Using R to Analyze Data from Probabilistic Monitoring in Oklahoma. Jean Lemmon
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1 Using R to Analyze Data from Probabilistic Monitoring in Oklahoma Jean Lemmon
2 Background Ambient monitoring program known as the Small Watershed Rotating Basin Monitoring Program o 414 USGS 11-digit HUC basins collated into 11 planning basins o Sampled every five weeks for two years with statewide coverage of all sites in five years 2008 Probabilistic component o 50 sites in each of 5 major basins 250 sites in State monitored in 5 years o Sites taken in order from master site list generated by Tony Olsen with the EPA Corvallis Lab o Water chemistry, fish, macroinvertebrates, and habitat monitored at same time as Rotating Basin fixed sites Data analysis using R Open Source (Free) Software scripts supplied by EPA
3 Probabilistic Site List 150 sites generated for 50 targeted sites Start with #1 and work down list using sites in order until 50 are available Possible reasons for not monitoring a site: o Landowner permission denied o Completely inaccessible o Dry o In the middle of an impoundment Data collected o Chemistry and bacteria o Habitat o Fish o Benthic macroinvertebrates (both winter and summer)
4 R Software Evaluating probabilistic data Define,, and conditions for: o Stressors (chemical and physical parameters) o Indicators (fish/bugs) Re-weight the sites actually monitored Run data through R open software (FREE) programs o Extent Estimates (% of stream length) o Relative Risk (strength of association between elevated stressor and degraded biota) o Attributable Risk (estimate of stressor effect on indicator)
5 Why do this? Extent Estimates give the percent of stream length in,, or condition Relative Risk assesses the relative importance of multiple stressors (Which stressors should be the major focus for remediation, restoration or protection?) Attributable Risk estimates the percent of improvement (if it were possible to completely address a stressor)
6 Stressors (chemical and physical parameters) Total Nitrogen Total Phosphorus Conductivity Turbidity Identified by EPA as most common problems Other chemical parameters Habitat observations
7 Indicators Fish Index of Biotic Integrity (IBI) calculated using: o Number of species o Number of sensitive benthic species o Number of sunfish species o Number of intolerant species o Proportion tolerant individuals o Proportion insectivorous cyprinid individuals o Proportion individuals as lithophilic spawners Benthic Macroinvertebrate IBI calculated using: o Taxa richness o Modified HBI (using NC tolerance values) o EPT/Total o EPT Taxa o % Dominant 2 Taxa o Shannon-Weaver
8 Oklahoma Ecoregions
9 Upper Red/Washita Basin
10 Lower Red River Basin
11
12 Southwestern Tablelands
13
14
15 Arkansas Valley
16
17 South Central Plains
18 Define,, EPA National Rivers and Streams Assessment (NRSA) EPA Wadeable Streams Assessment (WSA) by ecoregion % of Oklahoma Conservation Commission (OCC) high quality reference conditions Comparison with 1 or 2 times the standard deviation of OCC high quality reference conditions
19 EPA National Rivers & Streams Assessment (NRSA)
20 EPA Wadeable Streams Assessment (WSA)
21 OCC HQ Reference High quality reference sites chosen in each ecoregion because they have a healthy biological community and good habitat Percent of high quality reference conditions Comparison with 1 or 2 times the standard deviation of OCC high quality reference conditions Is a condition if it does not meet State of Oklahoma standards?
22 Total Nitrogen / NRSA (mg/l) WSA (mg/l) Southwest Tablelands Central Great Plains Cross Timbers Arbuckle Uplift South Central Plains Ouachita Mountains Arkansas Valley Ecoregion OCC HQ Ref (2 * StdDev) The average OCC high quality reference site does not always meet the NRSA and WSA conditions.
23 Total Phosphorus / NRSA (mg/l) WSA (mg/l) Southwest Tablelands Central Great Plains Cross Timbers Arbuckle Uplift South Central Plains Ouachita Mountains Arkansas Valley Ecoregion OCC HQ Ref (2 * StdDev) The average OCC high quality reference site does not always meet the NRSA and WSA conditions.
24 Conductivity / NRSA (µs/cm2) WSA (µs/cm2) Southwest Tablelands Central Great Plains Cross Timbers Arbuckle Uplift South Central Plains Ouachita Mountains Arkansas Valley Ecoregion OCC HQ Ref (2 * StdDev) The average OCC high quality reference site does not always meet the NRSA and WSA conditions.
