Termite Assemblage Pattern and Niche Partitioning in a Tropical Forest Ecosystem.

Termites are major plant decomposers in tropical forest ecosystems, but their cryptic nature poses an obstacle for studying their ecological roles in depth. In the current study, we quantified climatic and geographic information of 137 termite collection sites in the Kenting National Park, Taiwan, and described the ecological niches and assemblage patterns of 13 termite species of three families. Three major assemblage patterns are reported. First, the three termite families were found in most landcovering types with similar number of species, which indicated that each family played a unique role in the ecosystem. Second, average numbers of termite species were not different among collection sites, but the total number of termite species found in each landcovering type was different, which indicated that termite niche capacity in each small area was the same but some landcovering types were composed of diverse microhabitats to host more termite species. Third, termite species of every family showed distinct moisture preferences in their habitat choices. In addition to the three assemblage patterns, we found that niche size of the advanced termite family, Termitidae, was larger than that of the primitive termite families, Rhinotermitidae or Kalotermitidae. The broader choices of cellulosic materials as food sources may allow Termitidae to adapt to more diverse environments than exclusive wood feeders. Termite niche quantification could further be used to study termite pest adaption in urban areas, interspecific competition between native and invasive species, and plant decomposition processes.

Wolbachia infections in world populations of bean beetles (Coleoptera: Chrysomelidae: Bruchinae) infesting cultivated and wild legumes.

Wolbachia endosymbionts are widespread among insects and other arthropods, often causing cytoplasmic incompatibility and other reproductive phenotypes in their hosts. Recently, possibilities of Wolbachia-mediated pest control and management have been proposed, and the bean beetles of the subfamily Bruchinae are known as serious pests of harvested and stored beans worldwide. Here we investigated Wolbachia infections in bean beetles from the world, representing seven genera, 20 species and 87 populations. Of 20 species examined, Wolbachia infections were detected in four species, Megabruchidius sophorae, Callosobruchus analis, C. latealbus and C. chinensis. Infection frequencies were partial in M. sophorae but perfect in the other species. In addition to C. chinensis described in the previous studies, C. latealbus was infected with two distinct Wolbachia strains. These Wolbachia strains from the bean beetles were phylogenetically not closely related to each other. Among world populations of C. chinensis, some Taiwanese populations on a wild leguminous plant, Rhynchosia minima, exhibited a peculiar Wolbachia infection pattern, suggesting the possibility that these populations comprise a distinct host race or a cryptic species.

Testing and mapping non-stationarity in animal behavioral processes: a case study on an individual female bean weevil.

Statistical analysis based on two characteristics of a small-world network, and on Lempel-Ziv's measure of Kolmogorov-Chaitin's algorithmic complexity are first proposed to scan through an individual behavioral sequence for possible existence of non-stationarity. Due to fixed window width, these tests have drawbacks in mapping out regions of non-stationarity. A non-parametric approach based on sparse coding schemes is employed to segment the whole behavioral sequence into unequal length segments, thus resultant avoiding further efforts for grouping. Then attempts are made to entangle the resultant segmentation with other non-local behavioral patterns onto such sequence to ascertain that the non-stationarity corresponds to a sequence of different categories of underlying driving force. It is of potential importance that this segmentation, represented by a hierarchy of code sequences, provides a natural platform for detecting intrinsically coherent behavioral patterns based on continuously recorded data. Illustrations throughout the developments are made exclusively on data encoded from a nearly 4-h video-recording of a female bean weevil's behavior.