It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution.

For most herbivorous animals, group-living appears to incur a high cost by intensifying feeding competition. These costs raise the question of how gregariousness (i.e., the tendency to aggregate) could have evolved to such an extent in taxa such as anthropoid primates and ungulates. When attempting to test the potential benefits and costs, previous foraging models demonstrated that group-living might be beneficial by lowering variance in intake, but that it reduces overall foraging success. However, these models did not fully account for the fact that gregariousness has multiple experiences and can vary in relation to ecological variables and foraging competition. Here, we present an agent-based model for testing how ecological variables impact the costs and benefits of gregariousness. In our simulations, primate-like agents forage on a variable resource landscape while maintaining spatial cohesion with conspecifics to varying degrees. The agents' energy intake rate, daily distance traveled, and variance in energy intake were recorded. Using Morris Elementary Effects sensitivity analysis, we tested the sensitivity of 10 model parameters, of which 2 controlled gregarious behavior and 8 controlled food resources, including multiple aspects of temporal and spatial heterogeneity. We found that, while gregariousness generally increased feeding competition, the costs of gregariousness were much lower when resources were less variable over time (i.e., when calorie extraction was slow and resource renewal was frequent). We also found that maintaining proximity to other agents resulted in lower variance in energy intake when resources were more variable over time. Thus, it appears that the costs and benefits of gregariousness are strongly influenced by the temporal characteristics of food resources, giving insight into the pressures that shaped the evolution of sociality and group living, including in our own lineage.

LandS: Vegetation modeling based on Ellenberg's ecological indicator values.

We present LandS, a new version of the Gras Model. The Gras Model was designed to simulate grassland development at local scales based on Ecological Indicator Values (EIVs) for different grassland management practices. In LandS, we complemented the existing set of EIVs with a second set representing several environmental factors: light, moisture, temperature, soil pH and nitrogen, also known as Ellenberg's EIVs. These new EIVs make the model more versatile and applicable to a wide range of sites across Central Europe. For example, it can be used on sites with dry or moist, acidic or calcareous soils in grassland or forest environments. We have also improved the implementation of the model by introducing version control and moving species and site-specific variables to data input files, so that species sets can be easily swapped for application in new study sites. We demonstrate the use and behavior of the model in two simulation experiments exploring interactions mediated by Ellenberg's EIVs, using input files to apply the model to different landscapes. We also provide detailed guidance on species selection and calibration, and discuss model limitations.•LandS is an improved version of the GraS Model for simulating vegetation development at the local scale.•It includes Ellenberg-like indicator values for environmental variables for inverse prediction of species occurrence and composition.•The model is now flexible enough to be used for study sites throughout Central Europe, using data input files for species initialization.

Mechanistic population models for ecological risk assessment and decision support: The importance of good conceptual model diagrams.

The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment (ERA) is increasing. This growth has been facilitated by advances in technology, allowing the simulation of more complex systems, as well as by standardized approaches for model development, documentation, and evaluation. Mechanistic population models are particularly useful for simulating complex systems, but the required model complexity can make them challenging to communicate. Conceptual diagrams that summarize key model elements, as well as elements that were considered but not included, can facilitate communication and understanding of models and increase their acceptance as decision-support tools. Currently, however, there are no consistent standards for creating or presenting conceptual model diagrams (CMDs), and both terminology and content vary widely. Here, we argue that greater consistency in CMD development and presentation is an important component of good modeling practice, and we provide recommendations, examples, and a free web app (pop-cmd.com) for achieving this for population models used for decision support in ERAs. Integr Environ Assess Manag 2024;00:1-9. © 2023 SETAC.

Ecology needs to overcome siloed modelling.

Bahlburg et al. re-implemented eight growth models of Antarctic krill and showed that their predictions are all over the place. The authors discuss the reasons for this and how more coherence in modelling could be achieved through systematic model comparison and integration. For this, we need a common language.

Local buffer mechanisms for population persistence.

