|Titre||Planktonic food webs revisited : reanalysis of results from the linear inverse approach|
|Type de publication||Journal Article|
|Year of Publication||2014|
|Auteurs||Sakka-Hlaili, A, Niquil, N, Legendre, L|
|Journal||Progress in Oceanography|
Identification of the trophic pathway that dominates a given planktonic assemblage is generally based on the distribution of biomasses among food-web compartments, or better, the flows of materials or energy among compartments. These flows are obtained by field observations and a posteriori analyses, including the linear inverse approach. In the present study, we re-analysed carbon flows obtained by inverse analysis at 32 stations in the global ocean and one large lake. Our results do not support two “classical” views of plankton ecology, i.e. that the herbivorous food web is dominated by mesozooplankton grazing on large phytoplankton, and the microbial food web is based on microzooplankton significantly consuming bacteria; our results suggest instead that phytoplankton are generally grazed by microzooplankton, of which they are the main food source. Furthermore, we identified the “phyto-microbial food web”, where microzooplankton largely feed on phytoplankton, in addition to the already known “poly-microbial food web”, where microzooplankton consume more or less equally various types of food. These unexpected results led to a (re)definition of the conceptual models corresponding to the four trophic pathways we found to exist in plankton, i.e. the herbivorous, multivorous, and two types of microbial food web. We illustrated the conceptual trophic pathways using carbon flows that were actually observed at representative stations. The latter can be calibrated to correspond to any field situation. Our study also provides researchers and managers with operational criteria for identifying the dominant trophic pathway in a planktonic assemblage, these criteria being based on the values of two carbon ratios that could be calculated from flow values that are relatively easy to estimate in the field.