e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Spatial, temporal, and life history assumptions influence consistency of landscape effects on species distributions


Models describing relationships between landscape features and species distribution patterns often display inter-study inconsistencies. Identifying factors contributing to these inconsistencies is a vital step in clarifying the ecological importance of landscape features and synthesizing an effective knowledge base for use in conservation contexts. We examined the influence of several spatial, temporal, and life history assumptions on the outcomes of distribution versus landscape models (DLMs) relating wetland bird communities at 29 Massachusetts (USA) sites to independent urbanization, wetland, forest, and agricultural landscape gradients. We considered a bird specialization index as well as obligate and facultative species richness as response variables. Landscape gradients were quantified at 10 landscape extents (0-1000 m in 100 m increments) and three time periods (1971, 1985, 2005). Univariate models indicated that our specialization index showed: (1) the strongest response to landscape gradients at small extents (200 m); (2) a negative, threshold response to urbanization was superior to a linear fit; and (3) no evidence of time-lagged effects of landscape change. Interestingly, the form of our model (i.e. linear versus threshold) influenced the extent at which strongest effects were detected. Multivariate models relating the specialization index as well as obligate and facultative species richness to landscape gradients showed evidence of annual variability (i.e. composition, parameter estimates, and variability explained) that did not depend upon an organism's degree of specialization. Our results provide evidence that violations of common assumptions (e.g. selection of appropriate extent, lack of time-lagged effects, etc.) can impact the outcome of DLMs, which could lead to inter-study inconsistencies.

  • US
  • Tufts_Univ (US)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.