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Consequences of Didymo blooms in the transnational Kootenay River basin

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Abstract and Figures

Stream habitat changes that affect primary consumers often indirectly impact secondary consumers such as fishes. Blooms of the benthic algae Didymosphenia geminata (Didymo) represent one such habitat change known to affect stream macroinvertebrates. However, the potential indirect trophic impacts on fish consumers via modifications to their diet are poorly understood. The overall goal of this project was to determine if Didymo blooms in streams of the Kootenay River basin of British Columbia and Montana affect the condition and growth of fishes, and to see whether trophic mechanisms were responsible for any observed changes. We therefore quantified the diet, condition, and growth rate of trout, charr, and sculpin in a paired, Didymo vs. reference study, during the summer of 2018 and across a gradient of Didymo abundance in 2019. In the 2018 study, trout diets were 81% similar despite obvious differences in the composition of macroinvertebrate assemblages between the Didymo and reference streams. Trout abundance was higher in the stream with Didymo, but the amount of invertebrates in the drift was higher in the stream without Didymo. Growth rate and energy demand by individual trout was similar between the two streams. In the 2019 study, across a gradient of coverage, Didymo abundance was correlated only with the percent of aquatic invertebrates in trout diets and did not affect diets of charr or sculpin. Variation in fish condition was low across study streams. Thus, Didymo blooms may impact trout diets to a small extent, but we found no evidence this impact translates to changes in condition or growth. The relationship of fish abundance to Didymo blooms bears further study, but we found no obvious trophic mechanisms that would explain any differences. We suggest future studies prioritize research on potential impacts during winter months and on species with limited mobility that may be most greatly impacted by Didymo.
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Consequences of Didymo Blooms in the
transnational Kootenay River basin
Niall G. Clancy, Janice Brahney, Jeff Curtis, and Phaedra Budy
February 2020
1
Clancy, N. G., J. Brahney, J. Curtis, & P. Budy. 2020. Consequences of Didymo
blooms in the transnational Kootenay River basin. Report to BC Parks
from the Department of Watershed Sciences at Utah State University,
Logan, UT.
Abstract
Stream habitat changes that affect primary consumers often indirectly impact secondary
consumers such as fishes. Blooms of the benthic algae Didymosphenia geminata (Didymo)
represent one such habitat change known to affect stream macroinvertebrates. However, the
potential indirect trophic impacts on fish consumers via modifications to their diet are poorly
understood. The overall goal of this project was to determine if Didymo blooms in streams of the
Kootenay River basin of British Columbia and Montana affect the condition and growth of fishes,
and to see whether trophic mechanisms were responsible for any observed changes. We
therefore quantified the diet, condition, and growth rate of trout, charr, and sculpin in a paired,
Didymo vs. reference study, during the summer of 2018 and across a gradient of Didymo
abundance in 2019. In the 2018 study, trout diets were 81% similar despite obvious differences
in the composition of macroinvertebrate assemblages between the Didymo and reference
streams. Trout abundance was higher in the stream with Didymo, but the amount of invertebrates
in the drift was higher in the stream without Didymo. Growth rate and energy demand by individual
trout was similar between the two streams. In the 2019 study, across a gradient of coverage,
Didymo abundance was correlated only with the percent of aquatic invertebrates in trout diets and
did not affect diets of charr or sculpin. Variation in fish condition was low across study streams.
Thus, Didymo blooms may impact trout diets to a small extent, but we found no evidence this
impact translates to changes in condition or growth. The relationship of fish abundance to Didymo
blooms bears further study, but we found no obvious trophic mechanisms that would explain any
differences. We suggest future studies prioritize research on potential impacts during winter
months and on species with limited mobility that may be most greatly impacted by Didymo.
Acknowledgements
We would like to thank Jim Dunnigan and Chuck Hawkins for reviews of this manuscript.
Jon McFarland, Ryan West, Chris Clancy, and Marshall Wolf provided valuable assistance and
companionship in the field. Thank you to Jay DeShazer, Ryan Sylvester, Jared Lampton, Jordan
Frye, Brian Stephens, Monty Benner, and Mike Hensler for help at the Libby Field Office and to
Jeff Burrows, Greg Andrusak, Joe Thorley, and Murray Pearson in British Columbia. Gary Thiede
at Utah State, and Brett Roper and Mike Young of the US Forest Service, provided additional
support. The National Aquatic Monitoring Center in Logan, UT provided generous help with
identification of fish diet samples. This work was supported by the US Forest Service; Montana
Fish, Wildlife & Parks; British Columbia Ministry of the Environment; and the USU School of
Graduate Studies, Department of Watershed Sciences, and Ecology Center.
2
Introduction
Fish growth and production in coldwater systems is highly dependent on both
allochthonous and autochthonous sources of energy (Horton 1961; Huryn 1996; Bellmore et al.
