{stochLAB} is a tool to run Collision Risk Models (CRMs) for seabirds
on offshore wind farms.
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In addition, previous code underpinning core calculations for the extended model has been replaced by an alternative approach, resulting in significant gains in computational speed over Masden’s code.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!
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("MarineScotlandScience/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
To report any bugs, please log an ISSUE
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.
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 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 [119ms]
#> ℹ Preparing data✔ Preparing data [225ms]
#> ℹ Sampling parameters✔ Sampling parameters [374ms]
#> ⠙ Calculating collisions | 3/1000 iterations⠹ Calculating collisions | 59/1000 iterations⠸ Calculating collisions | 173/1000 iterations⠼ Calculating collisions | 292/1000 iterations⠴ Calculating collisions | 396/1000 iterations⠦ Calculating collisions | 519/1000 iterations⠧ Calculating collisions | 625/1000 iterations⠇ Calculating collisions | 746/1000 iterations⠏ Calculating collisions | 864/1000 iterations⠋ Calculating collisions | 980/1000 iterations✔ Calculating collisions | 1000/1000 iterations [2s]
#> ℹ Sorting outputs✔ Sorting outputs [701ms]
#> ✔ Job done!
#> $collisions
#> $collisions$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_A… 39.0 17.5 40.4 10.4 25.6 50.6 71.6 94.7
#> 2 b May_S… 57.9 26.1 58.7 15.9 39.1 76.0 108. 144.
#> 3 c Oct_D… 22.1 10.2 22.4 5.89 14.0 29.2 41.8 52.0
#>
#> $collisions$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_A… 1.02 1.79 0.550 0.0922 0.324 0.910 7.08 16.0
#> 2 b May_S… 1.51 2.61 0.836 0.142 0.480 1.37 9.82 21.2
#> 3 c Oct_D… 0.572 1.02 0.315 0.0515 0.181 0.509 3.91 9.22
#>
#> $collisions$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 a Jan_A… 0.489 1.10 0.199 0.0478 0.127 0.353 4.19 9.11
#> 2 b May_S… 0.723 1.59 0.303 0.0672 0.187 0.514 5.87 12.9
#> 3 c Oct_D… 0.275 0.625 0.114 0.0256 0.0730 0.203 2.22 5.40
#>
#>
#> $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 1.45 0.278 1.44 0.926 2.00
#> 2 Feb 0.970 0.280 0.961 0.448 1.52
#> 3 Mar 1.38 0.214 1.38 0.949 1.77
#> 4 Apr 1.49 0.208 1.50 1.10 1.91
#> 5 May 1.34 0.224 1.33 0.910 1.78
#> 6 Jun 1.05 0.272 1.05 0.469 1.60
#> 7 Jul 1.35 0.291 1.36 0.803 1.93
#> 8 Aug 0.872 0.291 0.875 0.303 1.47
#> 9 Sep 0.902 0.211 0.903 0.501 1.31
#> 10 Oct 1.31 0.225 1.31 0.876 1.75
#> 11 Nov 1.13 0.295 1.12 0.569 1.72
#> 12 Dec 1.18 0.239 1.18 0.708 1.64
#>
#> $sampled_pars$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 0.802 0.0207 0.802 0.762 0.839
#> 2 Feb 0.833 0.0207 0.833 0.793 0.871
#> 3 Mar 0.869 0.0204 0.869 0.827 0.910
#> 4 Apr 0.862 0.0196 0.861 0.824 0.899
#> 5 May 0.775 0.0195 0.775 0.737 0.812
#> 6 Jun 0.888 0.0198 0.888 0.848 0.926
#> 7 Jul 0.845 0.0199 0.845 0.804 0.885
#> 8 Aug 0.758 0.0194 0.759 0.721 0.796
