stochLAB is a tool to run Collision Risk Models (CRMs) for seabirds on offshore wind farms.
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). The package is for use by individuals modelling collision risk of seabirds at offshore wind farms. The primary functions take input information on the morphology, behaviour and densities of seabirds as well data pertaining to the proposed wind farm (i.e., turbine dimensions, speed and number).
These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, the package itself relies on generic biological and windfarm data and can be applied anywhere (i.e., in any marine environment) as long as the parameters are appropriate for the species and windfarms of interest.
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, the package lays down the code infrastructure for easier incorporation of new functionality, e.g. extra parameter sampling features, model expansions, etc.
In addition, previous code underpinning core calculations for the extended model has been replaced by an alternative approach, resulting in significant gains in computational speed over Masden’s code.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 core calculations are called repeatedly during simulations.
For a more detailed overview type ?stochLAB, once installed!
Installation
You can install the released version of stochLAB from CRAN with:
install.packages("stochLAB")You can install the development version with:
# install.packages("devtools")
devtools::install_github("HiDef-Aerial-Surveying/stochLAB")This package depends on the following packages, which should be installed automatically:
cli dplyr glue logr magrittr msm pracma purrr rlang stats tibble tidyr
Bug reports
To report any bugs, please log an ISSUE
Input parameters
Many of the input parameters for the stoch_crm() function need to be obtained from developers (e.g., blade pitch, rotor radius, wind speed, etc…). However, there are many parameters around the morphology and biology of birds that are built into the sCRM package for UK seabirds, which can be found HERE. sCRM is an R Shiny application that wraps up the stoch_crm() and band_crm() functions. These biological parameters can be accessed by installing the sCRM package and running sCRM::spp_dflts, which will bring up a tibble object with all the relevant information.
If performing a collision risk assessment in UK waters, default biological data for the following parameters can be obtained from the sCRM package:
flt_speed_pars, body_lt_pars, wing_span_pars, avoid_bsc_pars, avoid_ext_pars, noct_act_pars, flight_type, gen_fhd_boots
Other parameters around the species of interest need to be derived from site-based surveys:
prop_crh_pars, bird_dens_dt, and site_fhd_boots
All wind farm parameters need to be obtained by the wind farm developers:
n_blades, air_gap_pars, rtr_radius_pars, bld_width_pars, bld_pitch_pars, rtn_speed_pars, windspd_pars, trb_wind_avbl, trb_downtime_pars, wf_n_trbs, wf_width, wf_latitude, tidal_offset, season_specs, bld_chord_prf, lrg_arr_corr
The following parameters refer to the outputs:
out_format, out_sampled_pars, out_period, verbose, log_file
More information on input parameter specifics can be found in the vignettes for stoch_crm and band_crm.
Outputs
Once the collision risk model is run, the key outputs are presented as a table which contains the mean, standard deviation and median number of collisions summarised by month, season, or year. Quantiles of the bootstrapped collisions are also presented in the tables. These tables are accessed through calling from the model object. Run the Examples to view exemplar outputs.
stochOUT <- stochLAB::stoch_crm(...)
stochOUT$collisions$opt1 #For outputs from option 1 of the stochastic collision risk model
stochOUT$collisions$opt2 #For outputs from option 2 of the stochastic collision risk model
stochOUT$collisions$opt3 #For outputs from option 3 of the stochastic collision risk model Examples
Simple example
This is a basic example of running the stochastic collision model for one seabird species and one turbine/wind-farm scenario, with fictional input parameter data.
