Chinese Journal of Applied Ecology ›› 2024, Vol. 35 ›› Issue (9): 2392-2400.doi: 10.13287/j.1001-9332.202409.029
• Expert Insights • Previous Articles Next Articles
CONG Jiayi1,2, LI Xinzheng1,2,3,4, XU Yong1,2,3,4*
Received:
2024-01-02
Accepted:
2024-05-22
Online:
2024-09-18
Published:
2025-03-18
CONG Jiayi, LI Xinzheng, XU Yong. Application of species distribution models in predicting the distribution of marine macrobenthos[J]. Chinese Journal of Applied Ecology, 2024, 35(9): 2392-2400.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.cjae.net/EN/10.13287/j.1001-9332.202409.029
[1] Elith J, Leathwick JR. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systema-tics, 2009, 40: 677-697 [2] Engel M, Mette T, Falk W. Spatial species distribution models: Using Bayes inference with INLA and SPDE to improve the tree species choice for important European tree species. Forest Ecology and Management, 2022, 507: 119983 [3] Hellegers M, Ozinga WA, van Hinsberg A, et al. Eva-luating the ecological realism of plant species distribution models with ecological indicator values. Ecography, 2020, 43: 161-170 [4] Lecocq T, Harpke A, Rasmont P, et al. Integrating intraspecific differentiation in species distribution models: Consequences on projections of current and future climatically suitable areas of species. Diversity and Distributions, 2019, 25: 1088-1100 [5] Pellissier L, Pradervand JN, Pottier J, et al. Climate-based empirical models show biased predictions of butterfly communities along environmental gradients. Ecography, 2012, 35: 684-692 [6] Trew BT, Early R, Duffy JP, et al. Using near-ground leaf temperatures alters the projected climate change impacts on the historical range of a floristic biodiversity hotspot. Diversity and Distributions, 2022, 28: 1282-1297 [7] Fairbanks RG, Sverdlove M, Free R, et al. Vertical-distribution and isotopic fractionation of living planktonic-foraminifera from the Panama basin. Nature, 1982, 298: 841-844 [8] Beca-Carretero P, Teichberg M, Winters G, et al. Projected rapid habitat expansion of tropical seagrass species in the Mediterranean Sea as climate change progresses. Frontiers in Plant Science, 2020, 11: 555376 [9] Gallon RK, Robuchon M, Leroy B, et al. Twenty years of observed and predicted changes in subtidal red seaweed assemblages along a biogeographical transition zone: Inferring potential causes from environmental data. Journal of Biogeography, 2014, 41: 2293-2306 [10] Li JJ, Huang SH, Liu ZY, et al. Climate-driven range shifts of brown seaweed Sargassum horneri in the Northwest Pacific. Frontiers in Marine Science, 2020, 7: 570881 [11] Mendoza-Segura C, Fernandez E, Beca-Carretero P. Predicted changes in the biogeographical range of Gracilaria vermiculophylla under present and future climate scenarios. Journal of Marine Science and Engineering, 2023, 11: 367 [12] Neiva J, Assis J, Fernandes F, et al. Species distribution models and mitochondrial DNA phylogeography suggest an extensive biogeographical shift in the high-intertidal seaweed Pelvetia canaliculata. Journal of Biogeography, 2014, 41: 1137-1148 [13] Brun P, Kiorboe T, Licandro P, et al. The predictive skill of species distribution models for plankton in a changing climate. Global Change Biology, 2016, 22: 3170-3181 [14] Jensen LO, Mousing EA, Richardson K. Using species distribution modelling to predict future distributions of phytoplankton: Case study using species important for the biological pump. Marine Ecology, 2017, 38: e12427 [15] Zhang ZX, Mammola S, Xian WW, et al. Modelling the potential impacts of climate change on the distribution of ichthyoplankton in the Yangtze Estuary, China. Diversity and Distributions, 2020, 26: 126-137 [16] Asch RG, Sobolewska J, Chan K. Assessing the reliability of species distribution models in the face of climate and ecosystem