#> 9 Sep 0.860 0.0205 0.860 0.818 0.898
#> 10 Oct 0.877 0.0200 0.877 0.836 0.915
#> 11 Nov 0.864 0.0201 0.865 0.826 0.904
#> 12 Dec 0.834 0.0201 0.835 0.796 0.871
#>
#> $sampled_pars$downtime
#> # A tibble: 12 × 6
#> period mean sd median pctl_2.5 pctl_97.5
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jan 7.47 2.07 7.51 3.78 11.5
#> 2 Feb 9.98 2.07 9.99 6.13 13.9
#> 3 Mar 9.20 2.04 9.20 5.18 13.5
#> 4 Apr 9.39 1.96 9.43 5.61 13.2
#> 5 May 8.28 1.95 8.25 4.57 12.1
#> 6 Jun 8.44 1.98 8.41 4.58 12.4
#> 7 Jul 7.00 1.99 6.99 2.95 11.0
#> 8 Aug 9.22 1.94 9.18 5.41 13.0
#> 9 Sep 7.53 2.05 7.51 3.68 11.6
#> 10 Oct 7.11 2.00 7.13 3.31 11.2
#> 11 Nov 8.92 2.01 8.90 4.94 12.8
#> 12 Dec 9.60 2.01 9.50 5.89 13.4
#>
#> $sampled_pars$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
#> # ℹ 490 more rowsThis 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 with `by = join_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_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 984. 75.9 982. 847. 933. 1031. 1130. 1241.
#> 2 feeding Sep_D… 552. 41.6 551. 478. 523. 579. 634. 683.
#> 3 migrating Jan_A… 626. 47.7 623. 541. 591. 658. 722. 780.
#>
#> $scenario_1$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 60.7 67.3 41.9 2.41 25.3 65.7 293. 389.
#> 2 feeding Sep_D… 34.0 37.7 23.5 1.35 14.3 37.0 162. 217.
#> 3 migrating Jan_A… 38.6 42.8 26.8 1.55 16.2 42.4 186. 249.
#>
#> $scenario_1$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 17.9 24.8 10.5 0.394 5.92 17.9 106. 151.
#> 2 feeding Sep_D… 10.1 13.9 5.86 0.221 3.35 10.0 59.0 84.5
#> 3 migrating Jan_A… 11.4 15.8 6.77 0.251 3.79 11.5 66.9 93.8
#>
#>
#> $scenario_2
#> $scenario_2$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 1836. 137. 1831. 1586. 1743. 1921. 2098. 2301.
#> 2 feeding Sep_D… 1045. 76.4 1044. 909. 991. 1096. 1195. 1288.
#> 3 migrating Jan_A… 1181. 87.6 1177. 1025. 1118. 1241. 1356. 1463.
#>
#> $scenario_2$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 122. 132. 85.2 5.20 52.1 133. 574. 759.
#> 2 feeding Sep_D… 69.2 75.0 48.4 2.95 29.9 75.9 323. 430.
#> 3 migrating Jan_A… 78.3 84.8 55.1 3.37 33.7 86.5 369. 491.
#>
#> $scenario_2$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 35.9 48.7 21.4 0.848 12.2 36.1 208. 294.
#> 2 feeding Sep_D… 20.4 27.7 12.1 0.482 7.00 20.6 118. 167.
#> 3 migrating Jan_A… 23.1 31.3 13.9 0.547 7.91 23.5 133. 185.
#>
#>
#> $scenario_3
#> $scenario_3$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 2131. 143. 2126. 1875. 2034. 2223. 2415. 2690.
#> 2 feeding Sep_D… 1149. 76.8 1143. 1011. 1095. 1197. 1302. 1446.
#> 3 migrating Jan_A… 1300. 89.6 1292. 1131. 1239. 1357. 1491. 1638.
#>
#> $scenario_3$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 53.7 77.6 30.1 0.894 17.0 53.9 347. 477.
#> 2 feeding Sep_D… 28.9 41.8 16.1 0.496 9.16 29.0 185. 257.
#> 3 migrating Jan_A… 32.7 47.1 18.3 0.546 10.3 32.5 205. 291.
#>
#> $scenario_3$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 14.4 25.7 6.47 0.124 3.37 12.7 109. 162.
