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Susitna-Watana Hydroelectric Project Document
ARLIS Uniform Cover Page
Title:
SuWa 235
Fish and aquatics instream flow study (Study 8.5), evaluation of
relationships between fish abundance and specific microhabitat variables,
technical memorandum
Author(s) – Personal:
Author(s) – Corporate:
R2 Resource Consultants, Inc.
AEA-identified category, if specified:
September 17, 2014 technical memorandum filings
AEA-identified series, if specified:
Series (ARLIS-assigned report number): Existing numbers on document:
Susitna-Watana Hydroelectric Project document number 235
Published by: Date published:
[Anchorage, Alaska : Alaska Energy Authority, 2014] September 2014
Published for: Date or date range of report: Prepared for Alaska Energy Authority
Volume and/or Part numbers:
Final or Draft status, as indicated:
Attachment G
Document type: Pagination:
Technical memorandum v, 43 p.
Related work(s): Pages added/changed by ARLIS:
Cover letter to this report: Susitna-Watana Hydroelectric
Project, FERC Project no. 14241-000; Initial filing of September
2014 technical memoranda. (SuWa 228)
Attachments A-F and H-J (SuWa 229-234 and 236-238)
Added cover letter
Notes:
All reports in the Susitna-Watana Hydroelectric Project Document series include an ARLIS-
produced cover page and an ARLIS-assigned number for uniformity and citability. All reports
are posted online at http://www.arlis.org/resources/susitna-watana/
September 17, 2014
Ms. Kimberly D. Bose
Secretary
Federal Energy Regulatory Commission
888 First Street, N.E.
Washington, D.C. 20426
Re:Susitna-Watana Hydroelectric Project, Project No. 14241-000
Initial Filing of September 2014 Technical Memoranda
Dear Secretary Bose:
By letter dated January 28, 2014, the Federal Energy Regulatory Commission
(Commission or FERC) extended the procedural schedule for the preparation and review
of the Initial Study Report (ISR) for the proposed Susitna-Watana Hydroelectric Project,
FERC Project No. 14241 (Project).1 In particular, the Commission’s January 28 letter
established a deadline of June 3, 2014 for the Alaska Energy Authority (AEA) to file the
ISR, and provided a 120-day period for licensing participants to review the ISR prior to
the ISR meetings, which are scheduled to begin the week of October 13.2 The purpose of
this filing is to provide several technical memoranda to Commission Staff and licensing
participants prior to the ISR meetings.
As required by the Commission’s January 28 letter, AEA filed the ISR with the
Commission on June 3. Among other things, the ISR detailed AEA’s planned work
during the 2014 field season.3 As AEA was preparing this 2014 work plan, it recognized
that data gathered during the 2014 field season, together with other study work conducted
prior to the October 2014 ISR meetings,could assist Commission Staff, AEA, and other
licensing participants in developing the Project’s licensing study program for 2015. For
this reason, the ISR provided for AEA to prepare certain technical memoranda and other
information based on 2014 work.
AEA recognizes that Commission Staff and licensing participants need a
reasonable amount of time prior to the ISR meetings to review this additional
information. AEA and licensing participants consulted with Commission Staff on this
1 Letter from Jeff Wright, Federal Energy Regulatory Commission, to Wayne Dyok, Alaska Energy
Authority, Project No. 14241-000 (issued Jan. 28, 2014)[hereinafter, “January 28 letter”].
2 The full schedule for the ISR meetings appears in Section 1.5 of the ISR, as well as on AEA’s
licensing website, http://www.susitna-watanahydro.org/meetings/.
3 E.g., Initial Study Report §1.3 & Table 3, Project No. 14241-000 (filed June 3, 2014) [hereinafter,
“ISR”].
2
matter, and Staff directed that any additional information should be filed with the
Commission and made available to licensing participants no later than 15 days prior to
the ISR meetings, consistent with the typically applicable deadline under the
Commission’s Integrated Licensing Process regulations.4
With this letter, AEA is filing and distributing the first set of technical
memoranda and other information generated during the 2014 study season, as described
below. As part of its continued implementation of the study plan, AEA expects to file
certain additional technical memoranda prior to October 1, 2014, in accordance with
Commission Staff direction.
This first set of technical memoranda and other information consists of the
following:
Attachment A: Proposal to Eliminate the Chulitna Corridor from Further
Study. As explained in the ISR, throughout the licensing process AEA has
continually evaluated its proposal for Project development based on
environmental review, technical feasibility, practical considerations, and other
factors. As part of this iterative process, AEA notified the Commission and
licensing participants in the ISR that it was evaluating whether to continue
study of the Chulitna Corridor.5 Attachment A details AEA’s conclusion that
development of the Chulitna Corridor is not a reasonable alternative, and
therefore AEA proposes to eliminate the corridor from further study. AEA
seeks any comments or information on this proposal from federal and state
resource agencies and other participants in the licensing process.
Attachment B: Ice Processes in the Susitna River Study (Study 7.6),Detailed
Ice Observations October 2013 –May 2014 Technical Memorandum. The
ISR indicated that AEA would provide a summary of the 2014 break-up
observations.6 This technical memorandum describes all field activities and
observations between October 16, 2013 and May 15, 2014 for the Ice
Processes in the Susitna River Study (Study 7.6).
Attachment C: Study of Fish Distribution and Abundance in the Upper
Susitna River (Study 9.5), Proposed 2015 Modifications to Fish Distribution
and Abundance Study Plan Implementation Technical Memorandum. Based
on AEA’s experience in implementing the study plan for the Study of Fish
Distribution and Abundance in the Upper Susitna River (Study 9.5)during
2014, this technical memorandum proposes to continue certain modifications
to the implementation of this study during 2015.
4 See 18 C.F.R. §5.15(c)(2).
5 See ISR, ISR Overview §1.4.
6 See id., Ice Processes in the Susitna River Study,Study Plan 7.6, Part C §7.2.
3
Attachment D: Study of Fish Distribution and Abundance in the Middle and
Lower Susitna River Study (Study 9.6), 2013-2014 Winter Fish Study
Technical Memorandum. At the time the ISR was filed, AEA was still in the
process of conducting data entry, quality control, and analysis of winter
sampling for this study. AEA reported in the ISR that it would develop plans
for completing this study in a technical memorandum to be filed with the
Commission.7 This technical memorandum fulfills this commitment and sets
forth AEA’s proposal for winter efforts, including proposed methodologies
and modifications.
Attachment E: Characterization and Mapping of Aquatic Habitats (Study
9.9), 2013 and 2014 Aquatic Habitat Mapping Field Season Completion
Progress Technical Memorandum. In the ISR, AEA reported that its 2014
activities for the Characterization and Mapping of Aquatic Habitats Study
(Study 9.9) would consist of various ground-truthing surveys and collection of
habitat information for the 12 lakes within the potential reservoir inundation
zone.8 This technical memorandum reports on these activities.
Attachment F: Eulachon Run Timing, Distribution, and Spawning in the
Susitna River (Study 9.16), 2015 Proposed Eulachon Spawning Habitat Study
Modifications Technical Memorandum.After reviewing the 2013 and 2014
results from the Cook Inlet Beluga Whale Study (Study 9.17) and discussing
the results with the National Marine Fisheries Service, AEA has determined
that additional data are needed regarding eulachon spawning habitats. This
technical memorandum describes a proposed modification to the Study of
Eulachon Run Timing, Distribution and Spawning in the Susitna River (Study
9.16)to include an assessment of eulachon spawning habitats.
Attachment G: Fish and Aquatics Instream Flow Study (Study 8.5),
Evaluation of Relationships between Fish Abundance and Specific
Microhabitat Variables Technical Memorandum. Consistent with the
Commission’s study plan determination,9 this technical memorandum
provides a detailed evaluation of the comparison of fish abundance measures
with specific microhabitat variable measurements where sampling overlaps.
This memorandum is used to determine whether a relationship between a
specific microhabitat variable and fish abundance is evident.
Attachment H: Fish and Aquatics Instream Flow Study (Study 8.5), 2013-
2014 Instream Flow Winter Studies Technical Memorandum.In the ISR,
AEA reported that it would distribute its finding concerning the 2013-2014
7 See id., Study of Fish Distribution and Abundance in the Middle and Lower Susitna River Study,
Study Plan 9.6, Part C §7.1.2.5.
8 See id., Characterization and Mapping of Aquatic Habitats, Study Plan 9.9, Part C § 7.1.
9 See Study Plan Determination on 14 Remaining Studies for the Susitna-Watana Hydroelectric Project,
Appendix B at B-84 to B-86, Project No. 14241-000 (issued Apr. 1, 2013).
4
winter activities in 2014.10 This technical memorandum describes the
methods applied, and data and information collected, as part of the Instream
Flow Study 2013-2014 winter studies.
Attachment I: Geomorphology Study (Study 6.5), Susitna River Historical
Cross Section Comparison (1980s to Current) Technical Memorandum. As
specified in Revised Study Plan Section 6.5.4.1.2.3, this technical
memorandum describes changes within the main and side channels of the
Susitna River by comparing historical survey data from the 1980s with survey
data from the current Project.
Attachment J: Geomorphology Study (Study 6.5), 2014 Update of Sediment-
Transport Relationships and a Revised Sediment Balance for the Middle and
Lower Susitna River Segments Technical Memorandum. The purpose of this
technical memorandum is to update the sediment load rating curves and
preliminary estimates of the overall sediment balance in the Middle and
Lower River segments under pre-Project conditions that were initially
provided in “Development of Sediment-Transport Relationships and an Initial
Sediment Balance for the Middle and Lower Susitna River Segments,” (Tetra
Tech, Inc. 2013a). This update is based on additional data collected by the
U.S. Geological Survey in 2012 and 2013.
AEA appreciates the opportunity to provide this additional information to the
Commission and licensing participants, which it believes will be helpful in determining
the appropriate development of the 2015 study plan as set forth in the ISR. If you have
questions concerning this submission please contact me at wdyok@aidea.org or (907)
771-3955.
Sincerely,
Wayne Dyok
Project Manager
Alaska Energy Authority
Attachments
cc: Distribution List (w/o Attachments)
10 See ISR, Fish and Aquatics Instream Flow Study, Study Plan 8.5, Part C §7.5.2.
Susitna-Watana Hydroelectric Project
(FERC No. 14241)
Fish and Aquatics Instream Flow Study
(Study 8.5)
Evaluation of Relationships between Fish Abundance
and Specific Microhabitat Variables
Technical Memorandum
Prepared for
Alaska Energy Authority
Prepared by
R2 Resource Consultants, Inc.
