Wolves return: How to keep livestock safe?

KEEP THEIR GRAZING OFF PUBLIC  LANDS 

Research team investigates farmers’ willingness to implement options to protect grazing animals on pasture

THEY WOULD RATHER HUNT THE WOLVES

University of Göttingen

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image: 

Wolf-repelling fence for fields where cattle graze: an electric fence with five sturdy strands of wire is the design used here to prevent wolves from getting into the pastures.

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Credit: Friederike Riesch

Wolves had long been extinct in parts of Central Europe. Thanks to strict regulations to protect species, in recent decades they have become more widespread again. This brings new challenges: in many areas, protecting farm livestock is essential to prevent animals such as sheep, goats and cattle from being killed by hungry wolves. An international research team at the University of Göttingen, Humboldt-Universität zu Berlin (HU), Dresden University of Technology in Germany, together with KORA in Switzerland, conducted a survey to find out how farmers feel about measures such as wolf-repelling electric fences or guard dogs, and whether the availability of subsidies influences this. The survey showed that the willingness to protect livestock depends primarily on social pressure. Financial support is associated with a greater willingness to use electric fences against wolves. The study was published in the journal People and Nature.

 

In order to investigate perceptions and intentions regarding protecting their herds, the research team conducted an online survey in 2022 among farmers with grazing animals in Bavaria in Germany. The researchers evaluated the responses of 353 people using the “Theory of Planned Behaviour”. This psychological theory maintains that whether someone does something or not depends primarily on how strongly the person is committed to it. Their intention is influenced by three factors: their own attitude (“Do I think it makes sense?”), social pressure (“What do the people around me think?”), and perceived control (“Am I able to do it?”).

 

According to the survey results, the drivers behind farmers’ willingness to protect livestock vary depending on the measures. However, social pressure plays the most important role. “This means it is most important that farmers learn about examples of successful grazing in areas shared with wolves, and exchange knowledge and experiences with each other,” says Dr Friederike Riesch at Göttingen University’s Institute of Grassland Science, who led the study.

 

The study also shows that financial support can create additional incentives: in certain areas around the wolves’ territories, Bavaria promotes specially designed electric fences that deter wolves, as well as dogs to protect the herds. In the survey, livestock owners in such areas were more willing to install fences. The intention to take action is highest among those whose grazing animals are located directly in wolf territory. This is because farmers must demonstrate they have installed basic protection in order to receive compensation payments for damage caused by wolves. “The results show that subsidies encourage farmers to take measures to protect their livestock. This means that it is advisable to extend the promotion of electric wolf-repelling fences to the whole of Bavaria,” explains Dr Malte Möck, at the Agricultural and Food Policy Group at HU. According to the survey, however, the subsidy scheme has no influence on the intention to use guard dogs to protect grazing animals. The researchers conclude that the associated challenges cannot be solved with financial resources alone.

 

In addition to networking and promotion, the researchers recommend offering practical support to protect livestock in order to reduce additional work. Such actions have the added benefit of promoting exchange between people with different perspectives, which can help to defuse conflicts about wolves and grazing.

 

This study was supported by the German government's Special Purpose Fund held at Landwirtschaftliche Rentenbank – Germany’s development agency for agribusiness and rural areas – and by funds from Germany’s Federal Ministry of Research, Technology and Space (BMFTR) for the GreenGrass project.

 

Original publication: Riesch, F., et al. “How to reconcile pasture grazing and wolf recolonisation? Perceptions of management options by livestock farmers in Germany”. People and Nature (2025). DOI: 10.1002/pan3.70141

 

Contact:

Dr Friederike Riesch

University of Göttingen

Institute of Grassland Science

Department of Crop Sciences

Von-Siebold-Straße 8, 37075 Göttingen, Germany

Tel: +49 (0)551 39-26789

Email: friederike.riesch@agr.uni-goettingen.de

www.uni-goettingen.de/en/524567.html 

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Journal

People and Nature

DOI

10.1002/pan3.70141 

Method of Research

Survey

Subject of Research

Not applicable

Article Title

How to reconcile pasture grazing and wolf recolonisation? Perceptions of management options by livestock farmers in Germany

Article Publication Date

24-Sep-2025

 

Diversity in forest management promotes biodiversity

Researchers study “management zoning” in European beech woods using real-world data and virtual landscapes

University of Göttingen

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Intensively managed spruce forest in the Swabian Jura, a mountain range in Germany.

