README.md
In SCCWRP/CSCI: Tools for California Freshwater Biotic Assessment
CSCI
Raphael Mazor, raphaelm@sccwrp.org, Marcus W. Beck, marcusb@sccwrp.org, Mark W. Engeln
R package materials to calculate the California Stream Condition Index
(CSCI) based on O/E and pMMI scores using benthic macroinvertebrates.
Installation
Install the package as follows:
install.packages('devtools')
library(devtools)
install_github('SCCWRP/CSCI')
library(CSCI)
Citation
Please cite the package as follows:
Mazor, MD, Rehn, AC, Ode, PR, Engeln, M, Schiff, KC, Stein, ED, Gillett,
DJ, Herbst, DB, Hawkins, CP. 2016. Bioassessment in complex
environments: Designing an index for consistent meaning in different
settings. Freshwater Science 35(1): 249-271.
Usage
The core function is CSCI which requires taxonomic and site level
data.
#A list of two data frames: bugs and stations
data(bugs_stations)
# run the estimator
results <- CSCI(bugs = bugs_stations[[1]], stations = bugs_stations[[2]])
# see all the components of the report
ls(results)
## [1] "core" "Suppl1_grps" "Suppl1_mmi" "Suppl1_OE" "Suppl2_mmi"
## [6] "Suppl2_OE"
# preview report components
lapply(results, head)
## $core
## StationCode SampleID Count Number_of_MMI_Iterations
## 1 Site3 BadSample1 100 1
## 2 Site3 BadSample2 600 20
## 3 Site1 Sample1 556 20
## 4 Site2 Sample2 826 20
## 5 Site3 Sample3 607 20
## 6 Site3 Sample4 513 20
## Number_of_OE_Iterations Pcnt_Ambiguous_Individuals Pcnt_Ambiguous_Taxa
## 1 1 0.0000000 0.000000
## 2 1 83.3333333 50.000000
## 3 20 0.5395683 2.631579
## 4 20 0.9685230 1.666667
## 5 20 9.7199341 6.250000
## 6 1 37.6218324 41.025641
## E Mean_O OoverE OoverE_Percentile MMI MMI_Percentile
## 1 10.248486 1.00 0.09757538 0.00 0.1638082 0.00
## 2 10.248486 1.00 0.09757538 0.00 0.3488195 0.00
## 3 7.544418 8.95 1.18630752 0.84 0.8286403 0.17
## 4 12.953853 10.70 0.82600908 0.18 0.8253004 0.16
## 5 10.248486 13.25 1.29287384 0.94 1.1890926 0.85
## 6 10.248486 9.00 0.87817846 0.26 0.9030652 0.29
## CSCI CSCI_Percentile
## 1 0.1306918 0.00
## 2 0.2231975 0.00
## 3 1.0074739 0.52
## 4 0.8256547 0.14
## 5 1.2409832 0.93
## 6 0.8906218 0.25
##
## $Suppl1_mmi
## StationCode SampleID MMI_Score Clinger_PercentTaxa
## 1 Site3 BadSample1 0.1638082 0.0000000
## 2 Site3 BadSample2 0.3488195 0.0000000
## 3 Site1 Sample1 0.8286403 0.2729680
## 4 Site2 Sample2 0.8253004 0.4482318
## 5 Site3 Sample3 1.1890926 0.6710665
## 6 Site3 Sample4 0.9030652 0.6337662
## Clinger_PercentTaxa_predicted Clinger_PercentTaxa_score
## 1 0.6422118 0.0000000
## 2 0.6422118 0.0000000
## 3 0.3929307 0.5218131
## 4 0.6216008 0.4355838
## 5 0.6422118 0.7620922
## 6 0.6422118 0.7018676
## Coleoptera_PercentTaxa Coleoptera_PercentTaxa_predicted
## 1 0.00000000 0.07977832
## 2 0.00000000 0.07977832
## 3 0.11268908 0.08284403
## 4 0.07619408 0.05155909
## 5 0.12629335 0.07977832
## 6 0.07273219 0.07977832
## Coleoptera_PercentTaxa_score Taxonomic_Richness
## 1 0.2321037 1.00
## 2 0.2321037 2.00
## 3 0.7390277 34.60
## 4 0.7149352 32.20
## 5 0.8161136 41.45
## 6 0.5684343 26.80
## Taxonomic_Richness_predicted Taxonomic_Richness_score EPT_PercentTaxa
## 1 32.27143 0.0000000 0.0000000
## 2 32.27143 0.0000000 0.5000000
## 3 26.13860 0.8991626 0.2572269
## 4 32.79767 0.6548757 0.4576379
## 5 32.27143 0.9185017 0.5128822
## 6 32.27143 