iForest - Biogeosciences and Forestry

iForest - Biogeosciences and Forestry

Tree-ring-based reconstruction of larch budmoth outbreaks in the Central Italian Alps since 1774 CE

iForest - Biogeosciences and Forestry, Volume 12, Issue 3, Pages 289-296 (2019)
doi: https://doi.org/10.3832/ifor2533-012
Published: May 27, 2019 - Copyright © 2019 SISEF

Research Articles

The larch budmoth (Zeiraphera diniana Gn. - LBM) offers a unique example of cyclic fluctuations in insect populations. During regular LBM mass outbreaks, defoliation of European larch (Larix decidua Mill.) subalpine trees results in distinct ring-width reductions in the host trees. Although several observations, reconstructions and models suggest that LBM outbreaks travel from the southwest to the northeast along the Alpine arc, gaps in the underlying data still hamper our mechanistic understanding of the spatio-temporal system dynamics. Evidence of historical LBM outbreaks before 1964 is generally associated with uncertainty and is particularly scarce for the Central Italian Alps. Here, we introduce four new larch ring-width chronologies from Val di Sole in the Central Italian Alps and use time-series analyses and comparisons with non-host trees (Picea abies Karst.) to reconstruct LBM mass outbreaks. We identify distinct fingerprints of 15 spatially-synchronized LBM events that occurred between 1774 and 1964 CE. Our results are important for improving qualitative space-time models to simulate travelling wave dynamics of insect populations, and for correcting ring-width-based summer temperature reconstructions from this part of the Alpine arc.

European Larch, Insect Outbreaks, Larch Bud Moth, Tree-rings, Zeiraphera diniana Gn.


Tree growth at high-latitude and high-elevation sites is mainly limited by low summer temperatures. Tree-ring width (TRW) series from such sites have therefore been used to reconstruct temperature changes over different spatio-temporal scales ([30], [28], [27]). However, forest natural disturbances, such as wildfires, windstorms and geomorphological events and processes, as well as biotic attacks, such as insect outbreaks and fungal diseases, may also influence tree growth ([43]). European larch (Larix decidua Mill.) is one of the most commonly used species for dendroclimatic reconstructions in the Alps ([16], [17], [19], [29], [24]), although larch tree-ring patterns are affected by cyclic outbreaks of the larch bud moth (Zeiraphera diniana Gn., LBM hereafter - [48], [3], [26], [18]). The spatio-temporal identification of past LBM outbreaks is necessary to interpret the climatic signal of tree-ring chronologies.

The LBM, a moth belonging to Lepidoptera and characterized by periodic cyclic population fluctuations ([25]), has been studied since the early 1960s in the Engadin valley (Switzerland), and reports of outbreaks in several Alpine areas date back to 1815 ([3]). LBM population cycles are driven by trophic and non-trophic factors, such as host-parasite interactions, food quality, maternal effect, population density and climate ([2], [32], [10], [46]). The diet of LBM larvae is based on the raw fiber and protein in new larch foliage ([1], [10], [46]). The first effect of larvae feeding is defoliation. In the following two to three years, reduced needle length and a lower nitrogen content are typically observed ([4]). The decrease in crown mass reduces photosynthetic rates, and the related lower carbohydrate production and accumulation decrease TRW and maximum latewood density, leaving a typical fingerprint in the tree-ring series ([4]). LBM population dynamics are influenced by site characteristics, and a clear periodicity of outbreaks every 8-10 years is typically observed within the LBM optimum elevation belt between 1700 and 2000 m a.s.l. ([3], [37]). In the French Alps, the history of LBM outbreaks from 1750 to 1994 was reconstructed ([41]) and compared with climate ([42]) and land use changes ([7]). Tree-ring analyses conducted at the border between the Italian and French Alps were used to reconstruct LBM outbreaks that occurred from 1760 to 1999 ([39]), and in the Lötschental, Switzerland, a 1200-year tree-ring density chronology was used to reconstruct LBM outbreaks well back into medieval times ([26]).

While the presence of LBM was found to be synchronous at the valley scale, a wave pattern from west to east with a calculated velocity of 219.8 km · year-1 along the Alpine arc was described ([12], [36], [37]). Temporal and spatial data are essential to understand the dynamics of the LBM populations and to develop outbreak wave models. However, in some regions, such as the Central Italian Alps, LBM data from before 1964 are generally lacking ([3]).

Here, we develop four new TRW chronologies from high-elevation sites in the Central Italian Alps and use statistical approaches to analyze them in order to highlight the years characterized by anomalous growth. The aim is to reconstruct LBM outbreaks that occurred in this area in the past centuries, thus contributing to knowledge of the LBM cycle at locations above the ecological optimum elevation.

  Materials and methods 

Study area and sampling design

The study area is located in the Central Italian Alps, in the Adamello-Presanella and Ortles-Cevedale groups of the Central Italian Alps (46° 05′ 19″ N, 10° 22′ 45″ E). Where geomorphological processes enable the development of soils, associations of podzols, histosols and umbrisols, or of umbrisols, cambisols and podzols, were found depending on the slope aspect ([31]).

