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echolocation (Francis 1989). We therefore analyzed results for all bats and for a sub-
set of fruit bats. To further deduct patterns in the distribution of functional groups of
bat species in relation to landscape and habitat characteristics we used subsets of bat
species that primarily depend on forest habitat (forest bats), and bat species that roost
in caves (cave bats) (Heaney et al. 1998). Similar to birds, Philippine endemic bats
(Heaney et al. 1998) and globally threatened bats (IUCN 2006) were distinguished.
16.2.3.3
Landscape and Habitat Characterization Variables
In order to correlate observed bird and bat species richness and abundance with
landscape and structural habitat characteristics we determined two sets of variables.
Landscape variables were determined for the survey locality at large, including the
number of households in the nearest settlement, Euclidian (straight) distance from
the centre of the locality to contiguous forest of the Sierra Madre, Euclidian dis-
tance from the centre of the locality to the nearest cave known to have roosting bats
(Van der Lans 2005) and the size of Gmelina forests. Euclidian distances were cal-
culated using a GIS program and GPS-derived locality data. Gmelina forest size
was determined in the field using GPS.
Habitat characterization variables were determined using variable-sized plots
surrounding point count and mist-net locations. Plot sizes varied from 10 × 10 m to
100 × 100 m. with most plots measuring 20 × 20 m. Different plot sizes were used
to enable visual estimations of habitat characterizations from the centre of the plot.
Plots in forest were smaller than plots in open areas. Plot size was used to standard-
ize several variables to figures per ha. The study included a total of 64 habitat char-
acterization plots for which the following variables were determined.
Canopy cover was estimated as total percentage cover from the observer's height
upwards. No layers were distinguished. Ground cover was estimated as the percent-
age grass and herb cover. The height of the tallest tree within a characterization plot
was determined using an inclinometer. The number of trees with a diameter at
breast height (dbh) of more than 1 cm was counted. As a subset, the number of trees
with a dbh over 20 cm (“large trees”) was counted as well. These two variables were
standardized to number of trees/ha. The number of houses within a radius of 100 m
surrounding the point count or centre of the mist-net locality was counted.
16.2.4 Statistical Analysis
Heterogeneity in species detectability (Boulinier et al. 1998) and the practical
impossibility of infinite biodiversity surveys (Gotelli and Colwell 2001) lead to
incompleteness and variability of species richness assessments. We used the
computer package EstimateS (version 7.5, Colwell 2005) to calculate smoothed
sample-based species accumulation curves (Mao Tao, 100 randomized runs) from
point count results (birds: each point count is one sample) and mist-net results
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