2013). In the interior Columbia River basin, a long history of logging, mineral extraction, and river
impoundment has altered in-stream habitats and riparian areas (Hand et al. 2018), resulting in a
lack of structure and nutrients that alters the availability of food resources to aquatic organisms
(Meredith et al. 2014; Minshall et al. 2014). Habitat change can alter stream macroinvertebrate
assemblages and impact consumers of both larval and adult life-stages of aquatic insects (Power
et al. 1996; Nakano et al. 1999; Baxter et al. 2005; Malison and Baxter 2010). Such changes
within the interior Columbia River basin have indeed led to shifts in fish assemblage structure
(Frissell 1993). Understanding how specific habitat change alters the flow of in-stream energy
sources to fish consumers can thus be of great importance to conservation and management
efforts (Cross et al. 2011, 2013; Bellmore et al. 2012; Scholl et al. 2019).
Instream habitat components that alter primary and secondary production such as woody
debris and stream substrates are major topics of research, but ephemeral habitat components
such as macrophytes and algae are less often considered in restoration and management. In
recent years, increasing reports of severe blooms of the diatomaceous algae Didymosphenia
geminata (hereafter, Didymo) have led to significant concern about its causes and consequences
for freshwater organisms (Bickel and Closs 2008; Gillis and Chalifour 2010; James et al. 2010;
Anderson et al. 2014; James and Chipps 2016; Jellyman and Harding 2016). Overgrowths
(colloquially, blooms) of this North American-native are characterized by the production of a long
polysaccharide stalk from individual diatoms, which can lead to large areas of the substrate
becoming covered. However, the precise causes of Didymo blooms remain a current topic of
investigation (Taylor and Bothwell 2014).
At high Didymo bloom coverage, stream invertebrate assemblages originally dominated
by Ephemeroptera, Plecoptera, and Trichoptera (EPT taxa), typically shift towards dominance by
Chironomidae, Oligochaeta, Nematoda, or Cladocera, taxa generally associated with reduced
habitat quality in trout streams (Kilroy et al. 2009; Gillis and Chalifour 2010; James et al. 2010;
Byle 2014; Larned and Kilroy 2014; Jellyman and Harding 2016). There has been widespread
concern about the consequences of blooms for trout (Gillis and Chalifour 2010; James et al. 2010;
Jellyman and Harding 2016) because EPT taxa are often a primary food source for salmonid
species (Behnke 2010). However, to date, it is unclear if Didymo blooms have any significant
negative or positive impacts on trout species. Jellyman and Harding (2016) found that blooms in
several New Zealand rivers were correlated with lower trout abundances, dietary percent EPT,
and stomach fullness. In contrast, production of Atlantic Salmon (Salmo salar) in Icelandic and
Norwegian rivers has remained high despite the presence of severe Didymo blooms (Jonsson et
al. 2008; Lindstrøm and Skulberg 2008), and spawner abundance and escapement of Pacific
salmon and steelhead (Oncorhynchus spp.) in Vancouver Island streams either increased or did
not change in relation to blooms (Bothwell et al. 2008). In four South Dakota streams the condition
and feeding of large Brown Trout (Salmo trutta) was not correlated with Didymo blooms, while
body condition in juveniles was higher (James and Chipps 2010). However, the study was also
affected by drought, making causal inference difficult. As such, no individual study has
3
successfully examined the mechanistic links between Didymo blooms, macroinvertebrates, and
fishes necessary to make causal inference. Further, no studies have addressed the potential
effects of blooms on inland native trout populations or on nongame species such as members of
the family Cottidae.
To better understand the trophic consequences of Didymo blooms, we assessed the
relationship between blooms, fish diet, condition, and growth over two summers in a Columbia
River subbasin, the mountainous Kootenay (Kootenai in the U.S.) basin of British Columbia,
Idaho, and Montana (Fig. 1), much of which falls within the globally-rare, inland temperate
rainforest biome (Dellasala et al. 2011). We employed a multi-faceted research approach in which
we examined potential Didymo bloom impacts on fish: 1) temporally - in a reference-impact study
of two streams during one summer, and 2) spatially in a survey of fishes across Kootenay basin
streams representing a gradient of bloom severity.
Fig. 1. Location of study streams (red dots) within the Kootenay River basin (left) and the upper Libby Creek
subbasin (right). Inset A shows the location of the Kootenay basin within the larger Columbia River
watershed.
4
Study Location
To determine the potential effects of Didymo blooms on fishes, we combined a high frequency
sampling approach with a high spatial resolution approach. Twice-monthly through the summer
of 2018, we sampled two streams located in the Cabinet Mountains of northwestern Montana,
Bear Creek and nearby Ramsey Creek (Fig. 1). Both creeks have similar physical characteristics
(Table 1), but Bear Creek contains obvious Didymo blooms while Ramsey Creek does not. The
two streams thus offer an opportunity to examine potential effects of blooms on biotic communities
in a paired, reference-impact framework.
During both the summer of 2018 and 2019, we examined 131 locations on 103 individual
streams for the presence of Didymo blooms in the Kootenay River basin (Appendix A). In 2019,
we surveyed fishes in 28 of those streams (Fig. 1) representing large differences in bloom
coverage: 0 80% (Table 2). Ten of those streams were located in British Columbia provincial
parks (Fig. 2).