The climate of the study area is inner-alpine, with cold winters and temperate summers. The mean annual temperature of 0 °C is found at 2540 m a.s.l. Baroni et al. ([5]) calculated a mean vertical temperature gradient of 0.59 °C / 100 m elevation. The total amount of precipitation in the area spans from 810 mm to 1504 mm and is positively correlated with elevation and negatively correlated with latitude ([5]).

The valley floors of the study area are dominated by Norway spruce (Picea abies [L.] Karst.) forests, which transition to mixed forest stands (with European larch) above 1500 m a.s.l. Above 1900 m a.s.l. European larch becomes the dominant species and is associated with green alder (Alnus viridis [Chaix] D.C.). The sampling sites are located between 1900 and 2200 m a.s.l. in treeline ecotones, characterized by ericaceous species (Rhododendron ferrugineum L. and Vaccinium spp.) and Festuca scabriculmis (Hack.) K. Richt., where scattered larches and, in some cases, individual Swiss stone pine (Pinus cembra L.) trees grow ([6], [23], [20], [21]).

A total of 105 larch trees were sampled in four different valleys: “Val di Barco” (BARC hereafter) and “Val Palù di Vermiglio” (PALP) on the northern part of the Adamello-Presanella massif, “Val di Fumo” (FUMO) on the southern part of the same massif, and “Val Comasine” (ANBO) on the southern part of the Ortles-Cevedale group (Fig. 1). All valleys are N-S oriented. Two cores (five mm in diameter) were collected from each individual using an increment borer (Haglöf, Sweden). All trees were cored at breast height, perpendicular to the slope direction, to minimize the occurrence of reaction wood in the samples ([43]). Trees showing scars caused by mechanical disturbances, such as debris flows and avalanches, or other injuries were avoided.

Fig. 1 - Location map of the study area. The dotted circle, triangle, square and pentagon represent ANBO, BARC, PALP and FUMO, respectively.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Sample preparation and chronology development

The cores were sanded with progressively finer sand paper (P80, P150, P240, P400, P800 and P2000) to highlight the annual ring boundaries. TRW was measured using a sliding table (LINTAB™ mod. 3, RINNTECH®, Heidelberg, Germany), with a resolution of 0.01 mm, and TSAPWin Scientific 4.69h software (RINNTECH®). The individual TRW series were then visually compared and cross-dated, also against a published larch reference chronology ([8]). The visual cross-dating was additionally verified using the COFECHA program ([33]) and the “dplR” package in R ([15], [40]).

The raw TRW series were power transformed in order to attenuate the variance, and standardized indexes were then calculated as a ratio using a cubic smoothing spline with a 50% frequency cut-off at 32 years ([22]). Four site standard chronologies were formed as a bi-weighted mean of standard index series, and residual chronologies were formed using pre-whitening. These splines enabled the removal of medium and low frequencies due to age and long-term climatic trends while preserving the signal related to the pattern of LBM outbreaks, which was characterized by high-frequency rap- id growth variations ([22]). Owing to the uneven sample depth, the analyses were performed on the portion of the chronology that presented an Expressed Population Signal (EPS) value higher than 0.85 ([49], [22]).

Identification of pointer years

The years for which a group of well cross-dated tree-ring series indicated the occurrence of an event year, i.e., when a particularly narrow or large ring was formed, were identified as pointer years (PYs - [44]). The pointer year analysis was conducted using five methods described below, and only those years identified by at least two methods were considered as PYs and thus evaluated as possible LBM outbreaks.

(1) Be94: the TRW raw series were tested with the method proposed by ([9]). The mean sensitivity ([30]) of each series was multiplied by 0.85 (approximately equal to the lowest twentieth percentile) and used as the threshold for the mean growth variation. In each series a year was identified as PY if its growth variation is greater than the calculated threshold. A year was identified as PY if at least 75% of the series present a significant growth variation.

(2) HS96: all TRW standard series and the four standard chronologies were compared with a Norway spruce (Picea abies [L.] Karst.) chronology, following the methods I and IV described in Büntgen et al. ([18]) - [45], [11], [41]). To avoid a sensitivity bias ([29]), we used Norway spruce series from a forest stand located in Switzerland, about 100 km north-westerly from the sampling stands ([11]), rather than the Swiss stone pine series of a nearby valley ([21]). A 313 years long Norway spruce chronology was developed applying the same standardization applied to larch series. The obtained Norway spruce chronology, resulted sensitive to June-August mean summer temperatures as well as the larches in the study area ([20]), differently from the Swiss stone pine that shows a sensitivity to the May-September mean temperatures ([21]).

(3) No06: PYs were highlighted using the ring-width standard indexes (RWI), the RWI mean and the RWI standard deviation. PYs were identified as those years that satisfied both of the following conditions: (i) the RWI was lower than the RWI mean minus the RWI standard deviation multiplied by 0.85; and (ii) the RWI was lower than 0.6 of the RWI of the previous year ([39]).

