物种分布模型在海洋物种潜在分布预测中面临的大数据挑战
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摘要
利用物种分布模型(species distribution models or modeling,SDM)估计物种的真实和潜在分布区,已成为海洋保护区规划、外来、物种入侵预测、气候变迁或环境改变对物种分布影响等领域的研究热点。此外,在大数据技术迅速发展的背景下,国外已出现多个面向物种分布模型的大数据集以及应用大数据技术进行物种分布模型研究的案例,而我国,海洋方面应用大数据的物种分布模型的研究却寥寥无几。文章首先就物种分布模型、物种分布模型分类以及其研究现状做了较为详细的叙述,讨论了物种分布模型的大数据需求及所需数据类型,主要从大数据价值链的角度讨论了海洋领域物种分布模型与大数据的关系,具体从物种分布数据和环境数据的收集与大数据、物种数据和环境数据的集成与大数据、物种分布模型的预测分析与大数据3个方面展开。文章就物种分布模型所面临的大数据问题,从加强海洋生物多样性大数据平台和生态系统观测网络的建设以及鼓励海洋领域的物种分布模型应用研究项目的开展两方面给出作者的建议,对未来物种分布模型在我国海洋领域的研究和应用的前景作了展望。
Using Species Distribution Models(SDMs) to predict the real and potential distribution of species has become the hotspot research in ocean researches, especially in areas of marine protected area planning, invasive species prediction and climate changes or environmental changes influencing species distribution, etc. In addition, under the background of the rapid development of big data technology, there have been several SDMs oriented big data sets and research cases of SDM applying big data technology abroad. However, at home, there are few studies on species distribution models applying big data in the ocean. For this reason, this paper first described in detail the species distribution model, species distribution model classification and its research status. Then, requirements on big data and data types of species distribution model were further discussed. And the relationship between species distribution model and big data in the marine field was discussed from the perspective of big data value chain,including three specific aspects: 1) species distribution data and environmental data collection and big data; 2) integration of species data and environmental data and big data; 3) prediction and analysis of species distribution model and big data. Suggestions were given to solve the problems on big data faced by the SDMs: strengthening the construction of marine biodiversity big data platform and ecosystem observation network, and encouraging the development and application of species distribution models. Finally, the prospects of future research and application of SDMs in predicting marine species in China was proposed.
Using Species Distribution Models(SDMs) to predict the real and potential distribution of species has become the hotspot research in ocean researches, especially in areas of marine protected area planning, invasive species prediction and climate changes or environmental changes influencing species distribution, etc. In addition, under the background of the rapid development of big data technology, there have been several SDMs oriented big data sets and research cases of SDM applying big data technology abroad. However, at home, there are few studies on species distribution models applying big data in the ocean. For this reason, this paper first described in detail the species distribution model, species distribution model classification and its research status. Then, requirements on big data and data types of species distribution model were further discussed. And the relationship between species distribution model and big data in the marine field was discussed from the perspective of big data value chain,including three specific aspects: 1) species distribution data and environmental data collection and big data; 2) integration of species data and environmental data and big data; 3) prediction and analysis of species distribution model and big data. Suggestions were given to solve the problems on big data faced by the SDMs: strengthening the construction of marine biodiversity big data platform and ecosystem observation network, and encouraging the development and application of species distribution models. Finally, the prospects of future research and application of SDMs in predicting marine species in China was proposed.
引文
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[10] Downie AL, Numers MV, Bostr(o|¨)m C. Influence of model selection on the predicted distribution of the seagrass Zostera marina[J]. Estuarine Coastal&Shelf Science,2013, s121-122(2):8-19.
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[17] Garcia Alegre A, Sanchez F, Gomez Ballesteros M. Modeling and mapping the local distribution of representative species on the Le Danois bank, El Cachucho marine protected area[J]. Deep-Sea Research Part II:Topical Studies in Oceanography, 2014, 106:51-164.
[18] Hampton S E, Strasser C A, Tewksbury J J. Big data and the future of ecology[J]. Frontiers in Ecology and the Evvironment, 2013, 11(3):156-162.
[19] Hortal, J., de Bello, F, Diniz-Filho. Seven shortfalls that beset large-scale knowledge of biodiversity[J]. Annual Review of Ecology. Evolution, and Systematics, 2015,46:523-549.
[20] Hovel KA. Habitat fragmentation in marine landscapes:Relative effects of habitat cover and configuration on juvenile crab survival in California and North Carolinaseagrass beds[J]. Biological Conservation, 2003,110:401-402.
[21] Hull V, Xu W, Liu W. Evaluating the efficacy of zoning designations for protected area mamagement[J]. Biological Conservation, 2011, 144(12):3028-3037.
[22] Ierodiaconor D, Monk J, Rattray A, et al. Comparisonof automated c lassification techniques for predicting benthic biological communities using hydroacoustics and video observations[J]. Continental Shelf Research, 2011,31(2):S28-S38.
