Geology topics

Marielle L. van Toor

Publications and source records attributed to Marielle L. van Toor.

3 recordsLinked to original sources

USGS2021

Evidence for continental-scale dispersal of antimicrobial resistant bacteria by landfill-foraging gulls

Anthropogenic inputs into the environment may serve as sources of antimicrobial resistant bacteria and alter the ecology and population dynamics of synanthropic wild animals by providing supplemental forage. In this study, we used a combination of phenotypic and genomic approaches to characterize antimicrobial resistant indicator bacteria, animal telemetry to describe host movement patterns, and a novel modeling approach to combine information from these diverse data streams to investigate the acquisition and long-distance dispersal of antimicrobial resistant bacteria by landfill-foraging gulls. Our results provide evidence that gulls acquire antimicrobial resistant bacteria from anthropogenic sources, which they may subsequently disperse across and between continents via migratory movements. Furthermore, we introduce a flexible modeling framework to estimate the relative dispersal risk of antimicrobial resistant bacteria in western North America and adjacent areas within East Asia, which may be adapted to provide information on the risk of dissemination of other organisms and pathogens maintained by wildlife through space and time.

Article↗

USGS2018

Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity

Context High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance. Objectives We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range. Methods We used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature. Results Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species. Conclusions We show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

Landscape Ecology↗

USGS2016

Temporal segmentation of animal trajectories informed by habitat use

Most animals live in seasonal environments and experience very different conditions throughout the year. Behavioral strategies like migration, hibernation, and a life cycle adapted to the local seasonality help to cope with fluctuations in environmental conditions. Thus, how an individual utilizes the environment depends both on the current availability of habitat and the behavioral prerequisites of the individual at that time. While the increasing availability and richness of animal movement data has facilitated the development of algorithms that classify behavior by movement geometry, changes in the environmental correlates of animal movement have so far not been exploited for a behavioral annotation. Here, we suggest a method that uses these changes in individual–environment associations to divide animal location data into segments of higher ecological coherence, which we term niche segmentation. We use time series of random forest models to evaluate the transferability of habitat use over time to cluster observational data accordingly. We show that our method is able to identify relevant changes in habitat use corresponding to both changes in the availability of habitat and how it was used using simulated data, and apply our method to a tracking data set of common teal (Anas crecca). The niche segmentation proved to be robust, and segmented habitat suitability outperformed models neglecting the temporal dynamics of habitat use. Overall, we show that it is possible to classify animal trajectories based on changes of habitat use similar to geometric segmentation algorithms. We conclude that such an environmentally informed classification of animal trajectories can provide new insights into an individuals' behavior and enables us to make sensible predictions of how suitable areas might be connected by movement in space and time.

Ecosphere↗