September 2014
EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
TECHNICAL MEMORANDUM AND SPECIFIC MICROHABITAT VARIABLES
TABLE OF CONTENTS
1. Introduction ........................................................................................................................1
1.1. Relevancy of the Microhabitat Variables to Instream Flow Modeling ..............2
1.2. Biological Relevance of the Eight Microhabitat Parameters .............................3
1.2.1. Surface Flow and Groundwater Exchange Fluxes .......................... 4
1.2.2. Dissolved Oxygen (Intergravel and Surface Water) ....................... 4
1.2.3. Macronutrients (i.e., Nitrogen and Phosphorus) ............................. 5
1.2.4. Temperature (Surface Water and Intergravel) ................................ 5
1.2.5. pH .................................................................................................... 6
1.2.6. Dissolved Organic Carbon .............................................................. 6
1.2.7. Alkalinity ........................................................................................ 6
1.2.8. Chlorophyll-a .................................................................................. 7
1.3. Data Sources ......................................................................................................7
1.3.1. Fish Habitat Suitability Data ........................................................... 7
1.3.2. Fish Abundance Data ...................................................................... 8
1.3.3. Surface Flow and Groundwater Exchange Fluxes .......................... 8
1.3.4. Dissolved Oxygen (Intergravel and Surface Water) ....................... 9
1.3.5. Macronutrients (i.e., Nitrogen and Phosphorus) ............................. 9
1.3.6. Temperature (Intergravel and Surface Water) ................................ 9
1.3.7. pH .................................................................................................. 10
1.3.8. Dissolved Organic Carbon ............................................................ 10
1.3.9. Alkalinity ...................................................................................... 10
1.3.10. Chlorophyll-a ................................................................................ 10
2. Methods .............................................................................................................................11
2.1. HSC Analysis – Surface Water Dissolved Oxygen and Temperature .............11
2.2. FDA Analysis...................................................................................................11
2.2.1. pH Collected for FDA Study ........................................................ 12
2.2.2. Parameters Collected as Part of the Water Quality Study ............ 13
2.2.3. Benthic Chlorophyll-a Collected for River Productivity Study .... 15
3. Results ...............................................................................................................................15
3.1. HSC Analysis ...................................................................................................15
3.1.1. Surface Water Dissolved Oxygen ................................................. 16
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page i September 2014
EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
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3.1.2. Surface Water Temperature .......................................................... 16
3.2. FDA Analysis...................................................................................................16
3.2.1. pH collected for FDA Study ......................................................... 16
3.2.2. Parameters collected for Water Quality Study .............................. 17
3.2.3. Benthic Chlorophyll-a Collected for River Productivity Study .... 17
4. Discussion and Recommendations..................................................................................17
4.1. Macronutrients .................................................................................................18
4.2. pH .....................................................................................................................19
4.3. Dissolved Organic Carbon ...............................................................................19
4.4. Alkalinity .........................................................................................................19
4.5. Chlorophyll-a ...................................................................................................20
5. References .........................................................................................................................20
6. Tables ................................................................................................................................25
7. Figures ...............................................................................................................................32
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page ii September 2014
EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
TECHNICAL MEMORANDUM AND SPECIFIC MICROHABITAT VARIABLES
LIST OF TABLES
Table 1.1-1. Microhabitat variables requested for inclusion by NMFS in the FERC SPD (FERC
2013), noting Susitna River studies considering these variables. ......................................... 26
Table 1.3-1. Potential data sources for evaluation of relationships between microhabitat
variables and fish abundance measures. ............................................................................... 27
Table 1.3-2. Summary of relevant study sources and data used for the analyses of FERC
recommended variables. ....................................................................................................... 28
Table 3.2-1. Summary of pH model results ................................................................................. 29
Table 3.2-2. Summary of Water Quality model results ............................................................... 30
Table 3.2-3. Summary of benthic chlorophyll-a model results. .................................................. 30
Table 4.1-1. Evaluation of FERC requested variables and recommendations for inclusion in
future HSC curve development. ............................................................................................ 31
LIST OF FIGURES
Figure 1.3-1. Map of FA-104 (Whiskers Slough) showing example of fish abundance and water
quality samples from multiple sources that were combined to compare fish abundance with
habitat variables. ................................................................................................................... 33
Figure 2.2-1. Box plots of dissolved organic carbon concentrations for FDA-matched Water
Quality samples by Geomorphic Reach and macrohabitat type. .......................................... 34
Figure 2.2-2. Box plots of chlorophyll-a concentrations for FDA-matched Water Quality
samples by Geomorphic Reach and macrohabitat type. ....................................................... 34
Figure 2.2-3. Box plots of alkalinity for FDA-matched Water Quality samples by Geomorphic
Reach and macrohabitat type. ............................................................................................... 35
Figure 2.2-4. Box plot of River Productivity benthic chlorophyll-a samples by macrohabitat
type. ....................................................................................................................................... 35
Figure 3.2-1. Scatterplot with fitted models showing relationship between pH and log-
transformed (+1) counts for anadromous fry and juveniles. ................................................. 36
Figure 3.2-2. Scatterplot with fitted model showing relationship between pH and log-
transformed (+1) counts for resident salmonids. .................................................................. 37
Figure 3.2-3. Scatterplot showing relationship between pH and log-transformed (+1) counts for
resident non-salmonid fish. ................................................................................................... 38
Figure 3.2-4. Scatterplot with anadromous juvenile salmonid counts plotted against dissolved
organic carbon from WQ sampling. ...................................................................................... 38
Figure 3.2-5. Scatterplot with resident salmonid counts plotted against dissolved organic carbon
from WQ sampling. .............................................................................................................. 39
Susitna-Watana Hydroelectric Project Alaska Energy Authority
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EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
TECHNICAL MEMORANDUM AND SPECIFIC MICROHABITAT VARIABLES
Figure 3.2-6. Scatterplot with resident non-salmonid counts plotted against dissolved organic
carbon from WQ sampling. ................................................................................................... 39
Figure 3.2-7. Scatterplot with anadromous juvenile salmonid counts plotted against chlorophyll-
a from WQ sampling. ............................................................................................................ 39
Figure 3.2-8. Scatterplot with resident salmonid counts plotted against chlorophyll-a from WQ
sampling. ............................................................................................................................... 40
Figure 3.2-9. Scatterplot showing relationship between water column chlorophyll-a
concentrations and log-transformed counts of resident non-salmonid fish. ......................... 40
Figure 3.2-10. Scatterplot with resident salmonid counts plotted against alkalinity from WQ
sampling with fitted model. .................................................................................................. 41
Figure 3.2-11. Scatterplot with resident non-salmonid counts plotted against alkalinity from WQ
sampling with fitted model. .................................................................................................. 41
Figure 3.2-12. Scatterplot and fit models showing relationship between benthic chlorophyll-a
and resident salmonid counts in three Focus Areas. ............................................................. 42
Figure 3.2-13. Scatterplot and fit models showing relationship between benthic chlorophyll-a
and resident non-salmonid counts in three Focus Areas. ...................................................... 43
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page iv September 2014
EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
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LIST OF ACRONYMS AND ABBREVIATIONS
Abbreviation Definition
ADF&G Alaska Department of Fish and Game
AEA Alaska Energy Authority
DO Dissolved Oxygen
DOC Dissolved Organic Carbon
EPA U.S. Environmental Protection Agency
FA Focus Area
FDA Fish Distribution and Abundance
FERC Federal Energy Regulatory Commission
HSC Habitat Suitability Criteria
HSI Habitat Suitability Index
ICPMS Inductively Coupled Plasma-Mass Spectrometry
IFS Instream Flow Study
ISR Initial Study Report
LR Lower River
LWD Large Woody Debris
MR Middle River
N Nitrogen
NMFS NOAA National Marine Fisheries Service
P Phosphorus
PRM Project River Mile
Project Susitna-Watana Hydroelectric Project, FERC Project No. 14241
QAIC Version of Akaike’s Information Criteria (AIC) for overdispersed count data where
quasi-likelihood and sample size adjustments are made.
RSP Revised Study Plan
SPD Study Plan Determination
TKN Total Kjeldahl Nitrogen
TP Total Phosphorus
TWG Technical Workgroup
UR Upper River
USR Updated Study Report
VHG Vertical Hydraulic-head Gradient
WQ Water Quality
Susitna-Watana Hydroelectric Project Alaska Energy Authority
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EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
TECHNICAL MEMORANDUM AND SPECIFIC MICROHABITAT VARIABLES
1. INTRODUCTION
On April 1, 2013 the Federal Energy Regulatory Commission (FERC) issued its Study Plan
Determination (SPD) for 14 of the 58 proposed individual studies in the Alaska Energy
Authority’s (AEA) Revised Study Plan (RSP) for the Susitna-Watana Hydroelectric Project,
FERC Project No. 14241 (Project). When approving the Fish and Aquatics Instream Flow Study
(IFS) (Study 8.5), FERC’s April 1 SPD (FERC 2013) made recommendations for additional
exploratory analyses to look for strong relationships between fish abundance and microhabitat
variables that had not been planned for comparison to assess Project impacts. Responding to
agency requests, the FERC SPD made specific recommendations on additional microhabitat
variables that should be evaluated for possible inclusion in the HSC analyses (see Pages B-84-B-
86 of April 1, 2013 SPD; excerpts provided below):
Microhabitat Types, HSC and HSI Development
NMFS requests that the following microhabitat variables be collected: depth, velocity,
surface flow and groundwater exchange fluxes, upwelling/downwelling (determined by
vertical hydraulic gradient-head or VHG), substrate type, cover, woody debris, turbidity,
dissolved oxygen (intragravel and surface water), macronutrients (N, P), temperature
(intragravel and surface water), pH, dissolved organic carbon (DOC), alkalinity,
invertebrate drift density, benthic organic matter, algal biomass, and Chlorophyll-a.
FWS states that VHG, intragravel water quality, and groundwater are particularly
important microhabitats that are omitted from the assessment and should be included in
HSC/HSI development.
EPA states that relationships between the hierarchical habitat classifications proposed
by AEA and microscale habitat variables that are statistically relevant to fish use, have
not been tested and that the abundance and spatial distribution of microhabitat may not
necessarily depend on the spatial distribution of larger-scale aspects of channel
planform. EPA suggests that possible spatial relationship(s) between suitable
microhabitat conditions (e.g., substrate, depth, velocity, temperature, etc.) and channel
planform should be treated as hypotheses that require testing.
Discussion and Staff Recommendation
As noted above, AEA proposes to develop site-specific HSC by collecting microhabitat
data for depth, velocity, substrate, proximity to cover (including LWD), upwelling, and
turbidity for specific locations where target species and lifestages are observed.
Therefore, AEA is already addressing seven of the 18 microhabitat variables
recommended by the agencies for detailed analysis and preparation of HSC curves for
this study, and its proposed approach for developing HSC curves for these seven
variables is consistent with accepted practices for implementing an instream flow study
within the context of a hydroelectric licensing case (section 5.9(b)(6)).
In regard to three of the 11 microhabitat variables recommended by the agencies (i.e.,
invertebrate drift density, benthic organic matter, and algal biomass), HSC would be
developed for these resources as part of Study 9.8 (river productivity), in addition to
supplementary measures of Chlorophyll-a.
In regard to the remaining eight of the 11 microhabitat variables recommended by the
agencies for detailed analysis, there is insufficient information in the project record at
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EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
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this time to make a determination on whether to require AEA to develop preference
curves for these eight variables: surface flow and groundwater exchange fluxes,
dissolved oxygen (intragravel and surface water), macronutrients (i.e., nitrogen and
phosphorus), temperature (intragravel and surface water), pH, dissolved organic carbon,
alkalinity, and Chlorophyll-a. Additional information on fish distribution within the
project area would need to be obtained and compared to field measurements of these
parameters prior to making a determination on whether there is a need to develop
preference curves for the various target fish species and lifestages to be included in the
instream flow study, as part of the required analysis of project effects (section 5.9(b)(5)).
However, we envision that the initial analysis of any potential fish-habitat associations
for these parameters would be relatively low-cost (section 5.9(b)(7)) because AEA is
already proposing an intensive data collection effort within Middle River focus areas for
fish distribution as part of Study 9.6 (middle and lower river fish distribution) and water
quality sampling for these parameters as part of Study 5.5 (baseline water quality) and
Study 7.5 (groundwater). Therefore, AEA could conduct the evaluation by relying on the
extensive data collection already proposed in the RSP.