[23] Jetz W, McPherson JM, Guralnick RP. Integrating biodiversity distribution knowledge:toward a global map of life[J]. Trends in ecology&evolution, 2012, 27(3):151-159.
[24] Koehn JD, Hobday AJ, Pratchett MS.Climate change and Australian marine and freshwater environments, fises and fisheries:synthesis and options for adaptation[J].Marine and Freshwater Research, 2011, 62:1148-1164.
[25] Leclerc M, Saint Hilaire A, Bechara J. State-of-the-art and perspectives of habitat modelling for determining conservation flows[J]. Canadian Water Resources Journal, 2003,28(2):135-151.
[26] Tyberghein L, Verbruggen H, Pauly K. Bio-ORACLE:a global environmental dataset for marine species distribution modeling[J]. Global Ecology&Biogeography, 2012,21:272-281.
[27] Maravelias CD, Reid DG. Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalized additive models[J].Oceanographic Literature Review, 1997, 8:882-883.
[28] Menon S. Biodiversity Informatics-Big Data for Biodiversity Conservation and Ecological Forecasting[J]. 2014Presentations, 2014, 11:18-19.
[29] Mitrathakur S. Watching the aliens[J]. Engineering&Technology, 2014, 9(7):28-32.
[30] Naimi B, Araujo MB. A reproducible and extensible R platform for species distribution modeling[J]. Ecography,2016, 39:368-375.
[31] Nurnberg S, Aberhan M. Habitat breadth and geographic range predict diversity dynamics in marine Mesozoic bivalves[J]. Paleobiology, 2013, 39:360-372.
[32] Parsian M. Recipes for Scaling Up with Hadoop and Spark[J]. Oreilly Media, 2015, 8:360-361.
[33] Peterson AT, Soberon J. Species distribution modeling and ecological niche modeling:getting the concepts right[J]. Natureza&Conservacao, 2012, 10(2):102-107.
[34] Pollock L J, Tingley R, Morris W K. Understanding co-occurrence by modelling species simultaneously witha Joint Species Distribution Model[J]. Methods in Ecology and Evolution, 2014, 5(5):397-406.
[35] Qiao HJ, Escobar LE, Saupe EE. Using the KDE method to model ecological niches:A response to Blonder[J].Global Ecology And Biogeography, 2017,26:1076-1077.
[36] Qiao HJ, Escobar LE, Saupe EE. A cautionary note on the use of hypervolume kernel density estimators in ecological niche modeling[J]. Global Ecology And Biogeography, 2017, 26:1066-1070.
[37] Qiao HJ, Hu JH, Huang JH. Theoretical basis, future directions, and challenges for ecological niche models[J].SCIENTIA SINICA Vitae 2013, 43:915-927.
[38] Robinson LM, Elith J, Hobday AJ. Pushing the limits in marine species distribution modelling:Lessons from the land present challenges and opportunities[J]. Global Ecology and Biogeography, 2011, 20:789-802.
[39]乔慧捷,胡军华,黄继红.生态位模型的理论基础,发展方向与挑战[J].中国科学:生命科学,2013, 43:915-927.
[40] Sarr I, Naacke H, Bame N. Green and Distributed Architecture for Managing Big Data of Biodiversity[J]. Springer International Publishing, 2015, 6:21-39.
[41] Sbrocco EJ, Barber PH. MARSPEC:ocean climate layers for marine spatial ecology[J]. Ecology, 2013, 94:979-979.
[42] Schliep E M, Lany N K, Zarnetske P L. Joint species disttribution modelling for spatio-temporal occurrence and ordinal abundance data[J]. Global Ecology and Biogeog-raphy, 2017, 4:181-182.
[43] Sunday JM, Bates AE, Dulvy NK. Thermal tolerance and the global redistribution of animals[J]. Nature Climate Change, 2012,2:686-690.
[44] Topping D T, Lowe C G, Caselle J E. Home range and habitat utilization of adult california sheephead, semicossyphus pulcher, in a temperate no-take marine reserve[J].Marine Biology, 2005, 147(2):301-311.
[45] Valle M, Borja A, Chust G. Modelling suitable estuarine habitats for Zostera noltii, using Ecological Niche Factor Analysis and Bathymetric LiDAR[J]. Estuarine Coastal&Shelf Science, 2011, 94:144-154.
[46] Vanderwal J, Murphy HT, Kutt AS. Focus on poleward shifts in species'distribution underestimates the fingerprint of climate change[J]. Nature Climate Change, 2013,3, 239-243.
[47] Yoo JW, Lee YW, Lee CG. Effective prediction of biodiversity in tidal flat habitats using an artificial neural network[J]. Marine Environmental Research, 2013, 83(2):1-9.
