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# Potential of thinning to increase forest resilience and resistance to drought, pest, windstorm and fire: A meta-analysis

Catherine Chagnon<sup>a</sup>, Sébastien Dumont<sup>a, b</sup>, Alexandre Morin-Bernard<sup>a, b</sup>, Hervé Jactel<sup>b</sup>, Alexis Achim<sup>a</sup>, Guillaume Moreau<sup>a, c</sup>

## ARTICLE INFO

Keywords:

Forest resilience and resistance

Global change

Adaptive silviculture

Thinning

Forest management

Partial harvesting

Multi-risk

## ABSTRACT

As the pressure on forest ecosystems increases with the occurrence of more severe and frequent natural disturbances, the need for silvicultural treatments to mitigate multiple risks is becoming increasingly apparent. Thinning has been identified as a means of managing stands to enhance resilience and resistance to disturbances. However, the underlying mechanisms vary depending on the disturbance types and tree species and there is a lack of empirical evidence that thinning can effectively mitigate these risks at a broad scale. We conducted a meta-analysis of 50 studies quantifying the effects of thinning treatments on the resilience and resistance of forest stands to four categories of natural disturbances: drought, insects and pathogens, wind, and fire. Meta-analyses were conducted to examine the influence of various moderators, namely the response type (growth, survival, damage), thinning intensity, thinning type, time since the first treatment, stand age and pest type (for insects and pathogens). We found a positive broad-scale effect of thinning on forest resilience and resistance, while the disturbance-specific effect was positive for reducing the impact of drought, pests, and in some cases fire, but not significant for windstorms. Although responses varied among disturbance types, and in some cases response type, thinning type, and time since treatment, a key finding of this study is that no statistically significant negative effect of thinning has been detected with respect to our resilience and resistance indicators. Although thinning should not be considered as a tool that will singlehandedly increase the resilience of forests, our results suggest that for temperate and boreal ecosystems of North America and Europe, thinning can be expected to increase the resilience and resistance of forests to multiple stressors, in a wide range of sites and stand characteristics. Yet, empirical data from Asia, southern hemisphere and tropical forests are needed to enable global-scale conclusions. Moreover, potential detrimental effects of thinning on forest ecology should be carefully assessed before prioritizing thinning as a means of increasing forest resilience and resistance.

## 1. Introduction

An increasing occurrence of forest disturbances has been reported worldwide and has contributed to fragilizing the provision of forest ecosystem services by inducing reduction in productivity and broad-scale forest die-offs (Millar and Stephenson, 2015, Trumbore et al., 2015; Zhang et al., 2018; Anderegg et al., 2020; Patacca et al., 2023). For example, the recent increase in the frequency and severity of droughts around the globe has resulted in extensive tree mortality (Allen et al., 2015; Dai, 2012; Wolf and Paul-Limoges, 2023), whereas higher temperatures combined with droughts are promoting more frequent and severe fires, which has led to extensive increases in burned areas

(Andela et al., 2017; Piqué and Domènech, 2018; Príncipe et al., 2017). Rapid shifts in storm tracks towards forests previously subjected to milder wind regimes have also been reported, leading to increased wind damage in some forest ecosystems (Bengtsson et al., 2006; Kamimura et al., 2017). Moreover, changing climatic conditions have allowed the extension of the range of eruptive insects and pathogens into poorly adapted forests, thereby altering forests composition and productivity in several regions (Battisti et al., 2005; Klapwijk et al., 2012; Robinet and Roques, 2010; Wingfield et al., 2017; Patacca et al., 2023).

This concurrent increase in the frequency and severity of natural disturbances has also led to positive feedback and synergies between events of unprecedented severity, and increasing interactions between

<footnote><sup>a</sup> Centre d'étude de la forêt, Département des sciences du bois et de la forêt, Université Laval, 2405 rue de la Terrasse, Québec, QC G1V 0A6, Canada</footnote>

<footnote><sup>b</sup> INRAE, University of Bordeaux, BIOGECO, Cestas F-33610, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: guillaume.moreau@sbf.ulaval.ca (G. Moreau).</footnote>
drought, fire, wind, and pests have been reported worldwide (Seidl et al., 2017). While pre-fire droughts can weaken trees' ability to withstand fire damage and increase the likelihood of immediate fire-related mortality (van Mantgem et al., 2018; Furniss et al., 2020), drought-induced mortality can increase dead fuel accumulation, and in turn may lead to increased fire risks (Jactel et al., 2009; Stephens et al., 2018). Decline in vigour due to drought conditions can also increase the vulnerability of trees to insect outbreaks (Jactel et al., 2012; Scheller et al., 2018). Similarly, severe insect outbreaks can be triggered after windstorms due to the large number of fallen trees that become a suitable breeding substrate for bark beetles (Kärvemo et al., 2014; Stadelmann et al., 2013). An accumulation of dead trees following insect outbreaks can also lead to increased fire risk due to fuel accumulation (Woodall and Nagel, 2007), while outbreaks in the years following fires can significantly influence post-fire mortality (Hood and Bentz, 2007). Because of such intensifying pressures from various disturbances, it has been proposed that silvicultural strategies adopt a multi-risk approach, through the means of which silvicultural treatments are devised to simultaneously minimize the risks from multiple types of disturbances (Jactel et al., 2009, 2017).

Thinning, a silvicultural treatment used to control the density, structure, and species composition of a forest stand, has been proposed as such a tool that could help reduce the risk of forest damage for multiple types of disturbances (Jactel et al., 2009; Moreau et al., 2022). Thinning treatments involve removing a proportion of the trees of a stand in which the competition for resources would otherwise lead to natural mortality (Curtis et al., 1997; Zeide, 2001). It allows maintaining residual trees of desired characteristics (e.g. species, size, and shape), which in turn have better access to site resources such as light, nutrients, and water (Bréda et al., 1995; Medhurst et al., 2002). Thinning treatments may be broadly classified as thinning from above, which removes the largest trees in the diameter distribution to reduce the amount of competition for residual trees; thinning from below, which removes the smallest trees in the diameter distribution to recuperate imminent mortality; and selective thinning, which removes trees among all diameter classes based on characteristics deemed desirable or not, such as species, tree vigour or the occurrence of defects (Smith et al. 1997; Nyland et al., 2016). Selective thinning can focus on removing strong competitors for crop trees (crown thinning), low-vigour and low-quality trees (salvage thinning), or trees affected by insects and diseases (sanitation thinning; Smith et al. 1997; Nyland et al., 2016).

With a view to identifying silvicultural tools supporting a multi-risk approach, thinning is known to generally reduce the negative impacts of drought by increasing water availability to residual trees following the reduction of stand density (e.g. D'Amato et al., 2013; Sohn et al., 2016). Thinning may also decrease fire risk by limiting the accumulation of ladder fuels that may allow surface fires to transition to crown fires (e.g. Kalies and Yocom Kent, 2016; Tardós et al., 2019; Stoddard et al., 2021) through the removal of small trees that connect the forest floor to the canopy and are less fire-resistant than larger trees (Davis et al., 2024, Shive et al., 2024). By improving the overall stand growth and vigour, thinning can also limit mortality and productivity losses associated with insect and pathogen infestations (e.g., Fettig et al., 2007; Hood and Kimberley, 2009; Hood and Sala, 2016; Roberts et al., 2020). Low intensity thinning, by promoting the development of structural roots and radial growth of the stem, may also reduce vulnerability to wind damage, especially when performed at an early stage of stand development (e.g., Achim et al., 2005; Gardiner et al., 2013; Torita and Masaka, 2020) and at high levels of dispersed tree retention (Urgenson et al., 2013).

However, thinning does not systematically enhance the resistance or resilience of treated stands. Here we define resistance as the ability of a stand to resist change, and resilience as the ability to accommodate change and return to prior condition (Millar et al., 2007). The effect of thinning on drought vulnerability may indeed decline as stands mature, resulting in decreasing resilience over time (D'Amato et al., 2013; Moreau et al., 2022). Thinning may also lead to increased fire risk and

severity if post-treatment mortality is high or if debris are not removed after treatment (Kalies and Yocom Kent, 2016; Taylor et al., 2021). Increased wind penetration and air movement within the stand can also promote the spread of fire (Hood et al., 2020; Taylor et al., 2021), while the denser understory vegetation developing in thinned stands may act as ladder fuel, increasing the risk of crown fire (Kalies and Yocom Kent, 2016; Madrigal et al., 2017; Taylor et al., 2021). Thinned stands may also be more susceptible to root rot infections and other pathogens that are taking advantage of residual stumps to proliferate (Hood and Kimberley, 2009; Piri and Korhonen, 2008). Lastly, when not performed at an early stage, thinning, particularly when heavy, is known to initially destabilize stands by suddenly increasing wind exposure of residual trees, which results in reduced resistance to windstorms for multiple years (Gardiner et al., 2013). These findings suggest trade-offs may exist whereby the reduction of risk from one stressor may increase vulnerability to another. This reinforces the relevance of a multi-risk approach in silviculture. However, empirical quantification, and comparison of such trade-offs are still lacking, thereby limiting our capacity to draw broad-scale conclusions on the effectiveness of thinning for increasing the overall resistance and resilience of forests to global change.

