Biodiversity Informatics, 19, 2025, pp. 61-84 61 ECOLOGICAL NICHE MODELING APPLICATIONS TO INFECTIOUS DISEASES Shariful Islam1,2, Mariana Castaneda-Guzman1, Diego Soler-Tovar3, and Luis E. Escobar1,2,4* 1Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA 2Global Change Center, Virginia Tech, Blacksburg, VA, USA 3Faculty of Agricultural Sciences, Universidad de La Salle, Bogotá, Colombia 4Center for Emerging Zoonotic and Arthropod-Borne Pathogens, Virginia Tech, Blacksburg, VA, USA Abstract. Ecological niche modeling (ENM) is a widely used analytical approach for predicting species distri- butions and has been applied to study the spatial epidemiology of infectious diseases. Nevertheless, research evaluating the key components and assumptions of ENM in disease systems remains limited, raising concerns about its robustness, reproducibility, and transparency. To address this limitation, we conducted a systematic review and evaluated articles on ENM applications to infectious diseases between 2020 and 2022. We reviewed 78 articles to extract information following a standard protocol for reporting ENM analysis and summarized the information for each component (e.g., study subject, location, duration). The spatial extent of study areas varied from village to global scales, temporal duration ranged from 1 to 101 years, and the organismal levels ranged from individuals (57.7%) to populations (33.3%). Less frequently reported components included temporal au- tocorrelation tests (2.66%), algorithmic uncertainty (28.21%), temporal resolution (35.90%), background data selection (44.87%), coordinate reference system (41.02%), model performance from validation data (46.15%), and model averaging (20.51%). Our findings highlight a lack of consistency and transparency in disease ecolo- gy and disease biogeography studies, which may lead to misleading ENM applications in spatial epidemiology. Researchers and reviewers applying ENM to disease systems should clearly report key modeling components to ensure biologically sound outputs. This article identified trends and gaps in reporting ENM protocols for mapping disease transmission risk. Keywords: Biogeography, Disease, Ecological Niche, Epidemiology, Reproducibility, Spatial * Corresponding author: Address: 1015 Life Science Circle, Blacksburg, Virginia, 24061, United States. escobar1@vt.edu. ORCID: https://orcid.org/0000-0001-5735-2750 Introduction Emerging infectious diseases are increasing in fre- quency and represent a major threat to global public health and global economies (Dobson et al. 2020). Human-ani- mal interaction is considered an important driver of dis- ease emergence along with climate change, biodiversity loss, and socio-economic conditions (Keesing and Ostfeld 2024). Nevertheless, our ability to understand and ac- curately forecast disease emergence and spread remains limited (Escobar and Craft 2016). One primary reason for inaccurate disease forecasting is the limited standards in data availability, model parameterization, and the inherent stochasticity (i.e., randomness or unpredictability) of dis- ease transmission (Escobar 2020; Peterson 2014). Disease ecology and biogeography can be used to un- derstand why diseases emerge in some areas but not others (Peterson 2008). The “ecological niche”, defined as the set of favorable conditions (biotic and abiotic) allowing an organism to persist and disperse in the long term, is a key concept in disease ecology and disease biogeography (Escobar 2020; Peterson 2014). The ecological niche of pathogens, vectors, hosts, or disease transmission events is complex considering host-pathogen relationships. In some cases, the distribution of a pathogen is shaped by the distri- bution of its host. Nevertheless, there is also evidence that some pathogens do not follow the niches of their hosts and instead exhibit their own distinct ecological niche (Maher et al. 2010; Astorga et al. 2018). To estimate or character- mailto:escobar1@vt.edu https://orcid.org/0000-0001-5735-2750 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 62 ize the ecological niche of an organism causing an infec- tious disease, researchers commonly use ecological niche modeling (ENM). For example, Romero-Alvarez et al. (2023) assessed the potential spread of Oropouche virus in humans, and Escobar et al. (2017) identified areas of transmission risk for Heterosporis, a fish disease caused by the microsporidian parasite Heterosporis sutherlandae. A complete ENM framework consists of five major sec- tions (Overview/Conceptualization, Data, Model Fitting, Assessment, and Prediction), each containing components essential for building robust, transparent, and reproducible models (Zurell et al. 2020). With increasing access to ENM training materials, tools, and data, the implementation of ENM in the study of diseases is becoming more common (Frans and Liu 2024). For instance, a comprehensive open-access online course facilitates the learning and development of ENM for bio- diversity and disease (Peterson et al. 2019; Peterson and Ingenloff 2016). As the use of ENM in disease ecology and biogeography continues to expand (Escobar and Mo- rand 2021), it is increasingly important to assess whether studies are adhering to best practices and reporting im- portant modeling components transparently. Nevertheless, researchers may overlook key modeling components (e.g., study objects, location, and duration) and assumptions during ENM implementations (Escobar 2020), which can lead to an untrustworthy model and the misinterpretation of infectious diseases systems (Araújo et al. 2019; Peter- son 2014; Soley-Guardia et al. 2024). For instance, a study conducted by Brito-Hoyos et al. (2013) aiming to map the risk of bat-borne rabies outbreaks in Colombia employed a climate-based ENM. The authors, however, skipped critical steps of ENM (Escobar and Peterson 2013). One of them was the use of all available occurrence points for model calibration without considering spatial clustering and sampling bias. Additionally, Brito-Hoyos et al. (2013) used 15 climatic variables for their model building without considering the multicollinearity among the variables and spatial lags. Later, Escobar and Peterson (2013) revised the Brito-Hoyos et al. (2013) predictions and found that key biogeographic principles were missing, resulting in a misinformation of rabies transmission risk in Colombia. Similarly, studies have identified artifacts in four biocli- matic variables (Bio8, Bio9, Bio18, and Bio19), evidenced by unusual spatial anomalies and inconsistencies among adjacent pixels (Booth 2022; Escobar et al. 2014). There- fore, it has been recommended to assess the discontinuities among the four problematic variables for the specific study areas before final inclusion in an ENM. Nevertheless, Oli- vera et al. (2021) still included all the available bioclimat- ic variables in their ENM without quality check, which may fail to identify actual suitable areas. In a systematic review, Feng et al. (2019a) found a lack of critical mod- eling information (e.g., source of occurrence data, spatial extent, source of environmental data) in ENM applications to biodiversity. ENMs of infectious diseases is inherently more com- plex than traditional applications to biodiversity (Escobar and Craft 2016). Understanding disease ecology, partic- ularly transmission dynamics involving hosts, vectors, and pathogens, is often complicated by small sample sizes, sampling biases, and limited geographic and envi- ronmental data (Escobar 2020). Moreover, failing to in- corporate key biological components in ENM for disease systems may hinder the prediction of disease distribution and transmission risk (Escobar and Craft 2016; Peterson, 2006). Given the growing role of ENM in the understand- ing of the ecology of disease systems and informing spatial epidemiology, it is essential to evaluate how researchers incorporate fundamental components and assumptions to ensure robust and biologically meaningful model results. A standardized protocol may promote consistency and best practices in model development (Zurell et al. 2020). Previous studies have shown that the transparency and methodological rigor of individual-based or agent-based models have improved following the introduction of the Overview, Design Concepts, and Details (ODD) protocol (Grimm et al. 2010). Similarly, the adoption of the Darwin Core Standard has had a positive influence on data sharing practices (Wieczorek et al. 2012). Considering the impor- tance of having a standard checklist for ecological niche studies, and to support and guide key steps in ENM, Zurell et al. (2020) adapted the ODD protocol into the Overview/ Conceptualization, Data, Model Fitting, Assessment, and Prediction (ODMAP) framework. The ODMAP protocol complements and integrates the Range Model Metada- ta Standard (RMMS) by providing a step-by-step guide along with a comprehensive checklist of components required to build an ecological niche model (Merow et al. 2019, Zurell et al. 2020; Fitzpatrick et al. 2021). The ODMAP protocol also includes a Shiny web application that offers guidance for developing standardized ENM documentation using the RMMS dictionary and allows users to download the ODMAP table (Zurell et al. 2020; Fitzpatrick et al. 2021). As an updated and adaptable meta- data framework, ODMAP is well-suited for documenting ENM components and is designed to automatically ac- commodate future updates to the dictionary (Merow et al. 2019, Zurell et al. 2020; Fitzpatrick et al. 2021). Zurell et al. (2020) demonstrated the utility of their ODMAP pro- Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 63 tocol through nine case studies. It has been expected that ODMAP will have a similar impact like ODD protocol in biodiversity informatics (Zurell et al. 2020; Fitzpatrick et al. 2021). The ODMAP protocol is designed to assist beginners in ENM, provides a structured workflow for advanced modelers, and serves as an efficient tool for editors and reviewers to evaluate model-based studies (Zurell et a. 2020). The aim of this study was to assess recent ENM ap- plications to disease systems using the ODMAP checklist. To our knowledge, no studies have been conducted to evaluate the quality standards of the implementation of ENM in disease ecology and disease biogeography. Based on key ENM requirements, data limitations, and the com- plexities of disease transmission systems, we hypothesize that most articles fail to account for critical components during the modeling of infectious disease. Materials And Methods Search strategy We conducted a systematic literature review on Web of Sciences following the PRISMA framework (Moher et al. 2009) (Figure 1, date 2/15/2023). We recovered articles published between 2020 and 2022 using the terms “eco- logical niche modeling” OR “species distribution model- ing” AND “disease”. We selected this period since it corre- sponds to the release of a disease-specific article published with recommendations on ENM applications to describe and forecast disease (Escobar 2020). The study period also provides an overview of the early 2020s as a proxy of contemporary methods, including the period of explosion of COVID-19 articles among disciplines (Riccaboni and Verginer 2022). To avoid missing any articles, we repeated the search following the same terms on February 29, 2024. Figure 1: Flow diagram of systematic article search and selection for the data synthesis. From top to bottom, different background color indicates the article search on Web of Science, articles identification, article screening following inclusion and exclusion criteria, and final list of articles selected to include and data synthesis. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 64 After extracting information from all selected articles, we saved the dataset as a .csv file and imported it into R statistic software version 4.4.1 for the descriptive analy- sis (R Core Team 2024). We used the ggplot2 package for visualization (Wickham 2016). For components with dis- crete or categorical data (e.g. Yes/No), we used bar plot, while for continuing data, we used box plots. For cate- gorical components with only one observation, we exclud- ed that category from the bar plot. We used VOSviewer (www.vosviewer.com) version 1.6.20, to conduct keyword network analysis and visualization. We considered the minimum of 2 occurrences of keywords as a threshold for the keyword network in VOSviewer. Results We collected a total of 491 published articles between 2020 and 2022. After screening articles following inclusion and exclusion criteria, 78 articles were selected for review and evaluated for data collection. Results were categorized into five sections, including Overview, Data, Modelling, Assessment, and Prediction (ODMAP) following protocol by (Zurell et al. 2020). The journals in which the selected articles were published had impact factors ranging from 0.6 to 14.8 (n=75; mean=3.2) (Figure S1). Based on keywords network analysis, the application of ENM in the field of infectious disease research appears to be increasing. The larger node “ecological niche model” and “species distribu- tion model” highlight its recurrent and central themes in the analysis of the reviewed articles (Figure 2). Overview For the overview section there were twenty-two com- ponents, only eight components were explained by all 78 articles (36.4%; n=22) (Figure 3). For instance, only a small subset of articles provided information about code availability (9.0%; n=7), spatial extent (i.e., longitude, latitude) (26.9%; n=21), temporal resolution (35.9%; n= 8), data availability (36.4%; n=44), and model assembling (64.1%; n=50). Study selection All articles retrieved were downloaded and revised using detailed inclusion criteria (Table 1). We screened each article to assess its eligibility for this study, including language, publication period, research article, and appli- cation of ENM to understand the disease systems. Articles retained were read in full and data were extracted and stan- dardized in terms of units to allow comparisons. Data collection and preparation Following Zurell et al. (2020), we assessed and ex- tracted data from each article following five standard sec- tions to gather data or model parameterization, including an overview, data, modeling, assessment, and prediction (ODMAP) (Supplementary Material). For each section model components or parameters are required for robust ENM calibration and validation. Besides, we collected the most recent impact factor available for each journal in which the selected articles were published, using informa- tion from the respective journal websites. All data were entered and processed using MS Ex- cel (Microsoft Office ProfessionalPlus 2021, Microsoft Corporation, Washington) and saved as .csv file. Pieces of information were continuous or binary (yes or no) for some parameters. If the answer was “yes,” we recorded the corresponding details. For example, if the question was whether the author mentioned the statistical algorithm used for ENM building, and the answer was “yes,” we not- ed the algorithm employed in the study. Similarly, if the question was whether the author mentioned the temporal duration of the study, and the answer was “yes,” we re- corded the duration as a quantitative value. To standardize the geographical extent of the studies, we categorized them as regional if the study area included more than one country, or state-level if articles specified a sub-area within a country. Some articles reported spatial resolution in arcminutes. We converted the resolution from arcminutes to meters using conversion factors provided by the United States Geological Survey (USGS 2011). Table 1: Inclusion criteria considered selecting and removing the articles during article selection step. Inclusion criteria Exclusion criteria Articles written in English Articles written in a language other than English Articles published between 2020 to 2022 Removed all the articles outside the study period Research articles Removed review articles, commentaries, rebuttals, opinion letters or editorial notes Study used ENM to understand the ecology and distribution of a pathogen, or vector, or reservoir or definite host of an infectious disease Study fails to define a component of the disease system, non-in- fectious disease (e.g., cancer, mental health) Full text article is available Full text article is not available Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 65 Figure 2: Keyword network analysis for the selected articles (n=78). We used default settings in VOSviewer and considered minimum 2 occurrences of keywords as a threshold. The node size indicates its frequency in the analysis of the reviewed articles. Color gradient indicates the temporal trends of use of different keywords in reviewed articles along with the publication years. The spatial resolution of data ranged from 6 to 17,000 meters, with a median pixel size of 1000 meters (Figure S2A). Articles varied in spatial extent, from village to dis- trict, state, country, multi-country (regional), and global extents. Most of the studies were conducted in the United States (Figure S2B). The temporal extent of the articles varied between one and 101 years, with a median of 11 years (Figure S2C). Articles used different temporal res- olutions (time durations between two sequential data col- lections), from hourly to annual datasets. Most articles used data that were collected monthly, seasonally or annu- ally (Figure S2D). Researchers applied various statistical methods to im- plement ENM theory and examine the ecological niches of their disease study subjects. Sixty articles (76.9%) used only MaxEnt as a modeling technique. Other articles also used MaxEnt in combination with other modeling tech- niques, such as generalized linear models, random forest, boosted regression trees, and generalized additive models (Table S1). Considering subject organisms, most articles aimed to reconstruct the ecological niche of mosquitoes (20.5%; n=16) and ticks (14.1%; n=11). Only a limited number of articles used ENM to understand the ecologi- cal niches of pathogens in tandem with their hosts (Figure S2E). Out of 78 articles, six used biotic (e.g., human foot- print) in tandem with abiotic (e.g., temperature) variables to study infectious disease systems. Data Four of the 32 components used for modeling were present, including ecological level, taxon names, predictor variables and spatial extent, representing 12.5% (n=32) of Figure 3: Articles containing or missing information about different components of the Overview section. The Overview section contained key information about analysis. Cyan-blue: Number of articles with Overview information. Green-cyan: Number of articles missing Overview information. The most complete components in the overview section were types of ex- tent boundary, target output, response data type, predictor types, observation type, model objective, specific study location, fo- cal taxon, and affiliation of first author. While the least reported components were biotic variables, code availability, spatial ex- tent (lon, lat), temporal resolution, and data availability. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 66 the articles reviewed. A limited number of articles provid- ed information on background data (44.9%; n=35), sam- ple design (48.7%; n=38), addressing errors and biases (61.5%; n=48), temporal resolution (24.4%; n=19), and extent of transfer data (30.8%; n=24). Articles examined pathogens and their hosts at dif- ferent levels of biological organization. The majority of the research was done at the individual level (57.7%; n=45), followed by population (33.3%; n=26) and com- munity (9.0%; n=7) levels (Figure S3.1A). Articles used a variety of sampling designs, such as field surveys (n=8), random sampling (n=11), and data from online databas- es (n=7), but most (n=41) of the 78 articles reviewed did not disclose details regarding their sampling methodology (Figure S3.1C). The sample sizes used in ENM research varied from 7 (Bacillus cereus) to 1,394,279 (tuberculo- sis) occurrence records, with a median of 233 occurrences (Figure S3.1B). Most articles mentioned data partitioning procedures during model evaluation but did not mention percentage or number of observations or occurrences records used for model training, and testing. Data partitioning information for model validation was explained in 54 articles (69.2%), and training data in 59 (75.6%) articles (Figure 4). Articles employed either geographic coordinate ref- erence systems (n=12) or projected coordinate systems (n=16) to spatially identify occurrence records. Only 32 (42.2%) articles provided details regarding the coordi- nate reference system (Figure S3.2A). Of the 