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Research Article
Oak decline in southern Italy: environmental and climate parameters for modelling purposes*
expand article infoAntonio Luca Conte‡§, Romeo Di Pietro|§, Piera Di Marzio‡§, Sandro Strumia, Giuseppe Cillis#, Andrea Capuano, Paola Fortini‡§
‡ University of Molise, Pesche, Italy
§ NBFC, National Biodiversity Future Center, Palermo, Italy
| University of Rome Sapienza, Rome, Italy
¶ University of Campania “Luigi Vanvitelli”, Caserta, Italy
# CNR IMAA, Potenza, Italy

Corresponding author: Romeo Di Pietro ( romeo.dipietro@uniroma1.it )

Academic editor: Fotios Xystrakis
© 2025 Antonio Luca Conte, Romeo Di Pietro, Piera Di Marzio, Sandro Strumia, Giuseppe Cillis, Andrea Capuano, Paola Fortini.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Conte AL, Di Pietro R, Di Marzio P, Strumia S, Cillis G, Capuano A, Fortini P (2025) Oak decline in southern Italy: environmental and climate parameters for modelling purposes. Vegetation Ecology and Diversity 62: e160170. https://doi.org/10.3897/ved.160170
Open Access

Abstract

The future of the Mediterranean oak forests is under threat from the dangerous effects of global climate change, such as increasing droughts and heatwaves. The combined or individual action of certain climatic and environmental factors can lead to oak decline in various oak forest types. A study was conducted between 2015 and 2022 in southern Italy, encompassing thirty oak forest stands dominated by various Quercus species, including Q. cerris, Q. frainetto, Q. ilex, Q. pubescens, and affected by oak decline. The study employed field sampling, NDVI data, and remote sensing techniques. The distribution of the forest stands encompassed both the Temperate and Mediterranean bioclimatic regions. A total of 18 quantitative and 4 qualitative variables were recorded and subsequently compared with a damage severity scale based on field observations. The values of the variables were analyzed using both descriptive and multivariate statistics to ascertain their role in triggering oak decline episodes. It was found that eight variables were the most significant in explaining the occurrence of oak decline. These were the first-semester average rainfall, average maximum summer temperature, Rainfall anomaly index, Downward shortwave radiation, Root zone soil moisture, and three indicators concerning the number, amplitude, and duration of heatwaves. Quercus pubescens forests were found to be the most affected by oak decline. The years 2017 and 2022 were characterized by high levels of stress, with the combined effect of groups of diagnostic variables in exceeding the critical thresholds proving decisive in triggering episodes of oak decline. A vulnerability map was finally created reporting three vulnerability classes for oak decline: low, medium, and high. The analysis revealed that approximately 97% (116,700 hectares) of forest plots classified as vulnerable (31.7% of the total forest area in the study region) were categorized as medium or high vulnerability.

Keywords

Deciduous forest, Mediterranean basin, oak decline, Quercus, vulnerability map

Introduction

The Mediterranean basin, one of the most important global biodiversity hotspots, is characterized by 88 million hectares of forests (Olson et al. 2001; Underwood et al. 2009; FAO and Plan Bleu 2018; FAO 2024) and is home to 7.3 percent of the global human population (Khokhar and Kashiwase 2015). For over three decades, numerous authors have sounded the alarm about the future of European and Mediterranean forests, emphasizing that these are increasingly threatened by anthropogenic activities and unprecedented climatic events (Manion 1981; Brasier and Scott 1994; Ragazzi et al. 2000; Lindner et al. 2010; Grassi et al. 2013; Millar and Stephenson 2015; Doblas-Miranda et al. 2017; Fang et al. 2021; Jacobsen et al. 2023). According to the “State of the Mediterranean forests” report, 16% of forest habitat plants are threatened with extinction at a global or regional level, and 44% of these species are restricted to, or have their main centre of distribution in the Mediterranean forest habitats (FAO and Plan Bleu 2018; Kougioumoutzis et al. 2020). Mediterranean-type climates are generally characterized by warm and dry summers and mild winters, with precipitation concentrated in the winter months (Rivas Martínez 1996; Lionello et al. 2006; Gil-Pelegrín et al. 2017). The duration of the drought period ranges from one month (Meso-Mediterranean climate) to five months (Infra-Mediterranean climate) following the Worldwide Bioclimatic Classification System (Rivas-Martínez 1996; Rivas-Martínez et al. 2011). Oak species are the main component of both evergreen and deciduous forests within the geographical area of the Mediterranean Basin (Mucina et al. 2016; San-Miguel-Ayanz et al. 2016; Di Pietro et al. 2021).

Evergreen oak species (e.g. Quercus ilex L., Q. suber L.) have been shown to possess the capacity to withstand periods of drought and high summer temperatures, as well as cold winters. It is evident that there is a correlation between ecological stressors and the adaptation of morpho-physiological traits (Damesin and Rambal 1995; Fotelli et al. 2000; Chaves et al. 2003; Manes et al. 2006; San-Miguel-Ayanz et al. 2016; Jump et al. 2017; Kim et al. 2017; Lobo-Do-Vale et al. 2019). In contrast, the European deciduous oaks are more vulnerable, since they lack many of the drought adaptations exhibited by evergreen sclerophyllous species. This is especially evident in the case of Q. robur L., and to a lesser extent, Quercus petraea (Matt.) Liebl. which are predominantly located in the temperate regions of Europe (Jalas and Suominen 1976) and that, in the Mediterranean area, are classified as typically mesophilic species (Oberdorfer and Hofmann 1967; Biondi et al. 2002; Eaton et al. 2016). Between the evergreen oaks and the mesophilic deciduous oaks mentioned above, there is a large group of thermophilic/xerothermic deciduous oaks with a prevalent southern European distribution. Based on Denk et al. (2017), this group includes species belonging to both the section Cerris (e.g. Q. cerris L., Q. ithaburensis Decne. and Q. trojana Webb) and the section Quercus (e.g. Q. canariensis Willd., Q. faginea Lam., Q. frainetto Ten., Q. infectoria G.Olivier, Q. pubescens Willd., Q. pyrenaica Willd. and Q. vulcanica Boiss. ex Kotschy).

Oak decline is a syndrome affecting individuals and populations of species belonging to the genus Quercus which is characterized by a gradual worsening of tree health and vitality (Ciesla and Donaubauer 1994). Oak decline episodes involving Mediterranean forests composed of deciduous oaks belonging to Sect. Cerris and Sect. Quercus, have been well documented since the early 1980s (Brasier and Scott 1994; Brasier 1996; Thomas et al. 2002; Haavik et al. 2015; Oak et al. 2016; Rodríguez-Calcerrada et al. 2017a, 2017b; Caudullo and Barredo 2019; Hernández-Lambraño et al. 2019; Bussotti et al. 2023). As a multifactorial phenomenon, the occurrence of oak decline disease is characterized by abiotic predisposing factors that play a significant role in causing sharp environmental transformation. These factors can substantially be attributed to certain climatic variables that reach extreme values identifiable as being unexpected, unusual, severe, or out of season, when compared to their historical distribution (Fischer and Schär 2010; Richardson et al. 2013; Russo et al. 2014; Mariotti et al. 2015; Millar and Stephenson 2015; Gil-Pelegrin et al. 2018; Hammond et al. 2022). The variables involved in the occurrence of oak decline syndrome can be divided into three categories: a) predisposing variables linked to the geographic and environmental features of forest stands; b) inciting variables that are at least partially unpredictable and generally act rapidly and intensely, such as seasonal climatic anomalies, insect defoliation events, and human activities; c) contributing variables which generally comprise diseases caused by the poor health of trees and linked to cancer, fungi, viruses, and nematodes (Thomas et al. 2002; Denman et al. 2014; Brown et al. 2018; Hernández-Lambraño et al. 2019; Pinho et al. 2020; Denman et al. 2022). According to Arend et al. (2011), the European deciduous oak species may actually experience a positive response to an increase in temperature, but they are also susceptible to adverse effects when exposed to high temperatures and drought simultaneously. Drought stress is universally regarded as one of the most significant inciting variables involved in oak decline (see Choat et al. 2012; Natalini et al. 2016; Gentilesca et al. 2017; Rodríguez-Calcerrada et al. 2017a, 2017b; Brown et al. 2018; Bussotti and Pollastrini 2020; Senf et al. 2020). Conversely, it is also conceivable that the effects of drought, considered as a single-acting factor, are insufficient to elucidate a phenomenon impacting species whose ancestors established themselves in the Mediterranean Basin millions of years ago, and which, for this reason, should have adapted to its distinctive climatic characteristics and variations (Lindner et al. 2010; Keča et al. 2016; Doblas-Miranda et al. 2017; Kim et al. 2017; FAO and Plan Bleu 2018; Hernández-Lambraño et al. 2019; Bussotti and Pollastrini 2020). It is therefore reasonable to hypothesize that several additional variables may also contribute to the triggering of oak decline.

