Climatic determinants of the Carpathian treeline and its projected upward shifts in response to climate change

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Changes in temperature and precipitation regimes due to climate change affect the location of treelines, contingent on fine-scale variations in orographic and climatic conditions. Using high-resolution satellite imagery, we identified climatic treelines in the Carpathian Mountains, one of Europe’s largest contiguous forest ecosystems. We downscaled climate variables to a 30-meter resolution through a polynomial approximation of regression residuals with terrain attributes, then correlated climatic variables with the location of the climatic treeline. Growing degree days above 5°C demonstrated the strongest correlation with treeline location. Our growing degree threshold results in a total area of 1,370 km 2 above the current climatic treeline in the Carpathians. This area constitutes the climatic envelope for alpine ecosystems and comprises the highest ridges and peaks. Using future climate projections, this area will likely decrease to 410–515 km 2 by 2040, 100–320 km 2 by 2060, and 15–290 km 2 by 2080. The upward shift threatens the region's rare and endemic alpine species and will trigger substantial ramifications for ecosystems, water balance, and the carbon cycle in the Carpathian Mountains. A better understanding of the effects of climate change on treeline locations is crucial for informing ecosystem management and conservation planning, as well as to cushion the impacts of climate change on agriculture and forestry practices. Carpathians treeline global warming mountain ecosystems climate impacts Figures Figure 1 Figure 2 Figure 3 Introduction Trees disappear above a specific elevation, giving way to communities commonly called alpine meadows or alpine tundra, which have substantially lower aboveground biomass (Holtmeier 2009 ; Hansson et al. 2023 ). The line where trees disappear, the treeline, connects the highest patches of closed forest (Paulsen et al. 2000 ; Körner 2004 ; Czajka et al. 2015b ). Treelines constitute a transition belt (ecotone) between the closed continuous forest below and the treeless alpine zone above (Wieser and Tausz 2007 ; Holtmeier 2009 ). Typically, their location is determined by some threshold of tree height and canopy cover (Körner 1998 ; Wieser and Tausz 2007 ; Holtmeier 2009 ; Treml and Migoń 2015 ). The position of the treelines differs regionally and locally due to climatic and edaphic factors, local disturbance regimes, and anthropogenic land cover changes. Climatic treeline refers to the transitions determined by climatic conditions (Körner 2003 ; Holtmeier 2009 ). Climatic treelines exhibit consistent characteristics across various continents and latitudes, serving as crucial ecological divides and critical reference points for mountain life zones (Tranquillini 1979 ; Körner and Paulsen 2004 ; Körner et al. 2011 ). While temperature is widely acknowledged as the critical environmental determinant for the transition from forests to alpine shrubland and grassland, the precise climatic factors and physiological mechanisms governing treeline position remain unclear (Paulsen et al. 2000 ; Holtmeier and Broll 2007 ; Wieser and Tausz 2007 ; Smith et al. 2009 ). Factors such as precipitation and wind exposition that determine the duration of snow cover, atmospheric CO 2 levels, and soil nutrient status also shape treelines; however, they are either of lower significance or regionally and locally specific. Traditionally, alpine treelines have been associated with a mean air temperature of approximately 10 C during the warmest month in temperate mountains (Grace 2002 ; Wieser and Tausz 2007 ; Richardson and Friedland 2009 ; Körner 2021 ). This value is significantly lower in tropical regions, where the growing season extends almost throughout the year (Körner and Paulsen 2004 ). Treelines are also associated with the duration of the growing season (Ellenberg and Leuschner 2010 ) and the maximum daily temperatures in the summer (Daubenmire 1954 ), among others. Körner contends that the mean temperature of the growing season holds global significance in determining the treeline position (Körner 1998 , 2021 ). He attributes this to the suggested physiological mechanism inhibiting apical meristem activity in response to low temperatures, impeding tree tissue formation. He defines the growing season as the part of the year with average daily temperatures above 0.9⁰C (Körner et al. 2011 ). It can be assumed that no single overarching biological mechanism or quantitative parameter shapes global treeline positions. Instead, the diversity of regional and local climates, floral compositions, and the complexity of functional relationships result in distinct variations of altitudinal patterns of treelines (Smith et al. 2009 ). For separate regions with uniform ecological and topographical characteristics, the assumption that a single mechanism governs treeline location becomes more reasonable. However, the precise mechanisms and related climatic parameters determining regional climatic treeline locations remain an open question for most mountain ecosystems. Climate change, primarily through increased temperatures, prompts upward shifts in treelines, with significant implications for biodiversity, endemic alpine species, water balance, nutrient cycling, and carbon storage (Greenwood and Jump 2014; Hansson et al. 2021 ). Besides, both climate change and treeline shifts affect forestry management and land use systems, such as alpine pastures (Cannone et al. 2007 ). While ample evidence exists regarding climatically induced treeline ascent since the late 20th century (Holtmeier 2009 ), these changes manifest slowly due to trees' delayed response to climatic shifts. Therefore, most currently reported treeline changes resulted mainly from changes in land use practices, including declining transhumance and logging (Gehrig-Fasel et al. 2007 ; Weisberg et al. 2013 ). Our patchy understanding of climate change impacts on treeline alterations is unfortunate amidst accelerating climate change and associated extreme weather events, which increases pressure on mountain ecosystems, potentially driving faster upward shifts in treeline locations. We investigate the factors influencing climatic treeline position and potential shifts in the treeline locations for the Carpathian Mountains, which span several countries in the center of Europe. The Carpathians contain many ecosystems and climates and are a hotspot of European biodiversity, hosting numerous rare and endemic species (Mráz and Ronikier 2016 ). With peak elevations reaching only up to 2,655 meters, significant upward advancements in the treeline could eliminate alpine habitats across many of their mountain ranges. While recent trends in the Carpathian treeline have been assessed with satellite data (Mihai et al. 2007 ; Martazinova et al. 2011 ; Weisberg et al. 2013 ), there is currently a lack of quantification regarding the influence of historical climate and expected future climate change on treeline location in the Carpathians. Our primary objective in this paper is to identify the climatic factors that determine the location of the current climatic treeline in the Carpathians and to project the potential impacts of climate change on future treeline shifts. We delineated the extant current climatic treeline fragments on high-resolution remote sensing imagery to accomplish this. We downscaled the best available climatic data to account for the fine-scale variations in the determinants for the treeline location. These determinants permit quantifying the climatic variables above and below these fragments, revealing the variables with the strongest correlation with the treeline location. We then predict the potential climatic treeline locations in the entire Carpathian range and the appropriate areas of climatic envelopes for alpine ecosystems based on the threshold value of the most pertinent climatic variable. Finally, we use data from climate change scenarios to project treeline shifts for 2040, 2060, and 2080, considering different Shared Socioeconomic Pathways (SSPs) (Copernicus Climate Change Service 2021 ). Materials and methods Study area The Carpathian Mountains are the easternmost offshoot of the Central European highlands, stretching 1,500 km from East to West over five countries and covering 190,000 km 2 (Fig. 1 ). Most of this region is below the treeline, except for the highest mountain ranges. These ranges are approximately evenly distributed between three main subdivisions of the Carpathian Mountains, the north-western, eastern, and southern Carpathians (e.g., Kondracki 1989 ). We inspected the highest parts of the Carpathians (lying above 1,300 m) for the location of treelines. At the same time, we used the entire Carpathian region, including adjacent areas with a total size of 480,000 km 2, for downscaling climatic variables. Treeline delineation We define the treeline ecotone as the belt at the upper elevational limit of a closed-canopy forest with the gradual vegetation transition from closed tree stands to open grasslands and shrubs. Diffuse treelines are more often limited by climatic factors, unlike abrupt and krummholz treelines, which are more likely to be shaped by anthropogenic and natural disturbances (Harsch et al. 2009 ; Hansson et al. 2021 ). It is indirectly confirmed by gradual diffuse treelines being more responsive to climate warming (Tourville et al. 2023 ). We manually delineated treeline ecotones on high-resolution Bing images. To do so, we digitized tree stands on the respective elevations with visible and rapid but still gradual elevational deterioration of tree stature (see Fig. 4 , Appendix ). We identified 161 parcels of climatic treelines in the highest mountain ranges of the Carpathians, together encompassing an area of nearly 12 km 2 . The distribution of the parcels reveals three distinct clusters, each corresponding to a major subdivision of the Carpathian Mountains (Fig. 1 ). Delineated treeline parcels are distributed almost equally in the three subdivisions and cover the highest mountain ranges. Substantial disparities in treeline elevation were observed among the subdivisions, primarily along a north-south gradient, indicating that the characteristic elevation of treeline parcels changes with changing climatic conditions (Table 3 , Appendix ). Climatic variables and their spatial downscaling We utilized monthly climate data from 1970 to 2000 at 1 km 2 resolution from WorldClim 2.1 (Fick and Hijmans 2017 ). We derived 12 climatic variables to serve as determinants for treeline location. Using climatic values for the end of the last century is justified by the lag of several decades in trees' response to changing climate. We selected variables that capture various aspects of the thermal regime, namely the annual mean temperature, mean temperature of the warmest quarter of the year, mean temperature of the warmest month (July), mean October temperature, maximal temperature of the warmest month, and minimal temperature of the coldest month (the latter two reflect thermal and cold stress, respectively). Most of these variables indicate thermal resources available to plants during the most active growth phases, and all correlate with the altitude of treelines (Grace 2002 ; Körner and Paulsen 2004 ). We further incorporated the mean October temperature, which has been demonstrated to significantly predict treeline movement in the Northern Hemisphere (Hansson et al. 2023 ). We also included growing season duration, growing season mean temperature, and accumulated growing degree days (AGDD), a heat index based on the annual accumulation of daily temperatures surpassing a predefined threshold (Hansson et al. 2021 ). Hence, the AGDD is the cumulative sum of temperatures throughout the growing season. These variables were defined with a 0.9°C threshold, consistent