Chapter 2: Selection of Focus Areas
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South Fork Eel River SHaRP Plan 2. Chapter 2: Selection of Focus Areas The SHaRP approach is based on the concept maintaining a source of colonists for that focusing restoration in areas where occupying vacant habitats, and those steelhead and salmon still persist offers the populations demographically connecting and best potential for making measurable supporting each other as metapopulations is progress toward recovery overall, and toward well established in ecology and shown specific recovery needs for particular areas. graphically in Figure 2-1. (Levins 1969, The concept of larger populations Hanski and Gilpin 1996). Figure 2-1. Diagram of types of metapopulations and emigration from occupied to unoccupied areas (Levins 1969, Hanski and Gilpin 1996). Whether a species can persist in a patch or not loss, degradation, and fragmentation are depends on the quantity and spatial among the most serious threats to Pacific configuration of habitat within a network of salmon, the metapopulation concept has been connected patches. Under metapopulation embraced in salmon conservation and models, patch occupancy is dynamic and management (Schtickzelle and Quinn 2007, governed by local colonization and extinction McElhany et al. 2000). Salmon recovery processes (Hanski et al. 1998). As habitat efforts should focus not just on restoring Chapter 2: Selection of Focus Areas Page 2-1
South Fork Eel River SHaRP Plan
habitat but also on the proximity of habitat to Therefore, the first step of the SFER SHaRP
sources of potential colonists. As a first pilot effort was to identify the patches with
principle, the strongest local populations are the highest potential for recovery of
then the most critical watersheds to secure salmonids. This chapter describes the process
and recover in the short term to ensure the steering team followed to compare the 19
proximal habitats have the potential to be United States Geological Survey (USGS)-
colonized over the longer term when suitable delineated Hydrologic Unit Code (HUC)
habitat becomes available. These strategies HUC-12 sub-watersheds that make up the
are rooted in the idea of ‘protecting the best SFER sub-basin and determine which to
first’ and expanding restoration outward select for further detailed planning. Figure
(Beechie et al. 2008). The SHaRP process is 2-2 depicts the SHaRP process steps,
guided by this principle to focus limited including where and when the collaborative
salmonid recovery dollars in patches with the leveraged best available science, with the
highest potential for recovery of salmonids first box, outlined in red to demonstrate
and build out restoration from these core where we are in describing the process. We
areas. also use this figure in the following chapters
to highlight where subsequent aspects of
process are described.
Figure 2-2. The South Fork Eel River SHaRP process, with emphasis on selection of the focus areas.
Chapter 2: Selection of Focus Areas Page 2-2South Fork Eel River SHaRP Plan
Selecting Focus Areas provide a final score for each HUC-12 sub-
watershed.
The steering team compiled and evaluated
data from across the South Fork Eel River Data used for each category outlined in
(SFER) sub-basin that described the Bradbury et al. (1995), associated processing,
condition of salmonids and their habitat, as and description of any final adjustments are
well as human impacts. Only data sources described by category below. Appendix A
available for all or most of the 19 HUC-12s details the data used in the SHaRP process.
were included. Metrics were developed for
each data source, and each metric was Biological Importance
assigned to one of four categories inspired by
The steering team summarized publicly
Bradbury et al. (1995): Biological
available data on the current distribution and
Importance, Habitat Condition, Optimism
spawning density of salmonids as a ranking
and Potential, and Integrity and Risk. The
metric for the biological importance of each
team used an iterative process to assign
HUC-12 watershed. Current estimated
weights to each metric and added qualitative
Chinook and Coho Salmon and steelhead
scoring criteria to account for important
distribution was based on data collected from
aspects for which data were not available.
a variety of sources: CDFW, NMFS, private
For each category, each contributing metric timber companies, university research, local
was ranked relative to the distribution of watershed stewardship programs, and various
values for all sub-watersheds such that the stakeholders. This data has largely been
highest metric is given a percent rank of 100 compiled and available in CDFW’s
and the lowest metric is given a percent rank Biogeographic Information and Observation
of 0. Rank-weighted scores were then System (BIOS). The total length of stream
assigned by quartiles; metrics with a percent (km) within the current distribution of Coho
rank of 75-100 were given the highest score Salmon (Christy 2016), Chinook Salmon
of 4, metrics with a percent rank of 50-74 (Gavette 2005), and steelhead (Christy 2012)
received a score of 3, etc. These rank- were summed for each HUC-12 sub-
weighted scores were then adjusted to watershed. The total length of stream within
account for confidence in the data. Where Coho Salmon and steelhead distribution was
data was of questionable quality or adjusted based on CDFW staff assessment of
representation, a confidence multiplier (1.25- the most up-to-date salmonid distribution.
