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David B. Roy

Researcher at Natural Environment Research Council

Publications -  261
Citations -  30154

David B. Roy is an academic researcher from Natural Environment Research Council. The author has contributed to research in topics: Biodiversity & Population. The author has an hindex of 70, co-authored 250 publications receiving 26241 citations. Previous affiliations of David B. Roy include Rothamsted Research & University of Sheffield.

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Reduced-effort schemes for monitoring butterfly populations

TL;DR: Schemes with few sampling visits per year are cost-effective for expanding butterfly monitoring across Europe, and can be applied to national monitoring programmes and lead to effective assessment of continent-wide trends in populations.
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A new red list of British butterflies

TL;DR: Over the last century butterflies have undergone substantial changes in abundance and range in Great Britain and monitoring has improved markedly, rendering previous Red List assessments outdated.
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Spatial covariation between freshwater and terrestrial ecosystem services

TL;DR: It is indicated that relationships between indicators of services can change dramatically depending on the societal pressures and other regional conditions, and the delivery of multiple ecosystem services requires the development of regional strategies, or of national strategies that take account of regional variation.

Delivering Alien Invasive Species Inventories for Europe: DAISIE as a tool for addressing biological invasions

TL;DR: The DAISIE inventory, accounts, and distribution maps as mentioned in this paper provide the first qualified reference system on invasive alien species for the European region and can be used to synthesise current knowledge and trends in biological invasions in Europe.
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Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records

TL;DR: In this paper, a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends is presented, where the model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science program.