Prediction of invasion risk of pine wilt disease based on GIS spatial technology and MaxEnt model in western Sichuan
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摘要:
目的 松材线虫在我国主要以松墨天牛和云杉花墨天牛为传播媒介，感染林木后常导致森林毁灭性破坏。预测松材线虫病入侵风险不仅对森林保护与质量提升具有重要参考价值，还关乎我国生态安全与碳中和目标的实现。 方法 本文基于川西理县24个云杉花墨天牛和55个枯死松树（云杉花墨天牛羽化前载体）地理分布点以及20个生物与非生物因子数据，利用GIS分析工具和最大熵模型（MaxEnt）对该县云杉花墨天牛适生区和枯死松树潜在分布区进行预测，并通过MaxEnt软件内建的刀切法剖析影响云杉花墨天牛适生区与松树分布区的主要因子。考虑到松材线虫病发生至少需同时具备传播媒介（云杉花墨天牛）和载体（松树）二要素，将云杉花墨天牛适生区和枯死松树分布区数据进行加权求和，预测松材线虫病发生的潜在分布区，评估其入侵风险。 结果 研究发现MaxEnt模型对云杉花墨天牛适生区和枯死松树分布区的预测工作特征曲线的下面积值分别为0.993和0.969，表明模型的预测结果为优，可用于松材线虫病潜在入侵风险预测。松材线虫病潜在入侵风险评估发现距居民点1.5 km内、年均气温为7.8 ~ 10.1 ℃、最湿季降水量为345 ~ 358 mm时松材线虫病潜在发生风险最高。模型预估理县松材线虫病潜在发生高风险区面积为10 616 hm2，沿道路呈带状分布于各乡镇，占县域针叶林总面积7.1%。 结论 基于GIS空间技术和MaxEnt模型有助于预测川西林区松材线虫病入侵风险。但是，随着经济建设与气候变化，川西松材线虫传播与发生存在较大不确定性，应加强居民点、公路沿线松材线虫及其传播媒介的监测，完善防控应急预案，保障川西林区生态安全。 Abstract:Objective In China, the Pine wood nematode (Bursaphelenchus xylophilus) takes Monochamus alternatus and M. saltuarius as propagative materials, leading to catastrophic damage to forests since they infect the forest trees. Predicting the invasion risk of pine wilt disease has critical referred values for forest protection and quality improvement, and as a result, is related to national ecological security and carbon neutralization. Method Based on the data of 24 distribution points for M. saltuarius and another 55 points for dead pine trees (hosts of M. saltuarius before eclosion) as well as 20 abiotic and biotic variables in Lixian County, we predicted the potentially suitable distribution areas for M. saltuarius and dead pine trees using GIS analytical tool and the MaxEnt model. Furthermore, the MaxEnt jack-knife of variable importance was applied to analyze the influence of main factors on the areas of M. saltuarius and dead pine trees, respectively. Considering that the occurrence of pine wilt disease at least requires both elements (i.e., M. saltuarius and pine trees), we evaluated the invasion risk of pine wilt disease by predicting the potential occurred regions of B. xylophilus based on weighting and integrating data of the occurrence of the M. saltuarius and dead pine trees generated by MaxEnt models. Result The results showed that mean area values under curves of the suitable distribution areas of M. saltuarius and dead pines were 0.993 and 0.969 from the MaxEnt models respectively, which indicated that the model predictions were ideal and can be used to forecast the potential invasion risk of pine wilt disease. Assessing the invasion risk of pine wilt disease demonstrated that the risk was highest when mean annual temperature ranged from 7.8 ℃ to 10.1 ℃ and the precipitation in the wettest season was 348 mm to 358 mm as well as within 1.5 km from the nearest residential point. The model estimated that the high-risk area of pine wilt disease was 10 616 hm2, accounting for 7.1% of the total area of coniferous forests in the County, which was distributed zonally along the roads across villages and towns. Conclusion Using GIS spatial technology and MaxEnt modelling can benefit the prediction of the invasion risk of pine wilt disease for the forest zones in western Sichuan. However, there still exists great uncertainty in the transmission and occurrence of pine wilt disease due to economic construction and climate change. Therefore, it is necessary to strengthen the monitoring of pine wood nematodes (B. xylophilus) and their propagative materials along the residential areas and roads, promoting the preventive and controlled emergency response to ensure the ecological security of forest zones in western Sichuan. -
Key words:
- pine wilt disease /
- MaxEnt model /
- ArcGIS /
- invasion risk /
- Lixian County /
- western Sichuan
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图 1 四川理县云杉花墨天牛和枯死松树分布情况
Figure 1. Distributing pattern of Monochamus saltuarius and dead pine trees in Lixian County, Sichuan
图 2 MaxEnt模型应用ROC分析法检验云杉花墨天牛适生区和枯死松树发生区的模拟结果
Figure 2. Simulating results of potential target areas for M. saltuarius and occurred areas for dead pines examined using ROC curve in the MaxEnt models
图 3 四川理县云杉花墨天牛与枯死松树的预测概率对海拔、坡度与居民点的响应曲线
红线表明MaxEnt模型中云杉花墨天牛或枯死松树发生概率随各因子的变化趋势，蓝色部分为95%置信区间。下同。The red line indicates variation trend of occurrence probability of M. saltuarius or dead pine trees in the MaxEnt models as response to abiotic or biotic factors; The blue part represent 95% confidence intervals. The same below.
