DOI: 10.5061/dryad.6t1g1jx4h
title: Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent

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. 
description: 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. 
description: 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). 
description: # Data from 'Species representation in discrete habitats is patch
 size dependent' --- Contains the raw data and code used to reproduce
 the analysis and results in the manuscript. The simplest way to do this is
 to save all files provided to a single folder. Code needed to run the
 analyses in the paper are in scr_R_code_Dryad_R01.txt. Change the file
 extension from .txt to .R and then the script can be opened directly in
 R/R Studio and this includes code to load all objects described and run
 all analyses. ## Description of the data and file structure Description of
 data files: Data: **Datha.RData** - an R object of class 'list',
 each element of the list representing one of 202 p/a datasets obtained
 from published sources. Datasets are saved as sites x species dataframes,
 with patch area (in hectares) in the first column and species data in
 column numbers 2 to N+1, where N is the total number of species in that
 dataset (the +1 reflects the area data in the first column). Note, the
 number of rows (patches) and columns (area and species) differs for each
 element of the list (i.e., metacommunity dataset). **facVars_R01.RData** -
 an R object of class 'data.frame' with three columns giving the
 factor variables: 'mcom', or metacommunity type (island, habitat
 island or fragment); 'taxa', the broad taxonomic group (birds,
 non-avian vertebrate, plants, invertebrates); and 'datQual', or
 data quality (4 ordinal levels coded 4 &lt; 3 &lt; 2 &lt; 1, indicating a
 qualitative confidence level that the data for that patch represents a
 full census of all species present). **moddat_R01.RData** - an R
 data.frame object containing the untransformed model predictors calculated
 using the R script (scr_R_code_Dryad_R01). Column names refer to
 predictors as follows: * area (numeric) = patch area in hectares; * SR
 (integer) = species richness of the patch (integer count of species); *
 'zmips' (numeric) = MSLIP standardized effect size; *
 'omips' (numeric) = observed MSLIP; * 'res' (numeric)
 = residual deviation of patch richness from ISAR prediction; *
 'source' (factor) = code for distinct dataset; 'zsar'
 = slope (z parameter) of power law island species area relationship for
 that metacommunity; * 'ln_area' (numeric) = natural log of patch
 area in hectares; * 'ln_rich' (numeric) = natural log of SR; *
 'mcom' (factor) = metacommunity type (see Table 1, manuscript);
 * 'taxa' (factor) = broad taxonomic group; * 'datqual'
 (ordered factor) = ordinal factor indicating relative confidence (-4 &lt;
 -3 &lt; -2 &lt; -1) that data represent a full census for all patches
 within that metacommunity (see Table S2, Appendix S1 in manuscript
 Supporting Information for detail on assignment criteria).
 **mod.std_R01.RData** - an R data.frame with standardized data (mean = 0,
 SD = 1) for continuous predictors in moddat_R01.RData (see above). These
 are the data used to fit brmsfit object 'topmod'. **topmod** -
 an R object of class 'brmsfit' containing the top-ranking model
 used to generate results in the manuscript. ## Sharing/Access information
 Data was derived from multiple published grey and primary literature
 sources (i.e., all data are in the public domain). Full citations for the
 original source publications are detailed in the Supporting Information
 for the manuscript (see Appendix S1). Keywords and other search techniques
 used to collate the database are detailed in the following sources: *
 Deane and He (2018) Loss of only the smallest patches will reduce species
 diversity in most discrete habitat networks Global Change Biology
 24:5802-5814 * Deane (2022) Species accumulation in small-large vs
 large-small order: more species but not all species? Oecologia 200:273-284
 ## Code/Software scr_R_code_Dryad_R01.txt - is the R code to run the
 analyses, organised in three sections as follows (click on 'Show
 document outline' for ease of navigation in RStudio): 1.
 Pre-processing. Imports data and runs a for-loop to calculate the MSLIP
 standardized effect size. Can re-create the dataframe used in modelling.
 2. Modelling and diagnostics - gives code used to fit the final model, to
 check convergence and other diagnostics. 3. Analyse model - code to
 extract draws from the model posterior and create figures in the text.
 Uses R packages: brms, bayesplot, posterior, rstan, tidyverse, tidybayes.