Stream Condition Assessment of the Lake Tahoe Basin in 2009 and 2010 using the River Invertebrate Prediction and Classification System
Stream
Condition
Assessment
of
the
Lake
Tahoe
Basin
in
2009
and
2010
using
the
River
Invertebrate
Prediction
and
Classification
System
(RIVPACS)
1
Report PO79
Alison O’Dowd and
2
Andrew Stubblefield
1 Co ‐ Principal Investigator
Department of Environmental Science & Management
Humboldt State University
One Harpst St.
Arcata, CA 95521
Phone: 707 ‐ 826 ‐ 3438
Fax: 707 ‐ 826 ‐ 3501
Alison.ODowd@humboldt.edu
2 Co ‐ Principal Investigator
Department of Forestry and Wildland Resources
Humboldt State University
One Harpst St.
Arcata, CA 95521
Phone: 707 ‐ 826 ‐ 3258
Fax: 707 ‐ 826 ‐ 5634
Andrew.Stubblefield@humboldt.edu
December 2013
Funding for this research was provided by the Bureau of Land Management through the sale of public lands as authorized by the Southern Nevada Public Land Management Act (SNLPMA).
This Round 11 SNLPMA research grant was supported by an agreement with the USDA Forest Service Pacific Southwest Research Station.
1
T
ABLE
OF
C
ONTENTS
List of Tables .............................................................................................................................................................................
3
List of Figures............................................................................................................................................................................
4
ABSTRACT/SUMMARY ..............................................................................................................................................................
5
INTRODUCTION ........................................................................................................................................................................
5
Background ...........................................................................................................................................................................
5
Benthic Macroinvertebrates as Biological Indicators ...........................................................................................................
6
Study Objectives ...................................................................................................................................................................
7
METHODS .................................................................................................................................................................................
7
Study Sites ............................................................................................................................................................................
7
Sampling Design ...................................................................................................................................................................
9
Field and Laboratory Methods .............................................................................................................................................
9
Habitat and Water Quality data .....................................................................................................................................
10
Biological Data ................................................................................................................................................................
10
RIVPACS Methods ...............................................................................................................................................................
11
Analysis Methods ...............................................................................................................................................................
11
Status Analysis ................................................................................................................................................................
11
Trend Analysis ................................................................................................................................................................
12
Habitat Stressor Analysis ................................................................................................................................................
12
RESULTS AND DISCUSSION .....................................................................................................................................................
13
Conditional Threshold Analysis ..........................................................................................................................................
13
Status (2009 and 2010) ......................................................................................................................................................
13
Trend Analysis ....................................................................................................................................................................
15
Hydrologic Conditions ........................................................................................................................................................
17
Impervious Surface .............................................................................................................................................................
18
Habitat Stressors ................................................................................................................................................................
19
Habitat Characteristics of “Marginal” Sites ........................................................................................................................
21
Regional Summary ..............................................................................................................................................................
24
California Statewide Model ....................................................................................................................................................
27
Summary ................................................................................................................................................................................
27
Recommendations .................................................................................................................................................................
28
ACKNOWLEDGEMENTS ..........................................................................................................................................................
28
LITERATURE CITED ..................................................................................................................................................................
29
APPENDICES ...........................................................................................................................................................................
33
APPENDIX I: Attribute table of the eighty ‐ five sites sampled in the Lake Tahoe Basin in 2009 and 2010.
........................
33
APPENDIX II: A step ‐ by ‐ step guide for using the RIVPACS model to calculate O/E scores ................................................
36
APPENDIX III: Photos of Marginal sites sampled in 2009 and 2010 in the Tahoe Basin .....................................................
43
2
L
IST
OF
T
ABLES
Table 1.
Sampling schedule for the Lake Tahoe Basin showing number of stream sites sampled each year for status (new sites randomly selected each year), trend (sites randomly selected first two years, then in two ‐ year rotation), and reference (same eight sites sampled every year).
…….………………….………………………………..……………………………………….…………9
Table 2.
Lake Tahoe Region Average Monthly Precipitation (inches) during the Study Period (Western Regional Climate
Center)………………………………………………………………………………………………………………………………………………………………………………18
Table 3.
Significant (P<0.05) Linear regressions between site level habitat variables and benthic invertebrate O/E scores.
…………………………………………………………………………………………….……………………………………………………………………………………………19
Table 4.
Habitat variable analysis of marginal (O/E score < 0.7) sites…………………………………………………………………………………23
3
L
IST
OF
F
IGURES
Figure 1.
Locations of Lake Tahoe biossessment study sites sampled in 2009 and 2010 with last three digits of Site ID Code.
Prefix 08722, 634009 and 634010 have been omitted for clarity.
See Appendix 1 for full site IDs……………………………………….8
Figure 2.
Categories of ecological condition based on RIVPACS scores, Lake Tahoe Basin 2009 ‐ 2010.
................................
14
Figure 3.
Histogram of conditional categories showing sample size of each for 85 sites sampled in the Lake Tahoe Basin in
2009 and 2010.
.......................................................................................................................................................................
15
Figure 4.
Map of 2003 (yellow diamonds) and 2009 ‐ 2010 (black circles) benthic macroinvertebrate sampling sites in the
Lake Tahoe Basin.
...................................................................................................................................................................
16
Figure 5.
Comparison of 2009 ‐ 10 O/E scores (p>0.5) with 2003 O/E scores for sites sampled in nearby (<200 m) locations.
Site locations given in Figure 1.
Paired t ‐ test indicated no sIgnificant difference (p ‐ value = 0.92).
......................................
17
Figure 6.
Percentage of watershed in impervious land use category vs O/E score.
FIgure A represents imperviousness within entire watershed.
Figure B represents imperviousness within watershed <1 km upstream of site.
..........................
18
Figure 7.
Map of Lake Tahoe Basin with color ‐ coded symbols related to condition categories based on O/E scores.
boxes indicate regions of the Basin where patterns were observed (a – north shore/Incline Village, b – eastern shore (NV), c – south shore (Upper Truckee River and Trout Creek), and d ‐ the southwest shore…………….………………………………………………25
Figure 8.
Good and Excellent sites on Incline, Third, Wood and Griff Creeks on the north shore of Lake Tahoe.
O/E scores shown in boxes relative to urban development, 2009 and 2010.
This region shows Excellent quality despite adjacent and significant urbanization.
Green circles denote Excellent and blue circles denote Good O/E scores……………….…………………..26
Figure 9.
Low gradient reaches of Upper Truckee River and Trout Creek in the south shore of Lake Tahoe with Marginal conditions.
O/E scores in boxes.
Impervious surface from TRPA 2010 layer……………………………………………………………………...27
Figure 10.
Marginal conditions on the Upper Truckee River along Meyers Flat.
O/E scores shown in boxes.
Impervious surface from TRPA 2010 layer.
Marginal condition indicated by red circle, Good condition by blue circle and Excellent by
green circle…………………………………………………………………………………………………………………………………………………………………….…27
L
IST
OF
F
IGURES
IN
A
PPENDICES
Figure 11.
Photo of site on General Creek (634R10GNL, O/E=0.53) ......................................................................................
43
Figure 12.
Photo of site on the Upper Truckee River (CAT08722 ‐ 013, o/E=0.62) ..................................................................
43
Figure 13.
Photo of site on the Upper Truckee River (CAT08722 ‐ 017, O/E=0.61) .................................................................
44
Figure 14.
Photo of site on Glen Alpine Creek (CAT08722 ‐ 025, O/e=0.67) ............................................................................
44
Figure 15.
Photo of site on the Upper Truckee River (CAT08722 ‐ 041, O/E=0.46) .................................................................
45
Figure 16.
Photo of site on Trout Creek (CAT08722 ‐ 050, o/e=0.34) ......................................................................................
45
Figure 17.
Photo of site on the Upper Truckee River (CAT08722 ‐ 053, o/e=0.62) ..................................................................
46
Figure 18.
Photo of site on Trout Creek (CAT08722 ‐ 061, 0.59) .............................................................................................
46
Figure 19.
Photo of site on Cascade Creek (CAT08722 ‐ 070, o/e=0.53)..................................................................................
47
Figure 20.
Photo of site on the Upper Truckee River (CAT08722 ‐ 085, o/e=0.63) ..................................................................
47
Figure 21.
Photo of site on Mckinney Creek (CAT08722 ‐ 103, o/e=0.25) ...............................................................................
48
Figure 22.
Photo of site on Cascade Creek (CAT08722 ‐ 110, o/e=0.62)..................................................................................
48
Figure 23.
Photo of site on the Upper Truckee River (CAT08722 ‐ 114, o/e=0.50) ..................................................................
49
Figure 24.
Photo of site on the Upper Truckee River (CAT08722 ‐ 141, o/e=0.61) ..................................................................
49
Figure 25.
Photo of site on North Canyon Creek (p ‐ TACHAT08722 ‐ 063, o/e=0.69) ..............................................................
50
Figure 26.
Photo of site on Burke Creek (p ‐ TACHAT08722 ‐ 138, o/e=0.68) ...........................................................................
50
Figure 27.
Photo of site on Burke Creek (p ‐ TACHAT08722 ‐ 074, o/e=0.60) ...........................................................................
51
4
ABSTRACT/SUMMARY
Of the many efforts aimed at evaluating conditions in the Lake Tahoe Basin, the Stream Biological Integrity Monitoring
Program was designed to characterize the biological health of streams in the Lake Tahoe Basin by sampling benthic macroinvertebrates (BMIs) and a range of water quality and physical habitat parameters.
Benthic macroinvertebrates are the most commonly used biological indicator in stream monitoring programs because they are ubiquitous in stream environments and their species composition in a sample can indicate a range of anthropogenic disturbance levels.
The main objective of this project was to refine and develop data evaluation methods for benthic macroinvertebrate data collected in the Lake Tahoe Basin to better guide the consistent evaluation and reporting of stream conditions and to inform management and policy decisions.
The River Invertebrate Prediction and Classification System (RIVPACS) model was selected to evaluate the Tahoe Basin benthic macroinvertebrate data because it can be used in regional comparisons and is expected to be one of the primary tools used for bioassessment of California streams in the foreseeable future.
This study evaluated the status and trend of benthic macroinvertebrate data collected in 2009 and 2010 from 85 sites located within 29 watersheds of the Tahoe Basin.
Approximately 56% of streams sampled in 2009 and 2010 were categorized as “Excellent,” 18% were categorized as “Good”, while 26% were “Marginal.” Trend analysis of a sub ‐ set of sites revealed no significant differences between the 12 sites sampled in 2003 and nearby sites sampled in 2009 ‐ 2010 (p ‐ value = 0.92).
Precipitation and discharge were near average for the years examined, so climate was not likely to have exerted a bias on this assessment of the status and trends of aquatic resources for these years.
There was no apparent relationship between biological condition and the percentage of impervious surface in the upstream watershed; this may be a result of overall low levels of impervious surfaces Basin ‐ wide.
An investigation of habitat variables with biological condition found that Marginal sites tended to have higher water temperatures, more glide and pool habitat, larger coverage of non ‐ woody vegetation and more fine sediment.
Habitat variables associated with better biological condition included more riffle habitat, boulders, higher slope and dissolved oxygen.
Specific Marginal sites seemed to have common sources of degradation including lower stream flow discharge, more bank erosion and more sand and fine streambed substrate.
The stream evaluation methods presented in this study are meant to serve as a model for future assessment of streams in the Lake Tahoe Basin.
INTRODUCTION
B ACKGROUND
There are numerous efforts aimed at evaluating conditions in the Lake Tahoe Basin (hereafter Basin) in order to manage the natural resources and ecological integrity of the Basin.
In the Environmental Improvement Program (EIP) update document, key goals for 2008 ‐ 2018 included: “refine and implement monitoring and evaluation programs to assess the status of environmental conditions and determine the effectiveness of EIP restoration projects” (TRPA, 2010a).
Specifically, Subtheme 4b of the EIP seeks to identify environmental indicators and develop approaches for monitoring and evaluation.
One of the efforts working toward the EIP goals is the Lake Tahoe Status and Trend Monitoring & Evaluation (M&E)
Program, which is designed to assess the Lake Tahoe area’s environmental condition using a range of environmental indicators (www.tahoemonitoring.org).
Environmental indicators examined in the M&E program include: nutrients, fine sediment, air pollutants, forest vegetation, land use and streams.
More specifically, indicators of stream condition include pollutants, hydrology and biological integrity.
In 2009, the Stream Biological Integrity Monitoring Plan was developed and implemented to characterize the relative biological health of Lake Tahoe stream environments by sampling benthic macroinvertebrates (BMIs) and a range of water quality and habitat parameters throughout the Basin.
