Chemical control of water hyacinth by some herbicides and their effect on some aquatic invertebrate

Water Hyacinth (Eichhornia crassipes), regarded as one of worst aquatic weeds in the world, has been an invader in northern Iran, particularly in the Anzali Wetland. Herbicide application as a control method with respect to ecosystem health has been investigated. The effects of three herbicides, glyphosate (Roundup), Glufosinate-ammonium (Basta), and Bispyribac sodium (Nominee) were investigated on water hyacinth and on the survival of five aquatic invertebrates from the Anzali Wetland including Hemiptera, Amphipoda, Odonata, Ostracoda and Daphnia. The treatments consisted of 3 L/ha of glyphosate, 5 L/ha of Glufosinate-ammonium, and 0.3 L/ha of Bispyribac sodium. European Weed Research Council (EWRC) rating scale determining reduction of wet and dry weight of shoot was the basis of assessment to determine the effectiveness of the herbicides in the control of of water hyacinth. All herbicides were effective on water hyacinth while Roundup caused a significant reduction of shoot biomass and scored 98% on the EWRC scale. Bayesian mediation model was used to calculate total and decomposition effect of herbicides on animal groups. Based on the Bayesian mediation model, Basta showed the best performance with lowest probability of a negative effect (PEff<0=0.22). The accuracy of dosages and spraying of herbicides can be considered the most effective in inhibiting water hyacinth and the least destructive to living organisms.

Prior Predictive Checks for the Method of Covariances in Bayesian Mediation Analysis

Bayesian mediation analysis using the method of covariances requires specifying a prior for the covariance matrix of the independent variable, mediator, and outcome. Using a conjugate inverse-Wishart prior has been the norm, even though this choice assumes equal levels of informativeness for all elements in the covariance matrix. This paper describes separation strategy priors for the single mediator model, develops a Prior Predictive Check (PrPC) for inverse-Wishart and separation strategy priors, and implements the PrPC in a Shiny app. An empirical example illustrates the possibilities in the app. Guidelines are provided for selecting the optimal prior specification for the prior knowledge researchers wish to encode.