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Hu H, Zhou H, Li Y, Li Y, Yan Y, Yang J, Chen J, Chen Y, Cui D. The Involvement of Human Factors Brings New Findings for Predicting Global Suitability Habitat for Hyphantria cunea (Lepidoptera: Arctiidae). Ecol Evol 2025; 15:e71421. [PMID: 40421063 PMCID: PMC12104870 DOI: 10.1002/ece3.71421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 04/15/2025] [Accepted: 04/24/2025] [Indexed: 05/28/2025] Open
Abstract
Invasive pests have spread globally at an unprecedented scale, severely threatening biodiversity and resulting in significant economic losses, emerging as a global problem. This study utilizes the Maxent model, incorporating human and natural factors to predict the current and future potential global distribution of Hyphantria cunea, for comparison with climate change. Results indicate that under the influence of climate change, human factors have significantly altered the potential global distribution of H. cunea. In contrast to the potential distribution driven by climate change, this paper suggests that the suitable habitat area for H. cunea in Oceania, Southern Hemisphere, is expected to increase. Over the long term, under the SSP126 and 585 scenarios, there is a forecasted reduction of 25.2% and 33.2% in the suitable living area for H. cunea, whereas the SSP245 and 370 scenarios anticipate increases of 13.9% and 5.7%, respectively. Moreover, this research identifies areas of high suitability across continents and forecasts changes in the distribution patterns of H. cunea in the future. It offers crucial insights for developing more effective global quarantine strategies and pest management policies.
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Affiliation(s)
- Haochang Hu
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbinChina
| | - Hongwei Zhou
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbinChina
| | - Yuxi Li
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbinChina
| | - Yongzheng Li
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbinChina
| | - Yunbo Yan
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbinChina
| | - Jun Yang
- Forestry Grassland Investigation and Planning Institute of Heilongjiang ProvinceHarbinChina
| | - Jun Chen
- Fengcheng Forestry Pest Control and Quarantine StationFengcheng Forestry Development Service CenterFengchengChina
| | - Yumo Chen
- School of Materials Science and EngineeringNortheastern UniversityShenyangChina
| | - Di Cui
- Heilongjiang Forestry Technology Service CenterHarbinChina
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Du X, Fang Y, Zhao H, Xu X. Spatiotemporal evolution and driving forces of landscape structure and habitat quality in river corridors with ceased flow: A case study of the Yongding River corridor in Beijing, China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025; 374:123861. [PMID: 39778355 DOI: 10.1016/j.jenvman.2024.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 12/15/2024] [Accepted: 12/23/2024] [Indexed: 01/11/2025]
Abstract
Flow cessation leads to severe degradation of river corridor landscape structure, habitat quality, and ecological functions. This study focuses on the representative river with ceased flow in northern China, the Yongding River plain section. Utilizing long-term, high-resolution satellite remote sensing imagery and the InVEST model, we analyzed the spatiotemporal evolution of landscape structure and habitat quality (HQ) before and after river corridor flow cessation over the past 50 years. The study further employs partial least squares regression (PLSR) to explore the impact of landscape structural changes on HQ and uses generalized additive models (GAMs) and geographical detector (GeoDetector) to quantitatively identify key factors affecting habitat degradation and their interactive effects. Results indicate that from 1967 to 2018, mid-channel bar, floodplain, and waterbody decreased sharply from 37.4% to 3.8%. The mean HQ value dropped from 0.58 to 0.34 after flow cessation. Although HQ slightly recovered post-2004, high-quality habitat areas remain absent. Different landscape structures significantly influence HQ, with increased size and area of the waterbody and forest patches positively contributing, while cultivated land, barren land, and built-up land generally have negative impacts. PLAND, LPI, MPS, and AWMPFD are key metrics for optimizing landscape structure and implementing habitat restoration in river management. Anthropogenic activities emerged as the primary driver of river corridor habitat degradation post-flow cessation. Different drivers exhibit complex linear and nonlinear effects on HQ. Based on these findings, we propose ecological management strategies for river corridors with ceased flow. This study is essential for a deeper understanding of river corridors' structural dynamics and degradation mechanisms, providing a scientific basis for effective ecological restoration and management.
