the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Jan 2025
The significant role of snow in shaping alpine treeline responses in modelled boreal forests
Abstract. Treelines across the Northern Hemisphere are shifting upward and northward in response to global warming, particularly in boreal forests, where climate change progresses more rapidly at high elevations and latitudes. These shifts intensify competition for resources, threaten endemic alpine species, and disrupt established ecological relationships, leading to biodiversity loss. However, significant heterogeneity and regional variation exist in how treelines respond to environmental changes, with many underlying drivers and constraints still poorly understood.
This study aims to enhance understanding of alpine treeline dynamics and improve vegetation model predictions under changing climatic conditions. We evaluated the relative impact of key factors influencing treeline migration velocity and examined the effects of varying snow regimes on treeline migration within the alpine treeline ecotone. To achieve this, we incorporated a novel snow module into the vegetation model LAVESI (Larix Vegetation Simulator), enabling the integration of precipitation outside the growing season, snow accumulation, and snowmelt processes. This module allows for explicit modelling of the positive and negative impacts of snow depth on tree growth and treeline migration, while accounting for stochastically occurring extreme events and capturing full weather variability.
Our findings reveal site-specific responses to factors driving treeline shifts and forest expansion, with localised conditions playing a critical role in shaping migration dynamics. The Canadian and the Russian sites demonstrate clear insights into primary migration drivers, while the high variability at the Alaskan site indicates more complex local dynamics and greater predictive uncertainty. The study highlights the significant role of snow in modulating migration potential, as snow accumulation creates favourable conditions for seedling germination and growth while also posing risks of increased mortality from snow loads or avalanches. These results underscore the importance of incorporating snow-related processes into vegetation models to improve the accuracy of predictions for boreal forest dynamics.
Overall, this study provides valuable insights into tree migration processes, highlighting the varied predictability of treeline responses across regions. These findings carry significant implications for refining vegetation models and guiding conservation strategies to sustain alpine tundra resilience in the face of accelerating climate change.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(1592 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1592 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-4036', Anonymous Referee #1, 13 Feb 2025
-
AC1: 'Reply on RC1', Sarah Haupt, 14 Feb 2025
Thank you for carefully reading our manuscript. We will take the helpful comments into account in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-4036-AC1 -
AC3: 'Reply on RC1', Sarah Haupt, 18 Mar 2025
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC1
RC1: This article is investigating the role of snow on treeline response to climatic
warming by including snow (in addition to other abiotic and biotic variables) module in the
vegetation model LAVESI. I like the idea with the model LAVESI. Results showed site
specific response of treeline shifts and highlighted the role of snow in treeline dynamics.
The approach including additional variable in vegetation model is interesting. The data
are enough and relevant to test the authors’ hypothesis. I recommend a minor revision.
AC: We sincerely appreciate your valuable feedback and constructive comments
during the first review of our preprint. Your comments will significantly enhance
our manuscript. We will carefully address all referees' suggestions to further
improve the quality of our study and ensure it meets the high standards of
Biogeosciences.
RC1: I like your introduction, but it is a bit too long, it would very nice if you are able to
make it more concise by reducing it by 10-20 % pages.
AC: We will shorten the introduction while preserving all essential content.
RC1: Recently several studies reported role of biotic interactions on treeline dynamics.
Please see Bader et al. 2021, Ecography, Liang et al. 2016, PNAS, Sigdel et al. 2024,
Nature Plants.
AC: Thank you for the additional literature suggestions. We will consider to
include them where fitting in the text.
RC1: Overall, the discussion is general and comprehensive for me and mostly authors
focused on those influences and processes that have been investigated. It has been
neglected role of snow duration, i.e., the period when snow covers the treeline,
determines processes of tree regeneration and establishment.
AC: The snow module accounts for snow duration, i.e., the period during which
snow covers the treeline area:
Building on the approach of Sato et al. (2007), the processes of snow
accumulation and snowmelt were incorporated into the LAVESI model (see lines
231-232). As a result, the duration of snow cover is influenced by snow depth,
which is affected by both snow accumulation and snowmelt. All subsequent
influences on tree growth and treeline migration, which are dependent on snow
depth, are consequently impacted throughout the duration of the snow cover. The
effects of varying snow depths were implemented in the model in the following
abstracted manner:
If the height of the tree is higher than the current snow depth, the mortality rate of
the tree increases by 50%. This penalty decreases linearly with increasing tree
height and is eliminated once the tree reaches a height of 5 m.
When seeds are covered by snow, their germination is enhanced, exhibiting a
linear increase in germination rates with greater snow depth. Specifically, this
relationship begins at zero and reaches a 30% increase in germination at a snow
depth of 1 m.
If seedlings and saplings are covered by snow, their growth is enhanced by 30%.
The germination process is influenced linearly, either positively or negatively,
based on whether the snow melts before or after the 110th day of the year.
The actual day of snowmelt is divided by 110, and the deviation from 1.0 is added
to the germination probability (see lines 237-243).
In general, the growing season length and its influence on seedling germination
and tree growth are already incorporated into the vegetation model (see lines 187-
189).
We will provide a clearer explanation of these connections especially of the snow
period length in the updated version of the manuscript.
RC1: Related to this, snow fungi might also explain part of the spatio-temporal pattern.
AC: The influence of snow fungi was indirectly incorporated into the snow module,
as prolonged high snow load or a shortened growing season negatively affects
tree growth. In the discussion, we will highlight that snow fungi may also
contribute to the observed spatio-temporal pattern.
RC1: Also snow movements and avalanches may affect the observed species differently.
AC: The snow module includes snow load and avalanche occurrence. However,
we did not vary these effects by tree species, as the simulation areas in our study
– as well as the North American and Siberian treeline ecotones in general – are
predominantly dominated by spruce in Northwest Canada and Alaska, and by
larch in Siberia.
We will include species-dependent differences in treeline migration in the
discussion to provide further insights for studies focusing on species-specific
responses.
RC1: Additionally, even though, different plant traits and interactions are included in the
model, their influence on treeline sensitivity and migration is missing.
AC: Species-dependent plant traits are incorporated into the vegetation model.
Since the simulation areas are dominated by spruce or larch, we consider the
influence of other tree species on the sensitivity analysis as negligible.
RC1: It is not clear why site-specific specifics sensitivities are observed? What are the
mechanisms behind such discrepancies? It needs to be justified based on site specific
evidences. Authors discussed different factors (environmental and traits) but fail to link
site specific variability. Thus, it needs to synthesize the results rather than presenting
results directly.
AC: We acknowledge the need for a more integrated discussion of site-specific
sensitivities. Rather than treating the study sites separately, we will synthesize the
results to highlight overarching patterns and mechanisms driving the observed
differences. By linking environmental factors and species traits more explicitly, we
aim to provide a clearer, more comprehensive understanding of the site-specific
variability in treeline dynamics.
RC1: Generally, authors just compare their results with other similar studies. To make it
convincing, it needs deeper discussion with key scientific evidence and how it helps to
advance our understanding on treeline ecology under changing climate.
