3 Methods

fish_col_permit_num <- "SM24-882238"

3.1 Collaborative GIS Environment

Geographical Information Systems are essential for developing and communicating restoration plans as well as the reasons they are required and how they are developed. Without the ability to visualize the landscape and the data that is used to make decisions, it is difficult to conduct and communicate the need for restoration, the details of past and future plans as well as and the potential results of physical works.


To facilitate the planning and implementation of restoration activities, a collaborative GIS environment has been established using QGIS and is served on the cloud using source code stored here. This environment is intended to be a space where project team members can access, view, and contribute to the amalgamation of background spatial data and the development of restoration as well as monitoring for the project. The collaborative GIS environment allows users to view, edit, and analyze shared, up to date spatial data on personal computers in an office setting as well as on phones and tablets in the field. At the time of reporting, the environment was being used to develop and share maps, conduct spatial analyses, communicate restoration plans to stakeholders as well as to provide a central place to store methodologies and tools for conducting field assessments on standardized pre-developed digital forms. The platform can also be used to track the progress of restoration activities and monitor changes in the landscape over time, helping encourage the record keeping of past and future restoration activities in a coordinated manner.


The shared QGIS project was created using scripts currently kept in dff-2022 with the precise calls to project creation scripts tracked in the project_creation_and_permissions.txt document kept in the main QGIS project directory. Information about the scripts used for GIS project creation and updates can be viewed here with outcomes of their use summarized below:

  • Download and clip user specified layers from the BC Data Catalogue as well as data layers stored in custom Amazon Web Services buckets for an area of interest defined by a list of watershed groups and load to a geopackage called background_layers.gpkg stored in the main directory of the project.
  • A project directory is created to hold the spatial data and QGIS project information (ie. layer symbology and naming conventions, metadata, etc.).
  • Metadata for individual project spatial layers is kept in the rfp_tracking table within the background_layers.gpkg along with tables related to user supplied stream width/gradient inputs to bcfishpass to model potentially high value habitat that is accessible to fish species of interest.
# this is good info but need to figure out where it appropriately sits
# ### Issue Tracking
# "Issues" logged on the online github platform are effective ways to track tasks, enhancements, and bugs related to project components.
# They can be referenced with the scripts, text and actions used to address them by linking documentation to the issues with text comments
# or programatically through `git` commit messages.  Issues for this project are kept `r ngr::ngr_str_link_url(url_base = params$repo_url, url_resource = "issues", anchor_text = "here")`.

3.1.1 Mapping

The workflows to produce the georeferenced pdf maps include using a QGIS layer file defining and symbolizing all layers required and are continuously evolving. At the time of reporting - mapping scripts and associated layer file were kept under version control within bcfishpass here. Loading the QGIS layer file within a QGIS project, allows load and representation of all map component layers provided the user points to a postgresql database populated via bcfishpass outputs.

3.2 Planning

3.2.1 Habitat Modelling

Habitat modelling used to help guide planning for field assessments is generated by bcfishpass (Norris [2020] 2024) which has been designed to prioritize potential fish passage barriers for assessment or remediation by generating a simple model of aquatic habitat connectivity. We utilize the bcfishpass access model, linear spawning/rearing habitat model and lateral habitat connectivity for planning purposes. These models provide a valuable starting point, but their results are not definitive and should always be considered with professional judgment. Detailed information regarding model methodology, select parameters and known model limitations are detailed in Norris ([2020] 2024) with key documentation linked below:

Table 3.1 documents the custom species-specific thresholds for stream gradient and channel width applied to the linear spawning and rearing habitat model for this year’s project planning. Although parameter values were often modified to provide a more conservative estimate of habitat, the thresholds used in the model are loosely based on the references provided in Table 3.2.

