iForest - Biogeosciences and Forestry

The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7.272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.

National Forest Inventory, Sustainable Forest Management, Spatial Analysis, Forest Monitoring, Climatic Drivers

  Introduction 

The sustainable management of forest resources is acknowledged as one of the main issues for human well-being ([47]). Forests are fundamental for economic and productive aspects, as indicated by the growing interest in the bio-economy ([11]) and strategies for mitigating the effects of future climate. The Intergovernmental Panel on Climate Change (IPCC) defines climate change as “any change in climate over time, whether due to natural variability or because of human activity”([25]). For most scenarios, the expected increase in average annual temperature ranges between +2 and +4 °C for this century. The precipitation regime is predicted to be more discontinuous with precipitation concentrated in fewer and potentially dangerous extreme events ([45]). The combined temperature and precipitation interactions may threaten forest ecological processes leading to modifications of growth rates and delivery of ecosystem services ([42]). Moreover, changes in the frequency, intensity, duration and timing of “exogenous disturbances” such as wildfires, pests and diseases are expected.

Climate change effects have already been observed for tree species and ecological systems ([30]). For example, Boisvert-Marsh et al. ([4]) reported a latitudinal shift of the distribution of forest species in North America; similar studies have been conducted in Europe and specifically in the Mediterranean region ([33]). Chirici et al. ([10]) reported the effects of recent, unprecedented wind storms in Italy, and Allen et al. ([1]) conducted a global review of tree mortality following heat waves and water stresses.

Forest planning oriented on implementing strategies that adapt to climate change are central across all of Europe ([40]). The growth rate and resilience of forest systems to disturbances are directly connected to ecological requirements and adaptation capacity ([48]). Changes in species composition, reduction in biodiversity and smaller wood increments with reduced carbon sequestration are just few examples of the possible effects of climate change on forest ecosystems. In this sense, future provisioning of forest ecosystem services will be strongly influenced by the soil type, climatic drivers and forest management ([30], [42]).

The worldwide relevance of forests in climate change scenarios is acknowledged in international agreements, particularly by the IPCC ([26]), thanks to their ecosystem services such as Volatile Organic Compounds absorption and CO2 sequestration ([8]). Knowledge of the ecological plasticity of a given species is essential to support selection of suitable forest planning and management choices for mitigating the adverse effects of climate change ([37]). As a consequence, adequate and current information on forest tree species auto-ecology can be useful for adaptive forest management and for genetic selection ([48], [31]).

Recently, the Joint Research Centre (JRC) of the European Commission proposed a broad study on all forest tree species found in Europe: the European Atlas of Forest Species ([44]). This publication describes the main forest European tree species and their ecological and genetic characteristics. Predictive models have been applied to construct land suitability maps for each species. The spatial data were obtained starting from the European Forest Data Center - Forest Information Service for Europe (EFDAC-FISE - ⇒ http:/­/­forest.­jrc.­ec.­europa.­eu/­) data sets, while local bioclimatic variables were retrieved from publicly available datasets at the global scale. Using these data, a series of three bi-dimensional auto-ecology diagrams or climate space diagrams were drawn for each species. These graphs describe the distribution of species relative to pairs of bioclimatic factors: annual average temperature and total annual precipitation which are investigated for this study, potential solar irradiation during spring and summer season with the average temperature of the coldest month and the seasonal variation of the monthly precipitation. However, no numeric values have been publicly shared. Another extensive study regarding forest tree species is represented by the Climate Change Tree Atlas proposed by the USDA Forest Service ([27]). This Atlas is based on plot data acquired by the Forest Inventory and Analysis program of the USDA Forest Service and forms a spatial database for the 134 most common forest tree species in the eastern USA. The main aim of this database is to evaluate the current distribution of forest species and to forecast the possible impacts of climate change using regression tree analysis, Bagging Trees (BT) and Random Forest (RF) as predictive algorithms ([27]).

NFIs are the most extensive and comprehensive source of forest information suitable for spatial analysis, ecological modelling and statistical mapping of forest attributes ([28], [18], [32]). Raw georeferenced data for sampling units obtained in the field are fundamental for many research activities in the forestry field and are becoming publicly available in most countries ([5], [35]). At the same time, several large research projects in the last decade have made spatially interpolated climate variables available at different scales ([34], [21]). All the above-mentioned spatial sources of information are now available for the entire Italian territory, although no extensive analysis of the relationships between tree species distributions and current climate conditions have yet been conducted. Despite Italy being one of the most climate change prone countries in Europe and in the Mediterranean region, auto-ecological characterization of vegetation in Italy still relies on expert-based literature ([3]) and empirical observations based on the bioclimatic classification proposed early in the last century by Pavari ([38]), and later implemented by De Philippis ([13]).

