On the relationship between maximum tree height, elevation and climate in European mountain ranges

On the relationship between maximum tree height, elevation and climate in European mountain ranges | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article On the relationship between maximum tree height, elevation and climate in European mountain ranges Pere Gelabert, Marcos Rodrigues, Lluís Coll, Cristina Vega-Garcia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3062579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2024 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Mountain forests face significant threats from global change and spatio-temporal variation in tree height can help to monitor these effects. While tree height is typically measured through field inventories, remote sensing can provide accurate and extensive forest structure data. In this study, we used the Global Ecosystem Dynamics Investigation space-borne laser sensor (GEDI ) to examine the relationship between maximum tree height and elevation, temperature, and precipitation in the main European mountain ranges. We found a non-linear relationship between elevation and maximum tree height in all mountain ranges, supporting the existence of a common breakpoint that marks the beginning of the tree development limitation. Temperature and precipitation were identified as the most important drivers of tree height variation. Additionally, we predicted significant upward displacement of the breakpoint under climate change scenarios, potentially increasing the area without growth limitations for trees. However, the displacement of the breakpoint may not align with the movement of the treeline, impacting on alpine ecosystems. These findings contribute to understanding the impacts of global warming on mountain forest ecosystems and provide insights for their monitoring and managing. Earth and environmental sciences/Ecology/Forest ecology Earth and environmental sciences/Ecology/Forestry Figures Figure 1 Figure 2 Figure 3 Main Text Mountain forests account for 23% of the forested lands worldwide and sustain about half of the world’s population 1 . They provide a wide array of goods and services such as timber, shelter from animals, air purification and carbon sequestration. However, their persistence and profitability are seriously threatened by the advent of global change 2 . Tree height is a good indicator of biological productivity and site quality 3 and thus it has recently been used to produce continuous estimates of aboveground biomass and carbon storage 4 , 5 . It is also a proxy for ecosystem structure, being its heterogeneity an essential variable for predicting species richness at different scales 6 , 7 . Moreover, tree height can help monitor and anticipate the effects of global change on forest ecosystems 3 . Many studies point at climate as the main driver of the maximum height development of trees 3 , 8 , 9 . Overall, temperature seems to limit maximum tree height at high latitudes and elevations 8 – 10 , whereas water availability mostly influence tree development in the lowlands 11 , 12 . Tree height is often analyzed at the local scale via field-based forest inventories 13 , which are expensive and time-consuming. Recently, remote sensing methods, and particularly the use of LiDAR sensors, have been used to produce accurate and dense samplings of forest structure at large spatial extents 14 . In a recent study leveraging airborne LiDAR 15 – 17 we reported non-linear relations between elevation and maximum tree height in the Pyrenees 18 . In particular, we found piecewise response of maximum tree height with a sharp downward profile above a certain elevation threshold or breakpoint 18 , 19 . These results showed the existence of region-wide patterns in maximum tree height decline with elevation but whether or not this piecewise pattern is prevalent in other mountain regions has not been confirmed yet. In this regard, space-borne laser sensors, such as Global Ecosystem Dynamics Investigation (GEDI) offer a unique solution for tree height sampling at a global scale 20 . GEDI is an active remote sensing laser sensor onboard the International Space Station that provides detailed 3D information about forest structure and responses at the regional/global level. The GEDI program offers global coverage between 51.6 ºS-51.6ºN latitudes, making it ideal for evaluating hypotheses on the generality of patterns, and processes. In this work, we use the GEDI full-waveform space-borne laser instrument to investigate the relationship between maximum tree height and elevation, temperature, and rainfall for the main European mountainous ranges. Our main goals were (i) to ascertain whether the existence of a ‘breakpoint’ in the response of maximum tree height to elevation holds over the main European mountain ranges; (ii) to identify the climatic drivers of variations in maximum tree height across mountain ranges; and (iii) to foresee the displacement of the breakpoint under different climate change scenarios. We hypothesize that the breakpoint observed in the Pyrenees holds over the main European mountain ranges, making this breakpoint a useful indicator to monitor the early impacts of global warming on mountain forest ecosystems. Maximum tree-height decrease in elevation follows a non-linear profile We observed a non-linear response in the elevation - maximum tree height relationship along the main European mountain systems. The data supported the existence of a unique breakpoint in all mountain ranges but the elevation where it appears varied across mountain ranges (Figure 1). The Caucasus (1740.2 ± 3.9 m.a.s.l.) displayed the tipping point at higher elevations than the Pyrenees (1490.9 ± 5.7 m.a.s.l.), the Alps (1474.3 ± 10.8 m.a.s.l.) and the Carpathians (1416.2 ± 7.0 m.a.s.l.). Above this breakproint, maximum tree height declined faster in Eastern Mountain ranges – Carpathians, -3.31m /100m; Caucasus -2.28m/100m – as opposed to mountain ranges under greater oceanic influence – Alps, -1.4m/100m; Pyrenees, -1.24 m/100m. Non-linear modeling alternatives outperformed the linear regression baseline in all mountain ranges (Table1 and Table ED1) with both a gaussian and a segmented model providing a similar fit (R 2 >0.74). The drivers of maximum tree height decrease with elevation Models predicting maximum tree height from elevation were generally as good as the best model using climatic covariates, if not better (Table 1 and Table ED1). For the Alps, the Caucasus and the Carpathians, maximum temperature was the best climatic predictor of maximum tree height variation. In the Pyrenees, however, precipitation emerged as the most influential factor (Table 1). Non-linear relationships were observed for all climate factors, with strong evidence for piecewise trends and the existence of a breakpoint, except for precipitation in the Caucasus and the Carpathian Mountains (Table 1). Notwithstanding, the position of the elevation breakpoint was always closely related to either maximum or minimum temperature, precipitation, or both. For instance, the elevation breakpoint falls within the 1 st and 3 rd quartile of the