A random forest approach for predicting the presence of Echinococcus multilocularis intermediate host Ochotona spp. presence in relation to landscape characteristics in western China.

Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plateau pika (Ochotona spp.) small mammal intermediate host for the parasitic tapeworm Echinococcus multilocularis which is responsible for a significant burden of human zoonoses in western China. Landsat ETM+ satellite imagery and digital elevation model data were utilized to generate quantified measures of environmental characteristics across a study area in Sichuan Province, China. Land cover maps were generated identifying the distribution of specific land cover types, with landscape metrics employed to describe the spatial organisation of land cover patches. Random forests were used to model spatial patterns of Ochotona spp. presence, enabling the relative importance of the environmental characteristics in relation to Ochotona spp. presence to be ranked. An index of habitat aggregation was identified as the most important variable in influencing Ochotona spp. presence, with area of degraded grassland the most important land cover class variable. 71% of the variance in Ochotona spp. presence was explained, with a 90.98% accuracy rate as determined by 'out-of-bag' error assessment. Identification of the environmental characteristics influencing Ochotona spp. presence enables us to better understand distribution patterns of hosts implicated in the transmission of Em. The predictive mapping of this Em host enables the identification of human populations at increased risk of infection, enabling preventative strategies to be adopted.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Non-intersecting leaf insertion algorithm for tree structure models.

We present an algorithm and an implementation to insert broadleaves or needleleaves into a quantitative structure model according to an arbitrary distribution, and a data structure to store the required information efficiently. A structure model contains the geometry and branching structure of a tree. The purpose of this work is to offer a tool for making more realistic simulations of tree models with leaves, particularly for tree models developed from terrestrial laser scanning (TLS) measurements. We demonstrate leaf insertion using cylinder-based structure models, but the associated software implementation is written in a way that enables the easy use of other types of structure models. Distributions controlling leaf location, size and angles as well as the shape of individual leaves are user definable, allowing any type of distribution. The leaf generation process consist of two stages, the first of which generates individual leaf geometry following the input distributions, while in the other stage intersections are prevented by carrying out transformations when required. Initial testing was carried out on English oak trees to demonstrate the approach and to assess the required computational resources. Depending on the size and complexity of the tree, leaf generation takes between 6 and 18 min. Various leaf area density distributions were defined, and the resulting leaf covers were compared with manual leaf harvesting measurements. The results are not conclusive, but they show great potential for the method. In the future, if our method is demonstrated to work well for TLS data from multiple tree types, the approach is likely to be very useful for three-dimensional structure and radiative transfer simulation applications, including remote sensing, ecology and forestry, among others.

Spatial modelling and ecology of Echinococcus multilocularis transmission in China.

Recent research in central China has suggested that the most likely transmission mechanism for Echinococcus multilocularis to humans is via domestic dogs which are allowed to roam freely and hunt (infected) small mammals within areas close to villages or in areas of tented pasture. This assertion has led to the hypothesis that there is a landscape control on transmission risk since the proximity of suitable habitat for susceptible small mammals appears to be the key. We have tested this hypothesis in a number of endemic areas in China, notably south Gansu Province and the Tibetan region of western Sichuan Province. The fundamental landscape control is its effect at a regional scale on small mammal species assemblages (susceptible species are not ubiquitous) and, at a local scale, the spatial distributions of small mammal populations. To date the research has examined relationships between landscape composition and patterns of human infection, landscape and small mammal distributions and recently the relationships between landscape and dog infection rates. The key tool to characterize landscape is satellite remote sensing and these data are used as inputs to drive spatial models of transmission risk. This paper reviews the progress that has been made so far in spatial modeling of the ecology of E. multilocularis with particular reference to China, outlines current research issues, and describes a framework for building a spatial-temporal model of transmission ecology.

Landscape composition and spatial prediction of alveolar echinococcosis in southern Ningxia, China.

BACKGROUND: Alveolar echinococcosis (AE) presents a serious public health challenge within China. Mass screening ultrasound surveys can detect pre-symptomatic AE, but targeting areas identified from hospital records is inefficient regarding AE. Prediction of undetected or emerging hotspots would increase detection rates. Voles and lemmings of the subfamily Arvicolinae are important intermediate hosts in sylvatic transmission systems. Their populations reach high densities in productive grasslands where food and cover are abundant. Habitat availability is thought to affect arvicoline population dynamic patterns and definitive host-intermediate host interactions. Arvicoline habitat correlates with AE prevalence in Western Europe and southern Gansu Province, China. METHODS AND FINDINGS: Xiji County, Ningxia Hui Autonomous Region, borders southern Gansu. The aims of this study were to map AE prevalence across Xiji and test arvicoline habitat as a predictor. Land cover was mapped using remotely sensed (Landsat) imagery. Infection status of 3,205 individuals screened in 2002-2003 was related, using generalised additive mixed models, to covariates: gender; farming; ethnicity; dog ownership; water source; and areal cover of mountain pasture and lowland pasture. A Markov random field modelled additional spatial variation and uncertainty. Mountain pasture and lowland pasture were associated with below and above average AE prevalence, respectively. CONCLUSIONS: Low values of the normalised difference vegetation index indicated sub-optimality of lowland pasture for grassland arvicolines. Unlike other known endemic areas, grassland arvicolines probably did not provide the principal reservoir for Echinococcus multilocularis in Xiji. This result is consistent with recent small mammal surveys reporting low arvicoline densities and high densities of hamsters, pikas and jerboas, all suitable intermediate hosts for E. multilocularis, in reforested lowland pasture. The risk of re-emergence is discussed. We recommend extending monitoring to: southern Haiyuan County, where predicted prevalence was high; southern Xiji County, where prediction uncertainty was high; and monitoring small mammal community dynamics and the infection status of dogs.