library(stochLAB)
# ------------------------------------------------------
# Setting some of the required inputs upfront
b_dens <- data.frame(
month = month.abb,
mean = runif(12, 0.8, 1.5),
sd = runif(12, 0.2, 0.3))
# Generic FHD bootstraps for one species, from Johnson et al (2014)
fhd_boots <- generic_fhd_bootstraps[[1]]
# wind speed vs rotation speed vs pitch
wind_rtn_ptch <- data.frame(
wind_speed = seq_len(30),
rtn_speed = 10/(30:1),
bld_pitch = c(rep(90, 4), rep(0, 8), 5:22))
# wind availability
windavb <- data.frame(
month = month.abb,
pctg = runif(12, 85, 98))
# maintenance downtime
dwntm <- data.frame(
month = month.abb,
mean = runif(12, 6, 10),
sd = rep(2, 12))
# seasons specification
seas_dt <- data.frame(
season_id = c("a", "b", "c"),
start_month = c("Jan", "May", "Oct"), end_month = c("Apr", "Sep", "Dec"))
# ----------------------------------------------------------
# Run stochastic CRM, treating rotor radius, air gap and
# blade width as fixed parameters (i.e. not stochastic)
stoch_crm(
model_options = c(1, 2, 3),
n_iter = 1000,
flt_speed_pars = data.frame(mean = 7.26, sd = 1.5),
body_lt_pars = data.frame(mean = 0.39, sd = 0.005),
wing_span_pars = data.frame(mean = 1.08, sd = 0.04),
avoid_bsc_pars = data.frame(mean = 0.99, sd = 0.001),
avoid_ext_pars = data.frame(mean = 0.96, sd = 0.002),
noct_act_pars = data.frame(mean = 0.033, sd = 0.005),
prop_crh_pars = data.frame(mean = 0.06, sd = 0.009),
bird_dens_opt = "tnorm",
bird_dens_dt = b_dens,
flight_type = "flapping",
prop_upwind = 0.5,
gen_fhd_boots = fhd_boots,
n_blades = 3,
rtr_radius_pars = data.frame(mean = 80, sd = 0), # sd = 0, rotor radius is fixed
air_gap_pars = data.frame(mean = 36, sd = 0), # sd = 0, air gap is fixed
bld_width_pars = data.frame(mean = 8, sd = 0), # sd = 0, blade width is fixed
rtn_pitch_opt = "windSpeedReltn",
windspd_pars = data.frame(mean = 7.74, sd = 3),
rtn_pitch_windspd_dt = wind_rtn_ptch,
trb_wind_avbl = windavb,
trb_downtime_pars = dwntm,
wf_n_trbs = 200,
wf_width = 15,
wf_latitude = 56.9,
tidal_offset = 2.5,
lrg_arr_corr = TRUE,
verbose = TRUE,
seed = 1234,
out_format = "summaries",
out_sampled_pars = TRUE,
out_period = "seasons",
season_specs = seas_dt,
log_file = paste0(getwd(), "scrm_example.log")
)
#>
#> ── Stochastic CRM ──
#>
#> ℹ Checking inputs✔ Checking inputs [87ms]
#> ℹ Preparing data✔ Preparing data [152ms]
#> ℹ Sampling parameters✔ Sampling parameters [423ms]
#> ⠙ Calculating collisions | 3/1000 iterations⠹ Calculating collisions | 117/1000 iterations⠸ Calculating collisions | 272/1000 iterations⠼ Calculating collisions | 420/1000 iterations⠴ Calculating collisions | 570/1000 iterations⠦ Calculating collisions | 717/1000 iterations⠧ Calculating collisions | 870/1000 iterations⠇ Calculating collisions | 1000/1000 iterations✔ Calculating collisions | 1000/1000 iterations [1.6s]
#> ℹ Sorting outputs✔ Sorting outputs [761ms]
#> ✔ Job done!
#> $collisions
#> $collisions$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_Apr 28.0 12.7 28.8 7.15 18.2 36.4 52.0 67.3
#> 2 b May_Sep 56.4 25.3 58.1 15.4 37.2 73.5 105. 142.
#> 3 c Oct_Dec 19.3 8.92 19.5 5.13 12.2 25.5 36.6 45.4
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $collisions$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_Apr 0.728 1.28 0.393 0.0676 0.230 0.655 5.05 11.6
#> 2 b May_Sep 1.47 2.64 0.804 0.135 0.469 1.30 10.8 21.8
#> 3 c Oct_Dec 0.499 0.888 0.274 0.0448 0.158 0.445 3.43 8.06
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $collisions$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_Apr 0.350 0.790 0.143 0.0337 0.0913 0.256 3.05 6.66
#> 2 b May_Sep 0.710 1.61 0.294 0.0687 0.185 0.487 5.55 15.2
#> 3 c Oct_Dec 0.240 0.546 0.0995 0.0223 0.0636 0.177 1.93 4.73
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $sampled_pars
#> $sampled_pars$air_gap
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 36 0 36 36 36
#>
#> $sampled_pars$bld_width
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 8 0 8 8 8
#>
#> $sampled_pars$body_lt
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.390 0.00499 0.390 0.380 0.400
#>
#> $sampled_pars$flt_speed
