Restoring forests is pivotal for mitigation of climate change and biodiversity loss. This Data Explorer presents findings from research to identify opportunities for restoration of forest cover in Canada and where this restoration can best achieve multiple outcomes.

Source: Drever et al. 2025. Restoring forest cover at diverse sites across Canada can balance synergies and trade-offs. One Earth. https://doi.org/10.1016/j.oneear.2025.101177.

We identified 19.1 Mha as the area of opportunity for implementing tree planting across the country. Within this area, we examined eight scenarios that identify 1.2 Mha that each prioritize different possible outcomes, including high growth rates of trees, possibilities to restore habitat for species at risk, low planting costs, and potential to enhance provision of freshwater. No scenario excelled in all attributes, revealing important trade-offs but also synergies. Notably, prioritizing growth rates highlighted areas that overlapped with areas with many species at risk but had a trade-off with higher costs. Scenarios prioritizing low cost, accessible areas and high growth concentrated in southern Canada and coastal areas, overlapping in part with biodiversity-focused scenarios. It is possible to achieve multiple goals through tree planting by strategically concentrating efforts in locations that offer multiple positive outcomes for climate change, biodiversity conservation and people.

The Data Explorer displays:

1. Area of opportunity (19.1 Mha) identified for restoration of forest cover, differentiated by current land cover,

2. Input data in the area of opportunity used in the prioritization analyses,

3. Solution area (1.2 Mha) identified for each scenario.

All data are shown at 300-m resolution. Data can be downloaded here.

Please note: The spatial data are appropriate for identifying national- and provincial-level patterns but should not be used to identify pixel-level areas of opportunity.

This Priority Landscape (PL) shows priority areas for restoring forest ecosystem services damaged by wildfire. For areas that burned between 2008 and 2017, individual 30m pixels were assigned ranks from 1 (lowest need for restoration) to 5 (highest need for restoration) based on the composite scores from various assets and threats. Study Area: The area included in this priority landscape is limited to areas burned by wildfire in the years 2008 through 2017. The data layer "VegBurnSeverity18_1" identifies these areas. Assets: Surface water value: HUC12 watersheds from the National Hydrography Dataset (NHD) Wateshed Boundary Dataset (WBD) were ranked based on surface drinking water value from the USDA Forest Service's Forests to Faucet data, https://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml. The score of importance to surface drinking water was normalized to a 0-100 scale. This was then binned into 5 categories based on equal area quantiles for data within the study area. Site quality: This shows the productivity of timberland, based upon potential volume of wood (i.e. cubic feet) that can be produced per acre in a year. Spatial site class data was imputed by Barry Wilson from the USFS Forest Inventory and Analysis (FIA) data based on the attribute SITECLCD – site productivity class code. It shows the potential timber volume produced at culmination of mean annual increment, in the standard classes used by the USFS. See Wilson, B.T., C.W. Woodall, and D.M. Griffith, Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management, 2013. 8(1): p. 15. Threats: Erosion Hazard Potential: Erosion caused by the denudation of vegetation after a wildfire can threaten drinking water quality, or the operation of drinking water reservoirs. The Fire and Resource Assessment Program developed a post-fire erosion potential model based on the Revised Universal Soil Loss Equation with the assumption that the maximum amount of vegetation was removed by fire. The data was limited to the area burned between 2008 and 2017 and was binned into 5 categories representing lowest risk (1) to highest risk (5). High Severity Burn Areas: Higher severity fires result in more challenging conditions for forests to restore themselves. These areas represent the difficulty of achieving sufficient natural regeneration which would restore the forest land ecosystem services. These data were ranked into five categories representing easiest to regenerate (1) to hardest to regenerate (5). This ranking is based on the size and complexity of high severity burn areas, based on methodology and data developed by Jens Stevens (see publication https://www.fs.fed.us/psw/publications/north/psw_2017_north005_stevens.pdf) Composite Ranks and Final Ranking: All assets were combined and the result ranked from 1 to 5 to derive a composite asset. Likewise, all threats were combined the results ranked from 1 to 5 to create a composite threat. The composite asset layer and composite threat ranks were then combined and classified to a final priority landscape rank for each 30m pixel. 1 (lowest need for restoration) to 5 (highest need for restoration). This dataset was developed in 2018 to support the California Assessment of Forest and Rangelands.

Low-severity fires that killed few canopy trees played a significant historical role in dry forests of the western USA and warrant restoration and management, but historical rates of burning remain uncertain. Past reconstructions focused on on dating fire years, not measuring historical rates of burning. Past statistics, including mean composite fire interval (mean CFI) and individual-tree fire interval (mean ITFI) have biases and inaccuracies if used as estimators of rates. In this study, I used regression, with a calibration dataset of 96 cases, to test whether these statistics could accurately predict two equivalent historical rates, population mean fire interval (PMFI) and fire rotation (FR). The best model, using Weibull mean ITFI, had low prediction error and R2adj = 0.972. I used this model to predict historical PMFI/FR at 252 sites spanning dry forests. Historical PMFI/FR for a pool of 342 calibration and predicted sites had a mean of 39 years and median of 30 years. Short (< 25 years) mean PMFI/FRs were in Arizona and New Mexico and scattered in other states. Long (> 55 years) mean PMFI/FRs were mainly from northern New Mexico to South Dakota. Mountain sites often had a large range in PMFI/FR. Nearly all 342 estimates are for old forests with a history of primarily low-severity fire, found across only about 34% of historical dry-forest area. Frequent fire (PMFI/FR < 25 years) was found across only about 14% of historical dry-forest area, with 86% having multidecadal rates of low-severity fire. Historical fuels (e.g., understory shrubs and small trees) could fully recover between multidecadal fires, allowing some denser forests and some ecosystem processes and wildlife habitat to be less limited by fire. Lower historical rates mean less restoration treatment is needed before beginning managed fire for resource benefits, where feasible. Mimicking patterns of variability in historical low-severity fire regimes would likely benefit biological diversity and ecosystem functioning.

Data on recovery rates of woody vegetation in five restoring areas in North-Western Ecuador. Datasets include 1) environmental and landscape variables, 2) trees measuring > 5 cm dbh [individuals and biomass], 3) naturally regenerated trees measuring 2-5 cm dbh [individuals, species richness, dispersal mode], and 4) woody saplings measuring ≥ 20 cm height and < 2 cm dbh [individuals, species richness, dispersal mode]. All data recovery values are presented as plot-level rates over time. For regenerated trees and saplings, datasets on their dispersal syndromes are also available. Environmental data includes elevation, mean annual temperature and precipitation extracted from the CHELSA extrapolated database. This file also contains land-cover data extracted using Sentinel 2 on the distance of each forest monitoring plot to a natural forest patch, and percentage of landscape-scale natural forest cover. , , # Greater landscape-scale forest cover and animal-mediated seed dispersal syndromes associate with faster recovery rates in restoring tropical Andean forests

Dataset DOI: 10.5061/dryad.rxwdbrvp8

Description of the data and file structure

Data collected in circular plots measuring 8 m radius in Andean montane restoring forests in NW Ecuador. Tree plantation efforts took place in 2011 and 2013, and monitoring took place in 2019. Trees above 2 cm dbh were measured. Saplings higher than 20 cm and less than 2 cm dbh were measured.Â

Files and variables

File: env_landscape_restor_plots.csv

Description:Â Environmental and landscape attribute variables used in the study.

File: rec_saplings_disp_mode.csv

Description:Â Number of individuals in the sapling category (20 cm height, < 2 cm dbh) according to their dispersal mode: zoochory or other.

File: rec_saplings_ind_sp.csv

Description:Â Number of individuals and of species in the ...,

Climate projections for the southwestern US suggest a warmer, drier future and have the potential to impact forest carbon (C) sequestration and post-fire C recovery. Restoring forest structure and surface fire regimes initially decreases total ecosystem carbon (TEC), but can stabilize the remaining C by moderating wildfire behavior. Previous research has demonstrated that fire maintained forests can store more C over time than fire suppressed forests in the presence of wildfire. However, because the climate future is uncertain, I sought to determine the efficacy of forest management to moderate fire behavior and its effect on forest C dynamics under current and projected climate. I used the LANDIS-II model to simulate carbon dynamics under early (2010–2019), mid (2050–2059), and late (2090–2099) century climate projections for a ponderosa pine (Pinus ponderosa) dominated landscape in northern Arizona. I ran 100-year simulations with two different treatments (control, thin and burn) and a 1 in 50 chance of wildfire occurring. I found that control TEC had a consistent decline throughout the simulation period, regardless of climate. Thin and burn TEC increased following treatment implementation and showed more differentiation than the control in response to climate, with late-century climate having the lowest TEC. Treatment efficacy, as measured by mean fire severity, was not impacted by climate. Fire effects were evident in the cumulative net ecosystem exchange (NEE) for the different treatments. Over the simulation period, 32.8–48.9% of the control landscape was either C neutral or a C source to the atmosphere and greater than 90% of the thin and burn landscape was a moderate C sink. These results suggest that in southwestern ponderosa pine, restoring forest structure and surface fire regimes provides a reasonable hedge against the uncertainty of future climate change for maintaining the forest C sink.

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. Methods Literature search and data gathering 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).

Restoring forest ecosystems has become a global priority. Yet, soil dynamics is still poorly assessed among restoration studies and lacks knowledge on how soil is affected by forest restoration process. Here, we compile information on soil dynamics in forest restoration based on soil physical, chemical and biological attributes in temperate and tropical forest regions. It encompasses 50 scientific papers across 17 different countries and contains 1,469 quantitative information of soil attributes between reference (e.g., old-growth forest) and restored ecosystems (e.g. forests in their initial or secondary stage of succession) within the same study. To be selected, studies had to be conducted in forest ecosystems, to include multiple sampling sites (replicates) in both restored and reference ecosystems, and to encompass quantitative data of soil attributes for both reference and restored ecosystems.

We recorded in each study the following information: (i) study year; (ii) country;...

Aim: Colombia has committed to ambitious forest restoration targets which include a one million ha Bonn Challenge commitment and 6.47 - 8.31 million ha (rehabilitation and restoration, respectively) under the National Restoration Plan. Determining where and how to implement programs to achieve these targets remains a significant challenge. Location: Colombia Methods: We adopt a multi-objective optimisation framework for restoration planning and apply it to Colombia. We explore cost-effective solutions that leverage the potential for assisted natural regeneration benefits while accounting for opportunity and establishment costs of restoration and maximising biodiversity conservation and climate change mitigation benefits. We explore four politically relevant restoration areal targets (one, six, 6.47 and 8.31 million ha) and identify minimum cost, and suites of maximum benefit and cost-effective solutions. Results: We identify solutions that simultaneously perform well across biodiversity and carbon objectives, despite trade-offs between these objectives. We find that cost-effective solutions can achieve on average 91.1%, 90.8%, 90.5%, and 90.1% of maximum carbon benefit and 100% of the maximum biodiversity benefit while significantly reducing costs. On average, the maximum benefit solutions reduce the cost by 16.9%, 30.2%, 31.1%, and 34.4% when considering the one, six, 6.47 and 8.31 million ha restoration targets respectively. Main conclusions: Colombia has committed to bold restoration and conservation targets, such as those under the new 2030 Convention on Biological Diversity Global Biodiversity Framework. Strategic forest restoration planning will play an important role in achieving Colombia’s climate mitigation goals. We provide quantitative evidence to inform planning for environmentally and economically sensible restoration policy and practice in the country. Our framework and results can help guide Colombia towards meeting its ambitious forest restoration targets cost-effectively. Methods We used spatial prioritisation, the process of using computational tools for the informed spatial allocation of actions or placement land uses, to achieve an objective of restoring forest to maximise biodiversity and carbon sequestration benefits within selected priorities, while considering establishment and opportunity cost. Tree planting and extensive site preparation are popular restoration strategies and can be effective, but implementation can be prohibitively expensive for some sites or at large scales. Where ecological conditions are such that forests can grow back on their own or with low-cost assistance, natural regeneration methods can be less costly. To leverage these potential costs our establishment cost estimates account for the potential for natural regeneration by adjusting values relative to a spatially explicit random forest model.

This dataset is for the publication of Nitrogen and sulfur deposition reductions projected to partially restore forest soil conditions in the US Northeast, while understory composition continues to shift with future climate change. Manuscript is intended for the the journal of Water, Air, & Soil Pollution. This dataset is associated with the following publication: Leduc, S., C. Clark, J. Phelan, S. Belyazid, M. Bennett, K. Boaggio, J. Buckley, J. Cajka, and P. Jones. Nitrogen and Sulfur Deposition Reductions Projected to Partially Restore Forest Soil Conditions in the US Northeast, While Understory Composition Continues to Shift with Future Climate Change. WATER, AIR, & SOIL POLLUTION. Springer, New York, NY, USA, 233(376): 1-26, (2022).

AbstractCommunity-based tropical forest restoration projects, often promoted as a win-win solution for local communities and the environment, have increased dramatically in number in the past decade. Many such projects are underway in Andean cloud forests, which, given their extremely high biodiversity and history of extensive clearing, are understudied. This study investigates the efficacy of community-based tree-planting projects to accelerate cloud forest recovery, as compared to unassisted natural regeneration. This study takes place in northwest Andean Ecuador, where the majority of the original, highly diverse cloud forests have been cleared, in five communities that initiated tree-planting projects to restore forests in 2003. In 2011, we identified tree species along transects in planted forests (n = 5), naturally regenerating forests (n = 5), and primary forests (n = 5). We also surveyed 120 households about their restoration methods, tree preferences, and forest uses. We found that tree diversity was higher in planted than in unplanted secondary forest, but both were less diverse than primary forests. Ordination analysis showed that all three forests had distinct species compositions, although planted forests shared more species with primary forests than did unplanted forests. Planted forests also contained more animal-dispersed species in both the planted canopy and in the unplanted, regenerating understory than unplanted forests, and contained the highest proportion of species with use value for local people. While restoring forest increased biodiversity and accelerated forest recovery, restored forests may also represent novel ecosystems that are distinct from the region's previous ecosystems and, given their usefulness to people, are likely to be more common in the future., Usage notesCloud forest tree dataThe data in this file is at the site level. Each site (AP, SL, and so on) is described in the paper with which this data is associated. Size class is denoted by SE - seedling, SA - sapling, and TR - tree.Size_class_site_data.xlsx

Restoring degraded forest ecosystems is an important element in the ongoing challenge to sustain the integrity and functioning of the biosphere. However, the evaluation of restoration success is hampered by long lead times of management measures in forests. Moreover, forest change is accelerating in the absence of management because of ongoing climate change. Yet, because a counterfactual is frequently missing, it remains unclear whether restoration measures are aided or impeded by climate change. Here, we analyzed the pace and success of forest restoration under climate change, combining field data and simulation modelling. We focused on the management zone of Berchtesgaden National Park (BGNP), Germany, where restoration aims to restore homogeneous Norway spruce (Picea abies) forests to structurally diverse mixed mountain forests. We evaluated three alternative restoration strategies: Two active strategies focused on planting the currently underrepresented silver fir (Abies alba) and European beech (Fagus sylvatica) but differing in the creation of gap-cuts, and a third passive restoration strategy without interventions. Strategies were simulated with the forest landscape model iLand from 2020 to 2100 under different climate scenarios (historic, RCP 2.6, 4.5, and 8.5). The forests of BGNP developed into structurally diverse and mixed forests under all evaluated management strategies, and differences between active and passive restoration were generally small. While restoration goals for forest structure were largely met by 2100, forest composition remained far from target in all strategies. Climate change aided restoration by significantly increasing the prevalence of silver fir and European beech (+104.2 % to +258.6 %). Field data on short-term restoration effects were in line with simulated long-term trajectories.

Synthesis and applications. We here show that forest restoration efforts in Central European mountain forests will likely be accelerated by climate change. Nonetheless, the slow pace of restoration underscores the need for taking action. Our study highlights that active restoration measures such as tree planting can bring the system closer to restoration targets. However, it also demonstrates that passive restoration (no intervention) is a viable option for management, highlighting the need to evaluate restoration measures against the counterfactual of a no-intervention strategy.

High biodiversity and endemism combined with persistently high deforestation rates mark Madagascar as one of the hottest of biodiversity hotspots. Contemporary rising interest in large-scale reforestation, both globally and throughout Madagascar itself, presents a promising impetus for forest restoration and biodiversity conservation across the island. However, Madagascar may face unique restoration challenges due to its equally unique eco-evolutionary trajectory, which must be understood to enable successful ecological restoration. We conducted a systematic review of potential barriers to restoration for terrestrial forest biomes (rainforests, dry forests, subhumid highland forests) in Madagascar. Our results indicate that aboveground biomass recovery of Malagasy forests appears slower than other tropical forests. We suggest four key synergistic factors that inhibit restoration in Madagascar: (a) Lack of resilience to shifting nutrient and fire regimes arising from widespread high-intensity shifting cultivation; (b) Predominance of nutrient-poor, highly weathered ferralitic soils; (c) Vulnerability of regenerating native trees to competition with invasive species due to their evolutionary isolation; and (d) Low seed dispersal into regenerating forests due to the unique dependence of Malagasy trees on dispersal by forest-dependent endangered or extinct primates. However, we note that rigorous experimental study of regenerating forests in Madagascar is currently lacking. There is great opportunity and need for such research to disentangle drivers and interactions inhibiting forest restoration. These studies would enable reforestation practitioners to effectively capitalize on current global momentum to implement the large-scale restoration necessary for the conservation of Madagascar's numerous endemic species. Methods This dataset includes (1) a list of all references recovered in our literature search for the accompanying paper, and (2) data extracted from a subset of these papers. (1) We compiled peer-reviewed literature on forest restoration in Madagascar primarily through a systematic literature search of all ISI Web of Science (WOS) databases on English-language publications between 1 Jan 1990-1 March 2022 using the following search string: TS = (Madagascar AND forest AND (refor* OR restor* OR regen*)) Additional relevant papers (both in English and French) were added from the citations of the aforementioned studies, through expert referral, and through an additional search of WOS core collections using search terms related to our hypotheses, but not directly incorporating regeneration. The first datasheet in the excel file, "Included Studies", lists all studies included in our review, and a variety of relevant site and study data extracted from these papers. The second datasheet, "MasterList", includes all studies returned through our search and documents our screening process. (2) For papers included in our review, we extracted available quantitative data on aboveground biomass (AGB) recovery (eight unique datasets from nine studies - in the sheet "BiomassData") and sapling growth/survival (nine studies - in the sheet "SaplingData"). All biomass recovery data originated from naturally regenerating forests. For two studies reporting biovolume rather than biomass, we converted these values to biomass by multiplying by the average value for wood density across Madagascar (0.662 g cm-3; Chave et al. 2009, Zanne et al. 2009), as wood density was not reported for either study. We attempted to obtain supplementary data from authors when it was not available online.

ABSTRACT:

Hurricane Michael made landfall on Mexico Beach, Florida panhandle as a Category 5 storm on October 10th, 2018. The storm had a large impact on the forests in the Florida panhandle and into Georgia. In this study we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post hurricane Sentinel-2 imagery and compared the pre and post hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Our results provide a detailed map showing the extent of basal area loss across the Florida panhandle at 10m spatial scale. Plots were revisited to ground truth the modelled results and showed that the model performed well at categorizing forest hurricane damage. This study demonstrates the use of remotely sensed imagery and in-situ forest measurements to rapidly quantify, using common forestry metrics, forest damage from large natural disturbances at spatial resolution useful to inform disaster response management decisions.

METHODS:

The Restore .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) as a baseline for a Restore Act project focused on the Florida panhandle. A total of 248 plots were visited between December 2017 and March 2018. These temporary plots were navigated to using handheld GPS units and laid out in 36m squares containing four 9m diameter non overlapping subplots. Measurements of vegetative cover, tree species count, tree condition, and diameter at breast height were taken for all trees in the subplots. Post hurricane Michael 70 plots were revisited, measurements at these plots were a subjective plot hurricane damage categorization, and count and diameter of downed or damaged trees as well as miscellaneous notes regarding site damage.

The state parks .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) at Florida State parks post hurricane Michael. These plots are 20 m circular radius that include subjective plot hurricane damage categorization, and count of downed or damaged trees, herbaceous cover, as well as miscellaneous notes regarding site damage.

Several fields were added to these plot .csv files post field visit as a variables extracted from a principal component analysis that used Sentinel-2 imagery to inform a remote sensing analysis of basal area.

A file geodatabase is attached that contains the project area boundary, Apalachicola National Forest boundary, the three shapefiles of all restore plots, revisited restore plots, and state parks plots. Raster outputs from our analysis are also available in this gdb, they contain metadata in their item descriptions. The general metadata for these rasters follows:

A general linear regression model was built to estimate tree basal area across the study area of 11 counties in the Florida Panhandle. Basal area (BA) was calculated from Restore field plots where trees were present located in and around the Apalachicola National Forest. Plot measurements include all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Plot measurements of basal area per acre were related to the top ten principal components from a principal component analysis (PCA) at the spatial resolution of the Restore plots (40 m). The PCA used the normalized Sentinel-2 spectral values and two texture values from the 7 mosaicked images from two time periods, the winter of 2017-2018 and the spring of 2018, before Hurricane Michael. A softmax neural network model was built from the PCA and Restore plot datasets to identify areas where trees were present. The basal area model was applied to the pre and post hurricane PCA imagery to create modelled surfaces of estimated Basal Area in pixels that where over 50% likely to contain trees according to the softmax neural network model. The basal are linear regression model results and predictors for the model are in the tables below.

Table Linear Regression Model. Model fit and predictors for BAA.

N

RMSE

R2

Adjusted R2

231

2.811

0.51

0.50

Road, water and urban areas were masked out of this raster dataset using a 1 m landcover product created by the author and located here - https://doi.org/10.2737/RDS-2017-0014

Comprehensive dataset containing 204 verified Forest resort businesses in South Korea with complete contact information, ratings, reviews, and location data.