#> 2 feeding Sep_D… 7.74 13.8 3.48 0.0685 1.81 6.84 59.8 86.3
#> 3 migrating Jan_A… 8.74 15.6 3.93 0.0756 2.05 7.65 66.9 96.0
#>
#>
#> $scenario_4
#> $scenario_4$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 3720. 240. 3713. 3290. 3560. 3872. 4188. 4654.
#> 2 feeding Sep_D… 2037. 131. 2025. 1801. 1944. 2122. 2297. 2540.
#> 3 migrating Jan_A… 2297. 152. 2284. 2012. 2193. 2393. 2626. 2854.
#>
#> $scenario_4$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 100. 142. 57.0 1.79 32.6 101. 633. 867.
#> 2 feeding Sep_D… 54.8 77.7 31.0 1.01 17.8 55.5 342. 474.
#> 3 migrating Jan_A… 61.7 87.3 35.1 1.11 20.1 62.2 382. 531.
#>
#> $scenario_4$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 26.8 47.1 12.3 0.249 6.47 23.9 199. 295.
#> 2 feeding Sep_D… 14.7 25.7 6.69 0.140 3.53 13.1 111. 160.
#> 3 migrating Jan_A… 16.5 28.9 7.57 0.153 3.98 14.7 124. 177.
#>
#>
#> $scenario_5
#> $scenario_5$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 1176. 341. 1053. 825. 985. 1219. 2220. 2634.
#> 2 feeding Sep_D… 615. 176. 551. 430. 516. 640. 1136. 1338.
#> 3 migrating Jan_A… 703. 202. 630. 489. 591. 731. 1323. 1551.
#>
#> $scenario_5$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 0.837 2.75 0.131 0.000231 0.0341 0.390 8.68 31.9
#> 2 feeding Sep_D… 0.438 1.45 0.0687 0.000118 0.0180 0.202 4.66 17.0
#> 3 migrating Jan_A… 0.500 1.63 0.0782 0.000136 0.0209 0.230 5.20 18.4
#>
#> $scenario_5$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 0.221 0.795 0.0245 2.61e-5 0.00602 0.0786 2.41 8.88
#> 2 feeding Sep_D… 0.116 0.418 0.0129 1.29e-5 0.00319 0.0408 1.29 4.73
#> 3 migrating Jan_A… 0.132 0.472 0.0147 1.51e-5 0.00365 0.0462 1.44 5.12
#>
#>
#> $scenario_6
#> $scenario_6$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 2164. 568. 1961. 1557. 1840. 2263. 3874. 4500.
#> 2 feeding Sep_D… 1148. 298. 1043. 822. 977. 1210. 2021. 2323.
#> 3 migrating Jan_A… 1310. 341. 1188. 934. 1117. 1370. 2325. 2688.
#>
#> $scenario_6$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 1.64 5.28 0.266 0.000499 0.0711 0.779 17.1 59.5
#> 2 feeding Sep_D… 0.872 2.82 0.142 0.000254 0.0379 0.415 9.33 32.2
#> 3 migrating Jan_A… 0.991 3.18 0.162 0.000295 0.0437 0.472 10.4 34.7
#>
#> $scenario_6$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding May_A… 0.433 1.53 0.0503 5.79e-5 0.0126 0.157 4.77 16.6
#> 2 feeding Sep_D… 0.230 0.817 0.0271 2.91e-5 0.00672 0.0832 2.59 8.97
#> 3 migrating Jan_A… 0.261 0.921 0.0304 3.37e-5 0.00768 0.0947 2.88 9.68
#>
#>
#> $scenario_7
#> $scenario_7$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 429. 93.1 423. 270. 360. 490. 626. 767.
#> 2 feeding May_S… 697. 151. 685. 442. 588. 791. 1006. 1252.
#> 3 migrating Oct_D… 266. 59.2 262. 164. 223. 303. 390. 498.
#>
#> $scenario_7$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 257. 150. 223. 62.9 153. 320. 603. 1071.
#> 2 feeding May_S… 417. 244. 366. 100. 251. 517. 985. 1797.
