Time partitioning in pine and stone marten from the carnic pre-alps (NE Italy)

Fonda, F., Torretta, E., Balestrieri, A., Pavanello, M., 2017.

Abstract:

The pine marten (Martes martes) and the stone marten (M. foina) are the most similar sympatric carnivores in Europe (Larroque et al. 2015). Recently, they have been reported to coexist without spatial segregation thanks to slight differences in activity patterns (Monterroso et al. 2016), while cathemerality has been reported to enhance pine marten’s competitive ability with respect to the stone marten (Torretta et al. 2017). We investigated by camera-trapping the temporal activity patterns of the two martens in the Carnic Pre-Alps (NE Italy). The survey was carried out in 2015-2016, within a 1 x 1 km grid. We used 34 camera traps, one per cell. Within each cell, whenever possible camera traps were positioned in areas with dense forest cover and rock cavities, which were assumed to be preferred by both martens. All photos and videos of martens were subjected to a blind identification procedure by three of us. Diel activity patterns were estimated non-parametrically through the probability density function using Kernel Density Estimate and we tested the distribution uniformity using Watson’s test (U2). The overlap in the probability density functions of the two species was tested by Watson’s two- sample test. The pine marten was recorded 14 times at 5 sites, while the stone marten occurred in 12 sites, for a total of 19 videoclips. The first showed a non-uniform activity pattern (Wt = 0.34, P<0.01), being mainly nocturnal (79% of videoclips). The pine marten showed a cathemeral pattern (Wt = 0.084, P>0.1), i.e. was active during hours of both daylight (64% of videoclips) and darkness. The two patterns differed significantly (W2t = 0.23, P<0.05), suggesting time niche partitioning. Being potentially active within a broader period, cathemeral species may be more ecologically flexible than strictly nocturnal species and therefore able to adjust their activity patterns to local conditions.

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