Biology subjects

Rosenbaum, B.

Publications and source records attributed to Rosenbaum, B..

17 recordsLinked to original sources

bioRxiv2026

Forest degradation reshapes trophic functioning in vertebrate food webs across Amazonian forests

The Amazon is a mosaic of ecosystems, accounting for 13% of all known species globally, and responsible for providing a variety of ecosystem services, including a key role in global climate regulation. However, 18% of the Amazon forest cover has been lost completely and a further 38% degraded, threatening biodiversity and millions of local livelihoods. Currently, little is known about how this impacts the functioning of ecological communities. Here, we combine an energetic approach with biodiversity metrics to quantify ecosystem functioning in mammal and bird food webs across Amazonia, along a gradient of forest degradation linked to road proximity. We show that even under relatively low disturbance, ecosystem functions shift: carnivory increases closer to roads, driven by generalist species that persist under these conditions, whereas herbivory declines mainly due to reduced herbivore biomass and species richness. This suggests that processes associated with forest degradation can alter energy flow even where biodiversity metrics remain relatively unchanged, highlighting energetic approaches as sensitive indicators of ecosystem disruption.

ecology↗

bioRxiv2025

Earthworm invasion reduces above-belowground biodiversity and ecosystem multifunctionality

Global change alters abiotic and biotic conditions across the globe with unprecedented consequences for the functional integrity of affected ecosystems. However, most studies addressing global-change impacts focus on a very limited number of environmental variables, taxa, and ecosystem functions. Evidence is mounting that many belowground ecosystems are subject to an underappreciated aspect of global change: the invasion of earthworms. While we know that earthworm invasion can impact the physical, chemical, and biological properties of invaded ecosystems, it remains poorly understood how these changes are interconnected and how they concurrently affect the overall functioning of an ecosystem. To fill this gap, we collected data on six environmental variables, ten functional groups of microbes, plants, and animals, and 16 ecosystem functions from four forests in the USA and Canada, well-known hotspots of earthworm invasion. We used a multi-step Structural-Equation Modeling approach to disentangle the direct and indirect effects of earthworm invasion on environmental conditions and the ecosystem multidiversity and multifunctionality of invaded forests at different levels of resolution. Our analysis revealed that earthworm invasion reduced total multidiversity (combined microbial, plant, and animal multidiversity). Ecosystem multifunctionality was reduced via a combination of direct and indirect effects, with the latter involving both effects mediated by altered environmental conditions and by total multidiversity. In contrast, when resolving total multidiversity into taxon-specific multidiversity indices for microbes, plants, and animals, the above-mentioned effects of earthworm invasion on multifunctionality via multidiversity disappeared. Invasion effects on single ecosystem functions differed in their mediators but were net negative across the board. Given these differences across the differently-resolved analyses, the results suggest that a whole- ecosystem perspective is paramount to comprehensively understanding the impacts of biological invasions. Combining a multi-step analytical design with multiple biodiversity indices and multiple ecosystem functions assessed at the same time and place, our study represents the most complete assessment of the mechanisms and ecosystem-level consequences of earthworm invasion to date.

ecology↗

bioRxiv2024

The impacts of climate variability on the niche concept and distributions of species

1Ecological demographers know that year-to-year climate variability influences the long-term growth of populations and thus their viability. Despite this, species distribution models (SDMs), widely used to project species geographic distributions based on climate, typically ignore inter-annual climate variability. Here, we show that climate variability plays a crucial role in determining current and future distributions of species. We achieved this by developing a new SDM framework, XSDM, that accounts for variability when assessing the ecological niche and distribution of species. XSDM outperforms traditional SDMs in simulation studies. Using XSDM, we assessed the impacts of variability on 10 example species. Variability reduces species potential distributions by an average of 22%, up to 45%. Moreover, sensitivities of distributions to potential changes in average temperature and in its variability were comparable in magnitude. To avoid biases, future SDMs should consider the effects of variability through a demographic approach such as XSDM. As global change alters climate variability, e.g., through increased frequency of extreme events, XSDM provides better tools for countering biodiversity losses.

ecology↗

bioRxiv2023

Understanding interactions in a complex world: Design and analysis of multi-species functional response experiments

