skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on April 16, 2026

Title: The land-use land-cover change–emerging infectious disease nexus reconsidered
Abstract Deforestation due to land-use and land-cover (LULC) change has been linked to increased emerging zoonotic disease risk despite limited local level data on such outbreaks. This Forum reevaluates this risk inference using newly released data on zoonotic disease outbreaks, accounting for Structural One Health features, including socioeconomic development and armed conflict covariates. Event and time series data on disease and forest coverage anomalies at the 0.5-degree level for every month between January 2003 and December 2018 are used to estimate the relationship between LULC and zoonosis using Poisson generalized additive and generalized linear models. Once adjusted for Structural One Health features, outbreak risk is 7%–200% higher in areas that experienced forest cover reversion. These results highlight the importance of accounting for Structural One Health factors when analyzing complex socioecological phenomena such as the LULC–infectious disease nexus.  more » « less
Award ID(s):
2342105
PAR ID:
10596488
Author(s) / Creator(s):
;
Publisher / Repository:
BioScience
Date Published:
Journal Name:
BioScience
ISSN:
0006-3568
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Dar, Kamran Shaukat (Ed.)
    Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. parasites released from freshwater snails, the parasite’s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata,B.straminea, andB.tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails’ ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil. 
    more » « less
  2. Land-use and land-cover (LULC) change is a primary driver of terrestrial carbon release, often through the conversion of forest into agriculture or expansion of urban areas. Classification schemes are a key component of landscape analyses. This study creates a novel LULC classification scheme by incorporating ecological data to redefine classes of an existing LULC classification based on variation in above-ground tree carbon. A tree inventory was conducted for 531 plots within a subbasin of the Tampa Bay Watershed, Florida, USA. Above-ground tree carbon was estimated using the i-Tree model. Plots were classified using the Florida Land Use Cover Classification System. Mean quantities of above-ground tree carbon, by class, were tested for statistical differences. A reclassification was conducted based on these differences. Sub-classes within a given “land cover” class were similar for six of the seven classes. Significant differences were found within the “Wetlands” class based on vegetation cover, forming two distinct groups: “Forested Wetlands” and “Non-forested and Mangrove Wetlands”. The urban “land use” class showed differences between “Residential” and “Non-residential” sub-classes, forming two new classes. LULC classifications can sometimes aggregate areas perceived as similar that are in fact distinct regarding ecological variables. These aggregations can obscure the true variation in a parameter at the landscape scale. Therefore, a study’s classification system should be designed to reflect landscape variation in the parameter(s) of interest. 
    more » « less
  3. Abstract Zoonotic diseases represent 75% of emerging infectious diseases worldwide, and their emergence is mainly attributed to human‐driven changes in landscapes. Land use change, especially the conversion of natural areas to agricultural use, has the potential to impact hosts and vector dynamics, affecting pathogen transmission risk. While these links are becoming better understood, very few studies have investigated the opposite question—how native vegetation restoration affects zoonotic disease outbreaks.We reviewed the existing evidence linking native vegetation restoration with zoonotic transmission risk, identified knowledge gaps, and, by focusing on tropical areas, proposed forest restoration strategies that could help in limiting the spread of zoonotic diseases.We identified a large gap in information on the effects of native vegetation restoration on zoonotic diseases, especially within tropical regions. In addition, the few studies that exist do not consider environmental aspects that can affect the outcomes of restoration on disease risk, such as the land use history and landscape structural characteristics (as composition and configuration of native habitats). Our conceptual framework raises two important points: (1) the effects of forest restoration may depend on the context of the existing landscape, especially the percentage of native vegetation existing at the beginning of the restoration; and (2) these effects will also be dependent on the spatial arrangement of the restored area within the existing landscape. Furthermore, we propose important topics to be studied in the coming years to integrate zoonotic disease risk as a criterion in restoration planning.Synthesis and application. Our results contribute to a more comprehensive forest restoration planning, comprising multiple ecosystem services and resulting in healthier landscapes for both people and nature. Our framework could be integrated into the post‐2020 global biodiversity framework targets. 
    more » « less
  4. Abstract. The land of the conterminous United States (CONUS) hasbeen transformed dramatically by humans over the last four centuries throughland clearing, agricultural expansion and intensification, and urban sprawl.High-resolution geospatial data on long-term historical changes in land useand land cover (LULC) across the CONUS are essential for predictiveunderstanding of natural–human interactions and land-based climatesolutions for the United States. A few efforts have reconstructed historicalchanges in cropland and urban extent in the United States since themid-19th century. However, the long-term trajectories of multiple LULCtypes with high spatial and temporal resolutions since the colonial era(early 17th century) in the United States are not available yet. Byintegrating multi-source data, such as high-resolution remote sensingimage-based LULC data, model-based LULC products, and historical censusdata, we reconstructed the history of land use and land cover for theconterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results showwidespread expansion of cropland and urban land associated with rapid lossof natural vegetation. Croplands are mainly converted from forest, shrub,and grassland, especially in the Great Plains and North Central regions.Forest planting and regeneration accelerated the forest recovery in theNortheast and Southeast since the 1920s. The geospatial and long-termhistorical LULC data from this study provide critical information forassessing the LULC impacts on regional climate, hydrology, andbiogeochemical cycles as well as achieving sustainable use of land in thenation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022). 
    more » « less
  5. null (Ed.)
    Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential. 
    more » « less