Practical advice
David L Miller & Jason J Roberts
Real survey data is messy
Collecting and analysing data in the real world
- We've talked a lot about models
- We've also talked about assumptions
- Our example is relatively well-behaved
- What can we do about all the nasty real world stuff?
Aims
- Here we want to cover common questions
- Not definitive answers
- Some guidance on where to look for answers
What should my sample size be?
What do we mean by "sample size"?
- Number of animal (groups) recorded
- Number of segments
- Number of segments with observations
How would we know when we have enough samples?
- We don't
- Heavily context-dependent
- Go back to assumptions
Pilot studies and "you get what you pay for"
- Designing surveys is hard
Designing surveys is essential
Better to fail one season than fail for 5, 10 years
Get information early, get it cheap
- Inform design from a pilot study
Avoiding rules of thumb
- Think about assumptions
- Detection function
- Spatial model
- Think about design
- Spatial coverage
- Covariate coverage
"Which of X, Y, Z is correct?"
Alternatives problem
- When faced with options, try them all.
- Where does the sensitivity lie?
- What's really going on?
"How big should our segments be?"
Segment size
- If you think it's an issue test it
- Resolution of covariates also important
- Maybe species-/domain-dependent?
Model validation
- Some variety of cross-validation
- Temporal replication
- Leave out 1 year, fit to others, predict, assess
- Spatial “pseudo-jackknife”
- Leave out every \( n^{th} \) segment, refit, …
- (Maybe leave out 2, 3 etc…)
Resources
- Bibliography has pointers to these topics
- Distance sampling Google Group