Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach
Само за регистроване кориснике
2023
Аутори
Bosela, MichalRubio-Cuadrado, Álvaro
Marcis, Peter
Merganičová, Katarina
Fleischer Jr, Peter
Forrester, David I.
Uhl, Enno
Avdagić, Admir
Bellan, Michal
Bielak, Kamil
Bravo, Felipe
Coll, Lluís
Cseke, Klára
del Rio, Miren
Dinca, Lucian
Dobor, Laura
Drozdowski, Stanisław
Giammarchi, Francesco
Gömöryová, Erika
Ibrahimspahić, Aida
Kašanin-Grubin, Milica
Klopčič, Matija
Kurylyak, Viktor
Montes, Fernando
Pach, Maciej
Ruiz-Peinado, Ricardo
Skrzyszewski, Jerzy
Stajic, Branko
Stojanovic, Dejan
Svoboda, Miroslav
Tonon, Giustino
Versace, Soraya
Mitrovic, Suzana
Zlatanov, Tzvetan
Pretzsch, Hans
Tognetti, Roberto
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the patter...n of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process based models suggest that beech in European mountains will, on average, likely experience better growth conditions
under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.
Кључне речи:
Dendrochronology / Ecosystem dynamics / European beech / Global climate change / Process-based growth model / Tree growthИзвор:
Science of the Total Environment, 2023, 888, 164123-Издавач:
- Elsevier
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200026 (Универзитет у Београду, Институт за хемију, технологију и металургију - ИХТМ) (RS-MESTD-inst-2020-200026)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200197 (Институт за низијско шумарство и животну средину, Нови Сад) (RS-MESTD-inst-2020-200197)
- COST Action CA15226 CLIMO “Climate-Smart Forestry in Mountain Regions”
- Slovak Research and Development Agency ( projects APVV-15-0265, APVV-18-0390, APVV-18-0086 and APVV-19-0183)
- Slovenian Research Agency (ARRS) (the research core funding P4-0059 “Forest, forestry and renewable forest resources")”
- Ministry of Civil Affairs of Bosnia and Herzegovina
- Castilla and León regional government (Spain) excellence projects (CLU-2019-01 y CL-EI-2021-05)
- European Regional Development Fund (ERDF) - VA183P20
- OP RDE via grant no. CZ.02.1.01/0.0/0.0/16_019/0000803 “Advanced research supporting the forestry and wood-processing sector's adaptation to global change and the 4th industrial revolution”.
- Integrated Infrastructure Operational Programme funded by the ERDF - Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” (grant no. ITMS2014+ 313011W580)
- National Roadmap for Research Infrastructure (2020–2027), Ministry of Education and Science of the Republic of Bulgaria (agreements nos. DO1-405/18.12.2020 and DO1-163/28.07.2022 (LTER-BG))
Институција/група
IHTMTY - JOUR AU - Bosela, Michal AU - Rubio-Cuadrado, Álvaro AU - Marcis, Peter AU - Merganičová, Katarina AU - Fleischer Jr, Peter AU - Forrester, David I. AU - Uhl, Enno AU - Avdagić, Admir AU - Bellan, Michal AU - Bielak, Kamil AU - Bravo, Felipe AU - Coll, Lluís AU - Cseke, Klára AU - del Rio, Miren AU - Dinca, Lucian AU - Dobor, Laura AU - Drozdowski, Stanisław AU - Giammarchi, Francesco AU - Gömöryová, Erika AU - Ibrahimspahić, Aida AU - Kašanin-Grubin, Milica AU - Klopčič, Matija AU - Kurylyak, Viktor AU - Montes, Fernando AU - Pach, Maciej AU - Ruiz-Peinado, Ricardo AU - Skrzyszewski, Jerzy AU - Stajic, Branko AU - Stojanovic, Dejan AU - Svoboda, Miroslav AU - Tonon, Giustino AU - Versace, Soraya AU - Mitrovic, Suzana AU - Zlatanov, Tzvetan AU - Pretzsch, Hans AU - Tognetti, Roberto PY - 2023 UR - https://cer.ihtm.bg.ac.rs/handle/123456789/6267 AB - Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics. PB - Elsevier T2 - Science of the Total Environment T1 - Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach VL - 888 SP - 164123 DO - 10.1016/j.scitotenv.2023.164123 ER -
