@article {7088,
	title = {Quantitative food web modeling unravels the importance of the microphytobenthos-meiofauna pathway for a high trophic transfer by meiofauna in soft-bottom intertidal food webs.},
	journal = {Ecological Modelling},
	volume = {430},
	year = {2020},
	abstract = {Meiofauna are known to have an important role on many ecological processes, although, their role in food web dynamics is often poorly understood, partially as they have been an overlooked and under sampled organism group. Here, we used quantitative food web modeling to evaluate the trophic relationship between meiofauna and their food sources and how meiofauna can mediate the carbon flow to higher trophic levels in five contrasting soft-bottom intertidal habitats (including seagrass beds, mudflats and sandflats). Carbon flow networks were constructed using the linear inverse model-Markov chain Monte Carlo technique, with increased resolution of the meiofauna compartments (i.e. biomass and feeding ecology of the different trophic groups of meiofauna) compared to most previous modeling studies. These models highlighted that the flows between the highly productive microphytobenthos and the meiofauna compartments play an important role in transferring carbon to the higher trophic levels, typically more efficiently so than macrofauna. The pathway from microphytobenthos to meiofauna represented the largest flow in all habitats and resulted in high production of meiofauna independent of habitat. All trophic groups of meiofauna, except for selective deposit feeders, had a very high dependency on microphytobenthos. Selective deposit feeders relied instead on a wider range of food sources, with varying contributions of bacteria, microphytobenthos and sediment organic matter. Ecological network analyses (e.g. cycling, throughput and ascendency) of the modeled systems highlighted the close positive relationship between the food web efficiency and the assimilation of high-quality food sources by primary consumers, e.g. meiofauna and macrofauna. Large proportions of these flows can be attributed to trophic groups of meiofauna. The sensitivity of the network properties to the representation of meiofauna in the models leads to recommending a greater attention in ecological data monitoring and integrating meiofauna into food web models. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Carbon, Carbon flow networks, deposit feeder, Deposits, Ecological network analysis, Ecological process, Ecosystems, Feeding, Feeding ecology, food web, Food web model, intertidal community, intertidal habitats, Inverse problems, linear inverse model, Linear inverse models, Markov chain Monte Carlo techniques, Markov chains, Meiofauna, Microphytobenthos, Monte Carlo methods, Phytobenthos, quantitative analysis, Sediment organic matters, soft-bottom environment, stable isotope mixing models, trophic level, Trophic relationships, Trophic structure},
	issn = {03043800 (ISSN)},
	doi = {10.1016/j.ecolmodel.2020.109129},
	author = {van der Heijden, L.H. and Nathalie Niquil and Haraldsson, M. and Asmus, R.M. and Pacella, S.R. and Graeve, M. and Rzeznik-Orignac, J. and Asmus, H. and Saint-B{\'e}at, B. and Lebreton, B.}
}
@article {Meddeb201866,
	title = {Plankton food-web functioning in anthropogenically impacted coastal waters (SW Mediterranean Sea): An ecological network analysis},
	journal = {Progress in Oceanography},
	volume = {162},
	year = {2018},
	note = {cited By 4},
	pages = {66-82},
	publisher = {Elsevier Ltd},
	abstract = {The study is the first attempt to (i) model spring food webs in three SW Mediterranean ecosystems which are under different anthropogenic pressures and (ii) to project the consequence of this stress on their function. Linear inverse models were built using the Monte Carlo method coupled with Markov Chains to characterize the food-web status of the Lagoon, the Channel (inshore waters under high eutrophication and chemical contamination) and the Bay of Bizerte (offshore waters under less anthropogenic pressure). Ecological network analysis was used for the description of structural and functional properties of each food web and for inter-ecosystem comparisons. Our results showed that more carbon was produced by phytoplankton in the inshore waters (966{\textendash}1234 mg C m-2 d-1) compared to the Bay (727 mg C m-2 d-1). The total ecosystem carbon inputs into the three food webs was supported by high primary production, which was mainly due to \&gt;10 {\textmu}m algae. However, the three carbon pathways were characterized by low detritivory and a high herbivory which was mainly assigned to protozooplankton. This latter was efficient in channelling biogenic carbon. In the Lagoon and the Channel, foods webs acted almost as a multivorous structure with a tendency towards herbivorous one, whereas in the Bay the herbivorous pathway was more dominant. Ecological indices revealed that the Lagoon and the Channel food webs/systems had high total system throughput and thus were more active than the Bay. The Bay food web, which had a high relative ascendency value, was more organized and specialized. This inter{\textendash}ecosystem difference could be due to the varying levels of anthropogenic impact among sites. Indeed, the low value of Finn{\textquoteright}s cycling index indicated that the three systems are disturbed, but the Lagoon and the Channel, with low average path lengths, appeared to be more stressed, as both sites have undergone higher chemical pollution and nutrient loading. This study shows that ecosystem models combined with ecological indices provide a powerful approach to detect change in environmental status and anthropogenic impacts. {\textcopyright} 2018},
	keywords = {Algae, anthropogenic effect, Anthropogenic impacts, Anthropogenic pressures, Bizerte, Bizerte Bay, Chemical analysis, Chemical contamination, coastal water, community structure, Ecological network analysis, Ecology, ecosystem function, ecosystem modeling, Ecosystems, eutrophication, food web, Food webs, Functional properties, Inverse problems, Lakes, Linear inverse models, Markov processes, Mediterranean coastal waters, Mediterranean ecosystem, Mediterranean sea, Monte Carlo methods, network analysis, Phytoplankton, Plankton, primary production, Tunisia, Zooplankton},
	issn = {00796611},
	doi = {10.1016/j.pocean.2018.02.013},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0079661117300782},
	author = {Meddeb, M. and Grami, B. and Chaalali, A. and Haraldsson, M. and Nathalie Niquil and Pringault, O. and Sakka Hlaili, A.}
}
