ArticlePDF AvailableLiterature Review

Strategic roadmap to assess forest vulnerability under air pollution and climate change



Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly , most studies investigating effects of air pollutants on forests have generally neglected impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend 1) establishment of much denser array of monitoring sites, particularly in the South Hemisphere; 2) further integration of ground and satellite monitoring; 3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; 4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; 5) intensification of experimental studies, addressing combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~ 73,000 tree species on Earth; 6) more experimental focus on phenomics and genomics; 7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and 8) development of models integrating air pollution and climate change data from long-term monitoring programs.
Glob Change Biol. 2022;00:1–24.
Received: 6 October 2021 
Revised: 2 March 2022 
Accepted: 18 May 2022
DOI: 10.1111/gcb.16278
Strategic roadmap to assess forest vulnerability under air
pollution and climate change
Alessandra De Marco1| Pierre Sicard2| Zhaozhong Feng3| Evgenios Agathokleous3|
Rocio Alonso4| Valda Araminiene5| Algirdas Augustatis6| Ovidiu Badea7,8|
James C. Beasley9| Cristina Branquinho10| Viktor J. Bruckman11| Alessio Collalti12 |
Rakefet David- Schwartz13| Marisa Domingos14| Enzai Du15 | Hector Garcia Gomez4|
Shoji Hashimoto16| Yasutomo Hoshika17| Tamara Jakovljevic18| Steven McNulty19|
Elina Oksanen20| Yusef Omidi Khaniabadi21| Anne- Katrin Prescher22|
Costas J. Saitanis23| Hiroyuki Sase24| Andreas Schmitz25| Gabriele Voigt26|
Makoto Watanabe27| Michael D. Wood28| Mikhail V. Kozlov29 | Elena Paoletti16
1ENEA, CR Casaccia, SSPT- PVS, Rome, Italy
2ARGANS, Biot, France
3Key Laboratory of Agro- Meteorology of Jiangsu Province, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China
4Ecotoxicology of Air Pollution, CIEMAT, Madrid, Spain
5Lithuanian Research Centre for A griculture and Forestry, Kaunas, Lithuania
6Faculty of Forest Sciences and Ecology, Vytautas Magnus University, Kaunas, Lithuania
7“Marin Drăcea” National Institute for Research and Development in Forestry, Voluntari, Romania
8Faculty of Silviculture and Forest Engineering, “Transilvania” Universit y, Braşov, Romania
9Savannah River Ecology Laboratory and Warnell School of Forestry and Natural Resources, University of Georgia, Aiken, South Carolina, USA
10Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
11Commission for Interdisciplinary Ecological Studies, Austrian Academy of Sciences, Vienna, Austria
12Forest Modeling Lab., ISAFOM- CNR, Perugia, Italy
13Institute of Plant Sciences, AROVolcani Center, Rishon LeTsiyon, Israel
14Instituto de Botanica, Nucleo de Pesquisa em Ecologia, Sao Paulo, Brazil
15Faculty of Geographical Science, Beijing Normal University, Beijing, China
16Depar tment of Forest Soils, Forestry and Forest Products Research Institute, Tsukuba, Japan
17IRET- CNR, Sesto Fiorentino, Italy
18Croatian Forest Research Institute, Jastrebarsko, Croatia
19USDA Forest Service, Research Triangle Park, USA
20Depar tment of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, Finland
21Department of Environmental Health Engineering, Industrial Medial and Health, Petroleum Industry Health Organization (PIHO), Ahvaz, Iran
22Thuenen Institute of Forest Ecosystems, Eberswalde, Germany
23Lab of Ecology and Environmental Science, Agricultural University of Athens, Athens, Greece
24Ecological Impact Research Department, Asia Center for Air Pollution Research (ACAP), Niigata, Japan
25State Agency for Nature, Environment and Consumer Protection of North Rhine- Westphalia, Recklinghausen, Germany
26r.e.m. Consulting, Perchtoldsdorf, Austria
27Institute of Agriculture, Tokyo University of Agriculture and Technology (TUAT), Fuchu, Japan
28School of Science, Engineering and Environment, University of Salford, Salford, UK
29Department of Biology, University of Turku, Turku, Finland
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
    DE MARCO et al.
Forests cover ~30% of the world's land surface, store 45% of terres-
trial carbon (Bonan, 2008), and are home to 80% of global terrestrial
biodiversity (IUCN, 2021). Sustainable socioeconomic development
depends on the proper management of natural resources, includ-
ing forest ecosystems (Badea et al., 2013). Air pollution and climate
change have major impacts on and complex interactions with forest
health and productivity (Augustaitis & Bytnerowicz, 2008; Kozlov
et al., 2009). For example, tropospheric ozone (O3), which is both
a phytotoxic gas and a radiative forcer (Myhre et al., 2013), and ni-
trogen deposition (Du & de Vries, 2018), which causes forest de-
cline due to acidification (Augustaitis et al., 2010) and changes in
the frequency and intensity of climatic extremes (e.g., heat waves,
rainfall, wind storms), may impact the structure, composition, and
functioning of terrestrial ecosystems. These impacts can directly in-
fluence carbon cycling and its feedback to the climate system (Frank
et al., 2015; Matyssek et al., 2012; Paoletti et al., 2007; Serengil
et al., 2011; Sicard et al., 2020).
The future of global forests is a subject of public and politi-
cal concern due to extensive forest degradation worldwide (Hao
et al., 2018; Liu et al., 2018). Recently, environmental pollution
was identified as one of the five main drivers of biodiversity loss
(European Commission, 2020). Although environmental pollution
is an integral part of global change (Dale et al., 2000), most of the
research addressing the biotic effects of climate change do not con-
sider this issue. Furthermore, most studies on both the distribution
of pollutants and the biotic effects of pollution have neglected the
issue of climate change (Sicard, Augustaitis, et al., 2016). As a result,
studies exploring the combined effects of air pollution and climate
change remain uncommon.
A Web of Science search conducted in June 2021 identified only
74 peer- reviewed articles containing the keywords “climat* and pol-
lut*” and “tree* or forest*” in the title, 59 of which were relevant re-
search papers (Figure S1): In all, 11 studies used modeling to explore
the combined effects of air pollution and climate, 27 studies were
based on observations of forest health in either spatial or tempo-
ral gradients of air pollution and climate, and only one reported the
outcomes of a field experiment. The low number of experimental
studies with factorial design involving both airborne pollutants and
climate is alarming because it hampers our ability to identify cause-
and- effect relationships as well as to decipher the mechanisms un-
derlying the combined or interactive effects of pollution and climate
on the health of individual trees and forest ecosystems. As a result,
the quality of our predictions of the combined effects of climate
change and air pollution on future forest health is uncertain. To re-
spond to this global challenge, here we critically review the current
knowledge (and gaps) on air pollution and climate interactions in for-
ests, identify key research priorities, and suggest a strategic road-
map for future studies.
Alessandra De Marco, ENEA Casaccia, Via
Anguillarese 301, 00123 Rome, Italy.
Funding information
Academy of Finland, Grant/Award
Number: 276671, 311929 and 316182;
European Commission, Grant/Award
Number: LIFE15 ENV/IT/000183, LIFE19
ENV/FR/000086 and LIFE20 GIE/
IT/000091; JST SICORP, Grant/Award
Number: JPMJSC16HB; US Department of
Energy, Grant/Award Number:
DE- EM0005228
Although it is an integral part of global change, most of the research addressing the
effects of climate change on forests have overlooked the role of environmental pollu-
tion. Similarly, most studies investigating the effects of air pollutants on forests have
generally neglected the impacts of climate change. We review the current knowledge
on combined air pollution and climate change effects on global forest ecosystems
and identify several key research priorities as a roadmap for the future. Specifically,
we recommend (1) the establishment of much denser array of monitoring sites, par-
ticularly in the South Hemisphere; (2) further integration of ground and satellite
monitoring; (3) generation of flux- based standards and critical levels taking into ac-
count the sensitivity of dominant forest tree species; (4) long- term monitoring of
N, S, P cycles and base cations deposition together at global scale; (5) intensification
of experimental studies, addressing the combined effects of different abiotic factors
on forests by assuring a better representation of taxonomic and functional diversity
across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics
and genomics; (7) improved knowledge on key processes regulating the dynamics of
radionuclides in forest systems; and (8) development of models integrating air pollu-
tion and climate change data from long- term monitoring programs.
air pollution, climate change, forest ecosystem, forest nutrients, forest research roadmap,
forest vulnerability, radioactivity
DE MARCO et al.
Re giona l and nati ona l air qua lit y dir ec tive s and em issi ons contr ol po l-
icies (e.g., Japanese Air Pollution Control Act 1968/1970; European
Council Directive 2008/50/EC; United States Federal Register,
2015) led to the development of air quality monitoring stations.
Monitoring data are collated within national or regional databases,
such as the Acid Deposition Monitoring Network in East Asia, the
European Environment Agency Airbase system, and the Australia Air
Quality Network (AUSAQN; Schultz et al., 2017 ). Despite efforts to
moni tor air quality in Sout h America , the spat ial dist rib uti on of moni-
toring stations is still heterogeneous and insufficient to represent
the pollutant levels (Peláez et al., 2020).
Coordinated research networks of long- term experimental for-
est sites integrating monitoring and state- of- the- art methodological
and conceptual research to assess air pollution and global change
effects are not distributed in a way that represents all forest eco-
system types over the globe. Long- term forest monitoring and in-
frastructure networks are running regionally and worldwide, even
overlapping each other in their geographic expansions, and are likely
to further expand in the future. Here, we introduce some of the larg-
est networks of experimental forest sites, their research aims and
methodologies, and explore their capacities in view of the Supersite
definition (Mikkelsen et al., 2013).
International Long- Term Ecological Research (ILTER) is a “net-
work of networks” with research sites located in a wide array of
ecosystems aimed at developing a global understanding of envi-
ronmental change while also covering socioeconomical aspects
(known as LTSER). Expertise warrants the collection, management,
and analysis of spatiotemporally diverse datasets, such as DEIMS
(Drupal Ecological Information Management System), a central plat-
form providing information on sites and networks with ecological
long- term monitoring and experimentation at European and global
scales. Currently, ILTER encompasses 39 countries which together
operate more than 600 sites (Maass et al., 2016). Some sites main-
tain advanced continuous measurements, such as tower- based eddy
covariance assessments of CO2 and H2O fluxes. The ILTER network
includes the Ter res trial Eco sys tem Re search Netwo rk (TE RN), est ab-
lished in Australia, which provides a comprehensive metadata portal
containing information on continental scale datasets of measure-
ments describing fauna, flora, terrestrial ecosystems, ecological dy-
namics, land surfaces, soils, agricultural ecosystems, coasts, climate
observations and fluxes (Karan et al., 2016). Similarly, the Chinese
National Ecosystem Research Network (CNERN) is an integrated
platform of field stations supervised by various Chinese ministries.
