, Jasper McCutcheon
[aut] , Rileigh Hudock
[aut] , Noah Motz
[aut] , Madison
Windsor [aut] , Aaron
Carlisle [aut] ,
Edward Hale [aut] | Title: | Estimate User-Based Tagging Mortality and Tag Loss in Mark-Recapture Studies |
|---|---|
| Description: | We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an 'rmarkdown' printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies"). |
| Authors: | Brendan Campbell [aut, cre]
, Jasper McCutcheon
[aut] , Rileigh Hudock
[aut] , Noah Motz
[aut] , Madison
Windsor [aut] , Aaron
Carlisle [aut] ,
Edward Hale [aut] |
| Maintainer: | Brendan Campbell <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.0 |
| Built: | 2025-02-10 11:20:57 UTC |
| Source: | CRAN |
This model estimates the percent loss in tagged animals at large for
field-based recapture studies based on a linear decrease in survival and tag
retention (including lost tags and missidentified tags) for five weeks per
tagging cohort based on laboratory retention/survival studies. The
retentionmort() function can be used following a recapture field study to
estimate user-based tag loss in animals at large. The model is changed by
linear regression coefficients of weekly tag loss rate, weekly mortality
rate, and their respective intercepts. The coefficients used can be selected
from the currently included list using the err input or be customized.
This function is also capable of working with a cofactor with two conditions
(e.g. class1 individuals and large individuals) to improve resolution for
more specified studies.
retentionmort( nT, n_c1 = nT, TaL, c, R, err = 2, m_mort_c1 = NA, b_mort_c1 = NA, m_ret_c1 = NA, b_ret_c1 = NA, m_mort_c2 = NA, b_mort_c2 = NA, m_ret_c2 = NA, b_ret_c2 = NA )retentionmort( nT, n_c1 = nT, TaL, c, R, err = 2, m_mort_c1 = NA, b_mort_c1 = NA, m_ret_c1 = NA, b_ret_c1 = NA, m_mort_c2 = NA, b_mort_c2 = NA, m_ret_c2 = NA, b_ret_c2 = NA )
nT |
A vector of the number of tagged individuals for each tagging effort. |
n_c1 |
(optional) A vector of the number of tagged individuals in one of two categorical variables. If this is not being used then n_c1 will be equal to nT. |
TaL |
A vector of the cumulative number of tagged individuals following each effort. |
c |
One value for the total number of tagging efforts.This value must be greater than or equal to 6. |
R |
A vector of the number of recaptured individuals per effort. |
err |
A value (between 1 and 26) that represents the weekly mortality rate and weekly tag loss rate from a preloaded case study listed in the metadata. Alternatively, model coefficients can be manually included using a combination of the preceding parameters. While the preloaded data are based on weekly time stamps, customized model coefficients can reflect any time period specified and the projection will predict loss at 5 times the time interval. 1 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg + 95% CI) - McCutcheon et al. in prep
2 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg) - McCutcheon et al. in prep
3 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg - 95% CI) - McCutcheon et al. in
prep
4 = American Eel elvers (80 – 149 mm TL) tagged with 2 VIE tags in
anterior, posterior, central of body - Eissenhauer et al. 2024
https://doi.org/10.1002/nafm.11016
5 = Mummichogs (45 - 82 mm TL) tagged with 8mm PIT tags in abdominal
cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
6 = Mummichogs (45 - 82 mm TL) tagged with 12mm PIT tags in abdominal
cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
7 = Pinfish (45 - 82 mm TL) tagged with 8mm or 12mm PIT tags in
abdominal cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
8 = Cichlids (29 - 59 mm TL) tagged with VIE in various locations on
body - Jungwirth et al. 2019
https://doi.org/10.1007/s00265-019-2659-y
9 = River Shiners (36 - 49 mm TL) tagged with VIE using anesthesia in
various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
10 = River Shiners (50 - 56 mm TL) tagged with 8 mm PIT using
anesthesia in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
11 = River Shiners (40 - 51 mm TL) tagged with VIE using no anesthesia
in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
12 = River Shiners (50 - 55 mm TL) tagged with 8 mm PIT using no
anesthesia in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
13 = Delta Smelt (> 70 mm FL) tagged with injected acoustic tag -
Wilder et al. 2016
https://doi.org/10.1080/02755947.2016.1198287
14 = Delta Smelt (> 70 mm FL) surgically tagged with acoustic tag -
Wilder et al. 2016
https://doi.org/10.1080/02755947.2016.1198287
15 = Rohu Carp tagged with floy tags under dorsal fin - Hadiuzzaman
et al. 2015
https://www.researchgate.net/publication/289460932_Feasibility_study_of_using_floy_tag_and_visible_implant_fluorescent_elastomer_marker_in_major_carps
16 = Silver Carp tagged with floy tags under dorsal fin - Hadiuzzaman
et al. 2015
https://www.researchgate.net/publication/289460932_Feasibility_study_of_using_floy_tag_and_visible_implant_fluorescent_elastomer_marker_in_major_carps
17 = Black Bullhead (mean TL = 153.3 mm) tagged with VIE near dorsal
