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Abstract. Plague occurs episodically in many parts of the world, and some outbreaks appear to be related to increased abundance of rodents and other mammals that serve as hosts for vector fleas. Climate dynamics may influence the abundance of both fleas and mammals, thereby having an indirect effect on human plague incidence. An understanding of the relationship between climate and plague could be useful in predicting periods of increased risk of plague transmission. In this study, we used correlation analyses of 215 human cases of plague in relation to precipitation records from 1948 to 1996 in areas of New Mexico with history of human plague cases (38 cities, towns, and villages). We conducted analyses using 3 spatial scales: global (El Niño-Southern Oscillation Indices [SOI]); regional (pooled state-wide precipitation averages); and local (precipitation data from weather stations near plague case sites). We found that human plague cases in New Mexico occurred more frequently following winterspring periods (October to May) with above-average precipitation (mean plague years ϭ 113% of normal rain/ snowfall), resulting in 60% more cases of plague in humans following wet versus dry winter-spring periods. However, we obtained significant results at local level only; regional state-wide precipitation averages and SOI values exhibited no significant correlations to incidence of human plague cases. These results are consistent with our hypothesis of a trophic cascade in which increased winter-spring precipitation enhances small mammal food resource productivity (plants and insects), leading to an increase in the abundance of plague hosts. In addition, moister climate conditions may act to promote flea survival and reproduction, also enhancing plague transmission. Finally, the result that the number of human plague cases in New Mexico was positively associated with higher than normal winter-spring precipitation at a local scale can be used by physicians and public health personnel to identify and predict periods of increased risk of plague transmission to humans.Global climate dynamics are proposed by some to be responsible for recent outbreaks of infectious diseases, and others warn that long-term global warming could increase the risks of acquiring such diseases.

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size (NÌ‚) and effective sampling area (AÌ‚) are used to calculate density (DÌ‚ = NÌ‚/AÌ‚); and models applicable to sampling regimes using distance‐sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets (n = 11) were developed using nine rodent species from capture–recapture live‐trapping on both trapping grids and trapping webs in four replicate 4.2‐ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional “saturation” trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities. Density estimates (DÌ‚) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities (D) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this “blind” test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web‐based uniform and half‐normal models in program DISTANCE, and the grid‐based models Mb and Mbh in program CAPTURE (with AÌ‚ adjusted by species‐specific full mean maximum distance moved (MMDM) values). These models produced accurate DÌ‚ values (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's DÌ‚ values to the true D values over the range of observed test densities, only the web‐based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid‐based DÌ‚ analyses using full ...

The relationship between the risk of hantaviral pulmonary syndrome (HPS), as estimated from satellite imagery, and local rodent populations was examined. HPS risk, predicted before rodent sampling, was highly associated with the abundance of Peromyscus maniculatus, the reservoir of Sin Nombre virus (SNV). P. maniculatus were common in high-risk sites, and populations in high-risk areas were skewed toward adult males, the subclass most frequently infected with SNV. In the year after an El Niñ o Southern Oscillation (ENSO), captures of P. maniculatus increased only in high-risk areas. During 1998, few sites had infected mice, but by 1999, 18͞20 of the high-risk sites contained infected mice and the crude prevalence was 30.8%. Only 1͞18 of the low-risk sites contained infected rodents, and the prevalence of infection was lower (8.3%). Satellite imagery identified environmental features associated with SNV transmission within its reservoir population, but at least 2 years of high-risk conditions were needed for SNV to reach high prevalence. Areas with persistently high-risk environmental conditions may serve as refugia for the survival of SNV in local mouse populations.