Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Likely Habitats of The Yangtze Finless Porpoise
2.2. Wetland Landscape Extraction
2.3. Hydrodynamic Modeling
2.4. Stranding Risk Evaluation Indicators
2.4.1. Landscape Fragmentation
2.4.2. Days of Water Depth Decline
2.4.3. Water Depth Difference
2.4.4. Relief Degree of Lake Bathymetry
2.5. Delineation of the High Stranding Risk Area
3. Results
3.1. Landscape Variation within Habitats
3.2. Characteristics of Hydrological Conditions in the Habitat
3.3. Stranding Risk of the Yangtze Finless Porpoise
3.4. Characteristics of High Stranding Risk Areas
4. Discussion
4.1. Evaluation Indicator Selection
4.2. The Plight of the Yangtze Finless Porpoise
4.3. Suggested Implementation Measures
- The high-risk areas identified in this study require systematic and targeted investigations of the Yangtze finless porpoise, especially in years with low water level and rapid water decline.
- Governments of cities containing high-risk areas should strengthen training and logistical support for porpoise rescue and formulate sound rescue guidelines by learning from the successful experience of other wildlife conservation efforts.
- A wider social awareness of porpoise stranding protection requires widespread participation, including research institutes, universities, and NGOs. Because of the recent fishing ban, former fishers are ideal partners to provide critical support in the planning of surveys and rescue routes.
- It is necessary to explore the feasibility of technical interventions to help the porpoises escape from high stranding risk areas. Methods such as acoustic facilities, artificial landscape modifications, and water level regulation can all help prevent porpoise stranding, while other impacts they will cause cannot be underestimated.
4.4. Implications for Future Conservation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Gomez-Salazar, C.; Coll, M.; Whitehead, H. River dolphins as indicators of ecosystem degradation in large tropical rivers. Ecol. Indic. 2012, 23, 19–26. [Google Scholar] [CrossRef] [Green Version]
- Hunt, T.N.; Allen, S.J.; Bejder, L.; Parra, G.J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 2020, 10, 14366. [Google Scholar] [CrossRef] [PubMed]
- Huang, S.L.; Wang, X.; Wu, H.; Peng, C.; Jefferson, T.A. Habitat protection planning for Indo-Pacific humpback dolphins (Sousa chinensis) in deteriorating environments: Knowledge gaps and recommendations for action. Aquat. Conserv. Mar. Freshw. Ecosyst. 2021, 32, 171–185. [Google Scholar] [CrossRef]
- Barragán-Barrera, D.C.; Do Amaral, K.B.; Chávez-Carreño, P.A.; Farías-Curtidor, N.; Lancheros-Neva, R.; Botero-Acosta, N.; Bueno, P.; Moreno, I.B.; Bolaños-Jiménez, J.; Bouveret, L.; et al. Ecological Niche Modeling of Three Species of Stenella Dolphins in the Caribbean Basin, with Application to the Seaflower Biosphere Reserve. Front. Mar. Sci. 2019, 6, 10. [Google Scholar] [CrossRef] [Green Version]
- Coombs, E.J.; Deaville, R.; Sabin, R.C.; Allan, L.; O’Connell, M.; Berrow, S.; Smith, B.; Brownlow, A.; Doeschate, M.T.; Penrose, R.; et al. What can cetacean stranding records tell us? A study of UK and Irish cetacean diversity over the past 100 years. Mar. Mammal Sci. 2019, 35, 1527–1555. [Google Scholar] [CrossRef]
