Biology subjects

Pennekamp, F.

Publications and source records attributed to Pennekamp, F..

13 recordsLinked to original sources

bioRxiv2025

Biodiversity modulates the size-abundance relationship in changing environments

Organismal abundance tends to decline with increasing body size. Metabolic theory links this size structure with energy use and productivity, postulating a size-abundance slope of -0.75 that is invariant across environments. We tested the robustness of this relationship across gradients of protist species richness (1-6 species), temperature (15-25 {degrees}C), and time. Using replicated microcosms, we provide an empirical test of how temperature and biodiversity jointly shape the cross-community scaling relationship (CCSR). While our results support the expected of slope -0.75, we also found interactive effects showing the relationship is not invariant. Warming altered abundance scaling with size depending on richness; in high-richness communities, temperature favoured small protists, steepening the CCSR slope. These context-dependent responses emerged over time, suggesting a role of size-dependent species interactions in shaping responses to environmental change. Our findings demonstrate that cross-community size scaling is not fixed but shifts dynamically with ecological context.

ecology↗

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↗

bioRxiv2024

Causal discovery in biodiversity-mediated social-ecological systems

Global biodiversity loss and climate change exacerbate feedbacks within social-ecological systems, i.e., between ecosystems, their services and well-being of human societies. Our ability to mediate these feedbacks is hampered by incomplete understanding of the underlying causal links, which could benefit from interdisciplinary approaches to discover theoretical or empirical links from heterogeneous data characteristic of social-ecological studies. We propose a novel framework connecting literature-based causal knowledge with data-driven inference of causality. We test this framework for the highly biodiverse island of Borneo by conducting a systematic literature review of 7473 studies over 170 years, and a causal inference analysis for three conceptual causal diagrams connecting global change, socio-economics, ecosystem services, and biodiversity-ecosystem function using a set of 227 spatially explicit variables. We find that, while natural or social processes have been mostly studied independently, a set of studies already documents causal links across social-ecological domains for processes related to deforestation, food or energy. Causal discovery unveiled consistent negative causal links between global change, social-economic landscape, and biodiversity-ecosystem function, and positive causal links between global change and socio-economics, and these links were robust to indicator selection and addition. We detected few and weak links between social-economic landscape, global change, and ecosystem services. When comparing the data-driven inferred causal links to those documented by the literature, we find that links between biodiversity and ecosystem function with global change, and links between social-economic landscape and ecosystem services were also consistent, and causal analysis uncovered new (potential) causal links not yet described in the literature. Significance StatementAddressing climate change and biodiversity loss in the Anthropocene requires us to recognize that human societies and ecological systems are inherently interconnected in complex adaptive systems. Causal understanding in social-ecological systems enables understanding system dynamics and response to pressures and shocks. While promising, few studies have studied these systems using a combination of big literature which provides the state-of-the-knowledge and big data that provides the underlying information for causal discovery. With this framework, we can specify and rigorously test, causal links in biodiversity-mediated social-ecological processes under global change and examine potential interventions that lead to much needed sustainable outcomes.

ecology↗

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↗

bioRxiv2024

Food web context modifies predator foraging and weakens trophic interaction strength

Trophic interaction modifications (TIM) are widespread in natural systems and occur when a third species indirectly alters the strength of a trophic interaction. Past studies have focused on documenting the existence and relative magnitude of TIMs, however the underlying processes and long-term consequences often remain elusive. To address this gap, we experimentally quantified the density-dependent effect of a third species on a predators functional response to identify the processes impacted by, and consequences of TIMs. To do so we conducted short-term experiments with two ciliate communities each composed of a predator, prey, and non-consumable modifier species. In both communities, increasing modifier density weakened the trophic interaction strength, due to a negative effect on the predators search clearance rate, however the magnitude of the effect differed with prey species identity. Using parameters estimated from our experimental observation we simulated long-term dynamics and observed quantitative differences between models that account for TIMs or include only pairwise interactions. Our study is a clear demonstration that TIMs are important to understand and predict community dynamics and highlights the need to extend past pairs of focal species to understand the consequences of species interactions in communities.