maximum temperature isotherm altitude in all mountain ranges. However, in the Alps and the Pyrenees, the position of the breakpoint seems to be more closely related to Tmin and precipitation, respectively (Figure 2). This indicates that elevation can integrate the effects of the most influential climate driver(s), which makes it a powerful indirect indicator. Table 1: Summary of model performance and breakpoint location. Bold font indicates the highest correlation per variable; gray shading indicates the highest correlation per mountain system. R 2 , Pearson’s squared R; P, precipitation; Tmax, maximum temperature; Tmin, minimum temperature; Elev, elevation. R 2 Breakpoint (segmented only) Model P T max T min Elev P T max T min Elev Alps Linear 0.09±0.01 0.01±0.00 0.00±0.00 0.53±0.02 1535.97 ±3.62 11.08 ±0.01 1.79 ±0.01 1474 ±10.82 Segmented 0.24±0.01 0.84 ±0.01 0.77 ±0.00 0.78±0.00 Gaussian 0.14±0.01 0.82±0.01 0.74±0.00 0.80 ±0.00 Pyrenees Linear 0.28±0.01 0.00±0.00 0.10±0.01 0.40±0.01 1143.8 ±3.06 13.34 ±0.05 4.08 ±0.08 1490 ±5.74 Segmented 0.85 ±0.02 0.67±0.01 0.50±0.01 0.71 ±0.01 Gaussian 0.81±0.01 0.74 ±0.01 0.55 ±0.01 0.68±0.01 Carpathian Linear 0.36±0.04 0.53±0.01 0.61±0.00 0.58±0.00 - 7.4± 0.02 -0.21 ±0.01 1416 ±7.05 Segmented 0.61 ±0.04 0.86±0.01 0.84±0.00 0.85±0.00 Gaussian 0.36±0.01 0.87 ±0.00 0.85 ±0.00 0.88 ±0.00 Caucasus Linear 0.23±0.02 0.36±0.00 0.27±0.00 0.46±0.00 - 9.84 ±0.02 0.4 3±0.01 1740 ±3.95 Segmented 0.86 ±0.02 0.91 ±0.00 0.89 ±0.00 0.92 ±0.00 Gaussian 0.22±0.01 0.89±0.00 0.84±0.00 0.90±0.00 Climate change effects on the breakpoint position All the considered climate change models and scenarios foresee an increase in temperature. Since tree height was closely tied to temperature, we expect a generalized upward shift in the altitude breakpoints by the end of the century, tracking the increases in temperature predicted by all the scenarios. Under the Shared Socioeconomic Pathway 245 (SSP245; CO 2 emissions around current levels until 2050, then falling but not reaching net zero by 2100) the upward displacement of the breakpoint would lead to an increase of 20% in the surface where maximum tree height growth is not limited by temperature. Instead, the area without growth limitations under SSP 585 (CO 2 emissions triple by 2075) increased between 30% and 40%, reaching the or very close to the elevation of the estimated tree line. The general trend described above is not strictly observed for the Carpathians Mountains, where the current breakpoint we estimated is already quite close to the treeline, and hence does not allow for further expansion of the non-limited growth area (Figure 3). Discussion Maximum tree height showed a clear non-linear response along the altitudinal gradient in the four mountain ranges analyzed, supporting the existence of a common pattern in the control of the height development of tree vegetation. The non-linear, segmented trend allows the detection of an explicit breakpoint above which maximum tree height starts to drop linearly with elevation. Given the slim differences in performance between segmented and Gaussian models, we kept the segmented approach due to its ability to simplify the link into piecewise relationships and breakpoint thresholds. Besides, it facilitates further investigation regarding the strength of tree-height decrease along the elevation gradient. This breakpoint may reflect the climatic limit beyond which the suitability for vegetation growth decreases gradually 18 . In three out of the four mountain ranges we analyzed (Alps, Carpathian and Caucasus Mountains), the decrease in maximum tree height with elevation was due to thermal limitation as shown in Table 1 . Numerous studies have pointed out the limitation to tree development caused by temperature 8 , 9 , 21 , particularly in mountain ecosystems and high latitudes 8 . The strongest evidence of this phenomenon is the existence of the treeline, defined as the elevation limit of arboreal growth form 22 . However, such thermal limitation begins at much lower elevations 18 , 23 and the results of our study contribute to identify the elevation threshold where these processes start in different mountain ranges 18 , 23 . We recognize that elevation acts here as a proxy for climate variability. The uneven and sparse distribution of stations feeding climate interpolation models such as WorldClim 24 and their low spatial accuracy, hinders their reliability in mountain areas 8 , 25 . Although not without drawbacks, the high correlation of climate variables with elevation and the global availability of topographic data makes the latter a suitable variable for monitoring the effects of climate change in mountain areas. In contrast to the other mountain ranges, maximum tree height development in the Pyrenees seems to be more conditioned by precipitation than by thermal limitations. Due to its proximity to the Mediterranean Sea and its west-east disposition, a large part of the Pyrenees features a Mediterranean climate, characterized by a very marked summer drought period, especially on the southern slopes. However, even when precipitation was the main driver of maximum tree height development, elevation was also a good estimator of the breakpoint, which suggests it could be used globally as an indicator of the relationship between climate and tree height development 11 , 18 , 26 . A shift of the optimal growing conditions towards higher elevations is to be expected in response to global warming 27 (Fig. 3 ). Our results suggest an upward shift of the breakpoint, potentially reaching the current tree line, which would imply the lifting of restrictions on the growth of trees. As observed in boreal forests 28 – 30 , the upward displacement of such elevation point may imply a substantial increase in the productivity of these cold-limited forests 31 . Furthermore, these new and more developed forest communities may increase the provision of ecosystem services such as timber value and carbon sequestration 32 . Although upward shifts in the position of the treeline have already been observed as a response to global warming 33 , the displacement is far from being universal due to the complexity of factors and nuances influencing natural afforestation of treeless areas at the limit of their physiological tolerance 9 , 19 . The maximum height - elevation breakpoint, however, is more likely to relocate, since it is indicative of a physiological relationship between tree development and climate, not an actual physical limit. This may cause a mismatch between the upward shift of the breakpoint and that of the treeline, with potential consequences for the responses of alpine ecosystems to global change. Methods The study area of this research is restricted by the latitudinal scanning range of GEDI (51.6 ºS-51.6ºN). For this reason, we have analyzed the main European mountain ranges within GEDI’s reach, concretely, Pyrenees, Alps, Carpathians and Caucasus. Following the FAO global ecological zoning we established the lower limit of the analyzed mountain forests at 800m 3 4 . The Pyrenees are located along the border between Spain and France within longitudes 2.38ºW − 3.15ºE and