Estimating immigration in neutral communities : theoretical and practical insights into the sampling properties

1. Widening applications of neutral models of communities necessitates mastering the process of inferring parameters from species composition data. In a previous paper, we introduced the novel conditional GST(k) statistic based on community composition. We showed that it is a reliable basis for assessing migrant fluxes into local communities under a generalized version of the spatially implicit neutral model of SP Hubbell, which can accommodate non-neutral patterns at scales broader than the communities. 2. We provide here new insights into the sampling properties of the GST(k) statistic and on the derived immigration number, I(k). The analytical formulas for bias and variance are useful to assess estimation accuracy and investigate the variation of I(k) across communities. 3. Immigration estimation is asymptotically unbiased as sample size increases. We confirm the validity of our analytical results on the basis of simulated neutral communities. 4. We also underline the potential of using I(k) as a descriptive index of community isolation, without reference to any model of community dynamics. 5. We further propose a practical application of the bias and variance analysis for defining sampling designs for immigration quantification by efficiently balancing the number and size of community samples