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ZENODO
Dataset . 2023
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Data sources: ZENODO
DRYAD
Dataset . 2023
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Data used in: Assessing the resilience and viability of communities in a changing environment

Authors: Sæther, Bernt-Erik; Engen, Steinar; Solbu, Erik Blystad;

Data used in: Assessing the resilience and viability of communities in a changing environment

Abstract

# Data used in: Assessing the resilience and viability of communities in a changing environment [https://doi.org/10.5061/dryad.5dv41nsc2](https://doi.org/10.5061/dryad.5dv41nsc2) All population estimates are based on nest search or mapping of territorial individuals within a specific area. ## Description of data and file structure The data is structured into 'Raw' and 'Processed' data and R-code to process the data. The raw data is in different formats, some extracted from tables in puplications: * Ammarnäs: https://doi.org/10.34080/os.v14.20236 * Birdsong Valley: https://doi.org/10.34080/os.v4.23023 * Budal: https://doi.org/10.1111/j.1474-919X.2004.00338.x * Eastern Wood: https://doi.org/10.1002/9780470999592.app2 Some contains additional years of abundance. The processed data contains four columns: * **location:** one of four locations: Ammarnäs, Budal, Birdsong Valley and Eastern Wood * **year:** of sensus * **species:** * **abundance:** The raw data contains four datasets: * bird\_ammarnas.csv: three columns: Year, Species, Abundance * bird\_sussex.txt: three columns: Species, Year, Abundance * Birdsong Valley.xlsx: two sheets: "Data": five columns (unnamed): Species, Year, Abundance, Location, Reference. "Info": location and description * Budalen\_pr\_2018\_revised.xlsx: * one sheet: "Data": data in wide format, Norwegian abbreviated names, but corresponding scientific names are found in the R-code "data\_from\_raw\_to\_processed.R". ## Code/Software The code for importing and analysing the raw data as done in the paper can be found here: [https://doi.org/10.5281/zenodo.8252471](https://doi.org/10.5281/zenodo.8252471). The code for processing the raw data to one single dataset can be found in the R-code "data\_from\_raw\_to\_processed.R".

In order to assess community resilience, we propose to analyse how variation in overall abundance of individuals affect the number of species. We define community senstivity as the ratio between rate of change in log expected number of individuals in the community and the rate of change in the log expected number of species. Second, we define community resistance as the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. To illustrate these concepts we use four datasets of bird communities in European deciduous forests. We estimated the total variance of the species abundance distribution in order to calculate the community sensitivity and resistance. We found large differences in species heterogeneity and species-specific response to environmental fluctuations, the two major components of the total variance. The datasets are collected from various previously published sources, see references below and in the paper. The code for importing and analysing the raw data as done in the paper can be found here: https://doi.org/10.5281/zenodo.8252471, but we also provide better-structured datasets for easier applications to others.

The data consists of population estimates based on nest search or mapping of territorial individuals within a specific area. For the locations we have: Location Position Meters above sea level Study area (size) Time span Ammarnäs 65°58'-65°59'N, 15°58'-16°8E 540 - 720 m.a.s.l. 0.420 km2 1964-1999 Budal 62°45'N, 10°30'E 800 m.a.s.l. 0.3 km2 1967-2018 Birdsong Valley 55°43'N, 13°18'E 0.13 km2 1953-1992 Eastern Wood 51°30'N, -0°38'E 0.16 km2 1950-1979

Keywords

Birds, Population dynamics, Community ecology, FOS: Natural sciences

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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