Analysis of context-dependency issues related to the identification of keystone species in marine food webs
The concept of keystone species has been widely applied and discussed in the literature since its introduction. Keystone species are ecologically important species in marine food-webs, which have a disproportionately high trophic impact on their community, compared to their biomass. Thus, keystone species theoretically allow for the management of a single focal species with the aim of maintaining the whole ecosystem. In order to identify keystone species, indices measuring the potential of species to be keystone (or ‘keystoneness’, KS) have been proposed in the literature. Functional KS indices derived from Ecopath food-web models of marine ecosystems have recently been reviewed and a new index (KS3) has been proposed (Valls et al. 2015). Overall, cartilaginous fishes and toothed whales were the most frequently identified as potential keystone species. However, in some of the selected modeled ecosystems, no keystone species could be identified. Context-dependency may be the reason for discrepancies between ecosystems. The temporal and spatial scales considered may influence the identification of keystone species. Also, multiple effects of different biotic or abiotic factors may have an influence on species keystoneness. For instance, the KS3 index may be sensitive to natural or human-induced variability (e.g., different levels of fishing pressure), which can be estimated from existing ecosystem indices.
Reference: Valls, A., Coll, M., Christensen, V., 2015. Keystone species: toward an operational concept for marine biodiversity conservation. Ecological Monographs 85, 29-47.
o Implementation of the KS3 index in the Keystoneness Analysis plug-in of the Ecopath model and software
o Selection and application of relevant existing ecosystem indices for comparison with KS3
o Utilization of the EcoBase database to select a pool of Ecopath models relevant to the analysis and extract the required data
o Implementation of statistical analyses to explore context-dependency in keystone species identification using R and PRIMER
o Preparation of a scientific article to be submitted to a peer-reviewed journal in Ecology
o Master student available for a 6-months internship
o Basic notions and strong interest in ecosystem ecology and food-web theory applied to marine systems
o Basic notions in statistical analyses
o Programming skills in R for data handling and statistical analyses
o Background knowledge and computing skills in Ecopath with Ecosim and PRIMER Proficient in English
Supervision: Dr. Marta Coll (IRD, Sete, email@example.com) and Dr. Audrey Valls (CNRS, Moulis, firstname.lastname@example.org)
Starting date: as soon as possible Location: CRH, Sete, France with visits to Moulis.
Please contact us if you are interested!