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# Viscum album in French forests: Distribution, environmental drivers and impacts on tree health and growth

William Marchand, Jean-Baptiste Daubrée, Claire Depardieu

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William Marchand, Jean-Baptiste Daubrée, Claire Depardieu. Viscum album in French forests: Distribution, environmental drivers and impacts on tree health and growth. Forest Ecology and Management, 2025, 598, pp.123226. 10.1016/j.foreco.2025.123226. hal-05327925

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# *Viscum album* in French forests: Distribution, environmental drivers and impacts on tree health and growth

William Marchand<sup>a, *, †</sup>, Jean-Baptiste Daubrée<sup>b</sup>, Claire Depardieu<sup>c, d</sup>

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## ARTICLE INFO

### Keywords:

Mistletoe

Parasite

Forest health

French national forest inventory

Radial increment

Generalized additive models

Biodiversity

## ABSTRACT

The European mistletoe (*Viscum album*) is represented in France by three subspecies and plays a dual role in ecosystems. While it provides essential food and habitat for numerous animal species, it also reduces the economic value of timber and weakens trees, making them more vulnerable to biotic and abiotic stressors. This study leveraged the French National Forest Inventory dataset, comprising observations from over 737,000 trees across more than 80,000 plots, to investigate the environmental factors shaping mistletoe distribution and assess its impacts on tree health and growth performance. Our findings revealed that mistletoe distribution is not random within the range of its host species but is significantly influenced by climate and soil conditions, as well as by the history of forest use. *V. a. album* was primarily constrained by soil properties, whereas *V. a. abietis* and *V. a. austriacum* were strongly affected by isothermality. Additionally, mistletoe was more frequently observed on trees with a higher crown dieback and, for Scots pine and silver fir, trees with a high level of infestation exhibited a growth reduction of 23–34 % compared to non-parasitized individuals. Fir and pine mistletoe occurrences were significantly lower in species-rich stands and in younger forests compared to older ones. With the ongoing impacts of climate change, implementing fine-scale monitoring of mistletoe populations could help foresters mitigate mistletoe-induced economic losses while simultaneously supporting biodiversity conservation efforts.

## 1. Introduction

*Viscum album* L., or European mistletoe, is a hemiparasitic epiphytic plant found in mainland France in three distinct subspecies, each of which is specialized on a specific host tree. *Viscum album* L. *subsp. album* thrives on broadleaf trees, *Viscum album* *subsp. austriacum* (Wiesb.) Vollm. exclusively parasitizes pines, and *Viscum album* *subsp. abietis* (Wiesb.) Abrom. is restricted to fir trees (Nierhaus-Wunderwald and Lawrenz, 1997; Thomas et al., 2011). The *album* subspecies is predominantly found in agricultural and urban areas, where trees in hedgerows and forest patches provide environmental conditions close to the ecological optimum of the parasite (Krasylenko et al., 2020). This subspecies is also commonly observed in urban parks and forests (Czaja et al., 2020; Bilonozhko et al., 2025). The stressful conditions of urban environments, such as resource scarcity, soil compaction and chronic

pollution, make trees more susceptible to mistletoe infestations compared with individuals growing in rural environments (Czaja et al., 2020; Bilonozhko et al., 2025). All three subspecies share similar ecological requirements, notably high light availability, which plays a crucial role in seed germination and plant growth (Thomas et al., 2023). These light-related needs influence spatial distribution patterns: in closed forested environments, mistletoe is most commonly observed on edge trees and dominant, tall and large individuals (Durand-Gillmann et al., 2014; Nierhaus-Wunderwald and Lawrenz, 1997; Vallauri, 1997). Additionally, seed dispersal is more effective when host trees are isolated and tall, as these attract bird dispersers such as the mistle thrush (*Turdus viscivorus*) and Eurasian blackcap (*Sylvia atricapilla*), which feed on and distribute mistletoe seeds (Nierhaus-Wunderwald and Lawrenz, 1997; Thomas et al., 2023).

Mistletoe possesses photosynthetic organs that provide energy, but it

<footnote><sup>a</sup> Institut national de l'information géographique et forestière (IGN), Service de l'Information Forestière (SIF), 101, chemin du Château des barres, Nogent-sur-Vernisson 45290, France</footnote>

<footnote><sup>b</sup> Département Santé des Forêts, INRAe - URFM - Domaine Saint-Paul, site AGROPARC CS 40 509 - 84 914, AVIGNON CEDEX 9, France</footnote>

<footnote><sup>c</sup> Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Centre, Fredericton, NB, Canada</footnote>

<footnote><sup>d</sup> Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: william.marchand@ign.fr (W. Marchand), jean-baptiste.daubree@agriculture.gouv.fr (J.-B. Daubrée), claire.depardieu@nrcan-rncan.gc.ca (C. Depardieu).</footnote>
relies on its host for nutrients and water (Thomas et al., 2023). It extracts these resources by developing haustoria, specialized structures that penetrate the host's xylem to access raw sap. Wood from parasitized trees often exhibits deformities, cavities and discolorations, significantly reducing the merchantable value of the log when it is located on the trunk (Plagnat, 1955). Furthermore, mistletoe's water and nutrient uptake can severely impair the growth and climate resilience of the host trees. Studies on pine mistletoe in Germany (Kollas et al., 2017), Spain (Sangüesa-Barreda et al., 2012, 2013) and Turkey (Bilgili et al., 2018) have reported substantial growth reductions of 29-64% in parasitized trees, along with heightened climate sensitivity and increased defoliation compared with non-infested trees. Significant growth reductions have also been reported in parasitized silver fir (Abies alba) trees in Switzerland (Noetzli et al., 2003b), Spain (Camarero et al., 2025), and Eastern Carpathians (Barbu, 2009). In France, historical accounts from the 1950s describe severe impacts of mistletoe on fir plantations (Plagnat, 1955; Plagnat and Brossier, 1969). However, comprehensive and precise large-scale assessments of mistletoe's impact on tree growth rates in French forests remain scarce.

Because its distribution is constrained by host tree availability, local microclimatic conditions, and the presence of seed-dispersing birds, mistletoe tends to spread in spatial clusters (Ojeda et al., 2023). Once established in a given area, the parasite progressively infests all compatible host trees in close proximity. However, the expansion of its range across the landscape remains slow. Infested forest stands may exhibit a high proportion of heavily parasitized trees, which are more likely to succumb to additional stressors such as drought (Dobbertin and Rigling, 2006; Sangüesa-Barreda et al., 2012). Widespread major infestations can undermine forest resilience and potentially disrupt ecosystem dynamics (Griebel et al., 2017), a risk that may be further heightened by climate warming. Drought-stressed trees are more susceptible to die (DeSoto et al., 2020), potentially accelerating the degradation of mistletoe-infested forests. Weakened trees already exhibiting signs of decline (Teixeira-Costa et al., 2017) may be more susceptible to mistletoe infestation, which is likely to increase mistletoe prevalence in the future. However, the hypothesis of a higher

susceptibility of declining trees to mistletoe infestation still needs to be verified for *Viscum album* in temperate forest ecosystems. An accurate assessment of the current prevalence and impacts of mistletoe in French forests is urgently needed to help foresters anticipate and mitigate future changes.

