Breeding range shift of the red-crowned crane (Grus japonensis) under climate change

arc-minutes (~10 km spatial resolution). Andriamasimanana and Cameron also used this kind of data to predict the impacts of climate change on threatened birds in Madagascar [39]. There are many environmental factors that determine the geographic distribution of the red-crowned crane, such as food resource, availability of nesting site, vegetation cover, and cli- mate [2]. However, the spatial data of most factors are not available at this large scale in our study. Here we only studied the bioclimatic niche of this species using bioclimatic niche modeling. Coetzee et al. used bioclimatic niche modeling to predict range changes in relation to the Important Bird Areas (IBAs) network in southern Africa under climate change [40]. We also attempted to assess the effects of the change of bioclimatic conditions under changing cli- mate on the breeding range of this endangered species. The 19 bioclimatic variables used for bioclimatic niche modeling were obtained from WorldClim dataset (http://www.worldclim. org/) [41]. Bioclimatic variables represent the extreme and general trend of monthly tempera- ture and precipitation (Table 1). This dataset is widely used in predicting distributions of spe- cies. The future simulation data of bioclimatic variables was obtained from CCAFS GCM DATA PORTAL (http://www.ccafs-climate.org/data/). It is part of the International Centre for Tropical Agriculture (CIAT) and The CGIAR Research Program on Climate Change, Agricul- ture and Food Security (CCAFS) [42]. We chose simulations from General Circulation Models

(GCMs) used in IPCC 5th report. For emission scenarios, we used all four representative con- centration pathways (RCPs), i.e. RCP 2.6 (low emissions), RCP 4.5 (intermediate emissions), RCP 6 (intermediate emissions), and RCP 8.5 (high emissions). Those four RCPs are named based on how much heating they would produce in the year 2100–2.6, 4.5, 6.0, and 8.5 watts per square meter (W/m2), respectively [43]. The monthly maximum temperature, monthly minimum temperature and monthly total precipitation from GCMs were statistically down- scaled based on delta method. The 19 bioclimatic variables were then calculated from tempera- ture and precipitation variables (http://ccafs-climate.org/downloads/docs/Downscaling-WP- 01.pdf). For climate model, we chose HadGEM2-ES (Hadley Global Environment Model 2— Table 1. Bioclimatic variables. Abbreviation Description Bio_1 Annual Mean Temperature Bio_2 Mean Diurnal Range (Mean of monthly (max temp—min temp)) Bio_3 Isothermality (BIO2/BIO7) (� 100) Bio_4 Temperature Seasonality (standard deviation �100) Bio_5 Max Temperature of Warmest Month Bio_6 Min Temperature of Coldest Month Bio_7 Temperature Annual Range (BIO5-BIO6) Bio_8 Mean Temperature of Wettest Quarter Bio_9 Mean Temperature of Driest Quarter Bio_10 Mean Temperature of Warmest Quarter Bio_11 Mean Temperature of Coldest Quarter Bio_12 Annual Precipitation Bio_13 Precipitation of Wettest Month Bio_14 Precipitation of Driest Month Bio_15 Precipitation Seasonality (Coefficient of Variation) Bio_16 Precipitation of Wettest Quarter Bio_17 Precipitation of Driest Quarter

Bio_18 Precipitation of Warmest Quarter Bio_19 Precipitation of Coldest Quarter https://doi.org/10.1371/journal.pone.0229984.t001 PLOS ONE Breeding range shift of the red-crowned crane (Grus japonensis) under climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0229984 March 12, 2020 4 / 16