0.5234496 0.5559915
## EPT_PercentTaxa_predicted EPT_PercentTaxa_score Shredder_Taxa
## 1 0.5267920 0.0000000 0.0
## 2 0.5267920 0.6971410 0.0
## 3 0.3943971 0.4911140 0.0
## 4 0.5783728 0.5217916 4.9
## 5 0.5267920 0.7211863 3.7
## 6 0.5267920 0.8016522 1.0
## Shredder_Taxa_predicted Shredder_Taxa_score Intolerant_Percent
## 1 2.033700 0.2291321 0.00000000
## 2 2.033700 0.2291321 0.00000000
## 3 1.929400 0.2457120 0.01100000
## 4 3.760033 0.7336271 0.08855586
## 5 2.033700 0.8172945 0.15190000
## 6 2.033700 0.3880949 0.13820685
## Intolerant_Percent_predicted Intolerant_Percent_score
## 1 0.1696027 0.15600955
## 2 0.1696027 0.15600955
## 3 0.1440950 0.22556901
## 4 0.3143217 0.04899997
## 5 0.1696027 0.44543017
## 6 0.1696027 0.41934012
##
## $Suppl1_grps
## StationCode pGroup1 pGroup2 pGroup3 pGroup4 pGroup5 pGroup6 pGroup7
## 1 Site1 0.0002 0.0287 0.0065 0.0361 0.0007 0.0079 0.0000
## 2 Site2 0.1077 0.2976 0.0000 0.0056 0.0253 0.0994 0.3547
## 3 Site3 0.0192 0.1103 0.1226 0.1631 0.0068 0.0002 0.0001
## pGroup8 pGroup9 pGroup10 pGroup11
## 1 0.2169 0.0843 0.4991 0.1196
## 2 0.0078 0.1012 0.0006 0.0001
## 3 0.0530 0.2653 0.0700 0.1894
##
## $Suppl1_OE
## StationCode SampleID OTU CaptureProb MeanObserved
## 1 Site1 Sample1 Acari 0.8814250 5.55
## 2 Site2 Sample2 Acari 0.9585321 10.45
## 3 Site3 BadSample1 Acari 0.8678715 0.00
## 4 Site3 BadSample2 Acari 0.8678715 0.00
## 5 Site3 Sample3 Acari 0.8678715 18.85
## 6 Site3 Sample4 Acari 0.8678715 37.00
##
## $Suppl2_OE
## StationCode SampleID OTU CaptureProb
## 1 Site1 Sample1 Acari 0.881424961134976
## 2 Site1 Sample1 Argia 0.366983484848485
## 3 Site1 Sample1 Baetis 0.880591985549809
## 4 Site1 Sample1 Callibaetis 0.101509696969697
## 5 Site1 Sample1 Ceratopsyche_Hydropsyche 0.698923583910464
## 6 Site1 Sample1 Cheumatopsyche 0.193852461500248
## Iteration1 Iteration2 Iteration3 Iteration4 Iteration5 Iteration6
## 1 6 4 5 6 6 7
## 2 6 8 6 6 7 7
## 3 27 26 21 22 27 23
## 4 1 2 1 1 1 1
## 5 4 3 2 4 3 4
## 6 2 2 1 1 2 1
## Iteration7 Iteration8 Iteration9 Iteration10 Iteration11 Iteration12
## 1 6 5 4 4 6 7
## 2 6 7 5 7 9 8
## 3 27 25 23 20 22 25
## 4 1 2 2 2 2 1
## 5 5 5 4 4 4 4
## 6 1 2 2 1 1 2
## Iteration13 Iteration14 Iteration15 Iteration16 Iteration17 Iteration18
## 1 6 6 6 7 6 6
## 2 9 5 8 7 6 7
## 3 23 29 22 27 25 23
## 4 0 2 0 1 1 2
## 5 5 5 3 3 3 5
## 6 2 1 2 1 2 2
## Iteration19 Iteration20
## 1 4 4
## 2 5 6
## 3 24 22
## 4 1 1
## 5 3 3
## 6 1 2
##
## $Suppl2_mmi
## StationCode SampleID metric Iteration value
## 1 Site3 BadSample1 Clinger_PercentTaxa 1 0
## 2 Site3 BadSample1 Clinger_PercentTaxa 2 0
## 3 Site3 BadSample1 Clinger_PercentTaxa 3 0
## 4 Site3 BadSample1 Clinger_PercentTaxa 4 0
## 5 Site3 BadSample1 Clinger_PercentTaxa 5 0
## 6 Site3 BadSample1 Clinger_PercentTaxa 6 0
## predicted_value score
## 1 0.6422118 0
## 2 0.6422118 0
## 3 0.6422118 0
## 4 0.6422118 0
## 5 0.6422118 0
## 6 0.6422118 0
Metadata
Resources: SOP, paper
Contact: Raphael Mazor
SCCWRP/CSCI documentation built on Feb. 8, 2022, 11:25 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
CSCI
Raphael Mazor, raphaelm@sccwrp.org, Marcus W. Beck, marcusb@sccwrp.org, Mark W. Engeln
R package materials to calculate the California Stream Condition Index (CSCI) based on O/E and pMMI scores using benthic macroinvertebrates.
