ORCID Profile
0000-0003-1893-3960
Current Organisation
CNRS
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Public Library of Science (PLoS)
Date: 07-03-2014
Publisher: Wiley
Date: 04-12-2020
Abstract: The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of in idually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database ( www.spibirds.org )—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of in idually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million in idual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language). The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much‐needed large‐scale ecological data integration.
Publisher: Wiley
Date: 26-07-2020
Publisher: Wiley
Date: 07-08-2020
DOI: 10.1002/ECE3.6568
Abstract: Social network analyses allow studying the processes underlying the associations between in iduals and the consequences of those associations. Constructing and analyzing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using a priori knowledge about the species and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximized (a) the assortment of in iduals into previously known “breeding groups” (birds that contribute toward the same nest and maintain cohesion when foraging) and (b) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on a priori knowledge of the study species can be implemented in a erse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.
Publisher: Wiley
Date: 22-11-2013
DOI: 10.1002/ECE3.864
No related grants have been discovered for Claire Doutrelant.