Abstract
Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.
Highlights
Biodiversity scientists and ecologists work in different sub–domains including taxonomy, community ecology, behavioral ecology, conservation planning and many others
Reflecting on the semantic Bayesian network (BN) as a tool for knowledge elicitation and representation, we found that representing causal ecological knowledge enabled us to model behavioral interactions and estimate the probability associated with their occurrence
biodiversity and ecosystem informatics (BDEI) researchers have reflected on the field’s challenges [31] and the nature of the questions that they ask of biodiversity data [32], implying that more can be achieved with natural history occurrence data than merely a display of points on a map or the use of these to predict the potential distribution of a species
Summary
Biodiversity scientists and ecologists work in different sub–domains including taxonomy, community ecology, behavioral ecology, conservation planning and many others. In the field of flower–visiting ecology the process of knowledge production typically starts with analyses of observations of interacting plants and animals, either drawn from legacy natural history collection data [5] or collected de novo during field surveys [6]. At this point an expert can generate knowledge according to the traditions of natural science, by manually summarizing and analysing these data and interpreting the results using available or personal knowledge. Our objective is to PLOS ONE | DOI:10.1371/journal.pone.0166559 November 16, 2016
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