The primary scientific literature consulted to assess auto-ecological characteristics of forest tree species consists of a recent series of textbooks by Del Favero ([14], [15], [16]) and Pedrotti ([39]). However, no additional quantitative information about the auto-ecology of species beyond Bernetti ([3]) could be identified.

Climate is acknowledged to be one of the main factors accounting for the spatial distribution of forest tree species and represent one of the most important aspect to be carefully evaluated in forest monitoring efforts ([16], [19]). Thus, a detailed and current analysis of the relationship between vegetation and climate is essential for any investigation of the possible climate change effects on forest species distributions. The main aim of this study is to update knowledge on the climatic drivers related to the most important forest tree species in Italy. We used 7.272 field plots from the most recent Italian NFI (INFC2005) for which data are currently available, and the 1 km resolution climatic temperature and precipitation data from downscaled E-OBS gridded data (version 17.0) from the EU-FP6 project ENSEMBLES ([24]). We compared our findings with ecological niche information available in the literature. This analysis is intended as a starting point for further studies on future spatial distributions of tree species and growth models under climate change scenarios. In fact, adequate and current knowledge of ecological requirements for forest tree species represents the main source of information for future projections and forest ecosystem assessments.

  Materials and methods 

Materials

The spatial distributions of tree species in Italy were determined from the raw INFC2005 data freely available at ⇒ https:/­/­www.­inventarioforestale.­org/­ ([5]). INFC2005 was based on a three-phase sampling procedure with 13m-radius plots located at the intersections of a 1 × 1 km grid. Such scheme gave a statistical robustness to this dataset and can be used for further analysis. Here we used data for all 7.272 plots from the INFC2005 third phase that were visited in the field between 2006 and 2007. For each plot, data for the callipered trees are in the form of 230.874 tree records which served as a key source of information for species distribution analysis.

We considered 19 forest tree species selected as the most representative based on economic, ecological and landscape factors: European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.), Norway spruce (Picea abies Karst.), downy oak (Quercus pubescens Willd), Turkey oak (Quercus cerris L.), common chestnut (Castanea sativa Mill.), holm oak (Quercus ilex L.), European larch (Larix decidua Mill.), black pine (Pinus nigra Arnold), cork oak (Quercus suber L.), sessile oak (Quercus petraea Liebl.), Aleppo pine (Pinus halepensis Mill.), maritime pine (Pinus pinaster Ait.), Corsican pine (Pinus nigra Arnold subsp. laricio Palib. ex Maire - synon. Pinus laricio Poir.), stone pine (Pinus pinea L.), pedunculate oak (Quercus robur L.), arolla pine (Pinus cembra L.), Mediterranean cypress (Cupressus sempervirens L.) and Douglas fir (Pseudotsuga menziesii [Mirb.] Franco). The number of forest inventory plots by species is reported in Tab. 1. For this study, Bernetti ([3]) was considered the sole reference regarding the climatic limits of Italian forest tree species. Bernetti ([3]) describes 81 species with respect to botanical, geographic and ecological factors and includes potential climatic ranges based on mean annual temperature (MAT) and total annual precipitation (TAP - Tab. 1).

Tab. 1 - List of the studied tree species and their ecological ranges for temperature and precipitation as reported in the literature ([3]). (n): number of plots of the INFC2005 where the species was detected; (%): percentage of the total number of observations; (MinTmean, MaxTmean): minimum and maximum average annual temperatures, respectively; (MinPrec, MaxPrec): minimum and maximum total annual precipitation, respectively.

Code Species Observations Ecological range from literature
n % MinTmean MaxTmean MinPrec MaxPrec
10 Abies alba 210 2.8 6 12 1200 1500
280 Castanea sativa 865 11.4 10 14 700 2400
60 Cupressus sempervirens 42 0.6 12 17 800 1200
330 Fagus sylvatica 1003 13.2 6 12 1200 1500
80 Larix decidua 465 6.1 1 5 400 700
20 Picea abies 715 9.4 3 7 400 2000
40 Pinus cembra 58 0.8 1 5 400 2000
42 Pinus halepensis 155 2.0 15 23 300 400
45 Pinus laricio 104 1.4 7 12 1400 1800
49 Pinus nigra 329 4.3 7 12 1400 2900
47 Pinus pinaster 113 1.5 14 30 800 1200
43 Pinus pinea 93 1.2 14 18 350 600
90 Pseudotsuga menziesii 33 0.4 8 13 700 1500
300 Quercus cerris 1078 14.2 10 14 700 2400
311 Quercus ilex 494 6.5 12 17 800 1200
307 Quercus petraea 155 2.0 10 15 700 2400
308 Quercus pubescens 1392 18.4 10 14 700 2400
302 Quercus robur 89 1.2 10 15 700 2400
313 Quercus suber 179 2.4 14 18 600 800

