Cargando…
Remotely sensed between‐individual functional trait variation in a temperate forest
1. Trait‐based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field‐based approaches assessing traits at the level of plant individuals with limited spatial co...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366889/ https://www.ncbi.nlm.nih.gov/pubmed/34429885 http://dx.doi.org/10.1002/ece3.7758 |
_version_ | 1783738971798372352 |
---|---|
author | Guillén‐Escribà, Carla Schneider, Fabian D. Schmid, Bernhard Tedder, Andrew Morsdorf, Felix Furrer, Reinhard Hueni, Andreas Niklaus, Pascal A. Schaepman, Michael E. |
author_facet | Guillén‐Escribà, Carla Schneider, Fabian D. Schmid, Bernhard Tedder, Andrew Morsdorf, Felix Furrer, Reinhard Hueni, Andreas Niklaus, Pascal A. Schaepman, Michael E. |
author_sort | Guillén‐Escribà, Carla |
collection | PubMed |
description | 1. Trait‐based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field‐based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual‐tree crowns within a temperate forest site and then assigning RS‐derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between‐ and within‐species variation across contiguous space. 2. We used airborne imaging spectroscopy and laser scanning to collect individual‐tree RS data from a mixed conifer‐angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage‐height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within‐species trait variation into smaller‐scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between‐species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis. 3. On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage‐height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within‐site environmental gradients potentially contributing to the coexistence of the eight abundant species. 4. We conclude that with high‐resolution RS data it is possible to delineate individual‐tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field‐based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual‐based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests. |
format | Online Article Text |
id | pubmed-8366889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83668892021-08-23 Remotely sensed between‐individual functional trait variation in a temperate forest Guillén‐Escribà, Carla Schneider, Fabian D. Schmid, Bernhard Tedder, Andrew Morsdorf, Felix Furrer, Reinhard Hueni, Andreas Niklaus, Pascal A. Schaepman, Michael E. Ecol Evol Original Research 1. Trait‐based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field‐based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual‐tree crowns within a temperate forest site and then assigning RS‐derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between‐ and within‐species variation across contiguous space. 2. We used airborne imaging spectroscopy and laser scanning to collect individual‐tree RS data from a mixed conifer‐angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage‐height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within‐species trait variation into smaller‐scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between‐species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis. 3. On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage‐height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within‐site environmental gradients potentially contributing to the coexistence of the eight abundant species. 4. We conclude that with high‐resolution RS data it is possible to delineate individual‐tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field‐based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual‐based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests. John Wiley and Sons Inc. 2021-07-22 /pmc/articles/PMC8366889/ /pubmed/34429885 http://dx.doi.org/10.1002/ece3.7758 Text en © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Guillén‐Escribà, Carla Schneider, Fabian D. Schmid, Bernhard Tedder, Andrew Morsdorf, Felix Furrer, Reinhard Hueni, Andreas Niklaus, Pascal A. Schaepman, Michael E. Remotely sensed between‐individual functional trait variation in a temperate forest |
title | Remotely sensed between‐individual functional trait variation in a temperate forest |
title_full | Remotely sensed between‐individual functional trait variation in a temperate forest |
title_fullStr | Remotely sensed between‐individual functional trait variation in a temperate forest |
title_full_unstemmed | Remotely sensed between‐individual functional trait variation in a temperate forest |
title_short | Remotely sensed between‐individual functional trait variation in a temperate forest |
title_sort | remotely sensed between‐individual functional trait variation in a temperate forest |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366889/ https://www.ncbi.nlm.nih.gov/pubmed/34429885 http://dx.doi.org/10.1002/ece3.7758 |
work_keys_str_mv | AT guillenescribacarla remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT schneiderfabiand remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT schmidbernhard remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT tedderandrew remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT morsdorffelix remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT furrerreinhard remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT hueniandreas remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT niklauspascala remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest AT schaepmanmichaele remotelysensedbetweenindividualfunctionaltraitvariationinatemperateforest |