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.