Climate change is already altering the parasite's range, as mistletoe distribution is tightly constrained by temperature, being limited by both cold winters and cool summers (Iversen, 1944). Rising temperatures lift previous thermal barriers, allowing expansion both northward and to higher elevations. In the Swiss Alps, for instance, the upper altitudinal limit of pine mistletoe has shifted upward by approximately 200 m over the past century, with further increases anticipated (Dobbertin et al., 2005). Mistletoe's strong dependence on host species adds complexity to its distribution patterns. Each of the three subspecies parasitizes distinct hosts with specific ecological requirements, suggesting differentiated adaptations to environmental conditions (Walas et al., 2022). Improving our understanding of the factors currently shaping mistletoe distribution is therefore essential to better anticipate its future spread under changing climatic conditions.

Despite its negative effects on timber resources, tree health, and forest ecosystem dynamics, mistletoe fulfills a crucial and multifaceted ecological role. It contributes to nutrient cycling, enhances local species richness and understory biomass, and helps create cool microenvironments during heatwaves (Mutlu et al., 2016; March and Watson, 2010; Griebel et al., 2017; Ndagurwa et al., 2016; Urban et al., 2012). Additionally, its importance as a food source for numerous animal species highlights its role in supporting biodiversity and maintaining ecosystem functioning (Watson and Herring, 2012; Hood et al., 2025; Hódar et al., 2018). Growing recognition of mistletoe's positive role in supporting biodiversity has inspired recent reintroduction efforts of the parasite in a major Australian city (Watson et al., 2024). An improved understanding of the environmental factors that promote mistletoe occurrence will aid foresters in prioritizing forest areas for protection or restoration.

In this study, we used mistletoe observations spanning the entirety of mainland France and Corsica over a 15-year period to investigate the primary drivers and effects of this parasitic plant on French forests. The

<table><caption>**Table 1**

**Number of observations by host tree species.** The last column, "Retained in analyses," indicates whether the data were retained in the final analyses, on the basis of the "Number of parasitized trees".</caption><thead><tr><th>Viscum album<br>subspecies</th><th>Host tree species<br>group</th><th>Latin names</th><th>Number of<br>observed<br>individual tree</th><th>Number of<br>parasitized trees<br>(%)</th><th>Number of<br>parasitized trees<br>> 22.5 cm dbh</th><th>Retained in<br>analyses?</th></tr></thead><tbody><tr><td rowspan="12">abietis<br>album</td><td>Silver fir</td><td>Abies alba</td><td>34,706</td><td>1049 (3.0 %)</td><td>1046</td><td>YES</td></tr><tr><td>Cultivated<br>poplars</td><td>Populus tremuloides, Populus trichocarpa, Populus<br>canadensis</td><td>11,382</td><td>2723 (23.9 %)</td><td>2680</td><td>YES</td></tr><tr><td>Black locust</td><td>Robinia pseudoacacia</td><td>4702</td><td>375 (8.0 %)</td><td>313</td><td>YES</td></tr><tr><td>Fruit trees</td><td>Pirus malus, Pinus communis, Prunus amygdaloides,<br>Prunus domestica, Sorbus aria, Sorbus aucuparia, Sorbus<br>domestica, Sorbus latifolia, Sorbus mougeoti</td><td>1046</td><td>126 (12.0 %)</td><td>57</td><td>YES</td></tr><tr><td>Willow</td><td>Salix sp.</td><td>3200</td><td>160 (5.0 %)</td><td>147</td><td>YES</td></tr><tr><td>Wild poplar</td><td>Populus alba, Populus nigra</td><td>1900</td><td>82 (4.3 %)</td><td>81</td><td>YES</td></tr><tr><td>Small maple</td><td>Acer campestre, Acer opalus, Acer monspessulanum</td><td>3917</td><td>153 (3.9 %)</td><td>94</td><td>YES</td></tr><tr><td>Wild service tree</td><td>Sorbus torminalis</td><td>446</td><td>18 (4.0 %)</td><td>9</td><td>YES</td></tr><tr><td>Lime tree</td><td>Tillia cordata, Tillia platyphyllos</td><td>3402</td><td>67 (1.6 %)</td><td>48</td><td>YES</td></tr><tr><td>Walnut</td><td>Juglans regia, Juglans nigra</td><td>369</td><td>2 (0.5 %)</td><td>2</td><td>NO</td></tr><tr><td>Aspen</td><td>Populus tremula</td><td>5103</td><td>42 (0.8 %)</td><td>39</td><td>YES</td></tr><tr><td>Big maple</td><td>Acer pseudoplatanus, Acer platanoides</td><td>5807</td><td>42 (0.7 %)</td><td>40</td><td>YES</td></tr><tr><td>Cherry tree or<br>Wild cherry tree</td><td>Prunus avium, Prunus cerasus, Prunus serotina</td><td>4150</td><td>3 (0.1 %)</td><td>3</td><td>NO</td></tr><tr><td rowspan="12">austriacum</td><td>Red oak</td><td>Quercus rubra</td><td>1266</td><td>1 (0.1 %)</td><td>1</td><td>NO</td></tr><tr><td>Ash</td><td>Fraxinus excelsior, Fraxinus oxyphylla, Fraxinus ornus</td><td>18,596</td><td>8 (&lt;0.1 %)</td><td>8</td><td>NO</td></tr><tr><td>Alder</td><td>Alnus glutinosa, Alnus incana, Alnus cordata</td><td>6254</td><td>3 (&lt;0.1 %)</td><td>3</td><td>NO</td></tr><tr><td>Birch</td><td>Betula pubescens, Betula verrucosa</td><td>7755</td><td>3 (&lt;0.1 %)</td><td>3</td><td>NO</td></tr><tr><td>Hornbeam</td><td>Carpinus betulus</td><td>16,429</td><td>7 (&lt;0.1 %)</td><td>3</td><td>NO</td></tr><tr><td>Chestnut</td><td>Castanea sativa</td><td>24,109</td><td>5 (0.2 %)</td><td>2</td><td>NO</td></tr><tr><td>Scots pine</td><td>Pinus sylvestris</td><td>35,288</td><td>1592 (4.5 %)</td><td>1259</td><td>YES</td></tr><tr><td>Black pine</td><td>Pinus nigra ssp. nigricans or ssp. clusiana</td><td>5750</td><td>133 (2.3 %)</td><td>115</td><td>YES</td></tr><tr><td>Corsican pine</td><td>Pinus salzmannii or Pinus nigra ssp corsicana or calabrica</td><td>6468</td><td>54 (0.8 %)</td><td>53</td><td>YES</td></tr><tr><td>Hook pine</td><td>Pinus uncinata</td><td>1893</td><td>33 (1.7 %)</td><td>9</td><td>YES</td></tr></tbody></table>
objectives were 1) to assess the influence of environmental and tree level factors, as well as forest history, on mistletoe infestation, and 2) to investigate the impacts of mistletoe infestation on tree growth rates. Given that coniferous host species tend to occur at higher elevations than deciduous species, we hypothesize that pine and fir mistletoes are more sensitive to temperature gradients, and that their distribution may therefore be more closely tied to cooler environments than that of the album subspecies. We also hypothesize that high levels of mistletoe infestation coincide with a high proportion of crown dieback. Finally, we hypothesize that heavily infested trees exhibit reduced growth rates compared with mistletoe-free individuals, and that the strength of this relationship varies among host species.

## 2. Materials and methods

### 2.1. Data collection

#### 2.1.1. Sample plot network

We used data from the French National Forest Inventory (NFI, available at https://inventaire-forestier.ign.fr/dataifn/?lang=en), comprising dendrometric measurements of individual trees and detailed information on ecological and environmental site conditions, collected between 2008 and 2022. The French NFI network encompasses over 100,000 temporary sample plots, providing comprehensive coverage of mainland France. This network spans a latitudinal range of 9 degrees and a longitudinal range of 12 degrees, capturing the country's diverse climate conditions. The climate ranges from a warm and wet oceanic climate in western coastal regions to cold, rainy, and snowy environments in mountainous areas, as well as warm and dry climates in Mediterranean regions.

