野生动物监测技术和方法应用进展与展望

doi:10.17521/cjpe.2019.0165

植物生态学报 ›› 2020, Vol. 44 ›› Issue (4): 409-417.DOI: 10.17521/cjpe.2019.0165

所属专题：生态学研究的技术和方法

野生动物监测技术和方法应用进展与展望

肖文宏¹,周青松¹,朱朝东¹,吴东辉³,肖治术^1,^2,^*()

¹中国科学院动物研究所, 北京 100101
²中国科学院大学生命科学学院, 北京 100049
³中国科学院东北地理与农业生态研究所, 长春 130102

收稿日期:2019-06-27 接受日期:2019-10-14 出版日期:2020-04-20 发布日期:2020-03-26
通讯作者: 肖治术
基金资助:
国家重点研发项目(2016YFC0500105);国家自然科学基金(31700469);中国科学院生物多样性监测与研究网络兽类多样性监测网运行经费;河南南太行山水林田湖草生态保护修复试点工程(济源项目);2019年中央林业改革发展资金

Advances in techniques and methods of wildlife monitoring

XIAO Wen-Hong¹,ZHOU Qing-Song¹,ZHU Chao-Dong¹,WU Dong-Hui³,XIAO Zhi-Shu^1,^2,^*()

¹Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Received:2019-06-27 Accepted:2019-10-14 Online:2020-04-20 Published:2020-03-26
Contact: XIAO Zhi-Shu
Supported by:
National Key R&D Program of China(2016YFC0500105);National Natural Science Foundation of China(31700469);Operating Fund for the Biodiversity Monitoring and Research Network of the Chinese Academy of Sciences;Project for the Conservation and Restoration of the Henan Nantaihang Mountain Ecosystem, Composed of Mountains, Rivers, Forest, Farmland, Lakes, and Grassland (Jiyuan project);Central Forestry Reform and Development Fund for the Year of 2019

摘要/Abstract

摘要：

野生动物是生态系统研究和保护管理的重要生物类群, 对调控生态系统结构和功能, 维持生态系统健康平衡具有显著作用。科学监测数据是野生动物研究、保护、管理决策的前提基础, 但由于传统监测技术的限制, 野生动物的多样性特征及其与环境、生态系统的平衡机制未得到充分关注和研究。随着自动化、智能化、信息化技术的发展及应用, 野生动物监测技术和方法出现较大突破和变革。该文论述了近年来野生动物监测研究领域的重要新技术, 包括红外相机技术、全球定位系统(GPS)追踪技术、DNA条形码技术和高通量测序技术等。通过综合介绍相关的基础概念、基本原理, 该文总结了新技术的应用优势和重大应用进展, 同时探讨了新技术应用中存在的问题, 并对未来野生动物监测技术的发展趋势进行了展望。

关键词: 野生动物监测, 红外相机, GPS追踪, DNA条形码, 高通量测序技术

Abstract:

Wildlife as one major group in ecosystem research and conservation management, play a critical role in regulating the structure and function of ecosystems and maintaining the health and balance of ecosystems. Scientific monitoring data are the basis for wildlife research, protection and management decisions. However, the wildlife diversity, their relationship and related mechanisms with the environment and ecosystem balance have been paid insufficient attention due to the limitations of traditional monitoring technologies. With the development and application of automatical and information technologies, wildlife monitoring technology has achieved great breakthroughs and changes. In this paper, we described four new technologies widely used for wildlife monitoring recently, including camera-trapping technology, Global Positioning System (GPS) tracking technology, DNA-barcode technology and next-generation sequencing technology. We introduced the basic concepts and principles, then summarized the advantages and major application progress of these four key technologies as well as the problems existing in the application. Finally, we discussed the trend of the wildlife monitoring technologies.

Key words: wildlife monitoring, camera trap, GPS tracking, DNA barcodes, next-generation sequencing

肖文宏, 周青松, 朱朝东, 吴东辉, 肖治术. 野生动物监测技术和方法应用进展与展望. 植物生态学报, 2020, 44(4): 409-417. DOI: 10.17521/cjpe.2019.0165

XIAO Wen-Hong, ZHOU Qing-Song, ZHU Chao-Dong, WU Dong-Hui, XIAO Zhi-Shu. Advances in techniques and methods of wildlife monitoring. Chinese Journal of Plant Ecology, 2020, 44(4): 409-417. DOI: 10.17521/cjpe.2019.0165

