The data represents an assessment of biodiversity risk for the agricultural area of Alberta in 2002. Biodiversity risk refers to the loss of biological diversity, or the variety of plant and animal life in agricultural landscapes. This map, created in ArcGIS, tries to show where biodiversity could be threatened, such as in areas with significant habitat that coincide with areas of greater agricultural economic activity. Biodiversity is believed to affect the overall health of the environment.

Description:In the coming decades, warming and deoxygenation of marine waters are anticipated to result in shifts in the distribution and abundance of fishes, with consequences for the diversity and composition of fish communities. Here, we combine fisheries-independent trawl survey data spanning the west coast of the USA and Canada with high-resolution regional ocean models to make projections of how 34 groundfish species will be impacted by changes in temperature and oxygen in British Columbia (BC) and Washington. In this region, species that are projected to decrease in occurrence are roughly balanced by those that are projected to increase, resulting in considerable compositional turnover. Many, but not all, species are projected to shift to deeper depths as conditions warm, but low oxygen will limit how deep they can go. Thus, biodiversity will likely decrease in the shallowest waters (less than 100 m), where warming will be greatest, increase at mid-depths (100–600 m) as shallow species shift deeper, and decrease at depths where oxygen is limited (greater than 600 m). These results highlight the critical importance of accounting for the joint role of temperature, oxygen and depth when projecting the impacts of climate change on marine biodiversity.The rasters available in this dataset project the occurrence of each of the 34 groundfish species in a 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models (BCCM and NEP36). Each projection layer is provided as the mean projected occurrence as well as the lower and upper 95% confidence interval of projected occurrence.Methods:Estimated species response curves:We estimated how the observed distribution of groundfish species is determined by temperature, dissolved oxygen and seafloor depth using data from fisheries-independent scientific research trawls spanning the entire American and Canadian west coast. We included data from 4 surveys (NOAA West Coast, NOAA Alaska, NOAA Bering or DFO Pacific) from 2000 to 2019. For each species, we modelled occurrences in the coastwide trawl dataset using a generalized linear model (GLM) using the sdmTMB package in R v. 4.0.2. The predictors were temperature, log dissolved oxygen, log depth and survey. We included quadratic terms for temperature and log depth to allow species occurrences to peak at intermediate values. We fitted a breakpoint function for log dissolved oxygen to reflect the fact oxygen is a limiting factor. We assessed the forecasting accuracy of the SDM by comparing how well a model fitted to only data from 2000 to 2010 could forecast species’ occurrences in trawls within our focal region for the period of 2011–2019. We assessed all 77 groundfish species that were present in the overall trawl dataset, however the final analysis included only the 34 species for which the models had adequate forecasting ability.Projecting groundfish biodiversity changes:We based our groundfish biodiversity change projections on two regional models that downscale climate projections: the British Columbia Continental Margin model (BCCM) and the North-Eastern Pacific Canadian Ocean Ecosystem model (NEP36-CanOE). We used a historical baseline of 1986–2005 and future projected values for 2046–2065 based on RCP 4.5 and 8.5 emissions scenarios. Using the models that we validated in our forecasting accuracy assessment, we projected the occurrence of each species in each 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models.Uncertainties:Source survey data was collected by consistent methods with survey-grade GPS for all years included. Data quality is expected to be high. Modeled data are at 3 km resolution. Outputs are as accurate as source input models and are deemed to be of high quality and accurate based upon the precision of model inputs.Projecting biodiversity responses to climate change involves considerable uncertainty and our approach allows us to quantify some aspects of this. Of the uncertainty that we could quantify, roughly half was due to uncertainty in our SDMs and the remainder was due to regional ocean model uncertainty or scenario uncertainty. This amount of uncertainty in the SDMs is typical, stemming from the fact that contemporary species distributions are also influenced by other factors that we have not included in our model. In addition, although oxygen demand is understood to vary with temperature, limitations in the implementation of breakpoint models prevented us from estimating a temperature-dependent oxygen breakpoint. However, although somewhat unrealistic, this limitation is unlikely to have greatly increased the uncertainty in our SDMs because low oxygen concentrations occurred almost exclusively at depths where temperature variation and projected change was small.To reduce uncertainty due to year-to-year variation in climate, our model projections are based on 20-year climatologies with a future period that is far enough ahead to ensure that changes are unambiguously due to greenhouse gases. We have made projections based on two different emissions scenarios, and two different regional ocean models that are both downscaled from the same global model, the second generation Canadian Earth System Model (CanESM2), using different downscaling techniques. While the BCCM model was run inter-annually and then averaged to produce the climatologies, the NEP36 model used atmospheric climatologies with augmented winds to force the ocean model and produce representative climatologies. Comparing these regional projections provides an estimate of the uncertainty across different regional downscaling models and methods. We find that the projected impacts of climate change on the groundfish community are more sensitive to the differences in the regional ocean models than they are to the emissions scenarios used. However, these differences are in magnitude (changes tend to be larger based on NEP36 compared with the BCCM) rather than in direction, with both models resulting in similar overall patterns of biodiversity change and turnover for the groundfish community. Over the 60-year time period (1986–2005 versus 2046–2065) used in our study, our projections suggest that groundfish community changes are similar regardless of the scenario used.

