Shifts in Forest Species Composition and Abundance under Climate Change Scenarios in Southern Carpathian Romanian Temperate Forests

Abstract

The structure and functioning of temperate forests are shifting due to changes in climate. Foreseeing the trajectory of such changes is critical to implementing adequate management practices and defining long-term strategies. This study investigated future shifts in temperate forest species composition and abundance expected to occur due to climate change. It also identified the ecological mechanisms underpinning such changes. Using an altitudinal gradient in the Romanian Carpathian temperate forests encompassing several vegetation types, we explored forest change using the Landis-II landscape model coupled with the PnET ecophysiological process model. We specifically assessed the change in biomass, forest production, species composition and natural disturbance impacts under three climate change scenarios, namely, RCP 2.6, 4.5 and 8.5. The results show that, over the short term (15 years), biomass across all forest types in the altitudinal gradient will increase, and species composition will remain unaltered. In contrast, over the medium and long terms (after 2040), changes in species composition will accelerate, with some species spreading (e.g., Abies alba Mill.) and others declining (e.g., Fagus sylvatica L.), particularly under the most extreme climate change scenario. Some forest types (e.g., Picea abies (L.) karst forests) in the Southern Carpathians will notably increase their standing biomass due to climate change, compared to other types, such as Quercus forests. Our findings suggest that climate change will alter the forest composition and species abundance, with some forests being particularly vulnerable to climate change, e.g., F. sylvatica forests. As far as productivity and forest composition changes are concerned, management practices should accommodate the new conditions in order to mitigate climate change impacts.

Full text

Article
Shifts in Forest Species Composition and Abundance under
Climate Change Scenarios in Southern Carpathian Romanian
Temperate Forests
Juan García-Duro 1, *, Albert Ciceu 1,2 , Serban Chivulescu 1, Ovidiu Badea 1,2, Mihai A. Tanase 1,3 and
Cristina Aponte 1,4


Citation: García-Duro, J.; Ciceu, A.;
Chivulescu, S.; Badea, O.; Tanase,
M.A.; Aponte, C. Shifts in Forest
Species Composition and Abundance
under Climate Change Scenarios in
Southern Carpathian Romanian
Temperate Forests. Forests 2021,12,
1434. https://doi.org/10.3390/
f12111434
Academic Editor: Daniel J. Johnson
Received: 22 September 2021
Accepted: 18 October 2021
Published: 21 October 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1National Institute for Research and Development in Forestry “Marin Drăcea”, 077191 Voluntari, Ilfov,
Romania; albert.ciceu@icas.ro (A.C.); serban.chivulescu@gmail.com (S.C.); ovidiu.badea63@gmail.com (O.B.);
mihai@tma.ro (M.A.T.); cristinaaponte@gmail.com (C.A.)
2
Faculty of Silviculture and Forest Engineering, “Transilvania” University of Bra
s
,
ov, 500123 Bra
s
,
ov, Romania
3
Department of Geology, Geography and Environment, University of Alcala, 28802 Alcala de Henares, Spain
4
Department of Environment and Agronomy, Centro Nacional Instituto de Investigación y Tecnología Agraria
y Alimentaria, INIA-CSIC, 28040 Madrid, Spain
*Correspondence: juan.garcia.duro@icas.ro
Abstract:
The structure and functioning of temperate forests are shifting due to changes in climate.
Foreseeing the trajectory of such changes is critical to implementing adequate management practices
and defining long-term strategies. This study investigated future shifts in temperate forest species
composition and abundance expected to occur due to climate change. It also identified the ecological
mechanisms underpinning such changes. Using an altitudinal gradient in the Romanian Carpathian
temperate forests encompassing several vegetation types, we explored forest change using the
Landis-II landscape model coupled with the PnET ecophysiological process model. We specifically
assessed the change in biomass, forest production, species composition and natural disturbance
impacts under three climate change scenarios, namely, RCP 2.6, 4.5 and 8.5. The results show that,
over the short term (15 years), biomass across all forest types in the altitudinal gradient will increase,
and species composition will remain unaltered. In contrast, over the medium and long terms (after
2040), changes in species composition will accelerate, with some species spreading (e.g., Abies alba
Mill.) and others declining (e.g., Fagus sylvatica L.), particularly under the most extreme climate
change scenario. Some forest types (e.g., Picea abies (L.) karst forests) in the Southern Carpathians
will notably increase their standing biomass due to climate change, compared to other types, such
as Quercus forests. Our findings suggest that climate change will alter the forest composition and
species abundance, with some forests being particularly vulnerable to climate change, e.g., F. sylvatica
forests. As far as productivity and forest composition changes are concerned, management practices
should accommodate the new conditions in order to mitigate climate change impacts.
Keywords:
LANDIS-II; PnET; climate change; Southern Carpathians; forest biomass; production;
species composition; species abundance; Romanian temperate forests
1. Introduction
Human-induced climate change is one of the major processes affecting the global
environment nowadays [
1
]. However, the impacts are so complex and diverse that the net
effect of climate change on forest systems is still uncertain. For instance, while the increase
in atmospheric CO
2
concentrations often leads to greater productivity [
2
,
3
], the aridity
caused by warming [
4
,
5
] and the intensification of disturbances regimes [
6
] usually have
a negative impact on forests’ structure and productivity. Recent studies have suggested
that despite the positive climate change-associated effects [
7
,
8
], the negative ones often
prevail [9,10].
Forests 2021,12, 1434. https://doi.org/10.3390/f12111434 https://www.mdpi.com/journal/forests