AC: In the discussion, we will highlight the implications of our results for the
future of North America's treelines in the context of global warming.
RC1: Also, ecological implications of the modified model should be highlighted.
AC: In the discussion, we will also further elaborate on the ecological implications
for North American forests, including biodiversity, the albedo effect, and their role
as a carbon sink.
RC1: L13-14: As site wise results are presented, it is better to mention the areas/sites
considered in analysis.
AC: We will mention the study sites at this point in the abstract.
RC1: L64-66: Please see recent treeline studies used individual-based model to simulate
plot-based data including both biotic and abiotic variables (Zheng et al. 2024, Eco Lett).
AC: Thank you for the literature suggestion. We will carefully review the methods
and findings of this study.
RC1: L184: Please mention the sites considered in this study before this sentence.
AC: We will introduce the study sites earlier in the manuscript.
RC1: L307-308: Even though the trees traits data were adopted from previous
publications, better to elaborate how these data were retrieved and sources.
AC: We will compile a complete table of species traits and model variable values in
LAVESI and include it in the manuscript appendix.
RC1: Table 3 caption: impact factors could be replaced by predicting variables.
AC: We will change this term according to your suggestion.
RC1: No any parameter is significance from Road to central Alaska. What is mean? Is
this model not well able to predict the sensitivity of Alaskan treelines?
AC: At the Road to Central, Alaska site, no single factor was found to be
statistically significant. This may be due to stronger interactions between multiple
factors, which could obscure the effects of individual parameters. Another
possible explanation is that this site is more influenced by stochastic processes.
RC1: L392-395: It seems to methodology rather than results.
AC: We will relocate this section to the Methods section as suggested.
RC1: L464-469: Just speculations, no evidence supports the findings.
AC: As mentioned above, we will provide a more detailed explanation of the site-specific differences in the sensitivity analysis results, thereby clarifying or reassessing the significance of this section.
RC1: L532: either data or reference should be presented.
AC: We will insert a reference to ‘Table 1: Characteristics of the study sites.’ at this
point.
RC1: L550: How did species-specific stand structures varies across the sites. It should
be site and species specific. How population structure varied across the study sites? and
so on.
AC: Since spruce or larch clearly dominates at all three study sites, we have not
yet conducted a detailed comparison of species composition. However, we will
include a general comparison of the stand structure of the study sites in the
manuscript.
-
AC1: 'Reply on RC1', Sarah Haupt, 14 Feb 2025
-
RC2: 'Comment on egusphere-2024-4036', Anonymous Referee #2, 24 Feb 2025
The manuscript entitled "The significant role of snow in shaping alpine treeline responses in modelled boreal forests” aims to investigate the effect of snow on the treeline shift. However, in my opinion, reading the article looks like this is not the main focus of the manuscript. The structure of the article is center on the sensitivity analysis of the forest dynamic model LAVESI. In the methodology section is reported the description of a new way to incorporate the effect of the snow cover in the LAVESI model, but the results section is just partially focused on the outcomes of this process. Some issues in the incorporation of this process are detected and reported in the specific comments below. Moreover, the study areas are not adequately described and neither pictures or orthophoto are reported. For me it is complicate to understand the real characteristics of the three investigated sites.
The approach adopted in this manuscript is the progressive variation of the model inputs and so it is basically a sensitivity analysis of the LAVESI model. Furthermore, there is no comparison between the model outputs and the actual forest characteristics making impossible to evaluate the reliability of the model outcomes and consequently the future scenarios. The manuscript need to be deeply restructured and also the methodological approach deeply revised. In my opinion the final suggestion for the manuscript is a rejection encouraging the authors to work on it for a resubmission.
Below I report the specific comments.
Introduction: the section is too long and I suggest the authors to reduce it of about 50 lines. Moreover a general comment to this section is to restructure the description of the drivers and factors influencing the treeline shift. As it is, in my opinion it results a little bit complete to follow.
Line 187: for run, the authors intended years? So, the model time resolution is equal to 1 year if I understood correctly. Please specify it.
Section 2.1: how do the species specific parameters included in the model were derived? Please refer also to the species you are going to simulate reported in table 1.
Line 194: How does the tree mortality rate is included in the model? Please specify what tree and environmental related parameters where used to model this process.
Line 221: the threshold of 5°C for distinguish between rainfall and snowfall needs to be accurately motivate. The value of 5 sounds quite high in my opinion. How do you motivate it? Temperature daily fluctuation in case of precipitation is really low so I would expect that a mean daily temperature slightly less than 5°C will lead to rainfall precipitation. This is a crucial point, and it needs to be clarify and accurately motivated since it can majorly affect the simulation results.
Line 245-250: about avalanche induced mortality, the authors never mentioned the role of the slope as predisposing factor for snow avalanche release. It is generally defined that a slope between 27° and 55° is the range where avalanches can occur.
Section 2.3: For me this section results slightly complicate to follow because it is a pure description of the model and the relative processes. I would encourage the authors to use formulas and/or diagrams or flowcharts in order to have also a clear view of the developed model.
Table 1: are the locations reported in a local or a geographic coordinate system? It looks to me east north but the xmin etc. is confusing me. I would suggest to add also the total area modelled for each study site. A small map would be beneficial to clearly identify the location of the investigated sites.
Lines 304-308: Why do the authors compute the sensitivity analysis of the parameters of the model? For me it is unclear the motivation of this operation.
Section 5: for really understand the morphology of the sites and the condition it would be beneficial to add an image with the orthophoto of the investigated areas with contour lines or something similar.
Section 5: It would be of great value if the authors can compare the actual condition of the study site with the results of the spin up simulation. In this way it is possible to assess the reliability of the model and therefore the validity of the future scenarios.
Line 330: What is the definition of treeline? It is necessary to define how the authors have identified it. Is it based on a certain density of trees or something similar?
General comment for the methodology section: Authors investigated which factors are the most important/influent for the migration rate. I would say that the common approach for simulating future scenarios is to have a solid and reliable model either tested with field data or from literature in similar conditions. Consequently, models are used to predict future change by just modifying the environmental variables or the initial conditions, obtaining in this case the probable shift of the treeline. In this manuscript the procedure is not classical, and the authors look like they want to investigate which variable of the model can affect the treeline shift and how. To me this approach is more similar to a simple exercise respect to really understand the possible dynamic of the treeline shift for the three study sites. This feeling is corroborated by the absence of comparison between the current condition of the forests and simulation spin up for the three study areas. In other word the factors influencing the tree line shift are the environmental one and not the parameters of the model.
Line 359: please add a couple of sentences to introduce the results instead of directly showing Table 3.
Table 5: The table should be moved to the results section.
Lines 458-462: I would say this is a quite obvious result. The study areas are different, and I would expect exactly this result.
General comment to the results and discussion section: The authors report the outcomes of the modelled scenarios discussing and comparing the relative results, but the real problem is that a reliable scenario has not been identified. In the manuscript there is no identification of a modelled scenario that can be representative of current/actual forest conditions and of the future one. Without this definition is not possible to quantify the alpine treeline shift.
