Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species‐specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi‐dimensional assessments are still to be recommended.
Organismal movement is ubiquitous and facilitates important ecological mechanisms that drive community and metacommunity composition and hence biodiversity. In most existing ecological theories and models in biodiversity research, movement is represented simplistically, ignoring the behavioural basis of movement and consequently the variation in behaviour at species and individual levels. However, as human endeavours modify climate and land use, the behavioural processes of organisms in response to these changes, including movement, become critical to understanding the resulting biodiversity loss. Here, we draw together research from different subdisciplines in ecology to understand the impact of individual‐level movement processes on community‐level patterns in species composition and coexistence. We join the movement ecology framework with the key concepts from metacommunity theory, community assembly and modern coexistence theory using the idea of micro–macro links, where various aspects of emergent movement behaviour scale up to local and regional patterns in species mobility and mobile‐link‐generated patterns in abiotic and biotic environmental conditions. These in turn influence both individual movement and, at ecological timescales, mechanisms such as dispersal limitation, environmental filtering, and niche partitioning. We conclude by highlighting challenges to and promising future avenues for data generation, data analysis and complementary modelling approaches and provide a brief outlook on how a new behaviour‐based view on movement becomes important in understanding the responses of communities under ongoing environmental change.
Understanding host–pathogen dynamics requires realistic consideration of transmission events that, in the case of directly transmitted pathogens, result from contacts between susceptible and infected individuals. The corresponding contact rates are usually heterogeneous due to variation in individual movement patterns and the underlying landscape structure. However, in epidemiological models, the roles that explicit host movements and landscape structure play in shaping contact rates are often overlooked. We adapted an established agent‐based model of classical swine fever (CSF) in wild boar Sus scrofa to investigate how explicit representation of landscape heterogeneity and host movement between social groups affects invasion and persistence probabilities. We simulated individual movement both phenomenologically as a correlated random walk (CRW) and mechanistically by representing interactions of the moving individuals with the landscape and host population structure. The effect of landscape structure on the probability of invasion success and disease persistence depended remarkably on the way host movement is simulated and the case fatality ratio associated with the pathogen strain. The persistence probabilities were generally low with CRW which ignores feedbacks to external factors. Although the basic reproduction number R0, a measure of the contagiousness of an infectious disease, was kept constant, these probabilities were up to eight times higher under mechanistic movement rules, especially in heterogeneous landscapes. The increased persistence emerged due to important feedbacks of the directed movement on the spatial variation of host density, contact rates and transmission events to distant areas. Our findings underscore the importance of accounting for spatial context and group size structures in eco‐epidemiological models. Our study highlights that the simulation of explicit, mechanistic movement behaviour can reverse predictions of disease persistence in comparison to phenomenological rules such as random walk approaches. This can have severe consequences when predicting the probability of disease persistence and assessing control measures to prevent outbreaks.
91. Neutral landscape models (NLMs) simulate landscape patterns based 10 on theoretical distributions and can be used to systematically study 11 the effect of landscape structure on ecological processes. NLMs are 12 commonly used in landscape ecology to enhance the findings of field 13 studies as well as in simulation studies to provide an underlying land-14 scape. However, their creation so far has been limited to software 15 that is platform dependent, does not allow a reproducible workflow or 16 is not embedded in R, the prevailing programming language used by 17 ecologists. 18 2. Here, we present two complementary R packages NLMR and land-19 scapetools, that allow users to generate, manipulate and analyse NLMs 20 in a single environment. They grant the simulation of the widest col-21 lection of NLMs found in any single piece of software thus far while 22 allowing for easy manipulation in a self-contained and reproducible 23 workflow. The combination of both packages should stimulate a wider 24 usage of NLMs in landscape ecology. NLMR is a comprehensive col-25 lection of algorithms with which to simulate NLMs. landscapetools 26 provides a utility toolbox which facilitates an easy workflow with sim-27 ulated neutral landscapes and other raster data.283. We show two example applications that illustrate potential use cases 29 for NLMR and landscapetools: First, an agent-based simulation study 30 in which the effect of spatial structure on disease persistence was stud-31 ied. Here, spatial heterogeneity resulted in more variable disease out-32 comes compared to the common well-mixed host assumption. The 33 second example shows how increases in spatial scaling can introduce 34 35 4. Simplifying the workflow around handling NLMs should encourage an 36 uptake in the usage of NLMs. NLMR and landscapetools are both 37 generic frameworks that can be used in a variety of applications and 38 are a further step to having a unified simulation environment in R for 39 answering spatial research questions.40 Keywords: artificial pattern, landscape generator, neutral landscape 41 model, R, spatial visualisation, virtual landscape 42 48 tions and metrics of ecological patterns and processes at landscape scales (With 49
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