Fig. 2. British Columbia portion of the Kootenay River basin. Sample sites (red dots) are shown in provincial
parks (dark grey).
Table 1. Bear and Ramsey Creek habitat measurements - 2018.
Bear Ck.
(Didymo)
Ramsey Ck.
(No Didymo)
Temp. (°C) ±SD 9.79 ± 2.32 9.79 ± 2.40
Mesohabitat
Cascade 76% 83%
Riffle 16% 10%
Pool 8% 7%
Substrate Size 26.7 cm 23.2 cm
Wetted Width 7.24 m 7.17 m
Nutrients (μg/L) ±SD
SRP 1.995 ±0.368 1.530 ±0.409
Bromide below detection below detection
Fluoride below detection below detection
Nitrate 74.5 25
Phosphate below detection below detection
Sulfate 1235 930
2018 Habitat Measurements
5
Table 2. List of streams surveyed in 2019.
Methods
Didymo vs. Control Stream Study 2018
We selected a three-hundred meter long reach for study in both Bear and Ramsey Creeks.
The fish assemblages of both were predominantly composed of Columbia River Redband Trout
(O. mykiss gairdneri) and Bull Trout (Salvelninus confluentus). Ramsey Creek also contained a
small number of Columbia Slimy Sculpin (Uranidea cognata syn. Cottus cognatus). We measured
five habitat variables to ensure Bear and Ramsey Creeks were suitable for comparison: mean
substrate size (sensu Wolman 1954), channel width, mesohabitat composition (percent cascade,
riffle, & pool), water temperature (30-minute recording interval, Onset HOBO© data loggers), and
water chemistry (Lachat 8500 QuikChem FIA and IC). Every two weeks, we systematically
estimated percent-of-substrate covered by blooming Didymo using a five-gallon bucket with a
clear bottom, making five evenly-spaced estimates along lateral transects, each twenty paces
apart from reach-top to bottom. We then combined, twice-monthly estimates to form monthly
Didymo bloom coverage estimates.
Food-web structure was determined by macroinvertebrate and fish sampling concurrent
with Didymo coverage estimation. In conjunction with Didymo bloom measurements, we collected
drifting macroinvertebrates by placing two separate 10 x 18 inch drift nets in the stream for 30
minutes and pooling the combined samples in 70% ethanol. Samples were always taken between
the hours of 10:00 a.m. and 5:00 p.m. The day following each Didymo and macroinvertebrate
sampling event, we collected fishes through single-pass backpack electroshocking (LR-24
6
Backpack Shocker Smith-Root©, Vancouver, WA). We completed multiple passes during the final
sampling event (September) to maximize summer-long recapture. Each fish was anesthetized
with clove oil, weighed, measured, and marked by clipping a small section of the caudal fin. We
gastrically lavaged individuals larger than 100 mm to collect diets and, if captured during June or
July, implanted a uniquely-coded, 12 mm passive integrated transponder (PIT) tag (Model
HDX12, Biomark©, Boise, ID). Gut evacuation was assumed to be minimal due to cold
temperatures and processing generally less than an hour after capture. Using the average percent
growth between individuals measured in June and July, we back-calculated June weights for
individuals tagged in July. This represented 57% of Redband Trout in Bear Creek and 68% in
Ramsey Creek. For PIT-tagged Redband and Bull Trout captured in September, we also
compared summer growth to the total number of times that fish had been captured to test for
handling effects.
We identified and measured drift and diet macroinvertebrates to family and used published
length-to-mass conversions to estimate biomass (Benke et al. 1999; Sabo et al. 2002;
Baumgärtner and Rothhaupt 2003; Gruner 2007; Miyasaka et al. 2008) and caloric content
(Montana Fish, Wildlife & Parks, unpublished data). Conversions are provided in Appendix B.
Because a shift to a macroinvertebrate assemblage of smaller and more abundant
individuals may favor juvenile fishes (James and Chipps 2016), we identified large and small size-
classes of Redband and Bull Trout using length-frequency histograms (Appendix C). We then
calculated size-specific abundances using Lincoln-Petersen mark-recapture estimation in which
the final sampling date was the recapture event and all previous sampling events a single marking
event (Lincoln 1930). We determined this approach to be a reasonable because movement of
PIT-tagged fishes between the abutting upper and lower halves of Bear Creek was negligible and
thus assumed the closed population assumption of Lincoln-Petersen estimation was satisfied
(Appendix D).
We compared taxon-specific proportions of drifting macroinvertebrates in Bear and
Ramsey Creeks by calculating the monthly percent similarity (Schoener 1970):
(1) 

where Bi is the percent of invertebrates of taxa i in Bear Creek and Ri is the percent of
invertebrates of taxa i in Ramsey Creek. Using the same equation, we compared trout diets to
the availability of invertebrates in the drift as a measure of selection. Then, we also compared
trout diets between the two streams using percent energetic content for each diet taxa. To
evaluate how likely observed differences between groups were (drift vs. drift, diet vs. drift, and
diet vs. diet), we used Pearson’s chi-squared tests. We further report monthly and summer-long
gut fullness and relative number and energetic content of invertebrates in the drift between the
two streams.
