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Petchey, O.

Publications and source records attributed to Petchey, O..

9 recordsLinked to original sources
bioRxiv2026

Community performance curves predict community stability despite interaction effects

Predicting whether aggregate community properties remain stable as environments fluctuate is a central challenge in ecology. We asked how well species' fundamental niches predict this aspect of community stability. We developed community performance (CP) curves by aggregating species-specific performance across environmental conditions and tested whether their variability explains temporal variability in communities. In Lotka-Volterra and mechanistic consumer-resource simulations, CP variability explained, on average, 89% of variation in total abundance variability, and explanatory power remained above 75% across gradients of interaction strength and coexistence conditions. In a purpose-designed microcosm experiment, CP variability explained 67% of variation in total biomass variability. CP curves thus retain substantial predictive information even in strongly interacting systems, providing a tractable basis for forecasting community stability when species interactions are unknown.

ecology↗
bioRxiv2026

Micropollutant-driven bacterial adaptation enables resilient pharmaceuticals biodegradation at trace concentrations in biologically treated wastewater.

Pharmaceutical residues are persistent contaminants that resist conventional wastewater treatment and can disrupt ecosystems; however, microorganisms provide a promising biobased solution to transform or mineralize these complex xenobiotics. Whether pollutant-adapted communities maintain their degradative capacity under realistic environmental conditions remains a long-standing debate in environmental biotechnology. Here, microbial consortia enriched in six membrane bioreactors under high pharmaceutical concentration (100 mg/L) retained full biodegradation capacity across a 5000-fold concentration range. After prolonged exposure to six model compounds (atenolol, caffeine, diclofenac, enalapril, ibuprofen, and paracetamol) complete removal occurred for all except diclofenac. Degradation remained efficient even at lower and environmentally relevant concentrations (1 mg/L-20 {micro}g/L) and recovered rapidly upon re-exposure to higher loads (100 mg/L). Metagenomic profiling revealed enrichment of oxygenase-mediated catabolic pathways supporting this resilience. When transferred to a 7 liters bioreactor treating real wastewater, the adapted community removed targeted and untargeted pharmaceuticals, demonstrating robustness, scalability, and strong potential for sustainable micropollutant remediation. Environmental ImplicationPharmaceuticals and their metabolites are environmentally hazardous because these bioactive micropollutants are persistent and continuously discharged via wastewater, thereby endangering both ecosystem and human health. This study shows that pollutant-adapted microbial consortia can address this challenge, retaining strong degradative function across large concentration fluctuations, including environmentally relevant levels. It also demonstrates scalability: the adapted community can be transferred to real-wastewater operation to remove both targeted and additional pharmaceuticals, supporting a bio-based "polishing" step for wastewater treatment plants. Overall, these findings support more sustainable biological mitigation strategies to reduce micropollutant loads.

microbiology↗
bioRxiv2025

The imbalance of nature: The Role of Species Environmental Responses for Ecosystem Stability

Understanding the mechanisms underlying ecosystem stability is crucial in predicting ecological responses to environmental fluctuations. While the diversity-stability relationship has been widely studied, the role of species fundamental responses to the environment remains underexplored. Here, we investigate how the distribution of fundamental responses, captured by a novel metric--imbalance--drives ecosystem stability through asynchrony and population stability. Using a microcosm experiment with protist communities, we manipulated species richness and response distributions (defined as interspecific variation in species performance curves) under fluctuating temperature and different nutrient concentrations. Our results show that lower imbalance, achieved through asynchrony or high population stability, causes higher temporal stability, while richness has no effect on stability. Structural equation modelling revealed that imbalance decreases stability indirectly via increasing synchrony and decreasing population stability, explaining 90% of observed variation. Comparing imbalance derived from single versus multispecies communities demonstrates that fundamental species responses are primary drivers of stability, challenging traditional paradigms emphasizing interspecific interactions. This study provides mechanistic links between species responses, environmental variability, and ecosystem stability, offering new insights into the responses of ecological systems to environmental change.