CNERN represents a science and technology system that conducts
network observation and experimentation across China's ecosys-
tems, cutting across governmental departments, industrial sectors,
regions, and jurisdictions, and seeks to integrate observation equip-
ment and data resources and standardize research methods, tools,
and protocols. As a result, CNERN serves as a nexus for national
ecological research, promotes data sharing, and creates an educa-
tional center and collaborative base for ecological research. ILTER
networks are also present in Korea and Taiwan.
Another “network of regional networks” is represented by
FLUXNET, which is coordinating regional and global analyses con-
ducted at micrometeorological tower sites (eddy covariance) to
investigate the exchanges of carbon dioxide (CO2), water vapor,
and energy between terrestrial ecosystems and the atmosphere
(Pastorello et al., 2020). FLUXNET is divided into regional networks,
for example, the European Integrated Carbon Observation System
Research Infrastructure (ICOS RI) with more than 100 measuring
stations including 32 forest stations. In 2021, more than 800 sites
worldwide were operated on a long- term and continuous basis
within this network. Habitats included in this monitoring framework
include temperate conifer and broadleaf (deciduous and evergreen)
forests, tropical and boreal forests, crops, grasslands, chaparral,
wetlands, and tundra.
In Europe, the International Co- operative Programme on
Assessment and Monitoring of Air Pollution Effects was launched
in 1985 under the United Nations Convention on Long- Range
Transboundary Air Pollution (CLRTAP), with several units including
ICP Forests (Michel et al., 2018), ICP Vegetation, ICP Modelling and
Mapping, and ICP Integrated Monitoring (Lundin & Forsius, 2004).
Networks of monitoring stations are established within this frame-
work that continuously assess ecosystem responses to air pollution
and develop the associated modeling and assessment methods
(Forsius et al., 2021). ICP Forests currently monitor forest conditions
in Europe at two intensities: Level I is based on around 6000 ob-
servation plots within a systematic transnational grid of 16 × 16 km2.
Level II comprises around 800 plots in selected forest ecosystems
for clarifying cause– effect relationships, and also assesses foliar and
soil chemistry, tree growth, and conditions of ground vegetation.
Approximately 41 sites, depending on the parameters, also monitor
ambient air quality and meteorology.
ForestGEO is a global network of scientists and forest research
sites dedicated to advancing long- term study of the world's forests,
dedicated to the study of tropical and temperate forest function and
diversity. The multi- institutional network comprises 73 forest re-
search sites across the Americas, Africa, Asia, Europe, and Oceania.
ForestGEO monitors the growth and survival of approximately 6 mil-
lion trees and nearly 13,000 species that occur in the forest research
sites. This network also supports initiatives to monitor attributes
such as climate, carbon flux, vertebrates, insects, and soil microor-
ganisms. ForestGEO increases scientific understanding about the
potential effects of climate change on ecosystems, which is a priority
of the US Climate Change Science Program and highlighted by the
Intergovernmental Panel on Climate Change (IPCC) Working Group
II. Because of ForestGEO's extensive biological monitoring, unique
databases, and the partners' expertise, it promises to enhance soci-
ety's ability to evaluate and respond to the impacts of global climate
change. To date, unfortunately, the distribution of forest monitor-
ing sites within ForestGEO appears non- homogeneous (Figure 1).
    DE MARCO et al.
Indeed, boreal and tropical forest s are less represented among mon-
itoring sites and there is a disproportionate number of monitoring
sites in the Northern Hemisphere (NH), particularly in Europe, and
fewer sites in the Southern Hemisphere (SH).
The aforementioned monitoring networks may benefit from
data derived through remote sensing measurements (Lechner
et al., 2020). Remotely sensed imagery provides a synoptic view, and
is potentially available everywhere at a large range of spatial and
temporal scales with a high degree of homogeneity. Furthermore,
remote imagery provides digital images that can easily be integrated
with other spatial datasets in a geographic information system, and
per unit area remote sensing is an inexpensive way to acquire data.
The most used remote sensing sensors for assessing and monitoring
forest conditions are those on- board satellites, followed by airborne
(including Unmanned Aerial Vehicles) and terrestrial systems, or a
combination of these platforms (Torres et al., 2021). Previous studies
have demonstrated the utility of optical remote sensing for assessing
a variety of forest health indices, and are commonly used in forest
monitoring activities (Curran et al., 1992; Huang et al., 2019 ; Parent
& Verbyla, 2010 ). Landsat satellite images are still the most widely
used Earth Observation (EO) data in forest health studies (Torres
et al., 2021), which provide continuous time series data from the
1970s (i.e., Landsat 1 mission) until today (i.e., Landsat 8). Access to
Landsat images has been free since 2008, and the recently launched
Landsat 9 (September 2021) will be publicly available in early 2022.
In addition to Landsat imagery, imagery from sentinel missions
from the European Space Agency is also particularly important for
forest monitoring because of their high spatial and temporal reso-
lution. Furthermore, the availability of both active (Sentinel- 1) and
passive (Sentinel- 2) sensors might increase the precision of previ-
ous analytical methods that rely primarily on optical reflectance
indices. Similarly, forest health monitoring studies are increasingly
using Synthetic Aperture Radar (SAR) sensors. For example, C- band
data are sensitive to variations of Leaf Area Index, which are con-
nected to defoliation and hence forest status (Manninen et al., 2003;
Stankevich et al., 2017). SAR sensors are advantageous not just
because of their sensitivity to forest structural changes (Dobson
et al., 1992; Harrell et al., 1995; Le Toan et al., 1992), but also be-
cause of their ability to monitor the water content of the tree canopy
(Dobson et al., 199 2; Harrell et al., 1995; Le Toan et al., 199 2).
Specific remote sensing techniques that merge different spatial,
spectral, radiometric, and temporal resolutions are being increas-
ingly used to reduce data gaps and to characterize forest ecosystems
(Lausch et al., 2018). For example, Rogers et al. (2018) demonstrated
the potential of derived products based on Landsat, Advanced
Very High- Resolution Radiometer (AVHRR), and MODIS (Moderate
Resolution Imaging Spectroradiometer) data to detect early signals
of tree mortality. Modeling various biophysical indicators based on
aerial or ground- based LiDAR data can further expand the portfo-
lio of remote sensing- derived data, or at the very least allow their
validation in a more efficient manner than by means of traditional
monitoring and inventory. In this regard, a fusion of satellite spectral
data (e.g., Sentinel- 2) and LiDAR data (Global Ecosystem Dynamics
Investigations) could be the next step for global drought- induced
tree mortality assessment (Huang et al., 2019). More recently, the
Copernicus air- pollution monitoring satellite dedicated to trace gas-
ses assessment, such as O3, NO2, SO2, formaldehyde (HCHO), CO,
and CH4 (Sentinel- 5— Precursor/TROPOMI; Inness et al., 2019),
has been used for tracking pollution events and pollution sources
(Mesas- Carrascosa et al., 2020). By merging classical monitoring
techniques and state- of- the- art remote sensing, long- term studies
are facilitated (Tănase et al., 2019). Remote sensing use should be
FIGURE 1 Distribution of the most relevant monitoring network over the forested areas of the globe.
DE MARCO et al.
expand ed to vulnerable regio ns or ecosystem ty pes whi ch need spe-
cial protection from climate change and air pollution.
Highly instrumented forest research infrastructures (supersites)
provide long- term data series and promote integration of research
communities in a transcontinental collaboration network (Fischer
et al., 2011). For these supersites, the use of forest inventory data
together with remote sensing and EO data can provide valuable in-
formation on forest condition (Hartmann et al., 2018). As new forest
change detection algorithms based on EO sensors are developed,
they can be validated using data from long- term monitoring net-
works (Rodman et al., 2021).
To understand climate change and weather extremes, it is im-
portant to have observations of the Earth system going back as far
as possible in time. Reanalysis combines past short- range weather
forecasts with observations through data assimilation (Uppala
et al., 2005). The process mimics the production of day- to- day
weather forecasts. Reanalyses are usually produced at lower resolu-
tion than current weather forecasts, and they use the same modern-
data assimilation system and forecasting model throughout the
reanalysis period. The latest European Centre for Medium- Range
Weather Forecasts (ECMWF) reanalyses are produced through the
EU- funded Copernicus Climate Change Service (C3S). Forecasts are
freely available through the C3S Climate Data Store. The most recent
ECMWF reanalysis dataset is the ER A5 Back Extension, providing
data from 1950 to 1978. The Copernicus Atmosphere Monitoring
Service (CAMS) provides continuous data and information on atmo-
spheric composition. The service describes the current situation,
forecasts the situation a few days ahead, and analyses consistently
retrospective data records for recent years. CAMS supports many
applications in a variety of domains including health, environmen-
tal monitoring, renewable energies, meteorology, and climatology.
CAMS monitors and forecasts European air quality and worldwide
long- range transport of pollutants.
Various substances emitted from natural or anthropogenic sources
flow from the atmosphere into forest ecosystems by either wet or
dry deposition (Tørseth et al., 2012). Atmospheric deposition may
be harmful or beneficial for trees and other plants (Figure 2). Sulfur
(S) and nitrogen (N) compounds may function as either nutrients or
stressors for forests, even though they are derived from anthro-
pogenic air pollutants, such as sulfur oxides (SOx), nitrogen oxides
(NOx), and ammonia (NH3; Duan et al., 2016; Oksanen & Kontunen-
Soppela, 2021). When traveling through the canopy, acid deposi-
tion can cause direct damage to plant leaves (Du et al., 2017 ). When
deposited to the forest floor, N and S compounds are identified
as a cause of acidification and eutrophication (or N saturation) of
forest ecosystems (de Vries, 2021). Moreover, certain amounts of
phosphorus (P) and basic cations, such as calcium (Ca2+) and mag-
nesium (Mg2+), acting in forests as nutrients, are also derived from
anthropogenic emissions (Du et al., 2016, 2018). Climate change may
FIGURE 2 Main interactions of forest ecosystems with sulfur (S) and nitrogen (N) compounds. They may function as either stressors
(S) or nutrients (N), even when they are derived from anthropogenic air pollutants, such as sulfur oxides (SOx), nitrogen oxides (NOx), and
ammonia (NH3), with direct effects on forest canopy (Du et al., 2017 ) and indirect effects on acidification (Augustaitis et al., 2010) and
eutrophication (de Vries, 2021) including impacts on biodiversity (Clark et al., 2013), growth (Du et al., 2018), volatile emissions (Hansen
et al., 20 17; Liu & Greaver, 2009; Mushinski et al., 2 019; Schindler et al., 2020; Xie et al., 2018), and biogeochemistry (Gaudio et al., 2015;
Nakahara et al., 2010).