with the definition of the growing season in high-altitude treeline studies (Körner and Paulsen 2004 ; Paulsen and Körner 2014 ), and to a 5°C threshold to capture the growing season definition of trees (Körner 2003 ). To accomplish this, we temporally downscaled the time series of monthly mean temperatures to daily time series with a cubic smoothing spline. Subsequently, we calculated the number of days when the temperature exceeded the mentioned thresholds (the duration of the growing seasons), the mean temperature during the growing seasons, and the AGDD. We used elevation above mean sea level (AMSL) as another explanatory variable to verify if temperature-related variables were better predictors than differences in elevation alone. WorldClim data have already been used in the global analysis of treeline factors and distributions, but the 1 km resolution compromises the accuracy of the analysis (Körner et al. 2011 ; Haesen et al. 2023 ). We thus spatially downscaled the climate data to a resolution of 30 meters with a spatial variability model. This model accounts for local terrain effects, such as temperature changes with elevation and potential influences of terrain aspect; it also addresses large-scale spatial variability, including temperature changes in the north-south direction and the impact of continentality. Our downscaling approach for the 12 climatic variables involved the following steps: Regression models link the climatic variables to elevation and its two derivative components in the X and Y directions, which account for terrain aspect influences. Elevation data was sourced from the Shuttle Radar Topography Mission (SRTM) digital elevation model (version 3), available in 1-arcsecond (~ 30 meters) resolution (NASA 2014). We used Akaike and Bayesian information criteria to decide whether to include terrain aspects in each variable's model. The regression models predict the variables at 30-meter resolution and calculate the differences (residuals) between these predictions and the original 1-kilometer resolution variable. The residuals are likely mainly due to the remaining large-scale spatial variability not accounted for by terrain attributes. The residuals were smoothed using orthogonal polynomials in the form of a 6th-order trend surface. The emerging large-scale patterns were added to the regression models' predictions. The final prediction, therefore, combines the fine details contingent on local terrain conditions with the broader patterns associated with latitude and large-scale geographical features. We evaluated the downscaling accuracy with weather station data from the Global Historical Climatology Network dataset (Peterson and Vose 1997 ). Within the study region, we identified 28 weather stations with a minimum of 67% valid monthly records for 1970 to 1984 and 1985 to 1999 (Fig. 1 ). We computed the respective climatic variables for these station locations. Then, we determined the discrepancies between the observed weather station records, our downscaled data, and the original 1 km WorldClim data. This comparison enabled us to examine how much our downscaled data enhanced the original 1 km WorldClim dataset. Verifying relations between climatic variables and treeline location We superimposed the downscaled climatic variables on the delineated treeline parcels to assess their correspondence. We employed two approaches for the purpose. The first assumes that variables with a stronger association with the treeline exhibit greater homogeneity within the treeline ecotone compared to the surroundings across the Carpathians. To quantitively assess this correspondence, we calculated the ratio of standard deviations of the climatic variables within the treeline parcels to their background standard deviations in the elevation belt between 1,300 and 1,900 meters where most parcels are situated. The smaller the ratio for a climatic variable, the stronger the correspondence between this variable and the treeline location. The second approach involves running several machine learning models on climatic variables sampled over 13,331 pixels within treeline parcels and another 13,331 pixels randomly sampled over the background outside these parcels. These models were executed using the R Caret package; we automatically fine-tuned model parameters to select the “optimal” model (Kuhn 2008 ). The performance of each model was evaluated based on overall accuracy and Kappa statistics, which were calculated using five-fold cross-validation. We then calculated the variance importance of each climatic variable in each model using the Caret package's generic varImp function. Because this function employs model-specific variable importance measures (see Kuhn 2008 ), we ranked the variables according to their importance across different models to facilitate comparison. We did not explicitly calculate the statistical significance of our results; instead, we assume that variables consistently demonstrating better correspondence to the treeline across different models likely have a more robust association with the underlying processes responsible for treeline formation. Identifying the climatic variable most closely linked to the treeline location and determining its threshold value enables us to delineate the present-day climatic treeline. In addition, it permits the identification of areas where disturbances caused the treeline to descend and allows the definition of the extent of the climatic envelope for alpine ecosystems located above the treeline. We also forecast future climatic treeline shifts using climate projections. We used CMIP6 downscaled projections for four scenarios of the Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, SPP3-7.0 and SSP5-8.5, each averaged over 20-year periods (2021–2040, 241–2060, 2061–2080, 2081–2100); the scenario data are available through WorldClim (O’Neill et al. 2016 ; Fick and Hijmans 2017 ; Riahi et al. 2017 ). Averages and standard deviations were calculated for 23 global climate models (GCMs) for each SSP and period. We calculated the total area where the value of the indicative climatic variable is below the established threshold for the average of the models to estimate the extent of the future treeline ascent and the corresponding reduction in the climatic envelope for alpine ecosystems. Results Downscaling climatic variables The downscaling of climatic variables to a 30 m resolution substantially enhanced their spatial accuracy, as evidenced by comparing weather station data and original WorldClim data (Table 1 .). The extent of accuracy improvement differed among the variables: Minimal temperature showed only a modest gain, as anticipated, due to commonly occurring inversions causing inverted vertical temperature gradients at night. The accuracy gain exceeded 20% for 8 out of 12 variables, affirming the effectiveness of the employed downscaling method. Consequently, we utilized the downscaled climatic layers in subsequent analyses (refer to Fig. 5 , Appendix to illustrate the downscaling effect). Relationships between climatic variables and treeline location Standard deviations within treeline patches were significantly smaller for most climatic variables than those in the background elevation belt. Only for annual mean temperature and elevation AMSL were they roughly equal, with a ratio close to 1 (second column in Table 2 ; see also Table 4 , Appendix for complete data). This indicates that most climatic variables considerably better predict climatic treeline location than elevation alone. Among the variables, those characterizing accumulated growing degree days (AGDD) and temperatures during the warmer part of the year exhibited the smallest standard deviation ratios. Conversely, minimal and annual mean temperatures had a significantly poorer match to treelines. All machine learning models exhibited comparable performance measures, with Random Forest performing slightly better and Recursive Partitioning somewhat worse than others (Table 2 ). The variable importance of each climatic variable predictably differed among the models. However, AGDD of the growing season above 5°C ( GP5sum ) emerged as the most critical variable across all models while also demonstrating the most significant standard deviations ratio. The mean temperature of the warmest quarter ( Tquart) ranked second in terms of standard deviation ratio and in terms of variable importance in two machine learning models. Based on these findings, we use the AGDD of the growing season above 5°C to indicate current and future climatic treeline locations. The threshold value for climatic treeline was determined by averaging this variable across all treeline parcels, resulting in a value of 1,112 AGDDs (for threshold values of other variables, refer to column 2 of Table 4 , Appendix ). Based on this parameter, we estimate the total area of the climatic envelope for alpine ecosystems to be 1,370 km 2 (considering the mean 1970 to 2000 climate and the current ecosystem distribution). Prospective treeline projections We produced maps of projected treeline locations using downscaled averaged 23 CMIP6 projections of AGDD of the growing season above 5°C (Fig. 2 ). From these data, we generated a table and a graph illustrating the projected areas above the climatic treeline for four SSPs and three 20-year periods (Fig. 3 ; Table 6 , Appendix ). Two notable aspects of our projections deserve emphasis. Firstly, even under the most optimistic scenarios, the climatic envelope for alpine ecosystems is expected to shrink by more than half compared to its present extent. Secondly, under the worst-case scenarios, alpine ecosystems in the Carpathians could almost disappear entirely by the end of the century, with suitable climatic conditions persisting only on the highest isolated mountain tops and ridges. Discussion Factors determining the treeline position Treeline is a significant bioclimatic boundary for thermal life zones and aligns with the broader thermal constraints for temperate zone agriculture (Körner and Paulsen 2004 ). While altitudinal treelines are associated with decreasing temperatures at higher elevations (Wieser and Tausz 2007 ; Holtmeier 2009 ), there is less clarity about their underlying drivers (Grace 2002 ; Richardson and Friedland 2009 ). Our findings corroborate the results of several researches suggesting that warm season temperatures, rather than cold season lows, primarily dictate the upper bounds of tree growth (Tranquillini 1979 ; Wieser and Tausz 2007 ; Harsch et al. 2009 ; Davis et al. 2020 ). This explains higher treeline positions in central (core) parts of mountains compared to their peripheries as well as in continental climates versus adjacent maritime regions (Malinovsky 1980 ; Wieser and Tausz 2007 ; Holtmeier 2009 ; Czajka et al. 2015b ), given the positive impact of increased climate continentality on summer temperatures. We observe this phenomenon, which has been extensively documented in the Alps (Quervain 1904 ) and also in the Carpathians, where it manifests as an overall treeline elevation increase from west to east and from the peripheries to the cores of certain massifs (Malinovsky 1980 ; Czajka et al. 2015b ). We tested several suggestions regarding the driving factors behind climatic treelines in the case of Carpathians. Notably, temperature-related climatic variables are often strongly correlated, especially within a single region. This makes it difficult to single out the only “true” predictor of the climatic treeline. Our findings align with the threshold values of climatic variables suggested by several researchers. The mean July temperature recorded for our treeline parcels is 10.5⁰C (see Table 4 , Appendix ), which is close to the 10°C threshold suggested by the earlier authors (Grace 2002 ; Wieser and Tausz 2007 ; Richardson and Friedland 2009 ; Körner 2021 ). According to the model indicated in (Körner and Paulsen 2004 ), it corresponds to the tree root zone temperature