Ecology

Learn to access, manage and analyse Biodiversity data through Galaxy.

Requirements

Before diving into this topic, we recommend you to have a look at:

• Introduction to Galaxy Analyses

Material

Data and Metadata Management

These tutorials are focusing on data and metadata management in Ecology.

Data access

These lessons focus on ways to access data classicaly used in Ecology

Data preprocessing

These lessons focus on manners to preprocess Biodiversity data

Media annotation, manually or through AI

These lessons focus on manners to annotate media data (sounds, images, videos)

Data analysis

These lessons focus on ways to analyse Biodiversity data

Data visualization

These tutorials showcase Biodiveristy data visualization in Ecology

Learning Pathways

Introduction to Galaxy and Ecological data analysis

This learning path aims to teach you the basics of Galaxy and analysis of ecological data. You will learn how to use Galaxy for analysis, and will be guided through the common steps of biodiversity data analysis: download, check, filter and explore biodiversity data and analyze abundance data through modeling.

beginner ecology

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

Follow topic updates rss-feed with our RSS Feed

Community Resources

Editorial Board

This material is reviewed by our Editorial Board:

Yvan Le Bras

Marie Josse

Pauline Seguineau

Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!

Contributors

This material was contributed to by:

Marie Josse

Wolfgang Maier

Sophie Pamerlon

Martin Čech

Tanguy Genthon

Daniela Schneider

Gildas Le Corguillé

Wendi Bacon

Brigida Gallone

Jean-Baptiste Mihoub

Helena Rasche

Anthony Bretaudeau

Bérénice Batut

Pauline Seguineau

Arthur Barreau

Simon Benateau

Eric Coissac

Frédéric Boyer

Kevin Payet

Cristóbal Gallardo

Nicola Soranzo

Najat Amoukou

Niall Beard

Elisa Michon

Yvan Le Bras

Clara Urfer

Nadine Le Bris

William Durand

Tristan Reynolds

Mohammad Joudy

Olivier Norvez

Soumya Jha

Björn Grüning

Miguel Roncoroni

Coline Royaux

Nate Coraor

Clea Siguret

Ankerl Yassine

Anne Fouilloux

Verena Moosmann

Dominique Pelletier

Glinez Thibaud

Haswane Chei

Saskia Hiltemann

Laura Russell

John Beliveau

Perrine Jégousse

Elouan Le Mestric

Jean Le Cras

Funding

These individuals or organisations provided funding support for the development of this resource

ISYEB

Melbourne Bioinformatics

Fair-Ease

FAIR-EASE is a RIA project funded under HORIZON-INFRA-2021-EOSC-01-04, and it involves a consortium of 25 partners from all over Europe.

The University of Melbourne

MOOREV

Australian BioCommons

PNDB

SU

ABRomics

Gallantries

This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.

Antarctic and Southern Ocean Coalition

GBIF

MNHN

EuroScienceGateway

EuroScienceGateway was funded by the European Union programme Horizon Europe (HORIZON-INFRA-2021-EOSC-01-04) under grant agreement number 101057388 and by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee grant number 10038963.

FNSO

This project (AAPFNSO2019OpenMetaPaper-14026) is funded with the support of the national Fund for Open Science programme of the French Minister of Higher Education, Research and Innovation. Their funding has supported development of data/metadata management tools and related tutorial for Ecology and FAIR topics.

References

• Coline Royaux et al: Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology
  Numerous conceptual frameworks exist for best practices in research data and analysis (e.g., Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as “atoms” composing an analytical procedure. When generalized, “atoms” can be used in more than a single case analysis. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomized and generalized.
• Reto Schmucki et al: A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes
  The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within‐year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales.
• Vitor H. F. Gomes et al: Species Distribution Modelling: Contrasting presence-only models with plot abundance data
  Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model using inverse distance weighting (IDW)