Wildlife Institute of India, Dehradun, India.
Sci Rep. 2019 Aug 12;9(1):11627. doi: 10.1038/s41598-019-48193-2.
Great Indian Bustard (GIB) is listed as Critically Endangered, with less than 250 individuals surviving in three fragmented populations. The species is under tremendous threat due to various anthropogenic pressures. Effective management and conservation of GIB requires a proper monitoring protocol, which we propose using an occupancy framework approach to detect changes in the species' population. We used occupancy estimates from various landscape level surveys and simulated scenarios to evaluate the effectiveness of the proposed protocol. Our result showed there is >70% chance of detecting 100% change in the occupancy with 100 sampling sites and 10 temporal replicates. While with double sampling sites, the same change can be detected with 4-6 temporal replicates. In absence of a robust population estimation method, we argue for the use of occupancy as a surrogate to detect change in population as it provides better insights for rare elusive species such as GIB. Our proposed methodological framework is more precise than previous methods, which will help in evaluating efficacy of management interventions proposed and the implementation of species recovery plans.
大鸨（Great Indian Bustard，GIB）被列为极危物种，在三个碎片化的种群中幸存的个体数量不足 250 只。由于各种人为压力，该物种正面临巨大威胁。要对大鸨进行有效管理和保护，需要制定一个适当的监测方案，我们建议使用占有框架方法来检测物种种群的变化。我们使用了来自不同景观水平调查和模拟情景的占有估计值来评估拟议方案的有效性。我们的结果表明，在 100 个采样点和 10 个时间重复的情况下，有超过 70%的机会检测到占有变化 100%。而在采样点加倍的情况下，同样的变化可以通过 4-6 个时间重复来检测到。在缺乏稳健的种群估计方法的情况下，我们认为可以使用占有作为替代物来检测种群变化，因为它为像大鸨这样稀有而难以捉摸的物种提供了更好的了解。我们提出的方法框架比以前的方法更精确，这将有助于评估拟议的管理干预措施的效果和实施物种恢复计划。