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Climatic temperature and precipitation data were derived from a 1-km downscaled climatological maps for Italy for the 1981-2010 period developed from the E-OBS database. Specifically, these climatic data were derived using a downscaled procedure via a spatially-weighted regression model fully described by Maselli et al. ([34]). The significant underestimation of mapped rainfall reported by Maselli et al. ([34]) was corrected using ground measurements reported by Fibbi et al. ([20]).

Methods

Because INFC2005 plots may include multiple tree species ([6]), we omitted species representing less than 15% of the plot basal area ([22]). The dataset included a total of 7.272 tree species observations. For each georeferenced INFC2005 plot we further extracted the total rainfall and average annual temperatures from the downscaled E-OBS 1-km resolution maps.

All spatial analysis were done in the R statistical language ([41]).

  Results 

The distributions of 19 tree species from INFC2005 plots relative to TAP and MAT are graphically reported in Tab. 2 and Fig. 1 along with the comparisons to the potential limits for these variables reported by Bernetti ([3]). In Fig. 1 a bi-dimensional graph for each species is presented, with MAT values as the x-axis and TAP as the y-axis. On the side opposite to the axes, density distribution graphs have been added to characterize the frequency of records across the analyzed ecological ranges. Asymmetric distributions were often observed, mainly for rainfall. This is confirmed by the skewness and smaller ranges for the histograms, i.e., the distribution tails were often outside literature limits or were poorly characterized. The current observed MAT and TAP distribution limits for the 19 Italian forest tree species are reported in Fig. 2.

Tab. 2 - Results of the spatial overlay between INFC2005 plots and interpolated climatic data used in this study. (MinTmean, Tmean, MaxTmean): minimum, mean and maximum average annual temperatures, respectively; (MinPmean, Pmean, MaxPmean): minimum, mean and maximum average annual precipitation, respectively.

Species Temperature Range Precipitation Range
MinTmean Tmean MaxTmean MinPmean Pmean MaxPmean
Abies alba 2.10 8.03 15.78 676 1310 2002
Castanea sativa 3.80 11.76 17.20 669 1238 2257
Cupressus sempervirens 10.78 14.00 17.95 487 865 1359
Fagus sylvatica 3.07 9.15 15.78 742 1361 2708
Larix decidua -0.91 5.40 11.56 589 1067 1914
Picea abies -0.88 6.32 12.86 570 1170 2446
Pinus cembra 0.85 3.27 6.86 642 942 1213
Pinus halepensis 11.53 14.92 17.60 447 772 1310
Pinus laricio 9.46 11.81 15.20 752 1116 1543
Pinus nigra 5.44 11.31 16.11 663 1172 2441
Pinus pinaster 9.81 13.19 16.38 614 1039 1789
Pinus pinea 11.75 14.99 17.97 480 831 1345
Pseudotsuga menziesii 6.99 11.26 14.80 802 1261 1929
Quercus cerris 7.51 12.54 17.07 607 1011 1847
Quercus ilex 8.60 14.07 17.53 507 883 1529
Quercus petraea 5.78 11.73 16.18 546 1188 1999
Quercus pubescens 5.16 12.82 17.66 527 965 2098
Quercus robur 9.13 13.16 16.87 649 1002 1810
Quercus suber 12.37 15.00 18.01 473 751 1347

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Fig. 1 - Distribution of the 19 tree species in terms of average annual temperature (x-axis) and total annual precipitation (y-axis). The limits of the species’ ecological range from the literature are highlighted as a red square. Marginal histograms represent the frequency distribution of records.

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Fig. 2 - Box-plots for temperature (above) and precipitation (below) values retrieved from INFC data for the 19 different forest tree species. The limits of the species’ ecological range retrieved from the literature are reported as red rectangles.