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2019.0165

https://www.plant-ecology.com/CN/Y2020/V44/I4/409

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参考文献 71

[1]	Alexander JS, Gopalaswamy AM, Shi K, Riordan P (2015). Face value: towards robust estimates of snow leopard densities. PLOS ONE, 10, e0134815. DOI: 10.1371/journal.pone.0134815. DOI URL PMID
[2]	Barbi F, Bragalini C, Vallon L, Prudent E, Dubost A, Fraissinet- Tachet L, Marmeisse R, Luis P (2014). PCR primers to study the diversity of expressed fungal genes encoding lignocellulolytic enzymes in soils using high-throughput sequencing. PLOS ONE, 9, e116264. DOI: 10.1371/journal.pone.0116264. DOI URL PMID
[3]	Bohan DA, Caron-Lormier G, Muggleton S, Raybould A, Tamaddoni-Nezhad A (2011). Automated discovery of food webs from ecological data using logic-based machine learning. PLOS ONE, 6, e29028. DOI: 10.1371/journal.pone.0029028. URL PMID
[4]	Bohan DA, Vacher C, Tamaddoni-Nezhad A, Raybould A, Dumbrell AJ, Woodward G (2017). Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks. Trends in Ecology & Evolution, 32, 477-487. URL PMID
[5]	Bonada N, Prat N, Resh VH, Statzner B (2006). Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. Annual Review of Entomology, 51, 495-523. URL PMID
[6]	Bu HL, Wang F, McShea WJ, Lu Z, Wang DJ, Li S (2016). Spatial co-occurrence and activity patterns of mesocarnivores in the temperate forests of Southwest China. PLOS ONE, 11, e0164271. DOI: 10.1371/journal.pone.0164271. DOI URL PMID
[7]	Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335-336. URL PMID
[8]	Carugati L, Corinaldesi C, Dell’Anno A, Danovaro R (2015). Metagenetic tools for the census of marine meiofaunal biodiversity: an overview. Marine Genomics, 24, 11-20. URL PMID
[9]	Côrtes MC, Uriarte M (2013). Integrating frugivory and animal movement: a review of the evidence and implications for scaling seed dispersal. Biological Reviews, 88, 255-272. DOI URL PMID
[10]	Cotton TEA, Dumbrell AJ, Helgason T (2014). What goes in must come out: testing for biases in molecular analysis of arbuscular mycorrhizal fungal communities. PLOS ONE, 9, e109234. DOI: 10.1371/journal.pone.0109234. URL PMID
[11]	Cristescu ME (2014). From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends in Ecology & Evolution, 29, 566-571. URL PMID
[12]	D’Amore R, Ijaz UZ, Schirmer M, Kenny JG, Gregory R, Darby AC, Shakya M, Podar M, Quince C, Hall N (2016). A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genomics, 17, 55. DOI: 10.1186/s12864-015-2194-9. DOI URL PMID
[13]	Di Bella JM, Bao Y, Gloor GB, Burton JP, Reid G (2013). High throughput sequencing methods and analysis for microbiome research. Journal of Microbiological Methods, 95, 401-414. URL PMID
[14]	Didham RK, Basset Y, Leather SR (2010). Research needs in insect conservation and diversity. Insect Conservation and Diversity, 3, 1-4.
[15]	Drummond AJ, Newcomb RD, Buckley TR, Xie D, Dopheide A, Potter BC, Heled J, Ross HA, Tooman L, Grosser S, Park D, Demetras NJ, Stevens MI, Russell JC, Anderson SH, Carter A, Nelson N (2015). Evaluating a multigene environmental DNA approach for biodiversity assessment. GigaScience, 4, 46. URL PMID
[16]	Duangchantrasiri S, Umponjan M, Simcharoen S, Pattanavibool A, Chaiwattana S, Maneerat S, Kumar NS, Jathanna D, Srivathsa A, Karanth KU (2016). Dynamics of a low-density tiger population in Southeast Asia in the context of improved law enforcement. Conservation Biology, 30, 639-648. URL PMID
[17]	Dumbrell AJ, Ferguson RMW, Clark DR (2016). Microbial community analysis by single-amplicon high-throughput next generation sequencing: data analysis—From raw output to ecology//McGenity TJ, Timmis KN, Nogales B. Hydrocarbon and Lipid Microbiology Protocols. Springer, Heidelberg. 155-206.