latitudes 42.23ºN- 43.03ºN. The highest summits reach some 3,400 m above sea level. The lower elevations are dominated by a Mediterranean climate, with an average annual temperature around 8ºC and an average cumulative rainfall circa 800 mm year − 1 , characterized by summer drought. At higher altitude the conditions transition into “high mountain climate” with mild temperatures and rainfall over 2500 mm year − 1 , the wettest among the analyzed areas. The forests are dominated by Scots pine ( Pinus sylvestris ), beech ( Fagus sylvatica L. ), silver fir ( Albies alba Mill. ) and mountain pine ( Pinus uncinata ). The mountain forests of the Alps are characterized by a complex mosaic of mixed coniferous forests. The main species are spruce, fir, beech and a variety of pines. Climatically, the Alps are highly variable, with an uneven precipitation ranging from 2,300 mm near the Adriatic Sea to 800 mm in the central Alps. This mountain range is the natural border between Italy, Switzerland and France, Slovenia and Austria (5.12°E-16.1°E and 45.52°N-47.62°N), reaching nearly 4800 m at its highest summit. The Carpathians are a latitudinally oriented mountain range in central Europe, being the most continental among those analyzed. This range crosses part of northern Serbia, central Romania, eastern Ukraine, southern Poland, western Czech Republic, northern Hungary and an important part of Slovakia (18.43ºE-26.71ºE and 44.73ºN-49.1ºN). The highest peaks reach 2650 m.a.s.l.. Average yearly temperatures range from 7ºC at the lowest altitudes to 0ºC at the highest peaks. Rainfall is more abundant in the northern end (1400 mm/year) and rather scarce in the center of Romania (500 mm/year). The dominant tree species are sycamore ( Acer pseudoplatanus) , beech ( Fagus sylvatica) , silver fir (Abies alba) , and spruce (Picea abies, and Picea sylvatica) . The Caucasus Mountains are the natural border separating Georgia and Azerbaijan from Russia (39.13ºE-49.37ºE and 41.74ºN-43.13ºN). The Caucasus Mountains is the largest mountain range analyzed in terms of area and elevation, exceeding 5500 m.a.s.l. at the highest summits. At lower elevations, beech ( Fagus orientalis ) and a variety of Quercus spp. predominate. At higher elevations, forests are dominated by spruce ( Picea orientalis ), fir ( Abies nordmanniana ) and a variety of pines ( Pinus spp. ). It is the most arid mountain system analyzed, with temperatures above 10°C and precipitation of about 600 mm in lower elevations, while in the higher elevations low temperatures predominate, with an annual average of about 0°C, and precipitation of about 1000 mm/yr. 3.2. GEDI dataset We used the Global Ecosystem Dynamics Investigation (GEDI) dataset, collected between May 2019 and September 2020 35 . GEDI is a space-borne high-resolution laser ranging scanner aboard the International Space Station. GEDI was specifically designed to capture the vertical structure of the canopy layer across the temperate and tropical forests (between 51.6° latitude, northern and southern hemisphere) 20 . The GEDI instrument consists of 3 laser sensors, scanning 8 transects spaced 600 m along sensor acquisition range. Each transect collects waveforms at 25m radial footprints every 60 m along-track direction, with a geolocation error lower than 8m in X/Y coordinates and 10cm in Z. The GEDI science team derived the tree height subtracting the highest return (first received return) and the elevation, interpreted as the mode of the lowest value in the received waveform (Figure. ED1). We used the Level 2A product, which consists of footprint-level elevation and relative canopy heights (RH) referred to as the height above the ground of each energy percentile along the waveform profile. To avoid noise in canopy height detection, we selected only high quality footprints (quality flag = 1) collected during nighttime, with a sensitivity greater than 0.97, as recommended by the GEDI science team 36 . To reduce signal noise, we applied a sequence of filters. First, we restricted the analysis domain to mountain forests, i.e., locations higher than 800 m.a.s.l. according to the FAO criteria 34 . Since the noise is mainly located in the upper lands (due to the effect of steep slopes and snow cover 37 – 39 , we defined the maximum elevation where the tree stratum can be found, excluding the top 0.1% footprints to cleanse the upper tail of the distribution where the signal consists mostly of outliers and detection errors (Figure ED2 exemplifies the procedure). Then, we applied an outlier filter along the entire elevation gradient. We grouped GEDI’s footprints into 50m interval classes of elevation. Inside each 50m interval, we retained only those footprints comprised within the interval’s mean of the tree height (RH95) ± 3 standard deviations 40 . Overall, only 780,314 footprints remained after the quality control and noise reduction filtering processes. To retrieve the maximum tree height, we processed the remaining footprints retaining those with tree heights between the 90th and 95th percentiles of RH95 in each 25 m elevation interval. Temperature and precipitation data were subjected to the same procedure. In this case we defined the interval classes to cleanse noise and calculate maximum height using in their respective units (remaining 38,883). We adapted the interval size to retrieve approximately the same number of classes obtained in the case of elevation. 3.3. Ancillary data Since GEDI does not provide the actual elevation of the ground but only a baseline to calculate the relative heights, elevation data was retrieved from the NASADEM_HGT digital elevation model (DEM), an enhancement of the former STRM DEM with improved accuracy by incorporating data from SAR, LiDAR and optical sensors 41 . Climate data was retrieved from the WorldClim 2.1 24 https://www.zotero.org/google-docs/?rad2OO climate dataset. We extracted historical (1970–2000) information about minimum temperature (Tmin), maximum temperature (Tmax), and Precipitation (Prec), at a spatial resolution of 30 arc seconds (0.65 Km at latitude 45º). In addition, we also incorporated spatial predictions of Tmin and Tmax in the period 2080–2100. We retrieved and merged the predictions from eight global climate models (GCM): BCC-CSM2-MR 42 , CNRM-CM6-1 43 , CNRM-ESM2-1 44 , CanESM5 45 , IPSL-CM6A-LR 46 , MIROC-ES2L 4 7 , MIROC6 48 , MRI-ESM2-0 49 for the 245 and 585 shared socioeconomic pathways (SSPs). Since data were not available at 30 arc seconds, data were acquired at 2.5 minutes and resampled using bicubic interpolation. 2.5. Modeling After filtering the data, we ran a set of regression models to assess the strength and shape of the relationship between maximum tree height, elevation and climatic variable. To this purpose, we tested log-linear, segmented (Eq. 1 ), and Gaussian models (Eq. 2 ). Segmented models were parameterized searching for the optimal number of breakpoints through 50 iterations with 5 folds. To avoid the potential misspecification of the model due to spatial autocorrelation, we fitted 1000 models, in each of which we randomly retained half of the dataset as training dataset and the other half as test subsamples. We retrieved the median and the standard deviation of all parameter estimates across the 1000 models. In the case of segmented models, we retrieved the median and the standard deviation of the breakpoint position and the slope below and above the breakpoint. We computed the R-squared (R 2 ) and root mean standard error (RMSE) – calculated using the validation sample – as indicators of model performance. $$\text{log}\left(y\right)=\left\{\begin{array}{c}{\alpha }_{1}+{\beta }_{1}\bullet x \forall x \le \varPsi \\ {\alpha }_{2}+{\beta }_{2}\bullet x \forall x >\varPsi \end{array}\right.