0.25) could be applied to either increase or Some values were also adjusted to reflect
decrease the effect of that metric on the removal of mainstem Eel River distribution
overall ranking process. An adjustment for because in most instances this habitat was
confidence occurred only seven times in this used for migration rather than spawning or
process. All metric scores were then totaled rearing. Chinook Salmon values were not
and a final adjustment was applied to weigh adjusted in this way because this species uses
the final score for each category. The scores the mainstem Eel River for spawning and
for each category were then summed to rearing.
Chapter 2: Selection of Focus Areas Page 2-3South Fork Eel River SHaRP Plan
Adult salmonid spawner density was diminished but good quality habitat with
primarily determined using the CDFW CMP salmonids present, currently managed to
Adult Salmonid Spawning Ground Surveys protect natural resources with the possibility
completed annually since 2010. The CMP is to become high quality refugia) were
designed to describe the regional status of summed for each HUC-12 and used as a
Coho Salmon in coastal watersheds including contributing metric. Additionally, the SFER
the SFER (Adams et al. 2011). Coho Salmon, WA also included habitat suitability index
Chinook Salmon, and steelhead redd density (HSI) scores from an Ecosystem
(redds/km) from 2010-2016 (Starks and Management Decision Support (EMDS)–
Renger 2016) were standardized by year (z- based analysis of CDFW habitat typing
scored) and the mean redd density across all surveys completed in the 1990s and 2000s.
years for each HUC-12 sub-watershed was Outputs of the EMDS-based analysis that
used as the final contributing metric. were used as metrics in the SHaRP ranking
Standardizing by year ensured that the process included overall HSI scores, canopy
random samples drawn for surveying each cover scores, pool depth scores, pool shelter
year were fairly represented by the relative scores, and pool tail-out gravel
strength of each year’s salmonid return. Redd embeddedness scores. Standardized score
surveys followed CMP protocols and values ranged from -1 (fully unsuitable) to 1
targeted Coho Salmon, so results for Chinook (full suitable) and HUC-12 scores are based
Salmon and steelhead are incomplete. As on a length-weighted mean of the reach
suggested by Bradbury et al. (1995), scores within each sub-watershed (i.e. longer
Biological Importance was weighted twice as reaches have more influence on the average
high as the other categories of data to reflect than shorter reaches). Number of pieces of
the focus on salmonids in this process. large woody debris (LWD) per stream
kilometer from CDFW surveys, compiled by
Habitat Condition CDFW Fortuna for 2009-2010 and UC
The steering team assembled habitat data Hopland for 2002-2008 were also used as a
available from SFER Watershed Assessment contributing metric. The number of pieces of
(SFER WA) (CDFW 2014) and CDFW LWD in both size classes (6-20 ft. long and >
habitat inventories. The SFER WA included 20 ft. long) were summed for each surveyed
watershed-wide ranking of stream habitat reach and the final HUC-12 sub-watershed
suitability for Coho Salmon using a scores were based on a length-weighted mean
combination of available data and of the reach scores within each sub-
professional judgment (CDFW 2014). The watershed.
length of streams (km) which were ranked as The steering team also utilized modeled data
either “High Quality” (i.e. relatively including the Intrinsic Potential model (IP
undisturbed habitat with the range and model; Agrawal et al. 2005) and the
variability of conditions necessary to support NorWeST stream temperature model
species diversity and natural salmonid (Chandler et al. 2016) to rank the current
production) or “High Potential” (i.e. physical habitat available to salmonids based
Chapter 2: Selection of Focus Areas Page 2-4South Fork Eel River SHaRP Plan on methods utilized in the Nehalem Strategic mainstem portions of the Eel River were Action Plan process (2020). Both models removed from this analysis. were used to subset stream reaches which were ranked as “anchor-sites,” meaning that Data on the presence of mélange geology was they are expected to support all life stages of also used to characterize the inherent Coho Salmon. Anchor sites have mean hydrology of a watershed. Mélange layers are August temperatures
South Fork Eel River SHaRP Plan fragmentation and potential for water percentage of stream miles of small to diversions. The mean parcel size for each medium-sized streams (drainage area
South Fork Eel River SHaRP Plan Population census and parcel data were used sub-division, and expert opinion of marijuana to characterize the threats associated with cultivation impacts. This adjustment was increasing human population size including based on professional judgement and expert land development, increased water demands, opinion of the perceived diversion pressure and habitat fragmentation. We used census within each sub-watershed. Each sub- data from the 2010 U.S. Census which was watershed received a default adjustment of 1 first modified to better characterize the and a reduced score of either 0.5 or 0.75 wildland-urban interface and effects of depending on the perceived severity of water human development on natural areas diversion pressure. This adjustment score (Radeloff et al. 2005). The census blocks was