Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania

Daniel Wiese; Ananias A Escalante; Heather Murphy; Kevin A Henry; Victor Hugo Gutierrez-Velez

doi:10.1371/journal.pone.0223821

Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania

PLoS One. 2019 Oct 17;14(10):e0223821. doi: 10.1371/journal.pone.0223821. eCollection 2019.

Authors

Daniel Wiese¹, Ananias A Escalante², Heather Murphy³, Kevin A Henry¹, Victor Hugo Gutierrez-Velez¹

Affiliations

¹ Department of Geography and Urban Studies, College of Liberal Arts, Temple University, Philadelphia, Pennsylvania, United States of America.
² Department of Biology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania, United States of America.
³ Department of Biostatistics and Epidemiology, College of Public Health, Temple University, Philadelphia, Pennsylvania, United States of America.

Abstract

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus' presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus' presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aedes / physiology*
Aedes / virology
Animals
Area Under Curve
Ecosystem
Machine Learning*
Pennsylvania
Population Density
ROC Curve
Seasons
Temperature

Grants and funding

This work was made possible by funding from the Office of the Vice President for Research Administration and the Colleges of Liberal Arts, Public Health and Science and Technology at Temple University under the Targeted Funding Program. Publication of this article was funded in part by the Temple University Libraries Open Access Publishing Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.