{"id":5082,"date":"2021-09-16T14:24:21","date_gmt":"2021-09-16T12:24:21","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=5082"},"modified":"2021-09-16T14:24:21","modified_gmt":"2021-09-16T12:24:21","slug":"predicting-crown-width-and-length-using-nonlinear-mixed-effects-models-a-test-of-competition-measures-using-chinese-fir-cunninghamia-lanceolata-lamb-hook","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2021\/09\/16\/predicting-crown-width-and-length-using-nonlinear-mixed-effects-models-a-test-of-competition-measures-using-chinese-fir-cunninghamia-lanceolata-lamb-hook\/","title":{"rendered":"Predicting crown width and length using nonlinear mixed-effects models: a test of competition measures using Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.)"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-5085 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021-300x202.png\" alt=\"\" width=\"300\" height=\"202\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021-300x202.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021-768x516.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021-640x430.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/09\/Wang-2021.png 805w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">Including individual-tree competition indices as predictor variables could significantly improve the performance of crown width and length models for Chinese fir (<em>Cunninghamia lanceolata<\/em> (Lamb.) Hook.). Moreover, distance-dependent competition indices are superior to distance-independent ones when modeling crown width and length. Compared with crown width and length basic models with optimum competition indices, the performance of the two-level nonlinear mixed-effects models improved.<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p align=\"justify\"><strong>Context<\/strong> Crown width (CW) and crown length (CL) are two important variables widely included as the predictors in growth and yield models that contribute to forest management strategies.<br \/>\n<strong>Aims<\/strong> Individual-tree crown width and length models were developed with data from 1498 Chinese fir (<em>Cunninghamia lanceolata<\/em> (Lamb.) Hook.) trees in 16 sample plots located at Jiangle County, Fujian Province, southeastern China. Two hypotheses were proposed: (1) including individual-tree competition indices as predictor variables could significantly improve performance of both the CW\u2014DBH and CL\u2014DBH models; and (2) the distance-dependent competition indices would perform better than distance-independent ones.<br \/>\n<strong>Methods<\/strong> The models were fitted using generalized linear least squares or generalized nonlinear least squares methods. In addition, to prevent correlations between observations from the same sampling unit, we introduced age classes and sample plots as random effects to develop the two-level nonlinear mixed-effects models.<br \/>\n<strong>Results<\/strong> We found introduction of competition indices could significantly improve the performance of the CW\u2014DBH and CL\u2014DBH models. The distance-dependent competition index (i.e., competitor to subject tree distance) performed best in modeling the crown width and length models. Compared with crown width and length basic models with optimum competition indices, the performance of the two-level nonlinear mixed-effects models was significantly better.<br \/>\n<strong>Conclusion<\/strong> The two hypotheses were accepted. We hope these models will contribute to scientific management of Chinese fir plantations.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nCrown width and length models; Individual-tree competition indices; Optimum competition indices; Heteroskedasticity; Two-level nonlinear mixed-effects model<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1007\/s13595-021-01092-x'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nWang, W., Ge, F., Hou, Z. et al. Predicting crown width and length using nonlinear mixed-effects models: a test of competition measures using Chinese fir (<em>Cunninghamia lanceolata<\/em> (Lamb.) Hook.). Annals of Forest Science 78, 77 (2021). <a href=\"https:\/\/doi.org\/10.1007\/s13595-021-01092-x\">https:\/\/doi.org\/10.1007\/s13595-021-01092-x<\/a><\/p>\n<p><strong>For the read-only version of the full text:<\/strong><br \/>\n<a href=\"https:\/\/rdcu.be\/cxS8i\">https:\/\/rdcu.be\/cxS8i<\/a><\/p>\n<p><strong>Data availability<\/strong><br \/>\nThe datasets generated during and\/or analyzed during the current study are available from the corresponding author on reasonable request<\/p>\n<p><strong>Guest Editor<\/strong><br \/>\nC\u00e9line Meredieu<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message Including individual-tree competition indices as predictor variables could significantly improve the performance of crown width and length models for Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). Moreover, distance-dependent competition indices are superior to distance-independent ones when modeling crown width and length. Compared with crown width and length basic models with optimum competition indices, the [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,15],"tags":[],"class_list":["post-5082","post","type-post","status-publish","format-standard","hentry","category-article-type","category-research-paper","cat-14-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5082","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/users\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=5082"}],"version-history":[{"count":0,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5082\/revisions"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=5082"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=5082"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=5082"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}