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The spatial analysis shows that the climatic ranges proposed by Bernetti ([3]) are generally appropriate. Of the total number of observations for the 19 species, 46% fall within the joint temperature and precipitation ranges, 70% fell within the ranges for TAP alone, and 80% fell within the ranges for MAT alone. Similarities between current observed and Bernetti ([3]) potential ranges differed by species (Fig. 3). For the species of the Fagaceae family which represent almost the 70% of the observations, the limits of our current observed distributions are similar to the potential limits reported by Bernetti ([3]): for all species of this family the current observed limits fell within the Bernetti ([3]) limits (Fig. 2). For the genus Quercus, at least 60% of the observations (with the exception of the most Mediterranean species, Quercus ilex and Quercus suber) were usually within the potential limits for both MAT and TAP. Q. ilex tends to grow in drier conditions than those described by Bernetti ([3]) with current observed TAP of 883 mm versus a potential minimum of 800 mm, while Q. suber tends to be distributed in cooler and more humid areas than the potential limits of Bernetti ([3]).

Fig. 3 - Proportion of observations falling within the ecological range from the literature for each species and the whole dataset (“All sp.”) concerning temperature (red), precipitation (blue) and both (green).

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The current observed distribution of Castanea sativa is similar to the potential distribution, with 70% of the observations falling within both the temperature and precipitation potential limits. Also, for Fagus sylvatica the temperature limits are similar, while for precipitation the observations show that beech forests are also present in extremely rainy sites. From this perspective, the maximum potential TAP limit of 1500 mm reported by Bernetti ([3]) is too low.

For the Pinaceae family the situation is different. For the genus Pinus, except for Pinus cembra where current observed and potential limits were similar, our results demonstrated that these species tend to grow in conditions that differ from the potential limits reported by Bernetti ([3]). The limits of the current observed distribution of Pinus pinea, Pinus nigra and Pinus laricio are similar to the potential limits for temperature but not for precipitation. Pinus pinea tends to grow in conditions that are rainier than those predicted by Bernetti ([3]) who report a maximum potential of 600 mm versus the current observed average of 831 mm. On the contrary, Pinus nigra and Pinus laricio are currently distributed in drier conditions than those reported by Bernetti ([3]) with current observed TAP of 1172 and 1116 mm respectively for P. nigra and P. laricioversus a minimum potential of 1400 mm for both species.

The limits of the current observed distributions of Pinus pinaster and Pinus halepensis are generally similar to the potential precipitation limits but not the potential temperature limits. In fact, both these species tend to grow in warmer conditions than those reported by Bernetti ([3]) with 14 °C and 15 °C as minimum MAT value reported by Bernetti ([3]). Abies alba tends to be more plastic than reported by Bernetti ([3]) in that it can be found in conditions with both more or less rainfall than the potentials. Bernetti ([3]) reported a potential minimum TAP value of 1200 mm and a potential maximum of 1500 mm, while the observation averages are 1310 mm with minimum of 676 mm and maximum of 2002 mm. The current observed limits of the distributions for Picea abies are similar to the potential limits with almost all precipitation observations within the potential limits and almost 70% of the temperature observations within the potential limits. P. abies also tends to grow in slightly warmer conditions than the potential with observed MAT of 6.3 °C which is very close to the maximum limit of 7 °C reported by Bernetti ([3]).

Larix decidua tends to grow in warmer and more humid conditions than those reported by Bernetti ([3]). For precipitation the current observed average was 1067 mm versus a potential maximum of 700 mm, while for temperature the current observed average was 5.4 °C versus 5 °C as the potential maximum.

Finally, for Cupressus sempervirens current observed and potential distributions were generally similar, especially for temperature for which almost 90% of the observations were within the potential limits.

  Discussion 

Traditional knowledge about potential climatic limits for Italian forest tree species was found to be only partially consistent with the data we derived from the current observed spatial distributions, particularly for some species of the Pinaceae family. Unfortunately, it is difficult to determine if these inconsistencies are due to inadequate characterisations of species potential limits or to the results of forest management and reforestation programmes ([9], [17]). Actually, foresters often distributed forest tree species beyond their geographical limits (i.e., the expected ecological domain), especially after the First and the Second World Wars. In addition, it is important to note that such particular are represented, in our analysis, by a relatively limited number of observations from the NFI database and that some uncertainties may arise from the mappings of the climatic data. In particular, temperature is generally easier to map than rainfall whose distribution is more irregular and has a more complex dependence on altitude ([34]). This problem was partly addressed for this study using the correction proposed by Fibbi et al. ([20]), thereby reducing the inaccuracy of the rainfall estimates where the density of the original E-OBS stations was small.

