# Impact of defoliation on tree growth for sessile oak and Scots pine following heat-drought events

Théo Javoy, Sandrine Perret, Camille Couteau, Thomas Perot

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Théo Javoy, Sandrine Perret, Camille Couteau, Thomas Perot. Impact of defoliation on tree growth for sessile oak and Scots pine following heat-drought events. Forest Ecology and Management, 2025, 598, pp.123243. 10.1016/j.foreco.2025.123243. hal-05520906

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# Impact of defoliation on tree growth for sessile oak and Scots pine following heat-drought events

Théo Javoy*, Sandrine Perret, Camille Couteau, Thomas Perot

INRAE, UR Ecosystems Forestiers (EFNO) - UR1455, Domaine des Barres, Nogent-sur-Vernisson, 45290, France

## ARTICLE INFO

### Keywords:

- Defoliation

- Tree dieback

- Tree growth

- Sessile oak

- Scots pine

- Forest management

- Mixed forest

- Competition index

- Drought and heatwave

## ABSTRACT

European forests have experienced several droughts and heat waves in recent years leading to an increasing level of tree dieback. Quantifying the effects of dieback on tree growth is a crucial issue for forest managers. We monitored tree crown defoliation and tree growth for sessile oak and Scots pine from 2020 to 2023 in the OPTMix experimental network located in central France. We quantified the relationship between tree growth and tree crown defoliation under varying neighborhood conditions. We hypothesized that crown defoliation, tree size and neighborhood competition (local density and composition) would all affect tree growth. We carried out a magnitude analysis to quantify the importance of these different effects on tree growth. We showed that crown defoliation had a strong negative effect for sessile oak (-19.5 % of growth from 42 % to 57 % of defoliation) and a very strong negative effect for Scots pine (-29.5 % of growth from 39 % to 51 % of defoliation). Tree size and neighborhood density had the strongest effects on tree growth for both species, with, respectively, a positive and a negative effect. The proportion of heterospecific species in the neighborhood had the weakest effect, and was positive for sessile oak and negative for Scots pine. We found no interaction between crown defoliation and other predictors, thus indicating that they do not modify the relationship between crown defoliation and radial growth for either species. Forest managers can use defoliation as a rapid indicator of tree health and to help them assess the impact of defoliation on tree growth in various oak-pine forests.

## 1. Introduction

Climate change is modifying forest dynamics and sometimes results in forest dieback linked to droughts and high temperatures (Allen et al., 2015, 2010). More than 1300 such forest dieback events have been studied worldwide since 1970 (Hammond et al., 2022), and long-term forest monitoring reveals that tree mortality has doubled in Europe's temperate forests over the last three decades (Senf et al., 2018). In a few more decades, a warmer and drier climate will further exacerbate forest dieback (Allen et al., 2010; Gazol and Camarero, 2022; McDowell and Allen, 2015; Park Williams et al., 2013). Both growth and dieback are well-known predictors of tree mortality (e.g. Cailleret et al., 2017; Dobbertin, 2005). In the current context of global warming, monitoring tree crown defoliation or other crown indicators could provide a rapid assessment of forest health and vitality (Dobbertin, 2005; Ferretti et al., 2021b; Zarnoch et al., 2004).

Forest managers and researchers usually evaluate the level of tree dieback by quantifying tree crown defoliation. This method indicates a lack of foliage for a tree in comparison with a healthy reference tree with

full foliage (Eichhorn et al., 2016). Canopy defoliation, and forest dieback in general, may have many impacts, e.g. on forest microclimates (e.g., Zellweger et al., 2020), on forest biodiversity (Cours et al., 2022) and on tree growth (Ferretti et al., 2021a, 2021b). However, few studies have quantified the relationship between crown defoliation and tree radial growth during a forest dieback episode induced by several heat-drought events. Since 2000, some studies have focused on the relationship between tree growth and tree defoliation (Ferretti et al., 2021a, 2021b; Seidling et al., 2012). Ferretti et al. (2021a) demonstrated that crown defoliation had a negative effect on tree growth for ten important European tree species. Some studies support the use of defoliation as a rapid indicator of forest health and vitality along with tree growth and foliar composition (Dobbertin, 2005; Ferretti et al., 2021b; Seidling et al., 2012). However, this type of study usually refers to data collected on dominant and co-dominant trees located in even-aged monospecific forests, whereas it is likely that other silvicultural factors also affect the relationship between crown defoliation and tree growth. Few studies have accounted for tree size, neighborhood density and heterospecific interaction in the relationship between crown

<figure><img src="image_2.png" /></figure>

<footnote>* Corresponding author.
E-mail address: theo.javoy@inrae.fr (T. Javoy).</footnote>
defoliation and tree growth.

Larger trees generally have higher stem radial growth than smaller ones (Wykoff, 1990). Dominant trees generally have deeper coarse roots and a greater fine-root biomass than do suppressed trees, thus giving them better access to resources and potentially delaying water stress (Grote et al., 2016). Moreover, light interception is generally greater in the upper part of the canopy (Niinemets, 2010) so trees occupying the upper stratum benefit from increased photosynthesis and growth (Trouvé et al., 2017). Neighborhood competition for resources is also a key driver of tree growth and is generally estimated through competition indices (Canham et al., 2004). Previous studies have shown that reducing neighborhood stand density is an effective way to improve tree growth (Ngo Bieng et al., 2013; Perot et al., 2010; Sohn et al., 2016). Reducing stand density (i.e. the thinning effect) decreases competition and increases water and light availability for each tree (Breda et al., 1995). Competition and tree status may therefore modify the relationship between tree growth and crown defoliation. Ferretti et al. (2021a) mentioned that the effects of competition and tree status on tree growth could be confounding factors because they can act on both growth and defoliation.