78 articles, 74 used bioclimatic variables, either alone or in tandem with other variables. In addition to bioclimatic variables, articles used sociodemographic, topography, elevation, Normalized Difference Vegetation Index, and population density data as ENM predictor variables (Figure S3.2B). Variable data were mostly derived (N=57) from the World- Clim database, either by itself or in conjunction with other data sources (Figure S3.2C; Table S2). Articles also down- loaded bioclimatic data from MERRAclim (n=3), CHEL- SA (n=4), and other sources (Figure S3.2C; Table S2). Almost all articles (96.2%; n=75) mentioned model transference. A limited number of articles provided infor- mation about the spatial resolution and extent of variables for model transference. Similarly, a small number of ar- ticles mentioned the temporal resolution (24.4%; n=19) and temporal extent (30.8%; n=24) of their study. Only 38.5% of articles (n=30) provided information about envi- ronmental scenarios used in model transferring (Figure 4). Modelling There were twelve distinct components in the mod- eling section, but only one of them (8.3%, n=12) was ex- plained in 100% of articles (Figure 4). Information about nested data was provided in 2.7% (n=2), temporal autocor- relation in 2.7% (n=2), model averaging in 20.5% (n=16), model ensemble in 39.7% (n=31), and model setup for ex- trapolation 46.2% (n=36). Most articles (n=53) did not provide information about the pre-selection of the predictor variables. There were 15 articles merely referring to “correlation analysis” without explaining the precise methodology used to assess variable redundancy. Studies used seven different methods or techniques for the pre-selection of the variables (Fig- ure S4A). For variables, the most commonly used meth- ods were principal component analysis (n=13), Pearson correlation analysis (n=12), Spearman correlation (n=5), and variance inflation factor (n=4) (Figure S4A). Articles used eight different techniques to identify the variable of importance (Figure S4B). For identifying variable impor- tance, studies implemented the Jackknife test (n=20), cor- relation score (n=6), principal component analysis (n=6), Figure 4: Data section is divided into four different subsec- tions (Biodiversity data, Predictor variables, Data partition- ing and Transfer data). This sections summaries the informa- tion about species and predictor variables, and data processing. Cyan-blue: Number of articles with Data information. Green-cy- an: Number of articles missing Data information. The most com- plete components in the data section were taxon names, spatial extent (predictor), ecological level, predictor variables, and data sources. While the least reported components were temporal res- olution (transfer), temporal extent (transfer), absence data, quan- tification of novelty, and models and scenarios. * is for predictor, # is for transfer. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 67 permutation importance (n=6), and cross-validation (n=3) most commonly (Figure S4B). Articles mentioned that they checked for multicollinearity among the variables, though most of them did not mention specific methods they had used (n=26). We found that the articles used eight different techniques for multicollinearity check. The most common techniques for multicollinearity check in ENM were prin- cipal component analysis (n=7), Pearson correlation (n=6), and variance inflation factor (n=5) (Figure S4C). Assessment In the assessment section, five components were in- cluded, which are useful to explain the estimated relation- ship between biodiversity and environmental data (Figure 5). None of the five components of assessment section were explained in all articles (Figure 6). A limited number of ar- ticles provided information about expert judgement about ENM assessment (11.5%; n=9), performance on validation data (46.2%; n=36), and response shape of predictor vari- ables (62.8%; n=49). Prediction In ENM articles, seven components were considered for prediction. Only the information related to prediction unit was explained in all articles (Figure 7). Only a limit- ed number of articles addressed algorithmic uncertainty (28.2%; n=22), novel environment (38.2%; n=29), and sce- nario uncertainty (38.5%; n=30). Figure 5: Articles containing or missing information about different components of the Modelling section. The Modelling section contained information about repeatability of the model building. Cyan-blue: Number of articles with Modelling information. Green-cyan: Number of articles missing Modelling information. The most completely reported components in the modelling section were model setting fitting, threshold selection, model selection, vari- able importance, and parameter uncertainty. While the least reported components were temporal autocorrelation, nested data, model averaging, model ensembles, and coefficients. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 68 Figure 6: Articles containing or missing information about different components of the Assessment section. The Assessment section contained key information about the performance of the built ENM. Cyan-blue: Number of articles with Assessment informa- tion. Green-cyan: Number of articles missing Assessment information. The most complete components of Assessment section were performance of test data and performance on training data. While the least reported components were expert judgement and perfor- mance on validation data. Figure 7: Articles containing or missing information about different components of the Prediction section. The Prediction section contained information about inter or extrapolating of the developed ENM. Cyan-blue: Number of articles with Prediction in- formation. Green-cyan: Number of articles missing Prediction information. The most complete components in the prediction section were prediction unit, input data uncertainty, and parameter uncertainty. While the least reported components were algorithmic uncer- tainty, novel environment, and scenario uncertainty. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 69 Discussion We evaluated published articles for consistency and transparency in reporting key modeling and data compo- nents in ecological niche modeling (ENM) applications in disease ecology and disease biogeography. The use of ENM in infectious disease research is increasing with the journals where articles are being published varying in impact factor. A previous study reported that species distribution modeling research is published across a wide range of journals, with 12% annual growth (Vasconcelos et al. 2024). The increasing trends of articles publication in diverse journals reflect the interdisciplinary nature and broad applicability of ENM. We found that most articles missed important components during ENM building and were biased towards a certain modeling technique (i.e., MaxEnt), disease system (i.e., vector-borne), and study region (i.e., United States). Our study identified method- ological gaps of published articles, which could limit the replicability of studies. Our findings highlight an urgent need to improve ENM protocols and workflows among re- searchers, reviewers, and editors to as a means to improve rigor and reproducibility. Our analysis revealed that many infectious disease articles lacked comprehensive reporting of key compo- nents essential for the proper implementation of ENM across the ODMAP sections (Feng et al. 2019a; Zurell et al. 2020). For instance, temporal (n=19; 24.4%) and spa- tial resolution (n=41, 52.6%), which directly influence modeling outputs, were inadequately addressed in most articles (Figure 4). Ecological niches are highly context dependent and vary across different spatial and temporal scales. To understand pathogen transmission dynamics, comprehensive and scale-aware approaches are import- ant for understanding ecology and physiology of the or- ganisms involved in disease transmission (Escobar 2020; Zarzo-Arias et al. 2023). The absence of a clear description of model parame- terization limits the accurate validation and reproducibil- ity of ENM research, posing challenges considering the fields rapid growth and the lack of reproducibility stan- dards (Araújo et al. 2019; Feng et al. 2019a; Hallgren et al. 2019; Kass et al. 2025; Sandel et al 2011; Schmolke et al. 2010; Sillero et al. 2021; Soley-Guardia et al. 2024; Valavi et al. 2022; Veronica and Liu 2024). Previous case studies have similarly highlighted the unstructured and incom- plete explanation of components (e.g., Braga et al. 2014; Escobar and Peterson 2013; Escobar et al. 2014; Escobar et al. 2018; Kass et al. 2025; Merow et al. 2013; Peterson and Nakazawa 2008; Xu et al. 2024; Zurell et al. 2020). To address these issues, infectious disease modelers should adhere to a standardized framework that ensures detailed reporting of fundamental modeling components, facilitat- ing both effective communication among modelers and better understanding by readers. Numerous mathematical and statistical algorithms are available to estimate the ecological niches, with some be- ing more frequently used on the assumption that they are the most effective approaches (Araújo et al. 2019; Beery et al. 2021; Blonder et al. 2018; Drake 2015; Elith et al. 2008; Escobar 2020; Kass et al. 2025; Phillips et al. 2006; Qiao et al. 2015; Soley-Guardia et al. 2024; Valavi et al. 2022; Wiens et al. 2009). We found that most articles uti- lized MaxEnt as the predominant ENM algorithm (Table: S1). Nevertheless, evidences demonstrate that no single modeling algorithm is universally superior (Qiao et al., 2015; Valavi et al., 2022). MaxEnt is the most common algorithm used by researchers modeling ecological nich- es and spatial distributions (Barker and MacIsaac 2022; Campos et al. 2023; Feng et al. 2019b; Lippi et al. 2023a; Valavi et al. 2022). Previous studies have shown that the performance of ENM varies with changes in species or calibration areas (Elith et al., 2006; Qiao et al., 2019; Va- lavi et al., 2022) and algorithm employed (Escobar et al. 2018). Strikingly, the selection of modeling algorithms de- pends on the preferences of the modelers, instead of more scientific approaches, with some modelers preferring de- fault settings and others careful model tuning (Valavi et al. 2022). The preference maybe responds to the modelers’ limited understanding of how