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC2 -
AC2: 'Reply on RC2', Sarah Haupt, 18 Mar 2025
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC2
RC2: The manuscript entitled "The significant role of snow in shaping alpine treeline
responses in modelled boreal forests” aims to investigate the effect of snow on the
treeline shift. However, in my opinion, reading the article looks like this is not the main
focus of the manuscript. The structure of the article is center on the sensitivity analysis of
the forest dynamic model LAVESI. In the methodology section is reported the description
of a new way to incorporate the effect of the snow cover in the LAVESI model, but the
results section is just partially focused on the outcomes of this process. Some issues in
the incorporation of this process are detected and reported in the specific comments
below. Moreover, the study areas are not adequately described and neither pictures or
orthophoto are reported. For me it is complicate to understand the real characteristics of
the three investigated sites.
AC: Thank you for your insightful feedback and constructive critique during the
first review of our preprint. Your comments provide important guidance for
improving our manuscript. We will carefully consider all referees' suggestions to
enhance the clarity and quality of our study, ensuring it aligns with the high
standards of Biogeosciences.
RC2: The approach adopted in this manuscript is the progressive variation of the model
inputs and so it is basically a sensitivity analysis of the LAVESI model.
AC: We did not conduct a progressive variation of the model inputs but, as
correctly noted, performed a sensitivity analysis. In the context of pattern-oriented
modeling, this is a standard approach in ecological individual-based modeling
(see Grimm & Railsback, 2005, Individual-based Modeling and Ecology, Princeton
University Press; Grimm et al., 2005, Science, 310: 987-991).
RC2: Furthermore, there is no comparison between the model outputs and the actual
forest characteristics making impossible to evaluate the reliability of the model outcomes
and consequently the future scenarios.
AC: In pattern-oriented modeling, a simple model is iteratively refined to more
accurately reproduce observed patterns. As part of the validation process, model
outputs are compared with actual forest characteristics. While we have fitted the
treelines, this was not done in a spatially explicit manner. The original LAVESI
model (Kruse et al., 2016) has been systematically extended to incorporate
dispersal processes influenced by wind speed and direction (LAVESI-WIND 1.0;
Kruse et al., 2018), landscape topography and additional boreal forest species
(LAVESI-CryoGrid v1.0; Kruse et al., 2022), and forest fire dynamics (LAVESI-FIRE;
Glückler et al., 2024). Both the pattern-oriented modeling approach and the
sensitivity analysis have been consistently applied across these studies.
RC2: The manuscript need to be deeply restructured and also the methodological
approach deeply revised. In my opinion the final suggestion for the manuscript is a
rejection encouraging the authors to work on it for a resubmission.
AC: As also pointed out in Referee Comment 1, we will revise and clarify the
description while maintaining the methodology, as it aligns with established
ecological modeling standards.
RC2: Below I report the specific comments.
Introduction: the section is too long and I suggest the authors to reduce it of about 50
lines. Moreover a general comment to this section is to restructure the description of the
drivers and factors influencing the treeline shift. As it is, in my opinion it results a little bit
complete to follow.
AC: We will shorten the introduction while preserving all essential content.
Currently, the influencing factors are categorized into abiotic climatic factors,
species-dependent factors, abiotic non-climatic factors, and other biotic factors.
We will further emphasize this classification and illustrate it with a graphical
overview.
RC2: Line 187: for run, the authors intended years? So, the model time resolution is
equal to 1 year if I understood correctly. Please specify it.
AC: The model description outlines that within one simulation step, which is one
year, the relevant processes are incorporated consecutively as submodules (see
lines 175-176). We will reiterate this in line 187 for clarity.
RC2: Section 2.1: how do the species-specific parameters included in the model were
derived? Please refer also to the species you are going to simulate reported in table 1.
AC: We will compile a complete table of species traits and model variable values in
LAVESI and include it in the manuscript appendix. We will provide a clearer
explanation of the site-specific results by directly linking environmental factors
and species traits to site-specific variability, thereby enhancing the understanding
of the observed differences across sites.
RC2: Line 194: How does the tree mortality rate is included in the model? Please specify
what tree and environmental related parameters where used to model this process.
AC: The fundamental methodology for determining seed and tree mortality was
originally developed by Kruse et al. (2016) and has been updated since. We will
provide a clear explanation of how the tree mortality rate was calculated, detailing
the environmental parameters that influence this process and the mechanisms
behind their effects.
RC2: Line 221: the threshold of 5°C for distinguish between rainfall and snowfall needs
to be accurately motivate. The value of 5 sounds quite high in my opinion. How do you
motivate it? Temperature daily fluctuation in case of precipitation is really low so I would
expect that a mean daily temperature slightly less than 5°C will lead to rainfall
precipitation. This is a crucial point, and it needs to be clarify and accurately motivated
since it can majorly affect the simulation results.
AC: Our approach is based on the empirical function from Sato et al. (2007), which
in turn refers to the precipitation partitioning approach from Ichii et al. (2003). Ichii
et al. (2003) used a gradual transition, where snowfall accounts for 50% of total
precipitation at 2°C. In our model development, we initially incorporated this
approach but subsequently adjusted the threshold as part of a model tuning
process. Specifically, we compared simulated and observed snow accumulation
patterns and iteratively refined the temperature threshold to improve model
accuracy. However, we acknowledge that this value may vary depending on
regional climate conditions, and we will clarify this tuning process more explicitly
in the revised manuscript.
RC2: Line 245-250: about avalanche induced mortality, the authors never mentioned the
role of the slope as predisposing factor for snow avalanche release. It is generally
defined that a slope between 27° and 55° is the range where avalanches can occur.
AC: We implemented snow movements downslope in extreme years which happen
with a chance increasingly when exceeding the 99.9th percentile of snow height in
the full climate forcing series. This movement is simply implemented as
downslope independent from the slope angle. Further, in this simplification we
named this process avalanche. In the snow module description, we then described
the process and stated that mortality is added where a modelled avalanche
impacts the leading edge of tree stands, with its destructive force diminishing
downslope due to protecting tree growth (see lines 250-251). We will provide a
more detailed explanation that snow accumulation and snow movement were
modeled for each grid cell, considering the slope direction.
RC2: Section 2.3: For me this section results slightly complicate to follow because it is a
pure description of the model and the relative processes. I would encourage the authors
to use formulas and/or diagrams or flowcharts in order to have also a clear view of the
developed model.
AC: We will include additional formulas where needed and in-text formulas are not
sufficient. We will create a flowchart to clarify the interrelated processes.
Additionally, a graphical overview of all influencing factors will be provided to
facilitate a better understanding of the overall framework.
RC2: Table 1: are the locations reported in a local or a geographic coordinate system? It
looks to me east north but the xmin etc. is confusing me.
AC: We will update the names to 'Longitude' and 'Latitude' for clarity.
RC2: I would suggest to add also the total area modelled for each study site.
AC: We will also specify the total simulation area for each study site at this point.
RC2: A small map would be beneficial to clearly identify the location of the investigated
sites.