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7.28 1.47 7.28 4.30 10.0
#>
#> $sampled_pars$noct_actv
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.0333 0.00498 0.0333 0.0241 0.0436
#>
#> $sampled_pars$rtr_radius
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 80 0 80 80 80
#>
#> $sampled_pars$wing_span
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.08 0.0398 1.08 1.00 1.16
#>
#> $sampled_pars$hub_height
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 116 0 116 116 116
#>
#> $sampled_pars$dens_mth
#> # A tibble: 12 × 6
#> period mean sd median pctl_2.5 pctl_97.5
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jan 0.849 0.286 0.847 0.315 1.42
#> 2 Feb 0.833 0.224 0.826 0.415 1.28
#> 3 Mar 1.12 0.225 1.12 0.665 1.53
#> 4 Apr 1.11 0.242 1.11 0.647 1.59
#> 5 May 1.22 0.270 1.21 0.700 1.75
#> 6 Jun 0.835 0.252 0.838 0.299 1.35
#> 7 Jul 1.07 0.247 1.07 0.600 1.55
#> 8 Aug 1.04 0.279 1.05 0.490 1.61
#> 9 Sep 1.13 0.277 1.13 0.601 1.66
#> 10 Oct 1.04 0.222 1.04 0.616 1.48
#> 11 Nov 0.965 0.267 0.959 0.460 1.50
#> 12 Dec 1.21 0.199 1.21 0.813 1.59
#>
#> $sampled_pars$prop_oper_mth
#> # A tibble: 12 × 6
#> period mean sd median pctl_2.5 pctl_97.5
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jan 0.793 0.0207 0.792 0.752 0.829
#> 2 Feb 0.769 0.0207 0.769 0.729 0.808
#> 3 Mar 0.850 0.0204 0.850 0.807 0.890
#> 4 Apr 0.807 0.0196 0.807 0.769 0.845
#> 5 May 0.847 0.0195 0.847 0.809 0.884
#> 6 Jun 0.866 0.0198 0.866 0.827 0.905
#> 7 Jul 0.869 0.0199 0.869 0.829 0.910
#> 8 Aug 0.806 0.0194 0.806 0.768 0.844
#> 9 Sep 0.879 0.0205 0.879 0.838 0.917
#> 10 Oct 0.820 0.0200 0.820 0.779 0.858
#> 11 Nov 0.858 0.0201 0.858 0.819 0.898
#> 12 Dec 0.910 0.0201 0.911 0.872 0.947
#>
#> $sampled_pars$downtime
#> # A tibble: 12 × 6
#> period mean sd median pctl_2.5 pctl_97.5
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jan 6.09 2.07 6.12 2.39 10.1
#> 2 Feb 8.98 2.07 8.99 5.12 12.9
#> 3 Mar 8.54 2.04 8.54 4.52 12.8
#> 4 Apr 8.57 1.96 8.61 4.79 12.4
#> 5 May 7.83 1.95 7.81 4.12 11.7
#> 6 Jun 7.64 1.98 7.61 3.78 11.6
#> 7 Jul 9.43 1.99 9.42 5.37 13.5
#> 8 Aug 6.35 1.94 6.31 2.55 10.1
#> 9 Sep 7.46 2.05 7.44 3.62 11.6
#> 10 Oct 6.08 2.00 6.10 2.28 10.1
#> 11 Nov 7.56 2.01 7.54 3.58 11.4
#> 12 Dec 7.03 2.01 6.94 3.32 10.8
#>
#> $sampled_pars$wind_speed
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7.73 3.00 7.73 2.15 13.7
#>
#> $sampled_pars$rtn_speed
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.428 0.0574 0.417 0.345 0.556
#>
#> $sampled_pars$bld_pitch
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.329 0.635 0 0 1.57
#>
#> $sampled_pars$avoid_bsc
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.990 0.000985 0.990 0.988 0.992
#>
#> $sampled_pars$avoid_ext
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.960 0.00203 0.960 0.956 0.964
#>
#> $sampled_pars$prop_crh
#> # A tibble: 1 × 5
#> mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.0605 0.00904 0.0601 0.0441 0.0800
#>
#> $sampled_pars$gen_fhd
#> # A tibble: 500 × 6
#> height mean sd median pctl_2.5 pctl_97.5
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0.163 0.0188 0.166 0.109 0.187
#> 2 1 0.136 0.0129 0.138 0.0967 0.152
#> 3 2 0.114 0.00855 0.115 0.0863 0.124
#> 4 3 0.0950 0.00530 0.0963 0.0769 0.100
#> 5 4 0.0794 0.00296 0.0803 0.0686 0.0816
#> 6 5 0.0664 0.00146 0.0669 0.0606 0.0670
#> 7 6 0.0556 0.00116 0.0558 0.0530 0.0567
#> 8 7 0.0465 0.00166 0.0466 0.0439 0.0490
#> 9 8 0.0390 0.00216 0.0389 0.0357 0.0432
#> 10 9 0.0327 0.00250 0.0324 0.0290 0.0386
#> # … with 490 more rowsMultiscenario example
This is an example usage of stoch_crm() for multiple scenarios. The aim is two-fold:
Suggest how input parameter datasets used in the previous implementation can be reshaped to fit
stoch_crm()’s interface. Suggested code is also relevant in the context of multiple scenarios applications, since the wide tabular structure of these datasets is likely the favoured format for users to compile input parameters under different scenarios.Propose a functional programming framework to run
stoch_crm()for multiple species and wind-farm/turbines features.
Please note the example runs on fictional data.
