DOI: 10.5061/dryad.rv15dv4h8
title: Centennial recovery of recent human-disturbed forests

description: International commitments to restore degraded forests require global
 assessments of recovery timescales and trajectories of different forest
 attributes to inform restoration strategies. We use a meta-chronosequence
 approach including 125 forest chronosequences to reconstruct the past (*c.
 *300 years) and model future recovery trajectories of forests recovering
 from agriculture and logging impacts. We found recovering forests
 significantly differed from undisturbed ones after at least 150 years for
 ecosystem attributes like nitrogen stocks or species similarity and
 projected that difference to remain for up to 218 (38-745) or 494
 (92-2,039) years, respectively. These conservative recovery metrics,
 however, still fail to capture the complexity of forest ecosystems,
 suggesting longer recovery timescales. Global restoration strategies have
 now the opportunity to engage in planning for a restored world that
 incorporates ecologically meaningful centennial implementation timescales
 and monitoring frameworks.
description: Database construction We collected data from
 16,873 plots from 125 chronosequences of forest ecosystems recovering for
 50 to 295 years in 110 published primary studies. From these 125
 chronosequences, we extracted 634 recovery trajectories of quantitative
 measures of ecosystem attributes along time, related to the six most
 widely included recovery metrics with enough representation to be
 statistically meaningful. These included biodiversity metrics (organism
 abundance, species diversity, and species similarity) and biogeochemical
 functioning metrics (carbon cycling, nitrogen stock, and phosphorus
 stock). We also extracted factors related to the context of where recovery
 and restoration happened and included the restoration strategy (passive
 and active), the disturbance type [agriculture (including land recovering
 from cultivation, grazing or combinations of both), logging and mining],
 the latitude, and the climatic condition (i.e., aridity index).
 The trajectories related to organism abundance mainly contained
 biomass and density measurements. Species diversity included measurements
 of species richness and diversity indices. Species similarity trajectories
 contained information about species composition along the recovery
 trajectory, which were used to calculate pairwise compositional similarity
 at specific recovery times compared to a reference value. We used the
 Morisita-Horn similarity index, which accounts for species relative
 abundance. Abundance, diversity, and composition trajectories included
 five life forms: plants (including trajectories of woody plants, non-woody
 plants, and all plants combined), invertebrates, microorganisms other than
 fungi, fungi, and birds. Carbon cycling included pools and fluxes in soil,
 plants, litter, and microorganisms, whereas nitrogen and phosphorus stocks
 included bioavailable pools in soil, plants, and litter. From each
 trajectory, we collected all available recovery measures and compared them
 with a reference value. Statistical analysis
 To estimate the trajectory of forest recovery overtime, we fitted
 a separate linear mixed model (LMM) for the RR of each recovery metric. We
 included the recovery time as a fixed factor and as a random slope, and
 the trajectory identity as a random intercept, enabling a different slope
 and intercept for each trajectory (Eq. 1). As the recovery process over
 time may result in a wide range of trajectories from linear to more
 saturating shapes (<em>65</em>), we consider three functions
 to include the recovery time variable: one linear and two decelerating
 trends [ln(recovery time + 1) and √recovery time]. We then selected among
 the three options the one that best fit to the data of each recovery
 metric according to the minimum AICc. Their absolute values were square
 root transformed to meet the assumptions of general linear models and then
 multiplied by -1 to facilitate interpretation. Using
 the resulting LMMs, we predicted the RR after 73, 146, and 219 years of
 recovery [i.e., one, two, and three times the global life expectancy in
 2019, 73 years]. We then predicted the time needed for forest ecosystems
 to recover to 90% of reference values for each trajectory and recovery
 metric and calculated the median by metric. Also using
 the resulting LMMs, we predicted the RR after 50 and 100 years of recovery
 for each metric and trajectory (1) to know if the recovery completeness is
 dependent on the metric and (2) to understand the main explanatory
 variables underlying the recovery process for each metric. Predictions
 were performed by using function predict() from <em>stats</em>
 package. We fitted linear models (LM) to analyse the difference in the RR
 after 50 years and after 100 years of recovery among recovery
 metrics. We then fitted a separate LM for the effect of
 each explanatory variable studied (i.e., aridity, disturbance type or life
 form) on the RR predictions after 50 and 100 years of all recovery metrics
 together, and then for each recovery metric individually. In all the
 cases, the intercept of the LMMs for each trajectory was also included as
 a fixed factor to account for the effect of the initial state of
 degradation when recovery started. For the models fitted for the
 disturbance type and the life form, we excluded the categories with
 &lt;1% of the values (i.e., “mining” for disturbance and “bird” for
 life form) or those including data with mixing information from other
 categories (i.e., “cultivation and grazing and logging” for disturbance
 and “woody and non-woody” for life form). We could not test the effects of
 the restoration strategy on the RR predictions as 88% of the recovery
 trajectories belong to passively restored forests. To
 check the effect of the uncertainty in the recovery estimations, we first
 predicted the level of recovery after 50 and 100 years by using 999 random
 coefficients within the CI given by the models from the first stage and
 assuming a normal distribution. We then compared the original coefficients
 from the second-stage models with the resulting average coefficient
 calculated from 999 models coming from the randomizations. We did the
 comparison to check the effect of the recovery metric and of the recovery
 predictors (i.e. disturbance, aridity, and life form) after 50 and 100
 years.
