A data-driven approach to advancing the Wild Salmon Policy: Online Supplement
Stephanie Peacock & Bruno Carturan
2025-05-16
1 Data availability
All data that are used in the creation of figures and results are available through the Salmon Watershed Program’s Data Library. The specific datasets reported on in the paper and analysed in the associated code are the following:
Biological Status Summary for Salmon and Steelhead Conservation Units contains outcomes of PSF’s “data-driven” biological status assessments for Conservation Units.
Status Metrics and Benchmarks for Salmon and Steelhead Conservation Units contains the current spawner abundance metric and spawner-recruit and/or percentile benchmarks used to assess biological status by PSF.
Spawner Abundance for Salmon and Steelhead Conservation Units has the Conservation Unit-scale estimates of spawner abundance for all available years. The geometric mean spawner abundance over the most recent generation is used to assess biological status, while historical years are used in the estimation of benchmarks.
Trends in Spawner Abundance (Three Generations) for Salmon and Steelhead Conservation Units has the linear trend in estimated spawner abundance over the last three generations, used to supplement biological status based on current spawner abundance.
Habitat Assessment Threat Scores including the overall threat of degradation to salmon habitats from seven core habitat pressures.
Spawning Habitat including the spawning zone of influence (ZOI) for all salmon and steelhead Conservation Units (CUs) assessed in the Pacific Salmon Explorer.
External Status Assessments has additional information on the biological status of Conservation Units, including Wild Salmon Policy status assessments completed by Fisheries and Oceans Canada (DFO), as well as status assessments completed by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), where available.
The links above are to living datasets that will be continually updated post-publication. A snapshot of the data for the paper along with R code will be archived in Zenodo repository upon acceptance.
2 Biological status
2.1 Data sources
Table S1. List of Conservation Units that had spawner abundance estimates, along with the last year of data and source of data. See References for full citations of data sources with associated reports.
2.2 Summary of biological benchmarks and status outcomes
Figure S1. Summary of biological status outcomes by region for 407 CUs in BC. See main text Figure 3 for summary by species.
Figure S2. The number of Conservation Units with biological status assessments from three different sources: data-driven assessments shown in the Pacific Salmon Explorer (PSE), published assessments by Fisheries and Oceans Canada (DFO), and published assessments by COSEWIC. In (a), the total number of CUs with assessments is given regardless of the currency of those assessments. In (b), only assessments that include at least one spawner abundance estimate in the most recent generation (i.e., the generation ending in 2023 or more current) are included, greatly reducing the number of CUs with assessments. See Table S2 for specific CUs, assessment dates, and links to DFO and COSEWIC reports.
Table S2. Biological benchmarks and status outcomes for assessments by the Pacific Salmon Foundation (PSF) alongside published assessments from Fisheries and Oceans Canada (DFO) and the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). The percent change in spawner abundance over the most recent three generations is also shown. See variable descriptions below table.
Variables in Table S2:
region: Broad Region as defined in the Pacific Salmon Explorer.species_name: salmon species common name; one of Chinook, Chum, Coho, Pink, Sockeyeconservation_unit: The Conservation Unit name as displayed in the Pacific Salmon Explorer.psf_status: Biological status outcome ofgood,fair,poor,extinct,data-deficientornot-assessedbased on the data-driven assessment by PSF, as described in this paper.psf_status_yr: The most recent year of spawner abundance data for the Conservation Unit that was used to assesspsf_status.current_spawners: The geometric mean spawner abundance over the most recent generation, used as the metric to assesspsf_status.psf_status_type: The type of benchmarks against whichcurrent_spawnerswas assessed; one ofsr(spawner-recruit) orpercentile.bench_lower,bench_lower_025,bench_lower_975: the estimated lower benchmark and lower/upper 95% intervals on the lower benchmark.bench_upper,bench_upper_025,bench_upper_975: the estimated upper benchmark and lower/upper 95% intervals on the upper benchmark.dfo_status,dfo_status_yr,dfo_source: The status outcome from a published assessment by DFO, the most recent year of data for that assessment, and URL of the publication.cosewic_status,cosewic_status_yr,cosewic_source: The status outcome from a published assessment by COSEWIC, the most recent year of data for that assessment, and URL of the publication.trendis the percent change in spawner abundance over the most recent three generations, as predicted from a linear model fit to log-transformed smoothed spawner abundance data.
