Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.     

An improved approach for identifying suitable habitat of Sambar Deer (Cervus unicolor Kerr) using ecological niche analysis and environmental categorization: Case study at Phu-Khieo Wildlife Sanctuary,Thailand

To assess habitat suitability (HS) has become an increasingly important component of species/ecosystem management. HS assessment is usually based on presence/absence data related to environmental variables. An exception is Ecological Niche Factor Analysis (ENFA), which uses only presence data and which does not require absence data. Most HS modelling is based on input of all environmental parameters (EnvPs) without environmental categorization, and does not take into account species interaction and human intervention for an assessment of HS. In this study, the EnvPs are arranged into four features: geographical features, consumable features, human-factor features, and species–human interaction features. These features affect species with respect to movement, behavior and activity. The research presented here has used an already existing dataset of wildlife species and human activities/visitations, which was compiled during 2004–2006 in Phu-Khieo Wildlife Sanctuary (PKWS). Data from 2004 to 2005 were used to produce HS maps, while the data of 2006 were used for evaluating these maps. Sambar Deer (SD) was chosen to predict its own HS. Six HS maps of SD were analyzed using ENFA in the following manner: (1) inputting all EnvPs together, (2) inputting each feature, separately and (3) integrating the four resulting HS maps by model averaging. It was found that model averaging was capable of predicting the HS of SD more reliably than the model with all EnvPs put in together. Multiple linear regressions were computed between the HS map with all EnvPs and the HS maps with each feature. The results show that the HS map with only geographical features has the highest coefficient value (0.516) while the coefficient values of other HS maps with the above features are 0.296, 0.53 and −0.006, respectively. This indicates that the geographical features have an influence on the other features and that the predicting power is lower when all EnvPs are computed in the ENFA model. Therefore, in order to generate HS, each feature should at first be put into the model separately. Following that, the average of all features will be combined.     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

Principal components derived from soil physicochemical data explained a land degradation gradient,and suggested the applicability of new indexes for estimation of soil productivity in the Sakaerat Environmental Research Station,Thailand

To find a principal component (PC) that quantifies the degree of soil degradation, we analyzed various physicochemical characteristics of soils over a land degradation gradient related to aboveground vegetation in the Sakacrat Environmental Research Station (SERS), Thailand. The aboveground vegetative types representing the degradation gradient were bare ground (BG, highly degraded), dry dipterocarp forest (DDF, moderately disturbed) and dry evergreen forest (DEF, the original vegetation). Soils under these vegetative types were sampled in February (dry season). March just after temporal precipitation) and June (rainy season) 2001. Through the period of this research, the degradation was consistently explained by sandy texture, high bulk density, lower pH, high exchangeable acidity, poor mineral and organic nutrients and dryness. Principal component analysis (PCA) was applied to determine significant principal components (PCs) that clarify the differences in soil properties between the vegetative types and between the timing of soil sampling. The PC loadings suggested that the first PC was the component that indicates total fertility of soil in the site, while the fifth PC indicates the dry to wet seasonal transition. The first PC was named the total fertility component (TFC). The linear regression between the TFC score and recently proposed indexes, the soil fertility index (SFI) or the soil evaluation factor (SEF), was highly significant (p < 0.001), indicating that the SFI and the SEF are applicable to measuring total fertility of soils in the SERS.     

An optimization approach for sustainable release of e-flows for lake restoration and preservation: Model development and a case study of Baiyangdian Lake, China

In this paper, we describe the development of a model for the sustainable release of e-flows from the regional water resource infrastructure (e.g., reservoirs, rivers with available water) for lake restoration and preservation, and use the model in a case study of Baiyangdian Lake, China. First, we define the sustainable environmental flows (e-flows), with an emphasis on the ecological importance of temporal variation in factors such as water level (depth). By analyzing historical data on the suitable range of water levels in the lake, we evaluated fluctuations using canonical correspondence analysis and frequency distribution analysis. The temporal variations required by the ecosystem of the lake were also assessed. Based on this approach, we developed an optimization model for sustainable release of e-flows. We used the adaptive genetic algorithm approach to solve the model and determine the required release of e-flows. Scenario analysis then provided a range of potential management strategies for the e-flows. The optimal results are helpful to the lake managers to establish sustainable e-flow release schemes for the lake restoration and preservation.