It is envisioned that these data will be regularly collected, evaluated, and reported over time in order to inform and adjust policy and management actions (TRPA, 2009; TRPA, 2010b) to better meet Regional environmental quality goals.
5
B ENTHIC M ACROINVERTEBRATES AS B IOLOGICAL I NDICATORS
Benthic macroinvertebrates (or BMI; e.g., freshwater insect larvae, snails, worms, crustaceans) are one of the most commonly used biological indicators in stream monitoring programs (Merritt et al., 2008; Resh, 2008) because they can provide insight into current and past conditions, integrate the effects of cumulative stressors, and are directly related to beneficial uses prescribed by the Clean Water Act (Barbour et al., 1999; Bonada et al., 2006).
Benthic macroinvertebrates are long ‐ lived compared to algae and are ubiquitous in most stream environments compared to fish that can be limited by migrational barriers or poor water quality.
In addition, different BMI species can tolerate a range of disturbance levels in streams and are more cost ‐ effective to sample than toxicity and chemical testing (Rosenberg and Resh, 1993; Yoder and
Rankin, 1995).
There are several ways to explore and interpret BMI data for use in monitoring and evaluation programs.
A common practice is to simply calculate biological metrics (measures of biological condition that respond predictably and reliably to independent measures of human disturbance).
An example of a biological metric is the proportion of the sample that is made up of “tolerant” organisms.
A higher proportion of tolerant organisms would indicate a more degraded condition.
However, many metrics are generated at a coarse scale (e.g., the proportion of individuals from the generally sensitive
orders Ephemeroptera, Plecoptera and Trichoptera or ‘EPT’ reflects the taxonomic composition at the order level) and can therefore lead to generalizations about stream condition or be misleading (Lenat & Resh, 2001).
Another common practice used to interpret BMI data is to calculate multimetric indexes (MMIs) by converting metrics representing different tolerances, trophic levels or habits into to unitless scores and summing the scores to evaluate biological condition at a site (Karr and Chu, 1999).
California does not currently have a statewide MMI, but there are a few existing multi ‐ metric biological indices that can be referenced for use in the Tahoe Basin: 1) the benthic index of biological integrity (B ‐ IBI) developed for the Pacific Northwest (Karr, 1998; Karr and Chu, 1999), 2) a northern California
MMI (Rehn et al., 2005; Rehn et al., 2007) and 3) a Sierra Nevada MMI (Herbst and Silldorff, 2006).
In 2007, a MMI was developed for the Lake Tahoe Basin based on macroinvertebrate samples collected from 171 locations on 10 streams in 2003 (Fore, 2007).
The Tahoe Basin MMI was composed of six metrics (stonefly richness, caddisfly richness, number of long ‐ lived taxa, number of intolerant taxa, clinger richness, and % non ‐ insect taxa) that were
highly correlated with localized disturbances (Fore, 2007).
Although the Tahoe Basin MMI can be useful for evaluating conditions within the Tahoe Basin, it is less applicable for a comparison of conditions in other regions or climates.
Another method used in bioassessment that has greater potential for regional comparison is a predictive model called the
River Invertebrate Prediction and Classification System or RIVPACS (Hawkins et al., 2000).
This model was originally developed in the 1970’s by the Institute of Freshwater Ecology in Great Britain (Wright, 1994), but has since been adopted
by other countries and has influenced the European Union Water Framework Directive (WFD).
Models that predict BMI community structure based on environmental gradients at reference sites can be used to assess the health of disturbed streams by setting biological expectations as though the disturbance were absent.
These models reduce the influence of bias and can be used for regional assessments, detecting trends caused by land use or climate change, and evaluating site ‐ specific disturbances or restoration efforts.
RIVPACS uses cluster analyses to separate reference sites into groupings based on biology, and then predicts group membership based on physical variables unaffected by human stressors such as watershed area, latitude, geology, temperature, and precipitation.
Probabilities can then be generated for the likelihood of observing species given the stream’s physical setting (Hawkins et al., 2000).
The Expected score (E) is based on both the taxa present and the probability of detection (how commonly found).
Low ratios of Observed to Expected numbers of taxa (O/E) indicates degradation because many of the species predicted to occur may have been lost as a result of anthropogenic disturbance.
Conversely, high O/E scores (closer to 1.0) indicate that the assemblage sampled at a given site (O) was more similar to an undisturbed reference site (E) and therefore
indicates higher quality, more pristine conditions.
All of the bioassessment techniques discussed above were considered for use in analyzing the 2009 and 2010 benthic macroinvertebrate data collected in the Tahoe Basin, but after much consultation with bioassessment experts (R.
Mazor,
6
P.
Ode, A.
Rehn, T.
Suk, pers.
comm., 2011) it was decided that the RIVPACS model would be most prudent for the following reasons:
The observed to expected ratio (O/E) is less influenced by a priori bias related to site characteristics and it allows for the determination of reference sites a posteriori ;
RIVPACS is more appropriate for use in regional comparisons compared to biological metrics or MMIs; and
Biologists are currently working toward the development of a California statewide index that combines the ratio
of observed to expected taxa (O/E) and a predictive multimetric index (pMMI).
This combined index is called the
“California Stream Condition Index” (CSCI) and is expected to become the standard method for evaluating streams in California in the future (R.
Mazor, pers.
comm., 2013).
S TUDY O BJECTIVES
One element that was missing from the Stream Biological Integrity Monitoring Plan was clear guidance on how to
evaluate and report bioassessment information.
Consequently, the primary objectives of this project were to:
1) Refine and develop data evaluation methods for benthic macroinvertebrate (BMI) data in the Lake Tahoe Basin to better guide manager’s evaluation and reporting of stream condition information to decision makers and the public;
2) Select methods that can be used in comparisons with other regions in the future (e.g., California statewide
RIVPACS);
3) Analyze BMI data collected from the Lake Tahoe Basin in 2009 and 2010 to examine the status of streams from those years;
4) Compare 2009 and 2010 BMI results with earlier BMI data in the Tahoe Basin to look for possible trends;
5) Develop and recommend methods for future trend analysis; and
6) Explore potential relationships between habitat parameters, levels of urbanization, and O/E scores at each site sampled in 2009 and 2010.
This analysis is intended to determine if physical habitat variables gave satisfactory explanations for low O/E scores and determine if certain types of habitats or stream conditions were more likely to have lower O/E scores.
The resulting analysis from this effort will aid in identifying the current and relative condition of streams (or stream segments) so managers can better target restoration efforts.
In addition, this information will enable the characterization of trends in stream condition over time and may provide some evidence of the effectiveness of overall policy and management actions.
METHODS
S TUDY S ITES
Biological (i.e., benthic macroinvertebrate), water quality, and physical habitat data were collected between June and
September in 2009 and 2010 from 85 sites located within 29 watersheds of the Lake Tahoe Basin (see Figure 1 for site locations and Appendix I for site attributes).
Of the 85 sites sampled, 54 sites were located in California and were sampled by the Tahoe Regional Planning Agency (TRPA).
Thirty ‐ one sites located in Nevada were sampled by the Nevada
Department of Environmental Protection (NDEP).
Five sites were designated as reference sites.
Ten duplicate samples were collected for a total of 95 samples from 85 sites.
7
FIGURE 2.
LOCATIONS OF LAKE TAHOE BIOASSESSMENT STUDY SITES SAMPLED IN 2009 AND 2010 WITH LAST THREE DIGITS
OF SITE ID CODE.
PREFIX 08722, 634009 AND 634010 HAVE BEEN OMITTED FOR CLARITY.
SEE APPENDIX 1 FOR FULL SITE IDS.
8
S
AMPLING
D
ESIGN
The status and trend sampling was designed to examine the status of streams each year, trends over time, and conditions at targeted locations (Fore, 2007; TRPA, 2010b).
The status monitoring used a probabilistic sampling design to randomly select different sampling locations each year.
Random selection is intended to minimize potential influences of sampling bias (that can result from non ‐ random selection) and is more appropriate for use in regional comparisons (TRPA, 2010b).
Four sites sampled by NDEP in 2009 (i.e.
TAH03GlenBk ‐ 1, TAH03Logan ‐ 1,
TAH03MarlTrib ‐ 1, and TAH03NFKLogan ‐ 1) were not part of the probabilistic sampling, but were originally selected as potential reference sites.
Two of these four sites (TAH03GlenBk ‐ 1, TAH03NFKLogan ‐ 1) were designated as reference sites, while the other two were not.
For trend monitoring, sites were selected randomly in the first two years and then the same sites will be re ‐ sampled every two years to detect if changes in condition are occurring over time.
Lastly, targeted sites were selected in order to address specific questions such as those related to the effects of land use practices and/or restoration efforts in the watershed.
Of the 48 sites sampled each year, the monitoring plan targeted 33% (n=16) of sites to be designated for status monitoring, 50% (n=24) for trend monitoring and 17% (n=8) for targeted reference site monitoring (TRPA, 2010b) (Table 1).
Random selection of sites was done by using a protocol developed by EPA for selecting sites in a manner appropriate for status and trend monitoring (Paulsen et al., 1998; Olsen et al., 1999; USEPA, 2006).
Site locations used in the 2009 ‐ 2010 assessment are shown in Figure 1.
Table 1.
Sampling schedule for the Lake Tahoe Basin showing number of stream sites sampled each year for status (new sites randomly selected each year), trend (sites randomly selected first two years, then in two ‐ year rotation), and reference (same eight sites sampled every year).
Years with fewer sites sampled were a result of dry conditions (B.
Vollmer, pers.
comm.,
2013).
Table adapted from TRPA (2010b).
Year
2009
2010
Total Sites
39
48
Status (33%)
12*
16*
Trend
24*
(25%)
A
Trend B
(25%)
24*
Reference
(17%)
3
8
24 2011
2012
2013
2014
Etc.
48
45
48
48
16*
16*
16*
16*
24
22
24
8
7
8
8
* indicates randomly selected sites
Reference sites were selected randomly during the first two years (2009 ‐ 2010) and in subsequent years the same sites will be re ‐ sampled to make comparisons through time.
During the first few years of the sampling program, additional reference sites (5–10) will also be monitored to help establish expected scores for streams with little or no human influence (TRPA, 2010b).
In order to detect trends in urban/developed areas, 50% of sites were selected
from urban/developed areas and 50% from the non ‐ urban areas.
Urban boundaries were based on TRPA land use designation of either residential, tourist, or commercial.
F IELD AND L ABORATORY M ETHODS
Biological, water quality, and physical habitat field data were collected between June and September of 2009 and
2010 following the methods of California’s Surface Water Ambient Monitoring Protocol (SWAMP) (Ode, 2007).
The “basic” SWAMP method was used for collection of physical habitat and water quality data and the reachwide
benthos (multihabitat) procedure was used for collection of benthic macroinvertebrates (Ode, 2007).
9
H ABITAT AND W ATER Q UALITY DATA
For the SWAMP reachwide procedure, 11 equidistant transects, arranged perpendicular to the direction of stream flow, were established along a 150 ‐ meter reach in order to measure several habitat variables in a systematic manner (Ode, 2007).
Ten inter ‐ transects were established between transects as well.
At each transect, wetted width, bankfull width and heights were determined.
Substrate size distribution was measured by doing a Wolman
Pebble count (Wolman, 1954) of 105 particles collected across the 11 transects and 10 inter ‐ transects.
Cobble embeddedness, gradient, and sinuosity were also measured at each transect.
Canopy cover was measured using a densitometer at each transect.
The areal cover of each of the following channel features were recorded: 1) filamentous algae, 2) aquatic macrophytes, 3) boulders (>25 cm, intermediate axis length), 4) smaller woody debris
(<0.3
m diameter), 5) larger woody debris (>0.3
m diameter), 6) undercut banks, 7) overhanging vegetation, 8) live
tree roots, and 9) artificial structures and trash.
Areal cover for each channel feature is estimated as: 1) absent, 2) sparse (<10%), 3) moderate (10 ‐ 40%), 4) heavy (40 ‐ 75%), or 5) very heavy (>75%).
Human influence factors were measured within a 10m x 50m plot at every main transect.
The presence of 11 human influence categories were recorded relative to this zone: 1) walls/rip ‐ rap/dams, 2) buildings, 3) pavement/cleared lots, 4) roads/railroads, 5) pipes (inlets or outlets), 6) landfills or trash, 7) parks or lawns (e.g., golf courses), 8) row crops, 9) pasture/ rangelands, 10) logging/ timber harvest activities, 11) mining activities, 12) vegetative management (herbicides, brush removal, mowing), 13) bridges/ abutments, or 14) orchards or
vineyards.
