[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121616-en":3,"doc-seo-121616-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121616,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","A Double Machine Learning Trend Model for Citizen Science Data - Research Overview","Citizen and community-science datasets can reveal interannual population-change patterns at large geographic scales, but flexible sampling protocols usually do not stay consistent across years. As observation practices shift, interannual confounding arises, making changes in detection processes indistinguishable from real species population changes. This work introduces a Double Machine Learning–based framework that estimates trends while adjusting for confounding through machine-learned propensity scores and an additional simulation step for residual confounding.","A Double Machine Learning Trend Model for Citizen Science Data  \nDaniel Fink 1* , Alison Johnston2 , Matt Strimas-Mackey1 , Tom Auer1 , Wesley M. Hochachka 1 , Shawn Ligocki1 , Lauren Oldham Jaromczyk 1 , Orin Robinson1 , Chris Wood1 , Steve Kelling1 , and Amanda D. Rodewald1  \n1 Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA.  \n2 Centre for Research into Ecological and Environmental Modelling, School of Maths and Statistics, University of St Andrews, St Andrews, UK.  \n* Corresponding [author. ](author. daniel.fink@cornell.edu)[daniel.fink@cornell.edu](author. daniel.fink@cornell.edu)  \nAbstract  \n1. Citizen and community-science (CS) datasets have great potential for estimating interannual patterns of population change given the large volumes of data collected globally every year. Yet, the flexible protocols that enable many CS projects to collect large volumes of data typically lack the structure necessary to keep consistent sampling across years. This leads to interannual confounding, as changes to the observation process over time are confounded with changes in species population sizes.  \n2. Here we describe a novel modeling approach designed to estimate species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double Machine Learning, a statistical framework that uses machine learning methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. Additionally, we develop a simulation method to identify and adjust for residual confounding missed by the propensity scores. Machine learning makes it possible to use large feature sets to control for confounding and model heterogeneity in trends. Using this new method, we can produce spatially detailed trend estimates from citizen science data.  \n3. To illustrate the approach, we estimated species trends using data from the CS project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends in the face of real-world confounding. Results showed that the trend estimates distinguished between spatially constant and spatially varying trends at a 27km resolution. There were low error rates on the estimated direction of population change  \n(increasing/decreasing) and high correlations on the estimated magnitude of population change.  \n4. The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions without rigorous monitoring data.  \nSECTION 1: Introduction  \nInformation on population trends is essential for conservation monitoring and management. To date, the estimation of interannual trends has largely been restricted to the analysis of data from structured surveys where, ideally, the same observers follow the same survey protocols at the same locations, at same dates and times each year. This controlled survey structure is used to minimize the interannual variation in the observation process that can lead to confounding. This same structure also enables trends to be estimated using standard regression models (e.g. Kery & Royle, 2020; Link et al. , 2020) . However, these survey requirements have made it difficult to collect species-observation data at the scales necessary to monitor large groups of species across broad spatial extents , and at arbitrary times of year.  \nCitizen science projects are collecting increasingly large volumes of data for a variety of taxa (Pocock et al. , 2017) . The data collected by these projects have great potential to estimate population trends for species, regions, and times of year where structured data is lacking. However, one of the key challenges to using these data for trend estimation is controlling for confounding sources of interannual variation that lead to biased estima","cbCaip2o8wZvUmEN","https://ap.wps.com/l/cbCaip2o8wZvUmEN","pdf",1482698,1,28,"English","en",105,"# Abstract\n# Section 1: Introduction\n## Population trends and conservation monitoring\n## Challenges of structured surveys\n## Opportunities and confounding in citizen science data\n## Sources of interannual variation in observation\n## Double Machine Learning for causal adjustment","[{\"question\":\"Why is interannual confounding a major problem in citizen science trend estimation?\",\"answer\":\"Citizen science projects often change sampling protocols over time, so shifts in the observation process are confounded with changes in species populations. This biases estimated interannual trends if not controlled.\"},{\"question\":\"What is the core idea of the proposed model?\",\"answer\":\"The method uses Double Machine Learning to estimate population trends while controlling for interannual confounding. It leverages machine learning to model both population change and propensity scores used for adjustment.\"},{\"question\":\"How do the authors validate the approach in practice?\",\"answer\":\"They estimate species trends using eBird data and run a simulation study to evaluate performance under realistic confounding. Results show accurate direction classification and strong correlations for the magnitude of population change at a 27 km resolution.\"}]","A Double Machine Learning Trend Model for Citizen Science Data - Research Overview | PDF",1785805668,71,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-double-machine-learning-trend-model-for-citizen-science-data-research-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-double-machine-learning-trend-model-for-citizen-science-data-research-overview/121616/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is interannual confounding a major problem in citizen science trend estimation?","Question",{"text":75,"@type":76},"Citizen science projects often change sampling protocols over time, so shifts in the observation process are confounded with changes in species populations. This biases estimated interannual trends if not controlled.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core idea of the proposed model?",{"text":80,"@type":76},"The method uses Double Machine Learning to estimate population trends while controlling for interannual confounding. It leverages machine learning to model both population change and propensity scores used for adjustment.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors validate the approach in practice?",{"text":84,"@type":76},"They estimate species trends using eBird data and run a simulation study to evaluate performance under realistic confounding. Results show accurate direction classification and strong correlations for the magnitude of population change at a 27 km resolution.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]