library(stochLAB)
# --------------------------------------------------------- #
# ---- Reshaping into list-column data frames ----
# --------------------------------------------------------- #
#
# --- bird features
bird_pars <- bird_pars_wide_example %>%
dplyr::relocate(Flight, .after = dplyr::last_col()) %>%
tidyr::pivot_longer(AvoidanceBasic:Prop_CRH_ObsSD) %>%
dplyr::mutate(
par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"),
feature = gsub("SD|sd|Sd","", name)) %>%
dplyr::select(-name) %>%
tidyr::pivot_wider(names_from = par, values_from = value) %>%
tidyr::nest(pars = c(mean, sd)) %>%
tidyr::pivot_wider(names_from = feature, values_from = pars) %>%
tibble::add_column(prop_upwind = 0.5)
# --- bird densities: provided as mean and sd Parameters for Truncated Normal lower
# bounded at 0
dens_pars <- dens_tnorm_wide_example %>%
tibble::add_column(
dens_opt = rep("tnorm", nrow(.)),
.after = 1) %>%
tidyr::pivot_longer(Jan:DecSD) %>%
dplyr::mutate(
par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"),
month = gsub("SD|sd|Sd","", name)) %>%
dplyr::select(-name) %>%
tidyr::pivot_wider(names_from = par, values_from = value) %>%
tidyr::nest(mth_dens = c(month, mean, sd))
# --- FHD data from Johnson et al (2014) for the species under analysis
gen_fhd_boots <- generic_fhd_bootstraps[bird_pars$Species]
# --- seasons definitions (made up)
season_dt <- list(
Arctic_Tern = data.frame(
season_id = c("breeding", "feeding", "migrating"),
start_month = c("May", "Sep", "Jan"),
end_month = c("Aug", "Dec", "Apr")),
Black_headed_Gull = data.frame(
season_id = c("breeding", "feeding", "migrating"),
start_month = c("Jan", "May", "Oct"),
end_month = c("Apr", "Sep", "Dec")),
Black_legged_Kittiwake = data.frame(
season_id = c("breeding", "feeding", "migrating"),
start_month = c("Dec", "Mar", "Sep"),
end_month = c("Feb", "Aug", "Nov")))
# --- turbine parameters
## address operation parameters first
trb_opr_pars <- turb_pars_wide_example %>%
dplyr::select(TurbineModel, JanOp:DecOpSD) %>%
tidyr::pivot_longer(JanOp:DecOpSD) %>%
dplyr::mutate(
month = substr(name, 1, 3),
par = dplyr::case_when(
grepl("SD|sd|Sd", name) ~ "sd",
grepl("Mean|MEAN|mean", name) ~ "mean",
TRUE ~ "pctg"
)) %>%
dplyr::select(-name) %>%
tidyr::pivot_wider(names_from = par, values_from = value) %>%
tidyr::nest(
wind_avbl = c(month, pctg),
trb_dwntm = c(month, mean, sd))
## address turbine features and subsequently merge operation parameters
trb_pars <- turb_pars_wide_example %>%
dplyr::select(TurbineModel:windSpeedSD ) %>%
dplyr::relocate(RotorSpeedAndPitch_SimOption, .after = 1) %>%
tidyr::pivot_longer(RotorRadius:windSpeedSD) %>%
dplyr::mutate(
par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"),
feature = gsub("(SD|sd|Sd)|(Mean|MEAN|mean)","", name)
) %>%
dplyr::select(-name) %>%
tidyr::pivot_wider(names_from = par, values_from = value) %>%
tidyr::nest(pars = c(mean, sd)) %>%
tidyr::pivot_wider(names_from = feature, values_from = pars) %>%
dplyr::left_join(., trb_opr_pars)
#> Joining, by = "TurbineModel"
# --- windspeed, rotation speed and blade pitch relationship
wndspd_rtn_ptch_example
#> wind_speed rtn_speed bld_pitch
#> 1 0 0.0 90
#> 2 1 0.0 90
#> 3 2 0.0 90
#> 4 3 6.8 0
#> 5 4 6.8 0
#> 6 5 6.8 0
#> 7 6 6.8 0
#> 8 7 6.8 0
#> 9 8 8.1 0
#> 10 9 9.1 0
#> 11 10 9.3 0
#> 12 11 9.4 4
#> 13 12 9.5 7
#> 14 13 9.7 9
#> 15 14 9.7 11
#> 16 15 9.9 13
#> 17 16 10.2 15
#> 18 17 10.2 16
#> 19 18 10.2 18
#> 20 19 10.2 19
#> 21 20 10.2 20
#> 22 21 10.2 22
#> 23 22 10.2 23
#> 24 23 10.2 24
#> 25 24 10.2 25
#> 26 25 10.2 26
#> 27 26 10.2 27
#> 28 27 10.2 28
#> 29 28 10.2 29
#> 30 29 10.2 30
# --- windfarm parameters
wf_pars <- data.frame(
wf_id = c("wf_1", "wf_2"),
n_turbs = c(200, 400),
wf_width = c(4, 10),
wf_lat = c(55.8, 55.0),
td_off = c(2.5, 2),
large_array_corr = c(FALSE, TRUE)
)
# -------------------------------------------------------------- #
# ---- Run stoch_crm() for multiple scenarios ----
# -------------------------------------------------------------- #
# --- Set up scenario combinations
scenarios_specs <- tidyr::expand_grid(
spp = bird_pars$Species,
turb_id = trb_pars$TurbineModel,
wf_id = wf_pars$wf_id) %>%
tibble::add_column(
scenario_id = paste0("scenario_", 1:nrow(.)),
.before = 1)
# --- Set up progress bar for the upcoming iterative mapping step
pb <- progress::progress_bar$new(
format = "Running Scenario: :what [:bar] :percent eta: :eta",
width = 100,
total = nrow(scenarios_specs))
# --- Map stoch_crm() to each scenario specification via purrr::pmap
outputs <- scenarios_specs %>%
purrr::pmap(function(scenario_id, spp, turb_id, wf_id, ...){
pb$tick(tokens = list(what = scenario_id))
# params for current species
c_spec <- bird_pars %>%
