海南岛霸王岭热带天然林景观中主要木本植物关键种的潜在分布

doi:10.17521/cjpe.2007.0136

摘要/Abstract

摘要：

在较大的空间尺度上生态位模型是预测物种潜在分布的有效途径之一。为了探讨在热带天然林景观中木本植物(限于乔木和灌木)主要关键种的潜在分布,在对海南岛霸王岭的热带天然林进行按公里网格样方调查的基础上,采用演替地位和最大潜在高度两个功能性指标对物种进行了功能群划分,并在功能群框架下运用优势度指数法进行了关键种的确定;采用基于地理信息系统(Geographic information system, GIS)的基于规则集合预测的遗传算法(Algorithm for rule-set prediction, GARP)生态位模型对主要关键种的地理分布进行了预测,并应用受试者工作特征分析进行了模型精度验证;应用多元线性回归分析对影响各关键种潜在分布的关键因子进行了确定。结果表明:除了顶极次林层乔木功能群和顶极主林层乔木功能群外,在先锋种功能群、顶极灌木种功能群和顶极超冠层乔木功能群中采用优势度指数法划分出的关键种较为理想;一般来讲,在进行预测的8个关键种中,除了先锋主林层乔木种海南杨桐(Adinandra hainanensis),其它3个先锋种毛稔(Melastoma sanquiueum)、银柴(Aporosa chinensis)和枫香(Liquidambar formosana) 在研究区北部、西部以及西南部均具有较高的发生概率,而顶极种除了顶极超冠层乔木种南亚松(Pinus merkusii)外,九节(Psychotria rubra)、高脚罗伞(Ardisia quinquegona)和海南椎(Castanopsis hainanensis)具有相似的潜在分布格局,在研究区中部、东南部和南部地区具有较高的发生概率;相关分析表明极端最低温、年均温、极端最高温、年均降水量、海拔和坡向6大因子是影响研究区关键种潜在分布的关键因子;精度检验表明,GARP模型对8个关键种的潜在分布预测效果均较好,而其中又以银柴和海南椎的预测精度最高。

关键词: 木本植物功能群, 关键种, 潜在分布, 生态位模型, GARP, 热带森林

Abstract:

Aims Our major objectives were to 1) identify keystone species within the context of functional groups, 2) develop potential distributional predictions for keystone species using ecological niche model, 3) confirm factors determining potential distributions of keystone species, and 4) test if the performances of ecological niche model are better than those of a random model and differ in predicting different keystone species.

Methods Based on the investigation of 135 plots in a natural tropical forest landscape, we classified woody plant functional groups based on successional status and potential maximum height. Keystone species within each functional group were identified using a dominance index (DI). We used the genetic algorithm for rule-set prediction (GARP) to estimate the keystone species' potential distribution and then used the receiver operating characteristics to evaluate predictive performance. Applying multiple linear regression analysis, we identified major factors determining potential distributions of keystone species.

Important findings Identification of keystone species within pioneer species, climax shrub and emergent tree functional groups was clearer than within climax subcanopy and climax canopy tree functional groups. Generally, among the eight keystone species, pioneer species Melastoma sanquiueum, Aporosa chinensis and Liquidambar formosana (but not Adinandra hainanensis) have high probability of occurrence in the north, west and southwest regions of Bawangling. However, climax species Psychotria rubra, Ardisia quinquegona and Castanopsis hainanensis (but not Pinus merkusii) have high probability of occurrence in the central, southeast and south regions. Minimum and maximum temperature, mean annual temperature and precipitation, aspect and altitude were the key factors determining potential distributions of keystone species. Evaluation of GARP model's performance indicated excellent predictive ability of all eight keystone species' distribution. This study suggests the DI method is more suitable to identify keystone species within woody plant functional groups in which a single or a few species are dominant. Findings will assist decision makers in planning conservation and management policies in tropical rainforest areas.

