Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest
Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
William A. Weygint [verfasserIn] Jan U. H. Eitel [verfasserIn] Andrew J. Maguire [verfasserIn] Lee A. Vierling [verfasserIn] Daniel M. Johnson [verfasserIn] Colin S. Campbell [verfasserIn] Kevin L. Griffin [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: Ecosphere - Wiley, 2016, 14(2023), 3, Seite n/a-n/a |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2023 ; number:3 ; pages:n/a-n/a |
| Links: |
|---|
| DOI / URN: |
10.1002/ecs2.4465 |
|---|
| Katalog-ID: |
DOAJ08890704X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ08890704X | ||
| 003 | DE-627 | ||
| 005 | 20230410123249.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230410s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1002/ecs2.4465 |2 doi | |
| 035 | |a (DE-627)DOAJ08890704X | ||
| 035 | |a (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 050 | 0 | |a QH540-549.5 | |
| 100 | 0 | |a William A. Weygint |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. | ||
| 650 | 4 | |a point dendrometer | |
| 650 | 4 | |a stem radial growth | |
| 650 | 4 | |a thermal remote sensing | |
| 650 | 4 | |a tree growth | |
| 650 | 4 | |a tree water deficit | |
| 650 | 4 | |a tree water status | |
| 653 | 0 | |a Ecology | |
| 700 | 0 | |a Jan U. H. Eitel |e verfasserin |4 aut | |
| 700 | 0 | |a Andrew J. Maguire |e verfasserin |4 aut | |
| 700 | 0 | |a Lee A. Vierling |e verfasserin |4 aut | |
| 700 | 0 | |a Daniel M. Johnson |e verfasserin |4 aut | |
| 700 | 0 | |a Colin S. Campbell |e verfasserin |4 aut | |
| 700 | 0 | |a Kevin L. Griffin |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t Ecosphere |d Wiley, 2016 |g 14(2023), 3, Seite n/a-n/a |w (DE-627)635133679 |w (DE-600)2572257-8 |x 21508925 |7 nnns |
| 773 | 1 | 8 | |g volume:14 |g year:2023 |g number:3 |g pages:n/a-n/a |
| 856 | 4 | 0 | |u https://doi.org/10.1002/ecs2.4465 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 |z kostenfrei |
| 856 | 4 | 0 | |u https://doi.org/10.1002/ecs2.4465 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/2150-8925 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_636 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2037 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2144 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4367 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 14 |j 2023 |e 3 |h n/a-n/a | ||
| author_variant |
w a w waw j u h e juhe a j m ajm l a v lav d m j dmj c s c csc k l g klg |
|---|---|
| matchkey_str |
article:21508925:2023----::efeprtrsnevrnetlodtosrdcdiytmailaitos |
| hierarchy_sort_str |
2023 |
| callnumber-subject-code |
QH |
| publishDate |
2023 |
| allfields |
10.1002/ecs2.4465 doi (DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 DE-627 ger DE-627 rakwb eng QH540-549.5 William A. Weygint verfasserin aut Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology Jan U. H. Eitel verfasserin aut Andrew J. Maguire verfasserin aut Lee A. Vierling verfasserin aut Daniel M. Johnson verfasserin aut Colin S. Campbell verfasserin aut Kevin L. Griffin verfasserin aut In Ecosphere Wiley, 2016 14(2023), 3, Seite n/a-n/a (DE-627)635133679 (DE-600)2572257-8 21508925 nnns volume:14 year:2023 number:3 pages:n/a-n/a https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 kostenfrei https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/toc/2150-8925 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3 n/a-n/a |
| spelling |
10.1002/ecs2.4465 doi (DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 DE-627 ger DE-627 rakwb eng QH540-549.5 William A. Weygint verfasserin aut Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology Jan U. H. Eitel verfasserin aut Andrew J. Maguire verfasserin aut Lee A. Vierling verfasserin aut Daniel M. Johnson verfasserin aut Colin S. Campbell verfasserin aut Kevin L. Griffin verfasserin aut In Ecosphere Wiley, 2016 14(2023), 3, Seite n/a-n/a (DE-627)635133679 (DE-600)2572257-8 21508925 nnns volume:14 year:2023 number:3 pages:n/a-n/a https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 kostenfrei https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/toc/2150-8925 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3 n/a-n/a |
| allfields_unstemmed |
10.1002/ecs2.4465 doi (DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 DE-627 ger DE-627 rakwb eng QH540-549.5 William A. Weygint verfasserin aut Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology Jan U. H. Eitel verfasserin aut Andrew J. Maguire verfasserin aut Lee A. Vierling verfasserin aut Daniel M. Johnson verfasserin aut Colin S. Campbell verfasserin aut Kevin L. Griffin verfasserin aut In Ecosphere Wiley, 2016 14(2023), 3, Seite n/a-n/a (DE-627)635133679 (DE-600)2572257-8 21508925 nnns volume:14 year:2023 number:3 pages:n/a-n/a https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 kostenfrei https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/toc/2150-8925 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3 n/a-n/a |
