Good morning Lianne
I'm afraid there aren't simple answers to
your "cutoff" questions. Quite a few factors feed into the
determination of adequate modelling of the detection function.
Starting from the beginning: there is nothing sacred regarding the "60 observation" rule of thumb mentioned in Buckland et al. (2001). If you read Section 7.2.2 closely, you will see this comment regarding point transect data
Sample size in point transects can be misleading. One might detect 60 objects from surveying $k$ points and believe this sample contains a great deal of information about density. However, the area sampled increases with the square of distance, so that many of the observations are actually in the tail of $g(r)$ where detection probability is low. Detections at some distance from the point may be numerous partially because the area sampled is relatively large. Thus, sample size must be somewhat larger for point transect surveys than line transect surveys. As a rough guideline, the sample size for point transects should be approximately 25% larger than for line transect surveys to attain the same level of precision. This suggests a minimum sample size of around 75-100 for estimating a detection function, or average density within a study area.
The point in the study to consider
adequacy of sample size is during survey design (when formula
can be used to guide effort needed to achieve desired precision)
rather than at time of analysis. If rare species are the focus
of your investigation, design the surveys such that the
inference for those species is sound. If resources do not
permit the level of effort for those inferences to be sound,
redefine the objectives and abandon hope of making inference for
the rare species.
Using species as covariates to produce species-specific detection functions is useful, but again not a panacea. The premise of this covariate approach is that multiple species analysed in this fashion share a common detection function shape (hazard or half normal) but that the basic shape is altered as a function of the covariate. Hence it would be inappropriate to get far combining a species with a hazard rate and a species with a half normal via use of the covariate. This is the point at which a challenge arises: if a species has only 5 detections, it is improbable we can determine whether those detections follow a hazard or half normal shape. Therefore, as a practical matter, "enough" detections are needed to make an educated guess whether the underlying shape is hazard or half normal.
Your final question regarding number of rare species should also be viewed through the lens of which species share a common key function. If the three species that exceed the 60 observation threshold all have half normal key functions, then you will struggle with species with few detections that might have hazard rate key functions.
The discussion of sufficient number of
detections has a subjective element. If I were reviewing a
manuscript attempting to make inference for 35 species where the
sample was dominated by a small number of species (~10) with
most of the detections, I would suggest the inference for the
other 25 species is likely weak. As a researcher, you decide
whether to dilute the strength of your findings with weak
inference for under-represented species.
You received this message because you are subscribed to the Google Groups "distance-sampling" group.
To unsubscribe from this group and stop receiving emails from it, send an email to distance-sampl...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/distance-sampling/fc26cb7b-f3e2-46f2-8301-42de64da4e92n%40googlegroups.com.
-- Eric Rexstad Centre for Ecological and Environmental Modelling University of St Andrews St Andrews is a charity registered in Scotland SC013532
To view this discussion on the web visit https://groups.google.com/d/msgid/distance-sampling/882ce5fa-f640-6e47-a9ca-833f669a1f95%40st-andrews.ac.uk.
To view this discussion on the web visit https://groups.google.com/d/msgid/distance-sampling/CAFOBQArFkHSkJ2FVnaPzjjrs66%3D3yhZ6oPy8vG0kS9YpZbMTgw%40mail.gmail.com.