In a mixed forest, even though inter-specific interactions are a non-negligible factor for predicting growth, they generally have a weaker effect on growth than tree size and stand density (Ngo Bieng et al., 2013; Perot et al., 2010; Toïgo et al., 2015). To our knowledge, no studies have taken into account neighborhood composition in the relationship between tree growth and tree crown defoliation, whereas the area of mixed forests has increased in European forests in the last few years (Alberdi et al., 2015). Tree species diversity generally improves forest stand resistance to natural disturbances (Jactel et al., 2017). Sousa-Silva et al. (2018) demonstrated a greater resistance to drought for sessile oak in mixed stands. Furthermore, mixing tree species can enhance tree growth due to facilitation or reduced competition, leading to greater growth in mixtures than in monocultures (Ammer, 2019; Forrester and Bauhus, 2016). However, this is not always true since growth also depends on the species in the mixture and on growing conditions (Bello et al., 2019a, 2019b; Toïgo et al., 2015). Interactions among tree species may also modify the relationship between tree growth and crown defoliation. In order to obtain results that apply to the entire stand, and to avoid confounding factors, it is therefore important to establish the relationship between tree crown defoliation and tree growth while taking into account tree status and neighborhood competition (local density and composition). For forest managers, it is also important to rank the importance of the different effects on growth, so as to identify the factors with the greatest impact.

Europe experienced a record heat wave in 2019 and several other heat waves in more recent years (Xu et al., 2020). These events have damaged European forests and crown defoliation has increased (Knutzen et al., 2025). In July 2019, an air temperature of 42.8°C was recorded at the weather station near our study site (INRAE, Nogent-sur-Vernisson, France); it was the highest temperature recorded in the last thirty years. Following these heat-drought events, we monitored tree growth and tree crown defoliation for sessile oak (*Quercus petraea* L.) and Scots pine (*Pinus sylvestris* L.) in the OPTMix experiment located in central France (Korboulewsky et al., 2015). Both species are particularly important economic species in Europe, valued for a wide range of uses (Durrant et al., 2016; Eaton et al., 2016). In a previous study, we showed a significant increase in defoliation for sessile oak and Scots pine at the OPTMix site between 2020 and 2024 (Javoy et al., 2025). In the current study, we asked the following research questions:

1) How does tree crown defoliation affect radial growth for sessile oak and Scots pine? We hypothesized a strong negative effect of crown defoliation on stem radial growth for both species.

2) How important is the effect of crown defoliation on radial growth compared with tree size, neighborhood tree density and composition

effects? For both species, we hypothesized very strong effects for tree size and neighborhood tree density: larger trees would grow more and growth would be greater with lower neighborhood tree densities. We hypothesized a weaker neighborhood composition effect with a direction depending on tree species. We further hypothesized that crown defoliation would have a stronger effect than neighborhood composition.

3) Do tree size, neighborhood tree density and neighborhood composition modify the relationship between defoliation and growth? We hypothesized that all three factors could affect the relationship between defoliation and growth.

To answer these questions, we quantified the relationship between tree growth and tree crown defoliation for both species while taking into account the effect of tree size, neighborhood density and composition. In addition, we performed a magnitude analysis (Barbier et al., 2009; Camp et al., 2008) to quantify the importance of these different effects on tree growth.

## 2. Materials and methods

### 2.1. Study site and experimental design

This study was conducted at the OPTMix long-term experimental site (Oak Pine Tree Mixture, https://www.optmix.efno.fr, Korboulewsky et al., 2015) located in the Orléans National Forest (France, 47°49' N, 2°29' E) (Fig. 1). A complete description of the site is available in Javoy et al. (2025).

The study area is characterized by a temperate climate under oceanic influence with a mean annual temperature of 10.8°C and a mean annual rainfall of 729 mm (based on 1981–2010 data from the SAFRAN and ISBA analytical platforms, Météo-France, (Durand et al., 1993)). For the period covering this study (2020–2023), the monthly average temperature was 17.3°C and the monthly precipitation from May to September was 62 mm on average (see Appendix A for more details). At our study site, the forest stands experienced record temperatures during the severe heat-drought in 2019 and several other droughts and heat waves in recent years (see Appendix A, B, C).