A significant focus of current research is the precise evaluation of the extent to which these additional variables may be implicated.

Finally, it should not be underestimated that the efficiency of a variable in producing negative feedback in oak species, populations, and individuals is also related to local natural or anthropogenic factors, such as altitude, aspect, slope, meso- and micro-climate conditions, land-use etc.

Italy is one of the European countries that exhibit the highest degree of floristic biodiversity, boasting around 8300 taxa of native vascular plants (Bartolucci et al. 2024). From a bioclimatic point of view, the Italian Peninsula (south of the Po Valley) is included into two regions: the Mediterranean region, which mainly occupies coastal and subcoastal areas, and the Temperate region, which is defined by the Apennine range and a substantial portion of the sub-Apennines (Blasi et al. 2005). The forest pattern of the Italian peninsula is characterized by a high heterogeneity and diversity in both the Temperate and Mediterranean climates. The oak forests exhibit the most pronounced expression of this diversity, particularly in terms of dominant tree species and coenological diversity of the understory (Blasi et al. 2004; Ubaldi 2003; Biondi et al. 2014; Terzi et al. 2020).

This study analyses the effects of the oak decline phenomenon throughout a wide area of southern Italy, from 2015 to 2022, a particularly significant period with regards to the forest decline in the whole of the Mediterranean area (see Marques et al. 2025).

The primary objectives of this study are:

  1. To identify the oak-dominant forest type that is most affected by oak decline;
  2. To select the variables that are most active in triggering oak decline in the study area;
  3. To establish threshold values for these variables, the activation of which will result in oak decline;
  4. To develop a predictive GIS model based on the identified variables and threshold values, which will be used to create a vulnerability map of all the oak forests occurring in the study area.

Methods

Study area, taxonomical issues, and detection of forest stands

The study area covers the entire Basilicata administrative region in southern Italy (41°00'N – 16°45'E; 36°54'N – 15°21'E) which extends over an altitudinal range of 0 to 2,267 m. Most of the region is characterized by the Lucanian Apennines, a limestone mountain range encompassing approximately 170,000 hectares. This area includes 64 NATURA 2000 sites of the 92/43/EEC “Habitat” and “Bird” Directives (NATURA 2000 Viewer), two National Parks and seven Regional Parks. The vegetation landscape is characterized by evergreen and thermophilic deciduous oak forests (Fraxino orni-Quercion ilicis Biondi et al. 2003 and Carpinion orientalis Horvat 1958) within the coastal and hilly bioclimatic belt, as well as mixed woods and mesophilic oak forests (Fraxino orni-Ostryon carpinifoliae Tomazic 1940 and Melittio-Quercion frainetto Barbero et al. in Bonin et Gamisans 1976) within the submontane and lower montane belt. Beech woods (Geranio striati-Fagion Gentile 1970) are the dominant forest type throughout the upper montane belt of the whole region, with the co-dominance of Abies alba only in restricted areas of the Lucanian Apennines (Ruoti, Laurenzana) and Pollino massif. Only in the latter does a Pinus heldreichii open forest belt occur in the subalpine belt (Di Pietro and Fascetti 2005; Di Pietro et al. 2010; Fascetti et al. 2010; Biondi et al. 2014; Mucina et al. 2016; Di Pietro et al. 2021).

According to the checklist of the Italian vascular Flora (Bartolucci et al. 2024) 11 native oak species occur in Italy, whereas according to Flora of Italy (Pignatti et al. 2017-2019) this number rises to 19 species. This discrepancy can be attributed to the divergent systematic interpretations provided by various authors particularly concerning certain critical taxa belonging to the collective group of downy oaks (subgen. Quercus, sect. Quercus). Some of these taxa (e.g. Q. amplifolia Guss., Q. congesta C. Presl., Q. leptobalana Guss., etc.) are endemic to southern Italy, whereas others (e.g. Q. virgiliana (Ten.) Ten., Q. dalechampii Ten.) while maintaining their locus classicus in southern Italy they are also considered in the national floras of other European countries (Jordanov 1936–1979; Matyas 1970; Christensen 1997; Kaplan et al. 2022). Unfortunately, the analytical keys currently available do not yet allow for unambiguous distinction between the typical Q. pubescens and its taxonomically critical “sister species” within the collective group of Q. pubescens (Di Pietro et al. 2016, 2020a, 2020b). In view of the considerable morphological variability exhibited by this collective group, and the overlap in leaf and acorn morphological traits, when comparing different taxa within it, it was deemed most appropriate to make a generic reference to Q. pubescens s.l. in this paper.

To evaluate time variations, satellite images from the period 2015–2022 were used based on the four Sentinel-2 bands at 10 m × 10 m resolution: Blue, Green, Red, and NIR. The data were updated at intervals ranging from 8 to 16 days, and were acquired from 1 March 2015 to 31 August 2022. This data forms part of the European Space Agency’s Copernicus monitoring system (ECMWF 2018; Conte et al. 2019). The Normalized Difference Vegetation Index (NDVI) was downloaded from TerraClimate’s online database (Climate Hazard Sub-matrix) based on (Huntington et al. 2017) and available through the Climate Engine (2022–2024) web service (https://app.climateengine.com/climateEngine).

The presence and distribution of stands affected by oak decline syndrome, and their coenological identification, were assessed over the years between summer 2015 and summer 2022 (see Fig. 2). This was achieved by combining vegetation response data (NDVI), visible satellite image analysis, and field sampling based on the phytosociological approach (Braun-Blanquet 1964). Due to pragmatic considerations, each stand encompasses an area of 225 m2 (corresponding to a plot size of 15 m × 15 m), constituting a slight excess approximation compared to the 200 m2 proposed by Chytrý and Otýpková (2003) as a conventional standard plot-size for phytosociological relevés within European deciduous woodlands. The stands affected by oak decline are forest areas that clearly show symptoms of i) crown damage by die back (i.e. the death of twigs and other branches from the tips, starting from the apex of the branch and continuing down to the base) (Fig. 1); ii) leaf changes (i.e. the leaves may turn from yellow-green to yellow-orange or fall prematurely in September or October); iii) branch dieback and canopy thinning (i.e. the crown thins due to the loss of leaves, twigs and branches); and iv) cracks in the bark (i.e. cracks may appear in the bark, from which a watery fluid may run down). Furthermore, sprouts may also develop on the main branches and stems (epicormic branches) (Manion 1981; Ragazzi et al. 2000; Thomas et al. 2002; Choat et al. 2012; Keča et al. 2016; Colangelo et al. 2017; Conte et al. 2019).