3 Habitat status
3.1 Habitat pressure indicators
Table S3. Description of habitat pressure indicators, including the pressure metrics used to quantify each indicator.*
| Pressure indicator | Definition | Relevance | Pressure metric |
|---|---|---|---|
| Forest Harvest | Impacts on peak flows and other hydrological processes from areas within a watershed where trees have been cut and removed. | Forest harvest (logging) results in a reduction in forest cover that can change watershed hydrology by affecting rainfall interception, transpiration, and snowmelt processes (Guillemette et al. 2005; Coble et al. 2020; Cunningham et al. 2023). Such changes in hydrology can affect salmon habitats through altered peak flows, low flows, and annual water yields, as well as elevated stream temperatures (Tschaplinski and Pike 2017). | % of total watershed area that has been harvested in the last 60 years (with disturbance effects on watershed hydrology adjusted by time since forest harvest). |
| Fire | Impacts on peak flows and other hydrological processes from areas within a watershed where there has been combustion of forests and woodlands. | Wildfires can result in a reduction in forest cover that can change watershed hydrology by affecting rainfall interception, transpiration, and snowmelt processes. Such changes in hydrology can affect salmon habitats through altered peak flows, low flows, and annual water yields, as well as elevated stream temperatures (Isaak et al. 2010; Goeking and Tarboton 2020). | % of total watershed area that has burned in the last 60 years (with disturbance effects on watershed hydrology adjusted by both time since burn disturbance and severity of burn). |
| Insect Damage | Impacts on peak flows and other hydrological processes from areas in a watershed that have experienced intense and prolonged attacks of native or non-native xylophagous (e.g. bark beetles) or folivorous (e.g. defoliators) insect pests that have caused extensive tree mortality. | Damage to and death of trees due to insect attacks can result in a reduction in forest cover that can change watershed hydrology by affecting rainfall interception, transpiration, and snowmelt processes. Such changes in hydrology can affect salmon habitats through altered peak flows, low flows, and annual water yields, as well as increased water temperatures (Adams et al. 2012). | % of total watershed area damaged by insect pests in the last 60 years (with disturbance effects on watershed hydrology adjusted by both time since pest disturbance and severity of pest-induced forest mortality). |
| Roads | Linear corridors within a watershed that have been cleared of vegetation and engineered to allow travel by motorized vehicles on paved, gravel, or dirt surfaces. | Road development can interrupt subsurface flow, increase peak flows, and interfere with natural patterns of overland water flow in a watershed. Roads can be a significant cause of increased erosion and fine sediment deposition in streams, which can impact salmon spawning and rearing habitats (Smith and Redding 2012). | km/km2 density of roads, measured as total road length divided by total watershed area. |
| Urban and Industrial Development | Areas within a watershed that have been converted from natural landscapes to support urban or industrial development. | Urban and/or industrial land uses expand the area of impenetrable land surfaces (e.g. roads, parking lots, sidewalks), which can substantially increase watershed runoff. This can upset the dynamic equilibrium between the watershed and stream channel, resulting in dramatic changes in stream morphology, increased bank erosion, and overall habitat degradation. Urban and industrial land use also directly reduces stream water quality by delivering toxic materials and nutrients to the stream during storms (Poff et al. 2006). | % of total watershed area with urban or industrial development. |
| Agriculture | Areas within a watershed that have been converted from natural landscapes to support agricultural production (i.e. crops or livestock). | Agricultural development can increase runoff in a watershed, destabilizing flows, water temperatures, and channel morphologies in stream habitats used by salmon. Agriculture can also impact water and habitat quality through the introduction of contaminants and nutrients found in agricultural herbicides and fertilizers, and a reduced supply of coarse organic matter for spawning and rearing (Wang et al. 1997). | % of total watershed area with agricultural development. |
| Mines | Locations within a watershed that have been excavated for removal of coal, minerals, or aggregates. | The footprint of a mine and mining activity can change geomorphology and the hydrological processes of nearby water bodies. Runoff from mines can also introduce fine sediments and toxic contaminants into streams, impairing water quality, salmon prey densities, and overall survival and productivity of salmon (Daniel et al. 2015). | #/km2 density of mining, measured as the total number of coal, mineral, and aggregate mines, divided by total watershed area. |
*Table S4 for habitat data sources and Table S5 for benchmark values.
