Canadian Forest Service Publications
Local knowledge in ecological modeling. 2018. Bélisle, A.C.; Asselin, H.; LeBlanc, P.; Gauthier, S. Ecol. Soc. 23: 14.
Issued by: Laurentian Forestry Centre
Catalog ID: 39132
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Local people and scientists both hold ecological knowledge, respectively stemming from prolonged day-to-day contact with the environment and from systematic inquiry based on the scientific method. As the complementarity between scientific ecological knowledge (SEK) and local ecological knowledge (LEK) is increasingly acknowledged, LEK is starting to be involved in all branches of ecology, including ecological modeling. However, the integration of both knowledge types into ecological models raises methodological challenges, among which (1) consistency between the degree of LEK involvement and modeling objectives, (2) combination of concepts and methods from natural and social sciences, (3) reliability of the data collection process, and (4) model accuracy. We analyzed how 23 published studies dealt with those issues. We observed LEK reaches its full potential when involved at all steps of the research process. The validity of a modeling exercise is enhanced by an interdisciplinary approach and is jeopardized when LEK elicitation lacks rigor. Bayesian networks and fuzzy rule-based models are well suited to include LEK.
Plain Language Summary
Local communities and scientists gain their knowledge from daily contact with the environment and from systematic research based on the scientific method, respectively. The complementary relationship between the two types of knowledge is being increasingly recognized. For example, local knowledge can be used in ecological modelling. However, this integration raises some methodological challenges, including the reliability of the data collection process and the alignment of concepts and methods employed in natural sciences with those used in social sciences.
By analyzing 23 studies carried out on such integration issues, the researchers concluded that the integration of local ecological knowledge was optimized when done at each stage of the research. In addition, a multidisciplinary approach leads to better ecological models, while the lack of rigour in data collection results in model defects.