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Diversity and Distributions. 2020;26:1646–1662.wileyonlinelibrary.com/journal/ddi
Received: 26 March 2020
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Revised: 30 July 2020
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Accepted: 18 August 2020
DOI: 10.1111/ddi.13158
BIODIVERSITY RESEARCH
Protection gaps and restoration opportunities for primary
forests in Europe
Francesco M. Sabatini1,2,3 | William S. Keeton4 | Marcus Lindner5 |
Miroslav Svoboda6 | Pieter J. Verkerk7 | Jürgen Bauhus8 | Helge Bruelheide1,2 |
Sabina Burrascano9 | Nicolas Debaive10 | Inês Duarte11 | Matteo Garbarino12 |
Nikolaos Grigoriadis13 | Fabio Lombardi14 | Martin Mikoláš6,15 | Peter Meyer16 |
Renzo Motta12 | Gintautas Mozgeris17 | Leónia Nunes11,18 | Péter Ódor19 |
Momchil Panayotov20 | Alejandro Ruete21 | Bojan Simovski22 |
Jonas Stillhard23 | Johan Svensson24 | Jerzy Szwagrzyk25 | Olli-Pekka Tikkanen26 |
Kris Vandekerkhove27 | Roman Volosyanchuk28 | Tomas Vrska29 |
Tzvetan Zlatanov30 | Tobias Kuemmerle3,31
1Institut für Biologie, Martin-Luther-Universität Halle-Wittenberg, Halle, Germany
2German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
3Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
4Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, V T, USA
5Resilience Programme, European Forest Institute, Bonn, Germany
6Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6 – Suchdol, Czech Republic
7European Forest Institute, Joensuu, Finland
8Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
9Department of Environmental Biology, Sapienza University of Rome, Rome, Italy
10Réserves Naturelles de France, Dijon Cedex, France
11Centre for Applied Ecolog y “Professor Baeta Neves” (CEABN), InBIO, School of Agriculture, University of Lisbon, Lisbon, Portugal
12Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Grugliasco, Italy
13Forest Research Institute Thessaloniki, Thessaloniki, Greece
14Department of Agraria, Mediterranean University of Reggio Calabria – Feo Di Vito, Reggio Calabria, Italy
15PRALES, Rosina, Slovakia
16Northwest German Forest Research Institute, Göttingen, Germany
17Agriculture Academy, Institute of Forest Management and Wood Science, Vytautas Magnus University, Akademija, Lithuania
18CITAB, Centre of the Research and Technology of Agro-Environmental and Biological Science, University of Trás-os-Montes and Alto Douro, Vila Real,
Portugal
19Centre for Ecological Research Institute of Ecology and Botany, Vácrátót, Hungary
20University of Forestr y, Sofia, Bulgaria
21Greensway AB, Uppsala, Sweden
22Hans Em Faculty of Forest Sciences, L andscape Architecture and Environmental Engineering, Depar tment of Botany and Dendrology, Ss. Cyril and Methodius
University in Skopje, Skopje, Nor th Macedonia
23Forest Resources and Management, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Swit zerland
24Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
25Department of Forest Biodiversity, Universit y of Agriculture in Krakow, Krakow, Poland
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.
© 2020 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd.
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SABATINI eT A l.
26School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
27Research Institute for Nature and Forest (INBO), Geraardsbergen, Belgium
28NGO "Ecosphere" – Koshyts'ka, Uzhhorod, Ukraine
29Silva Tarouca Research Institute, Brno, Czech Republic
30Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria
31Integrative Research Institute on Transformation in Human-Environment Systems, Humboldt-Universität zu Berlin, Berlin, Germany
Correspondence
Francesco M. Sabatini, Institut für
Biologie, Martin-Luther-Universität Halle-
Wittenberg, Am Kirchtor 1, 06108 Halle,
Germany.
Email: francesco.sabatini@botanik.uni-halle.de
Funding information
European Commission, Grant/Award
Number: 658876; Portuguese Foundation
for Science and Technology, Grant/
Award Number: UID/AGR/04033/2019;
Naturvårdsverket, Grant/Award Number:
NV-03 5 01-15
Abstract
Aims: Primary forests are critical for forest biodiversity and provide key ecosystem
services. In Europe, these forests are particularly scarce and it is unclear whether they
are sufficiently protected. Here we aim to: (a) understand whether extant primary
forests are representative of the range of naturally occurring forest types, (b) iden-
tify forest types which host enough primary forest under strict protection to meet
conservation targets and (c) highlight areas where restoration is needed and feasible.
Location: Europe.
Methods: We combined a unique geodatabase of primary forests with maps of for-
est cover, potential natural vegetation, biogeographic regions and protected areas
to quantify the proportion of extant primary forest across Europe's forest types and
to identify gaps in protection. Using spatial predictions of primary forest locations
to account for underreporting of primary forests, we then highlighted areas where
restoration could complement protection.
Results: We found a substantial bias in primary forest distribution across forest types.
Of the 54 forest types we assessed, six had no primary forest at all, and in two-
thirds of forest types, less than 1% of forest was primary. Even if generally protected,
only ten forest types had more than half of their primary forests strictly protected.
Protecting all documented primary forests requires expanding the protected area
networks by 1,132 km2 (19,194 km2 when including also predicted primary forests).
Encouragingly, large areas of non-primary forest existed inside protected areas for
most types, thus presenting restoration opportunities.
Main conclusion: Europe's primary forests are in a perilous state, as also acknowledged
by EU's “Biodiversity Strategy for 2030.” Yet, there are considerable opportunities for
ensuring better protection and restoring primary forest structure, composition and
functioning, at least partially. We advocate integrated policy reforms that explicitly
account for the irreplaceable nature of primary forests and ramp up protection and
restoration efforts alike.
KEYWORDS
biodiversity conservation, conservation priorities, gap analysis, old-growth forest, primary
forest, protected areas, protection gap, restoration opportunities, strict protection, virgin
forest
1 | INTRODUCTION
Primary forests continue to disappear worldwide (FAO, 2016; Mackey
et al., 2015; Watson et al., 2016, 2018), even in regions where for-
ests are expanding (Potapov et al., 2017; Song et al., 2018). Their loss
is deeply concerning since primary forests are an irreplaceable part
of our natural heritage (Watson et al., 2018) and are critical for con-
serving forest biodiversity (Di Marco, Ferrier, Harwood, Hoskins, &
Watson, 2019; Dvořák et al., 2017; Gibson et al., 2011). Primary for-
ests provide important ecosystem services, such as carbon storage
and riparian functionality (Ford & Keeton, 2017; Warren, Keeton,
Bechtold, & Kraft, 2019; Watson et al., 2018). And while they have
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long been known to harbour high levels of biodiversity, particularly
for certain taxa such as bryophytes, fungi, lichens and saproxylic
beetles (Eckelt et al., 2018; Paillet et al., 2010; Watson et al., 2018),
recent research has shown that primary forests frequently also have
high functional trait diversity, which contributes to the resilience of
ecosystem service outputs to global change (Messier, Puettmann,
& Coates, 2013; Thom et al., 2019). Finally, where forest extent has
declined or forests have been heavily altered from historic baselines,
primary forests are also an important reference for guiding resto-
ration and adapting to global change (Kuuluvainen, 2002; Parviainen,
Bücking, Vandekerkhove, Schuck, & Päivinen, 2000).
