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The movement of animals affects the biodiversity, ecological processes, and resilience of an ecosystem. For the animals, moving has costs as well as benefits and the use of a given landscape provides insights into animal decisions and behavioral ecology. Understanding how animals use the landscape can thus clarify their effects on ecosystems and inform conservation measures aiming at preserving and restoring the ecological functions of animal dispersal. Here, we investigated the habitat preferences of African savanna elephants (Loxodonta africana) using GPS data from 155 individuals collected between 1998 and 2020 in Northern Kenya. In particular, we assessed how “energy landscapes”, i.e. the cost of locomotion due to the slope of the terrain and the animal body mass, together with elevation, vegetation productivity, water availability, and proximity to human settlements influence the habitat preferences of elephants. We found that the energy landscape is the most consistent predictor of elephants’ preferences, with individuals generally avoiding energetically costly areas and preferring highly productive habitats. We also found that other predictors such as elevation, water availability and human presence, are important in determining habitat usage, but varied greatly among elephants, with some individuals preferring habitats avoided by others. Our analysis highlights the importance of the energy landscape as a key driver of habitat preferences of elephants. Importantly, the enerscape modeling environment allowed us to develop testable hypotheses from rather coarse-grained data covering elephant movements and a few environmental parameters. Energy landscapes rely on fundamental biomechanical and physical principles and provide a mechanistic understanding of the observed preference patterns, allowing to disentangle key causal drivers of an animal’s preferences from correlational effects. This, in turn, has important implications for assessing and planning conservation and restoration measures, such as dispersal corridors, by explicitly accounting for the energy costs of moving.

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 fromhttps://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 onlinehttps://github.com/benjamin-rosenbaum/fittingdeterministic population models. Data (Fronhofer et al., 2020) will be provided via GitHub and Zenodo.

Biological invasions have major impacts on a variety of ecosystems and threaten native biodiversity. Earthworms have been absent from northern parts of North America since the last ice age, but non-native earthworms were recently introduced there and are now being spread by human activities. While past work has shown that plant communities in earthworm-invaded areas change towards a lower diversity mainly dominated by grasses, the underlying mechanisms related to changes in the biotic interactions of the plants are not well understood. Here, we used a trait-based approach to study the effect of earthworms on interspecific plant competition and aboveground herbivory. We conducted a microcosm experiment in a growth chamber with a full-factorial design using three plant species native to northern North American deciduous forests, Poa palustris (grass), Symphyotrichum laeve (herb), and Vicia americana (legume), either growing in monoculture or in a mixture of three. These plant community treatments were crossed with earthworm (presence or absence) and herbivore (presence or absence) treatments. Eight out of the eleven above- and belowground plant functional traits studied were significantly affected by earthworms, either by a general effect or in interaction with plant species identity, plant diversity level, and/or herbivore. Earthworms increased the aboveground productivity and the number of inflorescences of the grass P. palustris. Further, earthworms countervailed the increasing effect of herbivores on root tissue density of all species, and earthworms and herbivores individually increased the average root diameter of S. laeve in monoculture, but decreased it in mixture. In this study, earthworm presence gave a competitive advantage to the grass species P. palustris by inducing changes in plant functional traits. Our results suggest that invasive earthworms can alter competitive and multitrophic interactions of plants, shedding light on some of the mechanisms behind invasive earthworm-induced plant community changes in northern North America forests.

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.

Global warming over the next century is likely to alter the energy demands of consumers and thus the strengths of their interactions with their resources. The subsequent cascading effects on population biomasses could have profound effects on food web stability. One key mechanism by which organisms can cope with a changing environment is phenotypic plasticity, such as acclimation to warmer conditions through reversible changes in their physiology. Here, we measured metabolic rates and functional responses in laboratory experiments for a widespread predator-prey pair of freshwater invertebrates, sampled from across a natural stream temperature gradient in Iceland (4-18℃). This enabled us to parameterize a Rosenzweig-MacArthur population dynamical model to study the effect of thermal acclimation on the persistence of the predator-prey pairs in response to warming. Acclimation to higher temperatures either had neutral effects or reduced the thermal sensitivity of both metabolic and feeding rates for the predator, increasing its energetic efficiency. This resulted in greater stability of population dynamics, as acclimation to higher temperatures increased the biomass of both predator and prey populations with warming. These findings indicate that phenotypic plasticity can act as a buffer against the impacts of environmental warming. As a consequence, predator-prey interactions between ectotherms may be less sensitive to future warming than previously expected, but this requires further investigation across a broader range of interacting species.