- Obusan, M.C.M.; Rivera, W.L.; Siringan, M.A.T.; Aragones, L.V. Stranding events in the Philippines provide evidence for impacts of human interactions on cetaceans. Ocean Coast. Manag. 2016, 134, 41–51. [Google Scholar] [CrossRef]
- Huang, S.; Hao, Y.; Mei, Z.; Turvey, S.T.; Wang, D. Common pattern of population decline for freshwater cetacean species in deteriorating habitats. Freshw. Biol. 2012, 57, 1266–1276. [Google Scholar] [CrossRef]
- Braulik, G.T.; Reichert, A.P.; Ehsan, T.; Khan, S.; Northridge, S.P.; Alexander, J.S.; Garstang, R. Habitat use by a freshwater dolphin in the low-water season. Aquat. Conserv. Mar. Freshw. Ecosyst. 2012, 22, 533–546. [Google Scholar] [CrossRef]
- Braulik, G.T.; Noureen, U.; Arshad, M.; Reeves, R.R. Review of status, threats, and conservation management options for the endangered Indus River blind dolphin. Biol. Conserv. 2015, 192, 30–41. [Google Scholar] [CrossRef]
- Braulik, G.T.; Arshad, M.; Noureen, U.; Northridge, S.P. Habitat fragmentation and species extirpation in freshwater ecosystems; causes of range decline of the Indus river dolphin (Platanista gangetica minor). PLoS ONE 2014, 9, e101657. [Google Scholar] [CrossRef]
- Li, Q.; Lai, G.; Liu, Y.; Devlin, A.T.; Zhan, S.; Wang, S. Identifying the seasonal characteristics of likely habitats for the Yangtze finless porpoise in Poyang Lake. Aquat. Conserv. Mar. Freshw. Ecosyst. 2022, 32, 523–536. [Google Scholar] [CrossRef]
- Turvey, S.T.; Pitman, R.L.; Taylor, B.L.; Barlow, J.; Akamatsu, T.; Barrett, L.A.; Zhao, X.; Reeves, R.R.; Stewart, B.S.; Wang, K.; et al. First human-caused extinction of a cetacean species? Biol. Lett. 2007, 3, 537–540. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huang, J.; Mei, Z.; Chen, M.; Han, Y.; Zhang, X.; Moore, J.E.; Zhao, X.; Hao, Y.; Wang, K.; Wang, D. Population survey showing hope for population recovery of the critically endangered Yangtze finless porpoise. Biol. Conserv. 2020, 241, 108315. [Google Scholar] [CrossRef]
- Chen, T.; Wang, Y.; Gardner, C.; Wu, F. Threats and protection policies of the aquatic biodiversity in the Yangtze River. J. Nat. Conserv. 2020, 58, 125931. [Google Scholar] [CrossRef]
- Mei, Z.; Zhang, X.; Huang, S.; Zhao, X.; Hao, Y.; Zhang, L.; Qian, Z.; Zheng, J.; Wang, K.; Wang, D. The Yangtze finless porpoise: On an accelerating path to extinction? Biol. Conserv. 2014, 172, 117–123. [Google Scholar] [CrossRef]
- Liu, X.; Mei, Z.; Zhang, J.; Sun, J.; Zhang, N.; Guo, Y.; Wang, K.; Hao, Y.; Wang, D. Seasonal Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) movements in the Poyang Lake, China: Implications on flexible management for aquatic animals in fluctuating freshwater ecosystems. Sci. Total Environ. 2022, 807, 150782. [Google Scholar] [CrossRef] [PubMed]
- Li, Q.; Lai, G.; Devlin, A.T. A review on the driving forces of water decline and its impacts on the environment in Poyang Lake, China. J. Water Clim. Chang. 2021, 12, 1370–1391. [Google Scholar] [CrossRef]