In the initial phase of the study, the oak decline syndrome was assessed using the Normalized Difference Vegetation Index (NDVI) (Tucker 1979; Pettorelli 2013; Huang et al. 2021). It was utilized to identify plots showing a decrease in greenery and to monitor the spatio-temporal evolution of oak decline within the study area. In a subsequent step, visible satellite images were analyzed to rectify any issues identified by the vegetation response provided by the NDVI. The NDVI data were obtained from the Copernicus Sentinel-2 program (Climate Engine 2023). Field sampling was carried out in areas recognized as being affected by decline, and a Decline Severity Index (DES) was assessed in accordance with the protocol developed by Conte et al. (2019). The DES scale ranges from a value of 0 to 6, indicating the degree of damage observed in the stand due to oak decline syndrome. This observation was also utilized to elucidate the variability exhibited by the other variables.

Figure 1. 

Dieback damage to a Quercus cerris tree at the Gg28 site (Gorgoglione municipality, Basilicata region, S-Italy).

Table 1.
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List of the 18 quantitative and 4 qualitative variables employed in the study. Detailed descriptions can be found in the Suppl. material 1, specifically in Suppl. material 1: table S1.

Description of the variables
Climatic indicators PL First semester rainfall average
Rad Downward Shortwave Radiation (W/m2) (ERA5_AG)
TW Minimum temperature average of the meteorological winter (Dec-Jan-Feb)
TS Maximum temperature average of the meteorological summer (Jun-Jul-Aug)
Wind Surface wind speed (m/s)
Ecological stress indicators RAI Rainfall Anomaly Index
RZsm Root Zone soil moisture
HWh Heatwave Amplitude (daily mean 2-m temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile)
HWc Heatwave number (count of events satisfying the heatwave criteria of at least three consecutive days above the 90th percentile)
HWL Heatwave Duration (length of the longest number of consecutive days satisfying the heatwave criteria of at least three consecutive days above the 90th percentile)
Topographic indicators ELE Digital terrain model elevation
TRI Topographic Ruggedness Index
TPI Topographic Position Index
E Easterness (aspect)
S Southerness (aspect)
Atmospheric pollution indicators NO2 NO2 Nitrogen dioxide, Total Column (30% Cloud Screened) (OMNO2d v003)
SO2 SO2 Sulfur dioxide, Column Amount (OMSO2e v003)
HCHO CH₂O Formaldehyde, Column amount (OMHCHOd v003)
Forest stands features WoDs Dominant species
ForM Management’s Categories of forest
NATUR Degree of naturalness of the forest section
EcP Ecopedology
Figure 2. 

Location of the sampling stands (yellow dots) on the Basilicata region map. Also shown are: National and Regional Protected Areas, Natura 2000 Special Protection Areas (SPAs), and Special Areas of Conservation (SACs). Coordinate reference system: EPSG 32633. Coordinate values are reported in degrees.

The data set

The data set included 22 (18 quantitative and 4 qualitative) variables, collected over the period 2015–2022 (Table 1). The climatic variables were selected based on their potential role in triggering oak decline, as highlighted in numerous publications on forest decline and ecological stresses (Manion 1981; Lindner et al. 2010; Marques et al. 2025). These publications considered these variables individually (see Costa and Rodriguez 2017) or in association with other factors (see Brasier and Scott 1994; Colantoni et al. 2015; Kasim et al. 2025). The selection of the variables also considered certain of their attributes, such as the possibility of free use, a large spatial and temporal resolution and the accuracy and reliability of the data sources. The slope aspect (S and E in Table 1) was the only variable whose values were recorded directly in the field, while all the other data were obtained from online data sets (Funk et al. 2015; Climate Engine 2022–2024; Rodell et al. 2004; Rodell and Beaudoing 2007; MASE 2024). A comprehensive account of the forest stands, including their code, geographic coordinates, altitude, aspect, and dominant tree species is provided in the Suppl. material 1 (Suppl. material 1: tables S3, S4).

The following variables collected from the online data set: rainfall of the first semester: PL; surface incoming shortwave flux: Rad; average of the minimum temperature of the meteorological winter months: TW; average of the maximum temperature of the meteorological summer months: TS; surface wind speed: Wind; rain anomaly index: RAI; root zone soil moisture: RZsm; heatwave amplitude, as daily mean (2 m) temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile: HWh; heatwave number, the count of events satisfying the heatwave criteria of at least three consecutive days above the 90th percentile: HWc; longest number of consecutive days satisfying the heatwave criteria of at least three consecutive days above the 90th percentile: HWL; elevation from digital terrain model: ELE; topographic ruggedness index: TRI; topographic position index: TPI; nitrogen dioxide: NO2; sulfur dioxide: SO2; formaldehyde: HCHO) (Acker and Leptoukh 2007; Climate Engine 2023). The distribution of quantitative variables has been conducted within four major groups according to the following parameters: climatic, ecological stress, topographic, and atmospheric pollution indicators.

Finally, four qualitative variables, namely: the type of oak wood and the dominant species: WoDs; type of forest management: ForM; degree of naturalness: NATUR; Ecopedological category: EcP, were extrapolated from the Regional Forestry Map (Corona 2006) and the Ecopedological Map of Italy (Rusco et al. 2003). The complete descriptions of the 22 variables can be found in the Suppl. material 1 (Suppl. material 1: table S1).

Statistical analysis

A data matrix comprising values recorded between 2015 and 2022 (eight years) for each stand was subjected to statistical analysis (Suppl. material 1: table S2). This analysis was conducted using JASP software v. 0.19 (JASP Team 2024) and PAST software (Hammer et al. 2001).

The explanatory variables were transformed into a smaller set of principal components (PC), which account for most of the data variance. Principal component analysis (PCA) was performed on 18 explanatory variables using correlation matrix option in Past software (Hammer et al. 2001). In accordance with the scores expressed by the variables on the first three components, it was determined that only those with scores greater than 0.6 would be selected for further processing using descriptive and inferential statistics (Stevens 1992; Field 2005).

Descriptive statistics are a means of summarizing and graphically representing data, including box plots and summary tables. The central tendency (mean, median and mode) and dispersion (variance and standard deviation) of the data were assessed using descriptive statistics. The Shapiro-Wilk test was performed to ascertain whether the assumptions required for an inferential parametric test were satisfied. Based on the results of the Shapiro-Wilk test, the non-parametric Kruskal-Wallis test was employed in conjunction with a post-hoc Dunn’s Test to analyze the impact of DES on the number of years, for each designated variable.

Vulnerability map

A vulnerability map was created by combining environmental and climatic variables most strongly associated with oak decline. The identification of these variables was achieved through a multi-step process involving remote sensing, field data, and statistical modelling. Variables with scores greater than 0.6 in the first three PCA components were selected. Given the non-normal distribution of the data set, the median values were utilized as a metric of centrality and regarded as thresholds which were subsequently spatialized.