then multiplied by the sum of the were first intersected with each HUC-12 previously described contributing metrics watershed boundary using the ArcGIS (after accounting for rank and confidence), intersect tool (ESRI 2017). The population effectively reducing the final Integrity and density (persons per km2) reported for each Risk score by 25 or 50% for each sub- census block was then converted into the watershed with moderate to severe diversion count of persons for each census block using pressure, respectively. the new partial-block area. The population density for each HUC-12 sub-watershed was The results of the steering team ranking then calculated as the sum of persons within process are depicted in Figure 2-3. Scores each sub-watershed divided by the respective ranged from 136.5 for Hollow Tree Creek to sub-watershed area. This population density 49.5 for the Upper East Branch. The top was then used as a contributing metric. Land seven ranked sub-watersheds were as ownership, which was categorized as private, follows: Hollow Tree Creek, SFER non-timber company land per the SFER WA Headwaters (Elder Creek), Indian Creek, was used to characterize the potential for land Sproul Creek, Bull Creek, Redwood Creek, development and water extraction. The and Standley Creek. percentage of land within each sub-watershed that fell into this category was summarized and used as a contributing metric. The steering team incorporated an Integrity and Risk scoring adjustment to account for undocumented water diversion pressure which has been estimated to substantially impact dry season flow (Bauer et al. 2015). The steering team reviewed the Integrated Resource Water Management Plan’s data set of registered diversions and found it to be deficient of most water diversions. In lieu of a quantitative evaluation, the steering team used information on human population, land Chapter 2: Selection of Focus Areas Page 2-7
South Fork Eel River SHaRP Plan Figure 2-3. Stacked bar chart of the sub-watershed scores, by category, for each of the nineteen HUC-12 sub- watersheds of the SFER. The seven highest-scoring sub-watersheds, marked with yellow stars, were selected as focus areas for further SHaRP planning. The steering team presented the initial results selection of focus areas. A few participants of the sub-watershed ranking process at suggested other data to include in the public meetings in Bayside, Laytonville, and ranking, and the team explained that these Briceland, California in late 2017. We held data didn’t fit the criteria for inclusion two meetings in Briceland to maximize the because they did not apply to all 19 sub- opportunity for public input. At these watersheds. Participants didn’t suggest any meetings, the steering team described the other changes to the preliminary ranking SHaRP process and sought input on the methodology or results. preliminary sub-watershed ranking results. Meeting participants were neutral to Results of Focus Areas Selection supportive of the SHaRP concept and After the public meetings, the steering team generally agreed with the team’s preliminary finalized the SFER SHaRP focus areas which Chapter 2: Selection of Focus Areas Page 2-8
South Fork Eel River SHaRP Plan
are, from north to south: Bull Creek, temperatures and helps support west side
Redwood Creek, Sproul Creek, Indian Creek, redwood/Douglas fir forests. The distribution
Standley Creek, Hollow Tree Creek, and the and relative abundance of Coho Salmon and
SFER Headwaters. This last area was steelhead, which was explicitly accounted for
originally called Elder Creek after the name in scoring, may reflect generational success
of the HUC-12, but ultimately renamed in those west-side areas where summer
because the HUC contains many other rearing is more successful.
important tributaries in the headwaters area.
The location of these focus areas within the After the focus areas were selected, the
steering team began planning for each of the
SFER sub-basin is shown in Figure 2-4.
focus areas. This planning started with
All seven focus areas are on the west side of identifying the known “experts” with
the SFER (Figure 2-4). This outcome reflects experience in a particular focus area,
the spatial distribution of areas with high inquiring of these experts who else should be
underlying potential for salmonid habitat in included, and holding an Expert Panel and
the SFER. In addition, it is influenced by the Action Team meeting for that focus area.
geologic and hydrologic drivers of salmonid These important steps in the process are
habitat potential (detailed in Chapter 4): the described in Chapter 3. The steering team
underlying geology on the west side can hold came to refer to all the people that
water through the dry summer months that participated in at least one of the Expert Panel
result from the region’s Mediterranean and/or Action Team meetings (which
rainfall patterns, while differing geology on included the steering team members) as the
the east side has less water-holding capacity; Collaborative.
and dry season coastal fog moderates
Chapter 2: Selection of Focus Areas Page 2-9South Fork Eel River SHaRP Plan Figure 2-4. The 19 HUC-12 sub-watersheds of the SFER. The seven sub-watersheds shown in red were selected for further restoration planning using SHaRP and subsequently described as focus areas. Chapter 2: Selection of Focus Areas Page 2-10
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