| allfieldsGer |
10.1002/ecs2.4465 doi (DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 DE-627 ger DE-627 rakwb eng QH540-549.5 William A. Weygint verfasserin aut Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology Jan U. H. Eitel verfasserin aut Andrew J. Maguire verfasserin aut Lee A. Vierling verfasserin aut Daniel M. Johnson verfasserin aut Colin S. Campbell verfasserin aut Kevin L. Griffin verfasserin aut In Ecosphere Wiley, 2016 14(2023), 3, Seite n/a-n/a (DE-627)635133679 (DE-600)2572257-8 21508925 nnns volume:14 year:2023 number:3 pages:n/a-n/a https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 kostenfrei https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/toc/2150-8925 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3 n/a-n/a |
| allfieldsSound |
10.1002/ecs2.4465 doi (DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 DE-627 ger DE-627 rakwb eng QH540-549.5 William A. Weygint verfasserin aut Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology Jan U. H. Eitel verfasserin aut Andrew J. Maguire verfasserin aut Lee A. Vierling verfasserin aut Daniel M. Johnson verfasserin aut Colin S. Campbell verfasserin aut Kevin L. Griffin verfasserin aut In Ecosphere Wiley, 2016 14(2023), 3, Seite n/a-n/a (DE-627)635133679 (DE-600)2572257-8 21508925 nnns volume:14 year:2023 number:3 pages:n/a-n/a https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 kostenfrei https://doi.org/10.1002/ecs2.4465 kostenfrei https://doaj.org/toc/2150-8925 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 3 n/a-n/a |
| language |
English |
| source |
In Ecosphere 14(2023), 3, Seite n/a-n/a volume:14 year:2023 number:3 pages:n/a-n/a |
| sourceStr |
In Ecosphere 14(2023), 3, Seite n/a-n/a volume:14 year:2023 number:3 pages:n/a-n/a |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status Ecology |
| isfreeaccess_bool |
true |
| container_title |
Ecosphere |
| authorswithroles_txt_mv |
William A. Weygint @@aut@@ Jan U. H. Eitel @@aut@@ Andrew J. Maguire @@aut@@ Lee A. Vierling @@aut@@ Daniel M. Johnson @@aut@@ Colin S. Campbell @@aut@@ Kevin L. Griffin @@aut@@ |
| publishDateDaySort_date |
2023-01-01T00:00:00Z |
| hierarchy_top_id |
635133679 |
| id |
DOAJ08890704X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ08890704X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230410123249.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230410s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/ecs2.4465</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ08890704X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJac93aa551dda49c0ae614929e76c59a7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH540-549.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">William A. Weygint</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">point dendrometer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stem radial growth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">thermal remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree growth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree water deficit</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree water status</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ecology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan U. H. Eitel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Andrew J. Maguire</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lee A. Vierling</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel M. Johnson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Colin S. Campbell</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kevin L. Griffin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Ecosphere</subfield><subfield code="d">Wiley, 2016</subfield><subfield code="g">14(2023), 3, Seite n/a-n/a</subfield><subfield code="w">(DE-627)635133679</subfield><subfield code="w">(DE-600)2572257-8</subfield><subfield code="x">21508925</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:n/a-n/a</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/ecs2.4465</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/ecs2.4465</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2150-8925</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield><subfield code="h">n/a-n/a</subfield></datafield></record></collection>
|
| callnumber-first |
Q - Science |
| author |
William A. Weygint |
| spellingShingle |
William A. Weygint misc QH540-549.5 misc point dendrometer misc stem radial growth misc thermal remote sensing misc tree growth misc tree water deficit misc tree water status misc Ecology Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| authorStr |
William A. Weygint |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)635133679 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
QH540-549 |
| illustrated |
Not Illustrated |
| issn |
21508925 |
| topic_title |
