Abstract. Alternative community analyses, based on quantitative and presence/absence data, are comparable logically if the data type is the only factor responsible for differences among results. For presence/absence indices that consider mutual absences, no quantitative alternatives are known. To facilitate such comparisons, a new family of similarity coefficients is proposed for abundance data. Formally, this extension is achieved by generalizing the four cells of the usual 2 × 2 contingency table to the quantitative case. This implies an expanded meaning of absence: for a given species at a given site it is understood as the difference between the actual value and the maximum detected in the entire study. The correspondence between 10 presence/absence coefficients and their quantitative counterparts is evaluated by graphical comparisons based on artificial data. The behaviour of the new functions is also examined using field data representing post‐fire regeneration processes in grasslands and a chronosequence pertaining to forest regeneration after clear‐cut. The examples suggest that the new coefficients are most informative for data sets with low beta‐diversity and temporal background changes.

Since 1969, ten soil seed bank classification systems have been published. Among these systems, the number of recognized seed bank categories varies from three to twelve. Seed longevity is the main factor used for distinguishing categories, but dormancy and germination types are also important. Systems considering relatively few seed bank categories have been the most commonly proposed in contemporary plant ecology. In contrast, systems involving high numbers of categories have received limited interest because the detailed ecological knowledge of individual species required for their successful categorization is usually missing. A comprehensive table on the main features of seed bank classification systems is provided.

Summary In order to identify the most relevant environmental parameters that regulate flowering time of bulbous perennials, first flowering dates of 329 taxa over 33 yr are correlated with monthly and daily mean values of 16 environmental parameters (such as insolation, precipitation, temperature, soil water content, etc.) spanning at least 1 yr back from flowering. A machine learning algorithm is deployed to identify the best explanatory parameters because the problem is strongly prone to overfitting for traditional methods: if the number of parameters is the same or greater than the number of observations, then a linear model can perfectly fit the dependent variable (observations). Surprisingly, the best proxy of flowering date fluctuations is the daily snow depth anomaly, which cannot be a signal itself, however it should be related to some integrated temperature signal. Moreover, daily snow depth anomaly as proxy performs much better than mean soil temperature preceding the flowering, the best monthly explanatory parameter. Our findings support the existence of complicated temperature sensing mechanisms operating on different timescales, which is a prerequisite to precisely observe the length and severity of the winter season and translate for example, ‘lack of snow’ information to meaningful internal signals related to phenophases.