Background
The Tampa Bay Benthic Index (TBBI) [1,2]) is an assessment method that quantifies the ecological health of the benthic community in Tampa Bay. The index provides a complementary approach to evaluating environmental condition that is supported by other assessment methods currently available for the region (e.g., water quality report card, nekton index, etc.). The tbeptools package includes several functions described below to import data required for the index and plot the results to view trends over time. Each of the functions are described in detail below.
The TBBI uses data from the Tampa Bay Benthic Monitoring Program as part of the Environmental Protection Commission (EPC) of Hillsborough Country. The data are updated annually on a public site maintained by EPC, typically in December after Summer/Fall sampling. This is the same website that hosts water quality data used for the water quality report card. The required data for the TBBI are more extensive than the water quality report card and the data are made available as a zipped folder of csv files, available here. The process for downloading and working with the data are similar as for the other functions in tbeptools.
Data import and included datasets
Data for calculating TBBI scores can be imported into the current R
session using the read_importbenthic() function. This
function downloads the zipped folder of base tables used for the TBBI
from the EPC site if the data have not already been downloaded to the
location specified by the input arguments.
To download the data with tbeptools, first create a character path
for the location where you want to download the zipped files. If one
does not exist, specify a location and name for the downloaded file.
Here, we name the folder benthic.zip and download it on our
desktop.
path <- '~/Desktop/benthic.zip'
benthicdata <- read_importbenthic(path)Running the above code will return the following error:
#> Error in read_importbenthic() : File at path does not exist, use download_latest = TRUEWe get an error message from the function indicating that the file is
not found. This makes sense because the file does not exist yet, so we
need to tell the function to download the latest file. This is done by
changing the download_latest argument to TRUE
(the default is FALSE).
benthicdata <- read_importbenthic(path, download_latest = T)
#> File ~/Desktop/benthic.zip does not exist, replacing with downloaded file...
#> trying URL 'https://epcbocc.sharepoint.com/:u:/s/Share/EQUCWBuwCNdGuMREYAyAD1gBKC98mYtCHMWX0FYLrbT4KA?e=nDfnnQ&download=1' length 37122877 bytes (35.4 MB)Now we get an indication that the file on the server is being
downloaded. When the download is complete, we’ll have the data
downloaded and saved to the benthicdata object in the
current R session.
If we try to run the function again after downloading the data, we get the following message. This check is done to make sure that the data are not unnecessarily downloaded if the current file matches the file on the server.
benthicdata <- read_importbenthic(path, download_latest = T)
#> File is current..Every time that tbeptools is used to work with the benthic data,
read_importbenthic() should be used to import the data. You
will always receive the message File is current... if your
local file matches the one on the server. However, data are periodically
updated and posted on the server. If download_latest = TRUE
and your local file is out of date, you will receive the following
message:
#> Replacing local file with current...Calculating TBBI scores
After the data are imported, you can view them from the assigned object. The data are provided as a nested tibble that includes three different datasets: station information, field sample data (salinity), and detailed taxa information.
benthicdata
#> # A tibble: 3 × 2
#> name value
#> <chr> <list>
#> 1 stations <tibble [4,787 × 10]>
#> 2 fieldsamples <tibble [5,197 × 3]>
#> 3 taxacounts <tibble [149,657 × 7]>The individual datasets can be viewed by extracting them from the
parent object using the deframe() function from the tibble
package.