ecology↗
bioRxiv2023

Response diversity in the context of multifarious environmental change

1.Response diversity represents the inter- and intraspecific trait variation in organismal responses to the environment. Assemblages composed of organisms displaying large variation in their response to the environment (that is, having high response diversity) are expected to have higher temporal stability of aggregate community and ecosystem properties such as ecosystem functioning (i.e., an insurance effect). Yet, response diversity is not commonly measured in empirical studies, and when it is measured, this is done in different ways. Moreover, most proposed measures of response diversity concern situations with only one driver of environmental change. Thus far, no specific approach exists to measure response diversity in the context of multiple simultaneously changing (multifarious) environmental drivers. Here, we propose a new method to empirically quantify response diversity in the context of multifarious environmental change. First, we illustrate this method using simulated data. Next, we reveal the role of the direction of environmental change in shaping response diversity when multiple drivers of environmental change fluctuate over time. We show that, when the direction of the environmental change is unknown (that is, there is no information or a priori expectation about how an environmental condition has changed or will change in future), we can quantify the potential response diversity for a given community under any possible future environmental change scenario. That is, we can estimate the potential response capacity of a system under a range of extreme or realistic environmental changes, capturing its complete insurance capacity, with utility for predicting future responses to even multifarious environmental change. Finally, we investigate the drivers of response diversity in a multifarious environmental change context, showing how response diversity depends on: 1) the diversity of species responses to each environmental variable considered, 2) the relative effect of each environmental variable on species performance, 3) the correlation between the diversity in species responses to different environmental variables, and 4) the mean temporal value of the environmental variable. In doing so, we take an important step towards understanding the insurance capacity of ecological communities exposed to multifarious environmental change.

ecology↗
bioRxiv2023

Microxanox: an R package for simulating an aquatic MICRobial ecosystem that can occupy OXic or ANOXic states

Microxanox is an R package to simulate a three functional group ecosystem (cyanobacteria, phototrophic sulfur bacteria, and sulfate-reducing bacteria) with four chemical substrates (phosphorus, oxygen, reduced sulfur, and oxidized sulfur) using a set of ordinary differential equations. Simulations can be run individually or over a parameter range, to find stable states. The model can be implemented with different numbers of species per functional group. The package is constructed in such a way that the results contain the input parameter used, so that a saved results can be loaded again and the simulation be repeated. Furthermore, the package framework and code should serve as a useful starting point for making simulation models of other types of ecosystem.

ecology↗
bioRxiv2021

Stratified microbial communities are highly sensitive towards multiple combined global change factors, revealing antagonistic and synergistic effects

Microbial communities in many ecosystems are facing a broad range of global change drivers, such as nutrient enrichment, chemical pollution, and temperature change. These drivers can cause changes in the abundance of taxa, the composition of communities, and the properties of ecosystems. While the influence of single drivers is already described in numerous studies, the effect and predictability of multiple drivers changing simultaneously is still poorly understood. In this study, we used 240 highly replicable oxic/anoxic aquatic lab microcosms and four drivers (fertilizer, glyphosate, metal pollution, antibiotics) in all possible combinations at three different temperatures (20 {degrees}C, 24 {degrees}C, and 28 {degrees}C) to shed light into consequences of multiple drivers on different levels of organization, ranging from species abundance to community and ecosystem parameters. We found (i) that at all levels of ecological organisation, combinations of drivers can change the biological consequence and direction of effect compared to single drivers (ii), that effects of combinations are further modified by temperature, (iii) that a larger number of drivers occurring simultaneously is often quite closely related to their effect size, and (iv) that there is little evidence that any of these effects are associated with the level of ecological organisation of the state variable. These findings suggest that, at least in this experimental ecosystem approximating a stratified aquatic ecosystem, there may be relatively little scope for predicting the effects of combinations of drivers from the effects of individual drivers, or by accounting for the level of ecological organisation in question, though there may be some scope for prediction based on the number of drivers that are occurring simultaneous. A priority, though also a considerable challenge, is to extend such research to consider continuous variation in the magnitude of multiple drivers acting together.

microbiology↗
bioRxiv2018

Warming can destabilise predator-prey interactions by shifting the functional response from Type III to Type II

O_LIThe potential for climate change and temperature shifts to affect community stability remains relatively unknown. One mechanism by which temperature may affect stability is by altering trophic interactions. The functional response quantifies the per capita resource consumption by the consumer as a function of resource abundance and is a suitable framework for the description of nonlinear trophic interactions.\nC_LIO_LIWe studied the effect of temperature on a ciliate predator-prey pair (Spathidium sp. and Dexiostoma campylum) by estimating warming effects on the functional response and on the associated conversion efficiency of the predator.\nC_LIO_LIWe recorded prey and predator dynamics over 24 hours and at three temperature levels (15, 20 and 25{degrees}C). To these data we fitted a population dynamic model including the predator functional response, such that the functional response parameters (space clearance rate, handling time, and density dependence of space clearance rate) were estimated for each temperature separately. To evaluate the ecological significance of temperature effects on the functional response parameters we simulated predator-prey population dynamics. We considered the predator-prey system to be destabilised, if the prey was driven extinct by the predator.\nC_LIO_LIEffects of increased temperature included a transition of the functional response from a Type III to a Type II and an increase of the conversion efficiency of the predator. The simulated population dynamics showed a destabilisation of the system with warming, with greater risk of prey extinction at higher temperatures likely caused by the transition from a Type III to a Type II functional response.\nC_LIO_LIWarming-induced shifts from a Type III to II are not commonly considered in modelling studies that investigate how population dynamics respond to warming. Future studies should investigate the mechanism and generality of the effect we observed and simulate temperature effects in complex food webs including shifts in the type of the functional response as well as consider the possibility of a temperature dependent conversion efficiency.\nC_LI