To contribute to filling this gap, we performed a meta-analysis on published articles in peer-reviewed journals with the aim to better understand and quantify the potential effectiveness of thinning as a means of increasing resistance and resilience to multiple stressors. In doing so, we strived to produce a synoptic assessment of the effects of thinning on the resilience and resistance of forest stands to drought, pests, windstorms, and fire using a comprehensive set of direct quantitative indicators, including growth, survival, and resistance and resilience indices. Based on the evidence provided by a recent systematic review of the topic by Moreau et al. (2022), we first hypothesized that thinning, particularly when heavy (i.e. removing more than 40 % of the initial basal area) and applied from below, improves the resilience and resistance to drought, but that the positive effect of the treatment tends to decrease over time. We also hypothesized that in stands affected by pests, removing vulnerable and affected trees through selective thinning generally increases the resistance of treated stands, while thinning from below or above has no effect. Our third hypothesis was that thinning reduces the resistance to wind damage, and that this negative effect increases with treatment intensity and stand age. Our fourth and last hypothesis was that only heavy thinning and thinning from below are effective at increasing the resistance to fire.

## 2. Materials and methods

### 2.1. Data collection

Peer-reviewed publications were gathered using Web of Science. Four search queries, corresponding to the four disturbance types were elaborated, based on the following combinations of keywords: (title (TI) = (thinning OR selection cutting) OR keywords (KP) = (thinning OR selection cutting)) AND (resilience OR resistance OR survival OR mortality OR growth OR increment) AND (forest* OR stand* OR tree*)). An additional topic (TS) query was added to this common basis to describe each disturbance type: (drought OR water stress) for drought; (insect OR pest OR beetle OR woodwasp OR moth OR budworm OR defoliator OR scolytid* OR herbivor*) for insects; and (pathogen OR fung* OR disease) for pathogens, which were pooled as pest; (wind* OR storm) for wind; (fire OR wildfire) for fire. The search yielded 246 studies for drought, 355 studies for pests (which encompassed both insects (151) and pathogens (204)), 220 studies for wind, and 253 studies for fire, for a total of 1074 studies.

Relevance was first evaluated based on the title and abstract, and methods sections were also screened when necessary. Articles out of scope were removed at this stage (see PRISMA diagram; Figure S1), whereas retained full-text articles were then assessed for eligibility. To be included in this review, studies had to meet the following criteria: (1)
empirical study conducted in the field (no modelling); (2) reporting
original results (primary source of data); (3) direct assessment of the
effects of thinning on stand resilience and/or resistance to disturbances
using quantitative indices; (4) providing a comparison between at least
one treatment and a control (unthinned stand) at the stand level, with
replicates of each treatment (n ≥ 2); (5) peer reviewed article published
in English or French; (6) statistical information needed to perform a
meta-analysis, including stand-level mean, standard deviation of the
mean (SD), and sample size (number of stands). According to our third
criterion, indirect measurements of tree performance, such as the iso-
topic composition of tree rings, indirect indicators of photosynthesis or
stomatal conductance, were not included in the meta-analysis. A crucial
thing to consider in our fourth criteria was the level at which the effect
size (i.e. the difference in the effect of thinning on stand response to
disturbances between thinned and unthinned stands) is measured. In
classical (i.e. often medical) meta-analysis, there is generally only one
possible scale of measurements: the patient. In opposition, ecological
response can be reported at variable scale (Nakagawa et al., 2023).
When compiling the results, we encountered multiple papers in which
the measurements were at the tree level (mostly individual tree growth
responses in drought-focussed paper), and other in which the response
can only be compiled at the stand level (e.g. % tree mortality following a
fire). To include as many papers as possible, we conducted our analysis
at the stand level. Accordingly, papers that reported tree-scale mea-
surement were converted to stand-level responses when applicable by
pooling stands in order to compute a mean value and standard deviation
at the stand level. Tree-level measures without stand or block replicates
could not be included. Finally, to align with our study objective and
prevent confounding treatment effects, only the outcomes from thinning
applied as a standalone treatment—commonly referred to as "thin-only
"—were considered. Results from combinations of treatments, such as
thinning paired with prescribed burning for fire or stump removal for
pests, were excluded (refer to Table S1 for details on study exclusions).

All papers corresponding to these criteria were carefully screened,
including tables and figures, to extract stand-level mean response,
associated SD, and sample sizes. Statistical information reported as
figures was extracted using Web Plot Digitizer (version 4.6, Rohatgi
2022). When the information was presented as a boxplot, we used the
method described in Wan et al. (2014) to estimate the mean and SD. In
the final dataset, each observation (line) represents a case study, a direct
comparison between a control and a treatment. Studies often reported
more than one case studies, so there could be multiple lines per study.
This process resulted in 246 case studies from 50 studies, including 12
studies for drought (93 case studies; Table 1a), 25 studies for pests (88
case studies; Table 1b), 5 studies for wind (24 case studies; Table 1c),
and 8 studies for fire (41 case studies; Table 1d). Excluded studies are
classified by stressor and reason for their exclusion in Table S1.

For each retained study, we also extracted variables describing the
treatment and the environment in which the study was conducted.
Thinning intensity was expressed as the percentage of the stand basal
area (BA) removed and was extracted or calculated from the reported
stand characteristics. Based on the information provided in each study,
the thinning type was assigned to one of the previously described cat-
egories i.e. from below, from above, or selective. The stand age was
classified as either young (>40 years), mature (40–150 years), or old
(>150 years). The amount of time elapsed in years since the first thin-
ning treatment was also recorded when available, along with the num-
ber of thinning treatments when applicable. To describe the
environment, the phylogeny of the main species (broadleaf; conifer), the
dominant tree species, the regeneration method (planted; natural),
species composition (mixed; monospecific), and the location of the study
were extracted. The locations of studied retained for the meta-analysis
are presented in Fig. 1. The location was then used to determine the
biome, based on Dinerstein et al. (2017).

## 2.2. Calculating the effect size

To compute effect sizes, we first transformed response variables so
that all values were positive. Accordingly, we inverted mortality into
survival, and damage into a resistance index by subtracting the per-
centage of damaged trees from 100 %. Examples of resistance index are
the percentage of infected or defoliated trees by pests, the percentage of
trees with broken top or uprooted by wind, and the percentage of crown
consumed or scorched by fire. The type of response was then classified
into three groups (Table S2), corresponding to measures of either (i)
growth as computed by growth indices during or following a disturbance
event, (ii) survival proportion, or (iii) resistance indices defined as the
opposite of direct damage. For drought, the retained studies allowed to
further classified growth indices into three additional groups: (a) resi-
ience, which corresponded to growth in the years following a drought
compared to growth in the years before a drought (b) resistance, which
corresponds to growth during drought years compared to growth in the
years before a drought, and (c) recovery, which corresponds to growth
in the years after a drought compared to growth during a drought year.
We then computed the effect size for each case study as the standardized
mean difference (SMD; Hedge's *d*) between thinned and unthinned
(control) stands.

## 2.3. Global random effects model

We first calculated the overall effect of thinning on the stand-level
response to disturbances across all studies (grand mean effect size). To
do so, we performed a multi-level meta-analysis using the “rma.mv”
function of the “metafor” package (Viechtbauer, 2010) in the R statis-
tical computing environment (R Core Team, 2023). We first fitted a
random effects model (i.e. with only an intercept as a fixed effect), which
is analogous to a null model in linear mixed models. In such a model, an
intercept that is significantly different from zero indicates an overall
significant effect of thinning. We also accounted for different types of
non-independence between the effect sizes and between the sampling
errors using the recommendations of Nakagawa et al. (2023). First,
because studies often reported more than one case study, we added a
random effect to account for the non-independence of the effect sizes,
with case studies nested within studies. Second, we specified a
variance-covariance matrix of the sampling errors that accounted for the
non-independence of the case studies that corresponded to multiple
comparisons to the same control or that were measured on the same
experimental unit. The variance-covariance matrix was computed using
the “vcalc” function of the “metafor” package and assumed a 0.5 cor-
relation between the sampling errors of the outcomes that were
measured in the same stands. We also tested a more conservative cor-
relation of 0.8, which led to similar results (see Table S3). The results
reported in the paper are those computed using a 0.5 correlation. Models
were fitted using restricted maximum likelihood to compute
between-study heterogeneity (τ² and associated Q statistics), as well as
the standardized estimate of heterogeneity (I²; Higgins and Thompson,
2002) using the “orchard” package (Nakagawa et al., 2020) in R. The
latter statistic, which ranges between 0 and 1, allowed us to quantify the
relative amount of heterogeneity between and within the studies.

