Four forest watersheds (1 ha each) in the upper piedmont of Georgia were treated with hexazinone [3-cyclohexyl-6-(dimethylamino)-1-methyl-1,3,5-triazine-2,4(1H, 3H)-dione] pellets3 at a rate of 1.68 kg ai/ha. From the end of April, 1979, until May, 1980, 26 storms were monitored to determine movement of hexazinone and two of its metabolites [A: 3-(4-hydroxycyclohexyl)-6-(dimethylamino)-1-methyl-1,3,5-triazine-2,4(1H, 3H)-dione, and B: 3-cyclohexyl-6-(methylamino)-1-methyl-1,3,5-triazine-2,4(1H, 3H)-dione] in runoff water. Residues in runoff peaked in the first storm after application (mean concentration of 442 ± 53 ppbw), and declined with subsequent storms in a power curve function: Conc. (ppbw) = 405 × rate × (1 + 0.44 × days)-1.1. Loss of hexazinone in storm runoff averaged 0.53% of the applied herbicide, with Storms 1 and 17 accounting for 59.3% of the chemical exported. Storm 1 had high residue concentrations and low runoff volume, while Storm 17 contained only low residue levels but a very large stormflow. Hexazinone was the predominant compound in the runoff of all 26 storms. Metabolites A and B occurred in runoff in low-to-trace concentrations (<23 ppbw) for up to 7 months after application. Subsurface movement of hexazinone appeared in streamflow 3 to 4 months after application and produced an additional loss of 0.05%. A second-order perennial stream below the treated watersheds periodically contained hexazinone residues of <44 ppbw.

As marine protected areas (MPAs) are increasingly being utilised as a tool for fisherymanagement, their impact on the food web needs to be fully understood. However, little isknown about the effect of MPAs on fish assemblages, especially in the presence ofdifferent life history and ecological traits. Modelling the observed changes in fishpopulation structures may provide a mechanistic understanding of fish assemblage dynamics.In addition, modelling allows a quantitative estimate of MPA spill-over. To achieve thispurpose, we adapted an existing ecosystem model, OSMOSE (Object-oriented simulator ofmarine biodiversity exploitation), to the specific case of the presence of fish withmultiple life histories. The adapted model can manage 4 main categories of life historyidentified in an estuary MPA: fish that (1) spend their entire life cycle locally, (2) arepresent only as juveniles, (3) enter the area as juveniles and stay permanently exceptduring reproduction periods, which occur outside the estuary, and (4) are presentoccasionally and for a short time for foraging purposes. To take into account thesespecific life-history traits, the OSMOSE code was modified. This modelling approach wasdeveloped in the context of the Bamboung Bolong MPA, located in a mangrove area in theSine-Saloum Delta, Senegal. This was the ideal case to develop our approach as there hasbeen scientific monitoring of the fish population structure inside the MPA before fisheryclosure, providing a reference state, and continuous monitoring since the closure.Ecologically similar species were pooled by trophic traits into 15 groups that represented97% of the total biomass. Lower trophic levels (LTL) were represented by 6 compartments.The biomass of the model species was calibrated to reproduce the reference situationbefore fishery closure. Model predictions of fish assemblage changes after fishery closurecorresponding to the Bamboung MPA creation scenario were compared to field observations;in most cases the model reproduces observed changes in biomass (at least in direction). Wesuggest the existence of a “sanctuary effect”, that was not taken into account in themodel, this could explain the observed increase in biomass of top predators not reproducedby the model. Finally, the annual MPA fish spill-over was estimated at 11 tons (~33% ofthe fish biomass) from the model output, mainly due to diffusive effects.

The ecosystem approach to fisheries recognises the interdependence between harvested species and other ecosystem components. It aims to account for the propagation of the effects of harvesting through the food-web. The formulation and evaluation of ecosystem-based management strategies requires reliable models of ecosystem dynamics to predict these effects. The krill-based system in the Southern Ocean was the focus of some of the earliest models exploring such effects. It is also a suitable example for the development of models to support the ecosystem approach to fisheries because it has a relatively simple food-web structure and progress has been made in developing models of the key species and interactions, some of which has been motivated by the need to develop ecosystem-based management. Antarctic krill, Euphausia superba, is the main target species for the fishery and the main prey of many top predators. It is therefore critical to capture the processes affecting the dynamics and distribution of krill in ecosystem dynamics models. These processes include environmental influences on recruitment and the spatially variable influence of advection. Models must also capture the interactions between krill and its consumers, which are mediated by the spatial structure of the environment. Various models have explored predator-prey population dynamics with simplistic representations of these interactions, while others have focused on specific details of the interactions. There is now a pressing need to develop plausible and practical models of ecosystem dynamics that link processes occurring at these different scales. Many studies have highlighted uncertainties in our understanding of the system, which indicates future priorities in terms of both data collection and developing methods to evaluate the effects of these uncertainties on model predictions. We propose a modelling approach that focuses on harvested species and their monitored consumers and that evaluates model uncertainty by using alternative structures and functional forms in a Monte Carlo framework.

"Lake Life" is a software program designed to introduce the lay person to lacustrian ecology and to the basic concepts of hydraulic management. Having become familiar with the dynamics of the trophic system as well as the mechanisms leading to eutrophication, the user may experiment with managing the reservoir of his choice. Change over time in the principal elements in the ecosystem is calculated by a mathematical model. This paper first presents the successive steps in development of the model:- choice of variables: three plankton groups, fish, nutrients, oxygen ;- representation of complex mechanisms : for example, the vertical structure simulated in two layers ;- transcription into equations.This recapitulation will initiate the reader into the problems of modeling an ecosystem.We shall then analyse a few simulated situations: variations in plankton groups in three situations with increasing trophism, and one example of the impact of turbining on the fish and planktonic populations and on oxygenation of the hypolimnion. The quite realistic behavior of the simulations makes this software an excellent teaching tool.