To frame our results in a European context, a simple graphical comparison has been conducted using graphs provided in the JRC European Atlas of forest tree species ([44]). However, as already mentioned, no tables neither numerical supplementary data were delivered in addition to the full text file and the comparison was possible for all the species with some exceptions. This has been performed in order to include the “Italian forests” in a broader context. Pinus laricio is absent from the European Atlas, while Quercus petraea and Quercus robur are grouped therein as well as Pinus halepensis and Pinus brutia. The comparison is, therefore, only indicative and is reported here simply to provide hints about the comparison of Italian population relative to Europe populations. Indeed, sensible differences are possible between different meteorological data used. Nonetheless, italian tree species populations are generally within European Atlas limits, with some exceptions. Moreover, the climatic ranges that we observed in Italy are narrower than the Europe ranges for some species (as expected given the smaller study area), particularly for temperature. Concerning rainfall, a restricted range is clearly detectable for Italian populations of stone pine, Douglas fir and peduncolate oak for which Italian minima are greater than European minima, while the Italian maxima are less than the European maxima. Italian populations of arolla pine, Mediterranean cypress, cork oak and Norway spruce grow in conditions that are drier than the European range limit. The Italian populations of common chestnut, European beech, Turkey oak, black pine, maritime pine and Downy oak seem to be slightly shifted to more humid conditions, with Italian minima and maxima greater than the European limits. Finally, the observed precipitation ranges for Italian Silver fir populations were greater than the ranges reported in the European Atlas. For the other species differences relative to the European Atlas were less relevant.

Regarding temperature, the ecological range of Italian populations of the genus Quercus was shifted to slightly warmer conditions relative to European populations, with Italian climatic minima for these species greater than the European minima. Climatic maxima for Italian and European populations were similar, except for sessile and pedunculate oaks for which the Italian climatic maxima were greater than the European maxima. A similar situation was observed for species of the genus Pinus (black pine, maritime pine and stone pine), Mediterranean cypress and Douglas Fir.

As for European larch, Norway spruce and Arolla pine, European populations are located in slightly colder areas than Italian populations, with European climatic minima greater than Italian minima. Finally, Italian populations of common chestnut are shifted to slightly colder conditions relative to European populations with the current observed Italian temperature minimum smaller than the corresponding European minimum.

In recent years, marginal and peripheral forest populations have gained unique importance with respect to information they provide regarding the potential of forest tree species to adapt to ecological stresses ([23]). The new quantitative data provided by this study can be used to identify stands that may be adversely affected by the effects of climate change effects earlier than those located in the core of the geographic distribution. This information can be fundamental in Italy and more generally in the Mediterranean region, both of which are considered important European biodiversity hotspots featuring unique species richness ([36], [23], [32]). Moreover, the Mediterranean region is also considered to be seriously threatened by future climate change effects ([43], [29]). Mediterranean trees species are classified among many different taxa with a large biodiversity levels that, in part, originated as adaptive responses to previous climate changes ( [2]). Indeed, many recent research efforts have focused on populations living at marginal ecological domains in the Mediterranean region ([23], [33]). Both biodiversity conservation and sustainable forest management issues may be supported by the results of this study. Besides conservation, inaccurate characterization of environmental conditions characteristic of current growing zones may produce inaccurate future projections of ecosystem services and timber from productive forests, and consequently a loss of economic return ([42]). In such a context, the recently released georeferenced raw data from the last Italian NFI ([5]) represent a new source of consistent, empirical big-data in the form of real information regarding climatic and growth conditions for the most important Italian forest trees species that circumvents the traditional reliance on expert opinion and out-dated observations. In addition to climatic conditions, soil attributes, which are also a fundamental for describing forest species distributions, can mitigate or amplify climatic drivers ([7], [46]). Future analyses should also consider features such as soils, but a consistent source of quantitative soil information at the national level is still not publicly available in Italy.

  Conclusions 

For 7.272 plots of the Italian National Forest Inventory, we calculated average annual temperatures and precipitation from 1 km resolution climatic data. Using these data, we compared the current observed ecological distribution of the 19 most important tree species in Italy to the expert knowledge potential limits reported by Bernetti ([3]). We found that climatic limits and potential information should be probably revised for some of the species, particularly for some conifers and more frequently for precipitation data.

