Biology subjects

Petchey, O. L.

Publications and source records attributed to Petchey, O. L..

10 recordsLinked to original sources

bioRxiv2024

Biodiversity increases the forecastability of species abundances in changing environments

Accurate forecasts of ecological dynamics are critical for ecosystem management and conservation, yet the drivers of forecastability are poorly understood. Environmental change and diversity are considered major challenges to ecological forecasting. This assumption, however, has never been tested experimentally because forecasts have high data requirements. In a long-term microcosm experiment, we manipulated species richness of 30 experimental protist communities and exposed them to constant or gradually decreasing light levels. We collected finely-resolved time series (123 sampling dates over 41 weeks) of species abundances, community biomass, and oxygen concentrations. We then employed data-driven forecasting methods to forecast these variables. We found that species richness and light had a weak interactive effect on forecasts of species abundances: richness tended to reduce forecast accuracy in constant light but tended to increase forecast accuracy in declining light. These effects could partially be explained by differences among time series in variability and autocorrelation. Forecasts of aggregate properties (community biomass, oxygen), however, were unaffected by richness and light, and were not more accurate than those of species abundances. Our forecasts were based on time series that were detrended and standardized. Since real-world forecasting applications require predictions at the original scale of the forecasted variable, it is important to note that the results were qualitatively identical when back-transforming the forecasts to the original scale. Taken together, we found no strong evidence that higher diversity results in lower forecastability. Rather, our results imply that promoting diversity could make populations more predictable when environmental conditions change. From a conservation and management perspective, our findings suggest that diversity conservation might have beneficial effects on decision-taking by increasing the forecastability of species abundances in changing environments.

ecology↗

bioRxiv2023

Pollutome complexity determines the removal of recalcitrant pharmaceuticals

Organic pollutants are an increasing threat for wildlife and humans. Managing their removal is however complicated by the difficulties in predicting degradation rates. In this work we demonstrate that the complexity of the pollutome, the set of co-existing contaminants, is a major driver of biodegradation. We built representative assemblages out of one to five common pharmaceuticals (caffeine, atenolol, paracetamol, ibuprofen, and enalapril) selected along a gradient of biodegradability. We followed their individual removal by wastewater microbial communities. The presence of multichemical background pollution was essential for the removal of recalcitrant molecules such as ibuprofen. Crucially, high order interactions between pollutants were a determinant, with the addition of new molecules particularly impacting assemblages of multiple compounds. We explain these interactions by shifts in the microbiome, with degradable molecules such as paracetamol enriching species and pathways involved in the removal of several organic molecules. We conclude that pollutants should be treated as part of a complex system, with emerging pollutants potentially showing cascading effects and offering leverage to promote bioremediation.

microbiology↗

bioRxiv2023

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high-frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over- and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges. Open research statementData and code used for the analyses and figures are available on Zenodo: https://doi.org/10.5281/zenodo.10066786. Environmental (lake) data (Merkli et al. 2022) are available from ERIC: https://doi.org/10.25678/00066D.

ecology↗

bioRxiv2022

How to measure response diversity

O_LIThe insurance effect of biodiversity--that diversity stabilises aggregate ecosystem properties--is mechanistically underlain by inter- and intraspecific trait variation in organismal responses to the environment. This variation, termed response diversity, is therefore a potentially critical determinant of ecological stability. However, response diversity has yet to be widely quantified, possibly due to difficulties in its measurement. Even when it has been measured, approaches have varied. C_LIO_LIHere, we review methods for measuring response diversity and from them distil a methodological framework for quantifying response diversity from experimental and/or observational data, which can be practically applied in lab and field settings across a range of taxa. C_LIO_LIPrevious empirical studies on response diversity most commonly invoke response traits as proxies aimed at capturing species ecological responses to the environment. Our approach, which is based on environment-dependent ecological responses to any biotic or abiotic environmental variable, is conceptually simple and robust to any form of environmental response, including nonlinear responses. Given its derivation from empirical data on species ecological responses, this approach should more directly reflect response diversity than the trait-based approach dominant in the literature. C_LIO_LIBy capturing even subtle inter- or intraspecific variation in environmental responses, and environment-dependencies in response diversity, we hope this framework will motivate tests of the diversity-stability relationship from a new perspective, and provide an approach for mapping, monitoring, and conserving this critical dimension of biodiversity. C_LI

ecology↗

bioRxiv2021

Prey speed up, predators slow down: non-consumptive effects on movement behavior of a ciliate predator-prey pair

