# Warmer and brighter winters than before: Ecological and public health challenges from the expansion of the pine processionary moth (Thaumetopoea pityocampa)

Jean-Pierre Rossi, Andrea Battisti, Dimitrios N. Avtzis, Christian Burban, Noureddine Rahim, Jérôme Rousselet, Carole Kerdelhué, Kahraman İpekdal

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Jean-Pierre Rossi, Andrea Battisti, Dimitrios N. Avtzis, Christian Burban, Noureddine Rahim, et al. Warmer and brighter winters than before: Ecological and public health challenges from the expansion of the pine processionary moth (Thaumetopoea pityocampa). Science of the Total Environment, 2025, 978, pp.179470. 10.1016/j.scitotenv.2025.179470. hal-05054582

HAL Id: hal-05054582

https://hal.inrae.fr/hal-05054582v1

Submitted on 2 May 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# Warmer and brighter winters than before: Ecological and public health challenges from the expansion of the pine processionary moth (*Thaumetopoea pityocampa*)

Jean-Pierre Rossi<sup>a,*</sup>, Andrea Battisti<sup>b</sup>, Dimitrios N. Avtzis<sup>c</sup>, Christian Burban<sup>d</sup>, Noureddine Rahim<sup>e</sup>, Jérôme Rousselet<sup>f</sup>, Carole Kerdelhué<sup>a</sup>, Kahraman İpekdal<sup>g</sup>

## HIGHLIGHTS

* We model the geographic range of the pine processionary moth (PPM).

* Three species distribution models are constructed using tree-based methods.

* Interpretable machine learning reveals climate variables limiting the PPM's distribution.

* A significant northward expansion is projected for the coming decades.

* A contraction of suitable regions is expected in North Africa by 2040–2060.

## GRAPHICAL ABSTRACT

<figure><img src="image_5.png" /></figure>

## ARTICLE INFO

Editor: Fernando Pacheco

**Keywords:**

Climate change

Species range shift

Species distribution modeling

Health risk

Forest pest

## ABSTRACT

Assessing the species ecological responses to ongoing climate change is a critical challenge in environmental science. Rising temperatures, particularly in winter, are altering the distribution patterns of many species, including the pine processionary moth (PPM), *Thaumetopoea pityocampa* (Denis & Schiffermüller, 1775). This Mediterranean species, a significant defoliator of conifers, is expanding its range northward as winter temperatures increase. The larvae of PPM also pose serious public health risks due to their ability to induce allergic reactions in humans, pets, and livestock. To better understand these ecological shifts, we calibrated three distribution models (Bayesian Additive Regression Trees, Boosted Regression Trees, and Random Forest) based on historical and modern occurrence data compiling of 1769 points, and assessed climate suitability under

<footnote><sup>a</sup> CBGP (Centre de Biologie pour la Gestion des Populations), INRAE, CIRAD, IRD, Institut Agro, Montpellier, France</footnote>

<footnote><sup>b</sup> DAFNAE-Entomologia, Agripolis, Università di Padova, Legnaro, Italy</footnote>

<footnote><sup>c</sup> Hellenic Agricultural Organization "DEMETER", Thessaloniki, Greece</footnote>

<footnote><sup>d</sup> BIOGECO, INRAE, University of Bordeaux, Cestas, France</footnote>

<footnote><sup>e</sup> Higher National School of Biotechnology Taoufik KHAZNADAR, Nouveau Pôle Universitaire Ali Mendjeli, Constantine, Algeria</footnote>

<footnote><sup>f</sup> URZF, INRAE, Orléans, France</footnote>

<footnote><sup>g</sup> Department of Biology, Hacettepe University, Ankara, Türkiye</footnote>

<footnote>* Corresponding author at: CBGP, 755 avenue du Campus Agropolis, CS 30016, F-34988 Montferrier-sur-Lez cedex, France.</footnote>

<footnote>E-mail addresses: Jean-Pierre.Rossi@inrae.fr (J.-P. Rossi), andrea.battisti@unipd.it (A. Battisti), Christian.Burban@inrae.fr (C. Burban), Jerome.Rousselet@inrae.fr (J. Rousselet), Carole.Kerdelhué@inrae.fr (C. Kerdelhué), kipekdal@hacettepe.edu.tr (K. İpekdal).</footnote>
historical, current and future conditions. Our results show that winter minimum temperatures, summer maximum temperatures, and solar radiation significantly influence the life cycle, and shape the geographical distribution of PPM. Under current conditions, PPM could extend its range further north, but its limited flight capabilities hinder its ability to keep up with the pace of climate change. Future projections suggest continued northward expansion, although solar radiation is expected to limit the northernmost range of PPM. Certain host tree species of PPM are frequently used as ornamental plants, particularly in urban areas, which makes the careful selection of these species a potentially valuable tool for management. Our findings identify regions that are likely to become suitable for PPM colonization, where proactive measures could be implemented.

# 1. Introduction

Assessing the ecological responses of species to ongoing climate change presents a major challenge with significant implications for conservation (Harvey et al., 2023), disease ecology (Baker et al., 2022), agriculture (Jägermeyr et al., 2021), and food security (Singh et al., 2023; Molotoks et al., 2021).

Climate change influences species by altering a wide range of biotic and abiotic processes (Hannah, 2015). Among abiotic processes, one of the most widely predicted impacts of climate change on biodiversity is the shift in phenology and geographic range expansions, as species adjust their spatial distributions to track their climatic niches (Lawlor et al., 2024). While many studies have examined the biological effects of rising temperatures, most have focused on temperature increases during spring and summer—the growing season. However, climate change also affects other seasons, particularly winter, during which most arthropods in temperate regions enter diapause. As a result, temperature changes during the cold season have limited impacts on many species (but see Sturiale and Armbruster, 2023). Nonetheless, some gregarious insects construct communal silk nests that maintain a microclimate warmer than ambient winter temperatures, allowing larval development throughout winter (Fitzgerald et al., 2012). The population viability of such species often depends on minimum winter temperatures, and rising winter minima have direct consequences for their geographic distribution (Osland et al., 2021). Climate change also affects biotic processes, particularly interspecific relationships, by altering species interactions such as competition, predation, mutualism, and host-parasite dynamics. These changes can impact ecosystem functioning, alter food web structures, and cause biodiversity loss (Hannah, 2015).

Species Distribution Models (SDMs) are widely used to model species ecological niche and identify areas with favorable environmental conditions. They play a crucial role in assessing the effects of climate change on species distributions and are employed across various biological disciplines, including ecology, biogeography, epidemiology, conservation biology, and paleobiogeography (Guisan et al., 2017; Peterson, 2014). Species distribution modeling relies on key assumptions, including niche conservatism and the premise that species distributions are in equilibrium with climate (Guisan et al., 2017; Hui, 2023; Liu et al., 2020).

The pine processionary moth (PPM), *Thaumetopoea pityocampa* (Denis & Schiffermüller, 1775) (Lepidoptera: Notodontidae, Thaumetopoeinae), is native to the Western Mediterranean region. Its larvae develop gregariously mainly on pines, but some populations occur on cedars and occasionally on other conifers such as Douglas fir (Battisti et al., 2015). Egg-laying occurs in the summer, and both embryos and neonate larvae are sensitive to high temperatures (Santos et al., 2011; Rocha et al., 2017). Consequently, populations exposed to thermal stress have shifted their egg-laying period later in the season, especially in regions with increasingly hot summers (Robinet et al., 2015; Martin et al., 2022). Moreover, unlike most insects, the larval development of PPM occurs during autumn and winter, then forming highly visible silk nest, and making it particularly sensitive to winter temperature fluctuations.

PPM larvae feed nocturnally, leaving the nest only when temperatures exceed 0 °C, which represents the first thermal threshold

controlling larval feeding. The second threshold, referred to as the "activation temperature", is based on the temperature of the previous day within the nest, which must exceed 6 °C for larvae to become active (Battisti et al., 2005). When temperatures are too low, feeding ceases (a "starvation period"), weakening the colony and potentially leading to its collapse if unfavorable conditions persist (Hoch et al., 2009). Conversely, warmer temperatures shorten these starvation periods, accelerating development and improving survival rates. The increase in internal nest temperature is largely due to the absorption of solar radiation (Battisti et al., 2005; Poitou et al., 2021), which explains the higher density of nests at south-facing stand edges (Fitzgerald and Blas, 2003; Uemura et al., 2021). The importance of sunlight for PPM survival was recognized early on: Huchon and Demolin (1970) noted that PPM requires a minimum of 1800 h of annual sunlight, which they identified as a limiting factor for the species' distribution in northern France during the 1970s. Recent climate warming has resulted in both expansion and retraction of the species range. The northern part of the range of the PPM has expanded (Démolin et al., 1996; Battisti et al., 2005, 2006; Roques et al., 2015a, 2015b) as milder winters reduce thermal constraints on larval development. On the other hand, the southern margin of its range in Northern Africa appears to be retreating (Bourougaoui et al., 2021), likely due to increasingly harsh summer and early autumn conditions, which are detrimental to eggs and young larvae. Thus, PPM has emerged as a model species for studying the ecological impacts of climate change (IPCC, 2007, p. 105).

