Integrated Habitat Assessment of a Protected Fish Species in the Upper Yangtze River, China: Connectivity and Suitability

Abstract

In the context of anthropogenic climate change, dam construction, and other human activities, the biodiversity of freshwater fish is rapidly declining. The Upper Yangtze River Basin (UYRB) is a hotspot for hydropower development and is home to numerous endemic and rare freshwater fish species, most of which are on the brink of extinction. Schizothorax chongi is an endangered and protected fish species endemic to the UYRB, with significant economic and ecological value. However, the potential habitat of its wild population has not been reported, which hampers conservation efforts for this valuable species. This study utilized the Dendritic Connection Index (DCI) and Species Distribution Models (SDMs) to assess habitat connectivity in the UYRB and habitat suitability for S. chongi during the periods 1970-2000 and 2001-2020, respectively. The results show that S. chongi habitats underwent significant reduction during the 2001-2020 period, with the total length of medium and high suitability habitats decreasing by 51.7%. However, high suitability habitats in the southern section of the middle and lower Jinsha River, which is located in the upper and middle part of the UYRB, did not experience a noticeable reduction. Despite the relatively high habitat suitability maintained in the southern section of the middle and lower Jinsha River, connectivity has significantly declined. Restoring connectivity reduced by dam construction in this region is critically urgent. This study is the first to conduct a watershed-scale assessment of fish habitat integrating habitat suitability and connectivity providing valuable insights for local governments to develop specific conservation measures and plans. It can offer a valuable reference for researchers in the field of freshwater fish conservation.

Keywords

River connectivity / Ensemble modeling / Climate change / Dam construction / Schizothorax chongi

Acknowledgments

We especially thank Zhi Yang for providing fish occurrence data for this study.

Author Contributions

Conceptualization, P.Z.; Methodology, P.Z.; Software, X.B.; Data Curation, X.B., Y.Y., M.X. and L.Q.; Writing-Original Draft Preparation, X.B.; Writing-Review & Editing, X.B. and P.Z.; Visualization, X.B. and Z.N.; Supervision, P.Z.

Ethics Statement

This study does not involve human or animal subjects.

Informed Consent Statement

Not applicable.

Data Availability Statement

Original data and code are available from the corresponding author upon reasonable request.

Funding

This study was financially supported by the National Natural Science Foundation of China (Nos.52179142) and the Key R&D Program of Hubei Province (No. 2023BCB110).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Lundberg JG, Kottelat M, Smith GR, Stiassny ML, Gill AC. So many fishes, so little time: an overview of recent ichthyological discovery in continental waters. Ann. Mo. Bot. Gard. 2020, 87, 26-62.

[2] Nelson JS, Grande TC, Wilson MV. Fishes of the World, 5th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2016. ISBN:9871118342336.

[3] Reid GM, Contreras MacBeath T, Csatádi K. Freshwater Fishes and Their Conservation: Global Challenges. Int. Zoo Yb. 2013, 47, 6-45.

[4] Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003, 421, 37-42.

[5] Ziv G, Baran E, Nam S, Rodríguez-Iturbe I, Levin SA. Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. Proc. Natl. Acad. Sci. USA 2012, 109, 5609-5614.

[6] Dudgeon D, Arthington AH, Gessner MO, Kawabata Z, Knowler DJ, Lévêque C, et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. Camb. Philos. Soc. 2006, 81, 163-182.

[7] Cheng XY, Tao J, Wu RD, Chen LQ, Ding CZ. Functional ecology of freshwater fish: research progress and prospects. Acta Ecol. Sin. 2019, 39, 810-822. (In Chinese)

[8] Chen LQ, Wu Q, Zheng SM, Liu FJ. Development of embryo and yolk-sac larva of Schizothorax chongi. J. Fish. Sci. China 2008, 15, 927-934. (In Chinese)

[9] Duan B, Liu HY. Study on Isozymic Tissue-Specificity in Schizothorax chongi Fang. J. Southwest Univ. (Nat. Sci.Ed.) 2010, 32, 27-30. (In Chinese)

[10] Han JC, Liu GY, Zhuge YS, Huang YP, Liu DF. Ecological Protection of Fishery Resources Based on Development of Hydropower in Yalona River. J. China Three Gorges Univ. (Nat. Sci.Ed.) 2009, 31, 15-19. (In Chinese)