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Affiliation(s)
- Xintong Du
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
| | - Yan Fang
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
| | - Haiyue Zhao
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
| | - Xiaoming Xu
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
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Zhang Z, Wang C, Gong G, Chen Y, Ma S, Wu Y, Wang H, Li Y, Duan H. Biodiversity conservation and management of lake wetlands based on the spatiotemporal evolution patterns of crane habitats. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 353:120257. [PMID: 38330843 DOI: 10.1016/j.jenvman.2024.120257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024]
Abstract
The typical lake wetlands in the middle and lower reaches of the Yangtze River are important wintering sites of cranes in China. The spatiotemporal evolution of crane populations and their habitats has great value in clarifying the pivotal role of regional lake wetlands in biodiversity conservation. Therefore, 2562 data points of four crane species were selected in this study. The data reflected the distributional position of the cranes over the period 2000-2020. Twelve surrounding environmental factors were selected to investigate the spatiotemporal evolution in the study area by using the MaxEnt model. The Jackknife method was used to identify the main environmental factors affecting the choice of crane habitats. The results indicated that: (1) Developed land in the study area increased by 42,795.81 hm2. The crane populations were mainly distributed in the farmland and mudflat, and their number decreased yearly. (2) From 2000 to 2020, the area of suitable crane habitat experienced an overall decrease. Specifically, the mid-suitable area dwindled by 6234.23 hm2, marking a substantial reduction of 52.05 %. Similarly, the most suitable area saw a decline of 786.41 hm2, representing a noteworthy decrease of 71.09 %. (3) The findings from the analysis of influencing factors revealed a dynamic pattern over the years. Habitat type, water density, and distance to water were the main influencing factors in the study area from 2000 to 2020. This study provides a new perspective on the conservation and structural habitat restoration of crane populations in the middle and lower reaches of the Yangtze River.
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Affiliation(s)
- Zihan Zhang
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Cheng Wang
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Guanqing Gong
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Yangyang Chen
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Siyu Ma
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Yutong Wu
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Hanwei Wang
- School of Economics and Management, Anhui Agricultural University, Hefei, 230036, China.
| | - Yufeng Li
- School of Marine Science and Engineering, Nanjing Normal University, Nanjing, 210023, China.
| | - Houlang Duan
- Key Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
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Dou X, Guo H, Zhang L, Liang D, Zhu Q, Liu X, Zhou H, Lv Z, Liu Y, Gou Y, Wang Z. Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:166239. [PMID: 37572926 DOI: 10.1016/j.scitotenv.2023.166239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/14/2023]
Abstract
The Yellow River Delta (YRD) wetland is one of the largest and youngest wetland ecosystems in the world. It plays an important role in regulating climate and maintaining ecological balance in the region. This study analyzes the spatiotemporal changes in land use, wetland migration, and landscape pattern from 2013 to 2022 using Landsat-8 and Sentinel-1 data in YRD. Then wetland landscape changes and the impact of human activities are determined by analyzing correlation between landscape and socio-economic indicators including nighttime light centroid, total light intensity, cultivated land area and centroid, building area and centroid, economic and population. The results show that the total wetland area increased 1426 km2 during this decade. However, the wetland landscape pattern tended to be fragmented from 2013 to 2022, with wetlands of different types interlacing and connectivity decreasing, and distribution becoming more concentrated. Different types of human activities had influences on different aspects of wetland landscape, with the expansion of cultivated land mainly compressing the core area of wetlands from the edge, the expansion of buildings mainly disrupting wetland connectivity, and socio-economic indicators such as total light intensity and the centroid mainly causing wetland fragmentation. The results show the changes of the YRD wetland and provide an explanation of how human activities effect the change of its landscape, which provides available data to achieve sustainable development goals 6.6 and may give an access to measure the change of wetland using human-activity data, which could help to adject behaviors to protect wetlands.
