{"doi":"10.1101/2022.10.06.511086","title":"Mapping potential malaria vector larval habitats for larval source management: Introduction to multi-model ensembling approaches","abstract":"Abstract Mosquito larval source management (LSM) is a viable supplement to the currently implemented first-line malaria control tools for use under certain conditions for malaria control and elimination. Implementation of larval source management requires a carefully designed strategy and effective planning. Identification and mapping of larval sources is a prerequisite. Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats using ensemble modeling, which includes selection of models, ensembling method and predictors; evaluation of variable importance; prediction of potential larval habitats; and assessment of prediction uncertainty. The models were built and validated based on multi-site, multi-year field observations and climatic/environmental variables. Model performance was tested using independent multi-site, multi-year field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors were elevation, geomorphon class, and precipitation 2 months prior. Mapped distributions of potential malaria vector larval habitats showed different prediction errors in different ecological settings. This is the first study to provide a detailed framework for the process of multi-model ensemble modeling. Mapping of potential habitats will be helpful in LSM planning. Author’s summary Mosquito larval source management (LSM) is a viable supplement to the currently implemented first-line malaria control tools. Implementation of LSM requires a carefully designed strategy and effective planning. Identification and mapping of larval sources is a prerequisite. Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework for such a process, including selection of models, ensembling methods and predictors; evaluation of variable importance; and assessment of prediction uncertainty. We used predictions of potential malaria vector larval habitats as an example to demonstrate how the procedure works, specifically, we used multi-site multi-year field observations to build and validate the model, and model performance was further tested using independent multi-site multi-year field observations – this training-validation-testing is often missing from previous studies. The proposed ensemble modeling procedure provides a framework for similar biological studies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":302002,"datarank":0.17977087135468986,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.014979028054473393,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.014979028054473393,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":269980,"name":"Ming‐Chieh Lee","orcid":"0000-0002-2462-6089","position":1,"is_corresponding":false},{"id":313447,"name":"Xiaoming Wang","orcid":"0000-0003-1232-3125","position":2,"is_corresponding":false},{"id":313445,"name":"Daibin Zhong","orcid":"0000-0002-2771-9598","position":3,"is_corresponding":false},{"id":313451,"name":"Guiyun Yan","orcid":"0000-0001-8760-2925","position":4,"is_corresponding":false},{"id":313448,"name":"Guofa Zhou","orcid":"0000-0001-9283-5520","position":0,"is_corresponding":true}],"reference_count":71,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:06.309890Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}