CiteULike: neteler's Williams

Emerging infectious diseases in wildlife.

ES Williams — 2007-12-17T21:39:14-00:00

Rev Sci Tech, Vol. 21, No. 1. (April 2002), pp. 139-157.

The processes which give rise to emerging infectious diseases of wildlife can be categorised as follows: ecosystem alterations of anthropogenic or natural origin; movement of pathogens or vectors, via human or natural agency; and changes in microbes or in the recognition of emerging pathogens due to advances in the techniques of epidemiology. These are simplistic divisions because factors influencing the emergence of diseases of wild animals generally fall into more than one category. Mycoplasmosis among passerines is related to habitat changes and artificial feeding resulting in increased bird densities and subsequent disease transmission. The origin of this strain of Mycoplasma gallisepticum is not known. Hantavirus infections in rodents have emerged due to human-induced landscape alterations and/or climatic changes influencing population dynamics of hantavirus reservoir hosts, with disease consequences for humans. Movement of pathogens or vectors is a very important process by which diseases of wildlife expand geographic range. Although the origin of caliciviruses of rabbits and hares is somewhat obscure, their movement by humans, either deliberately or accidentally, has greatly expanded the distribution of these viruses. Rabies is an ancient disease, but geographic expansion has occurred by both natural and anthropogenic movements of wild animals. Human movement of amphibians may explain the distribution of the highly pathogenic chytrid fungus around the world. Newly recognised paramyxoviruses may reflect both changes in these pathogens and the development of techniques of identification and classification. Many more such examples of emerging diseases will arise in the future, given the extensive alterations in landscapes world-wide and movements of animals, vectors and pathogens. Those who study and diagnose diseases of wildlife must be alert for emerging diseases so that the impact of such diseases on wild animals, domestic animals and humans can be minimised.

Emerging infectious diseases in wildlife

ES Williams — 2007-12-17T21:25:08-00:00

Revue Scientifique et Technique de l'Office International des Epizooties, Vol. 21, No. 1. (April 2002), pp. 139-157.

The processes which give rise to emerging infectious diseases of wildlife can be categorised as follows: ecosystem alterations of anthropogenic or natural origin; movement of pathogens or vectors, via human or natural agency; and changes in microbes or in the recognition of emerging pathogens due to advances in the techniques of epidemiology. These are simplistic divisions because factors influencing the emergence of diseases of wild animals generally fall into more than one category. Mycoplasmosis among passerines is related to habitat changes and artificial feeding resulting in increased bird densities and subsequent disease transmission. The origin of this strain of Mycoplasma gallisepticum is not known. Hantavirus infections in rodents have emerged due to human-induced landscape alterations and/or climatic changes influencing population dynamics of hantavirus reservoir hosts, with disease consequences for humans. Movement of pathogens or vectors is a very important process by which diseases of wildlife expand geographic range. Although the origin of caliciviruses of rabbits and hares is somewhat obscure, their movement by humans, either deliberately or accidentally, has greatly expanded the distribution of these viruses. Rabies is an ancient disease, but geographic expansion has occurred by both natural and anthropogenic movements of wild animals. Human movement of amphibians may explain the distribution of the highly pathogenic chytrid fungus around the world. Newly recognised paramyxoviruses may reflect both changes in these pathogens and the development of techniques of identification and classification. Many more such examples of emerging diseases will arise in the future, given the extensive alterations in landscapes world-wide and movements of animals, vectors and pathogens. Those who study and diagnose diseases of wildlife must be alert for emerging diseases so that the impact of such diseases on wild animals, domestic animals and humans can be minimised.

Climate analysis with satellite versus weather station data

Robert Mendelsohn — 2007-03-05T08:59:25-00:00

Climatic Change, Vol. 81, No. 1. (March 2007), pp. 71-83.

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.

Novel methods improve prediction of species distributions from occurrence data

Jane Elith — 2006-04-13T15:45:22-00:00

Ecography, Vol. 29, No. 2. (April 2006), pp. 129-151.

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Evaluating presence-absence models in ecology: the need to account for prevalence

S Manel — 2005-07-24T20:03:05-00:00

Journal of Applied Ecology, Vol. 38, No. 5. (October 2001), pp. 921-931.

Summary1.Models for predicting the distribution of organisms from environmental data are widespread in ecology and conservation biology. Their performance is invariably evaluated from the percentage success at predicting occurrence at test locations.2.Using logistic regression with real data from 34 families of aquatic invertebrates in 180 Himalayan streams, we illustrate how this widespread measure of predictive accuracy is affected systematically by the prevalence (i.e. the frequency of occurrence) of the target organism. Many evaluations of presence–absence models by ecologists are inherently misleading.3.With the same invertebrate models, we examined alternative performance measures used in remote sensing and medical diagnostics. We particularly explored receiver-operating characteristic (ROC) plots, from which were derived (i) the area under each curve (AUC), considered an effective indicator of model performance independent of the threshold probability at which the presence of the target organism is accepted, and (ii) optimized probability thresholds that maximize the percentage of true absences and presences that are correctly identified. We also evaluated Cohen’s kappa, a measure of the proportion of all possible cases of presence or absence that are predicted correctly after accounting for chance effects.4.AUC measures from ROC plots were independent of prevalence, but highly significantly correlated with the much more easily computed kappa. Moreover, when applied in predictive mode to test data, models with thresholds optimized by ROC erroneously overestimated true occurrence among scarcer organisms, often those of greatest conservation interest. We advocate caution in using ROC methods to optimize thresholds required for real prediction.5.Our strongest recommendation is that ecologists reduce their reliance on prediction success as a performance measure in presence–absence modelling. Cohen’s kappa provides a simple, effective, standardized and appropriate statistic for evaluating or comparing presence–absence models, even those based on different statistical algorithms. None of the performance measures we examined tests the statistical significance of predictive accuracy, and we identify this as a priority area for research and development.