AC: As requested, we will include a map to visually illustrate the locations of the
study sites.
RC2: Lines 304-308: Why do the authors compute the sensitivity analysis of the
parameters of the model? For me it is unclear the motivation of this operation.
AC: As previously mentioned, this approach is a standard method in ecological
modeling (Grimm & Railsback, 2005; Grimm et al., 2005). The methods section
further explains that the sensitivity analysis is conducted to evaluate and compare
the influence of various factors on the migration rate of the alpine treeline and to
identify key drivers and assess how variations in these factors influence treeline
dynamics (see lines 299-300).
RC2: Section 5: for really understand the morphology of the sites and the condition it
would be beneficial to add an image with the orthophoto of the investigated areas with
contour lines or something similar.
AC: Satellite images of the study sites are already included in the preprint.
However, we have noticed that Figures A1 and B1 mistakenly display the same
image, which we will correct. We initially decided against including these images
in the main section to avoid excessive length but are happy to move them if
desired. Additionally, we can provide photos and full panorama images of the field
plots online.
RC2: Section 5: It would be of great value if the authors can compare the actual
condition of the study site with the results of the spin up simulation. In this way it is
possible to assess the reliability of the model and therefore the validity of the future
scenarios.
AC: The treeline was adjusted to match the current observed position through a
fitting process, which, although conducted, has not yet been explained in detail
within the manuscript. We will address this more thoroughly in the revised
version. However, it is important to note that achieving an exact match in a
stochastic model is inherently challenging. The model generates mean positions
based on multiple runs, with some simulations advancing further while others lag
behind. This variability is a result of the stochastic nature of the model, and it
allows us to explore the associated uncertainties. For further clarification, please
refer to the section on stochasticity in Grimm & Railsback (2005).
RC2: Line 330: What is the definition of treeline? It is necessary to define how the
authors have identified it. Is it based on a certain density of trees or something similar?
AC: The definition of the treeline in this study is described in lines 328-329:
Treelines were determined using threshold-based criteria of one hundred trees per
hectare.
RC2: General comment for the methodology section: Authors investigated which factors
are the most important/influent for the migration rate. I would say that the common
approach for simulating future scenarios is to have a solid and reliable model either
tested with field data or from literature in similar conditions. Consequently, models are
used to predict future change by just modifying the environmental variables or the initial
conditions, obtaining in this case the probable shift of the treeline. In this manuscript the
procedure is not classical, and the authors look like they want to investigate which
variable of the model can affect the treeline shift and how. To me this approach is more
similar to a simple exercise respect to really understand the possible dynamic of the
treeline shift for the three study sites. This feeling is corroborated by the absence of
comparison between the current condition of the forests and simulation spin up for the
three study areas. In other word the factors influencing the tree line shift are the
environmental one and not the parameters of the model.
AC: The aim of our study and sensitivity analysis was to evaluate and compare the
influence of various factors on the migration rate of the alpine treeline, identify key
drivers, and assess how variations in these factors influence treeline dynamics.
As outlined by Grimm & Railsback (2005) and Grimm et al. (2005), this approach is
a standard method for determining the influence of input factors on model output.
This methodology has also been applied in several other LAVESI studies, such as
Kruse et al. (2016, 2018, 2022) and Glückler et al. (2024). Similar approaches have
been used in other studies, including Zurell et al. (2011), who linked species
distribution models with an individual-based model to assess climate-induced
range shifts in black grouse, and Malchow et al. (2024), who used a spatially
explicit individual-based model to analyze red kite population dynamics.
Additionally, Zurell et al. (2018) applied species distribution models to evaluate the
risks faced by migratory birds due to climate and land cover changes. These
studies demonstrate that integrating process-based models with sensitivity
analyses is a widely used method for understanding ecological dynamics under
environmental change.
RC2: Line 359: please add a couple of sentences to introduce the results instead of
directly showing Table 3.
AC: In accordance with the suggestion, we will add an introductory sentence at
this point.
RC2: Table 5: The table should be moved to the results section.
AC: As suggested, we will move Table 5 to the results section.
RC2: Lines 458-462: I would say this is a quite obvious result. The study areas are
different, and I would expect exactly this result.
AC: We will provide a more detailed explanation of the site-specific differences in
the sensitivity analysis results, thereby clarifying or reassessing the significance
of this section.
RC2: General comment to the results and discussion section: The authors report the
outcomes of the modelled scenarios discussing and comparing the relative results, but
the real problem is that a reliable scenario has not been identified. In the manuscript
there is no identification of a modelled scenario that can be representative of
current/actual forest conditions and of the future one. Without this definition is not
possible to quantify the alpine treeline shift.
AC: We appreciate the reviewer’s concern regarding the identification of a
representative scenario for current and future forest conditions. However, our
study is not designed to provide deterministic predictions of future treeline
positions. Instead, it is a sensitivity study aimed at understanding the relative
influence of different factors on treeline migration dynamics. Our model was tuned
to represent observed patterns, but the primary objective is to assess the key
drivers and interactions shaping treeline shifts rather than to predict an exact
future distribution. By systematically varying input factors, we can identify the
processes most relevant for treeline migration, which can help refine future
projections and improve ecological understanding rather than provide a single
"correct" scenario. We will clarify this point more explicitly in the manuscript.
-
AC2: 'Reply on RC2', Sarah Haupt, 18 Mar 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-4036', Anonymous Referee #1, 13 Feb 2025
This article is investigating the role of snow on treeline response to climatic warming by including snow (in addition to other abiotic and biotic variables) module in the vegetation model LAVESI. I like the idea with the model LAVESI. Results showed site specific response of treeline shifts and highlighted the role of snow in treeline dynamics. The approach including additional variable in vegetation model is interesting. The data are enough and relevant to test the authors’ hypothesis. I recommend a minor revision.
I like your introduction, but it is a bit too long, it would very nice if you are able to make it more concise by reducing it by 10-20 % pages. Recently several studies reported role of biotic interactions on treeline dynamics. Please see Bader et al. 2021, Ecography, Liang et al. 2016, PNAS, Sigdel et al. 2024, Nature Plants.
Overall, the discussion is general and comprehensive for me and mostly authors focused on those influences and processes that have been investigated. It has been neglected role of snow duration, i.e., the period when snow covers the treeline, determines processes of tree regeneration and establishment. Related to this, snow fungi might also explain part of the spatio-temporal pattern. Also snow movements and avalanches may affect the observed species differently. Additionally, even though, different plant traits and interactions are included in the model, their influence on treeline sensitivity and migration is missing. It is not clear why site-specific specifics sensitivities are observed? What are the mechanisms behind such discrepancies? It needs to be justified based on site specific evidences. Authors discussed different factors (environmental and traits) but fail to link site specific variability. Thus, it needs to synthesize the results rather than presenting results directly. Generally, authors just compare their results with other similar studies. To make it convincing, it needs deeper discussion with key scientific evidence and how it helps to advance our understanding on treeline ecology under changing climate. Also, ecological implications of the modified model should be highlighted.
L13-14: As site wise results are presented, it is better to mention the areas/sites considered in analysis.
