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Mapping Evidence to Theory in Ecology

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Universität Bielefeld/P. Ottendörfer
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Graphic Resident Group
Design: C. Mehl/Büro Paschetag

Convenor

Tina Heger (Berlin, GER)

Contact at ZiF

Sabine Mende
zif-conferencesupport@uni-bielefeld.de

Mapping Evidence to Theory in Ecology

Addressing the Challenges of Generalization and Causality

September 2024 - February 2025

Ecology as a scientific discipline is expected to provide the knowledge needed for solving the biodiversity crisis. Accordingly, ecological research generates a steeply increasing amount of data and empirical findings, aimed at increasing knowledge about ecological systems. In principle, this rise in information should lead to a steady improvement of understanding of these systems, and thus also to a steady increase in information that can directly be used for improving biodiversity protection and management.

However, the translation of knowledge gained in single empirical studies into more general knowledge about ecological systems and into practically useful knowledge is not straightforward. Ecological systems are highly complex, and ecological processes are strongly context dependent. This leads to the fact that empirical results from single case studies are hard to extrapolate to other systems, and hard to translate into meaningful instructions for management. The core problem is how to synthesize the results of large amounts of case studies that are highly diverse with respect to research approach (e.g. field surveys or controlled lab experiments) as well as study systems (e.g. plants or insects, dry grasslands or oceans).

Meaningful synthesis needs to take ecological complexity into account and needs to assure that important information on the respective context of the study is not lost. What is needed, thus, are tools and workflows that allow developing 'case-specific generalizations'. Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context
dependence. Especially promising seems the idea to bring together advanced AI based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.

The work of the Resident Group continues beyond February 2025 as part of the Hi Knowledge Initiative. More information on ongoing activities can be found here on github.

  • Group photo fellows
    Universität Bielefeld/P. Ottendörfer
  • Photo during a workshop
    Universität Bielefeld/P. Ottendörfer
  • Photo during a workshop
    Universität Bielefeld/P. Ottendörfer

Meetings

Workshop I

Interdisciplinary Perspectives on Knowledge Synthesis in Ecology

4 – 6 November 2024

Convenor:

Tina Heger (Berlin)

Mobilizing scientific ecological knowledge is key for addressing the global biodiversity crisis. It is far from obvious, however, how to best approach this aim. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the current Resident Group “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. During the first day of the workshop, the group will present their current state of discussions and demonstrate preliminary results, opening the floor for feedback. The second day is dedicated to bringing in new perspectives from fellows newly arriving in Bielefeld, as well as from further workshop participants. Both days will provide plenty of time for discussions and hands-on sessions. On the third day, the fellows will explore joint next steps.

Hackathon: EcoHack Hackathon: EcoHack - AI & LLM Hackathon for Applications in Evidence-based Ecological Research & Practice

20 – 22 January 2025

Convenor:

Jennifer D'Souza (Hannover)

Can artificial intelligence help to make scientific literature for ecology more accessible, make better use of data and promote communication between researchers and practitioners? To answer this question, the ZiF research group ‘Mapping Evidence to Theory in Ecology. Addressing the Challenges of Generalization and Causality’ hosted a hackathon, a programming competition, from 20 to 22 January. There was a great deal of interest.

Workshop “From Ecological Knowledge to Restoration Action: Developing the Conceptual and Technological Basis for a Toolbox”

22 – 24 January 2025

Convenor:

Tina Heger

Making ecological knowledge usable is key for addressing the global biodiversity crisis. It is far from obvious, however, how to best approach this aim. In an interdisciplinary setting including ecologists, data scientists, computational linguists, and philosophers, the ZiF Resident Group "Mapping Evidence to Theory in Ecology" currently explores ways for extracting ecological knowledge from scientific papers and other sources (e.g. local and indigenous knowledge), representing it in the form of causal network graphs, linking it to evidence and providing means to interact with this integrated knowledge. The envisioned goal is the ‘EcoWeaveR’, a digital platform for synthesizing and representing ecological knowledge that can better support theorizing and restoration action in ecology.

Workshop “Mapping Evidence to Theory in Ecology”

24 – 28 February 2025

Convenor:

Tina Heger

Ecology as a scientific discipline is expected to provide the knowledge needed for solving the biodiversity crisis. Accordingly, ecological research generates a steeply increasing amount of data and empirical findings, aimed at increasing knowledge about ecological systems. However, the translation of knowledge gained in single empirical studies into more general knowledge about ecological systems and into practically useful knowledge is not straightforward. Ecological systems are highly complex, and ecological processes are strongly context dependent.