Riparian vegetation was also assessed within the 10m X 10m plot as follows.
The vegetation was divided into three zones: ground cover (<0.5
m), lower canopy (0.5
m ‐ 5 m), and upper canopy (>5 m).
The density of the following riparian classes were recorded: 1) upper canopy–trees and saplings, 2) lower canopy–woody shrubs and saplings,
3) woody ground cover–shrubs, saplings, 4) herbaceous ground cover–herbs and grasses, and 5) ground cover– barren, bare soil and duff.
For each vegetative class the areal cover was estimated as either: 1) absent, 2) sparse
(<10%), 3) moderate (10 ‐ 40%), 4) heavy (40 ‐ 75%), or 5) very heavy (>75%).
The percentage of the stream falling in each stream habitat type was identified at each inter ‐ transect.
The types included: 1) cascades/falls, 2) rapids, 3) riffles, 4) runs, 5) glides, 6) pools, 7) dry areas.
Discharge was measured
using the velocity area method, or if necessary, the neutral buoyant object method.
Common ambient water chemistry measurements (pH, DO, specific conductance, alkalinity, water temperature) were taken at the downstream end of each reach in accordance with the SWAMP protocol (Ode, 2007).
Most of the water chemistry measurements (pH, DO, specific conductance, water temperature, and salinity) were measured with a YSI© Professional Plus.
Alkalinity was measured with a Titration LaMotte© Field Kit.
B IOLOGICAL D ATA
Benthic macroinvertebrate samples were collected at each site using the SWAMP reachwide benthos
(multihabitat) procedure (Ode, 2007).
The reachwide procedure for sampling benthic macroinvertebrates used the same 11 transects from the physical habitat data collection to designate sampling locations.
Beginning with the furthest downstream transect, benthic macroinvertebrates were sampled approximately one meter downstream from each transect and the position in the stream channel alternated between 25%, 50% and 75% of the wetted width at each transect (Ode, 2007).
Each benthic macroinvertebrate sample was collected in a 1 ft
2
area using a 500 µm D ‐ net with the opening of the net oriented upstream.
The upstream substrate and/or vegetation was agitated thoroughly in the sampling area to dislodge benthic macroinvertebrates into the D ‐ net.
The 11 samples collected at each reach were composited into a single sample and preserved in 95% ethanol.
All benthic macroinvertebrate samples collected in 2009 and 2010 were sorted and identified by Aquatic Biology
Associates, Inc.
in Corvalis, Oregon.
All benthic macroinvertebrates were identified using the standard taxonomic effort (STE) level established by the Southwest Association of Freshwater Invertebrate Taxonomists (SAFIT)
(Richards and Rogers, 2006), which for insects was typically genus or species (including chironomids).
Upon
10
completion of the identification, Aquatic Biology Associates, Inc.
provided a spreadsheet with taxa and counts for each biological sample.
RIVPACS M ETHODS
In preparation for running the RIVPACS model, the ‘Predictive Models Primer’ on Utah State University’s Western
Center for Monitoring & Assessment of Freshwater Ecosystems website ( http://cnr.usu.edu/wmc/htm/predictive ‐ models/predictive ‐ models ‐ primer ) should be read to gain a more in ‐ depth understanding of how RIVPACS works.
Several tasks were performed in order to generate O/E scores for this project using the RIVPACS model (see
Appendix II for a more detailed step ‐ by ‐ step guide of how to run the RIVPACS model to generate O/E scores).
First, the benthic macroinvertebrate data collected in 2009 and 2010 (by TRPA and NDEP) were compiled and organized into a single spreadsheet.
The compiled data sheet retained as much information about each site as possible, including the minimum three variables needed to run the RIVPACS model: 1) the unique name of the sample, 2) the standard taxonomic effort (STE); and 3) the number of organisms of each taxon within each sample.
Second, each unique taxonomic name was matched with a corresponding Operational Taxonomic Unit (OTU).
The
OTU's for California are maintained by the California Department of Fish and Wildlife (formerly California
Department of the Fish and Game) Aquatic Bioassessment Lab.
The reason for matching the taxonomic names from the Tahoe Basin samples with the predefined OTU's is because the RIVPACS model has a standardized list of taxa that is used to run the model.
Therefore, there cannot be any unrecognizable taxa names in the file.
After matching the taxonomic names to the OTU's, the BMI data were subsampled to a standard of 300 organisms per sample by using an automated subsampling program in Fortran and then converted into a site by taxa matrix using a downloadable matrify.exe
program from the Utah State University’s Western Center for Monitoring &
Assessment of Freshwater Ecosystems website ( http://www.cnr.usu.edu/wmc/htm/predictive ‐
models/usingandbuildingmodels ).
The RIVPACS model is divided into three submodels based on the temperature and precipitation at each site.
Each of the Tahoe Basin sites were assigned to one of the three O/E submodels using the 30 ‐ year average (1961 ‐ 1990) of precipitation and temperature data from the PRISM Climate Group at Oregon State University
( http://prism.oregonstate.edu/ ).
Based on the temperature and precipitation results at each site, all of the sites in
Lake Tahoe Basin were located within submodel 3, so taxa were predicted on the basis of mean monthly temperature and log watershed area.
The resulting BMI matrix and habitat predictor file were used to run the
RIVPACS model.
Again, more detailed methods for running the RIVPACS model are included in Appendix II.
A
NALYSIS
M
ETHODS
S TATUS A NALYSIS
There were several types of analyses conducted to address the study objectives.
First, thresholds for conditional categories based on O/E scores were established based on the standard deviation of the reference site O/E scores
used to build the RIVPACS model (C.
Hawkins, pers.
comm., 2012).
Evaluation of status was conducted by examining the spatial arrangement of O/E scores throughout the Tahoe Basin and descriptive statistics to characterize status in 2009 and 2010.
11
T REND A NALYSIS
Trend analysis was conducted by comparing the results from 2009 and 2010 with benthic macroinvertebrate sampling conducted in 2003 (Fore, 2007).
However, different collection methods were used in 2003, which limits
the applicability of this analysis.
Overall and site ‐ specific trends were evaluated.
A comparison of 2009 and 2010 benthic macroinvertebrate data was also conducted and methods for long ‐ term trend analysis developed.
First, in order to determine the sampling error in 2009 and 2010, the standard deviation
of the differences of O/E scores for eight duplicates was calculated.
This standard deviation (0.11) was the minimum amount that a site needed to differ between two given years in order to account for the sampling error.
Next, a two ‐ tailed t ‐ test was used to compare all sites that were sampled in 2009 with those sampled in 2010.
The mean of O/E scores in 2009 (0.81
± 0.22) was significantly lower than the mean of O/E scores in 2010 (0.94
± 0.24), with a p ‐ value of 0.00697.
However, this type of analysis only gives a coarse look at the data and does not reveal
changes in condition at individual sites (i.e., if some sites improved and some sites got worse).
For analyzing changes at individual trend sites between two different years, a few analysis methods are recommended.
First, a direct comparison of the RIVPACS scores at the same site between two years (e.g., 2009 and 2011) should be examined.
An increase or decrease is only considered if the difference in O/E scores is larger than the sampling error (which can be calculated using the methods described above).
It should also be noted if the change in O/E score between two years puts the site into a different conditional category (marginal, good,
excellent).
Additionally, a paired t ‐ test can be used to see if conditions at the trend sites are showing an overall change in O/E score between two years.
For analyzing changes at individual trend sites over the long ‐ term (once several years of trend data are available), a linear mixed effects model is recommended.
When using the linear mixed effects model, the O/E score is defined as the dependent variable, site is the random factor and time is the fixed factor.
This analysis can be performed in
R using the lme4 package.
If time has a linear effect, you might be able to specify your model like this: lmer(OE ~
Year + (1 + Year|Site)).
In this specification, O/E is a function of Year, with Site as a random factor.
Each site has its own intercept, and year has a different coefficient at each station (R.
Mazor, pers.
comm, 2013).
It is important to keep in mind that trends may not actually exist in the data set and if they do, they are likely site specific.
Trends may also tend to be non ‐ linear.
For further data analysis, the ordination scores could be explored
in addition to the O/E scores.
H ABITAT S TRESSOR A NALYSIS
Lastly, analysis of stressors was conducted at both a landscape ‐ (using watershed attributes derived from GIS) and site ‐ level (habitat attributes derived from field data collected at the reach/site) scale.
At the landscape scale, the percentage of impervious surface cover was calculated to explore the impact of surrounding urbanized and developed land on individual sites.
Calculations were performed in ArcGIS 9.0
(ESRI, Redlands, CA).
The USGS 10m
DEM was used to generate watershed area.
Updated impervious surface coverages (coverage analysis completed in September 2012 of data captured in August of 2010) were obtained from the Spatial Informatics Group.
Percentage of impervious surface was calculated for two zones in each watershed: the entire watershed, and the portion of the watershed within a 1 ‐ km radius upstream of each study site.
Site ‐ level parameters included habitat
data that were collected at the same time as the benthic macroinvertebrates at each site.
Seventy ‐ seven habitat parameters were examined as described above.
12
RESULTS AND DISCUSSION
C ONDITIONAL T HRESHOLD A NALYSIS
There have been many attempts to determine conditional thresholds when evaluating biological metrics and indices (e.g., SWAMP, 2006; C.
Hawkins, pers comm., 2012; Western Center for Monitoring & Assessment of
Freshwater Ecosystems, 2013).
The thresholds for establishing conditional categories used in this study are consistent with other types of analysis being done in the Sierra Nevada Region (Furnish, 2013).
Boundaries in conditional categories were assigned by using a standard deviation of 0.15, which was based on the standard deviation of the reference site O/E scores originally used to build the RIVPACS submodel 3.
Therefore, sites with
O/E scores one standard deviation above and below 1.0
(between 1.15
and 0.85) were categorized as ‘Excellent’, sites with O/E scores between one and two standard deviations below 1.0
(0.7
– 0.85) were categorized as ‘Good’, and sites with O/E scores more than two standard deviations below 1.0
(<0.7) were categorized as ‘Marginal.’
Sites that were higher than 1.0
were also categorized as ‘Excellent', however if the sites scores were more than 2
standard deviations above an O/E score of 1.0
(>1.3) they were examined further.
S
TATUS
(2009
AND
2010)
The status of sites sampled in the Tahoe Basin in 2009 and 2010 found that the majority of sites were in Excellent or Good condition (see Figures 2 & 3).
Approximately 56% (n=48) of samples from 2009 and 2010 were categorized as Excellent, 18% (n=15) were categorized as Good, 26% (n=24) as Marginal.
The finding of generally
Good to Excellent conditions is not surprising as the majority of the watersheds are U.S.
Forest Service lands in the
Lake Tahoe Basin, have been managed for conservation or recreational purposes for the several decades, have
regional landuse policies that severely limit development or the destruction of stream habitat and have undergone extensive restoration efforts.
13
FIGURE 3.
CATEGORIES OF ECOLOGICAL CONDITION BASED ON RIVPACS SCORES, LAKE TAHOE BASIN 2009 ‐ 2010.
14
60
50
40
30
20
10
Histogram
Marginal n=24
of
Conditional
Good n=15
Categories
Excellent n=48
0
<0.7
0.7
‐ 0.85
>0.85
O/E score
FIGURE 4.
HISTOGRAM OF CONDITIONAL CATEGORIES SHOWING SAMPLE SIZE OF EACH FOR 85 SITES SAMPLED IN THE LAKE TAHOE BASIN IN
2009 AND 2010.
T
REND
A
NALYSIS
A benthic macroinvertebrate study was conducted in 2003 to investigate potential indices for assessing riverine ecological health (Fore, 2007).
A comparison of RIVPACS results from the 2003 study with the results from the first two years of the TRPA monitoring program (2009 ‐ 2010) provided an opportunity to look for trends in ecological condition over time.
Figure 4 indicates the spatial location of sites sampled during the two time periods.
The study design was different between time periods, with the 2003 study targeted riffle habitats along the major perennial streams, and the more recent study designed to capture conditions across the entire Tahoe Basin using a
probabilistic sampling design of multiple stream habitat types (reachwide protocol).
Studies have documented the comparability of these two sampling protocols (Gerth and Herlihy 2006, Herbst and Silldorff 2006, Rehn et al.
2007).
Despite this difference, it was possible to identify eleven sites sampled during both time periods that were within a few hundred meters of each other.
The data follow a general 1:1 relationship.
A paired t ‐ test indicated no significant differences between the 12 sites sampled in 2003 and nearby sites sampled in 2009 ‐ 2010 (p ‐ value =
0.92, see Figure 5 for individual site comparisons).
