Results
Of the 50 bats captured during this study, 39 were males, five of which
were sub-adults, and only 11 were females, four of which were
reproductive (lactating). All females and sub-adults were caught
exclusively in Juniperus procera or Erica arborea forests.
Only adult males were caught at the higher altitude Afroalpine moorlands
(Supplementary Table S1).
Ecological niche models
Models had very high discrimination ability (AUCtrain=0.997;
AUCtest=0.995 ±0.01). The main variables affecting the probability of
occurrence of P. balensis were maximum temperatures (highest
contribution to model and highest effect on gain when removed),
Ethiopian Afroalpine moorland and Ethiopian montane grassland and
woodland ecoregions, high topographic ruggedness and forest land cover
(Supplementary Table S2). All other variables, including anthropogenic
impact (human footprint), contributed very little to the model
(<0.5%), and were therefore removed. The model projected
suitable areas for P. balensis (i.e. falling above the threshold
that maximises model sensitivity and specificity) across the Ethiopian
Highlands, split into mountain ranges to the north and south of the Rift
Valley, as well as mountain ranges in Eritrea and Somaliland (Fig. 2;
Supplementary Fig. S2). However, only less than 1% of the Horn of
Africa was predicted to be suitable for P. balensis . Climatic
models projected across temporal scales predicted that the climatically
suitable range was 4.5 times larger during the LGM and 3.8 times larger
during the mid-Holocene. Only a quarter of the current range was
projected to remain suitable by the end of the century, equating to
5.6% of the LGM suitable range (based on the more severe RCP 8.5
scenario; Fig. 2; Supplementary Fig. S2; Table 1).
Genetic composition
We identified 32 unique cytb haplotypes and 34 unique HVI haplotypes
(Genbank Accession numbers to be added after acceptance). Each mountain
range had unique haplotypes and no haplotypes were shared between
mountain ranges. Overall, the Bayesian phylogenetic tree and haplotype
network showed a strong effect of mountain ranges on genetic population
structure and supported a split between haplotypes to the north-west and
south-east of the Rift Valley. The main divergence was identified
between haplotypes from Bale [Harenna Forest and Sanetti Plateau
(Bale-S)] and the remaining haplotypes, followed by the divergence of
Simien haplotypes (Fig. 3b). Population structure analysis of the
microsatellite dataset split individuals into two clusters (K=2), north
and south of the Rift Valley (Supplementary Fig. S3-S4). There were no
further splits within each cluster. There was some evidence of allele
sharing between both sides of the Rift Valley (Fig. 3c).
FST and Jost’s D values were strongly correlated (MRDM:
R2=0.684, P=0.018). In both cases, values confirmed
the separation between populations north and south of the Rift Valley,
with low values between the two Bale populations south of the valley and
highest values between populations on either side of the Rift Valley
(Supplementary Table S5).
Based on the mtDNA dataset, the Bale-S population had highest nucleotide
diversity, substantially higher than the rest of the populations. The
Simien population had lowest nucleotide diversity, but the highest
haplotype diversity, though differences in haplotype diversity between
populations were negligible (Table 2). Based on the microsatellite
dataset, all populations had relatively high and similar levels of
genetic diversity, with a particularly high number of private alleles
identified in the Guassa population. Abune Yosef and Bale-S had the
highest levels of inbreeding, while Simien and Guassa had the lowest,
though all values were low (Table 2).
Allelic richness corrected for sample size decreased as the proportion
of arable land in 5 km radius around the population capture location
increased (F=10.41, df=1,3, P=0.048, R2=0.717;
Supplementary Fig. S5). Levels of inbreeding did not relate to any of
the land-use variables (Supplementary Table S6).
Landscape barriers to gene
flow
The topographic hypothesis (effect of altitude) had the strongest
support (AICcmin=0.501, BICew=0.475), followed by the ecoregions
hypothesis (AICcmin=0.412, BICew=0.303). Confidence intervals of both
variables did not overlap zero, supporting their effect on gene flow
(Table 3). The exact same hypotheses received the strongest support
based on Jost’s D measure of genetic differentiation, though the
ecoregions hypothesis received the highest support (Supplementary Table
S7). Projected movement density maps based on the effect of these two
landscape variables highlight the strong effect of the Rift Valley on
genetic connectivity in P. balensis and the split between
populations to the north and south of the valley (Fig. 4). The remaining
hypotheses had very low support. Hypotheses that included multiple
variables had little support, likely due to the small number of nodes
(Table 3).
Demographic history
The best-supported scenario was of a recent approximately six-fold
decline of the south-eastern population but no change in the
north-western population (scenario 4; probability of scenario 0.988,
overall model error < 0.001, type 1 error = 0.095, type 2
error averaged over scenarios (± standard deviation) = 0.046 ±0.04;
Supplementary Fig. S6; Table S8). Model checking for the most probably
scenario (scenario 4) indicated a good fit between observed and
simulated datasets, whereby the observed dataset fell within the cloud
of simulated points (Supplementary Fig. S7). Rift valley split time was
estimated at a median of 89,600 years ago (95% CI: 35,000-155,400),
based on a generation time of two years, while the decline of the
south-eastern population was estimated to have occurred at a median of
150 years ago (95% CI: 30-1000). Bias in parameter estimation was low
(mean relative bias 0.18-0.26 for population size parameters, and 0.29
for time of decline of southern population; Table 4).