Introduction
Variation in home range size has long been a subject of ecological interest. Interspecific
variation in home range size has been related to body-size-dependent metabolic requirements
(McNab 1963), declining rates of utilisable energy in the environments with increaing body
weight (Harestad & Bunnell 1979), species overlap (Damuth 1981), biological time
(Lindstedt, Miller & Buskirk 1986) and rate of home range use (Swihart, Slade & Bergstrom
1988)
Intraspecific variation in home range size is less well understood. Minimum home range size
is a function of habitat productivity and resource distribution, as well as individual energy
requirements (Harestad & Bunnell 1979), so home range size is one indicator of habitat
quality (Tufto, Andersen & Linnell 1996). Range characteristics can also indicate the quality
of an animal. Previous studies have found that range size declines with increased food
availability in mammals (e.g. Boutin 1984, Kenward 1985, Wauters and Dondt 1992, Tew
1992, Wiegand 1995, Lucherini and Lovari 1996, Tufto et al 1996, Corp et al 1997, Hubbs
and Boostra 1998) and birds (Kenward 1982, Newton 1986, Johnstone 1992, Babcock 1995,
Zabel et al 1995, Marzluff et al. 1997). Therefore, the quality of an individual may be related
to its home range size. Moreover, range size is inversely related to survival, weight and
breeding success in squirrels (Kenward 1985, Wauters and Dhondt 1992). This adds credence
to the assumption that better individuals exploit denser or richer food sources and
consequently should have smaller home ranges. Smaller ranges therefore indicate that either
the habitat was better, and/or that the individuals was of higher quality (Kenward 1982, 1992,
1996,. Boutin 1984, Newton 1986, Fridell and Livaitis 1991, Johnstone 1992, Tew 1992,
Wauters and Dondt 1992, Babcock 1995, Wiegand 1995 Zabel et al 1995, Lucherini and
Lovari 1996, Tufto et al 1996, Marzluff et al. 1997).
In certain territorial birds, population density and home range size are inversely correlated,
and the latter seems to be affected by food supply (Krebs 1971, Simon 1975)
In this paper, we test the predictions that: (i) home range size increases with habitat
disturbance (ii) home range size decrease with increase in food availability (iii) better
individuals have smaller home ranges (iv) home ranges sizes are inversely proportional to
population density
Methods
Study area and species
This study was carried out in the tropical montane cloud forests of Taita Hills (03 20’S, 38
15’E and maximum altitude 2209m), south eastern Kenya. The hills are 150 km inland from
the Indian Ocean and are isolated from other highland blocks by over 80 km of semi-arid
plains in any direction (Lovett 1985). The forests are included in an endemic bird area with
the Eastern Arc Mountains of Tanzania (Stattersfield et al 1998), a global biodiversity hotspot
(Myers 1990, Mittermeier et al.1998). At present, less than 400 ha of indigenous cloud forest
is retained in the three larger fragments (Chawia (CH,50ha), Ngangao (NG, 92ha) and
Mbololo (MB,220ha)) and nine tiny remnants (2-4 and one of 12 ha) (Brooks et al; 1998;
Lens et al 1999). Indigenous forest loss since the early 1960s has been very substantial and is
estimated at 85% for CH, 50% for NG, <50% for MB and 95-99% for some of the smallest
remnants (Beentje 1987). The three forest fragments differ considerably in quality (Wilder et
al. 1999), where the least disturbed fragment (Mbololo, 200 ha) has more biomass, higher
equitability, greater canopy cover, more open shrub layer, higher leaf litter cover and less
herbaceous cover than the intermediate (Ngangao, 92 ha) and most disturbed ones (Chawia,
50 ha). The mutual distances between the three main fragments are NG-MB 11.3km, CH-MB
19.4 km and CH-NG 10.9 km.
Breeding subpopulations of the Taita Thrush are only found in these three larger fragments
where it is known to have survived (Brooks et al. 1998, Lens et al. 1998, Lens et al. 1999a),
although several individuals have consistently been recorded in Yale, a tiny well conserved
forest remnant 2.5 km from Ngangao. As a result of its limited range and small population
size, this specialised ground dwelling forest species (Zimmerman et al. 1996, Waiyaki in
prep) that is endemic to this highly fragmented and threatened forests of Taita Hills (Brooks et
al 1998, Lens et al 1998, Lens et al 1999a) is classified as critically endangered (Collar et al.
1994). The three subpopulations have genetically differentiated (Galbusera et al. 2000), vary
in sex ratios (Lens et al. 1998) and, levels of fluctuating asymmetry – FA (Lens et al 1999), a
measure of developmental stability) that increase with environmental and/or genetic stress
(Palmer & Strobeck 1986, but see e.g. Fowler & Whitlock 1994).