3.2 Data sources
Table S4. Sources of habitat pressure datasets used as inputs for habitat indicator assessments.
| Source | Indicator | Date of Data | Date Accessed |
|---|---|---|---|
| Agriculture Land Dispositions (GeoYukon) | Agriculture | 2023-11-21 | 2024-02-21 |
| Baseline Thematic Mapping (DataBC) | Agriculture | 1992 | 2024-02-18 |
| VRI (DataBC) | Agriculture | 2023 | 2024-10-01 |
| Historical Fire Polygons (DataBC) | Fire | 2024-04-01 | 2024-02-08 |
| Current Fire Polygons (DataBC) | Fire | 2024-09-24 | 2024-09-30 |
| Fire History (DataBC) | Fire | 2023-04-01 | 2024-09-30 |
| Burn Severity Polygons (DataBC) | Fire | 2023-11-24 | 2024-02-10 |
| Forest Inventory Zones (DataBC) | Fire | 2018-03-30 | 2024-10-01 |
| Harvested Areas in BC (Consolidated Cutblocks) (DataBC) | Forest Harvest | 2023-10-24 | 2024-02-08 |
| Results Inventory (for reserves) (DataBC) | Forest Harvest | 2024-03-30 | 2024-03-30 |
| Forest Disturbance in the Haida Gwaii Region (Gowgaia Institute) | Forest Harvest | 2021 | 2021-11-09 |
| Forest Openings (GeoYukon) | Forest Harvest | 2021-07-06 | 2023-09-06 |
| Historic Silviculture Inventory 50K (GeoYukon) | Forest Harvest | 2001-01-02 | 2023-09-06 |
| Pest Infestation Polygons (DataBC) | Insect Damage | 2022 | 2024-10-01 |
| VRI Dead Layer (DataBC) | Insect Damage | 2023 | 2024-10-01 |
| Forest Health Overview (GeoYukon) | Insect Damage | 2022 | 2024-03-04 |
| Minfile Inventory Database (DataBC) | Mines | 2024-02-12 | 2024-02-21 |
| Minfile Production Database (DataBC) | Mines | 2021 | 2024-02-21 |
| BC Aggregate Inventory Private Pits (2004) (British Columbia Geological Survey MapPlace) | Mines | 2004 | 2012 |
| Quartz Land Use Permits 50k (GeoYukon) | Mines | 2024-02-22 | 2024-04-06 |
| Quartz Mining Licenses 50K (GeoYukon) | Mines | 2013-09-20 | 2024-04-06 |
| Quartz Leases 50K (GeoYukon) | Mines | 2012-08-27 | 2024-04-06 |
| Mineral Claims Polygon Surveyed (GeoYukon) | Mines | 2017-04-24 | 2024-04-06 |
| Coal Leases 50K (GeoYukon) | Mines | 2017-04-18 | 2024-04-06 |
| Coal Exploration Licenses 50K (GeoYukon) | Mines | 2022-11-23 | 2024-04-06 |
| Gravel Pits 25K (GeoYukon) | Mines | 2023-09-16 | 2024-04-06 |
| BC Cumulative Effects Framework Integrated Roads (DataBC) | Roads | 2021 | 2023-09-06 |
| Yukon Road Network (GeoYukon) | Roads | 2022-03-08 | 2023-09-12 |
| Forestry Resource Roads 50K (GeoYukon) | Roads | 2021-09-20 | 2023-09-12 |
| BC Cumulative Effects Framework Human Disturbance (DataBC) | Urban & Industrial Development | 2021 | 2023-09-06 |
| Landcover circa 2000 (Government of Canada) | Urban & Industrial Development | 2000s | 2023-09-12 |
| Utilities Line 50K (GeoYukon) | Urban & Industrial Development | 2004-06-14 | 2023-09-12 |
| YEC Power Lines (GeoYukon) | Urban & Industrial Development | 2021-01-13 | 2023-09-12 |
| YEC Power Distribution Lines (GeoYukon) | Urban & Industrial Development | 2021-01-13 | 2023-09-12 |
| Railroads 50K Canvec (GeoYukon) | Urban & Industrial Development | 2017 | 2023-09-12 |
| Runways 25K (GeoYukon) | Urban & Industrial Development | 2023-09-16 | 2023-09-12 |
3.3 Summary of habitat benchmarks
For the seven habitat pressure indicators we use empirical benchmarks for habitat pressure indicators based on published literature. See Methods for Assessing Status and Trends in Pacific Salmon Conservation Units and their Freshwater Habitats for details of benchmark calculations.