Assessing and predicting the persistence of populations is essential for the conservation and control of species. Here, we argue that local mechanisms require a better conceptual synthesis to facilitate a more holistic consideration along with regional mechanisms known from metapopulation theory. We summarise the evidence for local buffer mechanisms along with their capacities and emphasise the need to include multiple buffer mechanisms in studies of population persistence. We propose an accessible framework for local buffer mechanisms that distinguishes between damping (reducing fluctuations in population size) and repelling (reducing population declines) mechanisms. We highlight opportunities for empirical and modelling studies to investigate the interactions and capacities of buffer mechanisms to facilitate better ecological understanding in times of ecological upheaval.

Digital twins: dynamic model-data fusion for ecology.

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

Simulation of Varroa mite control in honey bee colonies without synthetic acaricides: Demonstration of Good Beekeeping Practice for Germany in the BEEHAVE model.

The BEEHAVE model simulates the population dynamics and foraging activity of a single honey bee colony (Apis mellifera) in great detail. Although it still makes numerous simplifying assumptions, it appears to capture a wide range of empirical observations. It could, therefore, in principle, also be used as a tool in beekeeper education, as it allows the implementation and comparison of different management options. Here, we focus on treatments aimed at controlling the mite Varroa destructor. However, since BEEHAVE was developed in the UK, mite treatment includes the use of a synthetic acaricide, which is not part of Good Beekeeping Practice in Germany. A practice that consists of drone brood removal from April to June, treatment with formic acid in August/September, and treatment with oxalic acid in November/December. We implemented these measures, focusing on the timing, frequency, and spacing between drone brood removals. The effect of drone brood removal and acid treatment, individually or in combination, on a mite-infested colony was examined. We quantify the efficacy of Varroa mite control as the reduction of mites in treated bee colonies compared to untreated bee colonies. We found that drone brood removal was very effective, reducing mites by 90% at the end of the first simulation year after the introduction of mites. This value was significantly higher than the 50-67% reduction expected by bee experts and confirmed by empirical studies. However, literature reports varying percent reductions in mite numbers from 10 to 85% after drone brood removal. The discrepancy between model results, empirical data, and expert estimates indicate that these three sources should be reviewed and refined, as all are based on simplifying assumptions. These results and the adaptation of BEEHAVE to the Good Beekeeping Practice are a decisive step forward for the future use of BEEHAVE in beekeeper education in Germany and anywhere where organic acids and drone brood removal are utilized.

Large-scale PVA modeling of insects in cultivated grasslands: The role of dispersal in mitigating the effects of management schedules under climate change.

In many species, dispersal is decisive for survival in a changing climate. Simulation models for population dynamics under climate change thus need to account for this factor. Moreover, large numbers of species inhabiting agricultural landscapes are subject to disturbances induced by human land use. We included dispersal in the HiLEG model that we previously developed to study the interaction between climate change and agricultural land use in single populations. Here, the model was parameterized for the large marsh grasshopper (LMG) in cultivated grasslands of North Germany to analyze (1) the species development and dispersal success depending on the severity of climate change in subregions, (2) the additional effect of grassland cover on dispersal success, and (3) the role of dispersal in compensating for detrimental grassland mowing. Our model simulated population dynamics in 60-year periods (2020-2079) on a fine temporal (daily) and high spatial (250 × 250 m2) scale in 107 subregions, altogether encompassing a range of different grassland cover, climate change projections, and mowing schedules. We show that climate change alone would allow the LMG to thrive and expand, while grassland cover played a minor role. Some mowing schedules that were harmful to the LMG nevertheless allowed the species to moderately expand its range. Especially under minor climate change, in many subregions dispersal allowed for mowing early in the year, which is economically beneficial for farmers. More severe climate change could facilitate LMG expansion to uninhabited regions but would require suitable mowing schedules along the path. These insights can be transferred to other species, given that the LMG is considered a representative of grassland communities. For more specific predictions on the dynamics of other species affected by climate change and land use, the publicly available HiLEG model can be easily adapted to the characteristics of their life cycle.

Chronic and Acute Effects of Imidacloprid on a Simulated BEEHAVE Honeybee Colony.

Honeybees (Apis mellifera) are important pollinators for wild plants as well as for crops, but honeybee performance is threatened by several stressors including varroa mites, gaps in foraging supply, and pesticides. The consequences of bee colony longtime exposure to multiple stressors are not well understood. The vast number of possible stressor combinations and necessary study duration require research comprising field, laboratory, and simulation experiments. We simulated long-term exposure of a honeybee colony to the insecticide imidacloprid and to varroa mites carrying the deformed wing virus in landscapes with different temporal gaps in resource availability as single stressors and in combinations. Furthermore, we put a strong emphasis on chronic lethal, acute sublethal, and acute lethal effects of imidacloprid on honeybees. We have chosen conservative published values to parameterize our model (e.g., highest reported imidacloprid contamination). As expected, combinations of stressors had a stronger negative effect on bee performance than each single stressor alone, and effect sizes were larger after 3 years of exposure than after the first year. Imidacloprid-caused reduction in bee performance was almost exclusively due to chronic lethal effects because the thresholds for acute effects were rarely met in simulations. In addition, honeybee colony extinctions were observed by the last day of the first year but more pronounced on the last days of the second and third simulation year. In conclusion, our study highlights the need for more long-term studies on chronic lethal effects of pesticides on honeybees. Environ Toxicol Chem 2022;41:2318-2327. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Forecasting effects of transport infrastructure on endangered tigers: a tool for conservation planning.

The rapid development of transport infrastructure is a major threat to endangered species worldwide. Roads and railways can increase animal mortality, fragment habitats, and exacerbate other threats to biodiversity. Predictive models that forecast the future impacts to endangered species can guide land-use planning in ways that proactively reduce the negative effects of transport infrastructure. Agent-based models are well suited for predictive scenario testing, yet their application to endangered species conservation is rare. Here, we developed a spatially explicit, agent-based model to forecast the effects of transport infrastructure on an isolated tiger (Panthera tigris) population in Nepal's Chitwan National Park-a global biodiversity hotspot. Specifically, our model evaluated the independent and interactive effects of two mechanisms by which transport infrastructure may affect tigers: (a) increasing tiger mortality, e.g., via collisions with vehicles, and (b) depleting prey near infrastructure. We projected potential impacts on tiger population dynamics based on the: (i) existing transportation network in and near the park, and (ii) the inclusion of a proposed railway intersecting through the park's buffer zone. Our model predicted that existing roads would kill 46 tigers over 20 years via increased mortality, and reduced the adult tiger population by 39% (133 to 81). Adding the proposed railway directly killed 10 more tigers over those 20 years; deaths that reduced the overall tiger population by 30 more individuals (81 to 51). Road-induced mortality also decreased the proportion of time a tiger occupied a given site by 5 years in the 20-year simulation. Interestingly, we found that transportation-induced depletion of prey decreased tiger occupancy by nearly 20% in sites close to roads and the railway, thereby reducing tiger exposure to transportation-induced mortality. The results of our model constitute a strong argument for taking into account prey distributions into the planning of roads and railways. Our model can promote tiger-friendly transportation development, for example, by improving Environmental Impact Assessments, identifying "no go" zones where transport infrastructure should be prohibited, and recommending alternative placement of roads and railways.

Stabilizing microbial communities by looped mass transfer.

Building and changing a microbiome at will and maintaining it over hundreds of generations has so far proven challenging. Despite best efforts, complex microbiomes appear to be susceptible to large stochastic fluctuations. Current capabilities to assemble and control stable complex microbiomes are limited. Here, we propose a looped mass transfer design that stabilizes microbiomes over long periods of time. Five local microbiomes were continuously grown in parallel for over 114 generations and connected by a loop to a regional pool. Mass transfer rates were altered and microbiome dynamics were monitored using quantitative high-throughput flow cytometry and taxonomic sequencing of whole communities and sorted subcommunities. Increased mass transfer rates reduced local and temporal variation in microbiome assembly, did not affect functions, and overcame stochasticity, with all microbiomes exhibiting high constancy and increasing resistance. Mass transfer synchronized the structures of the five local microbiomes and nestedness of certain cell types was eminent. Mass transfer increased cell number and thus decreased net growth rates µ'. Subsets of cells that did not show net growth µ'SCx were rescued by the regional pool R and thus remained part of the microbiome. The loop in mass transfer ensured the survival of cells that would otherwise go extinct, even if they did not grow in all local microbiomes or grew more slowly than the actual dilution rate D would allow. The rescue effect, known from metacommunity theory, was the main stabilizing mechanism leading to synchrony and survival of subcommunities, despite differences in cell physiological properties, including growth rates.