- Xu, Z. The Study of the Yangtze Finless Porpoise Population Status and Behavior Characteristics and Protection in the Key Waters of Poyang Lak. Master’s Thesis, Nanchang University, Nanchang, China, 2015; p. 62. (In Chinese). [Google Scholar]
- Zhang, H.; Kang, M.; Shen, L.; Wu, J.; Li, J.; Du, H.; Wang, C.; Yang, H.; Zhou, Q.; Liu, Z.; et al. Rapid change in Yangtze fisheries and its implications for global freshwater ecosystem management. Fish Fish. 2020, 21, 601–620. [Google Scholar] [CrossRef]
- Xu, N. Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA. Atmosphere 2018, 9, 107. [Google Scholar] [CrossRef] [Green Version]
- Specht, M.; Specht, C.; Lewicka, O.; Makar, A.; Burdziakowski, P.; Dąbrowski, P. Study on the Coastline Evolution in Sopot (2008–2018) Based on Landsat Satellite Imagery. J. Mar. Sci. Eng. 2020, 8, 464. [Google Scholar] [CrossRef]
- Wang, X.; Liu, Y.; Ling, F.; Liu, Y.; Fang, F. Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015. ISPRS Int. J. Geo-Inf. 2017, 6, 68. [Google Scholar] [CrossRef] [Green Version]
- Maglione, P.; Parente, C.; Vallario, A. Coastline extraction using high resolution WorldView-2 satellite imagery. Eur. J. Remote Sens. 2014, 47, 685–699. [Google Scholar] [CrossRef]
- Li, Q.; Dai, X.; Liu, Y.; Devlin, A.T.; Lai, G.; Wang, W. Potential spawning grounds of phytophilic fish under a shifting hydrological regime in Poyang Lake, China. Fish. Manag. Ecol. 2022. [Google Scholar] [CrossRef]
- Han, X.; Chen, X.; Feng, L. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sens. Environ. 2015, 156, 426–437. [Google Scholar] [CrossRef]
- Smith, B.D. 1990 Status and conservation of the Ganges River dolphin Platanista gangetica in the Karnali River, Nepal. Biol. Conserv. 1993, 66, 159–169. [Google Scholar] [CrossRef]
- Lai, G.; Wang, P.; Li, L. Possible impacts of the Poyang Lake (China) hydraulic project on lake hydrology and hydrodynamics. Hydrol. Res. 2016, 47, 187–205. [Google Scholar] [CrossRef] [Green Version]
- Li, Q.; Lai, G.; Liu, Y.; Thomas Devlin, A.; Zhan, S.; Wang, S. Assessing the impact of the proposed Poyang lake hydraulic project on the Yangtze finless porpoise and its calves. Ecol. Indic. 2021, 129, 107873. [Google Scholar] [CrossRef]
- Zou, L.; Hu, B.; Qi, S.; Zhang, Q.; Ning, P. Spatiotemporal Variation of Siberian Crane Habitats and the Response to Water Level in Poyang Lake Wetland, China. Remote Sens. 2021, 13, 140. [Google Scholar] [CrossRef]
- Ferreira, I.J.M.; Ferreira, J.H.D.; Bueno, P.A.A.; Vieira, L.M.; Bueno, R.D.O.; Couto, E.V.D. Spatial dimension landscape metrics of Atlantic Forest remnants in Paraná State, Brazil. Acta Scientiarum. Technol. 2018, 40, 36503. [Google Scholar] [CrossRef]
- Wang, R.; Zhang, S.; Wang, R.; Pu, L.; Li, F.; Wang, Q.; Chen, D.; Yang, J.; Chang, L.; Bu, K. Analysis on the relief amplitude in Northeast China based on ASTER GDEM and mean change point method. Arid. Land Resour. Environ. 2016, 30, 49–54. (In Chinese) [Google Scholar]
- Xiao, C.; Li, P.; Feng, Z. Re-delineating mountainous areas with three topographic parameters in Mainland Southeast Asia using ASTER global digital elevation model data. J. Mt. Sci. 2018, 15, 1728–1740. [Google Scholar] [CrossRef]