    DE MARCO et al.
directly or indirectly affect the roles of these substances in forest
ecosystems (e.g., Mitchell & Likens, 2011; Nakahara et al., 2010).
Atmospheric deposition, especially of S and N compounds, has
declined over the last three decades (Sicard, De Marco, et al., 2016;
Tørseth et al., 2012; Zhong et al., 2020), despite many developing
nations still lacking effective SO2 emiss ion cont rol s. In Euro pe, depo-
sition of S and N peaked in the late 1970s and in the 1980s, respec-
tively (Engardt et al., 2017). In North America, deposition of S and
N peaked in the early 1970s and mid- 1990s, respectively (Mitchell
& Likens, 2011), when NH3 emission became more important
(Du, 2016). In South America, most average daily concentrations of
SO2 are below the World Health Organization air quality guidelines
(Peláez et al., 2020), and global atmospheric S deposition is lower
than in Europe, Asia, the United States, and Africa (Gao et al., 2018),
ranging around 4.96 ± 3.45 kgS ha−1 a−1 . In Asia, emissions of SO2
and NOx significantly increased from the early 1980s to the early
2000s (Ohara et al., 2007), 20 or 30 years later than in Europe and
the United States. The emissions of SO2 and NOx in China peaked in
2006 (Lu et al., 2011) and 2011– 2012 (Zheng et al., 2018), respec-
tively, and thereafter started decreasing. In China, emissions of NH3
reached a plateau in 1996 (Kang et al., 2016), although a gradual in-
crease in NH3 emissions in Asia (including China) was observed as of
2015 (Kurokawa & Ohara, 2020). A recent global analysis combined
inventory and modeling data to confirm that the total annual NOx
emissions finally stopped increasing in 2013, largely due to strict
control measures taken in China in recent years (Huang et al., 2017).
However, SO2 emissions in India overtook those in China in 2016 (Li
et al., 2017), and thus a focus should be placed on monitoring atmo-
spheric deposition in India and other developing countries. Major
air pollutants have been changing with industrialization in each re-
gion, from SO2 to NOX and NH3. With temporal changes of major
pollutants relative to industrialization, acidification, photochemical
formation of ozone, and excess N deposition appeared in sequence
as problems for forest ecosystems, as seen in the changes of main
causes of tree and forest decline in Northeast Asia (Takahashi
et al., 2020). Thus, emission reduction of S and/or N has been re-
flected gradually by the conditions of forest ecosystems.
In Europe and the United States, reduced S deposition resulted
in long- term declines in
concentrations in soil solutions (Berger
et al., 2016; Johnson et al., 2018) and freshwater (Garmo et al., 2014;
Vuorenmaa et al., 2017 ). However, since S compounds are retained
in forest ecosystems and released with changing environmental con-
ditions, changes in S leaching do not necessarily occur at the same
time as S deposition changes. Therefore, S output in forest catch-
ments often exceeds the atmospheric input due to legacy S pools
derived from past deposition (Vuorenmaa et al., 2017) or due to
changing climate (Mitchell & Likens, 2011), which might delay recov-
ery from acidification. In Asia, much of the deposited atmospheric
seems to be retained in forest soils (Duan et al., 2016; Sase
et al., 2019), which may imply a future risk of soil acidification under
changing climate. In fact,
concentrations and pH of river wa-
ters are related to the S emission/deposition rate (Duan et al., 2011;
Qiao et al., 2016; Sase et al., 2017, 2021). To understand the S cycle
in forest ecosystems, targeted studies on deposition trend and
changing climate are required (e.g., Mitchell & Likens, 2011; Sase
et al., 2019, 2021; Vuorenmaa et al., 2017 ).
Air pollution abatement may also reduce atmospheric inputs of
base cations (Tørseth et al., 2012), as reported for forest soil solu-
tions (Johnson et al., 2018) and freshwaters (Garmo et al., 2014;
Stoddard et al., 1999). Base cation nutrient s in China forest s neutral-
ized on average 76% of the potential acid load due to acid deposition
during 2001– 2015 (Du et al., 2018). Thus, base cation deposition
should be monitored simultaneously along with S and N deposition
as already done by several networks globally to assess nutrient sta-
tus and recovery from acidification in forest ecosystems.
Excess N inputs from the atmosphere have been disturbing
biogeochemical cycles in forest ecosystems (e.g., Aber et al., 1989;
Nakahara et al., 2010). With reduction in total N deposition mainly
due to NOX emissions, an improvement is expected in the NH.
However, high levels of NH3 deposition are still concerning because
NH3 emissions have not clearly reduced in many of the regions as
described above. Moreover, since emissions of SO2 and NOx have
been reduced resulting in significant decline of particulate forma-
tion (such as (NH4)2SO4 and NH4NO3), air concentrations of NH3
have been increasing and accordingly more localized NH3 deposi-
tion was identified in the United States (Butler et al., 2016). Even
though regional N deposition has gradually decreased, ecosystem
responses to N deposition appeared to show some degree of hyster-
esis (Gilliam et al., 2 019). In fact, there was no large- scale response
in understory vegetation, tree growth, or vitality to reduction of
N deposition in Europe, while a decline in
concentrations in
soil solutions and foliar N concentrations were partly observed
(Schmitz et al., 2019). In Asia, three decades of increase in N depo-
sition in China have exerted significant impacts on soil and water
acidification, understory biodiversity, forest growth, and carbon se-
questration (Qiao et al., 2016; Tian et al., 2018). However, recovery
from acidification and N saturation has already started following a
reduction in N deposition in Japan (Sase et al., 2019), where high
S and N deposition and climatic anomalies caused acidification and
N saturation in the 1990s (Nakahara et al., 2 010). Nitrogen leach-
ing from forest ecosystems is controlled not only by N deposition,
but also by various factors, including tree age, forest management,
climate, and other limiting nutrients such as phosphorus. Moreover,
emissions of NH3 (e.g., Hansen et al., 2017), N2O (e.g., Schindler
et al., 2020; Xie et al., 2018), and NOy (as NO + NO2+ HONO; e.g.,
Mushinski et al., 2 019) as well as microbial nitrification rate (e.g.,
Fang et al., 2015) in forest areas should be taken into consideration
for actual N fluxes. Since N deposition may increase gas N emissions
from ecosystems (e.g., Xie et al., 2018), a comprehensive study con-
sidering bilateral N fluxes (both deposition and emission) should be
promoted to evaluate whether a forest ecosystem is a sink or source
of reactive N species.
The analysis of N dynamics in Latin America is complex, due to
the enormous diversity of unmanaged and managed ecosystems,
including arid deserts as well as temperate and tropical forests.
Cunha- Zeri and Ometto (2021) stated the major input of N in Latin
DE MARCO et al.
American countries over the past decades occurred via natural bio-
logical fixation, compared to anthropic sources (fertilizers and fos-
sil fuel combustion). Nevertheless, human activities have currently
changed the N cycle of natural ecosystems in Latin America. For in-
stance, the conversion of unmanaged land to agriculture increased
biological N fixation up to twofold (Reis et al., 2020). Although the
highest total N deposition occurs in eastern and southern China,
Japan, Eastern US, and European forests, the highest dry deposi-
tion occurs in tropical forests (Schwede et al., 2018). For instance,
dry N deposition into the Atlantic Forest in the city of São Paulo
(Brazil) can exceed the critical N load found for most forests (Souza
et al., 2020).
Because of the continued increase in NH3 emission in some re-
gions (e.g., Kurokawa & Ohara, 2020) and stagnating values in oth-
ers (Maas & Grennfelt, 2016), N deposition is a per vasive issue that
impacts forest ecosystems. In addition, even relatively low levels of
N deposition affect the mycorrhizal association of trees (Lilleskov
et al., 2019; van der Linde et al., 2018) and may affect biodiversity of
sensitive species, such as lichens (Giordani et al., 2014). The magni-
tude and consequences of these human- induced changes in plant–
soil– microbe interactions as well as potential pathways for recovery
are currently open questions.
Moreover, excess N deposition may induce an imbalance of nutri-
ent ratios, such as N:P ratio (Krüger et al., 2020; Sardans et al., 2016).
However, the observational data on atmospheric P deposition are
still limited for forest areas (e.g., Chiwa, 2020; Du et al., 2016) and
N- P imbalances have been reported from various regions (Boccuzzi
et al., 2021; Krüger et al., 2020; Peñuelas et al., 2013). Taking into
account the global pattern of N and P limitation in forest areas (Du
et al., 2020), N and P deposition should be monitored together. Both
N and P cycles are listed as important Earth- system processes in the
concept of “Planetary boundaries” with N cycle already transgress-
ing its boundary (Rockström et al., 2009; Steffen et al., 2015).
Climate has an important role in regulating the global patterns of
terrestrial N and P limitation (Du et al., 2020). Specifically, there is a
shift from relative P to N limitation at lower mean annual tempera-
ture, temperature seasonality, mean annual precipitation, and higher
precipitation. Future climate change will likely reshape the spatial
pattern of nutrient limitation. For instance, climate warming will im-
prove N availability at mid- to- high latitudes via increasing biologi-
cal N fixation and N mineralization (Zaehle et al., 2010). Moreover,
growth stimulation by rising atmospheric CO2 concentration ([CO2])
will increase nutrient demand and, in turn, result in greater nutrient
limitation (Collalti et al., 2018; Wieder et al., 2015). The changing nu-
trient status under climate change will likely interact with the effects
of S and N deposit ion and thu s they should be considere d simulta ne-
ously when projecting future forest dynamics.
Background O3 concentrations have increased throughout the last
century due to the rising anthropogenic emissions of O3 precursors
from fossil fuel and biomass burning (Cooper et al., 2014; Monks
et al., 2015), although volatile organic compounds (VOCs) also are
major precursors (Wei et al., 20 14). Despite the decreasing trend of
other air pollutants in the last decades (e.g., S and N compounds,
heavy metals), global- scale background O3 concentrations increased
(Jakovljevet al., 2021; Sicard, 2021), but slight regional- scale de-
creases in peak concentrations were observed (Schaub et al., 2018).
Thus, O3 is nowadays one of the main phytotoxic air pollutants with
the potential to affect forest ecosystems worldwide (Agathokleous
et al., 2020; Bytnerowicz et al., 2016; De Marco et al., 2020; Feng,
Shang, Gao, et al., 2 019; Sicard, Augustaitis, et al., 2016).
