{"id":182,"date":"2019-12-05T16:00:55","date_gmt":"2019-12-05T16:00:55","guid":{"rendered":"https:\/\/esa.org\/stats\/?page_id=182"},"modified":"2024-07-25T18:54:28","modified_gmt":"2024-07-25T18:54:28","slug":"past-ecological-forecasting-award-winners","status":"publish","type":"page","link":"https:\/\/esa.org\/stats\/awards\/past-ecological-forecasting-award-winners\/","title":{"rendered":"Past Ecological Forecasting Award Winners"},"content":{"rendered":"
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2024 award winner<\/strong><\/em><\/p>\n

Barros, C., Luo, Y., Chubaty, A. M., Eddy, I. M. S., Micheletti, T., Boisvenue, C., Andison, D. W., Cumming, S. G., & McIntire, E. J. B. (2023). Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation. Methods in Ecology and Evolution, 14, 173\u2013188.\u00a0https:\/\/doi.org\/10.1111\/2041-210X.14034<\/a><\/p>\n

2023 award winner<\/em><\/strong><\/p>\n

Zwart, Jacob A., Oliver, Samantha K., Watkins, William David, Sadler, Jeffrey M., Appling, Alison P., Corson-Dosch, Hayley R., Jia, Xiaowei, Kumar, Vipin and Read, Jordan S.. 2023. \u201c Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions.\u201d Journal of the American Water Resources Association 59(2): 317\u2013337. https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1111\/1752-1688.13093<\/a><\/p>\n

2022 award winner<\/em><\/strong><\/p>\n<\/div>\n<\/div>\n

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Heilman, Kelly; Dietze, Michael; Arizpe, Alexis; Aragon, Jacob; Gray, Andrew; Shaw, John; Finley, Andrew; Klesse, Stefan; DeRose, Justin; Evans, Margaret. 2022. \u201cEcological forecasting of tree growth: regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty\u201d Global Change Biology 28(7):2442-2460 https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/gcb.16038<\/a><\/p>\n

2021 award winner<\/em><\/strong><\/p>\n<\/div>\n

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Saunders, SP, Cuthbert, FJ, Zipkin, EF. Evaluating population viability and efficacy of conservation management using integrated population models. J Appl Ecol. 2018; 55: 1380\u2013 1392. https:\/\/doi.org\/10.1111\/1365-2664.13080<\/a><\/span><\/div>\n<\/div>\n
\u00a0<\/div>\n
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2020 award winner<\/em><\/strong><\/div>\n
\u00a0<\/div>\n
Howell, P. E., B. R. Hossack, E. Muths, B. H. Sigafus, A. Chenevert-Steffler, and R. B. Chandler. 2019. A statistical forecasting approach to metapopulation viability analysis. \u00a0Ecological Applications<\/i> 30:1\u201310. DOI:10.1002\/eap.2038<\/a>.<\/div>\n<\/div>\n
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\u00a0<\/div>\n
2019 award winner<\/em><\/strong><\/div>\n

\n<\/em><\/strong>Paniw M, Maag N, Cozzi G, Clutton-Brock T, Ozgul A. (2019). Life history responses of meerkats to seasonal changes in extreme environments. Science<\/em> 363:633-.<\/div>\n<\/div>\n
\u00a0<\/div>\n
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2018 award winner<\/em><\/strong><\/p>\n

Thomas RQ, et al. (2017). Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments. Biogeosciences<\/em> 14:3525-3547.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

2024 award winner Barros, C., Luo, Y., Chubaty, A. M., Eddy, I. M. S., Micheletti, T., Boisvenue, C., Andison, D. W., Cumming, S. G., & McIntire, E. J. B. (2023). Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation. Methods in Ecology and Evolution, 14, 173\u2013188.\u00a0https:\/\/doi.org\/10.1111\/2041-210X.14034 2023 award winner Zwart, Jacob A., Oliver, Samantha…<\/p>\n","protected":false},"author":635,"featured_media":0,"parent":54,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ngg_post_thumbnail":0,"footnotes":""},"class_list":["post-182","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/pages\/182","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/users\/635"}],"replies":[{"embeddable":true,"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/comments?post=182"}],"version-history":[{"count":2,"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/pages\/182\/revisions"}],"predecessor-version":[{"id":283,"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/pages\/182\/revisions\/283"}],"up":[{"embeddable":true,"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/pages\/54"}],"wp:attachment":[{"href":"https:\/\/esa.org\/stats\/wp-json\/wp\/v2\/media?parent=182"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}