[18]	Edgar RC (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996-998. DOI URL
[19]	Frey S, Fisher JT, Burton AC, Volpe JP, Rowcliffe M (2017). Investigating animal activity patterns and temporal niche partitioning using camera-trap data: challenges and opportunities. Remote Sensing in Ecology and Conservation, 3, 123-132.
[20]	Gaby JC, Buckley DH (2012). A comprehensive evaluation of PCR primers to amplify the nifH gene of nitrogenase. PLOS ONE, 7, e42149. DOI: 10.1371/journal.pone.0042149. URL PMID
[21]	Gaston KJ, O’Neill MA (2004). Automated species identification: Why not? Philosophical Transactions: Biological Sciences, 359, 655-667.
[22]	Gibson JF, Shokralla S, Curry C, Baird DJ, Monk WA, King I, Hajibabaei M (2015). Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLOS ONE, 10, e0138432. DOI: 10.1371/journal.pone.0138432. URL PMID
[23]	Hebert PDN, Cywinska A, Ball SL (2003). Biological identifications through DNA barcodes. Proceedings of the Royal Society of London B, 270, 313-321.
[24]	Hebert PDN, Gregory TR (2005). The promise of DNA barcoding for taxonomy. Systematic Biology, 54, 852-859. URL PMID
[25]	Horne JS, Garton EO, Krone SM, Lewis JS (2007). Analyzing animal movements using Brownian bridges. Ecology, 88, 2354-2363. URL PMID
[26]	Ji Y, Ashton L, Pedley SM, Edwards DP, Tang Y, Nakamura A, Kitching R, Dolman PM, Woodcock P, Edwards FA, Larsen TH, Hsu WW, Benedick S, Hamer KC, Wilcove DS, Bruce C, Wang X, Levi T, Lott M, Emerson BC, Yu DW (2013). Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecology Letters, 16, 1245-1257. URL PMID
[27]	Jin Q, Hu XM, Han HL, Chen F, Cai WJ, Ruan QQ, Liu B, Luo GJ, Wang H, Liu X, Ward RD, Wu CS, Wilson JJ, Zhang AB (2018). A two-step DNA barcoding approach for delimiting moth species: moths of Dongling Mountain (Beijing, China) as a case study. Scientific Reports, 8, 14256. DOI: 10.1038/s41598-018-32123-9.
[28]	Jonsson KA, Tottrup AP, Borregaard MK, Keith SA, Rahbek C, Thorup K (2016). Tracking animal dispersal: from individual movement to community assembly and global range dynamics. Trends in Ecology & Evolution, 31, 204-214. URL PMID
[29]	Kays R, Crofoot MC, Jetz W, Wikelski M (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348, aaa2478. DOI: 10.1126/science.aaa2478. URL PMID
[30]	Lansdown K, McKew BA, Whitby C, Heppell CM, Dumbrell AJ, Binley A, Olde L, Trimmer M (2016). Importance and controls of anaerobic ammonium oxidation influenced by riverbed geology. Nature Geoscience, 9, 357. DOI: 10.1038/ngeo2684.
[31]	Lanzén A, Epelde L, Blanco F, Martin I, Artetxe Uand Garbisu C (2016). Multi-targeted metagenetic analysis of the influence of climate and environmental parameters on soil microbial communities along an elevational gradient. Scientific Reports, 6, 28257. DOI: 10.1038/srep28257. DOI URL PMID
[32]	Li J, Nedwell DB, Beddow J, Dumbrell AJ, McKew BA, Thorpe EL, Whitby C (2015). amoA gene abundances and nitrification potential rates suggest that benthic ammonia- oxidizing bacteria not archaea dominate N cycling in the Colne Estuary, United Kingdom. Applied and Environmental Microbiology, 81, 159-165. DOI URL PMID
[33]	Li S, McShea WJ, Wang D, Huang J, Shao L (2012). A direct comparison of camera-trapping and sign transects for monitoring wildlife in the Wanglang National Nature Reserve, China. Wildlife Society Bulletin, 36, 538-545.
[34]	Li S, Wang DJ, Xiao ZS, Li XH, Wang TM, Feng LM, Wang Y (2014). Camera-trapping in wildlife research and conservation in China: review and outlook. Biodiversity Science, 22, 685-695.
	[ 李晟, 王大军, 肖治术, 李欣海, 王天明, 冯利民, 王云 (2014). 红外相机技术在我国野生动物研究与保护中的应用与前景. 生物多样性, 22, 685-695.]
[35]	Li X, Bleisch WV, Jiang X (2018). Using large spatial scale camera trap data and hierarchical occupancy models to evaluate species richness and occupancy of rare and elusive wildlife communities in southwest china. Diversity and Distributions, 24, 1560-1572.