# see all
deframe(benthicdata)
#> $stations
#> # A tibble: 4,787 × 10
#> StationID StationNumber AreaAbbr FundingProject ProgramID ProgramName
#> <int> <chr> <chr> <chr> <int> <chr>
#> 1 448 02BBs301 MTB Apollo Beach 4 Benthic Monitoring
#> 2 449 02BBs305 MTB Apollo Beach 4 Benthic Monitoring
#> 3 450 02BBs307 MTB Apollo Beach 4 Benthic Monitoring
#> 4 451 02BBs364 MTB Apollo Beach 4 Benthic Monitoring
#> 5 452 02BBs369 MTB Apollo Beach 4 Benthic Monitoring
#> 6 453 02BBs395 MTB Apollo Beach 4 Benthic Monitoring
#> 7 454 02BBs397 MTB Apollo Beach 4 Benthic Monitoring
#> 8 455 02BBs398 MTB Apollo Beach 4 Benthic Monitoring
#> 9 456 02BBs401 MTB Apollo Beach 4 Benthic Monitoring
#> 10 457 02BBs402 MTB Apollo Beach 4 Benthic Monitoring
#> # ℹ 4,777 more rows
#> # ℹ 4 more variables: Latitude <dbl>, Longitude <dbl>, date <date>, yr <dbl>
#>
#> $fieldsamples
#> # A tibble: 5,197 × 3
#> StationID date Salinity
#> <int> <date> <dbl>
#> 1 448 2002-05-21 30.5
#> 2 449 2002-05-20 30.8
#> 3 450 2002-05-21 30.5
#> 4 451 2002-05-21 30.6
#> 5 452 2002-05-20 30
#> 6 453 2002-05-21 30.6
#> 7 454 2002-05-21 30.5
#> 8 455 2002-05-21 30.3
#> 9 456 2002-05-20 30.0
#> 10 457 2002-05-20 29.9
#> # ℹ 5,187 more rows
#>
#> $taxacounts
#> # A tibble: 149,657 × 7
#> StationID TaxaCountID TaxaListID FAMILY NAME TaxaCount AdjCount
#> <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 11321 152055 1198 Anomiidae Anomia … 1 25
#> 2 11321 152056 1224 Carditidae Cardite… 1 25
#> 3 584 233277 624 Cirratulidae Chaetoz… 1 25
#> 4 11321 152057 1306 Veneridae Chione … 3 75
#> 5 3132 239443 352 Pilargidae Sigambr… 14 350
#> 6 11321 152058 1088 Pyramidellidae Turboni… 6 150
#> 7 2541 140530 258 NULL NEMERTEA 8 200
#> 8 2542 140531 258 NULL NEMERTEA 2 50
#> 9 2992 140631 262 NULL Palaeon… 4 100
#> 10 3003 140635 285 Tetrastemmatidae Tetrast… 2 50
#> # ℹ 149,647 more rows
# get only station dat
deframe(benthicdata)[['stations']]
#> # A tibble: 4,787 × 10
#> StationID StationNumber AreaAbbr FundingProject ProgramID ProgramName
#> <int> <chr> <chr> <chr> <int> <chr>
#> 1 448 02BBs301 MTB Apollo Beach 4 Benthic Monitoring
#> 2 449 02BBs305 MTB Apollo Beach 4 Benthic Monitoring
#> 3 450 02BBs307 MTB Apollo Beach 4 Benthic Monitoring
#> 4 451 02BBs364 MTB Apollo Beach 4 Benthic Monitoring
#> 5 452 02BBs369 MTB Apollo Beach 4 Benthic Monitoring
#> 6 453 02BBs395 MTB Apollo Beach 4 Benthic Monitoring
#> 7 454 02BBs397 MTB Apollo Beach 4 Benthic Monitoring
#> 8 455 02BBs398 MTB Apollo Beach 4 Benthic Monitoring
#> 9 456 02BBs401 MTB Apollo Beach 4 Benthic Monitoring
#> 10 457 02BBs402 MTB Apollo Beach 4 Benthic Monitoring
#> # ℹ 4,777 more rows
#> # ℹ 4 more variables: Latitude <dbl>, Longitude <dbl>, date <date>, yr <dbl>The anlz_tbbiscr() function uses the nested
benthicdata to estimate the TBBI scores at each site. The
TBBI scores typically range from 0 to 100 and are grouped into
categories that describe the general condition of the benthic community.
