DOI: 10.5061/dryad.jh9w0vtq1
title: Disentangling seed availability and establishment filters at alpine treelines through a decade-long field manipulation

description: Climate warming is expected to increase upslope shifts of alpine treelines
 globally. However, the ecological filters controlling tree recruitment in
 the alpine belt remain poorly quantified, limiting accurate predictions of
 treeline responses to climate change. To fill this gap, we conducted a
 decade‑long, factorial field experiment at abrupt (Ordesa) and diffuse
 (Tessó) Pinus uncinata treelines located in the Spanish Pyrenees. Our
 experimental treatments manipulated seed addition, herbivory exclusion,
 shrub competition, and soil scarification. We monitored seedling presence
 and abundance annually and analyzed their interactions with climate
 variables, specifically growing-season temperature and snow depth.
 Seedling recruitment at alpine treelines was strongly filter-limited and
 varied between sites. Seed addition enhanced emergence at both treeline
 types, with a steeper, density-dependent response at the diffuse treeline.
 Herbivore exclusion (1-mm mesh) consistently increased densities across
 cohorts, underscoring herbivory as a critical biotic filter. Climatic and
 biotic factors interacted to shape establishment: at the abrupt treeline,
 warmer growing-season maxima and dense shrub cover suppressed recruitment,
 while snow depth exerted contrasting effects across cohorts, from
 protective to limiting. At the diffuse treeline, year-one seedlings peaked
 at intermediate growing-season maximum temperatures under low shrub cover,
 whereas older cohorts showed more variable responses, occasionally
 persisting under dense shrubs when cooler growing season and deeper snow
 provided facilitative microclimatic conditions. These patterns highlight
 that both propagule supply and the interplay of climatic stress and biotic
 interactions jointly determine early recruitment above treeline. Treeline
 advance under warming occurs only when multiple filters align—adequate
 seed supply, favourable microclimatic windows, moderated herbivory, and
 shrub effects that remain facilitative rather than competitive. Because
 these filters are context- and life-stage dependent, forecasts must move
 beyond climate envelopes to integrate fine-scale propagule dynamics,
 episodic heat/snow extremes, and density-dependent biotic interactions.
 Models coupling these processes with long-term observations will better
 predict forest–tundra change.
description: # Disentangling seed availability and establishment filters at alpine
 treelines through a decade-long field manipulation ## Description of the
 data and file structure This dataset contains all the necessary data for
 reproducing the results of the research, as described in the associated
 manuscript: ‘Disentangling seed availability and establishment filters at
 alpine treelines through a decade-long field manipulation’ ### Files and
 variables #### File: data_sgs.csv **Description:** This data records field
 observations from alpine and treeline ecological experiments. It contains
 variables describing site conditions, experimental treatments, seedling
 dynamics, and climatic factors. Each row represents data from one
 permanent plot in a given observation year. ##### Variables * Year: the
 year when the field observation was conducted * Site: two treeline types
 are abrupt (Las Cutas) and diffuse (Tessó) treelines * Zone: the
 ecological zone of treeline (Alpine tundra and transition) * PlotID: a
 unique identifier for each permanent plot, typically combining site name
 and plot number (e.g., Las CutasAT1) * Plot_number: plot number *
 Mesh_cage: indicates whether a mesh cage was installed the plot (Caged or
 Uncaged) * Treatment: the type of experimental treatment applied to the
 plot (Control, Seeded, Scarified and Seeded&Scarified) * 1YS: number
 of year-one-old seedlings recorded in the plot * 2YS: number of
 two-year-old seedlings recorded in the plot * 3YS: number of
 three-year-old seedlings recorded in the plot * prev1YS : the number of
 seedlings in the previous year corresponding to the current two-year-old
 cohort (i.e., 1YS from the prior year) * prev2YS: the number of seedlings
 in the previous year corresponding to the current three-year-old cohort
 (i.e., 2YS from the prior year) * seedlag: seed density in the plot,
 usually referring to the number of seeds sown per unit area (seeds per m²)
 * Shrub: shrub cover percentage (%) * GSMaxT: average maximum temperature
 during the growing season (°C) * GSMinT: average minimum temperature
 during the growing season (°C) * GSTP: total precipitation during the
 growing season (mm) * SD: Snow depth during the winter season (cm) *
 WMMWS: winter mean maximum wind speed (m/s) #### File: Code_brm.R
 **Description:** code for Bayesian generalized linear mixed-effects models
 with zero-inflated negative binomial distribution to examine the effects
 of treatment measures and environmental drivers on seedling recruitment.
 #### File: Code_seed.R **Description:** code for Bayesian generalized
 linear mixed-effects models with zero-inflated negative binomial
 distribution to examine the effects of seed density on year-one-old
 seedlings. #### File: Code_extract_interaction_summaries.R
 **Description:** code for extracting the posterior estimates, 89% credible
 intervals, and directional probabilities from each Bayesian generalized
 linear mixed-effects model. #### File: Code_BRT.R **Description:** code
 for exploring the relative importance of multiple influencing factors and
 the interaction effects among various variables using a boosted regression
 tree model. #### File: FIGURE_2.R **Description:** Code for plotting in
 FIGURE 2 shows temporal dynamics of seedling density (No. 0.25 m⁻²) from
 2014 to 2024 (except 2019) across treeline types, seedling age classes
 (1YS = year-one seedlings, 2YS, 3YS), and zones (transition vs. alpine
 tundra). #### File: FIGURE_S2S3.R **Description:** Code for plotting in
 FIGURE S2 and S3. Figure S2 shows the interannual variations in the
 growing season climate at two different treeline study sites in the
 Pyrenees from 2014 to 2024. Figure S3 shows the zero proportion plots of
 seedlings at different age classes (1YS = year-one-old seedlings, 2YS,
 3YS) under various treatments for the two tree species. #### File:
 brm_models.rds **Description:** This file contains the fitted Bayesian
 generalized linear mixed-effects models (zero-inflated negative binomial
 distribution) generated using Code_brm.R. These models quantify the
 effects of experimental treatments and environmental factors on seedling
 recruitment across sites and years. #### File: BRT_model.rds
 **Description:** This file stores the final boosted regression tree (BRT)
 models produced by Code_BRT.R. The models evaluate the relative importance
 and interaction effects of multiple ecological and climatic variables
 influencing seedling recruitment. ## Code/software All statistical
 analyses were conducted in R (version 4.4.3).