7
By pairing individual caloric demand with trout diet composition, we created energy-flow
food webs. We used a novel modification of the Benke-Wallace trophic-basis of production
method that accounts for thermal preferences to calculate energetic demand (Benke and Wallace
1980). The Benke-Wallace method was originally developed for use with benthic
macroinvertebrates and does not account for differential allocation of energy by organism size
and water temperature, factors known to strongly influence fish growth (Brown et al. 2004). Thus
we used two different numbers for the proportion of total assimilated energy allocated to growth
(net production efficiency or NPE) in large vs. small fishes as suggested by Bellmore et al. (2013).
We then modified this proportion by observed stream temperatures as compared to species’
thermal optimums such that a fish’s consumption in kilocalories was calculated
(2) 


where DietProportioni is the average proportion by kilocalories of food type i in the diet,
Growth is the summer growth (Jun.-Sept.) in grams of the average fish,
EnergyDensity is the energy density (kcal/gram) of the fish,
TissueAllocation is the theoretical maximum proportion of assimilated energy allocated to fish
tissue growth (net production efficiency), which was set as 0.22 for large size-class trout and 0.5
for small size-class trout and Slimy Sculpin
Digestiblei is the estimated digestible proportion of food type i, and
TempFactor is the temperature correction factor calculated according to the equation
(3) 
where StreamTemp is the average stream temperature for the measurement interval over which
growth was recorded and OptimTemp is the thermal optimum for the given species of fish. This
equation is an approximation of a fish’s thermal optimum curve that asymptotes at an energy-
allocation-to-tissue value of zero (Appendix E). We derived thermal optimum values from previous
field and laboratory studies: 13.1°C for Redband Trout (Bear et al. 2007), 12.0°C for Bull Trout
(Dunham et al. 2004), and 12.1°C for Slimy Sculpin (Wehrly et al. 2004).
We used estimated digestible proportions (Digestiblei) for each food type from Hanson et
al. (1997) and subtracted a value of 0.2Digestiblei to account for specific dynamic action (Hanson
et al. 1997). Thus Digestiblei 0.2Digestiblei is the assimilation efficiency of food type i.
To derive total estimated consumption by each species, we multiplied estimated summer
Benke-Wallace consumption values by calculated fish abundances in each stream. Then, we
multiplied the proportion of energy of each prey item in the average diet of each fish species by
the reach-level consumption estimates. Thus, we obtained estimates of total energy flow from all
prey to fish predators and compared results for Bear and Ramsey Creeks (Appendix F).
8
Multi-Stream Didymo Survey 2019
In a representative 30.5 meter (100 ft.) reach of each selected stream, we estimated
Didymo coverage using the same method as in 2018. We also recorded, six other habitat
variables: wetted-width (n=5), canopy density (n=5 using a densitometer [Strickler 1959]),
dominant vegetation type, substrate type (Cummins 1962), Rosgen channel type (Rosgen 1994),
number of large woody debris items (sensu Kershner et al. 2004), and stream temperature. From
reach top-to-bottom, we measured wetted width and canopy density, while we qualitatively
assessed vegetation, substrate, and channel type. We estimated mean August temperatures by
adding the time-specific difference of each stream’s temperature to a reference temperature
logger (Bear Creek for streams flowing into the Kootenay River below the Fisher River confluence,
Outlet Creek for those above the Fisher confluence, and Trail Creek for Fisher River tributaries).
In the same reach, we collected fishes through two-pass (one upstream, one downstream)
backpack electroshocking. We anesthetized, weighed and measured all fishes and then released
leuciscids and catostomids. Using an in-field assessment in which we gastrically-lavaged fish, we
assessed the diets of salmonids and cottids by spreading the diet contents in a 30 x 15 cm white
pan and recording the number of individuals of each invertebrate taxa. We identified insects to
order except for Simuliidae and Chironomidae, which we identified to family. Other invertebrates
we identified to Class or Phylum, and vertebrates to the lowest practical taxonomic level (usually
species).
We generated two response metrics of fish condition (Fulton’s K [Heincke 1908; Ricker
1975] and residual analysis of observed vs. predicted weights [Fechhelm et al. 1995]) and four
metrics of diet composition (%Diptera, %EPT, %Aquatics, and gut fullness [# Diet Items/Fish
Length]) for each fish. Using weighted, univariate logistic (%Diptera, %EPT, %Aquatics) and
linear regressions (gut fullness and fish condition) in which fish sample size was the relative
weight of each stream in the regression, we analyzed each response metric compared to Didymo
and the other six habitat variables. We removed four streams (Kokanee, Coffee, Mobbs & Solo
Joe Creeks) from regressions due to low sample size or substantially different substrate type. We
grouped fish by genus due to otherwise small sample size if compared only within species (charr
Salvelinus and sculpin Uranidea) or significant hybridization in the basin (trout Oncorhynchus),
which made some field ID’s difficult. For each comparison of a habitat variable to a diet metric,
we calculated an R2 (or Nagelkerke’s pseudo-R2 for logistic regression [Nagelkerke 1991]) and p-
value, and considered variables with an R2 greater than 0.2 and a p-value less than 0.2 to be a
non-spurious correlation.
