Periodic continuous-time movement models uncover behavioral changes of wild canids along anthropization gradients

Ranging ecology and resource selection of white-lipped peccaries (Tayassu pecari) in the world's largest tropical agricultural frontier

Agricultural commodity production is one the main drivers of deforestation in Legal Brazilian Amazonia resulting in a deforested and/or fragmented landscape formed by forest remnants of different sizes and shape embedded within the agricultural matrix. As an ecosystem engineer and a crucial seed predator, white-lipped peccaries (Tayassu pecari) play a pivotal role in forest structure, biodiversity, and nutrient cycling. However, they are highly sensitive to habitat fragmentation and hunting pressure. White-lipped peccaries are, therefore, a wide-ranging "landscape species," the spatial and ecological requirements of which can be used to guide conservation planning in human-modified landscapes. Using data from GPS-tracked individuals in large-scale mechanized agriculture landscapes in the state of Mato Grosso, Brazil's largest soybean and maize producer, we investiated the home range size and resource selection during both the crop and non-crop season. We observed a seasonal variation in home range size and an increased selection for croplands during the crop season. White-lipped peccaries favored native vegetation patches and also exhibited avoidance of locations distant from perennial water bodies and distant cropland locations far from forest remmants. This study can contribute to inform effective conservation strategies and land management practices aimed at preserving suitable habitats and promoting wildlife coexistence with working agricultural landscapes.

GPS tracking analyses reveal finely-tuned shorebird space use and movement patterns throughout the non-breeding season in high-latitude austral intertidal areas

BACKGROUND

Long-distance migratory birds spend most of their annual cycle in non-breeding areas. During this period birds must meet their daily nutritional needs and acquire additional energy intake to deal with future events of the annual cycle. Therefore, patterns of space use and movement may emerge as an efficient strategy to maintain a trade-off between acquisition and conservation of energy during the non-breeding season. However, there is still a paucity of research addressing this issue, especially in trans-hemispheric migratory birds.

METHODS

Using GPS-tracking data and a recently developed continuous-time stochastic process modeling framework, we analyzed fine-scale movements in a non-breeding population of Hudsonian godwits (Limosa haemastica), a gregarious long-distance migratory shorebird. Specifically, we evaluated if these extreme migrants exhibit restricted, shared, and periodic patterns of space use on one of their main non-breeding grounds in southern South America. Finally, via a generalized additive model, we tested if the observed patterns were consistent within a circadian cycle.

RESULTS

Overall, godwits showed finely-tuned range-residence and periodic movements (each 24-72 h), being similar between day and night. Remarkably, range-resident individuals segregated spatially into three groups. In contrast, a smaller fraction of godwits displayed unpredictable and irregular movements, adding functional connectivity within the population.

CONCLUSIONS

In coastal non-breeding areas where resource availability is highly predictable due to tidal cycles, range-resident strategies during both the day and night are the common pattern in a long-distance shorebird population. Alternative patterns exhibited by a fraction of non-resident godwits provide functional connectivity and suggest that the exploratory tendency may be essential for information acquisition and associated with individual traits. The methodological approach we have used contributes to elucidate how the composition of movement phases operates during the non-breeding season in migratory species and can be replicated in non-migratory species as well. Finally, our results highlight the importance of considering movement as a continuum within the annual cycle.

Spatial and temporal dynamics of space use by free-ranging domestic dogs Canis familiaris in rural Africa

Variation in the spatial ecology of animals influences the transmission of infections and so understanding host behavior can improve the control of diseases. Despite the global distribution of free-ranging domestic dogs Canis familiaris and their role as reservoirs for zoonotic diseases, little is known about the dynamics of their space use. We deployed GPS loggers on owned but free-ranging dogs from six villages in rural Chad, and tracked the movements of 174 individuals in the dry season and 151 in the wet season. We calculated 95% and core home ranges using auto-correlated kernel density estimates (AKDE₉₅ and AKDE_core ), determined the degree to which their movements were predictable, and identified correlates of movement patterns. The median AKDE₉₅ range in the dry season was 0.54 km² and in the wet season was 0.31 km² , while the median AKDE_core range in the dry season was 0.08 km² and in the wet season was 0.04 km² . Seasonal variation was, in part, related to owner activities; dogs from hunting households had ranges that were five times larger in the dry season. At least 70% of individuals were more predictably "at home" (<50 m from the household) throughout the day in the dry season, 80% of dogs demonstrated periodicity in activity levels (speed), and just over half the dogs exhibited periodicity in location (repeated space use). In the wet season, dogs mostly exhibited 24-h cycles in activity and location, with peaks at midday. In the dry season, dogs exhibited both 12- and 24-h cycles, with either a single peak at midday, or one peak between 06:00 and 12:00 and a second between 18:00 and 22:00. Strategies to control canine-mediated zoonoses can be improved by tailoring operations to the local spatial ecology of free-ranging dogs. Interventions using a door-to-door strategy in rural Chad would best conduct operations during the dry season, when access to dogs around their household more reliably exceeds 70% throughout the day. Given the importance of use in hunting for explaining variation in dog space-use, targeting approaches to disease control at the household level on the basis of owner activities offers potential to improve access to dogs.

