NIS-intro-detect-report

Date: 2024-02-29 18:38:01
Author: T.Gibson
Run Name: Scenario_1

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 higher 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 higher risk.

Input Parameters

##           Parameter Name Input Value
## 1                   user    T.Gibson
## 2               run_name  Scenario_1
## 3                   seed        2022
## 4                num_sim        1000
## 5              num_years          30
## 6              num_sites       10249
## 7         establish_risk  Scenario_1
## 8             intro_risk  Scenario_1
## 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        0.25
## 30              defaults     default
## 31              defaults         100
## 32              defaults          30
## 33              defaults exponential
## 34              defaults         0.8
## 35              defaults         0.8
## 36              defaults exponential
## 37              defaults           1
## 38              defaults         0.8
## 39              defaults           1
## 40              defaults           1
## 41              defaults         0.1
## 42              defaults    constant
## 43              defaults           1
## 44              defaults           1
## 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()`).
## Removed 2 rows containing missing values (`geom_bar()`).
## 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()`).
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## Warning: Removed 44 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 44 rows containing missing values (`geom_point()`).

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.

## Warning: Removed 44 rows containing missing values (`geom_line()`).

Summary of detection times:

A: Random surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000204  0.359571  0.934125  1.305789  1.831310 11.193175

B: Risk-based surveillance

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.00017  0.09448  0.25113  0.51271  0.56184 14.73416

C: Heavy risk-based surveillance

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000097  0.130853  0.374049  1.442853  1.066150 29.640294

Simulations with No Detections (%):

A: Random surveillance

## [1] 0

B: Risk-based surveillance

## [1] 0

C: Heavy risk-based surveillance

## [1] 4.4

Version

The version (sha) of https://github.com/CefasRepRes/NIS-intro-detect-sim repository used was: ddfdffc1ad06dd389230a1890fbe9cd13c140736

## 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     
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## other attached packages:
##  [1] readxl_1.4.2      dplyr_1.1.2       data.table_1.14.8 ReIns_1.0.12      EnvStats_2.7.0   
##  [6] patchwork_1.1.2   ggplot2_3.4.2     gtools_3.9.4      reshape2_1.4.4    truncnorm_1.0-9  
## [11] here_1.0.1        yaml_2.3.7       
## 
## loaded via a namespace (and not attached):
##  [1] bslib_0.5.0       tidyselect_1.2.0  xfun_0.39         splines_4.2.2     lattice_0.20-45  
##  [6] colorspace_2.1-0  vctrs_0.6.2       generics_0.1.3    htmltools_0.5.5   utf8_1.2.3       
## [11] survival_3.4-0    rlang_1.1.1       jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2       
## [16] withr_2.5.0       foreach_1.5.2     lifecycle_1.0.3   plyr_1.8.8        stringr_1.5.0    
## [21] munsell_0.5.0     gtable_0.3.3      cellranger_1.1.0  codetools_0.2-18  evaluate_0.21    
## [26] labeling_0.4.2    knitr_1.43        fastmap_1.1.1     doParallel_1.0.17 parallel_4.2.2   
## [31] fansi_1.0.4       highr_0.10        Rcpp_1.0.10       scales_1.2.1      cachem_1.0.8     
## [36] jsonlite_1.8.5    farver_2.1.1      digest_0.6.31     stringi_1.7.12    grid_4.2.2       
## [41] rprojroot_2.0.3   cli_3.6.1         tools_4.2.2       sass_0.4.6        magrittr_2.0.3   
## [46] tibble_3.2.1      pkgconfig_2.0.3   Matrix_1.6-5      rmarkdown_2.22    rstudioapi_0.14  
## [51] iterators_1.0.14  R6_2.5.1          compiler_4.2.2