regime shifts: Small pelagic fishes in the California current system. Frontiers in Marine Science, 2022, 9: 711522 [17] Champion C, Brodie S, Coleman MA. Climate-driven range shifts are rapid yet variable among recreationally important coastal-pelagic fishes. Frontiers in Marine Science, 2021, 8: 622299 [18] Fennie HW, Seary R, Muhling BA, et al. An anchovy ecosystem indicator of marine predator foraging and reproduction. Proceedings of the Royal Society B-Biological Sciences, 2023, 290: 20222326 [19] Harishchandra A, Xue HJ, Salinas S, et al. Thermal physiology integrated species distribution model predicts profound habitat fragmentation for estuarine fish with ocean warming. Scientific Reports, 2022, 12: 21781 [20] Karp MA, Brodie S, Smith JA, et al. Projecting species distributions using fishery-dependent data. Fish and Fisheries, 2023, 24: 71-92 [21] Schickele A, Goberville E, Leroy B, et al. European small pelagic fish distribution under global change scenarios. Fish and Fisheries, 2021, 22: 212-225 [22] Su S, Du J, Chen B, et al. Impact of climate change on the potential habitat distributions of eight pelagic fishes in the coastal waters of China. Acta Ecologica Sinica, 2022, 42: 4834-4846 [23] 刘尊雷, 杨林林, 袁兴伟, 等. 基于集成模型的小黄鱼越冬群体适宜生境及其环境影响因素. 应用生态学报, 2020, 31(6): 2076-2086 [24] El-Gabbas A, Van Opzeeland I, Burkhardt E, et al. Dynamic species distribution models in the marine realm: Predicting year-round habitat suitability of Baleen whales in the Southern Ocean. Frontiers in Marine Science, 2021, 8: 802276 [25] Pendleton DE, Holmes EE, Redfern J, et al. Using modelled prey to predict the distribution of a highly mobile marine mammal. Diversity and Distributions, 2020, 26: 1612-1626 [26] Thorne LH, Baird RW, Webster DL, et al. Predicting fisheries bycatch: A case study and field test for pilot whales in a pelagic longline fishery. Diversity and Distributions, 2019, 25: 909-923 [27] Tobena M, Prieto R, Machete M, et al. Modeling the potential distribution and richness of cetaceans in the azores from fisheries observer program data. Frontiers in Marine Science, 2016, 3: 202 [28] Robinson LM, Elith J, Hobday AJ, et al. Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities. Global Ecology and Biogeography, 2011, 20: 789-802 [29] Robinson NM, Nelson WA, Costello MJ, et al. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Frontiers in Marine Science, 2017, 4: 421 [30] 李新正. 我国海洋大型底栖生物多样性研究及展望: 以黄海为例. 生物多样性, 2011, 19(6): 676-684 [31] 李新正, 刘录三, 李宝泉, 等. 中国海洋大型底栖生物研究与实践. 北京: 海洋出版社, 2010 [32] Pillay D. Ecosystem engineering by thalassinidean crustaceans: Response variability, contextual dependencies and perspectives on future research. Diversity, 2019, 11: 64 [33] 张敬怀, 李小敏, 方宏达, 等. 珠江口海洋疏浚物倾倒区及附近海域大型底栖生物群落健康评价. 热带海洋学报, 2010, 29(5): 119-124 [34] 丁敬坤, 薛素燕, 李加琦, 等. 基于大型底栖动物的桑沟湾不同养殖区底栖生境健康评价. 中国水产科学, 2020, 27(12): 1393-1401 [35] 丁敬坤, 张雯雯, 李阳, 等. 胶州湾底栖生态系统健康评价: 基于大型底栖动物生态学特征. 渔业科学进展, 2020, 41(2): 20-26 [36] Guidetti P, Modena M, La Mesa G, et al. Composition, abundance and stratification of macrobenthos in the marine area impacted by tar aggregates derived from the Haven oil-spill (Ligurian Sea, Italy). Marine Pollution Bulletin, 2000, 40: 1161-1166 [37] Chatzinikolaou E, Mandalakis M, Damianidis P, et al. Spatio-temporal benthic biodiversity patterns and pollution pressure in three Mediterranean touristic ports. Science of the Total Environment, 2018, 624: 648-660 [38] Simboura N, Reizopoulou S. A comparative approach of assessing ecological status in two coastal areas of Eastern Mediterranean. Ecological Indicators, 2007, 7: 455-468 [39] 蔡立哲. 大型底栖动物污染指数(MPI). 环境科学学报, 2003, 23(5): 625-629 [40] Pinto R, Patrício J, Baeta A, et al. Review and evaluation of estuarine biotic indices to assess benthic condition. Ecological Indicators, 2009, 9: 1-25 [41] 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. 