DOI: 10.5061/dryad.3n5tb2rr2
title: Data from: Density-dependent species interactions modulate alpine treeline shifts

description: Species interactions such as facilitation and competition play a crucial
 role in driving species range shifts. However, density-dependence as a key
 feature of these processes has received little attention in both empirical
 and modelling studies. Herein, we used a novel, individual-based treeline
 model informed by rich in-situ observations to quantify the contribution
 of density-dependent species interactions to alpine treeline dynamics, an
 iconic biome boundary recognized as an indicator of global warming. We
 found that competition and facilitation dominate in dense versus sparse
 vegetation scenarios, respectively. The optimal balance between these two
 effects was identified at an intermediate vegetation thickness where the
 treeline elevation was the highest. Further, treeline shift rates
 decreased sharply with vegetation thickness and the associated transition
 from positive to negative species interactions. We thus postulate that
 vegetation density must be considered when modeling species range dynamics
 to avoid inadequate predictions of its responses to climate warming.
description: This dataset includes the code of the Sygera Treeline Model and
 the raw data for model results for validation and model
 experiments.  The Sygera Treeline Model is a new
 individual-based treeline model, which shares some key features and
 processes with the polar treeline simulator LAVESI (v. 1.01) (Kruse
 <em>et al.</em> 2016), and two classical forest gap models,
 FORCLIM (Bugmann 1994) and JABOWA (Botkin 1993). It is designed to explore
 how alpine treeline formation and dynamics under different vegetation
 scenarios with the background of climate change. In this model, trees were
 placed on a hypothetical mountain slope where the temperature gradually
 decreased with elevation, and had been impacted by alpine vegetation. All
 trees experience three physiological processes of establishment, growth,
 and mortality, and these processes were influenced by abiotic
 (temperature, drought) and biotic (intra- and interspecific interaction)
 environmental factors.  The another file includes the
 the raw data files that were analyzed to produce the statistics and
 figures reported in the paper. It includes model validation and model
 experiments. We validated only the last 50 years
 (1963–2012) of treeline shifts by comparing them with observed shift in
 test sets (plots LZ1, LZ3, RW2, RW3, BM1, and BM2). Average relative
 treeline elevation (in 2012) and elevational treeline shifts were obtained
 after 100 repeated simulations. Besides, a spatial analysis was performed
 by computing Ripley’s <em>K</em>(<em>t</em>)
 (Ripley 1977) from the <em>x</em> and <em>y</em>
 coordinates of adult trees in both simulated and measured treeline plots.
 The complete spatial randomness was also tested based on chi-squared
 tests, which indicate a cluster pattern when <em>P</em>
 &lt; 0.05. The R package “spatstat” version 3.0-7 (Gabriel 2017) was
 used for all spatial analyses.  In order to exclude
 direct effects of climate warming, we quantified the independent
 contributions of interspecific facilitation and competition, as well as
 their combined effects on treeline dynamics. Manipulative experiments were
 carried out with different model scenarios and vegetation scenarios in
 LZ2, as a typical treeline site. Firstly, we ran three model scenarios by
 gradually adding facilitation and competition processes into the model. In
 scenario A, we assumed that treeline dynamics were only determined by
 climate, thus we excluded the processes of vegetation facilitation and
 competition (i.e., the formula 13 in Appendix). The facilitation of alpine
 vegetation on tree growth and establishment was simulated in scenario B.
 Finally, both facilitation and competition were included in scenario C,
 with the formula 13 in Appendix set. Next, vegetation scenarios with
 different vegetation cover (0–1, with an interval of 0.1) and heights
 (0–300 cm, with an interval of 20 cm) were integrated into scenarios B and
 C to quantify the density-dependent species interactions. Various model
 scenarios and vegetation scenarios were simulated 50 times to reproduce
 the average treeline position in 2012 and treeline shift elevations over
 the last 50 years. Finally, the difference between scenarios A-C
 determines the total effect of interspecific interactions by eliminating
 the direct effects of climate. Similarly, the differences in treeline
 elevations and shifts between scenarios B and A represent the contribution
 of facilitation, whereas C-B represents the contribution of
 competition.
description: # Density-dependent species interactions modulate alpine treeline shifts
 Xiangyu Zheng1,2, Flurin Babst3,4, J. Julio Camarero5, Xiaoxia Li1,
 Xiaoming Lu1, Shan Gao1, Shalik Ram Sigdel1, Yafeng Wang6, Haifeng Zhu1,
 Eryuan Liang1* 1State Key Laboratory of Tibetan Plateau Earth System,
 Environment and Resources (TPESER), Institute of Tibetan Plateau Research,
 Chinese Academy of Sciences, Beijing 100101, China 2University of Chinese
 Academy of Sciences, Beijing 100049, China; 3School of Natural Resources
 and the Environment, University of Arizona, Tucson, AZ 85721; 4Laboratory
 of Tree-Ring Research, University of Arizona, Tucson, AZ 85721 5Instituto
 Pirenaico de Ecología (IPE-CSIC), 50059 Zaragoza, Spain 6College of
 Biology and the Environment, Nanjing Forestry University, Nanjing 210037,
 China **The file of “model code” is the code of the Sygera Treeline Model
 v1.0.** Containing files: 1\. Source code and header files:
 "....cpp" and "....h" and the makefile
 "Makefile" 2\. Parameter file: "parameter.txt" 3\.
