最大熵模型在物种生境预测中的应用研究进展

doi:10.13287/j.1001-9332.202502.025

摘要/Abstract

摘要： 气候变化和人类活动严重影响物种的分布范围和生境适宜性。近年来,利用模型进行物种潜在适宜性生境预测已成为该领域研究的焦点之一。最大熵模型(MaxEnt)是一种基于物种分布数据和环境变量数据的机器学习模型,在物种生境预测中得到了广泛应用。本文首先介绍了MaxEnt模型的机理、建立过程、优化方法以及评估体系,然后综述了该模型在濒危物种和外来入侵物种潜在生境预测以及未来气候变化下物种的潜在分布模拟领域的应用,最后对MaxEnt模型目前面临的挑战以及未来的发展前景进行了分析与展望,以期MaxEnt模型在预测物种在自然环境中的分布和变化方面发挥更加重要的作用,为生物多样性保护和管理提供技术参考。

关键词: 物种, MaxEnt模型, 生境, 适宜性, 生物多样性

Abstract: Climate change and anthropogenic activities are profoundly affecting species distribution range and habitat suitability. In recent years, using models to predict potential suitable habitats for different species has become one of the research focuses in this field. The maximum entropy model (MaxEnt), a machine learning model based on the data of species distribution and environmental variables, has been widely used in predicting species habitats. First, we introduced the mechanism, establishment process, optimization method and assessment system of the MaxEnt model. Then, we reviewed the application of the model in potential habitat prediction of endangered species and invasive species, and the simulation of the potential distribution of species under future climate change. Fina-lly, we proposed current challenges and future development prospects of the MaxEnt model, aiming to strengthen its role in predicting the natural distribution of species, and provide technical references for biodiversity conservation and management.

Key words: species; MaxEnt model; habitat; suitability; biodiversity

杨佳悦, 丁国玉, 田秀君. 最大熵模型在物种生境预测中的应用研究进展[J]. 应用生态学报, 2025, 36(2): 614-624.

YANG Jiayue, DING Guoyu, TIAN Xiujun. Research progress on the application of the MaxEnt model in species habitat prediction.[J]. Chinese Journal of Applied Ecology, 2025, 36(2): 614-624.

