Roland Kays

Terrestrial animal tracking as an eye on life and planet

A brave new world with a wider view Researchers have long attempted to follow animals as they move through their environment. Until relatively recently, however, such efforts were limited to short distances and times in species large enough to carry large batteries and transmitters. New technologies have opened up new frontiers in animal tracking remote data collection. Hussey et al. review the unique directions such efforts have taken for marine systems, while Kays et al. review recent advances for terrestrial species. We have entered a new era of animal ecology, where animals act as both subjects and samplers of their environments. Science, this issue 10.1126/science.1255642, 10.1126/science.aaa2478 BACKGROUND The movement of animals makes them fascinating but difficult study subjects. Animal movements underpin many biological phenomena, and understanding them is critical for applications in conservation, health, and food. Traditional approaches to animal tracking used field biologists wielding antennas to record a few dozen locations per animal, revealing only the most general patterns of animal space use. The advent of satellite tracking automated this process, but initially was limited to larger animals and increased the resolution of trajectories to only a few hundred locations per animal. The last few years have shown exponential improvement in tracking technology, leading to smaller tracking devices that can return millions of movement steps for ever-smaller animals. Finally, we have a tool that returns high-resolution data that reveal the detailed facets of animal movement and its many implications for biodiversity, animal ecology, behavior, and ecosystem function. ADVANCES Improved technology has brought animal tracking into the realm of big data, not only through high-resolution movement trajectories, but also through the addition of other on-animal sensors and the integration of remote sensing data about the environment through which these animals are moving. These new data are opening up a breadth of new scientific questions about ecology, evolution, and physiology and enable the use of animals as sensors of the environment. High–temporal resolution movement data also can document brief but important contacts between animals, creating new opportunities to study social networks, as well as interspecific interactions such as competition and predation. With solar panels keeping batteries charged, “lifetime” tracks can now be collected for some species, while broader approaches are aiming for species-wide sampling across multiple populations. Miniaturized tags also help reduce the impact of the devices on the study subjects, improving animal welfare and scientific results. As in other disciplines, the explosion of data volume and variety has created new challenges and opportunities for information management, integration, and analysis. In an exciting interdisciplinary push, biologists, statisticians, and computer scientists have begun to develop new tools that are already leading to new insights and scientific breakthroughs. OUTLOOK We suggest that a golden age of animal tracking science has begun and that the upcoming years will be a time of unprecedented exciting discoveries. Technology continues to improve our ability to track animals, with the promise of smaller tags collecting more data, less invasively, on a greater variety of animals. The big-data tracking studies that are just now being pioneered will become commonplace. If analytical developments can keep pace, the field will be able to develop real-time predictive models that integrate habitat preferences, movement abilities, sensory capacities, and animal memories into movement forecasts. The unique perspective offered by big-data animal tracking enables a new view of animals as naturally evolved sensors of environment, which we think has the potential to help us monitor the planet in completely new ways. A massive multi-individual monitoring program would allow a quorum sensing of our planet, using a variety of species to tap into the diversity of senses that have evolved across animal groups, providing new insight on our world through the sixth sense of the global animal collective. We expect that the field will soon reach a transformational point where these studies do more than inform us about particular species of animals, but allow the animals to teach us about the world. Big-data animal tracking. The red trajectory shows how studies can now track animals with unprecedented detail, allowing researchers to predict the causes and consequences of movements, and animals to become environmental sensors. Multisensor tracking tags monitor movement, behavior, physiology, and environmental context. Geo- and biosciences merge now using a multitude of remote-sensing data. Understanding how social and interspecific interactions affect movement is the next big frontier. Moving animals connect our world, spreading pollen, seeds, nutrients, and parasites as they go about the their daily lives. Recent integration of high-resolution Global Positioning System and other sensors into miniaturized tracking tags has dramatically improved our ability to describe animal movement. This has created opportunities and challenges that parallel big data transformations in other fields and has rapidly advanced animal ecology and physiology. New analytical approaches, combined with remotely sensed or modeled environmental information, have opened up a host of new questions on the causes of movement and its consequences for individuals, populations, and ecosystems. Simultaneous tracking of multiple animals is leading to new insights on species interactions and, scaled up, may enable distributed monitoring of both animals and our changing environment.

