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预测未来气候变化下松潘裸鲤（Gymnocypris potanini）和硬刺松潘裸鲤（G.p.firmispinatus）栖息地时空变化，为长江上游濒危特有鱼类栖息地评估提供重要参考。以2种裸鲤为研究对象，整合19种气候因子、7种人类活动相关因子和4种自然地理因子，基于人工神经网络（ANN）、分类树分析（CTA）、柔性判别分析（FDA）和最大熵模型（MAXENT）4种机器学习算法构建物种分布模型（SDMs），并筛选出平均昼夜范围（BIO2）、等温性（BIO3）和气温季节性（BIO4）为核心变量，结合历史时段（1970—2019年）数据建模，并预测未来SSP126、SSP585情景下2050、2070、2090年3个时段的栖息地变化。结果表明：（1）模型性能优秀，松潘裸鲤和硬刺松潘裸鲤模型的AUC（受试者曲线下面积）和TSS（真实技能值）分别为（0.981 ,0.912）和（0.945 ,0.843）;(2)历史时期，松潘裸鲤栖息地主要位于岷江流域和嘉陵江流域，其主要栖息地（适宜度> 0.8）占比54.3%，硬刺松潘裸鲤栖息地位于金沙江流域，主要栖息地占比22.6%，松潘裸鲤栖息地质量优于硬刺松潘裸鲤；（3）未来情景下，金沙江中下游东部将成为松潘裸鲤的主要栖息地，而硬刺松潘裸鲤新的主要栖息地将出现在金沙江中下游和上游的南部，且SSP585情景下的栖息地扩张程度大于SSP126情景。
Abstract：Gymnocypris potanini(GP) and G.p. firmispinatus(GPF), primarily inhabiting mountain rivers, are known as precious fishes in the production area and important cold-water economic fishes endemic to the upper Yangtze River basin(UYRB) in China. These two high-elevation, cold-water fish species are excellent models for understanding the adaptation of endemic genomes to climate change in the UYRB. However, the factors affecting their habitats and the alterations due to climate change and human activity are not yet clear. In this study, G. potanini and G.p. firmispinatus were selected for research, and we predicted the spatiotemporal changes in their habitat under future climate change scenarios, aiming to provide a critical reference for habitat assessment of endangered endemic fish species in the upper reaches of the Yangtze River. Species distribution models(SDMs) were constructed using four machine learning algorithms: Artificial Neural Networks(ANN), Classification Tree Analysis(CTA), Flexible Discriminant Analysis(FDA), and Maximum Entropy Modeling(MAXENT). After screening for covariance, importance, and response curves, the mean diurnal range(BIO2), isothermality(BIO3), and temperature seasonality(BIO4) were selected as the key variables for SDMs from a pool of 19 climatic factors, 7 human activity-related factors, and 4 natural geographic factors. These models were then calibrated using historical data(1970-2019) and subsequently applied to predict habitat changes under future SSP126 and SSP585 scenarios in 2050, 2070, and 2090. Results show:(1) Model performance was excellent, with AUC(Area Under the ROC Curve) and TSS(True Skill Statistic) metrics of 0.981, 0.912 for GP and 0.945, 0.843 for GPF, respectively.(2) The GP habitats during the historical period(1970-2020) were primarily in the Minjiang River basin and Jialing River basin, while the GPF habitat was primarily in the Jinsha River basin. The habitat quality of GP was higher than that of GPF, with 54.3% of primary habitat(suitability >0.8) for GP and 22.6% of primary habitat for GPF.(3)Under the SSP126 and SSP585 future scenarios across 2050, 2070, and 2090, the eastern middle and lower Jinsha River is likely to become the new primary habitat for GP, while new habitats for GPF will emerge in the southern upper Jinsha River and the middle-lower Jinsha River. Notably, habitat expansion under the SSP585 scenario is expected to be more extensive than under SSP126. Over the next century, no spatial overlap is anticipated between the habitats of GP and GPF. This study provides fundamental support for conserving the habitats of GP and GPF being altered by climate change and offers important insights for assessing the habitat of endangered endemic fish species in the upper Yangtze River under changing environmental conditions.
