Abstract
For plant–pollinator interactions to occur, the flowering of plants and the flying period of pollinators (i.e. their phenologies) have to overlap. Yet, few models make use of this principle to predict interactions and fewer still are able to compare interaction networks of different sizes. Here, we tackled both challenges using Bayesian structural equation models (SEM), incorporating the effect of phenological overlap in six plant–hoverfly networks. Insect and plant abundances were strong determinants of the number of visits, while phenology overlap alone was not sufficient, but significantly improved model fit. Phenology overlap was a stronger determinant of plant–pollinator interactions in sites where the average overlap was longer and network compartmentalization was weaker, i.e. at higher latitudes. Our approach highlights the advantages of using Bayesian SEMs to compare interaction networks of different sizes along environmental gradients and articulates the various steps needed to do so.
Highlights
Understanding how phenology determines species interactions is a central question in the case of mutualistic networks
Despite the high species diversity in Occitanie, the total number of interactions recorded in these sites (BF and F) is not the highest recorded in the field (Table 1): the maximum number of visits in the site of BF was 10 and 12 in the site of F
In this study we explored the effect of phenology overlap on a large network of species interactions in calcareous grasslands and how this effect could vary along a latitudinal gradient in France using empirical data on six planthoverfly networks
Summary
Understanding how phenology determines species interactions is a central question in the case of mutualistic networks. Phenological advances increase at higher latitudes, as a response to the acceleration of warming temperature along the same gradient (Post et al 2018), increase phenological mismatch, and have the potential to threaten the synchrony needed for effective pollination (Hutchings et al 2018) Such environmental changes can drastically alter pollinator interactions through modified temporal overlap between pollinators and their floral resources leading, in extreme cases, to local extinctions (Memmott et al 2007) and the ensuing absence of the partner species at the location and/or time at which the interaction should have taken place (Willmer 2012; MillerStruttmann et al 2015; Rafferty et al 2015; Hutchings et al 2018). Phenology predictably affects network compartmentalization as different phenophases likely correspond to different compartments when networks are considered on an annual scale (Martín González et al 2012)
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