The public availability of georeferenced, national forest inventory (NFI), plot-level data is fundamental for ecological forest studies ([12]). Further evidence concerning growth trends provided by the next inventory cycle, INFC 2015 which is still in progress, will increase the knowledge about existing adaptive traits across Italy and will allow comparison among and within the plots. On the other side, new interpolation techniques and methodological research on climate may increase accuracy and precision with respect to climatic information. Knowledge of the actual distribution of forest species and ecological niches is fundamental for both spatial and process-based simulation models used to deal with future scenarios. Thus, this study should motivate more detailed analyses on species distribution which could be used to identify country-level, future forest management strategies.

  Acknowledgments 

The Authors are very grateful Dr. Ronald E. McRoberts from USDA Forest Service for his careful and meticulous work in reviewing the draft manuscript and for all his comments which improved the structure and the content of this manuscript.

This study was partially supported by the PhD grant provided by the University of Florence to Matteo Pecchi in the framework of the project “Effetti di eventi estremi a seguito di cambiamenti climatici su ambienti naturali e semi-naturali. Impatto, mitigazione e resilienza”.

  References

(1)
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EHT, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim JH, Allard G, Running SW, Semerci A, Cobb N (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259: 660-684.
CrossRef | Gscholar
(2)
Benito Garzón M, Sánchez De Dios R, Sáinz Ollero H (2007). Predictive modelling of tree species distributions on the Iberian Peninsula during the Last Glacial Maximum and Mid-Holocene. Ecography 30: 120-134.
CrossRef | Gscholar
(3)
Bernetti G (1995). Selvicoltura speciale [Special silviculture]. UTET, Torino, Italy, pp. 415. [in Italian]
Gscholar
(4)
Boisvert-Marsh L, Périé C, De Blois S (2014). Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere 5: art83.
CrossRef | Gscholar
(5)
Borghetti M, Chirici G (2016). Raw data from the Italian National Forest Inventory are on-line and publicly available. Forest@ 13: 33-34. [in Italian with English summary]
CrossRef | Gscholar
(6)
Bravo-Oviedo A, Pretzsch H, Ammer C (2014). European mixed forests: definition and research perspectives. Forest Systems 23: 518-533.
CrossRef | Gscholar
(7)
Bréda N, Huc R, Granier A, Dreyer E (2006). Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Annals of Forest Science 63: 625-644.
CrossRef | Gscholar
(8)
Canadell JG, Raupach MR (2008). Managing forests for climate change mitigation. Science 320: 1456-1457.
CrossRef | Gscholar
(9)
Cantiani P, Marchi M (2017). A spatial dataset of forest mensuration collected in black pine plantations in central Italy. Annals of Forest Science 74: 50.
CrossRef | Gscholar
(10)
Chirici G, Bottalico F, Giannetti F, Del Perugia B, Travaglini D, Nocentini S, Kutchartt E, Marchi E, Foderi C, Fioravanti M, Fattorini L, Bottai L, McRoberts RE, Corona P, Gozzini B (2017). Assessing forest windthrow damage using single-date, post-event airborne laser scanning data. Forestry 1-11.
CrossRef | Gscholar
(11)
Corona P (2015). Forestry research to support the transition towards a bio-based economy. Annals of Silvicultural Research 38: 37-38.
CrossRef | Gscholar
(12)
Corona P, Chirici G, McRoberts RE, Winter S, Barbati A (2011). Contribution of large-scale forest inventories to biodiversity assessment and monitoring. Forest Ecology and Management 262: 2061-2069.
CrossRef | Gscholar
(13)
De Philippis A (1937). Classificazioni ed indici del clima in rapporto alla vegetazione forestale italiana [Classifications and indices of the climate in relation to the Italian forest vegetation]. Nuovo Giornale Botanico Italiano 44: 1-169. [in Italian]
Gscholar
(14)
Del Favero R (2004). I boschi delle regioni alpine italiane: tipologia, funzionamento, selvicoltura [The woods of Italian Alpine regions: typology, functioning, silviculture]. CLEUP, Padua, Italy, pp. 600. [in Italian]
Gscholar
(15)
Del Favero R (2008). I boschi delle regioni meridionali e insulari d’Italia: tipologia, funzionamento, selvicoltura [The woods of southern and insular regions of Italy: typology, functioning, silviculture]. CLEUP, Padua, Italy, pp. 472.
Gscholar
(16)
Del Favero R (2010). I boschi delle regioni dell’Italia centrale: tipologia, funzionamento, selvicoltura [The wood of central regions of Italy: typology, functioning, silviculture]. CLEUP, Padua, Italy, pp. 426. [in Italian]
Gscholar
(17)
Del Perugia B, Travaglini D, Bottalico F, Nocentini S, Rossi P, Salbitano F, Sanesi G (2017). Are Italian stone pine forests (Pinus pinea L.) an endangered coastal landscape? A case study in Tuscany (central Italy). Italian Journal of Forest and Mountain Environments 72: 103-121.
CrossRef | Gscholar
(18)
Di Biase RM, Fattorini L, Marchi M (2018). Statistical inferential techniques for approaching forest mapping. A review of methods. Annals of Silvicultural Research 42 (2): 46-58.
CrossRef | Gscholar
(19)
Ferrara C, Marchi M, Fares S, Salvati L (2017). Sampling strategies for high quality time-series of climatic variables in forest resource assessment. iForest - Biogeosciences and Forestry 10: 739-745.
CrossRef | Gscholar
(20)
Fibbi L, Chiesi M, Moriondo M, Bindi M, Chirici G, Papale D, Gozzini B, Maselli F (2016). Correction of a 1 km daily rainfall dataset for modelling forest ecosystem processes in Italy. Meteorological Applications 23: 294-303.