Given that our meta-analysis encompasses multiple contexts, we
tested the inclusion of biome, dominant tree phylogeny (deciduous/
coniferous), and dominant tree species as random intercepts. To assess
their effects, each model was fitted using maximum likelihood and
compared to a baseline model in which the random effects were speci-
fied as the case study nested in given study (~1|StudyID/CaseID). The
comparison among models was based on likelihood ratio tests imple-
mented with the "anova.rma" function, and the necessity to include the
tested random effect was assessed based on associated p-value and
model AIC. We also verified that no bias was included from pooling
different response types by testing the influence of response types on the
stand-level response to disturbances by running a meta-regression using
<caption>**Table 1**</caption>

a. Summary of studies retained for drought events and descriptive variables. T.Type is the type of thinning as described in the introduction (from below, from above, selective); T.Intensity is the % of basal area removed by thinning (Low: ≤ 30 %, moderate: 31–60 %, high: > 60 %); Regeneration is the regeneration method for the studied stands (planted or natural); The biome is based on Dinerstein et al. (2017).

<table><thead><tr><th>Authors</th><th>Year</th><th>Biome</th><th>Species</th><th>Phylogeny</th><th>T.Type</th><th>T.Intensity</th><th>Regeneration</th><th>Response</th><th>Case studies</th></tr></thead><tbody><tr><td>Bosela et al.</td><td>2021</td><td>Temperate</td><td>Fagus silvatica</td><td>Broadleaf</td><td>Above</td><td>Moderate</td><td>Natural</td><td>Resistance</td><td>2</td></tr><tr><td>D'Amato et al.</td><td>2013</td><td>Temperate</td><td>Pinus resinosa</td><td>Conifer</td><td>Selective</td><td>High</td><td>Plantation</td><td>Resistance Resilience</td><td>20</td></tr><tr><td>Diaconu et al.</td><td>2017</td><td>Temperate</td><td>Fagus silvatica</td><td>Broadleaf</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Resistance Resilience Recovery</td><td>6</td></tr><tr><td>Gavinet et al.</td><td>2019</td><td>Temperate</td><td>Quercus ilex</td><td>Broadleaf</td><td>Below</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>1</td></tr><tr><td>Manrique -Alba et al.</td><td>2020</td><td>Temperate</td><td>Pinus halepensis</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Planted</td><td>Resistance Resilience Recovery</td><td>6</td></tr><tr><td>Navarro-Cerrillo et al.</td><td>2019</td><td>Temperate</td><td>Pinus nigra</td><td>Conifer</td><td>Below</td><td>Low</td><td>Natural</td><td>Resistance Resilience Recovery</td><td>12</td></tr><tr><td>Navarro-Cerrillo et al.</td><td>2023</td><td>Temperate</td><td>Pinus sylvestris</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Planted</td><td>Resistance Resilience Recovery</td><td>18</td></tr><tr><td>Schmitt et al.</td><td>2020</td><td>Temperate</td><td>Quercus petraea</td><td>Broadleaf</td><td>Below</td><td>Moderate</td><td>Natural</td><td>Resistance Resilience Recovery</td><td>6</td></tr><tr><td>Sohn et al.</td><td>2013</td><td>Temperate</td><td>Picea abies</td><td>Conifer</td><td>Selective</td><td>Low</td><td>Planted</td><td>Resistance Recovery</td><td>16</td></tr><tr><td>Thomas and Waring</td><td>2015</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Below</td><td>Moderate</td><td>Natural</td><td>Resistance Resilience</td><td>2</td></tr><tr><td>Wang et al.</td><td>2019</td><td>Temperate</td><td>Pinus contorta</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Resistance</td><td>2</td></tr><tr><td>Zald et al.</td><td>2022</td><td>Temperate</td><td>Pinus lambertiana Calocedrus decurrens Abies concolor Pinus jeffreyi</td><td>Conifer</td><td>Below Above</td><td>Moderate High</td><td>Natural</td><td>Resistance</td><td>2</td></tr></tbody></table>

b. Summary of studies retained for pests and descriptive variables. T.Type is the type of thinning as described in the introduction (Below, Selective, Above); T.Intensity is the % of basal area removed by the treatment (Low: ≤ 30 %, moderate: 31–60 %, high: > 60 %); Regeneration is the regeneration method for the studied stands (planted or natural); The biome is based on Dinerstein et al. (2017).

<table><thead><tr><th>Authors</th><th>Year</th><th>Biome</th><th>Species</th><th>Phylogeny</th><th>T.Type</th><th>T. Intensity</th><th>Regeneration</th><th>Response</th><th>Pest</th><th>Case studies</th></tr></thead><tbody><tr><td>Baleshta et al.</td><td>2015</td><td>Temperate</td><td>Pseudotsuga menziesii</td><td>Conifer</td><td>Selective</td><td>High</td><td>Planted</td><td>Survival</td><td>Armillaria ostoyae</td><td>1</td></tr><tr><td>Egan et al.</td><td>2010</td><td>Temperate</td><td>Abies concolor</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>Scolytus ventralis</td><td>2</td></tr><tr><td>Fettig et al.</td><td>2012</td><td>Temperate</td><td>Pinus jeffreyi</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Survival</td><td>Dendroctonus brevicomis</td><td>3</td></tr><tr><td>Filip et al.</td><td>2009</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>Armillaria ostoyae</td><td>2</td></tr><tr><td>Filip et al.</td><td>2015</td><td>Temperate</td><td>Pseudotsuga menziesii; Abies procera</td><td>Conifer</td><td>Selective</td><td>High</td><td>Planted</td><td>Survival</td><td>Armillaria ostoyae; Heterobasidion occidentalis</td><td>2</td></tr><tr><td>Fuentealba and Beauce</td><td>2012</td><td>Temperate</td><td>Abies balsamea</td><td>Conifer</td><td>Selective</td><td>Low</td><td>Natural</td><td>Resistance Index</td><td>Choristoneura fumiferana</td><td>6</td></tr><tr><td>Hood and Sala</td><td>2016</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Below</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>Dendroctonus ponderosae</td><td>2</td></tr><tr><td>Hood and Kimberley</td><td>2009</td><td>Temperate</td><td>Pinus radiata</td><td>Conifer</td><td>Selective</td><td>High</td><td>Planted</td><td>Resistance Index</td><td>Armillaria novae-zelandiae</td><td>1</td></tr><tr><td>Jackson et al.</td><td>2016</td><td>Temperate</td><td>Larix occidentalis</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Resistance Index</td><td>Arceuthobium laricis</td><td>3</td></tr><tr><td>Kaitera</td><td>2002</td><td>Boreal</td><td>Pinus sylvestris</td><td>Conifer</td><td>Selective</td><td>NA</td><td>Planted</td><td>Resistance Index</td><td>Cronartium flaccidum</td><td>5</td></tr><tr><td>Knapp et al.</td><td>2021</td><td>Temperate</td><td>Abies concolor</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>Scolytus spp.</td><td>6</td></tr><tr><td>McIntire et al.</td><td>2018</td><td>Temperate</td><td>Pinus strobus</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Resistance Index</td><td>White Pine Needle Damage</td><td>4</td></tr><tr><td>Morris et al.</td><td>2022</td><td>Temperate</td><td>Pinus contorta</td><td>Conifer</td><td>Selective Above</td><td>Moderate; High</td><td>Natural</td><td>Survival</td><td>Dendroctonus ponderosae</td><td>4</td></tr><tr><td>Morrison et al.</td><td>2022</td><td>Temperate</td><td>Pseudotsuga menziesii, Pinus contorta</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Survival</td><td>Armillaria ostoyae</td><td>10</td></tr><tr><td>Negrón et al.</td><td>2017</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Above</td><td>High</td><td>Natural</td><td>Survival</td><td>Dendroctonus ponderosae</td><td>2</td></tr><tr><td>Oliva et al.</td><td>2010</td><td>Boreal</td><td>Picea abies</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Planted</td><td>Resistance Index</td><td>Heterobasidion annosum</td><td>4</td></tr><tr><td>Ostaff et al.</td><td>2006</td><td>Temperate</td><td>Abies balsamea</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Resistance Index</td><td>Neodiprion abietis</td><td>1</td></tr><tr><td>Piri and Korhonen</td><td>2008</td><td>Boreal</td><td>Picea abies</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Resistance Index</td><td>Heterobasidion annosum</td><td>1</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><caption>b. Summary of studies retained for pests and descriptive variables. T.Type is the type of thinning as described in the introduction (Below, Selective, Above); T.Intensity is the % of basal area removed by the treatment (Low: ≤ 30 %, moderate: 31–60 %, high: > 60 %); Regeneration is the regeneration method for the studied stands (planted or natural); The biome is based on Dinerstein et al. (2017).</caption><thead><tr><th>Authors</th><th>Year</th><th>Biome</th><th>Species</th><th>Phylogeny</th><th>T.Type</th><th>T. Intensity</th><th>Regeneration</th><th>Response</th><th>Pest</th><th>Case studies</th></tr></thead><tbody><tr><td>Robinson</td><td>2003</td><td>Mediterranean</td><td>Eucalyptus diversicolor</td><td>Broadleaf</td><td>Selective</td><td>High</td><td>Natural</td><td>Survival; Resistance Index</td><td>Armillaria luteobubalina</td><td>12</td></tr><tr><td>Scaria and Pinno</td><td>2024</td><td>Temperate</td><td>Pinus contorta</td><td>Conifer</td><td>Below</td><td>Low</td><td>Natural</td><td>Resistance Index</td><td>Cronartium harknessii</td><td>2</td></tr><tr><td>Smirnova et al.</td><td>2021</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Survival</td><td>Elytroderma deformans</td><td>2</td></tr><tr><td>Stark et al.</td><td>2013</td><td>Temperate</td><td>Abies concolor</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Survival</td><td>D.brevicomis; D. ponderosae; D. valens; Ips paraconfusus</td><td>6</td></tr><tr><td>Sturdevant et al.</td><td>2022</td><td>Temperate</td><td>Pseudotsuga menziesii</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Survival</td><td>Dendroctonus pseudotsugae</td><td>1</td></tr><tr><td>Whitney</td><td>1993</td><td>Temperate</td><td>Picea glauca</td><td>Conifer</td><td>Selective</td><td>High</td><td>Planted</td><td>Survival</td><td>Inonotus tomentosu</td><td>3</td></tr><tr><td>Woodruff et al.</td><td>2019</td><td>Temperate</td><td>Pinus ponderosa; Pinus jeffreyi</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Survival</td><td>Leptographium wageneri; Verticicdieliama wageneri</td><td>3</td></tr></tbody></table>