参考文献

[1] Aires T, Cúcio C, Brakel J, et al. Impact of persistently high sea surface temperatures on the rhizobiomes of Zostera marina in a Baltic Sea benthocosms. Global Change Biology, 2024, 30: e17337
[2] Reader MO, Eppinga MB, de Boer HJ, et al. Biodiversity mediates relationships between anthropogenic drivers and ecosystem services across global mountain, island and delta systems. Global Environmental Change, 2023, 78: 102612
[3] Zhang HL, Chase JM, Liao JB. Habitat amount modulates biodiversity responses to fragmentation. Nature Ecology & Evolution, 2024, 8: 1437-1447
[4] Wilson MC, Chen XY, Corlett RT, et al. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landscape Ecology, 2016, 31: 229-230
[5] Gracanin A, Mikac KM. Evaluating modelled wildlife corridors for the movement of multiple arboreal species in a fragmented landscape. Landscape Ecology, 2023, 38: 1321-1337
[6] Siebert J, Eisenhauer N, Poll C, et al. Earthworms modulate the effects of climate warming on the taxon richness of soil meso- and macrofauna in an agricultural system. Agriculture, Ecosystems and Environment, 2019, 278: 72-80
[7] Leclère D, Obersteiner M, Barrett M, et al. Bending the curve of terrestrial biodiversity needs an integrated stra-tegy. Nature, 2020, 585: 551-556
[8] Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecological Modelling, 2000, 135: 147-186
[9] Tuanmu MN, Viña A, Roloff GJ, et al. Temporal transferability of wildlife habitat models: Implications for habitat monitoring. Journal of Biogeography, 2011, 38: 1510-1523
[10] 唐书培, 穆丽光, 王晓玲, 等. 基于MaxEnt模型的赛罕乌拉国家级自然保护区斑羚生境适宜性评价. 北京林业大学学报, 2019, 41(1): 102-108
[11] Booth TH, Nix HA, Busby JR, et al. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 2014, 20: 1-9
[12] Mondanaro A, Di Febbraro M, Castiglione S, et al. ENphylo: A new method to model the distribution of extremely rare species. Methods in Ecology and Evolution, 2023, 14: 911-922
[13] Wainwright BJ, Leon J, Vilela E, et al. Wallace’s line structures seagrass microbiota and is a potential barrier to the dispersal of marine bacteria. Environmental Microbiome, 2024, 19: 23
[14] González-Miguéns R, Cano E, Pinto MGG, et al. A needle in a haystack: A new metabarcoding approach to survey diversity at the species level of Arcellinida (Amoebozoa: Tubulinea). Molecular Ecology Resour-ces, 2023, 23: 1034-1049
[15] He YT, Wang GJ, Ren YL, et al. MaxEnt modelling combined with fuzzy logic provides new insights into predicting the distribution of potato cyst nematodes with limited data. Computers and Electronics in Agriculture, 2024, 222: 109035
[16] Tang XG, Yuan YD, Li XM, et al. Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China. Frontiers in Plant Science, 2021, 12: 652500
[17] Pearson RG, Raxworthy CJ, Nakamura M, et al. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. Journal of Biogeography, 2007, 34: 102-117
[18] Beck J, Böller M, Erhardt A, et al. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics, 2014, 19: 10-15
[19] Rodrigues LD, Daudt NW, Cardoso LG, et al. Species distribution modelling in the Southwestern Atlantic Ocean: A systematic review and trends. Ecological Mode-lling, 2023, 486: 110514
[20] Kiran GS, Prajapati PC, Mohanta A. A systematic app-raisal of ecological niche modelling in the context of phytodiversity conservation. Environment Development and Sustainability, 2024, DOI: 10.1007/s10668-024-04994-8
[21] 刘晓彤, 袁泉, 倪健. 中国植物分布模拟研究现状. 植物生态学报, 2019, 43(4): 273-283
[22] 陈新美, 雷渊才, 张雄清, 等. 样本量对MaxEnt模型预测物种分布精度和稳定性的影响. 林业科学, 2012, 48(1): 53-59
[23] 周冬梅. 北方地区冬油菜潜在空间分布模拟研究. 博士论文. 兰州: 甘肃农业大学, 2014
[24] Brown JL. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution, 2014, 5: 694-700
[25] 孙淑霞. 气候变化下中国栎属物种及其丰富度潜在分布格局模拟预测研究. 博士论文. 济南: 山东大学, 2021
[26] Li XL, Wu KN, Hao SH, et al. Mapping cropland suita-bility in China using optimized MaxEnt model. Field Crops Research, 2023, 302, DOI: 10.1016/j.fcr.2023.109064
[27] Wan JZ, Wang CJ, Yu FH, et al. Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modelling for invasive plants. Biologia, 2019, 74: 757-766
[28] Guralnick RP, Hill AW, Lane M. Towards a collaborative, global infrastructure for biodiversity assessment. Ecology Letters, 2007, 10: 663-672
[29] Boakes EH, McGowan PJK, Fuller RA, et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biology, 2010, 8: e1000385
[30] Matutini F, Baudry J, Pain G, et al. How citizen science could improve species distribution models and their independent assessment. Ecology and Evolution, 2021, 11: 3028-3039