dplyr::filter(Species == {{spp}})
# density for current species
c_dens <- dens_pars %>%
dplyr::filter(Species == {{spp}})
# params for current turbine scenario
c_turb <- trb_pars %>%
dplyr::filter(TurbineModel == {{turb_id}})
# params for current windfarm scenario
c_wf <- wf_pars %>%
dplyr::filter(wf_id == {{wf_id}})
# inputs in list-columns need to be unlisted, either via `unlist()` or
# indexing `[[1]]`
# switching off `verbose`, otherwise console will be
# cramped with log messages
stoch_crm(
model_options = c(1, 2, 3),
n_iter = 1000,
flt_speed_pars = c_spec$Flight_Speed[[1]],
body_lt_pars = c_spec$Body_Length[[1]],
wing_span_pars = c_spec$Wingspan[[1]],
avoid_bsc_pars = c_spec$AvoidanceBasic[[1]],
avoid_ext_pars = c_spec$AvoidanceExtended[[1]],
noct_act_pars = c_spec$Nocturnal_Activity[[1]],
prop_crh_pars = c_spec$Prop_CRH_Obs[[1]],
bird_dens_opt = c_dens$dens_opt,
bird_dens_dt = c_dens$mth_dens[[1]],
flight_type = c_spec$Flight,
prop_upwind = c_spec$prop_upwind,
gen_fhd_boots = gen_fhd_boots[[spp]],
n_blades = c_turb$Blades,
rtr_radius_pars = c_turb$RotorRadius[[1]],
air_gap_pars = c_turb$HubHeightAdd[[1]],
bld_width_pars = c_turb$BladeWidth[[1]],
rtn_pitch_opt = c_turb$RotorSpeedAndPitch_SimOption,
bld_pitch_pars = c_turb$Pitch[[1]],
rtn_speed_pars = c_turb$RotationSpeed[[1]],
windspd_pars = c_turb$windSpeed[[1]],
rtn_pitch_windspd_dt = wndspd_rtn_ptch_example,
trb_wind_avbl = c_turb$wind_avbl[[1]],
trb_downtime_pars = c_turb$trb_dwntm[[1]],
wf_n_trbs = c_wf$n_turbs,
wf_width = c_wf$wf_width,
wf_latitude = c_wf$wf_lat,
tidal_offset = c_wf$td_off,
lrg_arr_corr = c_wf$large_array_corr,
verbose = FALSE,
seed = 1234,
out_format = "summaries",
out_sampled_pars = FALSE,
out_period = "seasons",
season_specs = season_dt[[spp]],
log_file = NULL
)
})
# --- close progress bar
pb$terminate()
# --- identify elements of output list
names(outputs) <- scenarios_specs$scenario_id
outputs
#> $scenario_1
#> $scenario_1$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 984. 75.9 982. 847. 933. 1031. 1130. 1241.
#> 2 feeding Sep_Dec 552. 41.6 551. 478. 523. 579. 634. 683.
#> 3 migrating Jan_Apr 626. 47.7 623. 541. 591. 658. 722. 780.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_1$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 60.7 67.3 41.9 2.41 25.3 65.7 293. 389.
#> 2 feeding Sep_Dec 34.0 37.7 23.5 1.35 14.3 37.0 162. 217.
#> 3 migrating Jan_Apr 38.6 42.8 26.8 1.55 16.2 42.4 186. 249.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_1$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 17.9 24.8 10.5 0.394 5.92 17.9 106. 151.
#> 2 feeding Sep_Dec 10.1 13.9 5.86 0.221 3.35 10.0 59.0 84.5
#> 3 migrating Jan_Apr 11.4 15.8 6.77 0.251 3.79 11.5 66.9 93.8
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_2
#> $scenario_2$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 1836. 137. 1831. 1586. 1743. 1921. 2098. 2301.
#> 2 feeding Sep_Dec 1045. 76.4 1044. 909. 991. 1096. 1195. 1288.
#> 3 migrating Jan_Apr 1181. 87.6 1177. 1025. 1118. 1241. 1356. 1463.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_2$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 122. 132. 85.2 5.20 52.1 133. 574. 759.
#> 2 feeding Sep_Dec 69.2 75.0 48.4 2.95 29.9 75.9 323. 430.
#> 3 migrating Jan_Apr 78.3 84.8 55.1 3.37 33.7 86.5 369. 491.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_2$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 35.9 48.7 21.4 0.848 12.2 36.1 208. 294.
#> 2 feeding Sep_Dec 20.4 27.7 12.1 0.482 7.00 20.6 118. 167.
#> 3 migrating Jan_Apr 23.1 31.3 13.9 0.547 7.91 23.5 133. 185.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_3
#> $scenario_3$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 2131. 143. 2126. 1875. 2034. 2223. 2415. 2690.
#> 2 feeding Sep_Dec 1149. 76.8 1143. 1011. 1095. 1197. 1302. 1446.
#> 3 migrating Jan_Apr 1300. 89.6 1292. 1131. 1239. 1357. 1491. 1638.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_3$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 53.7 77.6 30.1 0.894 17.0 53.9 347. 477.
#> 2 feeding Sep_Dec 28.9 41.8 16.1 0.496 9.16 29.0 185. 257.
#> 3 migrating Jan_Apr 32.7 47.1 18.3 0.546 10.3 32.5 205. 291.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_3$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 14.4 25.7 6.47 0.124 3.37 12.7 109. 162.
#> 2 feeding Sep_Dec 7.74 13.8 3.48 0.0685 1.81 6.84 59.8 86.3
#> 3 migrating Jan_Apr 8.74 15.6 3.93 0.0756 2.05 7.65 66.9 96.0
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_4
#> $scenario_4$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 3720. 240. 3713. 3290. 3560. 3872. 4188. 4654.
#> 2 feeding Sep_Dec 2037. 131. 2025. 1801. 1944. 2122. 2297. 2540.