The French NFI employs a systematic sampling strategy that ensures comprehensive annual coverage of the entire territory. Each year, approximately 7000 plots are sampled, which are distributed unevenly across the territory. Regions with low forest cover, areas that are already well-documented and present highly uniform forest structures and compositions, unproductive forests, and regions with challenging sampling conditions (e.g. montane forests) tend to be underrepresented, as a means to optimize sampling costs. To correct for this uneven sampling effort, sampling weights are assigned to each plot, ensuring accurate representation of forested areas at the national scale.

The sampling plots in the French NFI are concentric and vary in diameter, with trees measured according to progressively higher diameter thresholds. In the innermost plot (6 m radius), all trees with a diameter at breast height (DBH) of 7.5 cm or higher were recorded. This threshold increases to 22.5 cm in the intermediate 9 m-radius plot, and to 37.5 cm in the outermost 15 m-radius plot. Each sampled tree is assigned a weight that represents the number of similar trees per hectare, facilitating accurate estimates of the overall tree population.

For further methodological details on the French NFI sampling protocol, please refer to the methodology documentation (https://inventaire-forestier.ign.fr/IMG/pdf/180920_plaquette-methode_en.pdf).

#### 2.1.2. Tree-level data

Tree girth and species identity were recorded for each sampled tree. For the purpose of the analyses, the tree species were grouped on the basis of their botanical similarity. For example, in Table 1, the species group "small maple" includes the three species *Acer campestre*, *Acer opalus* and *Acer monspessulanum*. Diameter at breast height (DBH) was derived from girth measurements. The proportion of dead branches in the upper part of the tree crown, i.e. crown dieback, was assessed for dominant trees (individuals with full access to light) and categorized into five classes: 0–5 %, 5–25 %, 25–50 %, 50–95 % and more than 95 % dead branches.

Mistletoe infestations were recorded on trees through visual inspection. In this study, we used the word "cluster" to refer to an individual mistletoe ball. A tree was classified as parasitized if at least one

mistletoe cluster was visible. The trees were grouped into four categories depending on the number of visible mistletoe clusters: 0 (non-parasitized trees), 1–2 clusters, 3–5 clusters, and more than 5 clusters. This information was aggregated at the plot-level into a binary variable indicating the presence or absence of mistletoe and its host tree species groups. Initially, mistletoe clusters were recorded for all trees regardless of their DBH. However, as inventory protocols evolved, only trees with a DBH greater than 22.5 cm were evaluated for mistletoe infestation. Consequently, analyses were restricted to observations from trees exceeding 22.5 cm in DBH. To minimize biases, data from host tree species groups with fewer than 10 recorded mistletoe observations (including trees below 22.5 cm DBH) were excluded from the analyses. This ensured the exclusion of tree species groups where mistletoe infestation was extremely unlikely (e.g., mistletoe-resistant tree species). Ultimately, the dataset included 737,886 trees from 80,640 plots, of which 125,010 trees from 29,684 plots were retained for further analyses (Table 1, Table S1).

Partial cores of approximately 10 cm in length were taken from a randomly selected tree in each 1-cm DBH class, as well as from all trees with a DBH greater than 67.5 cm, for every tree species present in the sample plot (see Table S1 for the number of cored trees). The total width of the five outermost rings, excluding the ring of the sampling year (often partially formed) was measured. Full-length wood cores reaching the pith were taken from two individuals of the plot's dominant tree species, defined as the species with the highest canopy cover. Tree age was determined by ring count on the full length wood cores, and the average age per plot was used as a proxy for stand age. In poplar groves, where tree coring was prohibited, tree age was determined based on branch whorl counts.

#### 2.1.3. Plot-level information

Stem density, calculated per hectare by summing the weights assigned to each measured tree, served as a proxy for competition pressure among trees in the analyses. As stem density is an age-dependent metric, the total basal area, on a per hectare basis, was also computed as an alternative competition index. This metric was computed as the sum of the product between tree basal area and tree weight, by sample plot. The number of tree species contributing to more than 15 % of the total plot canopy cover was determined and categorized into a four-levels factorial variable: (1) stands with a single predominant species, (2) stands with two predominant species, (3) stands with three predominant species, and (4) stands with four or more predominant species. This index was used in our analyses as a proxy for tree species diversity. Sample plots were classified as either displaying signs of being a planted forest or not, based on visual inspection. A plot was considered planted if the trees were arranged in a regular pattern. Forest ownership was determined using spatial data provided by the French National Forest Office (Office National des Forêts, ONF, personal communication). Accordingly, forest plots were categorized as state-owned, other public, or private. The elevation data for each plot were obtained from the BD Alti digital elevation model provided by the French mapping agency (IGN; accessible at https://geoservices.ign.fr/bdalti). A trophic level index, reflecting the soil's overall nutritional capacity, and a hydric level index, assessing the extent to which plants' water needs are met at each plot location, were also incorporated into the analyses. Both indices were derived from floristic surveys and based on the presence of indicator plant species (Rameau et al., 1989). The trophic index spans from 0, denoting highly acidic sites, to 10, indicating strongly alkaline soils. Likewise, the hydric index ranges from 0, reflecting waterlogged conditions, to 10, representing extremely dry environments. At each plot location, a soil pit was dug to record soil parameters, and for a given inventory campaign approximately 7000 pits were excavated. The useful soil water reserve (SWR) was calculated using the following formula (Eq. 1):
where ROCK is the proportion of rocks and gravels, in 1/10th, of the total soil volume of the soil profile, DEPTH1 and DEPTH2 are depths of the upper and lower soil horizons, respectively, and TEXT1 and TEXT2 are coefficients determined according to the soil texture.

### 2.1.4. Climate data

We used the 1991–2020 climate normals for minimum and maximum annual temperatures, as well as total annual precipitation, obtained from the Aurelthy dataset (Sorel et al., 2022). This dataset provides high-resolution gridded data at a 1 km² spatial resolution for mainland France, and is generated through the interpolation of meteorological station data while accounting for variations in elevation (Canellas et al., 2014). Following Walas et al. (2022), we also integrated bioclimatic variables from WorldClim 2.1 (Fick and Hijmans, 2017) into our analyses: BIOiso (isothermality), BIOcoldm (minimum temperature of the coldest month), BIOtar (annual temperature range), and BIOprecw (precipitation of the warmest quarter).

### 2.1.5. Statistical analyses

Poplar groves are intensively managed monocultures with low species and structural diversity, short rotation cycles, and a maximum stand age of about 80 years. Predominantly located in lowland riparian areas of France, they show the highest infestation rates among tree species (Table 1). To account for this, we conducted separate analyses for poplar groves, resulting in two model sets for *Viscum album* subsp. *album*: one specific to poplar groves and another including all other forest types.