PPM is a defoliating insect of considerable economic importance in Mediterranean countries (Roques et al., 2015a, 2015b). Although recent studies have shown no increase in outbreaks linked to climate change in its native range in Spain (Camarero et al., 2022), outbreak frequency has increased in the expansion range (Tamburini et al., 2013). Moreover, PPM poses significant health risks for humans, pets, and livestock. Mature PPM larvae bear up to one million urticating setae that are dispersed by wind (Petrucco Toffolo et al., 2014) and serve as a defense against vertebrate predators (Battisti et al., 2017), but cause allergic reactions in non-target vertebrates upon contact with skin, eyes, or the respiratory tract (Moneo et al., 2015; Ricciardi et al., 2021). The ongoing expansion of PPM is leading to the colonization of densely populated areas, such as the Paris Basin (10 millions of inhabitants), thus exposing large populations to urtication risk. This risk is further exacerbated in urban areas compared to rural zones due to urban warming, which causes larvae to spend more time in the fifth and most dangerous instar (Backe et al., 2021). It is thus of prime importance to anticipate the possible changes in PPM distribution, and to decipher the contribution of different facets of the climatic and environmental conditions on environmental suitability.

The potential distribution of PPM under climate change has been explored using a variety of models (Roques et al., 2015a). Robinet et al. (2007) investigated the effects of minimal temperature rise during cold season on habitat suitability across France. Robinet et al. (2014) applied a reaction-diffusion model to simulate the active spread of PPM in response to rising winter temperatures. They also considered human-mediated accidental transport responsible for rare long-distance dispersal events far away to the north as a function of human population density (probability of introduction) and temperatures (probability of survival). Their findings suggest that the observed range expansion
may continue even if winter temperatures stabilize. Kriticos et al. (2013) used the CLIMEX algorithm to assess climate suitability, integrating it with a dispersal index and an economic impact model to create robust bioeconomic tools. These tools were used to provide a comprehensive assessment of the risk posed by a potential PPM invasion in New Zealand. However, none of these modeling efforts have accounted for the potentially limiting effects of solar radiation, which plays a critical role in regulating the activation temperature of the nest (Battisti et al., 2005; Poitou et al., 2021). Additionally, past assessments of potential range expansion have focused primarily on France. A more comprehensive analysis incorporating the entire geographic range of PPM and recent climate scenarios would more accurately explore the potential spread of this significant forest pest which also constitutes a public health problem.

This study aims to develop an SDM linking PPM occurrence data with a set of climatic variables. The objectives are twofold: (i) to use the model to estimate the potential range of PPM under historical climatic conditions (1961–1980), during the well-documented expansion period from 2001 to 2018, and to project the possible distribution of PPM from 2041 to 2060 under various climate change scenarios; and (ii) to identify and rank the climatic factors influencing the distribution of the insect, explicitly testing the hypothesis of a significant effect of solar radiation constraint. Our approach integrates three machine learning algorithms with a recent model-agnostic interpretability method (Lundberg and Lee, 2017) to deepen our understanding of the relationships between climatic variables and species distribution.

# 2. Material and methods

## 2.1. Analytical framework

Given its shift since the mid-1980s due to climate change, the current range may not be in equilibrium with current climatic conditions, particularly due to the limited dispersal capacity of the species (Battisti et al., 2015). We therefore calibrated the models based on occurrences that reflect the historical distribution of the species and corresponding climatic data (1960–1980). These models were then used to estimate the potential distribution under recent climatic conditions (2001–2018). We took advantage of a detailed record of the range of the PPM overtime available for France (Roques et al., 2015a, 2015b; Rossi, 2023) to compare the actual distribution with climate suitability in the recent period for that country. We used three algorithms to model the climate suitability of PPM: Bayesian Additive Regression Trees, Boosted Regression Trees, and Random Forest (see description below).

## 2.2. Occurrence data

In this study, we focused on the *T. pityocampa* species group as defined in Kerdelhué et al. (in press). This group occupies a broad range across the western Mediterranean basin and neighboring regions, extending from Morocco to Libya along the southern rim and from Portugal to western Türkiye in the northern part (Kerdelhué et al., 2009; Simonato et al., 2013; Ipekdal et al., 2020). The range of PPM is currently shifting, meaning that the species-environment equilibrium assumption is not met. We assume that this equilibrium held prior to the recent range shift, marked by expansion at the northern limit and contraction at the southern limit within the historical distribution of the species. Accordingly, we compiled a dataset documenting occurrence points across the range of the species prior to 1980s.

### 2.2.1. Presence points

A total of 1769 occurrence points were gathered from all over the PPM range and subsequently divided into two sets: 1562 points corresponding to the historical distribution (hereafter, “historical populations”), and 204 points representing populations that have established further north following the species expansion (hereafter,

“expanding populations”). The data were compiled from various sources, including published and unpublished surveys as well as databases, and are provided in the Supplementary file PPM-occurrence-data.ods. The occurrences were thinned to a single point per unique 2.5-min grid cell (Boria et al., 2014), resulting in 1242 occurrences for historical populations and 181 for expanding populations (Supplementary Fig. S1). The dataset containing the historical points was then subjected to geographic thinning to control for the possible sampling bias (Aiello-Lammens et al., 2015) using an arbitrary threshold of 10 km. The calculations were performed using the R package “spThin” (Aiello-Lammens et al., 2015), resulting in a dataset of 907 points available in the supplementary file PPM-occurrence-data-thinned-10 km.ods.

### 2.2.2. Pseudo-absence points for the historical range

The areas of historical PPM presence are known and have been mapped in France as well as in other European countries (Androić, 1956; Huchon and Demolin, 1970; Démolin et al., 1996; Roques et al., 2015a, 2015b), allowing us to identify regions where environmental conditions at the time did not allow the establishment of PPM. The historical distribution in France area and its evolution between the 1980s and 2020 are shown in Supplementary Fig. S2. We generated pseudo-absence points both to the north and south of the boundaries of the historical range, as well as within the mountainous region of the Massif Central in France (Supplementary Fig. S3). The method involved creating buffer zones of 1500 km to the north and 500 km to the south of the distribution limits of the species. While the northern range is well established (Roques et al., 2015a, 2015b), the southern range is less well known, particularly regarding its eastern limit, which includes Libya. Consequently, we restricted the southern buffer to longitudes corresponding to Morocco, Algeria, and Tunisia. We randomly generated 10,000 points within these two buffer zones and the Massif Central area. From these, we randomly selected a number of points proportional to the area of each region to create a set of pseudo-absences comparable in size to the presence points used for model calibration (Barbet-Massin et al., 2012). This resulted in 710 points for the northern buffer, eight points for the Massif Central region, and 192 points for the southern buffer.

## 2.3. Climate data

We used variables describing the mean temperatures of the warmest and coldest one- and three-month periods, obtained from the WorldClim database (Fick and Hijmans, 2017), to identify the limits within which PPM can complete its life cycle. These variables include the maximum temperature of the warmest month (bio5 in the WorldClim database), the minimum temperature of the coldest month (bio6), the mean temperature of the warmest quarter (bio10), and the mean temperature of the coldest quarter (bio11). These variables were selected for their ecological relevance, as they directly affect PPM survival during key periods of the life cycle, which is a highly recommended practice in species modeling (Hui, 2023). Extreme climatic events are not explicitly considered, as their impact is mitigated over time. Even if local abundances drop significantly due to a catastrophic event, recolonization from nearby areas can help counterbalance these effects. Moreover, prolonged diapause (up to eight years; Salman et al., 2019) provides an additional buffer against environmental disturbances. As a result, the long-term potential distribution range is shaped primarily by climatic conditions rather than short-term meteorological fluctuations.

We considered three time periods: 1961–1980 (the historical period), 2001–2018 (the recent period), and 2041–2050 (the future period) (Supplementary Table S1). We considered the bio5, bio6, bio10, and bio11 projections for the 2041–2060 period, based on six Global Circulation Models (GCMs): BCC-CSM2-MR, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, MIROC-ES2L, and MIROC6, under the Shared Socioeconomic Pathway SSP2-4.5.

As previously highlighted, the amount of incoming solar radiation plays a key role in maintaining the temperature of the PPM nest.
Therefore, we included a variable describing solar radiation in the model. We used the downward shortwave radiation flux (dsr), hereafter radiation, which refers to radiative energy in the wavelength range [0.3 µm – 4.0 µm] reaching the surface of the Earth per unit of time and area. These data, derived from the CORDEX regional climate model, were obtained from the Copernicus website (Copernicus Climate Change Service, Climate Data Store, 2019). We used the sum of the dsr values for the Northern Hemisphere winter months of December, January, and February, during which sunlight plays a critical role in the life cycle of PPM. We considered three time periods: 1951–1960 (the historical period, using the global and regional climate models CCCma-CanESM2 and CLMcom-CLM-CCLM4-8-17, respectively), 1981–1990 (the recent period, using the global and regional climate models ECMWF and CLMcom-ETH-COSMO-crCLIM, respectively), and 2041–2050 (the future period, using the global and regional climate models MPI-M-MPI-ESM-LR and GERICS-REMO2015, respectively). The scenario considered was RCP4.5. We used WorldClim data with a resolution of 2.5 min (≈22 km² at the equator) while the CORDEX data had a resolution of 0.11 degrees (6.6 min). The latter were adapted to the former using the R function “resample” from the package “raster” (Hijmans, 2023). Because they originate from different sources, the available datasets for radiation (dsr), and temperatures (bio5, bio6, bio10, bio11) for the various periods studied correspond to slightly different dates (see Supplementary Tables S1).