丁端华, 1994.四川鱼类志[M].成都：四川科学技术出版社.
乐佩琦, 2000.动物志:硬骨鱼纲鲤形目.下卷[M].北京：科学出版社.
梁思倩,杨坤,黎树,等, 2020.不同水系松潘裸鲤的形态特征分析[J].四川动物, 39(5):500-507.
聂媛媛, 2017.安宁河硬刺松潘裸鲤年龄、生长与繁殖特性研究[D].大连:大连海洋大学.
苏尚柯,杜建国,陈彬,等, 2022.气候变化对中国近海8种中上层鱼类潜在生境分布的影响[J].生态学报, 42(12):4834-4846.SU S K, DU J G, CHEN B, et al, 2022. Impact of climatechange on the potential habitat distributions of eight pelag-ic fishes in the coastal waters of China[J]. Acta EcologicaSinica, 42(12):4834-4846.
苏尚柯,杜建国,丁丽可,等, 2023.气候速度影响下中国周边海域鲨鱼保护优先区识别[J].生态学报, 43(22):9218-9231.SU S K, DU J G, DING L K, et al, 2023. Setting conservationpriorities for sharks in China’s surrounding seas under theimpact of climate velocity[J]. Acta Ecologica Sinica, 43(22):9218-9231.
许友卿,曹占旺,丁兆坤,等, 2010.高温对鱼类的影响及其预防研究[J].水产科学, 29(4):235-242.
许友卿,陈哲,丁兆坤, 2012.低温对鱼类的影响及其预防[J].广东农业科学, 39(15):132-136.
ALLOUCHE O, TSOAR A, KADMON R, 2006. Assessing theaccuracy of species distribution models:prevalence, kap-pa and the true skill statistic(TSS)[J]. Journal of AppliedEcology, 43(6):1223-1232.
ARAÚJO M B, NEW M, 2007. Ensemble forecasting of spe-cies distributions[J]. Trends in Ecology&Evolution, 22(1):42-47.
BARBET-MASSIN M, JIGUET F, ALBERT C H, et al, 2012.Selecting pseudo-absences for species distribution mod-els:how, where and how many?[J]. Methods in Ecologyand Evolution, 3(2):327-338.
BESACIER MONBERTRAND A L, TIMONER P, RAHMANK, et al, 2019. Assessing the vulnerability of aquatic mac-roinvertebrates to climate warming in a mountainous wa-tershed:supplementing presence-only data with speciestraits[J]. Water, 11(4):636.
BOOTH T H, NIX H A, BUSBY J R, et al, 2014. Bioclim:thefirst species distribution modelling package, its early ap-plications and relevance to most current MaxEnt studies[J]. Diversity and Distributions, 20(1):1-9.
BUISSON L, BLANC L, GRENOUILLET G, 2008. Modellingstream fish species distribution in a river network:the rela-tive effects of temperature versus physical factors[J]. Ecol-ogy of Freshwater Fish, 17(2):244-257.
BUISSON L, THUILLER W, CASAJUS N, et al, 2010. Uncer-tainty in ensemble forecasting of species distribution[J].Global Change Biology, 16(4):1145-1157.
CAISSIE D, 2006. The thermal regime of rivers:a review[J].Freshwater Biology, 51(8):1389-1406.
CARDINALE B J, EMMETT DUFFY J, GONZALEZ A, et al,2012. Biodiversity loss and its impact on humanity[J]. Na-ture, 486(7401):59-67.
CARPENTER S R, FISHER S G, GRIMM N B, et al, 1992.Global change and freshwater ecosystems[J]. Annual Re-view of Ecology and Systematics, 23:119-139.
COTE D, KEHLER D G, BOURNE C, et al, 2009. A new mea-sure of longitudinal connectivity for stream networks[J].Landscape Ecology, 24(1):101-113.
DA MATA R A, TIDON R, DE OLIVEIRA G, et al, 2017.Stacked species distribution and macroecological modelsprovide incongruent predictions of species richness forDrosophilidae in the Brazilian savanna[J]. Insect Conser-vation and Diversity, 10(5):415-424.
