Coupling scientific and local ecological knowledge network models for temperate coastal ecosystems

Abstract

Abstract There is an urgent need to analyse and understand small-scale fisheries environment under biotic and abiotic stressors. In this work, we use a kelp forest ecosystem in Baja California, Mexico to present a novel approach, comparing two network models based on different information sources. First, we developed a conventional scientific knowledge network model (CSK) parameterized with in-situ observations. Second, we used a local ecological knowledge network model (LEK) based on interviews with local fishers. Our main objectives were: (a) verify if the two knowledge sources generated comparable models, and (b) explore model responses to disturbance scenarios. The CSK model presented greater detail at lower trophic levels, contrary to the LEK model. Additionally, we simulated top-down and bottom-up ecological disturbances. With a top-down disturbance, the groups’ abundance increased following a cascade effect whereas, in the bottom-up disturbance, changes did not transfer uniformly. We also simulated anthropogenic disturbances through fishing pressure on three target species (lobsters, sea urchins, and sea bass). Our findings show similar patterns with the highest degree of change when lobsters are removed. Our findings highlight the potential of model complementarity and support the relevance of ecological network models to navigate future climate and anthropogenic uncertainty.