Smita Das, Prakash Basnet, Dominik Seidel, Alexander Röll, Martin Ehbrecht, Dirk Hölscher
{"title":"喜马拉雅安纳普尔纳地区山林的树木结构和结构复杂性","authors":"Smita Das, Prakash Basnet, Dominik Seidel, Alexander Röll, Martin Ehbrecht, Dirk Hölscher","doi":"10.1002/ece3.71341","DOIUrl":null,"url":null,"abstract":"<p>Mountain ranges comprise heterogeneous environments and high plant diversity, but little is known about the architecture and structural complexity of trees in mountain forests. We studied the relationship between tree architecture, environmental conditions, and tree structural complexity in forests of the Annapurna region in the Himalaya. We further asked whether and how tree structural complexity translates into forest stand structural complexity. The study covers 546 trees on 14 undisturbed study plots across wide ranges of elevation (1300 to 3400 m asl.) and annual precipitation (1180 to 3600 mm yr.<sup>−1</sup>). They were assessed by ground-based mobile laser scanning. We found that tree structural complexity, expressed as box-dimension (<i>D</i><sub><i>b</i></sub>), was lowest for the needle-leaved <i>Pinus wallichiana</i> and highest for the broad-leaved <i>Daphniphyllum himalense</i>. A high share of the variation in <i>D</i><sub><i>b</i></sub> was explained by tree architecture. In multivariate models, tree height, crown radius, and crown length explained more than 60% of the observed variation in <i>D</i><sub><i>b</i></sub>. Stem density of the plot accounted for 19% of the variation in <i>D</i><sub><i>b</i></sub>, and there was no influence of tree diversity. Precipitation explained l3% of the observed variation in tree <i>D</i><sub><i>b</i></sub>, but elevation and slope did not have significant influences. As expected, tree height decreased with increasing elevation, but small trees often had relatively high <i>D</i><sub><i>b</i></sub> values. The standard deviation of tree-level <i>D</i><sub><i>b</i></sub> within a plot explained 47% of the variation in stand-level structural complexity among plots, surpassing the maximum tree-level <i>D</i><sub><i>b</i></sub>. This suggests that both the sole removal of small or large trees would reduce the stand-level complexity by 36%. We conclude that in the Himalayan forests, species identity and tree architecture play a significant role in determining tree structural complexity, while environmental factors have a smaller role. Furthermore, structural variation among the trees within a plot plays a crucial role for the structural complexity at the stand level.</p>","PeriodicalId":11467,"journal":{"name":"Ecology and Evolution","volume":"15 4","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.71341","citationCount":"0","resultStr":"{\"title\":\"Tree Architecture and Structural Complexity in Mountain Forests of the Annapurna Region, Himalaya\",\"authors\":\"Smita Das, Prakash Basnet, Dominik Seidel, Alexander Röll, Martin Ehbrecht, Dirk Hölscher\",\"doi\":\"10.1002/ece3.71341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Mountain ranges comprise heterogeneous environments and high plant diversity, but little is known about the architecture and structural complexity of trees in mountain forests. We studied the relationship between tree architecture, environmental conditions, and tree structural complexity in forests of the Annapurna region in the Himalaya. We further asked whether and how tree structural complexity translates into forest stand structural complexity. The study covers 546 trees on 14 undisturbed study plots across wide ranges of elevation (1300 to 3400 m asl.) and annual precipitation (1180 to 3600 mm yr.<sup>−1</sup>). They were assessed by ground-based mobile laser scanning. We found that tree structural complexity, expressed as box-dimension (<i>D</i><sub><i>b</i></sub>), was lowest for the needle-leaved <i>Pinus wallichiana</i> and highest for the broad-leaved <i>Daphniphyllum himalense</i>. A high share of the variation in <i>D</i><sub><i>b</i></sub> was explained by tree architecture. In multivariate models, tree height, crown radius, and crown length explained more than 60% of the observed variation in <i>D</i><sub><i>b</i></sub>. Stem density of the plot accounted for 19% of the variation in <i>D</i><sub><i>b</i></sub>, and there was no influence of tree diversity. Precipitation explained l3% of the observed variation in tree <i>D</i><sub><i>b</i></sub>, but elevation and slope did not have significant influences. As expected, tree height decreased with increasing elevation, but small trees often had relatively high <i>D</i><sub><i>b</i></sub> values. The standard deviation of tree-level <i>D</i><sub><i>b</i></sub> within a plot explained 47% of the variation in stand-level structural complexity among plots, surpassing the maximum tree-level <i>D</i><sub><i>b</i></sub>. This suggests that both the sole removal of small or large trees would reduce the stand-level complexity by 36%. We conclude that in the Himalayan forests, species identity and tree architecture play a significant role in determining tree structural complexity, while environmental factors have a smaller role. 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Tree Architecture and Structural Complexity in Mountain Forests of the Annapurna Region, Himalaya
Mountain ranges comprise heterogeneous environments and high plant diversity, but little is known about the architecture and structural complexity of trees in mountain forests. We studied the relationship between tree architecture, environmental conditions, and tree structural complexity in forests of the Annapurna region in the Himalaya. We further asked whether and how tree structural complexity translates into forest stand structural complexity. The study covers 546 trees on 14 undisturbed study plots across wide ranges of elevation (1300 to 3400 m asl.) and annual precipitation (1180 to 3600 mm yr.−1). They were assessed by ground-based mobile laser scanning. We found that tree structural complexity, expressed as box-dimension (Db), was lowest for the needle-leaved Pinus wallichiana and highest for the broad-leaved Daphniphyllum himalense. A high share of the variation in Db was explained by tree architecture. In multivariate models, tree height, crown radius, and crown length explained more than 60% of the observed variation in Db. Stem density of the plot accounted for 19% of the variation in Db, and there was no influence of tree diversity. Precipitation explained l3% of the observed variation in tree Db, but elevation and slope did not have significant influences. As expected, tree height decreased with increasing elevation, but small trees often had relatively high Db values. The standard deviation of tree-level Db within a plot explained 47% of the variation in stand-level structural complexity among plots, surpassing the maximum tree-level Db. This suggests that both the sole removal of small or large trees would reduce the stand-level complexity by 36%. We conclude that in the Himalayan forests, species identity and tree architecture play a significant role in determining tree structural complexity, while environmental factors have a smaller role. Furthermore, structural variation among the trees within a plot plays a crucial role for the structural complexity at the stand level.
期刊介绍：
Ecology and Evolution is the peer reviewed journal for rapid dissemination of research in all areas of ecology, evolution and conservation science. The journal gives priority to quality research reports, theoretical or empirical, that develop our understanding of organisms and their diversity, interactions between them, and the natural environment.
Ecology and Evolution gives prompt and equal consideration to papers reporting theoretical, experimental, applied and descriptive work in terrestrial and aquatic environments. The journal will consider submissions across taxa in areas including but not limited to micro and macro ecological and evolutionary processes, characteristics of and interactions between individuals, populations, communities and the environment, physiological responses to environmental change, population genetics and phylogenetics, relatedness and kin selection, life histories, systematics and taxonomy, conservation genetics, extinction, speciation, adaption, behaviour, biodiversity, species abundance, macroecology, population and ecosystem dynamics, and conservation policy.
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