Statistical raster data (based on the median threshold values) were subjected to a process of normalization, with the aim of rendering them comparable to a range between 0 and 1. This process involved adjusting the values that lay above and below the threshold (Gemitzi et al. 2010). In this approach, all parameters were given equal weight. No estimation was made of the relative suitability of one site over another based on geostatistical scores. This approach enabled the standardization of the values associated with oak decline as they emerged from statistical analysis. To standardize the spatial resolution, it was necessary to scale the raster grid to a minimum horizontal pixel resolution of 20 m × 20 m. The NDVI was calculated using band 8A as opposed to band 8, on the basis that band 8A is more sensitive to changes in vegetation status. Given that band 8A has a resolution of 20 m × 20 m, it was necessary to scale band 4 to match this dimension. Consequently, an NDVI with a resolution of 20 m × 20 m was utilized to discern variations in forests with greater efficiency (Mandanici and Bitelli 2016; Zhang et al. 2017).

All operations were executed using the open-source software QGIS 3.16 (QGIS Development Team 2023).

Results

Identification and description of the stands affected by oak decline

Thirty stands affected by oak decline were identified within the Basilicata region between summer 2015 and summer 2022 based on NDVI satellite images and field surveys where different DES values were assigned (Fig. 2). The comprehensive description of the forest stands, including their codes, geographic coordinates, altitude, aspect, and dominant tree species is available in the Suppl. material 1 (Suppl. material 1: tables S3, S4).

According to the physiognomic characteristics of the studied forest types (Suppl. material 1: table S4, column WoDs), the dominant tree species in 60% stands affected by oak decline was Quercus pubescens Willd. This was followed by Q. cerris L. (30%), Q. frainetto Ten. (6.66%), and Q. ilex L. (3.33%).

Based on the naturalness class reported in the Regional Forestry Map (Suppl. material 1: table S4, Column NATUR), approximately 76.66% of the stands were found to be managed as standard coppice with short cutting cycles and were classified as included in the low naturalness category. The “young high forests” stands Rv11 and Cs16 and the “mature high forests” stands Ab10, Gg28 and Sc29 were classified as being in the medium naturalness category. Finally, the “young forests” stands Va07 and Tc27 were classified as “high naturalness category stands”.

The eco-pedological category “Apennine reliefs composed of undifferentiated tertiary sedimentary rocks and developed in Mediterranean-montane climate” (Suppl. material 1: table S4, column EcP), was found to be the most common substrate within the study area, accounting for 40% of the total analyzed stands.

Eleven, out of the thirty stands analyzed, fall within various protected areas, including two National Parks, seven Regional Parks and six NATURA 2000 sites, SPAs, or SACs (Suppl. material 1: table S4, column Protected area).

Oak decline-related damage to forest vegetation was observed during the specified time interval in 2017, 2019, 2020, 2021, and 2022 with a total of 57 events distributed unevenly across the 30 stands considered (Suppl. material 1: tables S5, S6). No occurrence of oak decline damage was recorded in the years 2015, 2016 or 2018. The highest number of oak decline episodes occurred in 2017, affecting all 30 stands. This was followed by 2020 (11 stands), 2019 (7 stands), 2021 (6 stands), and 2022 (3 stands). The calculation of the total number of oak decline episodes for each stand over the entire time interval (2015–2022) revealed that Pi05 (Quercus pubescens) experienced the most episodes (five), followed by Sm08 (Q. pubescens), Rv18 (Q. cerris), and Sc29 (Q. cerris) with four episodes each.

Statistical analysis

The data of 18 quantitative variables for 30 stands recorded over 8 years was combined to obtain a data matrix of 18 × 240 (columns × rows) (Suppl. material 1: table S2). PCA analysis of this matrix yielded an explanation of 49.26% of the total variance along the first three axes.

As illustrated in Figure 3, Component 1 of the PCA clearly differentiates between the observation years 2015, 2016, 2018, 2019, 2020, and 2021, all of which fall entirely or largely within the negative section of the diagram, and the observation years 2017 and 2022, which fall enterely within the positive section. Component 2 reveals the extent of the dispersion of the stands within the same year. As shown in Table 2, the variables most significantly correlated with Component 1 are primarily responsible for the spatial separation of the different years in the PCA diagram. It is evident that all these variables serve as indicators of climatic and ecological stress. Specifically, RAI, RZsm, and PL exhibit a negative relationship with Component 1, whereas Rad, TS, HWh, HWc, and HWL exhibit a positive one. Component 2 is more closely associated with topographical and atmospheric pollution indicators. The data indicates a positive correlation with TW, wind speed, E, NO2, and TPI. Nevertheless, Component 2 exhibits a negative correlation with SO2, S, ELE, and TRI. In general, Component 1 demonstrated a stronger relationship with inciting variables, while Component 2 showed a stronger relationship with predisposition variables (Manion and Lachance 1992; Brown et al. 2018). The biplot of PC1 and PC3 highlights the role of NO2 and TW (which are negatively correlated) in differentiating between stands along the third component. Conversely, a minimal variability was observed in the biplot PC2 and PC3 (Suppl. material 1: Sheet S11). The spatial separation of the ellipses relating to different years confirms the episodic nature of climatic and ecological stress. The PCA also reveals that precipitation and heat-related indices are the variables that most negatively affect oak decline patterns. According to the loadings presented in Table 2, eight variables (PL, Rad, TS, RAI, RZsm, HWh, HWC, and HWL) exhibited loadings that exceeded the statistically significant threshold of 0.6 (Stevens 1992; Field 2005). Consequently, these variables were subjected to further analysis using inferential statistics.

Figure 3. 

PCA biplot considering the role of the variables on the 30 identified stands. The latter are grouped per year of observation (as indicated by the ellipses). The value in brackets beside each Component indicates the proportion of variance explained by that Component. Both abbreviations and meaning of the environmental variables can be found in Suppl. material 1: table S1.

Table 2.
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The PCA loadings for each of the 18 variables are shown, divided according to PCA component. Variables with loadings greater than 0.60 are indicated in bold font. PL = rainfall of the first semester; Rad = surface incoming shortwave flux; TW = average of the minimum temperature of the meteorological winter months; TS = average of the maximum temperature of the meteorological summer months; Wind = Surface wind speed; RAI = Rainfall Anomaly Index; RZsm = Root Zone soil moisture; HWh = heatwave amplitude, daily mean temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile; HWc = Heatwave events; HWL = length of the longest number of consecutive periods satisfying the heatwave criteria of at least three consecutive days above the 90th percentile; ELE = digital terrain model elevation; TRI = topographic ruggedness index; TPI = topographic position index; E = Easterness; S = Southerness; NO2 = nitrogen dioxide; SO2 = sulfur dioxide; HCHO2 = formaldehyde.

Variables PC 1 PC 2 PC 3
PL -0.77969 0.00841 0.41898
Rad 0.80120 -0.05347 0.22796
TW 0.00080 0.60182 -0.53114
TS 0.66162 0.45230 0.06544
Wind 0.20051 0.53573 0.20685
RAI -0.80909 0.20057 0.41406
RZsm -0.81392 0.13087 0.25489
HWh 0.59485 0.01742 0.67905
HWc 0.73591 -0.18475 0.06661
HWL 0.82550 -0.22195 0.12434
ELE -0.17017 -0.78664 0.13410
TRI -0.06666 -0.44301 0.01626
TPI -0.02247 0.14043 -0.06898
E 0.06365 0.33272 -0.10389
S -0.02937 -0.39947 -0.01334
NO2 0.02319 0.32143 0.73277
SO2 -0.10606 -0.18874 -0.09366
HCHO 0.13366 -0.00563 0.36067

The Shapiro-Wilk test indicated that, except for TS, the variables were not normally distributed. Consequently, the median value was used instead of the mean value in all the descriptive statistical analyses (Suppl. material 1: table S7).