## 2.4. Algorithms

We used three different algorithms combined in an ensemble modeling approach to reduce uncertainty in estimating the potential distribution of PPM (Thuiller et al., 2019). The selected algorithms belong to the random trees category and are known for their ability to handle complex nonlinear relationships between species distribution data and both quantitative and qualitative environmental descriptors, as well as for their tolerance to collinearity (Dormann et al., 2013; Elith, 2019). The algorithms used are Bayesian Additive Regression Trees (BART) (Chipman et al., 2010; Carlson, 2020), Boosted Regression Trees (BRT) (Elith et al., 2008), and Random Forest (RF) (Breiman, 2001; Elith, 2019). They are ensemble methods that generate a set of decision trees, but they differ in how they construct and combine these trees.

The BART model was calibrated using the R package “embarcadero” (Carlson, 2020). We used 1000 trees and applied the function “bart.step” to perform stepwise variable selection, identifying the most relevant climate descriptors to be included in the model.

The BRT model calibration followed the guidelines provided in Elith et al. (2008), using the R package “dismo” (Hijmans et al., 2023) and “SDMtune” (Vignali et al., 2020). Climate variable selection was performed using the stepwise procedure implemented in the function “gbm. simplify”, ensuring that at least three variables were retained. The optimal number of trees to use was determined by the function “gbm. step”.

The RF model was calibrated using the R package “randomForest” (Liaw and Wiener, 2002) and “SDMtune” (Vignali et al., 2020), and the function “steprf” from the R package “steprf” (Li, 2022) was used for variable selection. The RF model was also calibrated with 1000 trees, and the node size and the number of explanatory variables sampled at each split were optimized using the R package “randomForestSRC” (Ishwaran and Kogalur, 2007).

## 2.5. Calibration

A random selection of 80 % of the historical occurrence points was performed for model calibration, while the remaining 20 % were reserved for model evaluation. The same selection was applied to all three models. The variable selection procedures were initiated using the five variables mentioned earlier (bio5, bio6, bio10, bio11, and dsr), and the resulting models were optimized by fine-tuning their

hyperparameters according to the specific procedures for each algorithm described above. The importance of the climate variables was calculated using the varimp function from the embarcadero package for the BART model. For the BRT and RF models, we used the varImp function from the R package SDMtune.

## 2.6. Model projection and ensemble modeling

Once the models were calibrated, they were used to produce estimates of climate suitability based on climate data including radiation. Each projection was then recoded as presence or absence, based on the suitability threshold at which the sum of sensitivity and specificity is highest. We computed an ensemble model based on the proportion of projections indicating suitable conditions, expressed as percentages (Guisan et al., 2017). The resulting map highlights areas of model agreement: high values indicate strong consensus on suitability, while low values indicate consensus on unsuitability. Disagreement is represented by intermediate values (e.g., 50 %). Computations were performed for historical, current, and future climate conditions. For future climate conditions, we combined the outputs of the three models for each of the six considered GCMs, resulting in a total of 18 projections.

## 2.7. Model evaluation

The evaluation of the three models was based on the 20 % of historical occurrence points not used during the calibration phase. Three evaluation metrics were calculated: the Area Under the Curve (AUC) of the receiver operating characteristic plot (Fielding and Bell, 1997), the true skill statistic (TSS) (Allouche et al., 2006), and the continuous Boyce index (Hirzel et al., 2006). AUC ranges from 0 to 1 and is threshold-independent, as it measures the ability of the model to discriminate between presence and absence across all possible thresholds. In contrast, TSS varies from -1 to +1 and is threshold-dependent, as it relies on a selected threshold to balance sensitivity and specificity. The continuous Boyce index relies exclusively on presence points and evaluates the agreement between model-predicted suitable areas and observed occurrences by assessing the distribution of presence points across different suitability classes. The continuous Boyce index varies from +1 (perfect prediction) to 0 (randomness) and -1 (counter-prediction). Additionally, we assessed ability of each model to predict the recent expansion (i.e., occurrence points corresponding to populations that have established between 2001 and 2018 in regions where the PPM was absent in historical time). To do this, we calculated the proportion of “expanding populations” (see above) that corresponded to pixels classified as suitable by the model for that recent period.

## 2.8. Model interpretability

Machine learning models, like those developed in this study, are often highly accurate and have a strong predictive power. However, their complexity can make them difficult to interpret, potentially limiting their explanatory power (Shmueli, 2010; Lundberg and Lee, 2017). Various methods have been developed to address this issue, and in this study, for BART, we used the spartial dependence plots function which reclassifies the climate descriptors based on their partial dependence plots to show the corresponding relative suitability of different regions (Carlson, 2020). As for BRT and RF models, we applied the Shapley method proposed by Lundberg and Lee in 2017, which assigns each explanatory variable a specific contribution to a given model output based on cooperative game theory. In our case, each pixel in the study area is associated with an estimate of climate suitability, and this method will provide an estimation of the contributions of the different climate variables to that value. This approach thus provides a clear understanding of the role of different explanatory variables and how their influence varies across geographical space. We used the function “treeshap” from the R package “treeshap” (Komisarczyk et al., 2024).
## 2.9. Environmental novelty and model extrapolation

Caution is needed when interpreting model projections under environmental conditions (projection data) that differ from those used for model calibration (training data), as such projections may lack biological relevance (Elith et al., 2010). To assess the degree of environmental novelty in the projection data, we used two methods: the Multivariate Environmental Similarity Surface (MESS) index and the Shape metric.

MESS index, proposed by Elith et al. (2010), provides a measure of environmental similarity. A negative MESS value indicates that at least one climate variable in the projection dataset falls outside the range of the reference data used for calibration, indicating environmental dissimilarity. Conversely, positive MESS values suggest no environmental novelty (i.e., no extrapolation). We calculated the MESS index for historical, recent and future climate scenarios. For each pixel of the map, we selected the lowest MESS value across estimates from the six GCMs. The calculations were performed using the “mess” function from the R package “dismo” (Hijmans et al., 2023).

The Shape metric, introduced by Velazco et al. (2024), is based on Mahalanobis distance, which is used to quantify the multivariate distance between training and projection data. Higher Shape values indicate greater environmental novelty at a projection point, corresponding to lower model reliability. We computed the Shape metric for historical, recent, and future climate scenarios. We selected the highest Shape value from the estimates across the six GCMs. The Shape metric was calculated using the “extra_eval” function from the R package “flexsdm” (Velazco et al., 2022).

# 3. Results

## 3.1. Model performances and variables contribution

The variable selection process resulted in the inclusion of bio10, bio11, and dsr for the BRT and RF models, and bio6, bio10, and dsr for the BART model. The AUC values were 0.996, 0.996, and 0.998 for the BART, BRT, and RF models, respectively. The TSS values were 0.950, 0.961, and 0.961 for the BART, BRT, and RF models, respectively. The continuous Boyce index values were 1, 0.986, and 0.955 for the BART, BRT, and RF models, respectively. The suitability threshold, at which the sum of sensitivity and specificity is highest, was 0.656, 0.702, and 0.752, respectively.

When projecting the models using climate data from the period 2001–2018, the proportion of presence points corresponding to expanding populations falling within suitable areas was 95.0 %, 95.6 %, and 98.3 % for the BART, BRT, and RF models, respectively. The variable importance is shown in Supplementary Fig. S4. The most influential variable across all three models was dsr, followed by low winter temperatures (either bio6 or bio11, depending on the model). The bio10 variable, representing high temperatures during the warmest period of the year, had the lowest contribution. The models produced smooth response curves displayed in Supplementary Fig. S5. The response curves for the dsr variable are very similar across all three models, whereas some divergences can be observed between RF and the other models for the remaining variables.

## 3.2. Model projection

Fig. 1 shows the consensus model based on the proportion of projections indicating suitable conditions according to historical, recent and future conditions. The projections of each individual model (BART, BRT, RF) are displayed in Supplementary Fig. S6 to S8. The corresponding binary maps (historical and recent periods) are shown in Supplementary Fig. S9 to S10. The three algorithms produced very similar results (Fig. 1), and their consensus predominantly displayed extreme values. Intermediate values are restricted to the margins of the potential range, where the interpretation of climate-distribution

relationships varies among the models.

During the historical period (1961–1980), climatically favorable areas for PPM were distributed around the Mediterranean Basin (Fig. 1A). The northern limit crossed France, traversing the Alps north of the Po Valley in Italy, and then continued along the coasts of Croatia, Montenegro, and Albania. Favorable regions encompassed Greece, including Crete, as well as parts of Macedonia, Bulgaria, Romania, Türkiye and the coasts of Syria, Lebanon, and Israel. To the south of the Mediterranean Basin, favorable areas were found in northern Morocco, Algeria, Tunisia, and parts of the Libyan coast, particularly the “Green Mountains” region (Jebel Akhdar) as well as the far eastern part of Egypt.

Climatically favorable regions for PPM have undergone significant alterations due to changing climate conditions in the recent period (2001–2018), as shown in Fig. 1B. The potential distribution area shifted northward, covering France, the extreme south of the United Kingdom, parts of Belgium, Luxembourg, and western Germany (Saarland, Rhineland-Palatinate, Baden-Württemberg). Favorable areas also extended into northern Italy and several Balkan countries. In the south and in the east, there was a contraction of suitable regions, confined to the Atlas Mountains in Morocco, Algeria, and Tunisia. Only a very small favorable area remained in Libya (Cyrenaica).