15
FIGURE 5.
MAP OF 2003 (YELLOW DIAMONDS) AND 2009 ‐ 2010 (BLACK CIRCLES) BENTHIC MACROINVERTEBRATE SAMPLING SITES IN THE
LAKE TAHOE BASIN.
16
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Site Name and Number
Fore Data 2003 TRPA Data 09 ‐ 10
FIGURE 6.
COMPARISON OF 2009 ‐ 10 O/E SCORES (P>.5) WITH 2003 O/E SCORES FOR SITES SAMPLED IN NEARBY (<200 M) LOCATIONS.
SITE
LOCATIONS GIVEN IN FIGURE 1.
PAIRED T ‐ TEST INDICATED NO SIGNIFICANT DIFFERENCE (P ‐ VALUE = 0.92).
H YDROLOGIC C ONDITIONS
Unusual weather conditions can influence the reproductive success and growth of benthic macroinvertebrates
(Resh et al., 2012).
Particularly dry years can lower invertebrate abundance by limiting habitat availability and quality, and food availability (Resh et al., 2012).
Wet years can lower abundances if flood events scour the channel beds during critical life stages.
We examined the average monthly precipitation during the period of the study,
2009 ‐ 2010, and during the previous study, 2003, and compared it to long term averages (1890 ‐ 2012, Table 2).
The results indicate that 2003 was slightly drier than average on the west (California Interior Basin) and east shores (NE
Nevada) of Lake Tahoe.
On the west shore, 2009 was wetter than average, while the climatic region influencing the east shore was drier than average that same year.
For the west and east shore, 2010 was near average, but slightly drier than average on the east shore.
The general trends of the precipitation data are supported by discharge data collected by the USGS.
For example, the total annual flows for Ward Creek at the mouth for Water Year 2003 (21.8
million m
3
) are comparable to 2009 and 2010 (18.2
and 21.6
million m
3
respectively).
We conclude that because precipitation and discharge were near average for all years examined, climate was not likely to have exerted a bias on this assessment of the status and trends of aquatic resources for these years.
17
TABLE 2.
LAKE TAHOE REGION AVERAGE MONTHLY PRECIPITATION (INCHES) DURING THE STUDY PERIOD (WESTERN REGIONAL CLIMATE
CENTER).
Basin
Average
2003
1
2009
2
1890 ‐ 2012
2010
2
West Shore
(California NE interior)
1.14*
0.89
Dry
1.88
Wet
0.9
Dry
East Shore
(NW Nevada)
0.78
0.68
Dry
0.53
Dry
0.8
Avg
* All data shown in inches.
collection (Fore, 2007).
2
1
Time
Time period shows monthly rainfall during the 2003 benthic macroinvertebrate pilot
period covers data from 2009 and 2010, the first two years of the TRPA monitoring plan.
I
MPERVIOUS
S
URFACE
The results of the percentage impervious surface analysis at full watershed scale and within a 1 ‐ km radius of the upstream watershed of each sample site did not show a significant correlation with O/E scores (Figure 6).
This finding is similar to what Fore (2007) reported for the data collected in 2003.
For example, there were high O/E scores at sites in the more urbanized Incline Village area, and low O/E scores in forested upper elevation sites along the east shore.
It also may reflect the fact that urbanization levels across Lake Tahoe remain very low (0.2
‐
13%).
1.4
1.4
1.2
1
1.2
1
O/E
Score
0.8
0.6
0.4
0.2
0
0% 5% 10% 15%
O/E
Score
0.8
0.6
0.4
0.2
0
0% 10% 20% 30%
A.
Impervious Area of Watershed
B.
Impervious Area in Watershed, <1 km upstream of site
FIGURE 7.
PERCENTAGE OF WATERSHED IN IMPERVIOUS LAND USE CATEGORY VS O/E SCORE.
FIGURE A REPRESENTS IMPERVIOUSNESS
WITHIN ENTIRE WATERSHED.
FIGURE B REPRESENTS IMPERVIOUSNESS WITHIN WATERSHED <1 KM UPSTREAM OF SITE.
Considering the percentage of impervious surface in the full upstream watershed, only two of 83 sites evaluated had a percentage of impervious surface greater than 5% (Figure 6).
Only six of 83 sites evaluated had impervious surface greater than 3%.
Examining the 1 ‐ km radius data, only three of 77 sites evaluated had impervious surface greater than 15% and only nine of 77 sites had impervious surface greater than 10% (although 0/E values were calculated for 85 sites, we were unable to calculate watershed area for all 85 due to modified drainage patterns in urban areas).
These statistics reflect the tendency towards lake ‐ level development and protection of upper elevations in National Forest, managed mainly for recreation and wildlife habitat.
Typically, watershed studies find aquatic impacts of watershed development initiating at 5 ‐ 10% impervious area (Schueler, 1994; Booth and
Jackson, 1997; Wang et al., 1997).
The principal agent of change is hydrological alteration such as the timing and
18
size of peak flows (Booth, 2005).
However, in large, urbanized metropolitan areas, low levels of imperviousness (5 ‐
10%) still cause marked degradation to stream communities (Cuffney et al., 2010).
The impervious surface analysis suggests that the majority of Lake Tahoe watersheds are under the threshold at which urbanization is expected to cause degradation of aquatic conditions (generally between 5 ‐ 10%) (e.g., Brabec et al., 2002).
TRPA policies in place since 1987, significantly regulate the rate of new development.
Regulations also limit the creation of new impervious coverage in stream zones (TRPA, 2012).
Furthermore, the overall undeveloped surrounding landscape in the Tahoe Basin held by the Lake Tahoe Basin Management Unit of the
United States Forest Service may serve as a ‘buffer’ to impacts of imperviousness.
We discuss exceptions to this
observation from the Upper Truckee and Trout Creek watersheds below.
H ABITAT S TRESSORS
We also investigated potential habitat stressors driving O/E scores at each site using two approaches.
For the first approach, we plotted each of the 77 site ‐ level habitat variables against corresponding O/E scores and then looked for associations between 0/E score and habitat variables (Table 3).
In the second approach, we examined the full suite of habitat variables at each site that scored in the Marginal (<0.7) range to see if explanatory stressors were present.
TABLE 3.
SIGNIFICANT (P<0.05) LINEAR REGRESSIONS BETWEEN SITE LEVEL HABITAT VARIABLES AND BENTHIC INVERTEBRATE O/E SCORES.
Habitat Variable
Linear Regression
r
2
*
Description
Glide Positive Linear Relationship
O/E = 1.04
‐ 0.00426
Glide (%) 16.1
Pool
O/E = 0.954
‐ 0.00941
Pool (%) 17.6
Negative Linear Relationship
Riffle
O/E = 0.714
+ 0.00415
Riffle (%) 19.6
Positive Linear Relationship
Fish Cover
Barren
Cover
Boulders
Ground
(<.5m)
O/E = 0.811
+ 0.00303
boulders (%)
O/E = 0.719
+ 0.00505
Barren Ground
Cover (<0.5m)
7
22.3
Positive
Positive
Linear
Linear
Relationship
Relationship
NonWoody
Ground
Plants
Cover (<.5)
O/E = 0.997
‐ 0.00392
NonWoody Plants
Grnd Cov (<.5m) 16.4
Negative Linear Relationship
Fines
O/E = 0.924
‐ 0.00917
Fines (%) 13.2
Negative Linear Relationship
Small Boulder
O/E = 0.801
+ 0.00794
Small Boulder (%) 9.5
Positive Linear Relationship
Large Boulder
O/E = 0.803
+ 0.00777
Large Boulder (%) 8.3
Slope
O/E = 0.805
+ 0.0140
Slope (%) 8.7
Positive Linear Relationship
Temperature Negative Linear Relationship
O/E = 1.28
‐ 0.0391
Temperature (°C) 19.6
*All regressions significant at the alpha = 0.05
level except for Fish Cover Boulders (P ‐ Value = 0.053) and Large
Boulders (P ‐ Value = 0.058)
19
Habitat Variable
Linear Regression
r
2
*
Description
Glide
Glide habitat was correlated with lower O/E scores
O/E = 1.04
‐ 0.00426
Glide (%) 16.1
Pool
Riffle
O/E
O/E
=
=
0.954
0.714
‐
+
0.00941
0.00415
Pool
Riffle
(%)
(%)
17.6
19.6
Pool habitat was correlated with lower O/E scores
Riffle habitat was correlated with higher O/E scores
Fish cover boulder habitat was correlated with higher O/E scores
Fish Cover Boulders
Barren Ground
Cover (<.5m)
O/E = 0.811
+ 0.00303
boulders (%)
O/E =
Cover
0.719
+
(<0.5m)
0.00505
Barren Ground
7
22.3
Barren ground cover was correlated with higher O/E scores
NonWoody Plants
Ground Cover (<.5)
O/E = 0.997
‐ 0.00392
NonWoody
Plants Grnd Cov (<.5m) 16.4
Grass cover was correlated with lower O/E scores
Fines
O/E = 0.924
‐ 0.00917
Fines (%) 13.2
Fines were correlated with lower
O/E scores
Small Boulder
Large Boulder
O/E = 0.801
+ 0.00794
Small Boulder(%) 9.5
O/E = 0.803
+ 0.00777
Large Boulder
(%) 8.3
Small and large boulders were correlated with higher O/E scores
Slope
O/E = 0.805
+ 0.0140
Slope (%) 8.7
Slope was correlated with higher O/E scores
Temperature
O/E = 1.28
‐ 0.0391
Temperature (°C) 19.6
Higher Temp.
was correlated with lower O/E scores
*All regressions significant at the alpha = 0.05
level except for Fish Cover Boulders (P ‐ Value = 0.053) and Large
Boulders (P ‐ Value = 0.058)
The significance of a linear relationship between the habitat variables and O/E scores was determined using the regression coefficient r
2
.
Nine habitat variables exhibited significant ( α = 0.05) correlations with O/E scores (see
Table 3), with an additional two variables just over 0.05.
The sign of the linear correlations shown in Table 3 indicate whether the correlations were positive or negative.
For stream reach types, higher percentages of glides and pools were associated with lower O/E scores, while higher percentages of riffles were associated with higher O/E scores.
This finding supports previous observations that higher densities of benthic macroinvertebrates are found in riffle habitat compared with other habitat types
(Brown & Brussock, 1991).
Moreover, increasing percentages of boulders providing fish cover, and increasing percentages of small and large boulders were associated with higher O/E scores compared with a negative association between percentage of fines and O/E scores.
This finding is supported by previous observations that higher benthic macroinvertebrate diversity is generally observed in coarser grain size channel beds and BMI communities are deleteriously affected by stream sedimentation (Jones et al., 2012).
Stream reaches with lower slopes or higher temperatures tended to have lower O/E scores.
The relationship with slope may be associated with the trends discussed previously, as steeper reaches are more able to flush away fine particles (Montgomery & Buffington, 1997).
Additionally low gradient sites tend to have more human disturbance.
20
Thus these sites are both vulnerable to disturbance because of their low gradients, and likely to be situated in areas of high human disturbance.
Temperature was negatively correlated with O/E scores.
Higher temperature sites did not support Expected (E) populations of benthic macroinvertebrates in the absence of disturbance.
These findings are consistent with previous studies that examined the effects of temperature on benthic macroinvertebrates (e.g., Burgmer et al., 2007).
Finally, an interesting correlation was observed between riparian ground cover and O/E scores.
Sites with higher non ‐ woody ground cover (grasses and herbaceous vegetation <0.5
m in height) were associated with lower O/E scores.
Moreover, sites with higher amounts of “barren ground” cover (vegetation <0.5
m in height) were associated with higher O/E scores.
Grass and riparian trees were inversely related.
We infer that the sites with high non ‐ woody plant ground cover have more grasses and fewer riparian trees.
Moreover, the sites with less barren ground (in the ground cover category, i.e.
<0.5
m in height) also have more grass and fewer riparian trees.
In summary, areas with dense shrubs and trees cover had greater O/E scores then areas that were more open, sunny and grassy.
Several factors may explain this result.
Open areas are more exposed to solar radiation and cause greater stream temperatures than stream segments with shade created by riparian shrubs and trees.
Thick riparian areas, in addition to providing shade, drop leaf litter into the stream supporting the base of the benthic macroinvertebrate food web (Delong & Brusven, 1994).
Also, the presence of grass may indicate former pasture.
Cattle and sheep grazing are reported for their adverse effects on stream channels (Trimble and Mendel, 1995;
Belsky et al., 1999).
These effects can persist for decades (Harding et al., 1998; Neff et al., 2005).