Figure 3. Response curves of predicted probability for M. saltuarius and dead pine trees to elevation, slope and residential variables in Lixian County, Sichuan
图 4 四川理县云杉花墨天牛与枯死松树的预测概率对气候因子与人为活动的响应曲线
Figure 4. Response curves of predicted probability for M. saltuarius and dead pine trees to climatic and anthropogenic variables in Lixian County, Sichuan
图 5 四川理县云杉花墨天牛适生区的MaxEnt模型预测
Figure 5. Prediction of MaxEnt model for potential target areas of M. saltuarius in Lixian County, Sichuan
图 6 四川理县枯死松树发生区的MaxEnt模型预测
Figure 6. Prediction of MaxEnt model for potential occurred areas of dead pines in Lixian County, Sichuan
图 7 四川理县耦合云杉花墨天牛与枯死松树的松材线虫病潜在风险MaxEnt模型预测
Figure 7. Prediction of MaxEnt model for potential risk of pine wilt disease based on integration of M. saltuarius and dead pines in Lixian County, Sichuan
表 1 四川理县生物与非生物因子对云杉花墨天牛适生区和枯死松树发生区MaxEnt模拟的贡献率
Table 1. Contribution of biotic and abiotic factors to MaxEnt model simulations of suitable areas for M. saltuarius and occurred areas for dead pine trees in Lixian County, Sichuan
影响因子
Impact factor贡献率
Contribution/%影响因子
Impact factor贡献率
Contribution/%云杉花墨天牛
M. saltuarius枯死松树
Dead pine trees云杉花墨天牛
M. saltuarius枯死松树
Dead pine trees年均温
Annual mean temperature20.40 5.50 最干季降水量
Precipitation of the driest month0.00 0.00 平均气温日较差
Mean temperature diurnal range0.10 0.00 最暖季平均降水量
Mean precipitation of the warmest season0.40 2.90 昼夜温差与年温差比值
Isothermality0.50 1.90 最冷季平均降水量
Mean precipitation of the coldest season0.00 0.10 温度季节变化
Temperature seasonality1.00 0.00 地表覆盖情况
Land surface coverage condition1.20 1.30 年温度变化范围 Annual temperature range 1.00 1.10 海拔 Elevation 7.00 2.10 年降水量 Annual precipitation 0.30 5.30 坡度 Slope 1.40 1.40 最湿月降水量
Precipitation of the wettest month19.00 4.20 坡向 Aspect 3.90 0.50 最干月降水量
Precipitation of the driest month0.10 2.10 距居民点距离
Distance to the nearest residential point28.60 29.00 降水量季节性变异系数
Variation coefficient of precipitation seasonality0.00 0.20 距道路距离
Distance to the nearest road2.00 24.90 最湿季降水量
Precipitation of the wettest month11.00 16.90 距水源距离
Distance to the water source2.00 0.70 
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[1] 孙红梅, 刘书剑. 黔西南州松材线虫病入侵规律及防范对策探究[J]. 植物检疫, 2019, 33(4): 13−20.Sun H M, Liu S J. Research on the invasion rules and prevention measures of Pine wilt disease in Qianxinan Prefecture[J]. Plant Quarantine, 2019, 33(4): 13−20. [2] 叶建仁. 松材线虫病在中国的流行现状、防治技术与对策分析[J]. 林业科学, 2019, 55(9): 1−10.Ye J R. Epidemic status of pine wilt didease in China and its prevention and control techniques and counter measures[J]. Scientia Silvae Sinicae, 2019, 55(9): 1−10. [3] 李计顺, 潘佳亮, 刘超, 等. 2020年全国松材线虫病疫情流行情况分析[J]. 中国森林病虫, 2021, 40(4): 1−4.Li J S, Pan J L, Liu C, et al. Analysis of the epidemic situation of pine wilt disease in China in 2020[J]. Forest Pest and Disease, 2021, 40(4): 1−4. [4] 张旭臣, 时勇, 范立淳, 等. 