On the Relevance and Moral Impediment of Digital Data Management, Data Sharing, and Public Open Access and Open Source Code in (Tropical) Research: The Rio Convention Revisited Towards Mega Science and Best Professional Research Practices

Huettmann, Falk

doi:10.1007/978-1-4939-2208-6_16

Cited by 24 publications

(8 citation statements)

References 26 publications

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“…Therefore, we here present a first-time analysis using data mining and machine learning to resolve these research questions. Specifically, we present an example for migratory passerine bird species in sub-Saharan Africa which may facilitate the use of these approaches in ornithology, conservation, and management (Huettmann, 2015;Zuckerberg et al, 2011).…”

Section: Predictors Associated With Declinesmentioning

confidence: 99%

“…We used and benefited from Open Access GIS-based data (sensu Ohse et al, 2009;Huettmann, 2015;see Huettmann, 2005;Huettmann, 2007;Zuckerberg et al, 2011) for best professional practices. These data are made publicly available to enhance scientific progress and to tackle new research questions, such as the ones posed in this study about declining, stable, and increasing bird populations.…”

Section: Limitations Of This Studymentioning

confidence: 99%

“…Previous studies Humphries and Huettmann, 2014) showed that this approach allows for reliable and meaningful predictions of spatial distributions, inferences of correlates of population, as well as relevant forecasting of the near future on a large scale. As argued in Huettmann and Czech (2006) and Huettmann (2015), our approach provides progressive solutions and new insights explicit in space and time that are highly relevant to a better understanding why these migratory species have been declining for decades and how to conserve them in the complex habitat and human-coupled landscape setting of Africa and beyond.…”

Section: Introductionmentioning

confidence: 99%

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Palearctic passerine migrant declines in African wintering grounds in the Anthropocene (1970–1990 and near future): A conservation assessment using publicly available GIS predictors and machine learning

Walther

Huettmann

2021

Science of The Total Environment

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The Anthropocene causes many massive and novel impacts, e.g., on migratory birds and their habitats. Many species of migratory birds have been declining on the Palearctic-African flyway in recent decades. To investigate possible impacts on a continental scale, we used 18 predictors extracted from 16 publicly available GIS layers in combination with machine learning methods on the sub-Saharan distributions of 64 passerine migrant species. These bird species were categorized as having experienced a 'Large Decline' (n = 12), a 'Moderate Decline' (n = 6) or 'No Decline' (n = 46) based on European census data from 1970 to 1990. Therefore, we present the first study for these species which uses publically available Open Access GIS-data and a multivariate (n = 18) and multi-species (n = 64) machine learning approach to deduce possible past impacts. We furthermore modelled likely future human population change and climate change impacts. We identified three predictor themes related to the distributions and declines of these migratory birds: (I) locations, represented by African ecosystems, countries, and soil types; (II) human population pressures and land-use intensities, the latter represented by land-use categories, habitat area, and cropland proportion; and (III) climatic predictors. This is the first study to relate migratory bird declines to human population pressures and land-use intensities using this type of analysis. We also identified areas of conservation concern, such as the Sahel region. Our models also predict that the declining trends of migratory birds will continue into the foreseeable future across much of Africa. We then briefly discuss some wider conservation implications in the light of the increasing drivers of biodiversity change associated with the Anthropocene as well as some possible solutions. We argue that only comprehensive systemic change can mitigate the impacts on the migratory birds and their habitats.

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Section: Predictors Associated With Declinesmentioning

confidence: 99%

Section: Limitations Of This Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Palearctic passerine migrant declines in African wintering grounds in the Anthropocene (1970–1990 and near future): A conservation assessment using publicly available GIS predictors and machine learning

Walther

Huettmann

2021

Science of The Total Environment

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“…Yet, all of this is ongoing in a digital world providing global access to information (Huettmann 2007(Huettmann , 2015b(Huettmann , 2015c. The search is on for new and innovative ways toward collaborative, respectful, place-informed, experiential, and sustainable education models that engage students in formal and informal learning while utilizing digital connectivity (Fig.…”

Section: Formal and Informal Learning And Place-based Educationmentioning

confidence: 99%

“…Digital explorations of museum objects are becoming more important (see Huettmann 2015bHuettmann , 2015c for real world examples and needs). The specimen-based, open-access database ARCTOS (http://arctos.database.museum) provides an example of how easily natural history museum collections can be accessed remotely.…”

Section: Introductionmentioning

confidence: 99%

Arctic science education using public museum collections from the University of Alaska Museum: an evolving and expanding landscape

et al. 2017

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Alaska faces unique challenges in STEM (science, technology, engineering, and mathematics) education, including limited accessibility to resources and learning opportunities, and a lack of place-based education resources. Museum education programs, traditionally focused on public outreach through docent-led tours, are playing an increasingly important role in both formal and informal aspects of STEM education to help address these challenges. The University of Alaska Museum (UAM) stands as a model in the Arctic region exemplifying how public natural history museum collections can be utilized to create active place-based learning experiences with the aim of increasing engagement in STEM literacy and building connections between museums and communities. These efforts take many forms, including the development of teaching materials involving physical objects and (or) online data from the open-access database ARCTOS, training preservice teachers, and implementing citizen science projects. Because many UAM specimens and objects are from Alaska, they are easily incorporated into place-based education, thereby demonstrating how the Arctic environment is unique at local and regional scales. Here, we showcase several programs that are either unique to UAM or part of larger national projects and include exemplar teaching modules in order to provide learning opportunities in the Arctic region and other rural settings.Key words : specimen-based science, STEM education, place-based education, citizen science, University of Alaska Museum.Résumé : L'Alaska est confronté à des défis exceptionnels en matière d'éducation en science, technologie, ingénierie et mathématiques (STIM), et parmi ces défis il y a l'accessibilité limitée aux ressources et aux possibilités d'apprentissage et un manque de ressources en éducation selon une approche locale. Les programmes éducatifs de musée, traditionnellement axés sur la sensibilisation du public au moyen de tours guidés, jouent un rôle de plus en plus important sur le plan tant formel qu'informel de l'enseignement de la STIM,

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The Forgotten Data: A Rather Short but Deep Story of Museums and Libraries in HKH and Similar Information Sources in Support of the Global Biodiversity Information System (GBIF.org) and Model-Predictions for Improved Conservation Management

Huettmann

2020

Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives

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On the Relevance and Moral Impediment of Digital Data Management, Data Sharing, and Public Open Access and Open Source Code in (Tropical) Research: The Rio Convention Revisited Towards Mega Science and Best Professional Research Practices

Cited by 24 publications

References 26 publications

Palearctic passerine migrant declines in African wintering grounds in the Anthropocene (1970–1990 and near future): A conservation assessment using publicly available GIS predictors and machine learning

Palearctic passerine migrant declines in African wintering grounds in the Anthropocene (1970–1990 and near future): A conservation assessment using publicly available GIS predictors and machine learning

Arctic science education using public museum collections from the University of Alaska Museum: an evolving and expanding landscape

The Forgotten Data: A Rather Short but Deep Story of Museums and Libraries in HKH and Similar Information Sources in Support of the Global Biodiversity Information System (GBIF.org) and Model-Predictions for Improved Conservation Management

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