{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T12:53:59Z","timestamp":1791982439154,"version":"4.4.0"},"reference-count":111,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019774","name":"Belmont Forum","doi-asserted-by":"publisher","award":["NSF-ICER 2024383 and NSF-ICER 2024385"],"award-info":[{"award-number":["NSF-ICER 2024383 and NSF-ICER 2024385"]}],"id":[{"id":"10.13039\/100019774","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"crossref","award":["2019\/23593-3"],"award-info":[{"award-number":["2019\/23593-3"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-DEB DEB-2011179"],"award-info":[{"award-number":["NSF-DEB DEB-2011179"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DEB-2011147"],"award-info":[{"award-number":["DEB-2011147"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM133439, R01AI168097, R01AI102918"],"award-info":[{"award-number":["R35GM133439, R01AI168097, R01AI102918"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["T32GM144273"],"award-info":[{"award-number":["T32GM144273"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"crossref","award":["2021\/10212-1"],"award-info":[{"award-number":["2021\/10212-1"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014496","name":"Stanford University Center for Innovation in Global Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014496","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014496","name":"Stanford University Center for Innovation in Global Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014496","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosglobalpublichealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Glob Public Health"],"abstract":"<jats:p>Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of <jats:italic>Schistosoma<\/jats:italic> spp. parasites released from freshwater snails, the parasite\u2019s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (<jats:italic>Biomphalaria glabrata<\/jats:italic>, <jats:italic>B<\/jats:italic>. <jats:italic>straminea<\/jats:italic>, and <jats:italic>B<\/jats:italic>. <jats:italic>tenagophila<\/jats:italic>). We used high-resolution (1km) climate, hydrology, land-use\/land-cover (LULC), and soil property data to describe the snails\u2019 ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.<\/jats:p>","DOI":"10.1371\/journal.pgph.0002224","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T17:25:35Z","timestamp":1722619535000},"page":"e0002224","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgph.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil"],"prefix":"10.1371","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-4798","authenticated-orcid":true,"given":"Alyson L.","family":"Singleton","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline K.","family":"Glidden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew J.","family":"Chamberlin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roseli","family":"Tuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1564-0871","authenticated-orcid":true,"given":"Raquel G. S.","family":"Palasio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriano","family":"Pinter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberta L.","family":"Caldeira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiane L. F.","family":"Mendon\u00e7a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar S.","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel V.","family":"Monteiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-0826","authenticated-orcid":true,"given":"Tejas S.","family":"Athni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5111-9322","authenticated-orcid":true,"given":"Susanne H.","family":"Sokolow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin A.","family":"Mordecai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulio A.","family":"De Leo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"issue":"1","key":"pgph.0002224.ref001","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1186\/s13071-023-05912-z","article-title":"Trends in mosquito species distribution modeling: insights for vector surveillance and disease control.","volume":"16","author":"CA Lippi","year":"2023","journal-title":"Parasit Vectors."