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Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model ( R 2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

The number of studies investigating links between among-individual differences in metabolic rate (MR) and behavior has grown dramatically in the past several years. A major and often untested assumption of these studies is that the selected measure of MR is a valid proxy for energetic constraints. We argue that without explicitly testing this assumption, observed patterns between MR and behavior are uninterpretable. We provide guidelines for evaluating how a given measure of MR relates to constraints on the acquisition or expenditure of energy. The approach we advocate will allow researchers to develop and test a priori predictions relating energy metabolism and behavior. We conclude by highlighting several exciting directions for future work in this rapidly growing field., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

We propose a new modeling approach for inspection data that provides a more useful interpretation of the patterns of detections of invasive pests, using cargo inspection as a motivating example. Methods that are currently in use generally classify shipments according to their likelihood of carrying biosecurity risk material, given available historical and contextual data. Ideally, decisions regarding which cargo containers to inspect should be made in real time, and the models used should be able to focus efforts when the risk is higher. In this study, we propose a dynamic approach that treats the data as a time series in order to detect periods of high risk. A regulatory organization will respond differently to evidence of systematic problems than evidence of random problems, so testing for serial correlation is of major interest. We compare three models that account for various degrees of serial dependence within the data. First is the independence model where the prediction of the arrival of a risky shipment is made solely on the basis of contextual information. We also consider a Markov chain that allows dependence between successive observations, and a hidden Markov model that allows further dependence on past data. The predictive performance of the models is then evaluated using ROC and leakage curves. We illustrate this methodology on two sets of real inspection data., (© 2013 Society for Risk Analysis.)