Category: One Ecosystem

Highly dependent on the different aspects of global change, variations in ecosystem services supply can also have direct impacts on human well being. A new article published in the open access journal One Ecosystem assesses the relationships between climate and land use change and ecosystem services supply in Europe, to pave the way on research connecting them to adaptation and human well being in a changing world.

Ecosystem services arise when ecological structures or functions contribute toward meeting a human demand. With global change impacting biodiversity and ecosystems properties, ecosystem services supply are also likely to be affected, consequently impacting various aspects of human well being.

In this context, assessing the possible bio-physical impacts of the ongoing and future changes in climate and land use becomes highly relevant for designing mitigation and adaptation policies.

While undergoing a comprehensive climate and land use impact assessment continues to be a demanding research challenge due to the large knowledge gaps, in their new paper, the team of scientists from the European Commission’s Joint Research Centre, Ispra, Italy and the Institute for Environmental Studies at the VU University Amsterdam, the Netherlands, present a first of its kind spatially explicit preliminary assessment of the changes in ecosystem services supply as a function of these global change drivers.

Carried out for the mainland of the 28 Member States of the European Union, the focus of this analysis is on regulating ecosystem services, due to their direct dependency on the proper functioning of ecosystems. Focusing on three regulating services: air quality regulation, soil erosion control, and water flow regulation, the new research presents an assessment of changes related to global change and their projected impacts, positive or negative, on human well being in the different European regions.

“Considering both land use projections and climate change scenarios in our research, in principle, enabled us to capture the main pressures acting on ecosystems and their services, thus enhancing the suitability of this approach to generate policy-relevant information,” explains the authors. “Yet, this study is only preliminary and a stepping stone for further research, needed not only to expand the analysis to other ES, but also to incorporate processes and scaling properties of the systems considered as they become available, and to account for spatial dependencies.”

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Original Source:

Polce C, Maes J, Brander L, Cescatti A, Baranzelli C, Lavalle C, Zulian G (2016) Global change impacts on ecosystem services: a spatially explicit assessment for Europe. One Ecosystem 1: e9990. https://doi.org/10.3897/oneeco.1.e9990

Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As one of its first publications, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data.

Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of ‘big data’ meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

“A wider adoption of machine-learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science,” explains the author of the study, Dr. Anne Thessen, the Ronin Institute for Independent Scholarship. “Despite these advantages, however, machine-learning techniques have not met their full potential in ecology and earth science”.

The present gap between the potential and actual use of machine-learning methods is mainly due to to the lack of communication and collaboration between the machine-learning research community and natural scientists; the current deficiency in graduate education in machine learning methods; and the requirement for a robust training and test data set.

However, according to the newly published review paper, these impediments can be overcome through financial support for collaborative work and education.

“For many researchers, machine learning is a relatively new paradigm that has only recently become accessible with the development of modern computing. In this paper I suggest several mechanisms through which this useful method can be quickly introduced within the ecological and earth science fields, to ensure their wider application.” adds Dr. Thessen.

“We are extremely happy to pioneer One Ecosystem publications with this particular article. Created as an innovator in the fields of Ecology and Sustainability Sciences, one of the journal’s main objectives is to answer the need for Open Access not only to the final research content, but also to all underpinning data. Tackling issues of the ‘big data’ era, this article provides a perfect match for being among the first publications in a journal that aims at innovation,” comments Benjamin Burkhard, Editor-in-Chief of One Ecosystem.

Original Source:

Thessen A (2016) Adoption of Machine Learning Techniques in Ecology and Earth Science. One Ecosystem 1: e8621. doi:10.3897/oneeco.1.e8621

Additional information:

The author would like to acknowledge NASA for financial support and the Boston Machine Learning Meetup Group for inspiration.