In terms of the trend in climatic variables, the average precipitation (PL) was recorded during the first semester of the 2015–2022 time series. At 243.2 mm, PL in 2020 was the lowest on record for this period. This was followed by 2022 (246.7 mm) and 2017 (255.1 mm) (Fig. 4). For all the other years in the time series, PL was greater than 300 mm (in 2019 and 2021) and greater than 400 mm (in 2015, 2016 and 2018) (Suppl. material 1: table S7). Rad values exceeded 280 W/m² in 2021 (286.3 W/m²) and 2017 (282.6 W/m²) (Fig. 4 and Suppl. material 1: table S7). In all other years, including in 2015, 2016, 2018, 2020, and 2022, Rad values were consistently below 280 W/m². The highest TS value was recorded in 2017 (37.4 °C), followed by 2015 (35.2 °C). In all the other years, the temperatures were below 35 °C (Fig. 4).

Regarding the trend of ecological stress variables, the RAI was found to be negative for the years 2022 (-2.1), 2020 (-1.5), 2017 (-1.3) and 2021 (-0.2). In contrast, positive values were recorded in 2015 (0.8), 2016 (0.9), 2018 (0.5), 2019 (0.3) and, most notably, in 2023 (2.5) (Fig. 5 and Suppl. material 1: table S7). From 2017 to 2022, RZsm showed negative values, with the following measurements: 2017 (-41.1 mm), 2020 (-26.5 mm), 2022 (-25.9 mm), 2021 (-9.5 mm), 2018 (-5.3 mm) and 2019 (3.9 mm). The only years to record positive values were 2015 (2.752 mm), 2016 (11.7 mm), and 2023 (46.1 mm) (Suppl. material 1: table S7). HWc showed its highest median value in 2022 with 9 events, followed by 6 events in 2021, 4 events in 2017 and 2019, 3 events in 2015 and one event in 2018 and 2020. No heat wave events were registered in 2016 (Fig. 5).

The median value recorded for HWh in 2017 was the highest in the series, at 31.7 °C. The lowest medians were recorded in 2018 and 2020 (24.3 and 24.4 °C respectively). A clear increase occurred between 2020 and 2021, rising from 24.4 to 30.3 °C. The median values for 2019, 2021 and 2022 were all high (> 29 °C; 29.2 °C, 30.3 °C and 29.4 °C respectively), suggesting an upward stabilization in recent years following the decline between 2018 and 2020. The HWL for 2017 showed a median value of 8.5 days. This was followed by 2022 (6.8 days) and 2019 (6.0 days), 2021 (5.2 days) and 2020 (4.6 days) (Fig. 5). No HWL was recorded in 2016, whereas it was 5.6 days in 2015 and 3.0 days in 2018 (Suppl. material 1: table S7).

The Kruskal-Wallis test, performed using either the years of observation or the DES as discriminatory factors, revealed non-causal variability in the dataset (Suppl. material 1: table S8), except for the comparison between HWc and DES, where the test was not significant. The results of the post-hoc test (Dunn’s test) demonstrate the disparities between the various years from 2015 to 2022 as determined by the eight variables selected by PCA.

PL underwent significant changes over the eight-year period, with the most profound changes occurring after 2017, particularly between 2020 and 2022. This suggests a temporal threshold effect, meaning a key event or trend that began around 2017 and significantly altered precipitation amounts, particularly in 2020 and 2022.

Rad shows robust and sustained change over time, with nearly all comparisons between pairs of years being statistically significant. Significant differences were observed between 2015 and 2016, 2017, 2018, 2019, and 2021. In contrast, the difference between 2015, 2020, and 2022, is not significant.

TS showed significant variation between 2015 and 2016, as well as between 2015 and 2018. 2019 marked a turning point, with no significant differences observed compared to subsequent years. From 2019 onwards, the results suggest that the TS has reached a state of equilibrium.

Significant variations in RAI occurred between 2015 and 2017, 2020, 2021, and 2022, respectively. The last three years of the series, 2020, 2021, and 2022, showed some convergence in rainfall anomalies.

RZsm has changed significantly over time, particularly during years when oak decline occurred in the different stands (2017, 2020, 2021, and 2022, except 2019).

HWh shows a significant difference between 2015, 2017, and 2021. Data for 2016 are unavailable because no heat waves were recorded in that year.

The analysis reveals a significant increase in HWc between 2015 and 2016, 2018, 2020, 2021, and 2022. This trend suggests an intensification of extreme weather events in the final years of the series.

HWL changed significantly in the series between 2015 and 2018. The years 2019, 2020, and 2021 exhibit more similar trends than almost all other years.

Figure 4. 

Boxplots reporting the values exhibited by the three climatic variables for each year investigated: a = PL (rainfall of the first semester); b = Rad (surface incoming shortwave flux); c = TS (average of the maximum temperature of the meteorological summer months). White circles indicate values that are outside the inner fences; asterisks indicate values that exceed three times the box height.

Table 3.
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Vulnerability classes and their respective threshold values. The relative area and percentage of each vulnerability class are shown.

Threshold Value Class hectares Percentage
<0.824 Low 3,416 2.9
0.824–1.329 Medium 68,144 58.4
>1.329 High 45,187 38.7
TOTAL 116,747 100
Figure 5. 

Boxplots reporting the values exhibited by the ecological stress variables for each year investigated: a = RAI (Rainfall Anomaly Index); b = RZsm (Root Zone soil moisture); c = HWh (heatwave amplitude, daily mean temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile); d = HWc (Heatwave events); e = HWL (length of the longest number of consecutive periods satisfying the heatwave criteria of at least three consecutive days above the 90th percentile). Grey colour (HWh 2016) is indicative of the non-availability of the data. In the same year (2016) both HWc and HWL yielded null results. White circles indicate values that are outside the inner fences; asterisks indicate values that exceed three times the box height.

The vulnerability map

Three vulnerability classes were identified for all the oak forests occurring in the study area based on the values exhibited by the most active variables in triggering oak decline. The median values were regarded as critical thresholds beyond which oak decline was deemed more probable (Suppl. material 1: table S9). The normalized raster layers were then combined by calculating the mean score for each spatial unit across all eight variables. This process yielded a composite vulnerability index for the forest areas (i.e., a single numerical index with different values for each pixel of the raster). The final value of the index, which ranged from 0 to 1.833 in the study area, was divided into three discrete values of ranges by statistical algorithms (from 0 to 0.701; from 0.702 to 1.430 and from 1.431 to 1.833). The aforementioned factors gave rise to three distinct vulnerability classes: low vulnerability (below 0.701), medium vulnerability (ranging between 0.702 and 1.430) and high vulnerability (above 1.430 up to 1.833). The resulting map is a raster file linked to a weighted linear combination of the theoretical binary variables. As illustrated in Table 3, approximately 58% of the vulnerable forest areas are classified as being “medium” class, 38.71% into the “high” class and 2.93% into the “low” class. A total of 31.7% of the oak forests in the Basilicata region (approximately 116,000 hectares out of 355,409 hectares of forest) have been classified as medium or high vulnerability (Fig. 6).

Figure 6. 

Vulnerability map of deciduous oak woodlands in the Basilicata region (Southern Italy). Also shown are National and Regional Protected Areas, Natura 2000 Special Protection Areas (SPAs), and Special Areas of Conservation (SACs). Coordinate reference system: EPSG 32633. Coordinate values are reported in degrees.

