Removal of the cationic dyes rhodamine B (RhB) and methylene blue (MB) by waste seeds Aleurites moluccana (WAM) was studied in a batch system. The adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), point of zero charge measurement, and the Boehm titration method. The effects of contact time and pH were investigated for the removal of cationic dyes. An increase in pH from 3 to 9 was accompanied by an approximately three-fold increase in the amount of dye adsorbed. The adsorptions equilibrium values were obtained and analyzed using the Langmuir, Freundlich, Sips, and Redlich–Peterson equations, the Sips isotherm being the one that showed the best correlation with the experimental values. The maximum adsorption capacities of the dyes were 178 mg/g for the MB and 117 mg/g for the RhB. The kinetic sorption was evaluated by the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models, where it was observed that sorption follows the pseudo-second-order kinetic model. The study of thermodynamics showed that the adsorption is a spontaneous and endothermic process. The results indicate that waste seeds of A. moluccana could be used as a low cost material for the removal of cationic dyes from wastewater.

Adsorption of rhodamine B and methylene blue dyes using waste of seeds of Aleurites Moluccana, a low cost adsorbent

Data-driven prospectivity modelling of greenfields terrains is challenging because very few deposits are available and the training data are overwhelmingly dominated by non-deposit samples.
 This could lead to biased estimates of model parameters. In the present study involving Random Forest (RF)-based gold prospectivity modelling of the Tanami region, a greenfields terrain in Western Australia, we apply the Synthetic Minority Over-sampling Technique to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50. An optimal threshold range is determined objectively using statistical measures such as the data sensitivity, specificity, kappa and per cent correctly classified. The RF regression modelling with the modified dataset of close to 50:50 sample ratio of deposit to non-deposit delineates 4.67% of the study area as high prospectivity areas as compared to only 1.06% by the original dataset, implying that the original “sparse” dataset underestimates prospectivity.

Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia

Conservation efforts for threatened or endangered species are challenging because the multi-scale factors that relate to their decline or inhibit their recovery are often unknown. To further exacerbate matters, the perceptions associated with the mechanisms of species decline are often viewed myopically rather than across the entire species range. We used over 80 years of fish presence data collected from the Great Plains and associated ecoregions of the United States, to investigate the relative influence of changing environmental factors on the historic and current truncated distributions of the Arkansas River shiner Notropis girardi. Arkansas River shiner represent a threatened reproductive ecotype considered especially well adapted to the harsh environmental extremes of the Great Plains. Historic (n = 163 records) and current (n = 47 records) species distribution models were constructed using a vector-based approach in MaxEnt by splitting the available data at a time when Arkansas River shiner dramatically declined. Discharge and stream order were significant predictors in both models; however, the shape of the relationship between the predictors and species presence varied between time periods. Drift distance (river fragment length available for ichthyoplankton downstream drift before meeting a barrier) was a more important predictor in the current model and indicated river segments 375–780 km had the highest probability of species presence. Performance for the historic and current models was high (area under the curve; AUC > 0.95); however, forecasting and backcasting to alternative time periods suggested less predictive power. Our results identify fragments that could be considered refuges for endemic plains fish species and we highlight significant environmental factors (e.g., discharge) that could be manipulated to aid recovery.

Backcasting the decline of a vulnerable Great Plains reproductive ecotype: identifying threats and conservation priorities

Various regression methods can be used to quantify the relationships between fish populations and their environment. Strong correlations often existing between environmental variables, however, can cause multicollinearity, resulting in overfitting in modeling. This study compares the performance of a regular generalized additive model (GAM) with raw environmental variables as explanatory variables (regular GAM) and a GAM based on principal component analysis (PCA-based GAM) in modeling the relationship between fish richness and diversity indices and environmental variables. The PCA-based GAM tended to perform better than the regular GAM in cross-validation tests, showing a higher prediction precision. The variables identified being significant in modeling differed between the two models, and differences between the two models were also found in the scope and range of predicted richness and diversity indices for demersal fish community. This implies that choices between these two statistical modeling approaches can lead to different ecological interpretations of the relationships between fish communities and their habitats. This study suggests that the PCA-based GAM is a better approach than the original GAM in quantifying the relationship between fish richness and diversity indices and environmental variables if the environmental variables are highly correlated.

A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices

Environmental change has and will con- tinue to adversely influence aquatic communities. Efforts to model impacts of environmental change on fisheries have largely focused on cold water, commer- cial, and recreationally valued species, even though warm water, non-game species have important roles in ecosystem services and processes. We developed species distribution models for fourteen warm water fish species native to the central United States and evaluated environmental drivers and predictive per- formance. We used an ensemble model approach produced by combining forecasts of five single-model techniques. Response plots and variable importance calculations were used to evaluate the influence of individual variables. The predictive performance of the ensemble models was assessed using area under the curve of the receiver operating characteristic plot (AUC). AUC values indicate ensemble models per- formed better than single-model types, suggesting ensemble models are more reliable and applicable for management purposes than single models. Most models were influenced by a mix of climate, land use, and geophysical variables; however, climate variables were the dominant environmental drivers across models. Given the high sensitivity of models to climate and land use, we expect future climate and land use changes to influence distributions.

/pdf/development-and-evaluation-of-species-distribution-models-fkdrgz17d6.pdf

Development and Evaluation of Species Distribution Models for Fourteen Native Central U.S. Fish Species

Species distribution models (SDMs) are often used in conservation planning, but their utility can be improved by assessing the relationships between environmental and species response variables. We constructed SDMs for 30 stream fishes of Maryland, USA, using watershed attributes as environmental variables and presence/absence as species responses. SDMs showed substantial agreement between observed and predicted values for 17 species. Most important variables were natural attributes (e.g., ecoregion, watershed area, latitude/longitude); land cover (e.g., %impervious, %row crop) was important for three species. Focused analyses on four representative species (central stoneroller, creek chub, largemouth bass, and white sucker) showed the probability of presence of each species increased non-linearly with watershed area. For these species, SDMs built to predict absent, low, and high densities were similar to presence/absence predictions but provided probable locations of high densities (e.g., probability of high-density creek chub decreased rapidly with watershed area). We applied SDMs to predict suitability of watersheds within the study area for each species. Maps of suitability and the environmental and species response relationships can help develop better management plans.

Species Distribution Models of Freshwater Stream Fishes in Maryland and Their Implications for Management

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Species Distribution Models of Freshwater Stream Fishes in Maryland and Their Implications for Management