Effects of in situ freezing on soil net nitrogen mineralization and net nitrification in fertilized grassland of northern China

Effects of soil freezing on nitrogen (N) mineralization have been the subject of increased attention in the ecological literature, though fewer studies have examined N mineralization responses to successive mild freezing, severe freezing and cyclic freeze–thaw events. Even less is known about relationships of responses to soil N status. This study measured soil N mineralization and nitrification in the field along an experimental N gradient in a grassland of northern China during the dormant season (October 2005–April 2006), a period in which freezing naturally occurs. Net N mineralization exhibited great temporal variability, with nitrification being the predominant N transformation process. Soil microbial biomass C and N and extractable NH4 + pools declined by 40, 52, and 56%, respectively, in April 2006, compared with their initial concentrations in October 2005; soil NO3– pools increased by 84%. Temporal patterns of N mineralization were correlated with soil microbial biomass C and N. N mineralization and nitrification increased linearly with added N. Microbial biomass C in treated soils increased by 10% relative to controls, whereas microbial N declined by 9%. Results further suggest that freezing events greatly alter soil N dynamics in the dormant season at this site, with considerable available N accumulating during this period.

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.