Fluctuations in Density-Dependent Selection Drive the Evolution of a Pace-of-Life Syndrome Within and Between Populations.

AbstractThe pace-of-life syndrome (POLS) hypothesis posits that suites of traits are correlated along a slow-fast continuum owing to life history trade-offs. Despite widespread adoption, environmental conditions driving the emergence of POLS remain unclear. A recently proposed conceptual framework of POLS suggests that a slow-fast continuum should align to fluctuations in density-dependent selection. We tested three key predictions made by this framework with an eco-evolutionary agent-based population model. Selection acted on responsiveness (behavioral trait) to interpatch resource differences and the reproductive investment threshold (life history trait). Across environments with density fluctuations of different magnitudes, we observed the emergence of a common axis of trait covariation between and within populations (i.e., the evolution of a POLS). Slow-type (fast-type) populations with high (low) responsiveness and low (high) reproductive investment threshold were selected at high (low) population densities and less (more) intense and frequent density fluctuations. In support of the predictions, fast-type populations contained a higher degree of variation in traits and were associated with higher intrinsic reproductive rate (r0) and higher sensitivity to intraspecific competition (γ), pointing to a universal trade-off. While our findings support that POLS aligns with density-dependent selection, we discuss possible mechanisms that may lead to alternative evolutionary pathways.

Energy-mediated responses to changing prey size and distribution in marine top predator movements and population dynamics.

Climate change is modifying the structure of marine ecosystems, including that of fish communities. Alterations in abiotic and biotic conditions can decrease fish size and change community spatial arrangement, ultimately impacting predator species which rely on these communities. To conserve predators and understand the drivers of observed changes in their population dynamics, we must advance our understanding of how shifting environmental conditions can impact populations by limiting food available to individuals. To investigate the impacts of changing fish size and spatial aggregation on a top predator population, we applied an existing agent-based model parameterized for harbour porpoises Phocoena phocoena which represents animal energetics and movements in high detail. We used this framework to quantify the impacts of shifting prey size and spatial aggregation on porpoise movement, space use, energetics and population dynamics. Simulated individuals were more likely to switch from area-restricted search to transit behaviour with increasing prey size, particularly when starving, due to elevated resource competition. In simulations with highly aggregated prey, higher prey encounter rates counteracted resource competition, resulting in no impacts of prey spatial aggregation on movement behaviour. Reduced energy intake with decreasing prey size and aggregation level caused population decline, with a 15% decrease in fish length resulting in total population collapse Increasing prey consumption rates by 42.8 ± 4.5% could offset population declines; however, this increase was 21.3 ± 12.7% higher than needed to account for changes in total energy availability alone. This suggests that animals in realistic seascapes require additional energy to locate smaller prey which should be considered when assessing the impacts of decreased energy availability. Changes in prey size and aggregation influenced movements and population dynamics of simulated harbour porpoises, revealing that climate-induced changes in prey structure, not only prey abundance, may threaten predator populations. We demonstrate how a population model with realistic animal movements and process-based energetics can be used to investigate population consequences of shifting food availability, such as those mediated by climate change, and provide a mechanistic explanation for how changes in prey structure can impact energetics, behaviour and ultimately viability of predator populations.

Collecting eco-evolutionary data in the dark: Impediments to subterranean research and how to overcome them.

