We present a population viability analysis for the metapopulation of the Antillean manatee Trichechus manatus manatus with the aim of predicting its tendencies under various hypothetical scenarios of conservation. Multiple individual Monte Carlo simulations of deterministic and stochastic factors were run on VORTEX 9.73 software. Populations were defined using genetic structure, geographic barriers, and typical ranging behavior. Demographic characteristics and life history parameters were inferred from the most recent compilation of information on the subspecies or were extrapolated from the Florida manatee T. m. latirostris. The baseline model describes a metapopulation with a positive growth. This model was sensitive to changes in mortality, but did not show any significant response to variations in assumed carrying capacity, age at first reproduction, maximum reproductive age, or initial population size. We simulated different scenarios by modifying human pressure, habitat fragmentation, and catastrophic events (i.e. hurricanes). Additional combined models were developed to simulate the best- and worst-case scenarios for human pressure level and fragmentation. The model suggested that the metapopulation would not be able to withstand an annual anthropogenically induced mortality rate >5%. A decrease in the survival of transient individuals could also lead to a decline of the population. Variations of the hurricane parameters did not yield important changes in the population curves, but other effects of climatic change are discussed. The extensive geographical area used by manatees requires international collaboration to ensure the protection of the metapopulation through effective conservation strategies across countries. © Inter-Research 2012.
CITATION STYLE
Castelblanco-Martínez, D. N., Nourisson, C., Quintana-Rizzo, E., Padilla-Saldivar, J., & Schmitter-Soto, J. J. (2012). Potential effects of human pressure and habitat fragmentation on population viability of the antillean manatee Trichechus manatus manatus: A predictive model. Endangered Species Research, 18(2), 129–145. https://doi.org/10.3354/esr00439
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