Forests 2021,12, 1434 2 of 18
Climate change effects, both positive and negative, will trigger quantitative and quali-
tative changes in forest composition, structure and functioning and will push for species
adaptive responses [
1
,
11
]. Plant responses to climate change will be species specific [
4
],
though species with similar vital attributes are expected to have close responses [
5
,
12
].
Thus, angiosperms and gymnosperms/conifers have different ecophysiological responses
to raised CO
2
concentrations, potentially affecting the regional response of each forest
type [12].
Temperate forests occupy 1097 M ha worldwide, retain 118.6 Pg of terrestrial car-
bon [
13
] and provide 40% of the world’s forest harvest [
14
], thus playing an important
role in the overall carbon balance. Shifts in temperate forests’ structure, functioning and
distribution, such as those driven by climate change and mitigation measures, will affect
their economic value [
15
] and will have notable consequences on their capacity to sequester
carbon and to provide other ecosystem services [
10
,
16
–
19
]. Forest carbon stocks and se-
questration primarily depend on forest productivity and disturbance regimes [
10
,
14
], both
of which are expected to change with climate change [
6
,
18
]. A study by Nabuurs et al. [
19
]
described the first signs of carbon sink saturation in European forests. Such weakening in
C sink/sequestration capacity may be related to aging forests, decreased summer humidity,
etc. A lower C sequestration capacity would also be related to the change in disturbance
regimes. The main disturbances in temperate forests include drought, insects, windthrows,
pathogens and fire [
6
]. However, disturbances are often interconnected and can generate
significant feedback effects [
6
]. This is, for instance, the case of bark beetle attacks, which
are secondary disturbances that tend to occur after drought, harvest or windthrows.
Other studies have already suggested that some temperate forests will eventually
decline due to summer droughts [
20
] and their habitat distribution will be reduced to
montane areas due to altitudinal shifts [
21
]. Some temperate forests will be more intensely
affected than others [
6
,
10
,
16
], with vulnerability being related to species composition [
16
],
health status, disturbances regimes [
22
] and the phytogeographical context [
23
]. In this
context, the evolution of European temperate forests under climate change is largely
uncertain, with studies suggesting that conifers will be severely damaged by climate
change [
16
], while beech (Fagus sylvatica L.) forests, already affected by drought in recent
decades [
20
], will be more sensitive to climate change. Given the gaps in the knowledge
and the relevance of mixed (broadleaf and conifer) forests [
24
–
26
], such discrepancies
require further research.
This study aimed to unveil the future effects of climate change and its interactions
with natural disturbances and land management on the structure and composition of
temperate forests by implementing a forest simulation modeling approach. The Southern
Carpathian forests were used as a case study as they harbor a large diversity of forest
types and are representative of European temperate forests. The forest simulation model
LANDIS-II [
27
] coupled with the PnET model [
28
,
29
], which have been highlighted for
their capacity to model complex forest dynamics under multiple interacting drivers (e.g.,
climate, management, pests, windthrows) [
30
–
33
], was used to increase our understanding
of the potential impact of natural disturbances on temperate forest composition, structure
and its productivity. In particular, this study addressed two main questions: (1) What
future changes in forest species composition and abundance are expected to occur due to
climate change? (2) What are the ecological mechanisms underpinning forest type and
species (variable effects—geographical and species dependent) changes?
We hypothesized that species in the lower part of the altitudinal gradient (e.g., oaks)
will grow in abundance and those in the high altitudes, particularly conifers, will be
constrained, and that the main mechanisms driving the change are warmer climate and
increments in droughts and disturbances, all of them modifying species interactions.

Forests 2021,12, 1434 3 of 18
2. Materials and Methods
2.1. Study Area
The study area is located in the Fagara¸s Mountains, in the Romanian Southern
Carpathians (Figure 1). It occupies around 6400 km
2
and comprises a large geograph-
ical gradient that extends 110 km north to south and 73 km east to west, covering the main
forested lands in the Arge¸s, Sibiu and Bra¸sov counties. The altitude ranges from 185 to
2544 m.a.s.l, with plain landscapes (c. 12% average slope, elevation < 500 m) on the south-
ern sector and steep and rough topography (c. 32% average slope, elevation > 1000 m) on
the high-altitude northern sector. The climate varies along the altitudinal gradient [
23
]:
the annual mean temperature in the highlands is lower than 4
◦
C, with a winter mean
temperature below
−
5
◦
C and a summer temperature around 14
◦
C. The precipitation
approaches 1000 mm, with summer rainfall of around 315 mm. The climate in the plains is
warmer and drier, with a 10.5
◦
C annual mean temperature, mean winter temperatures
below 0
◦
C and summer temperatures over 20
◦
C. The annual precipitation here is about
700 mm, with summer rainfall of around 260 mm. In general, September and October are
the driest months, while May and June are the wettest ones. Soil types in the area include
protosols, spodozols, cambisols, argiluvisols and hydromorphic soils with pseudogleic
properties [34,35].
Forests 2021, 12, x FOR PEER REVIEW 3 of 19
2. Materials and Methods
2.1. Study Area
The study area is located in the Fagaraş Mountains, in the Romanian Southern Car-
pathians (Figure 1). It occupies around 6400 km
2
and comprises a large geographical gra-
dient that extends 110 km north to south and 73 km east to west, covering the main for-
ested lands in the Argeş, Sibiu and Braşov counties. The altitude ranges from 185 to 2544
m.a.s.l, with plain landscapes (c. 12% average slope, elevation < 500 m) on the southern
sector and steep and rough topography (c. 32% average slope, elevation > 1000 m) on the
high-altitude northern sector. The climate varies along the altitudinal gradient [23]: the
annual mean temperature in the highlands is lower than 4 °C, with a winter mean tem-
perature below −5 °C and a summer temperature around 14 °C. The precipitation ap-
proaches 1000 mm, with summer rainfall of around 315 mm. The climate in the plains is
warmer and drier, with a 10.5 °C annual mean temperature, mean winter temperatures
below 0 °C and summer temperatures over 20 °C. The annual precipitation here is about
700 mm, with summer rainfall of around 260 mm. In general, September and October are
the driest months, while May and June are the wettest ones. Soil types in the area include
protosols, spodozols, cambisols, argiluvisols and hydromorphic soils with pseudogleic
properties [34,35].
Figure 1. (a) Location of the study area within Europe and (b) the Carpathians; (c) distribution of
the five forest types within the study area.
Temperate forests, whose composition changes across the altitudinal belts, cover 52%
of the total study area. There are five general forest types: (a) Picea abies (L.) karst forests,
(b) mixed Fagus sylvatica L.–conifer forests, (c) F. sylvatica forests, (d) mixed broadleaved
forests and (e) Quercus forests. Picea forests (Norway spruce) occupy the subalpine and
montane superior belts. Mixed Fagus–conifer forests, mainly Picea and Abies alba Mill.,
dominate the intermediate montane belt, with the beech abundance increasing as the alti-
tude decreases. Mixed Fagus–conifer forests are substituted by pure Fagus forests first and
mixed broadleaved forest (F. sylvatica and Carpinus betulus L.) later, both of them in the
inferior montane belt [24,36]. Oaks gradually appear in mixed broadleaved forests in the
colline belt and, in low-altitude areas, reach the point where Quercus forests dominate the
landscape. Within Quercus forests, there is an altitudinal transition from Quercus petraea
(Matt.) Liebl. to Quercus robur L., Quercus cerris L. and Quercus frainetto Ten. in the most
southern thermophilus areas [36].
Figure 1.
(
a
) Location of the study area within Europe and (
b
) the Carpathians; (
c
) distribution of the
five forest types within the study area.
Temperate forests, whose composition changes across the altitudinal belts, cover 52%
of the total study area. There are five general forest types: (a) Picea abies (L.) karst forests,
(b) mixed Fagus sylvatica L.–conifer forests, (c) F. sylvatica forests, (d) mixed broadleaved
forests and (e) Quercus forests. Picea forests (Norway spruce) occupy the subalpine and
montane superior belts. Mixed Fagus–conifer forests, mainly Picea and Abies alba Mill.,
dominate the intermediate montane belt, with the beech abundance increasing as the
altitude decreases. Mixed Fagus–conifer forests are substituted by pure Fagus forests first
and mixed broadleaved forest (F. sylvatica and Carpinus betulus L.) later, both of them in the
inferior montane belt [
24
,
36
]. Oaks gradually appear in mixed broadleaved forests in the
colline belt and, in low-altitude areas, reach the point where Quercus forests dominate the
landscape. Within Quercus forests, there is an altitudinal transition from Quercus petraea
(Matt.) Liebl. to Quercus robur L., Quercus cerris L. and Quercus frainetto Ten. in the most
southern thermophilus areas [36].