We recommend that AEA file with the Initial Study Report, a detailed evaluation of the
comparison of fish abundance measures (e.g., number of individuals by species and age
class) with specific microhabitat variable measurements where sampling overlaps, to
determine whether a relationship between a specific microhabitat variable and fish
abundance is evident. We expect the majority of locations where fish sampling and the
eight additional microhabitat variable sampling efforts would overlap at a scale where
they could be related would occur in focus areas where these sampling efforts are
concentrated. If results from these initial comparisons indicate strong relationships may
exist between a specific microhabitat parameter and fish abundance for a target species
and life stage, expanded sampling may be necessary in 2014 to investigate these
microhabitat relationships further. Accordingly, we recommend that AEA include in the
evaluation to be filed with the Initial Study Report, any proposals to develop HSC curves
for any of the 8 additional parameters as part of the 2014 study season.
This Technical Memorandum is in response to the FERC request, and first discusses the
relevancy of the eight microhabitat variables (surface flow and groundwater exchange fluxes,
dissolved oxygen (intergravel and surface water), macronutrients (i.e., nitrogen and phosphorus),
temperature (intergravel and surface water), pH, dissolved organic carbon, alkalinity, and
Chlorophyll-a) in the context of instream flow modeling, then presents the data that are available
for testing of the variables, and finally describes the subset of data that are synoptic with fish
presence or abundance measurements. In cases where statistical evaluation of the relationships
between biological data and these microhabitat variables is appropriate, these analyses are
provided. Finally, there is a summary discussion of the results and recommendations on whether
to include any of the variables in future HSC curve development.
1.1. Relevancy of the Microhabitat Variables to Instream Flow
Modeling
As background to this analysis, AEA lists the Susitna River licensing studies that are considering
the 18 microhabitat variables requested by NMFS (as noted by FERC in SPD excerpt above) in
Table 1.1-1.
The FERC noted in the SPD that AEA was already considering seven of the 18 parameters as
part of the ongoing IFS HSC/HSI data collection and modeling efforts. In addition, AEA notes
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that the IFS HSC analysis will also be evaluating surface water temperature and dissolved
oxygen concentrations. Intergravel temperature and DO are being studied as part of the IFS
Winter Studies for possible inclusion in the HSC and effective spawning habitat analyses. Thus,
the IFS analysis is actually incorporating nine of the 18 parameters.
The FERC noted that the River Productivity Study (Study 9.8) was addressing three other
variables (invertebrate drift density, benthic organic matter, and algal biomass). In addition,
AEA again notes that the River Productivity Study is also evaluating chlorophyll-a, which brings
the total number of variables being addressed by IFS and River Productivity up to 13.
Although the remaining five variables are not explicitly considered or integrated into HSC/HSI
functions within the IFS modeling framework, they are being evaluated and/or modeled as part
of other studies (Table 1.1-1). The results of these evaluations will be part of the overall effects
analysis on fish and aquatic habitats.
AEA agrees that the eight microhabitat variables listed by FERC for further consideration have
some biological relevance to fish, although some are more direct and pronounced than others
(e.g., pH versus chlorophyll-a). From an HSC/HSI and modeling perspective, one of the key
considerations is whether the microhabitat variables are directly flow dependent, and would or
could be modified due to Project operations such that the changes would have a direct influence
on the suitability of specific fish habitats. Only habitat variables meeting these criteria would
factor into the development of flow versus habitat relationships that are central to the IFS
modeling. The nine factors that AEA has incorporated directly into the IFS modeling are in fact
those that are flow dependent and that may have a direct influence on fish and fish habitats. The
remaining factors are those that would have more of an indirect effect and would not be expected
to impart an immediate response to a direct change in flow.
Nevertheless, AEA proceeded with an exploratory analysis of the eight variables specified in the
SPD. As stated by FERC, the overall objective of the analysis was to provide a comparison of
fish abundance measures with additional microhabitat variables where sampling efforts overlap
spatially and temporally. To achieve this, AEA applied appropriate statistical analysis to
determine whether “strong” direct relationships were evident between fish abundance and any of
the eight variables recommended by the FERC. Identification of such relationships may support
further sampling and evaluation, and possibly inclusion of one or more of the parameters within
the HSC/HSI model framework in 2015.
That one or more of the eight variables noted either singly or in combination with other variables
is related to fish abundance is to be expected. Most salmonids have a well-defined range of
parameter values within which they can function successfully. For many of the parameters,
certain threshold values have been defined, and these may be considered for inclusion as part of
the IFS modeling as either habitat indices or thresholds by which habitat quantities can be
adjusted.
1.2. Biological Relevance of the Eight Microhabitat Parameters
The suitability of habitat is defined as the degree to which habitat has the right characteristics to
support a target fish species during one or more life-history stages. Habitat suitability can be
affected directly, for example by changes in water depth or temperature, or influenced indirectly,
for example by changes in food availability due to changes in chlorophyll-a concentrations.
Ecological communities are shaped by a complex array of direct and indirect interactions or
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relationships. These interactions are spatially and temporally dynamic and can be challenging to
distinguish. In defining the relationship between fish abundance and the FERC variables, a
direct effect is defined as the direct impact of one individual variable on habitat selection that is
not influenced or mediated by another variable. An indirect effect is a general term referring to
an impact that can occur through several trophic levels or chains of interactions among species,
such as influences on food availability, predation or interference competition. It is much more
difficult to specify an indirect relationship between an environmental variable and habitat
selection or use, since that relationship is influenced by other unstable factors.
A brief review of each of the eight candidate variables and their potential influencing effect on
fish and fish habitat is provided below. This provides important context from which to formulate
hypotheses regarding Project operational effects on these variables and serves as a precursor to
the quantitative analysis that follows.
1.2.1. Surface Flow and Groundwater Exchange Fluxes
The exchange of groundwater and surface water is part of the natural process in most river
systems, and in the Susitna River it is quite pronounced, as evidenced by the number of
clearwater sloughs and side channels. The surface-groundwater exchange (i.e., upwelling and
downwelling) alters thermal and chemical regimes in aquatic habitats, which in turn affect
aquatic organisms (Soulsby et al. 2000, Ward and Tockner 2001; Malcolm et al. 2003). Within a
river system, the chemical characteristics of the source (either ground- or surface) can alter the
chemical characteristics in the recipient aquatic environment. Temperature regimes of ground-
and surface-sourced waters are also generally distinct. Groundwater is buffered from surficial
influences whereas surface water is often heavily influenced by annual climate regimes and daily
air temperatures. Therefore, thermal regimes in upwelling zones usually display less variability
in annual temperatures than those without upwelling.
Ambient water temperature affects physiological processes in fish (Carter 2005). The presence
of surface-groundwater exchange often provides cool (or warm depending on the season) water
refuge, influencing fish habitat use and distribution (Ebersole et al. 2001). Furthermore,
development and survival of intergravel eggs and embryos is dependent on intergravel thermal
and chemical regimes, which are controlled by ground-surface water exchange (Geist and
Dauble 1998, Baxter and Hauer 2000, Quinn 2005, Geist et al. 2006, Burril et al. 2010).
However, because groundwater chemical composition can vary significantly over space at a fine
scale, the presence of upwelling, by itself, does not always indicate that a site is appropriate for
incubation (Malcolm et al. 2009).
1.2.2. Dissolved Oxygen (Intergravel and Surface Water)
Dissolved oxygen (DO) is oxygen that is dissolved into water by diffusion from the surrounding
air, through hydrologic turbulence or as a waste product of photosynthesis by aquatic plants.
The gas-absorption capacity of water increases to saturation as water temperature decreases;
therefore, DO concentration can be limited by water temperature (Welch et al. 1998).
DO is essential to the survival of all aerobic aquatic organisms, and has a direct effect on habitat
suitability (Beschta et al. 1987), habitat use (Matthews and Berg 1997), physiology (Bjornn and
Reiser 1991), and survival (Heard 1991) of fishes and other aquatic organisms. Minimum DO
concentrations are required to satisfy metabolic needs of fish during all phases of the freshwater
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life cycle; therefore, fish abundance could be affected by extremely low levels of ambient DO.
For example, during the juvenile stage, salmonids may avoid habitats with low DO when
concentrations fell below 4.5 mg/L (Carter 2005). The State of Alaska recommends that water
column (surface) DO concentrations be maintained at a minimum of 7.0 mg/L, and that
intergravel (i.e., depth of 20 cm in the gravel) DO concentrations remain above 5.0 mg/L (DEC
2012).
Intergravel DO affects development of fish eggs and embryos. Reiser and Bjornn (1979)
reported that low DO concentrations during egg incubation may delay hatching, increase
anomalous development, premature hatching, and result in smaller size at emergence. Overall
survival of pre-emergent salmonids was significantly reduced when average intergravel DO
concentrations fell below approximately 8 milligrams per liter (Davis 1975). However,
development rate and growth performance of intergravel embryos does not necessarily relate to
proximate fish abundance during non-intergravel development stages.
1.2.3. Macronutrients (i.e., Nitrogen and Phosphorus)
Macronutrients, such as nitrogen (N) and phosphorus (P), are important in stream habitats
because both play a significant role in limiting the amount of photosynthesis and overall
productivity (Welch et al. 1998).
Most concern over macronutrient concentrations in streams relates to land use and development,
where large volumes of point-source nutrient enrichment from agricultural and stormwater
runoff can increase algal blooms, reduce fish habitat (Evans et al. 1996), and violate water
quality (WQ) standards. In stream environments less vulnerable to point-source enrichment,
concern over macronutrients often relates to nutrient cycling, the process during which
macronutrients, such as P and N, are assimilated into the aquatic environment and provide a
nutritional base for lower trophic organisms (e.g., periphyton and macroinvertebrates) (Chaloner
and Wipfli 2002).
The concentration of N and P at one location in a stream or river is unlikely to relate directly to
fish abundance at that same location, because N and P must first be assimilated into the food
chain by macroinvertebrates. Although fish will eventually be impacted, this assimilation
generally occurs along productivity gradients that vary broadly over space and time (Nakano and
Murakami 2001, Meyer et al. 2007).
1.2.4. Temperature (Surface Water and Intergravel)
Most fish are capable of inhabiting a broad range of water temperatures that naturally occur in
northern latitude river systems. Water temperature controls metabolic demands, waste costs, and
influences growth and development in freshwater fishes, so seemingly small incremental changes
in water temperature, both surface and intergravel, can have a significant effect on physiological
performance during each life stage (Hanson et al. 1997), and consequently could affect
distribution and abundance of fish communities (Lantz 1970, Power et al. 1988, Eliason et al.
2011). However, the detection of a surface water temperature effect on fish is sensitive to the
spatio-temporal scale at which the investigation is carried out (Jackson et al. 2000, Nakano and
Murakami 2001). Considering spatio-temporal constraints of sampling in the Susitna River
basin, presence or absence (not necessarily abundance) of thermally sensitive species (i.e., those
with a narrow range of ‘optimal’ temperatures) might be a more informative response variable to
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correlate with maximum or minimum temperature. Regardless, in the Susitna River basin, water
temperature could have significant influence on fish abundance because macrohabitat-associated
temperature regimes could vary widely.
