An Ecological Study of Helminths of Some Wyoming Voles (Microtus spp.) with a Description of a New Species of Nematospiroides (Heligmosomidae: Nematoda)

An ecological and taxonomic study of the helminth parasites of voles (Microtus spp.) in the Jackson Hole region of Wyoming is reported. Nematospiroides microti n. sp. from Microtus montanus nanus and M. richardsoni macropus is described and figured. A cestode, Paranoplocephala infrequens, and a nematode, Syphacia obvelata, were generally distributed throughout the region in all habitats except the sage flats. A trematode, Quinqueserialis hassalli, was recovered only from voles collected near streams at low altitudes. This was presumably due to the localized distribution of the molluscan intermediate host. Four helminths, viz., Hymenolepis horrida, Heligmosomum costellatum, Nematospiroides microti and Trichuris opaca, were restricted in their distribution to the alpine and sub-alpine meadows. Of these parasites, H. horrida and H. costellatum are reported for the first time from North America. Most of the other host and locality records are new. Available data indicate that host specificity was not a factor in restricting the distribution of parasites. Although the greatest numbers of parasites, both qualitative and quantitative, occurred in habitats where host density was greatest, it seems unlikely that host density is the only factor involved.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.