大连市松材线虫病疫区天牛种类调查[J]. 中国森林病虫, 2021, 40(3): 36-39.Zhang X C, Shi Y, Fan L C, et al. Investigation on long-horned beetle species in pine wilt disease epidemic area in Dalian[J]. Forest Pest and Disease, 2021, 40(3): 36−39. [5] Brockerhoff E G, Barbaro L, Castagneyrol B, et al. Forest biodiversity, ecosystem functioning and the provision of ecosystem services[J]. Biodiversity and Conservation, 2017, 26(13): 3005−3035. doi: 10.1007/s10531-017-1453-2 [6] 张旭, 赵京京, 闫峻, 等. 2017年中国大陆松材线虫病灾害经济损失评估[J]. 北京林业大学学报, 2020, 42(10): 96−106.Zhang X, Zhao J J, Yan J, et al. Economic loss assessment of pine wilt disease in mainland China in 2017[J]. Journal of Beijing Forestry University, 2020, 42(10): 96−106. [7] Rutherford T, Mamiya Y, Webster J. Nematode-induced pine wilt disease: factors influencing its occurrence and distribution[J]. Forest Science, 1990, 36(1): 145−155. [8] Rutherford T, Webster J. Distribution of pine wilt disease with respect to temperature in North America, Japan, and Europe[J]. Canadian Journal of Forest Research, 1987, 17(9): 1050−1059. doi: 10.1139/x87-161 [9] Hirata A, Nakamura K, Nakao K, et al. Potential distribution of pine wilt disease under future climate change scenarios[J/OL]. PLoS One, 2017, 12(8): e0182837[2022−10−20]. https://doi.org/10.1371/journal.pone.0182837. [10] Hunt D. Pine wilt disease: a worldwide threat to forest ecosystems[J]. Nematology, 2009, 11(2): 315−316. doi: 10.1163/156854109X404553 [11] Takeuchi Y, Futai K. Avirulent isolate of the pinewood nematode Bursaphelenchus xylophilus, survives 7 months in asymptomatic host seedlings[J]. Forest Pathology, 2007, 37(5): 289−291. doi: 10.1111/j.1439-0329.2007.00508.x [12] 陈卫东. 厦门市松材线虫病的危险性评估及松墨天牛的种群动态[J]. 安徽农学通报, 2020, 26(24): 120−123. doi: 10.3969/j.issn.1007-7731.2020.24.044Chen W D. Risk assessment of pine wood nematode disease and population dynamics of Monochamus alternatus in Xiamen City[J]. Anhui Agricultural Science Bulletin, 2020, 26(24): 120−123. doi: 10.3969/j.issn.1007-7731.2020.24.044 [13] 阮成俊, 谈家金, 叶建仁, 等. 越南枯死松树症状特征和体内寄生线虫种类调查[J]. 南京林业大学学报(自然科学版), 2016, 40(1): 44−52.Ruan C J, Tan J J, Ye J R, et al. A survey on the symptoms and endoparasite of the dead pine trees in Vietnam[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2016, 40(1): 44−52. [14] 廖太林, 叶建仁. 松树枯梢病的发生与土壤条件的关系[J]. 南京林业大学学报(自然科学版), 2005, 29(6): 126−128.Liao T L, Ye J R. Relationship between the epidemic of pine shoot blight and soil element[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2005, 29(6): 126−128. [15] 高瑞贺, 冀卫荣, 李宏, 等. 松材线虫病疫情指数与气候因素之间的关系[J]. 