c. Summary of studies retained for wind and descriptive variables. T.Type is the type of thinning as described in the introduction (Below, Selective, Above); T.Intensity is the % of basal area removed by the treatment (Low: ≤ 30 %, moderate: 31–60 %, high: > 60 %); Regeneration is the regeneration method for the studied stands (planted or natural); The biome is based on Dinerstein et al. (2017).

<table><thead><tr><th>Authors</th><th>Year</th><th>Biome</th><th>Species</th><th>Phylogeny</th><th>T.Type</th><th>T.Intensity</th><th>Regeneration</th><th>Response</th><th>Case studies</th></tr></thead><tbody><tr><td>Coates</td><td>1997</td><td>Temperate</td><td>Tsuga heterophylla; Tsuga plicata; Betula papyrifera; Pinus contorta; Abies lasiocarpa</td><td>Conifer</td><td>Selective</td><td>Low</td><td>Natural</td><td>Survival</td><td>2</td></tr><tr><td>Dagley et al.</td><td>2023</td><td>Temperate</td><td>Sequoia sempervirens; Pseudotsuga menziesii</td><td>Conifer</td><td>Below</td><td>Low</td><td>Natural</td><td>Resistance Index</td><td>12</td></tr><tr><td>Deligöz et al.</td><td>2022</td><td>Temperate</td><td>Pinus nigra</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Resistance Index</td><td>2</td></tr><tr><td>Picchio et al.</td><td>2020</td><td>Temperate</td><td>Pinus taeda</td><td>Conifer</td><td>Below</td><td>Low</td><td>Natural</td><td>Resistance Index; Survival</td><td>6</td></tr><tr><td>Wallentin et al.</td><td>2014</td><td>Temperate</td><td>Picea abies</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Planted</td><td>Resistance Index</td><td>2</td></tr></tbody></table>

d. Summary of studies retained for fire events and descriptive variables. T.Type is the type of thinning as described in the introduction (Below, Selective, Above); T.Intensity is the % of basal area removed by the treatment (Low: ≤ 30 %, moderate: 31–60 %, high: > 60 %); Regeneration is the regeneration method for the studied stands (planted or natural); The biome is based on Dinerstein et al. (2017).

<table><thead><tr><th>Authors</th><th>Year</th><th>Biome</th><th>Species</th><th>Phylogeny</th><th>T.Type</th><th>T. Intensity</th><th>Regeneration</th><th>Response</th><th>Case studies</th></tr></thead><tbody><tr><td>Brodie et al.</td><td>2024</td><td>Temperate</td><td>Pinus ponderosa; Abies concolor</td><td>Conifer</td><td>Selective Below</td><td>Moderate</td><td>Natural Planted</td><td>Resistance Index</td><td>8</td></tr><tr><td>Garcia-Jimenez et al.</td><td>2017</td><td>Temperate</td><td>Pinus halepensis</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Resistance Index</td><td>1</td></tr><tr><td>Pollet and Omi</td><td>2002</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Resistance Index</td><td>2</td></tr><tr><td>Prichard et al.</td><td>2010</td><td>Temperate</td><td>Pseudotsuga menziesii; Pinus contorta; Pinus ponderosa; Abies lasiocarpa</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Resistance Index; Survival</td><td>3</td></tr><tr><td>Raymond and Peterson</td><td>2005</td><td>Temperate</td><td>Pseudotsuga menziesii</td><td>Conifer</td><td>Below</td><td>High</td><td>Natural</td><td>Resistance Index; Survival</td><td>2</td></tr><tr><td>Ritchie et al.</td><td>2007</td><td>Temperate</td><td>Pinus ponderosa</td><td>Conifer</td><td>Selective</td><td>High</td><td>Natural</td><td>Resistance Index</td><td>8</td></tr><tr><td>Shive et al.</td><td>2024</td><td>Temperate</td><td>Pinus ponderosa; Pinus lambertiana; Pinus jeffreyi; Abies concolor Calocedrus decurrens; Pseudotsuga menziesii Quercus kelloggii</td><td>Conifer Broadleaf</td><td>Selective</td><td>NA</td><td>Natural</td><td>Resistance Index</td><td>3</td></tr><tr><td>Taylor et al.</td><td>2021</td><td>Temperate</td><td>Eucalyptus regnans; Eucalyptus delegatensis</td><td>Broadleaf</td><td>Selective</td><td>Moderate</td><td>Natural</td><td>Resistance Index</td><td>14</td></tr></tbody></table>

the response type as a moderator.

To visualise the results of our meta-analytic models, we used orchard plots built using the “orchaRd” package. The resulting figures are analogous to forest plots but optimized for environmental meta-analytic models by allowing visualization of the variance and weight of individual case studies for different groups (Nakagawa et al., 2020).

## 2.4. Sensitivity analyses and publication bias

We ran sensitivity analyses using a leave-one-out approach. We ran

the multi-level meta-analysis multiple times, each time leaving one of the included studies out of the dataset used to fit the model. We then plotted the estimated overall effect and associated prediction intervals to assess the relative influence of each study on the overall estimate and the stability of the analysis (Figure S2a). We also plotted the distribution of the resulting overall estimates to detect any outliers (Figure S2b) and used the DFBETA values (which correspond to the difference between the estimate with and without the study) to determine which of the studies were influential and should be removed from the dataset for sensitivity analyses. We used a threshold of 1 to identify influential
<figure><img src="image_2.png" /><figcaption>Fig. 1. Geographical location of the 50 studies by disturbance types. The symbol size represents the precision, i.e. the weight of each case study in the analysis. Note that the locations of the case studies were slightly dispersed to allow a better visualization.</figcaption></figure>

studies. Using a new dataset from which these studies had been removed, reran the overall models and report the overall estimates and associated 95 % CI in Table S3.

We then verified publication bias using the procedure proposed by Nakagawa et al. (2023), which is an adaptation of Egger's regression for detecting funnel plot asymmetry. We first investigated small-study effect size by fitting a multi-level model using a measure of sampling error based on the effective sampling size as moderator (equation 16 in Nakagawa et al., 2023). Small-study effect is present when effect sizes from small-sampling-sized studies is different (often larger) in magnitude than the “true effect.” We also tested for time-lag bias by fitting a model using the publication year (centred on 0) as moderator. Time-lag bias is present in the case where the effect declines over time and would manifest by a significant effect of publication year in the model. We tested for these two types of publication bias in separate models (Eqs. 1 and 2, respectively):

$$z_i = \beta_0 + \beta_1 \sqrt{\frac{1}{n_i}} + u_{j[i]} + e_i + m_i \quad (1)$$

$$z_i = \beta_0 + \beta_2 Year + u_{j[i]} + e_i + m_i \quad (2)$$

where $z_i$ is the response variable, $\beta_0$ is the overall estimate, $\beta_1$ is the slope associated with the small study effect ($\sqrt{\frac{1}{n_i}}$ being the sampling error based of the effective sampling size), $\beta_2 Year$ is the term associated with time-bias effect, $u_{j[i]}$ is the between-study effect (for the ith study and the ith effect size), $e_i$ is the within-study effect (for the ith effect size); and $m_i$ is the sampling error, related to the ith sampling variance.

Given that we detected small-study effect (-2.888 [-5.440, -0.337], p = 0.027; Table S3), we also ran another model based on Eq. 1 (see Table S3 caption for more details) to evaluate the “true effect”, which is reported in Table S3. Note that this small-study effect is negative, meaning that studies with larger sample size tends to detect a greater effect of thinning than smaller-size studies. The “true effect” would then be greater than the estimate of the original model. We detected no time-lag bias at this stage (0.029 [-0.059, 0.118], p = 0.515).

Because the overall estimates produced by models fitted with and without influential studies, as well as corrected for publication bias resulted in similar results, we consider our original model to be robust to

publication bias and to influential studies (Table S3). Accordingly, the results presented in the papers are based on the complete dataset. However, when small-study effect or time-lag bias was significant, the associated moderators were included in the following modelling steps to estimate heterogeneity while accounting for publication bias.

