Date: 2023-12-06 11:56:43
Author: T.Gibson
Run Name: Sim_2
Three scenarios for site surveillance to detect non indigenous species (NIS) introduction are presented and the time to detection is reported.
A. Random surveillance, where sites are visited at an equal rate independent of the risk of NIS introduction and establishment at each site.
B. Risk-based surveillance, where sites with a higher probability of NIS introduction and establishment are visited at a higher visit rate due to their elevated risk.
C. Heavy risk-based surveillance, where sites with higher probability of NIS introduction and establishment form the main focus of surveillance programmes and are visited at a substantially higher visit rate due to their elevated risk.
## Parameter Name Input Value
## 1 user T.Gibson
## 2 run_name Sim_2
## 3 seed 2022
## 4 num_sim 10000
## 5 num_years 30
## 6 num_sites 100
## 7 establish_risk random uniform
## 8 intro_risk random uniform
## 9 establish_prob 0.8
## 10 intro_prob 0.8
## 11 mean_visit_rate 1
## 12 detect_dynamic constant
## 13 det_prob 0.8
## 14 det_prob_min 0
## 15 det_prob_max 1
## 16 seed_n 1
## 17 detect_summary last
## 18 start_pop 1
## 19 start_possion F
## 20 pop_R 2
## 21 growth_model exponential
## 22 pop_cap 500
## 23 APrb 10
## 24 Abund_Threshold 1000
## 25 Prob_Below 0.1
## 26 Prob_Above 0.8
## 27 sensitivity_analysis FALSE
## 28 elasticity_analysis FALSE
## 29 elasticity_proportion 0.1
## 30 defaults default
## 31 defaults 100
## 32 defaults 30
## 33 defaults random uniform
## 34 defaults 0.8
## 35 defaults 0.8
## 36 defaults random uniform
## 37 defaults 1
## 38 defaults 0.8
## 39 defaults 0.9
## 40 defaults 1
## 41 defaults 0.1
## 42 defaults constant
## 43 defaults 1
## 44 defaults 10
## 45 defaults 1
## 46 defaults F
## 47 defaults 2
## 48 defaults exponential
## 49 defaults 500
## 50 defaults 10
## 51 defaults 1000
## 52 defaults 0.1
## 53 defaults 0.8
For all three surveillance scenarios a probability of establishment, probability of introduction, and probability of introduction and establishment are randomly generated or calculated per site. These are displayed below.
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
Note that for each of these surveillance strategies the overall visit
rate, as determined in the input parameters mean_visit_rate
(in this run: 1) remains the same. The site visit rate is artificially
increased for sites with higher probability of introduction and
establishment in risk-based surveillance and lowered for those with
lower probability of introduction and establishment. These plots
demonstrate this.
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
## Removed 2 rows containing missing values (`geom_bar()`).
The time it takes in years to visit a site where the NIS has been introduced and also detect it is illustrated in the histograms below. Where an NIS introduction is not detected within the surveillance period of 30 the time to detection reported is 1000 years.
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Warning: Removed 15 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## Warning: Removed 749 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 2 rows containing missing values (`geom_bar()`).
The time it takes in years to visit a site where the NIS has been introduced is also shown per simulation below.
## Warning: Removed 15 rows containing missing values (`geom_point()`).
## Warning: Removed 749 rows containing missing values (`geom_point()`).
The probability of detection an NIS introduction over time using the three surveillance methods is also shown for quick visual comparison.
## Warning: Removed 764 rows containing missing values (`geom_line()`).
A: Random surveillance
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000014 0.371364 0.889730 1.279498 1.766252 11.968989
B: Risk-based surveillance
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000017 0.231568 0.564472 1.152412 1.244430 29.753959
C: Heavy risk-based surveillance
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000108 0.172804 0.517811 1.903354 1.627596 29.857621
A: Random surveillance
## [1] 0
B: Risk-based surveillance
## [1] 0.15
C: Heavy risk-based surveillance
## [1] 7.49
The version (sha) of https://github.com/CefasRepRes/NIS-intro-detect-sim repository used was: 885e3d4e15b15628ccfa6a548f6359967cb7bb6e
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8 LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.2 data.table_1.14.8 ReIns_1.0.12 EnvStats_2.7.0 patchwork_1.1.2 ggplot2_3.4.2
## [7] gtools_3.9.4 reshape2_1.4.4 truncnorm_1.0-9 here_1.0.1 yaml_2.3.7
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 xfun_0.39 bslib_0.5.0 splines_4.2.2 lattice_0.20-45 colorspace_2.1-0
## [7] vctrs_0.6.2 generics_0.1.3 htmltools_0.5.5 utf8_1.2.3 survival_3.4-0 rlang_1.1.1
## [13] pillar_1.9.0 jquerylib_0.1.4 glue_1.6.2 withr_2.5.0 foreach_1.5.2 lifecycle_1.0.3
## [19] plyr_1.8.8 stringr_1.5.0 munsell_0.5.0 gtable_0.3.3 codetools_0.2-18 evaluate_0.21
## [25] labeling_0.4.2 knitr_1.43 fastmap_1.1.1 doParallel_1.0.17 parallel_4.2.2 fansi_1.0.4
## [31] highr_0.10 Rcpp_1.0.10 scales_1.2.1 cachem_1.0.8 jsonlite_1.8.5 farver_2.1.1
## [37] digest_0.6.31 stringi_1.7.12 grid_4.2.2 rprojroot_2.0.3 cli_3.6.1 tools_4.2.2
## [43] magrittr_2.0.3 sass_0.4.6 tibble_3.2.1 pkgconfig_2.0.3 Matrix_1.5-1 rmarkdown_2.22
## [49] rstudioapi_0.14 iterators_1.0.14 R6_2.5.1 compiler_4.2.2