},{"issue":"8","key":"pgph.0002224.ref002","doi-asserted-by":"crossref","first-page":"e0183626","DOI":"10.1371\/journal.pone.0183626","article-title":"Species distribution models: A comparison of statistical approaches for livestock and disease epidemics.","volume":"12","author":"T Hollings","year":"2017","journal-title":"PLOS ONE."},{"key":"pgph.0002224.ref003","doi-asserted-by":"crossref","first-page":"106335","DOI":"10.1016\/j.actatropica.2022.106335","article-title":"Predictive modeling of sand fly distribution incriminated in the transmission of Leishmania (Viannia) braziliensis and the incidence of Cutaneous Leishmaniasis in the state of Paran\u00e1Brazil.","volume":"229","author":"TM de Almeida","year":"2022","journal-title":"Acta Trop"},{"issue":"11","key":"pgph.0002224.ref004","doi-asserted-by":"crossref","first-page":"114041","DOI":"10.1088\/1748-9326\/ac9ece","article-title":"Linking Lyme disease ecology and epidemiology: reservoir host identity, not richness, determines tick infection and human disease in California","volume":"17","author":"AJ MacDonald","year":"2022","journal-title":"Environ Res Lett"},{"issue":"8","key":"pgph.0002224.ref005","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1111\/ecog.01028","article-title":"Geographical distribution, climatic variability and thermo-tolerance of Chagas disease vectors.","volume":"38","author":"GJ de la Vega","year":"2015","journal-title":"Ecography"},{"issue":"11","key":"pgph.0002224.ref006","doi-asserted-by":"crossref","first-page":"e0295149","DOI":"10.1371\/journal.pone.0295149","article-title":"Modelling the historical distribution of schistosomiasis-transmitting snails in South Africa using ecological niche models.","volume":"18","author":"N Ayob","year":"2023","journal-title":"PLOS ONE."},{"issue":"2","key":"pgph.0002224.ref007","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0304-3800(00)00354-9","article-title":"Predictive habitat distribution models in ecology","volume":"135","author":"A Guisan","year":"2000","journal-title":"Ecol Model"},{"issue":"1","key":"pgph.0002224.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1196\/annals.1439.002","article-title":"Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species","volume":"1134","author":"JM Jeschke","year":"2008","journal-title":"Ann N Y Acad Sci"},{"issue":"1","key":"pgph.0002224.ref009","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1146\/annurev.ecolsys.110308.120159","article-title":"Species Distribution Models: Ecological Explanation and Prediction Across Space and Time.","volume":"40","author":"J Elith","year":"2009","journal-title":"Annu Rev Ecol Evol Syst."},{"issue":"1","key":"pgph.0002224.ref010","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/1476-072X-9-54","article-title":"Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts.","volume":"9","author":"EF Lambin","year":"2010","journal-title":"Int J Health Geogr."},{"issue":"1782","key":"pgph.0002224.ref011","doi-asserted-by":"crossref","first-page":"20180335","DOI":"10.1098\/rstb.2018.0335","article-title":"Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil","volume":"374","author":"ML Childs","year":"2019","journal-title":"Philos Trans R Soc B Biol Sci"},{"issue":"6","key":"pgph.0002224.ref012","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1007\/s00477-017-1461-5","article-title":"Spatiotemporal modeling of relative risk of dengue disease in Colombia","volume":"32","author":"D Mart\u00ednez-Bello","year":"2018","journal-title":"Stoch Environ Res Risk Assess"},{"issue":"1","key":"pgph.0002224.ref013","doi-asserted-by":"crossref","first-page":"127","DOI":"10.4081\/gh.2006.287","article-title":"Bayesian modelling of geostatistical malaria risk data.","volume":"1","author":"L Gosoniu","year":"2006","journal-title":"Geospatial Health."},{"issue":"10","key":"pgph.0002224.ref014","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1111\/j.1365-2699.2008.01922.x","article-title":"Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives","volume":"35","author":"M Parviainen","year":"2008","journal-title":"J Biogeogr"},{"issue":"9","key":"pgph.0002224.ref015","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1111\/j.1461-0248.2009.01353.x","article-title":"Patterns and causes of species richness: a general simulation model for macroecology.","volume":"12","author":"NJ Gotelli","year":"2009","journal-title":"Ecol Lett."