#  <br>
#  
# The access model identifies natural barriers (ex. steep gradients for extended distances) and hydroelectric dams to classify the accessibility of streams for fish [@norris2021smnorrisbcfishpass]. On potentially accessible streams, scripts identify known barriers (ex. waterfalls >5m high) and additional anthropogenic features which are primarily road/railway stream crossings (i.e.
# culverts) that are potentially barriers. To prioritize these features for assessment or remediation, scripts report on
# how much modelled potentially accessible aquatic habitat the barriers may obstruct. The model can be refined with
# numerous parameters including known fish observations upstream of identified barriers and for each crossing location,
# the area of lake and wetland habitat upstream, species documented upstream/downstream, and an estimate of watershed area
# (on 2nd order and higher streams). Furthermore, mean annual precipitation weighted to upstream watershed area, stream
# discharge, and channel width can be collated using `bcfishpass`, `fwapg` and `bcfishobs`. This information can be used
# to provide an indication of the potential quantity and quality of habitat potentially gained should fish passage be
# restored, by comparing to user defined thresholds for the aforementioned parameters.
# 
#  <br>
#  
# The linear spawning and rearing habitat model uses species-specific thresholds for stream gradient (Table \@ref(tab:tab-fish-spawning-rearing)), channel width or discharge, network connectivity, and habitat type to assess the intrinsic potential of streams to support spawning and rearing. It also incorporates documented spawning locations and literature-derived parameters to refine habitat suitability estimates. This model helps guide field assessments and prioritize locations for fish passage restoration.
#  
#  <br>
#  
# <!-- Project specific fish species at the end of this paragraph. -->
# Regarding gradients, `bcfishpass` calculates the average gradient of BC Freshwater Atlas stream network lines at minimum 100m long intervals starting from the downstream end of the streamline segment and working upstream.  The network lines are broken into max gradient categories with new segments created if and when the average slope of the stream line segment exceeds user provided thresholds. For this phase of the project, the user provided gradient thresholds used to delineate "potentially accessible habitat" were based on estimated max gradients that rainbow trout (20%) and bull trout (25%) are likely to be capable of ascending.
# 
# <br>
# 
# Gradient, channel size and stream discharge are key determinants of channel morphology and subsequently fish distribution. High value rearing, overwintering, and spawning habitat preferred by numerous species/life stages of fish are often located within channel types that have relatively low gradients and large channel widths (also quantified by the amount of flow in the stream).
# 
# <br>
# 
# Following delineation of "potentially accessible habitat", the average gradient of each stream segment within habitat classified as below the 20% and 25% thresholds was calculated and summed within species and life stage specific gradient categories.  Average gradient of stream line segments can be calculated from elevations contained in the provincial freshwater atlas streamline dataset.

3.2.1.1 Statistical Support for bcfishpass Fish Habitat Modelling Updates

This project provided the statistical background for updates to bcfishpass that facilitated incorporation of channel width (observed or predicted) into species specific linear spawning/rearing habitat models. In early 2021, Bayesian statistical methods were developed to predict channel width in all provincial freshwater atlas stream segments where width measurements had not previously been measured in the field. The model was based on the relationship between watershed area and mean annual precipitation weighted by upstream watershed area (J. Thorley and Irvine 2021). In December of 2021, J. Thorley and Irvine (2021) methods were updated using a power model derived by Finnegan et al. (2005) which relates stream discharge to watershed area and mean annual precipitation resulting in J. L. Thorley, Norris, and Irvine (2021) which was utilized for channel width estimates within bcfishpass modelling at the time of reporting. More detailed documentation of the methodology used to facilitate both the data collection and statistical analysis can be sourced in Irvine ([2021] 2022) and J. L. Thorley, Norris, and Irvine (2021).


In 2024, in collaboration with Poisson Consulting - stream discharge and temperature causal effects pathways were mapped with the intent of focusing aquatic restoration actions in areas of highest potential for positive impacts on fisheries values (ie. elimination of areas from intrinic models where water temperatures are likely too cold to support fish production). The project began with a custom mechanistic model (visually represented here), but the model struggled to converge. The project then shifted to the air2stream model, which offers a middle ground between fully mechanistic models—often data-intensive and reliant on quantities that are difficult to measure or estimate—and purely statistical models, which lack physical justification and perform poorly when extrapolated to new conditions (Toffolon and Piccolroaz (2015)). After several adaptations, the expected stream temperatures were best modeled using the four-parameter version of the air2stream model, with added random effects by site for each of the four parameters (Hill, Thorley, and Irvine (2024)). The data used for the model were sourced from the following locations, for years 2019-2021:

  • Water temperature data collected in the Nechako Watershed were downloaded from Zenodo (Gilbert et al. 2022).
  • Hourly air temperature data were obtained from the ERA-5-Land dataset via the Copernicus Climate Change Service (Muñoz Sabater (2019))
  • Daily baseflow and surface runoff data were sourced from the Pacific Climate Impacts Consortium’s Gridded Hydrologic Model Output using the ACCESS1-0_rcp85 scenario (Pacific Climate Impacts Consortium (n.d.)).
#`r if(identical(gitbook_on, FALSE)){knitr::asis_output("<br><br><br>")}`

species <- c('CH', 'CM', 'CO', 'PK', 'SK', 'ST')

text_footnote <- ""
  #"Models for RB, GR and KO are under a process of development and have not yet been released.  All models parameters are preliminary and subject to collaborative development."