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Credit: Peter Schall

Uniform and intensive forest management approaches threaten European forest biodiversity. There are many different ways to manage forests, but the effects of different combinations of approaches are not well understood. An international research team led by the universities of Göttingen, Germany, and Jyväskylä, Finland, investigated how the Triad forest management framework can support biodiversity conservation in European beech forests. This framework proposes to balance economic and ecological objectives by dividing forest landscapes into three distinct zones: intensively managed forest for timber production, unmanaged areas for biodiversity conservation, and extensively managed forest for supporting biodiversity while producing timber. The researchers collected data from these three management categories and developed “virtual forest landscapes” for analysis. The results were published in the journal PNAS.

 

The research team quantified the effects of using different proportions of the three management zones in a landscape on the diversity of a wide range of species groups – including birds, beetles, plants, lichens and fungi. The original data, which were collected from nine sites in France, Germany, Italy and Czechia, were classified following the Triad forest management categories: intensively managed forests (with clearcutting – meaning harvesting all or nearly all the trees from an area), unmanaged areas (no harvesting in recent years) and extensively managed forest (partial harvests without clearcutting and dominated by native tree species). “The originality of our method was to develop ‘‘virtual” landscapes using computer analysis, in which data could be resampled. It enabled us to create landscapes that vary in proportion of extensively, intensively, and unmanaged forests, and explore the whole range of possibilities”, says researcher Dr Peter Schall, University of Göttingen.

 

The researchers found that species diversity was highest in landscapes composed of 60 percent unmanaged and 40 percent intensively managed forests. Diversity was lowest in purely intensively managed landscapes, but extensively managed forests contributed little to support species richness. However, it would be unrealistic to have 60 percent unmanaged forests in Europe due to the rising demand for wood, which is why the authors suggest focusing on improving the ecological performance of extensive management. “To preserve forest biodiversity in Europe, we recommend increasing the proportion of unmanaged forests and promoting forest heterogeneity in extensive management – for instance by providing a mosaic of open and closed forest patches and keeping large old trees and deadwood in the forests,” explains Dr Rémi Duflot, University of Jyväskylä. “Our study showed that it is possible to maintain a certain proportion of forest area dedicated to timber production without compromising overall forest biodiversity,” adds Schall.

Unmanaged beech forest at Hainich National Park in Germany

Credit

Peter Schall

Extensively managed beech forest at Hainich-Dün region in Germany.

Credit

Steffi Heinrichs

The study was supported by the EU Framework Programme Horizon 2020 via COST, the German Research Foundation (DFG), and the Kone Foundation, Finland.

 

Original publication: Duflot et al “Sustainable forest planning: assessing biodiversity effects of Triad zoning based on empirical data and virtual landscapes,” Proceedings of the National Academy of Sciences (PNAS) (2025). DoI: 10.1073/pnas.2512683122

 

Contact:

 

Queries welcome in English or German

Dr Peter Schall

University of Göttingen

Silviculture and Forest Ecology of the Temperate Zones

www.uni-goettingen.de/en/77271.html  

 

Queries welcome in English or French

Dr Rémi Duflot

University of Jyväskylä

Department of Biological and Environmental Science

www.remiduflot.com/

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Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2512683122 

Method of Research

Observational study

Subject of Research

Not applicable

Article Title

Sustainable forest planning: assessing biodiversity effects of Triad zoning based on empirical data and virtual landscapes

Article Publication Date

29-Sep-2025

 

U of A researchers reveal the importance of data choice in effective flood insurance

University of Arizona

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In a world covered with sensors and satellites, access to high-quality data that can help solve problems and improve systems is more widespread than ever. But, with such a wealth of information at our disposal, how do we know we're choosing the right data – and using it correctly? 

Researchers from the University of Arizona explored that question in the realm of flood insurance, and their findings could have implications for disaster response and recovery.  

In a study recently published in the journal Earth's Future, the researchers use a simulated flood insurance program in Bangladesh to compare real-world flood datasets gathered from five different sources. The team found that the type of data used affects not only the accuracy and speed of payouts but also the level of certainty in anticipating future insurance payouts, which directly influences program costs. 