Ozone burdens are higher in the Northern (O3 mean concentra-
tion 35– 50 ppb) than in the SH (O3 mean concentration <20 ppb;
Sicard et al., 2 017). For example, widespread O3- induced visible in-
jury, a specif ic damage ass ociated wit h O3 exposure, was foun d at 17
forest plots in Europe (Paoletti et al., 2019; Sicard et al., 2020). The
NH is more covered by land and terrestrial ecosystems, and more
inhabited by humans than the SH, and thus is more affected by an-
thropogenic activities. However, the SH is less monitored and thus
O3 burdens and effects may be underestimated. While there are
hundreds of papers on O3 effects on forest plants and forests in the
NH (i.e., Agathokleous et al., 2015; Feng, Shang, Gao, et al., 2019;
Izuta, 2017; Sicard et al., 2020) indicating various effects of O3 in
interaction with climate change (Figure 3), relevant research in the
SH remains scarce.
The analysis of O3 effects in Latin America is complex due to
the enormous diversity of natural and agricultural ecosystems.
Most monitoring studies on O3 effects on forest plants conducted
in the SH come from Brazil. Urban and industrial development has
been more intense along the Atlantic Brazilian coast, especially in
Southeastern region. Consequently, more severe O3 effects on the
Atlantic forest located in this subtropical region (mainly São Paulo
and Rio de Janeiro States) are expected (Domingos et al., 2003;
Moura et al., 2014, 2018). Ozone effects on native tree species from
the Atlantic Forest have recently been determined in the field or
experimentally, pointing to distinct tolerance levels and highlighting
the need to expand knowledge on this topic (Cassimiro et al., 2016;
Engela et al., 2021; Fernandes et al., 2019; Moura et al., 2018). In
the SH, the Amazon spans over 629 million hectares of rainfor-
est, accounting for 54% of the total rainforests left on Earth (Peng
et al., 2020). Recent modeling approaches have shown O3 concentra-
tions have increased above the Amazon and Cerrado biomes in Brazil
as a response to biomass burning and regional air pollution (Gerken
et al., 2016; Pope et al., 2 019). The lowest O3 exposures reported
are in Australia, New Zealand, southern parts of South America, and
some northern parts of Europe, Canada, and the United States (Mills
et al., 2018; Sicard et al., 2017). However, unfortunately, a proper O3
monitoring network does not currently exist. Despite the presence
of ground- level O3 monitoring networks in all the developed coun-
tries (Lefohn et al., 2018), there is still a lack of an integral network
of ground- level O3 monitoring across Asia, although 1500 monitor-
ing stations have recently been installed in China (Feng, Shang, Gao,
et al., 2019).
    DE MARCO et al.
Another challenge in monitoring O3 impacts on forests is the
choice of metrics. The AOT40 index (Accumulated Ozone over
Threshold of 40 ppb ozone), describing the exposure of plants to high
O3 concentrations, is the default measure for policy direc tives of the
European Union (Directive 2008/50/EC). However, AOT40 has been
criticized because it is not a proxy of gas uptake through leaf sto-
mata (stomatal flux), and flux- based indices have been applied (Anav
et al., 2022; De Marco & Sicard, 2 019; Paoletti et al., 2019; Sicard
et al., 2020) and showed O3 risks to vegetation would be different
from AOT40 (Anav et al., 2016; De Marco et al., 2015). The new
standard developed in Europe (Emberson et al., 2000) is the stoma-
tal O3 flux, defined as POD (Phytotoxic Ozone Dose). This standard
depends not only on O3 concentration, but also environmental (e.g.,
light intensity, air temperature, relative humidity, soil moisture) and
plant conditions (phenology, leaf morphological, and physiological
traits). A major impact of O3 is reduced aboveground and below-
ground carbon sequestration of forests (Agathokleous et al., 2016;
Gao et al., 2017; Figure 2). Ozone effects on biogenic volatile organic
compounds (BVOCs) are complex, as some compounds may decrease
(e.g., isoprene) while other compounds increase (e.g., monoter-
penes; Feng, Yuan, et al., 2019). Different BVOC compounds have
different capacity to generate O3, with isoprene having higher O3-
forming potential than monoterpenes (9.1 g 03 (g VOC)−1 and 3.8 g 03
(g VOC)−1, respectively; Benjamin & Winer, 1998). However, sesqui-
terpenes and some monoterpenes also contribute to the removal of
O3 at the canopy level and play an important role in the feedback
between stress- induced VOC emissions and O3 or aerosol forma-
tion (Calfapietra et al., 2013). The emission of isoprene, the most
abundant BVOC, can also be decreased by drought and CO2 and in-
creased by warming (Feng, Shang, Li, et al., 2019), indicating complex
O3- climate interactions that remain elusive in real- world forests. Soil
microbial processes contribute to emission of BVOCs and NOx that
act as O3 precursors (Gray et al., 2010). Overall, soils play an im-
portant role in forest VOC exchange, defining also carbon storage
by forest ecosystems, and fluxes depend upon BVOC compounds
and vegetation types (Mäki et al., 2019; Rinnan & Albers, 2020; for
details and values of fluxes in different vegetation types and envi-
ronmental media, see also Tani and Mochizuki (2021)). However, the
FIGURE 3 The Gordian Knot of the Forest– Ozone– Carbon interactions. In the pre- industrial epoch, carbon is stored via photosynthesis
(1) and leads to long- term carbon sequestration into aboveground and belowground (roots and soil) wood biomass (2) (Agathokleous
et al., 2016; Grantz et al., 2006). The higher CO2 levels, alone, in the atmosphere are expected to “feed” forest growth (Koike et al., 2018)
and have beneficial effects. The increased O3 levels, alone, depress forest trees, contributing to “forest decline syndrome,” that is, visible
injury, photosynthesis, carbon sequestration, carbon storage changes (7), and biomass decay, which also releases CO2 in the atmosphere (8)
(Agathokleous et al., 2016; Sandermann et al., 19 97; Sicard et al., 2021; Takahashi et al., 2020). In a positive feedback, the depressed forest
vegetation emits more BVOCs (4), further increasing O3 levels (Peñuelas & Staudt, 2010). Concurrent elevated concentrations of CO2 end
O3 may outcome to a sustained increase in Net Primary Productivity (NPP), while the adverse long- term effect of increased O3 on NPP may
be lesser than projected (Talhelm et al., 2 014). Elevated CO2 levels negate or even overcompensate the negative O3 effect on ecosystem
functions and the cycles of carbon and nitrogen. Anthropogenic emissions of CO2, NOx, and volatile organic compounds (VOCs) (3) as well
as biogenic VOCs (BVOCs) emitted by forests (4) contribute to increased O3 levels in the atmosphere (Yu & Blande, 2021). Soil microbial
processes contribute to soil- emitted BVOCs and NOx (O3 precursors; Gray et al., 2010 ) as well as CO2, N2O and CH4 (Yao et al., 2009; Zhang
et al., 2021) (5). Under advanced climate change, forest fires are expected to be more frequent and larger than in the pre- industrial epoch
(Zhang et al., 2021). These fires release carbon monoxide (CO), organic carbon (OC), NOx (all of which contribute to O3 formation), and
black carbon (BC; which influences photosynthesis by increasing diffuse radiation) as well as CO2 (which further intensifies global warming;
Flannigan et al., 2009; Kumar et al., 2019 ; Pellegrini et al., 2021; Yue & Unger, 2018) (9).
DE MARCO et al.
specific contribution of soil in VOC exchanges and O3 formation re-
mains poorly understood.
Heavy metal pollution was an important subject in widespread forest
decline in the 1980s– 1990s (Gawel et al., 1996), but more recently has
become a major item in phytoremediation (Pulford & Watson, 2003)
and environmental monitoring (Godzik, 2020). The term “heavy met-
als” is now discouraged, and these elements are now included more
broadly as “trace elements” (Pourret & Bollinger, 2018). Trace ele-
ments are a major component of particulate pollution (Antoniadis
et al., 2017; Grantz et al., 2003; Li et al., 2015; Schlutow et al., 2021;
Tóth et al., 2016). At the global scale, trees are important for their
role in retaining particulates (Yue et al., 2021). Nevertheless, in
some regions, soil contamination by trace elements remains so high
that it continues to kill trees and prevents natural recovery (Kozlov
et al., 2009). Among trace elements, radionuclides display the most
phytotoxic potential.
The use of nuclear energy or nuclear applications in health, ag-
riculture, environmental management, or industry/military resulted
in releases of radionuclides into the environment (Hong et al., 2012).
The first large- scale radioactive contamination from anthropogenic
sources occurred through global radioactive fallout from nuclear
weapons' tests conducted in the atmosphere during 1945– 1980
(Aoyama et al., 2006; United Nations, 2000). A variety of long- and
short- lived radionuclides were released during nuclear incidents; in
particular 137Cs with a relatively long half- life (~30 years) compared
to other radionuclides, such as 134Cs and 131Cs. Other major releases
of radionuclides occurred from the Chernobyl nuclear power plant
accident in 1986 (International Atomic Energy Agency, 2006) and
from the Fukushima Daiichi Nuclear Power plant accident in 2011
(Chino et al., 2011; Terada et al., 2020; Yoshida & Takahashi, 2012).
Radioactive contamination of forests has different types of
impacts (Figure 4). First, direct radiation can affect trees and an-
imals and occur at the level of DNA, cells, individuals, population
to whole ecosystems, and ranges from reparable DNA damage to
death of organisms (Committee on the Biological Effects of Ionizing
Radiation, 1990 ). An example of direct impacts of high radiation
doses to trees is the “Red forest” in the Chernobyl exclusion zone,
where pine trees became reddish brown and died following the ac-
cident (Beresford et al., 2016). Another visible impact of radiation
exposure in trees is the occurrence of morphological abnormalities
(Watanabe et al., 2015; Yoschenko et al., 2011, 2016). Compared
to the effects caused by high doses of radiation, those poten-
tially caused by relatively lower radiation dose are confounded by
many other factors and are still not clearly understood (Beresford
et al., 2020; Ji et al., 2019; Strand et al., 2017). In exposed areas, for-
est ecosystems are released from pressure by human existence, re-
sulting in creation of ecological niches and expansion of populations
of some species (Deryabina et al., 2015; Lyons et al., 2020; Perino
et al., 2019). Through intensive monitoring, it was confirmed that the
overall dynamics of 137Cs within forest ecosystems were similar be-
tween Chernobyl and Fukushima: tree canopies captured the depo-
sition of 137Cs and 137Cs migrated from the canopy to the soil surface
via water and litter fall, and most of it stays in the top layers of soil
(Itoh et al., 2015; Kato et al., 2019; Suchara et al., 2 016). However,
the migration velocity and distribution patterns of 137Cs within for-
ests and tree bodies differ substantially among forests and trees
(Imamura et al., 2017; Ohashi et al., 2017). It is essential to continue
experimental studies to identify the key processes influencing 137Cs
dynamics in forest systems, such as soil potassium concentrations
and fixation processes within soils (Kobayashi et al., 2019 ; Manaka
et al., 2019). Various models have been developed to characterize
137Cs dynamics in forests; however, improvements are necessary
to reproduce variations between forest types and species compo-
sitions (Hashimoto et al., 2020). Another aspect of radionuclide pol-
lution is that deposited radionuclides, which are easy to detect and
measure, provide an unintentional but useful opportunity to track
biogeochemical cycles in forest ecosystems (Fukuyama et al., 2008).