应用生态学报, 2017, 28(6): 2063-2072 [42] Kearney MR, Wintle BA, Porter WP. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conservation Letters, 2010, 3: 203-213 [43] Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecological Modelling, 2000, 135: 147-186 [44] 郭彦龙, 赵泽芳, 乔慧捷, 等. 物种分布模型面临的挑战与发展趋势. 地球科学进展, 2020, 35(12): 1292-1305 [45] Kearney M, Porter W. Mechanistic niche modelling: Combining physiological and spatial data to predict species' ranges. Ecology Letters, 2009, 12: 334-350 [46] Dormann CF, Schymanski SJ, Cabral J, et al. Correlation and process in species distribution models: Bridging a dichotomy. Journal of Biogeography, 2012, 39: 2119-2131 [47] Methorst J, Boehning-Gaese K, Khaliq I, et al. A framework integrating physiology, dispersal and land-use to project species ranges under climate change. Journal of Avian Biology, 2017, 48: 1532-1548 [48] DeAngelis DL, Mooij WM. Individual-based modeling of ecological and evolutionary processes. Annual Review of Ecology, Evolution and Systematics, 2005, 36: 147-168 [49] Ni J. Biome models: Main principles and applications. Acta Phytoecologica Sinica, 2002, 26: 481-488 [50] Razgour O. Beyond species distribution modeling: A landscape genetics approach to investigating range shifts under future climate change. Ecological Informatics, 2015, 30: 250-256 [51] Rougier T, Lassalle G, Drouineau H, et al. The combined use of correlative and mechanistic species distribution models benefits low conservation status species. PLoS One, 2015, 10(10): e0139194 [52] Reiss H, Cunze S, Konig K, et al. Species distribution modelling of marine benthos: A North Sea case study. Marine Ecology Progress Series, 2011, 442: 71-86 [53] Luan J, Zhang CL, Xu BD, et al. Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China. PLoS One, 2018, 13(11): e0207457 [54] Drewnik A, Weslawski JM, Wlodarska-Kowalczuk M. Benthic Crustacea and Mollusca distribution in Arctic fjord: Case study of patterns in Hornsund, Svalbard. Oceanologia, 2017, 59: 565-575 [55] Meissner K, Fiorentino D, Schnurr S, et al. Distribution of benthic marine invertebrates at northern latitudes: An evaluation applying multi-algorithm species distribution models. Journal of Sea Research, 2014, 85: 241-254 [56] Noble MM, Harasti D, Fulton CJ, et al. Identifying spatial conservation priorities using traditional and local ecological knowledge of iconic marine species and ecosystem threats. Biological Conservation, 2020, 249: 108709 [57] Zellmer AJ, Claisse JT, Williams CM, et al. Predicting optimal sites for ecosystem restoration using stacked-species distribution modeling. Frontiers in Marine Science, 2019, 6: 3 [58] 颜明艳, 李琼珍, 宋洁, 等. 基于MAXENT模型评估北部湾潮间带中国鲎和圆尾鲎稚鲎的潜在地理分布及种群保育对策. 生态学报, 2019, 39(9): 3100-3109 [59] 邢衍阔, 王林龙, 刘敏, 等. 基于物种分布模型的全球绿海龟空间分布及洄游廊道预测. 中国水产科学, 2021, 28(10): 1337-1345 [60] 邢衍阔, 康斌, 鹿志创, 等. 气候变化条件下太平洋丽龟的适宜生境及其变化. 应用生态学报, 2023, 34(8): 2267-2273 [61] Wright MK, Pompe LR, Mishra DR, et al. Hawksbill presence and habitat suitability of a marine reserve in Honduras. Ocean & Coastal Management, 2022, 225: 106204 [62] Xu Y, Ma L, Sui J, et al. Potential effects of climate change on the habitat suitability of macrobenthos in the Yellow Sea and East China Sea. Marine Pollution Bulletin, 2022, 174: 113238 [63] Xu Y, Ma L, Sui J, et al. Potential impacts of climate change on the distribution of echinoderms in the Yellow Sea and East China Sea. Marine Pollution Bulletin, 2023, 194: 115246 [64] Ceylan Y, Gül S. Potential habitats of an alien species (Asterias rubens Linnaeus, 1758) in the Black Sea: Its current and future distribution patterns. Environmental Science and Pollution Research, 2022, 29: 19563-19571 [65] Russell BD, Connell SD, Mellin C, et al. Predicting the distribution of commercially important invertebrate stocks under future climate. PLoS