INTRAGRO: A machine learning approach to predict future growth of trees under climate change
Abstract The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ece3.10626 |
| _version_ | 1797648348880044032 |
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| author | Sugam Aryal Jussi Grießinger Nita Dyola Narayan Prasad Gaire Tribikram Bhattarai Achim Bräuning |
| author_facet | Sugam Aryal Jussi Grießinger Nita Dyola Narayan Prasad Gaire Tribikram Bhattarai Achim Bräuning |
| author_sort | Sugam Aryal |
| collection | DOAJ |
| description | Abstract The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra‐annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra‐annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid‐elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra‐annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios. |
| first_indexed | 2024-03-11T15:30:52Z |
| format | Article |
| id | doaj.art-2b902358da2045008e62baada2d84c92 |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-03-11T15:30:52Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-2b902358da2045008e62baada2d84c922023-10-27T04:40:51ZengWileyEcology and Evolution2045-77582023-10-011310n/an/a10.1002/ece3.10626INTRAGRO: A machine learning approach to predict future growth of trees under climate changeSugam Aryal0Jussi Grießinger1Nita Dyola2Narayan Prasad Gaire3Tribikram Bhattarai4Achim Bräuning5Institut für Geographie Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Bayern GermanyInstitut für Geographie Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Bayern GermanyInstitute of Tibetan Plateau Research Chinese Academy of Sciences, State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE) Beijing ChinaDepartment of Environmental Science, Patan Multiple Campus Tribhuvan University Lalitpur NepalCentral Department of Biotechnology Tribhuvan University Kathmandu NepalInstitut für Geographie Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Bayern GermanyAbstract The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra‐annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra‐annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid‐elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra‐annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.https://doi.org/10.1002/ece3.10626dendrometergrowing‐season changeintra‐annual growthmachine learning algorithmPinus roxburghiisub‐tropical Nepal |
| spellingShingle | Sugam Aryal Jussi Grießinger Nita Dyola Narayan Prasad Gaire Tribikram Bhattarai Achim Bräuning INTRAGRO: A machine learning approach to predict future growth of trees under climate change Ecology and Evolution dendrometer growing‐season change intra‐annual growth machine learning algorithm Pinus roxburghii sub‐tropical Nepal |
| title | INTRAGRO: A machine learning approach to predict future growth of trees under climate change |
| title_full | INTRAGRO: A machine learning approach to predict future growth of trees under climate change |
| title_fullStr | INTRAGRO: A machine learning approach to predict future growth of trees under climate change |
| title_full_unstemmed | INTRAGRO: A machine learning approach to predict future growth of trees under climate change |
| title_short | INTRAGRO: A machine learning approach to predict future growth of trees under climate change |
| title_sort | intragro a machine learning approach to predict future growth of trees under climate change |
| topic | dendrometer growing‐season change intra‐annual growth machine learning algorithm Pinus roxburghii sub‐tropical Nepal |
| url | https://doi.org/10.1002/ece3.10626 |
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