L64-66: Please see recent treeline studies used individual-based model to simulate plot-based data including both biotic and abiotic variables (Zheng et al. 2024, Eco Lett).
L184: Please mention the sites considered in this study before this sentence.
L307-308: Even though the trees traits data were adopted from previous publications, better to elaborate how these data were retrieved and sources.
Table 3 caption: impact factors could be replaced by predicting variables.
No any parameter is significance from Road to central Alaska. What is mean? Is this model not well able to predict the sensitivity of Alaskan treelines?
L392-395: It seems to methodology rather than results.
L464-469: Just speculations, no evidence support the findings.
L532: either data or reference should be presented.
L550: How did species-specific stand structures varies across the sites. It should be site and species specific. How population structure varied across the study sites? and so on.
Key message and its ecological implications should be highlighted in the conclusion.
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC1 -
AC1: 'Reply on RC1', Sarah Haupt, 14 Feb 2025
Thank you for carefully reading our manuscript. We will take the helpful comments into account in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-4036-AC1 -
AC3: 'Reply on RC1', Sarah Haupt, 18 Mar 2025
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC1
RC1: This article is investigating the role of snow on treeline response to climatic
warming by including snow (in addition to other abiotic and biotic variables) module in the
vegetation model LAVESI. I like the idea with the model LAVESI. Results showed site
specific response of treeline shifts and highlighted the role of snow in treeline dynamics.
The approach including additional variable in vegetation model is interesting. The data
are enough and relevant to test the authors’ hypothesis. I recommend a minor revision.
AC: We sincerely appreciate your valuable feedback and constructive comments
during the first review of our preprint. Your comments will significantly enhance
our manuscript. We will carefully address all referees' suggestions to further
improve the quality of our study and ensure it meets the high standards of
Biogeosciences.
RC1: I like your introduction, but it is a bit too long, it would very nice if you are able to
make it more concise by reducing it by 10-20 % pages.
AC: We will shorten the introduction while preserving all essential content.
RC1: Recently several studies reported role of biotic interactions on treeline dynamics.
Please see Bader et al. 2021, Ecography, Liang et al. 2016, PNAS, Sigdel et al. 2024,
Nature Plants.
AC: Thank you for the additional literature suggestions. We will consider to
include them where fitting in the text.
RC1: Overall, the discussion is general and comprehensive for me and mostly authors
focused on those influences and processes that have been investigated. It has been
neglected role of snow duration, i.e., the period when snow covers the treeline,
determines processes of tree regeneration and establishment.
AC: The snow module accounts for snow duration, i.e., the period during which
snow covers the treeline area:
Building on the approach of Sato et al. (2007), the processes of snow
accumulation and snowmelt were incorporated into the LAVESI model (see lines
231-232). As a result, the duration of snow cover is influenced by snow depth,
which is affected by both snow accumulation and snowmelt. All subsequent
influences on tree growth and treeline migration, which are dependent on snow
depth, are consequently impacted throughout the duration of the snow cover. The
effects of varying snow depths were implemented in the model in the following
abstracted manner:
If the height of the tree is higher than the current snow depth, the mortality rate of
the tree increases by 50%. This penalty decreases linearly with increasing tree
height and is eliminated once the tree reaches a height of 5 m.
When seeds are covered by snow, their germination is enhanced, exhibiting a
linear increase in germination rates with greater snow depth. Specifically, this
relationship begins at zero and reaches a 30% increase in germination at a snow
depth of 1 m.
If seedlings and saplings are covered by snow, their growth is enhanced by 30%.
The germination process is influenced linearly, either positively or negatively,
based on whether the snow melts before or after the 110th day of the year.
The actual day of snowmelt is divided by 110, and the deviation from 1.0 is added
to the germination probability (see lines 237-243).
In general, the growing season length and its influence on seedling germination
and tree growth are already incorporated into the vegetation model (see lines 187-
189).
We will provide a clearer explanation of these connections especially of the snow
period length in the updated version of the manuscript.
RC1: Related to this, snow fungi might also explain part of the spatio-temporal pattern.
AC: The influence of snow fungi was indirectly incorporated into the snow module,
as prolonged high snow load or a shortened growing season negatively affects
tree growth. In the discussion, we will highlight that snow fungi may also
contribute to the observed spatio-temporal pattern.
RC1: Also snow movements and avalanches may affect the observed species differently.
AC: The snow module includes snow load and avalanche occurrence. However,
we did not vary these effects by tree species, as the simulation areas in our study
– as well as the North American and Siberian treeline ecotones in general – are
predominantly dominated by spruce in Northwest Canada and Alaska, and by
larch in Siberia.
We will include species-dependent differences in treeline migration in the
discussion to provide further insights for studies focusing on species-specific
responses.
RC1: Additionally, even though, different plant traits and interactions are included in the
model, their influence on treeline sensitivity and migration is missing.
AC: Species-dependent plant traits are incorporated into the vegetation model.
Since the simulation areas are dominated by spruce or larch, we consider the
influence of other tree species on the sensitivity analysis as negligible.
RC1: It is not clear why site-specific specifics sensitivities are observed? What are the
mechanisms behind such discrepancies? It needs to be justified based on site specific
evidences. Authors discussed different factors (environmental and traits) but fail to link
site specific variability. Thus, it needs to synthesize the results rather than presenting
results directly.
AC: We acknowledge the need for a more integrated discussion of site-specific
sensitivities. Rather than treating the study sites separately, we will synthesize the
results to highlight overarching patterns and mechanisms driving the observed
differences. By linking environmental factors and species traits more explicitly, we
aim to provide a clearer, more comprehensive understanding of the site-specific
variability in treeline dynamics.
RC1: Generally, authors just compare their results with other similar studies. To make it
convincing, it needs deeper discussion with key scientific evidence and how it helps to
advance our understanding on treeline ecology under changing climate.
AC: In the discussion, we will highlight the implications of our results for the
future of North America's treelines in the context of global warming.
RC1: Also, ecological implications of the modified model should be highlighted.
AC: In the discussion, we will also further elaborate on the ecological implications
for North American forests, including biodiversity, the albedo effect, and their role
as a carbon sink.
RC1: L13-14: As site wise results are presented, it is better to mention the areas/sites
considered in analysis.
AC: We will mention the study sites at this point in the abstract.
RC1: L64-66: Please see recent treeline studies used individual-based model to simulate
plot-based data including both biotic and abiotic variables (Zheng et al. 2024, Eco Lett).
AC: Thank you for the literature suggestion. We will carefully review the methods
and findings of this study.
RC1: L184: Please mention the sites considered in this study before this sentence.
AC: We will introduce the study sites earlier in the manuscript.
RC1: L307-308: Even though the trees traits data were adopted from previous
publications, better to elaborate how these data were retrieved and sources.
AC: We will compile a complete table of species traits and model variable values in
LAVESI and include it in the manuscript appendix.
RC1: Table 3 caption: impact factors could be replaced by predicting variables.
AC: We will change this term according to your suggestion.
