the open-source R package ctmm. R scripts used to carry out specific analyses for this study are openly available on GitHub at https://github.com/anagkrish/encounter_homerangeshift.

ecology↗

bioRxiv2023

Nomadic ungulate movements under threat: Declining mobility of Mongolian gazelles in the Eastern Steppe.

Increasing habitat fragmentation and disturbance threaten long distance movements of ungulates. While the effects of impermeable barriers on ungulate migrations have been well researched, quantitative evidence for gradual and long-term changes of mobility in response to anthropogenic disturbance remains relatively rare. We investigated changes in movement behavior of Mongolian gazelle Procapra gutturosa, a nomadic ungulate species native to the Mongolian steppe. Using GPS tracking data collected from 62 gazelle individuals between 2007 and 2021, we quantified 16-day displacement distances for each individual as a metric for long-distance movements. We used generalized linear mixed models, generalized additive models and additive quantile mixed models to assess how anthropogenic and environmental factors affected gazelle movement behavior. Long distance 16-day movements decreased significantly by up to 36 %, from 142 km in 2007 to 92 km in 2021. Changes in gazelle mobility were affected by the increasing number of vehicles in Mongolia, but could not be explained by concurrent changes in other environmental factors like temperature, precipitation or vegetation greenness that often drive ungulate migration behavior. Moreover, we found that gazelle movement decreased close to roads, and that gazelles stayed further away from roads during the snow-free season, when vehicular traffic likely is most intense. Conserving landscape permeability is essential for maintaining populations of highly mobile species. Our study provides evidence for a gradual decline in gazelle mobility over fifteen years as a response to increasing anthropogenic impact. To date, the transportation infrastructure permeating the Eastern Steppe does not pose physical barriers, yet our findings suggest that increasing traffic volume may create semipermeable barriers to gazelle movement. As human activity is increasing throughout the Eastern Steppe, interactions between ungulates and vehicle traffic need to be closely monitored in order to identify, localize, and mitigate semipermeable barrier effects before landscape permeability is severely altered.

ecology↗

bioRxiv2023

movedesign: Shiny R app to evaluate sampling design for animal tracking movement studies

O_LIProjects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space-use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner. C_LIO_LIWe developed movedesign, a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; i.e., the choices regarding data resolution (sampling interval) and battery life (sampling duration). C_LIO_LILeveraging the ctmm R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animals velocity remains in the data. C_LIO_LIThis application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. movedesign has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices. C_LI

ecology↗

bioRxiv2022

Clarifying space use concepts in ecology: range vs. occurrence distributions

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animals home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals. Open Research StatementTracking data on Aepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, and Ursus arctos are publicly archived in the Dryad repository (Noonan et al. 2018; https://doi.org/10.5061/dryad.v5051j2), as are data from Procapra gutturosa (Fleming et al. 2014a; https://doi.org/10.5061/dryad.45157). Data on Panthera onca were taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers: Canis latrans, 8159699; Canis lupus, 8159399; Chrysocyon brachyurus, 18156143; Felis silvestris, 40386102; Gyps africanus, 2919708; Lepus europaeus, 25727477; Martes pennanti, 2964494; Panthera leo, 220229; Papio cynocephalus, 222027; Syncerus caffer, 1764627; Tapirus terrestris, 443607536; Torgos tracheliotus, 2919708; and Ursus americanus, 8170674.

ecology↗

bioRxiv2022

Sampling bias and the robustness of ecological metrics for plant-damage-type association networks

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co-dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant-insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant-DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant-DT interactions. These results provide reassurance that cross-comparisons of plant-DT networks offer insights into network structure and function and support their widespread use in paleoecology. Moreover, these findings suggest novel opportunities for using plant-DT networks in neontological terrestrial ecology to understand functional aspects of insect herbivory across geological time, environmental perturbations, and geographic space.

