Wildlife hosts predict the distribution of reported coccidioidomycosis in the western United States

medRxiv · 2026-03-11

Abstract Global environmental change is reshaping human exposure to zoonotic and environmentally acquired pathogens, yet predicting disease risk remains challenging. High-resolution risk maps typically rely on human case data and environmental correlates, often overlooking ecological processes such as wildlife reservoirs. We evaluated whether mammalian reservoir distributions improve prediction of coccidioidomycosis (Valley fever), an emerging, environmentally-acquired fungal disease with a poorly characterized range. Using county-level coccidioidomycosis notification data from the Centers for Disease Control and Prevention, we developed a hierarchical Bayesian model of county-level endemicity, defined as >=10 cases per 100,000 population. We incorporated climatic, environmental, and vegetation covariates, a state-level reporting effect, and species distribution models for 22 mammalian species previously identified as Coccidioides reservoirs. We found that the number of endemic mammalian reservoirs in a county was the strongest predictor of coccidioidomycosis endemicity, with each standard deviation increase in reservoir species richness associated with substantially higher odds of endemicity (log-odds ratio = 1.702; 95% CI: 1.060-2.419). In contrast, maximum vapor pressure deficit, soil moisture, and land cover were not independently associated with endemicity after accounting for reservoir distributions. State-level reporting effects revealed substantial heterogeneity, and comparison of models with and without reporting effects identified regions likely to be endemic but underreported, including parts of Nevada, Utah, New Mexico, Texas, and Colorado. Our results establish reservoir diversity as a central predictor of zoonotic fungal disease risk and demonstrate a transferable framework for distinguishing between ecological drivers of infection from surveillance bias to improve disease risk mapping and identify areas of potential underreporting.

Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness

GIScience & Remote Sensing · 2026-02-09

Multiple landscape factors, including slope, vegetation density, and surface roughness, work together to affect pedestrian travel rates in off-path environments. Most previous pedestrian travel rate models have only quantified the effects of these factors at relatively coarse spatiotemporal resolutions, ignoring the fine-scaled information contained within LiDAR data and instantaneous travel trajectories. Previous studies have also relied on parametric function fitting to model off-path travel rates. This paper presents the first known examination of instantaneous travel rates using random forests—a machine learning algorithm well-suited to the presence of non-linear relationships and the ability to robustly evaluate variable importance. Principal components of airborne LiDAR-derived slope, vegetation density, and surface roughness were used as predictor variables in a random forest model. The model explained over 77% of the variance in observed travel rates from an independent test dataset (R² = 0.778). An analysis of permutation importance indicated that LiDAR-derived slope was the most important predictor of travel rate, followed by vegetation density, then surface roughness. Examining the importance of slope, vegetation density, and surface roughness together provides new insight beyond their individual effects, revealing how these landscape factors interact to shape pedestrian travel rates. While slope is the most important single predictor, our results show that vegetation density and surface roughness exert distinct and nonlinear influences that become clearer only when evaluated jointly. Notably, vegetation density reduces travel rate more sharply than surface roughness, and high vegetation density remains strongly limiting even when roughness is low, whereas the reverse is not true. These combined effects also exceed the explanatory power of individual median speed, underscoring the importance of environmental conditions over individual-level differences in exertion or fitness. Potential applications of this work include modeling instantaneous travel rates for wildland firefighter safety, search and rescue, and migration applications.

Multi-millennial reconstruction of fire return intervals from a fynbos – Afrotemperate forest ecotone in the Cape Floristic Region, South Africa: Paleoecological implications for present-day management

Global and Planetary Change · 2026-01-13

The impact of subsistence change on demography: Theory-informed Approximate Bayesian Computation reveals 4-fold increase in carrying capacity following economic intensification

Journal of Archaeological Science · 2026-03-05

Subsistence intensification is a major process in human history, however, quantifying its effects on demography is challenging given uncertainties regarding timing, population reconstructions, and ecological conditions. To help address these issues, we combine advances in ecological theory with Approximate Bayesian Computation (ABC) to generate a replicable, theory-informed, computational model for quantifying the effects of intensification on past population dynamics. Using the Colorado Plateau and Great Basin as case studies, we demonstrate how this ABC approach can 1) identify the timing of the adoption of intensified strategies to 2100 ± 117 and 2174 ± 395 cal BP, 2) show that intensification increased landscape carrying capacities by 4.1 ± 1 and 4.2 ± 1.9-fold, and 3) suggest that the process of intensification ranged over periods of 970 ± 244 and 1473 ± 416 years. In addition, the ABC method provides support for a core assumption of the ‘dates as data’ approach as only models built with simulated date sampling proportional to past population sizes are capable of matching observed output. Subsistence intensification has been tied to state formation, violence, inequality, sedentism, and numerous other influential phenomena. Our findings align with other prehistoric estimates from Europe and Africa to further suggest even this relatively small carrying capacity change coincides with significant social effects. • Approximate Bayesian Computation identifies timing and length of intensification. • Subsistence intensification increased carrying capacity of landscape 4-fold. • Subsistence intensification processes changed carrying capacity over ∼1000 years. • 4-fold population density increases correspond with drastic social change.