Our knowledge on combined effects of multiple factors on ecosys-
tem health originated primarily from temperate and boreal forests
of North America and Europe and is limited for tropical forests,
FIGURE 4 Diagram of direct and indirect effects of forest
radioactive contamination. The deposited radionuclides remain in
the forest and continue to circulate in the forest ecosystem, and
radiation can have adverse effects on forest biota (direct effects).
Restrictions on forest use and land abandonment to avoid exposure
can also affect forest ecosystems, including changes in vegetation
and wildlife populations. It has direct and indirect impacts on
ecosystems and local residents.
    DE MARCO et al.
especially of those in Africa (Matyssek et al., 2 017). In other words,
areas that have recently experienced the highest risk of forest deg-
radation are studied to a lesser extent than the areas where risk is
low. In addition, many communities whose food security and wealth
generation critically depend on forests are located in geographic re-
gions where our understanding of factors affecting forest ecosys-
tem health is poor. This geographic bias is typical for ecological and
environmental research (Archer et al., 2014), and its consequences
are generally seen as severe, because results obtained with one
study system may appear of little use in predicting the responses of
another, geographically distinct, study system (Haukioja et al., 1994).
Air pollution levels may become more harmful for plants as the
climate warms (Zvereva et al., 2008, 2010). More multi- factorial
manipulative studies are needed because effects of two or more
co- occurring factors on tree growth and forest productivity can-
not be adequately predicted from single- factor experiments
(Niinemets, 2010). The combined effects of two major abiotic aspects
of global change, mostly changes in CO2 and warming, on growth of
forests are studied in detail (Baig et al., 2015; Curtis & Wang, 1998;
Zvereva & Kozlov, 2006), and suggest air temperature may modify
plan t responses to elevate d CO2. Across 42 experiments with woody
plants, aboveground biomass increased significantly with both CO2
(the so called “fertilization effect”) and air temperature (by 21.4%
and 18.1%, respectively), whereas these two factors acting simulta-
neously showed a much smaller effect (8.2%) because of compen-
sating effects (Baig et al., 2015). Nitrogen fertilization enhances the
biomass response to elevated CO2 (Parrent & Vilgalys, 2007) despite
not universally (Terrer et al., 2019). The type of mycorrhiza was also
an important factor related to the effects of soil nutrient availability
on elevated CO2- induced growth enhancement (Baig et al., 2015).
However, two- factorial experiments involving both O3 exposure
and elevated CO2 are limited. Several studies under elevated CO2
showed a reduction in the negative effects of O3 because elevated
CO2 induced stomatal closure leading to lower O
3 uptake (Grams
et al., 1999; Watanabe et al., 2017 ). In contrast, the addition of N
alone exacerbated negative effec ts of O3 on photosynthesis of trees
(Feng, Shang, Li, et al., 20 19), while exposure to drought stress did
(Gao et al., 2 017) or did not protect plants from O3- induced effects
(Alonso et al., 2003, 2014).
Forest health also can be compromised by insect herbivory, in-
cluding both devastating outbreaks of forest pests and changes in
background herbivory. Despite relatively low levels of plant damage
(5%– 7% of leaf biomass annually: Kozlov et al., 2015), background
herbivory greatly reduces growth of woody plants (Shestakov
et al., 2020; Zvereva et al., 2012). Although warming, drought, CO2
increases, N deposition, and air pollution were repeatedly found to
increase herbivory (Lincoln et al., 199 3; Logan et al., 20 03), these
conclusions were likely affected by research and publication biases
(Zvereva & Kozlov, 2010) and/or were derived from results of short-
term laboratory experiments, which tend to overestimate the ef-
fects relative to natural ecosystems (Bebber, 2021). Within forest
ecosystems across the globe, no increase in insect herbivory was
observed from 1952 to 2013 (Kozlov & Zvereva, 2015). Similarly,
long- term monitoring did not reveal the effects of either pollution-
induced disturbance or 2.5°C climate warming on insect herbivory
in subarctic birch forests (Kozlov et al., 2017). Thus, the evidence
regarding combined effects of climate warming and air pollution on
insect herbivory remains somehow contradictory.
Other factors whose effects on forest trees have been stud-
ied in multi- factorial studies include (but are not limited to) cattle/
deer grazing, harvest of non- timber forest products, drought, flood-
ing, soil salinization, spring frost, heat waves, and increased ultra-
violet radiation (e.g., Mac Nally et al., 2011; Pliūra et al., 2019; Sugai
et al., 2019; Varghese et al., 2015). However, a low number of such
studies precludes any generalization regarding effects of these fac-
tors, combined with CO2 and air temperature increases or O3 and
insect herbivory on health of forest ecosystems. Modeling studies
jointly assessing the effects of climate change and air pollution can
greatly help for understanding and predicting future developments
of forests (Akselsson et al., 2016; Dirnböck et al., 2017; Etzold
et al., 2020; Fleck et al., 2 017; Rizzetto et al., 2016).
Air pollution, climate change, increased pests and pathogens, land-
use changes, and forest fragmentation can all reduce genetic diver-
sity and make forests more fragile and sensitive to other threats
(Gauthier et al., 2015). Current vegetation and forest growth models
are largely parameterized on direct growth and gas exchange meas-
urements or remote sensing, while information from biological and
genetic regulation mechanisms are still scarce. For example, part of
the carbon fixation products (i.e., photosynthates) that is not used
for biomass production is released in soil as root exudates, some is
stored, and some organic carbon is emitted as BVOCs affecting plant
and community ecology and atmospheric chemistry (Blande, 2021;
Collalti et al., 2020; Maja et al., 2015; Naidoo et al., 2019; Šimpraga
et al., 2019). Carbon sink strength of trees is known to be impaired
by limitations in water and nutrient availability, heath spells, air
pollutants, and increased herbivory. However, plant defense pro-
cesses against different abiotic and biotic factors are complex and
involve multiple signaling pathways (He et al., 2018), potentially af-
fecting how carbon is allocated to different organs (Merganičová
et al., 2019). Most of the underlying resistance mechanisms are de-
scribed or predicted from short- living herbaceous model systems,
whereas investigations on mechanisms of defense and adaptation
of forest trees are much more challenging due to long lifetime, high
genetic diversity, and variation of growth environments and climates
(Naidoo et al., 2019). There is an urgent need to intensify studies on
the mechanisms underlying the resilience of forest ecosystems to
current and long- term effects of air pollution and climate change,
utilizing genetic, species, and ecosystem- level functional diversity
as well as adaptive management, resistance breeding, and genetic
engineering (Naidoo et al., 2 019). Mechanistic understanding is
DE MARCO et al.
increasingly important also for efforts in afforestation and protec-
tion of primar y forests. In principle, there are two main approaches
for achieving resistance in forest trees: (i) selection of resistant
phenotypes identified in field experiments (Sniezko & Koch, 2017)
or polluted sites (Eränen et al., 2009; Kozlov, 2005); and (ii) struc-
tured breeding programs relying on multitude of omic techniques
(Naidoo et al., 2019). The databases for genetic information of tree
species have been rapidly increasing, and the most important model
systems for forest trees are Populus, Eucalyptus, Quercus, Castanea,
Pseudotsuga, Pinus, Picea, and Betula genuses (Falk et al., 2018;
Salojärvi et al., 2017). Genetic engineering efforts by forest bio-
technology companies have produced transgenic Eucalyptus and
Populus trees with enhanced growth and disease- resistant proper-
ties (Naidoo et al., 2019). Silver birch (Betula pendula Roth) is an ex-
cellent model system for elucidating the adaptation and acclimation
capacity of forest trees to rapidly changing climate due to its (i) wide
latitudinal and longitudinal distribution; (ii) recent advances in popu-
lation genomics and evolutionary history of birch species (Salojärvi
et al., 2017); and (iii) existence of well- characterized birch genotypes
that have been intensively studied for C and N economy, photosyn-
thetic efficiency, metabolism, chemistry, and phenology (Deepak
et al., 2018; Tenkanen et al., 2020). The population genomic analyses
of silver birch provide insights on natural selection mechanisms, with
candidate genes relevant for adaptation of trees to changing envi-
ronment, biotic stress, and growth regulation (Salojärvi et al., 2017 ).
Studies with birch have also shown the C- sink strength of trees can-
not be explained by physiological or genetic approaches alone, but
there are many negative and positive interactions with pollutants,
climate, pests, pathogens, microbiomes, and between plants that
should be understood in more detail (Naidoo et al., 2019; Silfver
et al., 2020; Wenig et al., 20 19).
Plant phenotypes are strongly affected by the environment, and
often genotype per environment interaction is the factor of greatest
interest. Methodologies have been developed for non- destructive
forest- level and individual tree- level phenotyping with remote sens-
ing techniques, which are particularly useful for identifying superior
genotypes under different stress conditions (Dungey et al., 2018;
Kefauver et al., 2012; Ludovisi et al., 2017 ). Recent advances in
metagenomics and the increasing knowledge of the importance
of microbiomes in plant health offer new opportunities for forest
health management (Imperato et al., 2019; Naidoo et al., 20 19;
Wenig et al., 2019). The regulatory networks of forest trees and the
beneficial non- pathogenic microbes living around and on the sur-
faces of plant roots (rhizosphere), leaves (phyllosphere), or in the
internal plant tissues (endosphere) can be particularly important for
carbon and nutrient dynamics of trees and the development of tree
immunity (Naidoo et al., 2019). Microbes are known to help plants
in water and nutrition acquisition, defense against pathogenic mi-
crobes, tolerance to abiotic stress, adaptation, promotion of the es-
tablishment of mycorrhizal association, and plant growth regulation,
forming a holobiont system with host trees (Imperato et al., 2019;
Naidoo et al., 2019; Wenig et al., 2019). Fungal and bacterial com-
munities in forest soils have been shown to respond to changes in
climate with a shift in their community composition as well as in their
diversity (Dubey et al., 2019; Jansson & Hofmockel, 2020; Milović
et al., 2021; Simard, 2010). For example, under elevated CO2, we
can observe alteration in relative abundances of bacteria and in-
creased bacterial to fungal ratio (Dubey et al., 2019), as well as an
increase in ectomycorrhizal colonization rate but a decrease in ecto-
mycorrhizal diversity (Wang et al., 2015). Warming and elevated O3
reduced ecto- and arbuscular mycorrhizal colonization and shifted
arbuscular mycorrhizal community composition in favor of the genus
Paraglomus, which has high nutrient- absorbing hyphal surface (Qiu
et al., 2021; Wang et al., 2015). At the same time, exposure to higher
levels of O3 is associated with lower soil microbial biomass and with
changes in the overall structure and composition of poplar rhizo-
sphere soil microbial communities (Li et al., 2021). The decreased
growth of roots and decrease in ectomycorrhizal colonization rate
and a shift in species abundance might be an early indicator of the
damaging impacts of O3 in some tree species, occurring prior to visi-
ble responses of aboveground tree parts (Katanić et al., 2014).