@article{ author = "Bosela, Michal and Rubio-Cuadrado, Álvaro and Marcis, Peter and Merganičová, Katarina and Fleischer Jr, Peter and Forrester, David I. and Uhl, Enno and Avdagić, Admir and Bellan, Michal and Bielak, Kamil and Bravo, Felipe and Coll, Lluís and Cseke, Klára and del Rio, Miren and Dinca, Lucian and Dobor, Laura and Drozdowski, Stanisław and Giammarchi, Francesco and Gömöryová, Erika and Ibrahimspahić, Aida and Kašanin-Grubin, Milica and Klopčič, Matija and Kurylyak, Viktor and Montes, Fernando and Pach, Maciej and Ruiz-Peinado, Ricardo and Skrzyszewski, Jerzy and Stajic, Branko and Stojanovic, Dejan and Svoboda, Miroslav and Tonon, Giustino and Versace, Soraya and Mitrovic, Suzana and Zlatanov, Tzvetan and Pretzsch, Hans and Tognetti, Roberto", year = "2023", abstract = "Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.", publisher = "Elsevier", journal = "Science of the Total Environment", title = "Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach", volume = "888", pages = "164123", doi = "10.1016/j.scitotenv.2023.164123" }
Bosela, M., Rubio-Cuadrado, Á., Marcis, P., Merganičová, K., Fleischer Jr, P., Forrester, D. I., Uhl, E., Avdagić, A., Bellan, M., Bielak, K., Bravo, F., Coll, L., Cseke, K., del Rio, M., Dinca, L., Dobor, L., Drozdowski, S., Giammarchi, F., Gömöryová, E., Ibrahimspahić, A., Kašanin-Grubin, M., Klopčič, M., Kurylyak, V., Montes, F., Pach, M., Ruiz-Peinado, R., Skrzyszewski, J., Stajic, B., Stojanovic, D., Svoboda, M., Tonon, G., Versace, S., Mitrovic, S., Zlatanov, T., Pretzsch, H.,& Tognetti, R.. (2023). Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach. in Science of the Total Environment Elsevier., 888, 164123. https://doi.org/10.1016/j.scitotenv.2023.164123
Bosela M, Rubio-Cuadrado Á, Marcis P, Merganičová K, Fleischer Jr P, Forrester DI, Uhl E, Avdagić A, Bellan M, Bielak K, Bravo F, Coll L, Cseke K, del Rio M, Dinca L, Dobor L, Drozdowski S, Giammarchi F, Gömöryová E, Ibrahimspahić A, Kašanin-Grubin M, Klopčič M, Kurylyak V, Montes F, Pach M, Ruiz-Peinado R, Skrzyszewski J, Stajic B, Stojanovic D, Svoboda M, Tonon G, Versace S, Mitrovic S, Zlatanov T, Pretzsch H, Tognetti R. Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach. in Science of the Total Environment. 2023;888:164123. doi:10.1016/j.scitotenv.2023.164123 .
Bosela, Michal, Rubio-Cuadrado, Álvaro, Marcis, Peter, Merganičová, Katarina, Fleischer Jr, Peter, Forrester, David I., Uhl, Enno, Avdagić, Admir, Bellan, Michal, Bielak, Kamil, Bravo, Felipe, Coll, Lluís, Cseke, Klára, del Rio, Miren, Dinca, Lucian, Dobor, Laura, Drozdowski, Stanisław, Giammarchi, Francesco, Gömöryová, Erika, Ibrahimspahić, Aida, Kašanin-Grubin, Milica, Klopčič, Matija, Kurylyak, Viktor, Montes, Fernando, Pach, Maciej, Ruiz-Peinado, Ricardo, Skrzyszewski, Jerzy, Stajic, Branko, Stojanovic, Dejan, Svoboda, Miroslav, Tonon, Giustino, Versace, Soraya, Mitrovic, Suzana, Zlatanov, Tzvetan, Pretzsch, Hans, Tognetti, Roberto, "Еmpirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: a multimodel approach" in Science of the Total Environment, 888 (2023):164123, https://doi.org/10.1016/j.scitotenv.2023.164123 . .