Uncertainty assessment of trophic flows in Hamilton Harbour : A linear inverse modellinganalysis

Ecopath with Ecosim has been extensively used to examine ecosystem attributes and the effects ofmanagement actions. One of the main limitations in using Ecopath to credibly guide managementdecisions lies in the quality and quantity of the data used. Linear Inverse Modelling treats the problemof ecosystem characterization in a rigorous mathematical way in which the foodweb is described as a(linear) function of the flows and model parameters are (inversely) derived from observed data. In thisstudy, our thesis is that Linear Inverse Modelling can be used as a complement to Ecopath applicationsto evaluate our confidence in typically reported ecosystem characterizations. Based on a simplifiedversion of a previously published foodweb topology (Hossain et al., 2012), we demonstrate that there isconsiderable uncertainty associated with the predicted energy flows within the ecosystem of HamiltonHarbour, Lake Ontario, Canada. Uncertainty related to external flows (e.g. respiratory and detritalflows) appears to be much higher than for internal flows associated with predator-prey relationships.Our Linear Inverse Modelling analysis reinforces earlier findings that most of the trophic flows areconcentrated within the first two trophic levels, while mass fluxes at the higher trophic levels aresignificantly lower. The intermediate ecotrophic efficiency for zooplankton suggests that planktivorousfishes do not fully capitalize upon the available food in the system. Our model estimates that asubstantial amount of the detrital material is being recycled by the microbial community within thesystem. Taken together with the significant detrital pool directly supporting zooplankton andoligochaetes/chironomids, this prediction is consistent with recent empirical evidence that particulateorganic matter from various allochthonous or autochthonous origins constitute important componentsof the energy transferred to higher trophic levels. Overall, our Linear Inverse Modelling analysis offersmeaningful insights that should contribute towards the development of a reliable ecosystem model forHamilton Harbour

Implementation of the Water Framework Directive: Lessons Learned and Future Perspectives for an Ecologically Meaningful Classification Based on Phytoplankton of the Status of Greek Lakes, Mediterranean Region

The enactment of the Water Framework Directive (WFD) initiated scientific efforts to develop reliable methods for comparing prevailing lake conditions against reference (or nonimpaired) states, using the state of a set biological elements. Drawing a distinction between impaired and natural conditions can be a challenging exercise. Another important aspect is to ensure that water quality assessment is comparable among the different Member States. In this context, the present paper offers a constructive critique of the practices followed during the WFD implementation in Greece by pinpointing methodological weaknesses and knowledge gaps that undermine our ability to classify the ecological quality of Greek lakes. One of the pillars of WDF is a valid lake typology that sets ecological standards transcending geographic regions and national boundaries. The national typology of Greek lakes has failed to take into account essential components. WFD compliance assessments based on the descriptions of phytoplankton communities are oversimplified and as such should be revisited. Exclusion of most chroococcal species from the analysis of cyanobacteria biovolume in Greek lakes/reservoirs and most reservoirs in Spain, Portugal, and Cyprus is not consistent with the distribution of those taxa in lakes. Similarly, the total biovolume reference values and the indices used in classification schemes reflect misunderstandings of WFD core principles. This hampers the comparability of ecological status across Europe and leads to quality standards that are too relaxed to provide an efficient target for the protection of Greek/transboundary lakes such as the ancient Lake Megali Prespa. © 2019, Springer Science+Business Media, LLC, part of Springer Nature