Ecological Complexity最新文献

Many authors have reported the risk of habitat fragmentation and the importance of connecting corridors between subpopulations (patches). However, we report that the connection of corridors may be harmful to species conservation. The paper deals with the birth and death processes of a single species living in a network composed of three patches. The disturbance due to a changing environment is assumed to affect only one patch. Two types of metapopulation models are applied. One is the lattice simulation model where we set a lattice as a patch. The other is based on metapopulation theory, which utilizes reaction-migration equations. The lattice simulation reveals that the connecting corridor between patches may be disadvantageous; the complete graph or a network with fully connected corridors is found not to be optimal for species conservation. Similar results are indicated by the application of metapopulation theory. We discuss the relationship between the risk of corridor construction and the effect of the hub patch.

Contacts between individuals are key for the spread of infectious disease. Although essential to understanding disease spread, contact rates are difficult to predict, based simply on population demographics in wildlife populations, because contact rates depend upon environmental features as well as the nature of social interactions within and between groups of individuals. We developed a detailed, behaviorally structured, individual-based model (IBM) in Netlogo to simulate contacts between- and within-groups of individual mule deer (Odocoileus hemionus), a species particularly susceptible to chronic wasting disease. The model tracks contacts (defined as two individuals coming within five meters of one another), recorded as between- or within-group depending on the social group membership of the two individuals (dyad). We parameterized the model with data from mule deer with global positioning systems (GPS) collars in east-central Alberta, Canada. Individuals move according to habitat preferences, home range attraction, and grouping behaviours. Animals were tracked at two-hour time steps and were modelled as selecting locations relative to preferred resources based on sex-specific integrated step-selection functions (iSSFs) with steps biased toward a home range centroid. Total within-group contacts increased with group size and were sensitive to changes in movement cohesion of the group and movement persistence, particularly movement cohesion. Total between-group contacts were sensitive only to the number of groups. We compared model predictions for where the locations of deer contacts occurred against an existing statistical model for the relative contact probabilities (RCP) on the same landscape (Dobbin et al. 2023). Predicted locations of deer contacts generally were consistent with higher predicted RCP values. When disease transmission is a function of contact rate, the model can be used to assess the interaction between model components (e.g., movement rates, grouping rules, home ranges, animal densities) and the spatial distribution of key natural and artificial resources that may attract deer and potentially increase disease spread.

The response threshold model explains the emergence of division of labor (i.e., task specialization) in an unstructured population by assuming that the individuals have different propensities to work on different tasks. The incentive to attend to a particular task increases when the task is left unattended and decreases when individuals work on it. Here we derive mean-field equations for the stimulus dynamics and show that they exhibit complex attractors through period-doubling bifurcation cascades when the noise disrupting the thresholds is small. In addition, we show how the fixed threshold can be set to ensure specialization in both the transient and equilibrium regimes of the stimulus dynamics. However, a complete explanation of the emergence of division of labor requires that we address the question of where the threshold variation comes from, starting from a homogeneous population. We then study a structured population scenario, where the population is divided into a large number of independent groups of equal size, and the fitness of a group is proportional to the weighted mean work performed on the tasks during a fixed period of time. Using a winner-take-all strategy to model group competition and assuming an initial homogeneous metapopulation, we find that a substantial fraction of workers specialize in each task, without the need to penalize task switching.

Dispersal is an ecological process central to population dynamics, describing one of the most important movement behaviours between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing and portraying plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. We establish that naturalistic kernels can emerge when simulating dispersal across irregular mosaic landscapes. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox; Vulpes vulpes). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.

Gymnophiona (caecilians) are inconspicuous, wormlike amphibians that are often hidden from human sight due to their aquatic or fossorial lifestyles. While Google Trends data have been widely used within conservation biology to provide information about the relative interest in species, and therefore of their flagship-making potential, as well as to identify current taxonomic biases. This study aimed to evaluate public interest in amphibians, with a particular focus on caecilians, and possible taxonomic biases of and within the class Amphibia. Google Trends data from amphibians, reptiles (sauropsids, excluding aves), and fishes (chondrichthyans + osteichthyans, excluding tetrapods) were analyzed and compared. In addition, a framework for a representation index and web representation index is presented. The introduced relative representation index was able to confirm taxonomic bias concerning Amphibia. Differences in worldwide public interest could also be evaluated within amphibians, indicating severe underrepresentation in public interest for caecilians.

Avoiding the ‘tragedy of the commons’ remains a challenge in many natural resource systems, and open-access fisheries are well-studied in this context. Here, an agent-based model is used to investigate how variation in fisher goals change what policies best solve the tragedy. When fishers’ goals are easily satisfied, commons problems are avoided without management interventions, but the imposition of quota limits triggers the tragedy. Thus, commons problems are not necessarily inevitable and sophisticated governance institutions or regulations are not always required to manage them; the same policy may prevent the tragedy or trigger it, depending on the fisher's goals. Given that it is difficult to ascertain them, by using a simulation model we can find patterns that help us identify fishers' goals and incorporate these patterns within our management procedure. This can assist adaptive management to better incorporate behaviour into policy evaluation.

Eco-epidemiology integrates ecological and epidemiological approaches to analyze both the impact of infectious diseases on ecological communities and how interspecific interactions can alter disease dynamics. With the aim of extracting general principles of eco-epidemiological dynamics, this article presents a review of the literature focusing on predator–prey type ordinary differential equation models with disease in one of the species. We included 81 articles that were categorized according to prey growth function, disease transmission function, epidemiological model compartments, and predator functional response. The findings reveal that these models share a common mathematical lineage, which in turn facilitates the construction of models based on the general assumptions identified in this study. The most prevalent models tend to assume logistic prey growth, a bilinear incidence rate for disease transmission, an epidemiological model of the Susceptible–Infected type, and a Holling Type II predator functional response.

Complexity has radically changed human understanding of the world environment and continues challenging our best scientific theories. In a rapidly changing research landscape, historical and philosophical insights into Complexity can heighten awareness of the proper theoretical perspectives scientists should adopt to advance the study of biocomplexity, including ecological complexity. The present work aims to deepen this awareness and disclose how researchers should generally approach, scientifically and philosophically, the question of what Complexity is, which is of great importance not only to the scientific community but also far beyond. First, this article reviews some critical historical turning points that led to Complexity. Second, the paper discusses philosophical-scientific approaches to the emergence as one of the most critical features of complex systems. The critical ideas behind attempts to understand the generators of complexity in nature are then presented, focusing on the living world. Finally, the review focuses on understanding the ecosystem- and organism-oriented perspectives of biocomplexity. We conclude that the genuine problem of the origin of complexity theory and biocomplexity will continue to inspire generations of researchers to search for new, more comprehensive mathematical and computational frameworks to explain biological hierarchies in order to further advance the scientific understanding of life.

Historically, the idea that ecosystems possess geographical boundaries has been dismissed as both naïve and impractical. But advancements in remote sensing have led to the reliable detection of spatial regimes that seem to provide early warning of a potential critical transition. This invites a reexamination of the role geographical boundaries play in explanations of the resilience concept. Despite apparent ontological imprecision, defining the boundaries of an ecosystem geographically, instead of dynamically (i.e., as collections of feedback mechanisms), dilates explanations of resilience to improve understanding of the history of contingent causal dynamics that culminate in emergent self-organization at a single scale. To demonstrate the utility of geographical boundaries, three related discussions connect spatial resilience theory with elements of island biogeography theory: (1) the function of stepping-stones as ecological filters, (2) mobile links as examples of the rescue effect, and (3) the way assembly rules and successive equilibria map onto the forward loop of the adaptive cycle heuristic.