[11] Qin XH, Liu GY, Wu YJ, Shi XT Wang XL. Effects of Light Intensity on the Hatching Rate of Fertilized Schizothorax chongi Eggs and on the Growth and Feeding of Larvae. J. Hydroecol. 2017, 38, 97-102. (In Chinese)

[12] Zhu QG, Tang HY, Lin H, Gong Y, Li XN, Yang Z. Age Structure, Growth Characteristics and Population Dynamic of Schizothorax chongi in Middle and Lower Jinsha River. J. Hydroecol. 2021, 42, 56-63. (In Chinese)

[13] Liu GY, Han JC, Tu ZY, Ma ZK, Shi XT, Huang YP. Influence of Water Velocity on Hematology Indices of Schizothorax chongi. J. Anhui Agric. Sci. 2011, 39, 7298-7300. (In Chinese)

[14] Yuan X, Tu ZY, Han JC, Wang XX, Shi XT, Liu GY, et al. Effects of Flow Rate on Swimming Behavior and Energy Consumption of Schizothorax chongi. Acta Hydrobiol. Sin. 2012, 36, 270-275. (In Chinese)

[15] Tu ZY. Research of Swimming Performance of Several Typical Fish in the Yalong River. Master’s thesis, Wuhan University, Wuhan, China, 2012. (In Chinese)

[16] Zhou HX, Gan WX. Study on the open feed for Schizothorax Chongi. Sci. Aquac. 2016, 11, 67-68. (In Chinese)

[17] Hu XW, Cao WS, Xu SH. The phenomenon of dead eggs in artificial breeding of Schizothorax chongi and prevention suggestions. Sci. Aquac. 2017, 39, 5-7. (In Chinese)

[18] Zuo PX, Jin FP, Leng Y, Wang ZF, Wu JX, Deng YL, et al. Prevention and control measures for wheel worm disease in the seedling stage of Schizothorax chongi. Aquaculture 2022, 43, 74-75. (In Chinese)

[19] Wang ZF, Deng YL, Xu YR, Zuo PX, Leng Y, Wang C. Acute Toxicity of Ammonia Nitrogen in Different Water Environments to Schizothorax chongi. Guizhou Agric. Sci. 2020, 48, 81-84. (In Chinese)

[20] Zhao CY, Wang HL, Zhang DX, Deng YL, Qiu CH, Zheng H, et al. Variation Trend of Fish Community in Upper and Under Longkaikou Dam. Yunnan Water Power 2022, 38, 224-229. (In Chinese)

[21] Zuo PX, Jin FP, Leng Y, Wang ZF, Wu JX, Yu HM, et al. Comparative analysis of nutritional value evaluation of muscle nutrient composition between wild and cultured Schizothorax chongi. Heilongjiang Anim. Sci. Vet. Med. 2023, 18, 126-132. (In Chinese)

[22] Zuo PX, Jin FP, Wang ZF, Wu JX, Zhao JX, Zhao LG, et al. A Preliminary Report on Hybridization and Growth Comparison Experiments of Schizothorax chongi (♀) and Percocypris Pingi Pingi (Tchang) (♂). Chin. Agric. Sci. Bull. 2024, 40, 159-164. (In Chinese)

[23] Zuo PX, Jin FP, Leng Y, Wang ZF, Wu JX, Yu HM, et al. Anesthetic Effect of Eugenol on Juvenile Schizothorax chongi. Chin. Agric. Sci. Bull. 2024, 40, 159-164. (In Chinese)

[24] Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AI, et al. Predicting species distributions for conservation decisions. Ecol. Lett. 2013, 16, 1424-1435.

[25] Domisch S, Jaehnig SC, Haase P. Climate-change winners and losers: Stream macroinvertebrates of a submontane region in Central Europe. Freshw. Biol. 2011, 56, 2009-2020.

[26] Kuemmerlen M, Schmalz B, Guse B, Cai Q, Fohrer N, Jähnig SC. Integrating catchment properties in small scale species distribution models of stream macroinvertebrates. Ecol. Model. 2014, 277, 77-86.

[27] Kaelin K, Altermatt F. Landscape-level predictions of diversity in river networks reveal opposing patterns for different groups of macroinvertebrates. Aquat. Ecol. 2016, 50, 283-295.

[28] Buisson L, Blanc L, Grenouillet G. Modelling stream fish species distribution in a river network: The relative effects of temperature versus physical factors. Ecol. Freshw. Fish 2008, 17, 244-257.

