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Abstract Tree canopies are known to elevate atmospheric inputs of both mercury (Hg) and methylmercury (MeHg). While foliar uptake of gaseous Hg is well documented, little is known regarding the temporal dynamics and origins of MeHg in tree foliage, which represents typically less than 1% of total Hg in foliage. In this work, we examined the foliar total Hg and MeHg content by following the growth of five individual trees of American Beech (Fagus grandifolia) for one growing season (April–November, 2017) in North Carolina, USA. We show that similar to other studies foliar Hg content increased almost linearly over time, with daily accumulation rates ranging from 0.123 to 0.161 ng/g/day. However, not all trees showed linear increases of foliar MeHg content along the growing season; we found that 2 out of 5 trees showed elevated foliar MeHg content at the initial phase of the growing season but their MeHg content declined through early summer. However, foliar MeHg content among all 5 trees showed eventual increases through the end of the growing season, proving that foliage is a net accumulator of MeHg while foliar gain of biomass did not “dilute” MeHg content. 

Missing data are ubiquitous in many domain such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR),missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs),we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Miss-ing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections.Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR.