Precision restoration: a necessary approach to foster forest recovery in the 21st century

We thank S. Tabik, E. Guirado, and Garnata Drone SL for fruitful debates about the application of remote sensing and artificial intelligence in restoration. E. McKeown looked over the English version of the manuscript. Original drawings were made by J. D. Guerrero. This work was supported by projects RESISTE (P18-RT-1927) from the Consejeria de Economia, Conocimiento, y Universidad from the Junta de Andalucia, and AVA201601.19 (NUTERA-DE I), DETECTOR (A-RNM-256-UGR18), and AVA2019.004 (NUTERA-DE II), cofinanced (80%) by the FEDER Program. F.M.-R. acknowledges the support of the Agreement 4580 between OTRI-UGR and the city council of La Zubia. We thank an anonymous reviewer for helpful comments that improved the manuscript.Forest restoration is currently a primary objective in environmental management policies at a global scale, to the extent that impressive initiatives and commitments have been launched to plant billions of trees. However, resources are limited and the success of any restoration effort should be maximized. Thus, restoration programs should seek to guarantee that what is planted today will become an adult tree in the future, a simple fact that, however, usually receives little attention. Here, we advocate for the need to focus restoration efforts on an individual plant level to increase establishment success while reducing negative side effects by using an approach that we term “precision forest restoration” (PFR). The objective of PFR will be to ensure that planted seedlings or sowed seeds will become adult trees with the appropriate landscape configuration to create functional and self-regulating forest ecosystems while reducing the negative impacts of traditional massive reforestation actions. PFR can take advantage of ecological knowledge together with technologies and methodologies from the landscape scale to the individual- plant scale, and from the more traditional, low-tech approaches to the latest high-tech ones. PFR may be more expensive at the level of individual plants, but will be more cost-effective in the long term if it allows for the creation of resilient forests able to providemultiple ecosystemservices. PFR was not feasible a few years ago due to the high cost and low precision of the available technologies, but it is currently an alternative that might reformulate a wide spectrum of ecosystem restoration activities.Junta de Andalucia P18-RT-1927European Commission AVA201601.19 A-RNM-256-UGR18 AVA2019.004OTRI-UGR 4580city council of La Zubia 458

Tropical Dry Forest climatic edaphic and vegetation Database

Selection of sample pointsWe created a 1 km2 grid for each of the five biogeographic regions and identified the centroid of each cell of the grid, which we used as a sample point. We selected 100 sample points in a random block design from the full population of centroids for a given ecoregion and repeated this for each of the 80 studied ecoregions. We then used the HILDA+ Global Land Use Change layer (HILDAplus_vGLOB-1.0; Winkler, Fuchs, Rounsevell, & Herold, 2021) to evaluate the land use types for the resulting grid of 8,000 points. The HILDA+ layer contains land cover information from 1960 to 2019, which allowed us to mask points defined as urban, cropland, ocean, water, or as missing data. We used the remaining unmasked points, defined as forest, shrubland, pasture, rangeland, or unmanaged grassland, in our analyses. We used a randomized block design to assess relationships between NDVI and climatic and edaphic variations found across TDL. All spatial analyses were conducted in ArcGIS 10.2.1.Normalized difference vegetation indexWe used 12 years (2005 to 2016) of NDVI data (250 m pixel resolution) collected with Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard US NASA’s Terra and Aqua satellites. The MODIS product was used because of its longevity, resolution, and frequent observations. Data analyses relied on 12-yr mean NDVI conditions for each of the 80 ecoregions sampled here. While data are collected sub-weekly, we used averaged monthly data to calculate mean annual NDVI for the 12-yr period. NDVI is dimensionless and ranges from -0.1 to 0.9, with higher values associated with denser vegetation and lower values associated with sparser vegetation. The lowest values represent bare ground. Climatic and edaphic metricsFor each sample point, we obtained rainfall, temperature, and evapotranspiration data from repositories including: NASA Earth Observations (https://neo.gsfc.nasa.gov/); Climatic Hazards Center - UC Santa Barbara (www.chc.ucsb.edu/about); Numerical Terradynamic Simulation Group (NTSG)-University of Montana (www.ntsg.umt.edu/); Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics (www.daac.ornl.gov/); and SOILGRID (www.soilgrids.org/). We examined a total of 17 climate variables: four temperature metrics, 10 precipitation metrics, an evapotranspiration metric, and two aridity metrics that integrate annual precipitation and temperature measures. All climate variables were summarized into mean annual averages representing from 1 up to 12 years of available data (Table S3). We also considered 17 edaphic metrics involving physical, chemical and biological properties of soils. These were all represented by a single point in time measurement, and so have no temporal averaging component. Each of the 34 metrics was processed and normalized to standard units.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)