\#$$ 1 Where α1 and α2 are the intercepts, β1 and β2 are the slopes of the ratios above and below the cut-off point, while ψ is the cut-off point. $$\text{y}={exp\left[-\frac{1}{2}\left(\frac{x-a}{b}\right)\right]}^{2}$$ 2 Where a and b are parameters estimated by the model. Finally, the effect exerted by climate change in the position of the elevation breakpoint was assessed by forecasting the position of the climatic breakpoints under the scenarios SSP245 and SSP585 projected using the ensemble of the general circulation models. we displaced the breakpoint elevation a reason of 0.65º/100m using the increment of mean temperature at the actual breakpoint. 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Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development 12 , 2727–2765 (2019). Yukimoto, S. et al. The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component. Journal of the Meteorological Society of Japan. Ser. II advpub , (2019). Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedData.docx Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2024 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3062579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":210154180,"identity":"baa630b2-c01c-4f16-b765-991c55cb9700","order_by":0,"name":"Pere Gelabert","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8020-4932","institution":"University of Lleida","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pere","middleName":"","lastName":"Gelabert","suffix":""},{"id":210154176,"identity":"d0df4c7c-c573-4c5c-9a3d-c437af5dc370","order_by":1,"name":"Marcos Rodrigues","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Rodrigues","suffix":""},{"id":210154177,"identity":"91493f50-1efb-45c8-9237-85e2b8c3657c","order_by":2,"name":"Lluís Coll","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lluís","middleName":"","lastName":"Coll","suffix":""},{"id":210154178,"identity":"12eb1fa4-a40a-4487-bc80-a2f0c6e35857","order_by":3,"name":"Cristina Vega-Garcia","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Vega-Garcia","suffix":""},{"id":210154179,"identity":"549c67e2-f6df-46fb-b138-6a091c357c0f","order_by":4,"name":"Aitor Ameztegui","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aitor","middleName":"","lastName":"Ameztegui","suffix":""}],"badges":[],"createdAt":"2023-06-14 11:01:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3062579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3062579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-024-01246-5","type":"published","date":"2024-02-15T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":38948890,"identity":"2793e6ae-6d28-4c36-8419-27bd6e21b6d3","added_by":"auto","created_at":"2023-06-22 21:03:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1008176,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum tree height prediction by elevation in all analyzed mountain ranges: Alps (green), Pyrenees (red), Caucasus Mountains (yellow) and Carpathian (blue). Solid colored lines display the bootstrapped median of the relationship between elevation and maximum tree height; points represent tree heights between the 90\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles. Vertical dashed lines mark the position of the average breakpoint with its corresponding the numerical value ± standard deviation above. Values below the solid lines report the slope of the relationship profile beyond the breakpoint. Natural earth’s relief base map in the background.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3062579/v1/d842a4f41cb64f26e52dd56b.png"},{"id":38948892,"identity":"9d99e112-a31d-4157-89d3-639cdae8c779","added_by":"auto","created_at":"2023-06-22 21:03:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":288199,"visible":true,"origin":"","legend":"\u003cp\u003eBox and violins plots depict the elevation values bounded by isohyet or isotherm defined at breakpoint of maximum tree height-temperature or maximum tree height-precipitation relations ± 1 standard deviation in each mountain range. Red points represent the breakpoint of the relation maximum tree height-elevation at each mountain range. In precipitation plot the Carpathians and Caucasus values are not represented due to the non-segmentable pattern of the data.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3062579/v1/33885571700390a444641fd6.jpeg"},{"id":38948891,"identity":"27d7bf75-43bf-4214-a156-5ee7b869b3bd","added_by":"auto","created_at":"2023-06-22 21:03:38","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":656143,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent and future areas without growth restrictions. Areas highlighted in light purple represent the area without growth restrictions under current conditions. Areas highlighted in dark purple represent the expected area without growth restrictions by 2100. Numeric values summarize the total area without growth restriction by 2100; values in brackets report the percent increase in unrestricted area; grey values refer to the area with restrictions defined as the remaining area between the breakpoint in 2100 and the estimated treeline. Esri hillside is used as a backdrop.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3062579/v1/ebeac1b5bcd0d8e699f85ad4.jpeg"},{"id":51216745,"identity":"dc763ed2-eec8-410b-842b-0777a1b5b265","added_by":"auto","created_at":"2024-02-16 08:09:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1721710,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3062579/v1/64dd6ad8-d797-4ccd-b33c-f2b4cf16151c.pdf"},{"id":38948889,"identity":"cf43c73d-d729-4424-84f2-a932d99afd54","added_by":"auto","created_at":"2023-06-22 21:03:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":192254,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-3062579/v1/f996aa571e6156037c481e06.