Machine Learning in Mode Choice Prediction as Part of MPOs’ Regional Travel Demand Models: Is It Time for Change?

Sustainability · 2025-04-16 · 5 citations

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning.

Transit to parks: An environmental justice study of transit access to large parks in the U.S. West

UNC Libraries · 2025-05-24

The new denial: climate <i>solution</i> misinformation on social media

Global Sustainability · 2025-01-01 · 4 citations

Abstract Non-technical summary A remarkable shift in climate change misinformation has taken over social media streams. The conversation is no longer totally absorbed with denying that climate change exists. Instead, the ‘New Denial’ is bent on condemning solutions to climate change and their supporters. Our study meticulously analyzed this shift, using extensive methods to untangle the content of over 200,000 Tweets from 2021 to 2023. We found that the New Denial is a heated political debate that often calls up common far-right arguments, falsely accuses climate solutions as ineffective and risky, and attacks climate solution supporters. Technical summary Over the past five years, a ‘New Denial’ has emerged in regards to climate change misinformation on social media. This shift marks a transition of the dominance of rhetoric centered around denial of climate change science to attacks that seek to undermine and cast doubt on proposed climate solutions and those who support them. While much of the academic literature to date has explored misinformation about climate science, there is a great need to explore this shift and seek out increased understanding of misinformation around climate change solutions specifically. In this paper, we employ a mixed-methods analysis, drawing on data from Twitter from 2021 to 2023, to analyze the content of climate solution misinformation. We find that the New Denial is frequently centered on politically-laden debates nestled in common narratives on the right, often attacking supporters of climate solutions as harbingering ulterior motives for climate solutions that are fundamentally flawed. We use these insights to reflect on targeted interventions for climate solution misinformation on social media. Social media summary A New Denial is sweeping social media, no longer bent on denying climate science. It's new target: climate solutions and the people pushing for them.

Do mixed-use developments optimize VMT and emissions reduction? Evidence from 36 regions

Transportation Research Part D Transport and Environment · 2025-08-07 · 1 citations

• MXDs generate about one-third less VMT than conventional developments of comparable size. • Longitudinal data shows that MXDs generate about 50% fewer vehicle trips over time. • Internal capture is a strong predictor of VMT and must be accounted for in trip generation models. • Even amongst MXDs, people walk more in those with rich built-environment characteristics. • MXDs in more compact regions generate about six times (6x) lower GHG emissions per trip. Mixed-use developments (MXDs) have dovetailed nicely as a development design strategy to lower VMT by concentrating diverse activities within walkable compact environments. Despite their conceptual appeal, estimating their actual impacts on vehicle trips and environmental quality remains both limited and challenging for agencies and local governments due to inconsistencies in existing traffic impact methodologies, which fail to account for regional variability. This study addresses these limitations by using advanced Gaussian multi-level regression models and K-fold cross-validation, incorporating the famous 7-D built environment variables to estimate comparative VMT and GHG emissions across 710 MXDs in 36 U.S. regions. We find that MXDs generate about one-third less VMT than conventional developments, with up to an 80% reduction in certain regions. Over time, MXDs generate even fewer vehicle trips (about 50% less) and significantly lower CO 2 e, suggesting positive impacts on lessening both traffic congestion and climate pollution in cities. Our study refines the methodology for estimating development-generated VMT while providing insights for shaping low-emission communities that align with modern sustainability goals.

Deep learning and machine learning enable broad-scale woodland height, cover, and biomass mapping

ISPRS Journal of Photogrammetry and Remote Sensing · 2025-05-23 · 4 citations

Targeted explorations of Pleistocene-Holocene transition archaeology on the Colorado Plateau in southern Utah

Quaternary International · 2025-10-01

The Pleistocene-Holocene transition (PHT) archaeological record on the Colorado Plateau has been interpreted as sparse, especially compared to the Great Basin to the west and the Rocky Mountains and Great Plains to the east. To explore whether this apparent sparsity is due to low PHT populations in the region, insufficient research targeting regional PHT archaeology, taphonomic processes, or cross-regional differences in lifeways, we surveyed ∼2400 acres of the San Rafael Desert on the Colorado Plateau in south-central Utah. Survey tracts were deemed likely to contain PHT archaeology by a random forest predictive model. We present the results of this survey, during which we encountered three sites and one isolate of likely PHT age, for a total of five diagnostic PHT points, including unfluted lanceolate points, a Scottsbluff point, a fluted point, and a stemmed point. In comparing the point types and PHT locality densities we recorded in the San Rafael Desert to those in areas of targeted PHT survey in surrounding regions, we conclude that while the San Rafael Desert's PHT record may be sparse, it demonstrates that the Colorado Plateau's PHT record is worthy of study given its potential to elucidate interaction between the Western Stemmed Tradition characteristic of the Great Basin and the fluted and lanceolate traditions characteristic of the Plains and Rockies.