Extinction debt refers to delayed species extinctions expected as a consequence of ecosystem perturbation. Quantifying such extinctions and investigating long-term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming. Additionally, the relative importance of eco-evolutionary processes varies across scales, because levels of ecological organization, i.e. individuals, (meta) populations and (meta)communities, respond hierarchically to perturbations. To summarize our current knowledge of the scales and mechanisms influencing extinction debts, we reviewed recent empirical, theoretical and methodological studies addressing either the spatio-temporal scales of extinction debts or the eco-evolutionary mechanisms delaying extinctions. Extinction debts were detected across a range of ecosystems and taxonomic groups, with estimates ranging from 9 to 90% of current species richness. The duration over which debts have been sustained varies from 5 to 570 yr, and projections of the total period required to settle a debt can extend to 1000 yr. Reported causes of delayed extinctions are 1) life-history traits that prolong individual survival, and 2) population and metapopulation dynamics that maintain populations under deteriorated conditions. Other potential factors that may extend survival time such as microevolutionary dynamics, or delayed extinctions of interaction partners, have rarely been analyzed. Therefore, we propose a roadmap for future research with three key avenues: 1) the microevolutionary dynamics of extinction processes, 2) the disjunctive loss of interacting species and 3) the impact of multiple regimes of perturbation on the payment of debts. For their ability to integrate processes occurring at different levels of ecological organization, we highlight mechanistic simulation models as tools to address these knowledge gaps and to deepen our understanding of extinction dynamics.The extinctions that comprise an extinction debt can be expected based on the assumption of a new equilibrium to be achieved. This new equilibrium is also a community state that depends on how much the perturbation changes environmental conditions and community properties. The changes in species richness will then emerge from the interactions of eco-evolutionary processes over time at multiple levels of ecological organization (Cabral et al. 2017(Cabral et al. , 2019. This reasoning emphasizes extinction debt as a community (or metacommunity) state. Therefore, we further refer to mechanisms of extinction debt as eco-evolutionary processes creating or prolonging this state, i.e. delaying extinctions and thus putting and maintaining the community into debt.Being a state, an extinction debt has to be first and foremost, detected. Once detected, it can be characterized (Fig. 1). The extinction debt itself is the number of extinctions expected to happen as consequence of a perturbation, therefore, the main m...

The ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, and visually appealing file that is easier to understand by nonspecialists. Traditionally used by data scientists when producing reports and informing decision-making, the use of this tool in research publication is not common, despite its potential to increase research impact and quality. For a single study, the content of such documentation partially overlaps with that of classical lab notebooks and that of the scientific manuscript reporting the study. Therefore, to minimize the amount of work required to manage all the files related to these contents and optimize their production, we present a starter kit to facilitate the implementation of computational notebooks in the research process, including publication. The kit contains the template of a computational notebook integrated into a research project that employs R, Python, or Julia. Using examples of ecological studies, we show how computational notebooks also foster the implementation of principles of Open Science, such as reproducibility and traceability. The kit is designed for beginners, but at the end we present practices that can be gradually implemented to develop a fully digital research workflow. Our hope is that such minimalist yet effective starter kit will encourage researchers to adopt this practice in their workflow, regardless of their computational background.

Implementing the FAIR principles for the curation of Integrative Biodiversity Research data 4 Transcript Jo: Welcome. You're listening to Access 2 Perspectives Conversation. Today with me here in the Zoom Room is Ludmilla Figueiredo. We met a couple of years ago through the Open Science Fellowship program by Wikimedia, Germany, where I had the pleasure of working with you, Ludmilla. And I'm very glad you're joining us today for this podcast. Welcome very much. Ludmilla: Thanks for having me, Jo. It's a pleasure. Jo: So for the preparation, also like you've done a lot of work. We've worked together in open science and open science realm, where during the Wikimedia fellowship you worked on a project developing an electronic lab notebook that's really simple to use or simple in structure for easy adaptability. We'll come to talk about that. You have a background in ecosystem conservation, broadly. Ludmilla: Yeah, broadly. More ecological modeling and ecological theory. Yes. With bills to conservation. Jo: Basically, your research informs ecosystem conservation. Can we say that? Ludmilla: You could say yes, because I used to work with extinction. So what I do is to try and understand when and how they go extinct. But to be fair, I have actually changed jobs recently and become much more involved with open science practices and helping researchers. So it's really fitting to my Wikimedia project, which is great. And also funny how fitting it is. Jo: Great. Yeah. It's funny to see how careers sometimes unfold just through fellowship or project opportunities that you're there to tap into, and then a whole new career path opens up afterwards. So would you guide us through some of your career steps and the turning points or what your research interest is from your undergraduate studies and what led you then to ecosystem modeling towards open science and open science practices and what you're now working on?Ludmilla: Sure. So I've always wanted to study biology. I was always concerned about environmental questions and conservation and so on and these type of issues that we have, since I was a kid, to be honest. So studying biology was pretty much a no brainer for me. I had decided much before we had to make a choice. And in my biology undergrad studies, I specialized in ecology. But honestly, I always found ecology a bit hard because sometimes it can be presented very case by case until I was introduced to ecological models which basically summarized the big ideas and showed you the main I mean, it's a model for a reason, right. They summarize the main processes, the main patterns that we can see. So this is what got me completely hooked on ecological modeling. And also I liked a bit of mathematics, so it made it easier for me. So from then on, during my undergraduate, I had an exchange program from the University where you could get a scholarship to go study abroad. And through this I went to France. And also by chance, I always said I'm sometimes very lucky that they were just starting a master's degree there in ecologica...

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