All the plots are located on the same soil type, characterized by an upper layer of sandy loam and a clay layer appearing at around 40–70 cm in depth (Brèthes, 1993). Two species were studied: sessile oak (*Quercus petraea* Matt. (Liebl.)) and Scots pine (*Pinus sylvestris* L.). These species are very common in French forests in both pure and mixed stands. The OPTMix experiment consists of 0.5-ha plots of mature forest (80–90 years old on average) composed of sessile oak and Scots pine distributed over a total area of 40 ha. Tree data were collected at 21 plots in the experiment, selected to enable us to cross stand density and stand composition with three plot replicates of each stand type: pure oak, pure pine and oak-pine mixture, in low and medium stand densities. Most of the data were collected from the same nine sites as one of our previous studies (Javoy et al., 2025). For oak-pine mixtures, we added three additional sites, each consisting in a single high-density plot (Table 1). The stand density of each 0.5 ha plot was defined by the Relative Density Index, RDI (Reineke, 1933). The RDI corresponds to the ratio between the current number of stems per hectare and the maximum number of stems for a given mean quadratic diameter according to the self-thinning rule (Reineke, 1933). A value of 0 corresponds to a target tree with no neighbors within a 10-meter circle and a value of 1 corresponds to a target tree with the maximum tree density in its neighborhood. We used data from the French National Forest Inventory to determine the self-thinning rules for Scots pine and sessile oak, then used the resulting coefficients to calculate RDI (see Appendix D for details). In mixed stands, RDI was calculated as the sum of the RDI values calculated independently per species. Low-density plots thinned in winter 2017 correspond to a target RDI of 0.4, medium-density plots to a target RDI of 0.7) and high-density plots to a target RDI of 1.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (A) Location of the OPTMix experimental study sites. Stands of each type (Oak, Pine, Mixture) are spread over twelve forest sites in the Orléans State Forest (grey area) (France, 47° 49'N, 2°29'E). Nine sites contain two experimental plots with the same stand composition but different stand densities, low and medium. Three sites correspond to mixed stands with one plot of high stand density. (B) Experimental design at the plot level (N = 21).</figcaption></figure>

<caption>**Table 1**</caption>

Plot characteristics based on the last full tree inventory at the end of the 2020 growing season. N.ha⁻¹ = Number of stems per hectare. BA tot. = total stand basal area. Dg = quadratic mean diameter. Ho = dominant height. Oak = sessile oak. Pine = Scots pine.

<table><thead><tr><th>Plot</th><th>Stand composition</th><th>Stand density</th><th>N. ha<sup>-1</sup></th><th>BA tot. (m<sup>2</sup>. ha<sup>-1</sup>)</th><th>BA oak (m<sup>2</sup>. ha<sup>-1</sup>)</th><th>BA pine (m<sup>2</sup>. ha<sup>-1</sup>)</th><th>Dg oak (cm)</th><th>Dg pine (cm)</th><th>Ho oak (m)</th><th>Ho pine (m)</th></tr></thead><tbody><tr><td>O12.2</td><td>Oak</td><td>Low</td><td>292</td><td>14.2</td><td>14.2</td><td>0</td><td>24.9</td><td>-</td><td>20.7</td><td>-</td></tr><tr><td>O214.2</td><td>Oak</td><td>Low</td><td>281</td><td>14.1</td><td>14.1</td><td>0</td><td>25.2</td><td>-</td><td>19.1</td><td>-</td></tr><tr><td>O593.2</td><td>Oak</td><td>Low</td><td>191</td><td>13.8</td><td>13.8</td><td>0</td><td>30.3</td><td>-</td><td>22.6</td><td>-</td></tr><tr><td>O12.3</td><td>Oak</td><td>Medium</td><td>485</td><td>22.8</td><td>22.8</td><td>0</td><td>24.4</td><td>-</td><td>22.1</td><td>-</td></tr><tr><td>O214.1</td><td>Oak</td><td>Medium</td><td>577</td><td>20.8</td><td>20.8</td><td>0</td><td>21.4</td><td>-</td><td>19</td><td>-</td></tr><tr><td>O593.1</td><td>Oak</td><td>Medium</td><td>416</td><td>22.8</td><td>22.8</td><td>0</td><td>26.4</td><td>-</td><td>21.4</td><td>-</td></tr><tr><td>O216.1</td><td>Mixed</td><td>Low</td><td>245</td><td>16.6</td><td>7.3</td><td>9.3</td><td>25.3</td><td>34.4</td><td>18.7</td><td>19.4</td></tr><tr><td>O57.3</td><td>Mixed</td><td>Low</td><td>205</td><td>16.2</td><td>7.1</td><td>9.1</td><td>25.4</td><td>41.9</td><td>20</td><td>22.9</td></tr><tr><td>O598.1</td><td>Mixed</td><td>Low</td><td>178</td><td>16.6</td><td>7.3</td><td>9.3</td><td>30.2</td><td>39.5</td><td>20.4</td><td>20.7</td></tr><tr><td>O216.4</td><td>Mixed</td><td>Medium</td><td>397</td><td>25.2</td><td>12.3</td><td>12.8</td><td>24.8</td><td>33.7</td><td>19.8</td><td>22.3</td></tr><tr><td>O57.1</td><td>Mixed</td><td>Medium</td><td>417</td><td>28.6</td><td>12</td><td>16.7</td><td>23.1</td><td>40.1</td><td>20.3</td><td>23.1</td></tr><tr><td>O598.4</td><td>Mixed</td><td>Medium</td><td>328</td><td>27.3</td><td>9.7</td><td>17.6</td><td>26.8</td><td>37.9</td><td>21.2</td><td>22.6</td></tr><tr><td>O108.1</td><td>Mixed</td><td>High</td><td>441</td><td>35.6</td><td>10.5</td><td>25</td><td>23.6</td><td>40</td><td>23.7</td><td>24.5</td></tr><tr><td>O178.1</td><td>Mixed</td><td>High</td><td>422</td><td>35.4</td><td>14</td><td>21.3</td><td>27</td><td>39.2</td><td>22.6</td><td>24</td></tr><tr><td>O255.1</td><td>Mixed</td><td>High</td><td>401</td><td>27.9</td><td>13.5</td><td>14.3</td><td>25.5</td><td>36.5</td><td>21.7</td><td>20.8</td></tr><tr><td>O200.2</td><td>Pine</td><td>Low</td><td>173</td><td>19</td><td>0</td><td>19</td><td>-</td><td>37.2</td><td>-</td><td>21.7</td></tr><tr><td>O333.2</td><td>Pine</td><td>Low</td><td>197</td><td>19.6</td><td>0</td><td>19.6</td><td>-</td><td>35.5</td><td>-</td><td>24.2</td></tr><tr><td>O83.2</td><td>Pine</td><td>Low</td><td>194</td><td>19.9</td><td>0</td><td>19.9</td><td>-</td><td>36.2</td><td>-</td><td>25.1</td></tr><tr><td>O200.1</td><td>Pine</td><td>Medium</td><td>343</td><td>31.1</td><td>0</td><td>31.1</td><td>-</td><td>33.9</td><td>-</td><td>22.5</td></tr><tr><td>O333.3</td><td>Pine</td><td>Medium</td><td>334</td><td>31.3</td><td>0</td><td>31.3</td><td>-</td><td>34.5</td><td>-</td><td>23.8</td></tr><tr><td>O83.1</td><td>Pine</td><td>Medium</td><td>283</td><td>31.2</td><td>0</td><td>31.2</td><td>-</td><td>37.4</td><td>-</td><td>24.9</td></tr></tbody></table>

