DOI: 10.5061/dryad.k6djh9wj4
title: Data from: Assessing the recovery gap in forest restoration within the Brazilian Atlantic Forest

description: Biodiversity serves as a proxy for numerous ecosystem services that can be
 realized through forest restoration, benefitting both people and the
 environment. We investigated the magnitude of biodiversity recovery
 incompleteness (i.e., the recovery gap) in forest restoration within the
 Brazilian Atlantic Forest, hereafter referred to as the Atlantic Forest.
 We conducted meta-analysis to analyze how species richness and species
 abundance of soil microorganisms, invertebrates and vascular plants, as
 well as the vegetation structure, recover across major gradients in
 environmental conditions and human-caused disturbances. Our study shows
 that forest restoration in the Atlantic Forest faces a notable
 biodiversity gap in species richness across both passive and active
 restoration areas. However, the vegetation structure could potentially
 reach reference levels within 25 to 50 years. Forest type influenced the
 recovery of species abundance in active restoration areas, with dense
 forests displaying the largest gaps. Likewise, taxonomic group influenced
 species richness gaps in passive restoration areas, with invertebrates
 showing the largest gap. Reference forest age and past land use did not
 significantly affect biodiversity outcomes in either restoration approach.
 However, biodiversity levels were lower than those of the reference forest
 at various levels of the moderating factors analyzed. 4. Synthesis and
 applications: The study shows that after 25–50 years, restoration sites
 develop a vegetation structure similar to that of reference forests,
 regardless of the restoration approach. Species richness also tends to
 recover over time, but the rate and pattern of recovery differ between
 approaches. Passive restoration follows a gradual, long-term decline in
 the recovery gap, while active restoration exhibits a less clear
 trajectory. Past land use is the strongest predictor of biodiversity
 recovery, particularly for vegetation structure. The restoration age,
 forest type, and taxonomic group play more moderate roles but explain
 significant variation within particular categories of each variable. These
 findings highlight the importance of targeted interventions to enhance
 restoration outcomes and the need to prioritize efforts based on specific
 restoration objectives. Our results emphasize the importance of setting
 realistic, taxon-specific goals and provide metrics to guide resource
 allocation based on recovery gaps and timelines.
description: <strong>Literature search and data
 gathering</strong> We searched the peer-reviewed
 literature available on Web of Science (SCI-E, SSCI, and ESCI), Scopus,
 CAB Direct, and SciELO. We also used Google Scholar as a search engine.
 Data was first collected on August 26th, 2020 (Supplementary Material S1).
 We employed the following search string to gather relevant literature:
 (restor* or recreat* or rehabilitat* or reforest* or afforest* or recover*
 or regenerat* or remediat* or revege*) AND (forest*) AND (Brazil* or
 Brasil*) AND (biodiversity or diversity) (see Supplementary Material S1).
 The searching and screening procedures closely resemble those employed by
 Romanelli et al. (2022). Only primary research meeting
 the following inclusion criteria was considered for full-text analysis.
 Eligible studies had to be conducted in the Brazilian Atlantic Forest,
 with interventions classified as either active (e.g., tree planting or
 seeding) or passive (minimal human intervention in forest regeneration)
 restoration, excluding highly degraded sites like mining areas.
 Comparators involved quantitative biodiversity outcomes, comparing
 restoration sites with a reference forest within the same assessment.
 Acknowledging variations in species assemblages and spatial arrangements,
 data from multiple reference forests were included (Suganuma &
 Durigan, 2015). Outcomes focused on statistical information related to
 species richness, species abundance, and vegetation structure (e.g.
 diameter, height, and basal area). Inclusion criteria also required direct
 data provision or indirect estimation, along with reporting restoration
 characteristics and environmental conditions (see Supplementary Material
 S1). We used the package metaDigitise in R (Pick,
 Nakagawa, & Noble, 2019) to extract data from figures, and used
 the Revtools package in R to remove article duplicates (Westgate,
 2019).