ecology↗

bioRxiv2021

Population-level inference for home-range areas

O_LIHome-range estimates are a common product of animal tracking data, as each range informs on the area needed by a given individual. Population-level inference on home-range areas--where multiple individual home-ranges are considered to be sampled from a population--is also important to evaluate changes over time, space, or covariates, such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous. C_LIO_LIHere, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. C_LIO_LIWe apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white-nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes. C_LIO_LIOur approach allows researchers to more accurately compare different populations with different movement behaviors or sampling schedules, while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests. C_LI

ecology↗

bioRxiv2021

A semi-variance approach to visualising phylogenetic autocorrelation

O_LIComparing traits across species has been a hallmark of biological research for centuries. While inter-specific comparisons can be highly informative, phylogenetic inertia can bias estimates if not properly accounted for in comparative analyses. In response, researchers typically treat phylogenetic inertia as a form of autocorrelation that can be detected, modelled, and corrected for. Despite the range of methods available for quantifying the strength of phylogenetic autocorrelation, no tools exist for visualising these autocorrelation structures. C_LIO_LIHere we derive variogram methods suitable for phylogenic data, and show how they can be used to straightforwardly visualise phylogenetic autocorrelation. We then demonstrate their utility for three empirical examples: sexual size dimorphism (SSD) in the Musteloidea, maximum per capita rate of population growth, r, in the Carnivora, and brain size in the Artiodactyla. C_LIO_LIWhen modelling musteloid SSD, the empirical variogram showed a tendency for the variance in SSD to stabilise over time, a characteristic feature of Ornstein-Uhlenbeck (OU) evolution. In agreement with this visual assessment, model selection identified the OU model as the best fit to the data. In contrast, the infinitely diffusive Brownian Motion (BM) model did not capture the asymptotic behaviour of the variogram and was less supported than the OU model. Phylogenetic variograms proved equally useful in understanding why an OU model was selected when modelling r in the Carnivora, and why BM was the selected evolutionary model for brain size in the Artiodactyla. C_LIO_LIBecause the variograms of the various evolutionary processes each have different theoretical profiles, comparing fitted semi-variance functions against empirical semi-variograms can serve as a useful diagnostic tool, allowing researchers to understand why any given evolutionary model might be selected over another, which features are well captured by the model, and which are not. This allows for fitted models to be compared against the empirical variogram, facilitating model identification prior to subsequent analyses. We therefore recommend that any phylogenetic analysis begin with a non-parametric estimate of the autocorrelation structure of the data that can be visualized. The methods developed in this work are openly available in the new R package ctpm. C_LI

evolutionary biology↗

bioRxiv2021

Animal soundscapes reveal key markers of Amazon forest degradation from fire and logging

Safeguarding tropical forest biodiversity requires solutions for monitoring ecosystem composition over time. In the Amazon, logging and fire reduce forest carbon stocks and alter tree species diversity, but the long-term consequences for wildlife remain unclear, especially for lesser-known taxa. Here, we combined data from multi-day acoustic surveys, airborne lidar, and satellite timeseries covering logged and burned forests (n=39) in the southern Brazilian Amazon to identify acoustic markers of degradation. Our findings contradict theoretical expectations from the Acoustic Niche Hypothesis that animal communities in more degraded habitats occupy fewer acoustic niches. Instead, we found that habitat structure (e.g., aboveground biomass) was not a consistent proxy for biodiversity based on divergent patterns of acoustic space occupancy (ASO) in logged and burned forests. Full 24-hr soundscapes highlighted a stark and sustained reorganization in community structure after multiple fires; animal communication networks were quieter, more homogenous, and less acoustically integrated in forests burned multiple times than in logged or once-burned forests. These findings demonstrate strong biodiversity co-benefits from protecting Amazon forests from recurrent fire activity. By contrast, soundscape changes after logging were subtle and more consistent with community recovery than reassembly. In both logged and burned forests, insects were the dominant acoustic markers of degradation, particularly during midday and nighttime hours that are not typically sampled by traditional field surveys of biodiversity. The acoustic fingerprints of degradation history were conserved across replicate recording locations at each site, indicating that soundscapes offer a robust, taxonomically inclusive solution for tracking changes in community composition over time. Significance StatementFire and logging reduce the carbon stored in Amazon forests, but little is known about how human degradation alters animal communities. We recorded thousands of hours of ecosystem sounds to investigate animal community assembly and the associations between biodiversity and biomass following Amazon forest degradation over time. 24-hr patterns of acoustic activity differed between logged and burned forests, and we observed large and sustained breakpoints in community structure after multiple burns. Soundscape differences among degraded forests were clearest during insect-dominated hours rarely sampled in field studies of biodiversity. These findings demonstrate that acoustic monitoring holds promise for routine biodiversity accounting, even by non-experts, to capture a holistic measure of animal communities in degraded tropical forests and benchmark change over time.

