TY  - JOUR
AU  - Bosela, Michal
AU  - Rubio-Cuadrado, Alvaro
AU  - Marcis, Peter
AU  - Merganicova, Katarina
AU  - Fleischer, Peter, Jr.
AU  - Forrester, David I.
AU  - Uhl, Enno
AU  - Avdagić, Admir
AU  - Bellan, Michal
AU  - Bielak, Kamil
AU  - Bravo, Felipe
AU  - Coll, Lluis
AU  - Cseke, Klara
AU  - del Rio, Miren
AU  - Dinca, Lucian
AU  - Dobor, Laura
AU  - Drozdowski, Stanislaw
AU  - Giammarchi, Francesco
AU  - Gomoryova, Erika
AU  - Ibrahimspahić, Aida
AU  - Kasanin-Grubin, Milica
AU  - Klopcić, Matija
AU  - Kurylyak, Viktor
AU  - Montes, Fernando
AU  - Pach, Maciej
AU  - Ruiz-Peinado, Ricardo
AU  - Skrzyszewski, Jerzy
AU  - Stajić, Branko
AU  - Stojanović, Dejan
AU  - Svoboda, Miroslav
AU  - Tonon, Giustino
AU  - Versace, Soraya
AU  - Mitrović, Suzana
AU  - Zlatanov, Tzvetan
AU  - Pretzsch, Hans
AU  - Tognetti, Roberto
PY  - 2023
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1417
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 environmen-tal 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 addi-tion, 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 explan-atory 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 empir-ical 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 sub-stantial 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.
T2  - Science of the Total Environment
T1  - Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach
VL  - 888
DO  - 10.1016/j.scitotenv.2023.164123
UR  - conv_931
ER  - 

@article{
author = "Bosela, Michal and Rubio-Cuadrado, Alvaro and Marcis, Peter and Merganicova, Katarina and Fleischer, Peter, Jr. and Forrester, David I. and Uhl, Enno and Avdagić, Admir and Bellan, Michal and Bielak, Kamil and Bravo, Felipe and Coll, Lluis and Cseke, Klara and del Rio, Miren and Dinca, Lucian and Dobor, Laura and Drozdowski, Stanislaw and Giammarchi, Francesco and Gomoryova, Erika and Ibrahimspahić, Aida and Kasanin-Grubin, Milica and Klopcić, Matija and Kurylyak, Viktor and Montes, Fernando and Pach, Maciej and Ruiz-Peinado, Ricardo and Skrzyszewski, Jerzy and Stajić, Branko and Stojanović, Dejan and Svoboda, Miroslav and Tonon, Giustino and Versace, Soraya and Mitrović, 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 environmen-tal 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 addi-tion, 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 explan-atory 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 empir-ical 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 sub-stantial 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.",
journal = "Science of the Total Environment",
title = "Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach",
volume = "888",
doi = "10.1016/j.scitotenv.2023.164123",
url = "conv_931"
}

Bosela, M., Rubio-Cuadrado, A., Marcis, P., Merganicova, K., Fleischer, P. Jr., 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., Gomoryova, E., Ibrahimspahić, A., Kasanin-Grubin, M., Klopcić, M., Kurylyak, V., Montes, F., Pach, M., Ruiz-Peinado, R., Skrzyszewski, J., Stajić, B., Stojanović, D., Svoboda, M., Tonon, G., Versace, S., Mitrović, S., Zlatanov, T., Pretzsch, H.,& Tognetti, R.. (2023). Empirical 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.
https://doi.org/10.1016/j.scitotenv.2023.164123
conv_931

Bosela M, Rubio-Cuadrado A, Marcis P, Merganicova K, Fleischer PJ, 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, Gomoryova E, Ibrahimspahić A, Kasanin-Grubin M, Klopcić M, Kurylyak V, Montes F, Pach M, Ruiz-Peinado R, Skrzyszewski J, Stajić B, Stojanović D, Svoboda M, Tonon G, Versace S, Mitrović S, Zlatanov T, Pretzsch H, Tognetti R. Empirical 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.
doi:10.1016/j.scitotenv.2023.164123
conv_931 .

Bosela, Michal, Rubio-Cuadrado, Alvaro, Marcis, Peter, Merganicova, Katarina, Fleischer, Peter, Jr., Forrester, David I., Uhl, Enno, Avdagić, Admir, Bellan, Michal, Bielak, Kamil, Bravo, Felipe, Coll, Lluis, Cseke, Klara, del Rio, Miren, Dinca, Lucian, Dobor, Laura, Drozdowski, Stanislaw, Giammarchi, Francesco, Gomoryova, Erika, Ibrahimspahić, Aida, Kasanin-Grubin, Milica, Klopcić, Matija, Kurylyak, Viktor, Montes, Fernando, Pach, Maciej, Ruiz-Peinado, Ricardo, Skrzyszewski, Jerzy, Stajić, Branko, Stojanović, Dejan, Svoboda, Miroslav, Tonon, Giustino, Versace, Soraya, Mitrović, Suzana, Zlatanov, Tzvetan, Pretzsch, Hans, Tognetti, Roberto, "Empirical 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),
https://doi.org/10.1016/j.scitotenv.2023.164123 .,
conv_931 .