Primary forests are naturally regenerated forests composed of
native species, where signs of past human use are minimal and eco-
logical processes, such as natural disturbances, operate dynamically
and with little impairment by anthropogenic influences (Barton &
Keeton, 2018; CBD, 2006; FAO, 2015). Globally, about one-third of
all forests can be considered primary, but most are located in re-
mote areas in the tropics, boreal zones or mountain regions (Potapov
et al., 2017). By contrast, primary forests are scarce in the sub-trop-
ical and temperate zones (Sabatini et al., 2018; Watson et al., 2016).
In Europe, millennia of land use deeply transformed the forested
landscapes (Kaplan, Krumhardt, & Zimmermann, 2009), so that very
few forests remain with minimal signs of human use (<4% of forest
area; FO REST EUROPE , 2015b). Yet, it is un cle ar wh eth er th ese rem-
nants are representative of the range of natural forest types found
in Europe (Sabatini et al., 2018), and whether they are effectively
protected.
Where primary forests still exist, ensuring that a sufficiently
large area is adequately protected should be the first priority from
a conservation perspective. Yet, there is a lack of consensus on how
much primary forest should be protected for safeguarding biodi-
versity (Lõhmus, Kohv, Palo, & Viilma, 2004; Mair et al., 2018; Noss
et al., 2012; Parviainen et al., 2000; Visconti et al., 2019). For in-
stance, the Aichi target #11 of the Convention of Biological Diversity
requires 17% of terrestrial land to be conserved in ecologically rep-
resentative systems of protected areas (CBD, 2010). In its National
Strategy on Biological Diversity, Germany committed to protecting
at least 5% of forested areas in wilderness areas (Schumacher, Finck,
Riecken, & Klein, 2018). Yet, most international agreements (CBD,
2010; European Commission, 1992; UN General Assembly, 2015)
do not explicitly refer to primary forest, which adds uncertainty
to conservation objectives (Chiarucci & Piovesan, 2019; Mackey
et al., 2015; Watson et al., 2018). Only recently the EU commission
released a new “Biodiversity Strategy for 2030,” which emphasizes
the need to define, map, monitor and strictly protect all of the EU's
remaining primary and old-growth forests (European Commission,
2020). Until this strategy comes into force, however, many pri-
mary forests remain unprotected (Mikoláš et al., 2019; Sabatini
et al., 2018), and it is unclear in which forest types such protection
gaps are largest.
Where protection does exist, it should be sufficiently strict to
avoid primary forest degradation. Many protected areas allow for
human activities (e.g. salvage logging) that could jeopardize natural
forest dynamics, such as successional recovery from natural dis-
turbance and carryover of biological legacies (Mikoláš et al., 2019;
Thorn et al., 2018). Such activities should thus be banned from pri-
mary forests, if the goal is to allow these forests to develop naturally.
Identifying upgrading gaps (i.e. protected areas requiring an upgrade
to strict protection) is therefore a second major priority to safeguard
primary forests in the long-term.
Finally, given the overall very small area still covered by primary
forest for most forest types, even protecting these areas entirely is
likely insufficient for meeting biodiversity targets for many forest
types (Keenelyside, Dudley, Cairns, Hall, & Stolton, 2012). Where
the area of extant primary forest is too low, promoting the devel-
opment of primary forest structure, composition and functioning in
non-primary (e.g. secondary and managed forests) forests is crucial.
Depending on the context and starting conditions (e.g. connectiv-
ity, presence of keystone species), restoration could happen either
passively (e.g. setting aside forest and discontinuing forest man-
agement, salvage logging or disturbance suppression) or actively
(e.g. removing non-native species, translocating species, restor-
ing natural hydrological conditions or promoting the development
of key structural elements, such as deadwood or veteran trees;
Keenelyside et al., 2012; Mazziotta et al., 2016; Mikoláš et al., 2019;
Schnitzler, 2014). Still, restoring conditions closer to those found in
primary forests faces many challenges, not the least of which is the
long timeframes involved. Where primary forests are scarce, lack
of regeneration material may impede restoration of compositional
diversity. Climate change adds uncertainty, as it is unclear where
species may thrive in the future (Cernansky, 2018). Yet, it provides
an additional argument for forest restoration, because increas-
ing the structural and compositional diversity of forests improves
their resistance and resilience to climate change effects (Barton &
Keeton, 2018; Betts, Phalan, Frey, Rousseau, & Yang, 2018; Mair
et al., 2018). Identifying where restoration gaps exist (i.e. areas
where restoring primary forests is needed and feasible) is therefore
a third conservation priority.
Building on a unique and comprehensive spatial database of
documented primary forests in Europe (Sabatini et al., 2018), as
well as on country-level statistics of primary forests (FOREST
EUROPE, 2015b), here we address three questions:
1. Are remaining primary forests representative of Europe's bio-
geographic diversity and forest types?
2. Which forest types have a sufficient proportion of primary forest
under strict protection and which forest types would meet differ-
ent conservation targets?
3. Where would primary forest restoration efforts best complement
protection to reach long-term conservation targets?
Compared to our previous work (Sabatini et al., 2018), which
focused on understanding the spatial determinants underlying the
current distribution of known primary forests, this study advances
existing knowledge on primary forests by (a) systematically assessing
their extent and distribution in relation to biogeographical regions
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SABATINI eT A l.
and forest types in Europe and (b) comprehensively characterizing
and mapping different conservation gaps. By identifying protection
and restoration gaps and priorities, in particular, we contribute to the
scientific knowledge urgently needed for developing an integrated
strateg y for protecting and restoring forest s with primary character-
istics across Europe's landscapes, as called for in the framework of
the new “EU Biodiversity Strategy for 2030” (European Commission,
2020).
2 | METHODS
2.1 | Input data
As acknowledged by the Convention of Biological Diversity, the
term “primary forest” has a different connotation in Europe com-
pared to the rest of the world. It refers to forests which have never
been completely cleared, at least throughout historical times, even
if traditional human disturbances (e.g. coppicing, burning, partial
logging) may have occurred (CBD, 2006). In line with the Food and
Agricultural Organization (FAO, 2015), here we consider a forest
as “primary” where the signs of former human impacts, if any, are
strongly blurred due to decades (at least 60–80 years) without for-
estry operations (Buchwald, 2005). We do not imply, therefore, that
these forests were never cleared nor disturbed by humans.
We used a novel database of primary forests in Europe, excluding
Russia (Sab ati ni et al., 2018). This ma p aggreg ate s and harm oni ze s in-
formation derived from existing local-to-regional maps and datasets,
scientific literature and original data from forest experts. In total, the
map includes 1.4 Mha of primary forest across 32 European coun-
tries and represents a comprehensive, spatially explicit database on
known primary forests in Europe (Sabatini et al., 2018).
To assess the distribution of Europe's total forested area, we used
a high-resolution (25 m) map of forest cover (Kempeneers, Sedano,
Seebach, Strobl, & San-Miguel-Ayanz, 2011), which we aggregated
at 250-m resolution (pixel size = 6.25 ha) for computational reasons.