fin - Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
18 = Bluegill (mean TL = 75.8 mm) tagged with VIE near dorsal fin -
Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
19 = Channel Catfish (mean TL = 127.9 mm) tagged with VIE near dorsal
fin - Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
20 = Juvenile Burbot (88 - 144 mm TL) tagged with coded wire tag on
snout, periocular region, nape, pectoral fin base, dorsal fin
base, and anal fin base - Ashton et al. 2013
https://doi.org/10.1080/02755947.2014.882458
21 = Delta Smelt adults (45 - 77 mm FL) and juveniles (20 - 40 mm FL)
tagged with calein markers - Castillo et al. 2014
https://doi.org/10.1080/02755947.2013.839970
22 = Juvenile Seabass (mean 173 g) tagged with dummy acoustic
transmitters in external or intraperitoneal cavity -
Begout Anras et al. 2003
https://doi.org/10.1016/S1054-3139(03)00135-8
23 = Juvenile American Eels (113 - 175 mm TL) tagged with
micro-acoustic transmitter in body cavity - Mueller et al. 2017
https://doi.org/10.1016/j.fishres.2017.06.017
24 = Juvenile European Eels (7 - 25 g) tagged with 12mm PIT tags -
Jepsen et al. 2022
https://doi.org/10.1111/jfb.15183
25 = Adult Atlantic Croaker (147 - 380 mm TL) tagged with VIE tags in
caudal fin - Torre et al. 2017
https://doi.org/10.1080/00028487.2017.1360391
26 = Adult Spot (65 - 222 mm FL) tagged with VIE tags in caudal fin -
Torre et al. 2017
https://doi.org/10.1080/00028487.2017.1360391
|
m_mort_c1 |
A value that represents the slope of the mortality rate
for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_mort_c1 |
A value that represents the intercept of the mortality
rate for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
m_ret_c1 |
A value that represents the slope of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_ret_c1 |
A value that represents the intercept of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
m_mort_c2 |
A value that represents the slope of the mortality rate
for the class2 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_mort_c2 |
A value that represents the intercept of the mortality
rate for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
m_ret_c2 |
A value that represents the slope of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
b_ret_c2 |
A value that represents the intercept of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
This returns a dataframe datacomp that contains summary
information from each mark-recapture effort, several parameters
used in the calculation of adjusted recaptures, and basic error
values between the expected and observed number of recaptures. The
datacomp dataframe can be used in the retentionmort_figure()
function to generate some preliminary figures that can be used to
assess model performance and factors that influence the error
between expected and observed recaptures.
Values that will be returned include:
week = The week in the study
nT = The number of tagged individuals per tagging effort
n_c1 = The number of tagged individuals in class1 per tagging
effort
TaL = The cumulative number of tagged individuals at large
TAsum = The weekly sum of adjusted number of tags at large
TDF = Tag depreciation factor
YSs = Resultant survival rate of class1
YSl = Resultant survival rate of class2
YMs = Resultant tag loss rate of class1
YMl = Resultant tag loss rate of class2
TaLs = The cumulative number of class1 individuals tagged at
large
TaLl = The cumulative number of class2 individuals tagged at
large
R = The number of recaptured individuals per effort
Rpercent = The proportion of recaptured individuals
RA = The adjusted number of recaptured individuals
PSE = The percent standard error between observed and
estimated recaptured individuals
#To formulate a dataset for each example ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env) #Using a preloaded set of model parameters datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, err=ret_env$err ) #Using custom model parameters for one class type datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05 ) #or datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #Using custom model parameters for two class types datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 )#To formulate a dataset for each example ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env) #Using a preloaded set of model parameters datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, err=ret_env$err ) #Using custom model parameters for one class type datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05 ) #or datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #Using custom model parameters for two class types datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 )
By inputing the datacomp dataframe, this function will save a markdown file
in the working directory named retentionmort.htmlthat provides helpful
information on the error associated with the number of recaptured individuals
compared to the expected number provided by the model. Some figures will be
less applicable for the field data application using the retentionmort()
function due to a low sample size, specifically figures 5 and 6.