- Boon, P.J.; Baxter, J.M. Editorial: Putting the conservation into Aquatic Conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 2021, 31, 1927–1928. [Google Scholar] [CrossRef]
- Hartman, K.L.; Fernandez, M.; Azevedo, J.M.N. Spatial segregation of calving and nursing Risso’s dolphins (Grampus griseus) in the Azores, and its conservation implications. Mar. Biol. 2014, 161, 1419–1428. [Google Scholar] [CrossRef]
- Hartel, E.F.; Noke Durden, W.; Corry-Crowe, G.O. Testing satellite telemetry within narrow ecosystems: Nocturnal movements and habitat use of bottlenose dolphins within a convoluted estuarine system. Anim. Biotelemetry 2020, 8, 13. [Google Scholar] [CrossRef]
- Truchon, M.H.; Measures, L.; L’Herault, V.; Brethes, J.C.; Galbraith, P.S.; Harvey, M.; Lessard, S.; Starr, M.; Lecomte, N. Marine mammal strandings and environmental changes: A 15-year study in the St. Lawrence ecosystem. PLoS ONE 2013, 8, e59311. [Google Scholar] [CrossRef] [Green Version]
- Prado, J.H.F.; Mattos, P.H.; Silva, K.G.; Secchi, E.R. Long-Term Seasonal and Interannual Patterns of Marine Mammal Strandings in Subtropical Western South Atlantic. PLoS ONE 2016, 11, e146339. [Google Scholar] [CrossRef] [Green Version]
- Smith, B.D.; Braulik, G.; Strindberg, S.; Mansur, R.; Diyan, M.A.A.; Ahmed, B. Habitat selection of freshwater-dependent cetaceans and the potential effects of declining freshwater flows and sea-level rise in waterways of the Sundarbans mangrove forest, Bangladesh. Aquat. Conserv. Mar. Freshw. Ecosyst. 2009, 19, 209–225. [Google Scholar] [CrossRef]
- Mei, Z.; Huang, S.; Hao, Y.; Turvey, S.T.; Gong, W.; Wang, D. Accelerating population decline of Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis). Biol. Conserv. 2012, 153, 192–200. [Google Scholar] [CrossRef]
- Prajapati, S. Stranding cases of endangered Ganges river dolphins in the Ghaghara-Sharada irrigation canals, Ganges river basin, India: Conservation implications. Mammalia 2021, 85, 39–46. [Google Scholar] [CrossRef]
- Costello, M.J.; Cheung, A.; De Hauwere, N. Surface Area and the Seabed Area, Volume, Depth, Slope, and Topographic Variation for the World’s Seas, Oceans, and Countries. Environ. Sci. Technol. 2010, 44, 8821–8828. [Google Scholar] [CrossRef]
- Dong, L.; Wang, D.; Wang, K.; Li, S.; Mei, Z.; Wang, S.; Akamatsu, T.; Kimura, S. Yangtze finless porpoises along the main channel of Poyang Lake, China: Implications for conservation. Mar. Mammal Sci. 2015, 31, 612–628. [Google Scholar] [CrossRef]
- Liu, Y.; Wu, G.; Zhao, X. Recent declines in China’s largest freshwater lake: Trend or regime shift? Environ. Res. Lett. 2013, 8, 14010. [Google Scholar] [CrossRef]
- Feng, L.; Hu, C.; Chen, X.; Cai, X.; Tian, L.; Gan, W. Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sens. Environ. 2012, 121, 80–92. [Google Scholar] [CrossRef]
- Ye, X.; Xu, C.; Zhang, Q.; Yao, J.; Li, X. Quantifying the Human Induced Water Level Decline of China’s Largest Freshwater Lake from the Changing Underlying Surface in the Lake Region. Water Resour. Manag. 2018, 32, 1467–1482. [Google Scholar] [CrossRef]
- De Leeuw, J.; Shankman, D.; Wu, G.; de Boer, W.F.; Burnham, J.; He, Q.; Yesou, H.; Xiao, J. Strategic assessment of the magnitude and impacts of sand mining in Poyang Lake, China. Reg. Environ. Chang. 2010, 10, 95–102. [Google Scholar] [CrossRef]
