Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, camera‐traps have dramatically improved our ...ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates.
To fill this gap in the camera‐trapper's statistical toolbox, we extended the existing Random Encounter Model (REM) to the multi‐species case in a Bayesian framework. This multi‐species REM can incorporate covariates and provides parameter estimates for even the rarest species. As input to the model, we used information directly available in the camera‐trap data. The model outputs posterior distributions for the REM parameters—movement speed, activity level, the effective angle and radius of the camera‐trap detection zone, and density—for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old‐growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters.
The model revealed a decrease in movement speeds, and therefore day‐range, across the species community in logged compared to old‐growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old‐growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was substantially higher in logged compared to old‐growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable in the model, revealing that larger‐bodied species were more active, had more variable speeds, and had larger detection zones.
Caution is warranted when estimating density for semi‐arboreal and fossorial species using camera‐traps, and more extensive testing of assumptions is recommended. Nonetheless, we anticipate that multi‐species density estimation could have very broad application.
High quality, contemporary data regarding patterns of chronic disease is essential for planning by health services, policy makers and local governments, but surprisingly scarce, including in rural ...Australia. This dearth of data occurs despite the recognition that rural Australians live with high rates of ill health, poor health behaviours and restricted access to health services. Crossroads-II is set in the Goulburn Valley, a rural region of Victoria, Australia 100-300 km north of metropolitan Melbourne. It is primarily an irrigated agricultural area. The aim of the study is to identify changes in the prevalence of key chronic health conditions including the extent of undiagnosed and undermanaged disease, and association with access to care, over a 15 year period.
This study is a 15 year follow up from the 2000-2003 Crossroads-I study (2376 households participated). Crossroads-II includes a similar face to face household survey of 3600 randomly selected households across four towns of sizes 6300 to 49,800 (50% sampled in the larger town with the remainder sampled equally from the three smaller towns). Self-reported health, health behaviour and health service usage information is verified and supplemented in a nested sub-study of 900 randomly selected adult participants in 'clinics' involving a range of additional questionnaires and biophysical measurements. The study is expected to run from October 2016 to December 2018.
Besides providing epidemiological and health service utilisation information relating to different diseases and their risk factors in towns of different sizes, the results will be used to develop a composite measure of health service access. The importance of access to health services will be investigated by assessing the correlation of this measure with rates of undiagnosed and undermanaged disease at the mesh block level. Results will be shared with partner organisations to inform service planning and interventions to improve health outcomes for local people.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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