[48]杨晓龙,杨超杰,胡成业,等.物种分布模型在海洋潜在生境预测的应用研究进展[J].应用生态学报,2017,28:2063-2072.
[49]于贵瑞,何洪林,周玉科.大数据背景下的生态系统观测与研究[J].中国科学院院刊,2018, 33(8):832-837.
[50]朱耿平,刘国卿,卜文俊,等.生态位模型的基本原理及其在生物多样性保护中的应用[J].生物多样性,2013,21:90-98.
[2] Beerling D J, Huntley B, Bailey J P. Climate and the distribution of Fallopia japonica:use of an introduced species to test the predictive capacity of response surfaces[J]. Journal of Vegetation Science, 1995, 6(2):269-282.
[3] Benedetti Cecchi L, Bulleri F, Dal Bello M. Hybrid datasets:integrating observations with experiments in the era of macroecology and big data[J]. Ecology, 2018, 99(12):2654-2666.
[4] Blonder B, Lamanna C, Violle C. Using n-dimensional hypervolumes for species distribution modeling:A response to Qiao et al[J]. Global Ecology And Biogeography,2017, 26:1071-1075.
[5] Blonder B, Lamanna C, Violle C. The n-dimensional hypervolume[J]. Global Ecology and Biogeography, 2014,23:595-609.
[6] Boulangeat I, Gravel D, Thuiller W. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances[J]. Ecology letters, 2012, 15(6):584-593.
[7] Busby J. BIOCLIM-a bioclimate analysis and prediction system[J]. Plant Protection Quarterly, 1991, 6(1):8-9.
[8] Candela L, Castelli D, Coro G. Species distribution modeling in the cloud[J]. Concurrency&Computation Practice&Experience, 2016, 28:1056-1079.
[9] Dong XL, Srivastava D. Big Data Integration[J]. Proceedings of the Vldb Endowment, 2013, 6:1245-1248.
[10] Downie AL, Numers MV, Bostr(o|¨)m C. Influence of model selection on the predicted distribution of the seagrass Zostera marina[J]. Estuarine Coastal&Shelf Science,2013, s121-122(2):8-19.
[11] Edgar GJ, Bates AE, Bird TJ. Webb TJ New Approaches to Marine Conservation Through Scaling Up of Ecological Data[J]. Annual Review of Marine Science, 2016, 8:435-461.
[12] Elith J, Leathwick J R. Species distribution models:ecological explanation and prediction across space and time[J]. Annual Review of Ecology Evolution&Systematics, 2009, 40:677-697.
[13] Elith J, Kearney M, Phillips S. The art of modelling range-shifting species[J]. Methods in ecology and evolution,2010, 1(4):330-342.
[14] Evans M E, Merow C, Record S. Towards processbased range modeling of many species[J]. Trends in ecology&evolution, 2016, 31(11):860-871.
[15] Fuentes D, Fiore N. The Life Watch approach to the exploration of distributed species information[J]. ZooKeys,2014, 463:132-133.
[15] Franklin J, Serra Diaz JM, Syphard. Big data for forecasting the impacts of global change on plant communities[J]. Global Ecology and Biogeography, 2017, 26(1):6-17.
[17] Garcia Alegre A, Sanchez F, Gomez Ballesteros M. Modeling and mapping the local distribution of representative species on the Le Danois bank, El Cachucho marine protected area[J]. Deep-Sea Research Part II:Topical Studies in Oceanography, 2014, 106:51-164.
[18] Hampton S E, Strasser C A, Tewksbury J J. Big data and the future of ecology[J]. Frontiers in Ecology and the Evvironment, 2013, 11(3):156-162.
[19] Hortal, J., de Bello, F, Diniz-Filho. Seven shortfalls that beset large-scale knowledge of biodiversity[J]. Annual Review of Ecology. Evolution, and Systematics, 2015,46:523-549.
[20] Hovel KA. Habitat fragmentation in marine landscapes:Relative effects of habitat cover and configuration on juvenile crab survival in California and North Carolinaseagrass beds[J]. Biological Conservation, 2003,110:401-402.
[21] Hull V, Xu W, Liu W. Evaluating the efficacy of zoning designations for protected area mamagement[J]. Biological Conservation, 2011, 144(12):3028-3037.
[22] Ierodiaconor D, Monk J, Rattray A, et al. Comparisonof automated c lassification techniques for predicting benthic biological communities using hydroacoustics and video observations[J]. Continental Shelf Research, 2011,31(2):S28-S38.
[23] Jetz W, McPherson JM, Guralnick RP. Integrating biodiversity distribution knowledge:toward a global map of life[J]. Trends in ecology&evolution, 2012, 27(3):151-159.
[24] Koehn JD, Hobday AJ, Pratchett MS.Climate change and Australian marine and freshwater environments, fises and fisheries:synthesis and options for adaptation[J].Marine and Freshwater Research, 2011, 62:1148-1164.