bcfishpass_spawn_rear_model |> 
  dplyr::filter(species_code %in% species) |>
  dplyr::mutate(Species = fishbc::fbc_common_name(species_code),
         spawn_gradient_max = round(spawn_gradient_max * 100 ,1),
         rear_gradient_max = round(rear_gradient_max * 100 ,1)) |>
  dplyr::select(Species,
         `Spawning Gradient  Max (%)`= spawn_gradient_max,
         `Spawning Width Min (m)` = spawn_channel_width_min,
         `Rearing Width Min (m)` = rear_channel_width_min,
         # `Spawning Width Max (m)` = spawn_channel_width_max,
         # `Spawning MAD Min (m3/s)` = spawn_mad_min,
         # `Spawning MAD Max (m3/s)` = spawn_mad_max,
         `Rearing Gradient Max (%)` = rear_gradient_max) |>
         # `Rearing MAD Min (m3/s)` = rear_mad_min,
         # `Rearing MAD Max (m3/s)` = rear_mad_max,
         # `Rearing Wetland Multiplier` = rear_wetland_multiplier,
         # `Rearing Lake Multiplier` = rear_lake_multiplier) |>
  t() |>
  as_tibble(rownames = "row_names") |>
  janitor::row_to_names(row_number = 1) |>
  rename(Variable = Species) |>
  fpr::fpr_kable(caption_text = 'Stream gradient and channel width thresholds used to model potentially highest value fish habitat.',
                 footnote_text = text_footnote,
                 scroll = F,
                 scroll_box_height = '300px')
Table 3.1: Stream gradient and channel width thresholds used to model potentially highest value fish habitat.
Variable Chinook Salmon Chum Salmon Coho Salmon Pink Salmon Sockeye Salmon Steelhead
Spawning Gradient Max (%) 4.5 6.5 5.5 6.5 2.5 4.5
Spawning Width Min (m) 4.0 2.1 2.0 2.1 2.0 4.0
Rearing Width Min (m) 1.5 – 1.5 – 1.5 1.5
Rearing Gradient Max (%) 5.5 – 5.5 – – 8.5
* 


bcfishpass_spawn_rear_model_references <- readr::read_csv(file = 'data/inputs_raw/bcfishpass_spawn_rear_model_ref.csv')

bcfishpass_spawn_rear_model_references |>
  dplyr::mutate(Species = fishbc::fbc_common_name(species_code)) |>
  dplyr::select(Species,
         `Spawning Gradient  Max (%)`= spawn_gradient_max,
         `Spawning Width Min (m)` = spawn_channel_width_min,
         # `Spawning Width Max (m)` = spawn_channel_width_max_ref,
         # `Spawning MAD Min (m3/s)` = spawn_mad_min,
         # `Spawning MAD Max (m3/s)` = spawn_mad_max,
         `Rearing Gradient Max (%)` = rear_gradient_max) |>
         # `Rearing Wetland Multiplier` = rear_wetland_multiplier,
         # `Rearing Lake Multiplier` = rear_lake_multiplier) |>
         # `Rearing MAD Min (m3/s)` = rear_mad_min,
         # `Rearing MAD Max (m3/s)` = rear_mad_max) |>
  t() |>
  as_tibble(rownames = "row_names") |>
  janitor::row_to_names(row_number = 1) |>
  dplyr::rename(Variable = Species) |>
  fpr::fpr_kable(caption_text = 'References considered for stream gradient and channel width thresholds used to model potentially highest value fish habitat. Preliminary and subject to revisions.',
                 footnote_text = 'The maximum gradient for steelhead rearing has been adjusted to 8.5% based on professional judgment, although references indicate 7.49%',
                 scroll = F)
Table 3.2: References considered for stream gradient and channel width thresholds used to model potentially highest value fish habitat. Preliminary and subject to revisions.
Variable Chinook Salmon Coho Salmon Steelhead Sockeye Salmon
Spawning Gradient Max (%) 0.03 (Kirsch et al. 2004, Busch et al. 2011, Cooney and Holzer 2006) 0.05 (Roberge et al. 2002, Sloat et al. 2017) 0.04 (Scheer and Steel 2006, Cooney and Holzer 2006) 0.02 (Lake 1999, Hoopes 1972)
Spawning Width Min (m) 3.7 (Busch et al. 2011, Cooney and Holzer 2006) 2 (Sloat et. al 2017) 3.8 (Cooney and Holzer 2006) 2 (Woll et al. 2017)
Rearing Gradient Max (%) 0.05 (Woll et al. 2017, Porter et al. 2008) 0.05 (Kirsch et al. 2004, Porter et al. 2008, Rosenfeld et al. 2000) 0.074 (Porter et al. 2008) –
* The maximum gradient for steelhead rearing has been adjusted to 8.5% based on professional judgment, although references indicate 7.49% 
xref_bcfishpass_names |>
  dplyr::filter(id_side == 1) |>
  dplyr::arrange(id_join) |>
  dplyr::select(Attribute = report, Definition = column_comment) |>
  fpr::fpr_kable(caption_text = 'bcfishpass outputs and associated definitions',
                 footnote_text = paste0(model_species_name, " uses a maximum gradient threshold of ", bt_network_gradient, "% to determine whether access is likely possible"),
                 scroll = gitbook_on)
#to quantify upstream habitat potentially available for salmonids and facilitate stream line symbology based on stream morphology.

