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Context
Environmental heterogeneity is considered an important mechanism of biodiversity. How environmental heterogeneity is characterised by the compositional, structural and functional variation of biotic and abiotic components is a central research theme in conservation.Objectives
We explore how environmental heterogeneity relates to the underlying physical landscape template and how that relationship changes over space and time. We examine how, in some areas, environmental heterogeneity may also be driven by dynamic ecological processes, and how this relates to patterns of plant species richness.Method
We use local geographically weighted regression to spatially partition environmental heterogeneity, measured as Landsat spectral variance, into the portion explained by stable physical landscape properties (R2) and the portion unexplained (1?R2) which we term landscape complexity. We explore how this relationship varies spatially and temporally as a function of dynamic ecological processes such as rainfall and season in Kruger National Park, as well as plant species richness at landscape scales.Results
The significance and direction of relationships varied over space and time and as a function of rainfall and season. R2 values generally decreased in higher rainfall summer months and revealed patterns describing the importance of known stable factors relative to unknown dynamic factors. Landscape complexity (1?R2) explained over 70 % of variation in species richness.Conclusions
Rainfall and seasonality are important drivers of environmental heterogeneity. The spatial arrangement and magnitude of model agreement helped disentangle the relative influence of the physical landscape template on environmental heterogeneity. Given the high correlation with species richness, landscape complexity provides complementary guidance to biodiversity research and monitoring prioritization.METHODS: Data included the source and destination of translocated specimens, the species and the dates the translocations were expected to occur. The data were used to construct a directed, non-weighted network in which a translocation event represented a tie in the network. Network density, in-degree (movements entering a node of interest) and out-degree (movements leaving a node of interest) and reciprocity were calculated.
RESULTS: The data analysed consisted of 692 unique translocations between 307 sites, with the majority (518; 73%) being for birds. The constructed network for bird, reptile and frog translocations comprised 260 nodes, with 34/260 (13%) having two-way movements and 47/260 (18%) non-reciprocal movements. The median degree score (sum of in- and out-degree) was two (min 0, max 36) with a mean of 3.5 in a right skewed distribution. Most sites acted as receivers or senders of consignments with only a few having both high in- and high out-degree, and thus had characteristics that made them sites of interest for surveillance activities. These included the National Wildlife Centre at Mount Bruce, Tiritiri Matangi Island and Te Kakahu (Chalky Island).
CONCLUSIONS: The presence of linking sites that join larger clusters within the network creates the potential for rapid disease spread if a pathogen were to be introduced. The important sites that supply or receive specimens for translocations are already well recognised by those performing translocations in New Zealand, and this paper provides further information by quantifying their role within the network. 相似文献
2. This increase can be prevented completely by spray‐cleaning carcasses during the various stages of evisceration.
3. If the carcasses are cleaned only at the end of the evisceration process, the numbers of Enterobacteriaceae are not reduced to initial levels and Salmonella contamination is less efficiently removed. 相似文献