Scientific methods in forestry, including empirical models of tree
growth, were primarily used for optimization of timber harvest
throughout the 20th century (Por& Bartelink, 2002). A more in-
tegrative modeling approach, acknowledging natural disturbances
(e.g., wind, fires, pests, diseases) as inherent elements of forest
ecosystem dynamics, was developed when computational advance-
ments allowed for the integration of greater complexity (Blanco
et al., 2020; Perera et al., 2015), although some biotic factors of for-
est disturbance such as herbivory are still rarely modeled (De Jager
et al., 2017). Since the forest dieback and acidification debate in
Europe in the 1980s, large efforts were put into improving under-
standing and prediction of anthropogenic disturbances on biogeo-
chemical dynamics of forests. Starting from models mainly targeting
the fate and effects of acid rain in forest ecosystems (Nilsson, 1988;
Sverdrup & De Vries, 1994), simulation tools have broadened to in-
clude other pressures, such as N deposition (De Vries et al., 2010),
O3 (Hoshika et al., 2015), and climate change and forest management
(Collalti et al., 2018). Modeling has been demonstrated to be a valu-
able tool for studying forest responses to present and future distur-
bances, allowing ecologists and foresters to deal with the study of
complex interactions and to evaluate future management strategies
(e.g., Collalti et al., 2018; Fleck et al., 2017) or policy options (e.g.,
Belyazid et al., 2010; Dirnböck et al., 2018).
Existing relationships between forest structure and compo-
sition and environmental variables were initially used to build
empirical models that describe past ecosystem behavior and ex-
trapolate to future conditions (Gustafson, 2013). Subsequent
modeling efforts simulated the causal biogeochemical mecha-
nisms that underlie the responses of ecosystems to these envi-
ronments (Kimmins et al., 2008). These so- called process- based
    DE MARCO et al.
models (PBMs) study the ecological processes and are considered
one of the most reliable approaches for modeling forest eco-
system dynamics under global change (Evans, 2012; Maréchaux
et al., 2020). However, forecasting forest growth is still a priority
in many studies, either for planning forestry activities under air
quality and climate change scenarios or as part of carbon storage
calculations (Blanco et al., 2020).
The general trend toward biodiversity conservation in interna-
tional policies (e.g., EU Biodiversity Strategy for 2030; European
Commission, 2020), its importance in preserving ecosystem ser-
vices, and the use of biodiversity metrics as indicators in risk
assessment (Coordination Centre for Effects, 2017) and pol-
icy evaluation (Hein et al., 2018) make the simulation of species
composition changes a decisive function for any model. When
dynamic PBMs are used for forecasting biodiversity shifts, they
are usually combined with vegetation response models based on
species niche suitability and competition (Belyazid et al., 2019;
Dirnböck et al., 2018). PBMs have been used at stand (e.g., Collalti
et al., 2016), landscape (Shifley et al., 2017), regional (Belyazid
et al., 2 019; De Marco et al., 2020; Santini et al., 2014), and global
scales (e.g., Krause et al., 2017 ). However, their implementation is
restricted at larger scales since PBMs need large, detailed input
datasets, which are often not available at national or continen-
tal scales. At these scales, a currently suitable approach is using
new models, mostly empirical, based on currently available large
datasets, such as species distribution models (SDMs; Maréchaux
et al., 2020; Noce et al., 2017). In the same way that PBMs rose
with the increasing computational power during the last decades
of the 20th century, SDMs have improved during the present de-
cade, in parallel to the increase in web available, reliable spatial-
referenced data, including environmental and meteorological
data, forest inventories, habitat distribution, aerial images, and
remote sensing (Pecchi et al., 2019; Urban, 2015). SDMs are usu-
ally statistical models that are currently used to support sustain-
able planning of forests at national and international scales (Zang
et al., 2012), with correlative SDMs using maximum entropy al-
gorithms being most frequently used (Noce et al., 2017; Pecchi
et al., 2019). Using forest decision support systems, climate
change scenarios and the balance of delivered ecosystem services
can be suggested as a methodological framework for validating
forest management alternatives aiming for more adaptiveness in
sustainable forestry (Marano et al., 2019; Mozgeris et al., 2019).
Mo reov er, some of the ve get ati on model s assoc iated wit h PBMs to
assess or forecast biodiversity are SDMs that may be applied from
site to regional scales (e.g., Wamelink et al., 2020). There are some
recent examples of SDMs implemented to assess forest biodiver-
sity respons e to at mospheri c pollution and climate change, such as
Hellegers et al. (2020) and Wamelink et al. (2020). However, these
models still lack essential information to feed their predictions,
since new field observations and experiments with novel set- ups
(e.g., Hansen & Turner, 2019) are needed to address the potential
successional and disturbance dynamics under the forthcoming cli-
mate conditions (McDermott, 2020). Therefore, there are several
possible approaches for different problems that scientists and
managers must deal with (Blanco et al., 2020; Fabrika et al., 2 019;
Maréchaux et al., 2020). The modeling process might be as com-
plex as needed by risk assessment objectives (Figure 5), providing
models and data are available and suitable. In general terms, empir-
ical models are good at predicting biomass and forest structure in
the shorter term, and consequently producing good management
recommendations for the present conditions, but are not reliable
in novel situations (i.e., future air pollution and climate change).
PBMs are good at studying effects and underlying processes of
change, particularly in the context of global change. However,
they still have low feasibility at broad scales, and the calibration
and validation processes are highly time- consuming (particularly
for the less- modeled species or regions). SDMs are appropriate for
early risk assessment on biodiversity conservation at the broad-
est scales, but still too empirical, which diminishes their reliability
in the long term (Urban et al., 2016). Mixing process- based with
empirical approaches (hybrid models), integrating, and connecting
different models (meta- and mega- models; Blanco, 2013) are ex-
cellent strategies to answer specific questions.
9.1 | Air pollution monitoring network
While conventional field- based monitoring plots will continue
to dominate the mainstay of air pollution and climate change re-
search in forests, they are costly and often logistically difficult to
conduct over large areas. Therefore, remote sensing techniques
will be more and more appropriate for large- scale monitoring pro-
grams, even though a more in- depth approach still needs to be
developed. Finer temporal intervals are required for in- depth un-
derstanding of some responses (e.g., stomatal O3 fluxes require
a continuous- monitoring approach). Highly instrumented field
sites are now cheaper and technically affordable. Integrating data
from transcontinental long- term ecological research infrastruc-
tures in tree- based models would lead to a better understanding
of how ecosystems work (Fischer et al., 2011). Long- term data
series can be integrated in existing big databases such as the
Global Atmosphere Watch (GAW) Program and the international
Tropospheric O3 Assessment Report (TOAR; Schultz et al., 2017;
WMO, GAW, 2003). These raw databases can lead to the devel-
opment of new products for temporal and spatial analysis (data
analysis, maps of data distributions, and data summaries) that are
freely accessible to the scientific community and other stakehold-
ers. Such databases can be used as tools for mechanistic and diag-
nostic understanding and upscaling.
The need for a global forest monitoring is irrefutable, and su-
persites” promote the integration of research communities in a
transcontinental collaboration network by upgrading existing
ground- based observation networks (e.g., FLUXNET, ICP, NEON)
DE MARCO et al.
covering all biogeographic areas (e.g., tropics, subtropics) and eco-
system types (e.g., woody savannas).
9.2 | Elements deposition in forests
The effects of S and N deposition on forest health have been
reducing gradually in many regions but problems have not been
solved. Legacy S pools remain, which could be affected by chang-
ing climate. Reduction of S deposition is associated with reduc-
tion of base cation deposition, which may alter nutrient status
and increase the risk of further soil acidification. The total inor-
ganic N deposition has been declining due to the implementation
of air pollution control policies, but the relative importance of
NH3 emissions and deposition is now higher (Butler et al., 2016;
Du, 2016), showing a relative increase of 0.38% per year over the
period 1985– 1999 (Du, 2016). Since ecosystem responses to de-
clining N deposition may show hysteresis (Gilliam et al., 20 19) and
key mechanisms of the N- induced changes in forest ecosystems
are not fully understood (Lilleskov et al., 20 19), long- term monitor-
ing of N- , S- , and P- cycles and base cations deposition should be
studied together to better understand biogeochemical processes
and plant biodiversity under climate change. Moreover, interac-
tions between nutrient deposition and rising O3 concentrations
should be considered in future studies (Shi et al., 2017). Long- term
monitoring should be continued even after significant air pollution
reductions to capture and understand the potential long- term ef-
fects of pollution and ecosystem recovery.
9.3  | Ground- level ozone
Surface O3 concentrations are generally higher in rural areas than in
urban areas (Sicard, 2021). However, as O3 levels are rising in cities
(Sicard, 2021), special attention should be paid to urban and peri-
urban forests, which offer services to local communities (Bruckman
et al., 2016) and can help meet air quality standards in cities (Sicard
et al., 2018). Because forest tree species play important (species-
dependent) dual roles as sinks and sources of O3 precursors (Geng
et al., 20 11; Saitanis, Agathokleous, et al., 2020), the O3 forming
potential (OFP) of the best regionally adapted forest tree species
should be investigated and taken into account by decision- makers to
select species with lower OFP for urban planning (Sicard et al., 2018).
The observed high O3 burdens, their high spatial heteroge-
neity, and the differential susceptibility of forest tree species to
O3, as well as their dual role as O3 sinks and precursor sources
(Agathokleous et al., 2020; Li et al., 2018), suggest an urgent
need for the establishment of a globally denser O3 monitoring
network in natural forest ecosystems in particular in the SH.
