Optimising sampling designs for habitat fragmentation studies
نویسندگان
چکیده
Habitat fragmentation has become one of the largest areas research in conservation biology. Empirical studies into habitat impacts typically measure ecological responses to metrics describing processes, for example ‘distance nearest forest edge’, ‘forest fragment area’ and ‘landscape amount’. However, these often fail sample across representative ranges characterising study region. They therefore lack data account correlation among multiple spatial autocorrelation sites, which reduces strength derived predictive models. Here, we draw on approaches used mining soil science industry develop standardised repeatable protocols designing optimised sampling schemes biodiversity fragmented landscapes that meet three criteria: distance between sites is maximised reduce autocorrelation, full range values interest are sampled confounding effects correlated minimised. We show our computational methods can optimise placement minimise, some cases entirely avoid, over- or under-sampling metrics. Our method flexible enough cater any continuous (e.g. maps percentage tree cover) categorical land use types) metric, simultaneously handle combinations types. implement as open-source code includes options mask invalid inaccessible regions, update designs adapt unforeseen constraints field suggest optimal numbers given design criteria. Using a case landscape, demonstrate how approach improves manually generated designs. also be applied landscape beyond fragmentation. introduce package novel tool able streamline experimental process monitoring at scales, leading improved quality representativeness.
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ژورنال
عنوان ژورنال: Methods in Ecology and Evolution
سال: 2021
ISSN: ['2041-210X']
DOI: https://doi.org/10.1111/2041-210x.13731