When projecting the models using climate data estimated for the 2041–2060 period, climatically favorable areas continued to shift northward, including southern parts of the United Kingdom, the southern Netherlands, and large parts of Germany and Poland, as well as regions along the northern shore of the Black Sea (Fig. 1C). The southern boundary of the potential distribution area also changed, with all of North Africa becoming unfavorable except for high-altitude areas in the Moroccan and Algerian Atlas Mountains. Southeastern Spain, part of Sicily (Italy), and Crete (Greece) also became climatically unfavorable. The area located to the east of the Mediterranean Basin, including the coasts of Syria, Lebanon, and Israel, became completely unfavorable.

There was a strong correspondence between the areas estimated as suitable by the models in France during the historical period and the known distribution of the species at that time (Supplementary Figs. S2 and S12A). However, the northern colonization front is well below the potential distribution range indicated by the models for the recent period (Supplementary Fig. S12B).

## 3.3. Model interpretability

The maps of the Shapley index for each climatic variable corresponding to the historical period and the RF model (chosen because it shows the highest AUC and TSS) are presented in Fig. 2. Similar maps corresponding to the BART and BRT models are shown in Supplementary Figs. S12 to S16. These maps allow for the visualization of the impact of different variables on the climate suitability estimated by the model.

The dsr variable positively affected climate suitability in regions around the Mediterranean Basin, where suitability was at its highest. Conversely, the amount of radiation was a limiting factor (negative) in the desert regions of North Africa (Morocco, Algeria, Tunisia, Libya, and Egypt), as well as in some areas of the Middle East. The negative effect was also very strong in northern Europe, where radiation levels are insufficient, particularly in northern France, the British Isles, Belgium, and the Netherlands.

The variable bio11 reflects the average temperature of the coldest quarter, a well-known limiting factor for the distribution of PPM. As shown in Fig. 2B, this variable plays a limiting role in northern Europe, while the Shapley index was close to zero elsewhere. The Shapley indices for the variable bio10 (mean temperature of the warmest quarter) (Fig. 2C) illustrated the effect of high temperatures in North Africa. Elsewhere, this variable had little impact on climate suitability and did not act as an ecological limiting factor. The BART and BRT models produced fairly similar patterns (Supplementary Figs. S12 to
<figure><img src="image_6.png" /><figcaption>b Recent climate data 2001-2018</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>c Future climate data 2041-2060</figcaption></figure>

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

**Fig. 1.** Consensus model based on the proportion of projections indicating suitable conditions, showing climate suitability for the pine processionary moth under (A) historical (1961–1980), (B) recent (2001–2018) and (C) future (2041–2060) climate conditions. Mapped values range from 0 % (all projections indicate unsuitable climate conditions) to 100 % (all projections indicate suitable climate conditions). Projection: EPSG:4326.
<figure><img src="image_9.png" /><figcaption>b Historical 1961-1980 bio11</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>c Historical 1961-1980 bio10</figcaption></figure>

<figure><img src="image_11.png" /></figure>

**Fig. 2.** Map of Shapley values for each climate variable used in the Random Forest (RF) model under historical climate conditions (1961–1980). (A) dsr: downward shortwave radiation flux (W/m²); (B) bio11: mean temperature of the coldest quarter (°C); (C) bio10: mean temperature of the warmest quarter (°C). Projection: EPSG:4326.
S16).

Maps of the Shapley index calculated using recent climate data (2001–2018) and the RF model are shown in Supplementary Figs. S14. It can be observed that the negative effect of dsr shifted northward in Europe (Supplementary Figs. S14A), which explains the northward expansion of the distribution range limit for this period. The Shapley index maps for bio11 and bio10 (Supplementary Figs. S14B, C) showed little change compared to the historical period (Fig. 2B, C). The corresponding maps for the BART and BRT models are presented in Supplementary Figs. S15 and S16.

Overall, dsr increased between the historical and recent periods, except for regions located at the highest latitudes of our study area (Supplementary Figs. S17). This increase is expected to continue between the recent period and the period covering 2041–2050 (Supplementary Figs. S17C) although less markedly. Maps showing the bio6, bio10, bio11, and dsr variables across the different time periods are provided in Supplementary Figs. S18 to S21.

### 3.4. Environmental novelty

The MESS index was positive across the study area except in Sweden, Finland, and the Russian Federation for all three periods studied (historical, recent, and future) (Supplementary Figs. S22). This indicates that potential issues related to model extrapolation were limited. The maps displaying the results of the SHAPE index are presented in Supplementary Figs. S23 and show the same pattern.

## 4. Discussion

Species distribution modeling is a widely used approach to study the geographic distribution of living organisms and to assess the impact of climate change on biological invasions (trans-continental invasions), the expansion of native species (within-continent invaders), and phytosanitary risks (Elith and Leathwick, 2009; Briscoe Runquist et al., 2019; Rossi and Rasplus, 2023). The pine processionary moth has been relatively understudied from this perspective, and our results for the current period align with those of Kriticos et al. (2013), who used the CLIMEX model. In our study, we carefully selected climatic variables that are closely linked to critical phases of the species life cycle. This practice is highly recommended (Araújo et al., 2019), as the choice of explanatory variables strongly influences model performance (Čengić et al., 2020). The inclusion of a variable representing solar radiation levels proved to be very useful as it appeared to be a limiting factor in northern Europe. While this had been previously hypothesized (Battisti et al., 2005), it had never been thoroughly analyzed until now. Techniques such as the SHAP index and spatial dependence indices have proven highly useful for identifying geographic areas where incident solar radiation is a limiting factor for the potential distribution of the pine processionary moth. These methods significantly enhance the explanatory power of complex models such as, those used in this study.

### 4.1. Solar radiation and temperature extremes constrain PPM distribution

Our results indicate that the level of radiation was the limiting factor for the range of PPM across the British Isles and northern Europe at longitudes below 10° East during the historical period (1961–1980). Elsewhere in northern Europe, the limiting factor was winter minimum temperatures. These findings align with the work of Huchon and Demolin (1970), and our models incorporate a variable representing incident solar radiation, confirming the importance of this factor as hypothesized by Battisti et al. (2005). Interestingly, Battisti et al. (2013) studied another species from the genus *Thaumetopoea*, *T. pinivora*, and demonstrated that solar radiation affects the performance of the insect either directly, by increasing body temperature, or indirectly, by altering host plant quality or the activity of natural enemies. This finding is significant as it highlights the potential role of incident solar radiation

in ecological analyses (Roderick, 2006), particularly in the context of distribution range modeling (Wang et al., 2025).

Solar irradiance varies according to season, latitude, and atmospheric transparency, which itself is influenced by cloud cover and aerosol loadings. Since the pioneering work of Stanhill and Cohen (2001), distinct phases of decrease (“dimming”) and increase (“brightening”) in solar radiation have been documented over the 20th century (Wild, 2016; Cohen and Stanhill, 2021). In the Northern Hemisphere, an “early brightening” phase occurred in the 1930s and 1940s, followed by a period from the 1950s to the 1990s characterized by a decline in radiation levels, known as “global dimming”, and subsequently a resurgence, termed “global brightening”, starting in the 2000s. Although there is ongoing debate, these multi-decadal variations appear to be linked to fluctuations in emissions of air pollutants, particularly aerosols (Wild et al., 2021), whose levels began to drop in the 1990s following the implementation of air quality regulations. This reduction in air pollution has enhanced atmospheric transparency and, consequently, the amount of incident solar radiation, especially in Europe (Wild et al., 2021), as well as in other regions globally (Cohen and Stanhill, 2021). Among the other physical factors affecting the downward solar radia-tions, cloudiness is considered significant (Voigt et al., 2021) and is undergoing notable changes: the world's reflective cloud cover has decreased over the past two decades (Voosen, 2025).

Due to its biology, PPM represents a unique example of a species responding to two distinct climatic variables, namely radiation and temperature, each influenced by different human-driven factors. Radiation plays a key role in regulating the nest temperature of the pine processionary moth (PPM) during the day, which explains the importance of this variable in our models (Poitou et al., 2021). The other thermal constraints affecting the life cycle of the PPM are well documented. Early instar larvae are particularly vulnerable to high summer temperatures, whereas later instar larvae are susceptible to minimum temperatures in autumn and winter. Although some phenological plasticity allows some degree of adaptation to these constraints (Battisti et al., 2015), the temperature within the nest during the day and ambient nighttime temperatures are key determinants of climate suitability at the northern and high-altitude edges of its range. Conversely, maximum temperatures in the warm season (summer/autumn) are limiting factors in the southern part of its distribution due to their impact on egg and larval survival (Bourougaoui et al., 2021). Winter minimum temperatures are increasing as a result of increasing CO₂ emissions and the resulting greenhouse effect, while downwelling shortwave radiation is rising due to improved management of aerosol emissions, which enhances atmospheric transmissivity. Atmospheric transmissivity, however, has multiple effects on the climate system.