Forests 2021,12, 1434 4 of 18
Some of the forested lands are actively managed for timber production, with F. sylvatica
providing 34% of the overall standing wood volume, P. abies 28%, Quercus spp. 17%,
A. alba 5% and other species the remaining 16% [
37
–
46
]. Depending on forest species
and conditions, different management systems are implemented including tree selection,
shelterwood and clearcutting. Clearcutting is practiced only for small areas (lower than
3 ha) in Norway spruce (P. abies) and non-natural forest stands [
47
,
48
]. The rest of the
forested land is either subjected to conservation management to protect forest health (e.g.,
phytosanitary felling, trees affected by small local windthrows) or is strictly protected
with no management actions allowed. The most common natural disturbances in the area
include windthrows and insect attacks [
49
]. Windthrow events mostly affect conifers in
the north sector of the study area [
50
], particularly Picea forests, with insect outbreaks
(Hylobius abietis L., Ips duplicatus Sahlberg and I. typographus L.) being a common secondary
disturbance following windthrows [51] and drought [49,52] in conifer forests.
2.2. Landis-II Model
Landis-II is a collection of spatially explicit forest landscape models [
53
] that simulate
forest change as a function of succession and disturbances [
33
]. The landscape in Landis-II
is defined as a grid of cells (here 200
×
200 m), each of which belongs to an ecoregion (i.e.,
areas of homogeneous soil and climate) and can contain multiple species cohorts that can
be independently killed by disturbances, competition or age-related mortality, as the suc-
cession progresses. Landis-II integrates a number of ecological process models through its
modular design. Here, we used the PnET succession extension [
54
] to underpin tree species
establishment, growth, mortality and decomposition. This extension embeds elements of
the PnET ecophysiology model of [
55
] and accounts for competition for available light
and water. Biomass growth is the result of a number of processes (e.g., photosynthesis,
evapotranspiration) controlled by species ecophysiological parameters (e.g., foliar N con-
centration, photosynthetic rates) given a number of conditions that include precipitation,
temperature and atmospheric CO
2
concentration. Climate change and CO
2
enrichment are
interwoven as change in environmental parameters over time, making the PnET extension
convenient for climate change modeling [30,56,57].
Landis-II interdependent disturbance extensions were used to model the impacts of
harvest [
31
,
58
], wind [
59
] and insect outbreaks [
60
,
61
]. The harvest module [
62
] implements
prescriptions in different management areas according to a temporal schedule, management
systems and stand characteristics, including resource availability, age structure or stand
composition. The biological disturbance agent module [
63
] implements insect impacts
based on pest species preferences and resource availability. The wind module [64], which
models windthrow events and operates independently of climate, was used to trigger
insect outbreaks.
2.3. Landscape Design: Ecoregions and Forest Communities
The initial landscape for Landis-II simulations was built based on the local manage-
ment plans that were available for the public forests (approximately 60% of the total forest
land) [
37
–
46
]. Management plans contained the spatial delimitation of forest stands and
information of their species composition and cohort ages, with stands ranging from 0.1 to
50 ha, the legal maximum allowed, with a median of 3.25 ha. Stands were classified into
14 ecoregions of homogeneous climate, soil type and tree species abundances (Supplemen-
tary Table S1; Supplementary Figure S1). Ecoregions were ascribed to one of the five forest
types according to species composition (Figure 1). To constrain the number of communities,
i.e., cohorts and species combinations that conform to a forest stand, a total of 223 initial
communities, comprising from 1 to 4 species and a range of 1 to 17 age cohorts, were identi-
fied in the management plans and assigned, based on their frequency, to the corresponding
stand and ecoregion in the simulated landscape. The area of private forest lands, for which
information was not available, was delimited based on Corine Land Cover 2012 [
65
]. To
ascribe ecoregions and initial communities in private forests, a random forest algorithm [
66
]