},{"issue":"1","key":"pgph.0002224.ref016","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/j.1472-4642.2010.00725.x","article-title":"A statistical explanation of MaxEnt for ecologists.","volume":"17","author":"J Elith","year":"2011","journal-title":"Divers Distrib"},{"issue":"2","key":"pgph.0002224.ref017","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1111\/j.2041-210X.2011.00172.x","article-title":"Selecting pseudo-absences for species distribution models: how, where and how many?","volume":"3","author":"M Barbet-Massin","year":"2012","journal-title":"Methods Ecol Evol"},{"issue":"1","key":"pgph.0002224.ref018","doi-asserted-by":"crossref","first-page":"e01486","DOI":"10.1002\/ecm.1486","article-title":"Predictive performance of presence-only species distribution models: a benchmark study with reproducible code","volume":"92","author":"R Valavi","year":"2022","journal-title":"Ecol Monogr"},{"issue":"2","key":"pgph.0002224.ref019","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1111\/2041-210X.13107","article-title":"blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.","volume":"10","author":"R Valavi","year":"2019","journal-title":"Methods Ecol Evol"},{"key":"pgph.0002224.ref020","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone.","volume":"202","author":"N Gorelick","year":"2017","journal-title":"Remote Sens Environ."},{"issue":"10","key":"pgph.0002224.ref021","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1111\/ele.13106","article-title":"Biodiversity monitoring, earth observations and the ecology of scale","volume":"21","author":"CB Anderson","year":"2018","journal-title":"Ecol Lett"},{"issue":"2","key":"pgph.0002224.ref022","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/jme\/tjad009","article-title":"Characterizing the Vector Data Ecosystem","volume":"60","author":"CA Lippi","year":"2023","journal-title":"J Med Entomol"},{"key":"pgph.0002224.ref023","unstructured":"GBIF. GBIF [Internet]. GBIF. [cited 2023 Jul 5]. Available from: https:\/\/www.gbif.org\/"},{"issue":"3","key":"pgph.0002224.ref024","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ecolmodel.2005.03.026","article-title":"Maximum entropy modeling of species geographic distributions.","volume":"190","author":"SJ Phillips","year":"2006","journal-title":"Ecol Model."},{"issue":"4","key":"pgph.0002224.ref025","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"J Elith","year":"2008","journal-title":"J Anim Ecol"},{"issue":"5","key":"pgph.0002224.ref026","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1111\/2041-210X.12523","article-title":"Fast and flexible Bayesian species distribution modelling using Gaussian processes.","volume":"7","author":"N Golding","year":"2016","journal-title":"Methods Ecol Evol"},{"issue":"3","key":"pgph.0002224.ref027","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0304-3800(02)00258-2","article-title":"Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters.","volume":"160","author":"YS Park","year":"2003","journal-title":"Ecol Model."},{"issue":"5","key":"pgph.0002224.ref028","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1111\/ddi.12892","article-title":"A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD.","volume":"25","author":"T Hao","year":"2019","journal-title":"Divers Distrib"},{"issue":"3","key":"pgph.0002224.ref029","doi-asserted-by":"crossref","first-page":"e01370","DOI":"10.1002\/ecm.1370","article-title":"A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels","volume":"89","author":"A Norberg","year":"2019","journal-title":"Ecol Monogr"},{"issue":"10","key":"pgph.0002224.ref030","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1111\/j.1600-0587.2013.07872.x","article-title":"A practical guide to MaxEnt for modeling species\u2019 distributions: what it does, and why inputs and settings matter.","volume":"36","author":"C Merow","year":"2013","journal-title":"Ecography"},{"issue":"1","key":"pgph.0002224.ref031","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/j.1600-0587.2008.05505.x","article-title":"Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models","volume":"32","author":"J Elith","year":"2009","journal-title":"Ecography"},{"issue":"5","key":"pgph.0002224.ref032","doi-asserted-by":"crossref","first-page":"e97122","DOI":"10.1371\/journal.pone.0097122","article-title":"Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias.","volume":"9","author":"Y Fourcade","year":"2014","journal-title":"PLOS ONE."