A global surge in ‘artisanal’, small-scale mining (ASM) threatens biodiverse tropical forests and exposes residents to dangerous levels of mercury. In response, governments, and development agencies are investing millions (USD) on ASM formalization; registering concessions and demarcating extraction zones to promote regulatory adherence and direct mining away from ecologically sensitive areas. This data publication contains data used to examine patterns of mining-related deforestation associated with ASM formalization efforts in the Department of Madre de Dios in the Peruvian Amazon. Using satellite images and government-issued spatial layers on mining formalization, we tracked changes in mining activities from 2001 to 2014 when agencies: (a) issued 1701 provisional titles and (b) tried to restrict mining to a > 5000 square kilometer (km²) ‘corridor’. The data reported in this publication are based on the centroids of a 25 hectare (ha) hexagon grid covering the 20,850 km² study area and includes variables related (1) mining deforestation from years 2001 to 2014, (2) mining concession status, (3) location relative to the mining corridor, as well as (4) location relative to time-invariant variables and access (geology, distance to river), administrative units (district, native communities), and conservation designation (protected areas).Data were compiled and analyzed to examine patterns of mining-related deforestation associated with formalization efforts in the Department of Madre de Dios, Perú.For more information about this study and these data, see Álvarez-Berríos and L'Roe (2021).

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled: "Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review) Abstract: To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration. This database was compiled from the following sources: 1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency. source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip 2. NMD. National Land Cover Data. Swedish Environmental Protection Agency. source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/ 3. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance. source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/ 4. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland. source: https://glad.earthengine.app 5. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN). source: https://doi.org/10.6084/m9.figshare.9828827.v2 6. POPULATION. Total Population in Sweden. Statistics Sweden. source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/ To learn more about the GRASS GIS database structure, see: https://grass.osgeo.org/grass82/manuals/grass_database.html

Intraspecific trait variability (ITV) provides the material for species adaptation to environmental changes. To advance our understanding of how ITV can contribute to species adaptation to a wide range of environmental conditions, we studied five widespread understory forest species exposed to both continental-scale climate gradients, and local soil and disturbance gradients. We investigated the environmental drivers of between-site leaf and root trait variation, and tested whether higher between-site ITV was associated with increased trait sensitivity to environmental variation (i.e. environmental fit). We measured morphological (specific leaf area: SLA, specific root length: SRL) and chemical traits (Leaf and Root N, P, K, Mg, Ca) of five forest understory vascular plant species at 78 sites across Canada. A total of 261 species-by-site combinations spanning ~4300 km were sampled, capturing important abiotic and biotic environmental gradients (neighbourhood composition, canopy structure, soil conditions, climate). We used multivariate and univariate linear mixed models to identify drivers of ITV and test the association of between-site ITV with environmental fit. Between-site ITV of leaf traits was primarily driven by canopy structure and climate. Comparatively, environmental drivers explained only a small proportion of variability in root traits: these relationships were trait-specific and included soil conditions (Root P), canopy structure (Root N) and neighbourhood composition (SRL, Root K). Between-site ITV was associated with increased environmental fit only for a minority of traits, primarily in response to climate (SLA, Leaf N, SRL). Synthesis. By studying how ITV is structured along environmental gradients among species adapted to a wide range of conditions, we can begin to understand how individual species might respond to environmental change. Our results show that generalizable trait-environment relationships occur primarily aboveground and only accounted for a small proportion of variability. For our group of species with broad ecological niches, variability in traits was only rarely associated with higher environmental fit, and primarily along climatic gradients. These results point to promising research avenues on the various ways in which trait variation can affect species performance along different environmental gradients.

The Mineral Deposit Inventory 2010 is a digital geoscience database that provides an overview of mineral occurrences within the province of Ontario. The database contains information on metallic and industrial mineral deposits, as well as some building stone and aggregate sites. This deposit information can be used as source data for geographic information systems and earth science analysis for research and mineral exploration.

The Ministry of Northern Development, Mines and Forestry is releasing an updated version of the Mineral Deposit Inventory (MDI). This database is an inventory of mineral deposits in the province of Ontario and supersedes previous MDI releases, including this one for 2010. Originally compiled in the early 1970s by the Resident Geologist Program (RGP), the database is continually being reviewed and updated by RGP staff. There are approximately 950 new records and 2000 updated records.

Each MDI record provides all or some of the following information: deposit name(s), location, status (e.g., occurrence, prospect, producer, past producer), commodities, character/classification, geological structure, lithology, minerals and mineral alteration, geochemistry, exploration history, and production and reserve data where available. Also included are notes on deposit visits and references to additional publications related to the deposit.

MDI data are provided in 2 formats in this release: 1) a relational database, and 2) MDI provincial coverage in a geospatial GIS format.

Supplementary tables can be used and are available for download from the additional documentation section. Supplementary look-up table descriptions are available in the data description document, which is available for download from the additional documentation section.