O_LIThe functional response describes feeding rates of consumers as a function of resource density. While models for feeding on a single resource species are well studied and supported by a large body of empirical research, consumers feeding on multiple resource species are ubiquitous in nature. However, laboratory experiments designed for parameterizing multi-species functional responses (MSFR) are extremely rare, mainly due to logistical challenges and the non-trivial nature of their statistical analysis. C_LIO_LIHere, we describe how these models can be fitted to empirical data in a Bayesian framework. Specifically, we address the problem of prey depletion during experiments, which can be accounted for through dynamical modeling. In a comprehensive simulation study, we test the effects of experimental design, sample size and noise level on the identifiability of four distinct MSFR models. Additionally, we demonstrate the methods versatility by applying it to a list of empirical datasets. C_LIO_LIWe identify experimental designs for feeding trials that produce the most accurate parameter estimates in two- and three-prey scenarios. Although noise introduces systematic bias in parameter estimates, model selection performs surprisingly well for the four MSFRs, almost always identifying the correct model even for small datasets. C_LIO_LIThis flexible framework allows the simultaneous analysis of feeding experiments from both single- and multi-prey scenarios, either with or without prey depletion. This will help to elucidate mechanisms such as prey selectivity, prey switching and their implications for food web stability and biodiversity. Our approach equips researchers with the appropriate statistical tools to improve the understanding of feeding interactions in complex ecosystems. C_LI

ecology↗

bioRxiv2023

Extreme in Every Way: Exceedingly Low Genetic Diversity in Snow Leopards Due to Persistently Small Population Size

Snow leopards (Panthera uncia) serve as an umbrella species whose conservation benefits their high-elevation Asian habitat. Their numbers are believed to be in decline due to numerous Anthropogenic threats; however, their conservation is hindered by numerous knowledge gaps. They are the least studied genetically of all big cat species with more to learn regarding their population structure, historical population size, and current levels of genetic diversity. Here, we use whole-genome sequencing data for 41 snow leopards (37 newly sequenced) to offer new insights into these unresolved questions. Among our samples, we find evidence of a primary genetic divide between the northern and southern part of the range around the Dzungarian Basin, as previously identified, and a secondary divide south of Kyrgyzstan around the Taklamakan Desert. Most noteworthy, we find that snow leopards have the lowest genetic diversity of any big cat species, due to a persistently small population size (relative to other big cat species) throughout their evolutionary history rather than recent inbreeding. Without a large population size or ample standing genetic variation to help buffer them from any forthcoming Anthropogenic challenges, snow leopard persistence may be more tenuous than currently appreciated.

evolutionary biology↗

bioRxiv2023

Quantitative description of six fish species' gut contents and abundances of their prey in the benthic food web of the Baltic Sea (1968-1978)

The dataset presents a compilation of stomach contents from six demersal fish species from two functional groups inhabiting the Baltic Sea. It includes detailed information on prey identities, body masses, and biomasses recovered from both the fishs digestive systems and their surrounding environment. Environmental parameters, such as salinity and temperature levels, have been integrated to enrich this dataset. The juxtaposition of information on prey found in stomachs and in the environment provides an opportunity to quantify trophic interactions across different environmental contexts and investigate how fish foraging behaviour adapts to changes in their environment, such as an increase in temperature. The compilation of body mass and taxonomic information for all species allows approaching these new questions using either a taxonomic (based on species identity) or functional trait (based on body mass) approach.

ecology↗

bioRxiv2023

Improving forest ecosystem functions by optimizing tree species spatial arrangement

Reforestation and afforestation programs are promoted as strategies to mitigate rising atmospheric CO2 concentrations and enhance ecosystem services. Planting diverse forests is supposed to foster such benefits, but optimal tree planting techniques, especially regarding species spatial arrangement, are underexplored. Here, using field measurements from the subtropical BEF-China experiment, we simulated leaf litterfall and decomposition, as a function of various spatial arrangements of tree species, from clusters of species to random distributions. We show that increasing tree species spatial heterogeneity in forests composed of nine tree species led to more evenly distributed litterfall, increased litter decomposition and associated nitrogen cycling by 45%. These effects were amplified with increasing plot species richness, while species functional trait identity and diversity modulated these relationships. The spatial arrangement of tree species is a critical component determining biodiversity-ecosystem functioning relationships, and considering such spatial aspects is crucial for operationalizing biodiversity-ecosystem functioning theory in realistic re-/afforestation projects.