Results
Didymo vs. Reference Stream Study - 2018
Differences in all four habitat variables were small between Bear (Didymo) and Ramsey
Creeks (No Didymo), giving us confidence the two were suitable for comparison (Table 1). Didymo
bloom severity in Bear Creek increased from 10.9% coverage in June to 22.6% coverage in
9
August before falling to 18.9% in September (Fig. 3). The June to August Didymo growth was
significant (p<0.01), but the decline from August to September was not (p = 0.21).
Fig. 3. Monthly, percent of stream substrate covered by Didymo in Bear Creek, 2018 (top). Pie charts show
proportions of major aquatic invertebrate taxa in the drift in Bear and Ramsey Creeks.
Percent composition of drifting invertebrates between the two streams generally became
less similar as Didymo coverage increased (June-September: 84.2%, 63.1%, 68.5% and 66.6%
similar; Fig. 3, Appendix G). Percent EPT in the drift was initially 12.1% higher in Ramsey Creek
but by September was 20.3% higher in Bear Creek. However, Ephemeroptera larvae were
proportionally more abundant in Ramsey Creek during all months (June-September: 10.5%,
0.7%, 11.8%, and 8.5% higher; Fig. 3). Percent of larval and pupal Diptera in Bear Creek was
17.8% higher than Ramsey Creek in June, 32.1% higher in July, 30.6% higher in August, and
12.0% lower in September. Both total drifting invertebrates and total energy of drifting
invertebrates similarly diverged later in the summer with the streams having similar numbers in
June, Ramsey Creek having higher numbers in July and August, and Bear Creek having higher
numbers in September. The summer-long amount of total energy of drifting invertebrates was 2.2
times higher in Ramsey Creek.
Reach abundance estimates for Redband and Bull Trout were higher in Bear Creek (Table
2). Slimy Sculpin (n=20) were only in Ramsey Creek. Relative growth of Redband Trout varied by
size-class. Summer relative growth of small trout (<105 mm) was estimated to be 0.0292 g/g/d in
Bear Creek but only 0.0033 g/g/d in Ramsey Creek, but this difference was likely driven by a very
small sample size of small Redband Trout during June (3 in Bear Creek and 1 in Ramsey Creek).
Relative growth of large size-class Redband Trout (>105 mm) was similar between the two
streams: 0.0027±0.0004 g/g/d in Bear Creek and 0.0029±0.0007 g/g/d in Ramsey Creek (mean
± standard error; Table 3). Growth for similar size class trout was likewise similar between the two
10
streams (Appendix H). We observed no negative impact of even frequent capture on growth of
PIT-tagged fish (Appendix I).
Table 3. Population (reach) abundance, growth, and consumption estimates for each fish species and size
class in Bear and Ramsey Creeks. Bioenergetics consumption estimates are shown for comparison to
Benke-Wallace estimates though population-level estimates used the Benke-Wallace method.
Redband Trout diets were 40.7% similar to the drift in Bear Creek 2 test: p < 0.01) and
40.1% similar to the drift in Ramsey Creek (χ2 test: p < 0.01; Fig. 4). By energetic content,
Redband diets were 81.2% similar between Bear and Ramsey Creeks for the whole summer (χ2
test: p = 0.84): 55.6% similar in June, 77.5% similar in July, 99.7% similar in August, and 75.0%
similar in September (Appendix G). Gut fullness was not significantly different between the two
streams in any month. Diets of small individual Redband Trout in Bear Creek had more EPT
(78.6%±8.4) than large individuals (46.4%±3.0), while gut fullness and %Diptera were similar.
Benke-Wallace consumption estimates for large, individual Redbands were 39% higher in
Bear Creek, while small size-class estimates were similar between the two streams (Table 3).
Reach-level energetic demand by all Redband Trout were estimated at 17,500 kcal in Bear Creek
and 6,111 kcal in Ramsey Creek (Table 3). The primary sources of energy (>5% of demand) for
Redband Trout in Bear Creek were Ephemeroptera (38.0% of energy intake), Hymenoptera
(15.1%), Trichoptera (14.4%), Plecoptera (9.5%) and Diptera (7.6%); (Fig. 5). Primary energy
sources for Ramsey Creek Redbands were Ephemeroptera (45.8%), Hymenoptera (15.7%),
Diptera (9.8%), Trichoptera (9.0%), and Plecoptera (6.3%); (Fig. 5). Primary sources of energy
for Bull Trout in Bear Creek were Ephemeroptera (48.0%), Trichoptera (13.1%), Nematoda
(7.2%), Plecoptera (6.3%), and Hymenoptera (5.1%); (Fig. 5). We collected only 4 Bull Trout 3
Slimy Sculpin diets in Ramsey Creek, and we did not consider this sufficient to draw conclusions
as to average diet compositions.