of 8.5⁰C, in line with their lower boundary for the Alps (9.2 ± 0.7⁰C) and equal to the 8.5⁰C estimated for the boreal zone (Körner and Paulsen 2004 ). The mean growing season above 0.9°C temperature converted to root zone is 7⁰C, consistent with the average value reported for the Alps (7.0 ± 0.4⁰C) and with the global estimate of 6.7 ± 0.8⁰C as detailed in (Körner and Paulsen 2004 ). It is a little higher than the 6.4°C suggested in the later work by these authors (Körner et al. 2011 ). Our findings are consistent with the notion that warm-season temperatures are predominant in governing the upper limits of tree growth (Tranquillini 1979 ; Wieser and Tausz 2007 ; Harsch et al. 2009 ). Our analysis revealed that the AGDD above 5°C, an indicator of cumulative heat during the warmest period of the year, exhibits the strongest correlation with treeline location in the Carpathians. The mean temperature of the warmest quarter was closely following in importance. This variable is more readily estimated and included in the standardized set of bioclimatic variables under the BIO10 code (Hijmans et al. 2005 ). Our findings align with the study of treeline changes in the northeastern United States, where AGDD was found to significantly account for variations in the extent of treeline advancement (Tourville et al. 2023 ). We do not, however, assert the universal applicability of these results. While it may be enticing to establish parameters and thresholds for climatic treelines that apply globally, it is probably unfeasible because actual biome distribution patterns are often divergent even under a fixed climate. Biome variations can emerge from diverse historical contexts, resulting in unique trait pools, differing ecosystem equilibrium states, and varied disturbance regimes (Higgins and Scheiter 2012 ; Moncrieff et al. 2016 ). However, the method that we apply for revealing the most relevant climatic factors determining treeline positions, namely – the identifications of the parcels of extant climatic treelines on high-resolution satellite images and the statistical analysis and machine learning modeling of the distribution of climatic variables within and outside these parcels – can be applied to other mountain regions as well. This would allow obtaining sufficiently large samples without the need to physically access remote locations in mountains with rugged terrain. Many European mountains with relatively dense and long-lasting human presence now feature treelines significantly lower than the climatic limits for tree growth since forests were cut for fuel and construction material and cleared out for pastures (Holtmeier and Broll 2007 ; Weisberg et al. 2013 ; Vincze et al. 2017 ). Traces of human influence that shaped the Carpathian treelines date back as far as 4,200 years and intensified around 2,000 years ago (Vincze et al. 2017 ). Since medieval times, practices like transhumance in the Carpathian Mountains involved slash-and-burn techniques to expand grazing fields (Vincze et al. 2017 ). As a result, the actual (anthropogenic) treeline in the Carpathians is commonly located 250–300 m lower than the climatic treeline, sometimes descending below 1,000 m AMSL, particularly on shallow slopes above densely populated valleys (Malinovsky 1980 ). In some parts of the Carpathians, treelines of anthropogenic origin are formed by deciduous European beech forests (Weisberg, Shandra, and Becker 2013), similar to those in southern Europe where Fagus species treelines are depressed hundreds of meters below the climatic treeline (Körner and Paulsen 2004 ). The effects of anthropogenic influences on treelines can persist long after their cessation (Holtmeier and Broll 2007 ; Malanson et al. 2011 ; Körner 2021 ). Some natural factors and disturbances interact with climatic factors to influence the location of treelines. Snow avalanches and debris flows significantly lower their position in susceptible concave terrain elements (Holtmeier and Broll 2007 ; Treml and Migoń 2015 ), while extremely convex and exposed terrain elements are not conducive to tree advancement either due to strong winds. Additionally, edaphic factors can affect treeline position, with treelines typically higher on carbonate rocks than on quartzite rocks and lower on coarse glacial deposits (Czajka et al. 2015b ; Treml and Migoń 2015 ). Treeline changes as a response to rising temperatures. Warming temperatures lead to shifts in species' and ecosystems' distributions, with a global meta-analysis estimating an average uphill movement rate of 11.1 meters per decade (Chen et al. 2011 ). The floras and faunas of Central European mountains are particularly vulnerable to climate change due to narrow habitat tolerances and marginal habitats for many species (e.g., Thuiller et al. 2005 ). Treelines have been observed to shift upwards in many regions worldwide, including Europe, Asia, and North America (Holtmeier 2009 ; see bibliography for additional sources). A global meta-analysis of data from 166 treeline sites found that 52% had notably advanced since 1900 AD, with only two sites showing receded treelines, both with evidence of disturbance (Harsch et al. 2009 ). Another study reported the advancement of 67% of 438 altitudinal treeline sites (Hansson et al. 2021 ). Data analysis from two surveys in the Swiss Alps in 1979/1985 and 1992/1997 revealed 893 hectares of new forests above the former treeline, with a median upward shift of 28 meters (Gehrig-Fasel et al. 2007 ). The Carpathian Mountains are no exception. A study comparing timberline altitudes in 2009 to those in 1964 on the south-facing slopes of the Babia Gora massif in the western Carpathians observed an upward progression of up to 30 meters (Czajka et al. 2015a ), while (Szwagrzyk 2015 ) noticed the young age of spruce trees on its north slopes, suggesting recent advances in the treeline. A comparison of historical maps from the 1930s with recent Landsat images of the Ukrainian Carpathians revealed significant treeline advancement on the highest ridges (Martazinova et al., 2011 ). It is, however, unclear how much of these effects can be attributed to climate change as opposed to the regeneration of forests previously destroyed by human activity below the climatic treeline. However, the observed changes are far more conservative than our projections of climatic treeline shifts, which, even under optimistic SSP scenarios, would typically rise by a few hundred meters. Apart from the accelerating nature of climate warming, this can be explained by the delayed ecological response, as trees have limited seed dispersal rates and require time to establish (Holtmeier and Broll 2007 ; Harsch et al. 2009 ; Holtmeier 2009 ; Smith et al. 2009 ). The extent to which the treeline lags behind climate change is debatable. While tree species migration rates were estimated to be less than 100 m per year, preventing them from occupying all suitable sites in Central and Northern Europe in the course of dispersion from the Last Glacial Maximum refugia (Svenning and Skov 2007 ), research on lake sediments in the central Alps near the modern treeline revealed a swift response of the treeline to climate changes at the Late Glacial-Holocene transition, suggesting that global warming could also trigger large-scale displacements of plant species and rapid upslope movements of the treeline (Tinner and Kaltenrieder 2005 ). Before the mid 20 century, tree radial stem increment noticeably decreased when approaching a treeline in the Swiss and Austrian Alps; by the end of the century, this relationship had almost vanished, indicating that these tree stands are now located far enough from the climatic treeline (Paulsen et al. 2000 ). Another study revealed that the rates of treeline advance reconstructed for study sites in the Canadian Rocky Mountains were much lower than expected based on changes in growing-season temperature, with a median percentage of observed versus expected movement of only 39% (Davis et al. 2020 ). Our study focused, like most others, on changes in average temperatures. However, knowledge about the impacts of extreme weather events on changes in treelines remains patchy, and they are predicted to increase in intensity and frequency with climate change (Smith et al. 2009 ). Climate change will also affect other treeline-related factors, such as snow avalanches, which are predicted to decrease in frequency and severity in a warming climate. Lower avalanche activity due to climate warming has already led to the expansion of forested areas in some Carpathian valleys (Kaczka et al. 2015 ). A study in Romania revealed significant forest regeneration on former avalanche paths, accounting for approximately 50% of the total increase in forest surface area (Mihai et al. 2007 ). Nonlinear relations created by positive feedback loops could further complicate the response of treelines to climatic factors, possibly resulting in alternative stable states of forest and alpine vegetation and abrupt treeline transitions driven by internal ecosystem dynamics (Malanson et al. 2011 ). Elevated atmospheric CO 2 concentrations could facilitate tree growth at treeline on their own through enhanced photosynthetic activity (Handa et al. 2005 ; Reyes-Fox et al. 2014 ), which could amplify and even outweigh the effects of predicted temperature increases (Higgins and Scheiter 2012 ). If our assumption of the summer temperatures as the leading factor behind climatic treelines is valid, the drastic decrease in the climatic envelope for alpine ecosystems in the Carpathians should be expected by the end of the century. This means that the efforts for the conservation of alpine communities, including their endemic and endangered species, should be spatially directed towards the highest ridges and peaks in the Carpathians, in particular Tatras and the southern Carpathians, where favorable climatic conditions for these ecosystems would sustain for a longer while. It can be seen in Fig. 3 that different SSPs indeed make a difference, and the divergence of predicted areas above the climatic treeline for four SSPs tends to grow larger with time. This means that global measures to slow climate warming would mean a lot in taming down the detrimental effects of climate change on ecosystems. Conclusions Our study reveals the climatic factors governing the current treeline position in the Carpathians and projects its upward shifts under climate change. Warm season temperatures are the most influential to the upper bounds of tree growth, and growing degree days above 5°C exhibit the strongest correlation with treeline location among the 12 climatic variables tested in our study. We used the estimated threshold value of the growing degree days to assess the current total area of the climatic envelope for alpine ecosystems at 1,370 km 2 . We then take CMIP6 climate projections to forecast the decrease of alpine ecosystems to less than a quarter of their current size by 2050. In the highest emission scenarios, alpine ecosystems in the Carpathians will almost disappear by 2080. Our work corroborates the importance of reducing greenhouse emissions to safeguard alpine ecosystems but also underscores the need for adaptation measures to support mountain regions. We fill a regional gap in quantifying treeline behavior under climate change; unfortunately, such estimates are still lacking for other critical mountain systems, such as the neighboring Alps and Dinaric mountains, which share comparable climate and ecosystem properties. Besides, we hope our results can contribute to more detailed studies that delve into physiological mechanisms and environmental interconnections for a deeper understanding of the ecological consequences of climate warming. Such knowledge is crucial for improving land-use management and adaptation strategies for mountain forestry, agriculture, and nature conservation amidst accelerating climate change. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. Both authors contributed to the study conception and design, data collection and analysis, writing the first draft of the manuscript and its subsequent editing. All authors read and approved the final manuscript. Data availability statement The research is based on open data mentioned and cited it the manuscript. Datasets created during the research process are accessible from Zenodo open repository by the following links: https://zenodo.org/records/11359344?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6Ijc5YzkwYzkwLWRhOTYtNGM2Zi1iNmNkLWVjYzg3NTcyNjkyMiIsImRhdGEiOnt9LCJyYW5 kb20iOiI2YTUyNjU1NTRkOThmMDRjMzJiNGJkZjk5YTg3NWQ0ZSJ9.KQREZlcL_F2FHzv-cyMLN7E3DpxGAi7JgDLfzeGH6FI -HJ2uj6ZUuvlJ1kCPZyIYIv01jsX3I2Aa3u-PchBj7Q https://zenodo.org/records/11358952?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImMwZjQ0MjFlLWY0YTMtNDAxMS1iYzliLWE3MzFkZjFjNzYwMyIsImRhdGEiOnt9LCJyY W5kb20iOiJiZTk2OGQyMmUyNGRiOGM1NmYwNDhlYzdlMTY5ZjJjYyJ9.sMM9MWmVjDbKuYvMsY_r-C134HU373cijc9ufC3E -5zTiIpcrSrbDpan2iv2HXcTF1gnqAZy5w0iD7MgYVn0SA https://zenodo.org/records/11320218?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjQxYmI2OTUxLWIzYjEtNGE0MS1iM2RiLTJmOWMzOGYzMmJiZCIsImRhdGEiOnt9LCJyY W5kb20iOiI5ZDBiZGQ5ZGZiZWQ1OGI5MWU4NzZlMWI0MWE5Y2ZhNCJ9.DVx9yJajAHwSkBneHqp9wtjAcEUWr64jsqx0QOHMgo MqKOC9B4uaoIvK2LA3piPs1CDtbbrnq3j7p1bW3FKuuQ Intermediate data and code are available from the corresponding author by request. 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Springer Netherlands, Dordrecht, pp 1–18 Tables Table 1 Downscaling summary : Climatic variables: Tann – Annual mean temperature; Tquart – Mean temperature of the warmest quarter; TJul – Mean July temperature; TOct – Mean October temperature; Tmax – Mean maximal temperature of the warmest month; Tmin – Mean minimal temperature of the coldest month; GP0.9leng – Duration of the growing season above 0.9°C; GP0.9mean – Mean temperature of the growing season above 0.9°C; GP0.9sum – AGDD of the growing season above 0.9°C; GP5leng – Duration of the growing season above 5°C; GP5mean – Mean temperature of the growing season above 5°C; GP5sum – AGDD of the growing season above 5°C. Explanatory terrain attributes (column 2): Elev – Elevation AMSL; AspX – elevation derivative in X direction (north-south); AspY – elevation derivative in Y direction (east-west). Climatic variable Regression model Root mean squared deviation from weather station data Accuracy gain for downscaled data, % Variables in the model, sign Multiple R-squared Original WorldClim data Downscaled data Tann Elev, AspX, AspY 0.745 0.777 0.731 5.92 Tquart Elev, AspY 0.823 0.605 0.475 21.5 TJul Elev, AspY 0.833 0.709 0.523 26.2 TOct Elev, AspX 0.721 0.543 0.424 20.3 Tmax Elev 0.796 1.037 0.722 30.4 Tmin Elev, AspY 0.506 1.42 1.4 1.4 GP0.9leng Elev 0.603 283.4 204.8 27.7 GP0.9mean Elev, AspY 0.845 0.26 0.213 18.1 GP0.9sum Elev 0.76 31,818 25,682 19.3 GP5leng Elev 0.465 320.9 159.4 50.3 GP5mean Elev, AspX 0.743 0.717 0.314 56.2 GP5sum Elev 0.742 37,010 28,452 23.1 Table 2 Measures of correspondence between treeline parcels and climatic variables : Variables codes are in Table 1. Downscaling summary : Climatic variables: Tann – Annual mean temperature; Tquart – Mean temperature of the warmest quarter; TJul – Mean July temperature; TOct – Mean October temperature; Tmax – Mean maximal temperature of the warmest month; Tmin – Mean minimal temperature of the coldest month; GP0.9leng – Duration of the growing season above 0.9°C; GP0.9mean – Mean temperature of the growing season above 0.9°C; GP0.9sum – AGDD of the growing season above 0.9°C; GP5leng – Duration of the growing season above 5°C; GP5mean – Mean temperature of the growing season above 5°C; GP5sum – AGDD of the growing season above 5°C. Explanatory terrain attributes (column 2): Elev – Elevation AMSL; AspX – elevation derivative in X direction (north-south); AspY – elevation derivative in Y direction (east-west). Machine learning models: Rpart – Recursive Partitioning for Classification Trees; Avnnet – Averaged Neural Network; MARS – Multivariate Adaptive Regression Spline; Gbm – Stochastic Gradient Boosting; RF – Random Forest. For machine learning models, ranks of variables are given since variable importance measures are model-specific and incomparable. "NA" denotes variables that were not included in a model. The three most important variables for each model are highlighted in colors. The elevation variable was not used in the machine learning models. The means and standard deviations of variables for treeline parcels and background and given in Table 4, Appendix . Raw variable importance measured with Caret's varImp function is shown in Table 5, Appendix. Variable Standard deviation ratios (parcel/background) Variable importance in machine learning model (ranks) Rpart Avnnet MARS Gbm RF Elevation 0.99 Tann 0.923 9 8 NA 11 10 Tquart 0.472 2 2 9 3 11 Tjul 0.487 3 3 7 12 9 Toct 0.549 8 11 8 10 8 Tmax 0.487 10 5 NA 9 7 Tmin 0.649 7 12 4 5 2 GP0.9leng 0.549 11 10 5 8 5 GP0.9mean 0.512 4 6 6 2 6 GP0.9sum 0.473 5 4 NA 6 12 GP5leng 0.513 6 7 1.5 4 3 GP5mean 0.559 12 9 3 7 4 GP5sum 0.468 1 1 1.5 1 1 Model Accuracy 0.86 0.87 0.87 0.89 0.93 Model Kappa 0.72 0.73 0.75 0.77 0.85 Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 22 May, 2025 Read the published version in Climatic Change → Version 1 posted Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 29 Jun, 2024 Editor assigned by journal 30 May, 2024 First submitted to journal 29 May, 2024 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. 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Left: north-western Carpathians (High Tatras); right: southern Carpathians (Făgăraș)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4487120/v1/98168a1087be33ec8092ff3e.png"},{"id":60917607,"identity":"8bd6c671-e4e3-43c3-8fda-7f15f4d87bf2","added_by":"auto","created_at":"2024-07-23 14:05:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64105,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated areas above the climatic treeline, derived from downscaled averages for 23 GCMs projections of growing degree days (AGDD) of the growing season above 5°C\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4487120/v1/3ffe7e0d25a22ed790d8469e.png"},{"id":83460944,"identity":"c196b0c3-d803-4fd2-90f9-6a8f9feb20ac","added_by":"auto","created_at":"2025-05-26 16:14:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4717287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4487120/v1/dd0d51da-d0ec-4b80-82ba-ff957d31d5a8.pdf"},{"id":60918262,"identity":"34268065-15d6-46e2-bc09-297acdbfdda9","added_by":"auto","created_at":"2024-07-23 14:13:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1129243,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4487120/v1/8ebe64e1b3b18dd0bcefab04.docx"}],"financialInterests":"","formattedTitle":"Climatic determinants of the Carpathian treeline and its projected upward shifts in response to climate change","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTrees disappear above a specific elevation, giving way to communities commonly called alpine meadows or alpine tundra, which have substantially lower aboveground biomass (Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hansson et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The line where trees disappear, the treeline, connects the highest patches of closed forest (Paulsen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; K\u0026ouml;rner \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Czajka et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). Treelines constitute a transition belt (ecotone) between the closed continuous forest below and the treeless alpine zone above (Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Typically, their location is determined by some threshold of tree height and canopy cover (K\u0026ouml;rner \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Treml and Migoń \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe position of the treelines differs regionally and locally due to climatic and edaphic factors, local disturbance regimes, and anthropogenic land cover changes. Climatic treeline refers to the transitions determined by climatic conditions (K\u0026ouml;rner \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Climatic treelines exhibit consistent characteristics across various continents and latitudes, serving as crucial ecological divides and critical reference points for mountain life zones (Tranquillini \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; K\u0026ouml;rner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While temperature is widely acknowledged as the critical environmental determinant for the transition from forests to alpine shrubland and grassland, the precise climatic factors and physiological mechanisms governing treeline position remain unclear (Paulsen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Holtmeier and Broll \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Factors such as precipitation and wind exposition that determine the duration of snow cover, atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels, and soil nutrient status also shape treelines; however, they are either of lower significance or regionally and locally specific.\u003c/p\u003e \u003cp\u003eTraditionally, alpine treelines have been associated with a mean air temperature of approximately 10 C during the warmest month in temperate mountains (Grace \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Richardson and Friedland \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K\u0026ouml;rner \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This value is significantly lower in tropical regions, where the growing season extends almost throughout the year (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Treelines are also associated with the duration of the growing season (Ellenberg and Leuschner \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the maximum daily temperatures in the summer (Daubenmire \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1954\u003c/span\u003e), among others. K\u0026ouml;rner contends that the mean temperature of the growing season holds global significance in determining the treeline position (K\u0026ouml;rner \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). He attributes this to the suggested physiological mechanism inhibiting apical meristem activity in response to low temperatures, impeding tree tissue formation. He defines the growing season as the part of the year with average daily temperatures above 0.9⁰C (K\u0026ouml;rner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt can be assumed that no single overarching biological mechanism or quantitative parameter shapes global treeline positions. Instead, the diversity of regional and local climates, floral compositions, and the complexity of functional relationships result in distinct variations of altitudinal patterns of treelines (Smith et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For separate regions with uniform ecological and topographical characteristics, the assumption that a single mechanism governs treeline location becomes more reasonable. However, the precise mechanisms and related climatic parameters determining regional climatic treeline locations remain an open question for most mountain ecosystems.