Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods
Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequentl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Colmanetti, Michel Anderson Almeida [verfasserIn] Weiskittel, Aaron [verfasserIn] Scolforo, Henrique Ferraço [verfasserIn] Sotomayor, Jaime Felipe Medina [verfasserIn] do Couto, Hilton Thadeu Zarate [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Forest ecology and management - Amsterdam [u.a.] : Elsevier Science, 1976, 473 |
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| Übergeordnetes Werk: |
volume:473 |
| DOI / URN: |
10.1016/j.foreco.2020.118306 |
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ELV004404882 |
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| 245 | 1 | 0 | |a Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods |
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| 520 | |a Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. | ||
| 650 | 4 | |a Biomass equations | |
| 650 | 4 | |a Tropical forests | |
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| 700 | 1 | |a do Couto, Hilton Thadeu Zarate |e verfasserin |4 aut | |
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10.1016/j.foreco.2020.118306 doi (DE-627)ELV004404882 (ELSEVIER)S0378-1127(20)31075-6 DE-627 ger DE-627 rda eng 570 630 640 DE-600 23 12 ssgn 48.00 bkl Colmanetti, Michel Anderson Almeida verfasserin aut Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor Weiskittel, Aaron verfasserin aut Scolforo, Henrique Ferraço verfasserin aut Sotomayor, Jaime Felipe Medina verfasserin aut do Couto, Hilton Thadeu Zarate verfasserin aut Enthalten in Forest ecology and management Amsterdam [u.a.] : Elsevier Science, 1976 473 Online-Ressource (DE-627)320572463 (DE-600)2016648-5 (DE-576)090956303 0378-1127 nnns volume:473 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 473 |
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10.1016/j.foreco.2020.118306 doi (DE-627)ELV004404882 (ELSEVIER)S0378-1127(20)31075-6 DE-627 ger DE-627 rda eng 570 630 640 DE-600 23 12 ssgn 48.00 bkl Colmanetti, Michel Anderson Almeida verfasserin aut Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor Weiskittel, Aaron verfasserin aut Scolforo, Henrique Ferraço verfasserin aut Sotomayor, Jaime Felipe Medina verfasserin aut do Couto, Hilton Thadeu Zarate verfasserin aut Enthalten in Forest ecology and management Amsterdam [u.a.] : Elsevier Science, 1976 473 Online-Ressource (DE-627)320572463 (DE-600)2016648-5 (DE-576)090956303 0378-1127 nnns volume:473 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 473 |
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10.1016/j.foreco.2020.118306 doi (DE-627)ELV004404882 (ELSEVIER)S0378-1127(20)31075-6 DE-627 ger DE-627 rda eng 570 630 640 DE-600 23 12 ssgn 48.00 bkl Colmanetti, Michel Anderson Almeida verfasserin aut Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor Weiskittel, Aaron verfasserin aut Scolforo, Henrique Ferraço verfasserin aut Sotomayor, Jaime Felipe Medina verfasserin aut do Couto, Hilton Thadeu Zarate verfasserin aut Enthalten in Forest ecology and management Amsterdam [u.a.] : Elsevier Science, 1976 473 Online-Ressource (DE-627)320572463 (DE-600)2016648-5 (DE-576)090956303 0378-1127 nnns volume:473 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 473 |
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10.1016/j.foreco.2020.118306 doi (DE-627)ELV004404882 (ELSEVIER)S0378-1127(20)31075-6 DE-627 ger DE-627 rda eng 570 630 640 DE-600 23 12 ssgn 48.00 bkl Colmanetti, Michel Anderson Almeida verfasserin aut Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor Weiskittel, Aaron verfasserin aut Scolforo, Henrique Ferraço verfasserin aut Sotomayor, Jaime Felipe Medina verfasserin aut do Couto, Hilton Thadeu Zarate verfasserin aut Enthalten in Forest ecology and management Amsterdam [u.a.] : Elsevier Science, 1976 473 Online-Ressource (DE-627)320572463 (DE-600)2016648-5 (DE-576)090956303 0378-1127 nnns volume:473 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 473 |
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10.1016/j.foreco.2020.118306 doi (DE-627)ELV004404882 (ELSEVIER)S0378-1127(20)31075-6 DE-627 ger DE-627 rda eng 570 630 640 DE-600 23 12 ssgn 48.00 bkl Colmanetti, Michel Anderson Almeida verfasserin aut Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor Weiskittel, Aaron verfasserin aut Scolforo, Henrique Ferraço verfasserin aut Sotomayor, Jaime Felipe Medina verfasserin aut do Couto, Hilton Thadeu Zarate verfasserin aut Enthalten in Forest ecology and management Amsterdam [u.a.] : Elsevier Science, 1976 473 Online-Ressource (DE-627)320572463 (DE-600)2016648-5 (DE-576)090956303 0378-1127 nnns volume:473 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.00 Land- und Forstwirtschaft: Allgemeines AR 473 |
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Enthalten in Forest ecology and management 473 volume:473 |
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Biomass equations Tropical forests Atlantic Forest Destructive sampling Best linear unbiased predictor |
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Colmanetti, Michel Anderson Almeida @@aut@@ Weiskittel, Aaron @@aut@@ Scolforo, Henrique Ferraço @@aut@@ Sotomayor, Jaime Felipe Medina @@aut@@ do Couto, Hilton Thadeu Zarate @@aut@@ |
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Colmanetti, Michel Anderson Almeida |
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calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native atlantic forest of brazil: a direct comparison of alternative approaches, sample sizes, and sample selection methods |
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Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods |
| abstract |
Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. |
| abstractGer |
Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. |
| abstract_unstemmed |
Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics. |
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Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods |
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| score |
7.4017506 |