Forests 2021,12, 1434 5 of 18
was trained using information from the management plans and environmental variables
(DEM, [
67
], climate [
68
], soil properties and classification [
34
] and distance to infrastructure
and populations [
69
,
70
]). The overall accuracy of the prediction reached 96.7% over the
independent testing dataset.
2.4. LANDIS-II Parameterization
The modeled species included Abies alba Mill. (silver fir), Alnus glutinosa (L.) Gaertn.
(European alder), Alnus incana (L.) Moench (grey alder), C. betulus (European hornbeam),
F. sylvatica (European beech), P. abies (Norway spruce), Q. cerris (Turkey oak), Q. frainetto
Ten. (Hungarian oak), Q. petraea (Matt.) Liebl. (sessile oak) and Q. robur L. (pedunculate
oak). Species contributing less than 2% to the overall wood volume in the study area
were not included in the simulation. Tree species parameters required by the model
were measured in the study area [
71
], compiled from unpublished data or obtained from
the local Romanian literature [
36
,
72
,
73
] and other international sources [
74
,
75
]. Model
parameterization was performed following the recommendations of [
29
]. The three main
background disturbances of the studied area were modeled to increase the accuracy of the
simulated landscape and were considered a key element in this study.
The wind disturbance extension required the windthrow return interval and severity,
which were modeled/introduced based on national historical records [
76
]. The aver-
age wind speed [
68
] and the area occupied by conifer forests were used to determine
the windthrow local impact on each ecoregion. The mortality of age cohorts was based
on Popa [
76
]. It was assumed that wind event occurrence will not be affected by cli-
mate change.
Bark beetle outbreaks were linked to windthrows and were followed by harvesting
interventions as per standard practices in the study area. Three main bark beetle species
were modeled: I. typographus and I. duplicatus, which target mature Picea stands [
49
,
77
],
and H. abietis, which targets stands with a high density of A. alba saplings [
78
]. Pest
species preferences (host species and age cohort) and impacts were defined based on
expert knowledge, forest health status monitoring [
79
–
81
] and national Romanian research
studies [49,51,52,82].
Harvesting prescriptions were defined following the national Romanian regulatory
framework [
47
,
48
,
83
,
84
] which establishes that logging cycles for the main species in the
study area are between 100 and 200 years for beech, Norway spruce, sessile oak and silver
fir, 70 to 160 years for other oaks and up to 80 years for broadleaved softwood species,
depending on the silvicultural system, forest function, site productivity and other specific
situations. The simulated landscape was divided into management areas, composed of
multiple stands, that included unmanaged, strictly protected forest (<1%), protected forest
with special conservation prescription practices (less than 10 m
3
/ha, approximately 40%),
selective logging (20%; with approximately half of the area dedicated to production of
mixed beech–conifer, mixed beech–broadleaved and oak forests), shelterwood forests (28%;
majority of Fagus, also the remaining mixed beech–conifer, mixed beech–broadleaved and
oak forests) and clearcut forests (12%; mostly Norway spruce pure stands). Within each
management area, management treatments (stand selection, harvesting periodicity, inten-
sity, targeting species and cycle duration) were implemented based on stand composition
and stand age.
2.5. Climate Change Scenarios
Three climate change scenarios were defined (Figure 2) based on the RCP 2.6, 4.5 and
8.5 emission scenarios [
85
]. For the model spin-up period (1901–2015), we used gridded cli-
mate data (precipitation, maximum and minimum temperature) from the Climate Research
Unit Time Series (CRU TS) [
86
] high-resolution dataset. For the simulation period between
2015 and 2100, we used RCP projections from Climatic Data Generator (ClimGen) [
87
]
based on the French National Centre for Meteorological Research CNRM-CN5 climate
model [
88
]. For the simulation period between 2100 and 2140, for which there were no cli-

Forests 2021,12, 1434 6 of 18
mate projections available, climate data were randomly resampled from the last 3 decades,
the last climate normal period, of the corresponding CNRM-CN5 series. Climate series
were extended to 2140 because of the species longevity and the long logging cycles used in
Romanian forestry.
Forests 2021, 12, x FOR PEER REVIEW 6 of 19
CN5 climate model [88]. For the simulation period between 2100 and 2140, for which there
were no climate projections available, climate data were randomly resampled from the
last 3 decades, the last climate normal period, of the corresponding CNRM-CN5 series.
Climate series were extended to 2140 because of the species longevity and the long logging
cycles used in Romanian forestry.
Gridded 0.5° climate data were statistically downscaled following Moreno and
Hasenauer [89] using a 30′′ resolution gridded climate dataset from WorldClim 1.4 and
2.0 [68]. Mean annual CO
2
concentrations taken from van Vuuren et al. [85] differed
among PCR scenarios but were assumed spatially constant. Similar to climate, CO
2
con-
centrations after 2100 were kept constant at the level of the last climate normal to prevent
inconsistencies with resampled climate series. Photosynthetically active radiation (PAR),
calculated from WorldClim 2.0 solar radiation [68], changed seasonally and spatially but
did not differ across climate change scenarios.
Figure 2. Comparison of climate change scenarios for the period 1970–2140 among the three RCP scenarios (RCP 2.6, RCP
4.5, RCP 8.5) in five main forest types representative of the altitudinal gradient (top to bottom). Precipitation (Prec), aver-
age temperature (Tavg) and standardized precipitation evapotranspiration index (SPEI index) (rows top to bottom repre-
sent RCP 2.6, RCP 4.5 and RCP 8.5, respectively).
2.6. Model Output and Validation
We simulated changes in forest biomass and species composition over a period of
125 years (2015–2140) under three climate change scenarios (RCP 2.6, RCP 4.5 and RCP
8.5), using 22 replicates of each scenario at a spatial resolution of the 200 m cell size. Model
outputs included annual live, dead and harvested biomass throughout the simulation pe-
riod. Forest net productivity was calculated as the change in biomass accounting for the
harvesting. Model outputs for the period 2010–2020, when the management plans were
released, were validated using Romanian forest yield tables [72,73] and forest manage-
ment plans of the study area [37–46]. The simulated initial biomass per hectare (Figure 3)
Figure 2.
Comparison of climate change scenarios for the period 1970–2140 among the three RCP scenarios (RCP 2.6, RCP
4.5, RCP 8.5) in five main forest types representative of the altitudinal gradient (top to bottom). Precipitation (Prec), average
temperature (Tavg) and standardized precipitation evapotranspiration index (SPEI index) (rows top to bottom represent
RCP 2.6, RCP 4.5 and RCP 8.5, respectively).
Gridded 0.5
◦
climate data were statistically downscaled following Moreno and Hase-
nauer [
89
] using a 30
00
resolution gridded climate dataset from WorldClim 1.4 and 2.0 [
68
].
Mean annual CO
2
concentrations taken from van Vuuren et al. [
85
] differed among PCR
scenarios but were assumed spatially constant. Similar to climate, CO
2
concentrations after
2100 were kept constant at the level of the last climate normal to prevent inconsistencies
with resampled climate series. Photosynthetically active radiation (PAR), calculated from
WorldClim 2.0 solar radiation [
68
], changed seasonally and spatially but did not differ
across climate change scenarios.
2.6. Model Output and Validation
We simulated changes in forest biomass and species composition over a period of
125 years (2015–2140) under three climate change scenarios (RCP 2.6, RCP 4.5 and RCP
8.5), using 22 replicates of each scenario at a spatial resolution of the 200 m cell size. Model
outputs included annual live, dead and harvested biomass throughout the simulation
period. Forest net productivity was calculated as the change in biomass accounting for the
harvesting. Model outputs for the period 2010–2020, when the management plans were
released, were validated using Romanian forest yield tables [
72
,
73
] and forest management
plans of the study area [
37
–
46
]. The simulated initial biomass per hectare (Figure 3) and

