Methods
We collected data on synanthropic free-tailed bat building use in the
Taita-Taveta county of south-eastern Kenya (East Africa), between
February and April 2022 (Figure 1). Topography of the county spans
low-lying savannah plains to mountain ranges, with elevations between
700 m (in the plains), up to 2200 m (highest peak of the Taita Hills)40. Human land-use in this county spans a gradient of
development, from rural areas with predominantly traditional building
practices, to more urbanised areas in the townships of Mwatate, Maktau,
Voi, and Wundanyi, with predominantly modern building practices.
Bat-borne viruses of zoonotic interest have been detected from
free-tailed bats in this area, including coronaviruses and Bombali virus
(genus: ebolavirus ) 41–43.
We assessed buildings for synanthropic free-tailed bat occupancy within
ten, 1x1 km sites (Figure 1). Sites were chosen to represent the
gradient of human landscapes utilised by free-tailed bats
(traditional-style housing to modern-style housing, Figure 2), and each
was centred on a single building roost identified to contain
synanthropic free-tailed bats. Building footprint maps, derived from
satellite imagery collected in 2020 and 2021, were used to identify all
buildings within sites 24. As public attitude towards
bats is negative in this region – being associated with witchcraft and
witch doctors (Mwasi & Mwakachola pers. comm.) – we endeavoured to
become familiarised and trusted by the community prior to surveying
sites and conducted all surveys with a local field assistant.
We individually evaluated each building within our 1
km2 sites for occupation by synanthropic free-tailed
bats. To do this, we (1) asked building owners whether they had recently
seen or heard bats inside the building during the day, (2) assessed the
building for signs of bat use
(e.g., bat faeces on the ground, stained ceiling, staining around
external roost entrance points, and smell, Appendix S1), and where
possible (3) assessed the building for physical presence of bats, for
sighting and/or auditory confirmation. We noted features of each
building that could impact roosting suitability for synanthropic
free-tailed bats (building type, roof design, and, where possible,
presence of a ceiling) (Figure 2). We classified modern buildings as
those with walls and flooring built from finished materials (e.g.,
cement and tiles), and traditional as those built from unfinished
materials (e.g., compacted earth). Buildings were considered occupied
if: the building owners confirmed bat occupation, the building had signs
of bat occupation, or bats were seen or heard inside the building. While
multiple bat species can occupy
buildings in this region, synanthropic free-tailed bats are the most
common and are distinctive in their building use (Appendix S1).
Buildings that were occupied by
species other than free-tailed bats were noted, but not included in
analyses of bat building use.
To identify building-level attributes of bat building use, we modelled
the response in building occupation relative to 1) building type (modern
or other), and 2) roof structure (triangular or other). Models were
generalized linear models (GLMs) with maximum likelihood (ML) estimation
and a binomial distribution with a logit-link, fit using the mgcv
package in R. We performed checks of standardised residuals to evaluate
model fit, as per Wood (2017) 44. Note that these
indicators of occupation may reflect past or current occupation by
synanthropic free-tailed bats, but nevertheless provide an indication of
building suitability.
To identify landscape-level attributes of bat-human exposure risk, we
modelled response in the number of occupied buildings per site, relative
to 1) the total number of buildings available, 2) the proportion of
those buildings that were a modern-build style, and 3) the proportion of
those buildings with a triangular roof style. To better reflect the
landscape of active roosts (and therefore, the landscape of human
exposure risk), we ran models on an additional dataset where building
occupation was rated based on the collective weight of evidence
indicating current free-tailed bat occupation; 0=very unlikely;
1-2=possible; 3=likely; 4=very likely; 5=certain (Appendix S1). Highly
weighted evidence for occupation included sighting and/or auditory
confirmation of free-tailed bats by the authors (inclusion into category
5, certain of occupation). Moderately weighted evidence for occupation
included owner confirmation of occupation, and signs of occupation.
Low-weighted evidence included building suitability, as per findings
relating to building-level attributes, described above. Buildings were
considered presently occupied if they were categorised as four or
greater (occupation very likely). All levels of evidence were evaluated
in addition to knowledge on where and how many bats were roosting, to
indicate synanthropic free-tailed bats, as detailed in Appendix S1.
Models were fitted as above, but with a Poisson distribution and log
link.
To provide an empirical estimate on landscape-scale building-roost
density, we calculated density as: 1) the total number of occupied
buildings (occupation category >=4) divided by the total
site area, and 2) the average of fixed-bandwidth kernel estimates,
estimated using the spatstat package in R 45. Kernel
estimates have the advantage of explicitly incorporating the
distribution of buildings into the density estimate, and can therefore
account for spatial heterogeneity in building aggregation46. Kernel values were estimated using roost building
location with Gaussian kernel smoothing and a smoothing bandwidth of 0.347. Bandwidth was selected by comparing projected
kernel density values to expected density values based on building
distances and survey area. Kernel averages were calculated per site
(pixel size = 0.008969 x 0.00896 meters).
Given the specific interest in the transition from traditional- to
modern-style housing, all analyses were repeated on a subset of data
that included traditional- and modern-style houses only, and houses with
triangle and flat roofing only (Appendix S3).