Installation
Install the package as follows:
install.packages('devtools')
library(devtools)
install_github('SCCWRP/CSCI')
library(CSCI)
Citation
Please cite the package as follows:
Mazor, MD, Rehn, AC, Ode, PR, Engeln, M, Schiff, KC, Stein, ED, Gillett, DJ, Herbst, DB, Hawkins, CP. 2016. Bioassessment in complex environments: Designing an index for consistent meaning in different settings. Freshwater Science 35(1): 249-271.
Usage
The core function is CSCI which requires taxonomic and site level
data.
#A list of two data frames: bugs and stations
data(bugs_stations)
# run the estimator
results <- CSCI(bugs = bugs_stations[[1]], stations = bugs_stations[[2]])
# see all the components of the report
ls(results)
## [1] "core" "Suppl1_grps" "Suppl1_mmi" "Suppl1_OE" "Suppl2_mmi"
## [6] "Suppl2_OE"
# preview report components
lapply(results, head)
## $core
## StationCode SampleID Count Number_of_MMI_Iterations
## 1 Site3 BadSample1 100 1
## 2 Site3 BadSample2 600 20
## 3 Site1 Sample1 556 20
## 4 Site2 Sample2 826 20
## 5 Site3 Sample3 607 20
## 6 Site3 Sample4 513 20
## Number_of_OE_Iterations Pcnt_Ambiguous_Individuals Pcnt_Ambiguous_Taxa
## 1 1 0.0000000 0.000000
## 2 1 83.3333333 50.000000
## 3 20 0.5395683 2.631579
## 4 20 0.9685230 1.666667
## 5 20 9.7199341 6.250000
## 6 1 37.6218324 41.025641
## E Mean_O OoverE OoverE_Percentile MMI MMI_Percentile
## 1 10.248486 1.00 0.09757538 0.00 0.1638082 0.00
## 2 10.248486 1.00 0.09757538 0.00 0.3488195 0.00
## 3 7.544418 8.95 1.18630752 0.84 0.8286403 0.17
## 4 12.953853 10.70 0.82600908 0.18 0.8253004 0.16
## 5 10.248486 13.25 1.29287384 0.94 1.1890926 0.85
## 6 10.248486 9.00 0.87817846 0.26 0.9030652 0.29
## CSCI CSCI_Percentile
## 1 0.1306918 0.00
## 2 0.2231975 0.00
## 3 1.0074739 0.52
## 4 0.8256547 0.14
## 5 1.2409832 0.93
## 6 0.8906218 0.25
##
## $Suppl1_mmi
## StationCode SampleID MMI_Score Clinger_PercentTaxa
## 1 Site3 BadSample1 0.1638082 0.0000000
## 2 Site3 BadSample2 0.3488195 0.0000000
## 3 Site1 Sample1 0.8286403 0.2729680
## 4 Site2 Sample2 0.8253004 0.4482318
## 5 Site3 Sample3 1.1890926 0.6710665
## 6 Site3 Sample4 0.9030652 0.6337662
## Clinger_PercentTaxa_predicted Clinger_PercentTaxa_score
## 1 0.6422118 0.0000000
## 2 0.6422118 0.0000000
## 3 0.3929307 0.5218131
## 4 0.6216008 0.4355838
## 5 0.6422118 0.7620922
## 6 0.6422118 0.7018676
## Coleoptera_PercentTaxa Coleoptera_PercentTaxa_predicted
## 1 0.00000000 0.07977832
## 2 0.00000000 0.07977832
## 3 0.11268908 0.08284403
## 4 0.07619408 0.05155909
## 5 0.12629335 0.07977832
## 6 0.07273219 0.07977832
## Coleoptera_PercentTaxa_score Taxonomic_Richness
## 1 0.2321037 1.00
## 2 0.2321037 2.00
## 3 0.7390277 34.60
## 4 0.7149352 32.20
## 5 0.8161136 41.45
## 6 0.5684343 26.80
## Taxonomic_Richness_predicted Taxonomic_Richness_score EPT_PercentTaxa
## 1 32.27143 0.0000000 0.0000000
## 2 32.27143 0.0000000 0.5000000
## 3 26.13860 0.8991626 0.2572269
## 4 32.79767 0.6548757 0.4576379
## 5 32.27143 0.9185017 0.5128822
## 6 32.27143 0.5234496 0.5559915
