ABSTRACT: Population viability analysis (PVA) is a predictive procedure that uses different modeling approaches to estimate species vulnerability to extinction. Using the stochastic modeling software VORTEX, we assessed the status of proboscis monkeys Nasalis larvatus in 3 fully or partially protected areas in Borneo, 1 in the Malaysian state of Sabah and 2 in the Indonesian provinces of Kalimantan. Species-specific life-history parameters were used when possible, and missing parameters were taken from other populations or species. We tested the sensitivity of final predicted population size to 20% variation around the possibly inaccurate parameter estimates. The model we used predicts that in the absence of any management, the Malaysian population will remain fairly stable, whilst the 2 Indonesian populations will decrease by more than half, the smallest going effectively extinct in 30 yr. We investigated whether management strategies, such as reforestation, corridors to reconnect sub-populations, harvesting, reduction of deforestation and controlling fires, might help forestall population extirpation at each site. Fire had the greatest impact on the Indonesian populations, although hunting is also likely to play an important role. Remedial action to reduce the frequency and extent of fires, perhaps by regulated land clearing for agriculture, should slow proboscis monkey population decline and provide more time to collect the data necessary to evaluate other management decisions. As information regarding mortality, the effect of fire, and hunting rates on proboscis monkeys is limited, further research should concentrate on these areas to improve PVAs for this species.
KEY WORDS: Population modeling · VORTEX · Extinction · Deforestation · Borneo · Kinabatangan · Kalimantan
Full text in pdf format | Cite this article as: Stark DJ, Nijman V, Lhota S, Robins JG, Goossens B
(2012) Modeling population viability of local proboscis monkey Nasalis larvatus populations: conservation implications. Endang Species Res 16:31-43. https://doi.org/10.3354/esr00385
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