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"On the relationship between maximum tree height, elevation and climate in European mountain ranges","fulltext":[{"header":"Main Text","content":"\u003cp\u003eMountain forests account for 23% of the forested lands worldwide and sustain about half of the world\u0026rsquo;s population\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. They provide a wide array of goods and services such as timber, shelter from animals, air purification and carbon sequestration. However, their persistence and profitability are seriously threatened by the advent of global change\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTree height is a good indicator of biological productivity and site quality\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and thus it has recently been used to produce continuous estimates of aboveground biomass and carbon storage\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. It is also a proxy for ecosystem structure, being its heterogeneity an essential variable for predicting species richness at different scales\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, tree height can help monitor and anticipate the effects of global change on forest ecosystems\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Many studies point at climate as the main driver of the maximum height development of trees\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Overall, temperature seems to limit maximum tree height at high latitudes and elevations\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, whereas water availability mostly influence tree development in the lowlands\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTree height is often analyzed at the local scale via field-based forest inventories \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, which are expensive and time-consuming. Recently, remote sensing methods, and particularly the use of LiDAR sensors, have been used to produce accurate and dense samplings of forest structure at large spatial extents\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In a recent study leveraging airborne LiDAR\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e we reported non-linear relations between elevation and maximum tree height in the Pyrenees\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In particular, we found piecewise response of maximum tree height with a sharp downward profile above a certain elevation threshold or \u003cem\u003ebreakpoint\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These results showed the existence of region-wide patterns in maximum tree height decline with elevation but whether or not this piecewise pattern is prevalent in other mountain regions has not been confirmed yet.\u003c/p\u003e \u003cp\u003eIn this regard, space-borne laser sensors, such as Global Ecosystem Dynamics Investigation (GEDI) offer a unique solution for tree height sampling at a global scale\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. GEDI is an active remote sensing laser sensor onboard the International Space Station that provides detailed 3D information about forest structure and responses at the regional/global level. The GEDI program offers global coverage between 51.6 \u0026ordm;S-51.6\u0026ordm;N latitudes, making it ideal for evaluating hypotheses on the generality of patterns, and processes. In this work, we use the GEDI full-waveform space-borne laser instrument to investigate the relationship between maximum tree height and elevation, temperature, and rainfall for the main European mountainous ranges. Our main goals were (i) to ascertain whether the existence of a \u0026lsquo;breakpoint\u0026rsquo; in the response of maximum tree height to elevation holds over the main European mountain ranges; (ii) to identify the climatic drivers of variations in maximum tree height across mountain ranges; and (iii) to foresee the displacement of the breakpoint under different climate change scenarios. We hypothesize that the breakpoint observed in the Pyrenees holds over the main European mountain ranges, making this breakpoint a useful indicator to monitor the early impacts of global warming on mountain forest ecosystems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaximum tree-height decrease in elevation follows a non-linear profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed a non-linear response in the elevation - maximum tree height relationship along the main European mountain systems. The data supported the existence of a unique breakpoint in all mountain ranges but the elevation where it appears varied across mountain ranges (Figure 1). The Caucasus (1740.2 \u0026plusmn; 3.9 m.a.s.l.) displayed the tipping point at higher elevations than the Pyrenees (1490.9 \u0026plusmn; 5.7 m.a.s.l.), the Alps (1474.3 \u0026plusmn; 10.8 m.a.s.l.) \u0026nbsp;and the Carpathians (1416.2 \u0026plusmn; 7.0 m.a.s.l.). Above this breakproint, maximum tree height declined faster in Eastern Mountain ranges \u0026ndash; Carpathians, -3.31m /100m; Caucasus -2.28m/100m \u0026ndash; as opposed to mountain ranges under greater oceanic influence \u0026ndash; Alps, -1.4m/100m; Pyrenees, -1.24 m/100m. Non-linear modeling alternatives outperformed the linear regression baseline in all mountain ranges (Table1 and Table ED1) with both a gaussian and a segmented model providing a similar fit (R\u003csup\u003e2\u003c/sup\u003e \u0026gt;0.74).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe drivers of maximum tree height decrease with elevation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModels predicting maximum tree height from elevation were generally as good as the best model using climatic covariates, if not better (Table 1 and Table ED1). For the Alps, the Caucasus and the Carpathians, maximum temperature was the best climatic predictor of maximum tree height variation. In the Pyrenees, however, precipitation emerged as the most influential factor (Table 1). Non-linear relationships were observed for all climate factors, with strong evidence for piecewise trends and the existence of a breakpoint, except for precipitation in the Caucasus and the Carpathian Mountains (Table 1). Notwithstanding, the position of the elevation breakpoint was always closely related to either maximum or minimum temperature, precipitation, or both. For instance, the elevation breakpoint falls within the 1\u003csup\u003est\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e quartile of the maximum temperature isotherm altitude in all mountain ranges. However, in the Alps and the Pyrenees, the position of the breakpoint seems to be more closely related to Tmin and precipitation, respectively (Figure 2). This indicates that elevation can integrate the effects of the most influential climate driver(s), which makes it a powerful indirect indicator.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Summary of model performance and breakpoint location. Bold font indicates the highest correlation per variable; gray shading indicates the highest correlation per mountain system. R\u003csup\u003e2\u003c/sup\u003e, Pearson\u0026rsquo;s squared R; P, precipitation; Tmax, maximum temperature; Tmin, minimum temperature; Elev, elevation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.081632653061225%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.89795918367347%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreakpoint (segmented only)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003csub\u003emax\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003csub\u003emin\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003csub\u003emax\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003csub\u003emin\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLinear\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1535.97\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;3.62\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.08\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;0.