Inside out: heart rate monitoring to advance the welfare and conservation of maned wolves (Chrysocyon brachyurus)

Anthropogenic change is a major threat to individual species and biodiversity. Yet the behavioral and physiological responses of animals to these changes remain understudied. This is due to the technological challenges in assessing these effects in situ. Using captive maned wolves (Chrysocyon brachyurus, n = 6) as a model, we deployed implantable biologgers and collected physiological data on heart rate (HR) and heart rate variability (HRV) over a 1-year period. To test for links between HR and changes in the environment we analysed HR daily rhythms and responses to potential stressors (e.g. physical restraint, change in housing conditions, short-distance transportation and unfamiliar human presence). The 2-min HR averages ranged from 33 to 250 bpm, with an overall rest average of 73 bpm and a maximum of 296 bpm. On average, HRV was higher in females (227 ± 51 ms) than in males (151 ± 51 ms). As expected, HR increased at dusk and night when animals were more active and in response to stressors. Sudden decreases in HR were observed during transportation in three wolves, suggestive of fear bradycardia. We provide the first non-anesthetic HR values for the species and confirm that behaviour does not always reflect the shifts in autonomic tone in response to perceived threats. Because strong HR responses often were not revealed by observable changes in behaviour, our findings suggest that the number and variety of stressors in ex situ or in situ environments for maned wolves and most wildlife species may be underestimated. Our study also shows that integrating biologging with behavioral observations can provide vital information to guide captive management. Similar technology can be used to advance in situ research for developing more effective welfare, management and conservation plans for the species.

A hierarchical machine learning framework for the analysis of large scale animal movement data

BACKGROUND

In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated.

METHODS

In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods.

RESULTS

While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries.

CONCLUSIONS

Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Scale-insensitive estimation of speed and distance traveled from animal tracking data

BACKGROUND

Speed and distance traveled provide quantifiable links between behavior and energetics, and are among the metrics most routinely estimated from animal tracking data. Researchers typically sum over the straight-line displacements (SLDs) between sampled locations to quantify distance traveled, while speed is estimated by dividing these displacements by time. Problematically, this approach is highly sensitive to the measurement scale, with biases subject to the sampling frequency, the tortuosity of the animal's movement, and the amount of measurement error. Compounding the issue of scale-sensitivity, SLD estimates do not come equipped with confidence intervals to quantify their uncertainty.

METHODS

To overcome the limitations of SLD estimation, we outline a continuous-time speed and distance (CTSD) estimation method. An inherent property of working in continuous-time is the ability to separate the underlying continuous-time movement process from the discrete-time sampling process, making these models less sensitive to the sampling schedule when estimating parameters. The first step of CTSD is to estimate the device's error parameters to calibrate the measurement error. Once the errors have been calibrated, model selection techniques are employed to identify the best fit continuous-time movement model for the data. A simulation-based approach is then employed to sample from the distribution of trajectories conditional on the data, from which the mean speed estimate and its confidence intervals can be extracted.

RESULTS

Using simulated data, we demonstrate how CTSD provides accurate, scale-insensitive estimates with reliable confidence intervals. When applied to empirical GPS data, we found that SLD estimates varied substantially with sampling frequency, whereas CTSD provided relatively consistent estimates, with often dramatic improvements over SLD.

CONCLUSIONS

The methods described in this study allow for the computationally efficient, scale-insensitive estimation of speed and distance traveled, without biases due to the sampling frequency, the tortuosity of the animal's movement, or the amount of measurement error. In addition to being robust to the sampling schedule, the point estimates come equipped with confidence intervals, permitting formal statistical inference. All the methods developed in this study are now freely available in the ctmmR package or the ctmmweb point-and-click web based graphical user interface.

Environmental Predictability as a Cause and Consequence of Animal Movement

The impacts of environmental predictability on the ecology and evolution of animal movement have been the subject of vigorous speculation for several decades. Recently, the swell of new biologging technologies has further stimulated their investigation. This advancing research frontier, however, still lacks conceptual unification and has so far focused little on converse effects. Populations of moving animals have ubiquitous effects on processes such as nutrient cycling and seed dispersal and may therefore shape patterns of environmental predictability. Here, we synthesise the main strands of the literature on the feedbacks between environmental predictability and animal movement and discuss how they may react to anthropogenic disruption, leading to unexpected threats for wildlife and the environment.

Circadian periodicity in space use by ungulates of temperate regions: How much, when and why?

When they visit and revisit specific areas, animals may reveal what they need from their home range and how they acquire information. The temporal dimension of such movement recursions, that is, periodicity, is however rarely studied, yet potentially bears a species, population or individual-specific signature. A recent method allows estimating the contribution of periodic patterns to the variance in a movement path. We applied it to 709 individuals from five ungulate species, looking for species signatures in the form of seasonal variation in the intensity of circadian patterns. Circadian patterns were commonplace in the movement tracks, but the amount of variance they explained was highly variable among individuals. It increased in intensity during spring and summer, when key resources were spatially segregated, and decreased during winter, when food availability was more uniformly low. Other periodicity-inducing mechanisms supported by our comparison of species- and sex-specific patterns involve young antipredator behaviour, territoriality and behavioural thermoregulation. Model-based continuous-time movement metrics represent a new avenue for researchers interested in finding individual-, population- or species-specific signatures in heterogeneous movement databases featuring various study designs and sampling resolutions. However, we observed large amounts of individual variation, so comparative analyses should ideally use both GPS and animal-borne loggers to augment the discriminatory power and be based on large samples. We briefly outline potential uses of the intensity of circadian patterns as a metric for the study of animal personality and community ecology.