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Program Atlas (http://www.cbr.washington.edu/analysis/apps/atlas) is a desktop application that provides the ability to analyze mark-recapture data from active-tag technology, and to adjust the observed survival estimates for potential tag-failure that may be misidentified as a mortality of the marked animal. The cbrATLAS R package was created to allow this analysis to be conducted in R. In addition, cbrATLAS incorporates an update of the original program to include the R package failCompare, increasing the number of model fitting options of tag-life data to predict tag survival of the active tags used in the study. The failCompare package also provides goodness-of-fit tests and rankings of the various models to assist in selection of the most appropriate model of tag-life, along with plotting the expected tag-life curve against observed tag failures.
cbrATLAS is currently capable of analyzing detection histories from the Single Release study design (with and without censoring events) that may or may not require correction for active-tag failure. Future iterations are planned to incorporate Paired Release, Virtual-Paired Release (ViPRe), and the Virtual Release Dead Fish Correction (ViRDCt) study designs.
library(cbrATLAS)
Load in the Single Release ATLAS Practice File. This can be a dataframe or the file name of a csv file.
data(single.rel) AdjSurv.fn(taghist.file=single.rel) # cjs unadjusted for taglife
The output from this run is a list object with the name of the detection history file and the unadjusted Cormack-Jolly-Seber survival estimates and standard errors.
To apply a taglife correction, add results from a tag-life study. This can be a data.frame object or the file name of a csv file. If a particular model is not pre-selected to fit the taglife data, failCompare will try a suite of models, print a ranking based on model fit, and automatically use the best fitting model to apply the corrections due to estimated tag-life.
data(taglife.data) # Taglife ATLAS Practice File tmp=AdjSurv.fn(taglife.file=taglife.data,taghist.file=single.rel,plot.taglife=F) # cjs adjusted for taglife tmp[-c(1:2)] # additional analysis performed
In addition to the unadjusted Cormack-Jolly-Seber estimates, the additional output includes the model fit results printed to the screen, the selected model for tag survival, the average probability and associated standard error that a tag was active upon arrival at each detection site in the study, and the adjusted CJS estimates based on those probabilities.
If a particular tag-life model is desired, an fc_obj object must be created first using the failCompare package. This object is then provided to AdjSurv.fn and applied to the given detection history.
TL_weib2=failCompare::fc_fit(time=taglife.data$tag_life_days,model="weibull") #'\dontrun{ #'AdjSurv.fn(taglife.file=taglife.data,taghist.file=single.rel,taglife.model=TL_weib2,plot.taglife=F) #'}
The mark-recapture method for the unadjusted Cormack-Jolly-Seber model is based on Skalski, J. R., S. G. Smith, R. N. Iwamoto, J. G. Williams, and A. Hoffmann. 1998. Use of passive integrated transponder tags to estimate survival of migrating juvenile salmonids in the Snake and Columbia Rivers. Canadian Journal of Fisheries and Aquatic Sciences 55:1484-1493.
The adjustment to those estimates is based on Townsend, R. L., J. R. Skalski, P. Dillingham, and T. W. Steig. 2006. Correcting bias in survival estimation resulting from tag failure in acoustic and radiotelemetry studies. Journal of Agricultural, Biological, and Environmental Statistics 11:183-196.
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