R package materials to calculate the Algal Stream Condition Index (ASCI) based on predictive MMI scores using diatom, soft-bodied algae, or a hybrid appproach.
Install the package as follows:
install.packages('devtools')library(devtools)install_github('SCCWRP/ASCI')library(ASCI)
The sample filesdemo_algae_tax
anddemo_station
are included to demonstrate the correct formats for the input data. Thedemo_algae_tax
file is adata.frame
of taxonomic data in long format (one row per sample). Thedemo_station
file is adata.frame
of GIS predictors in wide format, one row per station. See the help files for more information (e.g.,?demo_algae_tax
). Also see the help file forchkinp()
andcalcgis()
for requirements of each file to work with the ASCI.
head(demo_algae_tax)
## # A tibble: 6 x 7## StationCode SampleDate Replicate SampleTypeCode BAResult Result## <chr> <dttm> <dbl> <chr> <dbl> <dbl>## 1 909M24937 2016-06-22 00:00:00 1 Macroalgae NA 1.21e9## 2 909M24937 2016-06-22 00:00:00 1 Epiphyte 15 NA## 3 909M24937 2016-06-22 00:00:00 1 Epiphyte 83 NA## 4 909M24937 2016-06-22 00:00:00 1 Epiphyte 2 NA## 5 909M24937 2016-06-22 00:00:00 1 Integrated 5 NA## 6 909M24937 2016-06-22 00:00:00 1 Integrated 53 NA## # … with 1 more variable: FinalID <chr>
head(demo_station)
## # A tibble: 3 x 26## StationCode CondQR50 SITE_ELEV TEMP_00_09 KFCT_AVE AtmCa PPT_00_09## <chr> <int> <int> <int> <dbl> <dbl> <dbl>## 1 404M07357 NA 199 2456 0.278 0.0554 55570.## 2 801M16916 NA 197 2685 0.185 0.0652 25406.## 3 909M24937 NA 582 2442 0.202 0.106 37972.## # … with 19 more variables: MAX_ELEV <int>, CaO_Mean <dbl>,## # MgO_Mean <dbl>, S_Mean <dbl>, UCS_Mean <dbl>, LPREM_mean <dbl>,## # AtmMg <dbl>, AtmSO4 <dbl>, MINP_WS <dbl>, MEANP_WS <dbl>,## # SumAve_P <dbl>, TMAX_WS <dbl>, XWD_WS <dbl>, MAXWD_WS <dbl>,## # LST32AVE <dbl>, BDH_AVE <dbl>, PRMH_AVE <dbl>, PSA6C <chr>,## # XerMtn <lgl>
The output is in a wide format.
demo_results<-ASCI(demo_algae_tax,demo_station)demo_results
## # A tibble: 3 x 55## SampleID StationCode SampleDate Replicate SampleType## <chr> <chr> <dttm> <int> <chr>## 1 404M073… 404M07357 2016-06-13 00:00:00 1 Integrate…## 2 801M169… 801M16916 2016-05-25 00:00:00 1 Microalga…## 3 909M249… 909M24937 2016-06-22 00:00:00 1 Macroalga…## # … with 50 more variables: D_ValveCount <int>, S_EntityCount <int>,## # S_Biovolume <dbl>, D_NumberTaxa <dbl>, S_NumberTaxa <dbl>,## # H_NumberTaxa <dbl>, UnrecognizedTaxa <chr>, D_ASCI <dbl>,## # S_ASCI <dbl>, H_ASCI <dbl>, D_pct_att_prp_spp_BCG12 <dbl>,## # D_pct_att_prp_spp_OxRq_DO100_75 <dbl>,## # D_pct_att_prp_spp_Salinity_BF <dbl>,## # D_pct_att_prp_spp_Trophic_E <dbl>, D_prp_spp_BCG12_mod <dbl>,## # D_prp_spp_BCG12_mod_scr <dbl>, D_prp_spp_BCG12_pred <dbl>,## # D_prp_spp_OxRq_DO100_75_raw <dbl>,## # D_prp_spp_OxRq_DO100_75_raw_scr <dbl>,## # D_prp_spp_Salinity_BF_mod <dbl>, D_prp_spp_Salinity_BF_mod_scr <dbl>,## # D_prp_spp_Salinity_BF_pred <dbl>, D_prp_spp_Trophic_E_mod <dbl>,## # D_prp_spp_Trophic_E_mod_scr <dbl>, D_prp_spp_Trophic_E_pred <dbl>,## # H_OxRd_DO_30_richness_mod <dbl>, H_OxRd_DO_30_richness_mod_scr <dbl>,## # H_OxRd_DO_30_richness_pred <dbl>, H_pct_att_OxRd_DO_30_richness <dbl>,## # H_pct_att_prp_spp_BCG4 <dbl>, H_pct_att_prp_spp_IC_DOC_high <dbl>,## # H_pct_att_Salinity_BF_richness <dbl>, H_prp_spp_BCG4_mod <dbl>,## # H_prp_spp_BCG4_mod_scr <dbl>, H_prp_spp_BCG4_pred <dbl>,## # H_prp_spp_IC_DOC_high_raw <dbl>, H_prp_spp_IC_DOC_high_raw_scr <dbl>,## # H_Salinity_BF_richness_mod <dbl>,## # H_Salinity_BF_richness_mod_scr <dbl>,## # H_Salinity_BF_richness_pred <dbl>, S_cnt_spp_IC_DOC_high_raw <dbl>,## # S_cnt_spp_IC_DOC_high_raw_scr <dbl>,## # S_pct_att_cnt_spp_IC_DOC_high <dbl>, S_pct_att_prp_spp_BCG45 <dbl>,## # S_pct_att_prp_spp_Green <dbl>, S_prp_spp_BCG45_raw <dbl>,## # S_prp_spp_BCG45_raw_scr <dbl>, S_prp_spp_Green_raw <dbl>,## # S_prp_spp_Green_raw_scr <dbl>, Comments <chr>