DescriptionConservation of marine biodiversity requires understanding the joint influence of ongoing environmental change and fishing pressure. Addressing this challenge requires robust biodiversity monitoring and analyses that jointly account for potential drivers of change. Here, we ask how demersal fish biodiversity in Canadian Pacific waters has changed since 2003 and assess the degree to which these changes can be explained by environmental change and commercial fishing. Using a spatiotemporal multispecies model based on fisheries independent data, we find that species density (number of species per area) and community biomass have increased during this period. Environmental changes during this period were associated with temporal fluctuations in the biomass of species and the community as a whole. However, environmental changes were less associated with changes in species’ occurrence. Thus, the estimated increases in species density are not likely to be due to environmental change. Instead, our results are consistent with an ongoing recovery of the demersal fish community from a reduction in commercial fishing intensity from historical levels. These findings provide key insight into the drivers of biodiversity change that can inform ecosystem-based management.The layers provided represent three community metrics: 1) species density (i.e., species richness), 2) Hill-Shannon diversity, and 3) community biomass. All layers are provided at a 3 km resolution across the study domain for the period of 2003 to 2019. For each metric, we provide layers for three summary statistics: 1) the mean value in each grid cell over the temporal range, 2) the probability that the grid cell is a hotspot for that metric, and 3) the temporal coefficient of variation (i.e., standard deviation/mean) across all years.Methods:The analysis that produced these layers is presented in Thompson et al. (2022). The analysis uses data from the Groundfish Synoptic Bottom Trawl Research surveys in Queen Charlotte Sound (QCS), Hecate Strait (HS), West Coast Vancouver Island (WCVI), and West Coast Haida Gwaii (WCHG) from 2003 to 2019. Cartilaginous and bony fish species caught in DFO groundfish surveys that were present in at least 15% of all trawls over the depth range in which they were caught were included. This depth range was defined as that which included 95% of all trawls in which that species was present. The final dataset used in our analysis consisted of 57 species (Table S1 in Thompson et al. 2022).The spatiotemporal dynamics of the demersal fish community were modeled using the Hierarchical Modeling of Species Communities (HMSC) framework and package (Tikhonov et al. 2021) in R. This framework uses Bayesian inference to fit a multivariate hierarchical generalized mixed model. We modeled community dynamics using a hurdle model, which consists of two sub models: a presence-absence model and a biomass model that is conditional on presence. Our list of environmental covariates included bottom depth, bathymetric position index (BPI), mean summer tidal speed, substrate muddiness, substrate rockiness, whether the trawl was inside or outside of the ecosystem-based trawling footprint, and survey region (QCS & HS vs. WCVI & WCHG)), mean summer near-bottom temperature deviation, mean summer near-bottom dissolved oxygen deviation, mean summer cross-shore and along-shore current velocities near the seafloor, mean summer depth-integrated primary production, and local-scale commercial fishing effort.Layers are provided for three community metrics. All metrics should be interpreted as the value that would be expected in the catch from an average tow in the Groundfish Synoptic Bottom Trawl Research Surveys taken in a given 3 km grid cell. Species density (sometimes called species richness) should be interpreted as the number of the 57 species that would be caught in a trawl. Hill-Shannon diversity is a measure of diversity that gives greater weight to communities where biomass is spread equally across species. Community biomass is the total biomass across all 57 species that would be expected to be caught per square km in an average tow. Data Sources:Research data was provided by Pacific Science's Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2019 that occurred in four regions: Queen Charlotte Sound, Hecate Strait, West Coast Haida Gwaii, and West Coast Vancouver Island. Our analysis excludes species that are rarely caught in the research trawls and so our estimates would not include the occurrence or biomass of these rare species.Commercial fishing data was accessed through a DFO R script detailed here: https://github.com/pbsassess/gfdata. Local scale commercial fishing effort was calculated from this data. The substrate layers were obtained from a substrate model (Gregr et al. 2021). The oceanographic layers (bottom temperature, dissolved oxygen, tidal and circulation speeds, primary production) were obtained from a hindcast simulation of the British Columbia continental margin (BCCM) model (Peña et al. 2019).Uncertainties:Species that are not well sampled by the trawl surveys may not be accurately estimated by our model. The model did not include spatiotemporal random effects, which likely underestimates spatiotemporal variability in the region. It is also important to underline covariate uncertainty and model uncertainty. The hotspot estimates provide one measure of model uncertainty/certainty.

