Biology subjects

Travis, J. M. J.

Publications and source records attributed to Travis, J. M. J..

5 recordsLinked to original sources

bioRxiv2025

Landscape context drives within-species variation in dispersal kernels and limits transferability

Dispersal is a key ecological trait that ensures connectivity, gene flow, and range dynamics, yet empirical information about the dispersal process remains scarce. While interspecific differences have been synthesized, intraspecific variation driven by individual movement and landscape context remains poorly understood. Here, we use the individual-based modelling platform RangeShifter to simulate movement trajectories of four animal species, from insects to mammals, across neutral landscapes varying in habitat amount and fragmentation. From these simulations we derived dispersal kernels using five probability density functions and analysed their response to landscape context. Across species, log-normal (fat-tailed) kernels consistently best described dispersal, but both median and long-distance dispersal varied strongly with habitat configuration. Habitat loss consistently reduced abundances, whereas fragmentation per se more strongly shaped dispersal kernels, providing mechanistic insight into a central debate in landscape ecology. Our results highlight that dispersal kernels do not transfer reliably across environments and should be treated as emergent, context-dependent properties. Progress in predicting species responses to global change will require empirical studies explicitly integrated with mechanistic modelling to uncover generalizable links between dispersal, traits, and landscape context.

ecology↗

bioRxiv2025

Flexibility in movement strategies of neotropical nectarivorous birds: insights from high-Andean hummingbirds and flowerpiercers

Nectarivorous birds should have flexible movement behaviours in response to the presence of competitors and the spatiotemporal availability of flowering plants, particularly in tropical regions where flower blooms follow patterns of precipitation that are unpredictable across years. While pollinators such as hummingbirds (Trochilidae) have diet breadths that are constrained by trait-matching with flowers, nectar-robbing flowerpiercers are tanagers (Thraupidae) that typically drink nectar from holes they pierce near the flowers base. Consequently, distinct movement patterns for these two bird families would be expected from optimal foraging theory, yet little is known about how tropical nectarivores move in response to fluctuating conditions. We used fine-resolution tracking data from an automated radio telemetry grid to compare movement patterns between hummingbirds and flowerpiercers in high-Andean mountain ecosystems. We obtained an accumulated total of 435,513 location estimates and 452 tracking days from 22 individuals across six different bird species. Our results indicate that hummingbirds exhibit a greater diversity of movement behaviours in comparison to flowerpiercers, with varying space use and recursion patterns that are characteristic of sedentary, commuting/traplining and exploratory strategies, whereas most species of flowerpiercers were classified as central-place foragers. However, daily movement metrics show that there is substantial variation, and hierarchical clustering does not necessarily group together bird families, species, nor even individuals as more similar to each other. Flexibility in daily movement behaviours has seldom been described for neotropical nectarivorous birds in the wild. It emerges as an important trait to adjust behaviour to variable local contexts, and may be adaptive for persistence in challenging mountain ecosystems where weather conditions are harsh and floral resources are seasonal and limited. A better understanding of flexibility in movement behaviour can enhance our predictions about how animals respond to environmental change and anthropogenic pressures.

ecology↗

bioRxiv2024

Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems

Automated radio telemetry systems (ARTS) have the potential to revolutionise our understanding of animal movement by providing a near-continuous record of individual locations in the wild. However, localisation error in data generated by ARTS can be very high, especially in natural landscapes with complex vegetation structure and topography. This curtails the ecological questions that may be addressed with this technology. Here, we set up an ARTS grid in a valley with heterogeneous vegetation cover in the Colombian high Andes and applied an analytical pipeline to test the effectiveness of localisation methods. We performed calibration trials to simulate animal movement in high-or low-flight, or walking on the ground, and compared workflows with varying decisions related to signal cleaning, selection, smoothing, and interpretation, along with four multilateration approaches. We also quantified the influence of spatial features on the systems accuracy. We tested the grid by deploying tags on two high-altitude hummingbirds, the Great Sapphirewing (Pterophanes cyanopterus) and Bronze-tailed Thornbill (Chalcostigma heteropogon). Results showed large variation in localisation error, ranging from only 0.4-43.4 m from known locations up to 474-1929 m, depending on the localisation method used. The lowest average median error across calibration tracks was 105 m. In particular, we found that the selection of higher radio signal strengths and data smoothing based on the temporal autocorrelation in movement data are useful tools to improve accuracy. Moreover, the variables that significantly influence localisation error include terrain ruggedness, height of movement, vegetation type, and the location of animals inside or outside the grid area. In the case of our study system, thousands of location points were successfully estimated for two hummingbird species that previously lacked movement ecology data. Our case study on hummingbirds suggests ARTS grids can be used to estimate small animals home ranges, associations with vegetation types, and seasonality in occurrence. We present a comparative localisation pipeline, highlighting the variety of possible decisions while processing radio signal data. Overall, this study provides guidance to improve the resolution of location estimates, broadening the application of this tracking technology in the study of the spatial ecology of wild populations.

ecology↗

bioRxiv2021

Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography

Defining appropriate null expectations for species distribution hypotheses is important because sampling bias and spatial autocorrelation can produce realistic, but ecologically meaningless, geographic patterns. Generating null species occurrences with similar spatial structure to observed data can help overcome these problems, but existing methods focus on single or pairs of species and do not incorporate between-species spatial structure that may occlude comparative biogeographic analyses. Here, we describe an algorithm for generating randomised species occurrence points that mimic the within- and between-species spatial structure of real datasets and implement it in a new R package - fauxcurrence. The algorithm can be implemented on any geographic domain for any number of species, limited only by computing power. To demonstrate its utility, we apply the algorithm to two common analysis-types: testing the fit of species distribution models (SDMs) and evaluating niche-overlap. The method works well on all tested datasets within reasonable timescales. We found that many SDMs, despite a good fit to the data, were not significantly better than null expectations and identified only two cases (out of a possible 32) of significantly higher niche divergence than expected by chance. The package is user-friendly, flexible and has many potential applications beyond those tested here, such as joint SDM evaluation and species co-occurrence analysis, spanning the areas of ecology, evolutionary biology and biogeography.

ecology↗

bioRxiv2020

RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species' responses to environmental change

O_LIReliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses. C_LIO_LIWe present RangeShiftR, an R package that provides flexible and fast simulations of spatial eco-evolutionary dynamics and species responses to environmental changes. It implements the individual-based simulation software RangeShifter for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the packages functionality, describe the underlying model structure with its main components and present a short example. C_LIO_LIRangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with comprehensive documentation and elaborate tutorials to provide a low entry level. Thanks to the implementation of the core code in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. C_LIO_LIThe RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification, and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models. C_LI

ecology↗