(4) Ne07: the TRW raw series were filtered with a 13-year-long low-pass filter and then analyzed for PYs according to Schweingruber et al. ([44]) and Neuwirth et al. ([38]) using the function “pointer.norm” of the “pointRes” package in R ([47]). Following methods from Neuwirth et al. ([38]), standard deviation thresholds were set to 1.000, 1.280 and 1.645 to identify weak, strong and extreme events. The window of analysis was set to seven years and the number of series threshold was set to 75%.

(5) Ce19: site-specific calculated temperature anomalies ([13], [14]) were used to identify potential PYs. Moreover, the years that present mean summer temperature anomalies (June to August) below 1.28, 1.64 and 2.32 standard deviation thresholds (corresponding to the lowest 10th, 5th and 1st percentile) were considered particularly cold and thus as cause of climatically induced PYs. The climatic sensitivity of the standardized series and TRW chronologies was verified, and only those series showing a correlation index r > 0.224 with summer temperature were considered. The series were regressed using indexes calculated with the temperature series from the summer month that showed the highest correlation value and then subtracted from the temperature series. The standard deviation of the differences was calculated for each series and chronology. Years that included at least 25% of the series or a chronology difference value lower than the standard deviation multiplied by 1.96 were identified as PYs.

The analyses were performed for the years between 1774 and 1999, the maximum time interval covered by all the datasets.

To assess the presence of a cyclical function that could be ascribed to recurrent LBM outbreaks, wavelet analysis was conducted on the TRW standard chronologies using the “morlet” function of the “dplR” package. The spectra were analyzed considering the period between 20 and 28, using a significance level of 0.95.


Four larch chronologies from high-elevation sites were developed and analyzed (Fig. 2, Tab. 1). The descriptive statistics of each of the mean standard chronologies are reported in Tab. 2. The portions of the chronologies with an EPS > 0.85 span from 257 years (1759-2015 - FUMO) to 437 years (1579-2015 - PALP). All chronologies show a strong inter-series correlation (r = 0.44 ± 0.08), which indicates the presence of a common signal.

Fig. 2 - Chronologies (lines) and sample depth (area) from the four study sites. Bold lines represent the portion of the chronologies with an EPS > 0.85.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Tab. 1 - Characteristics of the four investigated sites.

Site name
Vegetation Elevation
(m a.s.l.)
Soil type
Val Comasine
Larix decidua 2167 ± 37 46.3326 10.6632 Podzol
Val di Barco
Larix decidua / Picea abies / Alnus viridis 1987 ± 95 46.2718 10.7070 Podzol
Val di Fumo
Larix decidua / Pinus cembra 2024 ± 61 46.0772 10.5537 Podzol
Val Palù di Vermiglio
Larix decidua / Picea abies 2149 ± 24 46.2697 10.6891 Podzol

  Enlarge/Reduce  Open in Viewer

Tab. 2 - Descriptive statistics of the four larch chronologies. (EPS): Expressed Population Signal; (rbt): mean inter-series correlation.

Site (Code) N° trees
Time span (length)
[EPS > 0.85 (length)]
r bt
Val Comasine
1420-2015 (596)
[1658-2015 (358)]
0.34 ± 0.05
Val di Barco
1531-2015 (485)
[1737-2014 (279)]
0.47 ± 0.10
Val di Fumo
1663-2015 (353)
[1759-2015 (257)]
0.40 ± 0.08
Val Palù di Vermiglio
1526-2016 (491)
[1579-2015 (437)]
0.53 ± 0.07
Mean - - 0.44 ± 0.08

  Enlarge/Reduce  Open in Viewer

The performed analyses enabled the identification of 116 PYs between 1774 and 1999. Of these, 36 years were highlighted by at least two methods and thus investigated as years in which an LBM outbreak could have taken place. The largest number of potential LBM outbreaks was reported for ANBO, with 23 occurrences, followed by FUMO (21), BARC (19) and PALP (18 - Fig. 3). Moreover, of the 36 highlighted years characterized by a potential LBM outbreak, 25 occurred synchronously in more than one chronology (Tab. 3). Ten of the synchronous PYs were highlighted in two different chronologies, ten in three chronologies and five (1813, 1821, 1830, 1843, 1926) in all the analyzed chronologies (Fig. 3, Tab. 3). Of the non-synchronous PYs, most were identified in equal number in BARC and FUMO (four out of 11), while two came from ANBO and one from PALP (Tab. 3).

Fig. 3 - Identified PYs plotted for single valley. Different colors identify the applied methods. Numbers underline years characterized by LBM outbreak.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Tab. 3 - Pointer years (PYs) identified with at least two methods in each valley. Daggered (†) values identify years classified as PYs in only one valley; starred (*) values identify climatic induced PYs according to meteorological data ([13], [14]), the letter that follow the star (a, b, c) identifies the significant level of the temperature anomalies outliers (90%, 95% and 99% respectively); double daggered (‡) values identify climatically induced PYs in Western Alps ([41]). Values between round brackets identify the number of PYs not climatically induced.