RC1: No any parameter is significance from Road to central Alaska. What is mean? Is
this model not well able to predict the sensitivity of Alaskan treelines?
AC: At the Road to Central, Alaska site, no single factor was found to be
statistically significant. This may be due to stronger interactions between multiple
factors, which could obscure the effects of individual parameters. Another
possible explanation is that this site is more influenced by stochastic processes.
RC1: L392-395: It seems to methodology rather than results.
AC: We will relocate this section to the Methods section as suggested.
RC1: L464-469: Just speculations, no evidence supports the findings.
AC: As mentioned above, we will provide a more detailed explanation of the site-specific differences in the sensitivity analysis results, thereby clarifying or reassessing the significance of this section.
RC1: L532: either data or reference should be presented.
AC: We will insert a reference to ‘Table 1: Characteristics of the study sites.’ at this
point.
RC1: L550: How did species-specific stand structures varies across the sites. It should
be site and species specific. How population structure varied across the study sites? and
so on.
AC: Since spruce or larch clearly dominates at all three study sites, we have not
yet conducted a detailed comparison of species composition. However, we will
include a general comparison of the stand structure of the study sites in the
manuscript.
-
AC1: 'Reply on RC1', Sarah Haupt, 14 Feb 2025
-
RC2: 'Comment on egusphere-2024-4036', Anonymous Referee #2, 24 Feb 2025
The manuscript entitled "The significant role of snow in shaping alpine treeline responses in modelled boreal forests” aims to investigate the effect of snow on the treeline shift. However, in my opinion, reading the article looks like this is not the main focus of the manuscript. The structure of the article is center on the sensitivity analysis of the forest dynamic model LAVESI. In the methodology section is reported the description of a new way to incorporate the effect of the snow cover in the LAVESI model, but the results section is just partially focused on the outcomes of this process. Some issues in the incorporation of this process are detected and reported in the specific comments below. Moreover, the study areas are not adequately described and neither pictures or orthophoto are reported. For me it is complicate to understand the real characteristics of the three investigated sites.
The approach adopted in this manuscript is the progressive variation of the model inputs and so it is basically a sensitivity analysis of the LAVESI model. Furthermore, there is no comparison between the model outputs and the actual forest characteristics making impossible to evaluate the reliability of the model outcomes and consequently the future scenarios. The manuscript need to be deeply restructured and also the methodological approach deeply revised. In my opinion the final suggestion for the manuscript is a rejection encouraging the authors to work on it for a resubmission.
Below I report the specific comments.
Introduction: the section is too long and I suggest the authors to reduce it of about 50 lines. Moreover a general comment to this section is to restructure the description of the drivers and factors influencing the treeline shift. As it is, in my opinion it results a little bit complete to follow.
Line 187: for run, the authors intended years? So, the model time resolution is equal to 1 year if I understood correctly. Please specify it.
Section 2.1: how do the species specific parameters included in the model were derived? Please refer also to the species you are going to simulate reported in table 1.
Line 194: How does the tree mortality rate is included in the model? Please specify what tree and environmental related parameters where used to model this process.
Line 221: the threshold of 5°C for distinguish between rainfall and snowfall needs to be accurately motivate. The value of 5 sounds quite high in my opinion. How do you motivate it? Temperature daily fluctuation in case of precipitation is really low so I would expect that a mean daily temperature slightly less than 5°C will lead to rainfall precipitation. This is a crucial point, and it needs to be clarify and accurately motivated since it can majorly affect the simulation results.
Line 245-250: about avalanche induced mortality, the authors never mentioned the role of the slope as predisposing factor for snow avalanche release. It is generally defined that a slope between 27° and 55° is the range where avalanches can occur.
Section 2.3: For me this section results slightly complicate to follow because it is a pure description of the model and the relative processes. I would encourage the authors to use formulas and/or diagrams or flowcharts in order to have also a clear view of the developed model.
Table 1: are the locations reported in a local or a geographic coordinate system? It looks to me east north but the xmin etc. is confusing me. I would suggest to add also the total area modelled for each study site. A small map would be beneficial to clearly identify the location of the investigated sites.
Lines 304-308: Why do the authors compute the sensitivity analysis of the parameters of the model? For me it is unclear the motivation of this operation.
Section 5: for really understand the morphology of the sites and the condition it would be beneficial to add an image with the orthophoto of the investigated areas with contour lines or something similar.
Section 5: It would be of great value if the authors can compare the actual condition of the study site with the results of the spin up simulation. In this way it is possible to assess the reliability of the model and therefore the validity of the future scenarios.
Line 330: What is the definition of treeline? It is necessary to define how the authors have identified it. Is it based on a certain density of trees or something similar?
General comment for the methodology section: Authors investigated which factors are the most important/influent for the migration rate. I would say that the common approach for simulating future scenarios is to have a solid and reliable model either tested with field data or from literature in similar conditions. Consequently, models are used to predict future change by just modifying the environmental variables or the initial conditions, obtaining in this case the probable shift of the treeline. In this manuscript the procedure is not classical, and the authors look like they want to investigate which variable of the model can affect the treeline shift and how. To me this approach is more similar to a simple exercise respect to really understand the possible dynamic of the treeline shift for the three study sites. This feeling is corroborated by the absence of comparison between the current condition of the forests and simulation spin up for the three study areas. In other word the factors influencing the tree line shift are the environmental one and not the parameters of the model.
Line 359: please add a couple of sentences to introduce the results instead of directly showing Table 3.
Table 5: The table should be moved to the results section.
Lines 458-462: I would say this is a quite obvious result. The study areas are different, and I would expect exactly this result.
General comment to the results and discussion section: The authors report the outcomes of the modelled scenarios discussing and comparing the relative results, but the real problem is that a reliable scenario has not been identified. In the manuscript there is no identification of a modelled scenario that can be representative of current/actual forest conditions and of the future one. Without this definition is not possible to quantify the alpine treeline shift.
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC2 -
AC2: 'Reply on RC2', Sarah Haupt, 18 Mar 2025
Citation: https://doi.org/10.5194/egusphere-2024-4036-RC2
RC2: The manuscript entitled "The significant role of snow in shaping alpine treeline
responses in modelled boreal forests” aims to investigate the effect of snow on the
treeline shift. However, in my opinion, reading the article looks like this is not the main
focus of the manuscript. The structure of the article is center on the sensitivity analysis of
the forest dynamic model LAVESI. In the methodology section is reported the description
of a new way to incorporate the effect of the snow cover in the LAVESI model, but the
results section is just partially focused on the outcomes of this process. Some issues in
the incorporation of this process are detected and reported in the specific comments
below. Moreover, the study areas are not adequately described and neither pictures or
orthophoto are reported. For me it is complicate to understand the real characteristics of
the three investigated sites.
AC: Thank you for your insightful feedback and constructive critique during the
first review of our preprint. Your comments provide important guidance for
improving our manuscript. We will carefully consider all referees' suggestions to
enhance the clarity and quality of our study, ensuring it aligns with the high
standards of Biogeosciences.
RC2: The approach adopted in this manuscript is the progressive variation of the model
inputs and so it is basically a sensitivity analysis of the LAVESI model.