Non-consumptive effects (NCEs) of predators on prey, such as induced defensive strategies, are frequently neglected in the analysis of predator-prey interactions. Yet these effects can have demographic impacts as strong as consumption. As a counterpart to NCEs, resource-availability effects (RAEs) can prompt changes in predators as well, e.g., in their foraging behavior. We studied NCEs and RAEs in the ciliate predator-prey pair Didinium nasutum and Paramecium caudatum. We examined the dependence of prey/predator swimming speed and body size on predator/prey presence. We also investigated prey spatial grouping behavior and the dependence of predator movement on local prey abundance. We collected individual movement and morphology data through videography of laboratory-based populations. We compared swimming speeds and body sizes based on their distributions. We used linear models to respectively quantify the effects of local prey abundance on predator displacements and of predator presence on prey grouping behavior. In the presence of prey, predator individuals swam more slowly, were bigger and made smaller displacements. Further, their displacements decreased with increasing local prey abundance. In contrast, in the presence of predators, proportionally more prey individuals showed a fast-swimming behavior and there was weak evidence for increased prey grouping. Trait changes entail energy expenditure shifts, which likely affect interspecific interactions and populations, as has been shown for NCEs. Less is known about the link between RAEs and demography, but it seems likely that the observed effects scale up to influence community and ecosystem stability, yet this remains largely unexplored. Significance StatementTo maximize their fitness, organisms balance investment in foraging and avoiding being eaten. The behaviors of prey and predators are thus expected to depend on the presence and absence of each other and serve either to boost the chances of predation evasion or to increase predation success. Here we provide an example of the co-dependence of behaviors in the predator-prey pair Didinium nasutum and Paramecium caudatum. We show that the predator slows down and searches in smaller areas when prey are present, while the prey speeds up and possibly groups more as a response to the presence of predators. Such behavioral changes are likely to have a demographic and community impact that is not accounted for with common measures of predators-prey interactions.

animal behavior and cognition↗

bioRxiv2021

Contrasting resistance and resilience of the coupled oxic and anoxic componentsof an experimental microbial ecosystem

Understanding how microbial communities of aquatic ecosystems respond to environmental change remains a critical challenge in microbial ecology. In this study, we used phototrophic oxic-anoxic micro-ecosystems to understand how the functioning and diversity of aerobic and anaerobic lake analog communities is affected by light deprivation. Continuous measurements were performed to describe oxygen dynamics (mean/min/max/amplitude) and time-series of full-length 16S rRNA sequencing were used to quantify changes in alpha- and beta-diversity. In the top oxic layer, oxygen concentration decreased significantly under light deprivation, but showed resilience in mean, minimum and maximum after light conditions were restored. Only the amplitude of diurnal fluctuations in oxygen concentrations did not recover fully, and instead tended to remain lower in treated ecosystems. Alpha-diversity of the top oxic layer communities showed a delayed increase after light conditions were restored, and was not resilient. In contrast, alpha-diversity of the anoxic bottom layer communities increased due to the stressor, but was resilient. Community composition changed significantly during light deprivation, showed resilience in the oxic layer and lack of resilience in the anoxic layer. Alpha-diversity and the amplitude of oxygen within and among treatments were strongly correlated, suggesting that higher diversity could lead to less variable oxygen concentrations, or vice versa. Our experiment showed that light deprivation induces multifaceted responses of community function and structure, hence focusing on a single stability component could potentially be misleading.

microbiology↗

bioRxiv2020

Large and interacting effects of temperature and nutrient addition on stratified aquatic ecosystems-results from a model micro-ecosystem

Aquatic ecosystems are often stratified, with cyanobacteria in oxic layers and phototrophic sulfur bacteria in anoxic ones. Changes in stratification caused by global environmental change are an ongoing concern. Increasing understanding how such aerobic and anaerobic microbial communities, and associated abiotic conditions, respond to multifarious environmental changes is an important endeavor in microbial ecology. Insights can come from observational and experimental studies of naturally occurring stratified aquatic ecosystems, from theoretical models of ecological processes, and from experimental studies of replicated microbial communities in the laboratory. Here we demonstrate a laboratory-based approach with small, replicated, and liquid dominated Winogradsky columns, with distinct oxic/anoxic strata in a highly replicable manner. Our objective is to apply simultaneous global change scenarios (temperature, nutrient addition) on this micro-ecosystem to report how the microbial communities (full-length 16SrRNA-seq.) and the abiotic conditions (O2, H2S, TOC) of the oxic/anoxic layer responded to these environmental changes. Composition of the strongly stratified microbial communities was greatly affected by temperature and by the interaction of temperature and nutrient addition, demonstrating the need of investigating global change treatments simultaneously. Especially phototrophic sulfur bacteria dominated the water column at higher temperatures, and may indicate the presence of alternative stable states. We show that the establishment of such a micro-ecosystem has potential to test global change scenarios in stratified eutrophic limnic systems.

