tgar3881 wrote:Forum,
I am currently debating using a model in order to find possible predictors or effects on the survival of GPS radio-collared ungulates that were translocated for restoration efforts. I have 2 years of data: 2 separate groups of animals (of the same species) were translocated from north Texas to west Texas during late-January and were equipped with GPS collars set to drop-off during late-November (300 days before drop-off post-release) of each year for 2013 and 2014, respectively. Each release site was spaced appr. 60 miles between each-other, and habitat was very similar. 59 animals were collared in 2013 and 62 were collared in 2014. We used aerial telemetry at least once per week for the 300 day period to monitor mortality (therefore all collared animals were encountered at least 1 time per week). GPS collars allowed us to determine appr. date of death. Therefore, we only monitored by flight to determine specific cause of mortality and locate animals.
After reading through the first 7 chapters of the Program MARK book, I learned a lot more than I expected to know about models, model selection, AIC, etc. Thus, I am still perplexed with the notion of even using MARK for estimating survival. I mainly want to find out if age (sub-adult, less than 4; adult, greater than or equal to 4), sex (Male or Female), study area, season (dry or monsoonal), or possibly daily/weekly rainfall, are predictors of survival and if using a model which of these effects play the largest role in survival of translocated animals. I know I will have to use a staggered-entry design, hence animals were collared and released within a 3 day period for each release year.
Hey Taylor,
Ok, well, you are where most studies start and the fact that you have read the first several chapters is a good start. First thing you have to figure out is what your objective is. It seems that you want to estimate survival based off a candidate model set that is a function of your 'predictors' and see which candidate model best fits the data you collected, and then make inference from that/that combination (including model selection uncertainty) of 'highest ranked models conditional on the model set' to make some statement about survival. I am going to lay out a few things here and below that you will need think about/discuss with your major prof.
1) You collected 'weekly' data on fate via your sampling design, thus the minimum period for which you could estimate S is weekly. However what is a relevant period to estimate survival for your species (which I am inferring is pronghorn as that is the only thing moved in Texas in that large of a quantity lately)? Typically, the sampling period for your data needs to have at least 1 'event' in it (e.g., death of a individual in your case), else the parameter of interest (S) is 1.00 for that period (and is technically not estimable). So, if say very few of your individuals die, then you are going to have a whole lot of weekly survival estimates of 1.0, which does not tell you much. Use of a wider interval (say month for instance) is fine, and may make more sense. But, I don't know if moving your critters from N. Tx to W. Tx just causes them to fall over or what. So, first thing, decide on appropriate interval for estimating survival.
2) It seems as if most of your predictors are binary (age, sex, season, site) with one that may be a continuous covariate (precip). I assume you have read the individual covariates chapter, as 99% of what you are asking about is detailed in there. What your specific analytical targets are should be decided by your major prof and you.
3) You mention staggered design, but I don't think you are understanding what it is. A staggered entry design is where you are capturing and bringing individuals into your sample over time, working under the assumption that the survivorship function for any animal entering the sample at time t is equal to the survivorship function of individuals entering the sample at t. All of your individuals enter in a period <3 days (less than the length of time of you sampling intervals), so this is not anything you need to worry about.
I have 3 main questions: (1) If I were to carry on with the previously mentioned question of survival, would I use the Known-Fate or the Nest Survival method in Program Mark; and (2) would going back to the ancient Kaplan-Meier staggered entry design (Pollock et al. 1999) or using some sort of ordinal logistic regression make more since with my question? To make a note on question (2), age and sex are the more important variables and to use precipitation or climate in my model I would just be pulling from NCDC data (in the given areas) for rainfall throughout the 300 day period (I didn't take rainfall or climate data during the study). And (3), Do I have enough data or variables to input in the model to do Known Fate or Nest Survival analysis?
Your comments and answers are greatly appreciated,
Taylor
1) Based on what you have told us, known fate models seem the obvious choice.
2) Calling 'Pollock et al. 1999' ancient is a little rough on those authors as it provides the foundation for the known fate approach as you see it. But, to answer your question, you can get the same answers using the approaches in MARK, using the 'ancient'
Pollock approach, or using ordinal logistic regression, if you know how they all work.
3) We have no idea if you have enough data, I mean, if all of them died, then survival for the period is 0, if none died its 1, otherwise we this goes back to my 1 above and it depends on your design and question. Do you have enough to estimate S, probably. As for variable(s), they really don't play a role in this. As for models, known fate is what you want based on what you said.
The analysis you are detailing is pretty straightforward with tons of examples in the book and elsewhere. Good luck.
\bret