},{"key":"pgph.0002224.ref033","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ecoinf.2016.11.004","article-title":"SPEDInstabR: An algorithm based on a fluctuation index for selecting predictors in species distribution modeling.","volume":"37","author":"C Guisande","year":"2017","journal-title":"Ecol Inform."},{"key":"pgph.0002224.ref034","volume-title":"Predicting species distributions with environmental time series data and deep learning [Internet]","author":"AM Smith","year":"2022"},{"issue":"24","key":"pgph.0002224.ref035","doi-asserted-by":"crossref","first-page":"E3359","DOI":"10.1073\/pnas.1521657113","article-title":"Spatial scale modulates the strength of ecological processes driving disease distributions","volume":"113","author":"JM Cohen","year":"2016","journal-title":"Proc Natl Acad Sci"},{"key":"pgph.0002224.ref036","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ecolmodel.2016.03.012","article-title":"On the dangers of model complexity without ecological justification in species distribution modeling.","volume":"330","author":"DM Bell","year":"2016","journal-title":"Ecol Model."},{"issue":"7","key":"pgph.0002224.ref037","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.tree.2019.03.004","article-title":"An ecological framework for modeling the geography of disease transmission","volume":"34","author":"EE Johnson","year":"2019","journal-title":"Trends Ecol Evol"},{"issue":"6","key":"pgph.0002224.ref038","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS).","volume":"43","author":"O Allouche","year":"2006","journal-title":"J Appl Ecol"},{"issue":"10","key":"pgph.0002224.ref039","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1038\/s41559-019-0972-5","article-title":"A checklist for maximizing reproducibility of ecological niche models.","volume":"3","author":"X Feng","year":"2019","journal-title":"Nat Ecol Evol."},{"issue":"1","key":"pgph.0002224.ref040","doi-asserted-by":"crossref","first-page":"eaat4858","DOI":"10.1126\/sciadv.aat4858","article-title":"Standards for distribution models in biodiversity assessments.","volume":"5","author":"MB Ara\u00fajo","year":"2019","journal-title":"Sci Adv."},{"issue":"2","key":"pgph.0002224.ref041","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1111\/j.2006.0906-7590.04596.x","article-title":"Novel methods improve prediction of species\u2019 distributions from occurrence data.","volume":"29","author":"J Elith*","year":"2006","journal-title":"Ecography"},{"issue":"6","key":"pgph.0002224.ref042","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1111\/j.1472-4642.2008.00496.x","article-title":"Not as good as they seem: the importance of concepts in species distribution modelling.","volume":"14","author":"A Jim\u00e9nez-Valverde","year":"2008","journal-title":"Divers Distrib."},{"key":"pgph.0002224.ref043","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ecolmodel.2013.12.012","article-title":"Spatial filtering to reduce sampling bias can improve the performance of ecological niche models.","volume":"275","author":"RA Boria","year":"2014","journal-title":"Ecol Model."},{"issue":"1","key":"pgph.0002224.ref044","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1111\/jbi.13734","article-title":"Model complexity affects species distribution projections under climate change","volume":"47","author":"P Brun","year":"2020","journal-title":"J Biogeogr"},{"issue":"1","key":"pgph.0002224.ref045","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1017\/S0376892997000088","article-title":"A review of methods for the assessment of prediction errors in conservation presence\/absence models","volume":"24","author":"AH Fielding","year":"1997","journal-title":"Environ Conserv"},{"issue":"1","key":"pgph.0002224.ref046","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.ecolmodel.2007.11.008","article-title":"Rethinking receiver operating characteristic analysis applications in ecological niche modeling.","volume":"213","author":"AT Peterson","year":"2008","journal-title":"Ecol Model."