Biased correlated random walk and foray loop: which movement hypothesis drives a butterfly metapopulation?

A rapid assessment methodology for quantifying and visualizing functional landscape connectivity

Context

The number of publications that evaluate or use landscape connectivity has grown dramatically in recent years. But the biological realism of common connectivity assessments remains limited. To address this shortcoming, I introduce a flexible methodology for evaluating functional landscape connectivity that can be quick to implement, biologically nuanced, and straightforward to interpret.

Methods

I combined a US Fish and Wildlife Service land cover map with information from existing empirical studies to develop a movement simulator for the Fender's blue butterfly, an endangered species in Oregon, USA. I use the resulting butterfly model to explore the concepts and mechanics behind my novel connectivity assessment methodology.

Results

My methods are able to identify clusters of connected resource patches, quantify and visualize movement rates between patches, and identify opportunities for enhancing connectivity through restoration and mitigation. My results include an emergent dispersal kernel that captures the influence of movement behavior on connectivity.

Discussion

The methods I introduce are capable of generating detailed yet practical connectivity analyses that can incorporate considerable biological and behavioral realism. My approach is simple to implement, and the requisite data can be modest. The toolkit I developed has the potential to standardize connectivity assessments that use either real or simulated movement data.

Differentiating spillover: an examination of cross-habitat movement in ecology spillover in ecology

Organisms that immigrate into a recipient habitat generate a movement pattern that affects local population dynamics and the environment. Spillover is the pattern of unidirectional movement from a donor habitat to a different, adjacent recipient habitat. However, ecological definitions are often generalized to include any cross-habitat movement, which limits within- and cross-discipline collaboration. To assess spillover nomenclature, we reviewed 337 studies within the agriculture, disease, fisheries and habitat fragmentation disciplines. Each study's definition of spillover and the methods used were analysed. We identified four descriptors (movement, habitat type and arrangement, and effect) used that differentiate spillover from other cross-habitat movement patterns (dispersal, foray loops and edge movement). Studies often define spillover as movement (45%) but rarely measure it as such (4%), particularly in disease and habitat fragmentation disciplines. Consequently, 98% of studies could not distinguish linear from returning movement out of a donor habitat, which can overestimate movement distance. Overall, few studies (12%) included methods that matched their own definition, revealing a distinct mismatch. Because theory shows that long-term impacts of the different movement patterns can vary, differentiating spillover from other movement patterns is necessary for effective long-term and inter-disciplinary management of organisms that use heterogeneous landscapes.

Influence of the large-Z effect during contact between butterfly sister species

Recently diverged butterfly populations in North America have been found to exhibit high levels of divergence on the Z chromosome relative to autosomes, as measured by fixation index, F st . The pattern of divergence appears to result from accumulation of incompatible alleles, obstructing introgression on the Z chromosome in hybrids (i.e., the large-Z effect); however, it is unknown whether this mechanism is sufficient to explain the data. Here, we simulate the effects of hybrid incompatibility on interbreeding butterfly populations using a model in which populations accumulate cross-incompatible alleles in allopatry prior to contact. We compute statistics for introgression and population divergence during contact between model populations and compare our results to those for 15 pairs of butterfly species interbreeding along a suture zone in central Texas. Time scales for allopatry and contact in the model are scaled to glacial and interglacial periods during which real populations evolved in isolation and contact. We find that the data for butterflies are explained well by an otherwise neutral model under slow fusion conditions. In particular, levels of divergence on the Z chromosome increase when interacting clusters of genes are closely linked, consistent with clusters of functionally related genes in butterfly genomes.

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

Integrating the influence of weather into mechanistic models of butterfly movement

BACKGROUND

Understanding the factors influencing movement is essential to forecasting species persistence in a changing environment. Movement is often studied using mechanistic models, extrapolating short-term observations of individuals to longer-term predictions, but the role of weather variables such as air temperature and solar radiation, key determinants of ectotherm activity, are generally neglected. We aim to show how the effects of weather can be incorporated into individual-based models of butterfly movement thus allowing analysis of their effects.

METHODS

We constructed a mechanistic movement model and calibrated it with high precision movement data on a widely studied species of butterfly, the meadow brown (Maniola jurtina), collected over a 21-week period at four sites in southern England. Day time temperatures during the study ranged from 14.5 to 31.5 °C and solar radiation from heavy cloud to bright sunshine. The effects of weather are integrated into the individual-based model through weather-dependent scaling of parametric distributions representing key behaviours: the durations of flight and periods of inactivity.

RESULTS

Flight speed was unaffected by weather, time between successive flights increased as solar radiation decreased, and flight duration showed a unimodal response to air temperature that peaked between approximately 23 °C and 26 °C. After validation, the model demonstrated that weather alone can produce a more than two-fold difference in predicted weekly displacement.

CONCLUSIONS

Individual Based models provide a useful framework for integrating the effect of weather into movement models. By including weather effects we are able to explain a two-fold difference in movement rate of M. jurtina consistent with inter-annual variation in dispersal measured in population studies. Climate change for the studied populations is expected to decrease activity and dispersal rates since these butterflies already operate close to their thermal optimum.

Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns, Scaling, and Mitigation

Epidemics caused by long-distance dispersed pathogens result in some of the most explosive and difficult to control diseases of both plants and animals (including humans). Yet the factors influencing disease spread, especially in the early stages of the outbreak, are not well-understood. We present scaling relationships, of potentially widespread relevance, that were developed from more than 15 years of field and in silico single focus studies of wheat stripe rust spread. These relationships emerged as a consequence of accounting for a greater proportion of the fat-tailed disease gradient that may be frequently underestimated in disease spread studies. Leptokurtic dispersal gradients (highly peaked and fat-tailed) are relatively common in nature and they can be represented by power law functions. Power law scale invariance properties generate patterns that repeat over multiple spatial scales, suggesting important and predictable scaling relationships between disease levels during the first generation of disease outbreaks and subsequent epidemic spread. Experimental wheat stripe rust outbreaks and disease spread simulations support theoretical scaling relationships from power law properties and suggest that relatively straightforward scaling approximations may be useful for projecting the spread of disease caused by long-distance dispersed pathogens. Our results suggest that, when actual dispersal/disease data are lacking, an inverse power law with exponent = 2 may provide a reasonable approximation for modeling disease spread. Furthermore, our experiments and simulations strongly suggest that early control treatments with small spatial extent are likely to be more effective at suppressing an outbreak caused by a long-distance dispersed pathogen than would delayed treatment of a larger area. The scaling relationships we detail and the associated consequences for disease control may be broadly applicable to plant and animal pathogens characterized by non-exponentially bound, fat-tailed dispersal gradients.