#> 3 migrating Oct_D… 159. 93.0 137. 37.8 94.0 199. 377. 649.
#>
#> $scenario_7$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 28.2 25.9 20.9 4.02 12.5 34.1 86.4 239.
#> 2 feeding May_S… 45.9 42.4 34.3 6.51 20.3 55.1 145. 409.
#> 3 migrating Oct_D… 17.5 16.0 12.8 2.48 7.78 21.8 54.6 144.
#>
#>
#> $scenario_8
#> $scenario_8$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 855. 186. 844. 539. 719. 977. 1248. 1531.
#> 2 feeding May_S… 1376. 299. 1352. 872. 1160. 1560. 1985. 2472.
#> 3 migrating Oct_D… 534. 119. 526. 329. 448. 608. 782. 999.
#>
#> $scenario_8$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 536. 307. 468. 135. 324. 668. 1246. 2183.
#> 2 feeding May_S… 862. 494. 760. 213. 524. 1067. 2013. 3624.
#> 3 migrating Oct_D… 334. 192. 290. 81.5 200. 418. 783. 1330.
#>
#> $scenario_8$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 58.6 52.8 43.8 8.62 26.4 71.2 178. 485.
#> 2 feeding May_S… 94.4 85.7 71.2 14.0 42.5 114. 295. 820.
#> 3 migrating Oct_D… 36.6 32.8 27.1 5.36 16.5 45.6 113. 293.
#>
#>
#> $scenario_9
#> $scenario_9$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 571. 123. 563. 360. 484. 643. 818. 1015.
#> 2 feeding May_S… 965. 210. 953. 615. 815. 1089. 1428. 1738.
#> 3 migrating Oct_D… 354. 78.1 349. 223. 299. 401. 511. 646.
#>
#> $scenario_9$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 194. 140. 157. 32.9 100. 245. 539. 1050.
#> 2 feeding May_S… 329. 238. 265. 55.1 169. 414. 905. 1788.
#> 3 migrating Oct_D… 121. 87.9 97.6 20.1 62.0 153. 332. 682.
#>
#> $scenario_9$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 23.0 25.6 15.7 2.36 8.83 27.4 79.6 265.
#> 2 feeding May_S… 38.9 43.5 26.3 3.96 15.0 47.4 137. 472.
#> 3 migrating Oct_D… 14.3 16.2 9.75 1.46 5.51 17.2 48.9 183.
#>
#>
#> $scenario_10
#> $scenario_10$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 1136. 245. 1120. 717. 963. 1279. 1628. 2019.
#> 2 feeding May_S… 1901. 413. 1875. 1212. 1605. 2145. 2811. 3422.
#> 3 migrating Oct_D… 709. 156. 698. 446. 598. 802. 1024. 1294.
#>
#> $scenario_10$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 404. 286. 328. 70.2 211. 509. 1101. 2131.
#> 2 feeding May_S… 676. 481. 547. 116. 351. 852. 1833. 3592.
#> 3 migrating Oct_D… 252. 181. 205. 43.2 131. 320. 686. 1393.
#>
#> $scenario_10$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 47.6 52.0 32.8 5.05 18.6 56.9 164. 535.
#> 2 feeding May_S… 79.8 87.6 54.3 8.36 31.2 95.8 279. 944.
#> 3 migrating Oct_D… 29.8 33.2 20.5 3.15 11.7 35.6 101. 373.
#>
#>
#> $scenario_11
#> $scenario_11$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 570. 121. 558. 366. 484. 647. 826. 979.
#> 2 feeding May_S… 981. 211. 957. 624. 830. 1115. 1431. 1682.
#> 3 migrating Oct_D… 347. 76.0 344. 221. 293. 395. 515. 606.
#>
#> $scenario_11$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 24.7 40.3 11.5 0.787 4.98 29.1 111. 440.
#> 2 feeding May_S… 42.5 69.2 19.8 1.35 8.56 48.3 189. 768.
#> 3 migrating Oct_D… 15.0 24.2 7.02 0.486 3.02 17.4 67.2 263.
