@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 {Fath20191,
	title = {Ecological network analysis metrics: The need for an entire ecosystem approach in management and policy},
	journal = {Ocean and Coastal Management},
	volume = {174},
	year = {2019},
	note = {cited By 8},
	pages = {1-14},
	publisher = {Elsevier Ltd},
	abstract = {In this paper, we identified seven ecological network analysis (ENA) metrics that, in our opinion, have high potential to provide useful and practical information for environmental decision-makers and stakeholders. Measurement and quantification of the network indicators requires that an ecosystem level assessment is implemented. The ENA metrics convey the status of the ecological system state variables, and mostly, the flows and relations between the various nodes of the network. The seven metrics are: 1) Average Path Length (APL), 2) Finn Cycling Index (FCI), 3) Mean Trophic level (MTL), 4) Detritivory to Herbivory ratio (D:H), 5) Keystoneness, 6) Structural Information (SI), and 7) Flow-based Information indices. The procedure for calculating each metric is detailed along with a short evaluation of their potential assessment of environmental status. {\textcopyright} 2019 Elsevier Ltd},
	keywords = {coastal zone, Cycling, Decision making, ecological approach, Ecological network analysis, ecosystem approach, ecosystem management, Ecosystems, environmental policy, food web, Food webs, Marine and coastal environments, Marine environment, network analysis, policy implementation, stakeholder, Trophic length},
	issn = {09645691},
	doi = {10.1016/j.ocecoaman.2019.03.007},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0964569118305969},
	author = {Fath, B.D. and H Asmus and R. Asmus and Baird, D. and Borrett, S.R. and de Jonge, V.N. and Ludovisi, A. and Nathalie Niquil and Scharler, U.M. and Sch{\"u}ckel, U. and Wolff, M.}
}
@article {Raoux2019728,
	title = {Measuring sensitivity of two OSPAR indicators for a coastal food web model under offshore wind farm construction},
	journal = {Ecological Indicators},
	volume = {96},
	year = {2019},
	note = {cited By 2},
	pages = {728-738},
	publisher = {Elsevier B.V.},
	abstract = {A combination of modelling tools was applied to simulate the impacts of the future Courseulles-sur-mer offshore wind farm (OWF) construction (Bay of Seine, English Channel) on the ecosystem structure and functioning. To do so, food-web models of the ecosystem under three scenarios were constructed to investigate the effect caused by the OWF of added substrate (reef effect), fishing restriction (reserve effect), and their combined effect. Further, Ecological Network Analysis indices and Mean Trophic Level were derived to investigate their suitability for detecting changes in the ecosystem state. Our analysis suggests changes in the ecosystem structure and functioning after the OWF construction, the ecosystem maturity was predicted to increase, but no alterations in its overall resilience capacity. {\textcopyright} 2018 Elsevier Ltd},
	keywords = {Barium compounds, Bay of Seine, coastal zone, Ecological network analysis, Ecopath with Ecosim, ecosystem function, ecosystem structure, Ecosystems, Electric utilities, English Channel, food web, France, in situ measurement, Marine environment, Marine renewable energy, measurement method, model, Offshore wind farms, Reefs, Reserve effect, sensitivity analysis, trophic level, wind farm},
	issn = {1470160X},
	doi = {10.1016/j.ecolind.2018.07.014},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S1470160X1830534X},
	author = {Raoux, Aurore and G{\'e}raldine Lassalle and Pezy, Jean-Philippe and Samuele Tecchio and Safi, Georges and Ernande, Bruno and Maz{\'e}, C. and Le Loc{\textquoteright}h, Francois and Lequesne, Justine and Girardin, Valerie and Jean-Claude Dauvin and Nathalie Niquil}
}
@article {Meddeb201967,
	title = {A new type of plankton food web functioning in coastal waters revealed by coupling Monte Carlo Markov chain linear inverse method and ecological network analysis},
	journal = {Ecological Indicators},
	volume = {104},
	year = {2019},
	note = {cited By 0},
	pages = {67-85},
	publisher = {Elsevier B.V.},
