New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

IF 1.7 3区 农林科学 Q2 FORESTRY Canadian Journal of Forest Research Pub Date : 2023-10-10 DOI:10.1139/cjfr-2023-0031
Steven Wagers, Guillermo Castilla, Mihai Voicu, Tyler Rea, G. Arturo Sanchez-Azofeifa
{"title":"New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada","authors":"Steven Wagers, Guillermo Castilla, Mihai Voicu, Tyler Rea, G. Arturo Sanchez-Azofeifa","doi":"10.1139/cjfr-2023-0031","DOIUrl":null,"url":null,"abstract":"Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (<5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha −1 ), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.","PeriodicalId":9483,"journal":{"name":"Canadian Journal of Forest Research","volume":"57 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Forest Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1139/cjfr-2023-0031","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0

Abstract

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (<5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha −1 ), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
加拿大西部泥炭地生态系统中小黑云杉的新的地上生物量组分方程
黑云杉是加拿大最常见的树种,在泥炭地占主导地位,在那里它们通常发育不良。我们利用来自56个样地的495棵破坏性采样树,对来自加拿大西部泰加平原和北方平原生态区泥炭地的小型(<5 m高)黑云杉建立了按组分(茎、枝和针)计算地上生物量的异速生长模型,目前尚无具体的组分生物量模型。我们使用“留一张图”交叉验证来评估可转移性,并将我们的模型与现有的国家和生态区特定方程进行比较。与国家模型相比,我们的模型预测树木总生物量具有更高的准确性和更少的偏差(相对RMSE: 30%对35%;相对偏差:+1%对-12%全国)。在其他外部数据集中也得到了类似的结果。现有的经济区方程表现得比我们的模型或国家的模型都差。当我们在样地水平上应用模型预测地上生物量密度(Mg·ha−1)时,我们的模型再次优于国家模型(相对RMSE: 15.9%,而全国为18.6%,相对偏差:+3.5%,全国为-13.6%)。这些结果表明,至少对于加拿大西部的泥炭地,我们的模型提供了比现有模型更好的小型黑云杉的地上生物量估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.20
自引率
9.10%
发文量
109
审稿时长
3 months
期刊介绍: Published since 1971, the Canadian Journal of Forest Research is a monthly journal that features articles, reviews, notes and concept papers on a broad spectrum of forest sciences, including biometrics, conservation, disturbances, ecology, economics, entomology, genetics, hydrology, management, nutrient cycling, pathology, physiology, remote sensing, silviculture, social sciences, soils, stand dynamics, and wood science, all in relation to the understanding or management of ecosystem services. It also publishes special issues dedicated to a topic of current interest.
期刊最新文献
Potential replacement understory woody plants for Robinia pseudoacacia plantations: Species composition and vertical distribution pattern Which factors influence consumers’ selection of wood as a building material for houses? Can partial-cut harvesting be used to extend the availability of terrestrial forage lichens in late-seral pine-lichen woodlands? Evidence from the Lewes Marsh (southern Yukon) silvicultural systems trial. Challenges and Opportunities Associated with Lifting the Zero COVID-19 Policy in China. Modelling diameter at breast height distribution of jack pine and black spruce natural stands in eastern Canada
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1