Forests 2021,12, 1434 7 of 18
the extracted timber volume were within the ranges recorded in the management plans
for all five forest types. As expected, compared to management, background natural
disturbances had minor effects on the current landscape. The simulated windthrow impact
was in accordance with volumes estimated from Popa [
76
] and the area affected by intense
windthrows during the period 1986–2016 [
79
–
81
]. Insect outbreak occurrence and impact
were also in accordance with the area affected by intense insect attacks in the study area
in the period 1986–2016 [
79
–
81
], and the harvested volumes were in agreement with the
management plans [37–46].
Forests 2021, 12, x FOR PEER REVIEW 7 of 19
and the extracted timber volume were within the ranges recorded in the management
plans for all five forest types. As expected, compared to management, background natural
disturbances had minor effects on the current landscape. The simulated windthrow im-
pact was in accordance with volumes estimated from Popa [76] and the area affected by
intense windthrows during the period 1986–2016 [79–81]. Insect outbreak occurrence and
impact were also in accordance with the area affected by intense insect attacks in the study
area in the period 1986–2016 [79–81], and the harvested volumes were in agreement with
the management plans [37–46].
Figure 3. Boxplot diagram comparing the modeled biomass in the five main forest types in the initial
Landis-II simulated landscape (year 2015) and the field-measured biomass data recorded from the
management plans.
2.7. Statistical Analysis
Biomass results were spatially aggregated for the five main forest types. Differences
across scenarios and forest types were assessed by linear mixed models (LME) [90], with
changes over time fit to polynomial functions (up to 5 terms) and simulation replicates as
a random factor. Natural disturbance outputs (i.e., mean damaged area, number of killed
cohorts and severity) and harvested biomass were analyzed for the entire study area.
Shifts in forest species composition at the landscape level were analyzed through the
change in species biomass, using constrained redundancy analysis [91] with year and cli-
mate change scenario as constraining variables.
3. Results
3.1. Disturbances
Windthrows affected a minor percentage of the forest area (1%), with all of the effect
located in the Norway spruce forest type. The affected area significantly increased over
time (p ≤ 0.05) such that by 2140, the area damaged was two-fold the area of 2015 (Figure
4a). A similar trend was observed in the severity and the number of cohorts killed by
wind, with values increasing over time, and the number of cohorts killed being signifi-
cantly lower for the RCP 8.5 scenario. These results are in accordance with the aging of
Norway spruce forests, as the susceptibility to windthrows increases with tree age.
Figure 3.
Boxplot diagram comparing the modeled biomass in the five main forest types in the initial
Landis-II simulated landscape (year 2015) and the field-measured biomass data recorded from the
management plans.
2.7. Statistical Analysis
Biomass results were spatially aggregated for the five main forest types. Differences
across scenarios and forest types were assessed by linear mixed models (LME) [
90
], with
changes over time fit to polynomial functions (up to 5 terms) and simulation replicates as a
random factor. Natural disturbance outputs (i.e., mean damaged area, number of killed
cohorts and severity) and harvested biomass were analyzed for the entire study area. Shifts
in forest species composition at the landscape level were analyzed through the change in
species biomass, using constrained redundancy analysis [
91
] with year and climate change
scenario as constraining variables.
3. Results
3.1. Disturbances
Windthrows affected a minor percentage of the forest area (1%), with all of the effect
located in the Norway spruce forest type. The affected area significantly increased over time
(p
≤
0.05) such that by 2140, the area damaged was two-fold the area of 2015
(Figure 4a)
. A
similar trend was observed in the severity and the number of cohorts killed by wind, with
values increasing over time, and the number of cohorts killed being significantly lower for
the RCP 8.5 scenario. These results are in accordance with the aging of Norway spruce
forests, as the susceptibility to windthrows increases with tree age.

Forests 2021,12, 1434 8 of 18
Forests 2021, 12, x FOR PEER REVIEW 8 of 19
Figure 4. Variation in the average area damaged by (a) wind (b) and insects and (c) the harvested biomass across climate
change scenarios. RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
Insect outbreaks annually affected around 200 ha of the forest area (~1%), mostly in
the spruce and mixed Fagus (beech)–conifer forests. The area and the number of cohorts
killed by Ips insects, which target mature P. abies trees, increased over time (p < 0.05), also
reflecting a change in the forest age structure. This trend was not observed for H. abietis
(Figure 4b).
Harvesting occurred mostly in the Fagus and Quercus forest types, where annual
means of 300,000 and 100,000 tons were extracted, respectively, with extraction rates of
about 10 and 15% every 10 years (Figure 4c). Harvesting in the spruce and mixed beech–
conifer forest types was mostly restricted to conservation works, with rates of extraction
below 2%. Differences between climate change scenarios were noted from 2070, with bio-
mass harvested in RCP 8.5 being greater, followed by RCP 4.5, and RCP 2.6, the lowest
among all the scenarios. Given that harvesting prescriptions were constant over time, fol-
lowing the national Romanian regulatory framework, this indicates that differences in
forest attributes developed over time among climate change scenarios.
3.2. Changes in Biomass across Climate Change Scenarios
Living aboveground biomass (Figure 5) tended to increase in all forest types and cli-
mate change scenarios throughout the simulation period, with fluctuations related to dis-
turbance or harvesting events. Comparatively larger increases in biomass were observed
for RPC 8.5, ranging from 21% in the Quercus forest type to 51% in the mixed beech–broad-
leaved forest type, than for RCP 4.5 (from 2% to 37%). The lowest change in biomass was
observed in RCP 2.6, ranging from a decrease in total biomass (−15%) in the Quercus forest
type to a 29% increase in the mixed beech–conifer forest type. The effect of climate varied
across forest types: mixed beech–broadleaved forests showed the largest differences in the
final biomass among scenarios, with RCP 8.5 showing a larger biomass than RCPs 4.5 and
2.6.
The analysis of the biomass at the species level revealed changes in species biomass
through the simulation timespan and among climate change scenarios (Figure 6). The five
Figure 4.
Variation in the average area damaged by (
a
) wind (
b
) and insects and (
c
) the harvested biomass across climate
change scenarios. RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
Insect outbreaks annually affected around 200 ha of the forest area (~1%), mostly in
the spruce and mixed Fagus (beech)–conifer forests. The area and the number of cohorts
killed by Ips insects, which target mature P. abies trees, increased over time (p< 0.05), also
reflecting a change in the forest age structure. This trend was not observed for H. abietis
(Figure 4b).
Harvesting occurred mostly in the Fagus and Quercus forest types, where annual means
of 300,000 and 100,000 tons were extracted, respectively, with extraction rates of about
10 and 15% every 10 years (Figure 4c). Harvesting in the spruce and mixed beech–conifer
forest types was mostly restricted to conservation works, with rates of extraction below
2%. Differences between climate change scenarios were noted from 2070, with biomass
harvested in RCP 8.5 being greater, followed by RCP 4.5, and RCP 2.6, the lowest among all
the scenarios. Given that harvesting prescriptions were constant over time, following the
national Romanian regulatory framework, this indicates that differences in forest attributes
developed over time among climate change scenarios.
3.2. Changes in Biomass across Climate Change Scenarios
Living aboveground biomass (Figure 5) tended to increase in all forest types and
climate change scenarios throughout the simulation period, with fluctuations related
to disturbance or harvesting events. Comparatively larger increases in biomass were
observed for RPC 8.5, ranging from 21% in the Quercus forest type to 51% in the mixed
beech–broadleaved forest type, than for RCP 4.5 (from 2% to 37%). The lowest change in
biomass was observed in RCP 2.6, ranging from a decrease in total biomass (
−
15%) in the
Quercus forest type to a 29% increase in the mixed beech–conifer forest type. The effect of
climate varied across forest types: mixed beech–broadleaved forests showed the largest
differences in the final biomass among scenarios, with RCP 8.5 showing a larger biomass
than RCPs 4.5 and 2.6.