To evaluate the environmental drivers influencing mistletoe presence, logistic generalized additive models (GAMs) were fitted using the *gam* function of the *mgcv* R package (Wood, 2017). A distinct model was developed for each mistletoe subspecies and, as specified above, separate models were fitted for the *V. a. album* subspecies in poplar groves and in other forests, resulting in a total of four models. The dataset included only plots where the host tree species were present, excluding plots without suitable hosts. Mistletoe cluster classes were converted into a binary variable indicating mistletoe presence at the plot scale, which was used as the response variable. The explanatory variables included climatic and bioclimatic factors, elevation, soil characteristics, stand-level competition metrics, and categorical variables representing species diversity and forest management practices. The models were weighted using plot weights to account for the sampling design. The full models were as follows:

$$P(A_j) \sim \text{div.}_j + \text{plantation}_j + \text{ownership}_j + s(G\text{tot}_j) + s(A\text{ge}_j) + s(\text{elev.}_j) + s(\text{dens.}_j) + s(\text{tmin}_j) + s(\text{tmax}_j) + s(\text{precip.}_j) + s(\text{SWR}_j) + s(\text{troph.}_j) + s(\text{hydr.}_j) + s(\text{BIOiso}_j) + s(\text{BIOcoldm}_j) + s(\text{BIOtar}_j) + s(\text{BIOprecw}_j) + \varepsilon \quad (2)$$

where $P(A_j)$ is the probability of observing one or more mistletoe clusters in plot j, $\text{div.}_j$ is a four-level factorial variable accounting for the number of predominant tree species within plot j, from a single predominant species to four or more predominant species, $\text{plantation}_j$ is a categorical variable indicating whether the forest stand displayed signs of being planted or not (note that this variable was not included in the model specific to poplar groves, as they are, by definition, always “planted forests”), $\text{ownership}_j$ is a variable describing the ownership class linked to the forest stand (either private, state-owned or other public forest), $G\text{tot}_j$ is the total basal area of the plot j, in m²/ha, $A\text{ge}_j$ is the age of the plot j, $\text{dens.}_j$ is the stem density per hectare associated with plot j, $\text{tmin}_j$ and $\text{tmax}_j$ are minimum and maximum temperature normals at plot j, $\text{precip.}_j$ is the precipitation normal of plot j, $\text{elev.}_j$ is the elevation at plot j, $\text{SWR}_j$ is the soil water reserve of plot j, $\text{troph.}_j$ and $\text{hydr.}_j$ are the trophic and hydric indices of plot j, as indicated by floristic surveys, and $\text{BIOiso}_j$, $\text{BIOcoldm}_j$, $\text{BIOtar}_j$ and $\text{BIOprecw}_j$ are the bioclimatic variables

isothermality, Minimum Temperature of Coldest Month, Temperature Annual Range and Precipitation of Warmest Quarter, respectively, at plot j. $\varepsilon$ is the residual error term. $s()$ denotes that the explanatory variable is considered a smooth term.

From the full models, a variable selection procedure was applied to keep only variables that, when removed from the models, led to an increase in AIC score of more than 2 units, starting with the least significant variable.

Logistic GAMs were applied at the tree level to analyze the relationship between canopy dieback and mistletoe infestation. The data were restricted to plots where at least one infested tree was observed, excluding plots without any mistletoe presence to reduce noise from unrelated environmental conditions. The response variable was a binary variable depicting mistletoe presence at the tree level. The explanatory variables included tree girth and a categorical factor representing the extent of crown dieback. The sampling weights were computed as the product of the plot weights and tree weights. Plot identity was incorporated as a random effect smooth term to account for spatial dependencies. The model formulas were as follows:

$$P(A_{ij}) \sim s(c13_{ij}) + \text{cat.mort}_{ij} + s(\text{plotID}_j, \text{bs} = \text{"re"}) + \varepsilon \quad (3)$$

where $P(A_i)$ is the probability of observing one or more mistletoe clusters in a tree i in a plot j, $c13_i$ is the girth of tree i, $\text{cat.mort}_i$ is the crown dieback category of the tree i, $\text{plotID}_j$ is the identification number of the plot j, and $\varepsilon$ is the residual error term.

We fitted generalized additive models (GAMs) to evaluate the impacts of mistletoe infestation on tree growth performances. Similar to the previous analyses, these models were restricted to plots where at least one tree hosted mistletoe. The response variable was the 5-year growth increment, which was log-transformed to ensure homoscedasticity of the residuals. The explanatory variables included the same predictors as in the first set of models (Eq. 2), with the addition of a categorical variable representing the number of mistletoe clusters observed on each tree. For the *austriacum* and *album* models, this variable was included in interaction with the identity of the host tree species. As no wood core was taken in poplar groves, there was no information on the 5-yr growth increments, and we did not fit models for poplar groves. Tree girth and stand age were also incorporated as explanatory variables. Plot identity was added as a random effect smooth term to account for spatial clustering. The sampling weights were computed as the product of the plot weights and tree weights. The model formulas were as follows:

$$\begin{aligned} \text{RW5}_{ij} & \sim \text{div.}_j + \text{speciesID}_{ij} + \text{ncluster}_{ij} + \text{speciesID}_{ij} \\ & \quad : \text{ncluster}_{ij} + \text{plantation}_j + \text{ownership}_j + s(G\text{tot}_j) + s(A\text{ge}_j) \\ & \quad \qquad + s(c13_{ij}) + s(\text{dens.}_j) + s(\text{elev.}_j) + s(\text{SWR}_j) + s(\text{troph.}_j) \\ & \quad \qquad + s(\text{hydr.}_j) + s(\text{BIOiso}_j) + s(\text{BIOcoldm}_j) + s(\text{BIOtar}_j) \\ & \quad \qquad + s(\text{BIOprecw}_j) + s(\text{precip.}_j) + s(\text{tmax}_j) + s(\text{tmin}_j) \\ & \quad \qquad + s(\text{plotID}_j, \text{bs} \\ & \quad = \text{"re"}) + \varepsilon \end{aligned} \quad (4)$$

where RW5ij is the 5-yr growth increment of tree i in plot j, speciesIDij is the species identity of tree i in plot j, nclusterij is the number of mistletoe clusters observed on tree i in plot j and Agej is the age of the stand in plot j. Please refer to the description of Eq. 2 for explanation on other terms.

Similarly to the procedure applied to the models depicted in Eq. 2, significant variables were selected based on AIC scores. In this case, the target models included species identity, the number of mistletoe clusters and the interaction between these two variables (i.e. these terms were systematically included, and not dropped if non-significant), as these variables were of importance to test our third hypothesis.

In all the models, the number of knots for the smooth terms was set to three for all the variables, except for tree girth, for which it was set to four. This adjustment aimed to minimize multicollinearity issues and
<figure><img src="image_4.png" /><figcaption>Album (other than poplar grove)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(B) Abietis</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(C) Austriacum</figcaption></figure>

<figure><img src="image_7.png" /></figure>

(caption on next page)
**Fig. 1.** Location of the study plots. Colored dots display locations where mistletoe has been observed, while gray dots represent locations where host species were present but the parasite was not detected. Additionally, plot locations across the climate space (1991–2020 average temperature and precipitation normals), together with probability density plots, are shown. Cohen's d effect sizes assess the magnitude of the difference between climate conditions in plots without any mistletoe observations (gray curves) and plots where at least one cluster was observed (coloured curves). The thresholds are as follows: |value| < 0.2: the difference is “negligible”, |value| < 0.5: “small”, |value| < 0.8: “medium”. In the density plots, the y-axis represents a probability density, meaning that the total area under each curve equals 1. The unit of the y-axis is the inverse of the x-axis unit.

produce biologically interpretable predicted curves. Variance inflation factors (VIFs) were assessed using the *vif* function of the R package *spatialRF* (Benito, 2022). Variables with VIF values exceeding 5 were excluded from the models to avoid multicollinearity. Visual inspection of the residuals confirmed that the assumptions of homoscedasticity and normality were satisfied. All the statistical analyses were performed using R v.4.4.1 (R Core Team, 2023). The predicted values and their 95 % confidence intervals were generated using the R package *ggeffects* (Lüdecke et al., 2024), and figures were created with *ggplot2* (Wickham et al., 2024).

