Cumulative Effects of Forestry Practices on Benthic Macroinvertebrate Assemblages in the Klamath National Forest Over a Range of Temporal and Spatial Scales Matthew R. Cover, CSU Stanislaus Juan de la Fuente, KNF Alison H. Purcell, Humboldt St. Rafael D. Mazor, SCCWRP Vincent H. Resh, U.C. Berkeley
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Cumulative Effects of Forestry Practices on Benthic
Macroinvertebrate Assemblages in the Klamath National Forest Over a Range of Temporal and
Spatial Scales Matthew R. Cover, CSU Stanislaus
Juan de la Fuente, KNF Alison H. Purcell, Humboldt St.
Rafael D. Mazor, SCCWRP Vincent H. Resh, U.C. Berkeley
CWE of BMP on BMI in KNF
by MRC of CSU STAN
JdlF of KNF AHP of HSU
RDM of SCCWRP VHR of UCB
Quantitative Linkages
Sediment Supply Channel
Conditions StreamBiota
Presenter
Presentation Notes
Upslope activities, sediment supply to stream channels, channel conditions, and biological responses. In this study we attempted to develop these quantitative linkages in order to inform a modeling approach.
Klamath National Forest
Presenter
Presentation Notes
This study took place in the KNF in Northwestern California. The Klamath River basin is an upside-down watershed. Whereas the upper basin has relatively flat topography (the site of the Klamath Lakes National Wildlife Refuge), the middle reaches of the river flow through…
Presenter
Presentation Notes
The KNF was selected as the study area because of existing work done by KNF geologist Juan de la Fuente, who has done extensive mapping and study of landslides throughout the forest. Landslides are the primary sediment sources in this steep terrain.
Effects of Forestry Management Practices (CWE) on BMI
Fine sediment1. Coarse: Regional scale
CA RivPACS model vs. KNF Sed supply model
Methods1. Assembled several sets of biological data
(Oregon Climate Center PRISM GIS layers)If mean monthly
Temperature < 9.9°C
If mean monthly
Temperature > 9.9°C
Submodel 3
Submodel 2Submodel 1
If log mean annual
Precipitation >2.952
If log mean annual
Precipitation <2.952
Assigning sites to appropriate submodel
Source: Chuck Hawkins
Then calculate predictor variables for sub-models (midges to subfamily)
Submodel 1Watershed Area
TemperatureLatitude
Submodel 2Longitude
% Sedimentary Geology
Precipitation
Submodel 3Watershed Area
Temperature
=% sedimentary geology (summarized from USGS maps,
John Olsen, Utah State University) Source: Chuck Hawkins
Presenter
Presentation Notes
Delineated catchments with mean annual precip in the background (high precip=blue, low precip=brown)
O/E vs Mass Wasting
Presenter
Presentation Notes
O/E50 and O/E100 are different capture probabilities. That is, when you use p >0.0, E is calculated for all taxa expected to occur at a site, as long as the probability is greater than 0. For p >=0.5 (yes, it's inclusive), E is calucalted only for those taxa expected to occur with a probability greater than or equal to 0.5. In general, people calculate both, but there seems to be greater support for scores based on p>=0.5. – email explanation from Rafi
O/E vs USLE Surface Erosion
Presenter
Presentation Notes
O/E50 and O/E100 are different capture probabilities. That is, when you use p >0.0, E is calculated for all taxa expected to occur at a site, as long as the probability is greater than 0. For p >=0.5 (yes, it's inclusive), E is calucalted only for those taxa expected to occur with a probability greater than or equal to 0.5. In general, people calculate both, but there seems to be greater support for scores based on p>=0.5. – email explanation from Rafi
Effects of Forestry Management Practices (CWE) on BMI
Fine sediment1. Coarse: Regional scale
CA RivPACS model vs. sed supply model
2. Medium: Watershed scaleGranitic watersheds w/ high sed. supply