Forests 2021,12, 1434 9 of 18
Forests 2021, 12, x FOR PEER REVIEW 9 of 19
main species, P. abies, A. alba, F. sylvatica, Q. petraea and Q. frainetto, showed significant
biomass increments during the succession (F. sylvatica up to 2050) (p < 0.05). Of those five,
all but F. sylvatica showed significant differences in biomass among the scenarios, with the
largest biomass under RCP 8.5 (p < 0.05). Three species, C. betulus, Q. petraea and particu-
larly F. sylvatica, showed short periods with strong reductions in biomass after 2050, which
co-occurred in time with low SPEI values, although they tended to recover later.
Figure 5. Mean (dashed line) and 95% confidence interval (shaded area) of the living biomass (-10
3
kg·ha
-1
) measured in the forest belts over time under the three climate change scenarios: RCP 2.6
(green), RCP 4.5 (orange) and RCP 4.5 (red).
Figure 5.
Mean (dashed line) and 95% confidence interval (shaded area) of the living biomass (
−
10
3
kg
·
ha
−1
) measured in
the forest belts over time under the three climate change scenarios: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
The analysis of the biomass at the species level revealed changes in species biomass
through the simulation timespan and among climate change scenarios (Figure 6). The five
main species, P. abies,A. alba,F. sylvatica, Q. petraea and Q. frainetto, showed significant
biomass increments during the succession (F. sylvatica up to 2050) (p< 0.05). Of those
five, all but F. sylvatica showed significant differences in biomass among the scenarios,
with the largest biomass under RCP 8.5 (p< 0.05). Three species, C. betulus,Q. petraea and
particularly F. sylvatica, showed short periods with strong reductions in biomass after 2050,
which co-occurred in time with low SPEI values, although they tended to recover later.
Forests 2021, 12, x FOR PEER REVIEW 9 of 19
main species, P. abies, A. alba, F. sylvatica, Q. petraea and Q. frainetto, showed significant
biomass increments during the succession (F. sylvatica up to 2050) (p < 0.05). Of those five,
all but F. sylvatica showed significant differences in biomass among the scenarios, with the
largest biomass under RCP 8.5 (p < 0.05). Three species, C. betulus, Q. petraea and particu-
larly F. sylvatica, showed short periods with strong reductions in biomass after 2050, which
co-occurred in time with low SPEI values, although they tended to recover later.
Figure 5. Mean (dashed line) and 95% confidence interval (shaded area) of the living biomass (-10
3
kg·ha
-1
) measured in the forest belts over time under the three climate change scenarios: RCP 2.6
(green), RCP 4.5 (orange) and RCP 4.5 (red).
Figure 6.
Mean (dashed line) and 95% confidence interval (shaded area) of the species living biomass (
·
10
3
kg
·
ha
−1
)
measured over time under the three climate change scenarios: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).

Forests 2021,12, 1434 10 of 18
The percentage of dead wood biomass relative to the total biomass showed a slight
increase in all the forest types over time (Figure 7). Whereas the living biomass of Norway
spruce and Quercus spp. differed among scenarios, the RCP scenario did not affect the
percentage of dead biomass in these forests, which also showed a trend with very small
interdecadal variations. Forest types with participation of F. sylvatica were subjected to
strong variations in dead biomass over time, usually within the range 2.5–5.0%, which is
also related to fluctuations in living biomass (Figure 5) and a low 6-month SPEI
(Figure 2)
.
The mortality events after drought temporally altered the proportion of live and dead
biomass and occurred with a higher intensity in the RCP 8.5 scenario.
Forests 2021, 12, x FOR PEER REVIEW 10 of 19
Figure 6. Mean (dashed line) and 95% confidence interval (shaded area) of the species living biomass (·10
3
kg·ha
−1
) meas-
ured over time under the three climate change scenarios: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
The percentage of dead wood biomass relative to the total biomass showed a slight
increase in all the forest types over time (Figure 7). Whereas the living biomass of Norway
spruce and Quercus spp. differed among scenarios, the RCP scenario did not affect the
percentage of dead biomass in these forests, which also showed a trend with very small
interdecadal variations. Forest types with participation of F. sylvatica were subjected to
strong variations in dead biomass over time, usually within the range 2.5–5.0%, which is
also related to fluctuations in living biomass (Figure 5) and a low 6-month SPEI (Figure
2). The mortality events after drought temporally altered the proportion of live and dead
biomass and occurred with a higher intensity in the RCP 8.5 scenario.
Figure 7. Percentage dead biomass relative to total biomass in the different forest types. Shaded areas indicate 95% confi-
dence interval for every decade and climate change scenario: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
3.3. Productivity
The main forest types showed different aboveground net productivities, with Nor-
way spruce and mixed forest productivity being typically lower than 15 kg/ha, that of
beech pure forests being around that value and that of Quercus being usually above it. The
aboveground net productivity tended to decrease slightly over time for all forest types.
Productivity also differed among climate change scenarios, with the magnitude of the dif-
ferences depending on forest type (Figure 8). Three forest types, Norway spruce, mixed
beech–broadleaved and Quercus, showed significantly (p < 0.05) higher productivity un-
der RCP 8.5 than under the other two scenarios. Beech forest productivity, despite having
high temporal variations, showed significant differences between RCP 8.5 and RCP 4.5 (p
< 0.02), usually being larger in RCP 8.5. The mixed beech–conifer forest productivity only
showed significant differences between the RCP 2.6 and RCP 4.5 scenarios (p = 0.035).
Figure 7.
Percentage dead biomass relative to total biomass in the different forest types. Shaded areas indicate 95%
confidence interval for every decade and climate change scenario: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
3.3. Productivity
The main forest types showed different aboveground net productivities, with Norway
spruce and mixed forest productivity being typically lower than 15 kg/ha, that of beech
pure forests being around that value and that of Quercus being usually above it. The
aboveground net productivity tended to decrease slightly over time for all forest types.
Productivity also differed among climate change scenarios, with the magnitude of the
differences depending on forest type (Figure 8). Three forest types, Norway spruce, mixed
beech–broadleaved and Quercus, showed significantly (p< 0.05) higher productivity under
RCP 8.5 than under the other two scenarios. Beech forest productivity, despite having high
temporal variations, showed significant differences between RCP 8.5 and RCP 4.5 (p< 0.02),
usually being larger in RCP 8.5. The mixed beech–conifer forest productivity only showed
significant differences between the RCP 2.6 and RCP 4.5 scenarios (p= 0.035).
3.4. Changes in Landscape Species Composition
Forest species composition varied over time and across climate change scenarios
(Figure 9). During the first half of the simulations, the landscape species composition was
similar among scenarios; in the following decades, the position of samples started to drift
with the displacement increasing over time. This resulted in a significant differentiation in
the species composition between RCP 8.5 and the other two scenarios. As shown by the
ordination, and in accordance with the biomass analysis, RCP 8.5 was characterized by a
greater biomass of Q. frainetto,P. abies and A. alba.