# 3. Results

## 3.1. Spatial distribution of mistletoe observations

Mapping mistletoe observations revealed striking differences in their distributions across the country (Fig. 1). Outside of poplar groves, *V. a. album* was observed across a large portion of the territory, with denser observations in the northern and western regions than in the southeast region. This subspecies thrived in significantly different climatic conditions from healthy host trees, favoring drier, warmer sites with a mean annual temperature of 11.5°C and 700 mm of annual precipitation (Fig. 1, top right panels). In poplar groves, infested cultivated poplars were located in significantly cooler environments compared with healthy individuals, but did not differ in terms of annual precipitation inputs (Fig. 1 top left panels). *V. a. abietis* was observed only in the eastern part of the country, predominantly in areas warmer than those typically inhabited by silver fir trees without mistletoe clusters (Fig. 1, bottom left panels). Moreover, the distribution of pine mistletoe (*V. a. austriacum*) was largely restricted to the alpine region, with a few additional observations in the Alsace plain. Notably, large portions of the distribution area of its host species remained free of the parasite. The subspecies was found at colder sites with higher precipitation compared to locations with non-infested pine trees (Fig. 1, bottom right panels).

## 3.2. Effects of the environment and silvicultural practices on mistletoe distribution

Outside of poplar groves, *V. a. album* was more frequently encountered in areas where the warmest quarter of the year receives substantial precipitation and where isothermia is high. The probability of the occurrence of the subspecies was maximized in regions where the minimum temperature normals reached 4°C. It was also more frequently found on strongly alkaline soils and on soils with a low hydric index, meaning waterlogged soils. It was more prevalent in young stands, in stands with a high total basal area, and at low elevation (Fig. 2, Table 2). This subspecies was also more frequently observed in stands showing signs of plantation compared to forests without obvious signs of plantation, as well as in privately-owned forests (Fig. 3, Table 2).

In poplar groves, observations of *V. a. album* were more frequent in regions where minimum temperature normals drop below 6°C, where precipitations of the warmest quarter are low and where temperature annual range and isothermia are high. The probability of occurrence was maximal in relatively old poplar groves having reached 40–80 years, in stands with low stem density and high basal area (Fig. 2, Table 2).

*V. a. abietis* was more prevalent in regions with high annual temperature ranges, low isothermia, and in stands located at an altitude

of around 600 m. This subspecies was more frequently found on acidic soils with low water reserves and in stands with low stem density, relatively high total basal area, and having reached an age of 150 years. Fir mistletoe was more prevalent in public forests, in forests with no signs of plantation, and in forests with a low species diversity i.e. the probability of occurrence was maximized in forests with only one predominant species (Fig. 3, Table 2).

*V. a. austriacum* was predominantly observed in areas with a low isothermia, low temperature normals, a high temperature annual range and low precipitation during the warmest quarter of the year. The probability of occurrence increased with stand age. Other environmental factors had minimal influence on the probability of occurrence of this subspecies (Fig. 2, Table 2). Pine mistletoe was more frequently observed in forests with no signs of plantation, and in forests with a low species diversity (Fig. 3, Table 2).

The tree-level models showed a significant relationship between tree size (girth) and the probability of mistletoe occurrence. In all mistletoe subspecies, bigger trees had higher probability of infestation compared with smaller-sized individuals. The relationship followed a sigmoid curve, with inflection points varying depending on the subspecies. In the case of *V. a. album* (both outside of poplar groves and in poplar groves), the curve started to plateau at a circumference of 1.25 m. For fir mistletoe, the effect of circumference started to slow-down at 2.5 m; and for pine mistletoe, the plateau started earlier, at a circumference value of 1.0 m (Supplementary Figure S3, Table 3).

## 3.3. Effects of mistletoe infestations on tree health

The models revealed a significant relationship between mistletoe infestation and crown dieback (Table 3). For *Viscum album album* outside of poplar groves, this relationship was nearly linear, with the probability of infestation increasing almost proportionally to the crown dieback proportion (Fig. 4). In poplar groves, the probability of infestation of the parasite followed a unimodal relationship, with a maximum for a crown dieback proportion of 25–50 % (Fig. 4). In the case of fir mistletoe, the relationship was more complex, showing an increase in infestation probability up to a crown dieback proportion of 25–50 %, followed by a decline at higher levels of dieback (Fig. 4, Supplementary Table S3). For pine mistletoe, the relationship was distinctly non-linear, with the highest probability of mistletoe infestation observed in trees exhibiting 50–95 % crown dieback (Fig. 4). In the case of pine and fir mistletoe, the observed patterns did not change when focusing on the geographic areas with the highest infestation rates (Figures S1 and S2).

## 3.4. Effects of mistletoe infestation on tree growth performance

The generalized additive models indicated that both silver fir and Scots pine trees with high levels of infestation (i.e., with more than five mistletoe clusters) displayed growth rates that were 34 % and 23 % lower, respectively, compared with mistletoe-free individuals (Fig. 5). Silver fir trees with 3–5 mistletoe clusters exhibited 5-yr growth 21 % lower compared to non-parasitized trees. For other species groups, the models did not detect significant differences in growth between parasitized and non-parasitized trees (Fig. 5, Table 4).
Predicted probability of mistletoe occurrence [unitless]

Album (poplar grove)

<figure><img src="image_8.png" /></figure>

Album (other than poplar grove)

<figure><img src="image_9.png" /></figure>

Abietis

<figure><img src="image_10.png" /></figure>

Austriacum

<figure><img src="image_11.png" /><figcaption>**Fig. 2.** Environmental drivers of mistletoe occurrence. The lines represent predicted values from generalized additive models, while the shaded areas indicate 95 % confidence intervals. “isothermality” refers to the BIOiso bioclimatic variable, “temp. annual range” to BIOtar, and “prec. warmest quarter” to BIOprecw.</figcaption></figure>
<table><caption>**Table 2**