Scores less than 73 are considered “degraded”, scores between 73 and 87
are “intermediate”, and scores greater than 87 are “healthy”. Locations
that were sampled but no organisms were found are assigned a score of
zero and a category of “empty sample”. The total abundance
(TotalAbundance, organisms/m2), species richness
(SpeciesRichness) and bottom salinity
(Salinity, psu) are also provided. Some metrics for the
TBBI are corrected for salinity and bottom measurements taken at the
time of sampling are required for accurate calculation of the TBBI.
tbbiscr <- anlz_tbbiscr(benthicdata)
tbbiscr
#> # A tibble: 4,787 × 15
#> StationID StationNumber AreaAbbr FundingProject ProgramID ProgramName
#> <int> <chr> <chr> <chr> <int> <chr>
#> 1 448 02BBs301 MTB Apollo Beach 4 Benthic Monitoring
#> 2 449 02BBs305 MTB Apollo Beach 4 Benthic Monitoring
#> 3 450 02BBs307 MTB Apollo Beach 4 Benthic Monitoring
#> 4 451 02BBs364 MTB Apollo Beach 4 Benthic Monitoring
#> 5 452 02BBs369 MTB Apollo Beach 4 Benthic Monitoring
#> 6 453 02BBs395 MTB Apollo Beach 4 Benthic Monitoring
#> 7 454 02BBs397 MTB Apollo Beach 4 Benthic Monitoring
#> 8 455 02BBs398 MTB Apollo Beach 4 Benthic Monitoring
#> 9 456 02BBs401 MTB Apollo Beach 4 Benthic Monitoring
#> 10 457 02BBs402 MTB Apollo Beach 4 Benthic Monitoring
#> # ℹ 4,777 more rows
#> # ℹ 9 more variables: Latitude <dbl>, Longitude <dbl>, date <date>, yr <dbl>,
#> # TotalAbundance <dbl>, SpeciesRichness <dbl>, TBBI <dbl>, TBBICat <chr>,
#> # Salinity <dbl>Plotting results
The TBBI scores can be viewed as annual averages for each bay segment
using the show_tbbimatrix() function. The
show_tbbimatrix() plots the annual bay segment averages as
categorical values in a conventional “stoplight” graphic. A baywide
estimate is also returned, one based on all samples across all locations
(“All”) and another weighted by the relative surface areas of each bay
segment (“All (wt)”). The input to show_tbbimatrix()
function is the output from the anlz_tbbiscr()
function.
show_tbbimatrix(tbbiscr)
The matrix can also be produced as a plotly interactive plot by setting
plotly = TRUE inside the function.
show_tbbimatrix(tbbiscr, plotly = T)Additional sediment data
In addition to biological data, sediment contaminant concentrations are measured at sites within Tampa Bay. These include over 100 different constituents grouped broadly as metals, organics, physical, or other. The concentrations of these constituents can be compared relative to Threshold Effects Levels (TEL) or Potential Effects Levels (PEL), when available, as relative indications of the likelihood that the concentrations will have toxic effects on invertebrates that inhabit the sediments. The functions in tbeptools can be used to retrieve the sediment data and provide an indication of the concentrations relative to the TEL or PEL thresholds.
The read_importsediment() function will retrieve all
sediment data for Tampa Bay collected annually by the Environmental
Protection Commission of Hillsborough County. The data are retrieved
from the same
location as the biological data used to calculate the TBBI.
path <- '~/Desktop/sediment.zip'
sedimentdata <- read_importsediment(path, download_latest = T)After the data are imported, you can view them from the assigned object.