\u003c/p\u003e \u003cp\u003eClimate change, primarily through increased temperatures, prompts upward shifts in treelines, with significant implications for biodiversity, endemic alpine species, water balance, nutrient cycling, and carbon storage (Greenwood and Jump 2014; Hansson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, both climate change and treeline shifts affect forestry management and land use systems, such as alpine pastures (Cannone et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While ample evidence exists regarding climatically induced treeline ascent since the late 20th century (Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), these changes manifest slowly due to trees' delayed response to climatic shifts. Therefore, most currently reported treeline changes resulted mainly from changes in land use practices, including declining transhumance and logging (Gehrig-Fasel et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Weisberg et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our patchy understanding of climate change impacts on treeline alterations is unfortunate amidst accelerating climate change and associated extreme weather events, which increases pressure on mountain ecosystems, potentially driving faster upward shifts in treeline locations.\u003c/p\u003e \u003cp\u003eWe investigate the factors influencing climatic treeline position and potential shifts in the treeline locations for the Carpathian Mountains, which span several countries in the center of Europe. The Carpathians contain many ecosystems and climates and are a hotspot of European biodiversity, hosting numerous rare and endemic species (Mr\u0026aacute;z and Ronikier \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). With peak elevations reaching only up to 2,655 meters, significant upward advancements in the treeline could eliminate alpine habitats across many of their mountain ranges. While recent trends in the Carpathian treeline have been assessed with satellite data (Mihai et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Martazinova et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Weisberg et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), there is currently a lack of quantification regarding the influence of historical climate and expected future climate change on treeline location in the Carpathians.\u003c/p\u003e \u003cp\u003eOur primary objective in this paper is to identify the climatic factors that determine the location of the current climatic treeline in the Carpathians and to project the potential impacts of climate change on future treeline shifts. We delineated the extant current climatic treeline fragments on high-resolution remote sensing imagery to accomplish this. We downscaled the best available climatic data to account for the fine-scale variations in the determinants for the treeline location. These determinants permit quantifying the climatic variables above and below these fragments, revealing the variables with the strongest correlation with the treeline location. We then predict the potential climatic treeline locations in the entire Carpathian range and the appropriate areas of climatic envelopes for alpine ecosystems based on the threshold value of the most pertinent climatic variable. Finally, we use data from climate change scenarios to project treeline shifts for 2040, 2060, and 2080, considering different Shared Socioeconomic Pathways (SSPs) (Copernicus Climate Change Service \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eThe Carpathian Mountains are the easternmost offshoot of the Central European highlands, stretching 1,500 km from East to West over five countries and covering 190,000 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of this region is below the treeline, except for the highest mountain ranges. These ranges are approximately evenly distributed between three main subdivisions of the Carpathian Mountains, the north-western, eastern, and southern Carpathians (e.g., Kondracki \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). We inspected the highest parts of the Carpathians (lying above 1,300 m) for the location of treelines. At the same time, we used the entire Carpathian region, including adjacent areas with a total size of 480,000 km\u003csup\u003e2,\u003c/sup\u003e for downscaling climatic variables.\u003c/p\u003e\u003cp\u003eTreeline delineation\u003c/p\u003e \u003cp\u003eWe define the treeline ecotone as the belt at the upper elevational limit of a closed-canopy forest with the gradual vegetation transition from closed tree stands to open grasslands and shrubs. Diffuse treelines are more often limited by climatic factors, unlike abrupt and krummholz treelines, which are more likely to be shaped by anthropogenic and natural disturbances (Harsch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hansson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is indirectly confirmed by gradual diffuse treelines being more responsive to climate warming (Tourville et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We manually delineated treeline ecotones on high-resolution Bing images. To do so, we digitized tree stands on the respective elevations with visible and rapid but still gradual elevational deterioration of tree stature (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified 161 parcels of climatic treelines in the highest mountain ranges of the Carpathians, together encompassing an area of nearly 12 km\u003csup\u003e2\u003c/sup\u003e. The distribution of the parcels reveals three distinct clusters, each corresponding to a major subdivision of the Carpathian Mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Delineated treeline parcels are distributed almost equally in the three subdivisions and cover the highest mountain ranges. Substantial disparities in treeline elevation were observed among the subdivisions, primarily along a north-south gradient, indicating that the characteristic elevation of treeline parcels changes with changing climatic conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClimatic variables and their spatial downscaling\u003c/p\u003e \u003cp\u003eWe utilized monthly climate data from 1970 to 2000 at 1 km\u003csup\u003e2\u003c/sup\u003e resolution from WorldClim 2.1 (Fick and Hijmans \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We derived 12 climatic variables to serve as determinants for treeline location. Using climatic values for the end of the last century is justified by the lag of several decades in trees' response to changing climate. We selected variables that capture various aspects of the thermal regime, namely the annual mean temperature, mean temperature of the warmest quarter of the year, mean temperature of the warmest month (July), mean October temperature, maximal temperature of the warmest month, and minimal temperature of the coldest month (the latter two reflect thermal and cold stress, respectively). Most of these variables indicate thermal resources available to plants during the most active growth phases, and all correlate with the altitude of treelines (Grace \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We further incorporated the mean October temperature, which has been demonstrated to significantly predict treeline movement in the Northern Hemisphere (Hansson et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also included growing season duration, growing season mean temperature, and accumulated growing degree days (AGDD), a heat index based on the annual accumulation of daily temperatures surpassing a predefined threshold (Hansson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, the AGDD is the cumulative sum of temperatures throughout the growing season. These variables were defined with a 0.9\u0026deg;C threshold, consistent with the definition of the growing season in high-altitude treeline studies (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paulsen and K\u0026ouml;rner \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and to a 5\u0026deg;C threshold to capture the growing season definition of trees (K\u0026ouml;rner \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To accomplish this, we temporally downscaled the time series of monthly mean temperatures to daily time series with a cubic smoothing spline. Subsequently, we calculated the number of days when the temperature exceeded the mentioned thresholds (the duration of the growing seasons), the mean temperature during the growing seasons, and the AGDD. We used elevation above mean sea level (AMSL) as another explanatory variable to verify if temperature-related variables were better predictors than differences in elevation alone.\u003c/p\u003e \u003cp\u003eWorldClim data have already been used in the global analysis of treeline factors and distributions, but the 1 km resolution compromises the accuracy of the analysis (K\u0026ouml;rner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Haesen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We thus spatially downscaled the climate data to a resolution of 30 meters with a spatial variability model. This model accounts for local terrain effects, such as temperature changes with elevation and potential influences of terrain aspect; it also addresses large-scale spatial variability, including temperature changes in the north-south direction and the impact of continentality. Our downscaling approach for the 12 climatic variables involved the following steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRegression models link the climatic variables to elevation and its two derivative components in the X and Y directions, which account for terrain aspect influences. Elevation data was sourced from the Shuttle Radar Topography Mission (SRTM) digital elevation model (version 3), available in 1-arcsecond (~\u0026thinsp;30 meters) resolution (NASA 2014). We used Akaike and Bayesian information criteria to decide whether to include terrain aspects in each variable's model.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe regression models predict the variables at 30-meter resolution and calculate the differences (residuals) between these predictions and the original 1-kilometer resolution variable. The residuals are likely mainly due to the remaining large-scale spatial variability not accounted for by terrain attributes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe residuals were smoothed using orthogonal polynomials in the form of a 6th-order trend surface. The emerging large-scale patterns were added to the regression models' predictions. The final prediction, therefore, combines the fine details contingent on local terrain conditions with the broader patterns associated with latitude and large-scale geographical features.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the downscaling accuracy with weather station data from the Global Historical Climatology Network dataset (Peterson and Vose \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Within the study region, we identified 28 weather stations with a minimum of 67% valid monthly records for 1970 to 1984 and 1985 to 1999 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We computed the respective climatic variables for these station locations. Then, we determined the discrepancies between the observed weather station records, our downscaled data, and the original 1 km WorldClim data. This comparison enabled us to examine how much our downscaled data enhanced the original 1 km WorldClim dataset.\u003c/p\u003e \u003cp\u003eVerifying relations between climatic variables and treeline location\u003c/p\u003e \u003cp\u003eWe superimposed the downscaled climatic variables on the delineated treeline parcels to assess their correspondence. We employed two approaches for the purpose. The first assumes that variables with a stronger association with the treeline exhibit greater homogeneity within the treeline ecotone compared to the surroundings across the Carpathians. To quantitively assess this correspondence, we calculated the ratio of standard deviations of the climatic variables within the treeline parcels to their background standard deviations in the elevation belt between 1,300 and 1,900 meters where most parcels are situated. The smaller the ratio for a climatic variable, the stronger the correspondence between this variable and the treeline location.