Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
Authors/Creators
- 1. La Trobe University
- 2. Stellenbosch University
Description
Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.
Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants).
The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups.
Notes
All provided files are intended for use within the R-programming environment. The raw database records required to run the analysis from scratch, along with processed data used to run regression models are saved as R data objects (i.e., extension '.RData'). The fitted model obtained in analysis and used to generate results is also an R object, but of class 'brmsfit' (requiring R package brms is loaded into the R-workspace). Both object types can be opened in R (R Studio, etc).
Funding provided by: Australian Research Council
Crossref Funder Registry ID: https://ror.org/05mmh0f86
Award Number: DP200101680
Funding provided by: National Research Foundation
Crossref Funder Registry ID: https://ror.org/05s0g1g46
Award Number: 89967
Methods
Details regarding keyword and other search strategies used to collate the raw database from published sources were presented in Deane, D. C. & He, F. (2018) Loss of only the smallest patches will reduce species diversity in most discrete habitat networks. Glob Chang Biol, 24, 5802-5814 and in Deane, D.C. (2022) Species accumulation in small-large vs large-small order: more species but not all species? Oecologia, 200, 273-284.
Minimum data requirements were presence absence records for all species in all patches and area of each habitat patch. The database consists of 202 published datasets. The first column in each dataset is the area of the patch in question (in hectares), other columns record presence and absence of each species in each patch. In the study, a metric was calculated for every patch that quantifies how the incidence of species in each patch compares with an expectation derived from the occupancy of all species in all patches (called mean species landscape-scale incidences per patch or MSLIP). This value was regressed on patch size and other covariates to determine whether the representation of widespread (or narrowly distributed) species changes with patch size.
In summary, the work flow proceeded in three steps.
1. Pre-processing. This stage consisted of calculating a standardized effect size (SES) for the MSLIP metric for every patch and extracting important covariates (taxon, patch type, total number of patches, total number of species, patch-level deviations from fitted island species area relationships, data quality) to be used in model building.
2. Model building. MSLIP SES was then modelled against patch area and other covariates using a multilevel Bayesian (meta-)regression model using Stan and brms in the statistical programming langauge R (Version 4.3.0).
3. Model analysis. The final model was analysed by running different scenarios and the patterns interpreted in light of the hypotheses under test and creating figures to illustrate these.
Files
scr_R_code_Dryad_R01.txt
Files
(12.1 kB)
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Additional details
Related works
- Is source of
- 10.5061/dryad.6t1g1jx4h (DOI)