},{"issue":"5","key":"pgph.0002224.ref047","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1111\/ecog.02909","article-title":"The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity.","volume":"41","author":"PJ Galante","year":"2018","journal-title":"Ecography"},{"issue":"4","key":"pgph.0002224.ref048","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1111\/geb.12007","article-title":"Discrimination capacity in species distribution models depends on the representativeness of the environmental domain","volume":"22","author":"A Jim\u00e9nez-Valverde","year":"2013","journal-title":"Glob Ecol Biogeogr"},{"key":"pgph.0002224.ref049","volume-title":"In: Advances in Neural Information Processing Systems [Internet].","author":"SM Lundberg","year":"2017"},{"key":"pgph.0002224.ref050","volume-title":"A simple and effective model-based variable importance measure [Internet]","author":"BM Greenwell","year":"2018"},{"key":"pgph.0002224.ref051","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ecoinf.2013.11.002","article-title":"Spatial bias in the GBIF database and its effect on modeling species\u2019 geographic distributions.","volume":"19","author":"J Beck","year":"2014","journal-title":"Ecol Inform."},{"issue":"6","key":"pgph.0002224.ref052","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1038\/s41559-023-02051-7","article-title":"Specimens trump field observations in capturing biodiversity trends.","volume":"7","author":"B Daru","year":"2023","journal-title":"Nat Ecol Evol."},{"issue":"7","key":"pgph.0002224.ref053","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1017\/S0031182001007703","article-title":"Schistosoma mansoni and Biomphalariapast history and future trends.","volume":"123","author":"J a. T Morgan","year":"2001","journal-title":"Parasitology"},{"key":"pgph.0002224.ref054","volume-title":"PAHO\/WHO | Schistosomiasis [Internet]","author":"C. Mitchell","year":"2014"},{"issue":"2","key":"pgph.0002224.ref055","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.actatropica.2008.07.002","article-title":"Socioeconomic studies of schistosomiasis in Brazil: A review","volume":"108","author":"H Kloos","year":"2008","journal-title":"Acta Trop"},{"key":"pgph.0002224.ref056","doi-asserted-by":"crossref","first-page":"105897","DOI":"10.1016\/j.actatropica.2021.105897","article-title":"Population-based, spatiotemporal modeling of social risk factors and mortality from schistosomiasis in Brazil between 1999 and 2018","volume":"218","author":"W Silva da Paz","year":"2021","journal-title":"Acta Trop"},{"key":"pgph.0002224.ref057","doi-asserted-by":"crossref","first-page":"e20200335","DOI":"10.1590\/0037-8682-0335-2020","article-title":"New epidemiological profile of schistosomiasis from an area of low prevalence in Brazil.","volume":"53","author":"A Santos IG de","year":"2020","journal-title":"Rev Soc Bras Med Trop."},{"issue":"2","key":"pgph.0002224.ref058","doi-asserted-by":"crossref","first-page":"e0008995","DOI":"10.1371\/journal.pntd.0008995","article-title":"A systematic literature review of schistosomiasis in urban and peri-urban settings.","volume":"15","author":"K Klohe","year":"2021","journal-title":"PLoS Negl Trop Dis."},{"issue":"7","key":"pgph.0002224.ref059","doi-asserted-by":"crossref","first-page":"e87892","DOI":"10.1371\/journal.pone.0087892","article-title":"The effect of simulating different intermediate host snail species on the link between water temperature and schistosomiasis risk.","volume":"9","author":"N McCreesh","year":"2014","journal-title":"PloS One"},{"key":"pgph.0002224.ref060","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1590\/S0074-02762001000900008","article-title":"The distribution of Biomphalaria spp. in different habitats in relation to physical, biological, water contact and cognitive factors in a rural area in Minas Gerais, Brazil","volume":"96","author":"H Kloos","year":"2001","journal-title":"Mem Inst Oswaldo Cruz"},{"issue":"1","key":"pgph.0002224.ref061","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/1476-072X-11-51","article-title":"Schistosomiasis transmission and environmental change: a spatio-temporal analysis in Porto de Galinhas, Pernambuco\u2014Brazil.","volume":"11","author":"E Gomes","year":"2012","journal-title":"Int J Health Geogr."