Goal 15Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity lossTarget 15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreementsIndicator 15.1.1: Forest area as a proportion of total land areaAG_LND_TOTL: Land area (thousands of hectares)AG_LND_FRSTN: Forest area (thousands of hectares)AG_LND_FRST: Forest area as a proportion of total land area (%)Indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem typeER_PTD_FRHWTR: Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)ER_PTD_TERR: Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)Target 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globallyIndicator 15.2.1: Progress towards sustainable forest managementAG_LND_FRSTBIOPHA: Above-ground biomass stock in forest (tonnes per hectare)AG_LND_FRSTCERT: Forest area under an independently verified forest management certification scheme (thousands of hectares)AG_LND_FRSTCHG: Forest area annual net change rate (%)AG_LND_FRSTMGT: Proportion of forest area under a long-term management plan (%)AG_LND_FRSTPRCT: Proportion of forest area within legally established protected areas (%)Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral worldIndicator 15.3.1: Proportion of land that is degraded over total land areaAG_LND_DGRD: Proportion of land that is degraded over total land area (%)Target 15.4: By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable developmentIndicator 15.4.1: Coverage by protected areas of important sites for mountain biodiversityER_PTD_MTN: Average proportion of Mountain Key Biodiversity Areas (KBAs) covered by protected areas (%)Indicator 15.4.2: Mountain Green Cover IndexER_MTN_GRNCVI: Mountain Green Cover IndexTarget 15.5: Take urgent and significant action to reduce the degradation of natural habitats, halt the loss of biodiversity and, by 2020, protect and prevent the extinction of threatened speciesIndicator 15.5.1: Red List IndexER_RSK_LST: Red List IndexTarget 15.6: Promote fair and equitable sharing of the benefits arising from the utilization of genetic resources and promote appropriate access to such resources, as internationally agreedIndicator 15.6.1: Number of countries that have adopted legislative, administrative and policy frameworks to ensure fair and equitable sharing of benefitsER_CBD_SMTA: Total reported number of Standard Material Transfer Agreements (SMTAs) transferring plant genetic resources for food and agriculture to the country (number)ER_CBD_NAGOYA: Countries that are parties to the Nagoya Protocol (1 = YES; 0 = NO)ER_CBD_ABSCLRHS: Countries that have legislative, administrative and policy framework or measures reported to the Access and Benefit-Sharing Clearing-House (1 = YES; 0 = NO)ER_CBD_ORSPGRFA: Countries that have legislative, administrative and policy framework or measures reported through the Online Reporting System on Compliance of the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA) (1 = YES; 0 = NO)ER_CBD_PTYPGRFA: Countries that are contracting Parties to the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA) (1 = YES; 0 = NO)Target 15.7: Take urgent action to end poaching and trafficking of protected species of flora and fauna and address both demand and supply of illegal wildlife productsIndicator 15.7.1: Proportion of traded wildlife that was poached or illicitly traffickedTarget 15.8: By 2020, introduce measures to prevent the introduction and significantly reduce the impact of invasive alien species on land and water ecosystems and control or eradicate the priority speciesIndicator 15.8.1: Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien speciesER_IAS_LEGIS: Legislation, Regulation, Act related to the prevention of introduction and management of Invasive Alien Species (1 = YES, 0 = NO)ER_IAS_NBSAP: National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (1 = YES, 0 = NO)Target 15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accountsIndicator 15.9.1: (a) Number of countries that have established national targets in accordance with or similar to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011–2020 in their national biodiversity strategy and action plans and the progress reported towards these targets; and (b) integration of biodiversity into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic AccountingER_BDY_ABT2NP: Countries that established national targets in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020 in their National Biodiversity Strategy and Action Plans (1 = YES; 0 = NO)ER_BDY_SEEA: Countries with integrated biodiversity values into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting (1 = YES; 0 = NO)Target 15.a: Mobilize and significantly increase financial resources from all sources to conserve and sustainably use biodiversity and ecosystemsIndicator 15.a.1: (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instrumentsDC_ODA_BDVDL: Total official development assistance for biodiversity, by donor countries (millions of constant 2018 United States dollars)DC_ODA_BDVL: Total official development assistance for biodiversity, by recipient countries (millions of constant 2018 United States dollars)Target 15.b: Mobilize significant resources from all sources and at all levels to finance sustainable forest management and provide adequate incentives to developing countries to advance such management, including for conservation and reforestationIndicator 15.b.1: (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instrumentsDC_ODA_BDVDL: Total official development assistance for biodiversity, by donor countries (millions of constant 2018 United States dollars)DC_ODA_BDVL: Total official development assistance for biodiversity, by recipient countries (millions of constant 2018 United States dollars)Target 15.c: Enhance global support for efforts to combat poaching and trafficking of protected species, including by increasing the capacity of local communities to pursue sustainable livelihood opportunitiesIndicator 15.c.1: Proportion of traded wildlife that was poached or illicitly trafficked

