Advances in wildlife abundance estimation using pedigree reconstruction

Area of Science:

• Wildlife ecology and conservation
• Population genetics and genomics
• Ecological modeling and simulation

Background:

• Accurate wildlife population metrics (abundance, growth rate, density) are crucial for conservation, especially for threatened and endangered species.
• Traditional abundance estimation methods involve trade-offs between effort and precision.
• Pedigree reconstruction is an emerging, noninvasive technique using genetic data to infer population characteristics.

Purpose of the Study:

• To evaluate the utility of pedigree reconstruction for wildlife population estimation.
• To assess the impact of sampling effort, spatial scale, and population density on the accuracy of pedigree reconstruction.
• To develop methods for quantifying the precision of pedigree reconstruction estimates.

Main Methods:

• Utilized a stochastic, spatially explicit simulation of a moose (Alces americanus) population with established relationships.
• Applied pedigree reconstruction to infer population size, trend, and density from simulated genetic sampling data.
• Developed and tested novel bootstrapped confidence intervals for estimate precision.

Main Results:

• Pedigree reconstruction accurately estimated adult moose population size and trend.
• Population estimates reflected the total extant population, not just the sampled area.
• Increased sampling effort and higher animal density required greater sampling effort for unbiased estimates.

Conclusions:

• Pedigree reconstruction is a viable and accurate method for estimating wildlife abundance and trends.
• The method shows particular promise for surveying smaller areas and populations with lower densities.
• Further research can refine precision measures and optimize sampling strategies for pedigree reconstruction.