microbiology↗

bioRxiv2020

Prior selection prevents the loss of an ecosystem cycle during acidification

Ecosystem processes vary temporally due to variation in environmental variables, such as when diurnal variation in sunlight causes diurnal cycles in net primary production. This variability can be characterized by its frequency and amplitude, used to define "normal" functioning of an ecosystem. Relatively little research has addressed how normal modes of variability, such as diurnal cycles, are lost or recovered, following anthropogenic stress. We conducted an aquatic mesocosm experiment to test whether prior application of environmental stress, in the form of moderate acidification, affected the diurnal cycle of dissolved oxygen when exposed to severe acidification. High-frequency data from sensor loggers deployed in 12 mesocosms showed that severe acidification caused a temporary loss of diurnal variation in dissolved oxygen concentration. However, pre-exposure to an acidic environment resulted in the persistence of the diurnal cycle. We hypothesize that pre-exposure shifted the community to acid tolerant genotypes and/or species of algae and other photosynthetic organisms. Our findings suggest that the stability of ecosystem cycles is intrinsically liked to the stress tolerance of the species assemblage.

ecology↗

bioRxiv2018

Evolution increases ecosystem temporal stability and recovery from a flood in grassland communities

Understanding factors that increase ecosystem stability is critical in the face of environmental change. Experiments simulating species loss from grassland ecosystems have shown that losing biodiversity decreases the ability of ecosystems to buffer negative effects of disturbances. However, as the originally sown experimental communities with reduced biodiversity develop, plant evolutionary processes or the assembly of interacting soil organisms may allow them to develop stability and resilience over time. We explored such effects in a long-term grassland biodiversity experiment with plant communities with either a history of co-occurrence (selected communities) or no such history (naive communities) over a four-year period in which a major flood disturbance occurred. We found selected communities had temporally more stable biomass than the same communities of naive plants, especially at low species richness. Furthermore, selected communities showed greater short-term biomass recovery after flooding, resulting in more stable post-flood productivity. In contrast to a previous study, the positive diversity-stability relationship was maintained after the flooding. Our results were consistent across three soil treatments simulating the presence or absence of co-selected microbial communities. We suggest that prolonged exposure of plant populations to a particular community context and abiotic site conditions can increase ecosystem temporal stability and resistance to disturbance. We argue that selection during the course of a biodiversity experiment is the most parsimonious explanation for these effects. A history of co-occurrence can in part compensate for species loss, as can high plant diversity in part compensate for the missing opportunity of such adaptive adjustments.

ecology↗

bioRxiv2017

Dynamic species classification of microorganisms across time, abiotic and biotic environments - a sliding window approach

1. Technological advances have greatly simplified to take and analyze digital images and videos, and ecologists increasingly use these techniques for trait, behavioral and taxonomic analyses. The development of techniques to automate biological measurements from the environment opens up new possibilities to infer species numbers, observe presence/absence patterns and recognize individuals based on audio-visual information.\n\n2. Streams of quantitative data, such as temporal species abundances, are processed by machine learning (ML) algorithms into meaningful information. Machine learning approaches learn to distinguish classes (e.g., species) from observed quantitative features (phenotypes), and in-turn predict the distinguished classes in subsequent observations. However, in biological systems, the environment changes, often driving phenotypic changes in behaviour and morphology.\n\n3. Here we describe a framework for classifying species under dynamic biotic and abiotic conditions using a novel sliding window approach. We train a random forest classifier on subsets of the data, covering restricted temporal, biotic and abiotic ranges (i.e. windows). We test our approach by applying the classification framework to experimental microbial communities where results were validated against manual classification. Individuals from one to six ciliate species were monitored over hundreds of generations in dozens of different species combinations and over a temperature gradient. We describe the steps of our classification pipeline and systematically explore the effects of the abiotic and biotic environments as well as temporal effects on classification success.\n\n4. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, because phenotypic differences driven by environmental change could be captured in the learning algorithm. Importantly, automatic classification showed comparable success compared to manual identifications.\n\n5. Our framework allows for reliable classification even in dynamic environmental contexts, and may help to improve long-term monitoring of species from environmental samples. It therefore has application in disciplines with automatic enumeration and phenotyping of organisms such as eco-toxicology, ecology and evolutionary ecology, and broad-scale environmental monitoring.

ecology↗