Since this map does not cover some Eastern European countries (e.g.
Ukraine, Belarus or Moldova), we integrated it with data on frac-
tional tree cover (original resolution 30 m) from the Global Forest
Watch (Hansen et al., 2013), which we also aggregated to a reso-
lution of 250 m. Percentage forest (or tree) cover estimated using
these two data sources had a good match in overlapping areas (i.e.
Poland, Slovakia and Romania), with Pearson's r correlation esti-
mated over 1,000 random points (with a 5 km minimum distance
between points) of 0.87 (p < .001). For our analysis, we defined
each 6.25 ha pixel as forest when forest\tree cover was >40%. This
threshold discriminates between open and closed forests as defined
by FAO (FAO, 2018).
We derived a map of forest types following a multi-step proce-
dure. We started with the map of the potential natural vegetation of
Europe (BfN, 2003), which reports potential zonal and azonal veg-
etation that would occur after a successional process undisturbed
by humans. Next, we cross-linked the >700 legend classes from this
map to the 13 forest categories (plantations excluded) defined by
the European Environmental Agency (EEA, 2006), as in Table S1.
By aggregating classes belonging to the same category, we could
then create a map with the potential distribution of forest catego-
ries in the absence of human disturbance. We then masked the map
of potential forest categories with the forest-cover map to quan-
tify the actual amount of forest area in each category (Figure S1).
Disaggregating categories across Europe's biogeographical regions
(BfN, 2003) yielded 54 forest types, defined as the combination be-
tween forest category and biogeographical region.
2.2 | Accounting for reporting gaps
To account for underreporting of primary forests data, we created a
composite dataset complementing different data sources. For each
country, we calculated the difference between the fraction of forests
contained in the map of primary forests (Sabatini et al., 2018), and
the country area estimates of forest undisturbed by man (FOREST
EUROPE, 2015b). The latter data are based on national interpreta-
tions of forest undisturbed by man and typically derive from for-
est inventories or individual studies (FOREST EUROPE, 2015a). We
considered this difference as an estimate of the amount of primary
forest not yet mapped for each country (Table S2). We then assigned
a corresponding fraction of forested area to primary forest, based
on the likelihood that each 250 m grid cell contains primary forests.
To calculate this likelihood, we trained a spatially explicit boosted
regression tree (BRT) model relating the presence of primary forests
(response variable) to a set of 15 non-collinear (Pearson's r < 0.7)
biophysical, socio-economic and historical land use predictors
(Table S3). This model is conceptually equivalent to the one pre-
sented in Sabatini et al. (2018), but downscaled to a 250 m resolu-
tion. Since spatial clustering might lead to inaccurate models (Phillips
et al., 2009), we rarefied primary forest presence points based on a
5 × 5-km grid. We selected 37,060 pseudo-absence points (i.e. ten
times the number of presences after rarefaction), stratified to con-
trol for the unequal sampling intensity across different European
countries or administrative regions. We set a learning rate of 0.02,
a tree complexity of 5 and a bag fraction of 0.7. We used the gbm.
step routine provided by the R dismo package (Hijmans, Phillips,
Leathwick, & Elith, 2011) to determine the optimal number of trees
(n = 1,650). We also reported the relative importance of each pre-
dictor, that is, the number of times that a variable was selected for
splitting in the BRT model, weighted by the squared improvement to
the model averaged over all trees (Elith et al., 2006) and produced
partial dependency plots for the most important predictors.
2.3 | Representativeness of primary forests
To evaluate the representativeness of primary forest distribution
along environmental gradients, we compared the probability–density
distributions between the forested area of Europe, and the database
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of documented primary forests (Sabatini et al., 2018), separately for
each biogeographical region. For this analysis, we used only the da-
tabase of documented primary forests (i.e. not the composite dataset
outlined above). We considered five environmental variables: elevation
(NA S A , 20 06), year l y solar ra d iatio n (N A SA , 20 0 6), gr ow i n g degre e da y s
(>5°C) (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), water availa-
bility (i.e. the ratio of actual over potential evapotranspiration, referred
to as Pries tley–Taylor alpha coeff icient in Trabu cco & Zomer, 2010) and
suitability to agricultural crops (Zabel, Putzenlechner, & Mauser, 2014).
We considered elevation as a proxy for forest accessibility. Yearly solar
radiation provides a quantitative estimation of topography-related
productivity at a given latitude. We preferred growing degree days
over mean annual temperature since it better represents the growing
conditions during the vegetative season. Similarly, we assumed the
ratio of actual over potential evapotranspiration to provide a better
representation of water availability compared to mean annual precipi-
tation. Finally, we used suitability to agricultural crops to account for
site productivity and land use competition.
To account for collinearity in the environmental data, we also com-
pared the distribution of forested area in Europe to that of primary
forest using a principal component analysis (PCA). After scaling each
variable to zero mean and unit standard deviation, we ran a PCA of
all the forested 250 m pixels of Europe. We then tested whether the
density estimates of the distributions of forested area pixels and pri-
mary forest pixels in the PCA space originated from the same (multi-
variate) distribution. We estimated the probability-density func tions in
the PCA space using a kernel density estimation and then compared
these between forested and primary forest pixels using a squared dis-
crepancy measure. As this comparison test is non-parametric and as-
ymptotically normal, it does not require any subjective decisions, nor
the usual resampling techniques to compute p-values. We used the
function kde.test in the R package ks (Duong, Goud, & Schauer, 2012).
To explore whether primary forests are representative of
Europe's forest types, we first attributed each primary forest pixel
to its respective forest type using the map of potential forest types
described above. We did this because compositional data were only
available for a subset of primary forests. This approach assumes,
therefore, that all primary forests belong to their respective poten-
tial forest type. For each forest type, we then calculated: (a) the cur-
rent extent of all forest, (b) the extent of primary forest and (c) the
fraction of forest in primary conditions. We limited the analysis to
forest types with a potential extent >1,000 km2 and ran this compar-
ison both using the primary forest database (documented primary
forests only) and the composite dataset.
2.4 | Quantifying protection, upgrading and
restoration gaps
Given the lack of consensus on how much primar y forest should be
conserved in Europe, we considered three alternative conservation
targets: 17% (according to the Aichi target #11; CBD, 2010), 10% and
5% of forest area in primary st ate. We deemed there to be a protection
gap for a given forest type when insufficient amounts of primary for-
ests were within protected areas to meet conservation targets, but
only when additional primary forests for those forest types occurred
outside protected areas. Similarly, we identified upgrading gaps for
those forest types where primary forests are formally protected, but
not yet included within strictly protected areas. We considered strict
protection (= IUCN category I and II) to be the only protection level
sufficient to ensure long-term conservation of primary forests, since
in some European countries forest management (e.g. partial cutting,
salvage logging) is allowed even in protected areas with lower protec-
tion level (e.g. Natura 2000 areas). Finally, we indicated as restoration
gaps those situations when not enough primary forest exists, so that
restoration is required to reach a conservation target.
