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

• Published in: PloS one Vol. 14; no. 10; p. e0223821
• Main Authors: Wiese, Daniel, Escalante, Ananias A, Murphy, Heather, Henry, Kevin A, Gutierrez-Velez, Victor Hugo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.10.2019; Public Library of Science (PLoS)
• Subjects: Aedes - physiology; Aedes - virology; Aedes albopictus; Algorithms; Animals; Aquatic insects; Area Under Curve; Biology and Life Sciences; Climate change; Dengue; Dengue fever; Earth Sciences; Ecology and Environmental Sciences; Ecosystem; Geographical distribution; Habitats; High humidity; Humidity; Infectious diseases; Introduced species; Invasive species; Learning algorithms; Machine Learning; Medicine and Health Sciences; Model accuracy; Mosquitoes; Neighborhoods; Pennsylvania; Physical Sciences; Population Density; Precipitation; Predictions; Public health; Research and Analysis Methods; ROC Curve; Rural areas; Seasons; Social Sciences; Socioeconomic factors; Spatial analysis; Spatial distribution; Suburban areas; Teaching methods; Temperature; Urban areas; Urban environments; Urban studies; Vector-borne diseases; Viral diseases; Viruses; West Nile virus; Pennsylvania; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0223821

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.