y = 0.0794x + 1.5997R2 = 0.716
0
2
4
6
8
10
12
14
16
0 50 100 150 200
Landslide Sediment Supply (m3 km-2 y-1)
Fine
Sed
imen
t Sup
ply
(m3 k
m-2
y-1
)
Sediment supply from USLE modeling (m3/km2/yr) / stream power index
0 5 10 15 20
Rea
ch a
vera
ge V
*
0.00
0.05
0.10
0.15
0.20
0.25
Y = 0.0084X + 0.046
r2 = 0.94 p = 0.001
2
Sediment supply from USLE modeling (m3/km2/yr) / stream power index
0.1 1 10 100
Sur
face
Fin
es in
Riff
les
(%)
1
10
100
Y = 5.15X0.41
r2 = 0.66 p<0.0001
2
Sediment supply from USLE modeling (m3/km2/yr) / stream power index
1 10 100
Med
ian
perm
eabi
lity
rate
(cm
/hr)
100
1000
10000
Y = 4622X-0.5
r2 = 0.75 p<0.026
39%
Salmonid Egg Survival
15%
2
3
0
10
20
30
40
50
60
70
80
90
0 5 10 15 20 25
Taxa
Ric
hnes
s
Percent Fines
3
Fine Sediment
Simple Linear Regression
Partial Correlation
Metrics and Taxa (Predicted Response to Fine Sediment)
Effects of Forestry Management Practices (CWE) on BMI
Fine sediment1. Coarse: Regional scale
CA RivPACS model vs. sed supply model
2. Medium: Watershed scaleGranitic watersheds w/ high sed. supply
3. Very Fine: Cobble scaleLarge predators and embeddedness
What is a debris flow?
Presenter
Presentation Notes
Concentrated mixtures of poorly sorted sediment and water. Significant natural hazard. Occur throughout world in mountainous regions.
Debris flows are catastrophic disturbances in mountain
streams
Hillslopes steeper than 100% (45°)
Presenter
Presentation Notes
Debris flows originate high on headwater hillslopes as shallow landslides. As landslides enter the channel network they can transform into exteremely high velocity flows of water, sediment, and wood.
Debris flows are catastrophic disturbances in mountain
streams
Scour headwater channels >10%
Presenter
Presentation Notes
In steep streams, usually greater than 10% slope, these water sediment mixtures can scour the stream channel to bedrock. In steep streams in the OCR, Christine May has showed how these channels undergo long-term cycles of sediment accumulation and removal. Following debris flow scour, sediment doesn’t begin to accumulate until LWD is recruited into the channel. Sediment accumulates until another debris flow scours to bedrock once again.
Debris flows are catastrophic disturbances in mountain
streams
Presenter
Presentation Notes
Further downstream, debris flows can strip vegetation, rearrange channels and valley bottoms, and deposit large amounts of sediment. The geomorphic effects of debris flows have been well described. However, less is known about the effects of these disturbances on stream biota.
19971997
1997
1997
1997
1964
1974
1974
CONTROL
CONTROL
10 Basins•10-20 km2
•6-9% Slope
•5 m wide
Natural Experiment
Debris Flows
5- 1997
2- 1974
2- None last 100+ years
1- 1964
5 km
Years since debris flow0 20 40 60 80 100 120
Rip
aria
n tre
e sp
ecie
s ric
hnes
s
0
2
4
6
8
10
12
14
Riparian TreesResults
white alder, willow, big leaf maple
doug fir, cedar
yew, madrone
Coarse benthic organic matter
Years since debris flow0 20 40 60 80 100 120
CBO
M B
iom
ass
(g m
-2)
0
10
20
30
40
50
60
Invertebrate Shredders1997 DF Older DF
Yoraperla (m-2) 3 169
Malenka (m-2) 36 131
Zapada columbiana (m-2) 5 152
Canopy Cover
Years since debris flow0 20 40 60 80 100 120
Can
opy
Cov
er (%
)
0
20
40
60
80
100
Primary Productivity- Dissolved Oxygen
Up to 5x greater in 1997 debris flow streams!
100
105
110
115
120
125
130
135
140
Dis
solv
ed O
xyge
n (%
Sat
urat
ion)
1997
1964-74
Control
Tompkins
Upper Elk
Invertebrate Grazers
1997 DF Older DF
Glossosma (m-2) 325 40
Epeorus (m-2) 44 226
Conclusions
• In steep mountains streams, even high sediment supply does not result in wholesale changes to the BMI assemblage
• A few taxa may show responses• Rare, catastrophic geomorphic
processes may have more significant and persistent impacts on stream communities