Maximising the informativeness of new records in spatial sampling design


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In building a robust knowledge base or validating existing models for use in ecological spatial modelling, having plentiful high-quality data is paramount. Careful survey design helps attain that goal and, in part due to financial constraints, such design requires the balancing of hard monetary costs and the intangible benefit of improved ecological models.We propose a framework that quantifies a location's value to the modeller by accounting for both the probability of obtaining new samples and their expected contribution to the model. The approach is illustrated on a citizen science database of roadkills in Taiwan, modelled as a Poisson point process on a linear road network.Our method has revealed some valuable locations that were not self-evident, for example, highlighting the possibility of sending volunteers to mountainous areas that despite being hard to reach, would provide valuable samples. We have also highlighted some ex situ sampling opportunities to avoid wasting resources by over-sampling hard to access locations.Our technique is not restricted to presence-only data, and in fact we present a general framework that can be applied to a wide range of settings by tuning its formulation. Our method is quite flexible and allows for more elaborate value functions, enabling managers to precisely quantify varied goals within the same framework.
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Key words
ecological modelling,linear network,Poisson point process,presence-only models,sampling bias,spatial modelling,species distribution models,survey design
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