The ongoing global brightening is driving an increase in global radiation reaching Earth's surface, leading to higher daily temperatures, greater evaporation rates, lower soil moisture, and, critically for PPM lower nighttime temperatures due to increased nocturnal radiative cooling (i.e. decreasing returning downwelling longwave radiation at night) (Cohen and Stanhill, 2021). Interestingly, these climatic shifts have potentially contradictory effects on PPM life cycle. The daytime radiation increase is beneficial, as it raises nest temperatures, but the associated drop in nighttime temperatures may negatively affect larval feeding activity. Overall, however, the current range expansion of the PPM shows that the concurrent rise in winter minimum temperatures driven by global climate warming, combined with the current global brightening significantly enhances climate suitability for that species in northern latitudes in Europe.

Projections from our models, based on recent climatic data (2001–2018), reveal a significant expansion of climatically suitable areas in Europe and a contraction in North Africa compared to both observations and models corresponding to the historical period (1960–1980). These findings align closely with empirical observations (Battisti et al., 2005; Roques et al., 2015a, 2015b; Bourougaoui et al., 2021) and can be attributed to increases in winter minimum and
summer maximum temperatures, as well as rising levels of incident solar radiation resulting from the ongoing brightening phenomenon. In Europe, Cohen and Stanhill (2021) report that surface solar radiation has increased by approximately +2 W/m² per decade during 1980–2000 and by +3 W/m² per decade after 2000. Over the same periods, temperatures have also risen, leading to a dramatic increase in climatically suitable areas for PPM across Europe.

Future climate projections suggest that these trends will persist in the coming decades (2041–2060). However, the estimated northward expansion of climatically suitable areas appears to remain limited to latitudes south of approximately 52°N, primarily constrained by short-wave radiation levels. The evolution of the ongoing brightening phenomenon will largely depend on atmospheric aerosol loadings and cloud cover (Norris and Wild, 2007). Naturally occurring aerosols, such as those released by volcanic eruptions (Wild et al., 2005) and megafires (Kochanski et al., 2019) may have a pronounced impact. However, projections using the CORDEX regional climate suggest a slight increase in downwelling shortwave radiation by 2050.

## 4.2. Shifting distribution and incomplete range filling

Model projections using recent climate data (2001–2018) suggest that climatic factors are no longer the primary constraints on the northern distribution of PPM. For France, where highly detailed datasets are available, the moth's expansion front remains well south of the northern limit of climatically suitable zones. While historical distribution patterns closely aligned with the northern boundary of favorable zones, this is no longer the case for the period 2001–2018. Several hypotheses may explain this discrepancy. PPM is a univoltine species with limited dispersal capabilities. Démolin (1969) estimated its maximum dispersal range at approximately 2 km per year, while Battisti et al. (2015) reported an average dispersal range of 5–6 km in flight mill experiments, consistent with the expansion rates observed in France (Battisti et al., 2005). Our findings suggest that recent climate changes have occurred too rapidly for the species to fully colonize newly favorable areas. Pioneer colonies, however, have been identified far from the established range of the moth over the past decades (Robinet et al., 2012), due to human-mediated dispersal. Although such events enable long-distance range jumps, they are too infrequent to allow PPM to occupy all newly favorable areas within its potential range. Nevertheless, they have accelerated the spread. In France, thirteen pioneer colonies were reported over the past two decades by plant health authorities (up to 200 km from the northern edge), compared to just one colony reported between 1960 and 1981 (<50 km; see Démolin et al., 1996; Kerdelhué et al., 2015). Moreover, among these introductions, two were recently detected in the northernmost part of France, near Belgium (Rousselet, pers. com.). This pattern of successful establishment following introduction aligns with the expansion of areas that have become warmer and brighter, making them suitable for colonization.

The distribution of host trees is an important factor influencing the spatial dynamics of phytophagous species and a lack of host could explain why PPM failed to track favorable climate change. However, this hypothesis is highly unlikely, as recent studies have shown that various host tree species of the PPM are present at significant densities in the form of trees outside forests within highly diverse landscapes. For example, in the open-fields region of Beauce region, France, Rossi et al. (2016a) reported that isolated host trees, often ornamental plantings in urban and peri-urban zones, formed a network sufficiently dense and continuous to facilitate the northward expansion of the PPM. An exhaustive survey in Orléans, France, revealed that PPM host trees are widespread and distributed continuously across the city (Rossi et al., 2016b). Correspondingly, PPM infestations were observed throughout the city, with some areas showing particularly high densities.

The geographic distribution of the natural enemies and competitors of the PPM, as well as its dynamics under climate change, may also influence the range of the moth. However, available data remain too

fragmented to incorporate these biotic factors into our models. Similarly, it would be highly relevant to investigate how climate change affects host tree performance, and how these changes may trigger cascading effects on PPM populations. Rising temperatures may induce stress, and increasing tropospheric CO₂ concentrations can alter the nutritional quality of foliage, as plants typically respond with increased productivity, leading to higher C:N ratios and reduced nitrogen concentrations in leaves (Hannah, 2015). Although herbivores often increase their feeding rates in response to lower nitrogen content, they are generally unable to fully compensate for this nutritional loss (Netherer and Schopf, 2010). Further research is needed to assess how different PPM host tree species respond to climate change and to understand the resulting effects on PPM population dynamics.

## 4.3. Public health risk in recently colonized areas

Given that PPM poses significant health risks to humans and animals, its recent expansion—and the likely continuation of this trend into densely populated northern European regions—raises serious public health concerns (Battisti et al., 2017). In areas that have recently become climatically favorable to the PPM, some potential host species are currently rare or absent due to their sensitivity to minimum temperatures and should be avoided, even if climate change might permit their planting in the future. The selection of ornamental tree species thus represents a key management strategy for mitigating human exposure. Knowledge of the host preferences of the PPM is therefore crucial for guiding these choices. Studies have long established that not all pine or cedar species are equally attractive to the moth (Calas, 1900; Démolin, 1969; Kerdelhué et al., 2024). Certain species, such as Austrian pine (Pinus nigra), are highly susceptible and have been widely planted across western Europe. Similarly, purely ornamental species like Himalayan cedar (Cedrus deodara) are also highly attractive (Rossi et al., 2016b). To minimize risks, these species should generally be avoided in public areas where human exposure is likely. However, the risk associated with a particular tree species depends on the broader composition of locally available host trees.

A recent study (Kerdelhué et al., 2024) highlights regional variability in host availability and selection by the moth in urban areas, making it challenging to establish universal rankings of host susceptibility, except for species consistently attacked, such as *P. nigra* and *C. deodara*. Regional studies are thus needed to inform tree selection policies for both urban managers and private landowners. Urban areas, with their unique climates and urban heat islands (Kuttler, 2008), offer particularly favorable conditions for PPM (Backe et al., 2021) with long periods in its fifth larval instar, the most hazardous stage for human health. Moreover, due to their plant diversity and ecological connectivity with peri-urban areas, cities may serve as bridgeheads for PPM in regions undergoing colonization. Our findings suggest that the ongoing range expansion of PPM in Europe will increase public health risks in both rural (e.g., recreational forests, forestry workers), peri-urban and urban areas. Although originally a Mediterranean species, PPM has become a significant issue far beyond its native range due to climate change. Co-ordinated efforts in urban planning, tree selection, and public health management are essential to mitigate these risks and manage the ecological and societal impacts of this expanding pest.

## CRediT authorship contribution statement

**Jean-Pierre Rossi:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization. **Andrea Battisti:** Writing – review & editing, Writing – original draft, Data curation, Conceptualization. **Dimitrios N. Avtzis:** Writing – review & editing, Data curation. **Christian Burban:** Writing – review & editing, Data curation. **Noureddine Rahim:** Writing – review & editing, Data curation. **Jérôme Rousselet:** Writing – review & editing, Data curation. **Carole Kerdelhué:** Writing – review & editing,
Writing – original draft, Conceptualization. **Kahraman İpekdal:**
Writing – review & editing, Writing – original draft, Data curation, Conceptualization.

Declaration of competing interest

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

Acknowledgements

We are grateful to Fina Lombardero (Department of Plant Production and Engineering Projects, University of Santiago de Compostela, Spain), M.-A. Auger-Rozenberg, A. Roques, and A. Bourougaaoui (URZF, INRAE Orléans, France), as well as Mirza Dautbasic (Faculty of Forestry, University of Sarajevo, Bosnia and Herzegovina). We thank the anonymous reviewers for their detailed and constructive feedback.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.179470.

Data availability

Raw data are provided as a supplementary file (occurrence data) and through links in the text (climate data). Model outputs are available as raster source files (GeoTIFF format) at https://doi.org/10.5281/zenodo.14755891, where the R code for computing the Shapley index can also be accessed.

References

Aiello-Lammens, M.E., Boria, R.A., Radosavljevic, A., Vilela, B., Anderson, R.P., 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545. https://doi.org/10.1111/ecog.01132.

Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS): assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x.

Androić, M., 1956. Contribution à l'étude de Cnethocampa pityocampa Schiff. Rev. Pathol. Vég. Entomol. Agric. France 35, 251–262.

Araújo, M.B., Anderson, R.P., Márcia Barbosa, A., Beale, C.M., Dormann, C.F., Early, R., Garcia, R.A., Guisan, A., Maiorano, L., Naimi, B., O'Hara, R.B., Zimmermann, N.E., Rahbek, C., 2019. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858. https://doi.org/10.1126/sciadv.aat4858.

Backe, K., Rousselet, J., Bernard, A., Frank, S., Roques, A., 2021. Human health risks of invasive caterpillars increase with urban warming. Landsc. Ecol. 36, 1475–1487. https://doi.org/10.1007/s10980-021-01214-w.