Not only is surface water temperature important to fish, but intergravel water temperature as
well. Intergravel water temperature is a critical variable controlling the development and
survival of salmonid embryos and alevins (Crisp, 1988, 1990). Warm water temperatures play
an important role in providing potential sanctuary for incubating eggs from lower surface water
temperatures and freezing substrate as well as accelerating the developmental rate of embryos
and increasing egg-to-smolt survival (Wangaard and Burger, 1983). Like intergravel DO,
intergravel temperature does not necessarily relate to proximate fish abundance during non-
intergravel development stages.
1.2.5. pH
The pH (acidity) of water directly affects physiology of fish (Herrmann et al. 1993, Marschall
and Crowder 1996), and is measured on a scale between 1 and 14, with 1 being extremely acidic,
7 neutral, and 14 extremely basic. Generally, an acceptable range of pH for viability of aquatic
life, particularly fish, depends on numerous factors, including acid neutralizing capacity (i.e.,
alkalinity, pH accumulation, water temperature, and dissolved oxygen levels (Wagner et al.
1997). Salmonids generally prefer a pH between 6.5 (slightly acidic) and 9 (slightly basic)
(USEPA 1986, 1999). The State of Alaska has set a water quality standard for pH of 6.5-8.5 for
growth and propagation of fish (DEC 2012).
1.2.6. Dissolved Organic Carbon
Dissolved organic carbon (DOC) is a term used to describe organic matter that is broken down
finely (0.2-0.5µm) by microbes and other biotic and physical processes, and is then made
available for assimilation into higher levels of the trophic food web in streams, such as
macroinvertebrates (Suberkropp 1998). While DOC is critical to productivity in streams, effects
of DOC concentration on fish abundance is unlikely to be evident in the Susitna River because
DOC tends to be stored in upstream habitats, then transported to downstream habitats where it
can be assimilated into higher trophic organisms (Bisson and Bilby 1998). Thus, spatial and
temporal variability in microbial processes and DOC would presumably confound the ability to
detect relationships between DOC and fish abundance (McGuire et al. 2014).
1.2.7. Alkalinity
Alkalinity is the name given to the quantitative capacity of an aqueous solution to neutralize an
acid. Measuring alkalinity is important in determining a stream's ability to neutralize acidic
pollution from rainfall or wastewater. It is one of the best measures of the sensitivity of a
waterbody to acid inputs. In most stream-fish populations, alkalinity of stream water alone is not
known to have a significant, direct effect on fish. However, as noted above, elevated acidity of
water (pH) directly hinders reproduction, development, growth, and survival of fish (Herrmann
et al. 1993, Marschall and Crowder 1996). Therefore, fish abundance could be lower where
water is acidic and alkalinity is low. Though alkalinity has few if any direct impacts on fish of
the Susitna River, a weakly buffered system is predisposed to fluctuations in pH.
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1.2.8. Chlorophyll-a
Chlorophyll-a is a biomolecule critical to photosynthesis, and is often measured in streams to
estimate algal production (e.g., Hynes 1970). While a valuable indicator of overall water quality
and productivity, chlorophyll-a concentration is unlikely to predict abundance of most fish
species in the Susitna River basin. However, chlorophyll-a concentrations could correlate with
abundance of some omnivorous fishes (e.g., longnose sucker) that feed extensively on algae.
1.3. Data Sources
Fish habitat use and abundance data for the Susitna River have been collected as part of the IFS
HSC Study (Study 8.5) and Fish Distribution and Abundance (FDA) Study (Studies 9.5 and 9.6).
If synoptic data for named microhabitat parameters are not available from these studies, then
habitat data from other studies have been considered if they were collected within the same
macrohabitat unit, and within two weeks of (frequency of Focus Area [FA] water quality
sampling), relevant fish abundance data. These other sources were the Water Quality Study
(Study 5.5), the River Productivity Study (Study 9.8), the Groundwater Study (Study 7.5), and
the IFS Winter Study (Study 8.5).
As part of the Baseline Water Quality Study, there were two types of monitoring programs used
to characterize surface water conditions: Baseline Water Quality Monitoring and Focus Area
Monitoring. These programs were distinguished by the frequency of water quality sampling and
the density of sampling efforts in a localized area (AEA 2012; RSP Section 5.5.4.4). Although
similar water quality variables were collected during each monitoring effort, water quality data
collected under the baseline monitoring occurred monthly from June-September 2013 at
mainstem transects spaced at approximate 5-mile intervals. In contrast, the Focus Area
monitoring included off-channel habitats, and had a higher frequency (every two weeks) and
density of sampling locations. The locations were coordinated with the IFS study to provide
some overlap with HSC and FDA sampling efforts (AEA 2014, Study 8.5 Section 4.4.2).
The River Productivity Study (AEA 2014; Study 9.8) also collected some relevant microhabitat
parameters at point sampling locations in selected macrohabitat units in Focus Areas.
The IFS and Groundwater Winter Studies collected data on several named microhabitat variables
in the same macrohabitat units where the FDA Winter Study (AEA 2014; Study 9.6) sampled for
fish abundance during the 2013-2014 winter period. However, the number of sampling locations
and overlapping time periods did not result in enough samples to evaluate relationships.
A summary of the fish habitat use and abundance and water quality data considered for this
analysis are presented in Table 1.3-1 and discussed briefly below.
1.3.1. Fish Habitat Suitability Data
The IFS HSC Study was designed to compare habitats in the Susitna River according to their
suitability for target species and life stages of fish. Suitability criteria for in-stream flow studies
typically use depth, velocity, substrate and/or cover for comparing suitable habitat because these
variables can be directly impacted by dam operations and impacts can be predicted by well-
established models (Bovee and Milhous 1978, Milhous et al. 1984, Bovee 1986, Bovee et al.
1998). In the Susitna River system, groundwater upwelling is also expected to have an influence
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on the selection of habitat by fish, and may be impacted by Project operations. The HSC study is
in the process of developing HSC curves that consider water depth, velocity, substrate, cover,
and groundwater upwelling, as well as surface water temperature, dissolved oxygen
concentration, and conductivity. These variables were measured during 210 sampling events in
the Middle River below Devils Canyon in 2013 (AEA 2014; Study 8.5). HSC field sampling is
ongoing to resample some 2013 locations as well as additional locations in the Lower River and
in the Middle River above Devils Canyon. At each site, habitat measurements have been taken
where fish were observed and at random transect locations to represent unused habitats. These
data are being used to estimate the probability that fish will use habitat units as a function of the
measured habitat variables. Because the study was designed for this purpose, the HSC Study is
more relevant for studying fish habitat preference than other data collection efforts. Because it is
clear from the FERC recommendation that FERC agrees with this characterization, habitat data
collected as part of the HSC study will be considered primary. Therefore, if data are available
from the HSC Study for any of the FERC recommended microhabitat variable, no additional
analyses are made here.
1.3.2. Fish Abundance Data
The FDA Study was designed to estimate spatial distribution and relative abundance of juvenile
anadromous salmonids and non-salmonid anadromous and resident fishes of the entire Susitna
River and some tributaries. FDA surveys were seasonal events during the ice-free seasons, with
various sampling methods chosen based on target species, life stage, and water conditions.
Snorkeling and electrofishing were preferred methods for juvenile fishes in clearwater areas
where velocities were safe. Minnow traps, beach seines, set nets, and fyke nets were employed
as alternatives in deeper waters and in habitats with limited access, low visibility, or high
velocities. Fish counts from baited minnow traps and fyke nets were not used as part of analysis
because these capture methods may not reflect selected habitat, but instead represent migrating
fish or fish drawn to an artificial food source.
Although microhabitat parameters were not an integral part of the FDA Study, some water
quality data was collected synoptic with fish surveys. Because these data are synoptic in space
and time, the microhabitat data collected as part of the FDA sampling will be used as a
secondary source of data for this study. In other words, if data for a FERC requested
microhabitat variable was not available from HSC sampling, but was available from FDA
sampling, the FDA data was used for the comparisons in this report.
1.3.3. Surface Flow and Groundwater Exchange Fluxes
As described in the ISR (AEA 2014; Study 8.5), micro-piezometers have been used as part of the
HSC study to locate areas of upwelling and downwelling in sample reaches (i.e., vertical
hydraulic-head gradient [VHG]). The use of micro-piezometers during HSC surveys have been
effective in detecting points of upwelling and downwelling within sample reaches, and have
therefore been used to characterize upwelling in macrohabitat units. However, these data are not
appropriate for estimating quantitative exchange flux (i.e., volume of groundwater exchange),
which is highly spatially variable based on substrate and flow. Native surface-groundwater
exchange is being studied at selected sites in the Susitna River (Groundwater Study 7.5);
however, these evaluations are being recorded at a spatio-temporal scale that cannot currently be
compared to HSC or fish distribution and abundance data.
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Although the presence of groundwater upwelling and downwelling are being incorporated into
the HSC curve development process (AEA 2014, Study 8.5 Appendix M), there are no surface
flow and groundwater exchange flux data available and so no analysis of this variable has been
completed.
1.3.4. Dissolved Oxygen (Intergravel and Surface Water)
Surface water DO has been collected as part of the HSC Study, the FDA Study, and the Water
Quality Study. The HSC data are most relevant (synoptic with fish data), and are already being
analyzed as part of the HSC study (AEA 2014; Study 8.5 Appendix M). The preliminary results
of this ongoing analysis are summarized in this Technical Memorandum.
There were no inchannel, intergravel (i.e., approximately 20-cm deep within the gravel)
dissolved oxygen data collected during the open-water period in 2013. Both the Water Quality
Study and the Groundwater Study collected groundwater dissolved oxygen concentrations in
floodplain wells, but these data would not be relevant for intergravel concentrations in the river.
Intergravel dissolved oxygen concentrations were recorded as part of the IFS Winter study at two
sites during September 2013 – April 2014. Although one fish sampling event occurred in
proximity to each dissolved oxygen monitoring site, these data are inadequate for describing
potential correlations between intergravel dissolved oxygen and fish utilization. Consequently,
no evaluation of the relationship between intergravel DO and fish abundance has been
completed. Intergravel DO thresholds will be developed (literature based) and used as part of the
effective spawning habitat analysis. This analysis will be used to determine potential impacts of
Project operations during the egg incubation period when intergravel conditions (DO and
temperature) are most critical to young salmonids.
1.3.5. Macronutrients (i.e., Nitrogen and Phosphorus)
Macronutrient concentrations were collected in 2013 as part of the Baseline and Focus Area
Water Quality Characterizations in 2013 (AEA 2014; Study 5.5). However, review of 2013
water quality results analyzed by the laboratory indicated overestimates for Total Phosphorus
(TP) concentrations and for Total Kjeldahl Nitrogen (TKN) concentrations. High turbidity levels
in the river water have a tendency to interfere with detection of specific nutrient particles using
ICPMS (Inductively Coupled Plasma-Mass Spectrometry) instrumentation and are difficult to
distinguish from the target analytes (e.g., TP and TKN). The 2013 results for these water quality
parameters are being re-sampled in 2014 and a correction factor identified to enable use of the
2013 data. As such, no analysis of potential relationships between macronutrients and fish
abundance measures could be completed.
1.3.6. Temperature (Intergravel and Surface Water)
Surface water temperature has been measured as part of the HSC Study, the FDA Study, and the
Water Quality Study. The HSC data are most relevant as they were synoptic and are already
being analyzed as part of the HSC study; preliminary results of that analysis are discussed here.