Understanding the formation of feeding links provides insights into processes underlying food webs. Generally, predators feed on prey within a certain body-size range, but a systematic quantification of such feeding niches is lacking. We developed a size-constrained feeding-niche (SCFN) model and parameterized it with information on both realized and non-realized feeding links in 72 aquatic and 65 terrestrial food webs. Our analyses revealed profound differences in feeding niches between aquatic and terrestrial predators and variation along a temperature gradient. Specifically, the predator-prey body-size ratio and the range in prey sizes increase with the size of aquatic predators, whereas they are nearly constant across gradients in terrestrial predator size. Overall, our SCFN model well reproduces the feeding relationships and predation architecture across 137 natural food webs (including 3878 species and 136,839 realized links). Our results illuminate the organisation of natural food webs and enables novel trait-based and environment-explicit modelling approaches.

Human activities put ecosystems under increasing pressure, often resulting in local extinctions. However, it is unclear how local extinctions affect regional processes, such as the distribution of diversity in space, especially if extinctions show spatial patterns, such as being clustered. Therefore, it is crucial to investigate extinctions and their consequences in a spatially explicit framework. Using highly controlled microcosm experiments and theoretical models, we ask here how the number and spatial autocorrelation of extinctions interactively affect metacommunity dynamics. We found that local patch extinctions increased local diversity ( α -diversity) and inter-patch diversity ( β -diversity) by delaying the exclusion of inferior competitors. Importantly, recolonization dynamics depended more strongly on the spatial distribution than on the number of patch extinctions: clustered local patch extinctions resulted in slower recovery, lower α -diversity and higher β -diversity. Our results highlight that the spatial distribution of perturbations should be taken into account when studying and managing spatially structured communities.

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 owing 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.

Despite intensive research on species dissimilarity patterns across communities (i.e. β-diversity), we still know little about their implications for variation in food-web structures. Our analyses of 50 lake and 48 forest soil communities show that, while species dissimilarity depends on environmental and spatial gradients, these effects are only weakly propagated to the networks. Moreover, our results show that species and food-web dissimilarities are consistently correlated, but that much of the variation in food-web structure across spatial, environmental, and species gradients remains unexplained. Novel food-web assembly models demonstrate the importance of biotic filtering during community assembly by (1) the availability of resources and (2) limiting similarity in species' interactions to avoid strong niche overlap and thus competitive exclusion. This reveals a strong signature of biotic filtering processes during local community assembly, which constrains the variability in structural food-web patterns across local communities despite substantial turnover in species composition.

Adaptative foraging behavior should promote species coexistence and biodiversity under climate change as predators are expected to maximize their energy intake, according to principles of optimal foraging theory. We test these assumptions using a dataset comprising 22,185 stomach contents of fish species across functional groups, feeding strategies, and prey availability in the environment over 12 years. Our results show that foraging shifts from trait-dependent prey selectivity to density dependence in warmer and more productive environments. This behavioral change leads to lower consumption efficiency as species shift away from their optimal trophic niche, undermining species persistence and biodiversity. By integrating this adaptive foraging behavior into dynamic models, our study reveals higher risk profiles for ecosystems under global warming.

Bacterial communities are often exposed to temporal variations in resource availability, which exceed bacterial generation times and thereby affect bacterial coexistence. Bacterial population dynamics are also shaped by bacteriophages, which are a main cause of bacterial mortality. Several strategies are proposed in the literature to describe infections by phages, such as “Killing the Winner”, “Piggyback the loser” (PtL) or “Piggyback the Winner” (PtW). The two temperate phage strategies PtL and PtW are defined by a change from lytic to lysogenic infection when the host density changes, from high to low or from low to high, respectively. To date, the occurrence of different phage strategies and their response to environmental variability is poorly understood. In our study, we developed a microbial trophic network model using ordinary differential equations (ODEs) and performed ‘in silico’ experiments. To model the switch from the lysogenic to the lytic cycle, we modified the lysis rate of infected bacteria and their growth was turned on or off using a density-dependent switching point. We addressed whether and how the different phage strategies facilitate bacteria coexistence competing for limiting resources. We also studied the impact of a fluctuating resource inflow to evaluate the response of the different phage strategies to environmental variability. Our results show that the viral shunt (i.e. nutrient release after bacterial lysis) leads to an enrichment of the system. This enrichment enables bacterial coexistence at lower resource concentrations. We were able to show that an established, purely lytic model leads to stable bacterial coexistence despite fluctuating resources. Both temperate phage models differ in their coexistence patterns. The model of PtW yields stable bacterial coexistence at a limited range of resource supply and is most sensitive to resource fluctuations. Interestingly, the purely lytic phage strategy and PtW both result in stable bacteria coexistence at oligotrophic conditions. The PtL model facilitates stable bacterial coexistence over a large range of stable and fluctuating resource inflow. An increase in bacterial growth rate results in a higher resilience to resource variability for the PtL and the lytic infection model. We propose that both temperate phage strategies represent different mechanisms of phages coping with environmental variability. Our study demonstrates how phage strategies can maintain bacterial coexistence in constant and fluctuating environments.