\u003c/p\u003e \u003cp\u003eThe second approach involves running several machine learning models on climatic variables sampled over 13,331 pixels within treeline parcels and another 13,331 pixels randomly sampled over the background outside these parcels. These models were executed using the R Caret package; we automatically fine-tuned model parameters to select the \u0026ldquo;optimal\u0026rdquo; model (Kuhn \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The performance of each model was evaluated based on overall accuracy and Kappa statistics, which were calculated using five-fold cross-validation. We then calculated the variance importance of each climatic variable in each model using the Caret package's generic \u003cem\u003evarImp\u003c/em\u003e function. Because this function employs model-specific variable importance measures (see Kuhn \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), we ranked the variables according to their importance across different models to facilitate comparison. We did not explicitly calculate the statistical significance of our results; instead, we assume that variables consistently demonstrating better correspondence to the treeline across different models likely have a more robust association with the underlying processes responsible for treeline formation. Identifying the climatic variable most closely linked to the treeline location and determining its threshold value enables us to delineate the present-day climatic treeline. In addition, it permits the identification of areas where disturbances caused the treeline to descend and allows the definition of the extent of the climatic envelope for alpine ecosystems located above the treeline.\u003c/p\u003e \u003cp\u003eWe also forecast future climatic treeline shifts using climate projections. We used CMIP6 downscaled projections for four scenarios of the Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, SPP3-7.0 and SSP5-8.5, each averaged over 20-year periods (2021\u0026ndash;2040, 241\u0026ndash;2060, 2061\u0026ndash;2080, 2081\u0026ndash;2100); the scenario data are available through WorldClim (O\u0026rsquo;Neill et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fick and Hijmans \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Riahi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Averages and standard deviations were calculated for 23 global climate models (GCMs) for each SSP and period. We calculated the total area where the value of the indicative climatic variable is below the established threshold for the average of the models to estimate the extent of the future treeline ascent and the corresponding reduction in the climatic envelope for alpine ecosystems.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDownscaling climatic variables\u003c/p\u003e\n\u003cp\u003eThe downscaling of climatic variables to a 30 m resolution substantially enhanced their spatial accuracy, as evidenced by comparing weather station data and original WorldClim data (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.). The extent of accuracy improvement differed among the variables: Minimal temperature showed only a modest gain, as anticipated, due to commonly occurring inversions causing inverted vertical temperature gradients at night. The accuracy gain exceeded 20% for 8 out of 12 variables, affirming the effectiveness of the employed downscaling method. Consequently, we utilized the downscaled climatic layers in subsequent analyses (refer to Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e to illustrate the downscaling effect).\u003c/p\u003e\n\u003cp\u003eRelationships between climatic variables and treeline location\u003c/p\u003e\n\u003cp\u003eStandard deviations within treeline patches were significantly smaller for most climatic variables than those in the background elevation belt. Only for annual mean temperature and elevation AMSL were they roughly equal, with a ratio close to 1 (second column in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; see also Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e for complete data). This indicates that most climatic variables considerably better predict climatic treeline location than elevation alone. Among the variables, those characterizing accumulated growing degree days (AGDD) and temperatures during the warmer part of the year exhibited the smallest standard deviation ratios. Conversely, minimal and annual mean temperatures had a significantly poorer match to treelines.\u003c/p\u003e\n\u003cp\u003eAll machine learning models exhibited comparable performance measures, with Random Forest performing slightly better and Recursive Partitioning somewhat worse than others (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The variable importance of each climatic variable predictably differed among the models. However, AGDD of the growing season above 5\u0026deg;C (\u003cstrong\u003eGP5sum\u003c/strong\u003e) emerged as the most critical variable across all models while also demonstrating the most significant standard deviations ratio. The mean temperature of the warmest quarter (\u003cstrong\u003eTquart)\u003c/strong\u003e ranked second in terms of standard deviation ratio and in terms of variable importance in two machine learning models.\u003c/p\u003e\n\u003cp\u003eBased on these findings, we use the AGDD of the growing season above 5\u0026deg;C to indicate current and future climatic treeline locations. The threshold value for climatic treeline was determined by averaging this variable across all treeline parcels, resulting in a value of 1,112 AGDDs (for threshold values of other variables, refer to column 2 of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e). Based on this parameter, we estimate the total area of the climatic envelope for alpine ecosystems to be 1,370 km\u003csup\u003e2\u003c/sup\u003e (considering the mean 1970 to 2000 climate and the current ecosystem distribution).\u003c/p\u003e\n\u003cp\u003eProspective treeline projections\u003c/p\u003e\n\u003cp\u003eWe produced maps of projected treeline locations using downscaled averaged 23 CMIP6 projections of AGDD of the growing season above 5\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). From these data, we generated a table and a graph illustrating the projected areas above the climatic treeline for four SSPs and three 20-year periods (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e). Two notable aspects of our projections deserve emphasis. Firstly, even under the most optimistic scenarios, the climatic envelope for alpine ecosystems is expected to shrink by more than half compared to its present extent. Secondly, under the worst-case scenarios, alpine ecosystems in the Carpathians could almost disappear entirely by the end of the century, with suitable climatic conditions persisting only on the highest isolated mountain tops and ridges.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFactors determining the treeline position\u003c/p\u003e \u003cp\u003eTreeline is a significant bioclimatic boundary for thermal life zones and aligns with the broader thermal constraints for temperate zone agriculture (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While altitudinal treelines are associated with decreasing temperatures at higher elevations (Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), there is less clarity about their underlying drivers (Grace \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Richardson and Friedland \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our findings corroborate the results of several researches suggesting that warm season temperatures, rather than cold season lows, primarily dictate the upper bounds of tree growth (Tranquillini \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harsch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Davis et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This explains higher treeline positions in central (core) parts of mountains compared to their peripheries as well as in continental climates versus adjacent maritime regions (Malinovsky \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Czajka et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e), given the positive impact of increased climate continentality on summer temperatures. We observe this phenomenon, which has been extensively documented in the Alps (Quervain \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1904\u003c/span\u003e) and also in the Carpathians, where it manifests as an overall treeline elevation increase from west to east and from the peripheries to the cores of certain massifs (Malinovsky \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Czajka et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe tested several suggestions regarding the driving factors behind climatic treelines in the case of Carpathians. Notably, temperature-related climatic variables are often strongly correlated, especially within a single region. This makes it difficult to single out the only \u0026ldquo;true\u0026rdquo; predictor of the climatic treeline. Our findings align with the threshold values of climatic variables suggested by several researchers. The mean July temperature recorded for our treeline parcels is 10.5⁰C (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e), which is close to the 10\u0026deg;C threshold suggested by the earlier authors (Grace \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Richardson and Friedland \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K\u0026ouml;rner \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to the model indicated in (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), it corresponds to the tree root zone temperature of 8.5⁰C, in line with their lower boundary for the Alps (9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7⁰C) and equal to the 8.5⁰C estimated for the boreal zone (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The mean growing season above 0.9\u0026deg;C temperature converted to root zone is 7⁰C, consistent with the average value reported for the Alps (7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4⁰C) and with the global estimate of 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8⁰C as detailed in (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It is a little higher than the 6.4\u0026deg;C suggested in the later work by these authors (K\u0026ouml;rner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings are consistent with the notion that warm-season temperatures are predominant in governing the upper limits of tree growth (Tranquillini \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Wieser and Tausz \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harsch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our analysis revealed that the AGDD above 5\u0026deg;C, an indicator of cumulative heat during the warmest period of the year, exhibits the strongest correlation with treeline location in the Carpathians. The mean temperature of the warmest quarter was closely following in importance. This variable is more readily estimated and included in the standardized set of bioclimatic variables under the BIO10 code (Hijmans et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our findings align with the study of treeline changes in the northeastern United States, where AGDD was found to significantly account for variations in the extent of treeline advancement (Tourville et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We do not, however, assert the universal applicability of these results. While it may be enticing to establish parameters and thresholds for climatic treelines that apply globally, it is probably unfeasible because actual biome distribution patterns are often divergent even under a fixed climate. Biome variations can emerge from diverse historical contexts, resulting in unique trait pools, differing ecosystem equilibrium states, and varied disturbance regimes (Higgins and Scheiter \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Moncrieff et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the method that we apply for revealing the most relevant climatic factors determining treeline positions, namely \u0026ndash; the identifications of the parcels of extant climatic treelines on high-resolution satellite images and the statistical analysis and machine learning modeling of the distribution of climatic variables within and outside these parcels \u0026ndash; can be applied to other mountain regions as well. This would allow obtaining sufficiently large samples without the need to physically access remote locations in mountains with rugged terrain.