[25] Leclerc M, Saint Hilaire A, Bechara J. State-of-the-art and perspectives of habitat modelling for determining conservation flows[J]. Canadian Water Resources Journal, 2003,28(2):135-151.
[26] Tyberghein L, Verbruggen H, Pauly K. Bio-ORACLE:a global environmental dataset for marine species distribution modeling[J]. Global Ecology&Biogeography, 2012,21:272-281.
[27] Maravelias CD, Reid DG. Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalized additive models[J].Oceanographic Literature Review, 1997, 8:882-883.
[28] Menon S. Biodiversity Informatics-Big Data for Biodiversity Conservation and Ecological Forecasting[J]. 2014Presentations, 2014, 11:18-19.
[29] Mitrathakur S. Watching the aliens[J]. Engineering&Technology, 2014, 9(7):28-32.
[30] Naimi B, Araujo MB. A reproducible and extensible R platform for species distribution modeling[J]. Ecography,2016, 39:368-375.
[31] Nurnberg S, Aberhan M. Habitat breadth and geographic range predict diversity dynamics in marine Mesozoic bivalves[J]. Paleobiology, 2013, 39:360-372.
[32] Parsian M. Recipes for Scaling Up with Hadoop and Spark[J]. Oreilly Media, 2015, 8:360-361.
[33] Peterson AT, Soberon J. Species distribution modeling and ecological niche modeling:getting the concepts right[J]. Natureza&Conservacao, 2012, 10(2):102-107.
[34] Pollock L J, Tingley R, Morris W K. Understanding co-occurrence by modelling species simultaneously witha Joint Species Distribution Model[J]. Methods in Ecology and Evolution, 2014, 5(5):397-406.
[35] Qiao HJ, Escobar LE, Saupe EE. Using the KDE method to model ecological niches:A response to Blonder[J].Global Ecology And Biogeography, 2017,26:1076-1077.
[36] Qiao HJ, Escobar LE, Saupe EE. A cautionary note on the use of hypervolume kernel density estimators in ecological niche modeling[J]. Global Ecology And Biogeography, 2017, 26:1066-1070.
[37] Qiao HJ, Hu JH, Huang JH. Theoretical basis, future directions, and challenges for ecological niche models[J].SCIENTIA SINICA Vitae 2013, 43:915-927.
[38] Robinson LM, Elith J, Hobday AJ. Pushing the limits in marine species distribution modelling:Lessons from the land present challenges and opportunities[J]. Global Ecology and Biogeography, 2011, 20:789-802.
[39]乔慧捷,胡军华,黄继红.生态位模型的理论基础,发展方向与挑战[J].中国科学:生命科学,2013, 43:915-927.
[40] Sarr I, Naacke H, Bame N. Green and Distributed Architecture for Managing Big Data of Biodiversity[J]. Springer International Publishing, 2015, 6:21-39.
[41] Sbrocco EJ, Barber PH. MARSPEC:ocean climate layers for marine spatial ecology[J]. Ecology, 2013, 94:979-979.
[42] Schliep E M, Lany N K, Zarnetske P L. Joint species disttribution modelling for spatio-temporal occurrence and ordinal abundance data[J]. Global Ecology and Biogeog-raphy, 2017, 4:181-182.
[43] Sunday JM, Bates AE, Dulvy NK. Thermal tolerance and the global redistribution of animals[J]. Nature Climate Change, 2012,2:686-690.
[44] Topping D T, Lowe C G, Caselle J E. Home range and habitat utilization of adult california sheephead, semicossyphus pulcher, in a temperate no-take marine reserve[J].Marine Biology, 2005, 147(2):301-311.
[45] Valle M, Borja A, Chust G. Modelling suitable estuarine habitats for Zostera noltii, using Ecological Niche Factor Analysis and Bathymetric LiDAR[J]. Estuarine Coastal&Shelf Science, 2011, 94:144-154.
[46] Vanderwal J, Murphy HT, Kutt AS. Focus on poleward shifts in species'distribution underestimates the fingerprint of climate change[J]. Nature Climate Change, 2013,3, 239-243.
[47] Yoo JW, Lee YW, Lee CG. Effective prediction of biodiversity in tidal flat habitats using an artificial neural network[J]. Marine Environmental Research, 2013, 83(2):1-9.
[48]杨晓龙,杨超杰,胡成业,等.物种分布模型在海洋潜在生境预测的应用研究进展[J].应用生态学报,2017,28:2063-2072.
[49]于贵瑞,何洪林,周玉科.大数据背景下的生态系统观测与研究[J].中国科学院院刊,2018, 33(8):832-837.
[50]朱耿平,刘国卿,卜文俊,等.生态位模型的基本原理及其在生物多样性保护中的应用[J].生物多样性,2013,21:90-98.