retentionmort_figure(datacomp)retentionmort_figure(datacomp)
datacomp |
The file generated from either the retentionmort() or retentionmort_generation() functions. |
This function will return one markdown file named retentionmort.html in your
current working directory listing some helpful information for analyzing
model data generated from the retentionmort() or retentionmort_generation()
functions
#Using retentionmort_generation() to produce multiple iterations of data to #run the model through datacomp = retentionmort_generation() Rmark = file.path(tempdir(),retentionmort_figure(datacomp)) unlink("retentionmort.Rmd") unlink("retentionmort.html") #Creating a dataset with test_dataset_retentionmort() and running the #retentionmort() function ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env) datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, err=ret_env$err) #Creating the markdown on datacomp Rmark = file.path(tempdir(),retentionmort_figure(datacomp)) unlink("retentionmort.Rmd") unlink("retentionmort.html")#Using retentionmort_generation() to produce multiple iterations of data to #run the model through datacomp = retentionmort_generation() Rmark = file.path(tempdir(),retentionmort_figure(datacomp)) unlink("retentionmort.Rmd") unlink("retentionmort.html") #Creating a dataset with test_dataset_retentionmort() and running the #retentionmort() function ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env) datacomp = retentionmort(n_c1=ret_env$n_c1, nT=ret_env$nT, TaL=ret_env$TaL, c=ret_env$c, R=ret_env$R, err=ret_env$err) #Creating the markdown on datacomp Rmark = file.path(tempdir(),retentionmort_figure(datacomp)) unlink("retentionmort.Rmd") unlink("retentionmort.html")
This model estimates the percent loss in tagged animals at large for
field-based recapture studies based on a linear decrease in survival and tag
retention (including lost tags and missidentified tags) that gets projected
for five weeks per tagging cohort based on laboratory retention/survival
studies. This retentionmort_generation() differs from retentionmort() because
it generates a mark-recapture dataset instead of relying on field data,
making it possible to estimate the expected error associated with an upcoming
field effort to provide insight on methods development. The model is changed
by linear regression coefficients of weekly tag loss rate, weekly mortality
rate, and their respective intercepts. The coefficients used can be selected
from the currently included list using the err input or be customized. This
function is also capable of working with a cofactor with two conditions
(e.g. class1 individuals and large individuals) to improve resolution for
more specified studies.
retentionmort_generation( n = 100, min_weeks = 6, max_weeks = 100, max_tags = 500, prop_class1 = 0, max_recap = 0.5, err = 2, m_mort_c1 = NA, b_mort_c1 = NA, m_ret_c1 = NA, b_ret_c1 = NA, m_mort_c2 = NA, b_mort_c2 = NA, m_ret_c2 = NA, b_ret_c2 = NA )retentionmort_generation( n = 100, min_weeks = 6, max_weeks = 100, max_tags = 500, prop_class1 = 0, max_recap = 0.5, err = 2, m_mort_c1 = NA, b_mort_c1 = NA, m_ret_c1 = NA, b_ret_c1 = NA, m_mort_c2 = NA, b_mort_c2 = NA, m_ret_c2 = NA, b_ret_c2 = NA )
n |
The number of iterations (i.e. generated mark-recapture datasets) the model will run through (default = 100). |
min_weeks |
The minimum number of efforts that each generated mark-recapture dataset will operate for (default = 6), must be at least 6. |
max_weeks |
The maximum number of efforts that each generated mark-recapture dataset will run for (default = 100). |
max_tags |
The maximum number of individuals that will be tagged per effort (default = 500). |
prop_class1 |
(Optional) The estimated proportion of tagged individuals that belong to the first of two classifications (use 1 or 0 if none; default = 0). |
max_recap |
The maximum proportion of recaptured individuals per effort (default = 0.5). |
err |
A value (between 1 and 26) that represents the weekly mortality rate and weekly tag loss rate from a preloaded case study listed in the metadata (default = 2). Alternatively, model coefficients can be manually included using a combination of the preceding parameters. While the preloaded data are based on weekly time stamps, customized model coefficients can reflect any time period specified and the projection will predict loss at 5 times the time interval. 1 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg + 95% CI) - McCutcheon et al. in prep