Intergravel water temperature data have been collected in the Middle River in association with
Groundwater and IFS winter studies programs, most broadly during winter 2013-2014 and to a
lesser extent during open water 2014. Although approximately 30 intergravel temperature sites
were maintained during 2013-2014, only two sites are co-located with fish sampling sites and
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have data records concurrent with fish sampling. There were multiple sampling events at one of
the two sites such that a total of five samples from two macrohabitats could be compared. This
is insufficient replication to evaluate evidence of a relationship between intergravel temperature
and fish presence. The available data are further constrained to the winter time period when
relatively small fluctuations in temperature occur. Similar to intergravel DO, intergravel
temperature thresholds will be developed as part of the effective spawning habitat analysis.
1.3.7. pH
The HSC Study collected and recorded pH only sporadically, so these data were not considered
for the analysis in this report. Although the collection of pH was not part of the sampling
protocol for FDA sampling, the multiparameter water quality meters used for sampling
temperature and dissolved oxygen concentrations automatically recorded pH. Therefore, the
numerous surface water pH measurements included in the FDA database (Table 1.3-2) have been
used for this analysis.
1.3.8. Dissolved Organic Carbon
Dissolved organic carbon was collected during the Water Quality Study as part of the Baseline
and Focus Area Water Quality Characterizations in 2013 (AEA 2014; Study 5.5). Laboratory
analyzed data were not reported in the ISR, but QC3 data are now available and have been used
for this analysis. Fish samples collected by FDA within the same macrohabitat unit within 2
weeks of the water quality samples were used for the comparisons in this Technical
Memorandum (Table 1.3-2, see example in Figure 1.3-1).
1.3.9. Alkalinity
Alkalinity was collected by the Water Quality Study as part of the Baseline Water Quality
Characterization in 2013, but not for the Focus Area Water Quality Characterization. Fish
samples collected by FDA within the same macrohabitat unit within 2 weeks of the water quality
samples were used for the comparisons in this Technical Memorandum (Table 1.3-2, see
example in Figure 1.3-1).
1.3.10. Chlorophyll-a
Chlorophyll-a in the water column was analyzed as part of the Water Quality Study for the
Baseline and Focus Area Water Quality Characterizations in 2013. The River Productivity Study
collected composite benthic algae samples from rock substrate at each benthic invertebrate site
location sampled in the Susitna River in 2013 (AEA 2014, Study 9.8). The River Productivity
and Water Quality chlorophyll-a samples could not be combined because the samples were
collected from different sources (substrate particle – River Productivity Study; mid-water
column – Water Quality Study) so they are analyzed separately in this report. Fish samples
collected by FDA within the same macrohabitat unit within 2 weeks of the River Productivity or
Water Quality samples were used for the comparisons in this report (Table 1.3-2, see example in
Figure 1.3-1).
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2. METHODS
Although the data for microhabitat variables come from multiple sources, the fish data that are
being correlated to the microhabitat variables come from only two main sources – HSC and FDA
Studies. A description of the methods used to compare microhabitat variables to each fish data
source are presented below.
2.1. HSC Analysis – Surface Water Dissolved Oxygen and
Temperature
As mentioned by FERC in the SPD, the HSC study is collecting a suite of habitat metrics (water
velocity, depth, temperature, DO, conductivity, turbidity, substrate composition, cover, and
presence of groundwater) that are being evaluated for development of HSC which can be used to
relate Project operations to changes in fish habitat. These data are collected with the objective of
determining the relationship between the habitat variables and fish habitat preference. For each
of the HSC variables, analyses are being conducted using logistic regressions with random
effects for sites, which allow the overall probability of fish presence to vary by site after
accounting for measured habitat variables. Using availability and utilization data, the HSC
regressions predict the probability of fish presence as a function of a set of habitat variables,
which include two of the additional variables (surface water dissolved oxygen and temperature)
requested by FERC. These models were compared based on weight of evidence using Akaike’s
Information Criteria (AIC). A description of these analyses and preliminary results are described
in the ISR (AEA 2014; Study 8.5, Appendix M), and are not further described here.
2.2. FDA Analysis
Most of the analyses presented in this report involve comparisons between habitat data collected
by various studies and fish abundance data collected by the FDA study. Fish abundance data
collected at random sites in the Upper, Middle, and Lower Rivers using electrofishing, seining,
and snorkeling were used for these comparisons. Survey locations within tributary mouths and
tributaries above the mouth were included, but results with and without these samples outside of
the Susitna River were compared to ensure that tributary conditions were not biasing results.
Subsets of this main dataset were used where synoptic data were available for each microhabitat
parameter.
To increase the number of samples with observed fish and to avoid conflicting results for
multiple species, fish counts were summed by species/life stage groups for the analyses, as
follows:
1) Anadromous salmon fry (Chinook [Oncorhynchus. tshawytscha], chum [O. keta], coho
[O. kisutch], sockeye [O. nerka])
2) Anadromous salmon juvenile fish (Chinook, coho, sockeye)
3) Resident salmonids (juvenile or adult; round whitefish [Prosopium cylindraceum], Arctic
grayling [Thymallus arcticus], rainbow trout [Oncorhynchus mykiss], Dolly Varden
[Salvelinus malma])
4) Resident non-salmonids (juvenile or adult; burbot [Lota lota], longnose sucker
[Catostomus catostomus])
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Adult anadromous species were not included because they were not targeted by FDA sampling,
and some sampling methods (i.e., electrofishing) were interrupted when anadromous adults were
encountered.
Total abundance for each of the fish groups varies longitudinally in the Susitna River,
particularly for the anadromous salmon groups. Because anadromous fish were not observed
during FDA sampling using the selected gear types in the mainstem Susitna River above Devils
Canyon, these zero count results are not included in the analysis for the anadromous categories,
as they would likely bias the results. To account for other longitudinal differences not related to
water quality, a fixed effect describing longitudinal location in the river was included as a
candidate predictor in the models when possible.
The fish abundance data, like most count data, are highly skewed, with many zero counts (no
fish captured or observed) and few large counts. Simple linear correlation or regression
techniques are generally not appropriate for data which are not approximately normally
distributed. For regression, log-transformed counts can be modeled using Poisson distribution,
but when there are excessive zero counts these models can fit poorly due to overdispersion (extra
variance). For the FDA data comparisons, a set of nested Poisson regression models are fit and
compared using Akaike Information Criteria corrected for overdispersion and sample size
(QAICc; Burnham and Anderson, 2002). The model with lowest QAICc is considered the “best
fit” model. Models with QAICc greater than the null model (i.e., the model with no predictors)
are considered to have “no evidence” of predictive capability, models with QAICc lower than
but within 2 units of the null model to have “weak evidence” of predictive strength, and models
with QAICc more than 2 units better (lower) than the null model to have “evidence” of
predictive strength. Akaike weights (Burnham and Anderson, 2002) are then used to evaluate
the weight of evidence for the best fit model over the null model when relevant.
2.2.1. pH Collected for FDA Study
In 2013, there were 220 pH measurements taken by FDA crews in the Middle River, ranging
from pH = 6.1 to pH = 9.6, and 102 measurements taken in the Lower River, ranging from pH =
5.5 to pH = 12.7 (Table 1.3-2). A total of 34 of the MR and LR pH observations are in tributary
mouths or in tributaries just above the mouth, and may be less relevant for habitat preferences in
the Susitna River. Therefore, results with and without these tributary samples are compared.
For pH analysis, anadromous salmon fry and juveniles were combined because there were few
locations where pH was sampled and anadromous fish were found (35 positive fry counts with
corresponding pH and 19 positive juvenile counts).
There are 14 pH observations outside the general preferred range of pH for fish (6.5-9), with 4 of
these samples corresponding to positive fish observations. One of these samples had an
anadromous fry and juvenile count greater than 300 fish observed. This point was very
influential in model fitting and generally caused nonsensical pH – abundance modeled
relationships. In addition, there were three observations with pH > 10.5 that may be considered
extreme. Although there is no clear indication that these pH measurements were caused by
equipment error, individual data points should not be allowed to greatly change regression
coefficients. Results with and without these observations were compared to measure the
influence of these high pH points.
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A longitudinal fixed effect was included to account for spatial differences in fish abundance that
may not be due to microhabitat variables. For pH models, geomorphic reaches were used,
although some had to be combined because of low sample sizes. The factor levels used were:
UR (all reaches combined), MR-1 and 2, MR-5 and 6, MR-7, MR-8, and LR (all reaches
combined).
The following models with Poisson errors were considered and compared for each of the three
fish groupings using QAICc:
1) Null Model
Log(Abundance) = intercept,
where intercept = the theoretical fish abundance value when pH = 0. This is a flat line at
the mean abundance; pH has no influence.
2) Reach Model
Log(Abundance) = reach,
where reach = a fixed 6-level factor describing longitudinal location on the Susitna River.
This is the null model within each longitudinal location in the river; pH has no influence.
3) Preference pH models
Log(Abundance) = intercept + a*pH + b*pH2
Log(Abundance) = reach + a*pH + b*pH2,
where a and b are estimated regression coefficients.
As discussed previously, if fish were to select habitat based on pH, there would be higher fish
abundance in the mid-range of pH, and fewer fish in the extremes. This would be a quadratic
model that is convex down (b<0) with a maximum value between pH of 6 and pH of 9.
Observed relationships contrary to this hypothesized shape are assumed to be extraneous and
likely due to variables other than pH.
Each of the three models described above were fit to 1) the entire pH dataset, 2) the dataset
without the tributaries, and 3) with and without outliers for comparison.
2.2.2. Parameters Collected as Part of the Water Quality Study
There were only four transects from the Baseline Water Quality Study, and 12 transects and 7
point sample locations from the Focus Area Water Quality Study that overlapped with at least
one FDA site within a two week time period. Average water quality concentrations (i.e., average
of six points along transect) for main channel transects were used to compare to overlapping
main channel FDA sites. If the overlapping FDA site was a plume or tributary mouth sample on
one bank of the river, the transect point samples closest to the overlapping FDA site (i.e., a
subset of the points along the transect) were averaged. A total of 26 FDA locations were
matched with the Water Quality samples in seven macrohabitat types, but most were main
channel sites with low numbers of fish observed. In some cases there were two water quality
samples within two weeks of an FDA survey event; in this case the closest sampling date was
used. When there were two fish observations within two weeks of a single water quality
measurement, both fish observations were used. With multiple dates and some Water Quality
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sites that were matched with multiple FDA sites, there were a total of 67 data points for
comparison (Table 1.3-2, see example in Figure 1.3-1).
Out of 67 data points, 56 had 0 observations of anadromous fry, 52 had 0 observations of
anadromous juveniles, 35 had 0 observations of resident salmonids, and 36 had 0 observations of
resident non-salmonids. Anadromous fry and juveniles were combined for the comparisons
because of the low capture rates. For alkalinity, which was only sampled as part of the Baseline
Water Quality Study, there were only 19 matched data points for comparison.
Because there were only three paired samples above Devils Canyon, these samples cannot be
used for any longitudinal groupings. Below Devils Canyon, the longitudinal factor had two
levels: MR-5 and 6 versus MR-7 and 8. If there were no differences between these two groups,
then the MR-2 data were included (for non-anadromous fish groups only). For alkalinity, there
was no longitudinal factor because of the small sample sizes.
For each habitat variable, the following models with Poisson errors were considered and
compared for each of the fish abundance groups using QAICc:
1) Null Model
Log(Abundance) = intercept,
where intercept = the theoretical fish abundance value when the water quality parameter
= 0. This is a flat line at the mean abundance with no influence of the water quality
parameter.
