Phenotypic and genetic diversity data recorded in island and mainland populations worldwide

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records.

Methods
Description of methods used for collection/generation of data: 
We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny.

First paper filter
To be useful, the papers must have met the following criteria:

Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites);

Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic);

Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms);

Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online
(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/);

Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions;

Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses;

The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering.

Second paper filter
In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,
allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species.

Methods for processing the data:
We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses.

References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0.
Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64.
Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12.
Tummers, B. (2006). DataThief III. https://datathief.org/