Results from the generalized additive models predicting the probability of mistletoe infestation at the plot scale (Eq. 2). Estimate, standard error ("Std. Error"), z value and p value ("Pr(>z|)".) are provided for the linear terms of the models. For the smooth terms, estimated ("edf") and reference ("Ref.df") degrees of freedom, chi-squared value ("Chi.sq") and p-value are provided. Adjusted R-squared values ("adj. R²") are also provided for each model. "NI" means that the variable was not retained in the model.</caption><thead><tr><th rowspan="2">Term</th><th rowspan="2">Variable</th><th colspan="3">abietis</th><th colspan="3">austriacum</th><th colspan="3">album (other than poplar grove)</th><th colspan="3">album (poplar grove)</th></tr><tr><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(>z|)</th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(>z|)</th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(>z|)</th></tr></thead><tbody><tr><td rowspan="7">linear</td><td>(Intercept)</td><td>-2.79</td><td>0.14</td><td>-20.53</td><td>0.00</td><td>-5.37</td><td>0.20</td><td>-26.99</td><td>0.00</td><td>-4.15</td><td>0.17</td><td>-24.96</td><td>0.00</td><td>-0.52</td><td>0.09</td></tr><tr><td>Plantation (planted)</td><td>-1.87</td><td>0.31</td><td>-6.06</td><td>0.00</td><td>-1.67</td><td>0.40</td><td>-4.13</td><td>0.00</td><td>-0.39</td><td>0.17</td><td>2.34</td><td>0.02</td><td>NI</td><td></td></tr><tr><td>Ownership (state-owned)</td><td>-0.33</td><td>0.14</td><td>-2.36</td><td>0.02</td><td>NI</td><td></td><td></td><td></td><td>0.36</td><td>0.29</td><td>1.24</td><td>0.22</td><td>NI</td><td></td></tr><tr><td>Ownership (private)</td><td>-0.51</td><td>0.12</td><td>-4.38</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>0.74</td><td>0.17</td><td>4.23</td><td>0.00</td><td>NI</td><td></td></tr><tr><td>Diversity (2 sp)</td><td>-0.31</td><td>0.11</td><td>-2.89</td><td>0.00</td><td>-0.45</td><td>0.12</td><td>-3.88</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>NI</td><td></td></tr><tr><td>Diversity (3 sp)</td><td>-0.72</td><td>0.16</td><td>-4.51</td><td>0.00</td><td>-1.27</td><td>0.25</td><td>-5.08</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>NI</td><td></td></tr><tr><td>Diversity (4 + sp)</td><td>-0.71</td><td>0.26</td><td>-2.71</td><td>0.01</td><td>-0.75</td><td>0.46</td><td>-1.63</td><td>0.10</td><td>NI</td><td></td><td></td><td></td><td>NI</td><td></td></tr><tr><td rowspan="10">smooth</td><td>edf</td><td>Ref.df</td><td>1.00</td><td>21.02</td><td>0.00</td><td>1.00</td><td>1.00</td><td>Ref.df</td><td>edf</td><td>Ref.df</td><td>Chi.sq</td><td>p-value</td><td>edf</td><td>Ref.df</td><td>Chi.sq</td></tr><tr><td>Stem density</td><td>1.00</td><td>1.00</td><td>21.02</td><td>0.00</td><td>NI</td><td></td><td>4.37</td><td>0.04</td><td>1.00</td><td>1.00</td><td>28.29</td><td>0.00</td><td>1.40</td><td>10.71</td></tr><tr><td>Total basal area</td><td>1.71</td><td>1.92</td><td>15.27</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>1.00</td><td>1.00</td><td>37.50</td><td>0.00</td><td>1.00</td><td>25.49</td></tr><tr><td>Stand age</td><td>1.86</td><td>1.98</td><td>30.85</td><td>0.00</td><td>1.55</td><td>1.80</td><td>39.40</td><td>0.00</td><td>1.39</td><td>1.63</td><td>56.75</td><td>0.00</td><td>1.76</td><td>18.27</td></tr><tr><td>Min. temp.</td><td>NI</td><td></td><td></td><td></td><td>1.97</td><td>2.00</td><td>289.10</td><td>0.00</td><td>1.92</td><td>1.99</td><td>38.46</td><td>0.00</td><td>1.80</td><td>25.20</td></tr><tr><td>Precip.</td><td>NI</td><td></td><td></td><td></td><td>1.89</td><td>1.99</td><td>15.90</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>NI</td><td></td></tr><tr><td>Elevation</td><td>1.99</td><td>2.00</td><td>137.59</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>1.00</td><td>1.00</td><td>17.77</td><td>0.00</td><td>NI</td><td></td></tr><tr><td>SWR</td><td>1.11</td><td>1.20</td><td>17.08</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>1.62</td><td>1.85</td><td>16.38</td><td>0.00</td><td>NI</td><td></td></tr><tr><td>Trophic index</td><td>1.00</td><td>1.00</td><td>14.31</td><td>0.00</td><td>1.00</td><td>1.00</td><td>34.79</td><td>0.00</td><td>1.00</td><td>1.00</td><td>164.35</td><td>0.00</td><td>NI</td><td></td></tr><tr><td>Hydric index</td><td>NI</td><td></td><td></td><td></td><td>1.00</td><td>1.00</td><td>26.60</td><td>0.00</td><td>1.95</td><td>2.00</td><td>47.22</td><td>0.00</td><td>NI</td><td></td></tr><tr><td>BIOtar</td><td>1.82</td><td>1.97</td><td>23.77</td><td>0.00</td><td>1.00</td><td>1.00</td><td>87.45</td><td>0.00</td><td>NI</td><td></td><td></td><td></td><td>1.91</td><td>1.99</td></tr><tr><td>BIOiso</td><td>1.00</td><td>1.00</td><td>56.16</td><td>0.00</td><td>1.00</td><td>1.00</td><td>57.92</td><td>0.00</td><td>1.00</td><td>1.00</td><td>58.31</td><td>0.00</td><td>1.20</td><td>14.20</td></tr><tr><td>BIOprecw</td><td>1.73</td><td>1.93</td><td>27.16</td><td>0.00</td><td>1.63</td><td>1.86</td><td>312.34</td><td>0.00</td><td>1.00</td><td>1.00</td><td>4.91</td><td>0.03</td><td>1.93</td><td>14.15</td></tr><tr><td>adj. R²</td><td>0.16</td><td></td><td></td><td></td><td>0.29</td><td></td><td></td><td></td><td>0.07</td><td></td><td></td><td></td><td>0.17</td><td>23.23</td></tr></tbody></table>

# 4. Discussion

## 4.1. Mistletoe distribution is shaped by environmental factors

The spatial distributions of the mistletoe observations and model outputs revealed that the parasite is not randomly distributed across the range of its host species. For the subspecies *V. a. album*, infestations were associated with warmer and drier areas compared to the average climate conditions experienced by its host tree species. Broadleaved tree species, and especially common hosts of *V. a. album*, naturally occur at relatively low elevations, where conditions are usually warm (Dumas, 2013). Trees in warm and dry locations may be weakened by more frequent drought episodes and heatwaves than individuals in cold and wet climates (Vitasse et al., 2023), making them more susceptible to mistletoe infestations. *V. a. album* was predominantly observed on strongly alkaline soils with a low hydric index. These clay-based soils have low infiltration capacity (Hopkins et al., 2007), suggesting that, even with high water availability during the warmest quarter of the year, trees at such sites could suffer from moisture deficits in case of severe drought episodes. Waterlogged soils, characterized by excess water retention (i. e. very low hydric index) and leading to extremely low oxygen availability, can also be detrimental to tree growth due to anoxic conditions. Weakened trees may have less energy to allocate to defense mechanisms compared with healthy trees (Monson et al., 2022), making it easier for mistletoe to establish haustoria. Another possible explanation is that deciduous species typically found in waterlogged or moisture-rich environments, such as aspen and willow, are also among the most susceptible to mistletoe infestation (Nierhaus-Wunderwald and Lawrenz, 1997).

For pine and fir mistletoe, our models identified isothermality as the primary environmental driver of infestation. This environmental factor represents the balance between diurnal and annual temperature variability (Fick and Hijmans, 2017). Mistletoe was more frequently found on pine and fir trees growing in areas with low isothermality. This pattern could be linked to the impacts of temperature variability on mistletoe fitness. First, mistletoe seeds are likely to have a relatively high survival rate in environments with low diurnal temperature variability. These environments minimize exposure to cold or heat extremes that can damage seeds and promote moisture retention which helps seed attachment and germination. Notably, seeds of *V. a. austriacum* are more sensitive to freezing temperatures than are those of *V. a. album* (Tikkanen et al., 2021). Additionally, areas with low diurnal temperature variation may be more favorable to seed dispersers (Chiatante, 2022). Furthermore, stable temperature conditions likely increase the growth and development of mistletoe clusters, reducing the risk of temperature extremes damaging adult plants (Walas et al., 2022).