ecology↗

bioRxiv2022

Heat dissipation drives the hump-shaped scaling of animal dispersal speed with body mass

Dispersal is critical to animal survival and thus biodiversity in fragmented landscapes. Increasing fragmentation in the Anthropocene necessitates predictions about the dispersal capabilities of the many species that inhabit natural ecosystems. This requires mechanistic, trait-based models of animal dispersal which are sufficiently general as well as biologically realistic. While larger animals should generally be able to travel greater distances, reported trends in their speeds across a range of body sizes suggest limited locomotor capacities among the largest species. Here, we show that this also applies to dispersal speeds and that this arises because of their limited heat-dissipation capacities. We derive a model considering how fundamental biophysical constraints of animal body mass associated with energy utilisation (i.e. larger animals have a lower metabolic energy cost of locomotion) and heat-dissipation (i.e. larger animals require more time to dissipate metabolic heat) limit sustained (i.e. aerobic) dispersal speeds. Using an extensive empirical dataset of animal dispersal speeds (531 species), we show that this allometric heat-dissipation model best captures the hump-shaped trends in dispersal speed with body mass for flying, running and swimming animals. This implies that the inability to dissipate metabolic heat leads to the saturation and eventual decrease in dispersal speed with increasing body mass as larger animals must reduce their realised dispersal speeds in order to avoid hyperthermia during extended dispersal bouts. As a result, the highest dispersal speeds are achieved by animals of intermediate body mass, whereas the largest species might suffer from stronger dispersal limitations in fragmented landscapes than previously anticipated. Consequently, we provide a mechanistic understanding of animal dispersal speed that can be generalised across species, even when the details of an individual species biology are unknown, to facilitate more realistic predictions of biodiversity dynamics in fragmented landscapes.

ecology↗

bioRxiv2021

Fitting deterministic population models tostochastic data: from trajectory matching tostate-space models

Population and community ecology traditionally has a very strong theoretical foundation with well-known dynamical models, such as the logistic and its variations, and many modification of the classical Lotka-Volterra predator-prey and interspecific competition models. More and more, these classical models are being confronted with data via fitting to empirical time series for purposes of projections or for estimating model parameters of interest. However, using statistical models to fit theoretical models to data is far from trivial, especially for time series data where subsequent measurements are not independent. This raises the question of whether statistical inferences using pure observation error models, such as simple (non-)linear regressions, are biased, and whether more elaborate process error models or state-space models have to be used to address this complexity. In order to help empiricists, especially researchers working with experimental laboratory populations in micro- and mesocosms, make informed decisions about the statistical formalism to use, we here compare different error structures one could use when fitting classical deterministic ODE models to empirical data. We consider a large range of biological scenarios and theoretical models, from single species to community dynamics and trophic interactions. In order to compare the performance of different error structure models, we use both realistically simulated data and empirical data from microcosms in a Bayesian framework. We find that many model parameters can be estimated precisely with an appropriate choice of error structure using pure observation error or state-space models, if observation errors are not too high. However, Allee effect models are typically hard to identify and state-space models should be preferred when model complexity increases. Our work shows that, at least in the context of low environmental stochasticity and high quality observations, deterministic models can be used to describe stochastic population dynamics that include process variability and observation error. We discuss when more complex state-space model formulations may be required for obtaining accurate parameter estimates. Finally, we provide a comprehensive tutorial for fitting these models in R. Open researchCode for stochastic individual-based simulations is available from https://doi.org/10.5281/zenodo.5500442. A tutorial for fitting ODE models to time series data in R is presented in the Supplementary Information and is also available online https://github.com/benjamin-rosenbaum/fitting deterministic population models. Data (Fronhofer et al., 2020) will be provided via GitHub and Zenodo.

ecology↗

bioRxiv2021

Warming decreases host survival with multiple parasitoids, but parasitoid performance also decreases

