Ecological Modelling最新文献

The impact of predators on prey activity patterns is routinely analyzed through the largely qualitative approach of comparing overlapping activity density plots. While this approach offers some insight into predator-prey dynamics, it precludes the direct estimation of a predator's impact on prey activity. We present a novel model that overcomes this shortcoming by using predator detections and an ideal prey activity curve to quantify the impact of predator activity on prey activity patterns. The model assumes that species strive to adhere to an ideal activity distribution and quantifies the degree to which a disturbance – in this case, a predator – prompts a departure from this ideal curve. We use spatially coincident camera trap records of mountain cottontail (Sylvilagus nuttallii), red fox (Vulpes vulpes), and coyote (Canis latrans) as a case study. We found that mountain cottontails limit their activity when red foxes are active, but do not alter their activity patterns to avoid coyotes. Critically, we also found that the model is sensitive to the a priori distribution used as an ideal activity curve. Therefore, preliminary testing of a priori distributions should be performed before running the model. This model improves our ability to quantify and predict predator-prey interactions as they pertain to activity patterns, but is presently limited to a single-predator system over a single active period.

Understorey communities in temperate forests have often been ignored in the study of the dynamics of forest structure and function, while evidence for the importance of this biotic layer is accumulating. Scarcity in understorey data with a high temporal resolution, and understorey data types that do not match popular vegetation modelling concepts, have limited previous modelling attempts to empirical models that are hard to extrapolate to new environmental conditions. Here we introduce a new process-based modelling approach designed specifically for understorey communities, whose dynamics are generally characterised by changes in (species-specific) cover data, while species characterisation is largely based on plant functional trait measurements. By confronting the model to data gathered in a large understorey mesocosm experiment, we show that our model concept is promising, and is able to predict performance differences within a species. Predictions across species were found to be more challenging, and will likely require new data on understorey traits and processes. In particular, new data on understorey carbon assimilation rates, vegetative phenology, plant architecture and belowground processes, are needed to advance the field of process-based understorey modelling.

Population models provide insights into population dynamics under diverse and untested chemical exposure scenarios, supporting their environmental risk assessment (ERA). In this study, we investigate the interplay of temperature and imidacloprid exposure on population dynamics using an Individual-Based Model (IBM) incorporating a dynamic energy budget (DEB) model for population dynamics and toxicokinetic-toxicodynamic models of the General Unified Threshold model for Survival (GUTS) framework to predict toxicity effects. For this, we tested different model configurations, where i) only the DEB parameters are corrected for temperature, as is common practice, and ii) also the TKTD parameters of the GUTS model are corrected for temperature. In doing so, we aim to evaluate the importance of temperature corrections in the GUTS model within an IBM framework. As expected, increased temperature amplitudes increase the range of simulated population sizes, and chemical exposure reduces the maximum population size. The combined effect of correcting both the DEB and TKTD parameters, however, yield an overall strongly negative effect on population sizes, particularly at lower temperatures. These results highlight the necessity of temperature-sensitive parameterization in population models for a protective risk assessment under the projected future climate conditions with increased temperatures and variability. Future considerations include incorporating local adaptations and acclimatization, particularly in different climate zones, to accurately interpret population model outcomes in the context of evolving environmental conditions. Such insights contribute to the refinement of ecological realism in ERA, enhancing the robustness of chemical risk management strategies.

The performance and transferability of species distribution models (SDMs) depends on a number of ecological, biological, and methodological factors. There is a growing body of literature that explores how the choice of climate covariate combinations and model parameters can affect predictive performance, but relatively few that delve into covariate reduction methods and the optimisation of model parameters, and the resulting spatial and temporal transferability of those models. The present work used the citrus pest, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), to illustrate how MaxEnt models trained on the insect’s native range in Asia varied in their predictions of climatic suitability across the introduced range when eight different covariate reduction methods were applied during model building. Additionally, it showed how model sensitivity varied across these different covariate combinations using three sets of independently validated occurrence points in the invaded range of the psyllid. Climatically suitable areas for D. citri differed by as much as two-fold between the best and worst-performing models in selected areas. Great care should be taken in the selection of the highest-performing predictor combinations and model parameter settings for SDMs, particularly in the case of invasive species where the assumption of environmental equilibrium is likely violated in the introduced range. Understanding how the predictive ability of SDMs can be influenced by the methodological choices made during the model building phase is vital to ensuring that ecological and invasion management programmes do not over- or underestimate climatic suitability and subsequent invasion risk.

This study, employing the AGRODEVS Agent-Based Model (ABM), systematically examined land use dynamics in Argentina's Pampas Region. Simulations under diverse scenarios highlighted the significant role of economic determinants, particularly crop price relationships, in influencing maize or wheat/soybean double cropping prevalence. Maize-dominated landscapes consistently achieved carbon sequestration goals, while wheat/soybean landscapes faced challenges, notably in ecotoxicity. Scenarios encompassed varying climatic conditions and soybean/maize price ratios, providing insights into the interplay shaping agricultural land use decisions among individual agents. The AGRODEVS model's robust performance underscored its effectiveness in integrating economic and environmental factors, contributing to a practical understanding of sustainable land use planning complexities.