Valley Year of potential LMB outbreak Total
ANBO 1777*a 1779*a 1783 1813*c,‡ 1821*a 1830 1843*b,‡ 1854 23 (8)
1871*a 1880 1886 1888†,‡ 1909 1913*a 1918 1923
1926*a 1932 1940*b,‡ 1948*b,‡ 1956*a 1962†,‡ 1964 -
BARC 1777*a 1779*a 1783 1813*c,‡ 1821*a 1830 1838 1843*b,‡ 19 (10)
1871*a 1880 1884 1889 1918 1920 1926*a 1933
1953 1973 1992 - - - - -
FUMO 1779*a 1801*a, † 1813*c,‡ 1821*a 1830 1843*b,‡ 1854 1871*a 21 (11)
1884 1902 1909 1913*a 1918 1923 1926*a 1932
1948*b, ‡ 1953 1956*a 1963 1971 - - -
PALP 1777*a 1783 1813*c,‡ 1819 1821*a 1830 1843*b,‡ 1854 18 (8)
1880 1886 1909 1923 1926*a 1933 1940*b,‡ 1956*a
1964 1992 - - - - - -

  Enlarge/Reduce  Open in Viewer

All the chronologies showed similar results from the wavelet analysis, with a significant return period of approximately 9-years occurring around the middle 20th century. On the same time span, a secondary peak significant at 95% was observed in BARC and FUMO on a period around 20-years. Considering the first half of the 19th century, ANBO, BARC and PALP report a significant peak on a return period around 20-years whereas FUMO showed a significant peak around a period of 13-years (Fig. 4). Despite these peaks constrained in time, the only chronology showing a period whose mean presented values above the significant threshold of 95% was BARC. In fact, two main peaks representing the mean of a 9-years and 20-years periods were identifiable, though they were due more likely to particular high values in the recent portion of the chronology than to the general high values along all the chronology (Fig. 4).

Fig. 4 - Wavelet analysis results for the standard chronologies and spectral analysis. Left column: Black contours identify the portions of the spectra significant at 95%; white shaded areas identify the cone of influence of the analysis. Right column: Black line identifies the mean power spectra over the same period, gray dashed line identifies the significance threshold of 95%.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

The results from the wavelet analysis are in good accordance with the statistical output from each chronology, highlighting a general lack of a regular cyclicity in time, cyclicity that is not inferable neither from the analysis of the PYs alone.


In this study, five different statistical approaches were applied to identify LBM outbreaks in four high-elevation larch chronologies from the Central Italian Alps between 1774 and 1999, the maximum time-span covered by all the involved datasets. The different statistical methods highlight different properties of the analyzed series, resulting in heterogeneous results depending on the parameters considered ([35]). This indicates that it is necessary to use several methods to identify LBM outbreaks with accuracy, as demonstrated in a previous study ([18]).

The statistical approaches Be94 ([9]), HS96 ([45], [34]) and No06 ([39]) provided a larger number of PYs than Ne07 ([38]) and Ce19 (this study). However, there is no specific threshold or method that can be used to identify LBM outbreaks definitively, even when a non-host chronology is used as a reference ([39]). In fact, different species could be characterized by different response to the same climatic conditions ([29]). Larch and spruce in the study area are known to have a similar response to climate ([29], [17], [23]). Although both spruce and larch chronologies showed a strong correlation with June temperature (up to mean r = 0.40) and in general with June-August mean summer temperature (mean r = 0.34 for larch chronologies and mean r = 0.37 for Norway spruce), correlation between the chronologies of the two different species was low and not significant (mean r = 0.15), suggesting a rather distinct response to environmental factors by the two species.

Moreover, all the statistical methods applied, excluding Ce19, considered tree growth as a parameter for identifying years in which growth is statistically different from the norm. Thus, they cannot distinguish between a climatically induced reduction in tree growth and a signal caused by an LBM outbreak. The synchronicity between the hatching of LBM eggs and larch needle sprouting is fundamental for the development of the larvae, and the survival of LBM eggs is negatively related to winter and early spring temperatures ([37], [42]). However, low spring and summer temperatures also induce narrow rings in larch, which could be interpreted as LBM outbreaks by statistical methods. When the highlighted PYs are compared with the temperature anomaly outliers (α = 0.90), 12 out of 36 PYs (Tab. 3) correspond to particularly cold summers in the area ([13], [14]). Climatically induced PYs have also been observed in the Western Alps, where other years characterized by cold summers were reported ([41], [7]). Of the 19 PYs identified as being climatically induced, 1813 and 1843 were particularly cold years (classified as outliers at 99% and 95%, respectively), and they were synchronously highlighted across all the analyzed chronologies. Beyond these years, also 1821 and 1926 were classified as cold years at 90% and both were highlighted as PYs in all chronologies as well as the more cold years (Tab. 3).