Stream
Pop
Abundance
Individual
Growth
Indiv Summer
Consumption
Pop Consumption
(g/g/d) (kcal) (kcal)
Small Bear 132 0.0292 20.9 2755.8
Ramsey 91 0.0033 18.8 1712.5
0.0000
Large Bear 196 0.0027 75.3 14724.5
Ramsey 81 0.0029 54.2 4398.1
Small Bear 60 0.0136 21.3 1277.0
Ramsey 2 NA NA NA
0.0000
Large Bear 45 0.0011 23.9 1080.5
Ramsey 3 NA NA NA
Ramsey 20 0.0030 8.8 176.8
Bull Trout
Slimy Sculpin
Redband Trout
11
Fig. 4. Invertebrate taxa in Bear and Ramsey Creek drift (top) and proportion in Redband Trout diets
(bottom) by month.
Fig. 5. Energy-flow food web
for fishes in Bear and Ramsey
Creeks. Line thickness
represents proportion of total
energy demand by the given
fish species met by each
invertebrate taxa. Only taxa
representing at least 5% of
energy demand are shown.
12
Multi-Stream Didymo Survey 2019
Between-site variation in Fulton’s K was too low to assess possible explanatory variables
(coefficients of variation [CV] ≤ 0.1; Appendix J). Between-site variation in fish relative condition,
calculated as a fish’s observed weight compared to its predicted weight, was similarly low for trout
and sculpin (CV of 0.12 and 0.04, respectively) and moderately low for charr (CV = 0.28). Despite
slightly more variation in charr relative condition between sites, there was no relationship between
condition and Didymo coverage (R2 = 0.03, p = 0.46).
For all diet metrics across all three fish taxa, percent Didymo cover was only correlated
with percent of aquatic invertebrates in Oncorhynchus diets (Fig. 6). Canopy cover, LWD, riparian
vegetation type, and stream temperature were also correlated with percent aquatic invertebrates
in Oncorhynchus diets, with LWD having the highest pseudo-R2 (Appendix J). In fact, few fish diet
metrics were correlated with any habitat variable (Appendix K). However, percent of aquatic
invertebrates in trout diets was positively associated with pine vegetation types (Fig. 7).
Fig. 6. Correlations of Didymo coverage to each fish taxa’s diet and condition from 2019. Each dot is the
average value for fish in a single stream. ρ2 is Nagelkerke’s pseudo-R2 value. *** indicates a p-value ≤0.05.
13
Fig. 7. Violin plot of percent aquatic invertebrates in trout diets showing the spread across different riparian
vegetation types.
Discussion
During the summers of 2018 and 2019, we examined the response of trout, charr, and
sculpin to Didymo blooms over space and time. While Didymo appeared to impact the
macroinvertebrate assemblage of Bear Creek, the macroinvertebrate food sources and
subsequent growth rates of trout did not appear to be affected. Across a gradient of Didymo bloom
coverages in 2019, Didymo was weakly correlated with percent of aquatic invertebrates in trout
diets but we observed little variation in condition of trout, charr, & sculpin.
As Didymo bloom coverage in Bear Creek increased to its maximum in August 2018, the
proportion of the invertebrate drift made up by larval Diptera (primarily Simuliidae and
Chironomidae) diverged between the two streams, remaining relatively high in Bear Creek while
decreasing in Ramsey Creek. Numerous other studies have similarly found high proportions of
Diptera, especially Chironomid larvae, where Didymo is in bloom (Marshall 2007; Kilroy et al.
2009; Gillis and Chalifour 2010; Anderson et al. 2014; Ladrera et al. 2015; Sanmiguel et al. 2016).
Yet despite their relative abundance in Bear Creek, Diptera comprised a disproportionately small
percent of Redband Trout diets in both streams, indicating strong negative selection.
Ephemeroptera, Hymenoptera and Nematoda were strongly selected for by Redband Trout in
both streams. Overall, Redband Trout diets were highly similar between the Didymo and
reference streams in 2018 (81.2% similar) despite differences in the availability of certain prey
taxa. In fact, diets were most similar in August (99.7% similar), when Didymo coverage was at its
peak. Correspondingly, major energy sources and growth rates of trout did not differ greatly
between Bear and Ramsey Creeks. It is however possible that Didymo coverage in Bear Creek
was not severe enough to cause the proportional shifts in macroinvertebrate composition such
that trout would have been impacted by food limitation. While a no-Didymo comparison was not
available for Bull Trout since so few were captured in Ramsey Creek, Bull Trout in Bear Creek
also did not utilize larval Diptera as a major energy source, which may be consequential only at
14
very high Didymo coverage. Further, sexually mature Bull Trout in these systems were likely
allocating energy to pre-spawn gamete production, which may have affected overall growth.