ecology↗
bioRxiv2018

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems intrinsic predictability - the highest achievable predictability given the degree to which system dynamics are the result of deterministic v. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. By means of simulations we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a dataset of 461 empirical ecological time series. We show how deviations from the expected PE-FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics.\n\nThese results demonstrate a theoretically-grounded basis for a model-free evaluation of a systems intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.\n\nGlossaryActive information: The amount of information that is available to forecasting models (redundant information minus lost information; Fig. 1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC=\"FIGDIR/small/350017_fig1a.gif\" ALT=\"Figure 1A\">\nView larger version (9K):\norg.highwire.dtl.DTLVardef@934d9eorg.highwire.dtl.DTLVardef@ccdc10org.highwire.dtl.DTLVardef@1839ed9org.highwire.dtl.DTLVardef@31bd70_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1A.C_FLOATNO The total information content of an observation of a system at a given state in time, St, is depicted by filled circles with past states (St-1 and St-2) represented by shades of grey, i) lack of overlap between past and present states illustrating a case where no information is transmitted from past states (i.e. a purely stochastic system), with low redundancy and high Shannon entropy rate, ii) intermediate overlap indicating a case when some information is transferred from past to present (i.e. a deterministic system strongly driven by stochastic forcing), with intermediate redundancy and Shannon entropy rate, iii) large overlap indicating a case when the current state is mostly determined by the previous state (i.e. a highly deterministic system), with high redundancy and low Shannon entropy rate. Note that both the redundancy and Shannon entropy rate of a system are intrinsic properties of the system and will only change if the system itself changes.\n\nC_FIG Forecasting error (FE): A measure of the discrepancy between a models forecasts and the observed dynamics of a system. Common measures of forecast error are root mean squared error and mean absolute error.\n\nEntropy: Measures the average amount of information in the outcome of a stochastic process.\n\nInformation: Any entity that provides answers and resolves uncertainty about a process. When information is calculated using logarithms to the base two (i.e. information in bits), it is the minimum number of yes/no questions required, on average, to determine the identity of the symbol (Jost 2006). The information in an observation consists of information inherited from the past (redundant information), and of new information.\n\nIntrinsic predictability: the maximum achievable predictability of a system (Beckage et al. 2011).\n\nLost information: The part of the redundant information lost due to measurement or sampling error, or transformations of the data (Fig. 1).\n\nNew information, Shannon entropy rate: The Shannon entropy rate quantifies the average amount of information per observation in a time series that is unrelated to the past, i.e., the new information (Fig. 1).\n\nNonlinearity: When the deterministic processes governing system dynamics depend on the state of the system.\n\nPermutation entropy (PE): permutation entropy is a measure of the complexity of a time series (Bandt & Pompe, 2002) that is negatively correlated with a systems predictability (Garland et al. 2015). Permutation entropy quantifies the combined new and lost information. PE is scaled to range between a minimum of 0 and a maximum of 1.\n\nRealized predictability: the achieved predictability of a system from a given forecasting model.\n\nRedundant information: The information inherited from the past, and thus the maximum amount of information available for use in forecasting (Fig. 1).\n\nSymbols, words, permutations: symbols are simply the smallest unit in a formal language such as the letters in the English alphabet i.e., {\"A\", \"B\",..., \"Z\"}. In information theory the alphabet is more abstract, such as elements in the set {\"up\", \"down\"} or {\"1\", \"2\", \"3\"}. Words, of length m refer to concatenations of the symbols (e.g., up-down-down) in a set. Permutations are the possible orderings of symbols in a set. In this manuscript, the words are the permutations that arise from the numerical ordering of m data points in a time series.\n\nWeighted permutation entropy (WPE): a modification of permutation entropy (Fadlallah et al., 2013) that distinguishes between small-scale, noise-driven variation and large-scale, system-driven variation by considering the magnitudes of changes in addition to the rank-order patterns of PE.

ecology↗
bioRxiv2017

Inferring processes of community assembly from macroscopic patterns: the case for inclusive and mechanistic approaches

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as the causal link between process and pattern is often lacking and processes known to be important are ignored. Here, we revisit current knowledge on community assembly across scales and, in line with several reviews that have outlined the features and challenges associated with current inference techniques, we identify a discrepancy between features of real communities and current inference techniques. We argue, that mechanistic eco-evolutionary models in combination with novel model fitting and model evaluation techniques can provide avenues for more accurate, reliable and inclusive inference. To exemplify, we implement a trait-based and spatially explicit dynamic eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources. This suggested approach can be computationally intensive, and model fitting and parameter estimation can be challenging. We discuss optimization of model implementation, data requirements and availability, and Approximate Bayesian Computation (ABC) as potential solutions to challenges that may arise in our quest for better inference techniques.

evolutionary biology↗