Does phenology explain plant–pollinator interactions at different latitudes? An assessment of its explanatory power in plant–hoverfly networks in French calcareous grasslands

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.

Phenological traits foster persistence of mutualistic networks by promoting facilitation

“…The benefits of phenological traits mainly occur because they decouple interactions in time, making the balance between facilitation and competition less negative than morphological traits. Our results provide a mechanism that might explain the importance of phenological traits relatively to morphological traits in seasonal pollination networks (CaraDonna et al, 2017 ; Gonzalez & Loiselle, 2016 ; Manincor et al, 2020 ; Sonne et al, 2020 ) and suggest that the seasonal structure is key to the maintenance of diversity in mutualistic communities. These findings can be generalised to other traits than phenology, while they allow to decouple interactions in time or space without leading to resource depletion.…”

Urbanization drives an early spring for plants but not for pollinators

“…To rank flowering species we used Braun–Blanquet's coefficients of abundance–dominance, ranging from i to 5 for least to most abundant respectively (van der Maarel 1979, Mucina et al 2000). We then converted these coefficients in percentage intervals and in mean values of percentage cover classes: i – 1 individual, + – few individuals less than 1%, 1 – 1–10%, 2 – 11–25%, 3 – 26–50%, 4 – 51–75%, 5 – 76–100% (de Manincor et al 2020). All estimations were performed by the same observer.…”

A methodological framework to analyse determinants of host–microbiota networks, with an application to the relationships between Daphnia magna's gut microbiota and bacterioplankton

“…Such limitations can also be partially removed by using node‐wise random effects to account for heterogeneity in node degrees (e.g. De Manincor et al, 2020). Second, the approximation‐based nature of the methods we propose allows an assessment of effects acting at different network scales, from community scale down to finer ones.…”

“…Recently, assessments of ecological networks have turned towards more sophisticated metrics and models encompassing, for example, motif counts, block models, degree equitability and abundance-corrected measures of specialization (Blüthgen, Menzel, & Blüthgen, 2006;Leger, Daudin, & Vacher, 2015;Stouffer, Camacho, Guimera, Ng, & Amaral, 2005). However, despite a few notable exceptions (Bartomeus, 2013;Bartomeus et al, 2016;CaraDonna et al, 2017;De Manincor et al, 2020;Joffard et al, 2019), ecological network analyses are still not assessing the amount of network variation driven by different environmental and biological factors.…”