[36]	Liu SL, Yang CT, Zhou CR, Zhou X (2017). Filling reference gaps via assembling DNA barcodes using high-throughput sequencing—Moving toward barcoding the world. Giga-Science, 6, gix104. DOI: 10.1093/gigascience/gix104. URL PMID
[37]	Lu A, Hou XW, Liu CL, Chen XL (2012). Insect species recognition using discriminative local soft coding. 1st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan. 1221-1224.
[38]	MacKenzie Dl, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE (2006). Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Academic Press, San Diego, USA.
[39]	Millspaugh JJ, Kesler DC, Kays RW, Gitzen RA, Schulz JH, Rota CT, Bodinof CM, Belant JL, Keller BJ (2012). Wildlife radio telemetry and remote monitoring//Silvy NJ. Wildlife Techniques Manual. Johns Hopkins Press, Baltimore. 258-283.
[40]	Moorcroft PR, Lewis MA (2006). Mechanistic Home Range Analysis. Princeton University Press, Princeton.
[41]	Morales JM, Moorcroft PR, Matthiopoulos J, Frair JL, Kie JG, Powell RA, Merrill EH, Haydon DT (2010). Building the bridge between animal movement and population dynamics. Philosophical Transactions of the Royal Society B Biological Sciences, 365, 2289-2301.
[42]	Nagy M, Akos Z, Biro D, Vicsek T (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464, 890-893. URL PMID
[43]	O’Brien TG, Baillie JEM, Krueger L, Cuke M (2010). The wildlife picture index: monitoring top trophic levels. Animal Conservation, 13, 335-343. DOI URL
[44]	Ollerton J, Erenler H, Edwards M, Crockett R (2014). Extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science, 346, 1360-1362. DOI URL PMID
[45]	Papaspyrou S, Smith CJ, Dong LF, Whitby C, Dumbrell AJ, Nedwell DB (2014). Nitrate reduction functional genes and nitrate reduction potentials persist in deeper estuarine sediments. Why? PLOS ONE, 9, e94111. DOI: 10.1371/journal.pone.0094111. DOI URL PMID
[46]	Pimm SL, Alibhai S, Bergl R, Dehgan A, Giri C, Jewell Z, Joppa L, Kays R, Loarie S (2015). Emerging technologies to conserve biodiversity. Trends in Ecology & Evolution, 30, 685-696. URL PMID
[47]	Polz MF, Cavanaugh CM (1998). Biasin template-to-product ratios in multi template PCR. Applied and Environmental Mircobiology, 64, 3724-3730.
[48]	Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite M, Berger J, Elmhagen B, Letnic M, Nelson MP, Schmitz OJ, Smith DW, Wallach AD, Wirsing AJ (2014). Status and ecological effects of the world’s largest carnivores. Science, 343, 1241484. DOI: 10.1126/science.1241484. DOI URL PMID
[49]	Ripple WJ, Newsome TM, Wolf C, Dirzo R, Everatt KT, Galetti M, Hayward MW, Kerley GI, Levi T, Lindsey PA (2015). Collapse of the world’s largest herbivores. Science Advances, 1, e1400103. DOI: 10.1126/sciadv.1400103. DOI URL PMID
[50]	Rodgers AR (2001). Recent telemetry technology//Millspaugh JJ, Marzluff JM. Radio Tracking and Animal Population. Academic Press, San Diego, USA. 79-121.
[51]	Rovero F, Martin E, Rosa M, Ahumada JA, Spitale D (2014). Estimating species richness and modelling habitat preferences of tropical forest mammals from camera trap data. PLOS ONE, 9, e103300. DOI: 10.1371/journal.pone.0103300. URL PMID
[52]	Shi ZY, Yang CQ, Hao MD, Wang XY, Ward RD, Zhang AB (2018). FuzzyID2: a software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods. Molecular Ecology Resources, 18, 666-675. DOI URL PMID
[53]	Sipos R, Szekely AJ, Palatinszky M, Revesz S, Marialigeti K, Nikolausz M (2007). Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis. FEMS Microbiology Ecology, 60, 341-350. URL PMID
[54]	Steenweg R, Hebblewhite M, Kays R, Ahumada J, Fisher JT, Burton C, Townsend SE, Carbone C, Rowcliffe JM, Whittington J, Brodie J, Royle JA, Switalski A, Clevenger AP, Heim N, Rich LN (2017). Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Frontiers in Ecology and the Environment, 15, 26-34. DOI URL