Stream resident trout are considered generalist invertivores (Behnke 1992), but strong
selection by Redband Trout in both Bear and Ramsey Creeks in 2018 for the same taxa indicates
this subspecies may show strong preferences for mayflies (Ephemeroptera). However, given
interior (non-steelhead) Redband Trout occupy only 42% of their historic range across the West
and only 2% of historic range in Montana (Muhlfeld et al. 2015), it is important to carefully evaluate
land management actions such as timber harvest or road construction that may impact sensitive
mayfly species.
In our 2019 survey of 28 streams with varying levels of coverage, Didymo bloom severity
was not correlated with most measures of fish diet and was only a weak predictor of aquatic
invertebrates in trout diets. In conjunction with the 81.2% similarity of diets between Bear and
Ramsey Creeks in 2018, this suggests Didymo may alter the composition of trout dietary
macroinvertebrates to a small extent, but that shift does not alter condition or growth rates of trout.
This disconnect may indicate trout in Kootenay River headwaters are not food limited during
summer months, or that much greater diet perturbations are necessary to affect trout growth.
Alternatively, the lack of variability in fish condition across streams may suggest fishes in these
populations conform to the theory of ideal-free distribution (Fretwell 1969; Sutherland et al. 1988)
such that fish condition between streams is relatively homogenous but abundances vary based
on where forage is most available. As such, distribution of fish condition in Kootenay basin
headwaters may be relatively stable - i.e. exist in a state of equilibrium (sensu Nash 1951).
Although not the impetus of our study, we observed interesting differences in percent of
aquatic invertebrates in trout diets in streams with differing riparian vegetation (Fig. 7). Allan et al.
(2003) found riparian communities dominated by alder in Alaska coastal temperate rainforests,
provided more terrestrial invertebrates to juvenile salmon (Oncorhynchus kisutch) than did those
dominated by a mix of hemlock and spruce (Picea spp.). Similarly, we observed higher
proportions of riparian invertebrates in trout diets in alder-dominated streams than in pine-
dominated streams (largely lodgepole pine Pinus contorta). In contrast to the finding of Allan et
al. (2003), trout in our inland temperate rainforest streams with riparian communities dominated
by cedar and hemlock, had similar aquatic-terrestrial ratios to alder-dominated streams (Fig. 7).
Our study examined the impacts of Didymo blooms only into early Fall during both years,
a time when terrestrial invertebrate inputs, and trout reliance upon them, are high (Nakano and
Murakami 2001). It is possible terrestrial inputs act as a buffer to shifts in aquatic invertebrate
composition caused by blooms and some negative or positive consequence of Didymo can only
be observed by studying fishes across seasons. In fact, some studies have reported severe
Didymo blooms during winter months (e.g. Kolmakov et al. 2008), and we observed severe
blooms in the Lardeau River during April of 2018 when snowpack was still high. Trout growth in
headwater streams is higher in summer months but foraging (Thurow 1997) and growth (Al-
Chokhachy et al. 2019) still occur over winter. We therefore suggest potential impacts of Didymo
on fishes be examined during winter. Further, due to the multitude of studies indicating impacts
15
to macroinvertebrate assemblages, the relationship of Didymo to imperiled invertebrates,
especially sedentary taxa that may not be able to avoid Didymo blooms such as freshwater
mussels, bears further study.
Implications for Management
Authors of previous studies have suggested nutrient amendments (James et al. 2015;
Coyle 2016) and dam releases (Cullis et al. 2015) as viable means to manage nuisance Didymo
blooms. Indeed, both methods show promise for reduction of blooms at local scales. The impetus
for this bloom reduction may be independent of concern for fishes, including aesthetics, fouling of
infrastructure, or to prevent hypoxia. However, we did not observe any major impacts of Didymo
blooms on the diet, condition, or growth of trout in Kootenay basin headwaters. This overall result
is similar to those for Brown Trout in a South Dakota stream (James and Chipps 2016). Therefore,
it is not clear efforts to control Didymo blooms in headwater streams will benefit fish condition.
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20
Appendix 1. All 131 unique stream locations examined for presence of Didymo blooms in the
Kootenai basin. Streams which we quantitatively assessed coverage are listed as ‘Y’. We visually
estimated covered for streams listed as ‘N’.
21
Appendix 1 (cont.)
22
Appendix 2. Redband Trout length-frequency histograms for Bear and Ramsey Creeks. The
black bar represents the cutoff for ‘small’ vs. ‘large’ fish.
23
Appendix 3. Biomass and energy conversions for fish prey items. Length (in millimeters)-to-
mass (gramsDryMass) conversions follow the equation: Mass = a*Lengthb
24
Appendix 4. Movement of trout between the abutting lower and upper halves of Bear Creek,
MT.
Appendix 5. Example thermal adjustment curve for the modified Benke-Wallace method for a
fish with a 13.1°C thermal optimum.
Month
Recaptured in
Adjacent Section
Recaptured in
Same Section
July 3.4% 96.6%
August 22.4% 77.6%
September 8.3% 91.7%
Percent of Tagged Fish
Fish Movement Between Bear Creek Sections
25
Appendix 6. Average percent-of-energy derived from different prey sources by Redband Trout in
Bear and Ramsey Creeks during the summer of 2018.
