Katrina, you generally need more observations in point transect sampling than in line transect sampling to get comparable precision, hence the suggestion of a larger sample size for point transects. But these are just cookbook rules. Whether you can get good estimation with smaller sample sizes depends on many factors. If you get good fits to your data and the shape of the detection function is plausible, given what you know about your species, I would not apply these rules rigidly.
If you use mcds, you can model data from multiple species, and include species as a covariate in your detection function model. Avoid pooling together species for which the detection process is expected to be quite different – e.g. mostly visual detections for one and mostly aural for another. But if a group of species all tend to be detected in much the same way, then it should be OK to pool, even if some have e.g. a louder song than others – that will be taken care of by including species as a factor in your model.
I’ll leave someone else to respond to your final question …
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Regarding fitting detection functions with covariates (whether or not the covariates are related to time), the first thing you should do is perform exploratory analysis of your data; before adding the covariates to the detection function.
The case study we have online, below, shows some exploratory plots
It may be the case that some covariates
contribute nothing to the fit of a detection function to your
data (the covariate does not influence detectability); or the
relationship of detectability and the covariate is highly
nonlinear, or there is an outlier in the data. Any of these
situations can result in the inability of the software to fit
models containing certain covariates. You'll need to dig in
your data to understand the nature of the challenge you are
presenting to the software.
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-- Eric Rexstad Centre for Ecological and Environmental Modelling University of St Andrews St Andrews is a charity registered in Scotland SC013532
I've not tried to fit a detection function with >10 parameters, so fitting a model with >60 parameters is an adventure. I'm not optimistic about your chances of success.
You shouldn't expect to produce reliable
estimates for species with a small number of detections even
using the covariate approach. Give thought to constraining your
analysis to species for which you will have some confidence in
the result. As a middle ground between "all species have same
detection function" and "each species has a unique detection
function, requiring 60 parameters to estimate", you could take a
"guild" approach and merge species that have similar vocal
properties, considering the possibility they share a common
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