1. Overview: 2005 Protocol Comparison Test Brett Roper National Aquatic Ecologist, USDA Forest Service broper@fs.fed.us (435) 755-3566

2. Analysis For Means, STD, CV proc glm data= ***; class stream; model BW = stream; run; To decompose variance proc mixed data=***; classes stream; model BW =; random stream; run; To determine stream means and difference among streams proc mixed data=***; classes stream ; model BW = stream; lsmeans stream /pdiff adjust=tukey; ods output diffs=ppp lsmeans=mmm; ods listing exclude diffs lsmeans; run; %include 'c:\BBRfile\stats\sasmacros\pdmix800.sas'; %pdmix800(ppp,mmm,alpha=0.1,sort=yes); run; No analysis yet to determine significant differences among groups

3. Proc GLM statement Source DF Squares Mean Square F Value Pr > F Model 11 254.5044526 23.1367684 148.70 <.0001 Error 24 3.7342700 0.1555946 Corrected Total 35 258.2387225 R-Square Coeff Var Root MSE grad Mean 0.985539 10.57097 0.394455 3.731490 Proc Mixed statement Covariance Parameter Estimates Cov Parm Estimate Stream 7.6604 Residual 0.1556

4. Output of the PDMIX statement for gradient from one group Standard Letter Obs Stream Estimate Error Group 1 Myrtle 9.8156 0.2277 A 2 Whisky 7.1933 0.2277 B 3 Indian 6.0044 0.2277 C 4 Crawfish 5.2956 0.2277 C 5 WF Lick 3.5644 0.2277 D 6 Tinker 2.9444 0.2277 DE 7 Potamus 2.7622 0.2277 DE 8 Trail 1.9511 0.2277 EF 9 Big 1.3800 0.2277 F 10 Crane 1.3756 0.2277 F 11 Camus 1.3601 0.2277 F 12 Bridge 1.1311 0.2277 F

5. Group 1 = Group 2 - 5 What should the results of an aquatic habitat protocol comparison look like?

6. What is a good attribute For categorization – Very little  Rosgen large classes For status and trend –Minimum S:N of around 2, % stream ≈ 70% (I would prefer S:N of 4 and stream ≈ 80% Coefficient of variation ≈ 20%

7. Attributes Gradient Bankfull Width Wetted Width Width-to-Depth Sinuosity Entrenchment % Pool Residual Pool Depth %Fines Median Particle Size D 84 Large Wood Large wood volume

8. Gradient Group 1Group 2Group 3Group 4Group 5Group 6 Mean 3.333.733.843.833.603.12 RMSE 0.240.390.531.130.490.91 CV 7.1310.5713.9129.6213.6828.86 Stream 7.027.668.825.976.954.27 Error 0.060.160.281.290.240.81 Total 7.087.829.107.257.195.09 S:N 124.4249.2330.994.6428.655.27 %Observer 0.010.020.030.180.030.16 %Stream 0.990.980.970.820.970.84 Groups 9.006.00 3.006.005.00

9. Myrtle Crane

10. Gradient; Results Can be shared? (R 2)

11. Bankfull Width Group 1Group 2Group 3Group 4Group 5Group 6 Mean 4.574.016.795.925.275.89 RMSE 0.330.570.962.481.891.37 CV 7.2614.2314.1241.9335.8623.21 Stream 6.376.5722.4618.958.8611.87 Error 0.110.320.926.173.571.87 Total 6.486.8923.3825.1212.4313.74 S:N 58.0620.2324.453.072.486.36 %Observer 0.020.050.040.250.290.14 %Stream 0.980.950.960.750.710.86 Groups 647234

13. truth GRP 1 GRP 2 GRP 3 GRP 4 GRP 5 GRP 6 truth 1.000.970.960.890.960.970.97 Group 11.000.990.900.970.960.97 Group 2 1.000.930.980.970.98 Group 3 1.000.890.950.95 Group 41.000.930.96 Group 5 1.000.99 Group 61.00 Correlations among all stream width groups and the truth (remember these are mean values compared to the truth)

14. Wetted Width Group 2Group 4Group 5Group 7Group 8 Mean 2.943.363.183.013.07 RMSE 0.200.630.170.650.26 CV 6.6818.835.4121.678.48 Stream 5.885.365.007.546.13 Error 0.040.400.030.430.07 Total 5.925.765.037.966.20 S:N 152.4913.43168.7917.6990.56 %Observer 0.010.070.010.050.01 %Stream 0.990.930.990.950.99 Groups 85765