DE MARCO JÚNIOR P, NÓBREGA C C, 2018. Evaluatingcollinearity effects on species distribution models:an ap-proach based on virtual species simulation[J]. PLoS One,13(9):e0202403.
DEVICTOR V, JULLIARD R, COUVET D, et al, 2008. Birdsare tracking climate warming, but not fast enough[J]. Pro-ceedings of the Royal Society B:Biological Sciences, 275(1652):2743-2748.
DOMISCH S, ARAÚJO M B, BONADA N, et al, 2013. Mod-elling distribution in European stream macroinvertebratesunder future climates[J]. Global Change Biology, 19(3):752-762.
DOMISCH S, JÄHNIG S C, HAASE P, 2011. Climate-changewinners and losers:stream macroinvertebrates of a sub-montane region in central Europe[J]. Freshwater Biology,56(10):2009-2020.
ELITH J, GRAHAM C H, ANDERSON R P, et al, 2006. Nov-el methods improve prediction of species’distributionsfrom occurrence data[J]. Ecography, 29(2):129-151.
ELITH J, LEATHWICK J R, 2009. Species distribution mod-els:ecological explanation and prediction across spaceand time[J]. Annual Review of Ecology, Evolution, andSystematics, 40:677-697.
FICK S E, HIJMANS R J, 2017. WorldClim 2:new 1-km spa-tial resolution climate surfaces for global land areas[J]. In-ternational Journal of Climatology, 37(12):4302-4315.
GAO J, 2019. Global Population Projection Grids Based onShared Socioeconomic Pathways(SSPs), Downscaled 1-km Grids, 2010—2100[J]. NASA Socioeconomic Dataand Applications Center(SEDAC), 10:H44747X44744.
GARRE A, FERNÁNDEZ P S, LINDQVIST R, et al, 2017.Bioinactivation:software for modelling dynamic microbi-al inactivation[J]. Food Research International, 93:66-74.
GRENOUILLET G, BUISSON L, CASAJUS N, et al, 2011.Ensemble modelling of species distribution:the effects ofgeographical and environmental ranges[J]. Ecography, 34(1):9-17.
GUISAN A, TINGLEY R, BAUMGARTNER J B, et al, 2013.Predicting species distributions for conservation decisions[J]. Ecology Letters, 16(12):1424-1435.
HANDELAND S O, IMSLAND A K, STEFANSSON S O,2008. The effect of temperature and fish size on growth,feed intake, food conversion efficiency and stomach evac-uation rate of Atlantic salmon post-smolts[J]. Aquaculture,283(1/2/3/4):36-42.
HARRIS I, OSBORN T J, JONES P, et al, 2020. Version 4 ofthe CRU TS monthly high-resolution gridded multivariateclimate dataset[J]. Scientific Data, 7(1):109.
HASTIE T, TIBSHIRANI R, BUJA A, 1994. Flexible discrimi-nant analysis by optimal scoring[J]. Journal of the Ameri-can Statistical Association, 89(428):1255-1270.
HIJMANS R J, CAMERON S E, PARRA J L, et al, 2005. Veryhigh resolution interpolated climate surfaces for globalland areas[J]. International Journal of Climatology, 25(15):1965-1978.
HIJMANS R J, GRAHAM C H, 2006. The ability of climateenvelope models to predict the effect of climate change onspecies distributions[J]. Global Change Biology, 12(12):2272-2281.
KAELIN K, ALTERMATT F, 2016. Landscape-level predic-tions of diversity in river networks reveal opposing pat-terns for different groups of macroinvertebrates[J]. Aquat-ic Ecology, 50(2):283-295.
KUEMMERLEN M, SCHMALZ B, GUSE B, et al, 2014. Inte-grating catchment properties in small scale species distri-bution models of stream macroinvertebrates[J]. EcologicalModelling, 277:77-86.
LOARIE S R, DUFFY P B, HAMILTON H, et al, 2009. The ve-locity of climate change[J]. Nature, 462(7276):1052-1055.
MUNGUÍA M, TREJO I, GONZÁLEZ-SALAZAR C, et al,2016. Human impact gradient on mammalian biodiversity[J]. Global Ecology and Conservation, 6:79-92.