# while high gradient sections typically  present  upstream  migration  barriers  and  less  available  habitat.  Additionally, the size of the stream (indicated by channel width) is an important determinant for habitat suitability for different species as well as specific life stages of those species. 

# `bcfishpass` was used to categorize and sum potentially accessible stream segments in the study area watersheds within gradient and width categories for each stream segment. 
# (0 - 3%, 3 - 5%, 5 - 8%, 8 - 15%, 15 - 20%) with these outputs further amalgamated to summarize and symbolize potential upstream habitat in three categories: riffle/cascade (0 - 5%), step-pool (5 - 15%) and step-pool very steep (15-20%) (Table \@ref(tab:tablethreshaverage)).  


#threshold and average gradient table
table_thresh_average <- tibble::tibble(`Gradient` = c('0 - 5%', '5 - 15%', '15 - 20%', '>20%'),
                                       `Channel Type` = c('Riffle and cascade pool', 'Step pool', 'Step pool - very steep', 'Non fish habitat'))

table_thresh_average |> 
    fpr::fpr_kable(caption_text = 'Stream gradient categories (threshold and average) and associated channel type.', scroll = F)

3.3 Fish Passage Assessments

3.3.1 Natural Barriers to Fish Passage

Our assessments may include natural features such as waterfalls that could limit fish passage. This informs whether upstream culvert upgrades would restore access for anadromous species (e.g., salmon) or primarily benefit resident fish already upstream. We document these features by measuring height, gradient, and pool depth, recording field observations, capturing site photographs, and reviewing background sources for context.

3.3.2 Road Stream Crossings

In the field, crossings prioritized for follow-up were first assessed for fish passage following the procedures outlined in “Field Assessment for Determining Fish Passage Status of Closed Bottomed Structures” (MoE 2011). The reader is referred to (MoE 2011) for detailed methodology. Crossings surveyed included closed bottom structures (CBS), open bottom structures (OBS) and crossings considered “other” (i.e. fords). Photos were taken at surveyed crossings and when possible included images of the road, crossing inlet, crossing outlet, crossing barrel, channel downstream and channel upstream of the crossing and any other relevant features. The following information was recorded for all surveyed crossings: date of inspection, crossing reference, crew member initials, Universal Transverse Mercator (UTM) coordinates, stream name, road name and kilometer, road tenure information, crossing type, crossing subtype, culvert diameter or span for OBS, culvert length or width for OBS. A more detailed “full assessment” was completed for all closed bottom structures and included the following parameters: presence/absence of continuous culvert embedment (yes/no), average depth of embedment, whether or not the culvert bed resembled the native stream bed, presence of and percentage backwatering, road fill depth, outlet drop, outlet pool depth, inlet drop, culvert slope, average downstream channel width, stream slope, presence/absence of beaver activity, presence/absence of fish at time of survey, type of valley fill, and a habitat value rating. Habitat value ratings were based on channel morphology, flow characteristics (perennial, intermittent, ephemeral), fish migration patterns, the presence/absence of deep pools, un-embedded boulders, substrate, woody debris, undercut banks, aquatic vegetation and overhanging riparian vegetation (Table 3.3).


fpr_table_habvalue |>
  knitr::kable(caption = 'Habitat value criteria (Fish Passage Technical Working Group, 2011).', booktabs = T, label = NA) |>
    kableExtra::column_spec(column = 1, width_min = '1.5in') |>
    kableExtra::kable_styling(c("condensed"), full_width = T, font_size = font_set)
Table 3.3: Habitat value criteria (Fish Passage Technical Working Group, 2011).
Habitat Value Fish Habitat Criteria
High The presence of high value spawning or rearing habitat (e.g., locations with abundance of suitably sized gravels, deep pools, undercut banks, or stable debris) which are critical to the fish population.
Medium Important migration corridor. Presence of suitable spawning habitat. Habitat with moderate rearing potential for the fish species present.
Low No suitable spawning habitat, and habitat with low rearing potential (e.g., locations without deep pools, undercut banks, or stable debris, and with little or no suitably sized spawning gravels for the fish species present).