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" rowspan=\"3\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.85\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.88\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaucasus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLinear\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;0.02\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003cp\u003e3\u0026plusmn;0.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1740\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;3.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSegmented\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86\u003c/strong\u003e\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.91\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.89\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92\u003c/strong\u003e\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGaussian\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate change effects on the breakpoint position\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the considered climate change models and scenarios foresee an increase in temperature. Since tree height was closely tied to temperature, we expect a generalized upward shift in the altitude breakpoints by the end of the century, tracking the increases in temperature predicted by all the scenarios. Under the Shared Socioeconomic Pathway 245 (SSP245; CO\u003csub\u003e2\u003c/sub\u003e emissions around current levels until 2050, then falling but not reaching net zero by 2100) the upward displacement of the breakpoint would lead to an increase of 20% in the surface where maximum tree height growth is not limited by temperature. Instead, the area without growth limitations under SSP 585 (CO\u003csub\u003e2\u003c/sub\u003e emissions triple by 2075) increased between 30% and 40%, reaching the or very close to the elevation of the estimated tree line. The general trend described above is not strictly observed for the Carpathians Mountains, where the current breakpoint we estimated is already quite close to the treeline, and hence does not allow for further expansion of the non-limited growth area (Figure 3). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaximum tree height showed a clear non-linear response along the altitudinal gradient in the four mountain ranges analyzed, supporting the existence of a common pattern in the control of the height development of tree vegetation. The non-linear, segmented trend allows the detection of an explicit breakpoint above which maximum tree height starts to drop linearly with elevation. Given the slim differences in performance between segmented and Gaussian models, we kept the segmented approach due to its ability to simplify the link into piecewise relationships and breakpoint thresholds. Besides, it facilitates further investigation regarding the strength of tree-height decrease along the elevation gradient. This breakpoint may reflect the climatic limit beyond which the suitability for vegetation growth decreases gradually\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In three out of the four mountain ranges we analyzed (Alps, Carpathian and Caucasus Mountains), the decrease in maximum tree height with elevation was due to thermal limitation as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Numerous studies have pointed out the limitation to tree development caused by temperature\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, particularly in mountain ecosystems and high latitudes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The strongest evidence of this phenomenon is the existence of the treeline, defined as the elevation limit of arboreal growth form\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, such thermal limitation begins at much lower elevations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and the results of our study contribute to identify the elevation threshold where these processes start in different mountain ranges\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We recognize that elevation acts here as a proxy for climate variability. The uneven and sparse distribution of stations feeding climate interpolation models such as WorldClim\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and their low spatial accuracy, hinders their reliability in mountain areas\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Although not without drawbacks, the high correlation of climate variables with elevation and the global availability of topographic data makes the latter a suitable variable for monitoring the effects of climate change in mountain areas. In contrast to the other mountain ranges, maximum tree height development in the Pyrenees seems to be more conditioned by precipitation than by thermal limitations. Due to its proximity to the Mediterranean Sea and its west-east disposition, a large part of the Pyrenees features a Mediterranean climate, characterized by a very marked summer drought period, especially on the southern slopes. However, even when precipitation was the main driver of maximum tree height development, elevation was also a good estimator of the breakpoint, which suggests it could be used globally as an indicator of the relationship between climate and tree height development\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA shift of the optimal growing conditions towards higher elevations is to be expected in response to global warming\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Our results suggest an upward shift of the breakpoint, potentially reaching the current tree line, which would imply the lifting of restrictions on the growth of trees. As observed in boreal forests\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, the upward displacement of such elevation point may imply a substantial increase in the productivity of these cold-limited forests\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, these new and more developed forest communities may increase the provision of ecosystem services such as timber value and carbon sequestration\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Although upward shifts in the position of the treeline have already been observed as a response to global warming\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, the displacement is far from being universal due to the complexity of factors and nuances influencing natural afforestation of treeless areas at the limit of their physiological tolerance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The maximum height - elevation breakpoint, however, is more likely to relocate, since it is indicative of a physiological relationship between tree development and climate, not an actual physical limit. This may cause a mismatch between the upward shift of the breakpoint and that of the treeline, with potential consequences for the responses of alpine ecosystems to global change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study area of this research is