QH540-549.5 Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest point dendrometer stem radial growth thermal remote sensing tree growth tree water deficit tree water status |
| topic |
misc QH540-549.5 misc point dendrometer misc stem radial growth misc thermal remote sensing misc tree growth misc tree water deficit misc tree water status misc Ecology |
| topic_unstemmed |
misc QH540-549.5 misc point dendrometer misc stem radial growth misc thermal remote sensing misc tree growth misc tree water deficit misc tree water status misc Ecology |
| topic_browse |
misc QH540-549.5 misc point dendrometer misc stem radial growth misc thermal remote sensing misc tree growth misc tree water deficit misc tree water status misc Ecology |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Ecosphere |
| hierarchy_parent_id |
635133679 |
| hierarchy_top_title |
Ecosphere |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)635133679 (DE-600)2572257-8 |
| title |
Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| ctrlnum |
(DE-627)DOAJ08890704X (DE-599)DOAJac93aa551dda49c0ae614929e76c59a7 |
| title_full |
Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| author_sort |
William A. Weygint |
| journal |
Ecosphere |
| journalStr |
Ecosphere |
| callnumber-first-code |
Q |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| author_browse |
William A. Weygint Jan U. H. Eitel Andrew J. Maguire Lee A. Vierling Daniel M. Johnson Colin S. Campbell Kevin L. Griffin |
| container_volume |
14 |
| class |
QH540-549.5 |
| format_se |
Elektronische Aufsätze |
| author-letter |
William A. Weygint |
| doi_str_mv |
10.1002/ecs2.4465 |
| author2-role |
verfasserin |
| title_sort |
leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| callnumber |
QH540-549.5 |
| title_auth |
Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| abstract |
Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. |
| abstractGer |
Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. |
| abstract_unstemmed |
Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4367 GBV_ILN_4700 |
| container_issue |
3 |
| title_short |
Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest |
| url |
https://doi.org/10.1002/ecs2.4465 https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7 https://doaj.org/toc/2150-8925 |
| remote_bool |
true |
| author2 |
Jan U. H. Eitel Andrew J. Maguire Lee A. Vierling Daniel M. Johnson Colin S. Campbell Kevin L. Griffin |
| author2Str |
Jan U. H. Eitel Andrew J. Maguire Lee A. Vierling Daniel M. Johnson Colin S. Campbell Kevin L. Griffin |
| ppnlink |
635133679 |
| callnumber-subject |
QH - Natural History and Biology |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1002/ecs2.4465 |
| callnumber-a |
QH540-549.5 |
| up_date |
2024-07-03T20:10:35.605Z |
| _version_ |
1803589974087434240 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ08890704X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230410123249.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230410s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/ecs2.4465</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ08890704X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJac93aa551dda49c0ae614929e76c59a7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH540-549.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">William A. Weygint</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">point dendrometer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stem radial growth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">thermal remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree growth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree water deficit</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tree water status</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ecology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan U. H. Eitel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Andrew J. Maguire</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lee A. Vierling</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel M. Johnson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Colin S. Campbell</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kevin L. Griffin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Ecosphere</subfield><subfield code="d">Wiley, 2016</subfield><subfield code="g">14(2023), 3, Seite n/a-n/a</subfield><subfield code="w">(DE-627)635133679</subfield><subfield code="w">(DE-600)2572257-8</subfield><subfield code="x">21508925</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:n/a-n/a</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/ecs2.4465</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/ac93aa551dda49c0ae614929e76c59a7</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/ecs2.4465</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2150-8925</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield><subfield code="h">n/a-n/a</subfield></datafield></record></collection>
|
| score |
7.400937 |