Biology subjects

Glidden, C. K.

Publications and source records attributed to Glidden, C. K..

6 recordsLinked to original sources

bioRxiv2026

Bird functional dispersion predicts dengue vector presence across tropical working landscapes in Costa Rica

Identifying reliable indicators of dengue vector habitat is critical for proactive surveillance and spatially targeted vector control. Bird communities are sensitive to habitat quality and landscape condition, and their functional diversity may track ecological conditions that support Aedes mosquito populations. We tested whether bird functional diversity predicts dengue vector presence across two working landscapes in Costa Rica, comparing bird-based predictors against landscape composition, vegetation structure, and plant diversity. Bird functional dispersion (FDis), the spread of species in multidimensional trait space, was the top-ranked predictor for Ae. aegypti and Ae. albopictus larval presence and for adult Ae. aegypti, outperforming all other predictor classes. Probability of presence declined with increasing FDis, where functionally homogeneous bird communities, characteristic of disturbed landscapes, co-occur with higher vector presence, consistent with shared habitat filtering. Functional diversity estimations may offer a practical signal for identifying habitats at elevated dengue vector risk.

ecology↗

bioRxiv2025

The impact of climate change on transmission season length: West Nile virus as a case study

BackgroundClimate change is accelerating the spread of temperature-sensitive vector-borne diseases such as West Nile virus (WNV), the most widespread mosquito-borne disease in the United States, with 2,400 reported cases in 2024. In New York State (NYS), where WNV first emerged in the U.S., mean temperatures have increased by 1.4{degrees}C since the early 1900s. Although temperature is a well-established driver of WNV transmission, its effect on transmission season length remains poorly quantified. This study examines whether WNV transmission seasons have lengthened in NYS and whether these changes are associated with increased disease risk, shifts in seasonal timing, and anthropogenic climate change. MethodsWe integrated daily county-level observational data from 1999-2024, including temperature, mosquito surveillance, and human case data, with climate model simulations spanning 1850-2024 to assess trends in transmission season length. FindingsBased on observed temperature suitability, the WNV transmission season in NYS has lengthened by an average of 20 days over the past 25 years, beginning 3.8 days earlier and ending 16.3 days later. Longer transmission seasons are positively associated with higher WNV prevalence in both mosquito vectors and humans. While such changes could occur in the absence of global warming, climate model analyses indicate that the observed increase in season length is 6.4 times more likely under historical climate forcing since 1900 than under pre-industrial conditions. InterpretationThese findings demonstrate that climate change is reshaping the phenology and burden of WNV transmission. FundingSupported by NIH, NSF, Stanford University, and the Fogarty International Center. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched Google Scholar for publications from 1999 to 2024 using the term "West Nile virus and season length," yielding 18,200 results. Previous studies focused on how climatic factors affect transmission and its seasonality in the United States, but none assessed how climate change influences transmission season duration. We analyzed over two decades of data to link extended WNV transmission seasons with increased vector and human prevalence. Additionally, we evaluated the causal role of climate warming using simulated climate data and counterfactual analyses. Added value of this studyUsing daily temperature and WNV case data from 1999- 2024, we found that the WNV transmission season in NYS has lengthened by an average of 20 days. The season now begins 3.8 days earlier and ends 16.3 days later. Longer seasons were associated with higher prevalence in mosquitoes and humans. Climate model simulations indicate that human-driven greenhouse gas emissions have significantly extended the WNV season, making observed trends in season length, onset, and conclusion 6, 2, and 16 times more likely, with implications for mosquito infection and human disease risk. Implications of all available evidenceOur findings suggest that climate change is lengthening WNV transmission seasons, a phenomenon likely relevant to other temperature-sensitive vector-borne diseases. As warming continues, extended transmission seasons may amplify risks for WNV and other vector-borne diseases, underscoring the need for climate-adaptive public health surveillance and intervention strategies.

ecology↗

bioRxiv2024

Climate variables and urbanization drive changes in the distribution of S. mansoni competent snails in Brazil: novel results from a counterfactual analysis

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria snails, so controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we leveraged machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of competent Biomphalaria throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution - while accounting for non-linearities that are difficult to detect from local case studies - can help inform schistosomiasis control strategies.

ecology↗

bioRxiv2023

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

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bioRxiv2022

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predicting the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles. Author summaryLeishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has targeted leishmaniasis for elimination and control by 2030, the disease remains a persistent threat. Across the Americas, particularly in Central America, the southeastern United States, and perimeters of the Amazon Basin, risk of infection is increasing in geographic extent and elevation. While it is known that Leishmania parasites, the causative agent of leishmaniasis, are maintained in the environment via a mammalian host, the full suite of wildlife hosts has yet to be documented, which significantly hinders control efforts. Here, we use machine learning and ecological and evolutionary trait profiles of known hosts to identify unrecognized potential wildlife hosts of Leishmania. We identify 136 mammals in the Americas that are likely to be exposed to and infected by zoonotic Leishmania in the wild. The high number of unrecognized potential hosts emphasizes a need to better invest in studying the ecological epidemiology of leishmaniasis. The study provides information and tools to support targeted intervention and management of this important poverty-associated disease.

ecology↗

bioRxiv2022

Data-Driven Predictions of Potential Leishmania Vectors in Latin America

The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a parasitic disease transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasite. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system. Author SummaryAmerican Cutaneous Leishmaniasis (ACL) is a neglected parasitic disease transmitted by sandflies in Latin America. There is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. In this study, the authors created a database of sandfly traits, then used predictive models to determine important factors in disease transmission and how different climate and environmental variables affect ACL transmission. The models suggest that transmission occurs at the interface between domestic habitats and well-preserved forests. The authors also generate predictions of which sandflies might be transmitting the parasite that are not known vectors at the time, specifically Psychodopygus amazonensis and Nyssomia antunesi. This new knowledge can lead to a better understanding of the system of transmission, and can point to possible hotspots of the disease. The analysis can also help direct researchers to areas of interest for sampling studies, as well as specific sandflies to focus their effort on.

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