The ratio of predator-to-prey biomass is a key element in food webs. Here, the authors report a unified analysis of predator-prey biomass scaling in complex food webs, finding general patterns of sub-linear scaling across ecosystems and levels of organization. The ratio of predator-to-prey biomass is a key element of trophic structure that is typically investigated from a food chain perspective, ignoring channels of energy transfer (e.g. omnivory) that may govern community structure. Here, we address this shortcoming by characterising the biomass structure of 141 freshwater, marine and terrestrial food webs, spanning a broad gradient in community biomass. We test whether sub-linear scaling between predator and prey biomass (a potential signal of density-dependent processes) emerges within ecosystem types and across levels of biological organisation. We find a consistent, sub-linear scaling pattern whereby predator biomass scales with the total biomass of their prey with a near 3/4-power exponent within food webs - i.e. more prey biomass supports proportionally less predator biomass. Across food webs, a similar sub-linear scaling pattern emerges between total predator biomass and the combined biomass of all prey within a food web. These general patterns in trophic structure are compatible with a systematic form of density dependence that holds among complex feeding interactions across levels of organization, irrespective of ecosystem type. info:eu-repo/semantics/publishedVersion

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.

Species adaptative foraging behaviour is now widely considered as enhancing species coexistence and hence biodiversity. However, most of the results are based on the hypothesis that species foraging maximises their energetic income, following the principles of optimal foraging theory. We here tested how the foraging behaviour of six fish species from two functional groups that differ by their body shape and hunting strategy responds to local ecosystem productivity and temperature using an allometric framework. We found that at higher temperatures, when the energetic stress of species increases because of their higher metabolic rates, species foraging is more driven by encounter rates than by trait selectivity. Contrary to classical hypotheses, we show that this change in behaviour leads to a lower consumption efficiency as species depart from their optimal trophic niche. We then analyse the consequences of this behavioural adaptation using a dynamic model and show that the incorporation of adaptive behaviour lowers species coexistence in food webs.

Animals require a certain amount of habitat to persist and thrive, and habitat loss is one of the most critical drivers of global biodiversity decline. While habitat requirements have been predicted by relationships between species traits and home-range size, little is known about constraints imposed by environmental conditions and human impacts on a global scale. Our meta-analysis of 395 vertebrate species shows that global climate gradients in temperature and precipitation exert indirect effects via primary productivity, generally reducing space requirements. Human pressure, however, reduces realised space use due to ensuing limitations in available habitat, particularly for large carnivores. We show that human pressure drives extinction risk by increasing the mismatch between space requirements and availability. We use large-scale climate gradients to predict current species extinction risk across global regions, which also offers an important tool for predicting future extinction risk due to ongoing space loss and climate change.