Trapping and handling
From July 1997 until September 1999, 49 adults were caught with mist nets (6 in Chawia, 25
in Ngangao and 18 in Mbololo) and equipped with backpack radio transmitters fitted with an
elastic harness (Biotrack,Wareham, Dorset, UK). All individuals were also weighed and the
wing and tarsus lengths measured. Additionally, birds were individually marked with
numbered aluminium bands and a unique colour combination of plastic bands. Individuals
who dropped radio tags could hence be identified on recapture and, estimation of distances
when tracking could be verified by observation and reading of colour bands especially at the
beginning. The transmitters did not appear to affect the flight or behaviour patterns of the
birds, and 95 % of the birds were recaptured in normal state within a week or longer after.
All transmitter were recovered within two months of fitting.
Measurement of condition
Individuals with a larger body size will inherently be heavier than their smaller bodied
counterparts. Since wing and tarsus lengths are highly correlated, and since wing length may
have large measurement error depending with the stage of moult, we scaled for weight by
regressing tarsus length on body weight, and used the residual weight as an indicator of
individuals condition.
Plot Habitat quality measurement
Habitat structure data were recorded in 10-m radius circular plots, based on a compass
oriented 25-m grid of the 9-ha plot. Structure of the habitat was assessed by quantifying
height, cover and density of structural elements within the forest layers (canopy, undergrowth
and ground). Within each 10-m circular sampling plot, counts were made of the number of
small (5 cm – 10 cm DBH), medium (11 cm – 30 cm) and large (>30 cm) trees. We counted
the number of dead fallen logs > 10 cm and also counted the number of cut stems in the 5-10
cm and > 10 cm categories. Estimates were made of the percentage cover at shrub, mid-storey
and high canopy layers. Two measures of shrub density were used: (a) half-sighting distances
were measured at 1m and 2m height on compass bearings directly north, south, east and west,
by measuring the distance from the centre point to where half of the 16 squares on the 40 cm
x 40 cm chequered board held by the second observer while walking away will be visible
(average of the four measures taken), (b) surface area of a continuous stand of shrub layer (if
> 2 stands occurred, areas were summed). We chose four points in a stratified random manner
(one on each direction – north west, north-east, south-west and south east) at which to
establish four 1m x 1m quadrats to estimate ground cover. In these quadrats we estimated the
percentage cover of leaf litter, herbaceous growth and also measured litter depth. We then
averaged the four scores to obtain a single value.
Habitat structure measurements produced sets of 17 variables. We then reduced the number of
variables by; (a) summing up variables that measured the same structural feature e.g. all
categories of live trees were added up and, (b) selecting variables that were known and or
suspected (from the literature) to be important to the thrush. Because the number of variables
was still high (posing the danger of Type 1 error) and many of the variables were highly
correlated, we used principal component analysis (PCA) to simplify the structure of the data
(STATS programme). The PCA was performed with varimax rotation and we selected two
axes with eigenvalues >1 (Pearson 1993), and which accounted for more than two-thirds of
the total variance (Table ). We then averaged the new variables per plot and used their means
to plot the 7 forest plots. The extent to which the original variables were correlated with the
components (factors loadings)) was used to interpret each axis.
Estimation of Invertebrates abundance
In exactly the same habitat structure sampling plots, we chose four points in a stratefied
random manner (one on each direction – north west, north-east, south-west and south east) at
which to establish four 1ft X Ift plots. In these quadrats we removed leaf litter (simulating the
thrushes) by placing it on the side, till we reached bare ground. We counted all invertebrates
larger than 3 cm in size encountered during litter removal and those that were on or even
slightly below the ground. We then averaged the scores to obtain a single value for each
variable for the sampling point.
The new vegetation variables and estimates of invertebrate abundance were then linked to a
Geographical Information System (GIS), and for each territory, corresponding values
calculated by over-laying territorial boundaries.
Radio-tracking
Of each radio-tagged thrush, two to three point-fixes were taken per day, with a minimum
time interval of 2h between fixes. Between 30 and 40 fixes were taken for each individual.
This number is chosen because a plot of home range size against the number of fixes reach an
asymptomic value at about 30 fixes. Only when these minima were equalled or exceeded was
a bird included in the analysis. The location of a thrush was recorded using west – east and
north – south coordinates with an accuracy of 5 m. All tracking was done betwen 0600 am
and 0600 pm.
Diurnal variation in activity is well known in birds (
). To represent all times of the day,
tracking was split into three equal sessions representing morning , afternoon and evening.
Estimation of Population Density
The population density was estimated using capture-recapture techniques (
)
As birds were caught by use of mist-nets, which are unbiased ( ), the assumption of equal
probability of recapturing marked and capturing unmarked birds should be held. Also,
because only the relative population density is of interest in this study, some bias in the
estimates can be tolerated..
Home range size analysis
All data on home range size were calculated using the RANGES V program (Kenward 1996
). For each thrush, a utilization distribution was calculated that allowed us to determine
wheather it had a mono- or multinuclear range (Kenward & Hodder 1995) and also to
determine area of regular use from excursions. Since they had multinuclear ranges,
multinuclear clustering (Kenward 1990) was used to calculate home range size. Area of
regular use was defined by 80 % of number of locations.