	abstract = {Plankton food webs (PFW) typology is based on different categories of functioning, according to the dominant processes and the role played by heterotrophic bacteria, small vs large phytoplankton, and small vs large zooplankton. Investigating the structure and the function of planktonic food webs in two SW Mediterranean waters (inshore and marine sites) at four seasons, using inverse (LIM-MCMC) and ecological network (ENA) analyses, we identified a new type of food web, called the {\textquotedblleft}bacterial multivorous food web{\textquotedblright}. This food web adds to the conventional trophic continuum as previously reported. The {\textquotedblleft}bacterial multivorous food web{\textquotedblright} present in winter showed the lowest primary production among seasons, but highest bacterial production. Several food web ratios characterized this new typology e.g. picophytoplankton net primary production to total primary production varied from 0.20 to 0.28; bacterial to primary production ratio is higher than values reported in global scale (≅1); bacterial net production to the potential protozoan prey net production was high (\&gt;0.2). In this special food web, carbon was mostly recycled, with a moderate fraction channeled to deep waters, which lead to a higher retention of carbon inside the ecosystem. This winter PFW also seemed to be the most organized, specialized, stable and mature, as related to common interpretations of ENA. The spring was characterized by herbivorous food web, with highest activity coinciding with low stability. Although less usual, the herbivorous pathway was also observed during summer, in inshore waters. The autumn food webs, which functioned as multivorous or microbial food webs, appeared to be stable and mature. Finally, our study demonstrates the usefulness of food web models derived ratios combined with ecological network analysis indices to conduct evaluation of the structure and functioning of ecosystems and potentially to support management decisions in marine environment. {\textcopyright} 2019},
	keywords = {Bacteria (microorganisms), bacterium, Carbon, Chemical contamination, coastal water, Coastal waters, ecological modeling, Ecology, ecosystem function, Ecosystems, Electric network analysis, Food microbiology, food web, Food web model, Food webs, inverse analysis, Inverse problems, Markov chain, Markov processes, Mediterranean sea, Monte Carlo analysis, Monte Carlo methods, net primary production, network analysis, Phytoplankton, picoplankton, Plankton, Protozoa, protozoan, Seasonal variation, trophic status, Trophic structure},
	issn = {1470160X},
	doi = {10.1016/j.ecolind.2019.04.077},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S1470160X19303243},
	author = {Meddeb, M. and Nathalie Niquil and Grami, B. and Mejri, K. and Haraldsson, M. and Chaalali, A. and Pringault, O. and Hlaili, A.S.}
}
@article {vanderHeijden201950,
	title = {Trophic importance of microphytobenthos and bacteria to meiofauna in soft-bottom intertidal habitats: A combined trophic marker approach},
	journal = {Marine Environmental Research},
	volume = {149},
	year = {2019},
	note = {cited By 2},
	pages = {50-66},
	publisher = {Elsevier Ltd},
	abstract = {Meiofauna can play an important role in the carbon fluxes of soft-bottom coastal habitats. Investigation of their feeding behavior and trophic position remains challenging due to their small size. In this study, we determine and compare the food sources used by nematodes and benthic copepods by using stable isotope compositions, fatty acid profiles and compound specific isotope analyses of fatty acids in the mudflats, seagrass beds and a sandflat of the Marennes-Ol{\'e}ron Bay, France, and the Sylt-R{\o}m{\o} Bight, Germany. Suspended particulate organic matter was much more 13C-depleted than other food sources and meiofauna, highlighting its poor role in the different studied habitats. The very low proportions of vascular plant fatty acid markers in meiofauna demonstrated that these consumers did not rely on this food source, either fresh or detrital, even in seagrass beds. The combined use of stable isotopes and fatty acids emphasized microphytobenthos and benthic bacteria as the major food sources of nematodes and benthic copepods. Compound specific analyses of a bacteria marker confirmed that bacteria mostly used microphytobenthos as a substrate. {\textcopyright} 2019 Elsevier Ltd},
