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      Contrasting spatial, temporal and environmental patterns in observation and specimen based species occurrence data

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          Abstract

          Species occurrence data records the location and time of an encounter with a species, and is valuable for many aspects of ecological and evolutionary analyses. A key distinction within species occurrence data is between (1) collected and preserved specimens that can be taxonomically validated (i.e., natural history collections), and (2) observations, which are more error prone but richer in terms of number and spread of observations. In this study we analyse the distribution in temporal, spatial, taxonomic and environmental coverage of specimen- and observation based species occurrence data for land plants in Norway, a region with strong climatic and human population density gradients. Of 4.8 million species occurrence records, the majority (78%) were observations. However, there was a greater species richness in the specimen record (N = 4691) than in the observation record (N = 3193) and most species were recorded more as specimens than observations. Specimen data was on average older, and collected later during the year. Both record types were highly influenced by a small number of prolific contributors. The species most highly represented in the observation data set were widespread or invasive, while in the specimen records, taxonomically challenging species were overrepresented. Species occurrence records were unevenly spatially distributed. Both specimen and observation records were concentrated in regions of Norway with high human population density and with high temperatures and precipitation, but in different regions within Norway. Observation and specimen records thus differ in taxonomic, temporal, spatial and environmental coverage for a well-sampled group and study region, potentially influencing the ecological inferences made from studies utilizing species occurrence data. The distribution of observation data dominates the dataset, so inferences of species diversity and distributions do not correspond to the evolutionary or physiological knowledge of species, which is based on specimen data. We make recommendations for users of biodiversity data, and collectors to better exploit the complementary strengths of these distinct biodiversity data types.

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          Rapid changes in flowering time in British plants.

          The average first flowering date of 385 British plant species has advanced by 4.5 days during the past decade compared with the previous four decades: 16% of species flowered significantly earlier in the 1990s than previously, with an average advancement of 15 days in a decade. Ten species (3%) flowered significantly later in the 1990s than previously. These data reveal the strongest biological signal yet of climatic change. Flowering is especially sensitive to the temperature in the previous month, and spring-flowering species are most responsive. However, large interspecific differences in this response will affect both the structure of plant communities and gene flow between species as climate warms. Annuals are more likely to flower early than congeneric perennials, and insect-pollinated species more than wind-pollinated ones.
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            New developments in museum-based informatics and applications in biodiversity analysis.

            Information from natural history collections (NHCs) about the diversity, taxonomy and historical distributions of species worldwide is becoming increasingly available over the Internet. In light of this relatively new and rapidly increasing resource, we critically review its utility and limitations for addressing a diverse array of applications. When integrated with spatial environmental data, NHC data can be used to study a broad range of topics, from aspects of ecological and evolutionary theory, to applications in conservation, agriculture and human health. There are challenges inherent to using NHC data, such as taxonomic inaccuracies and biases in the spatial coverage of data, which require consideration. Promising research frontiers include the integration of NHC data with information from comparative genomics and phylogenetics, and stronger connections between the environmental analysis of NHC data and experimental and field-based tests of hypotheses.
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              The ability of climate envelope models to predict the effect of climate change on species distributions

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                26 April 2018
                2018
                : 13
                : 4
                : e0196417
                Affiliations
                [001]Department of Natural History, NTNU University Museum, Norwegian University of Science and Technology, Trondheim, Norway
                State Museum of Natural History, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-0633-5595
                Article
                PONE-D-17-43585
                10.1371/journal.pone.0196417
                5919666
                29698441
                d953241b-2caa-4dc6-9756-edbb705fa870
                © 2018 Speed et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 December 2017
                : 12 April 2018
                Page count
                Figures: 5, Tables: 2, Pages: 17
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Taxonomy
                Computer and Information Sciences
                Data Management
                Taxonomy
                People and Places
                Geographical Locations
                Europe
                Norway
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Species Colonization
                Invasive Species
                Biology and Life Sciences
                Ecology
                Biodiversity
                Ecology and Environmental Sciences
                Ecology
                Biodiversity
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Density
                Biology and Life Sciences
                Plant Science
                Plant Taxonomy
                Biology and Life Sciences
                Taxonomy
                Plant Taxonomy
                Computer and Information Sciences
                Data Management
                Taxonomy
                Plant Taxonomy
                Ecology and Environmental Sciences
                Conservation Science
                Custom metadata
                All relevant data is available from public repository (GBIF Occurrence Download—6th October 2017, doi: 10.15468/dl.f2guqo. 2017).

                Uncategorized
                Uncategorized

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