Cronfa at Swansea University

    Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data

    Get PDF
    Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal's movement path. We define a ‘step’ to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques

    Evolutionary Fingerprinting of Genes

    No full text

    What is clinical leadership…and why is it important?

    No full text

    Seasonal Rainfall and Runoff Promote Coral Disease on an Inshore Reef

    No full text
    Declining water quality coupled with the effects of climate change are rapidly increasing coral diseases on reefs worldwide, although links between coral diseases and environmental parameters remain poorly understood. This is the first study to document a correlation between coral disease and water quality on an inshore reef.The temporal dynamics of the coral disease atramentous necrosis (AN) was investigated over two years within inshore populations of Montipora aequituberculata in the central Great Barrier Reef, in relation to rainfall, salinity, temperature, water column chlorophyll a, suspended solids, sedimentation, dissolved organic carbon, and particulate nitrogen, phosphorus and organic carbon. Overall, mean AN prevalence was 10-fold greater during summer wet seasons than winter dry seasons. A 2.5-fold greater mean disease abundance was detected during the summer of 2009 (44 ± SE 6.7 diseased colonies per 25 m2), when rainfall was 1.6-fold greater than in the summer of 2008. 1Two water quality parameters explained 67% of the variance in monthly disease prevalence in a Partial Least Squares regression analysis; disease abundance was negatively correlated with salinity (R2 = −0.6) but positively correlated with water column particulate organic carbon concentration (R2 = 0.32). Seasonal temperature patterns were also positively correlated with disease abundance, but explained only a small portion of the variance.The results suggest that rainfall and associated runoff may facilitate seasonal disease outbreaks, potentially by reducing host fitness or by increasing pathogen virulence due to higher availability of nutrients and organic matter. In the future, rainfall and seawater temperatures are likely to increase due to climate change which may lead to decreased health of inshore reefs

    A computational method of obtaining reliable measurement of periosteal cross-sectional area of human radii from laser scans

    No full text
    ABSTRACTThe accurate quantification of bones cross-sectional geometry provides valuable information about mechanical properties of bones such as rigidity to torsional, bending and compressive loading and also reveals insights into habitual activities of humans in the past. However, the use of current methods can produce large errors between measured and true cross-sectional areas. In this study the minimum cross sectional area was calculated at mid-shaft for a unique collection of laser scanned radii bones, recovered from Mary Rose warship, using a novel technique. A computational method was used to measure multiple cross-sectional areas for different orientations to then determine a minimum. This was then taken to represent a reliable mid-shaft cross-sectional area. The reliability of the process was tested using Bland and Altman plots to analyse the agreement between measurement trials. The systematic bias between the two measurement trials was 0.06mm2 (0.04% of the average cross-sectional area measurement) with 95% limits of agreement of 1.69mm2 (1.13%) and -1.57mm2 (1.05%). Consequently this method can be used as a reliable measure of periosteal cross-sectional area. The possibility also exists to transfer the methods described here to other imaging technologies for example, micro CT and magnetic resonance imaging. This would augment existing methods of computational analysis and produce accurate models.Keywords: computational modelling, bone topology, osteology, biomechanic
    Cronfa at Swansea University is based in GB