The trees studied herein were selected according to a stratified sampling with three stratification factors: tree size, neighborhood tree density and neighborhood composition as it has been demonstrated that radial growth for the two studied species depends on these factors (Perot et al., 2010). In each of the 21 plots, we selected nine trees per species representing three size classes. In each plot, local tree density and species proportion were recorded within a 10-meter circle centered on each sampling tree (see Bello et al., 2019b for a detailed description of the sampling protocol). We selected the trees that remained alive throughout the 2020–2023 period, i.e. the period during which we monitored tree crown defoliation. 241 trees were analyzed in this study (see Table 2 for the final number of trees classified by species, stand density and composition).

### 2.1.1. Monitoring tree crown defoliation

We started to monitor tree crown defoliation in 2020. Using

<caption>**Table 2**</caption>

Number of trees observed annually (Nobs.) for each species according to stand density and composition.

<table><thead><tr><th>Species</th><th>Stand composition</th><th>Stand density</th><th>Nobs.</th></tr></thead><tbody><tr><td rowspan="5"><i>Quercus petraea</i></td><td rowspan="2">Pure</td><td>Low</td><td>23</td></tr><tr><td>Medium</td><td>21</td></tr><tr><td rowspan="3">Mixed</td><td>Low</td><td>21</td></tr><tr><td>Medium</td><td>25</td></tr><tr><td>High</td><td>32</td></tr><tr><td rowspan="5"><i>Pinus sylvestris</i></td><td rowspan="2">Pure</td><td>Low</td><td>21</td></tr><tr><td>Medium</td><td>23</td></tr><tr><td rowspan="3">Mixed</td><td>Low</td><td>23</td></tr><tr><td>Medium</td><td>24</td></tr><tr><td>High</td><td>28</td></tr></tbody></table>
binoculars, we carried out one visual observation of each crown per year during June or July from 2020 to 2023 on the same sample of trees. We followed the European protocol defined by the ICP-Forests network (Eichhorn et al., 2016) to estimate tree crown defoliation. For each assessable tree crown, the percentage of defoliation relative to a healthy reference tree was visually estimated and classified in five-percent increments, from 0 % to 100 %. To limit observer effects, every effort was made for one and the same person to conduct all the observations for each one-year period and, as far as possible, for several consecutive years. Two main observers were involved: one conducted the observations from 2020 to 2022 and the other one joined the first operator to assess tree crown defoliation in 2023. The two operators calibrated their observations together at the beginning of the season to guarantee continuity in the measurements.

### 2.1.2. Monitoring tree radial growth

For both species, we started to monitor annual tree growth at breast height in 2013 for a sample of trees. We used manual dendrometers from 2013 to 2019 (DB20, Environmental Measuring Systems, Brno, Czech Republic). In 2019, two-thirds of the manual dendrometers were replaced by automatic dendrometers (DRS26, Environmental Measuring System, Brno, Czech Republic). Annual growth for year n was defined as the difference between the circumference at the end of year n and the circumference at the end of year n-1. The circumference at the end of year n was calculated by averaging the circumferences over the period from December of year n to January of year n + 1. Finally, we analyzed the mean annual radial growth (mm) for the 241 trees remaining alive throughout the 2020–2023 period (Table 2). We calculated mean annual growth and defoliation for a four-year growing period because Ferretti et al. (2021b) showed that the relationship between growth and defoliation is better for time windows longer than three years. A model with mean tree growth calculated over several years reduces the relative importance of errors that may arise from dendrometer measurements and crown defoliation observations (Ferretti et al., 2021a). It also makes it possible to smooth the effect of the year’s climatic conditions (Ferretti et al., 2021a).

### 2.1.3. Relative tree size

Relative tree size was calculated for each species based on tree circumference at breast height (1.3 m), and was defined as follows:

$$ (C_{i,sp} - C_{sp})/C_{sp} $$

Where $C_{i,sp}$ is the circumference of a target tree belonging to species sp in a plot and $C_{sp}$ is the mean quadratic circumference of all the trees on the plot belonging to species sp. We calculated relative tree size based on the last full tree-circumference inventory in 2020.