#> 3 migrating Jan_Apr 2297. 152. 2284. 2012. 2193. 2393. 2626. 2854.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_4$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 100. 142. 57.0 1.79 32.6 101. 633. 867.
#> 2 feeding Sep_Dec 54.8 77.7 31.0 1.01 17.8 55.5 342. 474.
#> 3 migrating Jan_Apr 61.7 87.3 35.1 1.11 20.1 62.2 382. 531.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_4$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 26.8 47.1 12.3 0.249 6.47 23.9 199. 295.
#> 2 feeding Sep_Dec 14.7 25.7 6.69 0.140 3.53 13.1 111. 160.
#> 3 migrating Jan_Apr 16.5 28.9 7.57 0.153 3.98 14.7 124. 177.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_5
#> $scenario_5$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 1176. 341. 1053. 825. 985. 1219. 2220. 2634.
#> 2 feeding Sep_Dec 615. 176. 551. 430. 516. 640. 1136. 1338.
#> 3 migrating Jan_Apr 703. 202. 630. 489. 591. 731. 1323. 1551.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_5$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 0.837 2.75 0.131 0.000231 0.0341 0.390 8.68 31.9
#> 2 feeding Sep_Dec 0.438 1.45 0.0687 0.000118 0.0180 0.202 4.66 17.0
#> 3 migrating Jan_Apr 0.500 1.63 0.0782 0.000136 0.0209 0.230 5.20 18.4
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_5$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 0.221 0.795 0.0245 0.0000261 0.00602 0.0786 2.41 8.88
#> 2 feeding Sep_Dec 0.116 0.418 0.0129 0.0000129 0.00319 0.0408 1.29 4.73
#> 3 migrating Jan_Apr 0.132 0.472 0.0147 0.0000151 0.00365 0.0462 1.44 5.12
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_6
#> $scenario_6$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 2164. 568. 1961. 1557. 1840. 2263. 3874. 4500.
#> 2 feeding Sep_Dec 1148. 298. 1043. 822. 977. 1210. 2021. 2323.
#> 3 migrating Jan_Apr 1310. 341. 1188. 934. 1117. 1370. 2325. 2688.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_6$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 1.64 5.28 0.266 0.000499 0.0711 0.779 17.1 59.5
#> 2 feeding Sep_Dec 0.872 2.82 0.142 0.000254 0.0379 0.415 9.33 32.2
#> 3 migrating Jan_Apr 0.991 3.18 0.162 0.000295 0.0437 0.472 10.4 34.7
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_6$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_Aug 0.433 1.53 0.0503 0.0000579 0.0126 0.157 4.77 16.6
#> 2 feeding Sep_Dec 0.230 0.817 0.0271 0.0000291 0.00672 0.0832 2.59 8.97
#> 3 migrating Jan_Apr 0.261 0.921 0.0304 0.0000337 0.00768 0.0947 2.88 9.68
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_7
#> $scenario_7$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 429. 93.1 423. 270. 360. 490. 626. 767.
#> 2 feeding May_Sep 697. 151. 685. 442. 588. 791. 1006. 1252.
#> 3 migrating Oct_Dec 266. 59.2 262. 164. 223. 303. 390. 498.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_7$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 257. 150. 223. 62.9 153. 320. 603. 1071.
#> 2 feeding May_Sep 417. 244. 366. 100. 251. 517. 985. 1797.
#> 3 migrating Oct_Dec 159. 93.0 137. 37.8 94.0 199. 377. 649.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_7$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 28.2 25.9 20.9 4.02 12.5 34.1 86.4 239.
#> 2 feeding May_Sep 45.9 42.4 34.3 6.51 20.3 55.1 145. 409.
#> 3 migrating Oct_Dec 17.5 16.0 12.8 2.48 7.78 21.8 54.6 144.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_8
#> $scenario_8$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 855. 186. 844. 539. 719. 977. 1248. 1531.
#> 2 feeding May_Sep 1376. 299. 1352. 872. 1160. 1560. 1985. 2472.
#> 3 migrating Oct_Dec 534. 119. 526. 329. 448. 608. 782. 999.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_8$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 536. 307. 468. 135. 324. 668. 1246. 2183.
#> 2 feeding May_Sep 862. 494. 760. 213. 524. 1067. 2013. 3624.
#> 3 migrating Oct_Dec 334. 192. 290. 81.5 200. 418. 783. 1330.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_8$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 58.6 52.8 43.8 8.62 26.4 71.2 178. 485.
#> 2 feeding May_Sep 94.4 85.7 71.2 14.0 42.5 114. 295. 820.
#> 3 migrating Oct_Dec 36.6 32.8 27.1 5.36 16.5 45.6 113. 293.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_9
#> $scenario_9$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 571. 123. 563. 360. 484. 643. 818. 1015.
#> 2 feeding May_Sep 965. 210. 953. 615. 815. 1089. 1428. 1738.
#> 3 migrating Oct_Dec 354. 78.1 349. 223. 299. 401. 511. 646.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_9$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 194. 140. 157. 32.9 100. 245. 539. 1050.
#> 2 feeding May_Sep 329. 238. 265. 55.1 169. 414. 905. 1788.
