Cascading effects of a disease outbreak in a remote protected area.

Disease outbreaks induced by humans increasingly threaten wildlife communities worldwide. Like predators, pathogens can be key top-down forces in ecosystems, initiating trophic cascades that may alter food webs. An outbreak of mange in a remote Andean protected area caused a dramatic population decline in a mammalian herbivore (the vicuña), creating conditions to test the cascading effects of disease on the ecological community. By comparing a suite of ecological measurements to pre-disease baseline records, we demonstrate that mange restructured tightly linked trophic interactions previously driven by a mammalian predator (the puma). Following the mange outbreak, scavenger (Andean condor) occurrence in the ecosystem declined sharply and plant biomass and cover increased dramatically in predation refuges where herbivory was historically concentrated. The evidence shows that a disease-induced trophic cascade, mediated by vicuña density, could supplant the predator-induced trophic cascade, mediated by vicuña behaviour, thereby transforming the Andean ecosystem.

Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species.

Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.

Spatial ecology of the Vicuña (Lama vicugna) in a high Andean protected area.

The study of animal space use is fundamental to effective conservation and management of wildlife populations and habitats in a rapidly changing world, yet many species remain poorly described. Such is the case for the spatial ecology of the Vicuña-a medium-sized wild camelid that plays a critical role, both as a consumer and as prey, in the high Andean food web. We studied patterns of space use of 24 adult female vicuñas from April 2014 to February 2017 at the southern edge of its range. Vicuñas showed strong fidelity to their home range locations across the study period and shared large portions of their home ranges with vicuñas from other family groups. Vicuña home ranges in our study were considerably larger than previous estimates across the range of the species. Variation in environmental and terrain factors and the associated risk of predation affected vicuña diel migration distance but not home range size or overlap. Our study offers new ecological insights into vicuña space use that can inform conservation and management efforts of vicuñas and other social ungulates.

El estudio del uso del espacio en animales es fundamental para la conservación y gestión eficaz de sus poblaciones y hábitats silvestres en un mundo que cambia rápidamente, sin embargo muchas especies siguen estando mal descritas. Tal es el caso del estudio de ecología espacial de la vicuña, un camélido silvestre de tamaño mediano que tiene un papel crítico en la red trófica altoandina. Estudiamos el uso del espacio de 24 hembras adultas de vicuñas desde abril de 2014 hasta febrero de 2017 en el extremo sur del área de distribución de la especie. Las vicuñas mostraron una gran fidelidad en la ubicación de sus áreas de acción durante el período de estudio y compartieron gran parte de sus áreas de acción con otros grupos familiars. Las áreas de acción de las vicuñas en nuestro estudio fueron considerablemente más grandes que las estimaciones previas en todo el rango de la especie. Variacíon en factores ambientales y topográficos, y el riesgo asociado de depredación afectaron las distancias de las migraciones diarias de las vicuñas, pero no afectaron el tamaño del área de acción. Nuestro estudio reporta nuevos datos ecológicos sobre el uso del espacio de vicuñas que pueden informar los esfuerzos de conservación y manejo de esta especie y otros ungulados sociales.

The plasticity of ungulate migration in a changing world.

Migratory ungulates are thought to be declining globally because their dependence on large landscapes renders them highly vulnerable to environmental change. Yet recent studies reveal that many ungulate species can adjust their migration propensity in response to changing environmental conditions to potentially improve population persistence. In addition to the question of whether to migrate, decisions of where and when to migrate appear equally fundamental to individual migration tactics, but these three dimensions of plasticity have rarely been explored together. Here, we expand the concept of migratory plasticity beyond individual switches in migration propensity to also include spatial and temporal adjustments to migration patterns. We develop a novel typological framework that delineates every potential change type within the three dimensions, then use this framework to guide a literature review. We discuss broad patterns in migratory plasticity, potential drivers of migration change, and research gaps in the current understanding of this trait. Our result reveals 127 migration change events in direct response to natural and human-induced environmental changes across 27 ungulate species. Species that appeared in multiple studies showed multiple types of change, with some exhibiting the full spectrum of migratory plasticity. This result highlights that multidimensional migratory plasticity is pervasive in ungulates, even as the manifestation of plasticity varies case by case. However, studies thus far have rarely been able to determine the fitness outcomes of different types of migration change, likely due to the scarcity of long-term individual-based demographic monitoring as well as measurements encompassing a full behavioral continuum and environmental gradient for any given species. Recognizing and documenting the full spectrum of migratory plasticity marks the first step for the field of migration ecology to employ quantitative methods, such as reaction norms, to predict migration change along environmental gradients. Closer monitoring for changes in migratory propensity, routes, and timing may improve the efficacy of conservation strategies and management actions in a rapidly changing world.