AC: We did not conduct a progressive variation of the model inputs but, as
correctly noted, performed a sensitivity analysis. In the context of pattern-oriented
modeling, this is a standard approach in ecological individual-based modeling
(see Grimm & Railsback, 2005, Individual-based Modeling and Ecology, Princeton
University Press; Grimm et al., 2005, Science, 310: 987-991).
RC2: Furthermore, there is no comparison between the model outputs and the actual
forest characteristics making impossible to evaluate the reliability of the model outcomes
and consequently the future scenarios.
AC: In pattern-oriented modeling, a simple model is iteratively refined to more
accurately reproduce observed patterns. As part of the validation process, model
outputs are compared with actual forest characteristics. While we have fitted the
treelines, this was not done in a spatially explicit manner. The original LAVESI
model (Kruse et al., 2016) has been systematically extended to incorporate
dispersal processes influenced by wind speed and direction (LAVESI-WIND 1.0;
Kruse et al., 2018), landscape topography and additional boreal forest species
(LAVESI-CryoGrid v1.0; Kruse et al., 2022), and forest fire dynamics (LAVESI-FIRE;
Glückler et al., 2024). Both the pattern-oriented modeling approach and the
sensitivity analysis have been consistently applied across these studies.
RC2: The manuscript need to be deeply restructured and also the methodological
approach deeply revised. In my opinion the final suggestion for the manuscript is a
rejection encouraging the authors to work on it for a resubmission.
AC: As also pointed out in Referee Comment 1, we will revise and clarify the
description while maintaining the methodology, as it aligns with established
ecological modeling standards.
RC2: Below I report the specific comments.
Introduction: the section is too long and I suggest the authors to reduce it of about 50
lines. Moreover a general comment to this section is to restructure the description of the
drivers and factors influencing the treeline shift. As it is, in my opinion it results a little bit
complete to follow.
AC: We will shorten the introduction while preserving all essential content.
Currently, the influencing factors are categorized into abiotic climatic factors,
species-dependent factors, abiotic non-climatic factors, and other biotic factors.
We will further emphasize this classification and illustrate it with a graphical
overview.
RC2: Line 187: for run, the authors intended years? So, the model time resolution is
equal to 1 year if I understood correctly. Please specify it.
AC: The model description outlines that within one simulation step, which is one
year, the relevant processes are incorporated consecutively as submodules (see
lines 175-176). We will reiterate this in line 187 for clarity.
RC2: Section 2.1: how do the species-specific parameters included in the model were
derived? Please refer also to the species you are going to simulate reported in table 1.
AC: We will compile a complete table of species traits and model variable values in
LAVESI and include it in the manuscript appendix. We will provide a clearer
explanation of the site-specific results by directly linking environmental factors
and species traits to site-specific variability, thereby enhancing the understanding
of the observed differences across sites.
RC2: Line 194: How does the tree mortality rate is included in the model? Please specify
what tree and environmental related parameters where used to model this process.
AC: The fundamental methodology for determining seed and tree mortality was
originally developed by Kruse et al. (2016) and has been updated since. We will
provide a clear explanation of how the tree mortality rate was calculated, detailing
the environmental parameters that influence this process and the mechanisms
behind their effects.
RC2: Line 221: the threshold of 5°C for distinguish between rainfall and snowfall needs
to be accurately motivate. The value of 5 sounds quite high in my opinion. How do you
motivate it? Temperature daily fluctuation in case of precipitation is really low so I would
expect that a mean daily temperature slightly less than 5°C will lead to rainfall
precipitation. This is a crucial point, and it needs to be clarify and accurately motivated
since it can majorly affect the simulation results.
AC: Our approach is based on the empirical function from Sato et al. (2007), which
in turn refers to the precipitation partitioning approach from Ichii et al. (2003). Ichii
et al. (2003) used a gradual transition, where snowfall accounts for 50% of total
precipitation at 2°C. In our model development, we initially incorporated this
approach but subsequently adjusted the threshold as part of a model tuning
process. Specifically, we compared simulated and observed snow accumulation
patterns and iteratively refined the temperature threshold to improve model
accuracy. However, we acknowledge that this value may vary depending on
regional climate conditions, and we will clarify this tuning process more explicitly
in the revised manuscript.
RC2: Line 245-250: about avalanche induced mortality, the authors never mentioned the
role of the slope as predisposing factor for snow avalanche release. It is generally
defined that a slope between 27° and 55° is the range where avalanches can occur.
AC: We implemented snow movements downslope in extreme years which happen
with a chance increasingly when exceeding the 99.9th percentile of snow height in
the full climate forcing series. This movement is simply implemented as
downslope independent from the slope angle. Further, in this simplification we
named this process avalanche. In the snow module description, we then described
the process and stated that mortality is added where a modelled avalanche
impacts the leading edge of tree stands, with its destructive force diminishing
downslope due to protecting tree growth (see lines 250-251). We will provide a
more detailed explanation that snow accumulation and snow movement were
modeled for each grid cell, considering the slope direction.
RC2: Section 2.3: For me this section results slightly complicate to follow because it is a
pure description of the model and the relative processes. I would encourage the authors
to use formulas and/or diagrams or flowcharts in order to have also a clear view of the
developed model.
AC: We will include additional formulas where needed and in-text formulas are not
sufficient. We will create a flowchart to clarify the interrelated processes.
Additionally, a graphical overview of all influencing factors will be provided to
facilitate a better understanding of the overall framework.
RC2: Table 1: are the locations reported in a local or a geographic coordinate system? It
looks to me east north but the xmin etc. is confusing me.
AC: We will update the names to 'Longitude' and 'Latitude' for clarity.
RC2: I would suggest to add also the total area modelled for each study site.
AC: We will also specify the total simulation area for each study site at this point.
RC2: A small map would be beneficial to clearly identify the location of the investigated
sites.
AC: As requested, we will include a map to visually illustrate the locations of the
study sites.
RC2: Lines 304-308: Why do the authors compute the sensitivity analysis of the
parameters of the model? For me it is unclear the motivation of this operation.
AC: As previously mentioned, this approach is a standard method in ecological
modeling (Grimm & Railsback, 2005; Grimm et al., 2005). The methods section
further explains that the sensitivity analysis is conducted to evaluate and compare
the influence of various factors on the migration rate of the alpine treeline and to
identify key drivers and assess how variations in these factors influence treeline
dynamics (see lines 299-300).
RC2: Section 5: for really understand the morphology of the sites and the condition it
would be beneficial to add an image with the orthophoto of the investigated areas with
contour lines or something similar.
AC: Satellite images of the study sites are already included in the preprint.
However, we have noticed that Figures A1 and B1 mistakenly display the same
image, which we will correct. We initially decided against including these images
in the main section to avoid excessive length but are happy to move them if
desired. Additionally, we can provide photos and full panorama images of the field
plots online.
RC2: Section 5: It would be of great value if the authors can compare the actual
condition of the study site with the results of the spin up simulation. In this way it is
possible to assess the reliability of the model and therefore the validity of the future
scenarios.