Technology on the Move: Recent and Forthcoming Innovations for Tracking Migratory Birds

Basic questions about the life histories of migratory birds have confounded scientists for generations, yet we are nearing an era of historic discovery as new tracking technologies make it possible to determine the timing and routes of an increasing number of bird migrations. Tracking small flying animals as they travel over continental-scale distances is a difficult logistical and engineering challenge. Although no tracking system works well with all species, improvements to traditional technologies, such as satellite tracking, along with innovations related to global positioning systems, cellular networks, solar geolocation, radar, and information technology are improving our understanding of when and where birds go during their annual cycles and informing numerous scientific disciplines, including evolutionary biology, population ecology, and global change. The recent developments described in this article will help us answer many long-standing questions about animal behavior and life histories.

A Comparison of Noninvasive Techniques to Survey Carnivore Communities in Northeastern North America

Abstract Carnivores are difficult to survey due, in large part, to their relative rarity across the landscape and wariness toward humans. Several noninvasive methods may aid in overcoming these difficulties, but there has been little discussion of the relative merits and biases of these techniques. We assess the value of 5 noninvasive techniques based on results from 2 multiyear studies of carnivores (including members of Carnivora and Didelphidae) in New York forests. Two metrics were particularly valuable in assessing the species-specific value of any particular survey technique: latency to initial detection (LTD) and probability of detection (POD). We found differences in the value of techniques in detecting different species. For midsized species (raccoon [Procyon lotor], fisher [Martes pennanti], opossum [Didelphis virginiana], and domestic cat [Felis catus]), camera traps and track-plates were approximately equivalent in detection efficiency, but the potential for wariness toward the survey apparatus resulted in higher LTD for track-plates than for cameras. On the other hand, track-plates detected small carnivores (marten [M. americana] and weasels [Mustela spp.]) more often than cameras and had higher PODs for small and midsized species than did cameras. Cameras were efficient mechanisms for surveying bears (Ursus americanus; low LTD, high POD) but functioned poorly for discerning presence of coyotes (Canis latrans; high LTD, low POD). Scat surveys and snowtracking were the best methods for coyotes, which avoided camera traps and artificial tracking surfaces. Our analysis of fecal DNA revealed that trail-based fecal surveys were inefficient at detecting species other than coyotes, with the possible exception of red foxes (Vulpes vulpes). Genetic analyses of feces and snowtracking revealed the presence of foxes at sites where other techniques failed to discern these species, suggesting that cameras and track-plates are inefficient for surveying small canids in this region. The LTD of coyotes by camera traps was not correlated with their abundance as indexed by scat counts, but for other species this metric may offer an opportunity to assess relative abundance across sites. Snowtracking surveys were particularly robust (high POD) for detecting species active in winter and may be more effective than both cameras and track-plates where conditions are suitable. We recommend that survey efforts targeting multiple members of the carnivore community use multiple independent techniques and incorporate mechanisms to truth their relative value.

A genome-wide perspective on the evolutionary history of enigmatic wolf-like canids

High-throughput genotyping technologies developed for model species can potentially increase the resolution of demographic history and ancestry in wild relatives. We use a SNP genotyping microarray developed for the domestic dog to assay variation in over 48K loci in wolf-like species worldwide. Despite the high mobility of these large carnivores, we find distinct hierarchical population units within gray wolves and coyotes that correspond with geographic and ecologic differences among populations. Further, we test controversial theories about the ancestry of the Great Lakes wolf and red wolf using an analysis of haplotype blocks across all 38 canid autosomes. We find that these enigmatic canids are highly admixed varieties derived from gray wolves and coyotes, respectively. This divergent genomic history suggests that they do not have a shared recent ancestry as proposed by previous researchers. Interspecific hybridization, as well as the process of evolutionary divergence, may be responsible for the observed phenotypic distinction of both forms. Such admixture complicates decisions regarding endangered species restoration and protection.