algorithms work (Joppa et al. 2013). To construct an ENM, the choice of modeling techniques and predictor variables should be informed by the physiology, ecology, and biogeographic history of the organism (Escobar 2020; Peterson et al. 2011). Joppa et al. (2013) recommended that the community should prioritize the understanding of the algorithms over the development of newer, fancier modeling workflows. We found that most studies used predictor variables from a particular data source (i.e., WorldClim) (Hijmans et al. 2005). Reliance on a specific source suggests that re- searchers are potentially biased on the use of similar types of readily available predictors, despite differences in study questions, model requirements, and mismatching study periods (Araújo et al. 2019; Booth 2022; Morales‐Barbe- ro et al. 2019; Oliver and Morecroft 2014; Regos et al. 2019). Previous studies also found WorldClim data to be the most frequently used (e.g., Barker and MacIsaac 2022; Bobrowski et al. 2021; Booth 2022; Datta et al. 2020; Es- cobar et al. 20014; Lippi et al. 2023a; Maria et al. 2017; Merkenschlager et al. 2023; Morales‐Barbero et al. 2019; Poggio et al. 2018). WorldClim temperature and precipi- Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 70 tation data are derived from global weather station aver- ages and interpolated with covariables such as elevation, distance to the coast, and satellite derived data to generate high-resolution bioclimatic variables (e.g., 30 arc seconds, approximately 1 kilometer) (Fick and Hijmans 2017; Hi- jmans et al. 2005; Waltari et al. 2014; Xu and Hutchinson 2011). Because weather station coverage varies geograph- ically (Hijmans et al. 2005; Waltari et al. 2014), World- Clim interpolations at ~1 km resolution generate 99.99% model-generated, interpolated climatic data (Peterson 2014). In contrast, remote sensing offers consistent, large- scale monitoring of surface temperature and precipitation, including day and night measurements (Adler et al. 2000; Jones et al. 2010; Waltari et al. 2014). A study reported that ENM built with predictor variables from MERRA performed as well as or better than models with World- Clim data (Waltari et al. 2014). As such, the selection of predictor variables for ENM should align with the study question and spatiotemporal scale of the calibration data (Pearson and Dawson 2003; Peterson et al. 2011; Regos et al. 2019). Using multiple predictors, including non-climat- ic predictors, improves ecological understanding of organ- isms (Regos et al. 2019). Employing diverse data sources, while considering species behavior and calibration area, helps reduce uncertainty and avoid overreliance in ENM construction (Regos et al. 2019). A next frontier in ENM applications to disease should be the combination of abiot- ic with biotic predictors, but generation of biotic variables is warranted (Peterson et al. 2019). We identified significant gaps in published articles regarding clear explanations of occurrence record data collection, cleaning processes, removal of spatial biases, and data partitioning into training/testing datasets. Data quality is a critical factor in building accurate and reliable ENM (Feng et al. 2019a; Escobar 2020; Soley-Guardia et al. 2024; Zurell et al. 2020). Ecological processes vary across spatial scales, and errors in selecting appropriate resolution and extent for occurrence and predictor vari- ables can violate biological and statistical assumptions (Feng et al. 2019a; McGill 2010; Soberón and Nakamu- ra 2009; Soley-Guardia et al. 2024). Biotic and abiotic predictors influence species distributions, but the level of influence vary with spatial resolution of variables, often requiring modification of original resolution of variables for consistency (Soberón and Nakamura 2009; Sunday et al. 2012). As such, researchers should follow standard pro- cedures to describe how spatial and environmental biases were addressed (Boria et al. 2014; Park and Davis 2017), how background data were selected (Barbet-Massin et al. 2012; Phillips et al. 2009), how calibration data were col- lected and prepared (Anderson and Raza 2010; Escobar 2020; Saupe et al. 2012), and how multicollinearity among predictors were managed (Bucklin et al. 2015; Pliscoff et al. 2014; Synes and Osborne 2011; Zeng et al. 2016). A higher number of articles utilized ENM to study the ecology and distribution of vector-borne disease caused by mosquitoes and ticks (Figure S2E). Our results align with Lippi et al. (2023b) and Van de Vuurst et al. (2023), who reported a growing focus on modeling vector distribution over the past decade. The higher number of ENM articles may be driven by factors such as the high disease burden posed by vectors, the critical need for risk area identifica- tion, policy mandates, and biased allocation of resources, or higher availability of vector datasets. ENM studies of infectious diseases are also geo- graphically biased. We found a strong focus of ENM ap- plications to disease in the Americas (Figure S2B). Lippi et al. (2023a) and Van de Vuurst et al. (2023) observed a higher number ENM modeling efforts in North Amer- ica and Europe. Paradoxically, vector-borne disease are predominantly identified in tropical, low-income coun- tries, highlighting the need for integrated, geographical- ly focused studies that account for environmental justice (Rosenberg et al. 2013). The limited number of ENM in regions like southeast Asia may stem from scarce publicly available data, limited research resources, and fewer re- search effort compared to North and South America, and Africa (Rosenberg et al. 2013). In contrast, the Americas benefit from robust systematic surveillance systems like the National Ecological Observatory Network, which pro- vide long-term, freely available pathogen, vector, and host data for the United States (Springer et al. 2016). Global databases like the Global Biodiversity Information Facili- ty also support parasite, vector, and host research, though their data quality and geographic coverage are influenced by research and funding biases (GBIF Secretariat and IAIA, 2020). More efforts may be needed to standardize and release pathogen occurrence data. Overall, the public health and economic significance of infectious diseases, availability of long-term environmental data, and exten- sive occurrence records are likely to promote more ENM studies in years to come. The use of ENM methods, such as MaxEnt, have been commonly used to study the ecology of vector-borne and directly transmitted zoonotic diseases. For instance, Max- Ent has been effectively applied to map the potential dis- tribution of schistosomiasis hosts and to guide eradication efforts for Tsetse flies and Trypanosomiasis (Dicko et al. 2014, Singleton et al. 2024). Nevertheless, ENM based solely on correlative relationships with climatic variables, Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 71 like those from WorldClim, may fall short in capturing the full complexity of disease ecology (Figure 8) (Cuervo et al. 2023). For example, a parsimonious study of avian in- fluenza could include only one viral lineage (e.g., H5N1), one primary host (e.g., one bird), and one secondary host (e.g., pig) in a low-dimensional environmental space (e.g., one environmental variable). This simple model to fore- cast transmission from birds to other species including humans, could identify just a portion of the actual trans- mission risk (Figure 8A). To develop more comprehensive and biologically meaningful models, it is essential to un- derstand the fundamental ecology of the disease systems and incorporate data on original hosts, secondary hosts, pathogen diversity, and relevant human features in ENM applications (Figure 8B). MaxEnt limitations derive from its vulnerability to extrapolate, which may produce overly simplistic or misleading results (Escobar et al. 2018; Qiao et al. 2019). For example, MaxEnt models can unrealisti- cally predict the survival of malaria vectors beyond 100°C (boiling water) temperature (Owens et al 2013). There- fore, future ENM to understand the ecology of infectious disease, including pathogen, vector, and host, should con- sider the physiology of the organisms to generate sound and realistic forecast. Caveats The ODMAP primarily serves as a framework to en- hance the reproducibility and transparency of spatial distri- bution models by standardizing the reporting of modeling procedures and data (Zurell et al. 2020). Its core purpose is to allow other researchers to evaluate, reproduce, and build upon published work rigorously, rather than inher- ently guaranteeing the ecological validity or comprehen- sive capture of complex disease ecological processes. Since pioneering applications to infectious diseases (i.e., Peterson et al. 2002), use of ENM in infectious disease research has emerged as a new theoretical and analytical framework (Srivastava et al. 2019; Escobar 2020; Vascon- celos et al. 2024). Nevertheless, key ENM components (e.g., study area, occurrence points, environmental vari- ables, analytical framework) could mislead model results (Araújo et al. 2019; Barker and MacIsaac 2022). Our anal- Figure 8: Schematic representation of simple and complex ecological niche model development. A. Building and geographical projection of ecological niche model using a subtype of avian influenza virus, a type of predictor, and a bird species may result in an incomplete understanding of the ecology of zoonotic avian influenza. B. Building and geographical projection of ecological niche using multiple subtypes of avian influenza virus, diverse predictors and multiple bird species for a more comprehensive understanding of the ecology of zoonotic avian influenza. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 72 ysis focused solely on description of the analytical frame- work in the literature based on the ODMAP protocol. We did not account for the theoretical framework employed for the biological interpretation of the models. ODMAP protocol suggests describing ENM param- eterization in the main article, so that we focused on the details in the main articles. It is plausible that ENM tuning was rigorous for some studies but described only in the supplementary material, not in the main article. To address this caveat, we thoroughly examined the supplementary materials available in the selected articles and found no methodological descriptions as supplementary materials. Overall, we found a considerable lack of details to foster reproducibility and also minimum efforts in model param- eterization to find the best model that reconstructs nature and allow the prediction of disease events (Levin 1992). This review covered articles published between 2000 and 2022 as a sample of current trends. Although, previous seminal work in the field and recent applications were not included, we argue that they likely suffer similar flaws in the analytical framework. Future ENM reviews should consider broader publication timeframes and future ENM applications should adopt ENM protocols to enhance re- producibility and transparency (Feng et al. 2019a; Zurell et al. 2020). Conclusions This study highlights flaws in ENM applications in disease ecology and disease biogeography. Specifically, many published articles provide insufficient explanations of key model components, such as spatial and temporal extent, biodiversity data sources, and mitigation of collin- earity, which reduce study reproducibility and transpar- ency. While ENM applications have advanced our under- standing of disease ecology and disease biogeography, our findings underscore the importance of adopting standard protocols to ensure consistency and reproducibility. Our study aimed to lay the groundwork for subsequent discus- sions and research on effectively incorporating important parameters in ENM applications to disease ecology and disease biogeography. Acknowledgements This study was supported by the National Science Foundation Human-Environment and Geographical Sciences Program (2116748) and CAREER (2235295) awards, and by the National Institute of Allergy and In- fectious Diseases of the National Institutes of Health (K01AI168452). This project was also supported by seed grants from the Virginia Tech Institute for Critical Tech- nology and Applied Science, Pandemic Prediction and Prevention Destination Area, and the Center for Emerging, Zoonotic, and Arthropod-borne Pathogens. The content is solely the authors’ responsibility and does not necessar- ily represent the official views of the National Institutes of Health. The authors would like to thank Dana Hawley, William Mark Ford, Sarah Karpanty, Paanwaris Paansri, and Paige Van de Vuurst for their support on the develop- ment and review of this manuscript. Data Availability The data table on which the analyses in this study are based is available via KU Scholarworks.1 Competing Interests The authors have declared that no competing interests exist. Author Contributions S.I. provided substantial input on the conceptualiza- tion, performed the formal analyses and wrote the first version of the manuscript and headed the review editing. M.C.G., D.S.T., L.E.E. provided methodological revision and gave considerable suggestions on writing – review and editing. L.E.E. developed the idea. All authors ap- proved the last version of this article. References Adler, Robert F., George J. Huffman, David T. Bolvin, Scott Curtis, and Eric J. Nelkin. 2000. “Tropical Rainfall Distributions Determined Using TRMM Combined with Other Satellite and Rain Gauge In- formation.” Journal of Applied Meteorology 39(12): 2007-2023. https://doi.org/10.1175/1520-0450(2001) 040%3C2007:TRDDUT%3E2.0.CO;2 Anderson, Robert P., and Ali Raza. 2010. “The Effect of the Extent of the Study Region on GIS Models of Species Geographic Distributions and Estimates of Niche Evolution: Preliminary Tests with Montane Rodents (Genus Nephelomys) in Venezuela.” Jour- nal of Biogeography 37(7): 1378–1393. https://doi. org/10.1111/j.1365-2699.2010.02290.x Araújo, Miguel B., Robert P. Anderson, A. Márcia Barbo- sa, Colin M. Beale, Carsten F. Dormann, Regan Early, Raquel A. Garcia, et al. 2019. “Standards for Distri- bution Models in Biodiversity Assessments.” Science Advances 5(1): eaat4858. https://doi.org/10.1126/ sciadv.aat4858 Astorga, Francisca, Luis E. Escobar, Daniela Poo-Muñoz, Joaquin Escobar-Dodero, Sylvia Rojas-Hucks, Mario Alvarado-Rybak, Melanie Duclos et al. 2018. “Distri- 1 https://hdl.handle.net/1808/36112. https://doi.org/10.1175/1520-0450(2001)040%3C2007:TRDDUT%3E2.0.CO;2 https://doi.org/10.1175/1520-0450(2001)040%3C2007:TRDDUT%3E2.0.CO;2 https://doi.org/10.1111/j.1365-2699.2010.02290.x https://doi.org/10.1111/j.1365-2699.2010.02290.x https://doi.org/10.1126/sciadv.aat4858 https://doi.org/10.1126/sciadv.aat4858 https://hdl.handle.net/1808/36112 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 73 butional Ecology of Andes Hantavirus: A Macroeco- logical Approach.” International Journal of Health Geographics 17: 1-12. https://doi.org/10.1186/ s12942-018-0142-z Barbet-Massin, Morgane, Frédéric Jiguet, Cécile Hélène Albert, and Wilfried Thuiller. 2012. “Selecting Pseu- do-Absences for Species Distribution Models: How, Where and How Many?” Methods in Ecology & Evo- lution 3(2): 327–338. https://doi.org/10.1111/j.2041- 210X.2011.00172.x Barker, Justin R., and Hugh J. MacIsaac. 2022. “Species Distribution Models Applied to Mosquitoes: Use, Quality Assessment, and Recommendations for Best Practice.” Ecological Modelling 472(10):110073. https://doi.org/10.1016/j.ecolmodel.2022.110073 Beery, Sara, Elijah Cole, Joseph Parker, Pietro Pero- na, and Kevin Winner. 2021. “Species Distribution Modeling for Machine Learning Practitioners: A re- view.” In ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (COMPASS ’21), June 28-July 2, 2021, Virtual Event, Australia. ACM, New York, NY, USA, 20 pages. https://doi. org/10.1145/3460112.3471966 Blonder, Benjamin, Cecina Babich Morrow, Brian Mait- ner, David J. Harris, Christine Lamanna, Cyrille Vio- lle, Brian J. Enquist, and Andrew J. Kerkhoff. 2018. “New Approaches for Delineating n‐dimensional Hy- pervolumes.” Methods in Ecology and Evolution 9(2): 305-319. https://doi.org/10.1111/2041-210X.12865 Bobrowski, Maria, Johannes Weidinger, and Udo Schick- hoff. 2021. “Is New Always Better? Frontiers in Glob- al Climate Datasets for Modeling Treeline Species in the Himalayas.” Atmosphere 12(5): 543. https://doi. org/10.3390/atmos12050543 Booth, Trevor H. 2022. “Checking Bioclimatic Variables that Combine Temperature and Precipitation Data Before their Use in Species Distribution Models.” Austral Ecology 47(7): 1506-1514. https://doi-org. ezproxy.lib.vt.edu/10.1111/aec.13234 Boria, Robert A., Link E. Olson, Steven M. Goodman, and Robert P. Anderson. 2014. “Spatial Filtering to Reduce Sampling Bias Can Improve the Performance of Ecological Niche Models.” Ecological Modelling 275(3): 73–77. https://doi.org/10.1016/j.ecolmod- el.2013.12.012 Braga, Guilherme Basseto, José Henrique Hildebrand Gri- si-Filho, Bruno Meireles Leite, Elaine Fátima de Sena, and Ricardo Augusto Dias. 2014. “Predictive Qualita- tive Risk Model of Bovine Rabies Occurrence in Bra- zil.” Preventive Veterinary Medicine 113(4): 536-546. https://doi.org/10.1016/j.prevetmed.2013.12.011 Brito-Hoyos, Diana Marcela, Edilberto Brito Sierra, and Rafael Villalobos Alvarez. 2013. “Geographic Dis- tribution of Wild Rabies Risk and Evaluation of the Factors Associated with its Incidence in Colom- bia, 1982-2010.” Pan American Journal of Public Health 33(1): 8–14. https://doi.org/10.1590/s1020- 49892013000100002 Bucklin, David N., Mathieu Basille, Allison M. Benscoter, Laura A. Brandt, Frank J. Mazzotti, Stephanie S. Ro- mañach, Carolina Speroterra, James I. Watling, and Wilfried Thuiller. 2015. “Comparing Species Distri- bution Models Constructed with Different Subsets of Environmental Predictors.” Diversity & Distributions 21(1): 23–35. https://doi.org/10.1111/ddi.12247 Campos, João C., Nuno Garcia, João Alírio, Salvador Are- nas-Castro, Ana C. Teodoro, and Neftalí Sillero. 2023. “Ecological Niche Models Using MaxEnt in Google Earth Engine: Evaluation, Guidelines and Recom- mendations.” Ecological Informatics 76(9):102147. https://doi.org/10.1016/j.ecoinf.2023.102147 Cuervo, Pablo Fernando, Patricio Artigas, Jacob Loren- zo-Morales, María Dolores Bargues, and Santiago Mas-Coma. 2023. “Ecological Niche Modelling Approaches: Challenges and Applications in Vec- tor-Borne Diseases.” Tropical Medicine and Infec- tious Disease 8(4): 187. https://doi.org/10.3390/trop- icalmed8040187 Datta, Arunava, Oliver Schweiger, and Ingolf Kuhn. 2020. “Origin of Climatic Data Can Determine the Trans- ferability of Species Distribution Models.” NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299 Dicko, Ahmadou H., Renaud Lancelot, Momar T. Seck, Laure Guerrini, Baba Sall, Mbargou Lo, Marc JB Vreysen et al. 2014. “Using Species Distribution Models to Optimize Vector Control in the Framework of the Tsetse Eradication Campaign in Senegal.” Proceedings of the National Academy of Sciences USA 111(28): 10149-10154. https://doi.org/10.1073/ pnas.1407773111 Dobson, Andrew P., Stuart L. Pimm, Lee Hannah, Les Kaufman, Jorge A. Ahumada, Amy W. Ando, Aaron Bernstein, et al. 2020. “Ecology and Economics for Pandemic Prevention.” Science 369(6502): 379–381. https://doi.org/10.1126/science.abc3189 Drake, John M. 2015. “Range Bagging: A New Method for Ecological Niche Modelling from Presence-only Data.” Journal of the Royal Society Interface 12(107): 20150086. http://dx.doi.org/10.1098/rsif.2015.0086 Elith, Jane, Catherine H. Graham*, Robert P. Anderson, Miroslav Dudík, Simon Ferrier, Antoine Guisan, Rob- ert J. Hijmans, et al. 2006. “Novel Methods Improve https://doi.org/10.1186/s12942-018-0142-z https://doi.org/10.1186/s12942-018-0142-z https://doi.org/10.1111/j.2041-210X.2011.00172.x https://doi.org/10.1111/j.2041-210X.2011.00172.x https://doi.org/10.1016/j.ecolmodel.2022.110073 https://doi.org/10.1145/3460112.3471966 https://doi.org/10.1145/3460112.3471966 https://doi.org/10.1111/2041-210X.12865 https://doi.org/10.3390/atmos12050543 https://doi.org/10.3390/atmos12050543 https://doi-org.ezproxy.lib.vt.edu/10.1111/aec.13234 https://doi-org.ezproxy.lib.vt.edu/10.1111/aec.13234 https://doi.org/10.1016/j.ecolmodel.2013.12.012 https://doi.org/10.1016/j.ecolmodel.2013.12.012 https://doi.org/10.1016/j.prevetmed.2013.12.011 https://doi.org/10.1590/s1020-49892013000100002 https://doi.org/10.1590/s1020-49892013000100002 https://doi.org/10.1111/ddi.12247 https://doi.org/10.1016/j.ecoinf.2023.102147 https://doi.org/10.3390/tropicalmed8040187 https://doi.org/10.3390/tropicalmed8040187 https://doi.org/10.3897/neobiota.59.36299 https://doi.org/10.1073/pnas.1407773111 https://doi.org/10.1073/pnas.1407773111 https://doi.org/10.1126/science.abc3189 http://dx.doi.org/10.1098/rsif.2015.0086 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 74 Prediction of Species’ Distributions from Occur- rence Data.” Ecography 29(2): 129–151. https://doi. org/10.1111/j.2006.0906-7590.04596.x Elith, Jane, John R. Leathwick, and Trevor Hastie. 