restricted by the latitudinal scanning range of GEDI (51.6 \u0026ordm;S-51.6\u0026ordm;N). For this reason, we have analyzed the main European mountain ranges within GEDI\u0026rsquo;s reach, concretely, Pyrenees, Alps, Carpathians and Caucasus. Following the FAO global ecological zoning we established the lower limit of the analyzed mountain forests at 800m\u003csup\u003e3\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Pyrenees are located along the border between Spain and France within longitudes 2.38\u0026ordm;W \u0026minus;\u0026thinsp;3.15\u0026ordm;E and latitudes 42.23\u0026ordm;N- 43.03\u0026ordm;N. The highest summits reach some 3,400 m above sea level. The lower elevations are dominated by a Mediterranean climate, with an average annual temperature around 8\u0026ordm;C and an average cumulative rainfall circa 800 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, characterized by summer drought. At higher altitude the conditions transition into \u0026ldquo;high mountain climate\u0026rdquo; with mild temperatures and rainfall over 2500 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the wettest among the analyzed areas. The forests are dominated by Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e), beech (\u003cem\u003eFagus sylvatica L.\u003c/em\u003e), silver fir (\u003cem\u003eAlbies alba Mill.\u003c/em\u003e) and mountain pine (\u003cem\u003ePinus uncinata\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe mountain forests of the Alps are characterized by a complex mosaic of mixed coniferous forests. The main species are spruce, fir, beech and a variety of pines. Climatically, the Alps are highly variable, with an uneven precipitation ranging from 2,300 mm near the Adriatic Sea to 800 mm in the central Alps. This mountain range is the natural border between Italy, Switzerland and France, Slovenia and Austria (5.12\u0026deg;E-16.1\u0026deg;E and 45.52\u0026deg;N-47.62\u0026deg;N), reaching nearly 4800 m at its highest summit.\u003c/p\u003e \u003cp\u003eThe Carpathians are a latitudinally oriented mountain range in central Europe, being the most continental among those analyzed. This range crosses part of northern Serbia, central Romania, eastern Ukraine, southern Poland, western Czech Republic, northern Hungary and an important part of Slovakia (18.43\u0026ordm;E-26.71\u0026ordm;E and 44.73\u0026ordm;N-49.1\u0026ordm;N). The highest peaks reach 2650 m.a.s.l.. Average yearly temperatures range from 7\u0026ordm;C at the lowest altitudes to 0\u0026ordm;C at the highest peaks. Rainfall is more abundant in the northern end (1400 mm/year) and rather scarce in the center of Romania (500 mm/year). The dominant tree species are sycamore (\u003cem\u003eAcer pseudoplatanus)\u003c/em\u003e, beech (\u003cem\u003eFagus sylvatica)\u003c/em\u003e, silver fir \u003cem\u003e(Abies alba)\u003c/em\u003e, and spruce \u003cem\u003e(Picea abies, and Picea sylvatica)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe Caucasus Mountains are the natural border separating Georgia and Azerbaijan from Russia (39.13\u0026ordm;E-49.37\u0026ordm;E and 41.74\u0026ordm;N-43.13\u0026ordm;N). The Caucasus Mountains is the largest mountain range analyzed in terms of area and elevation, exceeding 5500 m.a.s.l. at the highest summits. At lower elevations, beech (\u003cem\u003eFagus orientalis\u003c/em\u003e) and a variety of \u003cem\u003eQuercus spp.\u003c/em\u003e predominate. At higher elevations, forests are dominated by spruce (\u003cem\u003ePicea orientalis\u003c/em\u003e), fir (\u003cem\u003eAbies nordmanniana\u003c/em\u003e) and a variety of pines (\u003cem\u003ePinus spp.\u003c/em\u003e). It is the most arid mountain system analyzed, with temperatures above 10\u0026deg;C and precipitation of about 600 mm in lower elevations, while in the higher elevations low temperatures predominate, with an annual average of about 0\u0026deg;C, and precipitation of about 1000 mm/yr.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. GEDI dataset\u003c/h2\u003e \u003cp\u003eWe used the Global Ecosystem Dynamics Investigation (GEDI) dataset, collected between May 2019 and September 2020\u003csup\u003e35\u003c/sup\u003e. GEDI is a space-borne high-resolution laser ranging scanner aboard the International Space Station. GEDI was specifically designed to capture the vertical structure of the canopy layer across the temperate and tropical forests (between 51.6\u0026deg; latitude, northern and southern hemisphere)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The GEDI instrument consists of 3 laser sensors, scanning 8 transects spaced 600 m along sensor acquisition range. Each transect collects waveforms at 25m radial footprints every 60 m along-track direction, with a geolocation error lower than 8m in X/Y coordinates and 10cm in Z. The GEDI science team derived the tree height subtracting the highest return (first received return) and the elevation, interpreted as the mode of the lowest value in the received waveform (Figure. ED1). We used the Level 2A product, which consists of footprint-level elevation and relative canopy heights (RH) referred to as the height above the ground of each energy percentile along the waveform profile. To avoid noise in canopy height detection, we selected only high quality footprints (quality flag\u0026thinsp;=\u0026thinsp;1) collected during nighttime, with a sensitivity greater than 0.97, as recommended by the GEDI science team\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo reduce signal noise, we applied a sequence of filters. First, we restricted the analysis domain to mountain forests, i.e., locations higher than 800 m.a.s.l. according to the FAO criteria\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Since the noise is mainly located in the upper lands (due to the effect of steep slopes and snow cover \u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, we defined the maximum elevation where the tree stratum can be found, excluding the top 0.1% footprints to cleanse the upper tail of the distribution where the signal consists mostly of outliers and detection errors (Figure ED2 exemplifies the procedure). Then, we applied an outlier filter along the entire elevation gradient. We grouped GEDI\u0026rsquo;s footprints into 50m interval classes of elevation. Inside each 50m interval, we retained only those footprints comprised within the interval\u0026rsquo;s mean of the tree height (RH95)\u0026thinsp;\u0026plusmn;\u0026thinsp;3 standard deviations\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Overall, only 780,314 footprints remained after the quality control and noise reduction filtering processes.