A Scoping Review of Species Distribution Modeling Methods for Tick Vectors

Abstract

BackgroundGlobally, tick-borne disease is a pervasive and worsening problem that impacts human and domestic animal health, livelihoods, and numerous economies. Species distribution models are useful tools to help address these issues, but many different modeling approaches and environmental data sources exist.ObjectiveWe conducted a scoping review that examined all available research employing species distribution models to predict occurrence and map tick species to understand the diversity of model strategies, environmental predictors, tick data sources, frequency of climate projects of tick ranges, and types of model validation methods.DesignFollowing the PRISMA-ScR checklist, we searched scientific databases for eligible articles, their references, and explored related publications through a graphical tool (www.connectedpapers.com). Two independent reviewers performed article selection and characterization using a priori criteria.ResultsWe describe data collected from 107 peer-reviewed articles that met our inclusion criteria. The literature reflects that tick species distributions have been modeled predominantly in North America and Europe and have mostly modeled the habitat suitability for Ixodes ricinus (n = 23; 21.5%). A wide range of bioclimatic databases and other environmental correlates were utilized among models, but the WorldClim database and its bioclimatic variables 1–19 appeared in 60 (56%) papers. The most frequently chosen modeling approach was MaxEnt, which also appeared in 60 (56%) of papers. Despite the importance of ensemble modeling to reduce bias, only 23 papers (21.5%) employed more than one algorithm, and just six (5.6%) used an ensemble approach that incorporated at least five different modeling methods for comparison. Area under the curve/receiver operating characteristic was the most frequently reported model validation method, utilized in nearly all (98.9%) included studies. Only 21% of papers used future climate scenarios to predict tick range expansion or contraction. Regardless of the representative concentration pathway, six of seven genera were expected to both expand and retract depending on location, while Ornithodoros was predicted to only expand beyond its current range.ConclusionSpecies distribution modeling techniques are useful and widely employed tools for predicting tick habitat suitability and range movement. However, the vast array of methods, data sources, and validation strategies within the SDM literature support the need for standardized protocols for species distribution and ecological niche modeling for tick vectors.

A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks

“…For example, warmer and wetter winters or hotter and drier summers affect rates of survival in ticks [16][17][18][19][20]. Many species in Amblyomma, Dermacentor, Ixodes and other genera are expanding or expected to expand their ranges [21], making studies of tick ecology increasingly pertinent.…”

Climatic stress decreases tick survival but increases rate of host‐seeking behavior

“…Thus, further investigations of ticks' behavioral and physiological responses to abiotic stress are necessary to develop robust models of how climate change will affect the geographic expansion of tick vectors, the rate of host-finding, and the transmission of tick-borne diseases. Practically all species distribution models (SDMs) of tick vectors use correlative approaches to associate environmental variables with the current distribution, and about 21% of these published SDMs used future climate scenarios to predict tick range expansion (Kopsco et al, 2022). A mechanistic approach that incorporates behavioral and physiological responses of ticks is needed to improve SDMs that can reliably predict species range dynamics of ticks and other arthropod disease vectors (Kearney & Porter, 2009;Monz on et al, 2011).…”

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

“…Machine learning technology has recently been created, particularly for SDMs 108 . Numerous studies attest to the remarkable accuracy of algorithmically generated habitat suitability maps [109][110][111] . From our perspective, the main issue with the majority of these comparisons is that they only validate model performance (de ned as the match up among both predicted and observed species' distributions) against the needs under current conditions, despite the fact that most models are approximately accurate in trying to project distributions under present environmental conditions.…”