Table S5. Habitat pressure indicator metrics and benchmarks. (Note that watershed refers to the Province of British Columbia’s 1:20,000 Freshwater Atlas (FWA) Assessment Watersheds.)
| Indicator | Pressure Metric | Watershed Threat Benchmark | Benchmark Reference(s)† |
|---|---|---|---|
| Forest Harvest | % of total watershed area that has been harvested in the last 60 years with disturbance effects adjusted by time since forest harvest to reflect the progressive hydrological recovery of the watershed.* | <20% of watershed area harvested (low threat) 20 - 30% of watershed area harvested (moderate threat) >30% of watershed area harvested (high threat) |
Stednick 1996; MacDonald et al. 1997; Watertight Solutions Ltd. 2007; Guillemette et al. 2005; Buma and Livneh 2017; Winkler and Boon 2017; Winkler et al. 2015; Moore and Scott 2005; Zhang and Wei 2015; Winkler and Boon 2017; Crampe et al. 2020; Gronsdahl et al. 2019; Hou and Wei 2024; Hou et al. 2024 |
| Fire | % of total watershed area that has burned in the last 60 years with disturbance effects adjusted by time since burn disturbance (as for forest harvest) and also by severity of burn* | <20% of watershed area burned (low threat) 20 - 30% of watershed area burned (moderate threat) >30% of watershed area burned (high threat) |
Hallema et al. 2018; Goerking and Tarboton 2020; Williams et al. 2022 |
| Insect Damage | % of total watershed area damaged by insect pests in the last 60 years with disturbance effects adjusted by time since pest disturbance (as for forest harvest) and also by severity of pest-induced forest mortality* | <20% of watershed area with insect die-off (low threat) 20 - 30% of watershed area with insect die-off (moderate threat) >30% of watershed area with insect die-off (high threat) |
Carver et al. 2009; Adams et al. 2012; Buma and Livneh 2017 |
| Agriculture | % of total watershed area with agricultural development | <20% of watershed area with agricultural development (low threat) 20 - 50% of watershed area with agricultural development (moderate threat) >50% of watershed area with agricultural development (high threat) |
Wang et al. 1997; Fitzpatrick et al. 2001; Wang et al. 2003 |
| Urban & Industrial Development | % of total watershed area with urban or industrial development (with linear development features buffered for area-based calculation) | <10% of watershed area urbanized (low threat) 10 - 20% of watershed area urbanized (moderate threat) >20% of watershed area urbanized (high threat) |
Klein 1979; Steedman 1998; Wang et al. 1997; Yoder et al. 1999; Brenden et al. 2008; Daniel et al. 2015 |
| Roads | Total length of roads in a watershed, divided by total watershed area (km/km2) (density) | <1.5 km roads/km2 (low threat) 1.5 - 2.1 km roads/km2 (moderate threat) >2.1 km roads/km2 (high threat) |
Provincial Aquatic Ecosystems Technical Working Group (PAETWG) 2020 |
| Mines | Total # of mines of mineral, coal, and gravel mines in a watershed, divided by total watershed area (#/km2) (density) | <0.01 mines/km2 (low threat) 0.01 - 0.05 mines/km2 (moderate threat) >0.05 mines/km2 (high threat) |
Daniel et al. 2015 |
†See Appendix 10 of Pacific Salmon Foundation (2024) for the reference list specific to Table S4.