1. Models of natural processes necessarily sacrifice some realism for the sake of tractability. Detailed, parameter‐rich models often provide accurate estimates of system behaviour but can be data‐hungry and difficult to operationalize. Moreover, complexity increases the danger of ‘over‐fitting’, which leads to poor performance when models are applied to novel conditions. This challenge is typically described in terms of a trade‐off between bias and variance (i.e. low accuracy vs. low precision). 2. In studies of ecological communities, this trade‐off often leads to an argument about the level of detail needed to describe interactions among species. Here, we used data from a grassland biodiversity experiment containing nine locally abundant plant species (the Jena ‘dominance experiment’) to parameterize models representing six increasingly complex hypotheses about interactions. For each model, we calculated goodness‐of‐fit across different subsets of the data based on sown species richness levels, and tested how performance changed depending on whether or not the same data were used to parameterize and test the model (i.e. within vs. out‐of‐sample), and whether the range of diversity treatments being predicted fell inside or outside of the range used for parameterization. 3. As expected, goodness‐of‐fit improved as a function of model complexity for all within‐sample tests. In contrast, the best out‐of‐sample performance generally resulted from models of intermediate complexity (i.e. with only two interaction coefficients per species—an intraspecific effect and a single pooled interspecific effect), especially for predictions that fell outside the range of diversity treatments used for parameterization. In accordance with other studies, our results also demonstrate that commonly used selection methods based on AIC of models fitted to the full dataset correspond more closely to within‐sample than out‐of‐sample performance. 4. Synthesis. Our results demonstrate that models which include only general intra and interspecific interaction coefficients can be sufficient for estimating species‐level abundances across a wide range of contexts and may provide better out‐of‐sample performance than do more complex models. These findings serve as a reminder that simpler models may often provide a better trade‐off between bias and variance in ecological systems, particularly when applying models beyond the conditions used to parameterize them. This paper arose as part of the ‘BEF-Coexist’ workshop, organized by Y.F., C.R., and W.S.H., with funding from the German Research Foundation (RO2397/8). A.T.C. was supported by a ‘catalyst’ postdoctoral fellowship from sDiv. S.S. was supported by the Spanish Government under a Ramón y Cajal contract (RYC-2016- 20604). Funding and support for the Jena Experiment were provided by the German Research Foundation (FOR456/1451), Friedrich Schiller University of Jena, and Max Planck Society.

1. The inference of pairwise competitive outcomes (PCO) and multispecies competitive ranks and intransitivity from empirical data is essential to evaluate how competition shapes plant communities. Three categories of methods, differing in theoretical background and data requirements, have been used: (a) theoretically sound coexistence theory‐based methods, (b) index‐based methods, and (c) ‘process‐from‐pattern’ methods. However, how they are related is largely unknown. 2. In this study, we explored the relations between the three categories by explicitly comparing three representatives of them: (a) relative fitness difference (RFD), (b) relative yield (RY), and (c) a reverse‐engineering approach (RE). Specifically, we first conducted theoretical analyses with Lotka–Volterra competition models to explore their theoretical linkages. Second, we used data from a long‐term field experiment and a short‐term greenhouse experiment with eight herbaceous perennials to validate the theoretical findings. 3. The theoretical analyses showed that RY or RE applied with equilibrium data indicated equivalent, or very similar, PCO respectively to RFD, but these relations became weaker or absent with data further from equilibrium. In line with this, both RY and RE converged with RFD in indicating PCO over time in the field experiment as the communities became closer to equilibrium. Moreover, the greenhouse PCO (far from equilibrium) were only similar to the field PCO of earlier rather than later years. Intransitivity was more challenging to infer because it could be reshuffled by even a small competitive shift among similar competitors. For example, the field intransitivity inferred by three methods differed greatly: no intransitivity was detected with RFD; intransitivity detected with RY and RE was poorly correlated, changed substantially over time (even after equilibrium) and failed to explain coexistence. 4. Our findings greatly help the comparison and generalization of studies using different methods. For future studies, if equilibrium data are available, one can infer PCO and multispecies competitive ranks with RY or RE. If not, one should apply RFD with density gradient or time‐series data. Equilibria could be evaluated with T tests or standard deviations. To reliably infer intransitivity, one needs high quality data for a given method to first accurately infer PCO, especially among similar competitors. This study has been supported by the German Science Foundation (RO2397/8) in the framework of the Jena Experiment (FOR 456/1451). Y.H.F. was also supported by the Fundamental Research Funds for the Central Universities (lzujbky-2019-32). S.S. was supported by the Spanish Government under a Ramón y Cajal contract (RYC-2016-20604).

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 (β-) diversity by creating differences between perturbed and unperturbed patches and at the same time increased local (α-) 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 α-diversity in unperturbed patches and higher β-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.

