Date: 2023-12-06 12:01:07
Author: T.Gibson
Run Name: Sim_3


Three scenarios for site surveillance to detect non indigenous species (NIS) introduction are presented and the time to detection is reported.


Input Parameters

##           Parameter Name   Input Value
## 1                   user      T.Gibson
## 2               run_name         Sim_3
## 3                   seed          2022
## 4                num_sim         10000
## 5              num_years            30
## 6              num_sites           100
## 7         establish_risk equal uniform
## 8             intro_risk equal 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 equal uniform
## 34              defaults           0.8
## 35              defaults           0.8
## 36              defaults equal 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


Introduction and establishment probabilities across sites

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()`).
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## Removed 2 rows containing missing values (`geom_bar()`).


Site-specific visit rate for each surveillance scenario

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()`).


Time to detection for each site

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()`).
## 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 is also shown per simulation below.



Probability of NIS Detection Over Time Depending on Surveillance Method

The probability of detection an NIS introduction over time using the three surveillance methods is also shown for quick visual comparison.



Summary of detection times:

A: Random surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000075  0.360463  0.871461  1.252917  1.750302 13.315454


B: Risk-based surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000159  0.352136  0.862813  1.227108  1.710893 11.613501


C: Heavy risk-based surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000091  0.365390  0.880788  1.251818  1.714592 13.084177


Simulations with No Detections (%):

A: Random surveillance

## [1] 0

B: Risk-based surveillance

## [1] 0

C: Heavy risk-based surveillance

## [1] 0

Version

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      crayon_1.5.2      pkgconfig_2.0.3   Matrix_1.5-1     
## [49] rmarkdown_2.22    rstudioapi_0.14   iterators_1.0.14  R6_2.5.1          compiler_4.2.2