AC: The treeline was adjusted to match the current observed position through a
fitting process, which, although conducted, has not yet been explained in detail
within the manuscript. We will address this more thoroughly in the revised
version. However, it is important to note that achieving an exact match in a
stochastic model is inherently challenging. The model generates mean positions
based on multiple runs, with some simulations advancing further while others lag
behind. This variability is a result of the stochastic nature of the model, and it
allows us to explore the associated uncertainties. For further clarification, please
refer to the section on stochasticity in Grimm & Railsback (2005).
RC2: Line 330: What is the definition of treeline? It is necessary to define how the
authors have identified it. Is it based on a certain density of trees or something similar?
AC: The definition of the treeline in this study is described in lines 328-329:
Treelines were determined using threshold-based criteria of one hundred trees per
hectare.
RC2: General comment for the methodology section: Authors investigated which factors
are the most important/influent for the migration rate. I would say that the common
approach for simulating future scenarios is to have a solid and reliable model either
tested with field data or from literature in similar conditions. Consequently, models are
used to predict future change by just modifying the environmental variables or the initial
conditions, obtaining in this case the probable shift of the treeline. In this manuscript the
procedure is not classical, and the authors look like they want to investigate which
variable of the model can affect the treeline shift and how. To me this approach is more
similar to a simple exercise respect to really understand the possible dynamic of the
treeline shift for the three study sites. This feeling is corroborated by the absence of
comparison between the current condition of the forests and simulation spin up for the
three study areas. In other word the factors influencing the tree line shift are the
environmental one and not the parameters of the model.
AC: The aim of our study and sensitivity analysis was to evaluate and compare the
influence of various factors on the migration rate of the alpine treeline, identify key
drivers, and assess how variations in these factors influence treeline dynamics.
As outlined by Grimm & Railsback (2005) and Grimm et al. (2005), this approach is
a standard method for determining the influence of input factors on model output.
This methodology has also been applied in several other LAVESI studies, such as
Kruse et al. (2016, 2018, 2022) and Glückler et al. (2024). Similar approaches have
been used in other studies, including Zurell et al. (2011), who linked species
distribution models with an individual-based model to assess climate-induced
range shifts in black grouse, and Malchow et al. (2024), who used a spatially
explicit individual-based model to analyze red kite population dynamics.
Additionally, Zurell et al. (2018) applied species distribution models to evaluate the
risks faced by migratory birds due to climate and land cover changes. These
studies demonstrate that integrating process-based models with sensitivity
analyses is a widely used method for understanding ecological dynamics under
environmental change.
RC2: Line 359: please add a couple of sentences to introduce the results instead of
directly showing Table 3.
AC: In accordance with the suggestion, we will add an introductory sentence at
this point.
RC2: Table 5: The table should be moved to the results section.
AC: As suggested, we will move Table 5 to the results section.
RC2: Lines 458-462: I would say this is a quite obvious result. The study areas are
different, and I would expect exactly this result.
AC: We will provide a more detailed explanation of the site-specific differences in
the sensitivity analysis results, thereby clarifying or reassessing the significance
of this section.
RC2: General comment to the results and discussion section: The authors report the
outcomes of the modelled scenarios discussing and comparing the relative results, but
the real problem is that a reliable scenario has not been identified. In the manuscript
there is no identification of a modelled scenario that can be representative of
current/actual forest conditions and of the future one. Without this definition is not
possible to quantify the alpine treeline shift.
AC: We appreciate the reviewer’s concern regarding the identification of a
representative scenario for current and future forest conditions. However, our
study is not designed to provide deterministic predictions of future treeline
positions. Instead, it is a sensitivity study aimed at understanding the relative
influence of different factors on treeline migration dynamics. Our model was tuned
to represent observed patterns, but the primary objective is to assess the key
drivers and interactions shaping treeline shifts rather than to predict an exact
future distribution. By systematically varying input factors, we can identify the
processes most relevant for treeline migration, which can help refine future
projections and improve ecological understanding rather than provide a single
"correct" scenario. We will clarify this point more explicitly in the manuscript.
-
AC2: 'Reply on RC2', Sarah Haupt, 18 Mar 2025
Peer review completion
Journal article(s) based on this preprint
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 348 | 137 | 32 | 517 | 34 | 46 |
- HTML: 348
- PDF: 137
- XML: 32
- Total: 517
- BibTeX: 34
- EndNote: 46
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Josias Gloy
Luca Farkas
Katharina Schildt
Lisa Trimborn
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1592 KB) - Metadata XML
This article is investigating the role of snow on treeline response to climatic warming by including snow (in addition to other abiotic and biotic variables) module in the vegetation model LAVESI. I like the idea with the model LAVESI. Results showed site specific response of treeline shifts and highlighted the role of snow in treeline dynamics. The approach including additional variable in vegetation model is interesting. The data are enough and relevant to test the authors’ hypothesis. I recommend a minor revision.
I like your introduction, but it is a bit too long, it would very nice if you are able to make it more concise by reducing it by 10-20 % pages. Recently several studies reported role of biotic interactions on treeline dynamics. Please see Bader et al. 2021, Ecography, Liang et al. 2016, PNAS, Sigdel et al. 2024, Nature Plants.
Overall, the discussion is general and comprehensive for me and mostly authors focused on those influences and processes that have been investigated. It has been neglected role of snow duration, i.e., the period when snow covers the treeline, determines processes of tree regeneration and establishment. Related to this, snow fungi might also explain part of the spatio-temporal pattern. Also snow movements and avalanches may affect the observed species differently. Additionally, even though, different plant traits and interactions are included in the model, their influence on treeline sensitivity and migration is missing. It is not clear why site-specific specifics sensitivities are observed? What are the mechanisms behind such discrepancies? It needs to be justified based on site specific evidences. Authors discussed different factors (environmental and traits) but fail to link site specific variability. Thus, it needs to synthesize the results rather than presenting results directly. Generally, authors just compare their results with other similar studies. To make it convincing, it needs deeper discussion with key scientific evidence and how it helps to advance our understanding on treeline ecology under changing climate. Also, ecological implications of the modified model should be highlighted.
L13-14: As site wise results are presented, it is better to mention the areas/sites considered in analysis.
L64-66: Please see recent treeline studies used individual-based model to simulate plot-based data including both biotic and abiotic variables (Zheng et al. 2024, Eco Lett).
L184: Please mention the sites considered in this study before this sentence.
L307-308: Even though the trees traits data were adopted from previous publications, better to elaborate how these data were retrieved and sources.
Table 3 caption: impact factors could be replaced by predicting variables.
No any parameter is significance from Road to central Alaska. What is mean? Is this model not well able to predict the sensitivity of Alaskan treelines?
L392-395: It seems to methodology rather than results.
L464-469: Just speculations, no evidence support the findings.
L532: either data or reference should be presented.
L550: How did species-specific stand structures varies across the sites. It should be site and species specific. How population structure varied across the study sites? and so on.
Key message and its ecological implications should be highlighted in the conclusion.