2008. “A Working Guide to Boosted Regression Trees.” Journal of Animal Ecology 77(4): 802-813. https:// doi.org/10.1111/j.1365-2656.2008.01390.x Escobar, Luis E. 2020. “Ecological Niche Modeling: An Introduction for Veterinarians and Epidemiolo- gists.” Frontiers in Veterinary Science 7: 519059. https://www.frontiersin.org/articles/10.3389/ fvets.2020.519059 Escobar, Luis E., and Serge Morand. 2021. “Disease Ecology and Biogeography.” Frontiers in Veteri- nary Science 8: 765825. https://doi.org/10.3389/ fvets.2021.765825 Escobar, Luis E., Andrés Lira-Noriega, Gonzalo Medi- na-Vogel, and A. Townsend Peterson. 2014. “Potential for Spread of the White-Nose Fungus (Pseudogymno- ascus Destructans) in the Americas: Use of Maxent and NicheA to Assure Strict Model Transference.” Geospatial Health 9(1): 221. https://doi.org/10.4081/ gh.2014.19 Escobar, Luis E., and Meggan E. Craft. 2016. “Ad- vances and Limitations of Disease Biogeography Using Ecological Niche Modeling.” Frontiers in Microbiology 07(8):1174. https://doi.org/10.3389/ fmicb.2016.01174 Escobar, Luis E., and A. Townsend Peterson. 2013. “Spa- tial Epidemiology of Bat-Borne Rabies in Colom- bia.” Pan American Journal of Public Health 34(2): 135–136. Escobar, Luis E., Huijie Qiao, Javier Cabello, and A. Townsend Peterson. 2018. “Ecological Niche Mod- eling Re-Examined: A Case Study with the Darwin’s Fox.” Ecology and Evolution 8(10): 4757–4770. https://doi.org/10.1002/ece3.4014 Escobar, Luis E., Huijie Qiao, Christine Lee, and Nich- olas BD Phelps. 2017. “Novel Methods in Disease Biogeography: A Case Study with Heterosporo- sis.” Frontiers in Veterinary Science 4: 105. https:// doi.org/10.3389/fvets.2017.00105 Feng, Xiao, Daniel S. Park, Cassondra Walker, A. Townsend Peterson, Cory Merow, and Monica Papeş. 2019a. “A Checklist for Maximizing Reproducibili- ty of Ecological Niche Models.” Nature Ecology & Evolution 3(10): 1382–1395. https://doi.org/10.1038/ s41559-019-0972-5 Feng, Xiao, Daniel S. Park, Ye Liang, Ranjit Pandey, and Monica Papeş. 2019b. “Collinearity in Ecological Niche Modeling: Confusions and Challenges.” Ecol- ogy and Evolution 9(18): 10365–10376. https://doi. org/10.1002/ece3.5555 Fick, Stephen E., and Robert J. Hijmans. 2017. “World- Clim 2: New 1-Km Spatial Resolution Climate Sur- faces for Global Land Areas.” International Jour- nal of Climatology 37(12): 4302–4315. https://doi. org/10.1002/joc.5086 Fitzpatrick, Matthew C., Susanne Lachmuth, and Natalie T. Haydt. 2021. “The ODMAP Protocol: A New Tool for Standardized Reporting that Could Revolutionize Species Distribution Modeling.” Ecography 44(7): 1067-1070. https://doi.org/10.1111/ecog.05700 Frans, Veronica F., and Jianguo Liu. 2024. “Gaps and Op- portunities in Modelling Human Influence on Species Distributions in the Anthropocene.” Nature Ecology & Evolution 8(7): 1365-1377. https://doi-org.ezproxy. lib.vt.edu/10.1038/s41559-024-02435-3 GBIF Secretariat and International Association for Impact Assessment. 2020. “Best Practices for Publishing Bio- diversity Data from Environmental Impact Assess- ments.” https://doi.org/10.35035/DOC-5XDM-8762 Grimm, Volker, Uta Berger, Donald L. DeAngelis, J. Gary Polhill, Jarl Giske, and Steven F. Railsback. 2010. “The ODD Protocol: A Review and First Update.” Ecological Modelling 221(23): 2760-2768. https:// doi.org/10.1016/j.ecolmodel.2010.08.019 Hallgren, W., F. Santana, S. Low-Choy, Y. Zhao, and B. Mackey. 2019. “Species Distribution Models can be Highly Sensitive to Algorithm Configuration.” Ecological Modelling 408(9): 108719. https://doi. org/10.1016/j.ecolmodel.2019.108719 Hijmans, Robert J., Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis. 2005. “Very High Resolution Interpolated Climate Surfaces for Global Land Areas.” International Journal of Climatology 25(15): 1965–1978. https://doi.org/10.1002/joc.1276 Jones, Lucas A., Craig R. Ferguson, John S. Kimball, Ke Zhang, Steven Tsz K. Chan, Kyle C. McDonald, Eni G. Njoku, and Eric F. Wood. 2010. “Satellite Micro- wave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E.” IEEE Journal of Selected Topics in Applied Earth Ob- servations and Remote Sensing 3(1): 111–23. https:// doi.org/10.1109/JSTARS.2010.2041530 Joppa, Lucas N., Greg McInerny, Richard Harper, Lara Sa- lido, Kenji Takeda, Kenton O’Hara, David Gavaghan, and Stephen Emmott. 2013. “Troubling Trends in Sci- entific Software Use.” Science 340(6134): 814-815. https://doi.org/10.1126/science.1231535 Kass, Jamie M., Adam B. Smith, Dan L. Warren, Sergio Vignali, Sylvain Schmitt, Matthew E. Aiello‐Lam- https://doi.org/10.1111/j.2006.0906-7590.04596.x https://doi.org/10.1111/j.2006.0906-7590.04596.x https://doi.org/10.1111/j.1365-2656.2008.01390.x https://doi.org/10.1111/j.1365-2656.2008.01390.x https://www.frontiersin.org/articles/10.3389/fvets.2020.519059 https://www.frontiersin.org/articles/10.3389/fvets.2020.519059 https://doi.org/10.3389/fvets.2021.765825 https://doi.org/10.3389/fvets.2021.765825 https://doi.org/10.4081/gh.2014.19 https://doi.org/10.4081/gh.2014.19 https://doi.org/10.3389/fmicb.2016.01174 https://doi.org/10.3389/fmicb.2016.01174 https://doi.org/10.1002/ece3.4014 https://doi.org/10.3389/fvets.2017.00105 https://doi.org/10.3389/fvets.2017.00105 https://doi.org/10.1038/s41559-019-0972-5 https://doi.org/10.1038/s41559-019-0972-5 https://doi.org/10.1002/ece3.5555 https://doi.org/10.1002/ece3.5555 https://doi.org/10.1002/joc.5086 https://doi.org/10.1002/joc.5086 https://doi.org/10.1111/ecog.05700 https://doi-org.ezproxy.lib.vt.edu/10.1038/s41559-024-02435-3 https://doi-org.ezproxy.lib.vt.edu/10.1038/s41559-024-02435-3 https://doi.org/10.35035/DOC-5XDM-8762 https://doi.org/10.1016/j.ecolmodel.2010.08.019 https://doi.org/10.1016/j.ecolmodel.2010.08.019 https://doi.org/10.1016/j.ecolmodel.2019.108719 https://doi.org/10.1016/j.ecolmodel.2019.108719 https://doi.org/10.1002/joc.1276 https://doi.org/10.1109/JSTARS.2010.2041530 https://doi.org/10.1109/JSTARS.2010.2041530 https://doi.org/10.1126/science.1231535 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 75 mens, Eduardo Arlé et al. 2025. “Achieving High- er Standards in Species Distribution Modeling by Leveraging the Diversity of Available Software.” Ecography 2(2025): e07346. https://doi.org/10.1111/ ecog.07346 Keesing, Felicia, and Richard S. Ostfeld. 2024. “Emerging Patterns in Rodent-Borne Zoonotic Diseases.” Sci- ence 385(6715): 1305–1310. https://doi.org/10.1126/ science.adq7993 Levin, Simon A. 1992. “The Problem of Pattern and Scale in Ecology: The Robert H. Macarthur Award Lecture.” Ecology 73(6): 1943-1967. https://doi. org/10.2307/1941447 Lippi, Catherine A., Stephanie J. Mundis, Rachel Sippy, J. Matthew Flenniken, Anusha Chaudhary, Gavriella Hecht, Colin J. Carlson, and Sadie J. Ryan. 2023a. “Trends in Mosquito Species Distribution Modeling: Insights for Vector Surveillance and Disease Con- trol.” Parasites & Vectors 16(1): 302. https://doi. org/10.1186/s13071-023-05912-z Lippi, Catherine A, Samuel S C Rund, and Sadie J Ryan. 2023b. “Characterizing the Vector Data Ecosystem.” Journal of Medical Entomology 60(2): 247–254. https://doi.org/10.1093/jme/tjad009 Maher, Sean P., Christine Ellis, Kenneth L. Gage, Russell E. Enscore, and A. Townsend Peterson. 2010. “Range- wide Determinants of Plague Distribution in North America.” The American Journal of Tropical Medi- cine and Hygiene 83(4): 736. https://doi.org/10.4269/ ajtmh.2010.10-0042 Maria, Bobrowski, and Schickhoff Udo. 2017. “Why Input Matters: Selection of Climate Data Sets for Model- ling the Potential Distribution of a Treeline Species in the Himalayan Region.” Ecological Modelling 359(9): 92-102. https://doi.org/10.1016/j.ecolmod- el.2017.05.021 McGill, Brian J. 2010. “Matters of Scale.” Science 328(5978): 575–576. https://doi.org/10.1126/sci- ence.1188528 Merkenschlager, Christian, Freddy Bangelesa, Heiko Pa- eth, and Elke Hertig. 2023. “Blessing and Curse of Bioclimatic Variables: A Comparison of Different Calculation Schemes and Datasets for Species Dis- tribution Modeling within the Extended Mediterra- nean Area.” Ecology and Evolution 13(10): e10553. https://doi.org/10.1002/ece3.10553 Merow, Cory, Matthew J. Smith, and John A. Silander Jr. 2013. “A Practical Guide to Maxent for Modeling Species’ Distributions: what it Does, and why Inputs and Settings Matter.” Ecography 36(10): 1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x Merow, Cory, Brian S. Maitner, Hannah L. Owens, Ja- mie M. Kass, Brian J. Enquist, Walter Jetz, and Rob Guralnick. 2019. “Species’ Range Model Metadata Standards: RMMS.” Global Ecology and Biogeog- raphy 28(12): 1912-1924. https://doi-org.ezproxy.lib. vt.edu/10.1111/geb.12993 Moher, David, Alessandro Liberati, Jennifer Tetzlaff, Douglas G. Altman, and The PRISMA Group. 2009. “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.” PLOS Medicine 6(7): e1000097. https://doi.org/10.1371/ journal.pmed.1000097 Morales‐Barbero, Jennifer, and Julia Vega‐Álvarez. 2019. “Input Matters Matter: Bioclimatic Consistency to Map More Reliable Species Distribution Models.” Methods in Ecology and Evolution 10(2): 212-224. https://doi.org/10.1111/2041-210X.13124 Oliver, Tom H., and Mike D. Morecroft. 2014. “Interac- tions between Climate Change and Land Use Change on Biodiversity: Attribution Problems, Risks, and Op- portunities.” WIREs Climate Change 5(3): 317–335. https://doi.org/10.1002/wcc.271 Olivera, Leonela, Eugenia Minghetti, and Sara I. Mon- temayor. 2021. “Ecological Niche Modeling (ENM) of Leptoglossus clypealis a New Potential Global Invader: Following in the Footsteps of Leptoglos- sus occidentalis?” Bulletin of Entomological Re- search 111(3): 289-300. https://doi.org/10.1017/ S0007485320000656 Owens, Hannah L., Lindsay P. Campbell, L. Lynnette Dor- nak, Erin E. Saupe, Narayani Barve, Jorge Soberón, Kate Ingenloff et al. 2013. “Constraints on Interpre- tation of Ecological Niche Models by Limited Envi- ronmental Ranges on Calibration Areas.” Ecological Modelling 263(8): 10-18. https://doi.org/10.1016/j. ecolmodel.2013.04.011 Park, Daniel S., and Charles C. Davis. 