\u003c/p\u003e \u003cp\u003eMany European mountains with relatively dense and long-lasting human presence now feature treelines significantly lower than the climatic limits for tree growth since forests were cut for fuel and construction material and cleared out for pastures (Holtmeier and Broll \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Weisberg et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vincze et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Traces of human influence that shaped the Carpathian treelines date back as far as 4,200 years and intensified around 2,000 years ago (Vincze et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since medieval times, practices like transhumance in the Carpathian Mountains involved slash-and-burn techniques to expand grazing fields (Vincze et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a result, the actual (anthropogenic) treeline in the Carpathians is commonly located 250\u0026ndash;300 m lower than the climatic treeline, sometimes descending below 1,000 m AMSL, particularly on shallow slopes above densely populated valleys (Malinovsky \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). In some parts of the Carpathians, treelines of anthropogenic origin are formed by deciduous European beech forests (Weisberg, Shandra, and Becker 2013), similar to those in southern Europe where \u003cem\u003eFagus\u003c/em\u003e species treelines are depressed hundreds of meters below the climatic treeline (K\u0026ouml;rner and Paulsen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The effects of anthropogenic influences on treelines can persist long after their cessation (Holtmeier and Broll \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Malanson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; K\u0026ouml;rner \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome natural factors and disturbances interact with climatic factors to influence the location of treelines. Snow avalanches and debris flows significantly lower their position in susceptible concave terrain elements (Holtmeier and Broll \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Treml and Migoń \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while extremely convex and exposed terrain elements are not conducive to tree advancement either due to strong winds. Additionally, edaphic factors can affect treeline position, with treelines typically higher on carbonate rocks than on quartzite rocks and lower on coarse glacial deposits (Czajka et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Treml and Migoń \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTreeline changes as a response to rising temperatures.\u003c/p\u003e \u003cp\u003eWarming temperatures lead to shifts in species' and ecosystems' distributions, with a global meta-analysis estimating an average uphill movement rate of 11.1 meters per decade (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The floras and faunas of Central European mountains are particularly vulnerable to climate change due to narrow habitat tolerances and marginal habitats for many species (e.g., Thuiller et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Treelines have been observed to shift upwards in many regions worldwide, including Europe, Asia, and North America (Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; see bibliography for additional sources). A global meta-analysis of data from 166 treeline sites found that 52% had notably advanced since 1900 AD, with only two sites showing receded treelines, both with evidence of disturbance (Harsch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Another study reported the advancement of 67% of 438 altitudinal treeline sites (Hansson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data analysis from two surveys in the Swiss Alps in 1979/1985 and 1992/1997 revealed 893 hectares of new forests above the former treeline, with a median upward shift of 28 meters (Gehrig-Fasel et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Carpathian Mountains are no exception. A study comparing timberline altitudes in 2009 to those in 1964 on the south-facing slopes of the Babia Gora massif in the western Carpathians observed an upward progression of up to 30 meters (Czajka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), while (Szwagrzyk \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) noticed the young age of spruce trees on its north slopes, suggesting recent advances in the treeline. A comparison of historical maps from the 1930s with recent Landsat images of the Ukrainian Carpathians revealed significant treeline advancement on the highest ridges (Martazinova et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is, however, unclear how much of these effects can be attributed to climate change as opposed to the regeneration of forests previously destroyed by human activity below the climatic treeline.\u003c/p\u003e \u003cp\u003eHowever, the observed changes are far more conservative than our projections of climatic treeline shifts, which, even under optimistic SSP scenarios, would typically rise by a few hundred meters. Apart from the accelerating nature of climate warming, this can be explained by the delayed ecological response, as trees have limited seed dispersal rates and require time to establish (Holtmeier and Broll \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harsch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Holtmeier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The extent to which the treeline lags behind climate change is debatable. While tree species migration rates were estimated to be less than 100 m per year, preventing them from occupying all suitable sites in Central and Northern Europe in the course of dispersion from the Last Glacial Maximum refugia (Svenning and Skov \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), research on lake sediments in the central Alps near the modern treeline revealed a swift response of the treeline to climate changes at the Late Glacial-Holocene transition, suggesting that global warming could also trigger large-scale displacements of plant species and rapid upslope movements of the treeline (Tinner and Kaltenrieder \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Before the mid 20 century, tree radial stem increment noticeably decreased when approaching a treeline in the Swiss and Austrian Alps; by the end of the century, this relationship had almost vanished, indicating that these tree stands are now located far enough from the climatic treeline (Paulsen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Another study revealed that the rates of treeline advance reconstructed for study sites in the Canadian Rocky Mountains were much lower than expected based on changes in growing-season temperature, with a median percentage of observed versus expected movement of only 39% (Davis et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study focused, like most others, on changes in average temperatures. However, knowledge about the impacts of extreme weather events on changes in treelines remains patchy, and they are predicted to increase in intensity and frequency with climate change (Smith et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Climate change will also affect other treeline-related factors, such as snow avalanches, which are predicted to decrease in frequency and severity in a warming climate. Lower avalanche activity due to climate warming has already led to the expansion of forested areas in some Carpathian valleys (Kaczka et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A study in Romania revealed significant forest regeneration on former avalanche paths, accounting for approximately 50% of the total increase in forest surface area (Mihai et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Nonlinear relations created by positive feedback loops could further complicate the response of treelines to climatic factors, possibly resulting in alternative stable states of forest and alpine vegetation and abrupt treeline transitions driven by internal ecosystem dynamics (Malanson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations could facilitate tree growth at treeline on their own through enhanced photosynthetic activity (Handa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reyes-Fox et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which could amplify and even outweigh the effects of predicted temperature increases (Higgins and Scheiter \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf our assumption of the summer temperatures as the leading factor behind climatic treelines is valid, the drastic decrease in the climatic envelope for alpine ecosystems in the Carpathians should be expected by the end of the century. This means that the efforts for the conservation of alpine communities, including their endemic and endangered species, should be spatially directed towards the highest ridges and peaks in the Carpathians, in particular Tatras and the southern Carpathians, where favorable climatic conditions for these ecosystems would sustain for a longer while. It can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e that different SSPs indeed make a difference, and the divergence of predicted areas above the climatic treeline for four SSPs tends to grow larger with time. This means that global measures to slow climate warming would mean a lot in taming down the detrimental effects of climate change on ecosystems.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study reveals the climatic factors governing the current treeline position in the Carpathians and projects its upward shifts under climate change. Warm season temperatures are the most influential to the upper bounds of tree growth, and growing degree days above 5\u0026deg;C exhibit the strongest correlation with treeline location among the 12 climatic variables tested in our study. We used the estimated threshold value of the growing degree days to assess the current total area of the climatic envelope for alpine ecosystems at 1,370 km\u003csup\u003e2\u003c/sup\u003e. We then take CMIP6 climate projections to forecast the decrease of alpine ecosystems to less than a quarter of their current size by 2050. In the highest emission scenarios, alpine ecosystems in the Carpathians will almost disappear by 2080.\u003c/p\u003e \u003cp\u003eOur work corroborates the importance of reducing greenhouse emissions to safeguard alpine ecosystems but also underscores the need for adaptation measures to support mountain regions. We fill a regional gap in quantifying treeline behavior under climate change; unfortunately, such estimates are still lacking for other critical mountain systems, such as the neighboring Alps and Dinaric mountains, which share comparable climate and ecosystem properties. Besides, we hope our results can contribute to more detailed studies that delve into physiological mechanisms and environmental interconnections for a deeper understanding of the ecological consequences of climate warming. Such knowledge is crucial for improving land-use management and adaptation strategies for mountain forestry, agriculture, and nature conservation amidst accelerating climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eBoth authors contributed to the study conception and design, data collection and analysis, writing the first draft of the manuscript and its subsequent editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research is based on open data mentioned and cited it the manuscript.