[31] Gábor L, Moudry V, Barták V, et al. How do species and data characteristics affect species distribution models and when to use environmental filtering? International Journal of Geographical Information Science, 2020, 34: 1567-1584
[32] Fithian W, Elith J, Hastie T, et al. Bias correction in species distribution models: Pooling survey and collection data for multiple species. Methods in Ecology and Evolution, 2015, 6: 424-438
[33] Phillips SJ, Dudík M, Elith J, et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecological Applications, 2009, 19: 181-197
[34] Dubos N, Preau C, Lenormand M, et al. Assessing the effect of sample bias correction in species distribution models. Ecological Indicators, 2022, 145, DOI: 10.1016/j.ecolind.2022.109487
[35] Mudereri BT, Mukanga C, Mupfiga ET, et al. Analysis of potentially suitable habitat within migration connections of an intra-African migrant: The blue swallow (Hirundo atrocaerulea). Ecological Informatics, 2020, 57: 101082
[36] Tarabon S, Berges L, Dutoit T, et al. Environmental impact assessment of development projects improved by merging species distribution and habitat connectivity modelling. Journal of Environmental Management, 2019, 241: 439-449
[37] 刘晶晶, 张舟, 张权, 等. 基于参数优化Maxent模型的气候变化下青头潜鸭(Aythya baeri)在长江流域的潜在适生区预测. 生态与农村环境学报, 2024, DOI: 0.19741/j.issn.1673-4831.2024.0265
[38] Kass JM, Muscarella R, Galante PJ, et al. ENMeval 2.0: Redesigned for customizable and reproducible modelling of species’ niches and distributions. Methods in Ecology and Evolution, 2021, 12: 1602-1608
[39] Cobos ME, Peterson AT, Barve N, et al. kuenm: An R package for detailed development of ecological niche models using MaxEnt. PeerJ, 2019, 7: e6281
[40] Bao ZX, Zhang JY, Wang GQ, et al. The impact of climate variability and land use/cover change on the water balance in the Middle Yellow River Basin, China. Journal of Hydrology, 2019, 577: 123942
[41] Li XL, Wu KN, Hao SH, et al. Mapping of suitable habitats for earthworms in China. Soil Biology and Biochemistry, 2023, 184, DOI: 10.1016/j.soilbio.2023.109081
[42] Phillips SJ, Anderson RP, Dudík M, et al. Opening the black box: An open-source release of MaxEnt. Ecography, 2017, 40: 887-893
[43] 许仲林, 彭焕华, 彭守璋. 物种分布模型的发展及评价方法. 生态学报, 2015, 35(2): 557-567
[44] Liu CR, Newell G, White M. On the selection of thre-sholds for predicting species occurrence with presence-only data. Ecology and Evolution, 2016, 6: 337-348
[45] Yang HB, Viña A, Tang Y, et al. Range-wide evaluation of wildlife habitat change: A demonstration using giant pandas. Biological Conservation, 2017, 213: 203-209
[46] Zhao JC, Wang C, Shi XY, et al. Modeling climatically suitable areas for soybean and their shifts across China. Agricultural Systems, 2021, 192, DOI: 10.1016/j.agsy.2021.103205
[47] Zhao Y, Deng XW, Xiang WH, et al. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on MaxEnt model. Ecological Informatics, 2021, 64: 101393
[48] Bradie J, Leung B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography, 2017, 44: 1344-1361
[49] Ma L, Pan JH. Spatial identification and priority conservation areas determination of wilderness in China. Journal of Cleaner Production, 2024, 451: 142069
[50] Wang D, Cui B, Duan S, et al. Moving north in China: The habitat of Pedicularis kansuensis in the context of climate change. Science of the Total Environment, 2019, 697, DOI: 10.1016/j.scitotenv.2019.133979
[51] 罗玫, 王昊, 吕植. 使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现. 应用生态学报, 2017, 28(12): 4001-4006
[52] Ye PC, Zhang GF, Zhao X, et al. Potential geographi-cal distribution and environmental explanations of rare and endangered plant species through combined mode-ling: A case study of Northwest Yunnan, China. Ecology and Evolution, 2021, 11: 13052-13067
[53] 黄治昊, 周鑫, 张孝然, 等. 我国大陆黄檗潜在分布区及分布适宜性评价. 生态学报, 2018, 38(20): 7469-7476
[54] Zhang JJ, Jiang F, Li GY, et al. MaxEnt modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China. Ecology and Evolution, 2019, 9: 6643-6654
[55] Thapa A, Wu RD, Hu YB, et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution, 2018, 8: 10542-10554
[56] 张惠惠, 孟祥霄, 林余霖, 等. 基于GMPGIS系统和MaxEnt模型预测人参全球潜在生长区域. 中国中药杂志, 2023, 48(18): 4959-4966
[57] Xu ZH, Ren H, Wei X et al. Distribution and conservation status of Camellia longzhouensis (Theaceae), a critically endangered plant species endemic to southern China. Global Ecology and Conservation, 2021, 27, DOI: 10.1016/j.gecco.2021.e01585
[58] 塔旗, 李言阔, 范文青, 等. 基于最大熵生态位模型的中华穿山甲潜在适宜生境预测. 生态学报, 2021, 41(24): 9941-9952
[59] Neupane D, Baral S, Risch TS, et al. Broad scale functional connectivity for Asian elephants in the Nepal-India transboundary region. Journal of Environmental Management, 2022, 321: 115921