One, 2012, 7(12): e46554 [66] Brown LA, Furlong JN, Brown KM, et al. Oyster reef restoration in the Northern Gulf of Mexico: Effect of artificial substrate and age on nekton and benthic macroinvertebrate assemblage use. Restoration Ecology, 2014, 22: 214-222 [67] Hespanhol H, Cezon K, Felicisimo AM, et al. How to describe species richness patterns for bryophyte conservation? Ecology and Evolution, 2015, 5: 5443-5455 [68] Torres LG, Sutton PJH, Thompson DR, et al. Poor transferability of species distribution models for a pelagic predator, the grey petrel, indicates contrasting habitat preferences across ocean basins. PLoS One, 2015, 10(3): e0120014 [69] Rodriguez L, Garcia JJ, Carreno F, et al. Integration of physiological knowledge into hybrid species distribution modelling to improve forecast of distributional shifts of tropical corals. Diversity and Distributions, 2019, 25: 715-728 [70] Marques DA, Jones FC, Di Palma F, et al. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nature Ecology & Evolution, 2018, 2: 1128-1138 [71] Chen Y, Gao Y, Huang X, et al. Incorporating adaptive genomic variation into predictive models for invasion risk assessment. Environmental Science and Ecotechnology, 2024, 18: 100299 [72] Gallego R, Dennis TE, Basher Z, et al. On the need to consider multiphasic sensitivity of marine organisms to climate change: A case study of the Antarctic acorn barnacle. Journal of Biogeography, 2017, 44: 2165-2175 [73] Schulte PM. The effects of temperature on aerobic metabolism: Towards a mechanistic understanding of the responses of ectotherms to a changing environment. Journal of Experimental Biology, 2015, 218: 1856-1866 [74] Gamliel I, Buba Y, Guy-Haim T, et al. Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species. Ecography, 2020, 43: 1090-1106 [75] Bosch-Belmar M, Giommi C, Milisenda G, et al. Integrating functional traits into correlative species distribution models to investigate the vulnerability of marine human activities to climate change. Science of the Total Environment, 2021, 799: 149351 [76] Marchessaux G, Bosch-Belmar M, Cilenti L, et al. The invasive blue crab Callinectes sapidus thermal response: Predicting metabolic suitability maps under future warming Mediterranean scenarios. Frontiers in Marine Science, 2022, 9: 1055404 [77] Stephenson F, Gladstone-Gallagher RV, Bulmer RH, et al. Inclusion of biotic variables improves predictions of environmental niche models. Diversity and Distributions, 2022, 28: 1373-1390 [78] Bennington S, Rayment W, Dawson S. Putting prey into the picture: Improvements to species distribution models for bottlenose dolphins in Doubtful Sound, New Zealand. Marine Ecology Progress Series, 2020, 653: 191-204 [79] Liu S, Tian Y, Liu Y, et al. Development of a prey-predator species distribution model for a large piscivorous fish: A case study for Japanese Spanish mackerel Scomberomorus niphonius and Japanese anchovy Engraulis japonicus. Deep-Sea Research Part II-Topical Studies in Oceanography, 2023, 207: 105227 [80] Zhang Y, Zhang C, Xu B, et al. Impacts of trophic interactions on the prediction of spatio-temporal distribution of mid-trophic level fishes. Ecological Indicators, 2022, 138: 108826 [81] Zhang C, Chen Y, Xu B, et al. Comparing the prediction of joint species distribution models with respect to characteristics of sampling data. Ecography, 2018, 41: 1876-1887 [82] Fitzpatrick MC, Sanders NJ, Ferrier S, et al. Forecasting the future of biodiversity: A test of single- and multi-species models for ants in North America. Ecography, 2011, 34: 836-847 [83] Halpern BS, Walbridge S, Selkoe KA, et al. A global map of human impact on marine ecosystems. Science, 2008, 319: 948-952 [84] Gallardo B, Zieritz A, Aldridge DC. The importance of the human footprint in shaping the global distribution of terrestrial, freshwater and marine invaders. PLoS One, 2015, 10(5): e0125801 |