Baker, R.E., Mahmud, A.S., Miller, I.F., Rajeev, M., Rasambainarivo, F., Rice, B.L., Takahashi, S., Tatem, A.J., Wagner, C.E., Wang, L.-F., Wesolowski, A., Metcalf, C.J. E., 2022. Infectious disease in an era of global change. Nat. Rev. Microbiol. 20, 193–205. https://doi.org/10.1038/s41579-021-00639-z.

Barbet-Massin, M., Jiguet, F., Albert, C.H., Thuiller, W., 2012. Selecting pseudo-absences for species distribution models: how, where and how many?: how to use pseudo-absences in niche modelling? Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x.

Battisti, A., Avci, M., Avtzis, D.N., Jamaa, M.L.B., Berardi, L., Berretima, W., Branco, M., Chakali, G., El Alaoui El Fels, M.A., Frérot, B., Hódar, J.A., Ionescu-Mălăncuș, I., İpekdal, K., Larsson, S., Manole, T., Mendel, Z., Meurisse, N., Mirchev, P., Nemer, N., Paiva, M.-R., Pino, J., Protasov, A., Rahim, N., Rousselet, J., Santos, H., Sauvard, D., Schopf, A., Simonato, M., Yart, A., Zamoum, M., 2015. Natural History of the Processionary Moths (Thaumetopoea spp.): New Insights in Relation to Climate Change, in: Roques, A. (Ed.), Processionary Moths and Climate Change: An Update. Springer Netherlands, Dordrecht, pp. 15–79. doi:https://doi.org/10.1007/978-0-917-9340-7-2.

Battisti, A., Larsson, S., Roques, A., 2017. Processionary moths and associated urtication risk: global change-driven effects. Annu. Rev. Entomol. 62, 323–342. https://doi.org/10.1146/annurev-ento-031616-034918.

Battisti, A., Marini, L., Pitacco, A., Larsson, S., 2013. Solar radiation directly affects larval performance of a forest insect. Ecol. Entomol. 38, 553–559. https://doi.org/10.1111/een.12047.

Battisti, A., Stastny, M., Buffo, E., Larsson, S., 2006. A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly. Glob. Chang. Biol. 12, 662–671.

Battisti, A., Stastny, M., Netherer, S., Robinet, C., Schopf, A., Roques, A., Larsson, S., 2005. Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecol. Appl. 15, 2084–2096. https://doi.org/10.1890/04-1903.

Boria, R.A., Olson, L.E., Goodman, S.M., Anderson, R.P., 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012.

Bourougaoui, A., Ben Jamāa, M.L., Robinet, C., 2021. Has North Africa turned too warm for a Mediterranean forest pest because of climate change? Clim. Chang. 165, 46. https://doi.org/10.1007/s10584-021-03077-1.

Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.

Briscoe Runquist, R.D., Lake, T., Tiffin, P., Moeller, D.A., 2019. Species distribution models throughout the invasion history of Palmer amaranth predict regions at risk of future invasion and reveal challenges with modeling rapidly shifting geographic ranges. Sci. Rep. 9, 2426. https://doi.org/10.1038/s41598-018-38054-9.

Calas, J.E.A. (1900). Restauration et conservation des terrains en montagne. La processionnaire du pin (Cnethocampa pityocampa) : Meurs et métamorphoses, ravages, destruction. In Ministre de l'Agriculture, Administration des eaux et forêts (Ed.), Exposition universelle internationale de 1900 à Paris. Paris: Imprimerie Nationale, 116p.

Camarero, J.J., Tardif, J., Gazol, A., Conciatori, F., 2022. Pine processionary moth outbreaks cause longer growth legacies than drought and are linked to the North Atlantic oscillation. Sci. Total Environ. 819, 153041.

Carlson, C.J., 2020. Embarcadero: Species Distribution Modelling with Bayesian Additive Regression Trees in R. Evol. Methods Ecol. https://doi.org/10.1111/2041-210X.13389.

Cengić, M., Rost, J., Remenska, D., Janse, J.H., Huijbregts, M.A., Schipper, A.M., 2020. On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance. Ecol. Evol. 10, 12307–12317. https://doi.org/10.1002/ece3.6859.

Chipman, H.A., George, E.I., McCulloch, R.E., 2010. BART: Bayesian additive regression trees. Ann. Appl. Stat. 4. https://doi.org/10.1214/09-AOAS285.

Cohen, S., Stanhill, G., 2021. Changes in the sun's radiation: the role of widespread surface solar radiation trends in climate change: dimming and brightening, in Letcher, T.M. (Ed.) climate change. Elsevier, pp. 687–709. doi:https://doi.org/10.1016/j.bcor.2021.06.0032-3.

Copernicus Climate Change Service, 2019. CORDEX regional climate model data on single levels. 10.24381/CDS.CB91EDC3.

Démolin, G., 1969. Comportement des adultes de Thaumetopoea pityocampa Schiff. Dispersion spatiale, importance écologique. Ann. Sci. Forest. 26, 81–102. https://doi.org/10.1051/forest/19690104.

Démolin, G., Abgrall, J.F., Bouhot Delduc, L., 1996. Evolution de l'aire de la Processionnaire du pin en France. Les Cahiers du DSF 26–28. Les cahiers du DSF 1–1996, La santé des forêts en 1995. In: de l'Agriculture, de l'Alimentation, de la Pêche et des Affaires Rurales. Ministère, Paris, pp. 26–28.

Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., Garcia Marquez, J. R., Gruber, B., Lafourcade, B., Leitao, P.J., Muenkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schroeder, B., Skidmore, A.K., Zurell, D., Lautenbach, S., 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x.

Elith, J., 2019. Machine learning, random forests, and boosted regression trees. In: Brennan, L. (Ed.), Quantitative Analyses in Wildlife Science. Johns Hopkins University Press.

Elith, J., Kearney, M., Phillips, S., 2010. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342. https://doi.org/10.1111/j.2041-210X.2010.00036.x.

Elith, J., Leathwick, J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. https://doi.org/10.1146/annurev-ecolsys.110308.120159.

Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x.

Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086.

Fielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49.

Fitzgerald, T., Bias, X.P.I., 2003. Mid-winter foraging of colonies of the pine processionary caterpillar Thaumetopoea pityocampa Schiff. (Thaumetopoeidae). J Lepid Soc 57, 161–167.

Fitzgerald, T.D., Miller, S., Smith, M., 2012. Thermal properties of the tent of early instar colonies of the eastern tent caterpillar, *Malacosoma americanum* (Lepidoptera: Lasiocampidae). J. Them. Biol. 37, 615–624. https://doi.org/10.1016/j.jtberbio.2012.07.010.

Guisan, A., Thuiller, W., Zimmermann, N.E., 2017. Habitat Suitability and Distribution Models with Applications. R. Ecology, Biodiversity and Conservation. Cambridge University Press, Cambridge, United Kingdom; New York, NY.

Hannah, L.J., 2015. Climate Change Biology, Second Edition. ed. Academic Press, Elsevier, Amsterdam.

Hijmans, R.J., 2023. raster: Geographic Data Analysis and Modeling. package version 3.6–23, https://CRAN.R-project.org/package=raster.

Harvey, J.A., Tougeron, K., Gols, R., Heinen, R., Abarca, M., Abram, P.K., Basset, Y., Berg, M., Boggs, C., Brodeur, J., Cardoso, P., de Boer, J.G., De Snoo, G.R., Deacon, C.,
Dell, J.E., Desneux, N., Dillon, M.E., Duffy, G.A., Dyer, L.A., Ellers, J., Espíndola, A., Fordyce, J., Forister, M.L., Fukushima, C., Gage, M.J.G., García-Robledo, C., Gely, C., Gobbi, M., Hallmann, C., Hance, T., Harte, J., Hochkirch, A., Hof, C., Hoffmann, A., Kingsolver, J.G., Lamarre, G.P.A., Laurance, W.F., Lavandero, B., Leather, S.R., Lehmann, P., Le Lann, C., López-Urabe, J.M., M.M., Ma, C., Ma, G., Moiroux, J., Monticelli, L., Nice, C., Ode, P.J., Pincebourde, S., Ripple, W.J., Rowe, M., Sawmys, M.J., Sentis, A., Shah, A.A., Stork, N., Terblanche, J.S., Thakur, M.P., Thomas, M.B., Tylianakis, J.M., Van Baaren, J., Van de Pol, M., Van der Putten, W. H., Van Dyck, H., Verberk, W.C.E.P., Wagner, D.L., Weisser, W.W., Wetzel, W.C., Woods, H.A., Wyckhuys, K.A.G., Chow, S.L., 2023. Scientists' warning on climate change and insects. Ecol. Monogr. 93, e1553. https://doi.org/10.1002/ecm.1553.

Hijmans, R.J., Phillips, S., Leathwick, J.P., Elith, J., 2023. Dismo: species distribution modeling. R package version 1, 3-14. https://CRAN.R-project.org/package=dismo.

Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C., Guisan, A., 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142-152. https://doi.org/10.1016/j.ecolmodel.2006.05.017.

Hoch, G., Toffolo, E.P., Netherer, S., Battisti, A., Schopf, A., 2009. Survival at low temperature of larvae of the pine processionary moth *Thaumetopoea pityocampa* from an area of range expansion. Agri and Forest Entomol. 11, 313-320. https://doi.org/10.1111/j.1461-9563.2009.00431.x.

