Unexpected diversity in the reproduction behaviour of Calluna vulgaris

Abstract

For the first time, AI algorithms based on drone imagery allow us to deduce quantitative statements on the reproductive behaviour of Calluna vulgaris individuals at the population level – especially with regard to their spatial distribution at several reproductive phases – which can be done individual-specifically for a large number of individuals. The patterns thus extracted provide surprising insights into the variability of the flowering phenology of populations. For example, there is always an interplay within a population between profusely and weakly flowering individuals. However, their spatial configuration does not provide any recognizable patterns and its temporal appearance between the years does not remain constant. Even though a Calluna population reacts to changing environmental conditions with a median behaviour, i. e. a population flowers a lot or a little on average, no conclusions can be drawn from this as to the individual flowering behaviour. We are still unable to piece together the complex interplay of biotic and abiotic factors to draw a complete picture of the regulatory mechanisms in plant populations. In the future, the use of drones will allow us to generate further information and new knowledge about ecological relationships – in particular about the functioning of complex individual interactions in the realm of flowering plants and their effects on pollinating insects. At the same time, new AI-based evaluation methods are being developed that can map a variety of other plant characteristics and place them in the context of population dynamics so that – based on knowledge – the long-term protection and the development of natural habitats can be implemented.