Current global changes are reshaping ecological communities and modifying environmental conditions. We need to recognize the combined impact of these biotic and abiotic factors on species interactions, community dynamics, and ecosystem functioning. Specifically, the strength of predator-prey interactions often depends on the presence of other natural enemies: it weakens with competition and interference or strengthens with facilitation. Such effects of multiple predators on prey are likely to be affected by changes in the abiotic environment, altering top-down control, a key structuring force in natural and agricultural ecosystems. Here, we investigated how warming alters the effects of multiple predators on prey suppression using a dynamic model coupled with empirical laboratory experiments with Drosophila-parasitoid communities. While multiple parasitoids enhanced top-down control under warming, parasitoid performance generally declined when another parasitoid was present due to competitive interactions. This could reduce top-down control over multiple generations. Our study highlights the importance of accounting for interactive effects between abiotic and biotic factors to better predict community dynamics in a rapidly changing world and thus better preserve ecosystem functioning and services such as biological control.

ecology↗

bioRxiv2021

Adaptive foraging behaviour increases vulnerability to climate change

Higher temperatures are expected to reduce species coexistence by increasing energetic demands. However, flexible foraging behaviour could balance this effect by allowing predators to target specific prey species to maximize their energy intake, according to principles of optimal foraging theory. We test these assumptions using a large dataset comprising 2,487 stomach contents from six fish species with different feeding strategies, sampled across environments with varying prey availability over 12 years in Kiel Bay (Baltic Sea). Our results show that foraging shifts from trait-to density-dependent prey selectivity in warmer and more productive environments. This behavioural change leads to lower consumption efficiency at higher temperature as fish select more abundant but less energetically rewarding prey, thereby undermining species persistence and biodiversity. By integrating this behaviour into dynamic food web models, our study reveals that flexible foraging leads to lower species coexistence and biodiversity in communities under global warming.

ecology↗

bioRxiv2020

Spatial distribution of local patch extinctions drives recovery dynamics in metacommunities

Human activities lead more and more to the disturbance of plant and animal communities with local extinctions as a consequence. While these negative effects are clearly visible at a local scale, it is less clear how such local patch extinctions affect regional processes, such as metacommunity dynamics and the distribution of diversity in space. Since local extinctions may not be isolated events in space but rather cluster together, it is crucial to investigate their effects in a spatially explicit framework. Here, we use experimental microcosms and numerical simulations to understand the relationship between local patch extinctions and metacommunity dynamics. More specifically, we investigate the effects of the amount and spatial autocorrelation of extinctions in a full factorial design. Experimentally, we found that local patch extinctions increased inter-patch ({beta}-) diversity by creating differences between perturbed and unperturbed patches and at the same time increased local (-) diversity by delaying the competitive exclusion of inferior competitors. Most importantly, recolonization dynamics depended more strongly on the spatial distribution of patch extinctions than on the amount of extinctions per se. Clustered local patch extinctions reduced mixing between perturbed and unperturbed patches which led to slower recovery, lower -diversity in unperturbed patches and higher {beta}-diversity. Results from a metacommunity model matched the experimental observations qualitatively when the model included ranked competitive interactions, giving a hint at the underlying mechanisms. Our results highlight that local patch extinctions can increase the diversity within and between communities, that the strength of these effects depends on the spatial distribution of extinctions and that the effects of local patch extinctions can spread regionally, throughout a landscape. These findings are highly relevant for conservation and management of spatially structured communities under global change.

ecology↗

bioRxiv2018

The geometry of habitat fragmentation: effects of distribution patterns on short-term species persistence

Land-use changes cause habitat loss and fragmentation and are thus important drivers of anthropogenic biodiversity change. However, there is an ongoing debate about how fragmentation per se affects biodiversity in a given amount of habitat. We illustrate why it is important to distinguish two different aspects of fragmentation to resolve this debate: (i) geometric fragmentation effects, which exclusively arise from the spatial distributions of species and habitat fragments, and (ii) demographic fragmentation effects due to reduced fragment size, increased isolation, or edge effects. While most empirical studies are primarily interested in quantifying demographic fragmentation effects, geometric effects are typically invoked only as post-hoc explanations of biodiversity responses to fragmentation per se. Here, we present an approach to quantify geometric fragmentation effects on species persistence probability. We illustrate this approach using spatial simulations where we systematically varied the initial abundances and distribution patterns (i.e. random, aggregated, and regular) of species as well as habitat amount and fragmentation per se. As expected, we found no geometric fragmentation effects when species were randomly distributed. However, when species were aggregated, we found positive effects of fragmentation per se on persistence probability for a large range of scenarios. For regular species distributions, we found weakly negative geometric effects. These findings are independent of the ecological mechanisms which generate non-random species distributions. Our study helps to reconcile seemingly contradictory results of previous fragmentation studies. Since intraspecific aggregation is a ubiquitous pattern in nature, our findings imply widespread positive geometric fragmentation effects. This expectation is supported by many studies that find positive effects of fragmentation per se on species occurrences and diversity after controlling for habitat amount. We outline how to disentangle geometric and demographic effects of fragmentation, which is critical for predicting the response of biodiversity to landscape change.