A new approach to the global O3 monitoring network and alter-
native methods for monitoring O3 are feasible thanks to innova-
tive technologies (Saitanis, Sicard, et al., 2020), which will help
FIGURE 5 Simplified flux of information diagram in modeling approach for risk assessment of air pollution and climate change. The
modeling and risk assessment process might be as complex as the modelers need and the availability and suitability of models and data allow.
There are three main blocks (grey bands): atmospheric and ecological models (top two) are the tools to reach the objective of risk assessment
(bottom). Models are used both in the internal processing and description of the information contained in the boxes, and in the transmission
of information between them (model inputs and outputs). For example, habitat suitability can be modeled using vegetation response models
(such as VEG; Belyazid et al., 2019 or PROPS; Dirnböck et al., 2018), that are particularly designed to process output from process- based
models as input information, but it can be also modelled by species distribution models that are particularly designed for large- scaled input
datasets (e.g., Noce et al., 2017). Information generally needed in any environmental study (such as soil and terrain variables) has been
obviated in this diagram.
    DE MARCO et al.
to understand combined effects of O3 with other emerging envi-
ronmental factors. There is also an urgent need to generate flux-
base d standards and criti cal levels for fo res t prote ction taking into
account the sensitivity of dominant forest tree species. Because of
its limitations, the AOT40 index should not be adopted as default
for risk assessment (Agathokleous et al., 2019; Anav et al., 2022;
Sicard, Augustaitis, et al., 2016). Finally, the development of
countermeasures for controlling anthropogenic O3 precursor
emissions is also urgently needed.
Further research is still needed to develop O3- effect indicators
related to other ecosystem ser vices provided by forests such as bio-
diversity, soil protection, and water conservation. Nonlinear models
should be used for establishing cause– effect relationships under
experimental conditions (e.g., Agathokleous et al., 2019 ; De Marco
et al., 2013).
9.4 | Multiple stressors on forest ecosystems
For a better knowledge on combined effects of multiple factors on
ecosystem health, the selection of tree species for future studies
should account for their phylogenetic relatedness with already stud-
ied species. Ecological and environmental studies addressing the re-
sponses of tropical forests to combined effects of climate change and
air pollution should be intensified, in particular in areas at higher risk
of deforestation in the SH. This research domain is strongly biased
toward temperate and boreal forests of the NH. The evolutionary
changes in response to rising global CO2 levels and air temperature
elevation are known to occur in some plants, but the contribution of
evolutionary processes to the forest responses to steady CO2 and
air temperature rises remains unexplored. Experimental studies, ad-
dressing combined effects of different abiotic factors on forests,
should be intensified in the SH and should carefully select tree spe-
cies to assure a better representation of taxonomic and functional
diversity of the approximate 73,000 tree species now found on the
Earth (Cazzolla Gatti et al., 2022).
9.5 | Radioactive contamination of
forest ecosystems
Despite many papers reporting radioactivity effects on forest eco-
systems (Strand et al., 20 17; Tamaoki, 2016), there is still no con-
sensus on the mechanism through which radiation impacts forest
ecosystems or the dose rates at which impacts begin to occur
(Beresford et al., 2020; Strand et al., 2017). More robust and syn-
thesis studies are essential to inform (i) key processes regulating
the dynamics of radionuclides within forests; (ii) models for track-
ing radionuclides and prediction; (iii) holistic assessment of impacts
caused by radioactive contamination and its countermeasure devel-
opment; and (iv) use of 137Cs as a tracer. Furthermore, cost efficient
forest countermeasures must be developed and decisions must in-
clude locals, scientists, stakeholders, and governments.
9.6  | Genetic information of forest trees
More effort should focus on phenomics, combining high- throughput
capture of tree phenotypes, genotype information, data science,
and engineering (Falk et al., 2018; Naidoo et al., 2019). Future work
should include metadata integration and improved visualization for
comparative genomics. Characterizing the root traits and pheno-
types with association to genomics and shoot phenotyping is nec-
essary for whole- plant resistance breeding (Chuberre et al., 2018;
Tracy et al., 2020; Wiley et al., 2020). Rhizosphere phenotyping
opens new opportunities for experimental approaches, includ-
ing stress treatments, repeatability and combined use of imaging
techniques and machine learning to extract new traits from images,
within a systems approach (Tracy et al., 2020). The belowground net
primary production accounts for 40%– 70% of total terrestrial pro-
ductivity (Gherardi et al., 2020); therefore, more studies are needed
to explore response s of tree roots to climate and pol lution and quan-
tify root losses to belowground herbivores.
9.7 | Modeling and risk assessment
Model diversity constitutes a multi- purpose toolkit that can help
society to face the future challenges. Improving and enhancing
scientific communication in forest modeling is required as part of
this enterprise. The development of models integrating air pollution
and climate change data from long- term monitoring programs are
needed to improve forest research assessing interactions between
air pollution and climate change from the individual level to the stand
level. Future challenges include understanding of (i) the impacts of
air pollution on soil chemistry, (ii) the effects of climate change and
air pollution on plant phenol og y and reproductive fitness, (iii) the ca-
pacity of forests to sequester carbon under changing, and extremes,
climatic conditions and co- exposure to elevated levels of pollution,
and (iv) the effects of plant competitiveness (monocultures vs. mixed
cultures, single trees vs. community responses) on plant responses
to stressors.
This work was outlined in the framework of the Research Group
8.04.00 “Air Pollution and Climate Change” under the International
Union of Forest Research Organizations (IUFRO). IUFRO is the
largest international network of forest scientists, promoting global
cooperation in forest- related research and enhancing the under-
standing of the ecological, economic, and social aspects of forests
and trees. M.V.K. was supported by the Academy of Finland (pro-
jects 276671, 311929, and 316182). M.W. was supported by JST
SICORP (JPMJSC16HB). A.D.M., P.S., Y.H., and E.P. were supported
by the LIFE projects MODERN (LIFE20 GIE/IT/000091), MOTTLES
(LIFE15 ENV/IT/000183), and AIRFRESH (LIFE19 ENV/FR/000086).
Contributions of JCB were partially supported through funding from
the US Department of Energy under award number DE- EM0005228
to the University of Georgia Research Foundation. Open Access
DE MARCO et al.
Funding provided by ENEA Agenzia Nazionale per Le Nuove
Tecnologie l'Energia e lo Sviluppo Economico Sostenibile within the
CRUI- CARE Agreement.
The authors declare no conflict of interest.
Dat a sharing is not applicable to this article as no new dat a were cre-
ated or analyzed in this study.
Alessandra De Marco
Alessio Collalti
Enzai Du
Mikhail V. Kozlov
Elena Paoletti
Aber, J. D., Nadelhoffer, K. J., Steudler, P., & Melillo, J. M. (1989). Nitrogen
saturation in Northern forest ecosystems. BioScience, 39(6),
378– 386.
Agathokleous, E., Belz, R. G., Calatayud, V., De Marco, A., Hoshika, Y.,
Kitao, M., Saitanis, C. J., Sicard, P., Paoletti, E., & Calabrese, E. J.
(2019). Predicting the effect of ozone on vegetation via the linear
non- threshold (LNT), threshold and hormetic dose- response mod-
els. Science of the Total Environment, 6 49, 6174.
Agathokleous, E., Feng, Z., Oksanen, E., Sicard, P., Wang, Q., Saitanis,
C. J., Araminiene, V., Blande, J. D., Hayes, F., Calatayud, V.,
Domingos, M., Veresoglou, S. D., Peñuelas, J., Wardle, D. A ., De
Marco, A., Li, Z., Harmens, H., Yuan, X., Vitale, M., & Paoletti, E.
(2020). Ozone affects plant, insect, and soil microbial communi-
ties: A threat to terrestrial ecosystems and biodiversity. Scie nce
Advances, 6, eabc1176.
Agathokleous, E., Saitanis, C. J., Satoh, F., & Koike, T. (2015). Wild
plant species as subjects in O3 research. Eurasian Journal of Fores t
Research, 18, 1– 36.
Agathokleous, E., Saitanis, C. J., Wang, X., Watanabe, M., & Koike, T.
(2016). A review study on past 40 years of research on effects of
tropospheric O3 on belowground structure, functioning, and pro-
cesses of trees: A linkage with potential ecological implications.
Water, Air, & Soil Pollution, 227, 33.
Akselsson, C., Olsson, J., Belyazid, S., & Capell, R. (2016). Can increased
weathering rates due to future warming compensate for base
cation losses following whole- tree harvesting in spruce forests?
Biogeochemistry, 128, 89– 105.
Alonso, R., Elvira, S., González- Fernández, I., Calvete, H., García- Gómez,
H., & Bermejo, V. (2014). Drought stress does not protect Quercus
ilex L. from ozone effects: Results from a comparative study of two
subspecies differing in ozone sensitivit y. Plant Biology, 16, 375– 384.
Alonso, R., Elvira, S., Inclán, R., Bermejo, V., Castillo, F. J., & Gimeno, B.
S. (2003). Responses of Aleppo pine to ozone. In D. F. Karnosky,
K. E. Percy, A . H. Chappelka, & C. J. Simpson (Eds.), Air pollution,
global change and forests in the new millenium (pp. 211– 230). Els evier
Science Ltd.
Anav, A., De Marco, A., Collalti, A ., Emberson, L., Feng, Z., Lombardozzi,
D., Sicard, P., Verbeke, T., Viovy, N., Vitale, M., & Paoletti, E. (2022).
Legislative and functional aspects of different metrics used for
ozone risk assessment to forests. Environmental Pollution, 295,
Anav, A., De Marco, A., Proietti, C., Alessandri, A., Dell'Aquila, A., Cionni,
I., Friedlingstein, P., Khvorostyanov, D., Menut, L., Paoletti, E.,
Sicard, P., Sitch, S., & Vitale, M. (2016). Comparing concentration-
based (AOT40) and stomatal uptake (PODY) metrics for ozone
risk assessment to European forests. Global Change Biology, 22,
Antoniadis, V., Levizou, E., Shaheen, S. M., Ok, Y. S., Sebastian, A., Baum,
C., Prasad, M. N. V., Wenzel, W. W., & Rinklebe, J. (2017). Trace
elements in the soil- plant interface: Phytoavailability, transloca-
tion, and phytoremediation– A review. Earth- Science Reviews, 171,
621– 645.
Aoyama, M., Hirose, K., & Igarashi, Y. (2006). Re- construction and up-
dating our understanding on the global weapons tests 137Cs fallout.
Journal of Environmental Monitoring, 8, 431.
Archer, C. R., Pirk, C. W. W., Carvalheiro, L. G., & Nicolson, S. W.
(2014). Economic and ecological implications of geographic bias
in pollinator ecology in the light of pollinator declines. Oikos, 123,
401– 407.
