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Potential landscape-scale pollinator networks across Great Britain: structure, stability and influence of agricultural land cover
Understanding spatial variation in the structure and stability of plant-pollinator networks, and their relationship with anthropogenic drivers, is key to maintaining pollination services and mitigating declines. Constructing sufficient networks to examine patterns over large spatial scales remains challenging. Using biological records (citizen science), we constructed potential plant-pollinator networks at 10km resolution across Great Britain, comprising all potential interactions inferred from recorded floral visitation and species co-occurrence. We calculated network metrics (species richness, connectance, pollinator and plant generality) and adapted existing methods to assess robustness to sequences of simulated plant extinctions across multiple networks. We found positive relationships between agricultural land cover and both pollinator generality and robustness to extinctions under several extinction scenarios. Increased robustness was attributable to changes in plant community composition (fewer extinction-prone species) and network structure (increased pollinator generality). Thus, traits enabling persistence in highly agricultural landscapes can confer robustness to potential future perturbations on plant-pollinator networks
The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models
Visual storytelling is an intriguing and complex task that only recently
entered the research arena. In this work, we survey relevant work to date, and
conduct a thorough error analysis of three very recent approaches to visual
storytelling. We categorize and provide examples of common types of errors, and
identify key shortcomings in current work. Finally, we make recommendations for
addressing these limitations in the future.Comment: Accepted to the NAACL 2019 Workshop on Shortcomings in Vision and
Language (SiVL
Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes?
FUNDING This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1. ACKNOWLEDGMENT We thank the USGS Powell Center âNext Generation Microbesâ working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.Peer reviewedPublisher PD
A Quality Model for Actionable Analytics in Rapid Software Development
Background: Accessing relevant data on the product, process, and usage
perspectives of software as well as integrating and analyzing such data is
crucial for getting reliable and timely actionable insights aimed at
continuously managing software quality in Rapid Software Development (RSD). In
this context, several software analytics tools have been developed in recent
years. However, there is a lack of explainable software analytics that software
practitioners trust. Aims: We aimed at creating a quality model (called
Q-Rapids quality model) for actionable analytics in RSD, implementing it, and
evaluating its understandability and relevance. Method: We performed workshops
at four companies in order to determine relevant metrics as well as product and
process factors. We also elicited how these metrics and factors are used and
interpreted by practitioners when making decisions in RSD. We specified the
Q-Rapids quality model by comparing and integrating the results of the four
workshops. Then we implemented the Q-Rapids tool to support the usage of the
Q-Rapids quality model as well as the gathering, integration, and analysis of
the required data. Afterwards we installed the Q-Rapids tool in the four
companies and performed semi-structured interviews with eight product owners to
evaluate the understandability and relevance of the Q-Rapids quality model.
Results: The participants of the evaluation perceived the metrics as well as
the product and process factors of the Q-Rapids quality model as
understandable. Also, they considered the Q-Rapids quality model relevant for
identifying product and process deficiencies (e.g., blocking code situations).
Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model
enables detecting problems that take more time to find manually and adds
transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by
IEEE in the 44th Euromicro Conference on Software Engineering and Advanced
Applications (SEAA) 2018. The final authenticated version will be available
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