### 2.1.4. Neighborhood competition and composition

Neighborhood density and composition were calculated within a 10-meter circle centered on each target tree. This distance was chosen to evaluate neighborhood effects because competition indices calculated over a 10-meter radius best explain growth variability for both species (Perot et al., 2010). Neighborhood tree density was defined with the relative density index (RDI). Hereafter, the term neighborhood density will be used instead of RDI.

Neighborhood composition was defined as the proportion in number of the heterospecific individuals in the neighborhood of the target tree; 0 corresponds to no individuals of different species in the neighborhood and 1 corresponds to no individuals of the same species in the neighborhood. Neighborhood density and composition were calculated for each year based on the 2020 monitoring survey and taking into account annual tree mortality. We then calculated the average neighborhood density and composition for the four-year period 2020 – 2023.

## 2.2. Statistical analyses

### 2.2.1. Relationship between tree growth and tree crown defoliation

To establish the relationship between mean annual tree growth and mean annual tree crown defoliation while taking into account the effect of tree size, and neighborhood density and composition, we fitted a linear mixed effects model for each species with mean tree crown defoliation, relative tree size at the beginning of the study period, mean neighborhood density and mean neighborhood composition as explanatory variables. We conducted preliminary analyses, which showed that homoscedasticity and normality of the residuals were not achieved with mean annual tree radial growth (mm) as a response variable. Transforming the mean annual tree radial growth with the square root function solved these issues. We tested both the linear and quadratic effects for neighborhood density (RDI) and used the AIC to check whether the model with the quadratic effect should be retained. We added a plot random effect to the intercept to take into account the spatial structure of our sampling design. This random effect accounted for factors, such as soil characteristics, potentially affecting growth but not explicitly accounted for in the model and that could vary at the plot level. We tested whether there was a significant interaction between neighborhood composition, neighborhood density and relative tree size. We also tested whether there was any interaction between these factors and crown defoliation. We fitted different linear mixed-effects models with different interaction terms and rejected them when they were not significant. We used a stepwise model selection procedure and the final models were fitted according to the restricted maximum likelihood method (see Appendix E for the equations).

### 2.2.2. Analyzing the magnitude of the effects to understand the defoliation relative importance on stem radial growth

For each predictor, we analyzed the magnitude of the effect by quantifying the change in the response variable for a given change in an explanatory variable and evaluating whether this change was negligible or not (Barbier et al., 2009; Camp et al., 2008). To perform the magnitude analysis, we fitted our models with the centered explanatory variables (Barbier et al., 2009). We simulated a change for each explanatory variable and computed the change in growth relative to the mean growth predicted by the models for both species. For an explanatory variable X, the reference value was the average value of X ($\bar{X}$) and the change in X ($\Delta X$) was the standard deviation of X. We decided to use standard deviation because this method is simple, reproducible, and commonly used in magnitude analyses applied to ecology (Barbier et al., 2009; Gosselin et al., 2017). We also checked that this variation was realistic from an ecological and forest management point of view. We computed the change in tree growth when X changed from $\bar{X}$ to $\bar{X} + \Delta X$. We predicted 1000 values for stem radial growth by taking into account the variance-covariance matrix of the fitted model for each predictor. We calculated the mean and the 95 % confidence intervals of change in growth (%) for a given predictor. We fixed the other predictors to their average value. When a quadratic term is significant for one variable, it means that the magnitude of the effect changes along the predictor gradient. In this case, we calculated the change in growth for two different reference values along the X gradient.

We defined three levels of magnitude to determine the ecological importance of each effect on tree growth for both species. 1) We considered the magnitude of the effect negligible (even if statistically significant) if the change in the explanatory variable led to a relative change in growth that was less than 10 % of the mean growth. 2) The effect was considered strong if the change in the explanatory variable led to a difference of between 10 % and 20 %. 3) The effect was considered very strong if the change in the explanatory variable led to a difference in growth of more than 20 %. We interpreted our results based on Di Stefano (2004): we calculated the mean change in growth and the confidence intervals for each effect in comparison with the three levels
of magnitude to determine the ecological importance of each effect. The aim of the magnitude analysis was to better understand the ecological importance of crown defoliation on tree radial growth comparatively to the other predictors.

Data analyses were performed with the R software version 4.2.2. (R Core Team, 2022). All the linear mixed effects models were fitted with the lmer function of the lme4 package (Bates et al., 2015). Estimated marginal means and predictions from the linear mixed-effects models were computed with the emmeans package (Lenth, 2017).

## 3. Results

### 3.1. Crown defoliation effect on radial growth

Our results show a negative relationship between crown defoliation and radial growth for both species (Table 3, Fig. 2). The average defoliation of 42 % corresponded to a predicted growth of 1.07 mm.year⁻¹ for sessile oak with the other predictors set to their average observed values (Fig. 2). For Scots pine, the average defoliation of 39 % corresponded to 0.84 mm.year⁻¹ of stem radial growth with the other predictors set to their average observed values (Fig. 2). In the following presentation of the results, the magnitude of each effect is given relatively to these average growth values for the two species. Our magnitude analysis showed that crown defoliation effect was very strong for Scots pine and strong for sessile oak (Fig. 2, Table 3, Fig. 3). Crown defoliation had the second strongest effect on growth for Scots pine and the third strongest for sessile oak (Fig. 3). Scots pine lost 29.5 % of stem radial growth when defoliation increased from 39 % to 51 % and Sessile oak lost 19.5 % of stem radial growth when defoliation went from 42 % to 57 % (Fig. 3).