## EPT_PercentTaxa_predicted EPT_PercentTaxa_score Shredder_Taxa
## 1 0.5267920 0.0000000 0.0
## 2 0.5267920 0.6971410 0.0
## 3 0.3943971 0.4911140 0.0
## 4 0.5783728 0.5217916 4.9
## 5 0.5267920 0.7211863 3.7
## 6 0.5267920 0.8016522 1.0
## Shredder_Taxa_predicted Shredder_Taxa_score Intolerant_Percent
## 1 2.033700 0.2291321 0.00000000
## 2 2.033700 0.2291321 0.00000000
## 3 1.929400 0.2457120 0.01100000
## 4 3.760033 0.7336271 0.08855586
## 5 2.033700 0.8172945 0.15190000
## 6 2.033700 0.3880949 0.13820685
## Intolerant_Percent_predicted Intolerant_Percent_score
## 1 0.1696027 0.15600955
## 2 0.1696027 0.15600955
## 3 0.1440950 0.22556901
## 4 0.3143217 0.04899997
## 5 0.1696027 0.44543017
## 6 0.1696027 0.41934012
##
## $Suppl1_grps
## StationCode pGroup1 pGroup2 pGroup3 pGroup4 pGroup5 pGroup6 pGroup7
## 1 Site1 0.0002 0.0287 0.0065 0.0361 0.0007 0.0079 0.0000
## 2 Site2 0.1077 0.2976 0.0000 0.0056 0.0253 0.0994 0.3547
## 3 Site3 0.0192 0.1103 0.1226 0.1631 0.0068 0.0002 0.0001
## pGroup8 pGroup9 pGroup10 pGroup11
## 1 0.2169 0.0843 0.4991 0.1196
## 2 0.0078 0.1012 0.0006 0.0001
## 3 0.0530 0.2653 0.0700 0.1894
##
## $Suppl1_OE
## StationCode SampleID OTU CaptureProb MeanObserved
## 1 Site1 Sample1 Acari 0.8814250 5.55
## 2 Site2 Sample2 Acari 0.9585321 10.45
## 3 Site3 BadSample1 Acari 0.8678715 0.00
## 4 Site3 BadSample2 Acari 0.8678715 0.00
## 5 Site3 Sample3 Acari 0.8678715 18.85
## 6 Site3 Sample4 Acari 0.8678715 37.00
##
## $Suppl2_OE
## StationCode SampleID OTU CaptureProb
## 1 Site1 Sample1 Acari 0.881424961134976
## 2 Site1 Sample1 Argia 0.366983484848485
## 3 Site1 Sample1 Baetis 0.880591985549809
## 4 Site1 Sample1 Callibaetis 0.101509696969697
## 5 Site1 Sample1 Ceratopsyche_Hydropsyche 0.698923583910464
## 6 Site1 Sample1 Cheumatopsyche 0.193852461500248
## Iteration1 Iteration2 Iteration3 Iteration4 Iteration5 Iteration6
## 1 6 4 5 6 6 7
## 2 6 8 6 6 7 7
## 3 27 26 21 22 27 23
## 4 1 2 1 1 1 1
## 5 4 3 2 4 3 4
## 6 2 2 1 1 2 1
## Iteration7 Iteration8 Iteration9 Iteration10 Iteration11 Iteration12
## 1 6 5 4 4 6 7
## 2 6 7 5 7 9 8
## 3 27 25 23 20 22 25
## 4 1 2 2 2 2 1
## 5 5 5 4 4 4 4
## 6 1 2 2 1 1 2
## Iteration13 Iteration14 Iteration15 Iteration16 Iteration17 Iteration18
## 1 6 6 6 7 6 6
## 2 9 5 8 7 6 7
## 3 23 29 22 27 25 23
## 4 0 2 0 1 1 2
## 5 5 5 3 3 3 5
## 6 2 1 2 1 2 2
## Iteration19 Iteration20
## 1 4 4
## 2 5 6
## 3 24 22
## 4 1 1
## 5 3 3
## 6 1 2
##
## $Suppl2_mmi
## StationCode SampleID metric Iteration value
## 1 Site3 BadSample1 Clinger_PercentTaxa 1 0
## 2 Site3 BadSample1 Clinger_PercentTaxa 2 0
## 3 Site3 BadSample1 Clinger_PercentTaxa 3 0
## 4 Site3 BadSample1 Clinger_PercentTaxa 4 0
## 5 Site3 BadSample1 Clinger_PercentTaxa 5 0
## 6 Site3 BadSample1 Clinger_PercentTaxa 6 0
## predicted_value score
## 1 0.6422118 0
## 2 0.6422118 0
## 3 0.6422118 0
## 4 0.6422118 0
## 5 0.6422118 0
## 6 0.6422118 0
Metadata
Resources: SOP, paper Contact: Raphael Mazor
R Package Documentation
Browse R Packages
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