Forests 2021,12, 1434 11 of 18
Forests 2021, 12, x FOR PEER REVIEW 11 of 19
Figure 8. Productivity of the five main forest types (Picea, Fagus–conifers, Fagus, Fagus–broadleaved, Quercus) for the fore-
casts for every decade and climate change scenario: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
3.4. Changes in Landscape Species Composition
Forest species composition varied over time and across climate change scenarios (Fig-
ure 9). During the first half of the simulations, the landscape species composition was
similar among scenarios; in the following decades, the position of samples started to drift
with the displacement increasing over time. This resulted in a significant differentiation
in the species composition between RCP 8.5 and the other two scenarios. As shown by the
ordination, and in accordance with the biomass analysis, RCP 8.5 was characterized by a
greater biomass of Q. frainetto, P. abies and A. alba.
Figure 8.
Productivity of the five main forest types (Picea,Fagus–conifers, Fagus,Fagus–broadleaved, Quercus) for the
forecasts for every decade and climate change scenario: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
Forests 2021, 12, x FOR PEER REVIEW 11 of 19
Figure 8. Productivity of the five main forest types (Picea, Fagus–conifers, Fagus, Fagus–broadleaved, Quercus) for the fore-
casts for every decade and climate change scenario: RCP 2.6 (green), RCP 4.5 (orange) and RCP 4.5 (red).
3.4. Changes in Landscape Species Composition
Forest species composition varied over time and across climate change scenarios (Fig-
ure 9). During the first half of the simulations, the landscape species composition was
similar among scenarios; in the following decades, the position of samples started to drift
with the displacement increasing over time. This resulted in a significant differentiation
in the species composition between RCP 8.5 and the other two scenarios. As shown by the
ordination, and in accordance with the biomass analysis, RCP 8.5 was characterized by a
greater biomass of Q. frainetto, P. abies and A. alba.
Figure 9.
Ordination-based redundancy analysis of species biomass for the whole landscape, every
year in the period 2015–2140 and every Landis-II simulation obtained for climate change scenarios
RCP 2.6 (green), RCP 4.5 (orange) and RCP 8.5 (red), with the constraining variables year and
RCP scenario.
4. Discussion
The simulation model implemented for temperate forests in the Romanian Southern
Carpathians showed that climate change produces changes in forest biomass, productivity
and species abundance. Such changes were particularly noticeable under RCP 8.5, the most
extreme climate change scenario, and rendered an overall increase in the carbon carrying
capacity of the studied Carpathian forests.

Forests 2021,12, 1434 12 of 18
4.1. Forest Biomass and Productivity
The results of our Landis-II model adapted to Southern Carpathian forests showed
an overall increase in forest biomass over time, with significantly greater biomass accu-
mulation in the RCP 8.5 scenario. This is in line with Hubau et al. [
10
], who found that
contrary to some tropical forests, temperate forests maintain a certain capacity to keep
stocking carbon. The increased forest biomass accumulation and productivity predicted for
the study area with intense climate change point to the raised CO
2
concentration [
2
,
5
] and
warmer winters [
3
] as being the main drivers in the Carpathians, where precipitations do
not change significantly for most of the area [
23
]. However, our simulations also indicate
that the initial increase in biomass is limited, likely due to increasing drought and the
stabilization of climate and atmospheric CO
2
concentrations. This trend suggests that
the equilibrium already observed in some tropical forests [
10
] might occur in temperate
forests somewhere at the end of the current century. Indeed, other authors have reached
similar conclusions and timing, pointing directly to the proliferation of bark beetles as one
of the main natural disturbances limiting carbon sequestration associated with climate
change [
92
]. In the Southern Carpathians, climate change conditions overcompensate the
negative effects of the disturbances at the beginning of the period but may not be able to
do that after 2040, for most of the forests.
The main forest types studied responded differently to climate change, according
to the ecophysiology of the species. Norway spruce and Quercus spp. forests had low
productivity but higher stability, while mixed beech–conifer forests, beech forests and
mixed beech–broadleaved forests were more prone to biomass changes, particularly after
2050. Indeed, low- and medium-altitude forests in the Southern Carpathians are very likely
to be more affected by drought, while the precipitation in mountain tops, often covered
by Norway spruce forests, might increase slightly [
23
]. Bouriaud el al. [
16
] also predicted
forest biomass increments for the Romanian Eastern Carpathians but found a decreasing
trend for conifer forests. This discrepancy among Bouriaud et al.’s [
16
] results and ours is
likely due to three causes: (a) climate change effects on forests vary in type and intensity at
the global, regional and local levels [
15
,
23
,
93
], as do the local conditions and climate change
projections of the study areas in the Eastern and Southern Carpathians; (b) the modeling
approach, with PnET and Landis-II models also accounting for complex ecophysiological
responses to photosynthetically active radiation, atmospheric CO
2
concentration, etc.; and
(c) the climate models used [
16
,
94
–
97
]. Indeed, the CNRM-CN5 model [
87
,
88
] used here is
one of the least limiting climate models regarding water availability in the study area.
The predicted increase in the impact of the main natural disturbances in the Southern
Carpathians: windthrows and bark beetle attacks, particularly Ips sp., is in agreement
with other studies [
6
,
98
]. Even though some variations may be caused by harvest and
aging forests, the periods with low productivity are likely associated with drought, since
the SPEI value tends to decrease within the same periods. Drought events also occur
simultaneously with raised mortality and deadwood biomass increments. This is the case
of Norway spruce forests and beech forests after 2040. Natural disturbances induce organic
carbon release, and hence it is common that disturbance regimes and ecosystem resilience
determine forests’ carbon sequestration capacity [
10
,
99
–
102
]. A net increase in temperate
forest biomass caused by climate change has been forecasted for areas where disturbances
and extreme climate events have a limited impact [
54
,
103
]. These disturbances usually
damage conifer forests [6], and aging forests will likely contribute to it [52]. In our model,
H. abietis had a minor impact, as a consequence of the low biomass of the cohorts they target,
and because the selection harvesting management implemented in mixed beech and silver
fir (A. alba) stands prevents H. abietis outbreaks [
104
]. The change in wood harvest observed
here was also predicted by Bouriaud et al. [
16
], Ciceu et al. [
105
] and Chivulescu et al. [
33
]
and concentrated in the intermediate- and low-altitude forests, having consequences for
the forest structure. This increment in harvested timber is directly associated with both
forest aging and the application of logging cycles [
78
]. As a result of all previous processes,
around the year 2050, forests are expected to reach the carbon sequestration limit. Such