Discussion

From a Mediterranean landscape perspective, the increase in temperatures and the simultaneous decrease in rainfall over the last twenty years has led to more frequent episodes of oak decline. The rapid propagation of this phenomenon coupled with the severity of its consequences has prompted researchers from across Europe to undertake a multifaced examination of this issue. The result is a substantial corpus of scientific literature (Keča et al. 2016; Gentilesca et al. 2017; Acácio et al. 2021; Peñuelas and Sardans 2021; Alderotti and Verdiani 2023; Bussotti et al. 2023; Singh et al. 2023; Enderle et al. 2024; Gosling et al. 2024) which aims to establish the primary causes of oak decline and the most effective triggers. Accordingly, an ever-increasing number of variables related to natural and/or anthropogenic factors (e.g., climate, soil ecology, biotic interactions and pollution) have been gradually incorporated into consideration over time. The present study investigated the degree of correlation between these variables and their effectiveness in triggering oak decline processes in a specific area of southern Italy.

Geo-topographic, ecological and taxonomic insight on the oak woods involved in oak decline

A total of 57 episodes of oak decline has been documented over the past eight years. The highest number of episodes was observed in 2017, followed by 2019, 2020, 2021, and 2022. The majority of oak decline episodes within the study area were found to be concentrated in the hilly bioclimatic belt, and, to a lesser extent, in the sub-montane belt, i.e. within an altitudinal range between 400 and 800 m a.s.l. This range is characterized by a preponderance of thermo-mesophilic and xero-thermic oak woodlands. These results corroborate the findings of previous studies carried out in the Italian Peninsula (Sicoli et al. 1992; Ragazzi et al. 2000; Gentilesca et al. 2015; Gentilesca et al. 2017; Conte et al. 2019; Coluzzi et al. 2020).

Throughout southern Italy, the hilly bioclimatic belt is characterized by the co-dominance of evergreen sclerophyllous oak forests (which are predominantly represented by Quercus ilex) and thermophilic deciduous oak forests, which are characterized by a significant presence of Quercus pubescens s.l. Q. frainetto, and, to a lesser extent, Q. cerris. In fact, the Q. cerris forests become dominant in the submontane belt and in the first fringe of the lower montane belt (see Bonin and Gamisans 1976; Aita et al. 1977; Zanotti et al. 1995; Di Pietro et al. 2014).

About the distribution of the oak decline episodes within the investigated forest vegetation, it was found that 96% of affected populations were in deciduous woods.

It is hypothesized that an increase in summer aridity, average temperatures and the number of heatwaves will have a greater impact on deciduous forests than on sclerophyllous evergreen forests. Nevertheless, the low incidence of oak decline in Quercus ilex evergreen forests is noteworthy, particularly since such forests have been severely impacted by oak decline due to climate change and pathogen outbreaks (Frisullo et al. 2018; Encinas-Valero et al. 2022) in other areas of Italy and the Mediterranean basin (e.g. Spain and North Africa). It is evident that even a forest type that is notoriously well adapted to the Mediterranean climate is increasingly affected by damage caused by global warming. This is an unequivocal sign of the extent of the ongoing climate change. In this context, the unexpected emergence of Q. pubescens forests as the deciduous oak forest type characterized by the highest number of oak decline episodes in our study area could also be interpreted as a slightly alarming finding. In fact, it is widely accepted that Q. pubescens is one of the most xerothermic European deciduous oak species. It is known to be well adapted to the Mediterranean climate, showing a broad ecological spectrum, as well as drought tolerance strategies and effective protection against high solar radiation (Damesin and Rambal 1995; De Rigo et al. 2016; Tognetti et al. 2019).

However, according to some authors (Brullo et al. 1999; Bacchetta et al. 2004; Biondi et al. 2004) the “typical” Quercus pubescens Willd is best suited to the continental or steppic woodlands in Central and Eastern Europe. It would also occur in northern Italy and in some relict areas throghout the Apennines. Other pubescent oak species, such as Q. amplifolia, Q. congesta, Q. dalechampii, Q. leptobalana, and Q. virgiliana, would largely substitute it in the warmer Mediterranean areas of the Italian peninsula and its major islands. Based on this theory, we could hypothesize that most of the pubescent oak forest stands that we investigated were, in fact, relict communities dominated by the typical Q. pubescens with, eventually, only a minimal participation from the pubescent oak “sister” species from southern Italy mentioned above. From this perspective, i.e. considering Q. pubescens as a relict species in the Mediterranean context that is presumably unprepared for the xeric climate change, the high incidence of oak decline that we detected among the Q. pubescens stands would find a plausible explanation. However, several biosystematics studies published in the last decade and aimed at establishing the taxonomic consistency of all the southern Italian taxa currently included in the Quercus pubescens complex seem to exclude the possibility of more than one pubescent oak species being present in the study area and in Italy as a whole (Di Pietro et al. 2016, 2020a, 2020b, 2020c; Proietti et al. 2021; Fortini et al. 2022).

Conversely, the hypothesis that the Q. pubescens forests would be the most damaged, given that they were the most widespread forest type in the study area is unfounded. This is because Q. cerris is the dominant species in most oak forests in the Basilicata Region (Di Pietro et al. 2010; Fascetti et al. 2010; Borghetti et al. 2024). We would have expected the latter to be particularly susceptible to oak decline. However, despite being much more demanding in terms of soil moisture than Q. pubescens (Gellini and Grossoni 1997), Q. cerris appears (at least in this case) to well tolerate the negative effects of global warming. One possible explanation for the ability of Q. cerris to adapt to climate change in the Basilicata region is simply topographical: Q. cerris forests in southern Italy are mainly found in the submontane and lower montane bioclimatic belts (600–1200 m) and especially on north-facing slopes (Aita et al. 1977; Blasi et al. 2018). Within these altitudinal range and northern slopes preference, Q. cerris forests receive greater amounts of precipitation, air humidity, decreased values of direct insolation (consequently decreased rates of evapo-transpiration) than Q. pubescens s.l. forests in the hilly belt, where the latter species ecological optimum is found. The location of Q. cerris forests at this “high” altitude enables them to mitigate the negative effects of summer droughts, heatwaves and other climatic anomalies to some extent.

Oak decline as result of the combined effect of multiple factors

A more detailed analysis of the role played by the individual variables considered in this study, revealed that several of these were only slightly significant in triggering oak decline. The topographic variables (ELE, TRI, TPI, E, S) were found to have a negligible effect, accounting for a mere 12.4% of the expressed variability, as demonstrated in Component 2 of PCA. As Manion (1981) previously observed, these variables function as “predisposition variables”. They amplify the destabilizing effect of the anomalous values expressed by the climatic variables, thereby rendering forest stands more susceptible to oak decline. The most logical explanation for the absence of significance of these variables is that the majority of the oak decline episodes occurred within the same altitudinal belt (the hilly belt) and under similar environmental conditions. A much larger sampling area and/or a significantly greater number of sampling sites might have provided additional data for finer-scale comparisons.

Variables linked to atmospheric pollution (i.e., NO2, SO2, HCHO) were found to be scarcely significant too. It is likely that the spatialization of data produced by a limited number of isolated air quality control units might not be adequate to record variations in the response chemical parameters on a finer scale (see Brown et al. 2018). This is particularly evident in the context of sampling sites that exhibit significant natural features, where it is presumed that disparities in the number and incidence of pollutants between contaminated and uncontaminated sites are minimal.

