We need to talk about nonprobability samples

Brucella spp. in Wildlife of the Lombardy Region, Northern Italy

Surveillance data collected in the period 2017-20 for Brucella spp. in wildlife of the Lombardy Region in northern Italy were used to describe the exposure of the wildlife species to Brucella spp. in wild boar (Sus scrofa), European brown hare (Lepus europaeus), fallow deer (Dama dama), red deer (Cervus elaphus), and roe deer (Capreolus capreolus). Among the tested species, wild boar (n=6,440) showed the highest percentage of seropositive samples (5.9%). Notably, wild boars of perifluvial area of the Po River showed higher percentages of positivity than those of the pre-Alpine district. In addition, during the hunting season in 2018, 95 organs (uterus or testes, spleen, and submandibular lymph nodes) from wild boar of the perifluvial area of the Po River were collected for bacteriological examination. Brucella suis was isolated in culture from 18.9% of tested lymph nodes. These serological and microbiological results highlight the presence of B. suis in wild boar and suggest the importance of wild boar as a reservoir for B. suis. Comparison of the spatial distribution of Brucella-seropositive wild boars with the location of backyard swine farms revealed a higher chance of contact between the two populations only in the areas where the lower percentage of seropositive samples was observed. Conversely, the high percentage of seropositive samples observed in the Po River area coupled with positive microbiological cultures suggest a greater risk of infection for the humans directly or indirectly involved in wild boar hunting activity. These results may serve as a basis to establish sound wildlife management and to adopt education campaigns aimed at reducing the risk of human infection in people involved in wild boar hunting related activities.

Trust in Physicians and Hospitals During the COVID-19 Pandemic in a 50-State Survey of US Adults

Importance

Trust in physicians and hospitals has been associated with achieving public health goals, but the increasing politicization of public health policies during the COVID-19 pandemic may have adversely affected such trust.

Objective

To characterize changes in US adults' trust in physicians and hospitals over the course of the COVID-19 pandemic and the association between this trust and health-related behaviors.

Design, Setting, and Participants

This survey study uses data from 24 waves of a nonprobability internet survey conducted between April 1, 2020, and January 31, 2024, among 443 455 unique respondents aged 18 years or older residing in the US, with state-level representative quotas for race and ethnicity, age, and gender.

Main Outcome and Measure

Self-report of trust in physicians and hospitals; self-report of SARS-CoV-2 and influenza vaccination and booster status. Survey-weighted regression models were applied to examine associations between sociodemographic features and trust and between trust and health behaviors.

Results

The combined data included 582 634 responses across 24 survey waves, reflecting 443 455 unique respondents. The unweighted mean (SD) age was 43.3 (16.6) years; 288 186 respondents (65.0%) reported female gender; 21 957 (5.0%) identified as Asian American, 49 428 (11.1%) as Black, 38 423 (8.7%) as Hispanic, 3138 (0.7%) as Native American, 5598 (1.3%) as Pacific Islander, 315 278 (71.1%) as White, and 9633 (2.2%) as other race and ethnicity (those who selected "Other" from a checklist). Overall, the proportion of adults reporting a lot of trust for physicians and hospitals decreased from 71.5% (95% CI, 70.7%-72.2%) in April 2020 to 40.1% (95% CI, 39.4%-40.7%) in January 2024. In regression models, features associated with lower trust as of spring and summer 2023 included being 25 to 64 years of age, female gender, lower educational level, lower income, Black race, and living in a rural setting. These associations persisted even after controlling for partisanship. In turn, greater trust was associated with greater likelihood of vaccination for SARS-CoV-2 (adjusted odds ratio [OR], 4.94; 95 CI, 4.21-5.80) or influenza (adjusted OR, 5.09; 95 CI, 3.93-6.59) and receiving a SARS-CoV-2 booster (adjusted OR, 3.62; 95 CI, 2.99-4.38).

Conclusions and Relevance

This survey study of US adults suggests that trust in physicians and hospitals decreased during the COVID-19 pandemic. As lower levels of trust were associated with lesser likelihood of pursuing vaccination, restoring trust may represent a public health imperative.

Global insect herbivory and its response to climate change

Herbivorous insects consume a large proportion of the energy flow in terrestrial ecosystems and play a major role in the dynamics of plant populations and communities. However, high-resolution, quantitative predictions of the global patterns of insect herbivory and their potential underlying drivers remain elusive. Here, we compiled and analyzed a dataset consisting of 9,682 records of the severity of insect herbivory from across natural communities worldwide to quantify its global patterns and environmental determinants. Global mapping revealed strong spatial variation in insect herbivory at the global scale, showing that insect herbivory did not significantly vary with latitude for herbaceous plants but increased with latitude for woody plants. We found that the cation-exchange capacity in soil was a main predictor of levels of herbivory on herbaceous plants, while climate largely determined herbivory on woody plants. We next used well-established scenarios for future climate change to forecast how spatial patterns of insect herbivory may be expected to change with climate change across the world. We project that herbivore pressure will intensify on herbaceous plants worldwide but would likely only increase in certain biomes (e.g., northern coniferous forests) for woody plants. Our assessment provides quantitative evidence of how environmental conditions shape the spatial pattern of insect herbivory, which enables a more accurate prediction of the vulnerabilities of plant communities and ecosystem functions in the Anthropocene.

Population abundance estimates in conservation and biodiversity research

Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring.

Revealing uncertainty in the status of biodiversity change

Biodiversity faces unprecedented threats from rapid global change1. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets2-11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of "auxiliary variables." A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment-subsampling, quasirandomization, poststratification, superpopulation modeling, a "doubly robust" procedure, and multilevel regression and poststratification-to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.

Forecasting insect dynamics in a changing world

Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.

Uniting Experiments and Big Data to advance ecology and conservation

Many ecologists increasingly advocate for research frameworks centered on the use of 'big data' to address anthropogenic impacts on ecosystems. Yet, experiments are often considered essential for identifying mechanisms and informing conservation interventions. We highlight the complementarity of these research frameworks and expose largely untapped opportunities for combining them to speed advancements in ecology and conservation. With nascent but increasing application of model integration, we argue that there is an urgent need to unite experimental and big data frameworks throughout the scientific process. Such an integrated framework offers potential for capitalizing on the benefits of both frameworks to gain rapid and reliable answers to ecological challenges.

Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States

Importance

Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters.

Objective

To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors.

Design, Setting, and Participants

This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC.

Main Outcome and Measure

Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index.

Results

The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity.

Conclusions and Relevance

In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.