Forests 2021,12, 1434 13 of 18
an idea is supported by Bouriaud et al.’s [
16
] and Hubau et al.’s [
10
] studies. Nabuurs
et al. [19] also found evidence of carbon sink saturation in European forests.
4.2. Forest Composition
The Landis-II projections predicted shifts in forest species composition and abundance
under the RCP scenarios in Romanian Southern Carpathian temperate forests, with the
five main species showing significant biomass increments over time (F. sylvatica only up to
2040). Indeed, the reduction in the cohorts killed by windthrows under RCP 8.5 compared
to RCP 4.5 and RCP 2.6 is likely due to the success of A. alba and the intense modification
of the forest structure under RCP 8.5. However, only F. sylvatica showed differences among
the RCP scenarios, with the highest biomass accumulation under RCP 8.5. Some species,
particularly F. sylvatica, showed short periods with strong reductions in the biomass after
2050 that co-occurred, and thus are likely associated, with drought conditions. Climate
change may alter local environmental conditions, making them no longer suitable for
a given species and altering the interspecific competition [
18
,
96
], ultimately resulting
in a change in species abundance [
1
]. For instance, Musselman and Fox [
14
] indicated
that drought-tolerant species are expected to succeed at the global scale under the new
conditions, and this is likely going to occur for A. alba in the Southern Carpathians, as
predicted here.
Previous studies have indicated that the intensity of the change in climate is propor-
tional to the magnitude of the impact on forests [
16
,
33
]. The intensity of such a change in
the Southern Carpathians is expected to be relatively small, as the climate conditions do
not strongly depart from the current conditions under the analyzed RCPs. However, some
forest types are more vulnerable to changes than others [
18
,
96
]. Norway spruce forests at
the upper limit will gain from the temperature rise [
15
], and, as suggested by Landis-II
models, at the lower limit, they will also benefit from F. sylvatica’s vulnerability to drought.
At lower altitudinal belts, conifers coexist with beech. Beech and conifer mixed forests
are more successful than pure beech stands [
25
,
26
], but Norway spruce grows more and
remains relatively unaffected compared to F. sylvatica during drought [
106
–
108
]. A. alba
is also less susceptible to warmer and drier conditions than F. sylvatica [
25
]. Thus, at the
stand level, the association between F. sylvatica and A. alba is not only advantageous for
enduring drought [
25
,
26
,
106
,
107
] but also for sunlight use [
104
,
109
]. Our results for the
Southern Carpathians suggest that climate change will contribute to A. alba encroachment
in F. sylvatica pure forest stands. Indeed, there is strong evidence that beech forests have
already been affected by climate change-induced drought in recent decades [
20
]. Moreover,
Broadmeadow et al. [
4
] recently highlighted beech’s vulnerability in temperate forests
across Europe.
Conifer and beech forests aside, Quercus spp.-dominated forests will partially benefit
from the temperature rise and F. sylvatica decline. Quercus species are relatively drought
resistant. According to the Landis-II simulations, and as also supported by previous stud-
ies [
110
], the most thermophile and drought-resistant species in the Southern Carpathians
such as Q. frainetto [
36
] will benefit from climate change and increase their biomass during
succession, while Q. petraea will eventually suffer the impact of drought events.
5. Conclusions
Here, we simulated the changes in the composition and structure of the temperate
forests of the Southern Carpathians under three climate change scenarios. Our results
indicate that climate change may contribute, overall, to increasing temperate forest pro-
ductivity and biomass, mainly due to the combination of increasing temperatures and
thus extended growing periods in the uplands and only a mild reduction in rainfall across
the landscape. Fluctuations in this trend associated with strong biomass reductions and
temporary deadwood rise were related to drought periods. However, it is likely that
increased drought events may eventually counteract such positive trends.

Forests 2021,12, 1434 14 of 18
Climate change selectively affected areas and species, thus contributing to changes in
species abundance. P. abies benefitted from warmer conditions, whereas forests dominated
by F. sylvatica were vulnerable to climate change, with drought periods associated with
large mortality events. The A. alba abundance increased under the new climate conditions
at the expense of Fagus’s decline. Our results suggest that under the upcoming climate,
mixed F. sylvatica–conifer (namely Norway spruce and silver fir) stands will have greater
resistance and resilience than those of pure F. sylvatica stands, a finding that will help in
guiding future forest management practices.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/
10.3390/f12111434/s1, Figure S1: Distribution of the ecoregions within the study area. Ecoregions
represent areas of homogeneous climate, soil and forest types, Table S1: Characteristics of the defined
ecoregions within each forest type: main species composition, elevation range, soil attributes and
main natural disturbances affecting the ecoregion. Disturbances: It: I. typografus; Id: I. duplicatus; Ha:
H. abietis [34,35,111,112].
Author Contributions:
Conceptualization, C.A. and J.G.-D.; methodology, C.A. and J.G.-D.; soft-
ware, J.G.-D.; validation, J.G.-D. and C.A.; formal analysis, J.G.-D.; investigation, J.G.-D. and C.A.;
resources, J.G.-D., A.C. and S.C.; data curation, J.G.-D.; writing—original draft preparation, J.G.-D.
and C.A.; writing—review and editing, C.A., J.G.-D., A.C., S.C., O.B. and M.A.T.; visualization,
J.G.-D.; supervision, C.A.; project administration, M.A.T., C.A. and O.B.; funding acquisition, M.A.T.,
C.A. and O.B. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported financially by EO-ROFORMON project, ID P_37_651/SMIS
105058 and PN 19070101.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Acknowledgments:
The authors acknowledge the contribution of the panel of experts constituted
by I. Seceleanu, M. Paraschiv, N. Olenici and D. Chira.
Conflicts of Interest: The authors declare no conflict of interest.
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