Finally, we tested our dataset for scale dependence that could arise from differences in sampling unit sizes across the included studies. Scale dependence may be an issue in environmental meta-analysis, as the plot size may influence the outcome (Nakagawa et al., 2023). We thus verified if the effect of plot size had an influence on the effect sizes by modelling its effect as a moderator. We found no evidence of scale dependence in our dataset (0.631 [-0.569; 1.831], p = 0.303).

## 2.5. Disturbance-specific meta-analyses

In a second step, we tested whether the overall impact of thinning varied depending on the disturbance type (drought, pest, fire, or wind) by using this variable as a moderator and by running a likelihood ratio test to evaluate whether adding this moderator increased the explanatory power and evaluate differences between stressors. We then performed four independent multi-level meta-analyses corresponding to each stressor, as we expected that the effect of thinning would vary considerably for each stressor according to different treatment and stand characteristics (specific hypotheses are stated in the introduction), but also based on different ecological and physiological process. For each secondary meta-analysis, we first calculated the overall effect size for that specific disturbance by running a random-effect model. As in the overall meta-analytic model, we used a random effect to account for the non-independence between the effect sizes of a same study and computed a variance covariance matrix accounting for the non-independence between the sampling errors. We also evaluated the level of total, between-study, and within-study heterogeneity using the methods described above. We used this disturbance-specific overall model to test for publication bias and ran a sensitivity analysis as described in the previous section (Table S3). Given that we detected no small-study effect nor time-lag bias (Table S3), the moderators associated with each type of bias were not kept in following modelling steps.

We then verified the effect of several moderators, that varied according to our hypotheses. The selection of moderators to be tested was
also heavily influenced by the distribution of samples across the vari-
ables of interest. We ensured that each tested variable was distributed
adequately to assess its specific effect while minimizing confounding
effects. Consequently, the number of moderators tested for each
disturbance was limited. Other potentially relevant factors, such as tree
species, genus, or phylogeny, were excluded due to insufficient distri-
bution to rigorously evaluate their effects. Table S4 shows the contin-
gency tables of the multivariate frequency distribution of the case
studies and studies amongst the moderators investigated for each
disturbance-specific analysis. A specific model was fitted for each
moderator to assess its influence. We first tested if the type of response
influenced the effect sizes by adding response type as a moderator. Note
that we did not test for the influence of response type for wind distur-
bance because the unbalanced number of effect sizes did not permit it
(see Table S2). For drought, the response type corresponded to the type
of growth index (i.e. resilience, resistance, recovery). When testing
thinning type, thinning from above was removed from the analysis
because too few studies included this treatment (see Table 1 a-d). When
investigating the effect of continuous moderators (i.e. thinning intensity,
time since thinning) we tested their inclusion as a linear predictor and,
in the case where a non-linear relationship could be expected (based on
literature and preliminary analyses), we also tested a second-degree
polynomial model and a thin plate spline (computed using the "mgcv"
package, Wood, 2003). The choice of the final model for those variables
was based on AIC. The effect of each moderator was assessed using an
omnibus test based on F distribution (fitted with the "rma.mv" function,
using the "test = t" argument), according to which a significant
moderator succeeds in explaining a part of the between-study
heterogeneity.

3. Results

The overall effect of thinning on stand response to disturbances
(coefficient [95 % CI]) was significantly positive (1.0758 [0.453,
1.699], p < 0.001; Fig. 2), indicating that thinning successfully
increased the resilience and resistance of forest stands to disturbances.
Total heterogeneity was high ($I_{total}^2$ = 0.926), which was mainly due to
between-study heterogeneity ($I_{study}^2$ = 0.663) while within-study

heterogeneity was lower ($I_{effect}^2$ = 0.263). None of the random effects
(biome, tree phylogeny, dominant tree species) significantly increased
the model performance based on AIC and likelihood ratio tests, as they
accounted for low amounts of between study heterogeneity, with biome
and phylogeny explaining < 1 %, and species explaining 8 %. Consider-
ing fixed effects, thinning had a positive influence on all response type
(survival, resistance index or growth index; p = 0.001) and we found no
difference between the response types (p = 0.158). Overall, the thinning
resulted in a positive influence of stand's survival (1.018 [0.212,
1.823]), growth indices (1.572 [0.365, 2.779]) and resistance index
(0.540 [-0.233, 1.314]) following disturbances, although it was not
significant for the latter.

3.1. *Disturbance-specific models*

**Drought** - The general effect of thinning on resilience and resistance to drought was significantly positive (1.045 [0.317, 1.773], p = 0.005; Fig. 3). The total heterogeneity was 0.752, the between-study heterogeneity 0.330, and the within study heterogeneity 0.412. The effect of thinning on stand response varied according to the type of growth index considered (p = 0.008). Although positive in all cases, the effect of thinning was statistically significant for growth resilience (1.468 [0.640, 2.297], p < 0.001) and resistance (0.936 [0.203, 1.669], p = 0.013), whereas it was not significant, although marginal, for recovery (0.752 [-0.095, 1.599], p = 0.081; Fig. 4a). The effect of thinning intensity was not statistically significant (p = 0.628). Conversely, the effect of thinning type was significant (p < 0.001; Fig. 4b), with the effect of thinning from below being significantly positive and larger (2.321 [1.422, 3.220], p < 0.001) than that of selective thinning which was only marginal (0.454 [-0.057, 0.964], p = 0.08). Moreover, the time since the first thinning had a significant negative effect that was best modelled using a thin plate spline (p = 0.008). Accordingly, the predicted positive effect of thinning became non-significant after about 20 years, after which there is no more difference between thinned and unthinned stands (Fig. 4c).

**Pests** – The general effect of thinning on resistance to pests was significantly positive (0.883 [0.230, 1.535], p = 0.008, Fig. 3). Total heterogeneity was 0.931, with a between-study heterogeneity of 0.782,

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Orchard plot of the overall effect of thinning on stand responses to disturbances for the random-effect model. The open black dot shows the mean estimate, and the horizontal line represents the 95 % confidence interval on the standardised mean difference (SMD) scale. K shows the number of effect sizes, with the total number of studies between parentheses. Note that two effect sizes (with SMD >30) were removed from the figure to allow a better representation.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Orchard plot showing the general effect of thinning on stand responses to four types of disturbances based on the disturbance-specific models. The open black dots show the mean estimate, and the horizontal lines represent the 95 % confidence intervals on the standardised mean difference (SMD) scale. K shows the number of effect sizes, with the total number of studies between parentheses.</figcaption></figure>

and a within-study heterogeneity of 0.050. We found a significant influence of the response type (p < 0.001), whereby thinning positively influenced stand survival (1.234 [0.663,1.806], p < 0.001), but had a non-significant effect on the resistance index (0.006 [-0.568, 0.681], p = 0.857; Fig. 5a). The effect of thinning type was marginal (p = 0.069), with both types leading to positive results. Yet, only that of selective thinning was statistically positive (1.052 [0.093, 2.010], p = 0.032) whereas thinning from below was not significant (0.655 [-0.836, 2.146], p = 0.385; Fig. 5b). The effect of the pest type was also significant (p = 0.009), with the effect of thinning being significantly positive for insects (1.509 [0.494, 2.525], p = 0.004) but not for pathogens (0.409 [-0.363, 1.264], p = 0.274; Fig. 5c). We did not detect any effect of the intensity of the thinning on the response (0.004 [-0.045, 0.054], p = 0.861). Additionally, we ran supplementary analyses for

pathogens showing that stand age had a significant effect (p = 0.002), with the effect of thinning being significant for mature and old stands (>40 years; 0.965 [0.436, 1.495], p < 0.001), but not for young stands (-0.077 [-0.392, 0.237], p = 0.624). We also tested the effect of stand composition (mixed or monospecific) as an indicator of host density, but it was non-significant (p = 0.300).

**Wind** – The general effect of thinning on the resistance to wind damage was non-significant (0.133 [-3.280, 3.545], p = 0.939; Fig. 3). The total heterogeneity was 0.984, with a between-study heterogeneity of 0.926, and within-study heterogeneity of 0.027. The effect of thinning intensity was significantly negative (-1.978 [-3.639, -0.316], p = 0.022; Fig. 6), according to which the effect of thinning on stand response decreased with increasing thinning intensity. Nevertheless, the effect remains not significant across the whole range of treatment
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Orchard plot showing the effect of growth index type (a), thinning type (b), and time since the first thinning (c) on stand responses to drought for the random-effect model. In panels (a) and (b) the open black dots show the mean estimate, and the horizontal lines represent the 95 % confidence interval on the standardized mean difference (SMD) scale. In panel (c) the bold line represents the prediction, the dashed lines the 95 % confidence interval. K shows the number of effect sizes, with the total number of studies between parentheses.</figcaption></figure>

intensities covered. We also tested non-linear effect of thinning intensity using a second-order polynomial model as well as a thin plate spline, both of which also led to non-significant results, and showed lower performances than the linear model presented in Fig. 6. The effects of thinning type (p = 0.410) and age (p = 0.968) were both not-significant (Table 2).