Fish passage potential was determined for each stream crossing identified as a closed bottom structure as per MoE (2011). The combined scores from five criteria: depth and degree to which the structure is embedded, outlet drop, stream width ratio, culvert slope, and culvert length were used to screen whether each culvert was a likely barrier to some fish species and life stages (Tables 3.4 - 3.5). These criteria were developed based on data obtained from various studies and reflect an estimation for the passage of a juvenile salmon or small resident rainbow trout (Clarkin et al. 2005; Bell 1991; Thompson 2013). For crossings determined to be potential barriers or barriers based on the data, a culvert fix and recommended diameter/span was proposed.


tab <- as_tibble(t(fpr_table_barrier_scoring)) |>
  dplyr::mutate(V4 = names(fpr_table_barrier_scoring)) |>
  dplyr::select(V4, everything()) |>
  janitor::row_to_names(1) |>  ##turn the table sideways
  dplyr::mutate(Risk = case_when(Risk == 'Value' ~ '  Value',
                          T ~ Risk))

tab |>
  fpr::fpr_kable(caption_text = 'Fish Barrier Risk Assessment (MoE 2011).', scroll = F)
Table 3.4: Fish Barrier Risk Assessment (MoE 2011).
Risk LOW MOD HIGH
Embedded >30cm or >20% of diameter and continuous <30cm or 20% of diameter but continuous No embedment or discontinuous
Value 0 5 10
Outlet Drop (cm) <15 15-30 >30
Value 0 5 10
SWR <1.0 1.0-1.3 >1.3
Value 0 3 6
Slope (%) <1 1-3 >3
Value 0 5 10
Length (m) <15 15-30 >30
Value 0 3 6 


fpr_table_barrier_result |>
  fpr::fpr_kable(caption_text = 'Fish Barrier Scoring Results (MoE 2011).', scroll = F)
Table 3.5: Fish Barrier Scoring Results (MoE 2011).
Cumlative Score Result
0-14 passable
15-19 potential barrier
>20 barrier


The habitat gain index is the quantity of modelled habitat upstream of the subject crossing and represents an estimate of habitat gained with remediation of fish passage at the crossing. For this project, a gradient threshold between accessible and non-accessible habitat was set at 20% (for a minimimum length of 100m) intended to represent the maximum gradient of which the strongest swimmers of anadromous species (steelhead) are likely to be able to migrate upstream.


For reporting of Phase 1 - fish passage assessments within the body of this report (Table 3.4), a “total” value of habitat <20% output from bcfishpass was used to estimate the amount of habitat upstream of each crossing less than 20% gradient before a falls of height >5m - as recorded in MoE (2020) or documented in other bcfishpass online documentation. For Phase 2 - habitat confirmation sites, conservative estimates of the linear quantity of habitat to be potentially gained by fish passage restoration, steelhead rearing maximum gradient threshold (8.5%) was used. To generate estimates for area of habitat upstream (m2), the estimated linear length was multiplied by half the downstream channel width measured (overall triangular channel shape) as part of the fish passage assessment protocol. Although these estimates are not generally conservative, have low accuracy and do not account for upstream stream crossing structures they allow a rough idea of the best candidates for follow up.


Potential options to remediate fish passage were selected from MoE (2011) and included:

  • Removal (RM) - Complete removal of the structure and deactivation of the road.
  • Open Bottom Structure (OBS) - Replacement of the culvert with a bridge or other open bottom structure. Based on consultation with FLNR road crossing engineering experts, for this project we considered bridges as the only viable option for OBS type .
  • Streambed Simulation (SS) - Replacement of the structure with a streambed simulation design culvert. Often achieved by embedding the culvert by 40% or more. Based on consultation with FLNR engineering experts, we considered crossings on streams with a channel width of <2m and a stream gradient of <8% as candidates for replacement with streambed simulations.
  • Additional Substrate Material (EM) - Add additional substrate to the culvert and/or downstream weir to embed culvert and reduce overall velocity/turbulence. This option was considered only when outlet drop = 0, culvert slope <1.0% and stream width ratio < 1.0.
  • Backwater (BW) - Backwatering of the structure to reduce velocity and turbulence. This option was considered only when outlet drop < 0.3m, culvert slope <2.0%, stream width ratio < 1.2 and stream profiling indicates it would be effective..