- Waqas, U.; Malik, M.; Khokhar, L. Conservation of Indus River Dolphin (Platanista gangetica minor) in the Indus River System, Pakistan: An overview. Rec. Zool. Surv. Pak. 2012, 21, 82–85. [Google Scholar]
- Dronova, I.; Gong, P.; Wang, L. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sens. Environ. 2011, 115, 3220–3236. [Google Scholar] [CrossRef]
- Tian, H.; Li, W.; Wu, M.; Huang, N.; Li, G.; Li, X.; Niu, Z. Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data. Remote Sens. 2017, 9, 521. [Google Scholar] [CrossRef] [Green Version]
- Zhang, P.; Lu, J.; Feng, L.; Chen, X.; Zhang, L.; Xiao, X.; Liu, H. Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data. Remote Sens. 2015, 7, 4858–4879. [Google Scholar] [CrossRef] [Green Version]
Dry Condition | Normal Condition | Flood Condition | |||
---|---|---|---|---|---|
Year | Mean Water Level (m) | Year | Mean Water Level (m) | Year | Mean Water Level (m) |
1992 | 8.49 | 1991 | 9.95 | 1990 | 10.09 |
2004 | 8.65 | 1995 | 9.91 | 1993 | 10.91 |
2006 | 7.27 | 1996 | 9.66 | 1994 | 10.16 |
2007 | 8.32 | 1997 | 9.62 | 1998 | 11.81 |
2009 | 6.53 | 2003 | 9.65 | 1999 | 10.5 |
2011 | 7.66 | 2008 | 9.59 | 2000 | 10.84 |
2013 | 7.96 | 2010 | 9.39 | 2001 | 10.05 |
2018 | 8.49 | 2012 | 9.57 | 2002 | 10.21 |
2019 | 7.92 | 2014 | 9.18 | 2005 | 10.2 |
2015 | 9.35 | 2020 | 10.51 | ||
2016 | 9.09 | ||||
2017 | 9.28 | ||||
Average | 7.92 | Average | 9.52 | Average | 10.53 |
Mean Water Level in 2019 (m) | Mean Water Level in 2012 (m) | Mean Water Level in 2020 (m) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R | S | MAE | MRE | RMSE | R | S | MAE | MRE | RMSE | R | S | MAE | MRE | RMSE |
10.88 | 11.07 | 0.22 | 2.15 | 0.28 | 11.95 | 12.33 | 0.38 | 3.87 | 0.46 | 11.97 | 12.19 | 0.24 | 2.47 | 0.33 |
Dry Conditions | Normal Conditions | Flood Conditions | ||||
---|---|---|---|---|---|---|
City | High Risk Area (km2) | Proportion (%) | High Risk Area (km2) | Proportion (%) | High Risk Area (km2) | Proportion (%) |
DC | 92.74 | 32.59 | 70.99 | 28.28 | 29.06 | 32.24 |
HK | 29.82 | 10.48 | 23.89 | 9.52 | 4.84 | 5.37 |
JJ | 8.92 | 3.14 | 10.20 | 4.06 | 3.32 | 3.68 |
PY | 17.27 | 6.07 | 12.62 | 5.03 | 5.41 | 6.00 |
XJ | 39.91 | 14.03 | 50.68 | 20.19 | 20.01 | 22.21 |
XZ | 15.29 | 5.37 | 18.80 | 7.49 | 8.71 | 9.66 |
YX | 36.09 | 12.68 | 29.45 | 11.73 | 10.63 | 11.79 |
YG | 44.49 | 15.64 | 34.42 | 13.71 | 8.16 | 9.05 |
Total | 284.54 | 100 | 251.04 | 100 | 90.12 | 32.24 |
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Li, Q.; Li, W.; Lai, G.; Liu, Y.; Devlin, A.T.; Wang, W.; Zhan, S. Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling. Remote Sens. 2022, 14, 2455. https://doi.org/10.3390/rs14102455
Li Q, Li W, Lai G, Liu Y, Devlin AT, Wang W, Zhan S. Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling. Remote Sensing. 2022; 14(10):2455. https://doi.org/10.3390/rs14102455
Chicago/Turabian StyleLi, Qiyue, Wenya Li, Geying Lai, Ying Liu, Adam Thomas Devlin, Weiping Wang, and Shupin Zhan. 2022. "Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling" Remote Sensing 14, no. 10: 2455. https://doi.org/10.3390/rs14102455