3.4 Summary of habitat status outcomes by Conservation Unit
Table S6. Summary of cumulative threat to freshwater spawning habitats by Conservation Unit. Cumulative threat is quantified as the proportion of spawning habitat area with low, moderate, or high threat of degradation based on a roll up of seven core indicators (see main text and Pacific Salmon Foundation (2024) for details).
Variables in Table S5:
region: Broad Region as defined in the Pacific Salmon Explorer.species_name: salmon species common name; one of Chinook, Chum, Coho, Pink, Sockeyeconservation_unit: The Conservation Unit name as displayed in the Pacific Salmon Explorer.low_cumulative_threat: Proportion of spawning habitat area that has *low** cumulative threat of degradation based on comparison of metrics to benchmarks within each FWA Assessment Watershed that is within the spawning Zone of Influence for the Conservation Unit.mod_cumulative_threat: Proportion of spawning habitat area that has moderate cumulative threat of degradation.high_cumulative_threat: Proportion of spawning habitat area that has *high** cumulative threat of degradation.primary_threat: The habitat indicator with the *highest** spawning habitat area having moderate or high threat of degradation among all habitat indicators for the given Conservation Unit. A value ofNAindicates there are no spawning watersheds with moderate or high threat of degradation for any indicator.secondary_threat: The habitat indicator with the *second-highest** spawning habitat area having moderate or high threat of degradation among all habitat indicators for the given Conservation Unit.
Figure S3. Comparison of habitat status under WSP Strategy 2 (x-axis) and biological status under WSP Strategy 1 (y-axis) for individual CUs (points) among species and regions. Biological status of each CU was quantified as the sum of the possible outcomes of good (1), fair (2), or poor (3) weighted by their respective probabilities based on benchmark uncertainty. Habitat status for each CU was quantified as the sum of low (1), moderate (1), or high (3) threat of degradation weighted by the proportional spawning area in each outcome category. See main text Figure 6 for panels broken out by species.
3.5 Maps of habitat threats among all Assessment watersheds
Use the tabs below to navigate among the seven core habitat pressure indicators.
Forest harvest
Figure S4. Map showing the threat of habitat degradation for each Assessment Watersheds within the study area from forest harvest. Low/moderate/high threat is shown as green/amber/red colouring, respectively. Spawning watersheds are a subset of all watersheds, and are highlighted by darker shades.
Fire
Figure S5. Map showing the threat of habitat degradation for each Assessment Watersheds within the study area from fire. Low/moderate/high threat is shown as green/amber/red colouring, respectively. Spawning watersheds are a subset of all watersheds, and are highlighted by darker shades.
Insect damage
Figure S6. Map showing the threat of habitat degradation for each Assessment Watersheds within the study area from insect damage. Low/moderate/high threat is shown as green/amber/red colouring, respectively. Spawning watersheds are a subset of all watersheds, and are highlighted by darker shades.
Roads
Figure S7. Map showing the threat of habitat degradation for each Assessment Watersheds within the study area from roads. Low/moderate/high threat is shown as green/amber/red colouring, respectively. Spawning watersheds are a subset of all watersheds, and are highlighted by darker shades.
Urban & industrial development
Figure S8. Map showing the threat of habitat degradation for each Assessment Watersheds within the study area from urban and industrial development. Low/moderate/high threat is shown as green/amber/red colouring, respectively. Spawning watersheds are a subset of all watersheds, and are highlighted by darker shades.
Agriculture
