Stochastic migration collision risk model — mig_stoch

Run migration stochastic collision risk model for a single species and one turbine scenario

Usage

mig_stoch_crm(
  wing_span_pars,
  flt_speed_pars,
  body_lt_pars,
  prop_crh_pars,
  avoid_bsc_pars,
  n_turbines,
  n_blades = 3,
  rtn_speed_pars,
  bld_pitch_pars,
  rtr_radius_pars,
  bld_width_pars,
  wf_width,
  wf_latitude,
  flight_type,
  prop_upwind = 0.5,
  popn_estim_pars,
  season_specs,
  chord_profile = chord_prof_5MW,
  trb_wind_avbl,
  trb_downtime_pars,
  n_iter = 10,
  LargeArrayCorrection = TRUE,
  log_file = NULL,
  seed = NULL,
  verbose = TRUE
)

Arguments

wing_span_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species wingspan, in metres. Assumed to follow a tnorm-lw0 distribution.

flt_speed_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species flying speed, in metres/sec. Assumed to follow a Truncated Normal with lower bound at 0 (tnorm-lw0).

body_lt_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species body length, in metres. Assumed to follow a tnorm-lw0 distribution.

prop_crh_pars

A single row data frame with columns mean and sd. The mean and standard deviation of the proportion of flights at collision risk height.

avoid_bsc_pars

Single row data frame with columns mean and sd, the mean and standard deviation of the species avoidance rate to be used. Avoidance rate expresses the probability that a bird flying on a collision course with a turbine will take evading action to avoid collision, and it is assumed to follow a Beta distribution.

n_turbines

An integer. The number of turbines expected to be installed

n_blades

An integer. The number of blades per turbine (defaults to 3)

rtn_speed_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the rotation speed.

bld_pitch_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the blade pitch angle, i.e. the angle between the blade surface and the rotor plane, in degrees. Assumed to follow a tnorm-lw0 distribution.

rtr_radius_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the radius of the rotor, in metres. Assumed to follow a tnorm-lw0 distribution.

bld_width_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the maximum blade width, in metres. Assumed to be tnorm-lw0 distribution.

wf_width

Numeric value, the approximate longitudinal width of the wind farm, in kilometres (\(w\)).

wf_latitude

A decimal value. The latitude of the centroid of the windfarm, in degrees.

flight_type

A character. Either "flying" or "gliding" representing the type of flight most commonly used by the species

prop_upwind

Numeric value between 0-1 giving the proportion of flights upwind - defaults to 0.5.

popn_estim_pars

A single row data frame with columns mean and sd. The population estimate of the species expected to fly through the wind farm area.

season_specs

A data frame defining the seasons for aggregating over collision estimates. It must comprise the following columns:

season_id, (unique) season identifier,
start_month, name of the season's first month,
end_month, name of the season's last month.

chord_profile

A data frame with the chord taper profile of the rotor blade. It must contain the columns:

pp_radius, equidistant intervals of radius at bird passage point, as a proportion of rotor_radius, within the range \([0, 1]\).
chord, the chord width at pp_radius, as a proportion of blade_width. Defaults to a generic profile for a typical modern 5MW turbine. See chord_prof_5MW() for details.

trb_wind_avbl

A data frame with the monthly estimates of operational wind availability. It must contain the columns:

month, (unique) month names,
pctg, the percentage of time wind conditions allow for turbine operation per month.

trb_downtime_pars

A data frame with monthly estimates of maintenance downtime, assumed to follow a tnorm-lw0 distribution. It must contain the following columns:

month, (unique) month names,
mean, numeric, the mean percentage of time in each month when turbines are not operating due to maintenance,
sd, the standard deviation of monthly maintenance downtime.

n_iter

An integer > 0. The number of iterations for the model simulation.

LargeArrayCorrection

A boolean. Should the large array correction be calculated

log_file

Path to log file to store session info and main model run options. If set to NULL (default value), log file is not created.

seed

Integer, the random seed for random number generation, for analysis reproducibility.

verbose

boolean. TRUE for a verbose output

Value

Estimates of number of collisions per migratory season for the n number of iterations specified

Details

This function is an adaption of code from Masden(2015) used for estimating the collision risk of seabirds in offshore windfarm sites and is a further adaptation from Band(2012). It is a further adaptation of the stoch_crm function.

The collision risk model evaluates risk for each defined migratory period where flux rate is simply the number of birds travelling through the windfarm.