#> 3 migrating Oct_Dec 121. 87.9 97.6 20.1 62.0 153. 332. 682.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_9$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 23.0 25.6 15.7 2.36 8.83 27.4 79.6 265.
#> 2 feeding May_Sep 38.9 43.5 26.3 3.96 15.0 47.4 137. 472.
#> 3 migrating Oct_Dec 14.3 16.2 9.75 1.46 5.51 17.2 48.9 183.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_10
#> $scenario_10$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 1136. 245. 1120. 717. 963. 1279. 1628. 2019.
#> 2 feeding May_Sep 1901. 413. 1875. 1212. 1605. 2145. 2811. 3422.
#> 3 migrating Oct_Dec 709. 156. 698. 446. 598. 802. 1024. 1294.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_10$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 404. 286. 328. 70.2 211. 509. 1101. 2131.
#> 2 feeding May_Sep 676. 481. 547. 116. 351. 852. 1833. 3592.
#> 3 migrating Oct_Dec 252. 181. 205. 43.2 131. 320. 686. 1393.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_10$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 47.6 52.0 32.8 5.05 18.6 56.9 164. 535.
#> 2 feeding May_Sep 79.8 87.6 54.3 8.36 31.2 95.8 279. 944.
#> 3 migrating Oct_Dec 29.8 33.2 20.5 3.15 11.7 35.6 101. 373.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_11
#> $scenario_11$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 570. 121. 558. 366. 484. 647. 826. 979.
#> 2 feeding May_Sep 981. 211. 957. 624. 830. 1115. 1431. 1682.
#> 3 migrating Oct_Dec 347. 76.0 344. 221. 293. 395. 515. 606.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_11$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 24.7 40.3 11.5 0.787 4.98 29.1 111. 440.
#> 2 feeding May_Sep 42.5 69.2 19.8 1.35 8.56 48.3 189. 768.
#> 3 migrating Oct_Dec 15.0 24.2 7.02 0.486 3.02 17.4 67.2 263.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_11$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 3.52 11.1 0.933 0.0455 0.337 2.66 21.6 133.
#> 2 feeding May_Sep 6.04 19.0 1.57 0.0814 0.584 4.48 36.6 233.
#> 3 migrating Oct_Dec 2.13 6.60 0.568 0.0283 0.207 1.57 13.3 75.9
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_12
#> $scenario_12$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 1135. 241. 1111. 728. 964. 1287. 1645. 1949.
#> 2 feeding May_Sep 1932. 415. 1885. 1229. 1635. 2197. 2819. 3315.
#> 3 migrating Oct_Dec 696. 152. 689. 442. 587. 791. 1032. 1212.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_12$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 50.9 81.9 24.0 1.68 10.5 60.3 229. 887.
#> 2 feeding May_Sep 86.6 139. 41.0 2.85 17.8 99.1 384. 1534.
#> 3 migrating Oct_Dec 31.2 49.5 14.8 1.04 6.37 36.3 139. 535.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_12$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_Apr 7.17 22.2 1.95 0.0973 0.707 5.54 43.2 268.
#> 2 feeding May_Sep 12.2 37.7 3.25 0.172 1.22 9.21 73.2 464.
#> 3 migrating Oct_Dec 4.38 13.4 1.19 0.0606 0.439 3.28 27.1 154.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_13
#> $scenario_13$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 21.5 4.95 21.0 13.1 18.0 24.4 32.0 39.7
#> 2 feeding Mar_Aug 60.9 13.9 59.8 37.2 50.8 69.7 91.1 112.
#> 3 migrating Sep_Nov 24.4 5.70 24.0 14.9 20.3 27.8 37.0 45.4
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_13$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 21.6 4.72 21.3 13.2 18.4 24.6 31.5 36.8
#> 2 feeding Mar_Aug 61.3 13.3 60.9 37.6 52.2 69.8 89.0 106.
#> 3 migrating Sep_Nov 24.6 5.48 24.3 14.8 20.9 27.9 36.1 42.8
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_13$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 18.6 3.99 18.7 11.4 15.9 21.1 27.2 35.6
#> 2 feeding Mar_Aug 52.9 11.3 52.9 32.7 45.1 59.8 77.3 100.
#> 3 migrating Sep_Nov 21.2 4.68 21.1 13.1 18.0 24.0 31.4 41.3
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_14
#> $scenario_14$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 43.5 10.0 42.6 26.6 36.5 49.5 64.8 80.5
#> 2 feeding Mar_Aug 121. 27.6 119. 73.7 101. 138. 181. 223.
#> 3 migrating Sep_Nov 49.0 11.4 48.1 30.0 40.8 55.7 74.3 91.2
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_14$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 46.0 9.96 45.4 28.2 39.2 52.4 66.7 78.3
#> 2 feeding Mar_Aug 128. 27.6 127. 78.7 109. 145. 185. 219.
#> 3 migrating Sep_Nov 51.8 11.5 51.3 31.3 44.2 58.8 75.8 89.4
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_14$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 39.6 8.40 39.7 24.3 33.9 45.0 57.7 75.1
#> 2 feeding Mar_Aug 110. 23.3 110. 68.7 94.2 125. 160. 206.
