4.3 Article

Assessing reintroduction success in long-lived primates through population viability analysis: western lowland gorillas Gorilla gorilla gorilla in Central Africa

期刊

ORYX
卷 48, 期 2, 页码 294-303

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0030605312001391

关键词

Bateke Plateau; Congo; Gabon; Gorilla gorilla gorilla; population model; post-release monitoring; rehabilitation; reintroduction management

资金

  1. Ministry of Forest Economy of the government of Congo
  2. Ministry of Water and Forests and the National Agency for National Parks of the government of Gabon
  3. Aspinall Foundation, UK

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The use of population modelling has become an increasingly common tool in reintroduction planning and assessment. Although initial reintroduction success is often measured by quantifying post-release survival and reproduction, longer-term success is best assessed through measurements of population viability. Here we develop a population model capable of providing useful results for influencing management of a reintroduction programme for a long-lived and slow-reproducing primate, the western lowland gorilla Gorilla gorilla gorilla. We used post-release monitoring data from two reintroduced populations in the Bateke Plateau region of Congo and Gabon, complemented with published data on wild and captive populations, to develop a population model using Vortex. Sensitivity testing illustrated that the model was highly sensitive to changes in the input parameters for annual birth rates, the number of lethal equivalents, and for female annual mortality rates, especially for adults. The results of the population viability analysis suggested that the reintroduced gorilla populations have a reasonable chance of persistence (>90% over 200 years) but illustrated that reinforcement of the populations could significantly improve probabilities of population persistence and retention of genetic diversity. Equally, catastrophic events could have significant negative impacts. Continued monitoring of the populations should allow refinement of the model, improving confidence in its predictions and its relevance to decision-making.

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