Adaptive and selective seed abortion reveals complex conditional decision making in plants.

Behavior is traditionally attributed to animals only. Recently, evidence for plant behavior is accumulating, mostly from plant physiological studies. Here, we provide ecological evidence for complex plant behavior in the form of seed abortion decisions conditional on internal and external cues. We analyzed seed abortion patterns of barberry plants exposed to seed parasitism and different environmental conditions. Without abortion, parasite infestation of seeds can lead to loss of all seeds in a fruit. We statistically tested a series of null models with Monte Carlo simulations to establish selectivity and adaptiveness of the observed seed abortion patterns. Seed abortion was more frequent in parasitized fruits and fruits from dry habitats. Surprisingly, seed abortion occurred with significantly greater probability if there was a second intact seed in the fruit. This strategy provides a fitness benefit if abortion can prevent a sibling seed from coinfestation and if nonabortion of an infested but surviving single seed saves resources invested in the fruit coat. Ecological evidence for complex decision making in plants thus includes a structural memory (the second seed), simple reasoning (integration of inner and outer conditions), conditional behavior (abortion), and anticipation of future risks (seed predation).

Bias in phenology assessments based on first appearance data of butterflies.

Data on the first appearance of species in the field season are widely used in phenological studies. However, there are probabilistic arguments for bias in estimates of phenological change if sampling methods or population abundances change. We examined the importance of bias in three measures of phenological change: (1) the date of the first X appearances, (2) the date of the first Y% of all first appearances and (3) the date of the first Z% of the individuals observed during the entire flight period. These measures were tested by resampling the data of the Dutch Butterfly Monitoring Scheme and by simulations using artificial data. We compared datasets differing in the number of sampling sites, population abundance and the start of the observation period. The date of the first X appearances proved to be sensitive to the number of sampling sites. Both the date of the first X appearances and the date of the first Y% of all first appearances were sensitive to population trend. No such biases were found for estimates of the first Z% of the flight period, but all three measures were sensitive to changes in the start of the observation period. The conclusions were similar for both the study on butterfly data and the simulation study. Bias in phenology assessments based on first appearance data may be considerable and should no longer be ignored in phenological research.