We investigate the economic benefits of fundamental forest ecosystem services (FES) for the population in Germany at national level in monetary terms and estimate the spatial distribution of these benefits at county level. Specifically, we consider the benefits of timber production, of global climate protection due to carbon sequestration, of recreation for local residents, and of services for nature protection and landscape amenity. Combining information from official statistics and data from valuation studies that are compatible with economic demand theory, we identify spatial drivers of FES benefits and derive generic valuation functions for each of the services. Using a Geographic Information System, these valuation functions are applied to the conditions in the Local Administrative Units (municipalities), resulting in Benefit Function Transfer estimates for each service and each municipality. Afterwards, results are aggregated to NUTS-3 level (counties) and mapped. Aggregate annual benefits of timber production to society as a whole, of climate protection and of recreation services together exceed the ten billion Euro mark—far more than what is reflected in market statistics. Scenarios illustrate the potential for enhancing nature protection benefits particularly by restoring forest biodiversity, as measured by an avifaunistic indicator. The spatial analysis reveals distinct distributional patterns for each of the services. We conclude that a spatially explicit valuation for an entire country is possible even with limited data, which can help policy makers improve the institutional setting in a way that the protection and use of the forests become more sustainable and efficient. After pointing at several caveats, we finally suggest various possibilities for further model development.

Tropical biodiversity and associated ecosystem functions have become heavily eroded through habitat loss. Animal-mediated pollination is required in >94% of higher tropical plant species and 75% of the world´s leading food crops, but it remains unclear if corridors avert deforestation-driven pollination breakdown in fragmented tropical landscapes. Here, we used manipulative resource experiments and field observations to show that corridors functionally connect neotropical forest fragments for forest-associated hummingbirds and increase pollen transfer. Further, corridors boosted forest-associated pollinator availability in fragments by 14.3 times compared to unconnected equivalents, increasing overall pollination success. Plants in patches without corridors showed pollination rates equal to bagged control flowers, indicating pollination failure in isolated fragments. This indicates, for the first time, that corridors benefit tropical forest ecosystems beyond boosting local species richness, by functionally connecting mutualistic network partners. We conclude that small-scale adjustments to landscape configuration safeguard native pollinators and associated pollination services in tropical forest landscapes.

Series Name: Proportion of forest area under a long-term management plan (percent)Series Code: AG_LND_FRSTMGTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 15.2.1: Progress towards sustainable forest managementTarget 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globallyGoal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity lossFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Simulation modeling was used to examine long-term tradeoffs of alternative carbon management strategies by combining two wildfire management alternatives with three levels of contemporary forest restoration treatments on a 778,000-hectare landscape over 64 years using data from 2000-2019. Forest Service lands in the study area underwent restoration treatments on 237,218 hectares across the Kaibab and Coconino National Forests. The data within this package were either produced by the Landscape Simulator (LSim) or used to summarize or visualize these data. The foundation of the simulated data is built around forest growth and mortality simulations via the Forest Vegetation Simulator, and wildfire activity via the large Fire Simulator. Simulated data include a temporal accounting of forest stands, alongside the effects of mechanical thinning, prescribed fire, and area burned by wildfire. Data include tree stand characteristics by tree species (trees per acre; basal area, etc.), carbon stocks, and fire-induced mortality. Data needed to summarize the simulated data include stand characteristics and a record of which tree stands were included within our study area. Spatial data included for visualization are planning area polygons and tree stand polygons.To assess tradeoffs and synergies between traditional forest restoration and managing wildfire to meet forest management and carbon objectives.For more information about this study and these data, see Young et al. (2024).

Series Name: Forest area under an independently verified forest management certification scheme (thousands of hectares)Series Code: AG_LND_FRSTCERTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 15.2.1: Progress towards sustainable forest managementTarget 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globallyGoal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity lossFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/