To quantify these three conservation gaps, we calculated the
share of primary forest under different protection levels for each for-
est type. We used spatial information on protected areas from the
World Database on Protected Areas (UNEP-WCMC, & IUCN, 2019).
We conservatively considered those protected areas where the IUCN
category was not specif ied (e.g. Natur a 20 00 areas) as bei ng protecte d,
but not strictly. This yielded, for each forest type, the area and share
of primary forest currently unprotected (protection gaps) or outside
strictly protected areas (upgrading gaps). Similarly, we quantified the
area and share of forested land that would have to undergo restoration
to meet a given conservation target (re storation gaps) as the dif ference
between a conservation area target and the current amount of primar y
forests for that forest type. For visualization purposes, we used tree-
map graphs (Tennekes, 2017), where we show the 17%, 10% or 5%
forest area having the highest conservation status (two levels: primary,
non-primary) and protection status (three levels: strict—IUCN protec-
tion category I and II, other—IUCN categories III-VI, and no protection)
for each forest type. We ran this analysis both using our database of
documented primary forests and the composite dataset, which ac-
counts for underreporting of primary forest data.
The analyses based on documented primary forest alone or on
the composite dataset are highly complementary. The former re-
turns a more accurate representation of protection and upgrading
gaps, but overestimates the amount of restoration gaps. The latter
generates better estimates of restoration gaps, but quantifies pro-
tection and up gra ding gaps less ac cur ately due to the unce r tai n loc a-
tion of undocumented (=predicted) primary forests. Therefore, we
presented the results of both analyses, but gave them different em-
phases depending on the specific conservation gap. For protection
and upgrading gaps, we presented the results based on documented
primary forest alone in the main text, and those based on the com-
posite dataset in the supplementary material. For restoration gaps,
we did the opposite.
2.5 | Mapping restoration opportunities
To pinpoint the most favourable areas where restoration could com-
plement protection to reach primary forest conservation targets
(17%, 10 or 5%), we mapped restoration opportunities. We selected
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areas suitable for restoration by selecting forested areas with the
highest likelihood to contain primary forests, based on the BRT model
described above. Since our BRT model showed that socio-economic
(i.e. accessibility, population density) and land use (i.e. agricultural
suitability, wood increment) determinants were good predictors of
primary forest location, we interpreted areas with higher likelihood
of containing primary forest as areas having lower land use pressure
and thus greater suitability for primary forest restoration. We prior-
itized forests in protected areas, because we assume restoration has
lower opportunity costs and higher social acceptability there. We
mapped restoration gaps separately for each forest type, again using
both datasets (documented primary forests and composite). In the
first case, the areas with the highest likelihood of containing primary
forests were all considered as areas suitable for restoration. In the
second case, these areas were split between additional (predicted)
primary forest and forest suitable for restoration.
We visualized the output of these analyses in two ways. First,
we built a choropleth showing the share of forested pixels in need of
conservation action (i.e. protection, upgrading or restoration gaps)
at the level of first- or second-order (depending on country size)
administrative regions in Europe (Global Administrative Areas, 2012).
Second, we aggregated the results into hexagonal forest landscapes
(ca. 6,000 km2) and reported the biggest conservation gap per land-
scape, separately for each forest type. We ranked gaps as follows:
(a) unpr ote cted primary fo res t s (=protection gap), (b) primary forests
occurring in protected areas of IUCN category III or higher (=up-
grading gap), (c) areas favourable for restoration in protected areas
(=restoration gap) and (d) areas favourable for restoration outside
protected areas (=restoration + protection gap). These maps show
neither primary, nor non-primary forests in strictly protected areas,
as these areas do not require conservation actions.
3 | RESULTS
3.1 | Biogeographical bias in primary forest
distribution
Primary forests encompassed remarkably well the variability in
climate (solar radiation, growing degree days—GDD 5°, water
FIGURE 1 Distribution of total and primary forest cover along main environmental gradients. The y-axis represents the proportion of
250 m pixels covered with either forest (blue), or primary forest (pink), so that the areas under the curves are equivalent. We only considered
those biogeographical regions with more than 10,000 km2 of total forested area. Dots and horizontal bars, respectively, represent the
mean and standard deviation of the distributions. Outliers (<2.5th and >97.5th percentiles) are not shown [Colour figure can be viewed at
wileyonlinelibrary.com]
Total
forest
Primary
forest
Pannonian
Mediterranean
Continental
Boreal
Atlantic
Alpine
0.0 0.5 1.0 1.5
Elevation
(km)
−1.00−0.75−0.50−0.25
Solar Rad.
ln
(
Mj−1cm−2yr−1
)
100 200 300 400
GDD 5°C
(No. Days)
40 60 80 100
Water
Availability (%)
0255075
Crop
Suitability (%)
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SABATINI eT Al.
availability), topography (elevation) and soil productivity (agri-
cultural suitability) occurring in Europe's biogeographical regions
(Figure 1). However, there were some key differences between
the distribution of primary forests and total forest cover. Primary
forests were over-represented at high elevations (except for the
Alpine region) and at the low end of the solar radiation gradient in
the Alpine, Atlantic and Boreal biomes. They also occurred more
often where yearly solar radiation is low, that is, where topographi-
cal conditions are relatively unfavourable, such as on steep and/or
north-facing-slopes. Primary forests also occurred more frequently
in colder conditions (low GDD), where water availability is higher
(with the exception of the Alpine region), and on land less suitable
for agriculture, especially in the Alpine, Atlantic and Boreal biomes.
The tendenc y towards high elevation, cold an d wet conditions with
low yea rly solar radiation was also visible after accounting for colline ar-
ity between variables and comparing the distribution of primary and
total forest in the multivariate environmental space defined by a princi-
pal component analysis (PCA; Figure 2). The two multivariate distribu-
tions were significantly (z = 383,805, p < .001) different according to
a kernel density based on global two-sample comparison test (Duong
et al., 2012) referring to the first four principal components (97.3% of
variation explained). This difference was also significant when consid-
ering each biogeographical region separately (Figure S2).
We found a substantial geographic bias in the distribution of
primary forests across forest types, both when using the compos-
ite dataset and our primary forest database only. The composite
dataset contains information on 3.5 Mha of primary forest (1.4 Mha
from Sabatini et al. (2018), and 2.1 Mha predicted). The model un-
derlying the composite dataset had a relatively high cross-validated
area under the curve (AUC, mean ± SD range 0.86 ± 0.007) and
correlation between observed and predicted primary forest likeli-
hood (rcv = 0.63 ± 0.007). After controlling for spatial sorting bias
(Hijmans, 2012), AUC reduced to 0.65 and rcv to 0.29. The most im-
portant explanatory variables were forest growing stock (relative
influence 12.1%), population density (10.7%), forest cover in 1,850
(9.6%) and accessibility (8.3%). Specifically, the model stresses that
primary forests are more likely to occur in less productive areas
where current and historical anthropogenic pressure is low. Indeed,
the likelihood of a pixel containing primary forest was higher where
growing stock and human population density were lower, and for-
est cover in 1,850 AD was higher. The relationship with accessibility
was more complex: primary forest likelihood increased for increas-
ing travel time from major cities up to a certain threshold and then
decreased abruptly (Figure S3).