山西农业大学学报(自然科学版), 2019, 39(5): 32−40.Gao R H, Ji W R, Li H, et al. The relationship between pine wilt disease occurrence and climate variation[J]. Journal of Shanxi Agricultural University (Natural Sciences Edition), 2019, 39(5): 32−40. [16] Maehara N, Aikawa T, Kanzaki N. Relationship between pine wilt disease development in asymptomatic carrier trees of Bursaphelenchus xylophilus (Nematoda: Apelenchoididae) and their use by Monochamus alternatus (Coleoptera: Cerambycidae)[J]. Applied Entomology and Zoology, 2015, 50(1): 33−39. doi: 10.1007/s13355-014-0299-2 [17] 施雯, 朱恩骄, 王宇宸, 等. 基于MaxEnt模型预测戴褐臂金龟在中国的潜在适生区[J]. 生态学杂志, 2021, 40(9): 2936−2944.Shi W, Zhu E J, Wang Y C, et al. Prediction of potentially suitable distribution area of Propomacrus davidi Deyrolle in China based on MaxEnt model[J]. Chinese Journal of Ecology, 2021, 40(9): 2936−2944. [18] 吴思俊, 朱天辉, 谯天敏. 基于物种分布模型对未来气候变化下云南松毛虫在四川省适生区的预测[J]. 植物保护学报, 2021, 40(9): 2936−2944.Wu S J, Zhu T H, Qiao T M. Projections of Yunnan pine moth Dendrolimus houi in Sichuan Province under future climate change based on species distribution model[J]. Journal of Plant Protection, 2021, 40(9): 2936−2944. [19] Wittmann M E, Barnes M A, Jerde C L, et al. Confronting species distribution model predictions with species functional traits[J]. Ecology and Evolution, 2016, 6(4): 873−879. doi: 10.1002/ece3.1898 [20] Moreno R, Zamora R, Molina J R, et al. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent)[J]. Ecological Informatics, 2011, 6(6): 364−370. doi: 10.1016/j.ecoinf.2011.07.003 [21] Phillips S J, Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2): 161−175. doi: 10.1111/j.0906-7590.2008.5203.x [22] 魏淑婷, 李涛, 林玉成. 基于MaxEnt模型预测四川省松材线虫的潜在适生区[J]. 四川动物, 2019, 38(1): 37−46.Wei S T, Li T, Lin Y C. Prediction of the potential distribution of Bursaphelenchus xylophilus in Sichuan province using MaxEnt model[J]. Sichuan Journal of Zoology, 2019, 38(1): 37−46. [23] 刘晓梅, 蒲永兰, 李宏群, 等. 基于MaxEnt模型的重庆松材线虫病潜在生境分析[J]. 三峡生态环境监测, 2017, 3(3): 75−80.Liu X M, Pu Y L, Li H Q, et al. A study on potential biotope of pine wilt disease in Chongqing by using MaxEnt model[J]. Ecology and Environmental Monitoring of Three Gorges, 2017, 3(3): 75−80. [24] 韩阳阳, 王焱, 项杨, 等. 基于Maxent生态位模型的松材线虫在中国的适生区预测分析[J]. 南京林业大学学报(自然科学版), 2015, 39(1): 6−10.Han Y Y, Wang Y, Xiang Y, et al. Prediction of potential distribution of Bursaphelenchus xylophilus in China based on Maxent ecological niche model[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2015, 39(1): 6−10. [25] Xu G X, Chen H H, Shi Z M, et al. Mycorrhizal and rhizospheric fungal community assembly differs during subalpine forest restoration on the eastern Qinghai-Tibetan Plateau[J]. Plant and Soil, 2021, 458(1): 245−259. [26] 杨元合, 石岳, 孙文娟, 等. 中国及全球陆地生态系统碳源汇特征及其对碳中和的贡献[J]. 中国科学: 生命科学, 2022, 52(4): 534−574.Yang Y H, Shi Y, Sun W J, et al. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality[J]. Science China Life Science, 2022, 52(4): 534−574. [27] Zhou P, Luukkanen O, Tokola T, et al. Vegetation dynamics and forest landscape restoration in the upper Min river watershed, Sichuan, China[J]. Restoration Ecology, 2008, 16(2): 348−358. doi: 10.1111/j.1526-100X.2007.00307.x [28] 程功, 吕全, 冯益明, 等. 气候变化背景下松材线虫在中国分布的时空变化预测[J]. 林业科学, 2015, 51(6): 119−126.Cheng G, Lü Q, Feng Y M, et al. Temporal and spatial dynamic pattern of pine wilt disease distribution in China predicted under climate change scenario[J]. Scientia Silvae Sinicae, 2015, 51(6): 119−126. [29] 陈欢欢, 许格希, 马凡强, 等. 川西亚高山暗针叶林及其采伐次生林林下分层谱系结构[J]. 林业科学, 2020, 56(7): 1−11.Chen H H, Xu G X, Ma F Q, et al. Phylogenetic structure of undergrowth layers across subalpine dark coniferous forests and their post-harvesting secondary forests in western Sichuan[J]. Scientia Silvae Sinicae, 2020, 56(7): 1−11. [30] 杨楠, 马东源, 钟雪. 基于MaxEnt模型的四川王朗国家级自然保护区蓝马鸡栖息地适宜性评价[J]. 生态学报, 2020, 40(19): 7064−7072.Yang N, Ma D Y, Zhong X. Habitat suitability assessment of Blue Eared-Pheasant based on MaxEnt modeling in Wanglang National Nature Reserve, Sichuan Province[J]. Acta Ecologica Sinica, 2020, 40(19): 7064−7072. [31] 苏比奴尔·艾力, 热木图拉·阿卜杜克热木, 于苏云江·吗米提敏, 等. 基于MaxEnt模型的新疆鹅喉羚生境适宜性评价[J]. 野生动物学报, 2019, 40(1): 27−32.Eli S, Abdukerim R, Mamtimin Y, et al. Assessment of habitat suitability for Gazella subgutturosa in the Xinjiang based on the MaxEnt modeling[J]. Chinese Journal of Widelife, 2019, 40(1): 27−32. [32] 王鑫, 任亦钊, 黄琴, 等. 基于GIS和Maxent模型的赤水河地区濒危植物桫椤生境适宜性评价[J]. 生态学报, 2021, 41(15): 6123−6133.Wang X, Ren Y Z, Huang Q, et al. Habitat suitability assessment of endangered plant Alsophila spinulosa in Chishui River area based on GIS and Maxent model[J]. Acta Ecologica Sinica, 2021, 41(15): 6123−6133. [33] 吴曼菲, 胡湛波, 周岐海, 等. 基于MaxEnt模式的白头叶猴栖息地评价: 以广西崇左白头叶猴保护区为例[J]. 兽类学报, 2021, 41(1): 20−31.Wu M F, Hu Z B, Zhou Q H, et al. Habitat suitability assessment of the white-headed langur (Trachypithecus leucocephalus) based on MaxEnt modeling: a case study of the Chongzuo White-Headed Langur Nature Reserve, Guangxi[J]. Acta Theriologica Sinica, 2021, 41(1): 20−31. [34] 季英超, 张秋梅, 周成刚, 等. 松材线虫病在山东省的风险分析[J]. 山东农业大学学报(自然科学版), 2021, 52(2): 219−223.Ji Y C, Zhang Q M, Zhou C H, et al. Risk analysis of pine wilt disease in Shandong Province[J]. Journal of Shandong Agricultural University (Natural Science Edition), 2021, 52(2): 219−223. [35] 喻三鹏, 田茂娟, 张念念, 等. 入侵植物飞机草在贵州的适生区预测及风险分析[J]. 西部林业科学, 2021, 50(4): 125−130.Yu S P, Tian M J, Zhang N N, et al. Prediction and risk analysis of suitable growth area ofinvasive plant Chromolaena odorata in Guizhou[J]. Journal of West China Forestry Science, 2021, 50(4): 125−130. [36] 沈鹏, 李功权. 基于生态位因子模型的湖北省松材线虫病风险评估[J]. 