3.3.3 Cost Estimates

Cost estimates for structure replacement with bridges and embedded culverts were generated based on the channel width, slope of the culvert, depth of fill, road class and road surface type. Road details were sourced from FLNRORD (2020b) and FLNRORD (2020a) through bcfishpass. Interviews with Phil MacDonald, Engineering Specialist FLNR - Kootenay, Steve Page, Area Engineer - FLNR - Northern Engineering Group and Matt Hawkins - MoTi - Design Supervisor for Highway Design and Survey - Nelson were utilized to help refine estimates which have since been adjusted for inflation in 2020 and based on past experience.


Base costs for installation of bridges on forest service roads and permit roads with surfaces specified in provincial GIS road layers as rough and loose was estimated at $30000/linear m and assumed that the road could be closed during construction and a minimum bridge span of 15m. For streams with channel widths <2m, embedded culverts were reported as an effective solution with total installation costs estimated at $100k/crossing (pers. comm. Phil MacDonald, Steve Page then adjusted for inflation in 2020). For larger streams (>6m), estimated span width increased proportionally to the size of the stream. For crossings with large amounts of fill (>3m), the replacement bridge span was increased by an additional 3m for each 1m of fill >3m to account for cutslopes to the stream at a 1.5:1 ratio. To account for road type, a multiplier table was generated to estimate incremental cost increases with costs estimated for structure replacement on paved surfaces, railways and arterial/highways costing up to 15 times more than forest service roads due to expenses associate with design/engineering requirements, traffic control and paving. The cost multiplier table (Table 3.6) should be considered very approximate with refinement recommended for future projects.


sfpr_xref_road_cost() |>
  dplyr::mutate(cost_m_1000s_bridge = formatC(cost_m_1000s_bridge * 15000, format="d", big.mark=",")) |>
  dplyr::mutate(cost_embed_cv = formatC(cost_embed_cv * 1000, format="d", big.mark=",")) |>
  dplyr::rename(
    Class = my_road_class,
    Surface = my_road_surface,
    `Class Multiplier` = road_class_mult,
    `Surface Multiplier` = road_surface_mult,
    `Bridge $/15m` = cost_m_1000s_bridge,
    `Streambed Simulation $` = cost_embed_cv
  ) |>
  dplyr::filter(!is.na(Class)) |>
  dplyr::mutate(Class = dplyr::case_when(
    Class == 'fsr' ~ stringr::str_to_upper(Class),
    TRUE ~ stringr::str_to_title(Class)),
    Surface = stringr::str_to_title(Surface)
  ) |> 
  # filter(Class != 'FSR' & Surface != 'Paved') |>
  fpr::fpr_kable(caption_text = 'Cost multiplier table based on road class and surface type.', scroll = F)
Table 3.6: Cost multiplier table based on road class and surface type.
Class Surface Class Multiplier Surface Multiplier Bridge $/15m Streambed Simulation $
FSR Rough 1 1 450,000 100,000
FSR Loose 1 1 450,000 100,000
Resource Loose 1 1 450,000 100,000
Resource Rough 1 1 450,000 100,000
Permit Unknown 1 1 450,000 100,000
Permit Loose 1 1 450,000 100,000
Permit Rough 1 1 450,000 100,000
Unclassified Loose 1 1 450,000 100,000
Unclassified Rough 1 1 450,000 100,000
Unclassified Paved 1 2 750,000 150,000
Unclassified Unknown 1 2 750,000 150,000
Local Loose 4 1 1,500,000 200,000
Local Paved 4 2 3,000,000 400,000
Collector Paved 4 2 3,000,000 400,000
Arterial Paved 15 2 11,250,000 1,500,000
Highway Paved 15 2 11,250,000 1,500,000
Rail Rail 15 2 11,250,000 1,500,000

3.3.4 Climate Change Risk Assessment

In collaboration with the Ministry of Transportation and Infrastructure (MoTi), a new climate change replacement program aims to prioritize vulnerable culverts for replacement (pers. comm Sean Wong, 2022) based on data collected and ranked related to three categories - culvert condition, vulnerability and priority. Within the “condition” risk category - data was collected and crossings were ranked based on erosion, embankment and blockage issues. The “climate” risk category included ranked assessments of the likelihood of both a flood event affecting the culvert as well as the consequence of a flood event affecting the culvert. Within the “priority” category the following factors were ranked - traffic volume, community access, cost, constructability, fish bearing status and environmental impacts (Table 3.7). This project is still in its early stages with methodology changes going forward.