 Input file: “temp….txt” contains monthly temperature (°C) for all nine
 sites. Each column represents the average temperature from January to
 December, and each row represents the years during 1901–2012. “prec….txt”
 contains monthly precipitation and potential evapotranspiration (mm). The
 columns from 1 to 12 represent monthly precipitation from January to
 December, and the last column represents annual potential
 evapotranspiration. Each row represents the years during 1901–2012. 4\.
 Output file: it stores the running results of the model. Start a
 simulation: 1\. compile the code with the help of the makefile: "make
 all" 2\. update the settings of the simulation run in the parameter
 file 3\. execute the program: "./treeline" 4\. find output data
 in the file of "output"  **The file of “result_data&code”
 contains the raw data files that were analyzed to produce the statistics
 and figures reported in the paper.** The "code_R" file includes
 R code that is used to create all table and figure in the main text. To
 make easier for different readers, we provide three formats of code file,
 including R code (“Analyse_and_Plot.R”) and Quarto source code
 (“Analyse_and_Plot.qmd”). The descriptions of raw data:
 stabilization_0to500.csv: the output data of spin-up phase for every 5
 years. | **Variables** | **Description** | **Range** | **Unit** | |
 :------------ | :------------------------------------------- | :-------- |
 :------- | | SimulationNo | Simulation number | 1–100 | / | | idPlot |
 Treeline plot | 0–8 | / | | Timestep | The global time step | 0–500 | year
 | | StabilPeriod | Is it spin-up phase or real simulation phase | -1 | / |
 | Year | Randomly selected years for climate data | 1901–1930 | year | |
 NH201plus | The number of adult trees | &gt;0 | / | | Treeline | The
 relative treeline elevation | 0–200 | m |   realsimulation_1901to2012.csv:
 the output data of real simulation phase for every year. | **Variables** |
 **Description** | **Range** | **Unit** | | :------------ |
 :------------------------------------------- | :-------- | :------- | |
 SimulationNo | Simulation number | 1–100 | / | | idPlot | Treeline plot |
 0–8 | / | | Timestep | The global time step | 501–612 | year | |
 StabilPeriod | Is it spin-up phase or real simulation phase | 0–111 | / |
 | Year | Real simulation year | 1901–2012 | year | | NH201plus | The
 number of adult trees | &gt;0 | / | | Treeline | The relative treeline
 elevation | 0–200 | m |   treelist_2012.csv: the list of individual trees
 in 2012. | **Variables** | **Description** | **Range** | **Unit** | |
 :------------ | :-------------------------------------------------- |
 :-------- | :------- | | SimulationNo | Simulation number | 1–100 | / | |
 idPlot | Treeline plot | 0–8 | / | | StabilPeriod | Is it spin-up phase or
 real simulation phase | 111 | / | | Year | Real simulation year | 2012 |
 year | | X | X-axis coordinates of the individual | 0–30 | m | | Y |
 Y-axis coordinates of the individual | &gt;0 | m | | Elevation | Relative
 elevation of the individual | 0–200 | m | | Name | Numbers of all trees
 produced in the model | &gt;0 | / | | Height | Tree height | &gt;0 | cm |
 | DiaBasal | The basal diameter of the individual | &gt;0 | cm | |
 DiaBreast | The diameter of the individual at the breast height | ≥0 | cm
 | | Age | Tree age | ≥0 | year |   …_realsimulation_1901to2012.csv: the
 output data of different model scenarios. The A, B, and C represent the
 model scenarios of climate only, climate + facilitation, and climate +
 facilitation + competition, respectively. | **Variables** |
 **Description** | **Range** | **Unit** | | :------------ |
 :----------------------------------------------------------------- |
 :-------- | :------- | | SimulationNo | Simulation number | &gt;0 | / | |
 Shrubcover | The shrub covers above treeline in different vegetation
 scenarios | 0–1 | / | | Shrubheight | The shrub heights above treeline in
 different vegetation scenarios | 0–3 | m | | idPlot | Treeline plot | 0–8
 | / | | Timestep | The global time step | 500–612 | year | | StabilPeriod
 | Is it spin-up phase or real simulation phase | 0–111 | / | | Year | Real
 simulation year | 1901–2012 | year | | NH201plus | The number of adult
 trees | &gt;0 | / | | Treeline | The relative treeline elevation | 0–200 |
 m | | VTI | Shrubcover \* Shrubheight | 0–3 | m |