The 17 remaining statistically detected PYs (Tab. 4) that were classified as possible LBM outbreaks show high synchronicity among stands, especially between ANBO and both FUMO and PALP (Tab. 4). These chronologies share most of the ANBO highlighted PYs (six and seven out of eight, respectively), probably because the stands have a similar elevation, higher than BARC (Tab. 1). Moreover, all PYs reported for ANBO are synchronous with at least one other chronology, denoting a good synchronicity of the PYs of this stand with the LBM outbreaks highlighted in the area. A good synchronicity is observed also between FUMO and PALP, sharing six of the respectively highlighted PYs, whereas, probably due to the lower elevation, BARC present the lower synchronicity among all the considered chronologies and also the higher number of PYs highlighted in a single valley (four out of seven - Tab. 4). Most of the possible LBM outbreaks identified in this study are coherent with previous analyses, being synchronous with both weakly and strongly detected LBM outbreaks in the area ([3], [18], [42]). This synchronicity indicates that no time lag of LBM outbreaks between the stands occurred ([12], [36]). However, two of the PYs were identified for the first time in this study: 1884 (BARC, FUMO and PALP) and 1918 (ANBO, BARC and FUMO). In the first case, a high-elevation outbreak probably occurred in the area after a series of very strong outbreaks recorded during the preceding four years in nearby valleys ([18]). The 1918, instead, is synchronous with a weak outbreak recorded in two sites located slightly northmost in comparison to the study area. Thus, the high elevation sites act as LBM refugia before the massive outbreak that occurred in 1920, 1921 and 1923 in the area ([18]).

Tab. 4 - Newly identified potential LBM outbreaks (+) at the study sites. (PYs): pointer years.

1783 + + - +
1819 - - - +
1830 + + + +
1838 - + - -
1854 + - + +
1884 - + + -
1889 - + - -
1902 - - + -
1909 + - + +
1918 + + + -
1920 - + - -
1923 + - + +
1932/33 + + + +
1953 - + + -
1963/64 + - + +
1971 - - + -
1973 - + - -

  Enlarge/Reduce  Open in Viewer

Although several PYs were highlighted in the four chronologies, it is impossible to identify numerically a clear cyclicity in their occurrences. The absence of a clear cyclicity is in accordance with the recognized optimum for LBM between 1700 and 2000 m a.s.l., beyond which the outbreak occurrences tend to be sporadic and random ([3], [4]). However, the wavelet analysis highlights the presence of significant cycles with a return period around 20-years in the first half of the 19th century (ANBO, BARC and PALP), decreasing to around 9-years period in the second half of the 20th century (Fig. 4). The 20-years return period is hardly ascribable to the LBM or at least to an LBM regular cycle and is coherent with the PYs analysis results that show random intervals between outbreaks in the first half of the chronologies (Fig. 4, Tab. 3). Nevertheless, the relevant 9-years period peaks recorded in the second half of the 20th century, quite synchronous in all the analyzed valleys, is totally comparable with a normal LBM cycle ([3], [41], [39]). The presence of a return period ascribable to LBM in the last fifty years of the 20th century within chronologies that before do not show evidence of regular population fluctuation of this insect, can be related to climatic changes affecting the area in the last two centuries, as reported in the Western Alps ([42]). The first half of the 19th century was characterized by low temperatures with the coldest conditions occurred around the 1850s. In fact, in the Italian Central Alps, the 1850s is identified as the period during which the last phase of the Little Ice Age (LIA) occurred. The lower temperatures could have caused the synchronicity between larch needle sprouting and LBM egg hatching at lower elevation, and thus only massive LBM outbreaks could have influenced the high-elevation stands ([37], [42]). After the LIA, the increasing temperatures shifted the optimum belt of the LBM toward higher elevations. This shift can explain the significant return period of almost 9 years, observed in high-elevation stand chronologies until the 1990s (Fig. 4). Since the 1990s, the continuous increasing in mean summer temperature is considered one of the main factor that contribute to disrupt the LBM regular cycle in the Alps ([4], [37], [42]).

The comparison of the results provided by five different methods of PYs identification, enabled us to recognize 17 LBM outbreaks in the Central Italian Alps, 14 of which occurred before 1964. Moreover, two new outbreak years (1884 and 1918) were identified for the first time in the study region, and they corresponded to the timing of outbreaks already reported in the Eastern Alps ([3], [18]). Our results increase the knowledge about LBM population cycles at high-elevation sites in this part of the Alps. Further, they provide information that is useful for interpreting and correcting dendroclimatic reconstructions for this key area, from a climatic and glaciological point of view, and for a deeper understanding of the LBM population dynamic in the whole Alpine region.

  Authors’ contributions statement 

The study was conducted with contributions from all authors. RC and AC performed laboratory work and participated in field activities. CB and MCS contributed to the definition of the geomorphological context and conducted field sampling. RC and PC shaped the structure of the scientific design, and RC carried out data analysis. All authors contributed in an equal manner to the discussion and interpretation of the results. The text was written by RC and AC and revised by PC, UB, MCS and CB. The project was founded, directed and coordinated by CB.


