Macaroni Penguin (Eudyptes chrysolophus) - current and future species distribution models

• Other Authors: Jeremy James VanDerWal (hasAssociationWith), Jeremy James VanDerWal (hasCollector)
• Format: Dataset
• Language: unknown
• Published: James Cook University
• Subjects: geographic information system; spatial data; spatial analysis; climate change; GIS; Eudyptes chrysolophus; Macaroni penguin
• Online Access: https://researchdata.edu.au/macaroni-penguin-eudyptes-distribution-models/9576; https://researchdata.jcu.edu.au//published/68f955bbbb917bca0f16b339fc8074b5

Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling. Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - http://www.bom.gov.au/jsp/awap/ ) (Jones et al 2007, Grant et al 2008). Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on http://climascope.tyndall.ac.uk . Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at http://climascope.tyndall.ac.uk and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; http://www.bom.gov.au ). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at http://www.worldclim.org/bioclim . All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R ( http://www.r-project.org/ ). Used in the modelling were annual mean temperature, temperature seasonality, ...