Application of copper(II)-based chemicals induces CH3Br and CH3Cl emissions from soil and seawater.

Methyl bromide (CH3Br) and methyl chloride (CH3Cl) are major carriers of atmospheric bromine and chlorine, respectively, which can catalyze stratospheric ozone depletion. However, in our current understanding, there are missing sources associated with these two species. Here we investigate the effect of copper(II) on CH3Br and CH3Cl production from soil, seawater and model organic compounds: catechol (benzene-1,2-diol) and guaiacol (2-methoxyphenol). We show that copper sulfate (CuSO4) enhances CH3Br and CH3Cl production from soil and seawater, and it may be further amplified in conjunction with hydrogen peroxide (H2O2) or solar radiation. This represents an abiotic production pathway of CH3Br and CH3Cl perturbed by anthropogenic application of copper(II)-based chemicals. Hence, we suggest that the widespread application of copper(II) pesticides in agriculture and the discharge of anthropogenic copper(II) to the oceans may account for part of the missing sources of CH3Br and CH3Cl, and thereby contribute to stratospheric halogen load.

The MIAmaxent R package: Variable transformation and model selection for species distribution models.

The widely used "Maxent" software for modeling species distributions from presence-only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high-predictive performance but low-ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models-that is, models which are more complex but not necessarily predictively better-than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence-environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence-only data and logistic regression (GLM) for presence-absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.