},{"key":"pgph.0002224.ref062","doi-asserted-by":"crossref","first-page":"717261","DOI":"10.1155\/2015\/717261","article-title":"Environmental epidemiology of intestinal schistosomiasis in Uganda: population dynamics of biomphalaria (gastropoda: planorbidae) in Lake Albert and Lake Victoria with observations on natural infections with digenetic trematodes.","volume":"2015","author":"C Rowel","year":"2015","journal-title":"BioMed Res Int"},{"issue":"13","key":"pgph.0002224.ref063","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.ijpara.2014.06.010","article-title":"Geographical distribution of human Schistosoma japonicum infection in The Philippines: tools to support disease control and further elimination","volume":"44","author":"RJ Soares Magalh\u00e3es","year":"2014","journal-title":"Int J Parasitol"},{"issue":"3","key":"pgph.0002224.ref064","doi-asserted-by":"crossref","first-page":"S95","DOI":"10.4081\/gh.2012.127","article-title":"Spatial distribution of Biomphalaria spp., the intermediate host snails of Schistosoma mansoniin Brazil.","volume":"6","author":"RGC Scholte","year":"2012","journal-title":"Geospatial Health."},{"key":"pgph.0002224.ref065","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.actatropica.2013.12.007","article-title":"Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models","volume":"132","author":"RGC Scholte","year":"2014","journal-title":"Acta Trop"},{"key":"pgph.0002224.ref066","doi-asserted-by":"crossref","first-page":"e3401","DOI":"10.7717\/peerj.3401","article-title":"The South-American distribution and southernmost record of Biomphalaria peregrina\u2014a potential intermediate host of schistosomiasis.","volume":"5","author":"A Rumi","year":"2017","journal-title":"PeerJ"},{"issue":"3","key":"pgph.0002224.ref067","article-title":"Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil","volume":"11","author":"VS Barbosa","year":"2016","journal-title":"Geospatial Health [Internet]."},{"key":"pgph.0002224.ref068","doi-asserted-by":"crossref","first-page":"105764","DOI":"10.1016\/j.actatropica.2020.105764","article-title":"Modelling the present and future distribution of Biomphalaria species along the watershed of the Middle Paranapanema region, S\u00e3o Paulo","volume":"214","author":"RGS Palasio","year":"2021","journal-title":"Brazil. Acta Trop"},{"issue":"3","key":"pgph.0002224.ref069","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.actatropica.2008.10.012","article-title":"Spatial distribution of Biomphalaria mollusks at S\u00e3o Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures","volume":"109","author":"RJPS Guimar\u00e3es","year":"2009","journal-title":"Acta Trop"},{"key":"pgph.0002224.ref070","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1590\/S0074-02762010000400030","article-title":"A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil","volume":"105","author":"RJ de PS Guimar\u00e3es","year":"2010","journal-title":"Mem Inst Oswaldo Cruz"},{"key":"pgph.0002224.ref071","doi-asserted-by":"crossref","first-page":"e180347","DOI":"10.1590\/0074-02760180347","article-title":"The cost of a disease targeted for elimination in Brazil: the case of schistosomiasis mansoni","volume":"114","author":"GL Nascimento","year":"2019","journal-title":"Mem Inst Oswaldo Cruz"},{"key":"pgph.0002224.ref072","volume-title":"Schistosomiasis [Internet].","author":"R Tuan","year":"2012"},{"key":"pgph.0002224.ref073","doi-asserted-by":"crossref","first-page":"105984","DOI":"10.1016\/j.atmosres.2021.105984","article-title":"Urban rainfall in the Capitals of Brazil: Variability, trend, and wavelet analysis.","volume":"267","author":"JF de Oliveira-J\u00fanior","year":"2022","journal-title":"Atmospheric Res."},{"issue":"1","key":"pgph.0002224.ref074","article-title":"Gastr\u00f3podes dulciaqu\u00edcolas e helmintos associados, em cole\u00e7\u00f5es h\u00eddricas de Santo Andr\u00e9, S\u00e3o Paulo, Brasil","volume":"18","author":"FP Ohlweiler","year":"2012","journal-title":"Rev Bioci\u00eancias [Internet]."},{"key":"pgph.0002224.ref075","first-page":"e20140153","article-title":"The current distribution pattern of Biomphalaria tenagophila and Biomphalaria straminea in the northern and southern regions of the coastal fluvial plain in the state of S\u00e3o Paulo.","volume":"15","author":"RGS Palasio","year":"2015","journal-title":"Biota Neotropica."},{"issue":"668","key":"pgph.0002224.ref076","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3897\/zookeys.668.10562","article-title":"Molecular and morphological identification of Biomphalaria species from the state of S\u00e3o Paulo, Brazil.","author":"RGS Palasio","year":"2017","journal-title":"ZooKeys"},{"issue":"4","key":"pgph.0002224.ref077","doi-asserted-by":"crossref","first-page":"221","DOI":"10.12657\/folmal.026.019","article-title":"Genetic diversity of the freshwater snail Biomphalaria tenagophila (d\u2019Orbigny, 1835) (Gastropoda: Hygrophila: Planorbidae) across two coastal areas of southeast Brazil.","volume":"26","author":"RGS Palasio","year":"2018","journal-title":"Folia Malacol."