### 3.2. Effects of relative tree size and neighborhood competition on radial growth

Relative tree size had a significant positive effect on radial growth for both species (p-value < 0.001; Est. = 0.6 mm⁰.⁵.year⁻⁰.⁵, Table 3). The effect was very strong for Scots pine with 23.5 % additional growth for a larger tree than for an average-sized tree (Fig. 3, Fig. 4, see Appendix F for statistical values). For sessile oak, the relative tree size effect was also very strong (+31 % on radial growth), stronger than the defoliation effect (-19.5 %) (Fig. 3, see Appendix F for statistical values). Neighborhood density had the strongest effect on tree growth for both species (Table 3, Fig. 3). This effect depended on the reference value chosen to perform the magnitude analysis (Fig. 3) due to the non-linear response

of tree growth to neighborhood density (Table 3, significant quadratic term for both species). When the neighborhood density increased from a low density to an average density, the effect was very strong for both species, with a 60 % and 57.5 % loss of stem radial growth for oak and pine, respectively (Fig. 3, see Appendix F for statistical values). When the neighborhood density increased from an average density to a high density, the effect on tree radial growth remained strong for oak (-30 %) (Fig. 3, see Appendix F for statistical values). For pine, when the neighborhood density increased from an average density to a high density it was not possible to determine the magnitude level because the confidence interval of the effect covers the three levels of magnitude (Fig. 3, see Appendix F for statistical values).

Neighborhood composition had a significant, but opposite, effect on stem radial growth for the two tree species (Fig. 4). The greater the proportion of pine around a target oak, the greater the stem radial growth of the target oak (p-value < 0.001; parameter estimate. = 0.34 mm⁰.⁵.year⁻⁰.⁵, Table 3). Conversely, the greater the proportion of oak around a target pine, the lower the stem radial growth of the target pine (p-value = 0.002; parameter estimate. = -0.23 mm⁰.⁵.year⁻⁰.⁵, Table 3). Our magnitude analysis showed that the neighborhood composition led to a 17.5 % reduction in growth for pine, and conversely a 16.2 % increase in growth for oak; however, it was not possible to determine the magnitude level of the neighborhood composition effect because the confidence interval of the effect covers the three levels of magnitude (Fig. 3, see Appendix F for statistical values).

Relative tree size and neighborhood density had a stronger effect on stem radial growth than did crown defoliation for sessile oak (Fig. 3). For pine, low values of neighborhood density, but not high values had a stronger effect on radial growth than did crown defoliation (Fig. 3). Finally, we found no interaction between neighborhood composition, neighborhood density and relative tree size, nor did we find any interaction between these factors and crown defoliation (Appendix G).

## 4. Discussion

Our results showed a strong and a very strong negative effect of tree crown defoliation on tree radial growth for sessile oak and Scots pine, respectively. In this section, we discuss the effects of crown defoliation, tree size, neighborhood density and composition on radial growth. We then discuss the magnitude of these effects.

### Table 3

Estimated parameter values (mm⁰.⁵.year⁻⁰.⁵) from the linear mixed-effects models fitted for square root transformed stem radial growth. Nb obs. = number of observations. AIC = Akaike information criterion. RMSE = Root-mean-square error. Est. = Estimate. SE = Standard error. Df = Satterthwaite degree of freedom. The intercept of the models represents the stem radial growth of an average-sized tree (Relative size = 0) with a neighborhood density of zero and without heterospecific neighbors (Neighborhood composition = 0). Neighborhood density = Relative Density Index (RDI) in the tree's neighborhood, Neighborhood composition = Heterospecific tree density proportion in the tree's neighborhood.