Due to increasing climate uncertainties, optimizing plant traits is essential for sustainable agriculture. This article presents an approach that combines advanced modelling techniques to identify optimal plant traits under various agro-environmental conditions. By integrating a crop model, a climate generator, and our PEQI algorithm (Profile Expected Quantile Improvement), our method aims to create ideotype maps tailored to specific regions.

We use the SAMARA model (Simulator of crop trait Assembly, MAnagement Response, and Adaptation), calibrated with trials carried in Sahel on a set of local varieties, to simulate crop growth in diverse environments. The PEQI algorithm adjusts varietal parameters to maximize expected yield, defining precise selection objectives known as ideotypes, which are particularly important in regions with irregular rainfall patterns like the Sahel.

With the PEQI algorithm based on a kriging metamodel, we ensure effective adaptation to spatially variable environments. By leveraging a climate generator to simulate meteorological variability, our integrated approach optimizes crop yields in regions such as Senegal, southern Mali, Burkina Faso, and Guinea-Bissau. The outcome is an ideotype map for sorghum, providing breeders with a robust decision-support tool to enhance crop performance amidst climate uncertainty.

Population dynamics is influenced by between-individual variability. Dynamic Energy Budget (DEB) theory is an appealing framework for assessing such a variability, yet DEB parameters have rarely been estimated at the individual level. Bayesian hierarchical models show promise for inferring individual variability in DEB parameters, thought computational challenges have limited their use due to the need to solve differential equations. Timely, Stan has emerged as a general-purpose statistical tool for fitting dynamic models. This paper introduces an analytical strategy using Bayesian parametric inference and hierarchical modelling to estimate individual-specific DEB parameters. Two biologically relevant DEB parameters were successfully estimated for 69 Gilt-head breams (Sparus aurata) with up to 11 measures of length and wet weight each. The estimated between-individual variability in these two DEB parameters explained well the observed patterns in length and weight at between- and within-individual levels. Moreover, data-simulation experiments highlighted the potential and limitations of our approach, suggesting that improved data collection could enable to increase precision and the number of DEB parameters that can be estimated at the individual level. This strategy can better represent between-individual variability in DEB parameters, which ultimately may improve forecasting of population dynamics after integrating DEB into population models.

The metaphor of the Medawar zone describes the relationship between the difficulty of a scientific problem and the potential payoff of solving it. This zone represents the realm where questions offer high benefits relative to the effort required to address them. By harnessing the power of mechanistic modelling, scientists can navigate towards this zone, moving beyond known unknowns to discover unknown unknowns. This requires models to be realistic and reliable. Model usefulness, impact, and predictive power can be enhanced by achieving intermediate model complexity, where the trade-off between the realism and tractability of a model is optimised. To achieve these goals, we use the pattern-oriented modelling strategy (POM) to direct research into the Medawar zone by steering model structure towards intermediate complexity. We illustrate this strategy with a detailed conceptual process. Using example models from agri-ecological systems, we demonstrate how intermediate complexity can be attained through POM, and how pattern-oriented models of intermediate complexity that reproduce multiple patterns can uncover both known unknowns and unknown unknowns, which ultimately advances our understanding of complex systems and facilitates groundbreaking discoveries. In addition, we discuss the multidimensionality of the Medawar zone in the context of modelling philosophy and highlight the challenges and imperatives for achieving coherence in the modelling discipline. We emphasize the need for collaboration between end-users and modellers and the adoption of systematic modelling strategies such as POM.

As sciences mature, they transition from observation and description to explanation and prediction. This transition is associated with qualitative changes in the way quantitative mathematical formulations are constructed and interpreted, resulting in a ‘theory’. Such transitions from phenomenology to theory are happening in biology but the heuristic framework involved is rarely articulated. We here describe how the use of models in research sets model requirements, using Dynamic Energy Budget (DEB) theory to illustrate the more elaborate ones. We first make the distinction between mathematical formulae and models based on their relation to the abstract and real worlds. We then explain how the transition from models to theory affects model construction and parameter estimation, and discuss the concept of parameter estimation-in-context using the database Add_my_Pet on animal energetics. The transition comes with the need to develop auxiliary and meta- theory and to work with multiple datasets, implying constraints for the loss function that is used for parameter estimation. Finally, we discuss the extra requirements for general explanatory models: they need to be explicit on relevant general principles and to be embedded in a wider scientific context. We also discuss how we see theory’s relationship to observation and prediction change in the future as we use it to deal with theoretical and applied problems in biology.