The need for effective action on the global biodiversity crisis has never been more dire. Globally, nations are taking action to counteract severe landscape degradation. The United Nations launched its Decade on Ecosystem Restoration in 2021, kicking off further large-scale initiatives including the recently adopted EU Nature Restoration Law and the Kunming-Montreal Global Biodiversity Framework. However, despite global action, restoration activities remain scattered, disparate, and only loosely grounded in the bulk of available evidence. In order to conduct effective restoration and for ecological science to inform land management at the scale required, we need to develop a broad and deep evidence base that can be used for guiding effective restoration and management.
There are several reasons why no accessible, integrated evidence base exists yet. Even though restoration is a relatively young field, there is a vast trove of Grey literature and associated materials – professional reports, mapping, monitoring data, project summaries, and more. However, these are often uncatalogued and thus unavailable. Increasing volumes of peer-reviewed scientific literature contain knowledge useful for restoration as well, but much of it is not openly accessible. Further, traditional publications are not easily interpretable by non-scientists. These texts usually lack semantic annotations and therefore cannot be interpreted by machines, making automated information extraction challenging. Lastly, even if all these sources of information were readily available, it would still be challenging to decide how knowledge gathered in one specific ecosystem or location can be transferred to a different situation.
In theory, there is a lot of information that could be used for building up an encompassing knowledge base. Other fields (e.g. biomedicine) have started tackling the challenge of scattered knowledge using modern computer science practices (i.e. knowledge graphs, ontologies, AI), but ecology broadly lags behind. The EcoWeaver & TReK Community is an international and interdisciplinary collaborative that includes ecologists, computer scientists and philosophers, aiming at bringing these technologies to the field. The goal is to develop and implement a Toolkit for Restoration Knowledge (TReK) that will provide a deep and useful evidence base for ecological restoration. TReK will be the first core application of EcoWeaver, which is envisioned as the backbone infrastructure that collects and stores ecological knowledge and offers diverse options for on-demand synthesis, model development and data visualization for a variety of ecological sub-disciplines. It aims at supporting scientific progress, making scientific knowledge accessible and comprehensible, thus increasing trust in science and supporting decision making. It will allow making the most of the existing body of ecological knowledge.
EcoWeaver and the Toolkit for Restoration Knowledge (TReK): From ecological knowledge to restoration action
Our vision is to weave together, with the help of latest computational technologies, ecological knowledge from diverse sources, thus creating the EcoWeaver, a FAIR and openly accessible knowledge base that supports the generation of novel insights, enhances ecological understanding and empowers people to make informed decisions. The Toolkit for Restoration Knowledge, the core application of the EcoWeaver, is tailored for significantly enhancing the efficiency and effectiveness of ecosystem restoration. Communities of Practice, consisting of diverse teams of practitioners, scientists and local knowledge holders, are participating in the development of the EcoWeaver and TReK, ensuring their just and meaningful implementation.
| Positionality |
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| Acknowledging positionality recognizes the potential influence of a researcher’s social position and identity on various aspects of their research. Positionality refers to an individual’s worldview and the stance they take in relation to research and its context (Holmes, 2020). It encompasses beliefs about reality, knowledge, and values, which in turn influence the approach to research. These beliefs are shaped by personal experiences and various self-identifications, including national origin, geographic location, gender, ethnicity, disability status, social class, and other factors that contribute to the social position or location. |
| We express our positionality to provide the reader with a clear understanding of our background and identity. By being transparent, we aim to provide essential context for users of our research, acknowledging how our perspectives shape our approaches and outcomes. By offering a positionality statement, we strengthen our work through promoting transparency and reflexivity in our research. |
Our team shares the scientifically backed and evidence-based perspective that we are living in a triple planetary crisis and that we want to help tackle it. All members have a tertiary education, and work within an interdisciplinary space. Collectively we combine a variety of disciplinary backgrounds, including ecology, computer science, philosophy and social sciences. Team members consist of a diversity of career stages (e.g. Professors, senior and early career scientists, PhD, and MSc). As a team we have diverse cultural backgrounds, but with a majority of team members of European descent.
Our research affiliations are mostly in the Northern hemisphere, with project instigators based in Germany and Canada. We also acknowledge that to some extent the development of our ideas and outcomes are influenced by funding opportunities.
Figures in construction
Our positionality has impacted our research, for example shaping our understanding of causality. Furthermore, the selection of problems to solve could be biased and not reflect different local issues. Similarly, the solutions proposed underestimate local contexts with a risk to be irrelevant for certain populations, communities and local situations. Moreover, our position comports the risk to overlook certain stakeholders that would have been relevant to frame certain solutions. Furthermore, within subgroups of the team there are underlying interests in advancing the respective disciplines, for example, philosophers have adopted a practice-oriented approach with an interest in the approach of “Philosophy in Science” where the results of the philosophical investigation should be relevant to science. While ecologists in the group have a focus on advancing theory, e.g. understanding ecological mechanisms, and how these relate to practical implementation. Together, we have a common goal of making knowledge more accessible. Finally, for now, we have considered mostly English-language scientific literature in our work, but continue to be in discussion on ways to also include other languages and resources.
To minimize the impact of these biases we have adopted a specific approach. We organized interactive workshops with a core group designing the workshop process and invited broad participation from researchers within the different disciplines and from various geographical and institutional locations. These workshops created a safe space of exchange where ideas and opinions from everyone were heard, valued and considered in the project development. This interactivity also led to a collection of envisioned end ‘users’ with heterogeneous profiles, expanding to practitioners from various fields. We intend to reach out to some of these persons/communities to invite them to contribute input for further development of our work, and generally welcome expressions of interest. Since the initiation of the tool being developed by our group, special attention has been given to collective benefit and responsibility, following core ideas of the CARE principles (e.g., see our approach to incorporating grey literature - tab in construction).
For developing EcoWeaver and TReK in accordance with the vision [link], it is essential to address questions that go beyond issues directly related to the technical implementation of the required software. The group focuses on three overlapping areas of research: (i) Understanding users and user needs, (ii) causal and mechanistic modeling, and (iii) information acquisition and integration.
Key objectives:
Research questions:
Approach:
[Intersections with the other topics?]
Key objectives:
Research questions:
[Approach]
Intersection with other topics:
Key objectives:
Research questions:
[Approach]
Intersection with other topics
Given that the vision [link] is a very ambitious one, a single project could never be large enough to realize it. The idea is therefore to strive for a number of projects and individual activities all working in parallel towards this joint vision. The Venn diagram is meant as an overview, allowing localizing the diverse contributions within the overall effort.
We invite everyone interested in collaborative efforts towards the joint vision to take the initiative, and contribute with their own projects or activities.
[To be discussed: If you want to actively contribute, please ## [contact Tina Heger t.heger@tum.de ?? Join the github?]