Understanding how changes in biodiversity will impact the stability and functioning of ecosystems is a central challenge in ecology. Food‐web approaches have been advocated to link community composition with ecosystem functioning by describing the fluxes of energy among species or trophic groups. However, estimating such fluxes remains problematic because current methods become unmanageable as network complexity increases. We developed a generalisation of previous indirect estimation methods assuming a steady state system [1, 2, 3]: the model estimates energy fluxes in a top‐down manner assuming system equilibrium; each node's losses (consumption and physiological) balances its consumptive gains. Jointly, we provide theoretical and practical guidelines to use the fluxweb R package (available on CRAN at https://cran.r-project.org/web/packages/fluxweb/index.html We also present how the framework can merge with the allometric theory of ecology [4] to calculate fluxes based on easily obtainable organism‐level data (i.e. body masses and species groups ‐eg, plants animals), opening its use to food webs of all complexities. Physiological losses (metabolic losses or losses due to death other than from predation within the food web) may be directly measured or estimated using allometric relationships based on the metabolic theory of ecology, and losses and gains due to predation are a function of ecological efficiencies that describe the proportion of energy that is used for biomass production. The primary output is a matrix of fluxes among the nodes of the food web. These fluxes can be used to describe the role of a species, a function of interest (e.g. predation; total fluxes to predators), multiple functions, or total energy flux (system throughow or multitrophic functioning). Additionally, the package includes functions to calculate network stability based on the Jacobian matrix, providing insight into how resilient the network is to small perturbations at steady state. Overall, fluxweb provides a flexible set of functions that greatly increase the feasibility of implementing food‐web energetic approaches to more complex systems. As such, the package facilitates novel opportunities for mechanistically linking quantitative food webs and ecosystem functioning in real and dynamic natural landscapes.

Life-history theory posits that trade-offs between demographic rates constrain the range of viable life-history strategies. For coexisting tropical tree species, the best established demographic trade-off is the growth-survival trade-off. However, we know surprisingly little about co-variation of growth and survival with measures of reproduction. We analysed demographic rates from seed to adult of 282 co-occurring tropical tree and shrub species, including measures of reproduction and accounting for ontogeny. Besides the well-established fast-slow continuum, we identified a second major dimension of demographic variation: a trade-off between recruitment and seedling performance vs. growth and survival of larger individuals (≥ 1 cm dbh) corresponding to a 'stature-recruitment' axis. The two demographic dimensions were almost perfectly aligned with two independent trait dimensions (shade tolerance and size). Our results complement recent analyses of plant life-history variation at the global scale and reveal that demographic trade-offs along multiple axes act to structure local communities.

Since the seminal works of Bernstein (The coordination and regulation of movements. Pergamon Press, Oxford, 1967) several authors have supported the idea that, to produce a goal-oriented movement in general, and a movement of the organs responsible for the production of speech sounds in particular, individuals activate a set of coupling relations that coordinate the behavior of the elements of the motor system involved in the production of the target movement or sound. In order to characterize the configurations of the coupling relations underlying speech production articulator movements, we introduce an original method based on recurrence analysis. The method is validated through the analysis of simulated dynamical systems adapted to reproduce the features of speech gesture kinematics and it is applied to the analysis of speech articulator movements recorded in five German speakers during the production of labial and coronal plosive and fricative consonants at variable speech rates. We were able to show that the underlying coupling relations change systematically between labial and coronal consonants, but are not affected by speech rate, despite the presence of qualitative changes observed in the trajectory of the jaw at fast speech rate.

Climate warming is predicted to alter the structure, stability, and functioning of food webs1-5. Yet, despite the importance of soil food webs for energy and nutrient turnover in terrestrial ecosystems, warming effects on these food webs-particularly in combination with other global change drivers-are largely unknown. Here, we present results from two complementary field experiments testing the interactive effects of warming with forest canopy disturbance and drought on energy fluxes in boreal-temperate ecotonal forest soil food webs. The first experiment applied a simultaneous above- and belowground warming treatment (ambient, +1.7°C, +3.4°C) to closed canopy and recently clear-cut forest, simulating common forest disturbance6. The second experiment crossed warming with a summer drought treatment (-40% rainfall) in the clear-cut habitats. We show that warming reduces energy fluxes to microbes, while forest canopy disturbance and drought facilitates warming-induced increases in energy flux to higher trophic levels and exacerbates reductions in energy flux to microbes, respectively. Contrary to expectations, we find no change in whole-network resilience to perturbations, but significant losses of ecosystem functioning. Warming thus interacts with forest disturbance and drought, shaping the energetic structure of soil food webs and threatening the provisioning of multiple ecosystem functions in boreal-temperate ecotonal forests.