This work was financially supported by the Italian MIUR Project (PRIN 2010-11): “Response of morphoclimatic system dynamics to global changes and related geomorphological hazards” (national and local coordinator: C. Baroni) and by the project of strategic interest NEXTDATA (PNR National Research Programme 2011-2013; project coordinator: A. Provenzale CNR-IGG, WP 1.6, leader: C. Baroni UNIPI and CNR-IGG). U. Büntgen received a grant from “SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions” (CZ.02.1.01/0.0/0.0/ 16_019/0000797). We thank the Erasmus Programme Consortia Placement traineeship program for financial support. We are also very grateful to Dr. Gino Delpero, Luca Colato and Custodi Forestali from Comune di Vermiglio (Trento, Italy) for help with field activities and sampling. We thank Dr. Fabio Angeli, who is responsible for the Ufficio Distrettuale Forestale of Malè (Trento) for his support and sampling permissions. Finally, we thank PhD Melissa A. Dawes (WSL Swiss Federal Research Institute, Birmensdorf, Switzerland) for English grammar and spell editing. The authors are grateful to three anonymous reviewers for their valuable comments to this work.

  Conflict of Interest 

The authors declare that they have no conflict of interest.


Baltensweiler W, Fischlin A (1988). The larch budmoth in the Alps. In: “Dynamics of Forest Insect Populations” (AA Berryman ed). Springer, Boston, MA, USA, pp 331-351.
CrossRef | Gscholar
Baltensweiler W (1993). Why the larch bud-moth cycle collapsed in the subalpine larch-cembran pine forests in the year 1990 for the first time since 1850. Oecologia 94: 62-66.
CrossRef | Gscholar
Baltensweiler W, Rubli D (1999). Dispersal: an important driving force of the cyclic population dynamics of the larch bud moth, Zeiraphera diniana Gn. Forest Snow and Landscape Research 74: 3-153.
Baltensweiler W, Weber UM, Cherubini P (2008). Tracing the influence of larch-bud-moth insect outbreaks and weather conditions on larch tree-ring growth in Engadine (Switzerland). Oikos 117: 161-172.
CrossRef | Gscholar
Baroni C, Carton A, Seppi R (2004). Distribution and behaviour of rock glaciers in the Adamello-Presanella massif (Italian Alps). Permafrost and Periglacial Processes 15: 243-259.
CrossRef | Gscholar
Baroni C, Armiraglio S, Gentili R, Carton A (2007). Landform-vegetation units for investigating the dynamics and geomorphologic evolution of Alpine composite debris cones (Valle dell’Avio, Adamello Group, Italy). Geomorphology 84: 59-79.
CrossRef | Gscholar
Battipaglia G, Büntgen U, McCloskey SP, Blarquez O, Denis N, Paradis L, Brossier B, Fournier T, Carcaillet C (2014). Long-term effects of climate and land-use change on larch budmoth outbreaks in the French Alps. Climate Research 62: 1-14.
CrossRef | Gscholar
Bebber AE (1990). Una cronologia del larice (Larix decidua Mill.) delle alpi orientali italiane [A larch chronology (Larix decidua Mill.) of the Italian Eastern Alps]. Dendrochronologia 8: 119-140. [in Italian]
Becker M, Nieminen TM, Gérémia F (1994). Short-term variations and long-term changes in oak productivity in northeastern France. The role of climate and atmospheric CO2. Annales des Sciences Forestières 51: 477-492.
CrossRef | Gscholar
Berryman AA (1996). What causes population cycles of forest Lepidoptera? Trends in Ecology and Evolution 11: 28-32.
CrossRef | Gscholar
Bigler C (1999). Davos GR Dischma-Fluela - PCAB - ITRDB SWIT181. Web site.
Online | Gscholar
Bjørnstad ON, Peltonen M, Liebhold AM, Baltensweiler W (2002). Waves of larch budmoth outbreaks in the European alps. Science 298: 1020-1023.
CrossRef | Gscholar
Brunetti M, Lentini G, Maugeri M, Nanni T, Simolo C, Spinoni J (2012). Projecting North Eastern Italy temperature and precipitation secular records onto a high-resolution grid. Physics and Chemistry of the Earth, Parts A/B/C 40 (41): 9-22.
CrossRef | Gscholar
Brunetti M, Maugeri M, Nanni T, Simolo C, Spinoni J (2014). High-resolution temperature climatology for Italy: interpolation method intercomparison. International Journal of Climatology 34: 1278-1296.
CrossRef | Gscholar
Bunn AG (2010). Statistical and visual crossdating in R using the dplR library. Dendrochronologia 28: 251-258.
CrossRef | Gscholar
Büntgen U, Esper J, Frank DC, Nicolussi K, Schmidhalter M (2005). A 1052-year tree-ring proxy for Alpine summer temperatures. Climate Dynamics 25: 141-153.
CrossRef | Gscholar
Büntgen U, Frank DC, Nievergelt D, Esper J (2006). Summer temperature variations in the European Alps, A.D. 755-2004. Journal of Climate 19: 5606-5623.
CrossRef | Gscholar
Büntgen U, Frank DC, Liebhold AM, Johnson DM, Carrer M, Urbinati C, Grabner M, Nicolussi K, Levanič T, Esper J (2009). Three centuries of insect outbreaks across the European Alps. New Phytologist 182: 929-941.
CrossRef | Gscholar
Büntgen U, Tegel W, Nicolussi K, McCormick M, Frank DC, Trouet V, Kaplan JO, Herzig F, Heussner K-U, Wanner H, Luterbacher J, Esper J (2011). 2500 years of European climate variability and human susceptibility. Science 331: 578-582.
CrossRef | Gscholar
Cerrato R, Salvatore MC, Brunetti M, Coppola A, Baroni C (2018). Dendroclimatic relevance of “Bosco Antico”, the most ancient living European larch wood in the Southern Rhaetian Alps (Italy). Geografia Fisica e Dinamica Quaternaria 41: 35-49.
CrossRef | Gscholar
Cerrato R, Salvatore MC, Gunnarson BE, Linderholm HW, Carturan L, Brunetti M, De Blasi F, Baroni C (2019). A Pinus cembra L. tree-ring record for late spring to late summer temperature in the Rhaetian Alps, Italy. Dendrochronologia 53: 22-31.
CrossRef | Gscholar