During the past six months the group “Mapping Evidence to Theory in Ecology. Addressing the Challenges of Generalization and Causality” has been working towards ideas and tools to synthesize the results of large amounts of case studies and to make actual ecological knowledge more accessible to researchers and practitioners. In interdisciplinary conceptual discussions on ecological, philosophical, technical and other challenges for synthesizing and representing ecological knowledge they developed a digital toolbox, called EcoWeaver and its core application TReK.

In their closing workshop the fellows and guests of the research group will come together to wrap up the group’s results and plan for future cooperation. 

Members

PD Dr. Tina Heger
Leibniz Institute of Freshwater Ecology
and Inland Fisheries (IGB)

Freie Universität Berlin
Institute of Biology

Dr. Alejandra Parreno
​Technical University of Munich
TUM School of Life Sciences

Dr. Aline Potiron
Utrecht University /
Freudenthal Institute
Postdoctoral Researcher in the philosophy of the life sciences 

J.-Prof. Dr. Alkistis Elliott-Graves
Bielefeld University
Faculty of History, Philosophy and Theology / Department of Philosophy

Prof. Dr. Alsayed Algergawy
University of Passau
Faculty of Computer Science and Mathematics

Prof. Dr. Anika Groß
Anhalt University
Computer Science and Languages

Prof. Dr. Birgitta König-Ries
Friedrich Schiller University Jena
Faculty of Mathematics und Computer Science

Dr. Bruno Travassos-Britto
University of Toronto Mississauga
Biology Faculty

Dr. Carlos Arnillas Merino
University of Toronto Scarborough

Dr. Daniel Mietchen
FIZ Karlsruhe (Location Berlin)
Leibniz Institute for Information Infrastructure

Daphne Auer
Friedrich Schiller University Jena
Computer Scientist

Dr. Eleonora Louise Slabbert
Anhalt University
Agriculture, Ecotrophology, and Landscape Development

Prof. Dr. Eric Higgs
University of Victoria
School of Environmental Studies

Prof. Dr. Federica Russo
Utrecht University
Freudenthal Institute

Dr. Florencia Yannelli
Instituto Argentino de Investigaciones de las Zonas Áridas (IADIZA)
Mendoza, Argentina

Hrishikesh Jadhav
Junior Data Scientist
Universität Passau
Master degree Artificial Intelligence Engineering

Dr. Jennifer D'Souza
TIB, Hannover
Junior AI Research Group Lead

Dr. Jonas Wahl
TU Berlin
Postdoctoral Researcher

Prof. Dr. Jonathan Jeschke
Freie Universität Berlin
Institute of Biology

Leibniz Institute of Freshwater Ecology
and Inland Fisheries (IGB)

Dr. Josh Brian
Kings College, London
Department of Geography
Faculty of Social Science & Public Policy

Dr. Lars Vogt
TIB, Hannover
Comm. Head of ORKG Curation & Community Building

Léna Bureau
McGill University

Dr. Lena Neuenkamp
Institute of Landscape Ecology
Münster University

Dr. Lotte Korell
Helmholtz Centre for Environmental Research
Department of Species Interaction Ecology (SIE)

Dr. Manuel Pacheco-Romero
Leuphana University Lüneburg
Social Ecological Systems Institute

Marc Brinner
Bielefeld University
Faculty of Linguistics and Literary Studies 

Prof. Dr. Marie I. Kaiser
Bielefeld University
Faculty of History, Philosophy and Theology / Department of Philosophy

Moritz Plenz
Heidelberg University
Department of Computational Linguistics
Doctoral Student

Ass. Prof. Nancy Shackelford
University of Victoria
School of Environmental Studies

Prof. Dr. Phyllis Kristin Illari
University College London
Department of Science And Technology Studies

Dr. Robert Frühstückl
Faculty of History, Philosophy and Theology / Department of Philosophy
Bielefeld University

Prof. Dr. Sina Zarrieß
Bielefeld University
Faculty of Linguistics and Literary Studies 

Tarek Al Mustafa
Friedrich-Schiller Universität Jena
Faculty of Mathematics and Computer Science

Dr. Timothy James Alamenciak
University of Waterloo

Prof. Dr. Vicky Temperton
Leuphana University Lüneburg
Institute of Ecology

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