[55]	Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E (2012). Towards next-generation biodiversity assessment using DNA metabarcoding. Molecular Ecology, 21, 2045-2050. URL PMID
[56]	Tedersoo L, Abarenkov K, Nilsson RH, Schussler A, Grelet GA, Kohout P, Oja J, Bonito GM, Veldre V, Jairus T, Ryberg M, Larsson KH, Koljalg U (2011). Tidying up international nucleotide sequence databases: ecological, geographical and sequence quality annotation of ITS sequences of mycorrhizal fungi. PLOS ONE, 6, e24940. DOI: 10.1371/journal.pone.0024940. URL PMID
[57]	Thomas T, Gilbert J, Meyer F (2012). Metagenomics—A guide from sampling to data analysis. Microbial Informatics and Experimentation, 2, 3. URL PMID
[58]	Tomkiewicz SM, Fuller MR, Kie JG, Bates KK (2010). Global positioning system and associated technologies in animal behaviour and ecological research. Philosophical Transactions of the Royal Society B, 365, 2163-2176. DOI URL
[59]	Urbano F, Cagnacci F, Calenge C, Dettki H, Cameron A, Neteler M (2010). Wildlife tracking data management: a new vision. Philosophical Transactions of the Royal Society B, 365, 2177-2185. DOI URL
[60]	van der Weyde LK, Mbisana C, Klein R (2018). Multi-species occupancy modelling of a carnivore guild in wildlife management areas in the Kalahari. Biological Conservation, 220, 21-28. DOI URL
[61]	Wang F, McShea WJ, Wang D, Li S (2015). Shared resources between giant panda and sympatric wild and domestic mammals. Biological Conservation, 186, 319-325. DOI URL
[62]	Wang T, Feng L, Mou P, Wu J, Smith JLD, Xiao W, Yang H, Dou H, Zhao X, Cheng Y, Zhou B, Wu H, Zhang L, Tian Y, Guo Q, Kou X, Han X, Miquelle DG, Oliver CD, Xu R, Ge J (2016). Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landscape Ecology, 31, 491-503.
[63]	Wang T, Feng L, Yang H, Han B, Zhao Y, Juan L, Lu X, Zou L, Li T, Xiao W, Mou P, Smith JLD, Ge J (2017). A science-based approach to guide amur leopard recovery in china. Biological Conservation, 210, 47-55.
[64]	Wang T, Royle JA, Smith JL, Zou L, Lü X, Li T, Yang H, Li Z, Feng R, Bian Y, Feng L, Ge J (2018). Living on the edge: opportunities for Amur tiger recovery in China. Biological Conservation, 217, 269-279.
[65]	Wilson AM, Lowe JC, Roskilly K, Hudson PE, Golabek KA, McNutt JW (2013). Locomotion dynamics of hunting in wild cheetahs. Nature, 498, 185-189. URL PMID
[66]	Xiao ZS (2016). Wildife resource inventory using camera- trapping in natural reserves in China. Acta Theriologica Sinica, 36, 270-271.
	[ 肖治术 (2016). 红外相机技术促进我国自然保护区野生动物资源编目调查. 兽类学报, 36, 270-271.]
[67]	Xiao ZS, Li XH, Jiang GS (2014). Applications of camera trapping to wildlife surveys in China. Biodiversity Science, 22.
	[ 肖治术, 李欣海, 姜广顺 (2014). 红外相机技术在我国野生动物监测研究中的应用. 生物多样性, 22, 683-684.]
[68]	Yao Q, Lv J, Liu Q, Diao G, Yang B, Chen H, Tang J (2012). An insect imaging system to automate rice light-trap pest identification. Journal of Integrative Agriculture, 11, 978-985.
[69]	Yu DW, Ji Y, Emerson BC, Wang X, Ye C, Yang C, Ding Z (2012). Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods in Ecology and Evolution, 3, 613-623.
[70]	Zhang AB, Hao MD, Yang CQ, Shi ZY (2016). BarcodingR: an integrated R package for species identification using DNA barcodes. Methods in Ecology and Evolution, 8, 627-634.
[71]	Zhang L, Chen XL, Hou XW, Liu CL, Fan LM, Wang XJ (2011). Construction and testing of automated fruit fly identification system-bactrocera macquart (diptera: tephritidae). Acta Entomologica Sinica, 54, 184-196.
	[ 张蕾, 陈小琳, 侯新文, 刘成林, 樊利民, 汪兴鉴 (2011). 实蝇科果实蝇属昆虫数字图像自动识别系统的构建和测试. 昆虫学报, 54, 184-196.]

野生动物监测技术和方法应用进展与展望

Advances in techniques and methods of wildlife monitoring

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