ALBERT

Description: Rarity of species is often considered to set priorities for biodiversity conservation. Less abundant species are expected to be at higher risk of extinction and make significant contribution to food web functioning. However, the relationship between species abundance and position in food webs is still unclear. Here we tested possible correlations between species abundance and structural position in Prince William Sound food web. Species abundance was inferred from biomass data and structural position was characterized by 13 centrality indices. We found that less abundant species have higher trophic positions and display more generalist feeding strategies. However, positive correlations link most of the centrality indices to population size. Thus, being locally rare translates into more peripheral food web positions and implies marginal roles in the spread of indirect effects. Species characterized by largest population size are responsible for the transfer of largest amounts of biomass and regulate the transmission of indirect effects. Less abundant species are of marginal structural importance and are exposed to impacts mediated by larger populations. In Prince William Sound ecosystem, rarity is associated with critical food web positions and does not simply reflect a marginal contribution to biodiversity. We suggest that knowing the food web position of rare species might help to formulate more effective, system-level solutions for their conservation, rather than simply focusing on the direct treatment of symptoms.

Description: The sustainable management of social–ecological systems (SESs) requires that we understand the complex structure of relationships and feedbacks among ecosystem components and socioeconomic entities. Therefore, the construction and analysis of models integrating ecological and human actors is crucial for describing the functioning of SESs, and qualitative modeling represents an ideal tool since it allows studying dependencies among variables of diverse types. In particular, the qualitative technique of loop analysis yields predictions about how a system’s variables respond to stress factors. Different interaction types, scarce information about functional relationships among variables, and uncertainties in the values of the parameters are the rule rather than exceptions when studying SESs. Accordingly, loop analysis seems to be perfectly suitable to investigate them. Here, we introduce the key aspects of loop analysis, discuss its applications to SESs, and suggest it enables making the first steps toward the integration of the three dimensions of sustainability.