浙江农林大学学报, 2021, 38(3): 560−566.Shen P, Li G Q. Risk assessment of Bursaphelenchus xylophilus in Hubei Province based on ecological niche factor analysis model[J]. Journal of Zhejiang A&F University, 2021, 38(3): 560−566. [37] 理永霞, 张星耀. 松材线虫入侵扩张趋势分析[J]. 中国森林病虫, 2018, 37(5): 1−4.Li Y X, Zhang X Y. Analysis on the trend of invasion and expansion of Bursaphelenchus xylophilus[J]. Forest Pest and Disease, 2018, 37(5): 1−4. [38] 李静. 林业典型病虫害的分析与预测: 以四川省阿坝州理县、茂县为例[D]. 成都: 电子科技大学, 2017.Li J. Analysis and forecast of typical forestry disease: taking Sichuan Province’s Lixian County and Maoxian County as examples [D]. Chengdu: University of Electronic Science and Technology of China, 2017. [39] 潘佳亮, 姚翰文, 董赢谦, 等. 2019年全国松材线虫病疫情分析[J]. 中国森林病虫, 2021, 40(1): 32−37.Pan J L, Yao H W, Dong Y Q, et al. Analysis of the epidemic situation of pine wilt disease in China in 2019[J]. Forest Pest and Disease, 2021, 40(1): 32−37. [40] 陈红艳. 林业典型病虫害遥感监测: 以四川省阿坝州理县、茂县为例[D]. 成都: 电子科技大学, 2017.Chen H Y. Remote sensing monitoring of typical pests and diseases in forstry: a case study in Lixian and Maoxian, Aba State of Sichuan Province[D]. Chengdu: University of Electronic Science and Technology of China, 2017. [41] 叶江霞, 周汝良, 吴明山, 等. 云南省松墨天牛适生性空间模拟[J]. 林业科学研究, 2013, 26(4): 420−425.Ye J X, Zhou R L, Wu M S, et al. Spatial simulation of the adaptability of Monochamus alternatus Hope in Yunnan Pronvince[J]. Forest Research, 2013, 26(4): 420−425. [42] 庞帅, 陈本文, 陈桂芳, 等. 基于松墨天牛种群动态及其影响因素分析的松材线虫病风险评价[J]. 西北农林科技大学学报(自然科学版), 2018, 46(9): 81−90.Pan S, Chen B W, Chen G F, et al. Risk assessment of pine wood nematode disease based on population dynamics and impact factors of Monochamus alternatus[J]. Journal of Northwest A&F University (Natural Science Edition), 2018, 46(9): 81−90. [43] 徐瑞钧, 周汝良, 刘乾飞, 等. 气候变暖趋势下松墨天牛适生区分布模拟与预测[J]. 林业资源管理, 2020, 3(4): 109−116, 168.Xu R J, Zhou R L, Liu Q F, et al. Prediction and simulation of the suitable habitat of Monochamus alternatus under climate warming[J]. Forest Resources and Management, 2020, 3(4): 109−116, 168. [44] 何善勇, 骆有庆, 温俊宝, 等. 气候变暖对油松毛虫幼虫越冬及上下树发生期的影响[J]. 应用昆虫学报, 2012, 49(5): 1231−1242. doi: 10.7679/j.issn.2095-1353.2012.181He S Y, Luo Y Q, Wen J B, et al. Influence of climate warming on overwintering behaviour of the larva of Dendrolimus tabulaeformis[J]. Chinese Journal of Applied Entomology, 2012, 49(5): 1231−1242. doi: 10.7679/j.issn.2095-1353.2012.181 [45] 黄桂英, 贾丽萍, 王宏勋, 等. 红塔山自然保护区林业有害生物普查结果分析[J]. 林业调查规划, 2019, 44(5): 46−49.Huang G Y, Jia L P, Wang H X, et al. Analysis on survey results of forestry pests in Hongtashan Nature Reserve[J]. Forest Inventory and Planning, 2019, 44(5): 46−49. [46] Pennekamp F, Pontarp M, Tabi A, et al. Biodiversity increases and decreases ecosystem stability[J]. Nature, 2018, 563: 109−112. doi: 10.1038/s41586-018-0627-8 -
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