Cook ER, Kairiûkštis LA (1990). Methods of dendrochronology. Springer, Dordrecht, Netherlands, Vol. 23, pp. 120.
CrossRef | Gscholar
Coppola A, Leonelli G, Salvatore MC, Pelfini M, Baroni C (2013). Tree-ring-based summer mean temperature variations in the Adamello-Presanella Group (Italian Central Alps), 1610-2008 AD. Climate of the Past 9: 211-221.
CrossRef | Gscholar
Corona C, Guiot J, Edouard JL, Chalié F, Büntgen U, Nola P, Urbinati C (2010). Millennium-long summer temperature variations in the European Alps as reconstructed from tree rings. Climate of the Past 6: 379-400.
CrossRef | Gscholar
Dormont L, Baltensweiler W, Choquet R, Roques A (2006). Larch- and pine-feeding host races of the larch bud moth (Zeiraphera diniana) have cyclic and synchronous population fluctuations. Oikos 115: 299-307.
CrossRef | Gscholar
Esper J, Büntgen U, Frank DC, Nievergelt D, Liebhold AM (2007). 1200 years of regular outbreaks in Alpine insects. Proceedings of the Royal Society B: Biological Sciences 274: 671-679.
CrossRef | Gscholar
Esper J, George SS, Anchukaitis K, Arrigo R, Ljungqvist FC, Luterbacher J, Schneider L, Stoffel M, Wilson R, Büntgen U (2018). Large-scale, millennial-length temperature reconstructions from tree-rings. Dendrochronologia 50: 81-90.
CrossRef | Gscholar
Esper J, Krusic PJ, Ljungqvist FC, Luterbacher J, Carrer M, Cook ER, Davi N, Hartl-Meier C, Kirdyanov AV, Konter O, Myglan VS, Timonen M, Treydte K, Trouet V, Villalba R, Yang B, Büntgen U (2016). Ranking of tree-ring based temperature reconstructions of the past millennium. Quaternary Science Reviews 145: 134-151.
CrossRef | Gscholar
Frank DC, Esper J (2005). Characterization and climate response patterns of a high-elevation, multi-species tree-ring network in the European Alps. Dendrochronologia 22: 107-121.
CrossRef | Gscholar
Fritts HC (1976). Tree rings and climate. Elsevier, London, UK, pp. 582.
CrossRef | Gscholar
Galvan P, Ponge J-F, Chersich S, Zanella A (2008). Humus components and soil biogenic structures in Norway spruce ecosystems. Soil Science Society of America Journal 72: 548.
CrossRef | Gscholar
Ginzburg LR, Taneyhill DE (1994). Population cycles of forest Lepidoptera: a maternal effect hypothesis. Journal of Animal Ecology 63: 79.
CrossRef | Gscholar
Grissino-Mayer HD (2001). Evaluating crossdating accuracy: a manual and tutorial for the computer program COFECHA. Tree-Ring Research 57: 205-221.
Online | Gscholar
Holmes RL, Swetnam TW (1996). Program OUTBREAK users manual, dendrochronology program library. Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA.
Jetschke G, Van Der Maaten E, Van Der Maaten-Theunissen M (2019). Towards the extremes: a critical analysis of pointer year detection methods. Dendrochronologia 53: 55-62.
CrossRef | Gscholar
Johnson DM, Bjrnstad ON, Liebhold AM (2004). Landscape geometry and travelling waves in the larch budmoth. Ecology Letters 7: 967-974.
CrossRef | Gscholar
Johnson DM, Büntgen U, Frank DC, Kausrud KL, Haynes KJ, Liebhold AM, Esper J, Stenseth NC (2010). Climatic warming disrupts recurrent Alpine insect outbreaks. Proceedings of the National Academy of Sciences USA 107: 20576-20581.
CrossRef | Gscholar
Neuwirth B, Schweingruber FH, Winiger M (2007). Spatial patterns of central European pointer years from 1901 to 1971. Dendrochronologia 24: 79-89.
CrossRef | Gscholar
Nola P, Morales MS, Motta R, Villalba R (2006). The role of larch budmoth (Zeiraphera diniana Gn.) on forest succession in a larch (Larix decidua Mill.) and Swiss stone pine (Pinus cembra L.) stand in the Susa Valley (Piedmont, Italy). Trees - Structure and Function 20: 371-382.
CrossRef | Gscholar
R Core Team (2019). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar
Rolland C, Baltensweiler W, Petitcolas V (2001). The potential for using Larix decidua ring widths in reconstructions of larch budmoth (Zeiraphera diniana) outbreak history: dendrochronological estimates compared with insect surveys. Trees - Structure and Function 15: 414-424.
CrossRef | Gscholar
Saulnier M, Roques A, Guibal F, Rozenberg P, Saracco G, Corona C, Edouard JL (2017). Spatiotemporal heterogeneity of larch budmoth outbreaks in the French Alps over the last 500 years. Canadian Journal of Forest Research 47: 667-680.
CrossRef | Gscholar
Schweingruber FH (1988). Tree rings basic and applications of dendrochronology. Kluwer Academic Publishers. Springer, Dordrecht, Netherlands, pp. 276.
CrossRef | Gscholar
Schweingruber FH, Eckstein D, Serre-Bachet F, Bräker OU (1990). Identification, presentation and interpretation of event years and pointer years in dendrochronology. Dendrochronologia 8: 9-38.
Online | Gscholar
Swetnam TW, Lynch AM (1993). Multicentury, regional-scale patterns of western spruce budworm outbreaks. Ecological Monographs 63: 399-424.
CrossRef | Gscholar
Turchin P, Wood SN, Ellner SP, Kendall BE, Murdoch WW, Fischlin A, Casas J, McCauley E, Briggs CJ (2003). Dynamical effects of plant quality and parasitism on population cycles of larch budmoth. Ecology 84: 1207-1214.
CrossRef | Gscholar
Van Der Maaten-Theunissen M, Van Der Maaten E, Bouriaud O (2015). PointRes: an R package to analyze pointer years and components of resilience. Dendrochronologia 35: 34-38.
CrossRef | Gscholar
Weber UM (1997). Dendroecological reconstruction and interpretation of larch budmoth (Zeiraphera diniana) outbreaks in two central alpine valleys of Switzerland from 1470-1990. Trees - Structure and Function 11: 277-290.
CrossRef | Gscholar
Wigley TML, Briffa KR, Jones PD (1984). On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. Journal of Climate and Applied Meteorology 23: 201-213.
CrossRef | Gscholar