<table><thead><tr><th>Species</th><th>Nb obs.</th><th>AIC</th><th>RMSE</th><th>Parameter</th><th>Est.</th><th>SE</th><th>Df</th><th>t value</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="6">Quercus petraea</td><td rowspan="6">122</td><td rowspan="6">-26.1</td><td rowspan="6">0.17</td><td>Intercept</td><td>2.15</td><td>0.16</td><td>46</td><td>13.72</td><td>&lt; 0.001</td></tr><tr><td>Relative size</td><td>0.6</td><td>0.07</td><td>104</td><td>8.79</td><td>&lt; 0.001</td></tr><tr><td>Defoliation</td><td>-0.72</td><td>0.12</td><td>114</td><td>-5.77</td><td>&lt; 0.001</td></tr><tr><td>Neighborhood density</td><td>-2.01</td><td>0.48</td><td>60</td><td>-4.18</td><td>&lt; 0.001</td></tr><tr><td>(Neighborhood density)²</td><td>0.91</td><td>0.39</td><td>79</td><td>2.32</td><td>0.02</td></tr><tr><td>Neighborhood composition</td><td>0.34</td><td>0.1</td><td>26</td><td>3.35</td><td>&lt; 0.001</td></tr><tr><td rowspan="6">Pinus sylvestris</td><td rowspan="6">119</td><td rowspan="6">2.1</td><td rowspan="6">0.21</td><td>Intercept</td><td>2.3</td><td>0.14</td><td>43</td><td>16.21</td><td>&lt; 0.001</td></tr><tr><td>Relative size</td><td>0.63</td><td>0.13</td><td>109</td><td>5.02</td><td>&lt; 0.001</td></tr><tr><td>Defoliation</td><td>-1.25</td><td>0.18</td><td>104</td><td>-7.11</td><td>&lt; 0.001</td></tr><tr><td>Neighborhood density</td><td>-2.13</td><td>0.45</td><td>49</td><td>-4.75</td><td>&lt; 0.001</td></tr><tr><td>(Neighborhood density)²</td><td>1.29</td><td>0.39</td><td>59</td><td>3.29</td><td>0.002</td></tr><tr><td>Neighborhood composition</td><td>-0.23</td><td>0.06</td><td>14</td><td>-3.68</td><td>0.002</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Mean annual stem radial growth for sessile oak and Scots pine during the 2020 – 2023 period according to the tree crown defoliation. Solid lines correspond to the values predicted by the linear mixed-effects model. Dashed and dotted lines correspond to the 95 % confidence interval of the fitted values and the predictions. For each species, relative tree size was fixed at 0, which corresponds to the average circumference on the plot for a given species, and neighborhood density and composition were fixed to the mean observed value per species. Each point corresponds to one observed value (a tree).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Results of the magnitude analysis. Summary of predictor effects on stem radial growth for sessile oak and Scots pine. Squares and error bars are, respectively, mean and 95 % confidence intervals for the change in growth predicted by the linear mixed-effects model. Vertical grey lines are the limits of the effects on the stem radial growth for the three levels of magnitude: negligible (less than 10 % in absolute value), strong (between 10 % and 20 % in absolute value) and very strong (more than 20 % in absolute value). For each predictor X, we estimated their relative effects on stem radial growth (%) corresponding to a change from the observed average of X (X̄) to X + ΔX where ΔX was the standard deviation of X. For neighborhood density, the magnitude was evaluated for two reference values along the density gradient because of the non-linear response of tree growth to this predictor. For each calculation of the effect of predictor variation, we held the other predictors constant at their average observed values.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Mean annual stem radial growth for sessile oak (A, C, E) and Scots pine (B, D, E) for the 2020 – 2023 period according to relative tree size (A, B), neighborhood density (C, D) and neighborhood composition (E, F). Solid lines correspond to the values predicted by the linear mixed-effects model. Dashed and dotted lines correspond to the 95 % confidence interval of the fitted values and the predictions. Each point corresponds to an observed value (a tree). For each plot, we fixed the other predictors at their average observed value.</figcaption></figure>
## 4.1. Tree crown defoliation strongly reduces radial growth for sessile oak, very strongly for Scots pine

We found a significant negative correlation between tree crown defoliation and tree radial growth for both species, thus confirming our first hypothesis (Table 3).

In Europe, Michel et al. (2020) reported that 76.6 % of oaks and 73.9 % of Scots pine were suffering from a defoliation of between 15 % and 60 %. In our study, we observed higher values: 85 % of the oaks and 95 % of the pines fell within the 15-to-60 % range, with an average crown defoliation of close to 40 % for both species (42 % for oak and 39 % for pine).

The magnitude analysis we conducted showed that an increase along the defoliation gradient resulted in a strong loss of radial growth for oak and a very strong loss for Scots pine (Fig. 3). Tree crown defoliation reduced growth more for Scots pine (-29.5 % for 39–51 % of defoliation) than it did for sessile oak (-19.5 % for 42–57 % of defoliation) (Fig. 3). Further research is needed to determine the impact of defoliation on radial growth for only slightly defoliated trees (0–15 %) and for highly defoliated trees (85–100 %). At our study site, some highly defoliated oaks adapted their crown morphology with sprout growth on the main branches and along the trunk. These secondary organs could contribute to maintaining tree growth but were not assessed with the defoliation protocol we applied. Further research is also needed to quantify the role of these secondary organs on tree growth, which are not taken into account in tree crown observations.

## 4.2. Relative tree size and neighborhood competition very strongly affect radial growth for both species

We confirmed our hypothesis that tree growth is strongly affected by the individual's relative size and its neighborhood density (Fig. 3). We found that larger trees had a higher stem radial growth than smaller ones for both species (Fig. 4), a classical result (Wykoff, 1990). Dominant trees generally have deeper coarse roots and a greater fine-root biomass than do suppressed trees, thus giving them better access to resources in case of drought (water, light, nutrients) (Grote et al., 2016). However, this size effect could depend on water supply conditions and may well be modified during drought events, depending on the tree species (Zang et al., 2012). We found a negative relationship between tree growth and neighborhood density for both species (Fig. 3, Fig. 4), which is consistent with previous studies in the same forest (Ngo Bieng et al., 2013; Perot et al., 2010). Competition is a key driver for tree growth; higher neighborhood density increases the competition for water and light resources, leading to reduced growth. Our results show that this effect depends on the range of variations considered because of the non-linear response of growth to neighborhood density (Fig. 3). The neighborhood density effect is stronger for low levels of stand density (Fig. 3, Fig. 4).