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Affiliation(s)
- Xinyu Dou
- School of Earth and Space Sciences, Peking University, Beijing 100871, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
| | - Huadong Guo
- School of Earth and Space Sciences, Peking University, Beijing 100871, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.
| | - Lu Zhang
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.
| | - Dong Liang
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Zhu
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Xuting Liu
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Heng Zhou
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuoran Lv
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Liu
- School of Earth and Space Sciences, Peking University, Beijing 100871, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
| | - Yiting Gou
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhoulong Wang
- Signal & Communication Research Institute, China Academy of Railway Sciences Group Co., Ltd, Beijing 100081, China
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Zhang P, Zhang S, Zou Y, Wu T, Li F, Deng Z, Zhang H, Song Y, Xie Y. Integrating suitable habitat dynamics under typical hydrological regimes as guides for the conservation and restoration of different waterbird groups. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118451. [PMID: 37385199 DOI: 10.1016/j.jenvman.2023.118451] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/17/2023] [Accepted: 06/16/2023] [Indexed: 07/01/2023]
Abstract
The operation of the Three Gorges Project (TGP) has influenced the wetland ecosystems downstream, thereby affecting the distribution of habitats suitable for waterbirds. However, dynamic studies on habitat distribution under different water regimes are lacking. Here, using data from three successive wintering periods representing three typical water regimes, we modelled and mapped the habitat suitability of three waterbird groups in Dongting Lake, which is the first river-connected lake downstream of the TGP, and a crucial wintering ground for waterbirds along the East Asian-Australasian Flyway. The results showed that the spatial pattern of habitat suitability varied among the wintering periods and waterbird groups. The analysis estimated the largest suitable habitat area for the herbivorous/tuber-eating group (HTG) and the insectivorous waterbird group (ING) under a normal water recession pattern, whereas early water recession had a more adverse effect. The suitable habitat area for the piscivorous/omnivorous group (POG) was higher under late water recession than under normal conditions. The ING was the most affected by hydrological changes among the three waterbird groups. Further, we identified the key conservation and potential restoration habitats. The HTG exhibited the largest key conservation habitat area compared to the other two groups, while the ING showed a potential restoration habitat area larger than its key conservation habitat area, indicating its sensitivity to environmental changes. The optimal inundation durations from September 1 to January 20 for HTG, ING and POG were 52 ± 7 d, 68 ± 18 d, and 132 ± 22 d, respectively. Therefore, the water recession starting in mid-October may be favourable for waterbirds in Dongting Lake. Altogether, our results can be used as guidance for prioritising certain management actions for waterbird conservation. Moreover, our study highlighted the importance of considering habitat spatiotemporal variation in highly dynamic wetlands when implementing management practices.
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Affiliation(s)
- Pingyang Zhang
- Key Laboratory of Agro-ecological Processes in Subtropical Regions, Chinese Academy of Sciences, Changsha, 410125, China; Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China
| | - Siqi Zhang
- Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yeai Zou
- Key Laboratory of Agro-ecological Processes in Subtropical Regions, Chinese Academy of Sciences, Changsha, 410125, China; Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China.
| | - Ting Wu
- College of Resources and Environment, Hunan Agricultural University, Changsha, 410128, China
| | - Feng Li
- Key Laboratory of Agro-ecological Processes in Subtropical Regions, Chinese Academy of Sciences, Changsha, 410125, China; Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China
| | - Zhengmiao Deng
- Key Laboratory of Agro-ecological Processes in Subtropical Regions, Chinese Academy of Sciences, Changsha, 410125, China; Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China
| | - Hong Zhang
- Forestry Bureau of Yueyang, Yueyang, 414000, China
| | - Yucheng Song
- Administrative Bureau of Hunan East Dongting Lake National Nature Reserve, Yueyang, 414000, China
| | - Yonghong Xie
- Key Laboratory of Agro-ecological Processes in Subtropical Regions, Chinese Academy of Sciences, Changsha, 410125, China; Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China.
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