Alex E. Yuan

This study shows that methodological choices in correlation tests for ecological time series can lead to spurious results or missed associations.

The paper develops a quantitative genetics framework to analyze selection response in microbial communities, showing heritability alone cannot predict success due to nonlinearities and evolutionary dynamics.

The paper introduces permute-match tests to detect significant correlations between time series with nonstationarity and limited replicates, showing improved p-value bounds over traditional permutation tests.

The paper introduces a nonparametric statistical test for detecting correlations between time series, showing it outperforms existing methods in controlling false positives and detecting true relationships.

The paper reviews three causal inference methods for ecological time series and highlights their limitations and assumptions.