ecology↗

bioRxiv2018

Estimating parameters from multiple time series of population dynamics using Bayesian inference

O_LIEmpirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the respective parameter values may vary considerably. These issues limit the use of a priori model parametrizations.\nC_LIO_LIIn this study, we present a method suited for a direct estimation of model parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics.\nC_LIO_LIOur model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise parameter estimates. We detected significant variability among parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics.\nC_LIO_LIBy lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.\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

Fitting functional responses: Direct parameter estimation by simulating differential equations

O_LIThe feeding functional response is one of the most widespread mathematical frameworks in Ecology, Marine Biology, Freshwater Biology, Microbiology and related scientific fields describing the resource-dependent uptake of a consumer. Since the exact knowledge of its parameters is crucial in order to predict, for example, the efficiency of biocontrol agents, population dynamics, food web structure and subsequently biodiversity, a trustful parameter estimation is of utmost importance for scientists using this framework. Classical approaches for estimating functional response parameters lack flexibility and can often only serve as approximation for a correct parameter estimation. Moreover, they do not allow to incorporate side effects such as resource growth or background mortality. Both call for a new method to be established solving these problems.\nC_LIO_LIHere, we combined ordinary differential equation models (ODE models), that were numerically solved using computer simulations, with an iterative maximum likelihood fitting approach. We compared our method to classical approaches of fitting functional responses, using data both with and without additional resource growth and mortality.\nC_LIO_LIWe found that for classical functional response models, like the often used type II and type III functional response, the established fitting methods are reliable. However, using more complex and flexible functional responses, our new established method outperforms the traditional methods. Additionally, only our method allows to analyze experiments correctly when resources experience growth or background mortality.\nC_LIO_LIOur method will enable researchers from different scientific fields that are measuring functional responses to estimate parameters correctly. These estimates will enable community ecologists to parameterize their models more precisely, allowing for a deeper understanding of complex ecological systems, and will increase the quality of ecological prediction models.\nC_LI

ecology↗

bioRxiv2017

Interactive effects of shifting body size and feeding adaptation drive interaction strengths of protist predators under warming

Global change is heating up ecosystems fuelling biodiversity loss and species extinctions. High-trophic-level predators are especially prone to extinction due to an energetic mismatch between increasing feeding rates and metabolism with warming. Different adaptation mechanisms such as decreasing body size to reduce energy requirements (morphological response) as well as direct effects of adaptation to feeding parameters (physiological response) have been proposed to overcome this problem. Here, we use protist-bacteria microcosm experiments to show how those adaptations may have the potential to buffer the impact of warming on predator-prey interactions. After adapting the ciliate predator Tetrahymena pyriformis to three different temperatures (15{degrees}C, 20{degrees}C and 25{degrees}C) for approximately 20 generations we conducted functional response experiments on bacterial prey along an experimental temperature gradient (15{degrees}C, 20{degrees}C and 25{degrees}C). We found an increase of maximum feeding rates and half-saturation densities with rising experimental temperatures. Adaptation temperature had on average slightly negative effects on maximum feeding rates, but maximum feeding rates increased more strongly with rising experimental temperature in warm adapted predators than in cold adapted predators. There was no effect of adaptation temperature on half-saturation densities characterising foraging efficiency. Besides the mixed response in functional response parameters, predators also adapted by decreasing body size. As smaller predators need less energy to fulfil their energetic demands, maximum feeding rates relative to the energetic demands increased slightly with increased adaptation temperature. Accordingly, predators adapted to 25{degrees}C showed the highest feeding rates at 25{degrees}C experimental temperature, while predators adapted to 15{degrees}C showed the highest maximum feeding rate at 15{degrees}C. Therefore, adaptation to different temperatures potentially avoids an energetic mismatch with warming. Especially a shift in body size with warming additionally to an adaptation of physiological parameters potentially helps to maintain a positive energy balance and prevent predator extinction with rising temperatures.

ecology↗