% Total Energy Demand
Prey Source Bear Cr. Ramsey Cr.
Actinopterygii 0.2 0.0
Arachnida 0.8 0.1
Coleoptera 4.8 3.7
Collembola <0.1 <0.1
Diptera Adult 3.0 3.0
Diptera Larvae 4.6 6.8
Ephemeroptera Adult 5.3 2.0
Ephemeroptera Larvae 32.7 43.8
Hemiptera Adult 0.6 0.1
Hymenoptera 15.1 15.7
Lepidoptera 2.1 1.2
Nematoda 2.5 7.6
Oligochaeta 1.7 0.6
Plecoptera Adult 2.4 0.6
Plecoptera Larvae 7.1 5.7
Trichoptera Adult 0.4 0.2
Trichoptera Larvae 14.0 8.8
Other Insecta Adult 2.7 0.2
2018 Redband Trout Energy Sources
26
Appendix 7. Pearson’s Chi-squared test results comparing macroinvertebrate drift between Bear
and Ramsey Creeks, Redband Trout diets to drift in each stream, and diets between the streams. χ2
is the chi-squared test statistic and df is degrees of freedom.
χ2df p-value
June 5.9 30.11
July 6.4 30.09
August 22.5 3
5.2x10-5
September 3.2 30.37
Full Summer 5.0 30.17
χ2df p-value
Full Summer 79.1 19
2.7x10-9
χ2df p-value
Full Summer 82.9 16
4.9x10-11
χ2df p-value
June 53.4 12
3.5x10-7
July 16.9 15 0.32
August 18.2 15 0.25
September 26.5 13 0.01
Full Summer 12.2 18 0.84
Bear Cr. Drift vs. Ramsey Cr. Drift
Bear Cr. Redband Diets vs. Bear Cr. Drift
Ramsey Cr. Drift vs. Ramsey Cr. Redband Diets
Bear Cr. Redband Diets vs. Ramsey Cr. Diets
Results of Pearson's Chi-squared tests
27
Appendix 8. Redband Trout length (at first capture) compared to its summer long growth. Bear
Creek (Didymo) is in red and Ramsey Creek (No Didymo) is in blue.
Appendix 9. Relationship of handling pressure and growth of trout during summer 2018 in Bear
Creek.
Length at 1st Capture (mm)
Summer Growth (g)
28
Appendix 10. Statistics of spread for trout, charr, and sculpin condition (K) and gut fullness
across the 24 streams included in analyses of 2019 data.
Appendix 11. Univariate linear regression results for the five continuous and two categorical
habitat variables on trout, charr, and sculpin diet metrics. Categorical variables were assessed
with an anova and post-hoc Tukey test. COV is canopy cover, WW is wetted width, LWD is
large woody debris, TEMP is average August stream temperature, VEG is riparian vegetation
type, and CHAN is Rosgen channel type.
Mean Coefficient of Variation
Trout K0.943 0.0612
Fullness 0.0878 0.471
Charr K0.901 0.0764
Fullness 0.0756 0.447
Sculpin K1.08 0.112
Fullness 0.0494 0.677
Dispersion Statistics for Condition & Gut Fullness
... 10 For the downstream rivers, outflow discharges from a dam and/or its fluctuation should not be too small to suppress bloom of nuisance benthic algae and vegetation. 11,12 This is a requirement of discharge called environmental flows and has been a key concept in operating dam and reservoir systems in the modern world. 13 By contrast, too large outflow should be avoided for flood preventions. ...
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... For the downstream rivers, outflow discharges from a dam and/or its fluctuation should not be too small to suppress bloom of nuisance benthic algae and vegetation [11][12]. This is a requirement of discharge called environmental flows and has been a key concept in operating dam and reservoir systems in the modern world [13]. ...
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We deal with a new maximum principle-based stochastic control model for river management through operating a dam and reservoir system. The model is based on coupled forward-backward stochastic differential equations (FBSDEs) derived from jump-driven streamflow dynamics and reservoir water balance. A continuous-time branching process with immigration driven by a tempered stable subordinator efficiently describes clustered inflow streamflow dynamics. This is a completely new attempt in hydrology and control engineering. Applying a stochastic maximum principle to the dynamics based on an objective functional for designing cost-efficient control of dam and reservoir systems leads to the FBSDEs as a system of optimality equations. The FBSDEs under a linear-quadratic ansatz lead to a tractable model, while they are solved numerically in the other cases using a least-squares Monte-Carlo method. Optimal controls are found in the former, while only sub-optimal ones are computable in the latter due to a hard state constraint. Model parameters are successfully identified from a real data of a river in Japan having a dam and reservoir system. We also show that the linear-quadratic case can capture the real operation data of the system with underestimation of the outflow discharge. More complex cases with a realistic time horizon are analyzed numerically to investigate impacts of considering the environmental flows and seasonal operational purposes. Key challenges towards more sophisticated modeling and analysis with jump-driven FBSDEs are discussed as well.
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