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Modelling future growth of mountain forests under changing environments
ID
Bošela, Michal
(
Author
),
ID
Merganičová, Katarína
(
Author
),
ID
Torresan, Chiara
(
Author
),
ID
Cherubini, Paolo
(
Author
),
ID
Fabrika, M.
(
Author
),
ID
Heinze, Berthold
(
Author
),
ID
Höhn, Maria
(
Author
),
ID
Kašanin-Grubin, Milica
(
Author
),
ID
Klopčič, Matija
(
Author
),
ID
Mészáros, Ilona
(
Author
),
ID
Pach, Maciej
(
Author
),
ID
Střelcová, Katarina
(
Author
),
ID
Temperli, Cristian
(
Author
),
ID
Tonon, Giustino
(
Author
),
ID
Pretzsch, Hans
(
Author
),
ID
Tognetti, Roberto
(
Author
)
URL - Source URL, Visit
https://link.springer.com/chapter/10.1007/978-3-030-80767-2_7
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Abstract
Models to predict the effects of different silvicultural treatments on future forest development are the best available tools to demonstrate and test possible climate-smart pathways of mountain forestry. This chapter reviews the state of the art in modelling approaches to predict the future growth of European mountain forests under changing environmental and management conditions. Growth models, both mechanistic and empirical, which are currently available to predict forest growth are reviewed. The chapter also discusses the potential of integrating the effects of genetic origin, species mixture and new silvicultural prescriptions on biomass production into the growth models. The potential of growth simulations to quantify indicators of climate-smart forestry (CSF) is evaluated as well. We conclude that available forest growth models largely differ from each other in many ways, and so they provide a large range of future growth estimates. However, the fast development of computing capacity allows and will allow a wide range of growth simulations and multi-model averaging to produce robust estimates. Still, great attention is required to evaluate the performance of the models. Remote sensing measurements will allow the use of growth models across ecological gradients.
Language:
English
Keywords:
SDG 15
,
climate-smart forestry
,
CLIMO
,
forestry management
,
modelling
Typology:
1.16 - Independent Scientific Component Part or a Chapter in a Monograph
Organization:
BF - Biotechnical Faculty
Publication status:
Published
Publication version:
Version of Record
Year:
2022
Number of pages:
Str. 223-262
PID:
20.500.12556/RUL-133426
UDC:
630*61+630*22:630*111
DOI:
10.1007/978-3-030-80767-2_7
COBISS.SI-ID:
86700547
Publication date in RUL:
26.11.2021
Views:
1437
Downloads:
172
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Record is a part of a monograph
Title:
Climate-smart forestry in mountain regions
Editors:
Roberto Tognetti, Melanie Smith, Pietro Panzacchi
Place of publishing:
Cham
Publisher:
Springer
ISBN:
978-3-030-80766-5
COBISS.SI-ID:
86678787
Collection title:
Managing forest ecosystems (Online)
Collection numbering:
ǂvol. ǂ40
Collection ISSN:
2352-3956
Secondary language
Language:
Slovenian
Keywords:
gorski gozdovi
,
sonaravno gozdarstvo
,
podnebno pametno gozdarstvo
,
modeliranje
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