Key words: woody plant functional groups, keystone species, potential distribution, GARP, tropical forest

张志东, 臧润国. 海南岛霸王岭热带天然林景观中主要木本植物关键种的潜在分布. 植物生态学报, 2007, 31(6): 1079-1091. DOI: 10.17521/cjpe.2007.0136

ZHANG Zhi-Dong, ZANG Run-Guo. PREDICTING POTENTIAL DISTRIBUTIONS OF DOMINANT WOODY PLANT KEYSTONE SPECIES IN A NATURAL TROPICAL FOREST LANDSCAPE OF BAWANGLING, HAINAN ISLAND, SOUTH CHINA. Chinese Journal of Plant Ecology, 2007, 31(6): 1079-1091. DOI: 10.17521/cjpe.2007.0136

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2007.0136

https://www.plant-ecology.com/CN/Y2007/V31/I6/1079

图/表 8

参考文献 64

[1]	Anderson RP, Gomez-Laverde M, Peterson AT (2002). Geographical distributions of spiny pocket mice in South America: insights from predictive models. Global Ecology and Biogeography, 11,131-141.
[2]	Beerling DJ, Huntley B, Bailey JP (1995). Climate and the distribution of Fallopia japonica: use of an introduced species to test the predictive capacity of response surfaces. Journal of Vegetation Science, 6,269-282.
[3]	Berger WH, Parker FL (1970). Diversity of planktonic foraminifera in deep sea sediments. Science, 168,1345-1347. URL PMID
[4]	Blondel J (2003). Guilds or functional groups: does it matter? Oikos, 100,223-231.
[5]	Chazdon RL, Careaga S, Webb C, Vargas O (2003). Community and phylogenetic structure of reproductive traits of woody species in wet tropical forests. Ecological Monographs, 73,331-348.
[6]	Chen PF, Wiley EO, Mcnyset KM (2006). Ecological niche modeling as a predictive tool: silver and bighead carps in North America. Biological Invasions, 9,43-51.
[7]	Christianou M, Ebenman B (2005). Keystone species and vulnerable species in ecological communities: strong or weak interactors? Journal of Theoretical Biology, 235,95-103. DOI URL PMID
[8]	Chun WY (陈焕镛) (1964). Flora Hainanica, VolumeⅠ (海南植物志,第Ⅰ卷). Science Press, Beijing.167-362. (in Chinese)
[9]	Chun WY (陈焕镛) (1965). Flora Hainanica, VolumeⅡ (海南植物志,第Ⅱ卷). Science Press, Beijing.1-453. (in Chinese)
[10]	Coomes DA, Grubb PJ (2003). Colonization, tolerance, competition and seed-size variation within functional groups. Trends in Ecology & Evolution, 18,283-291.
[11]	Davic RD (2003). Linking keystone species and functional groups: a new operational definition of the keystone species concept. Conservation Ecology, 7(1),r11. http://www.consecol.org/vol17/iss11/resp11. Cited 10 Sep 2006.
[12]	Ebenman B, Jonsson T (2005). Using community viability analysis to identify fragile systems and keystone species. Trends in Ecology & Evolution, 20,568-575. DOI URL PMID
[13]	ESRI (2003). ARCGIS. Environmental Systems Research Institute, Inc., Redlands, California, USA.
[14]	Gili JM (2002). Variability of the keystone species concept in marine ecosystems. Trends in Ecology & Evolution, 17,499.
[15]	Guangdong Institute of Botany(广东省植物研究所) (1974). Flora Hainanica, Volume Ⅲ (海南植物志,第Ⅲ卷). Science Press, Beijing. (in Chinese)
[16]	Guangdong Institute of Botany(广东省植物研究所) (1977). Flora Hainanica, Volume Ⅳ (海南植物志,第Ⅳ卷). Science Press, Beijing. (in Chinese)
[17]	Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8,993-1009.
[18]	Hooper DU, Chapin FS III, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM, Loreau M, Naeem S, Schmid B, Setälä H, Symstad AJ, Vandermeer J, Wardle DA (2005). Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecological Monographs, 75,3-35.