2) Habitat Model
Log(Abundance) = reach,
where reach = a fixed factor describing longitudinal location on the Susitna River. This
is the null model within each reach with no influence of the water quality parameter.
3) Preference water quality models
Log(Abundance) = intercept + a*X
Log(Abundance) = reach + a*X,
where a is the estimated regression coefficient for X, the water quality parameter.
Dissolved organic carbon samples matched with FDA survey sites did not vary widely among
macrohabitats except for samples in MR-8 side sloughs (4 matched samples) that ranged from
1.5 to 10.5 mg/L, while the maximum of all other habitats was 3.7 mg/L (Figure 2.2-1). Results
with and without this high concentration value were compared.
The chlorophyll-a concentrations in the water column matched with FDA sites also did not vary
widely among macrohabitats, except for samples within backwaters (5 matched samples) that
ranged from 0 to 3.2 ug/L, while the maximum of all other habitats was 1.3 ug/L (Figure 2.2-2).
Results with and without this high concentration value were compared.
Alkalinity concentrations for 19 matched WQ-FDA samples ranged from 25 to 54 mg/L, with
most of the variability seen in MR-5 (Figure 2.2-3). Because of the small sample sizes,
longitudinal location in the river could not be included in the models for alkalinity.
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2.2.3. Benthic Chlorophyll-a Collected for River Productivity Study
The River Productivity Study collected composite benthic algae samples from wetted channel
substrate at each benthic invertebrate site location for a total of 309 composited algae samples in
the Susitna River in 2013 (AEA 2014, Study 9.8). There were seven River Productivity
sampling sites within three Focus Areas (FA-104 [Whiskers Slough], FA-173 [Stephan Lake
Complex], and FA-184 [Watana Dam]) that were sampled for benthic algae within two weeks of
an FDA survey and within the same macrohabitat. At each site there were two sampling times
that were concurrent with FDA samples. Most of these 14 sampling events could be closely
associated with multiple FDA samples taken from nearby mesohabitats within the same
macrohabitat unit. In total, there were 23 FDA samples matched with River Productivity benthic
chlorophyll-a estimates (Table 1.3-2, see example in Figure 1.3-1). The benthic chlorophyll-a
concentrations, taken from main channel, side channel, side slough, and tributary mouth habitats,
ranged from 0.042 to 66 mg/m2.
The River Productivity samples that could be matched with FDA samples were collected
between August 10 and September 27, 2013. There were no anadromous salmon fry, and a total
of 4 anadromous juvenile salmon captured at the matched sites during this time period.
Therefore, no comparisons for these fish groups were possible. There were a total of 94 resident
salmonids captured during 15 of the 23 matched sampling events, and a total of 24 resident non-
salmonids captured during 7 of the 23 matched sampling events.
The models shown for comparisons with Water Quality data in the previous section were also
used for the River Productivity benthic chlorophyll-a comparisons, with the following difference
for the reach (longitudinal fixed factor) variable.
The best candidate for a longitudinal fixed effect for the River Productivity chlorophyll-a models
would be comparing FA-104 (Whiskers Slough) to the Focus Areas above Devils Canyon (FA-
173 [Stephan Lake Complex] and FA-184 [Watana Dam]). However, there are no matched side
slough samples above Devils Canyon, and all FA-104 (Whiskers Slough) samples were from
side sloughs. Both the chlorophyll-a concentrations and the fish counts were highly variable
across macrohabitat types (Figure 2.2-4). Therefore it was most reasonable to combine the
longitudinal consideration with macrohabitat. A categorical factor with three groups was used:
1) side sloughs in FA-104 (Whiskers Slough); 2) side channels in FA-173 (Stephan Lake
Complex) and FA-184 (Watana Dam); and 3) main channel or tributary mouth in FA-173
(Stephan Lake Complex) and FA-184 (Watana Dam).
3. RESULTS
3.1. HSC Analysis
Preliminary HSC analysis results for chum salmon spawning and coho salmon fry are thoroughly
described in the Initial Study Report (AEA 2014, Study 8.5, Appendix M), and are only briefly
summarized here.
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3.1.1. Surface Water Dissolved Oxygen
In the initial analyses of 2013 HSC data, there was no evidence of a predictive relationship
between surface water DO and chum salmon spawning. For coho salmon fry, the data indicated
a negative relationship between dissolved oxygen and the presence of fish, which was unlikely
due to dissolved oxygen concentrations. Analysis for other target fish species/life stages will be
presented in the Updated Study Report (USR).
3.1.2. Surface Water Temperature
In the initial analyses of 2013 HSC data, there was no evidence of a predictive relationship
between water temperature and chum salmon spawning. However, there was strong evidence of
a predictive relationship between water temperature and coho fry presence. The fitted
relationship indicates that coho salmon fry prefer cooler water temperatures during the open
water period. This relationship and others will continue to be explored as part of the HSC
analysis in the USR.
3.2. FDA Analysis
3.2.1. pH collected for FDA Study
The pH model results for the three fish groups are summarized in Table 3.2-1. With the
influential point (single sample point with pH = 9.64 and 374 fish observed) removed, there is
evidence of a quadratic relationship between pH and log-transformed anadromous juvenile
counts. Anadromous fry and juvenile counts with the fitted pH preference models are displayed
in Figure 3.2-1. The pH preference models displayed in Figure 3.2-1 do not include the
influential point. Without this point, there is evidence that anadromous fry and juvenile
abundance is higher in locations with pH near 6.7 (6.5 when tributary sites are included). The
displayed model including a quadratic effect of pH on non-tributary samples has weight of
evidence more than 10 times the null model based on Akaike weights (Table 3.2-1). The
influential data point that was not included in this model contradicts this relationship – with this
data point included, the relationship dissolves completely. The remaining observations with pH
> 10 do not influence the model parameters or results.
For resident salmonids, there is strong evidence of a quadratic effect of pH, with preferred pH
near 7 (Figure 3.2-2). The observations with pH > 10 are plotted in red, but they do not
influence the model parameters or results. The model without tributary samples is nearly
identical to the model including tributary samples. The model including a quadratic effect of pH
on non-tributary samples has weight of evidence more than 20 times the null model based on
Akaike weights (Table 3.2-1).
For resident non-salmonids, there is no evidence of a relationship between pH and fish
abundance (Figure 3.2-3). The extreme observations with pH > 10 are plotted in red, but they do
not influence the model parameters or results. There are significant differences in abundances of
resident species among reaches, but adding pH to the model with a fixed reach effect did not
improve the model fit.
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3.2.2. Parameters collected for Water Quality Study
Model results for Water Quality parameters compared to abundance of species/life stage groups
are summarized in Table 3.2-2.
There is no evidence of a relationship between dissolved organic carbon and fish abundance. For
anadromous fry and juveniles (Figure 3.2-4), the null model has the lowest QAICc, both with
and without the samples with DO > 10 mg/L. There are differences among reaches for resident
salmonids but no relationship with DO (Figure 3.2-5), as the reach model has the lowest QAICc
both with and without the highest DO values included. The null model has the lowest QAICc for
resident non-salmonids, again indicating no relationship with DO (Figure 3.2-6).
For chlorophyll-a in the water column, the model fit to all data shows weak evidence of a
relationship with anadromous fry and juvenile fish (QAICc is 0.67 units less than QAICc for the
null model.) However, this relationship is driven by the extreme chlorophyll-a concentration,
and disappears when this value is excluded (i.e., the null model is the best fit; Figure 3.2-7). The
null model has the lowest QAICc for resident salmonids, indicating no evidence of a relationship
(Figure 3.2-8). However, there is a strong evidence (QAICc 3.6 – 9.9) of an increasing
relationship between chlorophyll-a in the water column and the presence of resident non-
salmonids (Figure 3.2-9). The chlorophyll-a model has weight of evidence 6 times that of the
null model based on Akaike weights. Without the single high chlorophyll-a concentration, the
weight of evidence ratio for the regression is increased to 142.
Although the alkalinity dataset is small (n=19), there is evidence of an increasing relationship
between alkalinity and counts of resident fish. For resident salmonids (Figures 3.2-10), the
weight of evidence for the alkalinity model is 7 times the weight of evidence for the null model.
For resident non-salmonids (Figure 3.2-11), the weight of evidence ratio is 8.
3.2.3. Benthic Chlorophyll-a Collected for River Productivity Study
Model results for benthic chlorophyll-a compared to abundance of species/life stage groups are
summarized in Table 3.2-3. Benthic chlorophyll-a concentrations have mixed results as a
predictor of fish abundance with little to no relationship for anadromous fry and juvenile and
resident salmonids, but strong evidence for a relationship for resident non-salmonids (Figure 3.2-
12 and Figure 3.2-13).
4. DISCUSSION AND RECOMMENDATIONS
Habitat suitability criteria curves and habitat suitability index (HSI) models have been utilized by
natural resources scientists for over two decades to assess the effects of habitat changes on biota.
HSC/HSI curves are designed to quantify changes in habitat under various flow regimes (Bovee
et al. 1998). HSC curves describe the instream suitability of habitat variables (typically depth,
velocity, substrate and cover) related to stream hydraulics and channel structure. HSC curves
can also be developed for other variables influenced by flow including water quality
(temperature, dissolved oxygen, turbidity, pH) and presence of groundwater upwelling or
downwelling. It is the goal of the HSC Study to develop the best predictive models possible for
habitat suitability of the target species and life stages (AEA 2014, Study 8.5). As previously
stated, water depth, velocity, groundwater upwelling/downwelling, substrate type, cover
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(including woody debris), turbidity, DO, temperature, and specific conductance are already
included in HSC curve development. The final HSC curves will be used with the hydraulic and
habitat models (AEA 2014, Study 8.5) to estimate the effects (habitat gain or loss) of alternative
operational scenarios at the Focus Area scale.
At the request of FERC, a detailed evaluation of fish abundance measures and eight additional
habitat variables (surface flow and groundwater exchange flux, surface and intergravel DO and
temperature, macronutrients, pH, DOC, alkalinity, and chlorophyll-a) was completed to
determine whether relationships were evident and if additional HSC curve development was
warranted.
There are three crucial requirements to be met for habitat variables to be included in HSC
development. The first is that there is a predictive and direct relationship between the habitat
variable and fish presence; second, that changes to the habitat variable as a function of flow can
be spatially and quantitatively predicted at the Focus Area scale; and third, that predicted
changes in the variable are observable at a temporal scale (hours to days) similar to changes in
flow conditions in response to Project operations. If any of these criteria cannot be met, then
AEA recommends that the individual variable not be included as part of site-specific HSC curve
development.
Of the eight variables requested by FERC for further investigation, three (VHG as a surrogate for
surface and groundwater exchange flux, surface water DO and temperature) will continue to be
collected in ongoing HSC sampling events, and analyzed as part of the HSC suitability curve
development process in the USR. Intergravel DO and temperature will also continue to be
collected, but this data will be used to develop threshold (highs and lows) that can be applied as
part of the effective spawning habitat analysis. Although not specifically requested by FERC,
specific conductance will continue to be collected and included as part HSC curve development.
For four of the remaining five variables (pH, DOC, alkalinity, and chlorophyll-a), statistical
analysis has been completed to estimate the probability that these variables are “strong”
predictors of habitat use by the target species and life stages. The remaining variable,
macronutrients, had no data from 2013 that could be used to compare to fish abundance
measures. A description of the predictive value of each of these five variables is presented
below along with a recommendation regarding inclusion in future HSC development activities
(Table 4.1-1).
