03605nas a2200301 4500008004100000245010800041210006900149300001400218490000700232520274600239653002002985653001803005653002203023653002003045100001703065700001403082700002003096700001703116700001503133700001503148700001703163700001503180700001603195700001603211700001503227700001703242856004403259 2011 eng d00aTracking Animal Location and Activity with an Automated Radio Telemetry System in a Tropical Rainforest0 aTracking Animal Location and Activity with an Automated Radio Te a1931-19480 v543 a

How do animals use their habitat? Where do they go and what do they do? These basic questions are key not only to understanding a species’ ecology and evolution, but also for addressing many of the environmental challenges we currently face, including problems posed by invasive species, the spread of zoonotic diseases and declines in wildlife populations due to anthropogenic climate and land-use changes. Monitoring the movements and activities of wild animals can be difficult, especially when the species in question are small, cryptic or move over large areas. In this paper, we describe an Automated Radio-Telemetry System (ARTS) that we designed and built on Barro Colorado Island (BCI), Panama to overcome these challenges. We describe the hardware and software we used to implement the ARTS, and discuss the scientific successes we have had using the system, as well as the logistical challenges we faced in maintaining the system in real-world, rainforest conditions. The ARTS uses automated radio-telemetry receivers mounted on 40-m towers topped with arrays of directional antennas to track the activity and location of radio-collared study animals, 24 h a day, 7 days a week. These receiving units are connected by a wireless network to a server housed in the laboratory on BCI, making these data available in real time to researchers via a web-accessible database. As long as study animals are within the range of the towers, the ARTS system collects data more frequently than typical animal-borne global positioning system collars (∼12 locations/h) with lower accuracy (approximately 50 m) but at much reduced cost per tag (∼10X less expensive). The geographic range of ARTS, like all VHF telemetry, is affected by the size of the radio-tag as well as its position in the forest (e.g. tags in the canopy transmit farther than those on the forest floor). We present a model of signal propagation based on landscape conditions, which quantifies these effects and identifies sources of interference, including weather events and human activity. ARTS has been used to track 374 individual animals from 38 species, including 17 mammal species, 12 birds, 7 reptiles or amphibians, as well as two species of plant seeds. These data elucidate the spatio-temporal dynamics of animal activity and movement at the site and have produced numerous peer-reviewed publications, student theses, magazine articles, educational programs and film documentaries. These data are also relevant to long-term population monitoring and conservation plans. Both the successes and the failures of the ARTS system are applicable to broader sensor network applications and are valuable for advancing sensor network research.

10aanimal tracking10aenvironmental10aobserving systems10asensor networks1 aKays, R., W.1 aTilak, S.1 aCrofoot, M., C.1 aFountain, T.1 aObando, D.1 aOrtega, A.1 aKuemmeth, F.1 aMandel, J.1 aSwenson, G.1 aLambert, T.1 aHirsch, B.1 aWikelski, M. uhttp://dx.doi.org/10.1093/comjnl/bxr07202017nas a2200169 4500008004100000245005800041210005700099490000900156520153600165653002001701653002701721100001101748700001301759700001201772700001701784856004601801 2010 eng d00aSwarm: Mining Relaxed Temporal Moving Object Clusters0 aSwarm Mining Relaxed Temporal Moving Object Clusters0 v20103 a

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.

10aanimal tracking10apositioning technology1 aLi, Z.1 aDing, B.1 aHan, J.1 aKays, R., W. uhttp://dl.acm.org/citation.cfm?id=192093400560nas a2200205 4500008004100000245001900041210001900060260003500079300001200114653002000126653001700146653001900163100001700182700002000199700001700219700001500236700002200251700001600273856006500289 2008 eng d00aRemote Cameras0 aRemote Cameras aWashington, D.C.bIsland Press a110-14010aanimal tracking10acamera traps10aremote cameras1 aKays, R., W.1 aSlauson, K., M.1 aLong, R., A.1 aMacKay, P.1 aZielinski, W., J.1 aRay, C., J. uhttps://www.nysm.nysed.gov/staff-publications/remote-cameras