Information or entropy analysis of diversity is used extensively in community ecology, and has recently been exploited for prediction and analysis in molecular ecology and evolution. Information measures belong to a spectrum (or q profile) of measures whose contrasting properties provide a rich summary of diversity, including allelic richness (q = 0), Shannon information (q = 1), and heterozygosity (q = 2). We present the merits of information measures for describing and forecasting molecular variation within and among groups, comparing forecasts with data, and evaluating underlying processes such as dispersal. Importantly, information measures directly link causal processes and divergence outcomes, have straightforward relationship to allele frequency differences (including monotonicity that q = 2 lacks), and show additivity across hierarchical layers such as ecology, behaviour, cellular processes, and nongenetic inheritance.

多样性的信息或熵分析广泛用于群落生态学，最近已被用于分子生态学和进化的预测和分析。信息度量属于一系列度量（或q分布），其对比特性提供了丰富的多样性总结，包括等位基因丰富度 ( q  = 0)、香农信息 ( q  = 1) 和杂合性 ( q = 2)。我们介绍了信息测量的优点，用于描述和预测组内和组间的分子变异，将预测与数据进行比较，以及评估散布等潜在过程。重要的是，信息测量直接将因果过程和分歧结果联系起来，与等位基因频率差异（包括q  = 2 缺乏的单调性）有直接关系，并在生态、行为、细胞过程和非遗传遗传等层次上显示可加性。