Decreasing reservoir water levels improve habitat quality for Asian elephants

Population health and habitat quality are intimately related and seasonal changes in habitat quality are likely to be reflected in the body condition of animals. We studied seasonal variation of body condition in free ranging Asian elephants (Elephas maximus) in Udawalawe National Park, Sri Lanka based on visual scoring of individually identified elephants. We assessed the body condition of 218 adult females and 329 adult males from January 2008 to November 2012 and examined its relation to monthly rainfall and water level of the Udawalawe reservoir. Contrary to expectations, body condition of elephants was higher in the dry season, when primary productivity decreases due to lack of rainfall. However, the body condition showed both a seasonal and inter-annual negative co-relation with reservoir water level. A possible explanation for improved body condition in the dry season is the greater availability of fresh grass due to the emergence of reservoir bed grasslands with the drawdown of water. Our results underscore the importance of water management of large irrigation reservoirs in elephant conservation in Sri Lanka

Multiple Imputation for Categorical Variables in Multilevel Data

Multiple Imputation (MI) is a technique that imputes a set of plausible values for each missing item using an imputation model. An imputation model predicts a value for the missing item given the observed data and should be compatible with any model that is fitted to the multiply imputed data which is known as the substantive analysis model. To hold the compatibility, the imputation model has to capture the complexities in the substantive analysis model such as the structure of the data set and complex terms (higher order, interaction) considering the type of incomplete variable and the number of incomplete variables in the data set. In this thesis, the application of MI for handling missing values in a set of level-1 categorical variables in a two-level data structure where the data can be fitted with a random intercept substantive analysis model is empirically investigated. Simulation studies considering the missing data rate, the number of clusters and the cluster sizes are based on data generated from a real multilevel educational data set

A Method for Cleaning International Food Trade Data For Regional Analysis: The Pacific Food Trade Database

International trade in food is an essential component of the global food system, with consequences ranging from environmental sustainability to public health. Evidence-based food policy requires analysis and interpretation of trade flows among countries. We describe a stepwise mixed-methods process to identify and correct errors in international trade data to develop a reliable food trade database for the Pacific region. The method profoundly changes estimates of regional food trade. Similar results would likely be identified in other global regions with trade data quality challenges. If so, improved data quality could have significant food and other policy ramifications