{"id":8141,"date":"2023-07-31T08:09:09","date_gmt":"2023-07-30T23:09:09","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&#038;p=8141"},"modified":"2023-08-09T21:48:59","modified_gmt":"2023-08-09T12:48:59","slug":"2023-08-11-jc","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-08-11-jc\/","title":{"rendered":"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems"},"content":{"rendered":"<p>We will discuss about \u201c Decomposing predictability to identify dominant causal drivers in complex ecosystems \u201d,Suzuki, Kenta, Shin-ichiro S. Matsuzaki, and Hiroshi Masuya., Proceedings of the National Academy of Sciences 119.42 (2022): e2204405119.<\/p>\n<p>&nbsp;<\/p>\n<p>Abstract<\/p>\n<section id=\"abstract\" role=\"doc-abstract\">\n<div role=\"paragraph\">Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series\u2013based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.<\/div>\n<\/section>\n<div class=\"signup-alert-ad\">\n<div class=\"d-flex justify-content-between align-items-center\">\n<div><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>We will discuss about \u201c Decomposing predictability to identify dominant causal drivers in complex ecosystems \u201d,Suzuki, Kenta, Shin-ichiro S. Matsuzaki, and Hiroshi Masuya., Proceedings of the National Academy of Sciences &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-08-11-jc\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems&#8221;<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-8141","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems - Biomedical Mathematics Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-08-11-jc\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"We will discuss about \u201c Decomposing predictability to identify dominant causal drivers in complex ecosystems \u201d,Suzuki, Kenta, Shin-ichiro S. Matsuzaki, and Hiroshi Masuya., Proceedings of the National Academy of Sciences &hellip; Continue reading &quot;Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-08-11-jc\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta property=\"article:modified_time\" content=\"2023-08-09T12:48:59+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2023-08-11-jc\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2023-08-11-jc\\\/\",\"name\":\"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"datePublished\":\"2023-07-30T23:09:09+00:00\",\"dateModified\":\"2023-08-09T12:48:59+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2023-08-11-jc\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2023-08-11-jc\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2023-08-11-jc\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/events\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\",\"name\":\"Biomedical Mathematics Group\",\"description\":\"\uae30\ucd08\uacfc\ud559\uc5f0\uad6c\uc6d0 \uc758\uc0dd\uba85\uc218\ud559\uadf8\ub8f9\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#organization\",\"name\":\"IBS Biomedical Mathematics Group\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2021\\\/02\\\/ibs-circle-1.png\",\"contentUrl\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2021\\\/02\\\/ibs-circle-1.png\",\"width\":250,\"height\":250,\"caption\":\"IBS Biomedical Mathematics Group\"},\"image\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems - Biomedical Mathematics Group","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-08-11-jc\/","og_locale":"en_US","og_type":"article","og_title":"Olive Cawiding, Decomposing predictability to identify dominant causal drivers in complex ecosystems - Biomedical Mathematics Group","og_description":"We will discuss about \u201c Decomposing predictability to identify dominant causal drivers in complex ecosystems \u201d,Suzuki, Kenta, Shin-ichiro S. 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