Abstract
Abstract Species composition assessment of ecological communities and networks is an important aspect of biodiversity research. Yet often ecological traits of organisms in a community are more informative than scientific names only. Furthermore, other properties like threat status, invasiveness, or human usage are relevant to many studies, but cannot be evaluated from taxonomy alone. Despite public databases collecting such information, it is still a tedious manual task to enrich community analyses with such, especially for large‐scaled data. Thus we aimed to develop a public and free tool that eases bulk trait mapping of community data in a web browser, implemented with current standard web and database technologies. Here, we present the Fennec (Functional Exploration of Natural Networks and Ecological Communities), a workbench that eases the process of mapping publicly available trait data to the user's communities in an automated process. Usage is either by a local self‐hosted or a public instance (https://fennec.molecular.eco) covering exemplary traits. Alongside the software, we also provide usage and hosting documentation as well as online tutorials. The Fennec aims to motivate public trait data submission and its reuse in meta‐analyses. Further, it is an open‐source development project with the code freely available to use and open for community contributions (https://github.com/molbiodiv/fennec).
Highlights
An important task in biodiversity research is the analysis of species composition of ecological communities and networks
Its database currently hosts trait data related to pollination and microbiomes from various sources
The database is subject to constant further extension with more traits, yet our main goal is to maintain high quality of the data available here
Summary
An important task in biodiversity research is the analysis of species composition of ecological communities and networks This can be done using traditional methods and more recently with analytical methods designed for large scale sample processing, like DNA metabarcoding (Keller, Danner, et al, 2015) or automated image analysis (Oteros et al, 2015) producing data volumes hard to cope with manually. Development of tools has already been initiated that aim to automatically map taxonomy information to functional traits, mostly through mapping on known genomes (Aßhauer et al, 2015; Edgar, 2017; Keller, Horn, et al, 2014; Langille et al, 2013) To our knowledge, it remains to date a manual effort to enrich eukaryotic communities with trait meta-data, such information is already publicly available
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