4.1. Macronutrients
Review of surface water quality samples collected in middle Susitna River Focus Areas in 2013
indicated that the concentrations of Total Phosphorus and Total Nitrogen were overestimated and
will need to be re-sampled in 2014. As such, no analysis of potential relationships between
macronutrients and fish abundance measures could be completed as part of this effort. Although
no site-specific macronutrient data was available, it is widely believed that the concentration of
N and P does not relate directly to fish abundance because it must first be assimilated into the
food web before utilized by fish (Nakano and Murakami 2001, Meyer et al. 2007). Furthermore,
the rate of P and N assimilation varies over space and time making it unrealistic to believe that
the water quality model can predict changes to total N and P concentrations within all
macrohabitat types of a Focus Area on an hourly or daily time-step in response to changes in
Project operations. Considering these facts, it is AEA’s recommendation that macronutrients are
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not added as a variable to predict fish habitat use as part of the HSC curve development process,
and that no additional data collection efforts are required.
4.2. pH
The pH of water can directly affect not only the habitat selection of fish but fish health as well.
The degree to which pH affects fish depends on numerous factors, including acid neutralizing
capacity (i.e., alkalinity, prior pH accumulation, water temperature, and dissolved oxygen levels
(Wagner et al. 1997). Although pH was not collected as part of the HSC surveys, it was largely
collected as part of FDA surveys (AEA 2014, Study 9.5 and 9.6). Results of this assessment
show no clear evidence of a relationship between pH and abundance of resident, non-salmonid
fish in the Susitna River. However, there is strong evidence that salmonids (resident and
anadromous fry and juvenile) are found most commonly in areas with pH near 7 in the Middle
and Lower River segments of the Susitna River. This result is not surprising given that
salmonids generally prefer a pH between 6.5 and 9 (USEPA 1999).
It is anticipated that Focus Area water quality modeling will estimate pH levels throughout the
Susitna River resulting from different flow release scenarios (AEA 2014, Study 5.5). The data
used in this Technical Memorandum show that 90-100% of salmonids are selecting habitats in
the range of 6.2-8.7, which is very similar to the USEPA determined preference range (USEPA
1999). Therefore, AEA is recommends that a pH range of 6.5-9 is used as a threshold by which
to evaluate the loss or gain in habitat area. Utilizing threshold values for pH would satisfy the
request by the agencies that pH be considered for suitable habitat, without requiring additional
data collection or modeling to develop predictive fish-habitat relationships.
4.3. Dissolved Organic Carbon
There is no evidence that DOC can be used as a predictor of fish abundance or habitat use in the
Susitna River. Dissolved organic carbon was collected during the Water Quality Study as part of
both the Baseline and Focus Area Water Quality Characterizations in 2013 (Study 5.5). Levels
of DOC can show considerable spatial and temporal variability depending on sample location
and assimilation into the trophic food web. A more meaningful indicator of the influence of
DOC on fish abundance might be macroinvertebrate productivity (relative abundance) and
species richness (AEA 2014, Study 9.8). As such, AEA recommends DOC not be added as a
variable to predict fish habitat use as part of the HSC curve development process.
4.4. Alkalinity
Alkalinity samples were not collected within middle Susitna River Focus Areas during the 2013
Water Quality Characterization Study (ISR Study 5.5). As a result, there were only 19 samples
(where FDA and alkalinity sampling overlapped) from which to evaluate a relationship between
alkalinity and fish abundance. Although in most stream-fish populations, alkalinity of stream
water alone is not known to have a significant, direct effect on fish, results of the statistical
analysis did show a weak relationship between alkalinity levels and both resident and non-
resident salmonids abundance. Since alkalinity levels are not being collected or modeled on a
Focus Area scale and the generally weak relationship between alkalinity and fish abundance,
AEA recommends alkalinity not be added as a variable to predict fish habitat use as part of the
HSC curve development process.
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4.5. Chlorophyll-a
In 2013, chlorophyll-a samples were collected by both the Water Quality Study (5.5) and River
Productivity Study (9.8). Unfortunately, the samples were collected from two different sources
(mid-water column and river substrate) and could not be combined as part of this analysis.
Similar to DOC, chlorophyll-a levels are generally not considered a direct indicator of fish
abundance (particularly for salmonids) or habitat use, but rather an indicator of overall water
quality and productivity. That said, statistical modeling did show a strong relationship between
chlorophyll-a levels and resident, non-salmonid fish species. This result is not entirely surprising
since most of the non-salmonid species that are included in this group consume algae.
Chlorophyll-a data are being collected as part of the River Productivity Study (AEA 2014, Study
9.8) to evaluate and model benthic macroinvertebrates and algal communities. Since both
macroinvertebrates and algae are direct food sources for several of the target fish species and life
stages, it is AEA’s recommendation to use the HSC curves developed from the River
Productivity Study for benthic macroinvertebrates and algae. To reduce duplication of effort, it
is AEA’s recommendation to not add chlorophyll-a in development of HSC curves for the IFS
Study.
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Milhous, R.T., D.L. Wegner, and T. Waddle. 1984. Users guide to the physical habitat
simulation system (PHABSIM). Instream Flow Information Paper No. 11. Washington,
DC: U.S. Fish and Wildlife Service (FWS/OBS-81/43). Revised.
Nakano, S. and M. Murakami. 2001. Reciprocal subsidies: dynamic interdependence between
terrestrial and aquatic food webs. Proceedings of the National Academy of Science
98:166-170.
Power, M.E., R.J. Stout, C.E. Cushing, P.P. Harper, F.R. Hauer, W.J. Matthews, P.B. Moyle, B.
Statzner, and I.R. Wais De Badgen. 1988. Biotic and abiotic controls in river and stream
communities. Journal of the North American Benthological Society 7:456-479.
Quinn, T.P. 2005. The Behavior and Ecology of Pacific Salmon and Trout. University of
Washington Press, Seattle, Washington. 378 pp.
Soulsby C., I. Malcolm, J. Petry, and A. Youngson. 2000. Groundwater-surface water
interactions in the hyporheic zone of headwater streams: assessing the influence on
salmonid habitats. Conference Proceedings, British Hydrological Society, Seventh
National Hydrology Symposium, the University of Newcastle Upon Tyne 4-6 September,
2000, 2.1-2.8.
Suberkropp, K.F. 1998. Microorganisms and matter decomposition. Pages 120-143 in Naiman,
R.J., and R.E. Bilby, eds., River ecology and management: lessons from the Pacific
coastal ecoregion. Springer Verlag.
USEPA (U.S. Environmental Protection Agency). 1986. Quality criteria for water 1986. Office
of Water Regulations and Standards. Washington, D.C. EPA/440/5-86-001.
USEPA (U.S. Environmental Protection Agency). 1999. National Recommended Water Quality
Criteria – Correction. Office of Water. EPA 822-Z-99-001. April 1999.
Susitna-Watana Hydroelectric Project Alaska Energy Authority
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EVALUATION OF RELATIONSHIPS BETWEEN FISH ABUNDANCE
TECHNICAL MEMORANDUM AND SPECIFIC MICROHABITAT VARIABLES
Wangaard, D.B., and C.V. Burger. 1983. The effects of various water temperature regimes on
the egg and alevin incubation of Susitna River chum and sockeye salmon. United States
Fish and Wildlife Service, National Fishery Research Center, Alaska Field Station,
Anchorage, Alaska.
Wagner, E.J., T. Bosakowski, and S. Intelmann. 1997. Combined effects of temperature and
high pH on mortality and the stress response of rainbow trout after stocking.
Transactions of the American Fisheries Society 126:985-998.
Ward, J.V., and K. Tockner. 2001. Biodiversity: towards a unifying theme for river ecology.
Freshwater Biology 46:807-819.
Welch, E.B., J.M. Jacoby, and C.W. May. 1998. Stream quality. Pages 69-94 in R.J. Naiman
and R.E. Bilby, eds., River ecology and management: lessons from the Pacific coastal
ecoregion. Springer Verlag.
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6. TABLES
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Table 1.1-1. Microhabitat variables requested for inclusion by NMFS in the FERC SPD (FERC 2013), noting
Susitna River studies considering these variables.
NMFS Requested Microhabitat
Variable
Variable Currently Included and/or Considered in IFS
Modeling
Variable Included, Considered or Modeled
by Other Resource Study
Water depth Yes Fluvial Geomorphology (Study 6.6)
Water velocity Yes Water Quality (Study 5.5)
Surface flow and groundwater flux Groundwater Study (Study 7.5)
Upwelling/downwelling (via VHG) Yes Groundwater Study (Study 7.5)
Substrate type Yes Fluvial Geomorphology (Study 6.6)
Cover Yes Geomorphology (LWD; Study 6.5)
Woody Debris Yes Geomorphology (Study 6.5)
Turbidity Yes River Productivity Study (TSS; Study 9.8);
Water Quality Modeling (Study 5.6)
Dissolved Oxygen (intergravel and
surface)
Yes
Water Quality Modeling (Study 5.6)
Groundwater Study (Study 7.5)
Effective Spawning Analysis (Study 8.5)
Macronutrients (N,P) Water Quality Modeling (Study 5.6)
Temperature (intergravel and surface)
Yes
Water Quality Modeling (surface; Study 5.6)
Groundwater Study (Study 7.5)
Effective Spawning Analysis (Study 8.5)
pH Water Quality (Study 5.5)
Dissolved Organic Carbon (DOC) Water Quality (Study 5.5)
Alkalinity Water Quality (Study 5.5)
Invertebrate drift density River Productivity Study (Study 9.8)
Benthic Organic Matter River Productivity Study (Study 9.8)
Algal biomass River Productivity Study (Study 9.8); Water
Quality Modeling (Study 5.6)
Chlorophyll-a River Productivity Study (Study 9.8); Water
Quality Modeling (Study 5.6)
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Table 1.3-1. Potential data sources for evaluation of relationships between microhabitat variables and fish
abundance measures.
Study
Named Variable(s)
Measured Collection Area
Collection
Period
Sample
Frequency
Sampling
Intensity
Water Quality
Monitoring –
Baseline (5.5)
Surface Water
Temperature, DO,
pH, Alkalinity,
Total Phosphorus,
Total Nitrogen,
Chlorophyll-a,
Dissolved Organic
Carbon
Mainstem from
PRM 29.9-235.2
June-September,
2013 Monthly Approximately
every 5-miles
Water Quality
Monitoring –
Focus Area (5.5)
Surface Water
Temperature, DO,
pH, Total
Phosphorus, Total
Nitrogen,
Chlorophyll-a,
Dissolved Organic
Carbon
FA-104, 113,
115, 128, 138,
141, and 144
Late July-Early
September, 2013 Every 2-weeks 3-4 stations per FA
Groundwater
Study (7.5)
Surface flow and
groundwater
exchange fluxes?
FA-104, FA-113,
FA-115, FA-128,
FA-138
August -
December, 2013 Continuous 1-7 stations per FA
Instream Flow
Study – HSC (8.5)
Surface Water
Temperature, DO
FA-104, 113,
115, 128, 138,
141, and 144
June-September,
2013 Every 2-weeks 6-12 sample sites
per FA
Instream Flow
Study – Winter
(8.5)
Surface Water
Temperature, DO FA-104 & 128 February-April,
2013 Monthly 6-12 sample sites
per FA
Groundwater
Study – Winter
(7.5)
Water
Temperature, DO FA-104 & 128 March & April,
2013 Continuous
1-intergravel D.O.
site per FA, 9-
intergravel temp.
sites in FA 104
Fish Distribution
and Abundance
(9.5 & 9.6)
Surface Water
Temperature, DO,
pH
Entire Susitna
River
July-October,
2013
Three seasonal
events Varies
River Productivity
Study (9.8)
Chlorophyll-a
(benthic)
4 Focus Areas, 1
LR Site
June-September,
2013
Three seasonal
events
3-5 Stations per
Site
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Table 1.3-2. Summary of relevant study sources and data used for the analyses of FERC recommended
variables.