[1] | LI Xinge, ZHU Lianqi, ZHU Wenbo, HAN Guangxuan. Effects of simulated precipitation changes on soil respiration:Progress and prospects [J]. Chinese Journal of Applied Ecology, 2024, 35(9): 2445-2454. |
[2] | ZHANG Juanjuan, LI Xingzhi, WANG Yanan, DENG Jiaojiao, ZHOU Li, ZHOU Wangming, YU Dapao, WANG Qingwei. Research advance in effects of solar radiation on litter decomposition in terrestrial ecosystems [J]. Chinese Journal of Applied Ecology, 2024, 35(9): 2463-2472. |
[3] | MA Yan, CHEN Tiexi, CHEN Xin, XIAO Yinmiao, ZHOU Shengjie, WANG Shengzhen. Change trend and attribution analysis of leaf area index in the East African Plateau from 1982 to 2020 [J]. Chinese Journal of Applied Ecology, 2024, 35(9): 2561-2570. |
[4] | ZHANG Haining, ZHANG Jun, ZHANG Dongjia, LI Luyao, TIAN Ruiping, WANG Chuankuan, QUAN Xiankui. Response of leaf anatomical structure of Larix gmelinii to climate warming and provenance variation [J]. Chinese Journal of Applied Ecology, 2024, 35(8): 2073-2081. |
[5] | FAN Yueyuan, GAO Huangjie, TAO Shaomin, YIN Chuanlin, YU Xiaoping. Potential distribution prediction of Pomacea canaliculata in China based on the Biomod2 [J]. Chinese Journal of Applied Ecology, 2024, 35(8): 2237-2246. |
[6] | TIAN Maohui, SHEN Lidong, SU Weici. Research progress on the effects of elevated atmospheric CO2 concentration on CH4 emission and related microbial processes in paddy fields [J]. Chinese Journal of Applied Ecology, 2024, 35(8): 2267-2281. |
[7] | YANG Shunting, WANG Huichun, JING Weikun, WANG Qigang, YAN Huijun, QIU Xianqin, JIAN Hongying. Simulation of climate change effect on the global distribution of Rosa multiflora [J]. Chinese Journal of Applied Ecology, 2024, 35(7): 1897-1906. |
[8] | LI Xiufen, WU Shuang, ZHAO Fang, ZHU Haixia, GONG Lijuan, JIANG Lixia, WANG Ping, ZHAO Huiying. Characteristics of soybean climate potential productivity in frigid region and its response to climate change [J]. Chinese Journal of Applied Ecology, 2024, 35(6): 1615-1624. |
[9] | GUO Daxin, LI Aoxiang, LIU Enke, WANG Juanling. Spatiotemporal variations and attribution analysis of reference evapotranspiration in the Fenwei Plain under climate change [J]. Chinese Journal of Applied Ecology, 2024, 35(6): 1625-1634. |
[10] | QI Yanying, KEYIMU Maierdang, LI Zongshan, ZENG Fanjiang. Radial growth response of Populus euphratica to climate change in the Cele desert oasis ecotone, China [J]. Chinese Journal of Applied Ecology, 2024, 35(5): 1187-1195. |
[11] | LI Jun, LIU Ze, WANG Pai, YANG Rui, SHI Fengming, DENG Jie, WANG Guoyan, SHI Songlin. Response of radial growth of Pinus wallichiana to climate change in Mount Qomolangma, Tibet, China [J]. Chinese Journal of Applied Ecology, 2024, 35(5): 1205-1213. |
[12] | WANG Ruoru, LI Xiaoma, GAN Dexin, LIU Huanyao, TANG Le, CAI Zhengwu. Characteristics and drivers of vegetation temporal dynamics in Hunan Province of China during 2002-2020 [J]. Chinese Journal of Applied Ecology, 2024, 35(5): 1312-1320. |
[13] | GONG Zhiyuan, WANG Chunlin, DONG Dandan, ZHANG Rui, ZHANG Xi. Influence of climate change and human activities on grassland phenology in Anhui Province [J]. Chinese Journal of Applied Ecology, 2024, 35(4): 1092-1100. |
[14] | JI Yinglu, YI Fan, QU Lin, LIU Hang, CHEN Jing, CHEN Linlin, LI Baoquan. Ecological characteristics and seasonal changes of macrozoobenthos in the waters of Miaodao Archipelago, China [J]. Chinese Journal of Applied Ecology, 2024, 35(4): 1131-1140. |
[15] | YANG Zhenkang, YANG Wanrong, LIU Zhijuan, GAO Weida, REN Tusheng, SHEN Yanjun, YANG Xiaoguang. Effects of climate change on wind erosion in the three provinces of Northeast China [J]. Chinese Journal of Applied Ecology, 2023, 34(9): 2429-2435. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||