High resolution forest change for Canada (Change Type) 1985-2011The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 27 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2011 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360 ). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035. https://authors.elsevier.com/sd/article/S0034425717301360Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

The Coastal Biodiversity Trawl Survey for the Passamaquoddy Bay was conducted annually between July to October from 2009 to 2019. This survey was intended to monitor long-term change in local species presence, habitat utilization, and health. The sampling activities support coastal research in fisheries, aquaculture, marine protected areas, and ecosystem change. Data collected prior to 2013 are generally not recommended for comparative analysis due to changes in vessel, sampling effort, and protocols.

In 2012 and 2013, Fisheries and Oceans Canada surveyed the benthos in two areas closed to bottom contact fishing, the Narwhal Overwintering and Coldwater Coral Zone (now the Disko Fan Conservation Area, DFCA), and the Hatton Basin Voluntary Coral Protection Zone (now the Hatton Basin Conservation Area, HBCA). Samples were collected following protocols recommended by the Arctic Council’s Circumpolar Biodiversity Monitoring Plan for the purposes of providing baseline data for future monitoring of benthic invertebrates in this sensitive region, and for facilitating pan-Arctic comparisons of benthic communities. Five biodiversity monitoring stations were established, four in the DFCA and one in the HBCA, each of which was fully sampled according to those protocols with Van Veen grabs or box corers, drop cameras and temperature recorders attached to the gear. This report summarises the grab/core-sampled benthic fauna collected during the 2012 survey of the Conservation Areas and complements another report documenting the epibenthos from the camera transects in the DFCA. Here we report on macrofauna in the 1-cm size fraction, and on foraminiferan meiofauna.The data provided is presented in the following report (see related link) :Jacobs, K., Bouchard Marmen, M., Rincón, B., MacDonald, B., Lirette, C., Gibb, O., Treble, M., and Kenchington, E. 2022. Biodiversity Monitoring Stations for Benthic Macrofauna and Meiofauna in the Disko Fan and Hatton Basin Conservation Areas. Can. Tech. Rep. Fish. Aquat. Sci. 3487: vi + 86 p.Cite this data as: Bouchard Marmen, Marieve; Rincon, Beatriz ; MacDonald, Barry; Lirette, Camille; Gibb, Olivia; Treble, Margaret ; Jacobs, Kevin; Kenchington, Ellen (2022). Biodiversity Monitoring Stations for Benthic Macrofauna and Meiofauna in the Disko Fan and Hatton Basin Conservation Areas. Published January 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7bcff18-698b-4d40-a7bd-13d39925cbeb

This dataset is a combination of landscape unit, biogeoclimatic zone/subzone/variants and Cariboo Chilcotin Land Use Plan (CCLUP) leading group type (PineGroup or FirGroup) used to roll up seral stage assessments in the Cariboo Natural Resource Region. Refer to the **Cariboo Regional Biodiversity Conservation Strategy Update Note #2: Amalgamation of Small NDT-BEC Units in Relation to Assessment of Seral Objectives and Old Growth Management Area Planning** and **Cariboo Regional Biodiversity Conservation Strategy Update Note #3: Definition of the Fir Group and Pine Group for Purposes of Seral Stage Assessments within NDT 4 of the Cariboo-Chilcotin** (see below under "Related Links") for more information on how seral stage assessment amalgamation units are derived.

Significant climate change impacts are highly likely in all Canadian marine and freshwater basins, with effects increasing over time (DFO 2012). Climate models project that ecosystems and fisheries across Canada will be disrupted into the foreseeable future (Lotze et al. 2019; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2024). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021; Pepin et al. 2022). The Climate Risk Index for Biodiversity (CRIB) was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable manner, supporting climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022), regionally (Lewis et al. 2023; Boyce et al. 2024; Keen et al. 2023), for fisheries (Boyce et al. 2024), and in support of spatial conservation planning (Keen et al. 2023). This dataset contains climate vulnerability and risk estimates from the CRIB framework adapted to consider warming at both the sea surface and its bottom for 145 marine species of conservation or fisheries interest across Canada’s marine territory. Climate risk is available at a 0.25-degree resolution under two contrasting emission scenarios to 2100. For each species, location, and scenario, 12 climate indexes, three vulnerability dimensions, and an overall vulnerability and risk score are provided. The accompanying report describes the data, methods, and workflow used to calculate risk. This report also guides the interpretation of these data to inform and support climate-informed decision-making in Canada.

Displays areas regulated as a conservation reserve in order to: * permanently protect representative ecosystems, biodiversity and significant elements of Ontario's natural and cultural heritage * provide opportunities for ecologically sustainable land uses, including traditional outdoor heritage activities and associated economic benefits * allow for scientific research and provide points of reference to support monitoring of ecological change on the broader landscape Official GEO title: Conservation Reserve Regulated

This dataset is a combination of landscape unit, biogeoclimatic zone/subzone/variants and Cariboo Chilcotin Land Use Plan leading group type (PineGroup or FirGroup) that patch size assessments are carried out on. Refer to the **Cariboo Regional Biodiversity Conservation Strategy Update Note #4: An Approach for Patch Size Assessments in the Cariboo Forest Region** (see below under "Related Links") for more information on how patch size assessment amalgamation units are derived.