Based on th e compos i t e da t aset , fo r only on e fo rest ty pe (no n - r i v-
erine alder, birch and aspen forest in the boreal biome), primary for-
est accounted for more than 17% of total forested area (Figure 3).
Of the remaining forest types, only one had a proportion of primary
forest >5%, and 13 forest types had a share of primary forest of
1%–5%. Another 33 forest types had between 0.01% and 1% of for-
est in primary state. For 13 of these, primary forest covered less
than 1,000 ha. No remaining primary forests were documented, or
predicted to exist, for the remaining six forest types, most of which
were located in the Atlantic and Alpine biomes (Figure 3). All these
results changed only marginally when considering our original data-
base of documented primary forests only (Figure S4). The number of
forest types having a relatively high proportion of primary forests
(1%–5%) decreased to seven, while those having little (0.01%–1%)
primary forest increased to 37. No primary forest was found in nine
forest types (Figure S4).
3.2 | Protection, upgrading and restoration gaps
When considering only our database of documented primary forests,
protection gaps were not particularly widespread across Europe's
FIGURE 2 Distribution of (a) all European forests, (b) primary
forests and (c) differences between the proportions of the two in
the multidimensional environmental space. The graphs are based on
a principal component analysis (PCA) based on elevation, growing
degree days (GDD 5°C), water availability, yearly solar radiation and
agricultural suitability. The first two principal components account
for 47.4% and 26.7% of the overall variation, respectively [Colour
figure can be viewed at wileyonlinelibrary.com]
Elevation
Water
availability
Solar
radiation
GDD
Crop
suitability
(a)
(b)
(c)
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SABATINI eT A l.
forest types. For only three forest types were there more than 80%
of remaining primary forests located outside protected areas, while
in an additional six forest types the proportion of unprotected pri-
mary forest was greater than 20% (Table S4; Figure S5). The situation
was considerably less favourable for primary forest protection when
basing the analysis on the composite dataset (Figure S6). In this case,
la r ge prot e c tion gap s (>80% of primary forest unprotected) occurred
in about one fourth of the forest types we considered (n = 12) and in
eight additional forest types, this proportion ranged between 50%
and 80% (Figure S6). Protecting all documented primary forests in
Europe would require expanding the current protected area net-
works by 1,132 km2. This area increased to 19,194 km2 when con-
sidering also undocumented (=predicted) primary forests (Table 1),
although this figure should be seen as an upper bound due to the
uncertain location of undocumented primary forests.
Upgrading gaps were very common, although for some countries
the IUCN category of protected areas is not consistently specified
(UNEP-WCMC, & IUCN, 2019). When considering documented pri-
mary forests only, there were 19 forest types where >80% of pri-
mary forest, albeit protected, was outside strict reserves of IUCN
category I or II (Figure 4; Figure S5; Table S4). In an additional six
and twelve forest types, this proportion was between 50%–80% and
20%–50%, respectively. More than half of the primary forest was
under strict protection in only ten forest types. A total of 5,109 km2
of documented primary forests qualified as in need of upgrading.
When considering our composite dataset, the number of forest types
with upgrading gaps exceeding 50% increased to eleven (Figure S6).
Based on our model, granting strict protection to all documented
and predicted primary forests in Europe would require upgrading an
additional 5,588 km2 of protected areas (0.1% of Europe's land area,
Table 1).
Meeting a 17% conservation target would require extensive res-
toration for most forest types (Figure 4). For most forest types, a high
fraction of protected non-primary forests was coupled with smaller
areas of primary forest (e.g. lowland, and montane beech forests in
the Alpine biome). For some other forest types, however, there was
neither enough primary forest, nor enough protected forest to fulfil
a 17% target (e.g. the taiga forest in the Atlantic biome). This general
situation neither changed for the least ambitious conservation target
(i.e. 5%) nor when repeating the analysis using the composite dataset
(Figure S7). Based on the composite dataset, an area approximately
the size of Romania (226,236 km2, 21.8% of Europe's forest area)
should undergo restoration if the goal would be to ensure that 17%
of Europe's forest approach primary, or close to primary conditions,
at some point in the future (Table 1). Of this area, 28.6% is currently
outside protected areas. Embracing conservation targets of 10% or
5% would decrease the required area to 107,440 and 30,331 km2,
respectively (Table 1).
3.3 | Restoration opportunities
We mapped the most favourable areas where restoration could
complement protection to reach primary forest conservation targets
FIGURE 3 Share and amount of primary forests across forest types. Numbers indicate the absolute extent of primary forests in
thousands of hectares as predicted when integrating data from Sabatini et al. (2018) and disaggregating data from FOREST EUROPE
(2015b). White cells represent either non-existing forest types, or forest types having an amount of total forest cover below 1,000 km2.
Biogeographical regions follow BfN (2003), and forest categories follow EEA (2006) [Colour figure can be viewed at wileyonlinelibrary.com]
88.14
0.24
1698
0.17
1787
0
0
568.6
8.81
577.4
280.2
2.44
1.14
283.8
1.79
6.82
24.23
15.49
0.94
<.01
49.27
7.05
0.58
1.2
8.86
1.48
0.17
19.33
39.9
1.95
75.19
0.13
117.2
291.5
2.82
18.87
14.1
329.4
11.69
0
5.31
11.23
0.06
29.45
0
5.69
5.69
14.01
3.87
17.88
1.1
0
67.75
6.06
0
74.91
1.02
0.48
<.01
0.39
0.11
0.16
2.18
116
8.14
114.2
238.3
852.5
21.02
2474
141.6
38.56
0.52
3528
Taiga
Hemiboreal−nemoral
Alpine coniferous
Acidophilus oak−birch
Mesophytic deciduous
Lowland Beech
Montane beech
Thermophilus deciduous
Broadleaved evergreen
Coniferous Mediterranean
Mire and swamp
Floodplain
Non−riverine
Alder−birch−aspen
Total
Total
Pannonian
Mediterranean
Continental
Boreal
Atlantic
Alpine
Share of
Primary for.
>17%
[5%, 17%)
[1%, 5%)
[0.1%, 1%)
[0.01, 0.1)
<0.01%
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SABATINI eT Al.
(Figure S8). The map showed that, for many forest types, favourable
areas were scattered throughout their respective biogeographical
regions. This is the case, for instance, for the mesophytic deciduous
forests in the continental region. For other forest types, we could
instead identify key regions for restoration. For the acidophilous
oak-birch forest s of the Continental biome, for instance, priority res-
toration areas were clustered along the Ukraine–Belarus border, in
Czech Republic, or in the western Cantabrian range. Similarly, for
thermophilous deciduous forests, priority areas for restoration were
widespread along the Apennines, as well as in the Spanish Pyrenees.