description: # Assessing the recovery gap in forest restoration within the Brazilian
 Atlantic Forest
 [https://doi.org/10.5061/dryad.k6djh9wj4](https://doi.org/10.5061/dryad.k6djh9wj4) ## Description of the data and file structure The data was collected through a systematic review of peer-reviewed literature to evaluate biodiversity outcomes in restored areas of the Brazilian Atlantic Forest. Studies were selected based on specific inclusion criteria, focusing on active and passive forest restoration efforts. The research analyzed quantitative biodiversity indicators, such as species richness, species abundance, and vegetation structure, comparing restored sites to reference forests. Data extraction and processing involved using metaDigitise for figure-based data retrieval and Revtools for duplicate removal. Missing data represented as N/A. ### Files and variables #### File: Data\_Abundance.csv **Description:** Data collected for species abundance ##### Variables * ID: Article ID * Authors * Year: publication year * Forest_study_description * Forest_type * RR_age: reference forest age * RR_age_groups * Lat.: latitude * Long.: longitude * Lat.Long * Soil: soil type * RT._age..years.: restoration sites age * RT.age.min: minimum age * RT.age.max: maximum age * RT_age_group_I: restoration group age I * RT_age_group_II: restoration group age II * Land_use * Approach: restoration approach * Tech_type: technique type * Dist_Ref_Rest_km..farest.restoration.site.way.from.the.reference: distance from the reference site * Spc_investigated: species investigated * Metric: metrics * Tax_group: taxonomic group * Metric.1: metrics * Mean_Rest: mean metric for restoration sites * SD_Rest: standard deviation for restoration * n_Rest: number of observations for restoration sites * Mean_Ref: mean metric for reference sites * SD_Ref: standard deviation for reference * n_Ref: number of observations for reference * LnRR: metric used for meta-analysis * yi: metric used for meta-analysis * vi: metric used for meta-analysis * index: metric used for meta-analysis * land: metric used for meta-analysis * wts: metric used for meta-analysis * fort: metric used for meta-analysis #### File: Data\_VegetationStructure.csv **Description:** Data collected for vegetation structure ##### Variables ID: Article ID * Authors * Year: publication year * Forest_study_description * Forest_type * RR_age: reference forest age * RR_age_groups * Lat.: latitude * Long.: longitude * Lat.Long * Soil: soil type * RT._age..years.: restoration sites age * RT.age.min: minimum age * RT.age.max: maximum age * RT_age_group_I: restoration group age I * RT_age_group_II: restoration group age II * Land_use * Approach: restoration approach * Tech_type: technique type * Dist_Ref_Rest_km..farest.restoration.site.way.from.the.reference: distance from the reference site * Spc_investigated: species investigated * Metric: metrics * Tax_group: taxonomic group * Metric.1: metrics * Mean_Rest: mean metric for restoration sites * SD_Rest: standard deviation for restoration * n_Rest: number of observations for restoration sites * Mean_Ref: mean metric for reference sites * SD_Ref: standard deviation for reference * n_Ref: number of observations for reference * LnRR: metric used for meta-analysis * yi: metric used for meta-analysis * vi: metric used for meta-analysis * index: metric used for meta-analysis * land: metric used for meta-analysis * wts: metric used for meta-analysis * fort: metric used for meta-analysis #### File: Data\_Richness.csv **Description:** Data collected for species richness ##### Variables ID: Article ID * Authors * Year: publication year * Forest_study_description * Forest_type * RR_age: reference forest age * RR_age_groups * Lat.: latitude * Long.: longitude * Lat.Long * Soil: soil type * RT._age..years.: restoration sites age * RT.age.min: minimum age * RT.age.max: maximum age * RT_age_group_I: restoration group age I * RT_age_group_II: restoration group age II * Land_use * Approach: restoration approach * Tech_type: technique type * Dist_Ref_Rest_km..farest.restoration.site.way.from.the.reference: distance from the reference site * Spc_investigated: species investigated * Metric: metrics * Tax_group: taxonomic group * Metric.1: metrics * Mean_Rest: mean metric for restoration sites * SD_Rest: standard deviation for restoration * n_Rest: number of observations for restoration sites * Mean_Ref: mean metric for reference sites * SD_Ref: standard deviation for reference * n_Ref: number of observations for reference * LnRR: metric used for meta-analysis * yi: metric used for meta-analysis * vi: metric used for meta-analysis * index: metric used for meta-analysis * land: metric used for meta-analysis * wts: metric used for meta-analysis - fort: metric used for meta-analysis Supplemental_material_S3_coded_revised_R2.txt: Codes used to perform the meta-analysis Supplementary_material_S1_Methods.docx: Summary of methods employed Supplementary_material_S2_-screening_process(1).xlsx: Primary studies selected for meta-analysis and reasons for excluding studies ## Code/software The steps and workflow employed are embedded in the .txt file. We computed weighted meta-analyses by the inverse variance of the response ratio in each study (Gurevitch & Hedges, 1999) using the "metafor" package in R software (Viechtbauer, 2010).