Where and when to hunt? Decomposing predation success of an ambush carnivore.

Predator-prey games emerge when predators and prey dynamically respond to the behavior of one another, driving the outcomes of predator-prey interactions. Predation success is a function of the combined probabilities of encountering and capturing prey, which are influenced by both prey behavior and environmental features. While the relative importance of encounter and capture probabilities have been evaluated in a spatial framework, temporal variation in prey behavior and intrinsic catchability are likely to also affect the distribution of predation events. Using a single-predator-single-prey (puma-vicuña) system, we evaluated which factors predict predation events across both temporal and spatial dimensions of the components of predation by testing the prey-abundance hypothesis (predators select for high encounter probability) and the prey-catchability hypothesis (predators select for high relative capture probability) in time and space. We found that for both temporal and spatial analyses, neither the prey-abundance hypothesis nor the prey-catchability hypothesis alone predicted kill frequency or distribution; puma kill frequency was static throughout the diel cycle and pumas consistently selected a single habitat type when hunting, despite temporal and spatial variation in encounter rates and intrinsic catchability. Our integrated spatiotemporal analysis revealed that an interaction between time of day and habitat influences kill probability, suggesting that trade-offs in the temporal and spatial components of predation drive the probability of predation events. These findings reinforce the importance of examining both the temporal and spatial patterns of the components of predation, rather than unidimensional measures of predator or prey behavior, to comprehensively describe the feedbacks between predator and prey in the predator-prey game.

Monitoring canid scent marking in space and time using a biologging and machine learning approach.

For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.

Habitat complexity mediates the predator-prey space race.

The spatial relationship between predator and prey is often conceptualized as a behavioral response race, in which prey avoid predators while predators track prey. Limiting habitat types can create spatial anchors for prey or predators, influencing the likelihood that the predator or prey response will dominate. Joint spatial anchors emerge when predator and prey occupy similar feeding habitat domains and risk and reward become spatially conflated, confusing predictions of which player will win the space race. These spatial dynamics of risk-foraging trade-offs are often obscured by habitat heterogeneity and community complexity in large vertebrate systems, fueling ambiguity regarding the generality of predictions from predator-prey theory. To test how habitat distribution influences the predator-prey space race, we examine correlation in puma and vicuña habitat selection and space use at two sites, one of which generates a distinct risk-foraging trade-off at a joint spatial anchor. The distribution of vegetation, which serves as both forage for vicuñas and stalking cover for pumas, differs between the sites; the llano contains a single central meadow that acts as a joint spatial anchor, while the canyon is characterized by more heterogeneous vegetation. Puma-vicuña habitat selection correlation was positive in the llano and negative in the canyon, and similarly, utilization distributions were more strongly correlated in the llano than the canyon. Vicuña locations occurred at higher values of puma habitat selection and utilization in the llano than in the canyon. Similarly, puma locations in the llano occurred at higher values of vicuña habitat selection and utilization than in the canyon. Although pumas consistently selected for and utilized vegetative and topographic cover regardless of habitat distribution, vicuñas only selected against vegetation in the heterogeneous canyon site, reducing spatial correlation with pumas. Our work suggests a joint spatial anchor favors pumas in the space race due to the inability for vicuñas to avoid crucial foraging habitat. The outcome of the predator-prey space race appears to be strongly informed by the distribution of habitat, whereby corresponding predictability of predator and prey favors predators in the spatial game.

Does the Treadmill Support Valid Energetics Estimates of Field Locomotion?

