Effects of habitat and land use on breeding season density of male Asian Houbara Chlamydotis macqueenii

Landscape-scale habitat and land-use influences on Asian Houbara Chlamydotis macqueenii (IUCN Vulnerable) remain unstudied, while estimating numbers of this cryptic, low-density, over-hunted species is challenging. In spring 2013, male houbara were recorded at 231 point counts, conducted twice, across a gradient of sheep density and shrub assemblages within 14,300 km² of the Kyzylkum Desert, Uzbekistan. Four sets of models related male abundance to: (1) vegetation structure (shrub height and substrate); (2) shrub assemblage; (3) shrub species composition (multidimensional scaling); (4) remote-sensed derived land-cover (GLOBCOVER, 4 variables). Each set also incorporated measures of landscape rugosity and sheep density. For each set, multi-model inference was applied to generalised linear mixed models of visit-specific counts that included important detectability covariates and point ID as a random effect. Vegetation structure received strongest support, followed by shrub species composition and shrub assemblage, with weakest support for the GLOBCOVER model set. Male houbara numbers were greater with lower mean shrub height, more gravel and flatter surfaces, but were unaffected by sheep density. Male density (mean 0.14 km-2, 95% CI, 0.12‒0.15) estimated by distance analysis differed substantially among shrub assemblages, being highest in vegetation dominated by Salsola rigida (0.22 [CI, 0.20‒0.25]), high in areas of S. arbuscula and Astragalus (0.14 [CI, 0.13‒0.16] and 0.15 [CI, 0.14‒0.17] respectively), lower (0.09 [CI, 0.08‒0.10]) in Artemisia and lowest (0.04 [CI, 0.04‒0.05]) in Calligonum. The study area was estimated to hold 1,824 males (CI: 1,645‒2,030). The spatial distribution of relative male houbara abundance, predicted from vegetation structure models, had the strongest correspondence with observed numbers in both model-calibration and the subsequent year’s data. We found no effect of pastoralism on male distribution but potential effects on nesting females are unknown. Density differences among shrub communities suggest extrapolation to estimate country- or range-wide population size must take account of vegetation composition

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

The climate of the Canary Islands by annual cycle parameters

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future