Assessing alternative methods of using population genomic data to measure changes in population size

Malaria remains a major global health burden, with traditional control methods facing challenges such as insecticide resistance and high operational costs. Genetic biocontrol offers a promising alternative for mosquito population suppression, but its field efficacy would require assessment. This study evaluates the role that population genomic statistics can play in detecting decreases in population size in the context of a cluster randomized control trial (cRCT), investigating the response of nucleotide diversity ( π ), Tajima’s D, segregating sites, and linkage disequilibrium (LD) under both constant and seasonal demographic scenarios. We simulated 90% and 99% population declines with various degrees of between-cluster heterogeneity, and assessed the detection power of each statistic over time and number of clusters per arm. Results show that Tajima’s D is highly sensitive and robust across crash severity, seasonality and heterogeneity scenarios. Segregating sites has similar power to Tajima’s D when baseline data are available. We further estimated that cRCTs require approximately 3 to 5 villages per treatment arm to achieve adequate statistical power. These findings provide recommendations for genetic monitoring of vector control interventions in wild populations.