15. Width to Depth Group 1Group 2Group 3Group 4Group 5Group 6 Mean 18.6327.2815.4516.7314.2623.43 RMSE 3.977.892.945.224.306.22 CV 21.3128.9119.0531.2030.1226.89 Stream 23.88102.3118.3212.5130.6665.63 Error 15.9062.188.6627.2418.4538.73 Total 39.78164.4926.9839.7449.11104.36 S:N 1.501.652.120.461.661.69 %Observer 0.400.380.320.690.38.37 %Stream 0.600.620.680.310.62.63 Groups 334233

16. Width to Depth Big Crawfish

17. Sinuosity Group 1Group 2Group 3Group 5 Mean 1.251.22 1.19 RMSE 0.11 0.040.06 CV 8.438.793.135.12 Stream 0.010.030.02 Error 0.01 0.00 Total 0.020.040.02 S:N 0.822.4413.005.54 %Observer 0.550.290.070.15 %Stream 0.450.710.930.85 Groups 2.004.005.004.00

18. Entrenchment Group 1Group 2Group 3Group 4Group 6 Mean 1.991.712.182.172.50 RMSE 0.590.480.951.061.22 CV 29.8228.2243.6848.7648.89 Stream 0.070.041.070.000.56 Error 0.330.230.911.031.48 Total 0.400.271.981.032.04 S:N 0.210.161.170.000.38 %Observer 0.820.860.461.000.73 %Stream 0.180.140.540.000.27 Groups 1.00 2.001.00

19. Entrenchment Big Crawfish

20. Percent Pool Group 1Group 2Group 3Group 4Group 5Group 7Group 8 Mean 29.4421.529.9917.7010.2822.5724.35 RMSE 12.9111.014.146.318.3010.425.24 CV 43.8551.1841.4535.6680.7346.2021.54 Stream 237.49227.3612.34201.18109.55122.13394.26 Error 165.87121.3217.1339.8568.86108.5927.47 Total 403.36348.6829.47241.03178.41230.72421.73 S:N 1.431.870.725.051.591.1214.35 %Observer 0.410.350.580.170.390.470.07 %Stream 0.590.650.420.830.610.530.93 Groups 2423226

21. Myrtle Crane Lets look at Percent Pool

22. Can data be shared? Some yes Some No. (R 2)

23. Residual Pool Depth ResPoDepRPD RESIDPDRPDAvgRPDmRPD Group 1Group 2Group 3Group 4Group 5Group 7Group 8 Mean 0.210.250.190.370.070.33 RMSE 0.03 0.060.010.080.05 CV 12.7211.4015.1116.8012.3924.6013.94 Stream 0.01 0.000.01 Error 0.00 0.010.00 Total 0.01 0.020.000.020.01 S:N 7.4310.138.653.4717.042.064.93 %Observer 0.120.090.100.220.060.330.17 %Stream 0.880.910.900.780.940.670.83 Groups 5454646

24. Median Particle Size (54.4 mm) Group 1Group 2Group 3Group 5Group 7 Mean 49.2827.9339.2536.4436.46 RMSE 13.8014.2018.1924.2922.98 CV 28.0150.8446.3366.6563.04 Stream 1144.90728.99807.29584.22601.60 Error 190.50201.63330.69589.85526.83 Total 1335.40930.621137.981174.071128.43 S:N 6.013.622.440.991.14 %Observer 0.140.220.290.500.47 %Stream 0.860.780.710.500.53 Groups 44433

25. D84 (155.1 mm) Group 1Group 2Group 3Group 5Group 7 Mean 143.81192.85158.28196.05118.12 RMSE 44.8186.3535.2764.1454.28 CV 31.1644.7722.2832.7245.95 Stream 11733.0038644.0012075.0028912.005519.79 Error 2008.007455.551243.924114.432942.16 Total 13741.0046099.5513318.9233026.438461.95 S:N 5.845.189.717.031.88 %Observer 0.150.160.090.120.35 %Stream 0.850.840.910.880.65 Groups 3.00 5.004.003.00