},{"key":"pgph.0002224.ref078","doi-asserted-by":"crossref","first-page":"e20190746","DOI":"10.1590\/1676-0611-bn-2019-0746","article-title":"Diversity of Biomphalaria spp. freshwater snails and associated mollusks in areas with schistosomiasis risk, using molecular and spatial analysis tools","volume":"19","author":"RGS Palasio","year":"2019","journal-title":"Biota Neotropica."},{"key":"pgph.0002224.ref079","doi-asserted-by":"crossref","first-page":"106132","DOI":"10.1016\/j.actatropica.2021.106132","article-title":"Spatial analysis of areas at risk for schistosomiasis in the Alto Tiet\u00ea Basin, S\u00e3o PauloBrazil.","volume":"224","author":"RGS Palasio","year":"2021","journal-title":"Acta Trop"},{"key":"pgph.0002224.ref080","author":"RHM Pereira","year":"2019","journal-title":"[Internet]2019 [cited 2024 May 10]. geobr: Loads Shapefiles of Official Spatial Data Sets of Brazil."},{"key":"pgph.0002224.ref081","unstructured":"IUCN. IUCN [Internet]. [cited 2023 Jul 5]. Available from: https:\/\/www.iucn.org\/"},{"issue":"9","key":"pgph.0002224.ref082","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"H He","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"pgph.0002224.ref083","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1111\/ecog.05615","article-title":"Modelling species presence-only data with random forests.","volume":"44","author":"R Valavi","year":"2021","journal-title":"Ecography"},{"issue":"1","key":"pgph.0002224.ref084","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1890\/07-2153.1","article-title":"Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.","volume":"19","author":"SJ Phillips","year":"2009","journal-title":"Ecol Appl"},{"key":"pgph.0002224.ref085","volume-title":"CHELSA [Internet]","author":"CHELSA","year":"2020"},{"issue":"6","key":"pgph.0002224.ref086","doi-asserted-by":"crossref","first-page":"5053","DOI":"10.1029\/2019WR024873","article-title":"MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset","volume":"55","author":"D Yamazaki","year":"2019","journal-title":"Water Resour Res"},{"key":"pgph.0002224.ref087","volume-title":"Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution [Internet]","author":"T Hengl","year":"2019"},{"key":"pgph.0002224.ref088","volume-title":"Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution [Internet]","author":"T. Hengl","year":"2018"},{"key":"pgph.0002224.ref089","volume-title":"Clay content in % (kg \/ kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution [Internet]","author":"T. Hengl","year":"2018"},{"key":"pgph.0002224.ref090","unstructured":"Open Spatial Demographic Data and Research. WorldPop [Internet]. WorldPop. [cited 2023 Jul 5]. Available from: https:\/\/www.worldpop.org\/"},{"issue":"17","key":"pgph.0002224.ref091","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.3390\/rs12172735","article-title":"Reconstructing three decades of land use and land cover changes in Brazilian biomes with landsat archive and Earth Engine.","volume":"12","author":"Souza CM","year":"2020","journal-title":"Remote Sens."},{"key":"pgph.0002224.ref092","doi-asserted-by":"crossref","first-page":"103312","DOI":"10.1016\/j.jue.2020.103312","article-title":"Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation.","volume":"125","author":"L Dijkstra","year":"2021","journal-title":"J Urban Econ."},{"issue":"0","key":"pgph.0002224.ref093","first-page":"1","article-title":"Reifications in disease ecology 2: Towards a decolonized pedagogy enabling science by, and for, the people.","volume":"0","author":"LF Chaves","year":"2023","journal-title":"Capital Nat Social."