is low constant apparent survival a problem here?

questions concerning analysis/theory using program MARK

is low constant apparent survival a problem here?

Postby shannonbarbermeyer » Tue Feb 14, 2023 2:20 pm

Based on a recent admin post, I think I originally posted this in the wrong help section...reposting here...thank you! Shannon

Hello, I'm running a POPAN capture-mark-recapture analysis on spotted dolphin data (photo ID data of marked adults) in MARK that were collected during line transect surveys from 2013-2017 with the primary aim being to obtain the Ni abundance estimates within each year. I evaluated the following combinations: survival constant, time varying; detection constant, time varying, and as a function of nautical mile effort; p_ent time varying; and N-hat. The "top" model was constant survival with varying prob of detection (no other models within 2 AICc). For the GOF tests that had sufficient data to evaluate, there were no statistically significant violation issues with heterogeneity of capture and unequal survival probabilities between sampling occasions. The Global GOF test was not significant, no issues of overdispersed data were detected. My question is since we are studying a long-lived mammal with survival that is more often considered close to 1 (and we had no reason during this period to expect survival not to be close to 1 for marked adults) - is the reported estimated constant survival of the model at 65% a problem or is that OK because it is apparent survival and so is also a reflection also of immigration / emigration and not a significant issue when estimating Ni for each year? I am hoping that this is a reflection of us surveying (consistently) just a portion of the expected range of this open population and so the low apparent survival is OK because it reflects the nature of our survey. Most CMR papers using POPAN report survival of various dolphin spp usually close to 1 unless there has been an event like a catastrophic oil spill. (I have read the manual multiple times but I apologize in advance if I missed something that would have helped answer this question. Also, I searched the forum before posting but was not able to find a post that answered my question - my apologies, again, if I missed anything). Thank you very much for suggestions / guidance :) Shannon
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Re: is low constant apparent survival a problem here?

Postby Eurycea » Thu Mar 30, 2023 11:56 am

I don't know the direct answer to your question about whether 65% is a good survival estimate for Dophins, but my experience with these types of models is that you need a reasonably robust estimate of detection probability before you can get any reasonable estimates out of these models. If you see a lot of boundary (zero estimates) for PENT that is often a red flag, at least in the data that I have looked at personally. How do your detection estimates look?

There are others on this forum who are much more knowledgeable than I am, maybe one of them will chime in.
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Re: is low constant apparent survival a problem here?

Postby shannonbarbermeyer » Mon Apr 03, 2023 3:20 pm

Thank you Eurycea,

I should clarify that I don't think 65% is a good survival estimate for dolphins - it isn't. But I was wondering if that meant the rest of the model outputs (including derived parameters) were, therefore, not useful. We recently finished our 2022 data processing so I re-analyzed an extended series (not just 2013-2017 but 2013-2022) and got 77%. This is still low but approximates constant survival for one group in another spotted dolphin analysis elsewhere.

Thank you for your reply. I had looked at probability of detection values (no boundary issues) - but not pents (probability of entry). There are two values of the time varying pents that have estimation issues (one near zero and one ranging from zero to one). I'm not sure how probability of entry may affect the low survival rate.

I've adjusted c-hat for overdispersion as indicated by GOF test. I'm not sure there is anything to be done to address the pent estimates, constant pent does not make sense here.

Thank you again,
Shannon
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