#>
#> $scenario_11$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 3.52 11.1 0.933 0.0455 0.337 2.66 21.6 133.
#> 2 feeding May_S… 6.04 19.0 1.57 0.0814 0.584 4.48 36.6 233.
#> 3 migrating Oct_D… 2.13 6.60 0.568 0.0283 0.207 1.57 13.3 75.9
#>
#>
#> $scenario_12
#> $scenario_12$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 1135. 241. 1111. 728. 964. 1287. 1645. 1949.
#> 2 feeding May_S… 1932. 415. 1885. 1229. 1635. 2197. 2819. 3315.
#> 3 migrating Oct_D… 696. 152. 689. 442. 587. 791. 1032. 1212.
#>
#> $scenario_12$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 50.9 81.9 24.0 1.68 10.5 60.3 229. 887.
#> 2 feeding May_S… 86.6 139. 41.0 2.85 17.8 99.1 384. 1534.
#> 3 migrating Oct_D… 31.2 49.5 14.8 1.04 6.37 36.3 139. 535.
#>
#> $scenario_12$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Jan_A… 7.17 22.2 1.95 0.0973 0.707 5.54 43.2 268.
#> 2 feeding May_S… 12.2 37.7 3.25 0.172 1.22 9.21 73.2 464.
#> 3 migrating Oct_D… 4.38 13.4 1.19 0.0606 0.439 3.28 27.1 154.
#>
#>
#> $scenario_13
#> $scenario_13$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 21.5 4.95 21.0 13.1 18.0 24.4 32.0 39.7
#> 2 feeding Mar_A… 60.9 13.9 59.8 37.2 50.8 69.7 91.1 112.
#> 3 migrating Sep_N… 24.4 5.70 24.0 14.9 20.3 27.8 37.0 45.4
#>
#> $scenario_13$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 21.6 4.72 21.3 13.2 18.4 24.6 31.5 36.8
#> 2 feeding Mar_A… 61.3 13.3 60.9 37.6 52.2 69.8 89.0 106.
#> 3 migrating Sep_N… 24.6 5.48 24.3 14.8 20.9 27.9 36.1 42.8
#>
#> $scenario_13$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 18.6 3.99 18.7 11.4 15.9 21.1 27.2 35.6
#> 2 feeding Mar_A… 52.9 11.3 52.9 32.7 45.1 59.8 77.3 100.
#> 3 migrating Sep_N… 21.2 4.68 21.1 13.1 18.0 24.0 31.4 41.3
#>
#>
#> $scenario_14
#> $scenario_14$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 43.5 10.0 42.6 26.6 36.5 49.5 64.8 80.5
#> 2 feeding Mar_A… 121. 27.6 119. 73.7 101. 138. 181. 223.
#> 3 migrating Sep_N… 49.0 11.4 48.1 30.0 40.8 55.7 74.3 91.2
#>
#> $scenario_14$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 46.0 9.96 45.4 28.2 39.2 52.4 66.7 78.3
#> 2 feeding Mar_A… 128. 27.6 127. 78.7 109. 145. 185. 219.
#> 3 migrating Sep_N… 51.8 11.5 51.3 31.3 44.2 58.8 75.8 89.4
#>
#> $scenario_14$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 39.6 8.40 39.7 24.3 33.9 45.0 57.7 75.1
#> 2 feeding Mar_A… 110. 23.3 110. 68.7 94.2 125. 160. 206.
#> 3 migrating Sep_N… 44.7 9.76 44.6 27.7 38.1 50.5 65.7 85.9
#>
#>
#> $scenario_15
#> $scenario_15$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 30.8 6.55 30.3 20.0 26.2 34.7 45.5 53.8
#> 2 feeding Mar_A… 88.3 18.8 86.9 57.3 75.0 99.5 130. 157.