#> 3 migrating Sep_Nov 44.7 9.76 44.6 27.7 38.1 50.5 65.7 85.9
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_15
#> $scenario_15$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 30.8 6.55 30.3 20.0 26.2 34.7 45.5 53.8
#> 2 feeding Mar_Aug 88.3 18.8 86.9 57.3 75.0 99.5 130. 157.
#> 3 migrating Sep_Nov 34.5 7.50 33.9 22.1 29.1 38.9 51.6 60.5
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_15$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 16.7 3.64 16.5 10.1 14.0 19.1 23.8 29.4
#> 2 feeding Mar_Aug 47.8 10.4 47.6 29.1 40.2 54.7 68.6 83.1
#> 3 migrating Sep_Nov 18.6 4.16 18.6 11.2 15.7 21.3 27.0 33.1
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_15$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 15.4 3.59 15.4 9.16 12.9 17.5 22.1 31.7
#> 2 feeding Mar_Aug 44.1 10.3 44.1 26.2 37.0 50.2 62.9 91.6
#> 3 migrating Sep_Nov 17.2 4.09 17.3 10.2 14.5 19.5 25.1 37.2
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_16
#> $scenario_16$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 62.3 13.3 61.3 40.4 53.0 70.3 92.2 109.
#> 2 feeding Mar_Aug 175. 37.3 172. 114. 149. 197. 258. 311.
#> 3 migrating Sep_Nov 69.1 15.0 68.0 44.2 58.3 78.1 104. 121.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_16$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 35.4 7.67 35.0 21.5 29.9 40.6 50.5 62.3
#> 2 feeding Mar_Aug 99.4 21.5 98.9 60.8 83.8 114. 143. 173.
#> 3 migrating Sep_Nov 39.2 8.67 39.1 23.6 33.1 44.8 56.8 69.4
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_16$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 32.7 7.54 32.7 19.6 27.6 37.1 46.9 66.3
#> 2 feeding Mar_Aug 91.9 21.1 91.8 54.9 77.1 104. 131. 188.
#> 3 migrating Sep_Nov 36.3 8.51 36.3 21.5 30.6 40.9 52.7 77.3
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_17
#> $scenario_17$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 27.1 6.46 26.8 16.4 22.5 31.3 40.4 51.6
#> 2 feeding Mar_Aug 81.3 19.3 80.0 49.3 67.2 93.3 122. 157.
#> 3 migrating Sep_Nov 31.0 7.53 30.4 18.5 25.4 35.7 47.4 58.1
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_17$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 1.10 0.470 1.04 0.417 0.761 1.34 2.18 3.68
#> 2 feeding Mar_Aug 3.28 1.40 3.12 1.24 2.27 4.02 6.51 11.0
#> 3 migrating Sep_Nov 1.25 0.541 1.19 0.471 0.869 1.54 2.51 4.42
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_17$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 0.774 0.408 0.725 0.283 0.520 0.957 1.56 4.85
#> 2 feeding Mar_Aug 2.32 1.21 2.18 0.855 1.56 2.88 4.64 14.1
#> 3 migrating Sep_Nov 0.884 0.467 0.840 0.333 0.593 1.10 1.78 5.34
#> # … with abbreviated variable name ¹pctl_97.5
#>
#>
#> $scenario_18
#> $scenario_18$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 54.9 13.1 54.2 33.2 45.5 63.3 81.7 104.
#> 2 feeding Mar_Aug 161. 38.2 159. 97.8 133. 185. 242. 310.
#> 3 migrating Sep_Nov 62.2 15.1 61.0 37.2 51.0 71.7 95.0 117.
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_18$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 2.33 0.992 2.22 0.889 1.62 2.84 4.63 7.73
#> 2 feeding Mar_Aug 6.84 2.90 6.50 2.61 4.73 8.38 13.6 22.7
#> 3 migrating Sep_Nov 2.64 1.13 2.52 0.998 1.83 3.24 5.29 9.20
#> # … with abbreviated variable name ¹pctl_97.5
#>
#> $scenario_18$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_9…¹ pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_Feb 1.65 0.855 1.54 0.606 1.11 2.03 3.31 10.1
#> 2 feeding Mar_Aug 4.83 2.49 4.55 1.79 3.26 6.00 9.54 28.7
#> 3 migrating Sep_Nov 1.86 0.969 1.77 0.707 1.25 2.31 3.68 11.0
#> # … with abbreviated variable name ¹pctl_97.5Band model example
This is an example usage of band_crm(). This is for a single species and single set of turbine parameters. This replicates the Band (2012) worksheet. The stoch_crm() function wraps around this function, where band_crm() acts in essence as a single draw of stoch_crm().
Please note the example runs on fictional data.
library(stochLAB)
# ------------------------------------------------------
# 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)
)
stochLAB::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 98.6 25.4 10.4
#> 2 Feb 53.2 13.7 5.61
#> 3 Mar 83.7 21.6 8.83
#> 4 Apr 61.4 15.8 6.48
#> 5 May 117. 30.2 12.4
#> 6 Jun 91.7 23.6 9.67
#> 7 Jul 114. 29.3 12.0
#> 8 Aug 91.1 23.5 9.61
#> 9 Sep 106. 27.3 11.2
#> 10 Oct 64.1 16.5 6.77
#> 11 Nov 60.7 15.7 6.40
#> 12 Dec 73.4 18.9 7.75