However, statistically significant variables were identified as triggers for oak decline. These were primarily climatic (PL, Rad, and TS) and ecological variables (RAI, RZsm, HWh, HWc, and HWL). As evidenced in previous studies (Brown et al. 2018; Colangelo et al. 2018; Senf et al. 2020; Bose et al. 2021; Bussotti et al. 2023; Gosling et al. 2024), this study found that variables related to drought and temperature anomalies (e.g. warm summers and high annual and seasonal drought) were the most significant in triggering oak decline episodes. In particular, the PCA revealed that the variables PL, Rad, TS, RAI, RZsm, HWh, HWc, and HWL displayed the highest correlation. The highest DES values were frequently detected in forest stands managed for closely spaced coppicing cycles (10–15 years) with a low number of standards left in place. In some cases, these standards were not reflected in the most mature trees (Fig. 7). The high values of TS, HWh, HWL and HWc (with extremes occurring in years when oak decline episodes were most evident and frequent) are consistent with this type of forest management.

Figure 7. 

Practice of coppicing in a Quercus frainetto wood in the Appennino Lucano National Park. This method involves the maintenance of very low number of young trees, whilst the shrubs and forest undergrowth are completely cut down. Coppicing is carried out in cycles of 10, 15 or 20 years.

As far as RZsm is concerned, it can be hypothesized that forest stands characterized by a short coppicing cycle (especially when coupled with a low number of remaining standards) may not adequately maintain sufficient humidity in the uppermost soil layers over extended periods.

The absence of a dominant tree layer and shrubby undergrowth renders the soil particularly susceptible to physical erosion and chemical leaching, resulting in a rapid decrease in litter thickness. The absence of continuous tree canopy and the scarcity of litter result in accelerated soil desiccation, slowed pedogenic processes and diminished nutrient availability. A forest that has been subjected to such a process is undoubtedly vulnerable to extreme weather conditions and, consequently, is more susceptible to oak decline. A salient finding of the study is the pivotal function of inappropriate land use and forest management. This finding aligns with the conclusions of earlier studies (Rambal and Debussche 1995; Hernández-Lambraño et al. 2019; Tognetti et al. 2019). It is estimated that approximately 76% of the forest stands affected by oak decline as identified in this study have undergone standard coppice management (Fig. 7). This has had a significant effect in reducing the structural complexity of forest ecosystems. Conversely, only negligible negative effects were recorded in the so-called “undisturbed stands”, i.e. those characterized by a higher degree of naturalness (Suppl. material 1: table S4).

One of the objectives of this study was to ascertain the threshold values of each variable that trigger oak decline. Our results demonstrated that oak decline is not triggered by the intensity of a solitary negative factor, but rather by the cumulative and combined effect of multiple factors.

As previously mentioned, the years 2017 and 2022 are particularly pertinent for comprehending this concept. Despite the PCA indicating comparable ecological stresses experienced during these two years, a significant disparity was observed in the number of oak decline events and the extent of recorded damage (Table 4 and Suppl. material 1: table S10).

Table 4.
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The highest and lowest values for the eight variables identified as the most significant in triggering oak decline, are shown, ordered by year (time interval 2015–2022). The green colour highlights the lowest values, while the orange colour highlights the highest values. PL = rainfall of the first semester; Rad = surface incoming shortwave flux; TS = average of the maximum temperature of the meteorological summer months; HWh = heatwave amplitude, daily mean temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile; RAI = Rainfall Anomaly Index; RZsm = Root Zone soil moisture; HWc = Heatwave events; HWL = length of the longest number of consecutive periods satisfying the heatwave criteria of at least three consecutive days above the 90th percentile.

Year Median Minimum Maximum Year Median Minimum Maximum
PL (mm) 2015 415.5 289.4 667.9 RAI 2015 0.830 0.390 1.600
2016 416.7 344.6 588.6 2016 0.925 0.500 1.350
2017 255.1 177.7 326.2 2017 -1.295 -1.710 -1.070
2018 405.5 291.7 544.2 2018 0.530 0.150 1.170
2019 363.0 282.5 541.8 2019 0.350 -0.320 0.750
2020 243.2 181.3 317.2 2020 -1.500 -1.820 -1.210
2021 336.6 257.6 481.9 2021 -0.265 -2.480 0.180
2022 246.7 165.3 288.6 2022 -2.160 -4.240 -1.640
Rad (W/m2) 2015 278.2 272.6 283.6 RZsm (mm) 2015 2.8 -7.9 18.7
2016 267.2 262.6 273.3 2016 11.8 -2.4 24.6
2017 282.7 278.5 288.1 2017 -41.2 -58.2 -26.9
2018 256.5 250.9 267.1 2018 -5.4 -19.0 5.3
2019 269.8 265.5 276.9 2019 -3.9 -12.1 12.9
2020 273.6 269.9 277.9 2020 -26.6 -34.7 -17.0
2021 286.3 281.3 287.9 2021 -9.6 -15.6 -3.5
2022 279.4 275.8 282.8 2022 -26.0 -36.9 -19.4
TS (°C) 2015 35.3 27.3 43.1 HWc 2015 3 3 4
2016 31.8 24.4 39.6 2016 0 0 0
2017 37.5 30.3 41.9 2017 4 3 4
2018 30.2 23.1 39.6 2018 1 1 2
2019 34.8 27.0 39.4 2019 4 2 5
2020 34.4 25.9 39.1 2020 1 1 3
2021 35.0 30.3 39.7 2021 6 5 7
2022 34.9 28.5 42.2 2022 9 7 10
HWh (°C) 2015 27.0 25.9 28.4 HWL 2015 5.7 5.3 6.0
2016 - - - 2016 0 0 0
2017 31.8 29.3 32.9 2017 8.5 7.0 8.5
2018 24.3 22.4 25.0 2018 3.0 3.0 4.5
2019 29.3 27.6 30.3 2019 6.0 4.5 6.3
2020 24.4 22.5 25.6 2020 4.7 3.0 6.0
2021 30.4 29.9 30.4 2021 5.2 5.0 7.5
2022 29.5 27.2 29.7 2022 6.9 5.2 7.7

A comparison of the two years in question reveals that 2017 is particularly noteworthy due to the heightened intensity of specific variables (i.e., very high summer temperatures TS and solar radiation Rad). However, the RAI (precipitation anomalies) does not attain extreme values, in contrast to the 2022 phenomenon. Conversely, 2022 exhibited drier conditions in terms of precipitation (RAI) and the frequency of heat waves (HWc), albeit with slightly lower intensity thermal anomalies (TS, Rad, HWh). It can thus be concluded that the severity of the oak decline episodes that occurred in 2017 appears to be linked to a synergic combination of the following factors: i) high average summer temperature (TS), ii) intense solar radiation (Rad), iii) particularly low soil moisture (RZsm), and iv) intense temperatures and the duration of heat waves (HWh and HWL). In contrast, although there was an increase in the number of heat waves in 2022, the intensity of thermal stress (TS, Rad, and HWh) and soil moisture (RZsm) were both lower than their respective critical thresholds. This resulted in a lower overall impact and more localized episodes of oak decline. The year 2017 serves as a paradigmatic example of a scenario in which multiple critical variables simultaneously exceeded their threshold values, thereby amplifying each other’s effects. In contrast, there was no similar peak in summer thermal stress in 2022, resulting in less pronounced mutual amplification. This is particularly noteworthy given the very low precipitation levels in 2022. These findings confirm once again that oak decline is not attributable to the anomalies of a single variable, but rather, it is the result of more complex interactions among multiple variables.

Table 4 clearly shows that, out of the eight identified variables as triggering oak decline, only four of them (Rad, RZsm, HWh and HWL) recorded the most critical values in 2017. However, it should be noted that the other two variables (PL and TS) exhibited values close to the highest or lowest critical values recorded in other years within the time interval considered.

In essence, each variable has a negative effect with a proportional relationship to its absolute value, contributing to an increase in the negative effects expressed by the other critical variables in the same proportion. In addition to the incremental contribution of a single variable to the effects caused by the others, an opposite trend was also identified: the “compensatory effect” between variables (Table 5).