Huchon, H., Demolin, G., 1970. La bioécologie de la Processionnaire du pin : dispersion potentielle, dispersion actuelle. Rev. For. Fr. 22, 220-234. https://doi.org/10.4267/2042/20421.

Hui, C., 2023. The dos and don'ts for predicting invasion dynamics with species distribution models. Biol. Invasions 25, 947-953. https://doi.org/10.1007/s10530-022-09276-3.

IPCC, 2007: Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change, M.L. parry, O.F. Canziani, J.P. Palutikof, P.J. Van der Linden and C. E. Hanson, Eds., Cambridge university press, Cambridge, UK, 976pp.

İpekdal, K., Burban, C., Sauné, L., Battisti, A., Kerdelhué, C., 2020. From refugia to contact: pine processionary hybrid zone in a complex biogeographic setting. Ecol. Evol. 10, 1623-1638. https://doi.org/10.1002/ece3.6018.

Ishwaran, H., Kogalur, U.B., 2007. Random survival forests for R News 7, 25-31.

Jägermeyr, J., Müller, C., Ruane, A.C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J.A., Fuchs, K., Guarin, J.R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A.K., Kelly, D., Khabarova, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S., Moyer, E.J., Okada, M., Phillips, M., Porter, C., Rabin, S.S., Scheer, C., Schneider, J.M., Schyns, J.F., Skalchy, R., Smerald, A., Stella, T., Stephens, H., Webber, H., Zabel, F., Rosenzweig, R., 2021. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food 2, 873-885. https://doi.org/10.1038/s43016-021-00400-y.

Kerdelhué, C., Battisti, A., Burban, C., Branco, M., Cassel-Lundhagen, K., Larsson, S., Lopez-Vaamonde, C., Magnoux, E., Mateus, E., Mendel, Z., Negrisolo, E., Paiva, M.-R., Pivotto, I.D., Rocha, S., Ronnâs, C., Roques, A., Rossi, J.-P., Rousselet, J., Salvato, P., Santos, H., Simonato, M., Zane, L., 2015. Genetic diversity and structure at different spatial scales in the processionary moths. In: Roques, A. (Ed.), Processional Moths and Climate Change : An Update. Springer Netherlands, Dordrecht, pp. 163-226. https://doi.org/10.1007/978-94-017-9340-7.

Kerdelhué, C., Rossi, J.-P., Bernard, A., Fanjas-Mercere, T., Gross, L., Nusillard, B., Pineau, P., Pradel, J., Talbi, A., Rousselet, J., 2024. Documenting prevalence and counts of pine processionary moth tents on local host trees in 3 regions of France with different climatic environments. Data Brief 53, 110208. https://doi.org/10.1016/j.dib.2024.110208.

Kerdelhué, C., Sauné, L., Burban, C., in press. New insights on the evolution of processionary moths (*Thaumetopoea* spp., Lepidoptera, Notodontidae) based on a RAD -seq phylogeny. Zool. Scr. doi:https://doi.org/10.1111/zsc.12712.

Kerdelhué, C., Zane, L., Simonato, M., Salvato, P., Rousselet, J., Roques, A., Battisti, A., 2009. Quaternary history and contemporary patterns in a currently expanding species. BMC Evol. Biol. 9, 220. https://doi.org/10.1186/1471-2148-9-220.

Kochanski, A.K., Mallia, D.V., Fearon, M.G., Mandel, J., Souri, A.H., Brown, T., 2019. Modeling wildfire smoke feedback mechanisms using a coupled fire-atmosphere model with a Radiatively active aerosol scheme. JGR-Atmos. 124, 9099-9116. doi:https://doi.org/10.1029/2019JD030558.

Komisarczyk, K., Kozminski, P., Maksymiuk, S., Biecek, P., 2024. treeshap: compute SHAP values for your tree-based models using the "TreeSHAP" algorithm. R package version 0.3.1.9000. https://github.com/ModelOriented/treeshap.

Kriticos, D.J., Leriche, A., Palmer, D.J., Cook, D.C., Brockerhoff, E.G., Stephens, A.E.A., Watt, M.S., 2013. Linking climate suitability, spread rates and host-impact when estimating the potential costs of invasive species. PLoS One 8, e54861. https://doi.org/10.1371/journal.pone.0054861.

Kuttler, W., 2008. The urban climate - Basic and applied aspects, in: Marzluff, J.M., Shulenberger, E., Endlicher, W., Alberti, M., Bradley, G., Ryan, C., Simon, U., ZumBrunnen, C. (Eds.), Urban Ecology: An International Perspective on the Interaction between Humans and Nature. Springer US, Boston, MA, pp. 233-248. doi:https://doi.org/10.1007/978-0-387-73412-5.

Lawlor, J.A., Comte, L., Grenouillet, G., Lenoir, J., Baecher, J.A., Bandara, R.M.W.J., Bertrand, R., Chen, I.-C., Diamond, S.E., Lancaster, L.T., Moore, N., Murienne, J., Oliveira, B.F., Pec, G.T., Pinsky, M.L., Rolland, J., Rubenstein, M., Scheffers, B.R., Thompson, L.M., van Amerom, J., Villalobos, F., Weiskopf, S.R., Sunday, J., 2024. Mechanisms, detection and impacts of species redistributions under climate change. Nat. Rev. Earth Environ. 5, 351-368. https://doi.org/10.1038/s43017-024-00527-z.

Li, J., 2022. Stepr: stepwise predictive variable selection for random Forest. R package version 1.0.2, https://CRAN.R-project.org/package=stepr.

Liw, A., Wiener, M., 2002. Classification and regression by randomForest. R News 2, 18-22.

Liu, C., Wolter, C., Xian, W., Jeschke, J.M., 2020. Most invasive species largely conserve their climatic niche. Proc. Natl. Acad. Sci. USA 117, 23643-23651. https://doi.org/10.1073/pnas.2004289117.

Lundberg, S.M., Lee, S.-I., 2017. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems 2017, pp. 4768-4777.

Martin, J., Mesmin, X., Buradino, M., Rossi, J., Kerdelhué, C., 2022. Complex drivers of phenology in the pine processionary moth: lessons from the past. Agric and Forest Entomol. 24, 247-259. https://doi.org/10.1111/afe.12488.

Molotoks, A., Smith, P., Dawson, T.P., 2021. Impacts of land use, population, and climate change on global food security. Food Energy Secur. 10, e261. https://doi.org/10.1002/fes.261.

Moneo, I., Battisti, A., Dufour, B., García-Ortiz, J.C., González-Muñoz, M., Moutou, F., Paolucci, P., Petruccio Toffolo, E., Rivière, J., Rodríguez-Mahillo, A.-I., Roques, A., Roques, L., Vega, J.M., Vega, J., 2015. Medical and veterinary impact of the urticating processionary larvae. In: Roques, A. (Ed.), Processional Moths and Climate Change : An Update. Springer Netherlands, Dordrecht, pp. 359-410. https://doi.org/10.1007/978-94-017-9340-7-8.

Netherer, S., Schopf, A., 2010. Potential effects of climate change on insect herbivores in European forests—general aspects and the pine processionary moth as specific example. Forest Ecol. Manag. 259, 831-838. https://doi.org/10.1016/j.foreco.2009.07.034.

Norris, J.R., Wild, M., 2007. Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar "dimming," and solar "brightening". J. Geophys. Res. 112, 2006JD007794. https://doi.org/10.1029/2006JD007794.

Osland, M.J., Stevens, P.W., Lamont, M.M., Brusca, R.C., Hart, K.M., Waddle, J.H., Langtimm, C.A., Williams, C.M., Keim, B.D., Terando, J.A., Reyier, E.A., Marshall, K. E., Loik, M.E., Boucek, R.E., Lewis, A.B., Seminoff, J.A., 2021. Tropicalization of temperate ecosystems in North America: the northward glob expansion of tropical organisms in response to warming winter temperatures. Glob. Chang. Biol. 27, 3009-3034. https://doi.org/10.1111/gcb.15563.

Petruccio Toffolo, E., Zovi, D., Perin, C., Paolucci, P., Roques, A., Battisti, A., Horvath, H., 2014. Size and dispersion of urticating setae in three species of processionary moths. Integr. Zool. 9, 320-327. https://doi.org/10.1111/1749-4877.201301.

Poitou, L., Robinet, C., Suppo, C., Rousselet, J., Laparie, M., Pincebourde, S., 2021. When insect pests build their own thermal niche: the hot nest of the pine processionary moth. J. Therm. Biol. 98, 102947. https://doi.org/10.1016/j.jtherbio.2021.102947.

Ricciardi, L., Giorgianni, C., Briguglio, G., Gangemi, S., Spatari, G., 2021. Processional caterpillar reactions in southern Italy forestry workers: description of three cases. Clin Mol Allergy 19, 15. https://doi.org/10.1186/s12948-021-00155-8.

Robinet, C., Baier, P., Pennerstorfer, J., Schopf, A., Roques, A., 2007. Modelling the effects of climate change on the potential feeding activity of *Thaumetopoea pityocampa* (Den. & Schiff.) (Lep., Notodontidae) in France. Glob. Ecol. Biogeogr. 16, 460-471. https://doi.org/10.1111/j.1466-8238.2006.00302.x.