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

Connecting higher order interactions with ecological stability in experimental aquatic food webs

Community ecology is built on theories that represent the strength of interactions between species as pairwise links. Higher order interactions occur when the presence of a third (or more) species changes the pairwise interaction between a focal pair. Recent theoretical work has highlighted the stabilizing role of higher order interactions for large, simulated communities, yet it remains unclear how important higher order effects are in real communities. Here we used experimental communities of aquatic protists to examine the relationship between higher-order interactions and community stability (as measured by the persistence of species in a community). We cultured a focal pair of consumers in the presence of additional competitors and a predator and collected time series data of their abundances. We then fitted competition models with and without HOIs to measure interaction strength between the focal pair across different community compositions. We used survival analysis to measure the persistence of individual species. We found evidence that additional species positively affected persistence of the focal species and that HOIs were present in most of our communities. However, persistence was only linked to HOIs for one of the focal species. Our results vindicate community ecology theory positing that species interactions may deviate from assumptions of pairwise interactions, opening avenues to consider possible consequences for coexistence and community stability.

ecology↗

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

An integrative study to understand the temperature-size rule with nonlinear temperature increase: One approach to rule them all?

The temperature-size rule (TSR) describes the inverse relationship between organism size and environmental temperature in uni- and multicellular species. Despite the TSR being widespread, the mechanisms for shrinking body size with warming remain elusive. Here, we experimentally test three hypotheses (differential development and growth [DDG], maintain aerobic scope and regulate oxygen supply [MASROS] and the supply-demand hypothesis [SD]) potentially explaining the TSR using the aquatic protist Colpidium striatum in three gradually changing and one constant temperature environment crossed with three different nutrient levels. We find that the constant and slowly warming environments show similar responses in terms of population dynamics, whereas populations with linear and fast warming quickly decline and show a stronger temperature-size response. Our analyses suggest that acclimation may have played a role in observing these differences among treatments. The SD hypothesis is most parsimonious with the data, however, neither the DDG nor the MASROS hypothesis can be firmly dismissed. We conclude that the TSR is driven by multiple ecological and acclimatory responses and hence multicausal. Author statementA.T. designed and led the experiment, and A.T. and A.G. performed the sampling. A.T. and F.P. analyzed and interpreted the data. All authors contributed to the writing of the manuscript.

ecology↗

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

Bottom-up and top-down control of dispersal across major organismal groups: a coordinated distributed experiment

Organisms rarely experience a homogeneous environment. Rather, ecological and evolutionary dynamics unfold in spatially structured and fragmented landscapes, with dispersal as the central process linking these dynamics across spatial scales. Because dispersal is a multi-causal and highly plastic life-history trait, finding general drivers that are of importance across species is challenging but highly relevant for ecological forecasting.\n\nWe here tested whether two fundamental ecological forces and main determinants of local population dynamics, top-down and bottom-up control, generally explain dispersal in spatially structured communities. In a coordinated distributed experiment spanning a wide range of actively dispersing organisms, from protozoa to vertebrates, we show that bottom-up control, that is resource limitation, consistently increased dispersal. While top-down control, that is predation risk, was an equally important dispersal driver as bottom-up control, its effect depended on prey and predator space use and whether dispersal occurred on land, in water or in the air: species that routinely use more space than their predators showed increased dispersal in response to predation, specifically in aquatic environments. After establishing these general causes of dispersal, we used a metacommunity model to show that bottom-up and top-down control of dispersal has important consequences for local population fluctuations as well as cascading effects on regional metacommunity dynamics. Context-dependent dispersal reduced local population fluctuations and desynchronized dynamics between communities, two effects that increase population and community stability.\n\nOur study provides unprecedented insights into the generality of the positive resource dependency of dispersal as well as a robust experimental test of current theory predicting that predator-induced dispersal is modulated by prey and predator space use. Our experimental and theoretical work highlights the critical importance of the multi-causal nature of dispersal as well as its cascading effects on regional community dynamics, which are specifically relevant to ecological forecasting.

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↗