Floral resource calculator

We present the Floral Resource Calculator — a tool designed to predict the daily production of nectar, sugars, and pollen for individual plant species, and to scale these estimates to habitats, ecosystems, and landscapes.

 

We present the Floral Resource Calculator — a tool designed to predict the daily production of nectar, sugars, and pollen for individual plant species, and to scale these estimates to habitats, ecosystems, and landscapes.

The tool was initially developed within the framework of the B-GOOD Horizon 2020 project and is being further developed within the Better-B Horizon Europe project.

Our framework integrates data on pollen, nectar, and sugar production by individual floral units (flowers or inflorescences) with information on floral unit abundance, flowering phenology, habitat composition, and the composition & configuration of habitats within ecosystems and landscapes.

This structure enables detailed predictions of the spatio-temporal availability of floral resources across species, habitats, and entire landscapes.

The framework explicitly captures spatio-temporal dynamics: daily flowering is determined by phenology models, resource production is allocated across flowering phases (initiation, full bloom, termination), and habitat composition data enable aggregation to per-area values. Landscape-level production is weighted by habitat cover.

A key strength is the framework’s flexibility — the “living databases” can be updated with new data, and users may integrate their own habitat composition, temperature records, or alternative phenology models.

The framework is implemented as an open-source tool, the Floral Resource Calculator, developed in Python 3.10 within a Jupyter Notebook environment. The tool, input data, and full documentation are freely available via GitLab.

To the GitLab repository

Better-B has received funding from the European Union, the Swiss State Secretariat for Education, Research and Innovation (SERI) and UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (grant number 10068544).

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union, European Research Executive Agency (REA), SERI or UKRI. Neither the European Union nor the granting authorities can be held responsible for them.

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