Functional exploration of natural networks and ecological communities

Ankenbrand, Markus J.; Hohlfeld, Sonja; Weber, Lorenz; Förster, Frank; Keller, Alexander

doi:10.1111/2041-210x.13060

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…The code for the Coral Traits database 32 (https:// github.com/jmadin/traits) could be modified to guide the creation of databases on other organisms. The FENNEC project provides a tool for accessing and viewing community trait data as a self-hosted website service 72 (https://github.com/molbiodiv/fennec). The OTN can act as a connector between developers and the broader community seeking to synthesize trait data, facilitating the training of scientists in all aspects of reproducible data management.…”

Section: Activity 1: Maintaining a Global Registry Of Trait-based Inimentioning

confidence: 99%

Open Science principles for accelerating trait-based science across the Tree of Life

et al. 2020

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raits, broadly speaking, are measurable attributes or characteristics of organisms. Traits related to function (for example, leaf size, body mass, tooth size or growth form) are often used to understand how organisms interact with their environment and other species via key vital rates such as survival, development and reproduction 1-5. Trait-based approaches have long been used in systematics and macroevolution to delineate taxa and reconstruct ancestral morphology and function 6-8 and to link candidate genes to phentoypes 9-11. The broad appeal of the trait concept is its ability to facilitate quantitative comparisons of biological form and function. Traits also allow us to mechanistically link organismal responses to abiotic and biotic factors with measurements that are, in principle, relatively easy to capture across large numbers of individuals. For example, appropriately chosen and defined traits can help identify lineages that share similar life-history strategies for a given environmental regime 12,13. Documenting and understanding the diversity and composition of traits in ecosystems directly contributes to our understanding of organismal and ecosystem processes, functionality, productivity and resilience in the face of environmental change 14-19. In light of the multiple applications of trait data to address challenges of global significance (Box 1), a central question remains: How can we most effectively advance the synthesis of trait data within and across disciplines? In recent decades, the collection, compilation and availability of trait data for a variety of organisms has accelerated rapidly. Substantial trait databases now exist for plants 20-23 , reptiles 24,25 , invertebrates 23,26-29 , fish 30,31 , corals 32 , birds 23,33,34 , amphibians 35 , mammals 23,36-38 and fungi 23,39 , and parallel efforts are no doubt underway for other taxa. Though considerable effort has been made to quantify traits for some groups (for example, Fig. 1), substantial work remains. To develop and test theory in biodiversity science, much greater effort is needed to fill in trait data across the Tree of Life by combining and integrating data and trait collection efforts.

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Section: Activity 1: Maintaining a Global Registry Of Trait-based Inimentioning

confidence: 99%

Open Science principles for accelerating trait-based science across the Tree of Life

et al. 2020

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“…Classification for clustering the plant species into groups was based on generalized minimum distance functions using k-means after cluster number selection according to the Elbow method which takes into account the percentage of variance explained by the clusters against the number of clusters (Kodinariya & Makwana, 2013). We used FENNEC v1.0.5 (Ankenbrand, Hohlfeld, Weber, Förster, & Keller, 2018) to add plant trait information to our pollen composition data and investigate possible phenological differences between the formed plant clusters.…”

Section: Discussionmentioning

confidence: 99%

“…Classification for clustering the plant species into groups was based on generalized minimum distance functions using k ‐means after cluster number selection according to the Elbow method which takes into account the percentage of variance explained by the clusters against the number of clusters (Kodinariya & Makwana, ). We used FENNEC v1.0.5 (Ankenbrand, Hohlfeld, Weber, Förster, & Keller, ) to add plant trait information to our pollen composition data and investigate possible phenological differences between the formed plant clusters. Subsequently, we used random forest analysis to assign bacterial communities of pollen and larvae (a) to host bee species and (b) to the defined pollen composition clusters and estimate the significance of these variables for correct classification (Junker & Keller, ; Prasad, Iverson, & Liaw, ) with the packages varSelRF v0.7‐8 (Diaz‐Uriarte, ) and randomForest v4.6‐14 (Liaw & Wiener, ).…”

Section: Methodsmentioning

confidence: 99%

Linking pollen foraging of megachilid bees to their nest bacterial microbiota

Voulgari-Kokota

Ankenbrand

Grimmer

et al. 2019

Ecology and Evolution

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Solitary bees build their nests by modifying the interior of natural cavities, and they provision them with food by importing collected pollen. As a result, the microbiota of the solitary bee nests may be highly dependent on introduced materials. In order to investigate how the collected pollen is associated with the nest microbiota, we used metabarcoding of the ITS2 rDNA and the 16S rDNA to simultaneously characterize the pollen composition and the bacterial communities of 100 solitary bee nest chambers belonging to seven megachilid species. We found a weak correlation between bacterial and pollen alpha diversity and significant associations between the composition of pollen and that of the nest microbiota, contributing to the understanding of the link between foraging and bacteria acquisition for solitary bees. Since solitary bees cannot establish bacterial transmission routes through eusociality, this link could be essential for obtaining bacterial symbionts for this group of valuable pollinators. Open Research Badges This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://www.ebi.ac.uk/ena/data/view/PRJEB27223, https://www.ebi.ac.uk/ena/data/view/PRJEB31610, and https://doi.org/10.5061/dryad.qk36k8q

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“…Much effort has already gone into creating definitions and protocols for traits collection. Yet, trait naming and corresponding definitions may differ between studies and trait databases (Ankenbrand et al, 2018;Dawson et al, 2021;Kunz et al, 2022). For example, specific leaf area (SLA) and leaf mass per area (LMA) are essentially the same trait, one being the inverse of the other.…”

Section: Be Aware Of Existing Trait Definitions and Homologiesmentioning

confidence: 99%

Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences

Keller

Ankenbrand

Bruelheide

et al. 2022

Preprint

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1. Traits have become a crucial part of ecological and evolutionary sciences, helping researchers understand the function of an organism's morphology, physiology, growth and life-history, with effects on fitness, behaviour, interactions with the environment, and ecosystem processes. However, compiling and analysing trait data comes with data-scientific challenges due to the complex nature of trait data.2. We offer 10 (mostly) simple rules, with some detailed extensions, as a guide in making critical decisions that consider the entire life cycle of trait data.3. This article is particularly motivated by its last rule, i.e. to propagate the good practices, and has the intention of bringing awareness of the different facets of a trait's life cycle to the community.4. When it comes to working with trait data, we gain particularly as an interdisciplinary community of field biologists, synthesis ecologists, computer scientists and database managers. We hope these basic guidelines can be useful as a starter for active communication in disseminating such integrative knowledge and how to make trait data future-proof.

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Functional exploration of natural networks and ecological communities

Cited by 7 publications

References 27 publications

Open Science principles for accelerating trait-based science across the Tree of Life

Open Science principles for accelerating trait-based science across the Tree of Life

Linking pollen foraging of megachilid bees to their nest bacterial microbiota

Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences

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