It is possible that these sites have yet to recover from the impacts of over a century of grazing (now banned in the Basin).
It is worth noting that although nine habitat variables exhibited significant ( α =0.05) correlations with O/E scores
(Table 3), a multi ‐ metric habitat index might better reflect the multiple factors related the BMI assemblages and result in higher R
2
values.
Multiple physical habitat factors influence BMIs (Barbour et al., 1999) and thus exploring the influence of a multiple habitat variables at once could be more informative for guiding policy and management.
Step ‐ wise or all ‐ possible multiple regression analysis are analytical methods that could be used in the future to narrow the list of habitat variables to those that explain most of the variation in O/E scores (i.e., identify those independent habitat variable that most consistently predict O/E scores).
H
ABITAT
C
HARACTERISTICS OF
“M
ARGINAL
”
S
ITES
We performed further habitat analysis for sites with O/E scores in the Marginal (O/E<0.7) range, (Table 4) to characterize each site and highlight the variables that were most descriptive of the degraded conditions and possible causative stressors.
The average of the values for the sites receiving Good and Excellent O/E scores are listed for comparison in Table 4.
In the Marginal sites, we were able to identify possible causes for low O/E scores.
Photos of selected Marginal sites are included in Appendix III.
Eleven of the 23 Marginal sites were located on the lower elevation reaches of the Trout Creek and Upper Truckee
River watersheds.
These sites, plus sites at Cascade Creek (634010110) and North Canyon Creek (08722 ‐ 030) had high levels of fines, sand or embeddedness, and bank erosion.
We attribute these stressors as the possible causative factors for the poor habitat conditions represented by the O/E scores.
Most of the low gradient sites also had very open canopy conditions, with limited riparian shade, and the implication of limited localized allocthonous loading (Delong & Brusven, 1994).
However, high levels of fine sediment/sand and open canopy conditions are natural features of low gradient streams and are factors that the RIVPACS model does not accurately assess.
However, the new California CSCI is anticipated to do a better job at evaluating low gradient sites because more low gradient sites were used to develop the CSCI and the model uses more input parameters to determine a score for a site (R.
Mazor, pers.
comm, 2013).
It will be helpful for TRPA to pay particular attention to the CSCI scores of low gradient sites in the Tahoe Basin relative to the amount of impact know about those sites as a way to gage how well the California statewide model performs for those sites.
21
The percentage of non ‐ woody plants in the <0.5
meter height ground cover category is an indication of grass and herbaceous cover.
Marginal sites had substantially higher grass cover than the average of the Good and Excellent
O/E score sites (Table 4).
We suspect that sites with higher grass cover may be former pasture areas recovering from the sedimentation and channel impacts of historical overgrazing as discussed earlier.
These sites also exhibited an open canopy cover because they are lower in the watershed, and thus are larger creeks, with the broader floodplains characteristic of lower gradient reaches.
Another group of sites with low O/E scores may be a result of a combination of sedimentation as described above
(elevated sand and fines or substantial bank erosion) and very low flow conditions leading to poor habitat quality
(Table 4).
Most of these sites with low flow had good canopy cover (6 of 7 sites with canopy data).
It is difficult to say, given the sedimentation present, if higher flow levels would have improved O/E scores.
The sites, with the exception of Cascade Creek were all from watersheds on the drier east shore of Lake Tahoe.
The eastern side of the Basin receives much less precipitation than the western side (Table 2).
General Creek (634R10GNL) and Glen Alpine Creek (634009025) sites had low O/E scores possibly resulting from low flow conditions and alteration of flow by beaver dams.
Percentages of fines and sand were quite low at these sites, despite some bank erosion.
The General Creek site was chosen as a reference site originally and in the 2003 assessment it had Excellent O/E scores.
The fact that there has been no site disturbance or change since the 2003 sampling, lends support to the proposition that the change in discharge led to the decline in O/E score at the
General Creek (634R10GNL) site.
At the Glen Alpine Creek (634009025) site, the low flow conditions may have led to elevated temperatures and low dissolved oxygen levels and resulted in a lower O/E score.
Three sites appeared to be impacted for other reasons.
A site on the Upper Truckee River (634010165) likely had a source of excess nutrients causing algal growth.
Little signs of degradation could be found for the Logan Creek site
(Tah3Logan ‐ 1) and discharge data were not available for this site.
As the North Fork Logan Creek had very low flow
(Table 4), which could be a causative factor for this site as well.
McKinney Creek (634010103) had low canopy cover, and was relatively warmer with little evidence of sedimentation.
Three sites, not in the Marginal categories (Griff Creek (63409040), Trib to Griff Creek (63409024), and Lonely
Gulch (63409075)) had low flow but reasonable O/E scores.
One explanation for these sites is that they are small, narrow creeks (~1.5
m bankfull width) as compared to 6m bankfull width for the General Creek site and Glen
Alpine site.
These sites had a smaller watershed area than the sites with large bankfull width and thus the RIVPACS model would predict fewer BMI taxa and the O/E ratio would be higher for the same Observed (O) benthic macroinvertebrate results.
Three sites had exceptionally high O/E scores (>1.3, Third Creek 08722 ‐ 028, First Creek 08722 ‐ 88, and Trout Creek
634R10TRT).
Examination of habitat variables for the sites indicated that the “Excellent” condition classification was warranted.
All three sites had low percentages of fines and sand, and high percentages of riparian canopy or dense cover of low canopy and shrubs (634R10TRT).
All three sites had stream water with adequate depth (15 ‐ 29 cm), flow and cool temperatures.
22
TABLE 4.
HABITAT VARIABLE ANALYSIS OF MARGINAL (O/E SCORE < 0.7) SITES.
Site / Site Number
O/E
Score
Discharge
#
(m
3
/s)
Canopy
Cover^
NonWoody
Gr.Cover
Erod.
Bank
Vuln.
Bank
Fines Sand Additional Factors
Possible source of
Degradation
+
Avg value for sites with O/E of Good or
Excellent (>0.7)
Trout /050
Upper
Upper
Trout
Upper
Upper
Truckee
Truckee
/061
Truckee
Truckee
/041
/114
/141
/017
Upper Truckee/013
Cascade/ 110
Upper Truckee /053
Upper Truckee /085
N Canyon /063
0.98
0.34
0.46
0.5
0.59
0.61
0.61
0.62
0.62
0.62
0.63
0.69
Slaughterhouse /111 0.47
Unnamed /010 0.53
Burke/ 074 0.60
Cascade /070 0.53
NFLogan/NFkLogan ‐ 1 0.67
Burke /138 0.68
Marlette /MarlTrib 0.69
0.35
0.57
0.42
0.55
0.46
0.99
0.18
1.47
0.28
0.19
0.08
NA
0*
0.002
0.003
0.03
0.01
0*
0.015
54
0
5
7
46
0
65
1
15
41
9
29
93
95
NA
59
82
94
76
27.7
86
50
64
54
75
5
50
31
56
33
85
11
20
85
6
67
76
0.0
6
0
18
14
0
0
0
14
32
NA
NA
NA
NA
14
NA
NA
NA
4.5
23
18
0
36
50
14
50
18
59
0
27
45
NA
NA
NA
NA
32
NA
NA
NA
10.5
39
9
10
17
3
3
25
22
4
16
88
86
44
100
0
30
48
8
24
60
23
60
20
36
16
30
47
15
48
0
8
56
0
4
54
42
42
13.7
o
C
38% fine gravel
14.2
o
C 29% hardpan
10.6% slope 53%
14.4
o
C 22% fine gravel
17.4
o
C
39%
38%
embed embedded
12.6
o
C embedded
Fines
Fines
Fines
Fines
Fines
Fines
Fines
Fines/Temp
Fines
Fines
Fines
Fines/Flow
Fines/Flow
Fines/Flow
Fines/Flow
Fines/Flow
Fines/Flow
Fines/Flow
General /R10GNL
Glen Alpine Cr./ 025
Logan /Logan ‐ 1
0.53
0.67
0.67
0.00
0.02
NA
¥
14
40
97
20
24
39
23
0
NA
45
50
NA
NA
17
4
10
7
24
DO: 3 mg/l 14.3
o
C
Flow
Flow/Temp
Flow?
McKinney /103 0.25
0.10
11 14 0 0 7 8 Temp
Upper Truckee /165 0.55
0.18
7 44 0 32 NA 22 Algae ‐ DO: 17 mg/l Nutrients
#
Bolded numbers were greatly different than average values for Good and Excellent sites.
^ All values in percent except for O/E scores and Discharge.
*Too shallow to measure flow.
NA indicates data not available.
DO indicates Dissolved Oxygen.
= excess fines; Temp = high temperatures; Nutr.
= excess nutrients.
¥
While flow was not
+
Suspected cause of degradation: Flow = insufficient discharge; Fines measured at Logan Creek, it is the suspected stressor as the wetted width was 58 cm and wetted depth 3.4
cm, and its tributary, the North Fork of Logan Creek, had low flow.
17.5
o
C
23
R
EGIONAL
S
UMMARY
Examining the entire Basin, a few regional trends were observed.
Figure 7 shows the four regions of the Lake
Tahoe Basin that are discussed below.
Figure 7.
MAP OF LAKE TAHOE BASIN WITH COLOR ‐ CODED SYMBOLS RELATED TO CONDITION CATEGORIES BASED ON O/E SCORES.
BOXES
INDICATE REGIONS OF THE BASIN WHERE PATTERNS WERE OBSERVED (A – NORTH SHORE/INCLINE VILLAGE, B – EASTERN SHORE (NV), C –
SOUTH SHORE (UPPER TRUCKEE RIVER AND TROUT CREEK), AND D ‐ THE SOUTHWEST SHORE.
24
Starting on the north shore of the Lake (Figure 7A), we uniformly observed O/E scores indicative of Good and
Excellent conditions, despite adjacent urbanization in the Incline Village area (Figure 8).
We hypothesize that the high O/E scores in this region were a result of completely protected upper watersheds, riparian buffers with good canopy cover in the areas with adjacent urbanization, and steep stream gradients right up to the lake which provide the ability to flush out fine sediment and maintain higher quality benthic habitat (Montgomery &
Buffington, 1997).
Figure 8.
GOOD AND EXCELLENT SITES ON INCLINE, THIRD, WOOD AND GRIFF CREEK ON THE NORTH SHORE OF LAKE TAHOE.
O/E SCORES
SHOWN IN BOXES RELATIVE TO URBAN DEVELOPMENT, 2009 AND 2010.
THIS REGION SHOWS EXCELLENT QUALITY DESPITE ADJACENT AND
SIGNIFICANT URBANIZATION.
GREEN CIRCLES DENOTE EXCELLENT AND BLUE CIRCLES DENOTE GOOD O/E SCORES.
On the eastern shore of Lake Tahoe located within Nevada (Figure 7B) we observed high O/E scores close to lake level, and scores indicative of Marginal conditions (<0.7) at higher elevations on the same streams.
All the sites in the region are located within forestland, with close to zero human disturbances.
The forests have not been logged for several decades, although they were heavily grazed and logged in the 19 th
and first half of the 20 th
centuries
(Leonard et al., 1979).
For the Marginal sites on the eastern side of the Basin, we observed high levels of fines and sands, which can clog interstitial spaces, reducing habitat availability for benthic macroinvertebrates (Von Bertrab et al., 2013) This high level of fine sediment reflects the parent material of highly erosive decomposed granite in the eastern watersheds (USDA ‐ NRCS, 2007).
However, the strongest stressor evident at these high elevation sites is very low stream flow levels.
The east shore of Lake Tahoe receives lower precipitation than the rest of the basin
(Table 2).
If the creeks are intermittent at the upper elevations they will not support diverse or abundant aquatic communities.
On the southern end of the Lake the trend is reversed (Figure 7C), with Excellent conditions (O/E > 0.85) in the higher elevation sites and Marginal conditions on the large, low ‐ gradient stream sites (primarily on the Upper
Truckee River and Trout Creek) close to lake level (Figure 9).
Further south, Marginal sites were observed in developed areas in the Meyers Flat area of the Upper Truckee River (Figure 10).
These sites at Meyers Flat, as described in Table 5, are characterized by channel beds with sand and fine particles, low riparian canopy cover percentages, and a high percentage of non ‐ woody, grass cover.
The Meyers Flat sites also have high levels of
25
adjacent urbanization.
The rivers and floodplains of this area have been altered to accommodate bridges, roads and houses, for grazing purposes as mentioned earlier, and for the South Lake Tahoe Airport.
In general, calculating benthic indices of ecological condition for low gradient reaches can be problematic because of the often high level of human disturbance and lack of low gradient undisturbed reference sites (Fore, 2007).
