Integrating Presence-only and Abundance Data to Predict Baobab (Adansonia digitata L.) Distribution: A Bayesian Data Fusion Framework

Integrating Presence-only and Abundance Data to Predict Baobab (Adansonia digitata L.) Distribution: A Bayesian Data Fusion Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Presence-only and Abundance Data to Predict Baobab (Adansonia digitata L.) Distribution: A Bayesian Data Fusion Framework Akoeugnigan Idelphonse Sode, Adande Belarmain Fandohan, Elias Teixeira Krainski, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871875/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Species distribution models (SDMs) are vital tools in ecology and conservation. The integration of increasingly available citizen science data with planned survey data offers a significant opportunity to enhance species distribution estimates. While integrated SDMs often combine presence-only and abundance data, the interdependence between the conditional distributions of these outcomes remains to be elucidated. This study proposes a Bayesian spatial fusion modelling framework to jointly analyse presence-only and abundance data for the African baobab in Benin. The aim was to understand and map the spatial variation in the species’ distribution. We briefly reviewed process-based models for count and point process data and explored various data fusion strategies using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differential Equations (SPDE) for inference. The results revealed a heterogeneous baobab distribution across Benin, characterised by a spatial autocorrelation range of 34.4 km (95% Bayesian credible interval, BCI = 27.59-42.52). Key drivers of this distribution include environmental factors such as annual temperature, rainfall of the driest month, soil texture (silt/clay fractions), and slope. A spatial fusion model incorporating shared latent components and common covariates' effects demonstrated the highest performance level, surpassing alternative fusion approaches. The model achieved the highest mean composite scores for the Area Under the ROC Curve (AUC) (0.85±0.02), accuracy (0.77±0.02), and True Skill Statistics (TSS) (0.66±0.05). The shared component model has the capacity to explain datasets' interdependence, estimate covariate effects missed by separate models, and enhance prediction precision. Despite relying on the assumption of an identically shared spatial signal across target responses, this research underscores the potential of spatial fusion modelling for integrating disparate data sources. The findings contribute to advancing SDM inference, particularly in data-limited contexts, and have wider applicability to spatial regression problems involving multisource outcomes. Log-Gaussian Cox process count data ISDM INLA joint spatial modelling shared latent components Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Distribution: A Bayesian Data Fusion Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-and-ecological-statistics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eest","sideBox":"Learn more about [Environmental and Ecological Statistics](http://link.springer.com/journal/10651)","snPcode":"10651","submissionUrl":"https://submission.nature.com/new-submission/10651/3","title":"Environmental and Ecological Statistics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Log-Gaussian Cox process, count data, ISDM, INLA, joint spatial modelling, shared latent components","lastPublishedDoi":"10.21203/rs.3.rs-7871875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7871875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpecies distribution models (SDMs) are vital tools in ecology and conservation. The integration of increasingly available citizen science data with planned survey data offers a significant opportunity to enhance species distribution estimates. While integrated SDMs often combine presence-only and abundance data, the interdependence between the conditional distributions of these outcomes remains to be elucidated. This study proposes a Bayesian spatial fusion modelling framework to jointly analyse presence-only and abundance data for the African baobab in Benin. The aim was to understand and map the spatial variation in the species\u0026rsquo; distribution. We briefly reviewed process-based models for count and point process data and explored various data fusion strategies using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differential Equations (SPDE) for inference. The results revealed a heterogeneous baobab distribution across Benin, characterised by a spatial autocorrelation range of 34.4 km (95% Bayesian credible interval, BCI = 27.59-42.52). Key drivers of this distribution include environmental factors such as annual temperature, rainfall of the driest month, soil texture (silt/clay fractions), and slope. A spatial fusion model incorporating shared latent components and common covariates' effects demonstrated the highest performance level, surpassing alternative fusion approaches. The model achieved the highest mean composite scores for the Area Under the ROC Curve (AUC) (0.85\u0026plusmn;0.02), accuracy (0.77\u0026plusmn;0.02), and True Skill Statistics (TSS) (0.66\u0026plusmn;0.05). The shared component model has the capacity to explain datasets' interdependence, estimate covariate effects missed by separate models, and enhance prediction precision. Despite relying on the assumption of an identically shared spatial signal across target responses, this research underscores the potential of spatial fusion modelling for integrating disparate data sources. The findings contribute to advancing SDM inference, particularly in data-limited contexts, and have wider applicability to spatial regression problems involving multisource outcomes.\u003c/p\u003e","manuscriptTitle":"Integrating Presence-only and Abundance Data to Predict Baobab (Adansonia digitata L.) Distribution: A Bayesian Data Fusion Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 23:24:38","doi":"10.21203/rs.3.rs-7871875/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-04T12:25:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T00:04:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231393167824223063652399239782242557365","date":"2025-10-28T23:51:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T08:30:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313350095282491807053449257656156351631","date":"2025-10-22T08:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T16:48:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T10:39:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T11:42:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental and Ecological Statistics","date":"2025-10-15T22:34:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-and-ecological-statistics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eest","sideBox":"Learn more about [Environmental and Ecological Statistics](http://link.springer.com/journal/10651)","snPcode":"10651","submissionUrl":"https://submission.nature.com/new-submission/10651/3","title":"Environmental and Ecological Statistics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7ed259f5-9a77-4003-ab81-37601be164eb","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T08:55:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 23:24:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7871875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871875","identity":"rs-7871875","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}