\u003c/p\u003e \u003cp\u003eTo retrieve the maximum tree height, we processed the remaining footprints retaining those with tree heights between the 90th and 95th percentiles of RH95 in each 25 m elevation interval. Temperature and precipitation data were subjected to the same procedure. In this case we defined the interval classes to cleanse noise and calculate maximum height using in their respective units (remaining 38,883). We adapted the interval size to retrieve approximately the same number of classes obtained in the case of elevation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Ancillary data\u003c/h2\u003e \u003cp\u003eSince GEDI does not provide the actual elevation of the ground but only a baseline to calculate the relative heights, elevation data was retrieved from the NASADEM_HGT digital elevation model (DEM), an enhancement of the former STRM DEM with improved accuracy by incorporating data from SAR, LiDAR and optical sensors\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Climate data was retrieved from the WorldClim 2.1\u003csup\u003e24\u003c/sup\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.zotero.org/google-docs/?rad2OO\u003c/span\u003e\u003cspan address=\"https://www.zotero.org/google-docs/?rad2OO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e climate dataset. We extracted historical (1970\u0026ndash;2000) information about minimum temperature (Tmin), maximum temperature (Tmax), and Precipitation (Prec), at a spatial resolution of 30 arc seconds (0.65 Km at latitude 45\u0026ordm;). In addition, we also incorporated spatial predictions of Tmin and Tmax in the period 2080\u0026ndash;2100. We retrieved and merged the predictions from eight global climate models (GCM): BCC-CSM2-MR\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, CNRM-CM6-1\u003csup\u003e43\u003c/sup\u003e, CNRM-ESM2-1\u003csup\u003e44\u003c/sup\u003e, CanESM5\u003csup\u003e45\u003c/sup\u003e, IPSL-CM6A-LR\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, MIROC-ES2L\u003csup\u003e4\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, MIROC6\u003csup\u003e48\u003c/sup\u003e, MRI-ESM2-0\u003csup\u003e49\u003c/sup\u003e for the 245 and 585 shared socioeconomic pathways (SSPs). Since data were not available at 30 arc seconds, data were acquired at 2.5 minutes and resampled using bicubic interpolation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Modeling\u003c/h2\u003e \u003cp\u003eAfter filtering the data, we ran a set of regression models to assess the strength and shape of the relationship between maximum tree height, elevation and climatic variable. To this purpose, we tested log-linear, segmented (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and Gaussian models (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Segmented models were parameterized searching for the optimal number of breakpoints through 50 iterations with 5 folds. To avoid the potential misspecification of the model due to spatial autocorrelation, we fitted 1000 models, in each of which we randomly retained half of the dataset as training dataset and the other half as test subsamples. We retrieved the median and the standard deviation of all parameter estimates across the 1000 models. In the case of segmented models, we retrieved the median and the standard deviation of the breakpoint position and the slope below and above the breakpoint. We computed the R-squared (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and root mean standard error (RMSE) \u0026ndash; calculated using the validation sample \u0026ndash; as indicators of model performance.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{log}\\left(y\\right)=\\left\\{\\begin{array}{c}{\\alpha }_{1}+{\\beta }_{1}\\bullet x \\forall x \\le \\varPsi \\\\ {\\alpha }_{2}+{\\beta }_{2}\\bullet x \\forall x \u0026gt;\\varPsi \\end{array}\\right.\\#$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere α1 and α2 are the intercepts, β1 and β2 are the slopes of the ratios above and below the cut-off point, while ψ is the cut-off point.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{y}={exp\\left[-\\frac{1}{2}\\left(\\frac{x-a}{b}\\right)\\right]}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere a and b are parameters estimated by the model.\u003c/p\u003e \u003cp\u003eFinally, the effect exerted by climate change in the position of the elevation breakpoint was assessed by forecasting the position of the climatic breakpoints under the scenarios SSP245 and SSP585 projected using the ensemble of the general circulation models. we displaced the breakpoint elevation a reason of 0.65\u0026ordm;/100m using the increment of mean temperature at the actual breakpoint.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrice, M., Gratzer, G., Alemayehu Duguma, L., Kohler, T. \u0026amp; Maselli, D. \u003cem\u003eMountain Forests in a Changing World: Realizing Values, Adressing Challenges\u003c/em\u003e. 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Ser. II\u003c/em\u003e \u003cstrong\u003eadvpub\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3062579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3062579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eMountain forests face significant threats from global change and spatio-temporal variation in tree height can help to monitor these effects. While tree height is typically measured through field inventories, remote sensing can provide accurate and extensive forest structure data. In this study, we used the Global Ecosystem Dynamics Investigation space-borne laser sensor (GEDI\u003c/b\u003e) \u003cb\u003eto examine the relationship between maximum tree height and elevation, temperature, and precipitation in the main European mountain ranges. We found a non-linear relationship between elevation and maximum tree height in all mountain ranges, supporting the existence of a common breakpoint that marks the beginning of the tree development limitation. Temperature and precipitation were identified as the most important drivers of tree height variation. Additionally, we predicted significant upward displacement of the breakpoint under climate change scenarios, potentially increasing the area without growth limitations for trees. However, the displacement of the breakpoint may not align with the movement of the treeline, impacting on alpine ecosystems. These findings contribute to understanding the impacts of global warming on mountain forest ecosystems and provide insights for their monitoring and managing.\u003c/b\u003e\u003c/p\u003e","manuscriptTitle":"On the relationship between maximum tree height, elevation and climate in European mountain ranges","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-22 21:03:33","doi":"10.21203/rs.3.rs-3062579/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"806c0dc1-002e-4677-aea9-5d30d05bdd61","owner":[],"postedDate":"June 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":22426015,"name":"Earth and environmental sciences/Ecology/Forest ecology"},{"id":22426016,"name":"Earth and environmental sciences/Ecology/Forestry"}],"tags":[],"updatedAt":"2024-02-16T08:09:23+00:00","versionOfRecord":{"articleIdentity":"rs-3062579","link":"https://doi.org/10.1038/s43247-024-01246-5","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2024-02-15 05:00:00","publishedOnDateReadable":"February 15th, 2024"},"versionCreatedAt":"2023-06-22 21:03:33","video":"","vorDoi":"10.1038/s43247-024-01246-5","vorDoiUrl":"https://doi.org/10.1038/s43247-024-01246-5","workflowStages":[]},"version":"v1","identity":"rs-3062579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3062579","identity":"rs-3062579","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}