**Fire** – The general effect of thinning on resilience to fire was positive but not significant (1.409 [-0.646, 3.464], p = 0.179; Fig. 3). The total heterogeneity was 0.984, with a between-study heterogeneity of 0.551, and a within-study heterogeneity of 0.433. We found no effect of response type on thinning effect (0.6742 [-2.333, 3.682], p = 0.653). Thinning intensity however had a significant negative influence on

stand response to fire, which was best modelled using a thin plate spline (p = 0.007). The effect of thinning was significantly positive when the treatment removed less than ~55 % of stand BA, but had a non-significant effect with greater intensities, as the confidence interval includes zero (Fig. 7). Lastly, the effect of thinning type was non-significant (p = 0.402), as were stand age (p = 0.532), and stand composition (p = 0.388).

## 4. Discussion

For temperate and boreal forests, results from our meta-regressions provide strong evidence that, overall, thinning tends to increase the
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Orchard plot showing the effect of response type (a) and thinning type (b) and pest type (c), on stand responses to insects and pathogens for the random-effect model. The open black dots show the mean estimate, and the horizontal lines represent the 95 % confidence interval on the standardized mean difference (SMD) scale. K shows the number of effect sizes, with the total number of studies between parentheses.</figcaption></figure>

resilience and resistance of forest stands to multiple stressors (Moreau et al., 2022). The effect of thinning on the response to disturbances across all studies (mean grand effect size) indicated that generally, thinning successfully increased the resilience and resistance of forest stands to disturbances. Although responses varied among disturbance types, and in some cases response type, thinning type and time since treatment, a key finding of this study is that no significant negative effects of thinning have been detected in our analyses. Should this silvicultural treatment be applied at a large scale across these biomes, the overall resilience and resistance of forests, as measured by our direct quantitative indices, should therefore be at least maintained or improved. However, silvicultural strategies are often applied at a more

local scale, where specific stressors may cause more concerns than others. Our results provide new insights on the effects of thinning on four types of stressors.

**Drought** – Our results support the conclusions of previous syntheses that thinning improve the resistance and resilience of the forest to drought (e.g. Sohn et al., 2016; Bottero et al., 2017; Moreau et al., 2022). While a previous broad scale study showed a positive effect of thinning on annual growth rate and indirect tree performance indicators during drought events (Sohn et al., 2016), we furthermore showed that the effect of thinning differed among different vulnerability indices computed from tree ring widths. Indeed, it was most efficient in increasing growth resistance and resilience, whereas its effect on growth
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Orchard plot showing the effect of thinning intensity on stand responses to wind for the random-effect model. The bold line represents the prediction and the dashed lines the 95 % confidence interval. K shows the number of effect sizes, with the total number of studies between parentheses.</figcaption></figure>

**Table 2**

Moderators' effects as assessed by disturbance-specific meta-regressions. The F and p-values are the results of the omnibus test for each moderator. Moderators in bold have a significant influence on the effect of thinning. The R² is the marginal R-squared specific to each significant moderator. The last two columns (grey background) present the test for residual heterogeneity (QE) and associated p-value. The meta-regressions were fitted using a variance-covariance matrix assuming a correlation of 0.5 between non-independent sampling errors.

<table><thead><tr><th>Disturbance</th><th>Tested moderators</th><th>F</th><th>p</th><th>R<sup>2</sup></th><th>QE</th><th>p</th></tr></thead><tbody><tr><td rowspan="4">Drought</td><td>Growth Index type</td><td>4.196</td><td>0.079</td><td>0.044</td><td>354.936</td><td>&lt; 0.001</td></tr><tr><td>Thinning intensity</td><td>0.236</td><td>0.628</td><td>0.003</td><td>359.933</td><td>&lt; 0.001</td></tr><tr><td><strong>Thinning type</strong></td><td>14.737</td><td><strong>&lt; 0.001</strong></td><td>0.385</td><td>334.872</td><td>&lt; 0.001</td></tr><tr><td><strong>Time since first treatment*</strong></td><td>4.809</td><td><strong>0.010</strong></td><td>0.131</td><td>320.433</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Pest</td><td><strong>Response type</strong></td><td>14.573</td><td><strong>&lt; 0.001</strong></td><td>0.190</td><td>303.266</td><td>&lt; 0.001</td></tr><tr><td><strong>Pest type</strong></td><td>4.969</td><td><strong>0.009</strong></td><td>0.107</td><td>307.515</td><td>&lt; .0001</td></tr><tr><td>Thinning intensity</td><td>0.031</td><td>0.861</td><td>0.001</td><td>315.466</td><td>&lt; 0.001</td></tr><tr><td>Thinning type</td><td>2.765</td><td>0.069</td><td>0.007</td><td>311.604</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Wind</td><td>Response type</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td><strong>Thinning intensity</strong></td><td>6.0948</td><td><strong>0.022</strong></td><td>0.015</td><td>77.487</td><td>&lt; 0.001</td></tr><tr><td>Thinning type</td><td>0.796</td><td>0.510</td><td>0.234</td><td>69.496</td><td>&lt; 0.001</td></tr><tr><td>Age</td><td>0.033</td><td>0.968</td><td>0.003</td><td>87.277</td><td>&lt; 0.001</td></tr><tr><td rowspan="4">Fire</td><td>Response type</td><td>1.144</td><td>0.329</td><td>0.007</td><td>671.259</td><td>&lt; 0.001</td></tr><tr><td><strong>Thinning intensity*</strong></td><td>7.452</td><td><strong>0.007</strong></td><td>0.516</td><td>611.371</td><td>&lt; 0.001</td></tr><tr><td>Thinning type</td><td>0.948</td><td>0.402</td><td>0.021</td><td>635.909</td><td>&lt; 0.001</td></tr><tr><td>Age</td><td>0.642</td><td>0.532</td><td>0.000</td><td>662.074</td><td>&lt; 0.001</td></tr></tbody></table>

recovery was also positive but statistically marginal. This suggests that thinning is effective in decreasing vulnerability to drought mostly by acting on the resistance, i.e., by limiting the magnitude of the growth decline during the event, rather than by increasing the rate of growth recovery following the event. As pointed out by Manrique-Alba et al. (2022), given that recovery index compares growth after the drought and during the drought, recovery is expected to be lower when resistance is higher and for trees which growth is little affected during the drought year. Herein, this trade-off between the recovery and resistance may result from the higher occurrence of gymnosperms among the included studies, which are known to have a lower recovery capacity than angiosperm (DeSoto et al., 2020); this effect might therefore not arise from the thinning treatment itself.

In line with our first hypothesis, thinning from below was the most effective type of thinning for increasing the resilience and resistance to drought. Although the positive effect of thinning from below was reported by several recent studies (e.g. Aldea et al., 2017; Cabon et al.,

2018; Vernon et al., 2018; Navarro-Cerillo et al., 2019; Low et al., 2021), it is the first time, to our knowledge, that the difference in efficiency between thinning types was quantified at a broad scale. By removing the smallest trees in the diameter distribution, thinning from below re-distributes site resources among larger trees, which have higher transpiration demand due to their larger crown surface (D'Amato et al., 2013). Thus, by improving conditions specifically for larger trees, which would be the most at risk of hydraulic failure during drought events (Bennett et al., 2015; McDowell and Allen, 2015, Stovall et al., 2019), thinning from below may have outperformed selective thinning, resulting in greater difference compared to unthinned stands. Moreover, larger trees may have a better capacity to benefit from a higher availability of resources due to larger root system and crown (Lopez et al., 2003; Pretzsch and Dieler 2012), which generally leads to a better growth potential (Stephenson et al., 2014).

Despite the generalized positive effect of thinning, some studies included in this meta-analysis reported negative effects of thinning on

<footnote>*Time since first treatment and thinning intensity were modelled using a thin plate spline for drought and fire, respectively</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Orchard plot showing the effect of thinning intensity on stand responses to fire for the random-effect model. The dashed lines represent the 95 % confidence interval. K shows the number of effect sizes, with the total number of studies between parentheses.</figcaption></figure>

drought resilience and resistance (whereas the effect of thinning from below was constantly positive). Accordingly, a certain degree of uncertainty remains regarding the relationship between tree and stand characteristics and their response to drought (Castagneri et al., 2022). Moreover, given that only two of the studies included thinning from above (Bosela et al., 2021; Zald et al., 2022), we were not able to consider this type of treatment in the analysis. Yet, in stands dominated by red pine (Pinus resinosa Aiton), Bosela et al. (2021) found that thinning from above had a greater positive effect on the resilience to drought than thinning from below. Similar results were obtained by Zald et al. (2022) in mixed conifer stands dominated by white fir (Abies concolor) in southern Sierra Nevada, California.

Variation among results is likely partly attributable to interspecies differences in functional strategies to cope with water deficits (Forrester et al., 2022; Gutierrez Lopez et al., 2021). In Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. H. Karst) stands in Sweden, Gutierrez Lopez et al. (2021) found that the reduction in whole-tree transpiration during drought was more pronounced in large pine trees compared to smaller ones, whereas the opposite was observed for spruce. Similarly, Colangelo et al. (2018) concluded to a higher probability of mortality for smaller than for larger oaks (Quercus frainetto Ten.) during drought episodes in Italy. Because of such differences in the effect of tree size on response to water stress between species and even among individuals of the same species, broad-scale conclusions on the performance of thinning from above compared to thinning from below should be interpreted with caution since tree size is only one of several factors at play (Forrester et al., 2022; Gutierrez Lopez et al., 2021). Indeed, our meta-analysis approach may have missed additional mechanisms by which thinning influences resilience and resistance to drought. For example, the site water status and the intensity and duration of drought events could not be incorporated directly into our meta-regressions. These factors are known to influence the beneficial effects of thinning (Trouvé et al. 2017), which may not be sufficient to enhance forest resilience during particularly extreme droughts (Elkin et al., 2015).