[29] Pringle C. What is hydrologic connectivity and why is it ecologically important? Hydrol. Process. 2003, 17, 2685-2689.

[30] Allan JD, Castillo MM, Capps KA. Stream Ecology: Structure and Function of Running Waters; Springer Nature: Cham, Switzerland, 2021.

[31] Nilsson C, Reidy CA, Dynesius M, Revenga C. Fragmentation and flow regulation of the world's large river systems. Science 2005, 308, 405-408.

[32] Liermann CR, Nilsson C, Robertson J, Ng RY. Implications of dam obstruction for global freshwater fish diversity. Bioscience 2012, 62, 539-548.

[33] Reid M, Reid M, Thoms M. Ecological significance of hydrological connectivity for wetland plant communities on a dryland floodplain river, MacIntyre River, Australia. Aquat. Sci. 2016, 78, 139-158.

[34] Lessmann J, Guayasamin JM, Casner KL, Flecker AS, Funk WC, Ghalambor CK, et al. Freshwater vertebrate and invertebrate diversity patterns in an Andean-Amazon basin: implications for conservation efforts. Neotrop. Biodivers. 2016, 2, 99-114.

[35] Dong X, Li B, He F, Gu Y, Sun M, Zhang H, et al. Flow directionality, mountain barriers and functional traits determine diatom metacommunity structuring of high mountain streams. Sci Rep. 2016, 6, 24711.

[36] Cote D, Kehler DG, Bourne C, Wiersma YF. A new measure of longitudinal connectivity for stream networks. Landsc. Ecol. 2009, 24, 101-113.

[37] Bai X, Zhang P, Cao X, Zhang D, Yang Z, Dong X, et al. Incorporating Implicit Information to Disentangle the Impacts of Hydropower Dams and Climate Change on Basin-Scale Fish Habitat Distribution. Ecol. Evol. 2024, 14, e70412.

[38] da Mata RA, Tidon R, de Oliveira G, Vilela B, Diniz-Filho JAF, Rangel TF, et al. Stacked species distribution and macroecological models provide incongruent predictions of species richness for Drosophilidae in the Brazilian savanna. Insect. Conserv. Divers. 2017, 10, 415-424.

[39] Boria RA, Olson LE, Goodman SM, Anderson RP. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 2014, 275, 73-77.

[40] Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 2015, 38, 541-545.

[41] Steen VA, Tingley MW, Paton PW, Elphick CS. Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data. Methods Ecol. Evol. 2021, 12, 216-226.

[42] Zhang P, Dong X, Grenouillet G, Lek S, Zheng Y, Chang J. Species range shifts in response to climate change and human pressure for the world's largest amphibian. Sci. Total Environ. 2020, 735, 139543.

[43] Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005, 25, 1965-1978.

[44] Harris I, Osborn TJ, Jones P, Lister D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. 2020, 7, 109.

[45] Fick SE, Hijmans RJ. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302-4315.

[46] De Marco PJ, Nóbrega CC. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 2018, 13, e0202403.

[47] Grenouillet G, Buisson L, Casajus N, Lek S. Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 2011, 34, 9-17.

[48] Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958, 65, 386-408.

[49] Booth TH, Nix HA, Busby JR, Hutchinson MF. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Divers. Distrib. 2014, 20, 1-9.

[50] Hastie T, Tibshirani R, Buja A. Flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 1994, 89, 1255-1270.

[51] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231-259.

[52] Thuiller W, Lafourcade B, Engler R, Araújo MB. BIOMOD-A platform for ensemble forecasting of species distributions. Ecography 2009, 32, 369-373.

[53] Swets JA. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285-1293.

[54] Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43, 1223-1232.

[55] Franklin J. Mapping Species Distributions: Spatial Inference and Prediction; Cambridge University Press: Cambridge, UK, 2009.

[56] Yang M, Zhao H, Xian X, Liu H, Li J, Chen L, et al. Potential global geographical distribution of Lolium temulentum L. under climate change. Front. Plant Sci. 2022, 13, 1024635.

[57] Araújo MB, New M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 2007, 22, 42-47.

[58] Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G. Uncertainty in ensemble forecasting of species distribution. Glob. Chang. Biol. 2010, 16, 1145-1157.

[59] Warren ML Jr., Pardew MG. Road crossings as barriers to small-stream fish movement. Trans. Am. Fish. Soc. 1998, 127, 637-644.