Alex Saunders, the paper's lead author and a PhD candidate in the U of A School of Geography, Development & Environment, worked alongside Kevin Anchukaitis, director of the Laboratory of Tree-Ring Research and professor of Earth systems geography; Andrew Bennett, assistant professor of hydrology and atmospheric sciences; Beth Tellman, former assistant professor; and Jonathan Giezendanner, a former research associate, both in the School of Geography, Development & Environment. The team also included researchers from Virginia Tech, the Bangladesh University of Engineering and Technology and the Bangladesh Water Development Board.

Saunders believes their findings could benefit residents in flood-prone areas around the world by highlighting to insurance providers how selecting the most appropriate data from a wide range of options can improve disaster response and financial protection. 

"More and more floods are happening every year, and that brings with it an increase in the total damage they cause," he said. "In the case of insurance, creating a more accurate tool that helps people receive more timely payouts could help them get through some of the worst times of their lives." 

Governments, nonprofits and industries like agriculture, insurance and energy increasingly rely on Earth observational data: information gathered from satellites, aircraft and ground-based sensors that monitor the planet. Experts use this data to guide sustainable resource management, predict environmental change and respond to natural disasters. 

In the case of flood insurance, companies increasingly rely on proxies – indirect indicators of damage, such as rainfall levels or river height – rather than directly assessing damage in insurance claims, which can be costly in both fees and the time it takes. These proxies are combined into indices, numerical thresholds that trigger payouts once crossed. Despite their growing use, Saunders noted that fundamental aspects of this system, such as which data sources are included in the insurance indices, often lack comprehensive testing. 

To address that issue, Saunders and his colleagues studied Bangladesh's monsoon seasons between 2004 and 2023. They analyzed rainfall data, river levels and flood maps from the national flood agency, along with two types of satellite data that measure water coverage. One satellite method used traditional surface water readings, while the other relied on an artificial intelligence model designed specifically to track monsoon flood patterns in Bangladesh. The AI-powered model successfully captured the progression of flood waters, while the traditional satellite method delivered unreliable estimates when there was a high level of cloud cover. 

The researchers then tested the five methods by evaluating when insurance was triggered over the 20-year period, how quickly those payouts occurred and how predictable they were.   

Their analysis revealed that no single dataset consistently outperformed the others, though data choice did impact outcomes. In some cases, different indices disagreed on whether, or when, payouts should occur. These discrepancies were even more pronounced at the regional level, where local variations made certain datasets less reliable. Combining or comparing multiple sources also improved confidence in payout decisions. 

"A stream gauge can tell us how high a river is, but that doesn't automatically mean there's flooding – or that people are nearby and at risk," Saunders said. "Satellites can show the surface of a whole region, but rainfall data can be just as useful, even though rain doesn't always lead to floods if the water flows somewhere else. That's why it can take multiple datasets to really understand floods." 

Saunders added that one of the study's most interesting results came from the AI-powered satellite model thanks to its ability to detect floods even during persistent cloud cover. Compared with the traditional satellite method the researchers tested, the AI approach triggered payouts an average of one week earlier. The newer approach also reduced the uncertainty in anticipated payouts by more than 20 percent, lowering potential insurance costs to customers. 

Saunders recommends that index-based flood insurance programs test a wide range of data sources before implementation, incorporate multiple indices to reduce the risk of missed or unnecessary payouts, and explore new technologies like AI to improve accuracy and timeliness. 

"Insurance providers may be guided in their decision making based on what data is most easily available, but there are a plethora of choices and more types of data through means like satellite sensors," Saunders said. "But just because a specific data set is easily available or based on the newest technology, doesn't mean it's the right one for a given scenario. People should consider and compare the full range of available information to look for the best solutions." 

The need for more accurate insurance systems is urgent. Between 2000 and 2023, only 16 percent of the $1.77 trillion in global economic flood losses were insured, leaving governments, businesses and households to absorb the overwhelming majority of costs. Insurance and reinsurance – the practice of insurers transferring risk to other insurers – depend on accurate and timely information to support effective relief and recovery in communities impacted by flooding 

By utilizing different sources of Earth observational data, Saunders said that governments, insurers and other organizations could have critical insight with which to make important, life-altering decisions – as long as they use that data wisely. 

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Journal

Earth's Future

DOI

10.1029/2025EF005966 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

Sensitivity to Data Choice for Index-Based Flood Insurance