Additionally, low gradient rivers naturally tend to have more sand and fines and fewer oxygen ‐ generating riffles, even without human disturbance, creating conditions for lower a diversity of benthic macroinvertebrates (Doisy and Rabeni, 2001).
FIGURE 9.
LOW GRADIENT REACHES OF UPPER TRUCKEE RIVER AND TROUT CREEK IN THE SOUTH SHORE OF LAKE TAHOE WITH MARGINAL
CONDITIONS.
O/E SCORES IN BOXES.
IMPERVIOUS SURFACE FROM TRPA 2010 LAYER.
FIGURE 10.
MARGINAL CONDITIONS ON THE UPPER TRUCKEE RIVER ALONG MEYERS FLAT.
O/E SCORES SHOWN IN BOXES.
IMPERVIOUS
SURFACE FROM TRPA 2010 LAYER.
MARGINAL CONDITION INDICATED BY RED CIRCLE, GOOD CONDITION BY BLUE CIRCLE AND EXCELLENT BY
GREEN CIRCLE.
26
The western shore of Lake Tahoe appears to have Good and Excellent conditions with four exceptions, all in the southwest (Figure 7D).
A site on General Creek (634R10GNL) had a decline in O/E score compared to 2003, evidently resulting from low flow conditions in 2010.
The other three southwest sites that had low O/E scores all had high stream temperatures.
A site on McKinney Creek (634010103) appears to have low canopy cover and very high water temperature.
A site on Glen Alpine Creek (634009025) had low flow and high temperatures despite a
40% canopy cover.
A site on Cascade Creek (634009070) in addition to having high water temperature, had high levels of bank erosion and channel bed materials embedded in fines.
The southwest part of the Basin is glaciated granitic terrain.
It is possible the overall stream shading is low from lack of tree cover on the many granitic outcrops.
C
ALIFORNIA
S
TATEWIDE
M
ODEL
The State of California is currently in the final stages of developing a new tool to analyze benthic macroinvertebrate data for bioassessment purposes.
This new tool is called the California Stream Condition Index
(CSCI) and will calculate both O/E scores based on a California statewide RIVPACS model and predictive MMI
(pMMI) scores (R.
Mazor, pers.
comm., 2013).
The O/E and pMMI scores will both be on a scale of 0 ‐ 1, so the final stream condition index (or CSCI) will be calculated by taking the average of the O/E and pMMI scores.
The State will provide documentation on how to use these new tools once they are publicly available.
Contact Raphael
Mazor (Southern California Coastal Water Research Project ‐ raphaelm@sccwrp.org), Peter Ode
(pode@ospr.dfg.ca.gov) or Andrew Rehn (CDFW ABL ‐ arehn@sbcglobal.net) for more information.
S
UMMARY
RIVPACS O/E scores were assigned to 85 stream sites within the Lake Tahoe Basin.
48 sites (56%) scored in the
Excellent category, 15 sites (18%) in the Good condition, and 24 (26%) Marginal.
Thus 74% of the sites in the basin were in Good or Excellent condition.
O/E scores in 2009 ‐ 2010 were compared with a pilot study performed in 2003.
Eleven sites were found that had been sampled during both time periods.
A paired t ‐ test indicated no significant differences between the
12 sites sampled in 2003 and nearby sites sampled in 2009 ‐ 2010 (p ‐ value = 0.92).
Impervious surface cover was calculated as a measure of urbanization and was calculated at the watershed scale and within a 1 ‐ km radius extracted from the watershed upstream of each site.
Impervious surface was not found to be correlated with O/E score.
Although some urbanized parts of the Basin such as South Lake
Tahoe had degraded water quality in urban areas, there were also regions such as Incline Village on the north shore with Good conditions in urban areas, and regions such as the east shore, where poor conditions were observed in non ‐ urban areas.
This is different than the findings of many scientific studies linking degradation of water quality with increased impervious area.
The overall impervious surface of Lake Tahoe Basin sites is extremely low, perhaps below the threshold at which impacts are observed.
Also it was observed that many factors lead to stream degradation in the Basin besides the level of urbanization.
Seventy ‐ seven habitat variables were collected for each site.
Significant positive correlations between habitat variables and O/E score were observed for dissolved oxygen, riffle habitat, fish cover provided by boulders, barren ground cover (implying greater canopy cover), small and large boulders, and slope.
Negative correlations were found for glide and pool habitat, nonwoody plant cover (grass and herbs), fines and temperature.
Correlation coefficients were not high because of the high variability between sites and diversity of factors affecting stream condition.
A multimetric habitat index or multiple linear regression approach would provide greater predictive ability.
Habitat variables for sites scoring in the Marginal categories were examined closely to determine possible stressors leading to the impaired conditions.
It was observed that a group of sites on the larger rivers in South
Lake Tahoe were impacted by sedimentation and bank erosion.
A group of sites on the east shore were affected by low flows and sedimentation.
On the southwest shore, a group of sites were affected by high stream temperatures.
27
Four regions were observed around the Lake with characteristic conditions.
O/E scores were high on the north shore of the Lake despite urbanization at lake level.
It was thought that steep stream gradients, riparian buffers and intact canopies created favorable conditions.
On the east shore, O/E values were high at lake level and worse at upper elevations.
Upper elevations were thought to be degraded from low flows and sedimentation from legacy land uses (pasture).
On the south shore, the trend was reversed with Excellent conditions at upper elevations and Marginal conditions on larger rivers with lower gradients.
The larger rivers were affected by sedimentation.
This was attributed to intact forest riparian cover at upper elevations, and channel modification and historic grazing at lower elevations.
Low gradient reaches are less able to flush out sand and fines to maintain good benthic habitat.
The northwest had uniformly good conditions.
The southwest had several streams affected by high temperatures, with one affected by low flow.
It was proposed that low forest cover over glacial outcrops may have led to increased solar heating.
The RIVPACS method for evaluating benthic invertebrate data, in conjunction with detailed habitat assessment according to SWAMP protocols, appears to be a powerful method for determining stream ecological condition in the Lake Tahoe Basin.
R
ECOMMENDATIONS
Based on the findings of this study we recommend the following:
For future statistical analysis it is more important to resample the same sites multiple times instead of sampling randomly selected status sites each year.
We recommend that all of the sites sampled become permanent sites.
Once the CSCI scores are acquired for the Tahoe sites (data for 2009, 2010, and 2011 have been submitted) we recommend that TRPA use the analysis methods described in this report to analyze those scores.
ACKNOWLEDGEMENTS
We thank the following individuals for their assistance in this project: Shane Romsos and Beth Vollmer (Tahoe
Regional Planning Agency), Nicole Shaw (Tahoe Environmental Research Center), Raphael Mazor (Southern
California Coastal Water Research Project), Peter Ode, James Harrington, and Andrew Rehn (CDFW ABL), Chad
Praul, P.E.
(Environmental Incentives, LLC), Thomas Suk (Lahontan RWQCB), Leska Fore (Statistical Design), Joseph
Furnish (USFS), Chuck Hawkins (Utah State University), and Marianne Denton (NDEP).
28
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32
APPENDICES
APPENDIX I: A TTRIBUTE TABLE OF THE EIGHTY ‐ FIVE SITES SAMPLED IN THE L AKE T AHOE B ASIN
IN
2009
AND
2010.
‘R
EF
’
INDICATES REFERENCE SITES
.
Site ID*
634R10BMW
634R10GNL
634R10SAX
634R10TRT
634R10UTR
CAT08722 ‐ 013
CAT08722 ‐ 017
CAT08722 ‐ 018**
CAT08722 ‐ 021
CAT08722 ‐ 023
CAT08722 ‐ 024
CAT08722 ‐ 025
CAT08722 ‐ 033
CAT08722 ‐ 038
CAT08722 ‐ 039
CAT08722 ‐ 040
CAT08722 ‐ 041
CAT08722 ‐ 042
CAT08722 ‐ 049**
CAT08722 ‐ 050**
CAT08722 ‐ 053
CAT08722 ‐ 054
CAT08722 ‐ 055
CAT08722 ‐ 058
CAT08722 ‐ 061
CAT08722 ‐ 070
CAT08722 ‐ 075
CAT08722 ‐ 085
CAT08722 ‐ 087
CAT08722 ‐ 090**
CAT08722 ‐ 103
CAT08722 ‐ 104
CAT08722 ‐ 105
CAT08722 ‐ 109
Waterbody Name
Big Meadow Creek (ref)
General Creek (ref)
Saxon Creek (ref)
Trout Creek (ref)
Upper Truckee River (ref)
Upper Truckee River
Upper Truckee River
Grass Lake Creek
Upper Truckee River
McKinney Creek
Unnamed
Glen Alpine Creek
Unnamed
Cascade Creek
Madden Creek
Griff Creek
Upper Truckee River
Cold Creek
Upper Truckee River
Trout Creek
Upper Truckee River
Cold Creek
Meeks Creek
Cold Creek
Trout Creek
Cascade Creek
Lonely Gulch Creek
Upper Truckee River
Quail Creek
Cold Creek
Mckinney Creek
Grift Creek
Osgood Creek
Saxon Creek
Agency
(State)
Date sampled Latitude
TRPA (CA) 8/12/2010 38.77979
Longitude
‐ 119.9982
TRPA (CA) 8/17/2010 39.03003
‐ 120.16003
TRPA (CA) 7/1/2010 38.8575
TRPA (CA) 7/7/2010 38.8546
‐ 119.98592
‐ 119.93976
TRPA (CA) 9/8/2010 38.77929
‐ 120.02834
TRPA (CA) 7/30/2009 38.881427
‐ 119.99828
TRPA (CA) 8/5/2009 38.753293
‐ 120.028038
TRPA (CA) 9/1/2009 38.796565
‐ 119.983644
TRPA (CA) 7/23/2009 38.822495
‐ 120.020192
TRPA (CA) 8/17/2009 39.065693
‐ 120.146568
TRPA (CA) 7/29/2009 39.257892
‐ 120.013288
TRPA (CA) 8/18/2009 38.875481
‐ 120.083476
TRPA (CA) 8/4/2009 38.763732
‐ 120.015882
TRPA (CA) 8/10/2009 38.927314
‐ 120.106425
TRPA (CA) 8/13/2009 39.089136
‐ 120.165736
TRPA (CA) 7/28/2009 39.250641
‐ 120.031554
TRPA (CA) 8/12/2009 38.859614
‐ 120.026852
TRPA (CA) 8/6/2009 38.90786
‐ 119.958649
TRPA (CA) 9/2/2009 38.7803
‐ 120.02855
TRPA (CA) 8/27/2009 38.936635
‐ 119.991903
TRPA (CA) 8/11/2009 38.807434
‐ 120.017092
TRPA (CA) 8/21/2009 38.899091
‐ 119.90403
TRPA (CA) 8/25/2009 39.005762
‐ 120.164983
TRPA (CA) 8/19/2009 38.898493
‐ 119.92051
TRPA (CA) 8/3/2009 38.884459
‐ 119.977805
TRPA (CA) 9/3/2009 38.951131
‐ 120.08044
TRPA (CA) 8/31/2009 39.01969
‐ 120.119285
TRPA (CA) 8/29/2009 38.829907
‐ 120.019649
TRPA (CA) 8/24/2009 39.075779
‐ 120.152675
TRPA (CA) 8/18/2010 38.90838
‐ 119.96027
TRPA (CA) 6/29/2010 39.04391
‐ 120.18303
TRPA (CA) 8/5/2010 39.23851
‐ 120.03065
TRPA (CA) 6/30/2010 38.85433
‐ 120.03468
TRPA (CA) 7/22/2010 38.84889
‐ 119.98768
33
Site ID*
CAT08722 ‐ 110
CAT08722 ‐ 113
CAT08722 ‐ 114
CAT08722 ‐ 117
CAT08722 ‐ 119
CAT08722 ‐ 122
CAT08722 ‐ 123
CAT08722 ‐ 125
CAT08722 ‐ 133**
CAT08722 ‐ 134**
CAT08722 ‐ 136
CAT08722 ‐ 139
CAT08722 ‐ 141
CAT08722 ‐ 143
Waterbody Name
Cascade Creek
Upper Truckee River
Upper Truckee River
Upper Truckee River
Meeks Creek
Heavenly Valley Creek
Ward Creek
Trout Creek
Echo Creek
Rubicon Creek
Watson Creek
Lonely Gulch Creek
Upper Truckee River
Ward Creek
Agency
(State)
Date sampled Latitude Longitude
TRPA (CA) 7/27/2010 38.92223
‐ 120.11441
TRPA (CA) 8/10/2010 38.71745
‐ 120.00666
TRPA (CA) 8/24/2010 38.92193
‐ 119.98848
TRPA (CA) 7/26/2010 38.81871
‐ 120.01887
TRPA (CA) 7/20/2010 39.01967
‐ 120.14709
TRPA (CA) 6/22/2010 38.91724
‐ 119.94593
TRPA (CA) 9/2/2010 39.1327
‐ 120.15781
TRPA (CA) 7/6/2010 38.87902
‐ 119.98085
TRPA (CA) 7/21/2010 38.84208
‐ 120.03818
TRPA (CA) 6/24/2010 38.98923
‐ 120.10772
TRPA (CA) 8/25/2010 39.21944
‐ 120.09324
TRPA (CA) 6/21/2010 39.01618
‐ 120.12245
TRPA (CA) 7/29/2010 38.89151
‐ 119.99315
TRPA (CA) 7/19/2010 39.13057
‐ 120.22485
CAT08722 ‐ 145
CAT08722 ‐ 151
CAT08722 ‐ 153
Upper Truckee River
Quail Creek
Angora Creek
CAT08722 ‐ 155
CAT08722 ‐ 161
CAT08722 ‐ 165 p ‐ TACHAT08722 ‐ 010
Burton
Upper
Upper
Creek
Truckee
Truckee
Unnamed
River
River
Tahoe p ‐ TACHAT08722 ‐ 012** 3rd Creek ‐ Mt.