ecology↗

bioRxiv2021

Spatial microbial community dynamics using a continuous species interaction model

The assembly and maintenance of microbial diversity in natural communities, despite the abundance of toxin-based antagonistic interactions, presents major challenges for biological understanding. A common framework for investigating such antagonistic interactions involve cyclic dominance games with pairwise interactions. The incorporation of higher-order interactions in such models permits increased levels of microbial diversity, especially in communities where antibiotic producing, sensitive, and resistant strains co-exist. However, most such models involve a small number of discrete species, assume a notion of pure cyclic dominance, and focus on low mutation rate regimes, none of which well represents the highly interlinked, quickly evolving, and continuous nature of microbial phenotypic space. Here, we present an alternative vision of spatial dynamics for microbial communities based on antagonistic interactions--one in which a large number of species interact in continuous phenotypic space, are capable of rapid mutation, and engage in both direct and higher-order interactions mediated by production of and resistance to antibiotics. Focusing on toxin production, vulnerability, and inhibition among species, we observe highly divergent patterns of diversity and spatial community dynamics. We find that species interaction constraints (rather than mobility) best predict spatiotemporal disturbance regimes, whereas community formation time, mobility, and mutation size best explain patterns of diversity. We also report an intriguing relationship among community formation time, spatial disturbance regimes, and diversity dynamics. This relationship, which suggests that both higher-order interactions and rapid evolution are critical for the origin and maintenance of microbial diversity, has broad-ranging links to the maintenance of diversity in other systems. Significance StatementPersistently diverse microbial communities are one of biologys great puzzles. Using a novel continuous trait space modeling framework that accommodates high mutation rates, elevated species richness, and direct and higher-order antagonistic species interactions, we find that two parameters characterizing mutation size and mobility best explain patterns of microbial diversity. Moreover, community formation time (the duration of the transient phase in community assembly) provides an unexpectedly clear guide to the diversity profiles of the resulting communities. These discoveries showcase how complex, antagonistic interactions mediated by the production of, inhibition of, and vulnerability to toxins (antibiotics) can shape microbial communities, allowing for extraordinarily high levels of diversity and temporal persistence.

systems biology↗

bioRxiv2020

Were Neoarchean atmospheric methane hazes and early Paleoproterozoic glaciations driven by the rise of oxygen in surface environments?

Geochemical evidence suggests methane was the predominant greenhouse gas in the Archean and early Proterozoic eons. Consequently, fluctuations in methane concentration in Earths atmosphere (i.e., methane hazes) would have contributed to climate change and influenced the flux of UV radiation reaching surface environments. If correct, understanding what factors (e.g., O2 and/or resource concentration) drove the biological methane cycle might shed light on the repetition of biological, atmospheric and climatic events preserved in the sedimentary rock record from ~2.8 to 2.0 BYA. To explore these interdependencies, we developed a novel dynamical model of microbial ecological interactions to investigate the conditions under which methane is preferentially released to the atmosphere. We found that the interplay between resource and O2 availability results in complex cyclic methane dynamics unrelated to the functional groups or efficiencies of microbial communities, to initial conditions, or to other model constraints. Based on these results, we propose that the cyclicity of methane haze events and glacial episodes in the Neoarchean and early Paleoproterozoic may have been linked to the progressive increase in oceanic and atmospheric O2 through the interval.

systems biology↗

bioRxiv2020

Effects of substrate availability on growth and metabolism in soil microbes: Insights from theoretical modeling of studies of the Warburg effect and substrate-induced respiration

Carbon Use Efficiency (CUE) is a popular concept for measuring the efficiency of biomass production in different biological systems and, is frequently employed to understand effects of microbial processes on soil carbon dynamics. CUE in soil microbes is often measured through respiration-based studies, especially through the addition of a labile carbon substrate such as glucose. Therefore, exploring the response of microbial respiration to availability of labile substrates is crucial to understand microbial CUE in soils. In this work, we build upon a cellular model of the Warburg effect, where cells simultaneously utilize inefficient aerobic glycolysis/fermentation and efficient oxidative phosphorylation pathways for energy synthesis even at high oxygen availability, to predict microbial community response to various levels of substrate availability. We test our predictions systematically using a series of substrate-induced respiration (SIR) experiments to demonstrate prevalence of the Warburg effect in soil microbial communities. We further discuss the relevance of the underlying metabolic processes behind the Warburg effect in interpreting soil microbial CUE.

microbiology↗