Authors’ Affiliation

Riccardo Cerrato 0000-0002-4192-8839
Anna Coppola
Maria Cristina Salvatore
Carlo Baroni 0000-0001-5905-4650
Dipartimento di Scienze della Terra, University of Pisa, Pisa (Italy)
Paolo Cherubini 0000-0002-9809-250X
Ulf Büntgen 0000-0002-3821-0818
WSL Swiss Federal Research Institute, Birmensdorf (Switzerland)
Paolo Cherubini 0000-0002-9809-250X
Department of Forest and Nature Conservation, Faculty of Forestry, University of British Columbia, Vancouver BC (Canada)
Ulf Büntgen 0000-0002-3821-0818
Department of Geography, University of Cambridge, Cambridge (United Kingdom)
Ulf Büntgen 0000-0002-3821-0818
CzechGlobe and Department of Geography, Masaryk University, Brno (Czech Republic)
Maria Cristina Salvatore
Carlo Baroni 0000-0001-5905-4650
CNR-IGG, National Research Council - Institute of Geosciences and Earth Resources, Pisa (Italy)

Corresponding author



Cerrato R, Cherubini P, Büntgen U, Coppola A, Salvatore MC, Baroni C (2019). Tree-ring-based reconstruction of larch budmoth outbreaks in the Central Italian Alps since 1774 CE. iForest 12: 289-296. - doi: 10.3832/ifor2533-012

Academic Editor

Emanuele Lingua

Paper history

Received: Jun 23, 2017
Accepted: Apr 02, 2019

First online: May 27, 2019
Publication Date: Jun 30, 2019
Publication Time: 1.83 months

© SISEF - The Italian Society of Silviculture and Forest Ecology 2019

  Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Creative Commons Licence

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 33303
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 25908
Abstract Page Views: 2107
PDF Downloads: 2542
Citation/Reference Downloads: 4
XML Downloads: 2742

Web Metrics
Days since publication: 1850
Overall contacts: 33303
Avg. contacts per week: 126.01

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2019): 5
Average cites per year: 1.00


Publication Metrics

by Dimensions ©

List of the papers citing this article based on CrossRef Cited-by.


iForest Similar Articles


This website uses cookies to ensure you get the best experience on our website. More info