Robinet, C., Imbert, C.-E., Rousselet, J., Sauvard, D., Garcia, J., Goussard, F., Roques, A., 2012. Human-mediated long-distance jumps of the pine processionary moth in Europe. Biol. Invasions 14, 1557-1569. https://doi.org/10.1007/s10530-011-9979-9.

Robinet, C., Laparie, M., Rousselet, J., 2015. Looking beyond the large scale effects of global change: local phenologies can result in critical heterogeneity in the pine processionary moth. Front. Physiol. 6. https://doi.org/10.3389/fphys.2015.00334.

Robinet, C., Rousselet, J., Roques, A., 2014. Potential spread of the pine processionary moth in France: preliminary results from a simulation model and future challenges. ANNALS OF FOR. Sci. 71, 149-160. https://doi.org/10.1007/s13595-013-0287-7.

Rocha, S., Kerdelhué, C., Ben Jamaa, M.L., Dhahri, S., Burban, C., Branco, M., 2017. Effect of heat waves on embryo mortality in the pine processionary moth. Bull. Entomol. Res. 107, 583-591. https://doi.org/10.1017/S0007485317000140.

Roderick, M.L., 2006. The ever-flickering light. Trends Ecol. Evol. 21, 3-5. https://doi.org/10.1016/j tree.2005.11.005.

Roques, A., Rousselet, J., Avci, M., Avtzis, D.N., Basso, A., Battisti, A., Ben Jamaa, M.L., Bensidi, A., Berardi, L., Berretima, W., Branco, M., Chakali, G., Çota, E., Dautbasić, M., Delb, H., El Alaoui El Fels, M.A., El Merchit, F., El Mokhefi, M., Forster, B., Garcia, J., Georgiev, G., Glavendekić, M.M., Goussard, F., Halbig, P., Henke, L., Hernández, R., Hódar, J.A., İpekdal, K., Jurc, M., Klimetzek, D., Laparie, M., Larsson, S., Mateus, E., Matosević, D., Meier, F., Mendel, Z., Meurisse, N., Mihajlović, L., Mirchev, P., Nasceski, S., Nussbaumer, C., Paiva, M.-R., Papazova, I., Pino, J., Podlesnik, J., Poirot, J., Protasov, A., Rahim, N., Sánchez Peña, G., Santos, H., Sauvard, F., Schopf, A., Simonato, M., Tsankov, G., Wagenhoff, E., Yart, A., Zamora, R., Zamoum, M., Robinet, C., 2015b. Climate Warming and Past and Present Distribution of the Processional Moths (*Thaumetopoea* spp.) in Europe, Asia Minor and North Africa, in: Roques, A. (Ed.), Processional Moths and Climate Change: An Update. Springer Netherlands, Dordrecht, pp. 81-161. doi:https://doi.org/10.1007/978-94-017-9340-7-3.

Roques, L., Rossi, J.-P., Berestycki, H., Rousselet, J., Garnier, J., Roquejoffre, J.-M., Rossi, L., Soubeyrand, S., Robinet, C., 2015a. Modeling the Spatio-temporal dynamics of the pine processionary moth. In: Roques, A. (Ed.), Processional Moths and Climate Change : An Update. Springer Netherlands, Dordrecht, pp. 227-263. https://doi.org/10.1007/978-94-017-9340-7-5.

Rossi, J.-P., 2023. Climat et invasions biologiques. In: Lannou, C., Rasplus, J.-Y., Soubeyrand, S., Gautier, M., Rossi, J.-P. (Eds.), Crises sanitaires en agriculture: les espèces invasives sans surveillance. Editions QUAE, Versailles, pp. 71-78.

Rossi, J.-P., Garcia, J., Roques, A., Rousselet, J., 2016a. Trees outside forests in agricultural landscapes: spatial distribution and impact on habitat connectivity for
forest organisms. Landsc. Ecol. 31, 243–254. https://doi.org/10.1007/s10980-015-0239-8.

Rossi, J.-P., Imbault, V., Lamant, T., Rousselet, J., 2016b. A citywide survey of the pine processionary moth *Thaumetopoea pityocampa* spatial distribution in Orléans (France). Urban For. Urban Green. 20, 71–80. https://doi.org/10.1016/j.ufug.2016.07.015.

Rossi, J.-P., Rasplus, J.-Y., 2023. Climate change and the potential distribution of the glassy-winged sharpshooter (*Homalodisca vitripennis*), an insect vector of *Xylella fastidiosa*. Sci. Total Environ. 860, 160375. https://doi.org/10.1016/j.scitotenv.2022.160375.

Salman, M.H.R., Bonsignore, C.P., El Fels, A., El Alaoui, Giomi, F., Hodar, J.A., Laparie, M., Marini, L., Merel, C., Zalucki, M.P., Zamoum, M., Battisti, A., 2019. Winter temperature predicts prolonged diapause in pine processionary moth species across their geographic range. PeerJ 7, e6530. https://doi.org/10.7717/peerj.6530.

Santos, H., Paiva, M.R., Tavares, C., Kerdelhué, C., Branco, M., 2011. Temperature niche shift observed in a Lepidoptera population under allochronic divergence: niche shift under allochronic speciation. J. Evol. Biol. 24, 1897–1905. https://doi.org/10.1111/j.1420-9101.2011.02318.x.

Shmueli, G., 2010. To explain or to predict? Stat. Sci. 25, 289–310. https://doi.org/10.1214/10-STS330.

Simonato, M., Battisti, A., Kerdelhué, C., Burban, C., Lopez-Vaamonde, C., Pivotto, I., Salvato, P., Negrisolo, E., 2013. Host and phenology shifts in the evolution of the social moth genus Thaumetopoea. PLoS One 8, e57192. https://doi.org/10.1371/journal.pone.0057192.

Singh, B.K., Delgado-Baquerizo, M., Egidì, E., Guirado, E., Leach, J.E., Liu, H., Trivedi, P., 2023. Climate change impacts on plant pathogens, food security and paths forward. Nat. Rev. Microbiol. 21, 640–656. https://doi.org/10.1038/s41579-023-00900-7.

Stanhill, G., Cohen, S., 2001. Global dimming: a review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric. For. Meteorol. 107, 255–278. https://doi.org/10.1016/S0168-1923(00)00241-0.

Sturiale, S.L., Armbruster, P.A., 2023. Contrasting effects of an extended fall period and winter heatwaves on the overwintering fitness of diapausing disease vector *Aedes albopictus*. Curr. Res. Insect Sci. 4, 100067. https://doi.org/10.1016/j.cris.2023.100067.

Tamburini, G., Marini, L., Hellrigl, K., Salvadori, C., Battisti, A., 2013. Effects of climate and density-dependent factors on population dynamics of the pine processionary

moth in the southern Alps. Clim. Chang. 121, 701–712. https://doi.org/10.1007/s10584-013-0966-2.

Thuiller, W., Guéguen, M., Renaud, J., Karger, D.N., Zimmermann, N.E., 2019. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446. https://doi.org/10.1038/s41467-019-09519-w.

Uemura, M., Zalucki, M.P., Battisti, A., 2021. Behavioural plasticity and tree architecture shapes tent and foraging locations of pine processionary larval colonies. Entomologia 41, 121–136. https://doi.org/10.1127/entomologia/2020/1091.

Velazco, S.J.E., Rose, M.B., de Andrade, A.F.A., Minoli, I., Franklin, J., 2022. FLEXSDM: an R package for supporting a comprehensive and flexible species distribution modelling workflow. Methods Ecol. Evol. 13, 1661–1669. https://doi.org/10.1111/2041-210X.13874.

Velazco, S.J.E., Rose, M.B., De Marco, P., Regan, H.M., Franklin, J., 2024. How far can I extrapolate my species distribution model? Exploring shape, a novel method. Ecography e06992. https://doi.org/10.1111/ecog.06992.

Vignali, S., Barras, A.G., Arlettaz, R., Braunisch, V., 2020. SDMtune: an R package to tune and evaluate species distribution models. Ecol. and Evol. 10, 11488–11506. https://doi.org/10.1002/ece3.6786.

Voigt, A., Albern, N., Ceppi, P., Grise, K., Li, Y., Medeiros, B., 2021. Clouds, radiation, and atmospheric circulation in the present-day climate and under climate change. WIREs Climate Change 12, e694. https://doi.org/10.1002/wcc.694.

Voosen, P., 2025. Earth's clouds are shrinking, boosting global warming. Science 387, 17. https://doi.org/10.1126/science.adv6047.

Wang, L., Diao, C., Lu, Y., 2025. The role of remote sensing in species distribution models: a review. Int. J. Remote Sens. 46, 661–685. https://doi.org/10.1080/01431161.2024.2421949.

Wild, M., 2016. Decadal changes in radiative fluxes at land and ocean surfaces and their relevance for global warming. WIREs Clim. Change 7, 91–107. https://doi.org/10.1002/wcc.372.

Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C.N., Dutton, E.G., Forgan, B., Kallis, A., Russak, V., Tsvetkov, A., 2005. From dimming to brightening: decadal changes in solar radiation at earth's surface. Science 308, 847–850. https://doi.org/10.1126/science.1103215.

Wild, M., Wacker, S., Yang, S., Sanchez-Lorenzo, A., 2021. Evidence for clear-sky dimming and brightening in Central Europe. Geophys. Res. Lett. 48, e2020GL092216. https://doi.org/10.1029/2020GL092216.