TABLE 1 Summary statistics for protection, upgrading and restoration gaps in Europe (excluding Russia). Only biogeographical regions
hosting >10,000 km2 of forest shown. These estimates are based on a composite dataset merging data from Sabatini et al. (2018) and
country-level estimates from FOREST EUROPE (2015b)
Alpine Atlantic Boreal Continental Mediterranean Pannonian Total
Land area
km2674, 547 855,030 983,369 1,858,760 93 7,114 151,20 5 5,7 71, 245
Forest area
km2226,962 126,722 662,233 570,294 150,355 18 ,4 41 1,770,381
%33.65 14.82 67. 3 4 30.68 16 .04 12.20 30.68
Primary forest area
km28,525 210 24,772 1,416 386 535 ,314
% of land area 1.26 0.02 2.52 0.08 0.04 0.00 0. 61
% of forest area 3.76 0.17 3.74 0.25 0 .26 0.03 1.9 9
Protection gapsa
km23,304 146 14,855 642 247 119,194
% of land area 0.49 0.02 1. 51 0.03 0.03 0.00 0.33
% of forest area 1.46 0.12 2 .24 0 .11 0.16 0.0 0 1.08
Upgrading gapsa
km22,618 16 2,573 299 79 35,588
% of land area 0.39 0.00 0. 26 0.02 0.01 0.00 0.10
% of forest area 1.15 0.01 0.39 0.05 0.05 0.02 0.32
Restoration gaps
Target 17%
km217,0 4 3 19,196 79,736 86,936 18,926 2,432 226,236
% of land area 2.5 2.2 8.1 4.7 2.0 1.6 3.9
% of forest
area
7.5 15.1 12.0 15.2 12.6 13.2 12.8
% not
protected
1.4 12.5 76.2 0.0 0 .1 0.0 28.6
Target 10%
km25,353 10,620 33,732 47,13 5 8,485 1,147 1 07,44 0
% of land area 0.8 1.2 3.4 2.5 0.9 0.8 1 .9
% of forest
area
2.4 8.4 5.1 8.3 5.6 6.2 6 .1
% not
protected
1.2 9.2 47. 1 0.0 0.0 0.0 16.3
Target 5%
km2708 4,495 3,585 18,839 2,044 391 30,331
% of land area 0.1 0.5 0.4 1.0 0.2 0.3 0.5
% of forest
area
0.3 3.5 0.5 3.3 1.4 2.1 1.7
% not
protected
0.0 2.3 1.3 0.0 0.0 0.0 0.9
aDue to the uncertain location of undocumented (=predicted) primary forests, these figures should be taken with caution and seen as possible upper
bounds, as we expect that a higher than random proportion of undocumented primary forests occur in protected areas.
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SABATINI eT A l.
For taiga forests, restoration opportunities were concentrated
primarily in southern Finland (Figure S8).
When considering our composite dataset and all forest types
jointly, restoration gaps dominated (Figure 5). Assuming a 17% tar-
get, a strong contrast emerged between the lowlands of Southern
and Central Europe on the one hand, and Fennoscandia and the main
European mountain ranges on the other. In Western Europe, for in-
stance Great Britain, the Iberian Peninsula, Northern Italy and the
lowland areas of France, Germany and Poland, little or no primary
forest remains so that restoration gaps prevailed. In Fennoscandia
and in the Alpine, Carpathian and Balkan mountain ranges, instead,
not all primary forests were adequately protected, according to our
analyses. These were either outside protected areas (e.g. Sweden
or eastern Romania), or not strictly protected (e.g. Slovakia, Bosnia
and Herzegovina, or Bulgaria) or their protection level was not con-
sistently reported (e.g. Finland). Running the same analysis using
our database of documented primary forests showed some marked
shifts in conservation priorities, especially for data poor areas. In
Sweden, Belarus, Albania and the Alpine range, for instance, gaps in
restoration replaced protection gaps (Figure S9). Differences were
also substantial for the mountain regions of Southern Europe. Here,
most documented primary forests were effectively protected (blue
tones in Figure S9). Yet, these regions were also predicted to con-
tain additional primary forests, which were either located outside
strictly protected areas (see for instance the pink shades of the
Italian Apennines in Figure 5) or were unprotected altogether (e.g.
brown shades in Albania, Montenegro or southern Serbia).
4 | DISCUSSION
Primary forests are essential for biodiversity (Di Marco et al., 2019;
Gibson et al., 2011; Watson et al., 2018), but are declining globally
(Potapov et al., 2017; Watson et al., 2016). Yet, major uncertainties
remain concerning the distribution of primary forests in Europe, their
protection status, and for which areas and forest types restoration
FIGURE 4 Distribution of forest area between primary and non-primary status, across protection levels and forest types. Only
documented primary forest data from Sabatini et al. (2018) considered. Each square represents 17% of the area of each forest type. For each
square, the size of the coloured rectangles is proportional to the area of forest in a given protection status (strict protection = IUCN I-II,
other protection = IUCN III-VI, not protected) or conservation status (primary, non-primary). Squares are further divided in three rectangles,
which cumulatively represent a 5% (left bar), 10% (left bar + bottom bar) and 17% (all square) of total forest. Rectangles are progressively
filled considering forest area based on the following order: (a) strictly protected primary forest, (b) primary forest occurring in other
protected areas, (c) unprotected primary forest, (d) strictly protected non-primary forest, (e) non-primary forest in other protected areas
and (f) unprotected non-primary forest. In each rectangle, forest area in higher categories is only shown if the amount of forest area in lower
categories does not reach the respective (5%, 10% or 17%) threshold. Only forest types with a total forest cover above 1,000 km2 are shown
[Colour figure can be viewed at wileyonlinelibrary.com]
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efforts are most needed. By combining available data on the dis-
tribution of primary forests with a modelling approach, our study
addresses these knowledge gaps, and pinpoints areas and forest
types where restoration efforts would best complement protection
to help reach long-term conservation targets.
Remaining primary forests are not evenly distributed across for-
est types and are only partially representative of the full range of
environmental conditions in Europe. Almost three-quarters of all
forest types (39 of 54) have no or less than 1% of primary forest
remaining, which is likely insufficient to preserve the majority of
species associated with these forests (Lõhmus et al., 2004; Swanson
et al., 2011). This is particularly critical in light of the fact that primary
forests are crucial for the long-term persistence of many organismal
groups and red-listed species in Europe, including insects (Eckelt
et al., 2018), fungi and lichens (Ardelean, Keller, & Scheidegger, 2016;
Moning & Müller, 2009).