description: # Centennial recovery of recent human-disturbed forests
 [https://doi.org/10.5061/dryad.rv15dv4h8](https://doi.org/10.5061/dryad.rv15dv4h8) This dataset includes the information compiled in a meta-analysis about global long-term recovery trajectories of forest ecosystems, in terms of their biodiversity (i.e., organism abundance, species diversity, and Morisita-Horn species similarity) and biogeochemical functions (i.e., cycling of carbon, nitrogen stock, and phosphorus stock); and the response ratios computed to estimate the recovery completeness of each of these metrics. ## Description of the data and file structure Data are provided in two .csv data files: * dataset_forest_recovery_metaanalysis.csv: dataset needed to run all the previous and new analysis of this study, updated after the review process. * dataset_forest_recovery_metaanalysis_posneg.csv: dataset needed to run a sensitivity analysis to separately model the recovery of trajectories that had starting points over 100% recovery completeness from those with starting points below 100%. Both files have the same column names, which are defined below: Columns related to each selected article in the meta-analysis: *code*: code given to each selected article in the meta-analysis *citation*: an abbreviated version of each selected article's citation Columns related to each forest chronosequence: *chrono*: number to identify each chronosequence in each article (this value is typically "1", as there was usually only one chronosequence per article) *latitude*: latitude (decimal degrees) *longitude*: longitude (decimal degrees) *arid*: Lang aridity index *disturb_category*: main anthropogenic disturbance ocurring prior to forest recovery *disturb_specific*: specific anthropogenic disturbance ocurring prior to forest recovery *restoration_activity*: type of restoration strategy (passive/active) *n*: number of subplots (i.e., replicates within forest plots) measured in each chronosequence. It was used to estimate the study precision. *plot_size*: subplot area (m2). It was used to estimate the study precision. Columns related to each recovery trajectory inside each chronosequence: *response_variable*: the specific variable measured along a recovery trajectory (as it was literally called in the primary article) *n_variable*: the code use to differentiate each response variable from the same chronosequence *metric_type*: organism abundance, species diversity, Morisita-Horn species similarity, cycling of carbon, nitrogen stock and phosphorus stock: *life_form*: the type of life form characterized with each response variable related to organism abundance, species diversity and Morisita-Horn species similarity. Columns related to each data-point inside each recovery trajectory: *age*: time since forest recovery started (years) *y*: the value of the response ratio, computed as ln (*X*res/*X*ref), where *X*res is the value of each response variable at each age and *X*ref is the reference value of the response variable in the reference forest. *age_log*: natural logarithm [time since forest recovery started] (years) *age1_log*: natural logarithm [time since forest recovery started + 1] (years) age_sqrt: square root [time since forest recovery started] (years) ## Code/Software * 02_analysis_resub_clean.R: This R code enables to fit the meta-analytic models and to predict the time forests need to recover their biodiversity (i.e., organism abundance, species diversity and Morisita-Horn species similarity) and functions (i.e., cycling of carbon, nitrogen stock and phosphorus stock); and the effect of aridity, disturbance category, restoration strategy and life form on their recovery.