The stronger influence of isothermality on pine and fir mistletoe occurrence compared to *V. album* album occurrence may stem from the distribution of their host species. Pine and fir typically grow at higher altitudes compared to broadleaved species, where cooler climates and more frequent freezing episodes prevail. Additionally, tree populations in low-isothermality environments, with reduced exposure to stress, are less likely to develop traits favoring stress resistance (Serra-Maluquer et al., 2022), increasing their susceptibility to mistletoe infestations.

In poplar groves, the relationship between *Viscum album* subsp. album occurrence and isothermality was relatively strong. The probability of mistletoe occurrence was higher in poplar groves characterized by a high isothermality, contrasting with the patterns observed for pine and fir mistletoes. This discrepancy may be attributed to the typical location of poplar groves in lowland areas, where exposure to cold extremes is less frequent than in high-altitude stands. High isothermality may indicate increased risk of late frosts and drought episodes, both of which can compromise the vitality of poplar trees and increase susceptibility to mistletoe infestation (Teixeira-Costa et al., 2017).
<figure><img src="image_12.png" /><figcaption>**Fig. 3.** Effects of silvicultural practices and tree species diversity on mistletoe occurrence. The dots represent predicted values from generalized additive models, while the error bars indicate 95 % confidence intervals. Mistletoe subspecies absent from a panel indicate that the variable was not retained in the model following selection based on AIC scores.</figcaption></figure>

## 4.2. The role of host traits and seed dispersers

Beyond climate variables, host tree size was a key factor influencing infestation probability at the individual level. Larger trees were those showing the highest probability of being infested (Table S3, Figure S3), a pattern widely documented in previous studies (Larrieu et al., 2018; Lech et al., 2020; Matula et al., 2015; Vallauri, 1997). Larger trees often are dominant, taller individuals in the stand, that are more likely to attract disperser birds and provide optimal conditions for mistletoe establishment, particularly in terms of incident light.

The two primary bird dispersers of mistletoe, the blackcap and the mistle thrush, favor habitats with high light availability, which are also considered favourable for mistletoe. The mistle thrush usually nests in forest edges and clearings, and is especially common in hilly and mountainous areas of the Mediterranean region (Skórka and Wójcik, 2005). The blackcap, whose population increased by 30 % between 2001 and 2021 in mainland France, preferentially occupies open forested areas such as forest edges, clearings, thinned plots and windfalls (Linossier, 2017). Observation records show that mistle thrush activity peaks between December and May, aligning with the maturation and germination periods of mistletoe seeds (MNHN, 2003b). In contrast, the blackcap is largely absent from French forests during much of this period, as it migrates to southern regions (mainly Spain and North Africa) from September to March (MNHN, 2003a). Consequently, its role in mistletoe dispersal is mainly limited to its overwintering areas, which may explain why pine mistletoe is observed at significantly higher rates in Spain compared to the French Pyrenees (MNHN, 2003c).

## 4.3. The influence of past and present management practices on mistletoe occurrence

Beyond environmental factors, past and present forest management practices can significantly influence mistletoe occurrence. Our models

showed that mistletoe was predominantly observed in pure pine and fir stands (i.e., stands dominated by a single tree species; see Fig. 3). Given that pine and fir mistletoes are highly specialized parasites restricted to a narrow range of host species, monospecific stands offer a higher density of compatible hosts compared to species-rich forests. Supporting this, previous research in Spanish pine forests reported lower mistletoe infestation levels in mixed stands compared to pure forests (van Halder et al., 2019). Fir mistletoe was also significantly more prevalent in public forests, possibly due to silvicultural practices that favor pure over mixed stands. In France, pure fir stands account for approximately 4 % of the total public forest area, compared to only 1 % in privately owned forests (Supplementary Figure S4). In contrast, the influence of forest management and composition was less pronounced for the *album* subspecies, likely due to its broader host range. Model predictions indicate significantly higher occurrences of pine and fir mistletoe in forests without any sign of plantation compared to stands displaying obvious signs of being planted. One plausible explanation is that naturally re-generated forests tend to be less intensively managed, potentially fa-voring mistletoe infestation. Alternatively, this finding may result from limitations in the inventory protocol. Plantation signs are identified through visual inspection, which can become unreliable in the oldest stands where tree mortality and natural regeneration obscure the reg-ular spacing typical of planted forests. Consequently, forest stands classified as non-planted were generally older than those identified as plantations (Figure S5), and the observed higher mistletoe occurrence in the former may be attributable to stand age rather than management practices.

We observed that pine mistletoe was primarily observed in restricted areas of northeastern and southeastern France, despite the widespread presence of its host species across the country. Sangüesa-Barreda et al. (2018) identified large portions of the French territory, particularly in central and western regions, as climatically suitable for pine mistletoe. This mismatch between predicted habitat suitability and observed
<table><caption>**Table 3**
Results from the generalized additive models predicting the probability of mistletoe infestation at the tree scale (Eq. 3). Estimate, standard error ("Std. Error"), z value and p value ("Pr(> z|)".) are provided for the linear terms of the models. For the smooth terms, estimated ("edf") and reference ("Ref.edf") degrees of freedom, chi-squared value ("Chi.sq") and p-value are provided. Adjusted R-squared values ("adj. R²") are also provided for each model.</caption><thead><tr><th rowspan="2">Term</th><th rowspan="2">Variable</th><th colspan="3">abietis</th><th colspan="3">austriacum</th><th colspan="3">album (other than poplar grove)</th><th colspan="3">album (poplar grove)</th></tr><tr><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(> z|)</th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(> z|)</th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(> z|)</th></tr></thead><tbody><tr><td rowspan="5">linear</td><td>(Intercept)</td><td>-0.52</td><td>0.97</td><td>-0.54</td><td>0.59</td><td>2.11</td><td>0.82</td><td>2.58</td><td>0.01</td><td>2.53</td><td>0.54</td><td>4.72</td><td>2.34</td></tr><tr><td>cat.mort 5-25 %</td><td>0.73</td><td>0.05</td><td>14.24</td><td>0.00</td><td>1.17</td><td>0.04</td><td>28.22</td><td>0.00</td><td>0.50</td><td>0.05</td><td>10.24</td><td>-0.20</td></tr><tr><td>cat.mort 25-50 %</td><td>1.10</td><td>0.09</td><td>12.77</td><td>0.00</td><td>0.71</td><td>0.07</td><td>10.93</td><td>0.00</td><td>1.37</td><td>0.09</td><td>15.14</td><td>1.08</td></tr><tr><td>cat.mort 50-95 %</td><td>0.33</td><td>0.12</td><td>2.70</td><td>0.01</td><td>3.89</td><td>0.16</td><td>24.50</td><td>0.00</td><td>1.88</td><td>0.11</td><td>17.09</td><td>1.01</td></tr><tr><td>cat.mort > 95 %</td><td>-57.10</td><td>3535579.76</td><td>0.00</td><td>1.00</td><td>-0.96</td><td>0.25</td><td>-3.76</td><td>0.00</td><td>3.01</td><td>0.24</td><td>12.80</td><td>0.18</td></tr><tr><td rowspan="3">smooth</td><td>edf</td><td>2.99</td><td>Ref.df</td><td>Chi.sq</td><td>8205.15</td><td>p-value</td><td>Ref.df</td><td>Chi.sq</td><td>p-value</td><td>edf</td><td>Ref.df</td><td>Chi.sq</td><td>p-value</td></tr><tr><td>PlotID</td><td>487.10</td><td>496.00</td><td>12103.05</td><td>0.00</td><td>2.99</td><td>3.00</td><td>3186.03</td><td>0.00</td><td>2.90</td><td>2.99</td><td>3447.41</td><td>2.98</td></tr><tr><td>adj. R<sup>2</sup></td><td>0.34</td><td></td><td></td><td>0.32</td><td>546.59</td><td>575.00</td><td>17546.30</td><td>0.00</td><td>582.38</td><td>643.00</td><td>8704.00</td><td>681.22</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Fig. 4.** Relationship between crown dieback and mistletoe infestation. Dots display the predicted probabilities from generalized additive models, by mistletoe subspecies and crown dieback class. The error bars are 95 % confidence intervals. Fir trees with more than 95 % crown dieback were never observed with mistletoe clusters, leading to a predicted probability of mistletoe observation of zero and a highly negative estimated coefficient in our model (Table 3).</figcaption></figure>