	keywords = {Article, Bacillariophyta, bacteria, bacterium, Benthic copepods, Benthic diatoms, Biological materials, biomarker, Biomass, carbon 13, coastal zone, controlled study, Copepoda, delta carbon 13, delta nitrogen 15, Diatom, Ecosystems, fatty acids, Feeding Behavior, food supply, France, Free livings, Freeze Drying, Frisian Islands, Germany, high performance liquid chromatography, intertidal environment, isotope analysis, Isotopes, lipid composition, Marennes-Oleron Bay, Meiofauna, microbial activity, Microphytobenthos, mudflat, Mudflats, Nematoda, nitrogen 15, nonhuman, North Frisian Islands, Nouvelle-Aquitaine, Organic matter, Phytobenthos, Plants (botany), Sandflats, seagrass, Seagrass beds, Sediment, soft-bottom environment, species habitat, substrate, suspended particulate organic matter, Sylt-Romo Bight, trophic environment, Trophic markers, Trophic structure, unclassified drug},
	issn = {01411136},
	doi = {10.1016/j.marenvres.2019.05.014},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0141113618304744},
	author = {van der Heijden, L.H. and Graeve, M. and R. Asmus and Rzeznik-Orignac, J. and Nathalie Niquil and Bernier, Q. and Guillou, G. and H Asmus and Lebreton, B.}
}
@article {Safi2019116,
	title = {Vitamine ENA: A framework for the development of ecosystem-based indicators for decision makers},
	journal = {Ocean and Coastal Management},
	volume = {174},
	year = {2019},
	note = {cited By 5},
	pages = {116-130},
	publisher = {Elsevier Ltd},
	abstract = {The Water Framework Directive (article 2, paragraph 21) as well as the Marine Strategy Framework Directive (MSFD, Descriptor 4) stress the need for assessing the quality of the structure and the functioning of ecosystems. The MSFD also underlines the urgent need for development, testing, and validation of ecosystem state indicators. Holistic function-based criteria and indicators as provided by Ecological Network Analysis (ENA) could be used to define and assess the {\textquoteleft}Good Environmental Status{\textquoteright} of marine ecosystems. This approach also feeds Ecosystem Based Management (EBM). ENA generally analyses the fluxes{\textquoteright} quality of a single medium such as here the carbon fluxes in a food web and produces a number of useful metrics that indicate, inter alia, the total carbon flow through the system, the quality of the functioning of the system or the trophic efficiency of system. A short list of indices [i.e. Detritivory over Herbivory ratio (D/H), Connectance Index (CI), Transfer Efficiency (TE) over trophic levels, System Omnivory Index (SOI), Finn{\textquoteright}s Cycling Index (FCI), relative Redundancy (R/DC), Average Mutual Information (AMI) and Interaction Strength (IS)] is proposed for practical use. This paper presents a first framework for OSPAR Regional Sea Convention food web indicators based on ENA. These are presented here focusing on their applicability and what is needed for implementation, illustrating their potential use by case studies. {\textcopyright} 2019 Elsevier Ltd},
	keywords = {Average mutual information, Carbon, Carbon flux, Criteria and indicators, Decision making, Ecological network analysis, ecosystem function, ecosystem management, ecosystem structure, Ecosystem-based management, Ecosystems, Efficiency, environmental indicator, Environmental management, Environmental regulations, food web, Good environmental status, holistic approach, Interaction strength, marine ecosystem, Marine pollution, Marine strategy framework directives, Water conservation, Water Framework Directives},
	issn = {09645691},
	doi = {10.1016/j.ocecoaman.2019.03.005},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S096456911830591X},
	author = {Safi, Georges and Giebels, D. and Arroyo, N.-L. and Heymans, J.J. and Preciado, I and Raoux, Aurore and Sch{\"u}ckel, U. and Samuele Tecchio and de Jonge, V.N. and Nathalie Niquil}
}
@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.}
}