2 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg) - McCutcheon et al. in prep
3 = Large (> 61mm TL) and class1 (< 61mm TL) Mummichogs tagged with
VIE in caudal peduncle (avg - 95% CI) - McCutcheon et al. in
prep
4 = American Eel elvers (80 – 149 mm TL) tagged with 2 VIE tags in
anterior, posterior, central of body - Eissenhauer et al. 2024
https://doi.org/10.1002/nafm.11016
5 = Mummichogs (45 - 82 mm TL) tagged with 8mm PIT tags in abdominal
cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
6 = Mummichogs (45 - 82 mm TL) tagged with 12mm PIT tags in abdominal
cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
7 = Pinfish (45 - 82 mm TL) tagged with 8mm or 12mm PIT tags in
abdominal cavity - Kimball & Mace 2020
https://doi.org/10.1007/s12237-019-00657-4
8 = Cichlids (29 - 59 mm TL) tagged with VIE in various locations on
body - Jungwirth et al. 2019
https://doi.org/10.1007/s00265-019-2659-y
9 = River Shiners (36 - 49 mm TL) tagged with VIE using anesthesia in
various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
10 = River Shiners (50 - 56 mm TL) tagged with 8 mm PIT using
anesthesia in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
11 = River Shiners (40 - 51 mm TL) tagged with VIE using no anesthesia
in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
12 = River Shiners (50 - 55 mm TL) tagged with 8 mm PIT using no
anesthesia in various locations - Moore & Brewer 2021
https://doi.org/10.1002/nafm.10607
13 = Delta Smelt (> 70 mm FL) tagged with injected acoustic tag -
Wilder et al. 2016
https://doi.org/10.1080/02755947.2016.1198287
14 = Delta Smelt (> 70 mm FL) surgically tagged with acoustic tag -
Wilder et al. 2016
https://doi.org/10.1080/02755947.2016.1198287
15 = Rohu Carp tagged with floy tags under dorsal fin - Hadiuzzaman
et al. 2015
https://www.researchgate.net/publication/289460932_Feasibility_study_of_using_floy_tag_and_visible_implant_fluorescent_elastomer_marker_in_major_carps
16 = Silver Carp tagged with floy tags under dorsal fin - Hadiuzzaman
et al. 2015
https://www.researchgate.net/publication/289460932_Feasibility_study_of_using_floy_tag_and_visible_implant_fluorescent_elastomer_marker_in_major_carps
17 = Black Bullhead (mean TL = 153.3 mm) tagged with VIE near dorsal
fin - Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
18 = Bluegill (mean TL = 75.8 mm) tagged with VIE near dorsal fin -
Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
19 = Channel Catfish (mean TL = 127.9 mm) tagged with VIE near dorsal
fin - Schumann et al. 2013
https://benthamopen.com/contents/pdf/TOFISHSJ/TOFISHSJ-6-41.pdf
20 = Juvenile Burbot (88 - 144 mm TL) tagged with coded wire tag on
snout, periocular region, nape, pectoral fin base, dorsal fin
base, and anal fin base - Ashton et al. 2013
https://doi.org/10.1080/02755947.2014.882458
21 = Delta Smelt adults (45 - 77 mm FL) and juveniles (20 - 40 mm FL)
tagged with calein markers - Castillo et al. 2014
https://doi.org/10.1080/02755947.2013.839970
22 = Juvenile Seabass (mean 173 g) tagged with dummy acoustic
transmitters in external or intraperitoneal cavity -
Begout Anras et al. 2003
https://doi.org/10.1016/S1054-3139(03)00135-8
23 = Juvenile American Eels (113 - 175 mm TL) tagged with
micro-acoustic transmitter in body cavity - Mueller et al. 2017
https://doi.org/10.1016/j.fishres.2017.06.017
24 = Juvenile European Eels (7 - 25 g) tagged with 12mm PIT tags -
Jepsen et al. 2022
https://doi.org/10.1111/jfb.15183
25 = Adult Atlantic Croaker (147 - 380 mm TL) tagged with VIE tags in
caudal fin - Torre et al. 2017
https://doi.org/10.1080/00028487.2017.1360391
26 = Adult Spot (65 - 222 mm FL) tagged with VIE tags in caudal fin -
Torre et al. 2017
https://doi.org/10.1080/00028487.2017.1360391
|
m_mort_c1 |
A value that represents the slope of the mortality rate
for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_mort_c1 |
A value that represents the intercept of the mortality
rate for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
m_ret_c1 |
A value that represents the slope of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_ret_c1 |
A value that represents the intercept of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
m_mort_c2 |
A value that represents the slope of the mortality rate
for the class2 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
b_mort_c2 |
A value that represents the intercept of the mortality
rate for the class1 individuals. While all the preloaded
