A National Forest Fire Behaviour Prediction (FBP) Fuel Type (FT) layer, representing fuel types as described in the Canadian Forest Fire Danger Rating System (CFFDRS) Canada-wide. This data is developed through the Spatialized Canadian National Forest Inventory (SCANFI) dataset, specifically the updated SCANFI version 2.0. The Fuel Type layer is produced at variable resolution to best meet the needs of users: 30m (default) and 100m (ideal for fire growth modelling).The purpose of this dataset is to characterize Canadian forests into Fuel Types for use in various applications, including wildland fire management, research, fuel management, and more. This layer is primarily developed to support national scale applications and uses but can be utilized for localized landbases and adapted as required.This data represents the most up-to-date national product and is updated annually prior to the start of the fire season to incorporate burned area and additional data from the previous year. This data will be updated annually and published online by April 30th of each year.Note: In this product Slash fuel types (S-1, S-2, and S-3) are not represented. Conifer Plantation (C-6) is not represented. Raster Grid Value 13 represents vegetated fuels without an effective match to an existing FBP Fuel Type under this ruleset. Grid Value 11 (corresponding to D-1 Leafless Aspen) is assumed to represent true Deciduous fuels as described in the FBP system.

A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

Background:More than 80% of the heat produced in the Earth's crust comes from granitoid rocks. When granitoid rocks form they naturally concentrate radioactive elements such as U, Th, and K, and the radiogenic decay of these elements is an exothermic reaction. The radioactive decay of these elements within a granitoid body may generate local heat anomalies and elevated geothermal gradient at relatively shallow crustal levels. In combination with other local rock properties (e.g, porosity, permeability, thermal conductivity), radiogenic heat has the potential to generate a geothermal resource. The decay of radioactive elements converts mass into radiation energy, which in turn gets converted to heat. While all naturally radioactive isotopes generate some heat, significant heat generation only occurs from the decay of 238 U ,235 U ,232 Th and 40 K. Therefore, potential heat production is governed by the concentrations of U ,Th and K in the rock. In igneous rocks, radiogenic heat production is dependent on the bulk chemistry of the rock and decreases from acidic (e.g. granite) through basic to ultra basic rock types. Therefore, granites with anomalously high concentrations of U ,Th and K are targets for calculating potential radiogenic heat production. Potential radiogenic heat production (A)from plutonic rocks can be calculated using this equation:A (\\u03BCW/m 3 )=10 -5 \\u1D29 (9.52c u +2.56c K +3.48c Th )where "c" is the concentration of radioactive elements "U" and "Th" in ppm, and "K" in %; and "\\u1D29" is the rock density. Heat production constants of the natural radio-elements U, Th, K are 9.525x10 -5 , 2.561x10 -5 and 3.477x10 -9 W/kg, respectively.Data and Methods:Geochemical data from \~1760 samples of plutonic rocks from Yukon are used to calculate potential heat production. The calculated values for radiogenic heat production (A) are plotted over the mapped distribution of Paleozoic and younger plutonic rocks and major crustal faults are also shown for reference.

This dataset is a compilation of numerous data files recovered as part of a data request in 2024 from a historical archive of DFO research data. Sediment and Suspended Particulate Matter (SPM) samples were collected over more than a decade as part of Dr. Douglas Loring’s research program while working at the Bedford Institute of Oceanography (BIO). Samples were collected as part of individual and collaborative research projects, and links to the relevant primary science publications and reports are included in the data in an effort to provide context to the data, as well as describe the field and laboratory techniques used to generate the attached data.

Historical finds of Operophtera brumata

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

The large, habitat-forming bubblegum coral, Paragorgia arborea, is a vulnerable marine ecosystem indicator with an antitropical distribution. Dense aggregations of the species have been protected from bottom-contact fishing in the Scotian Shelf bioregion off Nova Scotia, Canada in the northwest Atlantic Ocean. Recently, basin-scale habitat suitability ensemble modeling has projected an alarming loss of 99% of suitable habitat for this species across the North Atlantic by 2100. Here, a regional reassessment of the predicted distribution of this species in the bioregion, using both machine learning (random forest) and generalized additive model (GAM) frameworks, including projection to 2046−2065, was undertaken. Extrapolation diagnostics were applied to determine the degree to which the models projected into novel covariate space (i.e., extrapolation) in order to avoid erroneous inferences. The best predictors of the species’ distribution were a suite of temporally-invariant terrain variables that identified suitable habitat along the upper continental slope. Additional predictors, projected to vary with future ocean climatologies, identified areas of the upper slope in the eastern portion of the study area that will remain within suitable ranges for P. arborea at least through to the mid-century. Additionally, 3-D Lagrangian particle tracking simulations indicated potential for both connectivity among known occurrence sites and existing protected areas, and for colonization of unsurveyed areas predicted to have suitable habitat, from locations of known occurrence. These results showed that extirpation of this iconic species from the Scotian Shelf bioregion is unlikely over the next decades. Potential climate refugia were identified and results presented in the context of protected area network design properties of representativity, connectivity, adequacy, viability and resilience. Here we provide the interpolated environmental predictor variables used in present day and future species distribution models. Predicted surfaces from random forest (RF) and generalized additive models (GAM) are provided for present day and projected (RCP4.5 and RCP8.5 2045-2065) time frames. Outputs after applying extrapolation diagnostic tools (calculated using the R package dsmextra) are provided for each model. Climate change refugia for this species under the RCP4.5 2045-2065 projections showing analogue conditions are also provided. Explanations for the surfaces can be found in Wang, Murillo & Kenchington (2022) and in the associated supplementary files. Reference:Wang S, Murillo FJ and Kenchington E (2022) Climate-Change Refugia for the Bubblegum Coral Paragorgia Arborea in the Northwest Atlantic. Front. Mar. Sci. 9:863693. doi: 10.3389/fmars.2022.863693

Historical finds of Profenusa thomsoni

Historical finds of Coleophora serratella

Wildfire ignition density rasters separated by cause (human, natural (lightning)) for mean and normalized mean summaries from 1980 to 2023.