Many primary forests in Europe are unprotected, which necessi-
tates expansion of the current protected areas network. Protecting
primary forests is more cost-effective than their restoration once
they have been degraded (IUCN, 2016). Primary forests store more
FIGURE 5 Distribution of conservation gaps regarding primary forests across European administrative units. For each unit, we
highlighted the share of forested pixels classified as protection gaps (=unprotected primary forests), upgrading gaps (=protected primary
forests outside strict reserves) and restoration gaps (=forests in areas favourable for restoration for forest types with less than 17% primary
forest). All forest types are shown together. Only administrative units having more than 5 km2 in any of the three gaps are shown. Each black
dot in the triangular colour legend represents one administrative unit. Please note the axes of the triangular colour gradients are scaled
differently to improve data visualization. This graph is based on a composite dataset integrating data from Sabatini et al. (2018) and FOREST
EUROPE (2015a) [Colour figure can be viewed at wileyonlinelibrary.com]
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SABATINI eT A l.
carbon per hectare compared to logged, degraded or planted forests
in ecologically comparable locations (Burrascano, Keeton, Sabatini, &
Blasi, 2013; Watson et al., 2018) and often remain major net carbon
sinks late into forest succession (Luyssaert et al., 2008). Granting
them with adequate protection would therefore provide important
climate benefits, besides enhancing biodiversity (Moomaw, Masino,
& Faison, 2019). According to our analysis, designating 0.3% of
Europe's land area (=1,132 km2) as additional protected areas would
be sufficient to safeguard all documented primary forest fragments,
but protection would still be heavily biased towards the alpine and
the boreal biomes. Similarly, urgent is the need to upgrade the pro-
tection level in about 5,109 km2 of existing protected areas, where
primary forest patches are not yet strictly protected. We consider
these area estimates as lower bounds, since only about two fifths of
Europe's primary forests have been mapped so far. When accounting
for undocumented primary forests using a composite dataset based
on modelling, the areas in need of protection and upgrade in pro-
tection increased to 19,194 and 5,600 km2, respectively. Due to the
uncertain location of undocumented (=predicted) primary forests,
however, these figures should be seen as possible upper bounds, as
we expect that a higher than random proportion of undocumented
primary forests occur in protected areas. There is therefore the need
to further improve our knowledge of the distr ib utio n of Europe's pri-
mary forests to reduce the uncertainty concerning these estimates.
Upgrading protected areas to ensure the long-term maintenance
of primary forests requires a substantial change in conservation
objectives, especially in the Natura 2000 network. The recently re-
leased “EU Biodiversity Strategy for 2030” explicitly mentions the
need to effectively protect all remaining primary and old-growth
forests in Europe and designate at least 10% of Europe's land under
strict protection (European Commission, 2020). Although moving in
the right direction, this strategy falls short by not ensuring that net-
works of strictly protected areas are fully representative of Europe's
forest types. Even where the proportion of extant primary forests
is low, existing protected areas contain large forest areas and thus
provide important opportunities for restoration. Restoring exist-
ing forests towards their ecological potential represents a low-cost
complement to other land-based solutions (e.g. afforestation, refor-
estation) to mitigate climate change, which promises to maximize
biodiversity co-benefits (Griscom et al., 2017; Moomaw et al., 2019).
We found that the areas with the most favourable socio-economic
conditions for restoration coincide with those of low forest harvest-
ing intensity and roundwood production (Levers et al., 2014; Verkerk
et al., 2019). Prioritizing restoration in these areas would reduce the
opportunity costs arising from taking forests out of timber produc-
tion (Keenelyside et al., 2012). Particularly, favourable are those
areas where harvesting intensity has been low in recent history (e.g.
northern Fennoscandia, parts of the Carpathians, the Balkan region
and the Apennines). For forest types mostly located in densely in-
habited areas with high land use pressure, however, restoring the
attributes of primary forests remains challenging. This is the case,
for instance, for the lowland areas in the Atlantic or Mediterranean
biomes. Yet, some of the areas highlighted by our model in these
regions are currently following a trajectory of land use de-intensi-
fication (Levers et al., 2018), such as the Trossachs in Scotland and
the foothills of the southern Carpathians. In this context, abandon-
ment of forest management in economically marginal areas may
provide clear opportunities for restoring future primary forests at
least in small forest patches. This would provide important benefits
to biodiversity, since these restored patches might serve as refuges
for rare or endangered species in these highly fragmented regions
(Vandekerkhove et al., 2011).
Yet, restoring primary forests has many unsettled concep-
tual, economic and technical challenges (Bauhus, Puettmann,
& Messier, 2009; Fahey et al., 2018; Keeton, Lorimer, Palik, &
Doyon, 2019; Schnitzler, 2014) and requires long timeframes. Where
the starting point is relatively natural forest, such as in long-estab-
lished protected areas, passive rewilding approaches (Navarro &
Pereira, 2012; Perino et al., 2019) may be sufficient to promote
the redevelopment of the structure, function and composition of
primary forest ecosystems (Thorn et al., 2018). Active restoration
may instead prove more useful when the starting conditions are
less favourable (e.g. young even-aged stands, non-adapted or
non-native tree species composition, low genetic diversity; Keeton
et al., 2019). Managing for old-growth characteristics, such as struc-
tural complexity, is an option, as it can accelerate stand development
processes, establishment of late-successional biodiversity and eco-
system services such as carbon storage and flood resilience (Bauhus
et al., 2009; Ford & Keeton, 2017; Keeton et al., 2019). Restoring
natural disturbance regimes could be likewise desirable where pri-
mary forests, and the biodiversity therein, depend on infrequent,
high-severity disturbance events, but this requires a careful con-
sideration of possible drawbacks given the specific socio-ecological
context (Kuuluvainen, 2002; Swanson et al., 2011). In all cases, in-
creasing the diversity and complexity of Europe's forest ecosystems
may reduce the future negative impacts of climate change (Barton &
Keeton, 2018; Mair et al., 2018). Primary forests, for instance, have
been shown to effectively buffer forest-floor summer temperatures
compared to simplified forests (Frey et al., 2016), therefore mitigat-
ing climate change impacts for those species with the highest sensi-
tivity to temperature increases (Betts et al., 2018).
Our work represents the first systematic analysis of the repre-
sentativeness, conservation gaps and restoration opportunities of
Europe's primary forests. Yet, some uncertainties need to be men-
tioned. First, the quality of the currently available data varies across
countries (Sabatini et al., 2018). Nevertheless, no biogeographical
region was systematically under-sampled, and the inclusion of ad-
ditional country-level information to derive a composite dataset
on primary forest (FOREST EUROPE, 2015b) further mitigates
this potential bias. Yet, the location of predicted primary forests
remains uncertain, so that figures based on the composite dataset
should be taken with caution. Second, there is considerable incon-
sistency surrounding the application of IUCN protection categories
for protected forest areas in Europe (Frank et al., 2007; Parviainen
& Frank, 2003). At least for certain countries, some protected
areas or alternative forms of protection (e.g. voluntary set-asides,
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or certification schemes outside protected areas) may be granting
adequate protection to primary forest patches, even without being
categorized with the highest IUCN levels (Parviainen et al., 2000).
This is, for instance, the case of Finland where many Natura 2000
areas, although not currently categorized as strict protected areas,
may grant a sufficient level of protection to primary forests. If this is
true, then the current upgrading gap of primary forests might change
to restoration or protection gap in many areas in Finland (from pink
to blue or brown in Figure 5). By contrast, in certain contexts even
national parks may provide insufficient protection to primary for-
ests, for instance where widespread salvage logging is allowed after
insect, wind and fire disturbances (Mikoláš et al., 2019; Schickhofer
& Schwarz, 2019). Finally, when prioritizing areas for restoration,
our analysis neither explicitly accounted for opportunity costs, land
tenure, productivity or rent, nor did we treat the uneven distribu-
tion of threatened species and biodiversity hotspots. Aligning resto-
ration and conservation targets (e.g. habitat of threatened species),
as well as other ecosystem services (e.g. timber provisioning) would
be a useful follow-up undertaking for some biomes (Mönkkönen
et al., 2014; Sabatini et al., 2019).