2017. “Implica- tions and Alternatives of Assigning Climate Data to Geographical Centroids.” Journal of Biogeogra- phy 44(10): 2188–2198. https://www.jstor.org/sta- ble/26626941 Pearson, Richard G., and Terence P. Dawson. 2003. “Predicting the Impacts of Climate Change on the Distribution of Species: Are Bioclimate Envelope Models Useful?” Global Ecology and Biogeogra- phy 12(5): 361–371. https://doi.org/10.1046/j.1466- 822X.2003.00042.x Peterson, A. Townsend. 2008. “Biogeography of Diseas- es: A Framework for Analysis.” Naturwissenschaften 95(6): 483–491. https://doi.org/10.1007/s00114-008- 0352-5 https://doi.org/10.1111/ecog.07346 https://doi.org/10.1111/ecog.07346 https://doi.org/10.1126/science.adq7993 https://doi.org/10.1126/science.adq7993 https://doi.org/10.2307/1941447 https://doi.org/10.2307/1941447 https://doi.org/10.1186/s13071-023-05912-z https://doi.org/10.1186/s13071-023-05912-z https://doi.org/10.1093/jme/tjad009 https://doi.org/10.4269/ajtmh.2010.10-0042 https://doi.org/10.4269/ajtmh.2010.10-0042 https://doi.org/10.1016/j.ecolmodel.2017.05.021 https://doi.org/10.1016/j.ecolmodel.2017.05.021 https://doi.org/10.1126/science.1188528 https://doi.org/10.1126/science.1188528 https://doi.org/10.1002/ece3.10553 https://doi.org/10.1111/j.1600-0587.2013.07872.x https://doi-org.ezproxy.lib.vt.edu/10.1111/geb.12993 https://doi-org.ezproxy.lib.vt.edu/10.1111/geb.12993 https://doi.org/10.1371/journal.pmed.1000097 https://doi.org/10.1371/journal.pmed.1000097 https://doi.org/10.1111/2041-210X.13124 https://doi.org/10.1002/wcc.271 https://doi.org/10.1017/S0007485320000656 https://doi.org/10.1017/S0007485320000656 https://doi.org/10.1016/j.ecolmodel.2013.04.011 https://doi.org/10.1016/j.ecolmodel.2013.04.011 https://www.jstor.org/stable/26626941 https://www.jstor.org/stable/26626941 https://doi.org/10.1046/j.1466-822X.2003.00042.x https://doi.org/10.1046/j.1466-822X.2003.00042.x https://doi.org/10.1007/s00114-008-0352-5 https://doi.org/10.1007/s00114-008-0352-5 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 76 Peterson, A. Townsend, Robert P. Anderson, Marlon E. Cobos, Martín Cuahutle, Angela P. Cuervo-Robayo, Luis E. Escobar, Marc Fernández et al. 2019. “Curso Modelado de Nicho Ecológico, Versión 1.0.” Biodi- versity Informatics 14: 1-7. https://doi.org/10.17161/ bi.v14i0.8189 Peterson, A. Townsend, and Kate Ingenloff. 2016. “Bio- diversity Informatics Training Curriculum, ver- sion 1.2.” Biodiversity Informatics 11:1. https://doi. org/10.17161/bi.v11i1.5008 Peterson, A. T., and Y. Nakazawa. 2008. “Environmen- tal data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri.” Global Ecology and Biogeography 17(1): 135-144. https://doi-org.ezproxy.lib.vt.edu/10.1111/ j.1466-8238.2007.00347.x Peterson, A. Townsend. 2014. Mapping Disease Transmis- sion Risk. Baltimore: Johns Hopkins University Press. https://doi.org/10.1353/book.36167 Peterson, A. T., Victor Sánchez-Cordero, C. Ben Beard, and Janine M. Ramsey. 2002. “Ecologic Niche Mod- eling and Potential Reservoirs for Chagas Disease, Mexico.” Emerging Infectious Diseases 8(7): 662– 667. https://doi.org/10.3201/eid0807.010454 Peterson, A.T., J. Soberón, R.G. Pearson, R.P. Ander- son, E. Martínez-Meyer, M Nakamura, and M.B. Araújo. 2011. “Ecological Niches and Geograph- ic Distributions.” Princeton University Press, New Jersey, USA. https://press.princeton.edu/books/pa- perback/9780691136882/ecological-niches-and-geo- graphic-distributions-mpb-49 Phillips, Steven J., Miroslav Dudík, Jane Elith, Cather- ine H. Graham, Anthony Lehmann, John Leathwick, and Simon Ferrier. 2009. “Sample Selection Bias and Presence-Only Distribution Models: Implica- tions for Background and Pseudo-Absence Data.” Ecological Applications 19(1): 181–197. https://doi. org/10.1890/07-2153.1 Phillips, Steven J., Robert P. Anderson, and Robert E. Schapire. 2006. “Maximum Entropy Modeling of Species Geographic Distributions.” Ecological Mod- elling 190(3-4): 231-259. https://doi.org/10.1016/j. ecolmodel.2005.03.026 Pliscoff, Patricio, Federico Luebert, Hartmut H. Hilger, and Antoine Guisan. 2014. “Effects of Alternative Sets of Climatic Predictors on Species Distribution Mod- els and Associated Estimates of Extinction Risk: A Test with Plants in an Arid Environment.” Ecological Modelling 288(9):166–177. https://doi.org/10.1016/j. ecolmodel.2014.06.003 Poggio, Laura, Enrico Simonetti, and Alessandro Gimo- na. 2018. “Enhancing the WorldClim Data Set for National and Regional Applications.” Science of the Total Environment 625: 1628-1643. https://doi. org/10.1016/j.scitotenv.2017.12.258 Qiao, Huijie, Xiao Feng, Luis E. Escobar, A. Townsend Peterson, Jorge Soberón, Gengping Zhu, and Moni- ca Papeş. 2019. “An Evaluation of Transferability of Ecological Niche Models.” Ecography 42(3): 521– 534. https://doi.org/10.1111/ecog.03986 Qiao, Huijie, Jorge Soberón, and A. Townsend Peterson. 2015. “No Silver Bullets in Correlative Ecological Niche Modelling: Insights from Testing among Many Potential Algorithms for Niche Estimation.” Methods in Ecology and Evolution 6(10): 1126–1136. https:// doi.org/10.1111/2041-210X.12397 R Core Team. 2024. “R: A Language and Environment for Statistical Computing.” R Foundation for Statistical Computing. 2024. https://www.R-project.org/ Regos, Adrián, Laura Gagne, Domingo Alcaraz-Segura, João P. Honrado, and Jesús Domínguez. 2019. “Ef- fects of Species Traits and Environmental Predictors on Performance and Transferability of Ecological Niche Models.” Scientific Reports 9(1): 4221. https:// doi.org/10.1038/s41598-019-40766-5 Riccaboni, Massimo, and Luca Verginer. 2022. “The Impact of the COVID-19 Pandemic on Scientific Research in the Life Sciences.” PLoS One 17(2): e0263001. https://doi.org/10.1371/journal.pone.0263001 Romero-Alvarez, Daniel, Luis E. Escobar, Albert J. Au- guste, Sara Y. Del Valle, and Carrie A. Manore. 2023. “Transmission Risk of Oropouche Fever Across the Americas.” Infectious Diseases of Poverty 12(1): 47. https://doi.org/10.1186/s40249-023-01091-2 Rosenberg, Ronald, Michael A. Johansson, Ann M. Pow- ers, and Barry R. Miller. 2013. “Search Strategy Has Influenced the Discovery Rate of Human Viruses.” Proceedings of the National Academy of Sciences USA 110(34): 13961–13964. https://doi.org/10.1073/ pnas.1307243110 Sandel, Brody, L. Arge, Bo Dalsgaard, R. G. Davies, K. J. Gaston, W. J. Sutherland, and J-C. Svenning. 2011. “The influence of Late Quaternary Climate-Change Velocity on Species Endemism.” Science 334(6056): 660-664. https://doi.org/10.1126/science.1210173 Saupe, E. E., V. Barve, C. E. Myers, J. Soberón, N. Barve, C. M. Hensz, A. T. Peterson, H. L. Owens, and A. Lira-Noriega. 2012. “Variation in Niche and Distri- bution Model Performance: The Need for a Priori Assessment of Key Causal Factors.” Ecological Mod- elling 237–238(7):11–22. https://doi.org/10.1016/j. ecolmodel.2012.04.001 Schmolke, Amelie, Pernille Thorbek, Donald L. DeAnge- lis, and Volker Grimm. 2010. “Ecological Models Sup- https://doi.org/10.17161/bi.v14i0.8189 https://doi.org/10.17161/bi.v14i0.8189 https://doi.org/10.17161/bi.v11i1.5008 https://doi.org/10.17161/bi.v11i1.5008 https://doi-org.ezproxy.lib.vt.edu/10.1111/j.1466-8238.2007.00347.x https://doi-org.ezproxy.lib.vt.edu/10.1111/j.1466-8238.2007.00347.x https://doi.org/10.1353/book.36167 https://doi.org/10.3201/eid0807.010454 https://press.princeton.edu/books/paperback/9780691136882/ecological-niches-and-geographic-distributions-mpb-49 https://press.princeton.edu/books/paperback/9780691136882/ecological-niches-and-geographic-distributions-mpb-49 https://press.princeton.edu/books/paperback/9780691136882/ecological-niches-and-geographic-distributions-mpb-49 https://doi.org/10.1890/07-2153.1 https://doi.org/10.1890/07-2153.1 https://doi.org/10.1016/j.ecolmodel.2005.03.026 https://doi.org/10.1016/j.ecolmodel.2005.03.026 https://doi.org/10.1016/j.ecolmodel.2014.06.003 https://doi.org/10.1016/j.ecolmodel.2014.06.003 https://doi.org/10.1016/j.scitotenv.2017.12.258 https://doi.org/10.1016/j.scitotenv.2017.12.258 https://doi.org/10.1111/ecog.03986 https://doi.org/10.1111/2041-210X.12397 https://doi.org/10.1111/2041-210X.12397 https://www.R-project.org/ https://doi.org/10.1038/s41598-019-40766-5 https://doi.org/10.1038/s41598-019-40766-5 https://doi.org/10.1371/journal.pone.0263001 https://doi.org/10.1186/s40249-023-01091-2 https://doi.org/10.1073/pnas.1307243110 https://doi.org/10.1073/pnas.1307243110 https://doi.org/10.1126/science.1210173 https://doi.org/10.1016/j.ecolmodel.2012.04.001 https://doi.org/10.1016/j.ecolmodel.2012.04.001 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 77 porting Environmental Decision Making: A Strategy for the Future.” Trends in Ecology & Evolution 25(8): 479–486. https://doi.org/10.1016/j.tree.2010.05.001 Sillero, Neftalí, Salvador Arenas-Castro, Urtzi Enriquez‐ Urzelai, Cândida Gomes Vale, Diana Sousa-Guedes, Fernando Martínez-Freiría, Raimundo Real, and A. Márcia Barbosa. 2021. “Want to Model a Species Niche? A Step-By-Step Guideline on Correlative Ecological Niche Modelling.” Ecological Modelling 456(9): 109671. https://doi.org/10.1016/j.ecolmod- el.2021.109671 Singleton, Alyson L., Caroline K. Glidden, Andrew J. Chamberlin, Roseli Tuan, Raquel GS Palasio, Adri- ano Pinter, Roberta L. Caldeira et al. 2024. “Species Distribution Modeling for Disease Ecology: A Multi- Scale Case Study for Schistosomiasis Host Snails in Brazil.” PLoS Global Public Health 4(8): e0002224. https://doi.org/10.1371/journal.pgph.0002224 Soberón, Jorge, and Miguel Nakamura. 2009. “Niches and Distributional Areas: Concepts, Methods, and Assumptions.” Proceedings of the National Academy of Sciences USA 106(supplement_2): 19644–19650. https://doi.org/10.1073/pnas.0901637106 Soley-Guardia, Mariano, Diego F. Alvarado-Serrano, and Robert P. Anderson. 2024. “Top Ten Hazards to Avoid When Modeling Species Distributions: A Didactic Guide of Assumptions, Problems, and Recommen- dations.” Ecography 2024(4): e06852. https://doi. org/10.1111/ecog.06852 Springer, Yuri P., David Hoekman, Pieter T. J. Johnson, Paul A. Duffy, Rebecca A. Hufft, David T. Barnett, Brian F. Allan, et al. 2016. “Tick-, Mosquito-, and Ro- dent-Borne Parasite Sampling Designs for the Nation- al Ecological Observatory Network.” Ecosphere 7(5): e01271. https://doi.org/10.1002/ecs2.1271 Srivastava, Vivek, Valentine Lafond, and Verena C. Griess. 2019. “Species Distribution Models (SDM): Applica- tions, Benefits and Challenges in Invasive Species Management.” CABI Reviews 2019: 1-13. https://doi. org/10.1079/PAVSNNR201914020 Sunday, Jennifer M., Amanda E. Bates, and Nicholas K. Dulvy. 