the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tales from the past: remapping dynamic tree- and forest lines in response to changing climate and current land use
Abstract. Average temperatures are rising more rapidly in high-latitude and alpine regions than elsewhere, leading to a gradual compression of the alpine bioclimatic zone due to the upward shift of the tree- and forest lines (TFLs). While most studies on TFL dynamics indicate advance, the rate at which regional and local treelines respond to climate warming remains uncertain. Furthermore, not every empirical TFL is determined by climate alone; edaphic conditions, species traits, and, in particular, land use, affect tree growth and distribution. In many regions, domestic grazing and other forms of traditional mountain summer farming have historically depressed forest lines. Previous research has been limited by the comparison of data sampled with mixed methods and poor temporal data coverage. Additionally, there is still a lack of studies accounting for time-lags, thus including data spanning a long time.
In this study, we used consistently remapped in situ measurements of mountain birch (Betula pubescens ssp. czerepanovii) in Norway, dating from 50 to 130 years back, to: (1) document the rate of TFL change; (2) understand the impact of land use and climate change on regional TFL dynamics; and (3) discuss regional aspects and quality components of the data.
We find that Norwegian TFLs are advancing at rates exceeding 0.5 m yr−1, primarily driven by climate change for TLs and land use for FLs. Still, the rates of TFL change vary considerably between regions, likely due to stochastic disturbances (e.g. snow avalanches, insect outbreaks, pests, landslides, rockfalls).
We highlight the need for better quantification of time lags in treeline responses and for consistent definitions and methodologies when assessing long-term TFL dynamics in boreal–alpine ecotones.
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- RC1: 'Comment on egusphere-2026-2717', Anonymous Referee #1, 15 Aug 2026 reply
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- 1
The manuscript by Tjessem et al. reports changes in treelines (TLs) and forest lines (FLs) and investigates the roles of climate change and land use using long-term in situ observations of mountain birch in Norway. The authors find that these tree- and forest lines (TFLs) have been advancing at rates comparable to those reported in previous studies. Based on regression analyses, they further conclude that changes in TLs are primarily driven by climate change, whereas changes in FLs are primarily driven by land use.
While this study leverages a unique long-term in situ dataset to investigate changes in TFLs, I found that some of the main conclusions are not sufficiently supported by the analyses presented. In addition, several aspects of the methodology and datasets are not described clearly enough to allow the reader to fully understand or evaluate the analyses. Please see my detailed comments below.
Major comments:
1. Key datasets (TFLs and land use) are not described with sufficient clarity.
For the TFL data, how frequently were these sites surveyed, and how are the observations distributed temporally? The description in L150 (“the first, second, and third TFL record”) and the three registrations shown in Fig. 2 are somewhat confusing. L204 mentions the change is calculated with only the first and last record, were other records used in the analysis? Please clarify the temporal sampling scheme and explain how these different records/registrations are defined and used in the analysis.
For the land-use data, are observations available only for the years listed in L163 and L166? If so, how are the 20-year averages prior to each TFL record calculated if land-use information is only available for selected years? A clearer description of the temporal coverage, interpolation/aggregation procedure, and how these data are matched with the TFL observations is needed.
2. The explanatory power of the statistical analyses is very low, making the attribution of TFL changes to specific drivers difficult to support.
For example, the correlation in Figure 4 is less than 0.25. The linear regression models also just explain very limited variability in TFL changes (R2 up to 0.2). Although the authors also mention other factors such as disturbance, the poor model performance makes it difficult to draw any convincing conclusions regarding the drivers of TFL changes.
Minor comments:
L24: TL and FL should be spelled out at first appearance. Likewise in the main text.
L53: This sentence is ambiguous. It is unclear which region the factors described after “due to” refer to. Please clarify.
Table 1: The selection of growing season length variables (Monthly temperature > 5, 5.5, 6, … °C) appears somewhat arbitrary. Please provide a justification for the choice and range of these temperature thresholds.
Table 1: “Annual temperature deviation” is confusing by its name, for example, it could mean the standard deviation of temperature within each year or across years, although Table A1 provides a more detailed definition. Since this variable represents the temperature difference relative to the 1981–2010 reference period, a term such as “temperature change” may be clearer.
L159: Could you briefly explain why NNE 22.5° is least favourable?