stochLAB: Stochastic Collision Risk Model

stochLAB is a tool to run stochastic (and deterministic) Collision Risk Models (CRM) for seabirds on offshore wind farms.

The main functions of stochLAB are:

• stoch_crm(): Stochastic Collision Risk Model,

• band_crm(): Deterministic Collision Risk Model,

• mig_stoch_crm(): Stochastic Migration Collision risk Model

Overview

The {stochLAB} package is an adaptation of the R code developed by Masden (2015) to incorporate variability and uncertainty in the avian collision risk model originally developed by Band (2012).

Code developed under {stochLAB} substantially re-factored and re-structured Masden's (heavily script-based) implementation into a user-friendly, streamlined, well documented and easily distributed tool. Furthermore, this package lays down the code infrastructure for easier incorporation of new functionality, e.g. extra parameter sampling features, model expansions, etc.

Previous code underpinning key calculations for the extended model has been replaced by an alternative approach, resulting in significant gains in computational speed over Masden's code. This optimization is particularly beneficial under a stochastic context, when Band calculations are computed repeatedly. See generate_rotor_grids() for further details.

Function stoch_crm()

stoch_crm() is essentially a replacement function for script BandModel.r in Masden's approach. Main changes in terms of user interface include:

• Collision model runs for one single scenario (i.e. one species for one turbine scenario) at each stoch_crm() call. Apart from gains in development code structure, this unit-based approach was considered to offer greater end user flexibility for setting up and managing multiple scenarios (including parallel computation).

• Input parameters now entered explicitly into feature-specific arguments, instead of bundled together into wide-column tables. This improves code readability and reduces the quantity of hard-coded parameter names, therefore making referencing errors less likely.

• Outputs no longer saved automatically to external files.

Seasonal Outputs

stoch_crm() now provides an option for user-defined seasonal outputs, allowing for country/region specific seasonal definitions.

Currently implemented CRM calculations produce collision estimates on a monthly basis to reflect changing bird abundance within the windfarm area. Seasonal estimates are obtained by aggregating monthly estimates over each season definition, with uncertainty in collision estimates being suitably propagated to seasonal outputs.

Sampling distributions

Masden's implementation used Normal and Truncated Normal distributions to generate random parameter values. However, the Normal distribution is unbounded, and so there was the risk of randomly drawing inappropriate values in some cases.

stoch_crm() extends the use of bounded probability distributions to all model parameters. Strictly positive features (e.g. bird densities, body length, turbine downtime, etc.) are sampled from Truncated Normal distributions, while features constrained between 0 and 1 (e.g. nocturnal activity, proportion of flights at collision risk height, etc) are assumed to follow Beta distributions.

In addition, new functionality has been incorporated to allow the use of bird density resamples (e.g. bootstrap samples) or quantile estimates from density estimation models in the simulations.

Function band_crm()

band_crm() performs the core CRM calculations required to estimate the number of collisions, as per Band (2012). While being the computing workhorse of the stoch_crm() function, it can also be used alone, providing backward compatibility with the original Band spreadsheet outputs.

See also

Useful links:

• https://github.com/HiDef-Aerial-Surveying/stochLAB

• Report bugs at https://github.com/HiDef-Aerial-Surveying/stochLAB/issues

Examples

# ------------------------------------------------------
# Run with arbitrary parameter values, for illustration
# ------------------------------------------------------

# Setting a dataframe of parameters to draw from
params <- data.frame(
  flight_speed = 13.1,         # Flight speed in m/s
  body_lt = 0.85,              # Body length in m
  wing_span = 1.01,            # Wing span in m
  flight_type = "flapping",    # flapping or gliding flight
  avoid_rt_basic = 0.989,      # avoidance rate for option 1 and 2
  avoid_rt_ext = 0.981,        # extended avoidance rate for option 3 and 4
  noct_activity = 0.5,         # proportion of day birds are inactive
  prop_crh_surv = 0.13,        # proportion of birds at collision risk height (option 1 only)
  prop_upwind = 0.5,           # proportion of flights that are upwind
  rotor_speed = 15,            # rotor speed in m/s
  rotor_radius = 120,          # radius of turbine in m
  blade_width = 5,             # width of turbine blades at thickest point in m
  blade_pitch = 15,            # mean radius pitch in Radians
  n_blades = 3,                # total number of blades per turbine
  hub_height = 150,            # height of hub in m above HAT
  n_turbines = 100,            # number of turbines in the wind farm
  wf_width = 52,               # width across longest section of wind farm
  wf_latitude = 56,            # latitude of centroid of wind farm
  tidal_offset = 2.5,          # mean tidal offset from HAT of the wind farm
  lrg_arr_corr = TRUE          # apply a large array correction?
)

# Monthly bird densities
b_dens <- data.frame(
  month = month.abb,
  dens = runif(12, 0.8, 1.5)
)

# flight height distribution from Johnston et al
gen_fhd_dat <- Johnston_Flight_heights_SOSS %>%
  dplyr::filter(variable=="Gannet.est") %>%
  dplyr::select(height,prop)


# monthly operational time of the wind farm
turb_oper <- data.frame(
  month = month.abb,
  prop_oper = runif(12,0.5,0.8)
)


band_crm(
  model_options = c(1,2,3),
  flight_speed = params$flight_speed,
  body_lt = params$body_lt,
  wing_span = params$wing_span,
  flight_type = params$flight_type,
  avoid_rt_basic = params$avoid_rt_basic,
  avoid_rt_ext = params$avoid_rt_ext,
  noct_activity = params$noct_activity,
  prop_crh_surv = params$prop_crh_surv,
  dens_month = b_dens,
  prop_upwind = params$prop_upwind,
  gen_fhd = gen_fhd_dat,
  site_fhd = NULL,  # Option 4 only
  rotor_speed = params$rotor_speed,
  rotor_radius = params$rotor_radius,
  blade_width = params$blade_width,
  blade_pitch = params$blade_pitch,
  n_blades = params$n_blades,
  hub_height = params$hub_height,
  chord_prof = chord_prof_5MW,
  n_turbines = params$n_turbines,
  turb_oper_month = turb_oper,
  wf_width = params$wf_width,
  wf_latitude = params$wf_latitude,
  tidal_offset = params$tidal_offset,
  lrg_arr_corr = params$lrg_arr_corr
  )
#> # A tibble: 12 × 4
#>    month  opt1  opt2  opt3
#>    <chr> <dbl> <dbl> <dbl>
#>  1 Jan    63.0  16.3  6.65
#>  2 Feb    63.9  16.5  6.74
#>  3 Mar    74.0  19.1  7.81
#>  4 Apr   110.   28.4 11.6 
#>  5 May    98.4  25.4 10.4 
#>  6 Jun   113.   29.2 11.9 
#>  7 Jul    73.3  18.9  7.73
#>  8 Aug    52.6  13.6  5.54
#>  9 Sep    91.3  23.5  9.63
#> 10 Oct    70.5  18.2  7.43
#> 11 Nov    67.9  17.5  7.16
#> 12 Dec    70.2  18.1  7.40