Suppr超能文献

绘制树冠层氮素分布图以评估混交林中一种易危树栖食叶动物的觅食栖息地。

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species forests.

作者信息

Wagner Benjamin, Baker Patrick J, Moore Ben D, Nitschke Craig R

机构信息

School of Ecosystem and Forest Sciences The University of Melbourne Richmond, Victoria Australia.

Hawkesbury Institute for the Environment The Western Sydney University Penrith, NSW Australia.

出版信息

Ecol Evol. 2021 Dec 16;11(24):18401-18421. doi: 10.1002/ece3.8428. eCollection 2021 Dec.

Abstract

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species forests in southeastern Australia. We surveyed temperate forests across an elevational and topographic gradient from sea level to high elevation (50-1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.

摘要

食草动物的觅食决策与植物营养质量密切相关。对于具有特殊食性的树栖食叶动物,如易危的大滑翔机(),适宜觅食树木的丰度会影响栖息地适宜性和物种出现情况。因此,对叶片氮含量进行建模和制图的能力将有助于我们在更精细尺度上理解食叶动物的栖息地利用情况。我们测试了由轻型低成本商用无人机(UAV)收集的高分辨率多光谱影像,能否根据澳大利亚东南部混合物种森林梯度上的叶尺度化学测量值,在树冠层和林分尺度上预测叶片总氮和可消化氮(N和digN)。我们在从海平面到高海拔(海拔50 - 1200米)的海拔和地形梯度上调查了温带森林的森林结构、叶片化学性质以及大滑翔机的出现情况。利用多光谱叶片反射率和光谱指数的测量值,我们估算了N和digN,并使用机器学习算法绘制了N和适宜觅食栖息地的地图。我们的调查涵盖了17个物种,其叶片氮含量(干物质,DM)范围为0.63%至1.92%,可消化氮含量(DM)范围为0.45%至1.73%。在模型交叉验证中,多光谱叶片反射率和光谱指数都是N和digN的强预测指标。在树木水平上,观测氮含量与预测氮含量之间79%的变异性得到了解释。一个空间监督分类模型正确识别了80%与高氮浓度(≥1% DM)相关的树冠像素。我们开发了一种成功的方法,利用无人机多光谱影像在树冠层和林分尺度上估算一系列温带物种的叶片氮含量。利用无人机影像在空间上量化觅食栖息地的能力,使得能够在与支持澳大利亚东南部易危大滑翔机的地面调查、管理和保护相关的尺度上,对大滑翔机栖息地进行远程评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d087/8717341/05e2adfcb640/ECE3-11-18401-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验