SYNOPSIS: Quantifying animal energy expenditure during locomotion in the field is generally based either on treadmill measurements or on estimates derived from a measured proxy. Two common proxies are heart rate (ƒH) and dynamic body acceleration (accelerometry). Both ƒH and accelerometry have been calibrated extensively under laboratory conditions, which typically involve prompting the animal to locomote on a treadmill at different speeds while simultaneously recording its rate of oxygen uptake (V̇o2) and the proxy. Field estimates of V̇o2 during locomotion obtained directly from treadmill running or from treadmill-calibrated proxies make assumptions about similarities between running in the field and in the laboratory. The present study investigated these assumptions, focusing on humans as a tractable species. First we investigated experimentally if and how the rate of energy expenditure during treadmill locomotion differs to that during field locomotion at the same speeds, with participants walking and running on a treadmill, on tarmac, and on grass, while wearing a mobile respirometry system. V̇o2 was substantially higher during locomotion in both of the field conditions compared with on a level treadmill: 9.1% on tarmac and 17.7% on grass. Second, we included these data in a meta-analysis of previous, related studies. The results were influenced by the studies excluded due to particulars of the experiment design, suggesting that participant age, the surface type, and the degree of turning during field locomotion may influence by how much treadmill and field locomotion V̇o2 differ. Third, based on our experiments described earlier, we investigated the accuracy of treadmill-calibrated accelerometry and ƒH for estimating V̇o2 in the field. The mean algebraic estimate errors varied between 10% and 35%, with the ƒH associated errors being larger than those derived from accelerometry. The mean algebraic errors were all underestimates of field V̇o2, by around 10% for fH and varying between 0% and 15% for accelerometry. Researchers should question and consider how accurately a treadmill-derived proxy calibration of V̇o2 will estimate V̇o2 during terrestrial locomotion in free-living animals.

Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags.

BACKGROUND: Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. DESCRIPTION: This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space. CONCLUSIONS: Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.

Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.

Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

On higher ground: how well can dynamic body acceleration determine speed in variable terrain?

INTRODUCTION: Animal travel speed is an ecologically significant parameter, with implications for the study of energetics and animal behaviour. It is also necessary for the calculation of animal paths by dead-reckoning. Dead-reckoning uses heading and speed to calculate an animal's path through its environment on a fine scale. It is often used in aquatic environments, where transmission telemetry is difficult. However, its adoption for tracking terrestrial animals is limited by our ability to measure speed accurately on a fine scale. Recently, tri-axial accelerometers have shown promise for estimating speed, but their accuracy appears affected by changes in substrate and surface gradients. The purpose of the present study was to evaluate four metrics of acceleration; Overall dynamic body acceleration (ODBA), vectorial dynamic body acceleration (VDBA), acceleration peak frequency and acceleration peak amplitude, as proxies for speed over hard, soft and inclined surfaces, using humans as a model species. RESULTS: A general linear model (GLM) showed a significant difference in the relationships between the metrics and speed depending on substrate or surface gradient. When the data from all surface types were considered together, VeDBA had the highest coefficient of determination. CONCLUSIONS: All of the metrics showed some variation in their relationship with speed according to the surface type. This indicates that changes in the substrate or surface gradient during locomotion by animals would produce errors in speed estimates, and also in dead-reckoned tracks if they were calculated from speeds based entirely on a priori calibrations. However, we describe a method by which the relationship between acceleration metrics and speed can be corrected ad hoc, until tracks accord with periodic ground truthed positions, obtained via a secondary means (e.g. VHF or GPS telemetry). In this way, dead-reckoning provides a means to obtain fine scale movement data for terrestrial animals, without the need for additional data on substrate or gradient.

The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems.

Numerous methods are currently available to track animal movements. However, only one of these, dead-reckoning, has the capacity to provide continuous data for animal movements over fine scales. Dead-reckoning has been applied almost exclusively in the study of marine species, in part due to the difficulty of accurately measuring the speed of terrestrial species. In the present study we evaluate the use of accelerometers and a metric known as overall dynamic body acceleration (ODBA) as a proxy for the measurement of speed for use in dead-reckoning. Data were collated from previous studies, for 10 species locomoting on a treadmill and their ODBA measured by an attached data logger. All species except one showed a highly significant linear relationship between speed and ODBA; however, there was appreciable inter- and intra-specific variance in this relationship. ODBA was then used to estimate speed in a simple trial run of a dead-reckoning track. Estimating distance travelled using speed derived from prior calibration for ODBA resulted in appreciable errors. We describe a method by which these errors can be minimised, by periodic ground-truthing (e.g., by GPS or VHF telemetry) of the dead-reckoned track and adjusting the relationship between speed and ODBA until actual known positions and dead-reckoned positions accord.