# This line can be removed once Table.R is run. Just a work around for now. 
xref_moti_climate_names <- sfpr_xref_moti_climate_names()

xref_moti_climate_names  %>%
  dplyr::slice(7:nrow(.)) |>
  dplyr::select(spdsht, report) |>
  dplyr::rename(Parameter = spdsht, Description = report) |>
  fpr::fpr_kable(caption_text = 'Climate change data collected at MoTi culvert sites', scroll = gitbook_on)
Table 3.7: Climate change data collected at MoTi culvert sites
Parameter Description
erosion_issues Erosion (scale 1 low - 5 high)
embankment_fill_issues Embankment fill issues 1 (low) 2 (medium) 3 (high)
blockage_issues Blockage Issues 1 (0-30%) 2 (>30-75%) 3 (>75%)
condition_rank Condition Rank = embankment + blockage + erosion
condition_notes Describe details and rational for condition rankings
likelihood_flood_event_affecting_culvert Likelihood Flood Event Affecting Culvert (scale 1 low - 5 high)
consequence_flood_event_affecting_culvert Consequence Flood Event Affecting Culvert (scale 1 low - 5 high)
climate_change_flood_risk Climate Change Flood Risk (likelihood x consequence) 1-6 (low) 6-12 (medium) 10-25 (high)
vulnerability_rank Vulnerability Rank = Condition Rank + Climate Rank
climate_notes Describe details and rational for climate risk rankings
traffic_volume Traffic Volume 1 (low) 5 (medium) 10 (high)
community_access Community Access - Scale - 1 (high - multiple road access) 5 (medium - some road access) 10 (low - one road access)
cost Cost (scale: 1 high - 10 low)
constructability Constructibility (scale: 1 difficult -10 easy)
fish_bearing Fish Bearing 10 (Yes) 0 (No) - see maps for fish points
environmental_impacts Environmental Impacts (scale: 1 high -10 low)
priority_rank Priority Rank = traffic volume + community access + cost + constructability + fish bearing + environmental impacts
overall_rank Overall Rank = Vulnerability Rank + Priority Rank
priority_notes Describe details and rational for priority rankings


3.4 Habitat Confirmation Assessments

Following fish passage assessments, habitat confirmations were completed in accordance with procedures outlined in the document “A Checklist for Fish Habitat Confirmation Prior to the Rehabilitation of a Stream Crossing” (Fish Passage Technical Working Group 2011). The main objective of the field surveys was to document upstream habitat quantity and quality and to determine if any other obstructions exist above or below the crossing. Habitat value was assessed based on channel morphology, flow characteristics (perennial, intermittent, ephemeral), the presence/absence of deep pools, un-embedded boulders, substrate, woody debris, undercut banks, aquatic vegetation and overhanging riparian vegetation. Criteria used to rank habitat value was based on guidelines in Fish Passage Technical Working Group (2011) (Table 3.3).


During habitat confirmations, to standardize data collected and facilitate submission of the data to provincial databases, information was collected on digital field forms adapted from provincial “Site Cards”. Habitat characteristics recorded included channel widths, wetted widths, residual pool depths, gradients, bankfull depths, stage, temperature, conductivity, pH, cover by type, substrate and channel morphology (among others). When possible, the crew surveyed downstream of the crossing to a minimum distance 300m and upstream to a minimum distance of 500 - 600m. Any potential obstacles to fish passage were inventoried with photos, physical descriptions and locations recorded on site cards. Surveyed routes were recorded with time-signatures on handheld GPS units.


3.4.1 Fish Sampling

3.4.1.1 Electrofishing

Fish sampling was conducted on a subset of sites when biological data was considered to add significant value to the physical habitat assessment information. Electrofishing was utilized for fish sampling according to stream inventory standards and procedures found in the Reconnaissance (1:20 000) Fish and Fish Habitat Inventory Manual (Resources Inventory Committee 2001). A Haltech 2000 backpack electrofisher was used within discrete site units both upstream and downstream of the subject crossing with electrofisher settings and seconds, water quality parameters (i.e. conductivity, temperature and ph), start and end locations, length of site and wetted widths (average of a minimum of three) recorded.

3.4.1.2 Fish Handling and Processing

Captured fish were held in buckets with sufficient water to minimize stress until processing, and multiple buckets were used when catch numbers were high. For each fish captured, fork length, weight and species was recorded with results documented in the fish data submission spreadsheet.

