Abstract
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.
Highlights
Great Indian Bustard (GIB), India’s heaviest flying bird, was once abundant in dry grasslands of India
The fragmented populations of GIBs were once distributed throughout the grasslands of northern India and the Deccan landscape in 11 states[23], but are confined to few fragmented populations[4]
GIBs depend on India’s semi-arid open landscape, which is rapidly being converted into agricultural lands and a significant loss in grassland habitats has been observed due to various anthropogenic factors[24]
Summary
Great Indian Bustard (GIB), India’s heaviest flying bird, was once abundant in dry grasslands of India. Owing to the nomadic behaviour, long-life span, isolated population, rarity and long-ranging behavior[12] of the GIB, it is very difficult to infer population trends using existing survey protocol and the reasons therein to serve as a rationale for conservation actions Species with such natural history characteristics create unique challenges for both the design and implementation of population estimation surveys[13]. Occupancy data are considered more effective for the study of rare species where abundance estimation is logistically and financially challenging[14,19] Under such circumstances, the need is to design a survey protocol which is logistically viable and able to detect change in number or occupancy within a given time frame. In order to detect any change in GIB occupancy, we simulated different combinations of number of replicates and sites to reach the “acceptable” statistical power (typically 0.8) with a robust and feasible sampling design
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