sedimentdata
#> # A tibble: 221,597 × 24
#> ProgramId ProgramName FundingProject yr AreaAbbr StationID StationNumber
#> <int> <chr> <chr> <int> <chr> <int> <chr>
#> 1 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 2 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 3 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 4 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 5 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 6 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 7 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 8 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 9 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> 10 4 Benthic Moni… TBEP 1993 HB 2463 93HB15
#> # ℹ 221,587 more rows
#> # ℹ 17 more variables: Latitude <dbl>, Longitude <dbl>, Replicate <chr>,
#> # SedResultsType <chr>, Parameter <chr>, ValueAdjusted <dbl>, Units <chr>,
#> # Qualifier <chr>, MDLnum <dbl>, PQLnum <dbl>, TEL <dbl>, PEL <dbl>,
#> # BetweenTELPEL <chr>, ExceedsPEL <chr>, PELRatio <dbl>,
#> # PreparationDate <chr>, AnalysisTimeMerge <chr>The show_sedimentmap() function can be used to create
maps of selected parameters relative to TEL and PEL values. Green points
show concentrations below the TEL, yellow points show concentrations
between the TEL and PEL, and red points show concentrations above the
PEL. The applicable TEL and PEL values for the parameter are indicated
in the legend. The selected stations are those that are sampled in the
years within the yrrng argument.
show_sedimentmap(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022))A single year of data can be shown as well.
show_sedimentmap(sedimentdata, param = 'Arsenic', yrrng = 2022)A map showing only the concentrations is returned if TEL and PEL values are not available for a parameter.
show_sedimentmap(sedimentdata, param = 'Selenium', yrrng = c(1993, 2022))Maps for total contaminant values (e.g., Total DDT, Total PAH, Total
PCB, Total LMW PAH, Total HMW PAH) can also be returned. Although the
totals are not included in the sedimentdata object, they
are calculated by tbeptools using the anlz_sedimentaddtot()
function. Simply entering the name of the total parameter in the
show_sedimentmap() function will produce the summary
map.
show_sedimentmap(sedimentdata, param = 'Total DDT', yrrng = c(1993, 2022))The PEL ratio can also be used to assess relative sediment quality
given the measured contaminants. The show_sedimentpelmap()
function creates a map of average PEL ratios graded from A to F for
benthic stations monitored in Tampa Bay. The PEL ratio is the
contaminant concentration divided by the Potential Effects Levels (PEL)
that applies to a contaminant, if available. Higher ratios and lower
grades indicate sediment conditions that are likely unfavorable for
invertebrates. The station average combines the PEL ratios across all
contaminants measured at a station.
show_sedimentpelmap(sedimentdata, yrrng = c(1993, 2022))The average PEL ratios and grades used to create the map can also be
returned as a data frame using anlz_sedimentpel().
anlz_sedimentpel(sedimentdata, yrrng = c(1993, 2022))
#> # A tibble: 2,233 × 7
#> yr AreaAbbr StationNumber Latitude Longitude PELRatio Grade
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <fct>
#> 1 1993 HB 93HB15 27.8 -82.4 0.0157 B
#> 2 1993 HB 93HB16 27.8 -82.5 0.0261 C
#> 3 1993 HB 93HB23 27.9 -82.4 0.0174 B
#> 4 1993 LTB 93LTB24 27.7 -82.6 0.0124 B
#> 5 1993 LTB 93LTB25 27.6 -82.6 0.0189 B
#> 6 1993 LTB 93LTB26 27.6 -82.7 0.00997 B
#> 7 1993 LTB 93LTB27 27.6 -82.7 0.0125 B
#> 8 1993 LTB 93LTB28 27.6 -82.7 0.0887 D
#> 9 1993 LTB 93LTB29 27.6 -82.6 0.0350 C
#> 10 1993 LTB 93LTB30 27.6 -82.6 0.0496 C
#> # ℹ 2,223 more rowsPlots of bay segment averages of sediment concentrations for selected
parameters can be created with show_sedimentave(). The plot
includes appropriate lines for the TEL and PEL values, as well the grand
mean across all segments. The former are omitted from the plot if
unavailable for a selected parameter.
show_sedimentave(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022))
The same plot can be returned as an interactive plotly object using
plotly = T.
show_sedimentave(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022), plotly = T)The values used in the plot can be returned with
anlz_sedimentave().
