How fishing cats Prionailurus viverrinus Bennett, 1833 fish: describing a felid’s strategy to hunt aquatic prey
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modified on 2021-12-26, 13:54 Abstract: The fishing cat’s persistence in a ‘semi-aquatic
niche’ suggests the evolution of a successful hunting
strategy. We describe it for the first time by analysing 197
camera-trap video-clips, collected from a participatoryscience initiative, within an ethogram framework.
The cats
spent ∼52% of the time sitting and waiting for prey (fishes)
to come nearer and took limited attempts to hunt (3.89%) in
deeper waters (in which the upper portions of the cat’s body
were submerged), where its hunting success was found to
be 42.86%. In shallow waters, it adopted a predominantly
active mode of hunting (∼96%) to flush out prey.
Study area, data collection, analysis: Our study area consists of three sites, including the Howrah
district (22°27′40.66″N, 88°0′32.71″E) in the lower Gangetic
floodplains of West Bengal and two coastal wetlands in the
Mahanadi floodplains along the eastern coast – Paradip
(20°18′50.63″N, 86°36′45.49″E) and Chilika (Figure 1). All
are human-dominated and/or human-used landscapes.
We initiated a participatory-science initiative, ‘Know
Thy Neighbour’, in which we used 20 camera traps (14
Covert Illuminator 9 and 6 Browning IR) at each site opportunistically over 2.5 years, starting from November
2016. We consulted local people and as suggested by them
we searched for probable sites to place camera traps along
shallow ponds or water channels by locating scats and/or
tracks of felids. The cameras were set on video mode and
were active from 5:30 p.m.–6:00 a.m. Each trained volunteer picked up traps in the early morning and repositioned
them at the same location at night to reduce chances of
misplacement/theft. We assumed that we would not fail to
miss fishing cat activity when camera traps were nonactive, since fishing cats are primarily nocturnal (Hunter
2019) and villagers mostly reported seeing them after dark.
We collected over 200 camera-trap video recordings
from the participatory-citizen-science initiative of which 197
videos were selected to analyse hunting behaviours of
fishing cats. All videos were analysed by the first author to
avoid introducing observer bias. Consecutive videos from
the same camera trap on the same night, which were taken
less than a minute apart, were considered as single data, as
we assumed lack of independence between closely occurring activities so that we could minimise bias towards individual behavioural tendencies in our analysis. ‘Behaviour
States’ were defined as behavioural patterns of relatively
long duration (Martin and Bateson 1993). These were further
divided into ‘behaviour sub-states’. Behavioural patterns
of relatively short duration were classified as ‘Behaviour Events’ (Martin and Bateson 1993). We calculated the proportion of time spent performing each State and the frequency of each Event using an ethogram framework. If two
separate individuals were detected in the videos, these were
considered independent behavioural events and analysed
as two different samples.
To examine activity patterns, videos (n = 98) were
selected, which were recorded at least 30 min apart to ensure
independence. These videos were then used to generate line
graphs to understand peak activity patterns of fishing cats.
In total, we analysed 134.43 min from 197 camera trap
video footages constituting 198 data points from all three
study sites combined; 77.48 min of video footage from
Howrah, 15.95 min of video footage from Paradip and
41 min of video footage from Chilika.
