Description: This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution. Methods: These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset. Abstract from Thompson et al. 2023: Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys. Data Sources: Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available. Uncertainties: These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.

Description Conservation 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.

Description: Spatial information on the distribution of juvenile Pacific salmon is needed to support Marine Spatial Planning in the Pacific Region of Canada. Here we provide spatial estimates of the distribution of juvenile fish in the Strait of Georgia for all five species of Pacific salmon. These estimates were generated using a spatiotemporal generalized linear model and are based on standardized fishery-independent survey data from the Strait of Georgia mid-water juvenile salmon mid-water trawl survey from 2010 to 2020. We provide predicted catch per unit effort (CPUE), year-to-year variation in CPUE, and prediction uncertainty for both summer (June–July) and fall (September–October) at a 0.5 km resolution, covering the majority of the strait. These results show that the surface 75 m of the entire Strait of Georgia is habitat for juvenile salmon from June through early October, but that distributions within the strait differ across species and across seasons. While there is interannual variability in abundances and distributions, our analysis identifies areas that have consistently high abundances across years. The results from this study illustrate juvenile habitat use in the Strait of Georgia for the five species of Pacific salmon and can support ongoing marine spatial planning initiatives in the Pacific region of Canada. Methods: Juvenile Salmon Survey Data This analysis is based on surveys conducted between 2010 and 2020. Sets that lasted between 12 and 50 minutes and at depths less than or equal to 60 m (head rope depth) were included. The resulting survey dataset consists of 1588 sets. The analysis included all five species of Pacific salmon. For pink salmon, only even year surveys were included as they have a two-year life cycle and are effectively absent from the Strait in odd years. Geostatistical model of salmon abundance and Predictions We estimated the spatial distribution and abundance of each species of Pacific Salmon using geostatistical models fit with sdmTMB (Anderson et al. 2022). For each species, we modelled the number of individuals caught in a set, at a location and time using a negative binomial observation model with a log link. Predictions were made for each survey season (summer and fall) in each year from 2010 to 2020 over a 500 m by 500 m grid based on a 3 km buffer around the outer concave hull of the trawl coordinates. The concave hull was calculated using the ‘sf_concave_hull’ function from the sf package using a concavity ratio of 0.3, and excluding holes. Predictions were made as catch per unit effort (CPUE, for 60 minutes) for tows conducted in the surface waters (i.e., head rope at 0 m). Continuous estimates are provided at a 0.5 km resolution throughout the Strait of Georgia. These estimates consist of 1) mean catch per unit effort (CPUE), 2) year-to-year coefficient of variation (CV) of CPUE as a measure of the temporal variability, 3) binned biscale measures of mean vs. CV of CPUE to distinguish areas where abundance is consistently high vs. areas where it is high on average, but with high year-to-year variability, and 4) mean standard error in CPUE as a measure of uncertainty. See Thompson and Neville for full method details. Uncertainties: Although the models had relatively low uncertainty and the estimated spatial patterns reflected the spatial and temporal variation in CPUE in the surveys, it is important to understand the limitations of these model predictions. Because juvenile salmon are often aggregated, there is high variability in the CPUE in the survey data. Our model predictions represent the geometric mean CPUE and so are an average expectation, but do not reproduce the high inter-tow variability that is present in the survey data. Spatially, our predictions have low uncertainty in areas that are central within the standard survey track line. However, uncertainty is higher on the margins of the survey area, where there are fewer sets to inform those predictions. Data Sources: Juvenile salmon survey database from Salmon Marine Interactions Program, REEFF, ESD, Pacific Biological Station. Data is also available through Canadian Data Report of Fisheries and Aquatic Sciences publications.