Tracking Ocean Wanders (PDF, 5 MB) - BirdLife International

Tracking ocean wanderers: the global distribution of albatrosses and petrels – Methods
2.2.2 Deriving density distribution maps
In order to identify areas which are highly utilised by
albatrosses and petrels, some indication of density needs to
be derived from the PTT tracking data. However the
sampling regime of these data is dependent on several
factors: the speed at which the bird is travelling, its latitude
(Georges et al. 1997), and the performance of the device
itself. In order to provide a more regular sampling regime it
was assumed that a bird flew in a straight line at constant
velocity between two successive uplinks, where these uplinks
were less than 24 hours apart. The path of the bird was then
resampled at hourly intervals, any remaining time being
added to the first segment of the next path between
successive uplinks. If the interval between uplinks was more
than 24 hours, no assumptions were made about the bird’s
behaviour and these paths were not resampled. In this way
devices with long duty cycles were also catered for as no
assumptions were made about the bird’s location during
‘OFF’ cycles. The resampling method also ensured that each
trip was weighted by its duration when calculating density
distributions. The process produced locations for the
individual at hourly intervals, and thus density distribution
maps derived from these locations were indicative of time
spent (‘bird hours’) in a particular area.
Albatrosses and petrels are central place foragers when
breeding, so in any density distribution map the uplinks around
the colony could potentially outweigh any more distant
foraging area. If the commuting points to and from foraging
areas are removed, this high density around the colony should
be reduced. However this requires making assumptions about
the bird’s activity based solely on the tracking data. In addition,
commuting birds could still be at risk from fisheries interactions
if they encounter a fishing vessel and stop to forage. To assess
the effect of excluding commuting data, foraging points for
Wandering Albatrosses were assumed to be those resampled
points occurring between sunrise and sunset where the
velocity was less than 20 km/hr. By excluding all other
points, a density distribution of foraging locations was
produced. This did not noticeably reduce the density around
the colony, and there was little difference in areas of importance
(Figure 2.2). Therefore foraging and commuting points
were not separated out in the final maps. It is thus recognised
that not all ‘hot spots’ identified by the kernel analysis will
be foraging areas, but they still represent areas of risk.
Kernel density estimators have been successfully used in
several tracking studies to quantify habitat use and identify
home ranges (e.g. Wood et al. 2000). The single most
important step when using these estimators is the selection
of the smoothing (or h) parameter. This can greatly
influence the home range size and can also highlight or
smooth over areas of high density (Annex 3), so it is
necessary to explicitly report the methods used to ensure
transparency and objectivity. Care needs to be taken when
comparing different datasets, but current experience and
practice is encouraging (e.g. Matthiopoulos 2003).
As this study does not attempt to estimate range sizes,
aiming instead to identify core areas of utilisation for
conservation manage-ment, the selection of h was done by
identifying the smallest practical unit of management on
the high seas. For present purposes the workshop
participants agreed this to be a 1 degree grid square.
Although the use of 1 degree as a smoothing parameter
means the shape of the kernel will vary with changes in
latitude, it was agreed that the effects of this would be small
in relation to the scales at which the data would be
presented, and that this latitude-related distortion is widely
understood.
Kernel density distribution maps were derived using the
kernel function in ArcGIS 8.2. The grid size was set at onetenth
of the value of h i.e. 0.1 of a degree. If sample sizes
were sufficiently large, separate kernel density distribution
maps were produced for birds of different ages (juvenile,
adult), breeding status, sex and breeding stage.
2.2.3 Combining density grids (weighting)
The density grids derived by kernel analysis of the
resampled PTT locations for each part of the population
were adjusted to reflect an index of ‘seabird at sea hours’ as
follows: the kernel density estimate of each cell was divided
by the number of resampled PTT locations for the dataset,
and then weighted by the number of individuals at sea for
that particular colony and breeding status/stage (e.g. Figure
Figure 2.2. Kernel density distribution contours of breeding Wandering Albatrosses tracked from Iles Crozet, showing density contours using
all locations (A) and foraging locations only (B).
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