#> 3 migrating Sep_N… 34.5 7.50 33.9 22.1 29.1 38.9 51.6 60.5
#>
#> $scenario_15$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 16.7 3.64 16.5 10.1 14.0 19.1 23.8 29.4
#> 2 feeding Mar_A… 47.8 10.4 47.6 29.1 40.2 54.7 68.6 83.1
#> 3 migrating Sep_N… 18.6 4.16 18.6 11.2 15.7 21.3 27.0 33.1
#>
#> $scenario_15$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 15.4 3.59 15.4 9.16 12.9 17.5 22.1 31.7
#> 2 feeding Mar_A… 44.1 10.3 44.1 26.2 37.0 50.2 62.9 91.6
#> 3 migrating Sep_N… 17.2 4.09 17.3 10.2 14.5 19.5 25.1 37.2
#>
#>
#> $scenario_16
#> $scenario_16$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 62.3 13.3 61.3 40.4 53.0 70.3 92.2 109.
#> 2 feeding Mar_A… 175. 37.3 172. 114. 149. 197. 258. 311.
#> 3 migrating Sep_N… 69.1 15.0 68.0 44.2 58.3 78.1 104. 121.
#>
#> $scenario_16$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 35.4 7.67 35.0 21.5 29.9 40.6 50.5 62.3
#> 2 feeding Mar_A… 99.4 21.5 98.9 60.8 83.8 114. 143. 173.
#> 3 migrating Sep_N… 39.2 8.67 39.1 23.6 33.1 44.8 56.8 69.4
#>
#> $scenario_16$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 32.7 7.54 32.7 19.6 27.6 37.1 46.9 66.3
#> 2 feeding Mar_A… 91.9 21.1 91.8 54.9 77.1 104. 131. 188.
#> 3 migrating Sep_N… 36.3 8.51 36.3 21.5 30.6 40.9 52.7 77.3
#>
#>
#> $scenario_17
#> $scenario_17$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 27.1 6.46 26.8 16.4 22.5 31.3 40.4 51.6
#> 2 feeding Mar_A… 81.3 19.3 80.0 49.3 67.2 93.3 122. 157.
#> 3 migrating Sep_N… 31.0 7.53 30.4 18.5 25.4 35.7 47.4 58.1
#>
#> $scenario_17$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 1.10 0.470 1.04 0.417 0.761 1.34 2.18 3.68
#> 2 feeding Mar_A… 3.28 1.40 3.12 1.24 2.27 4.02 6.51 11.0
#> 3 migrating Sep_N… 1.25 0.541 1.19 0.471 0.869 1.54 2.51 4.42
#>
#> $scenario_17$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 0.774 0.408 0.725 0.283 0.520 0.957 1.56 4.85
#> 2 feeding Mar_A… 2.32 1.21 2.18 0.855 1.56 2.88 4.64 14.1
#> 3 migrating Sep_N… 0.884 0.467 0.840 0.333 0.593 1.10 1.78 5.34
#>
#>
#> $scenario_18
#> $scenario_18$opt1
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 54.9 13.1 54.2 33.2 45.5 63.3 81.7 104.
#> 2 feeding Mar_A… 161. 38.2 159. 97.8 133. 185. 242. 310.
#> 3 migrating Sep_N… 62.2 15.1 61.0 37.2 51.0 71.7 95.0 117.
#>
#> $scenario_18$opt2
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 2.33 0.992 2.22 0.889 1.62 2.84 4.63 7.73
#> 2 feeding Mar_A… 6.84 2.90 6.50 2.61 4.73 8.38 13.6 22.7
#> 3 migrating Sep_N… 2.64 1.13 2.52 0.998 1.83 3.24 5.29 9.20
#>
#> $scenario_18$opt3
#> # A tibble: 3 × 10
#> season_id period mean sd median pctl_2.5 pctl_25 pctl_75 pctl_97.5 pctl_99
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 breeding Dec_F… 1.65 0.855 1.54 0.606 1.11 2.03 3.31 10.1
#> 2 feeding Mar_A… 4.83 2.49 4.55 1.79 3.26 6.00 9.54 28.7
#> 3 migrating Sep_N… 1.86 0.969 1.77 0.707 1.25 2.31 3.68 11.0This 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