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USGS2020

A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems

Given the public health, economic and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi‐tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues. We developed a multi‐state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained). We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal and genital mucosa of 510 feral swine Sus scrofa during the years 2014–2016 in Florida, USA. The constrained model was better supported by data. PrV prevalence estimates varied among tissues and were higher than the naïve estimates, ranging from to 0.06 (CI: 0.02–0.14) in genital to 0.54 (CI: 0.14 ‐ 0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06–0.18) in nasal to 0.51 (CI: 0.21–0.81) in genital tissue. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue‐specific prevalence and detection probabilities similar to ours. Synthesis and applications . We focused on inferences about pathogen prevalence in multi‐tissue disease systems, dealing with both nondetection and potential dependencies among tissues in infection status. We found strong evidence of variation in both prevalence and detection probabilities among tissues. Our results emphasize the importance of sampling multiple tissues and of applying inference methods that account for imperfect detection in the surveillance of systemic diseases. The multi‐state modelling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and can be used even when the infection status of the pathogen in one tissue may depend on the infection status of the pathogen in other tissue(s).

Journal of Applied Ecology↗

USGS2026

What is the (real) rate of soil health practice adoption? Making sense of three data sources

Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [ Zea mays L.], soybean [ Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.

Journal of Soil and Water Conservation↗

USGS2010

Ranking Alaska moose nutrition: Signals to begin liberal antlerless harvests

We focused on describing low nutritional status in an increasing moose ( Alces alces gigas ) population with reduced predation in Game Management Unit (GMU) 20A near Fairbanks, Alaska, USA. A skeptical public disallowed liberal antlerless harvests of this moose population until we provided convincing data on low nutritional status. We ranked nutritional status in 15 Alaska moose populations (in boreal forests and coastal tundra) based on multiyear twinning rates. Data on age-of-first-reproduction and parturition rates provided a ranking consistent with twinning rates in the 6 areas where comparative data were available. Also, short-yearling mass provided a ranking consistent with twinning rates in 5 of the 6 areas where data were available. Data from 5 areas implied an inverse relationship between twinning rate and browse removal rate. Only in GMU 20A did nutritional indices reach low levels where justification for halting population growth was apparent, which supports prior findings that nutrition is a minor factor limiting most Alaska moose populations compared to predation. With predator reductions, the GMU 20A moose population increased from 1976 until liberal antlerless harvests in 2004. During 1997–2005, GMU 20A moose exhibited the lowest nutritional status reported to date for wild, noninsular, North American populations, including 1) delayed reproduction until moose reached 36 months of age and the lowest parturition rate among 36-month-old moose (29%, n = 147); 2) the lowest average multiyear twinning rates from late-May aerial surveys ( x̄ = 7%, SE = 0.9%, n = 9 yr, range = 3–10%) and delayed twinning until moose reached 60 months of age; 3) the lowest average mass of female short-yearlings in Alaska ( x̄ = 155 ± 1.6 [SE] kg in the Tanana Flats subpopulation, up to 58 kg below average masses found elsewhere); and 4) high removal (42%) of current annual browse biomass compared to 9–26% elsewhere in boreal forests. When average multiyear twinning rates in GMU 20A (sampled during 1960–2005) declined to <10% in the mid- to late 1990s, we began encouraging liberal antlerless harvests, but only conservative annual harvests of 61–76 antlerless moose were achieved during 1996–2001. Using data in the context of our broader ranking system, we convinced skeptical citizen advisory committees to allow liberal antlerless harvests of 600–690 moose in 2004 and 2005, with the objective of halting population growth of the 16,000–17,000 moose; total harvests were 7–8% of total prehunt numbers. The resulting liberal antlerless harvests served to protect the moose population's health and habitat and to fulfill a mandate for elevated yield. Liberal antlerless harvests appear justified to halt population growth when multiyear twinning rates average ≤10% and ≥1 of the following signals substantiate low nutritional status: <50% of 36-month-old moose are parturient, average multiyear short-yearling mass is <175 kg, or >35% of annual browse biomass is removed by moose.

Journal of Wildlife Management↗