Caves and other subterranean habitats fulfill the requirements of experimental model systems to address general questions in ecology and evolution. Yet, the harsh working conditions of these environments and the uniqueness of the subterranean organisms have challenged most attempts to pursuit standardized research.Two main obstacles have synergistically hampered previous attempts. First, there is a habitat impediment related to the objective difficulties of exploring subterranean habitats and our inability to access the network of fissures that represents the elective habitat for the so-called "cave species." Second, there is a biological impediment illustrated by the rarity of most subterranean species and their low physiological tolerance, often limiting sample size and complicating laboratory experiments.We explore the advantages and disadvantages of four general experimental setups (in situ, quasi in situ, ex situ, and in silico) in the light of habitat and biological impediments. We also discuss the potential of indirect approaches to research. Furthermore, using bibliometric data, we provide a quantitative overview of the model organisms that scientists have exploited in the study of subterranean life.Our over-arching goal is to promote caves as model systems where one can perform standardized scientific research. This is important not only to achieve an in-depth understanding of the functioning of subterranean ecosystems but also to fully exploit their long-discussed potential in addressing general scientific questions with implications beyond the boundaries of this discipline.

While shoot herbivores reduce, root herbivores increase nutrient enrichment's impact on diversity in a grassland model.

Nutrient enrichment is widespread throughout grassland systems and expected to increase during the Anthropocene. Trophic interactions, like aboveground herbivory, have been shown to mitigate its effect on plant diversity. Belowground herbivory may also impact these habitats' response to nutrient enrichment, but its influence is much less understood, and likely to depend on factors such as the herbivores' preference for dominant species and the symmetry of belowground competition. If preferential toward the dominant, fastest growing species, root herbivores may reduce these species' relative fitness and support diversity during nutrient enrichment. However, as plant competition belowground is commonly considered to be symmetric, root herbivores may be less impactful than shoot herbivores because they do not reduce any competitive asymmetry between the dominant and subordinate plants. To better understand this system, we used an established, two-layer, grassland community model to run a full-factorially designed simulation experiment, crossing the complete removal of aboveground herbivores and belowground herbivores with nutrient enrichment. After 100 yr of simulation, we analyzed communities' diversity, competition on the individual level, as well as their resistance and recovery. The model reproduced both observed general effects of nutrient enrichment in grasslands and the short-term trends of specific experiments. We found that belowground herbivores exacerbate the negative influence of nutrient enrichment on Shannon diversity within our model grasslands, while aboveground herbivores mitigate its effect. Indeed, data on individuals' above- and belowground resource uptake reveals that root herbivory reduces resource limitation belowground. As with nutrient enrichment, this shifts competition aboveground. Since shoot competition is asymmetric, with larger, taller individuals gathering disproportionate resources compared to their smaller, shorter counterparts, this shift promotes the exclusion of the smallest species. While increasing the root herbivores' preferences toward dominant species lessens their negative impact, at best they are only mildly advantageous, and they do very little reduce the negative consequences of nutrient enrichment. Because our model's belowground competition is symmetric, we hypothesize that root herbivores may be beneficial when root competition is asymmetric. Future research into belowground herbivory should account for the nature of competition belowground to better understand the herbivores' true influence.

Movement and Seasonal Energetics Mediate Vulnerability to Disturbance in Marine Mammal Populations.

AbstractIn marine environments, noise from human activities is increasing dramatically, causing animals to alter their behavior and forage less efficiently. These alterations incur energetic costs that can result in reproductive failure and death and may ultimately influence population viability, yet the link between population dynamics and individual energetics is poorly understood. We present an energy budget model for simulating effects of acoustic disturbance on populations. It accounts for environmental variability and individual state, while incorporating realistic animal movements. Using harbor porpoises (Phocoena phocoena) as a case study, we evaluated population consequences of disturbance from seismic surveys and investigated underlying drivers of vulnerability. The framework reproduced empirical estimates of population structure and seasonal variations in energetics. The largest effects predicted for seismic surveys were in late summer and fall and were unrelated to local abundance, but instead were related to lactation costs, water temperature, and body fat. Our results demonstrate that consideration of temporal variation in individual energetics and their link to costs associated with disturbances is imperative when predicting disturbance impacts. These mechanisms are general to animal species, and the framework presented here can be used for gaining new insights into the spatiotemporal variability of animal movements and energetics that control population dynamics.

Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach.

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Honey bee colony performance affected by crop diversity and farmland structure: a modeling framework.

Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics, PH and PP, which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower, PH and PP were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, and PH (269.5 kg) and PP (108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent-Based Population Models for Ecological Risk Assessment.

Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521-540. © 2020 SETAC.