Jeffrey, many thanks for the very helpful advice!
As to help clarify the data, it was collected at multiple sample locations at 12 individual sites for EIA purposes. These sites are around 1-2km square and their locations and the sample points within each site have not been randomly generated. However, the sites are reasonably spread across the north and central areas of England. At each of the 12 sites, between 5 and 8 observations were made at sample points across the site, giving a total of 92 sample points across all sites e.g. site 1 contains 6 sample points. To give you an idea of the sampling effort involved, the data contains 10,000+ nocturnal hours of recording effort over 950+ nights, containing 50,000+ records of bat passes. What I think I would prefer to do is analyse the 92 sample points, and perhaps summarise by site. would be sufficient power for a regression framework?
I should note that all sample points were on the exterior of woodland, but the sample points and the woodland patches lie within the same landscape. Woodland metrics have been calculated using V-late for patches within a range of buffer zones up to 1 km from each sample point. The woodland patches within this 1 km buffer zone, range from 25m sq to 19 hectares.
Unfortunately, I had no hand in the design of the surveys, I have simply been given access to the results of surveys for EIAs, so you could define it as secondary data. Not all the surveys were carried out to the same methodology e.g. period of survey, time of year, repetition. Each static remote detector collected between 4 and 14 nights of data per survey session at the sample points and not all sample point survey sessions were repeated on multiple occasions over the bat activity season. The sample points were not randomly generated but the location chosen to collect information about the specific location. Any thoughts on whether the unrepeated sample points should be excluded from the study? This would reduce the number of sample points to 46.
Thanks again Jeffrey