2012. “Thermal Tolerance and the Global Redistribution of Animals.” Nature Climate Change 2(9): 686–690. https://doi.org/10.1038/nclimate1539 Synes, Nicholas W., and Patrick E. Osborne. 2011. “Choice of Predictor Variables as a Source of Uncertainty in Continental-Scale Species Distribution Modelling un- der Climate Change.” Global Ecology and Biogeog- raphy 20(6): 904–914. https://doi.org/10.1111/j.1466- 8238.2010.00635.x United States Geological Survey. 2011. “USGS OFR 2011-1127: Construction of a 3-Arcsecond Digital Elevation Model for the Gulf of Maine, Conversion Factors.” Conversion Factors. 2011. https://pubs. usgs.gov/of/2011/1127/convert.html Valavi, Roozbeh, Gurutzeta Guillera-Arroita, José J. Lahoz-Monfort, and Jane Elith. 2022. “Predictive Performance of Presence-Only Species Distribu- tion Models: A Benchmark Study with Reproduc- ible Code.” Ecological Monographs 92(1): e01486. https://doi.org/10.1002/ecm.1486 Van de Vuurst, Paige, and Luis E. Escobar. 2023. “Climate Change and Infectious Disease: A Review of Evidence and Research Trends.” Infectious Diseases of Poverty 12(1): 51. https://doi-org.ezproxy.lib.vt.edu/10.1186/ s40249-023-01102-2 Vasconcelos, Rodrigo N., Taimy Cantillo-Pérez, Washing- ton JS Franca Rocha, William Moura Aguiar, Deor- gia Tayane Mendes, Taíse Bomfim de Jesus, Caro- lina Oliveira de Santana, Mariana MM de Santana, and Reyjane Patrícia Oliveira. 2024. “Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review.” Earth 5(4): 963-989. https://doi. org/10.3390/earth5040050 Waltari, Eric, Ronny Schroeder, Kyle McDonald, Robert P. Anderson, and Ana Carnaval. 2014. “Bioclimatic Variables Derived from Remote Sensing: Assessment and Application for Species Distribution Modelling.” Methods in Ecology and Evolution 5(10): 1033–1042. https://doi.org/10.1111/2041-210X.12264 Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. Second edition. Use R! Cham: Spring- er International Publishing. Wieczorek, John, David Bloom, Robert Guralnick, Stan Blum, Markus Döring, Renato Giovanni, Tim Rob- ertson, and David Vieglais. 2012. “Darwin Core: An Evolving Community-Developed Biodiversity Data Standard.” PLoS One 7(1): e29715. https://doi. org/10.1371/journal.pone.0029715 Wiens, John A., Diana Stralberg, Dennis Jongsomjit, Christine A. Howell, and Mark A. Snyder. 2009. “Niches, Models, and Climate Change: Assessing the Assumptions and Uncertainties.” Proceedings of the National Academy of Sciences USA 106(2): 19729- 19736. https://doi.org/10.1073/pnas.0901639106 Xu, Tingbao, and Michael Hutchinson. 2011. “ANUCLIM Vversion 6.1 User Gguide.” Fenner School of Envi- ronment and Society, Australian National University. Xu, Quanli, Xiao Wang, Junhua Yi, and Yu Wang. 2024. “Bias Correction in Species Distribution Models https://doi.org/10.1016/j.tree.2010.05.001 https://doi.org/10.1016/j.ecolmodel.2021.109671 https://doi.org/10.1016/j.ecolmodel.2021.109671 https://doi.org/10.1371/journal.pgph.0002224 https://doi.org/10.1073/pnas.0901637106 https://doi.org/10.1111/ecog.06852 https://doi.org/10.1111/ecog.06852 https://doi.org/10.1002/ecs2.1271 https://doi.org/10.1079/PAVSNNR201914020 https://doi.org/10.1079/PAVSNNR201914020 https://doi.org/10.1038/nclimate1539 https://doi.org/10.1111/j.1466-8238.2010.00635.x https://doi.org/10.1111/j.1466-8238.2010.00635.x https://pubs.usgs.gov/of/2011/1127/convert.html https://pubs.usgs.gov/of/2011/1127/convert.html https://doi.org/10.1002/ecm.1486 https://doi-org.ezproxy.lib.vt.edu/10.1186/s40249-023-01102-2 https://doi-org.ezproxy.lib.vt.edu/10.1186/s40249-023-01102-2 https://doi.org/10.3390/earth5040050 https://doi.org/10.3390/earth5040050 https://doi.org/10.1111/2041-210X.12264 https://doi.org/10.1371/journal.pone.0029715 https://doi.org/10.1371/journal.pone.0029715 https://doi.org/10.1073/pnas.0901639106 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 78 Based on Geographic and Environmental Character- istics.” Ecological Informatics 81(7): 102604. https:// doi.org/10.1016/j.ecoinf.2024.102604 Zarzo-Arias, Alejandra, Britta Uhl, Daniel S. Maynard, and Manuel B. Morales. 2023. “The Ecological Niche at Different Spatial Scales.” Frontiers in Ecology and Evolution 11: 1296340. https://doi.org/10.3389/ fevo.2023.1296340 Zeng, Yiwen, Bi Wei Low, and Darren C. J. Yeo. 2016. “Novel Methods to Select Environmental Variables in MaxEnt: A Case Study Using Invasive Cray- fish.” Ecological Modelling 34(12):5–13. https://doi. org/10.1016/j.ecolmodel.2016.09.019 Zurell, Damaris, Janet Franklin, Christian König, Phil J. Bouchet, Carsten F. Dormann, Jane Elith, Guillermo Fandos, et al. 2020. “A Standard Protocol for Report- ing Species Distribution Models.” Ecography 43(9): 1261–1277. https://doi.org/10.1111/ecog.04960 https://doi.org/10.1016/j.ecoinf.2024.102604 https://doi.org/10.1016/j.ecoinf.2024.102604 https://doi.org/10.3389/fevo.2023.1296340 https://doi.org/10.3389/fevo.2023.1296340 https://doi.org/10.1016/j.ecolmodel.2016.09.019 https://doi.org/10.1016/j.ecolmodel.2016.09.019 https://doi.org/10.1111/ecog.04960 Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 79 Figure S1: Distribution of Impact Factors of the journals where selected articles for this study were published. Supplemental Material Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 80 Figure S2: Some of the selected components from the Overview section for a comprehensive understanding. A. Boxplot shows the ranges of spatial resolutions (meters) used in the 78 articles; B. Bar plot shows the spatial extent of the study area mentioned in 78 articles; C. Boxplot shows the temporal extent considered in different articles; D. Bar plot shows the temporal resolutions used in different articles; and E. Bar plot shows the study subjects considered in different articles to study the ecological niche. We plotted the bars having value more than one only. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 81 Figure S3.1: Some of the selected components related to biodiversity data from the Data section for a comprehensive understand- ing. A. Bar plot shows the articles using ecological niche models at different biological organization levels; B. Box plot shows the ranges of sample sizes used in different articles; and C. Bar plot shows the different sampling techniques used in different articles. We plotted the bars having value more than one only. igure S3.2: Some of the selected components related to predictor data from the Data section for a comprehensive understanding. A. Bar plot shows the articles using different coordinate reference system; B. Bar plot shows the different predictor variables used in different articles for ecological niche model development; and C. Bar plot shows the different sources for the predictor data used in different articles. We plotted the bars having value more than one only. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 82 Figure S4: Some of the selected components from the Modeling section for a comprehensive understanding. A. Bar plot shows the articles using different techniques used to preselect the predictor variables; B. Bar plot shows the different techniques used to identify the variable importance in ecological niche modeling in different articles; and C. Bar plot shows the different techniques used to check for multicollinearity among the predictor variable during ecological niche modeling in different articles. We plotted the bars having value more than one only. Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 83 Serial no Modeling techniques Number of articles 1 Maximum Entropy Modeling (MaxEnt) 60 2 Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), General Linear Models (GLM), General Additive Models (GAM), General Boosted Models (GBM), Classification Tree Analysis (CTA), Multiple Adaptive Regression Splines (MARS), Ran- dom Forests (RF), and MaxEnt 1 3 ANN, SRE, FDA, GLM, GAM, GBM, CTA, MARS, RF, and MaxEnt 1 4 Bayesian additive regression trees (BARTs) 1 5 Bioclimatic envelope, MaxEnt, Logistic regression, and Domain model 1 6 Bioclimatic envelope (BIOCLIM), MaxEn, GLM, MARS, CART, mixture discriminant analysis (MDA), RF, BRT, and GAM 1 7 BRT, GAM, MARS, and Maxent. 1 8 BIOCLIM, and BIOCLIM True/False 1 9 SRE, MaxEnt, GLM, GAM, MARS, CTA, RF, and GBM 1 10 GBM, GLM, RF, and MARS 1 11 GLM, MARS, RF, and MaxEnt 1 12 GLM, GBM, RF, and MaxEnt 1 13 Logistic regression model 1 14 MaxEnt, SVM, Environmental Distance (ED) and Climate Space Model (CSM) 1 15 MAXENT, GLM, CTA, MARS, SVM, GAM, GBM, and ANN 1 16 MaxEnt, RF, and BRT 1 17 GLM, GAM, MaxEnt, R), and BRT 1 18 Simple logistic regression model; RF 1 19 None 1 Table S1: Different ecological niche modeling techniques used to study the ecological niches of diseases Shariful Islam et al. – Ecological Niche Modeling Applications to Infectious Diseases 84 Serial No Sources of the predictor variables Number of articles WorldClim 36 CHELSA 2 CHELSA; ISRO 1 CHELSA; ISRIC; MODIS; NASA; SoilGrid; WorldGrids 1 MERRAclim 1 MERRAclim; SoilGrids 1 MERRAclim; NASA; SoilGrids 1 AAFC; CWI; NRCan 1 Agri-Geomatics Service of Agriculture and Agri-Food Canada; Agriculture and Agri-Food Canada; Canada Centre for Mapping and Earth Observation 1 Argentinian Space Agency 1 AWS Open Data Terrain Tiles; IUCN; PRISM; SRIC SoilGrids 1 Brazilian Geomorphometric Database; WorldClim 1 CalEnviroscreen; GAP; USGS 1 CGIAR-CSI; CGLS; DHS; Ethiopia’s survey data in 2016; ISRIC; SDSM; WorldClim 1 CGIAR-CSI; Gridded Population of the World, MODIS; WorldClim; WorldGrids 1 CGIAR-CSI; FAO; HydroSHEDS; WorldClim 1 CGIAR-CSI; WorldClim 1 China Resource and Environment Science and Data Center; WorldClim 1 ClimateNA 1 Copernicus Global Land Service archive; WorldClim 1 DEM; DIVA-GIS; ISRIC; MODIS; NASA; NOAA; WorldClim 1 DEM; DIVA-GIS; IUCN; WorldClim 1 Digital Soil Mapof the World; SGS; SGS FEWS NET; WorldClim 1 DTM; WorldClim 1 Geographical Survey Institute 1 Global land cover database; WorldClim 1 Government databases; WorldClim 1 ISRIC; SoilGrids; WorldClim 1 MODIS; NLCD; PRISM; TIGER 1 NASA; SPOT4; WorldClim 1 National Land Numerical Information 1 National Mapping Organization; WorldClim 1 Pakistan Bureau of Statistics; WorldClim 1 PaleoClim 1 SEDAC; SRT; WorldClim 1 Shuttle Radar Topography Mission; WorldClim 1 Soilgrids; Worldclim 1 SRTM 1 SRTM; Worldlcim 1 USGS; WorldClim 1 None 2