birdnet_iNEXT: UNDER DEVELOPMENT

In nationalparkservice/NSNSDAcoustics: Streamline National Park Service Bioacoustics Analysis

UNDER DEVELOPMENT

Description

TBD

TBD

Usage

birdnet_iNEXT(data, confidence.threshold = 0, n.detections = 1)

birdnet_iNEXT(data, confidence.threshold = 0, n.detections = 1)

Arguments

data	Data.frame / data.table with....
confidence.threshold	Threshold below which BirdNET results should be excluded.
n.detections	Number of detections of a species within a day to conclude presence.

Details

This function was developed by the National Park Service Natural Sounds and Night Skies Division to support species biodiversity characterization in bioacoustics projects. This function is under development.

This function was developed by the National Park Service Natural Sounds and Night Skies Division Scientists in Parks intern Kayley Dillon to support species biodiversity characterization in bioacoustics projects.

Value

DESCRIBE... additional columns:

• insert col name here: tbd

• insert col name here: tbd

• insert col name here: tbd

DESCRIBE... additional columns:

• stuff: tbd

• stuff: tbd

• stuff: tbd

Examples

## Not run: 

# Read in example BirdNET data
data(exampleBarchartData)

dat <- add_time_cols(
  dt = exampleBarchartData,
  recording.id.col = 'recordingID',
  tz.recorder = 'America/Los_Angeles',
  tz.local = 'America/Los_Angeles'
)

result <- birdnet_iNEXT(
  data = dat,
  confidence.threshold = 0,
  n.detections = 3
)

# We can use this result to plot species rarefaction curves
# Possibly just script this instead of functionizing

# Choose "sized-based" vs "coverage-based"
size.based <- data.table(
  Days = result$iNextEst$size_based$t,
  Richness = result$iNextEst$size_based$qD,
  Lower_95 = result$iNextEst$size_based$qD.LCL,
  Upper_95 = result$iNextEst$size_based$qD.UCL,
  Method = result$iNextEst$size_based$Method
)

cov.based <- data.table(
 Days = result$iNextEst$coverage_based$t,
 Richness = result$iNextEst$coverage_based$qD,
 Lower_95 = result$iNextEst$coverage_based$qD.LCL,
 Upper_95 = result$iNextEst$coverage_based$qD.UCL,
 Method = result$iNextEst$coverage_based$Method
)

cov.based[,locationID := 'Rivendell']
size.based[,locationID := 'Rivendell']

cov.based[,Filter := 'Coverage']
size.based[,Filter := 'Size']

dat <- rbind(cov.based, size.based)

# Richness values are essentially the same;
# it's the confidence values that are different
# size.based conf ranges will be much tighter

# Separate out by method
interp <- dat[Method == 'Rarefaction']
extrap <- dat[Method == 'Extrapolation']
obs <- dat[Method == 'Observed']

# Transform data to order plots properly
levs <- c('Coverage', 'Size')
dat$Filter <- factor(dat$Filter, levels = levs)
interp$Filter <- factor(interp$Filter, levels = levs)
extrap$Filter <- factor(extrap$Filter, levels = levs)
obs$Filter <- factor(obs$Filter, levels = levs)

# Graph Results
ggplot() +
 facet_grid(cols = vars(Filter), scales = "free_x") +
 geom_ribbon(dat, mapping =
               aes(x = Days, ymin = Lower_95,
                   ymax = Upper_95, fill = locationID),
             alpha = 0.5) +
 geom_point(data = obs, mapping =
              aes(x = Days, y = Richness,
                  color = locationID), size = 3) +
 geom_line(data = interp, mapping =
             aes(x = Days, y = Richness,
                 color = locationID,
                 linetype = "dashed"), size = 1) +
 geom_line(data = extrap, mapping =
             aes(x = Days, y = Richness,
                 color = locationID,
                 linetype = "solid"), size = 1) +
 theme_classic() +
 scale_fill_manual(name = "locationID",
                   values = c("#66c2a5", "#fc8d62")) +
 scale_color_manual(name = "locationID",
                    values = c("#66c2a5", "#fc8d62")) +
 labs(x = "Days Sampled", y = "Species Richness") +
 guides(linetype = "none")



## End(Not run)
## Not run: 

# Read in example BirdNET data
data(exampleBarchartData)

dat <- add_time_cols(
  dt = exampleBarchartData,
  recording.id.col = 'recordingID',
  tz.recorder = 'America/Los_Angeles',
  tz.local = 'America/Los_Angeles'
)

result <- birdnet_iNEXT(
  data = dat,
  confidence.threshold = 0,
  n.detections = 3
)

# We can use this result to plot species rarefaction curves
# Possibly just script this instead of functionizing

# Not sure if this should be "sized-based" vs "coverage-based"
# Size-based looks more right
size.based <- data.table(
  Days = result$iNextEst$size_based$t,
  Richness = result$iNextEst$size_based$qD,
  Lower_95 = result$iNextEst$size_based$qD.LCL,
  Upper_95 = result$iNextEst$size_based$qD.UCL,
  Method = result$iNextEst$size_based$Method
)

cov.based <- data.table(
 Days = result$iNextEst$coverage_based$t,
 Richness = result$iNextEst$coverage_based$qD,
 Lower_95 = result$iNextEst$coverage_based$qD.LCL,
 Upper_95 = result$iNextEst$coverage_based$qD.UCL,
 Method = result$iNextEst$coverage_based$Method
)

cov.based[,locationID := 'Rivendell']
size.based[,locationID := 'Rivendell']

cov.based[,Filter := 'Coverage']
size.based[,Filter := 'Size']

dat <- rbind(cov.based, size.based)

# Okay, so the richness values are essentially the same;
# it's the confidence values that are different
# size.based conf ranges will be much tigher -- why?

# Separate out by method
interp <- dat[Method == 'Rarefaction']
extrap <- dat[Method == 'Extrapolation']
obs <- dat[Method == 'Observed']

# Transform data to order plots properly
levs <- c('Coverage', 'Size')
dat$Filter <- factor(dat$Filter, levels = levs)
interp$Filter <- factor(interp$Filter, levels = levs)
extrap$Filter <- factor(extrap$Filter, levels = levs)
obs$Filter <- factor(obs$Filter, levels = levs)

## Graph ####
ggplot() +
 facet_grid(cols = vars(Filter), scales = "free_x") +
 geom_ribbon(dat, mapping =
               aes(x = Days, ymin = Lower_95,
                   ymax = Upper_95, fill = locationID),
             alpha = 0.5) +
 geom_point(data = obs, mapping =
              aes(x = Days, y = Richness,
                  color = locationID), size = 3) +
 geom_line(data = interp, mapping =
             aes(x = Days, y = Richness,
                 color = locationID,
                 linetype = "dashed"), size = 1) +
 geom_line(data = extrap, mapping =
             aes(x = Days, y = Richness,
                 color = locationID,
                 linetype = "solid"), size = 1) +
 theme_classic() +
 scale_fill_manual(name = "locationID",
                   values = c("#66c2a5", "#fc8d62")) +
 scale_color_manual(name = "locationID",
                    values = c("#66c2a5", "#fc8d62")) +
 labs(x = "Days Sampled", y = "Species Richness") +
 guides(linetype = "none")



## End(Not run)

nationalparkservice/NSNSDAcoustics documentation built on March 4, 2025, 10:24 p.m.