International audience; Predator-prey interactions in natural ecosystems generate complex food webs that have a simple universal body-size architecture where predators are systematically larger than their prey. Food-web theory shows that the highest predator-prey body-mass ratios found in natural food webs may be especially important as they create weak interactions with slow dynamics that stabilize communities against perturbations and maintain ecosystem functioning. Identifying these vital interactions in real communities typically requires arduous identification of interactions in complex food webs. Here, we overcome this obstacle by developing predator-trait models to predict average body-mass ratios based on a database comprising 290 food webs from freshwater, marine and terrestrial ecosystems across all continents. We analyzed how species traits constrain body-size architecture by changing the slope of the predator-prey body-mass scaling. Across ecosystems, we found high body-mass ratios for predator groups with specific trait combinations including (1) small vertebrates and (2) large swimming or flying predators. Including the metabolic and movement types of predators increased the accuracy of predicting which species are engaged in high body-mass ratio interactions. We demonstrate that species traits explain striking patterns in the body-size architecture of natural food webs that underpin the stability and functioning of ecosystems, paving the way for community-level management of the most complex natural ecosystems.

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 fragmentationper seaffects 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 fragmentationper 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 fragmentationper se.As expected, we found no geometric fragmentation effects when species were randomly distributed. However, when species were aggregated, we found positive effects of fragmentationper seon 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 fragmentationper seon 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.

Empirical 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.In 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.Our 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.By 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.

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 vs. 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 data set 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. These results demonstrate a theoretically grounded basis for a model‐free evaluation of a system's 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., Ecological Monographs, 89 (2), ISSN:0012-9615, ISSN:1557-7015, ISSN:1741-7015

Integrating mechanistic models of movement and behavior into large-scale movement ecology and biodiversity research is one of the major challenges in current ecological science. This is mainly due to a large gap between the spatial scales at which these research lines act. Here, we propose to apply trait-based movement models to bridge this gap and generalize movement trajectories across species and ecosystems. We show how to use species traits (e.g., body mass) to generate allometric random walks and illustrate in two worked examples how this facilitates general predictions of species-interaction traits, meta-community structures, and biodiversity patterns. Thereby, allometric random walks foster a closer integration of movement ecology and biodiversity research by scaling up from small-scale mechanistic measurements to a predictive understanding of movement and biodiversity patterns in different landscapes.

The 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. Here, 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. We 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. Our 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.

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 predatorTetrahymena pyriformisto three different temperatures (15°C, 20°C and 25°C) for approximately 20 generations we conducted functional response experiments on bacterial prey along an experimental temperature gradient (15°C, 20°C and 25°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°C showed the highest feeding rates at 25°C experimental temperature, while predators adapted to 15°C showed the highest maximum feeding rate at 15°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.

Surrogate models are used for global approximation of responses generated by expensive computer experiments like CFD applications. In this paper, we make use of structural similarities which are shared by a class of related problems. We identify these structures by applying statistical shape models. They are used to build a generic surrogate model approximation to sample data of a new problem of the same class. In a variable fidelity framework the generic surrogate model is combined with the sample data to generate an efficient and globally accurate interpolation model, which requires less costly sample evaluations than ordinary response surface methods. We demonstrate our method with an aerodynamic test case and show that it significantly improves the approximation quality.

In aerodynamic applications often evaluations of an expensive computer simulation like a CFD solver are needed for a whole range of input parameters. Dense computations to describe the global behavior of an objective function are out of reach due to limited computational resources. Surrogate models like the Kriging method allow an interpolation of collected data and a global approximation. Adaptive sampling strategies can reduce the number of required samples for accurate and efficient surrogate models by automatically identifying critical or too coarse sampled regions of the input domain. We compare different existing sampling strategies as well as new theoretical methods using a dense set of validation data in order to gain a deeper understanding of optimal sample distributions and lower error boundaries.

Response surface methods for the approximation of outputs of computer experiments such as the Kriging method often suffer from a lack of accuracy or efficiency. Many computationally expensive samples are needed for the globally correct reproduction of an unknown response. We investigate adaptive sampling strategies, which can automatically identify critical regions of an input-parameter domain and require less samples than traditional one-stage approaches like Latin hypercube designs. Furthermore, we propose a new method which makes use of the assumption that the aerodynamic responses are not of arbitrary structure, but rather related to other instances of a mutual problem class. Both approaches are validated with numerical test cases, showing that they produce more accurate surrogate models using less samples than traditional approaches.

An electronic set up was established for the study of very short-lived (> 10 μs) isotopes arising from nuclear reactions. This was done by using a mass spectrometer on-line with an accelerator's beam.