[60] 杜素洁, 郭建洋, 赵浩翔, 等. 近十年我国入侵生物预防与监控研究. 植物保护, 2023, 49(5): 410-418
[61] 生态环境部. 2020年中国生态环境状况公报(摘录). 环境保护, 2021, 49(11): 47-68
[62] 王丽丽, 杨采青, 王瑛, 等. 全球入侵物种马铃薯块茎蛾生态位转移及适生区扩展. 应用生态学报, 2024, 35(3): 797-805
[63] Enders M, Havemann F, Ruland F, et al. A conceptual map of invasion biology: Integrating hypotheses into a consensus network. Global Ecology and Biogeography, 2020, 29: 978-991
[64] Yang WJ, Sun SX, Wang NX, et al. Dynamics of the distribution of invasive alien plants (Asteraceae) in China under climate change. Science of the Total Environment, 2023, 903: 166260
[65] 李晓霞, 胡宽义, 曾安逸, 等. 基于MaxEnt模型预测海南外来入侵植物新记录种: 沼生金纽扣在中国的潜在适生区. 热带作物学报, 2024, 45(9): 1989-1997
[66] Bailey SA, Deneau MG, Jean L, et al. Evaluating efficacy of an environmental policy to prevent biological invasions. Environmental Science & Technology, 2011, 45: 2554-2561
[67] Liu DS, Wang, R, Gordon DR, et al. Predicting plant invasions following China’s water diversion project. Environmental Science & Technology, 2017, 51: 1450-1457
[68] Anderson AB, Silva JP, Sorvilo R, et al. Population expansion of the invasive Pomacentridae Chromis limbata (Valenciennes, 1833) in southern Brazilian coast: Long-term monitoring, fundamental niche availability and new records. Journal of Fish Biology, 97: 362-373
[69] 塞依丁·海米提, 努尔巴依·阿布都沙力克, 阿尔曼·解思斯, 等. 人类活动对外来入侵植物黄花刺茄在新疆潜在分布的影响. 生态学报, 2019, 39(2): 629-636
[70] 李丽鹤, 刘会玉, 林振山, 等. 基于MaxEnt和ZONATION的加拿大一枝黄花入侵重点监控区确定. 生态学报, 2017, 37(9): 3124-3132
[71] Ding WC, Li HY, Wen JB. Response of the invasive plant Ailanthus altissima (Mill.) Swingle and its two important natural enemies (Eucryptorrhynchus scrobiculatus (Motschulsky) and E. brandti (Harold)) to climate change. Ecological Indicators, 2022, 143, DOI: 10.1016/j.ecolind.2022.109408
[72] Anibaba QA, Dyderski MK, Jagodzinski AM. Predicted range shifts of invasive giant hogweed (Heracleum mantegazzianum) in Europe. Science of the Total Environment, 2022, 825: 154053
[73] Rewicz A, Mysliwy M, Rewicz T, et al. Contradictory effect of climate change on American and European populations of Impatiens capensis Meerb: Is this herb a global threat? Science of the Total Environment, 2022, 850: 157959
[74] World Meteorological Organization (WMO). State of the Global Climate 2024. Geneva: WMO, 2024
[75] Warren R, Price J, Graham E, et al. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 ℃ rather than 2 ℃. Science, 2018, 360: 791-795
[76] Williams JW, Jackson ST, Kutzbacht JE, et al. Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104: 5738-5742
[77] Wallner WE. Factors affecting insect population dyna-mics: Differences between outbreak and non-outbreak species. Annual Review of Entomology, 1987, 32: 317-340
[78] Xian XQ, Zhao HX, Guo JY, et al. Estimation of the potential geographical distribution of a new potato pest (Schrankia costaestrigalis) in China under climate change. Journal of Integrative Agriculture, 2023, 22: 2441-2455
[79] Lü JJ, Wu JG. Advances in the effects of climate change on the distribution of plant species and vegetation in China. Environmental Science & Technology, 2009, 32: 85-95
[80] Guo YL, Zhao Z, Li XF. Moderate warming will expand the suitable habitat of Ophiocordyceps sinensis and expand the area of O. sinensis with high adenosine content. Science of the Total Environment, 2021, 787: 147605
[81] Zhan P, Wang FY, Xia PG, et al. Assessment of sui-table cultivation region for Panax notoginseng under different climatic conditions using MaxEnt model and high-performance liquid chromatography in China. Industrial Crops and Products, 2022, 176: 114416
[82] Wang E, Lu ZG, Rohani ER, et al. Current and future distribution of Forsythia suspensa in China under climate change adopting the MaxEnt model. Frontiers in Plant Science, 2024, 15: 1394799
[83] Li JJ, Fan G, He Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Science of the Total Environment, 2020, 698: 134141
[84] Zhao YH, Wen YF, Zhang WQ, et al. Distribution pattern and change prediction of Phellodendron habitat in China under climate change. Ecology and Evolution, 2023, 13: e10374
[85] Mukul SA, Alamgir M, Sohel MSI, et al. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Science of the Total Environment, 2019, 663: 830-840
[86] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 2006, 190: 231-259
[87] Pan S, Peng DL, Li YM, et al. Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling. Journal of Integrative Agriculture, 2023, 22: 2138-2150
[88] 唐中海, 罗华林, 王建华, 等. 基于GIS和MaxEnt模型的白唇鹿(Cervus albirostris)潜在适宜生境及保护GAP分析. 生态学报, 2022, 42(22): 9394-9403