Variable(s) Analyzed Most Relevant Study/Source Total Number of Samples Available Number of Samples Matched to Fish Data
Surface flow and
groundwater exchange
fluxes
Groundwater Study (7.5)
Temporally Continuous Data
at Limited Number of
Stations; Not designed to
measure flux
n/a
Dissolved Oxygen (Surface) Instream Flow Study – HSC
(8.5)
Chum Spawning (n=960);
Coho Fry (n=669)
Chum Spawning (n=960);
Coho Fry (n=669)
Dissolved Oxygen
(Intergravel)
Instream Flow Study –
Winter (8.5) Multiple samples at two sites 2 total (not used)
Macronutrients: Total
Phosphorus, Total Nitrogen
Water Quality Monitoring –
Baseline and Focus Area
(5.5)
Samples rejected for Quality
Control issues n/a
Water Temperature
(Surface)
Instream Flow Study – HSC
(8.5)
Chum Spawning (n=992);
Coho Fry (n=833)
Chum Spawning (n=992);
Coho Fry (n=833)
Water Temperature
(Intergravel)
Instream Flow Study –
Winter (8.5) and
Groundwater Study – Winter
(7.5)
Multiple samples at 30
stations 5 total (not used)
pH Fish Distribution and
Abundance (9.5 & 9.6) 322 322
Dissolved Organic Carbon
Water Quality Monitoring –
Baseline and Focus Areas
(5.5)
Multiple samples at 45 sites 67
Alkalinity Water Quality Monitoring –
Baseline (5.5) Multiple samples at 18 sites 19
Chlorophyll-a (Water)
Water Quality Monitoring –
Baseline and Focus Areas
(5.5)
Multiple samples at 45 sites 67
Chlorophyll-a (benthic) River Productivity Study (9.8) Multiple samples at 25 sites 23
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Table 3.2-1. Summary of pH model results
Species/Lifestage Dataset Best Fit Model
Delta (Null or Reach Model – Best Fit Model)
Ratio of Akaike Weights (Best Fit/Null or Reach Model)
Anadromous Fry +
Juv
All Samples Null 0 1
Remove Influential Point Reach and Quadratic Effect of
pH 21 33000
pH< 9.5 Reach and Quadratic Effect of
pH 20 27000
No Trib Samples Null 0 1
No Trib Samples; Remove
Influential Point Quadratic Effect of pH 5 11
No Trib Samples; pH<9.5 Quadratic Effect of pH 5 9.8
Resident
Salmonids
All Samples Reach and Quadratic Effect of
pH 10 150
pH< 10 Reach and Quadratic Effect of
pH 9.5 110
No Trib Samples Reach and Quadratic Effect of
pH 6.2 22
No Trib Samples; pH<10 Reach and Quadratic Effect of
pH 5.9 19
Resident Non-
Salmonids
All Samples Reach 0 n/a
pH< 10 Reach 0 n/a
No Trib Samples Reach 0 n/a
No Trib Samples; pH<10 Reach 0 n/a
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Table 3.2-2. Summary of Water Quality model results
Parameter Species/Lifestage Dataset Best Fit Model
Delta (Null or Reach Model – Best Fit Model)
Ratio of Akaike Weights (Best Fit/Null or Reach Model)
DOC
Anadromous
Fry+Juv
All data Null 0 n/a
without high DOC
value Null 0 n/a
Resident Salmonids
All data Reach Model 0 n/a
without high DOC
value Reach Model 0 n/a
Resident Non-
Salmonids
All data Null 0 n/a
without high DOC
value Null 0 n/a
Chlorophyll-a
Anadromous
Fry+Juv
All data Chlorophyll-a 0.67 1.4
without high
chlorophyll value Null 0 n/a
Resident Salmonids
All data Reach Model 0 n/a
without high
chlorophyll value Reach Model 0 n/a
Resident Non-
Salmonids
All data Chlorophyll-a 3.6 6.2
without high
chlorophyll value Chlorophyll-a 9.9 142
Alkalinity
Resident Salmonids All data Alkalinity 3.9 7
Resident Non-
Salmonids All data Alkalinity 4.2 8.1
Table 3.2-3. Summary of benthic chlorophyll-a model results.
Species/Lifestage Dataset Best Fit Model
Delta (Null or Reach Model –
Best Fit Model)
Ratio of Akaike Weights (Best Fit/Null or
Reach Model)
Resident
Salmonids All Samples Habitat and Linear Effect of
Benthic Chlorophyll-a 33 1.7E+07
Resident Non-
Salmonids All Samples Habitat and Linear Effect of
Benthic Chlorophyll-a 21 3.2E+04
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Table 4.1-1. Evaluation of FERC requested variables and recommendations for inclusion in future HSC
curve development.
Variable
Relationship with Fish Abundance
Measures (Strong, Weak, None)
Direct Link to Fish Habitat Use
Modeled at Focus Area Scale
Recommended for Future HSC Analysis
Macronutrients: Total Phosphorus, Total Nitrogen Insufficient Data Unknown No No
pH Strong Yes Yes Yes
Dissolved Organic Carbon None No Yes No
Alkalinity Weak No No No
Chlorophyll-a Strong No Yes No
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7. FIGURES
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Figure 1.3-1. Map of FA-104 (Whiskers Slough) showing example of fish abundance and water quality samples from multiple sources that were
combined to compare fish abundance with habitat variables.
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MC MC CWP MC SC SSB US BW TM CWP MC SC BW MC SC SS0246810
HabitatDissolved Organic Carbon MR-2 MR-5 MR-6 MR-7 MR-8
Figure 2.2-1. Box plots of dissolved organic carbon concentrations for FDA-matched Water Quality samples
by Geomorphic Reach and macrohabitat type.
Notes:
MC = main channel, CWP = clearwater plume, SC = side channel, SSB = side slough beaver, US = upland slough,
BW = backwater, TM = tributary mouth, SS = side slough.
MC MC CWP MC SC SSB US BW TM CWP MC SC BW MC SC SS0.00.51.01.52.02.53.0HabitatChlorophyll-a (ug/L)MR-2 MR-5 MR-6 MR-7 MR-8
Figure 2.2-2. Box plots of chlorophyll-a concentrations for FDA-matched Water Quality samples by
Geomorphic Reach and macrohabitat type.
Notes:
MC = main channel, CWP = clearwater plume, SC = side channel, SSB = side slough beaver, US = upland slough,
BW = backwater, TM = tributary mouth, SS = side slough.
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MC MC CWP MC SC SSB US BW TM CWP01020304050
HabitatAlkalinity (mg/L)MR-2 MR-5 MR-6
Figure 2.2-3. Box plots of alkalinity for FDA-matched Water Quality samples by Geomorphic Reach and
macrohabitat type.
Notes:
MC = main channel, CWP = clearwater plume, SC = side channel, SSB = side slough beaver, US = upland slough,
BW = backwater, TM = tributary mouth, SS = side slough.
MC SC SS TM0102030405060
MacroHabitatChlorophyll-a (mg/m2)
Figure 2.2-4. Box plot of River Productivity benthic chlorophyll-a samples by macrohabitat type.
Notes:
MC = main channel, CWP = clearwater plume, SC = side channel, SSB = side slough beaver, US = upland slough,
BW = backwater, TM = tributary mouth, SS = side slough.
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6 8 10 120123456
pHLN (Count+1) Anadromous Fry + Juvex
Best Model Without Trib
Best Model All Data
Figure 3.2-1. Scatterplot with fitted models showing relationship between pH and log-transformed (+1)
counts for anadromous fry and juveniles.
Symbol “x” is over point that is removed from analysis for undue influence. Points displayed with red circles do not
influence fitted parameters.
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6 8 10 1201234
pHLN (Count+1) Resident Salmonids
Figure 3.2-2. Scatterplot with fitted model showing relationship between pH and log-transformed (+1) counts
for resident salmonids.
6 8 10 1201234
pHLN (Count+1) Resident Non-salmon
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Figure 3.2-3. Scatterplot showing relationship between pH and log-transformed (+1) counts for resident non-
salmonid fish.
0 2 4 6 8 1001234
Dissolved Organic Carbon (mg/L)LN(Count+1) Anadromou
Figure 3.2-4. Scatterplot with anadromous juvenile salmonid counts plotted against dissolved organic carbon
from WQ sampling.
0 2 4 6 8 1001234
Dissolved Organic Carbon (mg/L)LN(Count+1) Resident S
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Figure 3.2-5. Scatterplot with resident salmonid counts plotted against dissolved organic carbon from WQ
sampling.
0 2 4 6 8 100.00.51.01.52.0Dissolved Organic Carbon (mg/L)LN(Count+1) Resident N
Figure 3.2-6. Scatterplot with resident non-salmonid counts plotted against dissolved organic carbon from
WQ sampling.
0.0 0.5 1.0 1.5 2.0 2.5 3.001234
Chlorophyll-a (ug/L)LN(Count+1) Anadromo
Figure 3.2-7. Scatterplot with anadromous juvenile salmonid counts plotted against chlorophyll-a from WQ
sampling.
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0.0 0.5 1.0 1.5 2.0 2.5 3.001234
Chlorophyll-a (ug/L)LN(Count+1) Resident S
Figure 3.2-8. Scatterplot with resident salmonid counts plotted against chlorophyll-a from WQ sampling.
0.0 0.5 1.0 1.5 2.0 2.5 3.00.00.51.01.52.0Chlorophyll-a (ug/L)LN(Count+1) Resident Nonxx
Without High Chlorophyll-a Value
All Data Points
Figure 3.2-9. Scatterplot showing relationship between water column chlorophyll-a concentrations and log-
transformed counts of resident non-salmonid fish.
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25 30 35 40 45 5001234
Alkalinity (mg/L)LN(Count+1) Resident Salm
Figure 3.2-10. Scatterplot with resident salmonid counts plotted against alkalinity from WQ sampling with
fitted model.
25 30 35 40 45 500.00.51.01.52.0Alkalinity (mg/L)LN(Count+1) Resident Non-
Figure 3.2-11. Scatterplot with resident non-salmonid counts plotted against alkalinity from WQ sampling
with fitted model.
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0 10 20 30 40 50 600.00.51.01.52.02.53.03.5Chlorophyll-a (mg/m2)LN (Count+1) Resident SalmonidsMC Above Devils Canyon
SC Above Devils Canyon
SS Below Devils Canyon
Figure 3.2-12. Scatterplot and fit models showing relationship between benthic chlorophyll-a and resident
salmonid counts in three Focus Areas.
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0 10 20 30 40 50 600.00.51.01.52.02.5Chlorophyll-a (mg/m2)LN (Count+1) Resident Non-salmoniMC Above Devils Canyon
SC Above Devils Canyon
SS Below Devils Canyon
Figure 3.2-13. Scatterplot and fit models showing relationship between benthic chlorophyll-a and resident
non-salmonid counts in three Focus Areas.
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