A multiple data source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy

摘要: When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical theoretical evaluation multiple data source as a flexible effective way to improve the performance models for estimation populations. integrated three sources estimate size relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, supported our results simulations assess robustness violations main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on grid 41 5 × km cells, moving trap locations between five sessions. also live-trapped, ear-tagged, genotyped 17 (2004–2008), resights marked family units (July–September 2008) into dataset. Population was estimated at 40 (95% CI = 37–52) bears, corresponding closure-corrected density 32 bears/1000 km2 28–36). Given average capture probability obtained all combined (pˆ=0.311), suggested that expected degree correlation among did not seriously affect model performance, level bias <5%. Our refine previous simulation work larger populations, cautioning effect lack independence low application very (N < 100).