25 Turbidity / Ecoregion WSA (NTU) OCC HQ Ref (2 * StdDev) Southwest Tablelands 20 Central Great Plains Cross Timbers Arbuckle Uplift 7 21 South Central Plains Ouachita Mountains Arkansas Valley The average OCC high quality reference site does not always meet the NRSA and WSA conditions.
26 Chemistry Comparison Total Ni (WSA) Total Ni (OCC) Total P (NRSA) Total P (WSA) Total P (OCC) Conductivity (NRSA) Conductivity (WSA) Conductivity (OCC) Turbidity (WSA) Turbidity (OCC) Turbidity Total Ni (NRSA) Conductivity Ecoregion Total P siteid Total N OKR Cross Timbers OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Central Great Plains OKR Cross Timbers OKR Cross Timbers OKR Central Great Plains OKR Central Great Plains OKR Cross Timbers OKR Cross Timbers
27 Conductivity Extent Estimate OCC NRSA WSA Percent of stream length
28 Upper Red / Washita 8% 50% 37% 22% 42% 41% Macroinvertebrate Condition (% of Total Stream Length) Fish Condition (% of Total Stream Length)
29 Upper Red/Washita Relative Risk Extent Estimates Attributable Risk Habitat (OCC) TSS DO % Saturation Alkalinity Conductivity (OCC) Turbidity (OCC) Total P (OCC) Total N (OCC) Fish IBI (Extent stream length in condition = 22.5%)
30 Upper Red/Washita Relative Risk Extent Estimates Attributable Risk Habitat (OCC) TSS DO % Saturation Alkalinity Conductivity (OCC) Turbidity (OCC) Total P (OCC) Total N (OCC) Macroinvertebrate IBI (Extent of stream length in condition = 8.2%)
31 Lower Red River Basin 5% 6% 28% 33% 61% Macroinvertebrate Condition (% of Total Stream Length) 67% Fish Condition (% of Total Stream Length)
32 Lower Red River Basin Relative Risk Extent Estimates Attributable Risk Pool Variability Channel Alteration Bank Stability Total Habitat TKN TDS DO % Saturation Conductivity (OCC) Turbidity (OCC) Total P (OCC) Total N (OCC) Fish IBI (Extent of stream length in condition = 5.0%)
33 Lower Red River Basin Extent Estimates Relative Risk Attributable Risk Pool Variability Channel Alteration Bank Stability What happened? Total Habitat TKN TDS DO % Saturation Conductivity (OCC) Turbidity (OCC) Total P (OCC) Total N (OCC) Macroinvertebrate IBI (Extent of stream length in condition = 5.68%)
34 Lower Red River Basin Relative Risk summary(subset(all.dat.rr, select=c(resp.var.bugs,stres.vars))); MIBI_Cond Not:26 : 1 NA's :25 TN_NRSA_Cond Not:26 :23 NA's : 3 TN_EcoR_Cond Not:22 :29 NA's : 1 TN_OCC_Cond Not:35 :13 NA's : 4 TP_NRSA_Cond Not:27 :22 NA's : 3 TP_EcoR_Cond Not:29 :22 NA's : 1 TP_OCC_Cond Not:38 :10 NA's : 4 Conductivity_NRSA_Cond Not:49 : 2 NA's : 1 Conductivity_EcoR Not:43 : 8 NA's : 1 Conductivity_OCC Not:40 :11 NA's : 1 Turbidity_EcoR Not:33 :18 NA's : 1 Turbidity_OCC Not:43 : 8 NA's : 1
35
36 Conclusions
37 Conclusions
38 R Software Relatively easy to use Challenge: Identifying,, conditions Helpful hints: 1. Work in Excel or Minitab or a familiar program 2. Save the file as CSV 3. Use R scripts 4. Save the results as CSV 5. Work in Excel or Minitab or a familiar program to graph the results
39 Conclusions Probabilistic monitoring is useful for a quick and relatively inexpensive method to identify stressors putting streams at risk (visit each site once or twice) Ambient monitoring provides much more information and allows identification of high quality reference conditions (visit each site twenty times in two years)
40 Questions?
41 No time for questions!
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