[60] Oldford G, Cote D, Kehler DG, Riefesel GR, Wiersma YF. FIPEX v10. 4: An ArcGIS Desktop Add-in for assessing impacts of fish passage barriers and longitudinal connectivity of rivers. SoftwareX 2023, 23, 101469.

[61] Caissie D. The thermal regime of rivers: a review. Freshw. Biol. 2006, 51, 1389-1406.

[62] Dala-Corte RB, Giam X, Olden JD, Becker FG, Guimarães TdF, Melo AS. Revealing the pathways by which agricultural land-use affects stream fish communities in South Brazilian grasslands. Freshw. Biol. 2016, 61, 1921-1934.

[63] Keppeler FW, de Souza AC, Hallwass G, Begossi A, de Almeida MC, Isaac VJ, et al. Ecological influences of human population size and distance to urban centres on fish communities in tropical lakes. Aquatic. Conserv. Mar. Freshw. Ecosyst. 2018, 28, 1030-1043.

[64] Su G, Logez M, Xu J, Tao S, Villéger S, Brosse S. Human impacts on global freshwater fish biodiversity. Science 2021, 371, 835-838.

[65] Wu J, Xu HG, Chen L. A Review of Impacts of Climate Change on Species. J. Ecol. Rural. Environ. 2011, 27, 1-6. (In Chinese)

[66] Sun HY, Xue LF, Jiang H, Chen YF. Fish-Based Evaluation of Protected Areas in the Jinsha River Basin. Environ. Impact Assess. 2019, 41, 13-16. (In Chinese)

[67] Hermoso V, Filipe AF, Segurado P, Beja P. Freshwater conservation in a fragmented world: Dealing with barriers in a systematic planning framework. Aquatic. Conserv. Mar. Freshw. Ecosyst. 2018, 28, 17-25.

[68] Hermoso V, Kennard MJ, Linke S. Integrating multidirectional connectivity requirements in systematic conservation planning for freshwater systems. Divers. Distrib. 2012, 18, 448-458.

[69] Yujun Y, Yanning G, Shanghong Z. The impact of dams on the river connectivity of the two largest river basins in China. River Res. Appl. 2022, 38, 185-193.

[70] Chu C, Mandrak NE, Minns CK. Potential impacts of climate change on the distributions of several common and rare freshwater fishes in Canada. Divers. Distrib. 2005, 11, 299-310.

[71] Ramankutty N, Evan AT, Monfreda C, Foley JA. Global Agricultural Lands: Croplands, 2000; NASA Socioeconomic Data and Applications Center (SEDAC) [Dataset]: Palisades, NY, USA, 2010.

[72] Ramankutty N, Evan AT, Monfreda C, Foley JA. Farming the Planet: 1. Geographic Distribution of Global Agricultural Lands in the Year 2000. Glob. Biogeochem. Cycles. 2008, 22, GB1003.

[73] Oakleaf JR, Kennedy CM, Baruch-Mordo S, West PC, Gerber JS, Jarvis L, et al. Development Threat Index; NASA Socioeconomic Data and Applications Center (SEDAC) [Dataset]: Palisades, NY, USA, 2019.

[74] Oakleaf JR, Kennedy CM, Baruch-Mordo S, West PC, Gerber JS, Jarvis LJ, et al. A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion. PLoS ONE 2015, 10, e0138334.

[75] Wang P, Huang C, Brown de Colstoun EC, Tilton JC, Tan B. Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat; NASA Socioeconomic Data and Applications Center (SEDAC) [Dataset]: Palisades, NY, USA, 2017.

[76] Center for International Earth Science Information Network—CIESIN—Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11; NASA Socioeconomic Data and Applications Center (SEDAC) [Dataset]: Palisades, NY, USA, 2018.

[77] Center for International Earth Science Information Network—CIESIN—Columbia University, Centro Internacional de Agricultura Tropical—CIAT. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid; NASA Socioeconomic Data and Applications Center (SEDAC)[Dataset]: Palisades, NY, USA, 2005.

[78] Wildlife Conservation Society—WCS, Center for International Earth Science Information Network—CIESIN—Columbia University. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Influence Index (HII) Dataset (Geographic); NASA Socioeconomic Data and Applications Center (SEDAC) [Dataset]: Palisades, NY, USA, 2005.