Aspects of reproduction of the brown mussel Perna perna at the Iture rocky beach near Cape Coast,Ghana

Aspects of the reproductive biology of the brown mussel Perna perna at the Iture rocky beach near Cape Coast, Ghana, were studied from September 2014 to August 2015. The current study was aimed at providing information useful for managing the mussel fishery in this locality and also that would form the basis for designing appropriate culture methods for the species. Microscopic examination of fresh smears of gonadal material, as well as histological preparations of the gonad, were used to study the sexuality and breeding pattern of the species. Monthly gonadal and condition indices were also determined. Perna perna exhibited gonochoristic sexuality with a sex ratio of approximately 1:1 throughout the study period. Sexes were identifiable at shell lengths of 15.0–19.9 mm. Five stages of gonadal development were identified in both sexes. Gametogenic activity was continuous throughout the year, with two major spawning activities, from April to June and from August to December. These periods coincided with the major and minor rainy seasons, respectively, as well as the major upwelling period in August. Condition indices suggest that the mussels were in better condition for harvesting in March and August prior to the major spawning events.     

Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application

Abstract

The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419.     

Spatial variability estimation and risk assessment of the aquifer level at sparsely gauged basins using geostatistical methodologies

The spatial variability evaluation of the water table level of an aquifer provides useful information in water resources management plans. Three different approaches are applied to estimate the spatial variability of the water table in the study basin. All of them are based on the Kriging methodology. The first is the classical Ordinary Kriging approach, while the second involves information from a secondary variable (surface elevation) and the application of Residual Kriging. The third calculates the probability to lie below a certain groundwater level limit that could cause significant problems in groundwater resources availability. The latter is achieved by means of Indicator Kriging. A recently developed non-linear normalization method is used to transform both data and residuals closer to normal distribution for improved prediction results. In addition, the recently developed Spartan variogram model is applied to determine the spatial dependence of the measurements. The latter proves to be the optimal model, compared to a series of models tested, which provides in combination with the Kriging methodologies the most accurate cross validation estimations. The variogram form is explained with respect to the radius of influence of the pumping wells representing the spatial impact of the pumping activity. Groundwater level and probability maps are developed providing the ability to assess the spatial variability of the groundwater level in the basin and the risk that certain locations have in terms of a safe groundwater level limit that has been set for the sustainability of the groundwater resources of the basin.     

An application of Spartan spatial random fields in environmental mapping: focus on automatic mapping capabilities

This paper investigates the potential of Spartan spatial random fields (SSRFs) in real-time mapping applications. The data set that we study focuses on the distribution of daily gamma dose rates over part of Germany. Our goal is to determine a Spartan spatial model from the data, and then use it to generate “predictive” maps of the radioactivity. In the SSRF framework, the spatial dependence is determined from sample functions that focus on short-range correlations. A recently formulated SSRF predictor is used to derive isolevel contour maps of the dose rates. The SSRF predictor is explicit. Moreover, the adjustments that it requires by the user are reduced compared to classical geostatistical methods. These features present clear advantages for an automatic mapping system. The performance of the SSRF predictor is evaluated by means of various cross-validation measures. The values of the performance measures are similar to those obtained by classical geostatistical methods. Application of the SSRF method to data that simulate a radioactivity release scenario is also discussed. Hot spots are detected and removed using a heuristic method. The extreme values that appear in the path of the simulated plume are not captured by the currently used Spartan spatial model. Modeling of the processes leading to extreme values can enhance the predictive capabilities of the spatial model, by incorporating physical information.     

Ecosystem modelling in the southern Benguela: comparisons of Atlantis,Ecopath with Ecosim,and OSMOSE under fishing scenarios

Ecosystem-based management of marine fisheries requires the use of simulation modelling to investigate the system-level impact of candidate fisheries management strategies. However, testing of fundamental assumptions such as system structure or process formulations is rarely done. In this study, we compare the output of three different ecosystem models (Atlantis, Ecopath with Ecosim, and OSMOSE) applied to the same ecosystem (the southern Benguela), to explore which ecosystem effects of fishing are most sensitive to model uncertainty. We subjected the models to two contrasting fishing pressure scenarios, applying high fishing pressure to either small pelagic fish or to adult hake. We compared the resulting model behaviour at a system level, and also at the level of model groups. We analysed the outputs in terms of various commonly used ecosystem indicators, and found some similarities in the overall behaviour of the models, despite major differences in model formulation and assumptions. Direction of change in system-level indicators was consistent for all models under the hake pressure scenario, although discrepancies emerged under the small-pelagic-fish scenario. Studying biomass response of individual model groups was key to understanding more integrated system-level metrics. All three models are based on existing knowledge of the system, and the convergence of model results increases confidence in the robustness of the model outputs. Points of divergence in the model results suggest important areas of future study. The use of feeding guilds to provide indicators for fish species at an aggregated level was explored, and proved to be an interesting alternative to aggregation by trophic level.