Swarm: mining relaxed temporal moving object clusters

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. The goal of our paper is to find all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.

Does the resource dispersion hypothesis explain group living

The resource dispersion hypothesis (RDH) asserts that, if resources are heterogeneous in space or time, group living might be less costly than was previously thought, regardless of whether individuals gain direct benefits from group membership. The RDH was first proposed more than 20 years ago and has since accumulated considerable support. However, it is sometimes discredited because a priori tests of specific predictions are few, relevant variables have proved difficult to define and measure, and because its assumptions and predictions remain unclear. This is unfortunate because the RDH provides a potentially powerful model of grouping behavior in a diversity of conditions. Moreover, it can be generalized to predict other phenomena, including spacing behavior in nonsocial animals and utilization of resources other than food. Here, we review the empirical support, clarify the predictions of the RDH and argue that they can be used to provide better tests.

Mining periodic behaviors for moving objects

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.

Thieving rodents as substitute dispersers of megafaunal seeds

The Neotropics have many plant species that seem to be adapted for seed dispersal by megafauna that went extinct in the late Pleistocene. Given the crucial importance of seed dispersal for plant persistence, it remains a mystery how these plants have survived more than 10,000 y without their mutualist dispersers. Here we present support for the hypothesis that secondary seed dispersal by scatter-hoarding rodents has facilitated the persistence of these large-seeded species. We used miniature radio transmitters to track the dispersal of reputedly megafaunal seeds by Central American agoutis, which scatter-hoard seeds in shallow caches in the soil throughout the forest. We found that seeds were initially cached at mostly short distances and then quickly dug up again. However, rather than eating the recovered seeds, agoutis continued to move and recache the seeds, up to 36 times. Agoutis dispersed an estimated 35% of seeds for >100 m. An estimated 14% of the cached seeds survived to the next year, when a new fruit crop became available to the rodents. Serial video-monitoring of cached seeds revealed that the stepwise dispersal was caused by agoutis repeatedly stealing and recaching each other’s buried seeds. Although previous studies suggest that rodents are poor dispersers, we demonstrate that communities of rodents can in fact provide highly effective long-distance seed dispersal. Our findings suggest that thieving scatter-hoarding rodents could substitute for extinct megafaunal seed dispersers of tropical large-seeded trees.

A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animals movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

BackgroundThe movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.ResultsThis paper contributes to movement ecology research by presenting a new publicly available system, Environmental-Data Automated Track Annotation (Env-DATA), that automates annotation of movement trajectories with ambient atmospheric observations and underlying landscape information. Env-DATA provides a free and easy-to-use platform that eliminates technical difficulties of the annotation processes and relieves end users of a ton of tedious and time-consuming tasks associated with annotation, including data acquisition, data transformation and integration, resampling, and interpolation. The system is illustrated with a case study of Galapagos Albatross (Phoebastria irrorata) tracks and their relationship to wind, ocean productivity and chlorophyll concentration. Our case study illustrates why adult albatrosses make long-range trips to preferred, productive areas and how wind assistance facilitates their return flights while their outbound flights are hampered by head winds.ConclusionsThe new Env-DATA system enhances Movebank, an open portal of animal tracking data, by automating access to environmental variables from global remote sensing, weather, and ecosystem products from open web resources. The system provides several interpolation methods from the native grid resolution and structure to a global regular grid linked with the movement tracks in space and time. The aim is to facilitate new understanding and predictive capabilities of spatiotemporal patterns of animal movement in response to dynamic and changing environments from local to global scales.