## 4.3. An opposite effect of neighborhood composition on stem radial growth for the two species

We confirmed our hypothesis that neighborhood composition has a weaker effect than tree size and neighborhood density (Fig. 3). We found an opposite effect of neighborhood composition on stem radial growth for the two species; however, our results did not allow us to determine whether this effect was negligible or strong (Fig. 3, Fig. 4). Several processes simultaneously affect tree growth in mixtures and could therefore affect tree growth for oak and pine: nutrient or light availability can be greater in mixtures due to reduced competition or facilitation (Augusto et al., 2015), and species water use efficiency can increase in mixtures compared to monocultures (Forrester, 2015; Grossiord, 2020). Bigelow et al., (2023) found that neighborhood oak reduced competition with pine, thus increasing pine growth, but our findings show that pines had better growth with a low proportion of oaks in the neighborhood (Fig. 4). In contrast, oaks had better growth with a

high proportion of pines in the neighborhood (Fig. 4). As in previous studies (Bello et al., 2019b; Ngo Bieng et al., 2013; Perot et al., 2010), we found that in oak-pine mixtures, competition increases or decreases depending on the considered species. Mixing is beneficial to the more competitive species (sessile oak) and detrimental to the other (Scots pine). Further research is needed to determine the ecological significance of this effect on radial growth compared to the other factors studied.

## 4.4. Relative importance of crown defoliation effect in comparison with other effects on radial growth

For both species, increasing neighborhood density had the strongest negative effect on tree radial growth: a reduction in growth from 30 % to 60 % for oak, and from 18.5 % to 57.5 % for pine, depending on the reference value chosen to perform the magnitude analysis (Fig. 3).

For both species, relative tree size had the strongest positive effect on tree radial growth with a 30 % increase in growth for larger oaks and 20 % for larger pines, compared to average-sized trees (Fig. 3).

Crown defoliation had the second strongest effect on growth for pine (a 29.5 % reduction in growth) and the third strongest for oak (a 19.5 % reduction in growth) (Fig. 3). We found that crown defoliation had a stronger effect than tree size for pine, though not for oak. Neighborhood composition has the weakest effect, and the only one showing an opposite effect on tree growth depending on the considered species: a 17.5 % reduction in growth for pine, and conversely a 16.2 % increase in growth for oak (Fig. 3). We therefore confirmed our hypothesis that crown defoliation has a stronger effect than neighborhood composition.

Some studies have already provided evidence for a growth-defoliation relationship across a range of species in Europe and elsewhere (Ferretti et al., 2021a). However, few studies have shown how tree crown defoliation affects tree growth while taking into account other factors which also affect tree growth (local competition and tree size). Studies usually refer to data collected on dominant and co-dominant trees located in even-aged monospecific stands (Bussotti et al., 2024; Ferretti et al., 2021a, 2021b). We show that defoliation rate appears to be a very important factor to consider when explaining tree growth, as are relative tree size and neighborhood density, which also had a strong or very strong effect on tree growth in our study. Contrary to our last hypothesis, we found no significant interaction between defoliation and the other predictors, meaning that these factors do not modify the relationship between tree crown defoliation and tree radial growth (Appendix G). An interesting future course of research might be to focus on the interactions between defoliation and these others factors by taking into account their direct and indirect effects on tree growth. Our study does show, however, how defoliation affects tree growth comparatively to other factors and highlights the role of defoliation as an important indicator of tree health in various neighborhood conditions.

## 5. Conclusion

We established the relationship between tree growth and tree crown defoliation, for a range of defoliation from 15 % to 85 %, for sessile oak and Scots pine of varying sizes and in varying neighborhood conditions. We showed that tree growth, expressed in terms of annual radial growth, was strongly negatively correlated to crown defoliation for sessile oak and very strongly for Scots pine. We also demonstrated that relative tree size and neighborhood density very strongly affected tree radial growth, more than tree crown defoliation. Neighborhood composition also affected tree growth but to a lesser extent than did tree crown defoliation. We found no interaction between crown defoliation and the other predictors, indicating that neighborhood competition and tree size do not modify the relationship between tree crown defoliation and tree radial growth. When working on growth projections, it seems important to take into account crown defoliation in order to correctly estimate
radial growth, given the strong effect we found. We generally support using defoliation as an indicator of tree health in various neighborhood conditions and our findings can help forest managers assess the impact of defoliation on tree growth in oak-pine forests. Further studies including very severely defoliated trees (approaching 100 %) and un-defoliated trees (0 %) are needed to obtain a more general model of the impact of defoliation on tree growth. Further research is also needed to extend our results to a diverse range of tree species on different soils and climatic conditions.

## CRediT authorship contribution statement

**Théo Javoy** : Writing – review & editing, Writing – original draft, Visualization, Formal analysis, Conceptualization. **Camille Couteau**: Methodology, Data curation, Conceptualization. **Sandrine Perret**: Supervision, Methodology, Data curation, Conceptualization. **Thomas Perot**: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

None.

## Acknowledgments

This work was carried out at the OPTMix (Oak Pine Tree Mixture) experimental site (http://optmix.efno.fr/) managed by the INRAE and supported by the Centre-Val de Loire Region and the French National Forest Office (ONF). This research also received support under the Sycomore Programme (https://www.sycomore-cvl.fr/en/), with the financial support of the Centre-Val de Loire Region (France). Field measurements and observations were funded in part by the French National Forest Office (ONF). The site belongs to the French national research infrastructure, ANAE-F (http://www.anaee-france.fr/fr/) and the GIS Coop network (https://giscoop.hub.inrae.fr/), which is supported by the French Ministry for Agriculture and Food. We thank all the members of the OPTMix team for their technical help. We thank Marion Gosselin and Frédéric Gosselin for their statistical assistance on the magnitude analysis. We thank Victoria Moore for her assistance in correcting the English version of the manuscript.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.foreco.2025.123243.

## Data availability

Data will be made available on request.

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