Rose
Creek
TRPA (CA) 8/3/2010 38.76539
‐ 120.03040
TRPA (CA) 6/28/2010 39.07126
‐ 120.15467
TRPA (CA) 6/23/2010 38.88142
‐ 120.03544
TRPA (CA) 8/26/2010 39.20423
‐ 120.16278
TRPA (CA) 8/16/2010 38.78562
‐ 120.01859
TRPA (CA) 8/31/2010 38.8036
‐ 120.01621
NDEP (NV) 7/16/2009 38.961442
‐ 119.92057
NDEP (NV) 7/24/2009 39.28863
‐ 119.934259
p ‐ TACHAT08722 ‐ 016 p ‐ TACHAT08722 ‐ 028 p ‐ TACHAT08722 ‐ 032 p ‐ TACHAT08722 ‐ 036
Incline Creek NDEP (NV) 7/21/2009 39.249417
‐ 119.93352
3rd Creek @ Mountain Golf Course NDEP (NV) 7/23/2009 39.266759
‐ 119.946488
Incline Creek
Logan House Creek
NDEP
NDEP
(NV)
(NV)
7/22/2009
7/27/2009
39.247308
39.066105
‐
‐
119.936653
119.934114
p ‐ TACHAT08722 ‐ 044** 3rd Creek p ‐ TACHAT08722 ‐ 047 Secret Harbor Creek p ‐ TACHAT08722 ‐ 052 Glenbrook Creek
NDEP
NDEP
NDEP
(NV)
(NV)
(NV)
7/23/2009
7/20/2009
7/14/2009
39.25641
39.139575
39.087756
‐
‐
‐
119.942203
119.92432
119.935123
p ‐ TACHAT08722 ‐ 056 p ‐ TACHAT08722 ‐ 060 p ‐ TACHAT08722 ‐ 063 p ‐ TACHAT08722 ‐ 074 p ‐ TACHAT08722 ‐ 076 p ‐ TACHAT08722 ‐ 080 p ‐ TACHAT08722 ‐ 084 p ‐ TACHAT08722 ‐ 088 p ‐ TACHAT08722 ‐ 092 p ‐ TACHAT08722 ‐ 096 p ‐ TACHAT08722 ‐ 100
Incline Creek Headwater NDEP (NV) 7/28/2009 39.282843
‐ 119.921032
Unnamed Creek by Fairview Blvd NDEP (NV) 8/3/2009 39.263879
‐ 119.938139
North Canyon
Burke Creek
Unnamed Tahoe Creek
NDEP
NDEP
NDEP
(NV)
(NV)
(NV)
7/30/2009
8/4/2009
6/23/2010
39.109287
38.97255
39.277904
‐
‐
‐
119.916893
119.939706
119.941259
Incline Creek
Zephyr Creek
First Creek
Wood Creek
Rosewood Creek
North Logan House Creek
NDEP
NDEP
NDEP
NDEP
(NV)
(NV)
(NV)
(NV)
6/25/2010
7/8/2010
7/7/2010
6/28/2010
39.24508
39.01637
39.24738
39.06861
‐
‐
‐
‐
119.93926
119.9336
119.94465
119.94049
NDEP (NV) 6/24/2010 39.25827
‐ 119.98603
NDEP (NV) 6/22/2010 39.26905
‐ 119.96555
34
Site ID* Waterbody Name p ‐ TACHAT08722 ‐ 108** Deer Creek p ‐ TACHAT08722 ‐ 111 Slaugherhouse Creek p ‐ TACHAT08722 ‐ 112 p ‐ TACHAT08722 ‐ 124 p ‐ TACHAT08722 ‐ 138
Wood
Rosewood
Burke
Creek
Creek
Creek
Agency
(State)
Date sampled Latitude Longitude
NDEP (NV) 6/29/2010 39.25701
119.94044
NDEP (NV) 7/13/2010 39.1241
‐ 119.91155
NDEP (NV) 7/1/2010 39.24657
‐ 119.95778
NDEP (NV) 6/30/2010 39.25551
‐ 119.94968
NDEP (NV) 7/6/2010 38.98018
‐ 119.91818
p ‐ TACHAT08722 ‐ 140 p ‐ TACHAT08722 ‐ 152
TAH03GlenBk ‐ 1
TAH03Logan ‐ 1
TAH03MarlTrib ‐ 1**
Third
Second
Glenbrook
Logan
Creek
Creek
Creek
House
Unnamed
Trib.
Upper
Trib to
Upper
Marlette
Lake
NDEP
NDEP
NDEP
NDEP
NDEP
(NV)
(NV)
(NV)
(NV)
(NV)
7/5/2010
7/9/2010
7/22/2009
7/20/2009
7/31/2009
39.276619
39.25549
39.0758981
39.05579
39.160889
‐
‐
‐
‐
‐
119.947277
119.97428
119.89900
119.91100
119.897905
TAH03NFKLogan ‐ 1 NF Logan House NDEP (NV) 7/13/2009 39.072731
‐ 119.919112
*The last 3 digits of the Site ID is used in Fig 1 and Tables 4 & 6.
**Duplicate sample taken at this site.
O/E values at these sites were calculated by taking the mean of the primary and duplicate samples.
35
APPENDIX
II:
A
STEP
‐
BY
‐
STEP GUIDE FOR USING THE
RIVPACS
MODEL TO CALCULATE
O/E
SCORES
In preparation for running the RIVPACS model, the ‘Predictive Models Primer’ on Utah State University’s Western
Center for Monitoring & Assessment of Freshwater Ecosystems website ( http://cnr.usu.edu/wmc/htm/predictive ‐
models/predictive ‐ models ‐ primer ) should be read to gain a more in ‐ depth understanding of how RIVPACS works.
S TEP 1 – C OMPILATION AND ORGANIZATION OF BENTHIC MACROINVERTEBRATE DATA
First, all of the benthic macroinvertebrate data for this study were compiled into a single spreadsheet.
This required re ‐ formatting data from two different entities (TRPA and NDEP) into a single file.
The file was formatted with the samples as row headers and the sample attributes as column headers.
In this format, each row had information about a single taxon from a single sample.
Therefore each sample had as many rows as number of
taxa that were collected in that sample.
In the compilation of data from TRPA and NDEP, an effort was made to retain as much information about each sample as possible from each source (e.g., sample date, stream name, latitude, longitude, “sampling agency, etc.), but the three fields that were absolutely required to run RIVPACS were: 1) the unique name of the sample (e.g.,
‘SampleID’ or ‘ActivityID’ – this is different from the ‘StationID’ because some sites will have multiple replicates collected); 2) a field that includes the standard taxonomic effort (ste) level such as genus or species (e.g., ‘FinalID’ or ‘Characteristic’); and 3) the number of organisms of that taxon in the sample (e.g., ‘BAResult’ or ‘Results Value’).
Common fields (sometimes with different names) from each data source were matched and given a common column header (Table A).
See Table B for an example of this format.
Table A.
Fields matched between NDEP and TRPA data sets
NDEP field name TRPA field name Combined dataset field name
StationID
ActivityStartDate
ActivityID
Characteristic
ResultsValue
Site_Name
StationCode
SampleDate
SampleID
FinalID
BAResult
LabSampleID
StationCode
SampleDate
SampleID
FinalID
BAResult
LabSampleID
36
Table B.
Example of spreadsheet that combines data from two sources (NDEP and TRPA).
StationID SampleDate AgencyCode Replicate SampleID
NDEP09M001
NDEP09M001
NDEP09M001
NDEP09M001
NDEP09M002
NDEP09M002
NDEP09M002
NDEP09M002
7/28/2009
7/28/2009
7/28/2009
7/28/2009
8/3/2009
8/3/2009
8/3/2009
8/3/2009
NDEP
NDEP
NDEP
NDEP
NDEP
NDEP
NDEP
NDEP
1
1
1
1
1
1
1
1 p p p p p p p p
‐
‐
‐
‐
‐
‐
‐
‐
TAHCAT08722
TAHCAT08722
TAHCAT08722
TAHCAT08722
TAHCAT08722
TAHCAT08722
TAHCAT08722
TAHCAT08722
‐
‐
‐
‐
‐
‐
‐
‐
056
056
056
056
056
056
056
056
FinalID
Ameletus
Apatania 2
Baetis bicaudatus 3
BAResult StreamName
2 Incline Creek
Incline
Incline
Creek
Creek
Baetis tricaudatus 1
Ephydridae 12
Empididae.
Ferrissia
2
31
Helicopsyche
Helodon
10
Incline
Logan
Logan
Logan
Logan
Logan
Creek
House
House
House
House
House
Creek
Creek
Creek
Creek
Creek
Serratella teresa
Simulium.
Turbellaria
1
137
21
3
NDEP09M002
634009018
634009018
634009018
8/3/2009
9/1/2009
9/1/2009
9/1/2009
NDEP
TRPA
TRPA
TRPA
1
1
1
1 p ‐ TAHCAT08722 ‐ 056
CAT08722 ‐ 018
CAT08722 ‐ 018
CAT08722 ‐ 018
634009018
634009018
634009018
634009018
9/1/2009
9/1/2009
9/1/2009
9/1/2009
9/1/2009
TRPA
TRPA
TRPA
TRPA
TRPA
2
2
1
2
2
CAT08722 ‐ 018
CAT08722 ‐ 018_Dup
CAT08722 ‐ 018_Dup
CAT08722 ‐ 018_Dup
CAT08722 ‐ 018_Dup
Ephydridae
Hygrobates
Lebertia
Sperchon
Sperchonopsis
46
4
19
2
1
634009018
Legend
StationID = the sampling site location name
SampleDate = the date the sample was collected
AgencyCode = the agency that collected the data
Replicate = the replicate number (1 indicates the first sample taken at that site, 2 indicates a duplicate sample at the same site)
SampleID = the unique identification name for the sample collected (a site can have multiple samples take)
FinalID = the standard taxonomic level effort for the taxa collected
BAResult= the number of individuals collected of that corresponding taxa in the same row
StreamName = the name of the waterbody where the sample was collected
37
S TEP 2 – M ATCHING O PERATIONAL T AXONOMIC U NITS (OTU S )
Once all of the benthic macroinvertebrate data were compiled into a single Microsoft Excel spreadsheet (called
‘combined.xls’) and formatted as described above, the standard taxonomic effort identification (in this case ‘Final
ID’) column was matched with the proper Operational Taxonomic Unit or OTU (‘OTUName2’ to run the no midge model) in a file from the California Department of Fish & Game (Contact Andy Rehn; arehn@sbcglobal.net
for an updated version of the OTU file).
Microsoft Access was used to initially match the taxa with identical names in the FinalID column from the combined spreadsheet with the OTU names.
In order to do this, the combined NDEP/TRPA spreadsheet and the
OTU spreadsheet were separately imported into Microsoft Access (the ‘External Data’ tab in Access was used to get to the ‘Excel’ icon to import the excel spreadsheet).
The combined table initially included 445 unique FinalID
taxonomic names.
Next, a ‘Make Table Query’ was created in Access (by going to the ‘Create’ tab and then ‘Query Design’ button).
The ‘combined’ and ‘OTUs’ tables were added to the Query.
