Annual spatio-temporal migration patterns of Hooded Cranes wintering in Izumi based on satellite tracking and their implications for conservation

Background: The Hooded Crane(Grus monacha) is listed as a vulnerable species by IUCN. Knowledge about the migration of the Hooded Crane is still limited. Here we reported the spatio-temporal migration patterns of Hooded Cranes wintering in Izumi, Japan, as well as important stopover areas for their conservation.Methods: Four adult and five subadult cranes, all wintering in Izumi, Japan, were fitted with satellite transmitters(GPS–GSM system) at their stopover sites in northeastern China in 2014 and 2015. We analyzed the time and duration of adults and subadults in spring and autumn migration, as well as the time and duration they stayed in breeding and wintering ground. In addition, we analyzed the land use of the cranes in stopover areas.Results: Adult cranes took much longer time to migrate both north in spring(mean days) compared with subadult cranes(15.3 and 5.2 days, respectively). H= 44.3 days) and south in fall(mean = 54.0 owever, the subadults had longer wintering(mean = 149.8 days) and nomadic(breeding season for adults) seasons(mean d with adults(133.8 and 122.3 days, respectively). Three important stopover areas have been= 196.8 days) compare identified: the region around Muraviovka Park in Russia, the Songnen Plain in China, and the west coast of South Korea, where cranes spent most of their migration time(62.2 and 85.7% in spring and autumn, respectively). During migration, nomadic period and winter, Hooded Cranes usually stay in croplands for resting and feeding. In non-wintering season, less than 6% of stopover sites were located within protected areas.Conclusion: Overall, our results contribute to understanding the annual spatio-temporal migration patterns of Hooded Cranes in the eastern flyway, and planning conservation measures for this species.

Optimizing protected areas to boost the conservation of key protected wildlife in China

To meet the challenge of biodiversity loss and reach the targets of the proposed Post-2020 Global Biodiversity Framework,the Chinese government updated the list of national key protected wildlife in 2021 and has been continually expanding the protected areas(PAs).However,the status of protected wildlife in PAs remains unclear.In this study,we conducted a national assessment of the status of protected wildlife and suggested an optimization plan to overcome these shortcomings.From 1988 to 2021,the number of protected species almost doubled,and the area of PAs increased by 2.4 times,covering over 92.8%of the protected species.Nonetheless,some protected species have less than 10%of their habitat included in PAs.Despite the significant addition of amphibians and reptiles to the latest protection list,they are the fewest species and are the least covered by PAs compared with birds and mammals.To fix these gaps,we systematically optimized the current PAs network by adding another 10.0%of China’s land area as PAs,which resulted in 37.6%coverage of protected species’habitats in PAs.In addition,26 priority areas were identified.Our research aimed to identify gaps in current conservation policies and suggest optimization solutions to facilitate wildlife conservation planning in China.In general,updating the list of key protected wildlife species and systematically optimizing PA networks are essential and applicable to other countries facing biodiversity loss.

Time and energy minimization strategy codetermine the loop migration of demoiselle cranes around the Himalayas

Tens of thousands of demoiselle cranes’crossing the Himalayas to the Indian subcontinent have been reported for decades,but their exact spring migration route remained a mystery until our previous study found they made a detour in spring along the western edge of the Himalayas and crossed the Mongolian Plateau to their breeding areas based on satellite telemetry of 3 birds.To corroborate the loop migration pattern and explore whether demoiselle crane’s loop migration route is shaped by time-and energy-minimization strategies in spring and autumn and how the temporal and spatial variation of environmental conditions contribute to crane’s selection of migration routes,we tracked 11 satellite-tagged demoiselle cranes from their breeding area in China and Russia,simulated 2 pseudo migration routes,and then compared the environmental conditions,time,and energy cost between true and pseudo routes in the same season.Results show that demoiselles’spring migration obeyed time-minimization hypothesis,avoiding the colder Qinghai-Tibet Plateau,benefited by abundant food and higher thermal and orographic uplift along the route;autumn migration follows energy-minimization hypothesis with the shorter route.Our research will contribute to uncover the mechanical reasons why demoiselle crane avoids crossing the giant barrier of the Himalayas in spring,and shapes a loop migration route.

滦河上游——白枕鹤西部种群的重要停歇地

白枕鹤(Grus vipio)为国家Ⅱ级重点保护野生动物,被IUCN列为易危(VU)物种。白枕鹤西部种群繁殖于中蒙俄交界处的达乌尔地区,数量呈下降趋势。我们于2017–2018年在蒙古国东部给白枕鹤西部种群的50只个体佩戴了GPS-GSM跟踪设备。截至2019年5月,获得春季和秋季迁徙路径各48条。分析结果显示:春季91.67%和秋季72.91%的跟踪个体在滦河上游(河北省沽源–内蒙古正蓝旗–多伦区域)停歇,春季停留时间36.16±15.00天、秋季20.26±11.08天,分别占春季和秋季迁徙时间的75%和67%,确定了这一区域是西部种群迁徙途中最重要的停歇地。迁徙路线栖息地选择模型结果显示,白枕鹤常在距离湖泊较近(<210 km)、海拔1,200–1,500 m,且坡度小(<1°)的区域停歇。而滦河上游和整条迁徙路线停歇位点比较的模型结果显示,滦河上游停歇地的海拔1,200–1,500 m与整条迁徙路线栖息地选择模型的结果一致;此外这个区域离河流更近(<70km),不仅有湿地和水体的栖息环境,还有草地和农田可供觅食和栖息。保护空缺分析发现滦河上游现有四处保护地,但在保护地内的迁徙停歇点不超过总位点的1.63%。综上,我们建议将滦河上游整体纳入保护地体系进行管理,为这一受胁物种及其栖息地管理和保护提供可靠保障。

Obtaining the best possible predictions of habitat selection for wintering Great Bustards in Cangzhou, Hebei Province with rapid machine learning analysis

Great Bustards(Otis tarda dybowskii)are one of the world’s heaviest flying birds,occupying grassland habitats in Eastern Asia.Our study is located at the most eastern Chinese wintering site in Cangzhou,Hebei Province,where approximately 100 individuals are concentrated in a small area(17.53 km2).Solid information is still lacking about the wintering areas for this subspecies in its eastern range and specifically for China.The study area consists of intensely used farmland in proximity to humans and is lacking conservation areas and wild,open fields.Here,we present our results from two years of field data collection on habitat selection.We choose a machine learning model approach based on a rapid assessment methodology for the winter habitat of the Great Bustard.It is based on a spatial analysis of the best available environmental data,which were collected relatively quickly.These relatively new methods in ecology are based on an ensemble of decision trees and include algorithms such as TreeNet,Random Forest and CART used in parallel.In this study,we collected bustard droppings(presence only)from 48 locations between December 2011 and January 2012 and used the sites as training data.Droppings from 23 locations were collected in November 2012,and those sites were used as test data.We used eight environmental variables as predictor layers for the response variable of bustard presence/availability.We employed a Geographic Information System(ArcGIS 10.1and Geospatial Modelling Environment)and Google Earth.Compared with the other three models,we found that predictions from Random Forest obtained a significant difference between presence and absence.According to this model,the three most important factors for wintering Great Bustards are distance to residential area,distance to water pools,and farmland area.Our model shows that wintering Great Bustards prefer locations that are over 400 m away from residential areas,within 900 m of water pools and on areas of farmland smaller than 0.5 km2.We think we can apply our analysis to Great Bustard management in our study area and the adjacent region and that this work sets a baseline for future research.