},{"key":"pgph.0002224.ref094","doi-asserted-by":"crossref","DOI":"10.1017\/9781139028271","volume-title":"Habitat suitability and distribution models: with applications in R [Internet]","author":"A Guisan","year":"2017"},{"issue":"2","key":"pgph.0002224.ref095","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1111\/2041-210X.13525","article-title":"Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data","volume":"12","author":"VA Steen","year":"2021","journal-title":"Methods Ecol Evol"},{"issue":"3","key":"pgph.0002224.ref096","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/geb.13628","article-title":"Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth","volume":"32","author":"AB Smith","year":"2023","journal-title":"Glob Ecol Biogeogr"},{"issue":"4","key":"pgph.0002224.ref097","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1037\/a0016973","article-title":"An introduction to recursive partitioning: rationale, applicationand characteristics of classification and regression trees, bagging, and random forests.","volume":"14","author":"C Strobl","year":"2009","journal-title":"Psychol Methods."},{"key":"pgph.0002224.ref098","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning [Internet]","author":"T Hastie","year":"2001"},{"key":"pgph.0002224.ref099","doi-asserted-by":"crossref","DOI":"10.1057\/9780230509993","article-title":"Classification and Regression by randomForest.","volume":"2","author":"A Liaw","year":"2002","journal-title":"R News."},{"key":"pgph.0002224.ref100","first-page":"1","article-title":"Package \u201cdismo\u201d","author":"R Hijmans","year":"2017","journal-title":"Circles"},{"issue":"11","key":"pgph.0002224.ref101","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1111\/2041-210X.12261","article-title":"ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models.","volume":"5","author":"R Muscarella","year":"2014","journal-title":"Methods Ecol Evol"},{"key":"pgph.0002224.ref102","first-page":"5372","article-title":"Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest","author":"A. Brenning","year":"2012","journal-title":"In: 2012 IEEE International Geoscience and Remote Sensing Symposium [Internet]."},{"issue":"2","key":"pgph.0002224.ref103","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1556\/ComEc.10.2009.2.9","article-title":"The effect of prevalence and its interaction with sample size on the reliability of species distribution models.","volume":"10","author":"A Jim\u00e9nez-Valverde","year":"2009","journal-title":"Community Ecol."},{"key":"pgph.0002224.ref104","author":"L. Osorio-Olvera","journal-title":"luismurao\/ntbox: From getting biodiversity data to evaluating species distribution models in a friendly GUI environment version 0.7.1 from GitHub [Internet]."},{"key":"pgph.0002224.ref105","article-title":"pdp: An R package for constructing partial dependence plots.","author":"BM Greenwell","year":"2017","journal-title":"R J."},{"issue":"1","key":"pgph.0002224.ref106","first-page":"343","article-title":"Package \u201cvip\u201d","volume":"12","author":"B Greenwell","journal-title":"Var Importance Plots"},{"issue":"3","key":"pgph.0002224.ref107","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions.","volume":"41","author":"E \u0160trumbelj","year":"2014","journal-title":"Knowl Inf Syst."},{"issue":"3","key":"pgph.0002224.ref108","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3390\/insects13030221","article-title":"Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination.","volume":"13","author":"CG Rhodes","year":"2022","journal-title":"Insects"},{"issue":"1","key":"pgph.0002224.ref109","doi-asserted-by":"crossref","first-page":"20604","DOI":"10.1038\/srep20604","article-title":"Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination.","volume":"6","author":"Z Ren","year":"2016","journal-title":"Sci Rep."},{"key":"pgph.0002224.ref110","author":"RP Anderson","year":"2016","journal-title":"Final report of the task group of GBIF data fitness for use in distribution modelling"},{"issue":"12","key":"pgph.0002224.ref111","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1111\/ecog.00845","article-title":"What do we gain from simplicity versus complexity in species distribution models?","volume":"37","author":"C Merow","year":"2014","journal-title":"Ecography"}],"container-title":["PLOS Global Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pgph.0002224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T17:27:09Z","timestamp":1722619629000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pgph.0002224"}},"subtitle":[],"editor":[{"given":"Kamran Shaukat","family":"Dar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":111,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pgph.0002224","relation":{},"ISSN":["2767-3375"],"issn-type":[{"value":"2767-3375","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,2]]}}}