Ricerc@Sapienza

Modelling landscape connectivity represents one of the central challenges for conservation of natural resources,
especially in human dominated landscapes. Many different methods have been developed to this effect, but their
assumptions and limitations have been largely ignored. Using high resolution GPS tracking data from brown bears
(Ursus arctos) in central Italy, we investigated the influence of behavioural state (movement vs other behaviours), sex,
and algorithms, namely least cost path and circuit theory, on the identification of structural corridors. In particular,
considering that most studies do not account for behavioural states and/or individual characteristics, and that basically
all studies consider only a single corridor algorithm, we performed (1) a within-algorithm comparison, under the
hypothesis that both behavioural states and sex would influence prediction of structural corridors, and (2) a betweenalgorithm
comparison, under the hypothesis that different algorithms would predict different corridors. We found that
the impact of sex and/or behavioural state was substantial. On average, least cost path corridors for moving females
were 4.7 km apart (st.dev = 7.6 km) from corridors for moving males, and 5.0 km apart (st.dev = 7.2 km) from corridors
not considering sex and behaviour. The same was true for circuit theory corridors. The between-algorithm comparison
showed that the two corridor models yielded almost identical results, with >80% of the least cost path corridors falling
into the two top deciles for the corresponding circuit theory corridors. Our results suggest that the failure to consider an
animal’s behavioural state and/or sex may result in misidentification of corridors, with potential misallocation of the
limited conservation resources available.