The existence of this compensatory effect can be demonstrated through an examination of the trend in the values expressed by the variables PL, RAI, RZsm, and TS, Rad, HWh, HWL, and HWc in 2015, in comparison with other years. Despite the presence of anomalously high temperatures throughout the summer months (Table 4), 2015 does not appear to be among the years characterized by oak decline, for any of the forest stands considered. In this case, the abundant rainfall experienced in the first semester of the year served to counterbalance the deleterious effects of the unusually high summer temperatures.

Table 5.
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Comparison of key climatic and ecological variables between 2017 and 2022. PL = rainfall of the first semester; RAI = Rainfall Anomaly Index; RZsm = Root Zone soil moisture; TS = average of the maximum temperature of the meteorological summer months; Rad = surface incoming shortwave flux; HWh = heatwave amplitude, daily mean temperature on hottest day satisfying the heatwave criteria of at least three consecutive days above the 90th percentile; HWc = Heatwave events; HWL = length of the longest number of consecutive periods satisfying the heatwave criteria of at least three consecutive days above the 90th percentile.

Variable 2017 median value 2022 median value Notes
PL 255.1 mm 246.7 mm Both below the critical threshold of 335,86 mm
RAI -1.295 -2.160 2022 drier than 2017
RZsm -41.2 mm -26.0 mm 2017 showed lower (drier) values
TS 37.5 °C <35 °C 2017 was warmer
Rad 282.7 W/m² <280 W/m² 2017 more radiation
HWh 31.8 °C 29.5 °C 2017 HW more intense
HWc 4 events 9 events 2022 more frequent
HWL 8.5 days 6.8 days 2017 more length

The vulnerability map

A vulnerability map which considers three oak decline risk categories (low, medium and high), was created for the entire Basilicata region (Fig. 6). It summarizes all the concepts and assessments that have been previously expressed. The situation appears quite alarming. A large proportion of the forest heritage in the study area is classified as medium or high risk. Unfortunately, the plots in the high-vulnerability category far outnumber those in the low-vulnerability category. Nevertheless, caution is required when drawing conclusions or hypothesizing future scenarios, as this constitutes merely the first proposal of its nature. As there are no vulnerability maps available for previous years, it is not possible to determine whether the forest situation of oak decline in the study area is worsening, remaining stable, or improving over time. Nevertheless, we can be certain of excluding at least the last one of these three hypothetical options. The constant increase in global warming over the last two decades, together with the ever-worsening desertification of the Mediterranean region (Colantoni et al. 2015; Carvalho et al. 2022), does not seem to support such an optimistic hypothesis. However, according to some recent estimates, a promising future seems to be opening for the Quercus genus. Some authors (Hanewinkel et al. 2013; Holland et al. 2014; Früchtenicht et al. 2018) suggest that the Quercus group could expand its potential distribution range in Europe (at the expense of other forest types) from 11% to 28–40% by 2100. While we don’t want to dampen enthusiasm, our vulnerability map indicates that prospective episodes of oak decline, on a regional scale, could affect 63.43% of mesophilic and meso-thermophilic oak forests. Given that future climate change scenarios indicate an increase in heatwaves and drought periods (ESOTC 2024), it is more likely that a significant proportion of the land acquired by oak forests, as postulated in the aforementioned studies, will be heavily taxed by oak decline.

Conclusions

The health of Mediterranean oak forests is being adversely affected by increasing global warming attributable to both anthropogenic and natural causes, thus rendering them increasingly vulnerable to oak decline. Notwithstanding the ongoing taxonomical debates, the oak forests of southern Italy are significant in terms of Europe’s biodiversity heritage. These precious ecosystems, therefore, need to be monitored over time to ensure they are managed and protected in a sustainable manner.

In this study, we analyzed the effects of the oak decline phenomenon throughout the whole Basilicata administrative Region, a sector of southern Italy which has a high concentration of oak forests. The Quercus pubescens forests in the hilly belt were found to be the forest type most affected by oak decline. Therefore, from a southern European perspective, our findings stand in contrast, at least in part, with those of analogous studies from Central Europe, which suggest Q. pubescens as a probable beneficiary of global warming.

As has been previously highlighted in the literature (Thomas et al. 2002; Brown et al. 2018; Bose et al. 2021; Gosling et al. 2024), all variables associated with drought and temperature anomalies (PL, RAI, RZsm, Rad, TS, HWh, HWc, and HWL) were found to be the most influential in triggering episodes of oak decline.

The combined effect of groups of diagnostic variables in exceeding the critical thresholds was found to be a decisive factor in triggering oak decline episodes. Meanwhile abundant rainfall in the first half of the year compensated for anomalous dryness or high temperatures in summer. This study confirms that sustainable forest management is crucial in enhancing the resilience of oak forests. The utilization of management techniques based on coppicing in close rotations have been shown to be harmful, as they amplify the negative effects of climatic anomalies. Consequently, it is essential to promote forest management practices that guarantee a substantial extent of forest coverage and structural diversity. This objective can be achieved by including individuals of all age groups and ensuring a wide occurrence of pre-renewal. In the present study, the most resilient forests were identified as those exhibiting a high degree of naturalness in terms of structure and floristic composition. At this time, a specific approach for sustainable forest management that is universally applicable cannot be proposed, due to the high number of variables involved. However, several valuable guidelines supported by studies carried out under similar conditions are now available and should be given greater consideration (e.g. Lindner et al. 2014; Colangelo et al. 2017; Gavinet et al. 2019; Ripullone et al. 2020).

The vulnerability map, which summarizes the entire body of research, marks an important turning point. It raises awareness of the scale of oak decline and is a valuable tool for planning the management and protection of forest resources. In addition to quantifying and visualizing the impact of oak decline in a specific area (the territory of Basilicata Region), the vulnerability map could serve as a basic model for active monitoring policies aimed at counteracting the negative effects of this phenomenon. Once the results have been interpreted, tested and hopefully improved, they could soon be extended to other areas or applied on a broader scale when integrated with new data.

Funding

This study was supported by (a) MUR PRIN 2022, project code: 2022B3HA72, project title: “Xerothermic deciduous oaks, a still-unsolved genetic, taxonomic and coenological issue of a valuable forest resource in Mediterranean countries. From the safeguard of landscape identity to the planning of urban forests” (PI. R. Di Pietro, UO P. Fortini) CUP B53D23011890006; (b) the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2: Investment 1.4—Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of the Italian Ministry of University and Research; Award Number: project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, project funded by the European Union—“NextGenerationEU NBFC”; project title “National Biodiversity Future Center”; A) R. Di Pietro: CUP B83C22002950007 (B) P. Fortini and P. Di Marzio CUP H73C22000300001.

Acknowledgements

The authors wish to acknowledge the two anonymous referees and the Editor for the valuable feedback on an earlier version of the manuscript.

References

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Topical Collection: "Species and community variability in vegetation dynamics and plant diversity conservation". Edited by Gianmaria Bonari, Silvia Del Vecchio, Fotios Xystrakis and Federico Fernández-González.

Supplementary material

Supplementary material 1 

Supplementary tables and images

Antonio Luca Conte, Romeo Di Pietro, Piera Di Marzio, Sandro Strumia, Giuseppe Cillis, Andrea Capuano, Paola Fortini

Data type: xlsx

Explanation note: The supplementary files include tables and matrices reporting: information on the study area, descriptioon of variables involved in oak decline, matrices used for multivariate analyses procedures.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
https://doi.org/10.3897/ved.160170.suppl1
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