Contrary to both our first hypothesis and the conclusion from a recent synthesis (Moreau et al., 2022; del Campo et al., 2022), the positive effect of thinning on resilience and resistance to drought did not increase with treatment intensity. While the literature review of Moreau et al. (2022) included results from indirect measurements of tree performance and modelling results, our meta-regressions revealed a lack of

direct empirical evidence to conclude that stand resilience and resistance to drought generally tend to increase with thinning intensity. Our results rather showed a constant positive effect of thinning from very low to heavy thinning intensities (ranging from 12 % to 95 % of BA removal) when compared to unthinned stands.

The effect of thinning on drought resilience and resistance declined significantly with time since the first thinning and reach the same as untreated stands after approximately 40 years. Similar diminishing effect was observed in stands treated with a single thinning treatment (Gavinet et al., 2019; Diaconu et al., 2017; Navarro-Cerrillo et al., 2019) as well as in stands where repeated thinnings were applied (Schmitt et al., 2020; Sohn et al., 2013; Bosela et al., 2021; D'Amato et al., 2013; Manrique-Alba et al., 2020). This declining effect has been attributed to the development of higher leaf-to-sapwood area ratios for trees released by thinning (D'Amato et al., 2013; Mausolf et al., 2018). At some point, the higher leaf area may result in increased water demand and vulnerability to hydraulic failure for released trees, a process that repeated thinnings do not seem to prevent (D'Amato et al., 2013; Mausolf et al., 2018; Bottero et al., 2021). However, the diminishing positive effect of thinning over time remains sparsely documented and poorly understood (Moreau et al., 2022).

**Pests** – Our meta-regressions revealed several opportunities to increase the resistance of forests to insects and pathogens through thinning. First, our results showed that selective thinning generally increased stand resistance in infected stands, which is in line with our third hypothesis. Indeed, removing the most vulnerable trees (salvage thinning) or trees already infected (sanitation thinning) may both contribute to increasing tree vigour while also reducing pathogens inoculum or removing brood trees for insects (Muzika, 2017). However, our results showed that removing only the smallest trees in the diameter distribution (thinning from below) without direct consideration for the vulnerability of trees or their level of infection was not sufficient to improve resistance. Our results suggest that targeting vulnerable and infected trees is more important than enhancing resource availability to increase resistance to pests. Although thinning from above was found to have potential for increasing stand resistance to mountain pine beetle (Negrón et al., 2017), only two of the retained studies tested this type of treatment (Negrón et al., 2017; Morris et al., 2022), which was not sufficient to test the efficiency of thinning from above in our analysis. Again, further studies including thinning from above are needed to
assess the effects of this treatment at a broader scale.

An important finding of our meta-regressions is that thinning significantly increases resistance to pests by reducing stand mortality, but not by reducing the level of infection (resistance index) within treated stands. These results are in line with previous reviews (Roberts et al., 2020; Moreau et al., 2022) which concluded that thinning can rarely prevent infections, but rather delay the spread and development of some pests. Over time, the level of infected trees remains equivalent in most cases (Roberts et al., 2020). However, by significantly increasing tree vigour (including both growth and survival), thinning can promote resistance in infected stands (Bulman et al., 2016; Muzika, 2017; Roberts et al., 2020; Moreau et al., 2022). Such a positive effect on survival dynamics was confirmed at a broad scale and with a large variety of pests by our meta-regressions.

Overall, we found a stronger and more consistent effect of thinning in stands threatened by insects. Thinning was particularly effective against beetles (Egan et al., 2010; Knapp et al., 2021) and spruce budworm outbreaks (Fuentealba and Bauce, 2012). A considerable body of evidence from studies that could not be included in the analysis due to non-compliance with our selection criteria also suggests that thinning is effective at increasing resistance to bark beetles (Hood and Sala, 2016; Scheller et al., 2018; Stadelmann et al., 2013; Steel et al., 2021), as well as woodwasp *Sirex noctilio* (Dodds et al., 2014) and to other defoliators such as spongy moths (Fajvan and Gottschalk, 2012). Regarding pathogens, thinning had a positive effect against foliar fungal pathogens such as the white pine needle damage complex (McIntire et al., 2018) and infection by Tomentosus root rot (Whitney, 1993). Although not incorporated in our meta-analysis, there is also evidence that selective thinning can help increase the resistance to Dothistroma needle blight (Bulman et al., 2016). However, results for Armillaria root rot infections were less conclusive, with positive effects (Filip et al., 2009; 2015; Hood and Kimberley, 2009), but also non-significant (McIntire et al., 2018) and negative effects (Morrison et al., 2022). Logging operations may facilitate root rot infections due to contamination from stumps or direct damage to the stems and roots of residual trees (Filip et al., 2015). The inconsistency effect of thinning alone for Armillaria and other root rot infections certainly contributed to negate the general effect for this group of pests.

Given that the biology and population dynamics of eruptive organisms vary tremendously for both pathogens and insects, the efficacy of thinning will likely vary accordingly (Moreau et al., 2022). Although our analysis is broad and encompasses many sites and climatic conditions, our conclusions are limited to pests included in our meta-regressions. Moreover, the information about host density, tree size or stand composition were too inconsistent or unbalanced in the retained studies to allow robust and comprehensive analysis. For pathogens, we were able to include stand age and stand composition (mixed or mono-specific) as proxies for host density. However, only stand age was significant, with thinning being effective in older stands (> 40 years). These older stands were dominated by Douglas-fir (Filip et al., 2015), pine species (McIntire et al., 2018; Woodruff et al., 2019), western larch (Jackson et al., 2016) and spruce species (Whitney, 1993; Piri and Korhonen, 2008). However, direct comparisons of stand age within studies were often not provided and confounding effects could be involved. Indeed, response types were slightly unbalanced, as survival analyses (for which thinning was more efficient) were more represented in case studies with higher host densities (Appendix 1). Other variables, such as the nature of the pathogen infecting the stands was not considered herein and could also lead to confounding effects, according to which these results must be interpreted carefully. Yet, further research on a wider range of insects, pathogens, and hosts is needed to better assess the effects of thinning on forest resilience (Prospero and Cleary, 2017; Roberts et al., 2020; Moreau et al., 2022).

**Wind** – The potential negative effect of thinning on the risk of wind damage has been identified as one of the main potential limitations of the treatment for increasing overall forest resistance to multiple hazards

(Moreau et al., 2022). Contrary to our third hypothesis, we found no evidence of this negative effect of thinning on resilience to wind damage. These results are based on data from 5 studies and 23 case studies distributed across Europe and North America. The studied stands were part of temperate biome and consisted of both natural and planted forests composed of a total of 10 distinct coniferous species, with stand age ranging from 15 to more than 300 years. Given that wind resistance is known to vary substantially among species, as well as within the same species depending on stand characteristics and location, and it is plausible that the effect of thinning on wind resistance is equally variable and explains the non-significance of our results (Canham et al., 2001; Ribeiro et al., 2016).

Overall, very few case studies reported negative effects of thinning on wind resistance, and none were observed in stands where less than ~30% of the basal area had been removed. With such low-intensity treatments, we rather observed positive responses following thinning that could suggest a non-linear response as a function of thinning intensity. This was however not supported by our results, maybe because of the small weight of these studies due to the lower precision (large standard deviation of the mean) of the reported results. Yet, combined with the absence of a significant negative effect of thinning, these results suggests that thinning, especially when applied at a low-intensity, is a relatively low-risk treatment with regards to the occurrence of wind damage. Nevertheless, there is evidence that thinning should be avoided in dense, mature stands composed of conifers with high height-to-diameter ratios (Gardiner et al., 2013; Pukkala et al., 2016; Albrecht et al., 2012, 2015), although our analyses could not add significant support to these previous findings at a broad scale. Further results from experimental studies are however needed to confirm whether pre-commercial and low-intensity thinning can effectively improve resilience and resistance to windstorm as the stand matures (Gardiner et al., 2013), and to assess how forest composition and structure, spatial aggregation of retained trees, as well as site characteristics including exposure to winds, may alter the relationship between thinning and forest vulnerability to wind damage (Hanewinkel et al., 2014; Kamimura et al., 2008; Maguire et al., 2006; Urgenson et al., 2013).