\u003c/p\u003e\n\u003cp\u003eDatasets created during the research process are accessible from Zenodo open repository by the following links:\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/11359344?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6Ijc5YzkwYzkwLWRhOTYtNGM2Zi1iNmNkLWVjYzg3NTcyNjkyMiIsImRhdGEiOnt9LCJyYW5\u003cbr\u003ekb20iOiI2YTUyNjU1NTRkOThmMDRjMzJiNGJkZjk5YTg3NWQ0ZSJ9.KQREZlcL_F2FHzv-cyMLN7E3DpxGAi7JgDLfzeGH6FI\u003cbr\u003e-HJ2uj6ZUuvlJ1kCPZyIYIv01jsX3I2Aa3u-PchBj7Q\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/11358952?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImMwZjQ0MjFlLWY0YTMtNDAxMS1iYzliLWE3MzFkZjFjNzYwMyIsImRhdGEiOnt9LCJyY\u003cbr\u003eW5kb20iOiJiZTk2OGQyMmUyNGRiOGM1NmYwNDhlYzdlMTY5ZjJjYyJ9.sMM9MWmVjDbKuYvMsY_r-C134HU373cijc9ufC3E\u003cbr\u003e-5zTiIpcrSrbDpan2iv2HXcTF1gnqAZy5w0iD7MgYVn0SA\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/11320218?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjQxYmI2OTUxLWIzYjEtNGE0MS1iM2RiLTJmOWMzOGYzMmJiZCIsImRhdGEiOnt9LCJyY\u003cbr\u003eW5kb20iOiI5ZDBiZGQ5ZGZiZWQ1OGI5MWU4NzZlMWI0MWE5Y2ZhNCJ9.DVx9yJajAHwSkBneHqp9wtjAcEUWr64jsqx0QOHMgo\u003cbr\u003eMqKOC9B4uaoIvK2LA3piPs1CDtbbrnq3j7p1bW3FKuuQ\u003c/p\u003e\n\u003cp\u003eIntermediate data and code are available from the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCannone N, Sgorbati S, Guglielmin M (2007) Unexpected impacts of climate change on alpine vegetation. 5:360\u0026ndash;364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen I-C, Hill JK, Ohlem\u0026uuml;ller R et al (2011) Rapid Range Shifts of Species Associated with High Levels of Climate Warming. 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Holocene 27:1613\u0026ndash;1630. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0959683617702227\u003c/span\u003e\u003cspan address=\"10.1177/0959683617702227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeisberg PJ, Shandra O, Becker ME, Arctic (2013) Antarct Alp Res 45:404\u0026ndash;414. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1657/1938-4246-45.3.404\u003c/span\u003e\u003cspan address=\"10.1657/1938-4246-45.3.404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieser G, Tausz M (2007) Current Concepts for Treelife Limitation at the Upper Timberline. In: Wieser G, Tausz M (eds) Trees at their Upper Limit. Springer Netherlands, Dordrecht, pp 1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eDownscaling summary\u003c/em\u003e: Climatic variables: \u003cstrong\u003eTann\u003c/strong\u003e \u0026ndash; Annual mean temperature; \u003cstrong\u003eTquart\u003c/strong\u003e \u0026ndash; Mean temperature of the warmest quarter; \u003cstrong\u003eTJul\u003c/strong\u003e \u0026ndash; Mean July temperature; \u003cstrong\u003eTOct\u003c/strong\u003e \u0026ndash; Mean October temperature; \u003cstrong\u003eTmax\u003c/strong\u003e \u0026ndash; Mean maximal temperature of the warmest month; \u003cstrong\u003eTmin\u003c/strong\u003e \u0026ndash; Mean minimal temperature of the coldest month; \u003cstrong\u003eGP0.9leng\u003c/strong\u003e \u0026ndash; Duration of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP0.9mean\u003c/strong\u003e \u0026ndash; Mean temperature of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP0.9sum\u003c/strong\u003e \u0026ndash; AGDD of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP5leng\u003c/strong\u003e \u0026ndash; Duration of the growing season above 5\u0026deg;C; \u003cstrong\u003eGP5mean\u003c/strong\u003e \u0026ndash; Mean temperature of the growing season above 5\u0026deg;C; \u003cstrong\u003eGP5sum\u003c/strong\u003e \u0026ndash; AGDD of the growing season above 5\u0026deg;C. Explanatory terrain attributes (column 2): Elev \u0026ndash; Elevation AMSL; AspX \u0026ndash; elevation derivative in X direction (north-south); AspY \u0026ndash; elevation derivative in Y direction (east-west).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClimatic variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eRegression model\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eRoot mean squared deviation from weather station data\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eAccuracy gain for downscaled data, %\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVariables in the model, sign\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMultiple R-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOriginal WorldClim data\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDownscaled data\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTann\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspX, AspY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTquart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTJul\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTmin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP0.9leng\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP0.9mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP0.9sum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP5leng\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP5mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev, AspX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP5sum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eMeasures of correspondence between treeline parcels and climatic variables\u003c/em\u003e: Variables codes are in Table 1. \u003cem\u003eDownscaling summary\u003c/em\u003e: Climatic variables: \u003cstrong\u003eTann\u003c/strong\u003e \u0026ndash; Annual mean temperature; \u003cstrong\u003eTquart\u003c/strong\u003e \u0026ndash; Mean temperature of the warmest quarter; \u003cstrong\u003eTJul\u003c/strong\u003e \u0026ndash; Mean July temperature; \u003cstrong\u003eTOct\u003c/strong\u003e \u0026ndash; Mean October temperature; \u003cstrong\u003eTmax\u003c/strong\u003e \u0026ndash; Mean maximal temperature of the warmest month; \u003cstrong\u003eTmin\u003c/strong\u003e \u0026ndash; Mean minimal temperature of the coldest month; \u003cstrong\u003eGP0.9leng\u003c/strong\u003e \u0026ndash; Duration of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP0.9mean\u003c/strong\u003e \u0026ndash; Mean temperature of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP0.9sum\u003c/strong\u003e \u0026ndash; AGDD of the growing season above 0.9\u0026deg;C; \u003cstrong\u003eGP5leng\u003c/strong\u003e \u0026ndash; Duration of the growing season above 5\u0026deg;C; \u003cstrong\u003eGP5mean\u003c/strong\u003e \u0026ndash; Mean temperature of the growing season above 5\u0026deg;C; \u003cstrong\u003eGP5sum\u003c/strong\u003e \u0026ndash; AGDD of the growing season above 5\u0026deg;C. Explanatory terrain attributes (column 2): Elev \u0026ndash; Elevation AMSL; AspX \u0026ndash; elevation derivative in X direction (north-south); AspY \u0026ndash; elevation derivative in Y direction (east-west). Machine learning models: \u003cstrong\u003eRpart\u003c/strong\u003e \u0026ndash; Recursive Partitioning for Classification Trees; \u003cstrong\u003eAvnnet\u003c/strong\u003e \u0026ndash; Averaged Neural Network; \u003cstrong\u003eMARS\u003c/strong\u003e \u0026ndash; Multivariate Adaptive Regression Spline; \u003cstrong\u003eGbm\u003c/strong\u003e \u0026ndash; Stochastic Gradient Boosting; \u003cstrong\u003eRF\u003c/strong\u003e \u0026ndash; Random Forest. For machine learning models, ranks of variables are given since variable importance measures are model-specific and incomparable. \u0026quot;NA\u0026quot; denotes variables that were not included in a model. The three most important variables for each model are highlighted in colors. The elevation variable was not used in the machine learning models. The means and standard deviations of variables for treeline parcels and background and given in Table 4, \u003cem\u003eAppendix\u003c/em\u003e. \u003cem\u003eRaw variable importance measured with Caret\u0026apos;s varImp function is shown in\u003c/em\u003e Table 5, Appendix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStandard deviation ratios (parcel/background)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eVariable importance in machine learning model (ranks)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRpart\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAvnnet\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMARS\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGbm\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRF\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElevation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP5sum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eModel Accuracy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eModel Kappa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carpathians, treeline, global warming, mountain ecosystems, climate impacts","lastPublishedDoi":"10.21203/rs.3.rs-4487120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4487120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTreelines represent a significant ecological boundary in mountainous regions. Changes in temperature and precipitation regimes due to climate change affect the location of treelines, contingent on fine-scale variations in orographic and climatic conditions. Using high-resolution satellite imagery, we identified climatic treelines in the Carpathian Mountains, one of Europe\u0026rsquo;s largest contiguous forest ecosystems. We downscaled climate variables to a 30-meter resolution through a polynomial approximation of regression residuals with terrain attributes, then correlated climatic variables with the location of the climatic treeline. Growing degree days above 5\u0026deg;C demonstrated the strongest correlation with treeline location. Our growing degree threshold results in a total area of 1,370 km\u003csup\u003e2\u003c/sup\u003e above the current climatic treeline in the Carpathians. This area constitutes the climatic envelope for alpine ecosystems and comprises the highest ridges and peaks. Using future climate projections, this area will likely decrease to 410\u0026ndash;515 km\u003csup\u003e2\u003c/sup\u003e by 2040, 100\u0026ndash;320 km\u003csup\u003e2\u003c/sup\u003e by 2060, and 15\u0026ndash;290 km\u003csup\u003e2\u003c/sup\u003e by 2080. The upward shift threatens the region's rare and endemic alpine species and will trigger substantial ramifications for ecosystems, water balance, and the carbon cycle in the Carpathian Mountains. A better understanding of the effects of climate change on treeline locations is crucial for informing ecosystem management and conservation planning, as well as to cushion the impacts of climate change on agriculture and forestry practices.\u003c/p\u003e","manuscriptTitle":"Climatic determinants of the Carpathian treeline and its projected upward shifts in response to climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 14:05:09","doi":"10.21203/rs.3.rs-4487120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-23T18:54:20+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-29T21:15:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-31T02:54:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2024-05-29T17:28:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"831c6d3b-196b-48d0-bfd6-cecb90a85ea9","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:13:35+00:00","versionOfRecord":{"articleIdentity":"rs-4487120","link":"https://doi.org/10.1007/s10584-025-03947-y","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2025-05-22 15:58:27","publishedOnDateReadable":"May 22nd, 2025"},"versionCreatedAt":"2024-07-23 14:05:09","video":"","vorDoi":"10.1007/s10584-025-03947-y","vorDoiUrl":"https://doi.org/10.1007/s10584-025-03947-y","workflowStages":[]},"version":"v1","identity":"rs-4487120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4487120","identity":"rs-4487120","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}