Statistical sensitivity for detection of spatial and temporal patterns in rodent population densities.

A long-term monitoring program begun 1 year after the epidemic of hantavirus pulmonary syndrome in the U.S. Southwest tracked rodent density changes through time and among sites and related these changes to hantavirus infection rates in various small-mammal reservoir species and human disease outbreaks. We assessed the statistical sensitivity of the program's field design and tested for potential biases in population estimates due to unintended deaths of rodents. Analyzing data from two sites in New Mexico from 1994 to 1998, we found that for many species of Peromyscus, Reithrodontomys, Neotoma, Dipodomys, and Perognathus, the monitoring program detected species-specific spatial and temporal differences in rodent densities; trap-related deaths did not significantly affect long-term population estimates. The program also detected a short-term increase in rodent densities in the winter of 1997-98, demonstrating its usefulness in identifying conditions conducive to increased risk for human disease

Knowing the Biosphere: Documentation, Specimens, Archives, and Names Reveal Environmental Change and Emerging Pathogens

One Health programs and trajectories are now the apparent standard for exploring the occurrence and distribution of emerging pathogens and disease. By definition, One Health has been characterized as a broadly inclusive, collaborative, and transdisciplinary approach with connectivity across local to global scales, which integrates the medical and veterinary community to recognize health outcomes emerging at the environmental nexus for people, animals, plants, and their shared landscapes. One Health has been an incomplete model, conceptually and operationally, focused on reactive and response-based foundations, to limit the impact of emerging pathogens and emerging infectious diseases and, as such, lacks a powerful proactive capacity. A proactive, predictive One Health is necessary, emanating in part from geographically/taxonomically broad and temporally deep biological collections of pathogen-host assemblages. The DAMA protocol (Document, Assess, Monitor, Act), the operational extension of the Stockholm paradigm (SP), accomplishes this task by encompassing holistic and strategic biological sampling of reservoir host assemblages and pathogens at environmental interfaces and more extensively through resurveys, with development of informatics resources digitally linked to physical specimens held in publicly accessible museum biorepositories. Archives of specimens are the foundations for accumulating interrelated archives of information (the baselines against which change can be identified and tracked), with collections serving as fundamental resources for biodiversity informatics under the conceptual evolutionary and ecological umbrella of the SP. A cultural and conceptual transformation is essential among the diverse practitioners in the One Health community, one that recognizes the necessity of placing pathogens in an evolutionary, ecological, and environmental context by integrating specimens and associated informatics into an infrastructure and networks for actionable information. As a community, it is essential to abandon response-based business as usual while looking forward toward proactive transboundary approaches that maximize our conceptual and taxonomic view of diversity across interconnected planetary scales that influence the complexity of pathogen-host interfaces. Evolution, where the past always influences the present and the future, defines our trajectory, as the need for sustained archives that describe the biosphere becomes more acute with each passing day

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

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 ((N) over cap) and effective sampling area ((A) over cap) are used to calculate density ((D) over cap = (N) over cap/(A) over cap); 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 ((D) over cap) 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 M-b and M-bh in program CAPTURE (with A adjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate (D) over cap 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 (D) over cap 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 (D) over cap analyses using full MMDM values for (W) over cap area adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based approaches.Our results support the contention that web-based approaches for density estimation of small-mammal populations are both theoretically and empirically superior to grid-based approaches, even when sample size is far less than often recommended. In view of the increasing need for standardized environmental measures for comparisons among ecosystems and through time, analytical models based on distance sampling appear to offer accurate density estimation approaches for research studies involving small-mammal abundances.</p

Transformational Principles for NEON Sampling of Mammalian Parasites and Pathogens: A Response to Springer and Colleagues

Seen as an opportunity to establish a nationwide web of environmental monitoring sites (Kao et al. 2012), the National Environmental Observatory Network (NEON) is now releasing a series of protocols presented with apparently broad community support. Springer and colleagues (2016) outlined sampling designs aimed at understanding how changing environmental conditions will affect mammals and associated parasites

Transformational Principles for NEON Sampling of Mammalian Parasites and Pathogens: A Response to Springer and Colleagues

Seen as an opportunity to establish a nationwide web of environmental monitoring sites (Kao et al. 2012), the National Environmental Observatory Network (NEON) is now releasing a series of protocols presented with apparently broad community support. Springer and colleagues (2016) outlined sampling designs aimed at understanding how changing environmental conditions will affect mammals and associated parasites