26. % Fines Group 1Group 2Group 3Group 4Group 5Group 6Group 7 Mean 18.8329.9010.7720.1519.5222.3429.15 RMSE 4.888.229.557.596.4513.4516.93 CV 25.9227.4988.6737.6433.0360.1758.97 Stream 170.39106.9444.36124.53149.1863.0339.11 Error 23.8367.5491.2857.5341.56183.18284.35 Total 194.22174.48135.64182.06190.74246.21323.46 S:N 7.151.580.492.163.590.340.14 %Observer 0.120.390.670.320.220.740.88 %Stream 0.880.610.330.680.780.260.12 Groups 3412421

27. Bank Stability stab2pct100-unstabBNK100-pcterosionPctStab Group 1Group 2Group 4Group 7 Mean 78.2579.5695.6696.80 RMSE 11.129.951.737.10 CV 14.2112.511.817.33 Stream 115.65462.8626.855.17 Error 123.6199.042.9950.41 Total 239.26561.9029.8555.57 S:N 0.944.678.970.10 %Observer 0.520.180.100.91 %Stream 0.480.820.900.09 Groups 2441

28. Large Wood >=3m_Cnt1 00mLWD3x30LWDlwdpiece1LWD_L Group 1Group 2Group 3Group 4Group 7Group 8 Mean18.6026.4814.7221.184.632.50 RMSE4.2712.863.356.524.921.35 CV22.9748.5722.7830.79106.1554.16 Stream248.18334.9472.17271.2835.145.78 Error18.25165.4311.2542.6124.191.82 Total266.43500.3783.42313.8959.337.60 S:N13.602.026.416.371.453.18 %Observer0.070.330.130.140.410.24 %Stream0.930.670.870.860.590.76 Groups646425

29. Large Wood (volume) LWD_Cat1- 2_allLen_Vol100m LWD_Cat1_allLen_ Vol100mVLWDlwdvol1 Group 1Group 2Group 3Group 4 Mean 19.969.6012.5912.21 RMSE 6.893.908.247.06 CV 34.5040.5865.4457.80 Stream 158.2438.8770.1531.20 Error 47.6715.1867.9051.07 Total 205.9154.05138.0582.28 S:N 3.322.561.030.61 %Observer 0.230.280.490.62 %Stream 0.770.720.510.38 Groups 4431

30. How I summarized A: S:N >9, stream variability 90%, CV < 20% B: S:N >4, stream variability 80%, CV < 20% C: S:N > 2, Stream variability 70% or CV around 20% D: S:N close to 2, stream variability more than 50%, or CV around 20%. F: Anything lower.

31. GRP 1 GRP 2 GRP 3 GRP 4 GRP 5 GRP 6 GRP 7 GRP 8 GradientA(1)AABAB BF WidthA(1)AACCB Wetted Width A AA(1) AA WDDFC(1)FFF SinuosityDCA(1) B EntrenchmentFFF(1)F F % PoolFFFBF DA(1) Res Pool DepthAAABA(1) CB D50B(1)CC F F D84BBA(1) B C FinesB(1)FFCBFF Bank StabilityDB A(1) F LWD #A(1)CBB DC LWD VolumeB(1)CFF

32. Preliminary Observations Some attributes everyone does passable at: gradient, bankfull width, wetted width, residual pool depth. –For these attributes it is likely that cross walks can be determined not only with e ach other but with the truth. Only one attribute that nobody does well at; entrenchment. –Although nobody does well with this one it may not matter since it will only be used for classification; but if that is true why not as a group agree on an AML. For remaining attributes – width to depth, sinousity, % pool, D 50 D 84, fines, bank stability, and large wood volume – some groups have better (more consistent within the group) protocols than others.

33. Some thoughts on why certain attributes were done better when there was variation in the protocols Ratio things (Sinuosity, width-to-depth, and entrenchment) were done better with laser level. Extensive training resulted in more consistent sediment (fines and particle distributions, and large wood (counts and volume). Pools were best done with a fixed length.

34. How do we decide what to measure Bankfull width vs wetted width Bankfull width has hydrologic meaning, can be measured if the stream is dry. Not affect by season. Wetted with more consistently measured, is a measure of summer aquatic habitat (nice to know a stream is dry)

35. What’s next? LiDAR Determine what steps should be taken to standardize protocols. –Continue efforts to develop crosswalks. –Should data quality control recommendations be made. –Seek consensus on the best protocol(s) to use. Determine which attributes provide useful data. Proposal was submitted for BPA funding for follow up work. Publication of the John Day basin protocol test results.

36. Questions ?