Statistical analyses
The main statistical comparisons tested with our empirical data were: 1)
whether frequency of occupation is greater for subplots in ‘core’ areas
of the roost compared with subplots in irregularly occupied ‘peripheral’
areas (defined by occupation greater than or less than 80% of surveys
respectively (Appendix S1); 2) whether bat occupation decreases with
distance from the roost centre (per species); 3) whether bat species
segregate in vertical space; and 4) whether dominant individuals occupy
the centre of roosts, and subdominant individuals the outer area (per
species). We also provide qualitative comparisons of 5) seasonal
patterns of abundance and occupancy per species; and 6) whether bat
species segregate in horizontal space.
We utilised generalized additive models for all statistical comparisons
to allow for nonlinearity, with random effects modelled with smooth
functions. Roost site and subplot were modelled using a standard random
effects smoothing function. Session was modelled using a cyclic cubic
regression spline in cases where seasonality in the time series was
evident (all comparisons except those involving the proportion of male
black, male grey-headed and combined male bats per tree), otherwise
session was modelled with a standard random effects smoothing function.
We accounted for non-independence (nesting) of random effects by
including an autoregressive model for errors in the model (Yang et
al. 2012; Laurinec 2017). For the comparisons involving evaluation of
species, models were run separately for each species owing to
differences in seasonality of occupation (and so, differences in the fit
of cyclic cubic regression splines). Error distribution for comparisons
were specified according to data type and extent of zero-inflation (as
per Crawley 2013). We fit the models and performed checks of
standardised residuals in R (Version 4.0.2), using the ‘mgcv’ package
(functions ‘gamm’ and ‘gam.check’) (as per Wood 2017). See Appendix S1
in the Supporting Information for more detailed information on modelling
decisions and a summary table of comparisons. Summarised data and
annotated R code are available on GitHub at: <
https://github.com/TamikaLunn/FF-roost-ecology >.
Results
From our review of management, recovery and restoration documents
published by state government, we highlighted 31 commonly held
understandings relevant to flying-fox roosting structure (Table 2). From
our systematic search for empirical literature we generated a total of
79 search results. Of these, 52 were removed through screening (10 being
outside the Australian mainland, 4 on non-Pteropus species, and 38
focused on topics other than roost structure). An additional 18
published studies and 4 honours/PhD theses were included from citations
and the author’s reference collections, giving 49 included studies in
total (Appendix S1). Lastly, we generated an empirical dataset
consisting of 13 monthly repeat measures from 2,522 trees across eight
roost sites. Roost sites contained 118-474 measured and tagged trees
each, with an average of 2 (sparsely structured) to 75 (densely
structured) trees per 20x20 meter subplot. Tree roosting height and
count was recorded for 9,056 trees out of 32,206 repeat measures. (Note
that our total repeat measures were less than 32,786 owing to cases of
tree removal through the duration of the survey.) We report model
outputs of main interest in the main text, but see Appendix S2 in the
Supporting Information for full model output.
Below, and in Table 2, we synthesise how commonly held understandings
compare with existing literature and new data from our study.