Wildlife tracking data management: a new vision

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioral data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals’ environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.

Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.

Non-intrusive tracking of commercial broiler chickens in situ at different stocking densities

Unlike several other farm animal species, the broiler chicken remains unprotected by species-specific legislation. The densities at which broilers should be kept is a highly contentious issue-some studies have demonstrated increased welfare problems at higher densities, whilst a few others have, contrary to expectations, suggested that broilers may actually find crowds of other birds attractive. A tracking method was developed and used to provide an insight into the social preferences of commercial broiler chickens in situ-inside commercial, closed-system broiler houses. The aim was to simultaneously assess the relative impact of global measures of density, such as target and actual stocking densities and local measures of the social environment on the behaviour and route taken to feed by focal birds. Birds were tracked inside 20 commercial broiler houses across the UK. Results from this study show that stocking density per se seems to have little direct effect on the individual behaviours of focal broiler chickens. However, there may still be an indirect effect of stocking density on broiler behaviour, mediated through the local social environment. (C) 2007 Elsevier B.V. All rights reserved.

Tracking leatherback turtles (Dermochelys coriacea) during consecutive inter-nesting intervals: Further support for direct transmitter attachment

Over recent years there have been substantial efforts to record and interpret the post-nesting movements of leatherback turtles (Dermochelys coriacea) breeding in tropical regions. Less well documented are the movements undertaken by individual turtles during the breeding season itself, or more specifically between sequential nesting events. Such movements are of interest for two reasons: (1) gravid female leatherbacks may range extensively into the territorial waters and nesting beaches of neighbouring countries, raising questions for conservationists and population ecologists; and (2) the magnitude of movements themselves help elucidate underlying reproductive strategies (e.g. whether to rest near to the nesting or forage extensively). Here, satellite relay data loggers are used (SRDLs) to detail the movements and behaviour of two female leatherback turtles throughout three consecutive inter-nesting intervals in the Commonwealth of Dominica, West Indies. Both near-shore residence and extensive inter-nesting movements were recorded, contrasting previous studies, with movements away from the nesting beach increasing towards the end of the nesting season. Using this behavioural study as a backdrop, the suitability of attaching satellite transmitters directly to the carapace was additionally explored as an alternative approach to conventional harness deployments. Specifically, the principal aims were to (1) gather empirical data on speed of travel and (2) assess dive performance (aerobic dive limit) to enable comparisons with turtles previously fitted with harnesses elsewhere in the Caribbean (n = 6 turtles; Grenada, WI). This produced mixed results with animals bearing directly attached transmitters travelling significantly faster (55.21 km day(-1): SD 6.68) than harnessed individuals (39.80 km day(-1); SD 6.19); whilst no discernable difference in dive performance could be found between the two groups of study animals. (C) 2009 Elsevier B.V. All rights reserved.

Satellite tracking of the World's largest bony fish, the ocean sunfish (Mola mola L.) in the North East Atlantic

Satellite-linked archival transmitters were used to record the movements of three ocean sunfish (Mola mola) in the North East Atlantic. Patterns of depth use and temperature experienced by individual fish were integrated into 4-hour intervals throughout the tracking period and relayed via the Argos system. Data were recorded for 42, 90 and 54 days respectively from the three fish. The first two were tagged off southern Portugal at the end of February 2007 and travelled principally northward, while the third fish was tagged off west Ireland in August 2007 and travelled southward. These patterns are consistent with seasonal migration of ocean sunfish to high latitudes and their Subsequent return south. Maximum depths recorded by the three fish were 432 m, 472 m and 320 m respectively. All three individuals showed a diel pattern in depth use, occurring deeper during the day and shallower at night, a pattern consistent with sunfish tracking normally vertically migrating prey. Sunfish sometimes remained continuously at deeper (>200 m) depths during the day, but at other times they showed extensive movement through the water column typically travelling between their maximum depth and the surface within each 4-h period. The overall pattern to emerge was that ocean sunfish travel extensively in both horizontal and vertical dimensions, presumably in search of their patchily-distributed jellyfish prey.

The Movebank data model for animal tracking

Studies of animal movement are rapidly increasing as tracking technologies make it possible to collect more data of a larger variety of species. Comparisons of animal movement across sites, times, or species are key to asking questions about animal adaptation, responses to climate and land-use change. Thus, great gains can be made by sharing and exchanging animal tracking data. Here we present an animal movement data model that we use within the Movebank web application to describe tracked animals. The model facilitates data comparisons across a broad range of taxa, study designs, and technologies, and is based on the scientific questions that could be addressed with the data.