3.4.1.3 Pit Tagging

Fish with a fork length greater than 60 mm and belonging to species approved under the scientific fish collection permit SM24-882238 were tagged with Passive Integrated Transponders (PIT tags) using the Abdominal Cavity method outlined by Biomark. To anesthetize fish prior to pit tagging, we used a solution of approximately 0.1 mL of clove oil per 1 L of water (1:10,000). This concentration was selected for its efficiency in providing effective sedation with minimal residual effects, making it ideal for studies in which fish are released back into their natural habitats (Fernandes et al. 2017). The clove oil solution was prepared in advance by dissolving pure clove oil in ethyl alcohol in a 1:9 ratio (clove oil: ethyl alcohol) to enhance solubility, then mixed into the water bucket (Fernandes et al. 2017). Fish were immersed in this solution until they reached an appropriate level of anesthesia for handling and then were tagged. To maintain needle sharpness and minimize injury risk, needles were replaced approximately every 10 fish. Each tagged fish was scanned with the PIT reader, and both the PIT tag ID and row ID were recorded. Once tagged, fish were placed into a bucket of fresh water and allowed to recover before being released back into the stream. Fish information and habitat data will be submitted to the province under scientific fish collection permit SM24-882238.

3.4.2 Aerial Imagery

Scripted processing and serving of UAV imagery collected during the project is available at https://github.com/NewGraphEnvironment/stac_uav_bc/ (Irvine [2025] 2025). OpenDroneMap was utilized to produce orthomosaics, digital surface models (DSMs), and digital terrain models (DTMs) (OpenDroneMap Authors [2014] 2025). To support efficient web-based access - imagery products were converted to cloud-optimized GeoTIFFs (COGs) using rio-cogeo, then collated accordiong to the SpatioTemporal Asset Catalog (STAC) specification with pystac and uploaded to S3 storage Amazon Web Services (2025). A titiler tile server was set up to facilitate interactive viewing of the orthoimagery and an Application Program Interface (API) leveraging stac-fastapi-pgstac is served at https://images.a11s.one to enable linking of collection images through QGIS as well as remote spatial and temporal querying using open source software such as rstac (Development Seed [2019] 2025; stac-utils 2025; Simoes et al. 2021).

3.5 Engineering Design

Engineering designs were signed and sealed by professional engineers. When possible - completed designs are loaded to the PSCIS data portal.

3.6 Remediations

Structure replacement was conducted by project specific contractors. If not already completed, as-built drawings will be loaded to the PSCIS data portal.

3.7 Monitoring

Monitoring of fish passage restoration sites — both proposed and completed - is essential to ensure restoration investments lead to meaningful ecological outcomes. Monitoring enables evaluation of whether remediation actions improve connectivity for fish and provides critical feedback to refine future prioritization and restoration strategies.


Baseline data collection, including fish sampling and aerial surveys via drone, are core components of this monitoring. While detailed methods for these activities are included in the previous habitat confirmation section, they are also fundamental to effectiveness monitoring, as they provide context to not only facilitate prioritization and communications but also for detecting change following restoration.


To support consistent and targeted assessment, a custom field form was developed for routine effectiveness monitoring based loosely on Forest Investment Account (2003) but tailored specifically for fish passage projects. Table 3.8) outlines the monitoring metrics used.


readr::read_csv('data/inputs_raw/form_monitoring_desc.csv') |> 
 mutate(Parameter = stringr::str_to_title(Parameter)) |> 
  mutate(Parameter = case_when( Parameter == "Uav_flight" ~ "UAV Flight",
                                T ~ Parameter)) |> 
  fpr::fpr_kable(scroll = F,
                 caption_text = 'Description of monitoring metrics used for effectiveness monitoring.')
Table 3.8: Description of monitoring metrics used for effectiveness monitoring.
Parameter Description
Dewatering Have the remediation works led to dewatering of the channel due to substrate aggradation or other factors?
Velocity Are flow velocities similar to those within the natural channel? Are they expected to exceed swim speeds of particular fish species/life stages of interest?
Constriction Have the remediation works led to constriction of the channel. Compare channel width underneath structure and within construction footprint to average channel widths upstream and downstream?
Substrate Is the substrate within/under and adjacent to the remediated structure generally equivalent to that found upstream and downstream where natural channel conditions exist?
Riparian What is the condition of the riparian area within the construction footprint?
UAV Flight Was a flight conducted with unmmanned aerial vehicle to document conditions at time of monitoring?
Flow_depth What are the flow depths at the time of assessment within project footprint. Are depths expected to be sufficient to facilitat upstream passage for specific species/life stages of interest?
Stability Does the structure appear to be stable or is there evidence of erosion/shifting?
Revegetation How were riparian areas rehabillitated and are they improving fish habitat value?
Cover Is cover available for fish within the construction footprint in the form of overhanging vegetation, large/small woody debris, boulders, undercut banks, etc?
Maintenance If required, provide maintenance recommendations.
Recommendations General recommendations for follow up. Could include revegetation, addition of substrate, fish sampling, etc.