anlz_sedimentave(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022))
#> # A tibble: 7 × 8
#> AreaAbbr TEL PEL Units ave lov hiv grandave
#> <fct> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 BCB 7.24 41.6 mg/kg 2.64 2.34 2.94 2.57
#> 2 HB 7.24 41.6 mg/kg 2.93 0.665 5.20 2.57
#> 3 LTB 7.24 41.6 mg/kg 2.93 2.47 3.40 2.57
#> 4 MR 7.24 41.6 mg/kg 1.66 1.37 1.95 2.57
#> 5 MTB 7.24 41.6 mg/kg 2.17 1.86 2.48 2.57
#> 6 OTB 7.24 41.6 mg/kg 3.24 0.707 5.78 2.57
#> 7 TCB 7.24 41.6 mg/kg 2.40 1.92 2.88 2.57As before, the total contaminant values (e.g., Total DDT, Total PAH,
Total PCB, Total LMW PAH, Total HMW PAH) can also be returned even
though they are not included in the sedimentdata object.
The anlz_sedimentaddtot() function is used to calculate the
totals within anlz_sedimentave().
anlz_sedimentave(sedimentdata, param = 'Total DDT', yrrng = c(1993, 2022))
#> # A tibble: 7 × 8
#> AreaAbbr TEL PEL Units ave lov hiv grandave
#> <fct> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 BCB 3.89 51.7 ug/kg 0.570 0.382 0.757 0.700
#> 2 HB 3.89 51.7 ug/kg 1.77 0.870 2.67 0.700
#> 3 LTB 3.89 51.7 ug/kg 0.218 0.105 0.332 0.700
#> 4 MR 3.89 51.7 ug/kg 0.838 0.517 1.16 0.700
#> 5 MTB 3.89 51.7 ug/kg 0.434 0.285 0.582 0.700
#> 6 OTB 3.89 51.7 ug/kg 0.490 0.274 0.706 0.700
#> 7 TCB 3.89 51.7 ug/kg 0.582 0.202 0.962 0.700A similar plot of the bay segment averages for the average PEL ratios
can be created with show_sedimentpelave(). The colors
indicate the grades for A (green) to F (red).
show_sedimentpelave(sedimentdata, yrrng = c(1993, 2022))
The same plot can be returned as an interactive plotly object using
plotly = T.
show_sedimentpelave(sedimentdata, yrrng = c(1993, 2022), plotly = T)The values used in the plot can be returned with
anlz_sedimentpelave().
anlz_sedimentpelave(sedimentdata, yrrng = c(1993, 2022))
#> # A tibble: 7 × 5
#> AreaAbbr ave lov hiv grandave
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 BCB 0.0535 0.0445 0.0625 0.0522
#> 2 HB 0.118 0.0663 0.169 0.0522
#> 3 LTB 0.0281 0.0247 0.0315 0.0522
#> 4 MR 0.0446 0.0375 0.0516 0.0522
#> 5 MTB 0.0374 0.0327 0.0421 0.0522
#> 6 OTB 0.0506 0.0371 0.0642 0.0522
#> 7 TCB 0.0337 0.0265 0.0409 0.0522The show_sedimentalratio() function creates a plot of a
selected metal parameter against Aluminum. This plot provides
information on the concentration of the parameter relative to background
levels, where Aluminum is present as a common metal in the Earth’s
crust. An elevated ratio of a metal parameter relative to aluminum
suggests it is higher than background concentrations [3]. The linear fit of a log-log model is shown
as a solid black line, with 95% prediction intervals. The TEL/PEL
values, if available, are also shown as horizontal red lines.
show_sedimentalratio(sedimentdata, param = 'Zinc', bay_segment = c('HB', 'LTB'))
The same plot can be returned as an interactive plotly object using
plotly = T.
show_sedimentalratio(sedimentdata, param = 'Zinc', bay_segment = c('HB', 'LTB'), plotly = T)