CrossRef | Gscholar
(21)
Fick SE, Hijmans RJ (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37: 4302-4315.
CrossRef | Gscholar
(22)
Giannetti F, Barbati A, Mancini L, Travaglini D, Bastrup-Birk A, Canullo R, Nocentini S, Chirici G (2018). European Forest types: toward an automated classification. Annals of Forest Science 75: 6.
CrossRef | Gscholar
(23)
Hampe A, Petit RJ (2005). Conserving biodiversity under climate change: the rear edge matters. Ecology Letters 8: 461-467.
CrossRef | Gscholar
(24)
Haylock MR, Hofstra N, Klein Tank AM, Klok EJ, Jones PD, New M (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 113 (D20): 1691.
CrossRef | Gscholar
(25)
IPCC (2001). climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Interngovernmental Panel on Climate Change (Houghton, JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 881.
Gscholar
(26)
IPCC (2014). Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Field CB, Barros VR, Dokken DJ, Mach KI, Manstrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1132.
Gscholar
(27)
Iverson LR, Prasad AM, Matthews SN, Peters M (2008). Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management 254: 390-406.
CrossRef | Gscholar
(28)
Johnson KD, Birdsey R, Finley AO, Swantaran A, Dubayah R, Wayson C, Riemann R (2014). Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance and Management 9: 1-11.
CrossRef | Gscholar
(29)
Lelieveld J, Hadjinicolaou P, Kostopoulou E, Chenoweth J, El Maayar M, Giannakopoulos C, Hannides C, Lange MA, Tanarhte M, Tyrlis E, Xoplaki E (2012). Climate change and impacts in the Eastern Mediterranean and the Middle East. Climatic Change 114: 667-687.
CrossRef | Gscholar
(30)
Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, Lexer MJ, Marchetti M (2010). Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management 259: 698-709.
CrossRef | Gscholar
(31)
Marchetti M, Vizzarri M, Lasserre B, Sallustio L, Tavone A (2015). Natural capital and bioeconomy: challenges and opportunities for forestry. Annals of Silvicultural Research 38: 62-73.
CrossRef | Gscholar
(32)
Marchi M, Ducci F (2018). Some refinements on species distribution modelling using tree-level National Forest Inventories for supporting forest management and marginal forest population detection. iForest - Biogeosciences and Forestry 11: 291-299.
CrossRef | Gscholar
(33)
Marchi M, Nocentini S, Ducci F (2016). Future scenarios and conservation strategies for a rear-edge marginal population of Pinus nigra Arnold in Italian central Apennines. Forest Systems 25: e072.
CrossRef | Gscholar
(34)
Maselli F, Pasqui M, Chirici G, Chiesi M, Fibbi L, Salvati R, Corona P (2012). Modeling primary production using a 1 km daily meteorological data set. Climate Research 54: 271-285.
CrossRef | Gscholar
(35)
Mauri A, Strona G, San-Miguel-Ayanz J (2017). EU-Forest, a high-resolution tree occurrence dataset for Europe. Scientific Data 4: 160123.
CrossRef | Gscholar
(36)
Médail F, Quézel P (1997). Hot-spots for conservation of plant diversity in the Mediterranean Basin. Annals of the Missouri Botanical Garden 84: 122-127.
Gscholar
(37)
Nocentini S, Buttoud G, Ciancio O, Corona P (2017). Managing forests in a changing world: the need for a systemic approach. A review. Forest Systems 26 (1): eR01.
CrossRef | Gscholar
(38)
Pavari A (1916). Studio preliminare sulla coltura di specie forestali esotiche in Italia. Prima parte generale [Preliminary study on the cultivation of exotic forest species in Italy. First general part]. Annali del Regio Istituto Superiore Nazionale Forestale vol. I - 191, pp. 221. [in Italian]
Gscholar
(39)
Pedrotti F (2013). Geobotanical mapping and its levels of study. In: “Plant and Vegetation Mapping. Geobotany Studies (Basics, Methods and Case Studies)”. Springer- Verlag Berlin Heidelberg, Germany, pp. 1-6.
CrossRef | Gscholar
(40)
Petr M, Boerboom L, Ray D, Van Der Veen A (2014). An uncertainty assessment framework for forest planning adaptation to climate change. Forest Policy and Economics 41: 1-11.
CrossRef | Gscholar
(41)
R Development Core Team (2018). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar
(42)
Ray D, Petr M, Mullett M, Bathgate S, Marchi M, Beauchamp K (2017). A simulation-based approach to assess forest policy options under biotic and abiotic climate change impacts: a case study on Scotland’s National Forest Estate. Forest Policy and Economics [in press].
CrossRef | Gscholar
(43)
Resco De Dios V, Fischer C, Colinas C (2007). Climate change effects on mediterranean forests and preventive measures. New Forests 33: 29-40.
CrossRef | Gscholar
(44)
San-Miguel-Ayanz J, De Rigo D, Caudullo G, Durrant HT, Mauri A (2016). European atlas of forest tree species. Publication Office of the European Union, Luxembourg, pp. 202.
CrossRef | Gscholar
(45)
Ummenhofer CC, Meehl GA (2017). Extreme weather and climate events with ecological relevance: a review. Philosophical Transactions of the Royal Society B: Biological Sciences 372: 20160135.
CrossRef | Gscholar
(46)
Van Der Maaten-Theunissen M, Bümmerstede H, Iwanowski J, Scharnweber T, Wilmking M, Van Der Maaten E (2016). Drought sensitivity of beech on a shallow chalk soil in northeastern Germany - a comparative study. Forest Ecosystems 3: 24.
CrossRef | Gscholar
(47)
Wagner S, Nocentini S, Huth F, Hoogstra-Klein M (2014). Forest Management Approaches for Coping with the Uncertainty of Climate Change: Trade-Offs in Service Provisioning and Adaptability. Ecology and Society 19: 397-412.
CrossRef | Gscholar
(48)
Williams MI, Dumroese RK (2013). Preparing for climate change: forestry and assisted migration. Journal of Forestry 111: 287-297.
CrossRef | Gscholar