5 | CONCLUSIONS
Our work clearly highlights the overall perilous state of Europe's pri-
mary forests. The strong biogeographical bias we found highlights
the urgent need for concerted, cross-national and multiscale con-
servation planning for Europe's forests. For instance, where primary
forests are still relatively widespread, such as in parts of Eastern
Europe, managers must be aware of the uniqueness of these forests
in a broader biogeographical context. Recent reports of primary for-
est loss from these key areas (Mikoláš et al., 2017, 2019; Schickhofer
& Schwarz, 2019) are, therefore, of greatest concern and require
prompt and coordinated action. Likewise, even small regions could
make important contributions to restoring missing primary forests
for some forest types at the European scale. Systematic conserva-
tion planning (Margules & Pressey, 2000) provides an operational
framework to prioritize areas for protection or restoration, with the
goal of creating a functional and representative network of strictly
protected primary forests, in synergy with other national to conti-
nental conservation initiatives (Perino et al., 2019; Schnitzler, 2014;
Schumacher et al., 2018). The surge in demands for materials and
bioenergy we experienced over recent years in Europe has trans-
lated into intensifying wood harvesting in many regions, including
some that are crucial for primary forest conservation (Searchinger
et al., 2018). This conjuncture further increases the urgency to pro-
tect and restore primary forests. The “decade of ecosystem resto-
ration”, as recently declared by the United Nations for 2021–2030,
may provide momentum to set ambitious restoration goals. For ex-
ample, this includes setting aside large areas where redevelopment
towards forest landscapes composed of complex mosaics of seral
habitats and late-successional stand structures will be encouraged,
either actively or passively.
Primary forests are scarce and highly fragmented in Europe,
which may engender vulnerability to anthropogenic stress and
disturbance, impair species' and ecosystems' adaptive responses,
and compromise species' capacity for refugial retreat (Angelstam
et al., 2020; Mikoláš et al., 2019; Svensson, Andersson, Sandström,
Mikusiński, & Jonsson, 2019), especially under the expected increase
in disturbances under climate change (Seidl et al., 2017). Managed
forests should play a key role in this regard. Retention forestry, for
instance, integrates primary forest structures (e.g. deadwood, large
trees, natural tree species composition) into managed forests, there-
fore increasing connectivity between forest reserves and contrib-
uting to preserve forest biodiversity across large scales (Gustafsson
et al., 2012). Diversified forest management strategies efficiently
balancing the trade-offs between timber production and biodiver-
sity impacts are therefore a crucial complement to protection and
restoration efforts in Europe (Eyvindson, Repo, & Mönkkönen, 2018;
Mönkkönen et al., 2014; Sabatini et al., 2019).
The recently released “Biodiversity Strategy for 2030” has the
merit of explicitly recognizing the irreplaceable nature of primary
forests. Yet, this strategy should be coupled with an integrated for-
est policy reforms to prevent the continued loss of Europe's most
valuable forests and in parallel ramp up both protection and resto-
ration efforts for these forests. Only an effective management and
governance of forest landscapes and resources, and a full recogni-
tion of the values and contributions of diverse states of forests can
strategically ensure the maintenance and restoration of key ecosys-
tem services and the fulfilment of human well-being in the long term
(Chazdon, 2018).
ACKNOWLEDGEMENTS
This research was funded by the European Union under the Marie
Skłodowska-Curie Project FORESTS & CO, Grant Agreement no.
658876. Additional support was provided by FCT—Portuguese
Foundation for Science and Technology, under the project UID/
AGR/04033/2019, the Swedish Environmental Protection Agency,
Stockholm, grant NV-03501-15. We are grateful to handling editor
and three anonymous reviewers for thoughtful, constructive com-
ments on a prior manuscript version that has helped to improve this
paper. Op en access fun ding enab led and organized by Projekt DE AL.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
PEER REVIEW
The peer review history for this article is available at https://publo
ns.com/publo n/10.1111/ddi.13158.
DATA AVAIL ABILIT Y STAT EME NT
The data on primary forests presented here remain the property
of the institutions, organizations or persons who created or col-
lected them. The custodian of each dataset (i.e. the person or in-
stitution owning or representing the contributed data) is listed in
Sabatini et al., 2018 – https://doi.org/10.1111/ddi.12778. Data
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SABATINI eT A l.
are available from the corresponding author upon request for
research or application purposes, subject to approval from the
respective custodians. The composite dataset of primary forest
is available at https://idata.idiv.de/ddm/Data/ShowD ata/1841
together with the maps of conservation gaps and restoration op-
portunities. All statistical code is available upon request from the
corresponding author.
ORCID
Francesco M. Sabatini https://orcid.org/0000-0002-7202-7697
Marcus Lindner https://orcid.org/0000-0002-0770-003X
Pieter J. Verkerk https://orcid.org/0000-0001-5322-8007
Helge Bruelheide https://orcid.org/0000-0003-3135-0356
Matteo Garbarino https://orcid.org/0000-0002-9010-1731
Fabio Lombardi https://orcid.org/0000-0003-3517-5890
Peter Meyer https://orcid.org/0000-0003-4200-4993
Gintautas Mozgeris https://orcid.org/0000-0002-8480-6006
Leónia Nunes https://orcid.org/0000-0002-2617-0468
Péter Ódor https://orcid.org/0000-0003-1729-8897
Alejandro Ruete https://orcid.org/0000-0001-7681-2812
Bojan Simovski https://orcid.org/0000-0003-2905-1971
Johan Svensson https://orcid.org/0000-0002-0427-5699
Kris Vandekerkhove https://orcid.org/0000-0003-1954-692X
Tzvetan Zlatanov https://orcid.org/0000-0003-4205-3429
Tobias Kuemmerle https://orcid.org/0000-0002-9775-142X
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BIOSKETCH
Francesco M. Sabatini is a forest ecologist. Within the frame-
work of the Marie Skłodowska-Curie Project FORESTS & CO
(Grant Agreement no. 658876), he established the Informal
Network of Forest Scientists—F&CO-NET, as a means to bring
together forest scientists and experts working on primary and
old-growth forests. The main aim of this network is maintaining
a harmonized geodatabase on the spatial distribution of primary
forests in Europe and adjacent areas, and facilitating its use for
non-commercial purposes, mainly academic and conservation-
relevant research.
Author contributions: F.M.S. and T.K. designed the study. F.M.S.
ran the statistical analyses. F.M.S., T.K. W.S.K., M.S., P-J.V., H.B.,
J.B., K.V., J.Sv. and M.S. drafted the first version of the manu-
script. S.B., N.D., M.G., N.G., F.L., M.M., P.M., R.M., G.M., L.N.,
P.Ó., M.P., A.R., B.S., J.St., J.Sz., K.V., R.V., T.V. and T.Z. contrib-
uted data. All authors contributed to the writing.
SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Sabatini FM, Keeton WS, Lindner M,
et al. Protection gaps and restoration opportunities for
primary forests in Europe. Divers Distrib. 2020;26:1646–
1662. https://doi.org/10.1111/ddi.13158
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