occurrences may be linked to historical land-use changes. In France, grasslands historically used for pasture were largely abandoned after World War II (Prévosto et al., 2003). Scots pine, a pioneer species that regenerates naturally in France, colonized these areas extensively, leading to a significant expansion of Scots pine stands (Prévosto et al., 2003). Today, these post-war Scots pine stands are approximately 70 years old, which is considered a suboptimal age for mistletoe infestation. Indeed, our models showed that stand age is a significant factor influencing mistletoe occurrence, with fir and pine mistletoe being more prevalent in older stands than in younger forests (Fig. 2). These findings suggest that pine mistletoe may expand its current range in the future, particularly in the Massif central and Atlantic West regions, where host species are abundant.

### 4.4. Effects of mistletoe infestation on tree health and growth performance

The link between mistletoe infestation and canopy dieback is well known (e.g. Plagnat, 1955; Rigling et al., 2010; Barbu, 2012; Camarero et al., 2025). Historical reports from the 1950s in France already documented significant canopy dieback in fir plantations heavily infested with mistletoe (Plagnat, 1955). A recent study reported a significant positive relationship between mistletoe infestation and crown defoliation in silver fir trees (Camarero et al., 2025). For pine and fir mistletoes, as well as in poplar groves infested with the album subspecies, the models revealed a significant decline in mistletoe occurrence for trees with the highest proportions of dead branches. Although our dataset lacks repeated crown dieback measurements, limiting detailed analysis of temporal dynamics, this pattern likely reflects reduced host suitability. Severely damaged trees may offer suboptimal conditions for mistletoe establishment and growth. Dead branches are unable to deliver nutrients and water to the parasite, and remaining living tissues may be functionally impaired, with reduced sap quality (Fan et al., 2022). Physiological changes induced by the mistletoe infestation itself may exacerbate host decline. Mistletoe infestation induces swelling at the attachment point on the host branch, followed by the gradual death of tissues distal to the attachment site (Tennakoon and Pate, 1996). The parasite can reduce leaf size and abundance, limiting photosynthetic capacity and carbohydrate availability in infested branches (Rigling
<figure><img src="image_14.png" /><figcaption>**Fig. 5.** Effect of mistletoe infestation on tree growth rates. The dots are the average predicted values from the generalized additive models, by species group and number of mistletoe clusters. The error bars represent 95 % confidence intervals. Note that "cultivated poplar" refers to individuals of cultivated poplar species that are growing outside of designated poplar groves.</figcaption></figure>

et al., 2010). This crown degradation has been linked with reductions in canopy volume in lodgepole pine (Godfree et al., 2003) and loss of fo- liage in the crown of severely infested Scots pine (Dobbertin and Rigling, 2006). Griebel et al. (2022) observed a canopy recovery in eucalypt trees following drought-induced mistletoe mortality, suggesting that mistletoe removal may restore host vigor under certain conditions. Additionally, heavily declining trees without mistletoe clusters may have been impacted by other biotic agents, such as bark beetles. Given the negative relationship between mistletoe occurrence and bark beetle infestations in silver fir trees (Durand-Gillmann et al., 2014), this could also explain the absence of mistletoe clusters in fir trees with over 95 % crown dieback (Fig. 3).

Highly parasitized (i.e. with more than five mistletoe clusters) fir and pine trees presented 5-yr growth increments 23–34 % lower compared with healthy, non-infested individuals. Our analysis, based on 5-year average growth increments without repeated measurements, does not allow for a direct attribution of these differences to mistletoe infestation. However, previous studies using traditional dendrochronological data and temporal assessments of mistletoe infestation severity have reported comparable ranges of mistletoe-induced growth losses in pine and fir trees (Camarero et al., 2025; González de Andrés et al., 2024). Growth reductions in mistletoe-infested trees are due to carbon, water and nutrient imbalances (González de Andrés et al., 2024; Sangüesa-Barreda et al., 2013). The high evapotranspiration rate of the parasite can exacerbate drought stress in the host, potentially damaging the hy- draulic system of the tree and depleting its carbon reserves (Sangüesa-Barreda et al., 2013). The lack of a significant impact of *Viscum album album* on growth rates of broadleaved trees may be attributed to the small sample size.

Mistletoe-induced growth losses typically begin 1–5 years after infestation (Noetzli et al., 2003a). Determining the exact timing of infestation is challenging, and the interplay of numerous factors affecting radial growth complicates efforts to isolate the proportion of growth loss attributable to mistletoe. Large, dominant trees are more susceptible to mistletoe infestation than smaller, subcanopy individuals

(Lech et al., 2020; Matula et al., 2015). These larger trees often benefit from more favorable growing conditions, particularly in terms of inci- dent light, which can mitigate any reduction in growth rates caused by mistletoe infestation during an extended time period. Additionally, the limited number of trees for which 5-yr growth increments were measured further adds to this complexity.

4.5. Conclusions

This study reveals that mistletoe infestations are shaped by specific environmental factors and tree-level characteristics. By examining the interactions between climate, soil properties and host traits, our findings underscore the complexity of mistletoe distribution and the need to consider multiple ecological variables when assessing its spread. In particular, climate conditions, along with forest management history and tree size and health, emerged as key predictors of infestation probability. Ongoing climate change - characterized by milder winters and reduced freeze risk - is likely to enhance mistletoe seed survival and germination success. As a result, mistletoe prevalence in French forests may increase in the coming decades, similarly to what is already observed in Spain (Suárez-Herrera et al., 2025). Our observations indi- cate that warm, stable climate conditions were particularly favorable for fir mistletoe, which, among the three subspecies, could benefit the most from climate warming. However, the long-term trends in mistletoe distribution require further investigation, as some regions may eventu- ally become too warm to support either the parasite or its host tree species, potentially shifting its range northward. Monitoring these changes is essential to reduce economic risks for forest owners while preserving biodiversity, with proactive management, such as adapted harvesting methods, playing a crucial role in limiting mistletoe spread under climate change. Our findings further support the notion that silvicultural approaches promoting species diversity can reduce mistletoe infestation in pine and fir stands.
manuscript.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.foreco.2025.123226.

## Data availability

R script used for analyses as well as datasets are available on Github (https://github.com/WilliamMarchand36/Mistletoe-French-NFI) and Figshare (10.6084/m9 figshare.28742006) repositories.

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