A relationship was created between the fields
‘FinalID’ in the combined table and ‘OTUName2’ in the OTUs table.
A ‘Make Table’ query was selected and the
new table was named ‘OTUmatch.’ The Make Table query included the fields and criteria listed in Table C.
Table C.
Make Table query format to automatically match OTU names.
Field: FinalID
Table: Combined
Taxon_Code_Name*
OTUs
OTUName2
OTUs
Total: Group By Group By Group By
*this field included the lowest taxonomic level (usually genus or species).
This contrasts with the OTU is a designated taxonomic level (not necessarily the lowest taxonomic level).
The field ‘Taxon_Code_Name’ was included to help see not only which taxa were identical to the OTU, but also the taxa names that were not identical, but Taxon_Code_Name’ would show how FinalID and OTU were connected.
The resulting Make Table was named “Match_FinalID_TaxonCodeName” and was exported to Microsoft Excel.
This table matched 155 taxa that had identical names to that of ‘Taxon_Code_Name’.
By having all three columns it was possible to see how the FinalID was related to the OTU column.
Only the FinalID names that were absolutely consistent with the Taxon_Code_Name or OTU column matched up in this process, the remainder needed to be matched manually.
In order to investigate the taxa that were not matched automatically in Microsoft Access (because the taxa names were identical), a new table was created in Excel to determine how unmatched ‘FinalID’ names could be renamed to match the appropriate OTU.
A new column was added to the spreadsheet named “OTUmatch” to provide a link between the original FinalID name and the appropriate OTU name.
Therefore ‘OTUmatch’ would be used to create the bug file for further use in the process.
An example of the manual matching table format used is shown in Table
D.
38
Table D.
Table created to manually match FinalID with OTUName2.
Original FinalID (n=445) OTUmatch OTUName2 Notes
Ablabesmyia
Acari
Tanypodinae
Acari
Tanypodinae
Acari
Genus Ablabesmyia changed to subfamily
Tanypodinae
Automatic match
Acentrella
Allocosmoecus
Ametor
partitus scabrosus
Apataniidae
Apedilum
Apsectrotanypus
Acentrella
Allocosmoecus
Ametor
Chironominae
Tanypodinae
Acentrella
Allocosmoecus
Ametor
Chironominae
Automatic match
Species Allocosmoecus partitus changed to genus Allocosmoecus
Species Ametor scabrosus changed to genus
Ametor
Deleted Apataniidae because family level too high
Genus Apedilum changed to subfamily
Chironominae
Apsectrotanypus is a genus in the subfamily
Tanypodinae.
Even though it was not explicitly linked to an OTUName, the match was Tanypodinae, which is included as an
OTU.
Automatic match Arctopsyche Arctopsyche Arctopsyche
Arctopsyche grandis
Arctopsychinae
Arctopsyche
Rhyacophila Betteni Gr.
Rhyacophila_betteni_group
Rhyacophila betteni group Rhyacophila_betteni_group
Rhyacophila Brunnea Gr.
Rhyacophila_brunnea_vemna_groups
Arctopsyche
Species Arctopsyche grandis was changed to genus Arctopsyche
Deleted Apataniidae because family level too high
Manually matched OTU
Rhyacophila_betteni_group Manually matched OTU
Manually matched OTU
39
Some of the FinalID names were eliminated from the data set because the taxonomic level was too high and did not have a corresponding OTU (e.g., Apataniidae was deleted in Table 5 because family level was too high).
Once all of the FinalIDs were either matched or deleted a new table with only the fields ‘FinalID’ and ‘OTUmatch’ was imported into Access.
Another Make Table query was created and linked the field ‘FinalID’ in the combined table with ‘FinalID’ in the OTUmatch table.
Table E shows the format of the Make Table query to create the bug
file.
Table E.
Make Table query format to create bug file.
Field: SampleID OTUmatch BAResult
Table: combined
Total: Group By
OTUmatch
Group By combined
Group By
Criteria: Is Not Null
The table created from this query had 3,279 rows and 162 unique taxa names that were linked to OTUs.
This table was exported and for further processing.
It is possible that the above steps could be done by the taxonomist if
they have access to the appropriate OTU file.
S TEP 3 ‐ S UBSAMPLE
The RIVPACS model for mountainous California requires 300 ‐ count samples, so the Tahoe Basin samples were subsampled electronically to meet that target.
The electronic subsample process was done by downloading a
Fortran subsampling program created by Dr.
Dave Roberts that runs in a DOS window under Windows®
( subsample.exe
from the Utah State University’s Western Center for Monitoring & Assessment of Freshwater
Ecosystems website http://www.cnr.usu.edu/wmc/htm/predictive ‐ models/usingandbuildingmodels ).
This program randomly ‘samples’ individuals from each of the samples in the original data file and creates a new file with no more 300 organisms in each sample.
Samples with original counts less than 300 will not be affected.
The original data must be a tab ‐ or comma ‐ delimited text file, such as Windows Notepad, in the format shown in Table
F (3 columns in which the first column contains sample names/codes, the second column contains OTU names, and the third column contains counts).
This file cannot include taxa with zero counts (WCMAFE, 2012) and the filename
cannot contain any punctuation.
Table F.
Format required to run the subsample.exe
program
Site
634R10BMW
634R10BMW
634R10BMW
Taxon
Acari
Ameletus
Baetis
Count
4
1
2
Etc.
To run subsample.exe
, the bug file (in 3 column format) was placed in the directory containing the subsample.exe
file or on the desktop.
The subsample.exe
program was launched by double clicking on the program name.
In a new window the name of the file containing the bug data was clicked and dragged into the program window.
Next, the name of the file to create was entered.
Then the number of individuals to include in each sample (300) was entered.
Finally, a random number was entered to initiate the program.
The program created a new file in which
each sample contained 300 individuals, except for those original samples that contained fewer than 300 individuals
(WCMAFE, 2012).
The Program also will prompt: “Do you want to debug?
Y/N?”
The 163 unique taxa names were reduced to 154 names after the subsample process was completed.
40
S TEP 4 – M ATRIFY
The subsampled three column format was converted into a site by taxa matrix by using the matrify.exe
program from the Utah State University’s Western Center for Monitoring & Assessment of Freshwater Ecosystems website
( http://www.cnr.usu.edu/wmc/htm/predictive ‐ models/usingandbuildingmodels ).
This program was also created by Dr.
Dave Roberts.
The matrify.exe
program was used by placing it in the same directory as the subsampled bug file.
After launching the program the original and new file names were entered when prompted.
The file was in comma ‐ separated format (.csv), so this was entered when prompted.
Finally it was indicated that a zero (0) should be used for absences.
The program created a rectangular matrix in which the OTUs and the sample names/codes are sorted in alphabetical order.
The resulting file was inspected to make sure the
program ran successfully.
At the completion of this step, the 154 taxa formed the column headers and the 95 samples filled the rows.
S TEP 5 – D ETERMINE SUBMODEL
Each of the sites were then assigned to one of the three O/E submodels using 30 ‐ year average (1961 ‐ 1990) of precipitation and temperature data from the PRISM Climate Group at Oregon State University
( http://prism.oregonstate.edu/ ).
Mean monthly temperature was determined by taking the average of PRISM maximum and minimum mean monthly temperatures.
PRISM output is available as a geospatial coverage, so that climatic variables could be determined for each specific site location within the Tahoe Basin.
When using the
PRISM raster layer, the values given need to be divided by 10 to get the mean monthly temperature in Celsius.
The criteria for determining submodel were:
Submodel 1: mean monthly temperature >9.3
C and log mean monthly precipitation >2.952
cm
Submodel 2: mean monthly temperature >9.3
C and log mean monthly precipitation <2.952
cm
Submodel 3: mean monthly temperature <9.3
C
All of the sites in Lake Tahoe Basin fell under submodel 3, so taxa were predicted on the basis of mean monthly temperature and log watershed area.
Watershed area for each site was calculated from from 10 m digital
elevation maps using the hydrology tools package in ArcGIS.
To calculate the watershed area of each site a series of steps need to be executed using the spatial analyst tools in
ArcGIS.
First, load a 10m DEM map and a layer of stream survey locations.
Second, after obtaining a license to run the spatial analyst tools, open the Hydrology toolbox to fill the sinks in th DEM layer.
Third, use the flow direction tool.
The output will be a layer that when clicking on a point on the map, it will calculate how many other cells
flow into that particular cell.
Find how many cells flow into all the stream sites using the flow accumulation layer.
Using an excel table, divide this number by 1000 to calculate the area of accumulation in km
2
.
Then calculate the log of the area in km
2
to get the log watershed area.
S TEP 6 – P REPARE PREDICTOR FILE
A three ‐ column comma ‐ separated (.csv) file was created that included the fields: 1) SiteCode, 2) log watershed area (LOGWSA), and 3) monthly mean temperature (TMEAN_SITE).
The sample names/codes had to be the same and in the same order as those in the prepared bug file.
The labels for the predictor variables and their order have
to be exactly the same as expected by the model (case sensitive) as listed above in parentheses.
The units of measure and any transformations had to be the same as expected by the model.
S TEP 7 – R UN RIVPACS
In order to run RIVPACS via Utah State University’s Western Center for Monitoring & Assessment of Freshwater
Ecosystems website, it is necessary to first obtain a login and password.
Because the Center and its web resources
41
are no longer supported by grants, user fees are required to support software and web maintenance.
All users except those using the software for educational purposes must pay a yearly fee prior to accessing the software.
Users must contact the Center's Director regarding fees and to request a username and password
( http://www.usu.edu/contact/ ).
Once a login and password have been obtained, the user can login at: http://cnr.usu.edu/wmc/htm/predictive ‐ models/predictivemodelsoftware.
The predictive model software will ask which model the user would like to use.
In this study, we used the mountainous California submodel 3 “no midge” model (because midges were primarily grouped by subfamily instead of genus or species) or “CA_R3_NOMIDGES.” The delimiter (tab ‐ , comma ‐ , or space ‐
) was then specified.
In this study the data were comma ‐ delimited.
Next the model asks to upload the bug file and habitat (predictor) file to run the model.
Once those two files are uploaded, simply press “submit data” to run the model.
The RIVPACS model generates four output files.
To read a description of each of these files and how to interpret them go to: http://www.cnr.usu.edu/wmc/htm/predictive ‐ models/usingandbuildingmodels
The index was calculated as the number of predicted taxa Observed at the site (O) divided by the number of taxa
Expected to occur (E) or O/E.
The O/E scores reported in this study were based on a probability of detection threshold of >0.5, because this threshold has been found to be more precise and more sensitive in detecting
effects of stressors (Hawkins et al., 2000) and it de ‐ emphasizes rare taxa.
42
APPENDIX III: P HOTOS OF M ARGINAL SITES SAMPLED IN 2009 AND 2010 IN THE T AHOE B ASIN
Figure 11.
Photo of site on General creek (634R10GNL, O/E=0.53)
FIGURE 12.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 013, O/E=0.62)
43
FIGURE 13.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 017, O/E=0.61)
FIGURE 14.
PHOTO OF SITE ON GLEN ALPINE CREEK (CAT08722 ‐ 025, O/E=0.67)
44
FIGURE 15.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 041, O/E=0.46)
FIGURE 16.
PHOTO OF SITE ON TROUT CREEK (CAT08722 ‐ 050, O/E=0.34)
45
FIGURE 17.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 053, O/E=0.62)
FIGURE 18.
PHOTO OF SITE ON TROUT CREEK (CAT08722 ‐ 061, 0.59)
46
FIGURE 19.
PHOTO OF SITE ON CASCADE CREEK (CAT08722 ‐ 070, O/E=0.53)
FIGURE 20.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 085, O/E=0.63)
47
FIGURE 21.
PHOTO OF SITE ON MCKINNEY CREEK (CAT08722 ‐ 103, O/E=0.25)
FIGURE 22.
PHOTO OF SITE ON CASCADE CREEK (CAT08722 ‐ 110, O/E=0.62)
48
FIGURE 23.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 114, O/E=0.50)
FIGURE 24.
PHOTO OF SITE ON THE UPPER TRUCKEE RIVER (CAT08722 ‐ 141, O/E=0.61)
49
FIGURE 25.
PHOTO OF SITE ON NORTH CANYON CREEK (P ‐ TACHAT08722 ‐ 063, O/E=0.69)
FIGURE 26.
PHOTO OF SITE ON BURKE CREEK (P ‐ TACHAT08722 ‐ 138, O/E=0.68)
50
FIGURE 27.
PHOTO OF SITE ON BURKE CREEK (P ‐ TACHAT08722 ‐ 074, O/E=0.60)
51