datasets work in weekly time intervals, these can be
customized to any time interval to match the sampling
interval. The resulting model will then project mortality
and tag loss for 5 times the time interval. If this value
is added, then, at minimum, |
m_ret_c2 |
A value that represents the slope of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
b_ret_c2 |
A value that represents the intercept of the tag loss
(represented as tag loss and missidentification) rate for
the class1 individuals. While all the preloaded datasets
work in weekly time intervals, these can be customized to
any time interval to match the sampling interval. The
resulting model will then project mortality and tag loss
for 5 times the time interval. If this value is added,
then, at minimum, |
This returns a dataframe datacomp that contains summary
information from each mark-recapture effort, several parameters
used in the calculation of adjusted recaptures, and basic error
values between the expected and observed number of recaptures. The
datacomp dataframe can be used in the retentionmort_figure()
function to generate some preliminary figures that can be used to
assess model performance and factors that influence the error
between expected and observed recaptures.
Values that will be returned include:
week = The week in the study
c = The total number of efforts within the dataset
iteration = The iteration number of the dataset
nT = The number of tagged individuals per tagging effort
nlc1 = The number of tagged individuals in class1 per
tagging effort
TaL = The cumulative number of tagged individuals at large
TAsum = The weekly sum of adjusted number of tags at large
TDF = Tag depreciation factor
YSs = Resultant survival rate of class1
YSl = Resultant survival rate of class2
YMs = Resultant tag loss rate of class1
YMl = Resultant tag loss rate of class2
TaLs = The cumulative number of class1 individuals tagged
at large
TaLl = The cumulative number of class2 individuals tagged
at large
R = The number of recaptured individuals per effort
Rpercent = The proportion of recaptured individuals
RA = The adjusted number of recaptured individuals
PSE = The percent standard error between observed and
estimated recaptured individuals
#Using only default variables datacomp = retentionmort_generation() #Using custom model parameters for one class type datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05 ) #or datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #Using custom model parameters for two class types datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #or datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = 2, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #err gets overrided by customized coefficients#Using only default variables datacomp = retentionmort_generation() #Using custom model parameters for one class type datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05 ) #or datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #Using custom model parameters for two class types datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = NA, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #or datacomp = retentionmort_generation(n = 100, max_weeks = 100, prop_class1 = 0, max_recap = 0.5, err = 2, m_mort_c1=-0.0625, b_mort_c1=1.06, m_ret_c1=-0.113, b_ret_c1=1.05, m_mort_c2=-0.0203, b_mort_c2=1.03, m_ret_c2=-0.0541, b_ret_c2=0.993 ) #err gets overrided by customized coefficients
Using the example code verbatim, this function will produce a series of outputs that can be directly input into the retentionmort() function.
test_dataset_retentionmort()test_dataset_retentionmort()
c = One value for the total number of tagging efforts that is 6 or greater.
err = A value (between 1 and 26) that represents the weekly mortality rate and weekly tag loss rate from a preloaded case study listed in the metadata.
n_c1 = A vector of the number of tagged individuals in one of two categorical variables.
nT = A vector of the number of tagged individuals for each tagging effort.
R = A vector of the number of recaptured individuals per effort.
TaL = A vector of the cumulative number of tagged individuals following each effort.
#Run this verbatim to produce a single mark-recapture dataset ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env)#Run this verbatim to produce a single mark-recapture dataset ret_env <- new.env() data<- test_dataset_retentionmort() list2env(data, envir = ret_env)