Augustaitis, A., Augustaitienė, I., Kliučius, A., Pivoras, G., Šopauskienė,
D., & Girgždienė, R. (2010). The seasonal variability of air pollu-
tion effects on pine conditions under changing climates. European
Journal of Forest Researc, 129, 431– 441.
Augustaitis, A., & Bytnerowicz, A. (2008). Contribution of ambient
ozone to Scots pine defoliation and reduced growth in the Central
European forests: A Lithuanian case stud. Environmental Pollution,
155, 436– 445.
Badea, O., Silaghi, D., Taut, I., Neagu, S., & Leca, S. (2013). Forest
monitoring- assessment, analysis and warning system for forest
ecosystem status. Notulae Botanicae Horti Agrobotanici Cluj- Napoca,
41, 613– 625.
Baig, S., Medlyn, B. E., Mercado, L., & Zaehle, S. (2015). Does the growth
response of woody plants to elevated CO2 increase with tempera-
ture? A model- oriented meta- analysis. Global Change Biology, 21,
4303– 4319.
Bebber, D. P. (2021). The gap between atmospheric nitrogen deposi-
tion experiments and reality. Science of the Total Environment, 801,
Belyazid, S., Phelan, J., Nihlgård, B., Sverdrup, H., Driscoll, C., Fernandez,
I., Aherne, J., Teeling- Adams, L. M., Bailey, S., Arsenault, M.,
Cleavitt, N., Engstrom, B., Dennis, R., Sperduto, D., Werier, D., &
Clark, C. (2019). Assessing the effects of climate change and air
pollution on soil properties and plant diversity in northeastern U.S.
hardwood forests: Model setup and evaluation. Water, Air, & Soil
Pollution, 230, 1– 33.
Belyazid, S., Sverdrup, H., Kurz, D., & Braun, S. (2010). Exploring ground
vegetation change for different deposition scenarios and methods
for estimating critical loads for biodiversity using the for SAFE- VEG
model in Switzerland and Sweden. Water, Air, & Soil Pollution, 216 ,
2 8 9 3 1 7 .
Benjamin, M. T., & Winer, A. M. (1998). Estimating the ozone e forming
potential of urban trees and shrubs. Atmospheric Environment, 32,
5 3 6 8 .
Beresford, N. A., Fesenko, S., Konoplev, A., Skuterud, L., Smith, J. T., &
Voigt, G. (2016). Thirty years after the Chernobyl accident: What
lessons have we learnt? Journal of Environmental Radioactivity, 157,
7 7 8 9 .
Beresford, N. A ., Scott, E. M., & Copplestone, D. (2020). Field effects
studies in the Chernobyl Exclusion Zone: Lessons to be learnt.
Journal of Environmental Radioactivity, 211, 105893.
Berger, T. W., Türtscher, S., Berger, P., & Lindebner, L. (2016). A slight
recovery of soils from acid rain over th e last thre e dec ade s is not re-
flected in the macro nutrition of beech (Fagus sylvatica) at 97 forest
stands of the Vienna woods. Environmental Pollution, 216, 6 2 4 6 3 5 .
Blanco, J. A. (2013). Modelos ecológicos: Descripción, explicación y pre-
dicción. Ecosistemas, 22(3), 1– 5.
    DE MARCO et al.
Blan co, J. A., Ameztegui, A., & Rodríg uez, F. (2020) . Mod ellin g forest eco -
systems: A crossroad between scales, techniques and applications.
Ecological Modelling, 425, 109030.
Blande, J. D. (2021). Effects of air pollution on plant- insect interac-
tions mediated by olfactory and visual cues. Current Opinion in
Environmental Science & Health, 19, 100228.
Boccuzzi, G., Nakazato, R. K., Pereira, M. A. G., Rinaldi, M. C. S., Lopes,
M. I. M. S., & Domingos, M. (2021). Anthropogenic deposition in-
creases nitrogen- phosphorus imbalances in tree vegetation, litter
and soil of Atlantic Forest remnants. Plant and Soil, 461, 341– 354.
Bonan, G. B. (2008). Forests and climate change: Forcings, feedbacks,
and the climate benefits of forests. Science, 320, 1444– 1449.
Bruckman, V. J., Terada, T., Fukuda, K., Yamamoto, H., & Hochbichler, E.
(2016). Overmature periurban QuercusCarpinus coppice forests in
Austria and Japan: A comparison of carbon stocks, stand charac-
teristics and conversion to high forest. European Journal of Forest
Research, 135, 857– 869.
Butler, T., Vermeylen, F., Lehmann, C. M., Likens, G. E., & Puchalski, M.
(2016). Increasing ammonia concentration trends in large regions
of the USA derived from the NADP/AMoN network. Atmospheric
Environment, 146, 132– 140.
Bytnerowicz, A., Hsu, Y. M., Percy, K., Legge, A., Fenn, M. E., Schilling,
S., Frączek, W., & Alexander, D. (2016). Ground- level air pollution
changes during a boreal wildland mega- fire. Science of the Total
Environment, 572, 755– 769.
Calfapietra, C., Fares, S., Manes, F., Morani, A., Sgrigna, G., & Loreto,
F. (2013). Role of biogenic volatile organic compounds (BVOC)
emitted by urban trees on ozone concentration in cities: A review.
Environmental Pollution, 183, 71– 80.
Cassimiro, J. C., Moura, B. B., Alonso, R., Meirelles, S. T., & Moraes, R. M.
(2016). Ozone stomatal flux and O3 concentration- based metrics
for Astronium graveolens Jacq., a Bra zi li an native forest tr ee species.
Environmental Pollution, 213, 1007– 1015.
Cazzolla Gatti, R., Reich, P. B., Gamarra, J. G. P., Crowther, T., Hui, C.,
Morera, A., Bastin, F., de Miguel, S., Nabuurs, G.- J., Svenning, J.-
C., Serra- Diaz, J. M., Merow, C., Enquist, B., Kamenetsky, M., Lee,
J., Zhu, J., Fang, J., Jacobs, D. F., Pijanowski, B., Liang, J. (2022).
The number of tree species on Earth. Proceedings of the National
Academy of Sciences of the United States of America, e2115329119.
https://doi.or g/10.1073/pnas.21153 29119
Chino, M., Nakayama, H., Nagai, H., Terada, H., Katata, G., & Yamazawa,
H. (2011). Preliminary estimation of release amounts of 131I and
137Cs accidentally discharged from the Fukushima Daiichi Nuclear
Power Plant into the atmosphere. Journal of Nuclear Science and
Technology, 48(7), 1129– 1134.
Chiwa, M. (2020). Ten- year determination of atmospheric phospho-
rus deposition at three forested sites in Japan. Atmospheric
Environment, 223, 117247.
Chuberre, C., Plancot, B., Driouich, A., Moore, J. P., Bardor, M., Gügi, B.,
& Vicré, M. (2018). Plant immunity is compartmentalized and spe-
cialized in roots. Frontiers in Plant Science, 9, 1692.
Clark, C. M., Bai, Y., Bowman, W. D., Cowles, J. M., Fenn, M. E., Gilliam,
F. S., Phoenix, G. K., Siddique, I., Stevens, C. J., Sverdrup, H.
U., & Throop, H. L. (2013). Nitrogen Deposition and Terrestrial
Biodiversity. In S. A. Levin (Ed.), Encyclopedia of biodiversity (2nd ed.,
pp. 519– 536). Elsevier Inc.
Collalti, A., Ibrom, A., Stockmarr, A., Cescatti, A., Alkama, R., Fernández-
Martínez, M., Ciais, P., Sitch, S., Friedlingstein, P., Goll, D. S., Nabel,
J. E. M. S., Pongratz, J., Arneth, A., Haverd, V., & Prentice, I. C.
(2020). Forest production efficiency increases with growth tem-
perature. Nature Communications, 11, 5322.
Collalti, A ., Marconi, S., Ibrom, A., Trotta, C., Anav, A., D'Andrea,
E., Matteucci, G., Montagnani, L., Gielen, B., Mammarella, I.,
Grünwald, T., Knohl, A., Berninger, F., Zhao, Y., Valentini, R., &
Santini, M. (2016). Validation of 3D- CMCC Forest Ecosystem
Model (v.5.1) against eddy covariance data for 10 European forest
sites. Geoscientific Model Development, 9, 479– 504.
Collalti, A., Trotta, C., Keenan, T., Ibrom, A., Bond- Lamberty, B., Grote, R.,
Vicca, S., Reyer, C. P. O., Migliavacca, M., Veroustraete, F., Anav, A.,
Campioli, M., Scoccimarro, E., Grieco, E., Cescatti, A., & Matteucci,
G. (2018). Thinning can reduce losses in carbon use efficiency and
carbon stocks in managed forests under warmer climate. Journal of
Advances in Modelling Earth Systems, 10(10), 2427– 2452.
Committee on the Biological Effects of Ionizing Radiation. (1990). Health
effects of exposure to low levels of ionizing radiation: BEIR V. National
Research Council, National Academy Press.
Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M.,
Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J.- F.,
Naik, V., Oltmans, S. J., Schwab, J., Shindell, D. T., Thompson, A. M.,
Thouret, V., Wang, Y., & Zbinden, R. M. (2014). Global distribution
and trends of tropospheric ozone: An observation- based review.
Elementa: Science of the Anthropocene, 2, 000029.
Coordination Centre for Effects. (2017). European critical loads:
Database , biodiversity and ecosystems at risk. CCE final report 2017
(J.- P. Hettelingh, M. Posch, & J. Slootweg, Eds.). Rijksinstituut voor
Volksgezondheid en Milieu.
Cunha- Zeri, G., & Ometto, J. (2021). Nitrogen emissions in Latin America:
A conceptual framework of drivers, impacts, and policy responses.
Environmental Development, 38, 100605.
Curran, P. J., Dungan, J. L., & Gholz, H. L. (1992). Seasonal LAI in slash
pine estimated with Landsat TM. Remote Sensing of Environment,
39(1), 3– 13.
Curtis, P. S., & Wang, X. Z. (1998). A meta- analysis of elevated CO2 ef-
fects on woody plant mass, form, and physiology. Oecologia, 113,
299– 313.
Dale, V. H., Joyce, L. A., McNulty, S., & Neilson, R. P. (2000). The interplay
between climate change, forests, and disturbances. Science of the
Total Environment, 262, 201204.
De Jager, N. R., Drohan, P. J., Miranda, B. M., Sturtevant, B. R., Stout, S. L.,
Royo, A. A., Gustafson, E. J., & Romanski, M. C. (2017). Simulating
ungulate herbivory across forest landscapes: A browsing extension
for LANDIS- II. Ecological Modelling, 350, 11– 2 9.
De Marco, A., Anav, A., Sicard, P., Feng, Z., & Paoletti, E. (2020). High