NEWBOLD T, 2018. Future effects of climate and land-usechange on terrestrial vertebrate community diversity un-der different scenarios[J]. Proceedings Biological Scienc-es, 285(1881):20180792.
NUNEZ S, ARETS E, ALKEMADE R, et al, 2019. Assessingthe impacts of climate change on biodiversity:is below2℃enough?[J]. Climatic Change, 154(3):351-365.
PETERSON A T, PAPEŞM, EATON M, 2007. Transferabilityand model evaluation in ecological niche modeling:acomparison of GARP and Maxent[J]. Ecography, 30(4):550-560.
PHILLIPS S J, ANDERSON R P, SCHAPIRE R E, 2006. Max-imum entropy modeling of species geographic distribu-tions[J]. Ecological Modelling, 190(3/4):231-259.
SALA O E, CHAPIN F S 3rd, ARMESTO J J, et al, 2000.Global biodiversity scenarios for the year 2100[J]. Sci-ence, 287(5459):1770-1774.
SCHLOSS C A, NUÑEZ T A, LAWLER J J, 2012. Dispersalwill limit ability of mammals to track climate change inthe Western Hemisphere[J]. Proceedings of the NationalAcademy of Sciences of the United States of America, 109(22):8606-8611.
SEBASTIÁN-GONZÁLEZ E, BARBOSA J M, PÉREZ-GARCÍA J M, et al, 2019. Scavenging in the Anthropo-cene:human impact drives vertebrate scavenger speciesrichness at a global scale[J]. Global Change Biology, 25(9):3005-3017.
SENAY S D, WORNER S P, IKEDA T, 2013. Novel three-steppseudo-absence selection technique for improved speciesdistribution modelling[J]. PLoS One, 8(8):e71218.
STEEN V A, TINGLEY M W, PATON P W C, et al, 2021. Spa-tial thinning and class balancing:key choices lead to varia-tion in the performance of species distribution modelswith citizen science data[J]. Methods in Ecology and Evo-lution, 12(2):216-226.
SWETS J A, 1988. Measuring the accuracy of diagnostic sys-tems[J]. Science, 240(4857):1285-1293.
THERNEAU T, ATKINSON B, RIPLEY B, et al, 2015. Rpackage‘rpart’[R/OL].[2024-05-10].http://cran.rproject.org/web/package/rpart/rpart.pdf.
THOMAS C D, CAMERON A, GREEN R E, et al, 2004. Ex-tinction risk from climate change[J]. Nature, 427(6970):145-148.
THUILLER W, LAVERGNE S, ROQUET C, et al, 2011. Con-sequences of climate change on the tree of life in Europe[J]. Nature, 470(7335):531-534.
TIMONER P, FASEL M, ASHRAF VAGHEFI S S, et al, 2021.Impacts of climate change on aquatic insects in temperatealpine regions:complementary modeling approaches ap-plied to Swiss rivers[J]. Global Change Biology, 27(15):3565-3581.
VENABLES W N, RIPLEY B D, 2002. Random and mixed ef-fects[M]//Modern Applied Statistics with S. New York,NY:Springer New York:271-300.
ZHANG P, DONG X H, GRENOUILLET G, et al, 2020. Spe-cies range shifts in response to climate change and humanpressure for the world’s largest amphibian[J]. Science ofthe Total Environment, 735:139543.
基本信息:
DOI：10.15928/j.1674-3075.202405100180
中图分类号:Q958.8
引用信息:
[1]柏雄风,张鹏,杨志,等.气候变化影响下长江上游松潘裸鲤和硬刺松潘裸鲤潜在栖息地变化预测研究[J].水生态学杂志,2026,47(01):65-75.DOI:10.15928/j.1674-3075.202405100180.
基金信息:
国家自然科学基金项目（52179142）; 中国三峡建工（集团）有限公司技术服务项目（JG-EP-0421003）; 重庆嘉陵江利泽航电开发有限公司科研项目（LZ-DL-2024-021）
2024-08-14
2024-08-14
2024-08-14