Authors’ Affiliation

(1)
Matteo Pecchi
Francesca Giannetti 0000-0002-4590-827X
Iacopo Bernetti 0000-0003-2297-1070
Davide Travaglini 0000-0003-0706-2653
Gherardo Chirici 0000-0002-0669-5726
geoLAB - Laboratory of Forest Geomatics, Department of Science and Technology in Agriculture, Food, Environment and Forestry, University of Florence, Firenze (Italy)
(2)
Maurizio Marchi 0000-0002-6134-1744
Piermaria Corona 0000-0002-8105-0792
CREA - Research Centre for Forestry and Wood, Arezzo (Italy)
(3)
Marco Bindi 0000-0002-8968-954X
Department of Science and Technology in Agriculture, Food, Environment and Forestry, University of Florence, Florence (Italy)
(4)
Marco Moriondo 0000-0002-8356-7517
Fabio Maselli 0000-0001-6475-4600
Luca Fibbi 0000-0001-6985-6809
CNR-IBIMET, via Madonna del Piano 10, 50019, Sesto Fiorentino, Florence (Italy)

Corresponding author

 
Matteo Pecchi
matteo.pecchi@unifi.it

Citation

Pecchi M, Marchi M, Giannetti F, Bernetti I, Bindi M, Moriondo M, Maselli F, Fibbi L, Corona P, Travaglini D, Chirici G (2019). Reviewing climatic traits for the main forest tree species in Italy. iForest 12: 173-180. - doi: 10.3832/ifor2835-012

Academic Editor

Marco Borghetti

Paper history

Received: May 04, 2018
Accepted: Feb 01, 2019

First online: Mar 15, 2019
Publication Date: Apr 30, 2019
Publication Time: 1.40 months

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

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