Global woodland structure from local interactions:new nearest-neighbour functions for understanding the ontogenesis of global forest structure

Background:A number of hypotheses and theories,such as the Janzen-Connell hypothesis,have been proposed to explain the natural maintenance of biodiversity in tropical and temperate forest ecosystems.However,to date the details of the processes behind this natural maintenance are still unclear.Recently two new nearest-neighbour characteristics were proposed and in this paper we demonstrate how they can contribute to a better understanding of the ontogenesis of global forest structure from localised neighbourhoods.Methods:We applied the new species and size segregation functions together with appropriate test procedures to four example woodland data sets from China at Daqingshan,Jiaohe,Jiulongshan and Xiaolongshan forest regions.In addition we quantified the morphology of the new characteristics and modelled a neighbourhood allometric coefficient linking the two functions.Results:The results revealed quite different species segregation patterns with both conspecific and heterospecific attraction.We found these to be generally matched by equivalent size segregation patterns of attraction of similar and different sizes.It was straightforward to model the size segregation function from the knowledge of the species segregation function by estimating a neighbourhood allometric coefficient.Conclusions:The new characteristics have helped to quantify the extent and rate of decline of neighbourhood interactions in terms of spatial species and size diversity.Through the allometric neighbourhood coefficient the analysis highlighted once more how closely related species and size segregation are,thus supporting the minglingsize hypothesis.Using both a traditional and a restricted random-labelling test has provided a valuable tool for understanding the exact nature of species-mingling and size-inequality relationships.

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.