**Fire** – We observed contrasting responses to thinning among the 41 case studies, with most studies reporting positive effects (Garcia-Jimenez et al., 2017; Pollet and Omi, 2002; Ritchie et al., 2007; Brodie et al., 2024; Shive et al., 2024), although an absence of effect (Prichard et al., 2010), and negative effects of thinning (Raymond and Peterson, 2005; Taylor et al., 2021) were also observed. This inconsistency resulted in an overall positive but non-significant relationship between thinning and stand resistance to fire, which support the conclusions of a recent syntheses conducted in western United-States (Davis et al., 2024). Indeed, Davis et al. (2024) highlighted that the effectiveness of thinning alone was inconsistent and restricted to the first years following the thinning. Our results also highlighted that treatment type was not significant, which refutes our last hypothesis based on which thinning from below would be more effective. Yet, the sylvicultural treatments applied to stands in a fire-risk context shared the aim of reducing ladder fuels and modifying stand structure to limit connectivity, which may partly attenuate the differences between treatment types. Although stand composition is associated with variable stand resistance to fire, we did not find a significant effect of stand composition on thinning effectiveness. Yet, most of the included articles are from the western United States, where Davis et al. (2024) found similar results even when accounting for a variety of forest types.

Our meta-regressions showed a significant positive effect of thinning when less than 55% of the initial basal area was removed, which corresponds to a usual thinning intensity as of the literature, whereas greater intensities are rather infrequent. Although this result should be interpreted with caution since treatment intensity was only available in about 40% of the case studies, it suggests that thinning of low to medium intensity, have the potential to increase resistance to fire. We observed such a positive effect in conifer-dominated stands from the
western United-States dominated by ponderosa pine and white fir, from natural and planted forests, and following both selective thinning and thinning from below (e.g., Brodie et al., 2024; Garcia-Jimenez et al., 2017; Pollet and Omi, 2002).

In general, the documented positive effects of thinning were related mainly to the reduction of fuel loads and the disruption of fuel continuity in treated stands (Safford et al., 2012; Prichard and Kennedy, 2014; Thomas and Waring, 2015). Contrary to the above, the accumulation of harvest slash following thinning operations and the quick densification of the understory due to the recruitment of new seedlings can increase surface fuels, leading to increasing fire risk (Arellano-Pérez et al., 2020; Kalies and Yocom Kent, 2016; Madrigal et al., 2017; Taylor et al., 2021). Given that large canopy opening can increase wind flow, solar radiation and near-surface temperature altogether (Russell et al., 2018), removing most of the stand basal area with severe thinning may exacerbate the adverse effect of post-harvest slash accumulation by reducing both canopy fuel and slash moisture, thereby increasing fire risk.

To address this potential drawback, harvest slash can be eliminated through prescribed burning (Innes et al. 2006; Piqué and Domènech, 2018) or mechanical removal (Dell and Green, 1968; Butler et al., 2013). There is substantial evidence that thinning combined with fuel treatment, such as slash burning or removal and understory clearing, reduces the risk and severity of fire (Safford et al., 2012; Collins et al., 2014; Kalies and Yocom Kent, 2016; Piqué and Domènech, 2018; Volkova and Weston, 2019; Stoddard et al., 2021; Davis et al., 2024, Shive et al., 2024). Yet, because prescribed burning is a silvicultural treatment in itself, thinning treatment paired with prescribed burning was not included in the present meta-analysis, in order to discriminate the specific effect of thinning alone. Given the recent accumulation of recent studies on fuel treatments (e.g., Davis et al., 2024, Shive et al., 2024), a meta-analysis on the impacts of thinning combined with different slash and understory management approaches would be timely as it could help confirm the benefits of such approaches at a broader scale.

Overall, the voluntary broad focus of our study encompasses a variety of factors, including historic fire regimes, fire-adaptive species traits, ecosystem productivity gradients, stand structure, and tree spatial patterning and more—each of which can influence the effectiveness of treatments (Safford et al., 2012; Collins et al., 2014; Davis et al., 2024, Shive et al., 2024). In many cases, these factors were only represented once in the retained articles, which prevented a direct analysis. Still, the sensitivity analysis we ran using a leave-one-out method (Figure S7) indicates that the removal of any study does not have a particular influence on the general results. While we do not have sufficient information to conclude on differences in thinning effect due to these factors, this analysis suggests that our main results are not biased because of these differences. Nevertheless, to enable a better understanding of the broader impacts of these factors on subsequent fire severity, additional studies across a broad range of forest ecosystems are needed.

## 4.1. Perspectives for multi-risk management and concluding remarks

Generating broad-scale conclusions to inform forest management decisions in the face of global change is highly challenging. Although our analyses showed that negative responses may occur, in general, thinning can be expected to increase the resilience and resistance of forests to multiple stressors across North America and Europe. This was documented especially for the temperate and boreal biomes, and for a wide range of sites and stands characteristics, in forests that are affected by multiple disturbances such as drought, pests, windstorms, and fire. However, our article survey revealed that almost no studies were available in Asia and the southern hemisphere, as well as in subtropical and tropical forests. Empirical evidence from these regions is needed to allow global-scale conclusions.

An interesting finding of this study is the absence of an overall negative effect of thinning on the resistance to wind damage. Such

negative effect in the first years following its application was identified as the main potential limitation of thinning for increasing overall forest resistance to multiple disturbances (Moreau et al., 2022). Although more investigation is needed to identify the precise contexts in which thinning exacerbates the risk of damage excessively, our results demonstrate that this treatment, at least when basal area removal is relatively low (i.e. ≤ 30 %), is generally a low-risk intervention with respect to wind damage. Thus, thinning offers an opportunity to manage stands that are threatened by disturbances such as drought, pests, and fire, without necessarily increasing their susceptibility to windthrow events. For example, forest stands in windy areas that are threatened by increasing pressure from both drought and eruptive organisms like bark beetles could be subjected to selective thinning, thus increasing their overall resilience to these multiple disturbances.

Nevertheless, thinning should not be considered as a tool that will singlehandedly increase the resilience and resistance of forests (Moreau et al., 2022). While we found a positive effect of thinning on forest resilience to drought, the effect of thinning alone was less conclusive for stands affected by some pests, mainly Armillaria root rot infections. In stands that are threatened by Armillaria, thinning must be complemented by stumps treatments to be efficient at increasing forest resilience (Oliva et al., 2010). Similarly, in cases where fire is an additional threat, thinning should be complemented by prescribed burning or mechanical slash removal (Collins et al., 2014; Kalies and Yocom Kent, 2016; Davis et al., 2024). Moreover, we found that the positive effect of thinning in a given stand, even when repeated, was limited in time. Thus, at the stand scale, thinning should be seen as an effective tool to temporary reinforce and stabilise forest resources, while opening a range of opportunities to implement longer-term adaptive management strategies (Bradford and Bell, 2017).

Although the development of silviculture strategies focusing on enhancing forest adaptation to global change has attracted a lot of attention in the last decades, and although it is one of the most common silvicultural treatments applied worldwide, we found only few studies bringing robust empirical data on the direct impacts of thinning on forest resilience and resistance to multiple disturbances. For many disturbances, especially wind and fire, recent research often focussed on modelling simulations (e.g. Hirsch, 1996; Gardiner et al., 2000; Hale et al., 2015). While these mechanistic models have undoubtedly brought important insights on the factors related to forest resilience after thinning, empirical data remain important for their validation, and assembling such data can help draw conclusions at a broader scale. To help collect more empirical data, efforts must be made to revisit silvicultural trials and conduct a *posteriori* experiments in a way that key stand attributes linked to forest resilience and resistance can be identified (Seidl et al., 2017). Using remote sensing and dendrochronological approaches, these studies could focus on how pre-disturbance stands history and characteristics affect forest resilience and vulnerability to multiple disturbances (Achim et al., 2022). There is also a need for implementing new silvicultural trials that include a variety of treatments (including thinning), in which a range of adaptive silvicultural strategies are tested and compared. Both types of research efforts (revisiting and establishing trials) are required to further our understanding of the efficacy of silvicultural treatments such as thinning for meeting the challenge of stabilizing the provision of forest ecosystem services in the face of global change. In the case of thinning, these efforts should bring additional support on its efficiency at increasing forest resistance and resilience to global change.

## Authors' contributions

Guillaume Moreau, Catherine Chagnon and Hervé Jactel conceived the ideas and designed methodology; Catherine Chagnon and Sébastien Dumont collected the data and organized the dataset; Catherine Chagnon performed the statistical analysis and data visualization; Catherine Chagnon and Guillaume Moreau wrote the first draft of the manuscript,
with contributions from Sébastien Dumont, Alexis Achim, Alexandre Morin-Bernard and Hervé Jactel. All authors contributed critically to the drafts and gave final approval for publication.

### CRediT authorship contribution statement

**Achim Alexis:** Writing – review & editing, Validation, Resources, Funding acquisition. **Moreau Guillaume:** Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Conceptualization. **Jactel Hervé:** Writing – review & editing, Validation, Methodology, Investigation, Conceptualization. **Dumont Sébastien:** Writing – review & editing, Methodology, Investigation, Data curation. **Morin-Bernard Alexandre:** Writing – review & editing. **Chagnon Catherine:** Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization.

### Funding

This research was conducted as part of the Silva21 Alliance Grant project (NSERC ALLRP 556265-20) funded by the Natural Sciences and Engineering Research Council of Canada.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors are thankful to Bastien Castagneyrol for his help with the statistical analysis.

### Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.foreco.2025.122788.

### Data availability

No data was used for the research described in the article.

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