Changes in relation to previous top-line function stoch_crm

function will run only option 1 for migratory species

Examples

# ------------------------------------------------------
# Run with arbitrary parameter values, for illustration
# ------------------------------------------------------
season_specs <- data.frame(
  season_id = c("PrBMigration", "PoBMigration","OMigration"),
  start_month = c("Mar", "May", "Oct"), end_month = c("Apr", "Sep", "Feb")
)

# wind availability
windavb <- data.frame(
  month = month.abb,
  pctg = runif(12, 85, 98)
)
head(windavb)
#>   month     pctg
#> 1   Jan 97.12456
#> 2   Feb 96.62743
#> 3   Mar 91.24142
#> 4   Apr 85.79908
#> 5   May 92.52050
#> 6   Jun 97.63880

# maintenance downtime
dwntm <- data.frame(
  month = month.abb,
  mean = runif(12, 6, 10),
  sd = rep(2, 12))
head(dwntm)
#>   month     mean sd
#> 1   Jan 6.073612  2
#> 2   Feb 8.547489  2
#> 3   Mar 8.498627  2
#> 4   Apr 8.426751  2
#> 5   May 6.449581  2
#> 6   Jun 7.975182  2


mig_stoch_crm(
  # Wing span in m,
  wing_span_pars = data.frame(mean = 1.08, sd = 0.04),
  # Flight speed in m/s
  flt_speed_pars = data.frame(mean = 7.26, sd = 1.5),
  # Body length in m,
  body_lt_pars = data.frame(mean = 0.39, sd = 0.005),
  # Proportion of birds at CRH
  prop_crh_pars = data.frame(mean = 0.06, sd = 0.009),
  # avoidance rate
  avoid_bsc_pars = data.frame(mean = 0.99, sd = 0.001),
  n_turbines = 150,
  n_blades = 3,
  # rotation speed in m/s of turbine blades
  rtn_speed_pars = data.frame(mean = 13.1, sd = 4),
  # pitch in degrees of turbine blades
  bld_pitch_pars = data.frame(mean = 3, sd = 0.3),
  # sd = 0, rotor radius is fixed
  rtr_radius_pars = data.frame(mean = 80, sd = 0),
  # sd = 0, blade width is fixed
  bld_width_pars = data.frame(mean = 8, sd = 0),
  wf_width = 100,
  wf_latitude = 54.1,
  prop_upwind = 0.5,
  flight_type = "flapping",
  # population flying through windfarm,
  popn_estim_pars = data.frame(mean = 21584, sd = 2023),
  season_specs = season_specs,
  chord_profile = chord_prof_5MW,
  trb_wind_avbl = windavb,
  trb_downtime_pars = dwntm,
  n_iter = 1000,
  LargeArrayCorrection = TRUE,
  log_file = NULL,
  seed = 1234,
  verbose = TRUE)
#> 
#> ── Stochastic CRM for Migratory birds ──
#> 
#> ℹ Checking for manditory arguments
#> ✔ Checking for manditory arguments [10ms]
#> 
#> ℹ Data validation
#> ✔ Data validation [19ms]
#> 
#> ℹ Sampling data
#> ✔ Sampling data [58ms]
#> 
#> ℹ Running simulation...
#> ✔ Running simulation... [1.7s]
#> 
#> ℹ Creating outputs
#> ✔ Creating outputs [15ms]
#> 
#> $collisions
#> # A tibble: 1,000 × 3
#>    PrBMigration PoBMigration OMigration
#>           <dbl>        <dbl>      <dbl>
#>  1        0.192        0.202      0.205
#>  2        0.199        0.209      0.212
#>  3        0.221        0.233      0.236
#>  4        0.186        0.196      0.198
#>  5        0.203        0.214      0.217
#>  6        0.267        0.281      0.284
#>  7        0.221        0.232      0.235
#>  8        0.172        0.181      0.183
#>  9        0.242        0.254      0.258
#> 10        0.374        0.393      0.398
#> # … with 990 more rows
#> 
#> $flux_rates
#> # A tibble: 1,000 × 3
#>    PrBMigration PoBMigration OMigration
#>           <dbl>        <dbl>      <dbl>
#>  1        4015.        4015.      4015.
#>  2        3959.        3959.      3959.
#>  3        4240.        4240.      4240.
#>  4        3946.        3946.      3946.
#>  5        4080.        4080.      4080.
#>  6        4028.        4028.      4028.
#>  7        4269.        4269.      4269.
#>  8        3899.        3899.      3899.
#>  9        3915.        3915.      3915.
#> 10        4145.        4145.      4145.
#> # … with 990 more rows
#>