Code and synthetic data for analysis of relation between species sizes and local abundances trends
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- English (en)
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- utf8
- Dataset Reference Date ()
- 2024-05-01
- Identifier
- doi: / 10.5285/1f7687de-d68e-4349-b4d3-b7d3a127a7df
- Other citation details
- Terry, J.C.D., O'Sullivan, J.D., Rossberg, A.G. (2024). Code and synthetic data for analysis of relation between species sizes and local abundances trends. NERC EDS Environmental Information Data Centre 10.5285/1f7687de-d68e-4349-b4d3-b7d3a127a7df
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- If you reuse this data, you should cite: Terry, J.C.D., O'Sullivan, J.D., Rossberg, A.G. (2024). Code and synthetic data for analysis of relation between species sizes and local abundances trends. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/1f7687de-d68e-4349-b4d3-b7d3a127a7df
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- Biota
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Report
- Dataset Reference Date ()
- 2010-12-08
- Statement
- To generate the assemblage time series, we downloaded all studies available in the ‘open’ component of the BioTIME database of community time series from Zenodo. We identified studies as ‘multi-site’ or ‘single-site’ based on the number of coordinates in the BioTIME database. Single-site studies were considered as one combined assemblage, whilst widely dispersed ‘multi-site’ studies were portioned into assemblages based on a global hexagonal grid of 96 km2 cells using the dggridR package in R. We retained records from assemblages with abundance or biomass data of at least 10 distinct species and at least 5 years between the first and last record. We used four separate trait databases that include some measure of organism size, but we did not mix information between databases. For amniotes, an amniote life history database was downloaded. For plants, we used the TRY database. For fish, we downloaded a curated database of fish traits, which in turn is largely based on data from the FishBase database. It is focused on the North Atlantic and Pacific continental shelf, but this represents the majority of the relevant BioTIME studies. For marine species, we downloaded size data from the WoRMS database. Data cleaning was performed in order to match up species names in the trait databases and BioTIME database. We assessed each assemblage–trait combination where ≥40% and ≥5 of the species had data for that trait and >80% of year samples contained at least 5 species. We excluded transitory species within each assemblage by including only those species that were seen in over half of the year samples. Where this filtering left data from less than 1% of the cells in the original study, we removed the whole study. Where a study included both ‘abundance’ and ‘biomass’ data, we preferentially used the abundance data. Studies with only presence–absence data were not used. To calculate the relative change in abundance of each species, we fitted the square-root transformed and scaled species totals as a function of year for each assemblage using ordinary least-squares regression models and calculate the slope β for each species. The main response variable τ for each assemblage was then computed as Kendall’s rank correlation coefficient between size trait values and the set of βs. Species with missing trait values were excluded from the calculation of τ. Where there were multiple assemblages per study, study-level τ was taken as a simple arithmetic mean of all assemblage-level τ values. We also test two alternative transformations of the population data. To examine study-level determinates of τ within each size trait, for each study we calculated: (1) the mean total species richness of each assemblage over the time frame, (2) the mean assemblage-level trait data completeness, (3) the mean number of years from which there were data, (4) the mean span of years from which there were data, (5) the log10-transformed number of assemblages within the study (that is, the spatial extent), (6) the absolute latitude of the centre of the study and (7) the range of traits in the assemblage (log10(max) − log10(min)). We fitted a set of linear models to assess whether these factors could predict either τ or τ2. All analysis used the R language, and scripts are included in the KnittedScripts folder. More information is provided in the supporting information.
Metadata
- File identifier
- 1f7687de-d68e-4349-b4d3-b7d3a127a7df XML
- Metadata Language
- English (en)
- Character set
- ISO/IEC 8859-1 (also known as Latin 1)
- Resource type
- application
- Hierarchy level name
- application
- Metadata Date
- 2024-05-08T15:19:57
- Metadata standard name
- UK GEMINI
- Metadata standard version
- 2.3