We present a multispecies stochastic model that suggests optimal fishing policy for two species in a three-species predator–prey ecosystem in the Barents Sea. We employ stochastic dynamic programming to solve a three-dimensional model, in which the catch is optimized by using a multispecies feedback strategy. Applying the model to the cod, capelin, and herring ecosystem in the Barents Sea shows that the optimal catch for the stochastic interaction model is more conservative than that implied by the deterministic model. We also find that stochasticity has a stronger effect on the optimal exploitation policy for prey (capelin) than for predator (cod).

Bioeconomic analyses of spatial fishery models have established that marine reserves can be economically optimal (i.e., maximize sustainable profit) when there is some type of spatial heterogeneity in the system. Analyses of spatially continuous models and models with more than two discrete patches have also demonstrated that marine reserves can be economically optimal even when the system is spatially homogeneous. In this note we analyze a spatially homogeneous two-patch model and show that marine reserves can be economically optimal in this case as well. The model we study includes the possibility that fishing can damage habitat. In this model, marine reserves are necessary to maximize sustainable profit when dispersal between the patches is sufficiently high and habitat is especially vulnerable to damage.

The presence of sediments in a river is one of the major factors that characterize the river. The presence of sediment in any water resource is detrimental to its design purpose and it scratches any structure such as bridge foundations, conduit pipes, and turbine blades it comes into contact with while in motion and this leads to their eventual failure under load. The correct estimation of sediment yield transported by a river is indispensable in water resources engineering as sediment affects its hydraulic structure. The use of mathematical modeling algorithms such as genetic algorithms (GA) has proved to be very accurate in predicting sediment load in a river. The analogy behind GA is that genes in DNA functions are manipulated in specific ways through specific transcription operations. Therefore, applying the same logical operators to selected parameters relevant to sediment loads in rivers leads to mathematical prediction of the sediment load. This review article discusses the dynamic of sedimentation and analyses the use of GA as a hydrological model for accurately predicting sediment yield in a river, its potentials and shortcomings while recommending its modification.

Evaluation and forecasting of water-level fluctuation for one river is of increasing importance since it is intimately associated with human welfare and socioeconomic sustainability development. In this study, it is found that time series of monthly water-level fluctuation exhibits annual cyclical variation. Then with annual periodic extension for monthly water-level fluctuation, the so-called “elliptic orbit model” is proposed for describing monthly water-level fluctuation by mapping its time series into the polar coordinates. Experiments and result analysis indicate potentiality of the proposed method that it yields satisfying results in evaluating and forecasting monthly water-level fluctuation at the monitoring stations in the Yangtze River of China. It is shown that the monthly water-level fluctuation is well described by the proposed elliptic orbit model, which offers a vivid approach for modeling and forecasting monthly water-level fluctuation in a concise and intuitive way.

The purpose of this paper is to retrace the evolution of mathematical models focused on relation and interaction between economic growth, sustainable development, and natural environment conservation. First, generic defensive expenditures are introduced into a common-property harvesting model in order to favor the species growth. Second, a transition model comprising both harvesting and nonharvesting values of wildlife biological species emerges. The latter gives rise to a group of purely nonharvesting models where anthropic activities and economic growth may have positive or negative impact on the natural evolution of wildlife species. Several scholars have proved that optimal strategies that are relatively good for harvesting purposes are not simply “transferrable” to the context of conservation of wildlife biological species with no harvesting value. In addition, the existence of optimal policies for long-term conservation of all biological species (with or without harvesting value) cannot be guaranteed without having relatively large species populations at the initial time. Therefore, all such strategies are incapable of enhancing the scarce populations of endangered species and, therefore, cannot save these species from eventual (local) extinction. As an alternative, policymakers may soon be compelled to design and implement short-term defensive actions aimed at recovery and enhancement of endangered wildlife species.

In this paper, we present some new properties of the Mitra-Wan forestry model written as a discrete-time optimal control problem. For this problem, the set of stationary states is characterized. For the optimal long-run management, we consider the following optimality criteria: *average optimality*, *good control policies*, *bias optimality*, and *overtaking optimality*. We establish relationships between these criteria and show that the value of average optimal policies is constant and equals the value in the optimal stationary state.

In this paper, we propose a two-stage structured population model subject to component Allee effects in fecundity and maturation, and with two disturbances (predation only and harvest and predation) acting on both stages. It is shown that this combination leads to a demographic Allee effect—a characteristic that could be exploited in pest biological control, but on the other hand, it represents a bane in conservation biology. The analysis is performed for disturbances with functional responses type 2 and 3, and the models show that they yield qualitatively similar results. This characteristic is discussed from the species conservation and biological control point of view, together with possible extensions of this work.

Performance of a multispecies age-structured assessment (MSASA) model in the Gulf of Alaska (GOA) relative to changes in data and model assumptions was examined through simulation exercises. Species included arrowtooth flounder (*Atheresthes stomias*), Pacific cod (*Gadus macrocephalus*), walleye pollock (*Theragra chalcogramma*), Pacific halibut (*Hippoglossus stenolepis*), and Steller sea lion (*Eumetopias jubatus*). Age-specific predation mortality was estimated as a flexible function of predator and prey abundances and fitted to diet data. Simulated data sets were constructed by applying random error to estimates of catch, survey, and diet data from an operating model, whose structure was identical to that of the estimating model. Simulations explored the effects of data variability, mismatched assumptions regarding model structure, and lack of diet data on model performance. Model misspecification and uninformative diet data had the greatest influence on model performance. Given the current emphasis on the development of ecosystem-based models and management, prioritizing the rigorous sampling of diet data would best facilitate the development of predation models useful to management agencies.