[19]	Hu YJ (胡玉佳), Li YX (李玉杏) (1992). Tropical Rain Forest of Hainan Island (海南岛热带雨林). Guangdong Higher Education Press, Guangzhou. (in Chinese)
[20]	Huang JH (黄建辉), Han XG (韩兴国) (2001). Keystone species: what is keystoneness? Acta Phytoecologica Sinica (植物生态学报), 25,505-509. (in Chinese with English abstract)
[21]	Hurlbert SH (1997). Functional importance vs keystoneness: reformulating some questions in theoretical biocenology. Australian Journal of Ecology, 22,369-382.
[22]	Hutchinson GE (1957). Concluding remarks. Cold Spring Harbor Symposium. Quantitative Biology, 22,415-427.
[23]	Hutchinson MF (1999). ANUDEM Version 4.6 User Guide. The Australian National University, Centre for Resource and Environmental Studies, Canberra.
[24]	Hutchinson MF (2000). ANUSPLIN Version 4.1 User Guide. The Australian National University, Centre for Resource and Environmental Studies, Canberra.
[25]	Jiang YX (蒋有绪), Wang BS (王伯荪), Zang RG (臧润国), Jin JH (金建华), Liao WB (廖文波) (2002). Biodiversity and Mechanism of Maintenance of the Tropical Forest in Hainan Island (海南岛热带林生物多样性及其形成机制). Science Press, Beijing,219-324. (in Chinese)
[26]	Jordan F, Takacs-Santa A, Molnar I (1999). Are liability theoretical quest for keystones. Oikos, 86,453-462.
[27]	Køhler P, Ditzer T, Huth A (2000). Concepts for the aggregation of tropical tree species into functional types and application to Sabah's lowland rain forest. Journal of Tropical Ecology, 16,591-602.
[28]	Krogh SN, Zeisset MS, Jackson E, Whitford WG (2002). Presence/absence of a keystone species as an indicator of rangeland health. Journal of Arid Environments, 50,513-519.
[29]	Li HM (李红梅), Han HX (韩红香), Xue DY (薛大勇) (2005). Prediction of potential geographic distribution areas for the pine bark scale, Matsucoccus matsumurae(Kuwana)(Homoptera: Margarodidae) in China using GARP modeling system. Acta Entomologica Sinica (昆虫学报), 48,95-100. (in Chinese with English abstract)
[30]	Li W, Wang Z, Ma Z, Tang H (1997). A regression model for the spatial distribution of red-crown crane in Yancheng Biosphere Reserve, China. Ecological Modelling, 103,115-121.
[31]	Li YD (李意德) (1993). Comparative analysis for biomass measurement of tropical mountain rain forest in Hainan Island, China. Acta Ecologica Sinica (生态学报), 13,313-320. (in Chinese with English abstract)
[32]	Loreau M (2000). Biodiversity and ecosystem functioning: recent theoretical advances. Oikos, 91,3-17.
[33]	Loreau M (2004). Does functional redundancy exist? Oikos, 104,606-611.
[34]	Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, Hooper DU, Huston MA, Raffaelli D, Schmid B, Tilman D, Wardle DA (2001). Biodiversity and ecosystem functioning: curreent knowledge and future challenges. Science, 294,804-808. DOI URL PMID
[35]	Lu Y (陆阳), Li MG (李鸣光), Huang YW (黄雅文), Chen ZH (陈章和), Hu YJ (胡玉佳) (1986). Vegetation of Bawangling Gibbon Natural Reserve, in Hainan Island. Acta Phytoecologica et Geobotanica Sinica (植物生态学与地植物学学报), 10,106-114. (in Chinese with English abstract)
[36]	Monica P, Peterson AT (2003). Predicting the potential invasive distribution for Eupatorium adenophorum Spreng. in China. Journal of Wuhan Botanical Research (武汉植物学研究), 21,137-142.
[37]	Nix HA (1986). A biogeographic analysis of Australian elapid snakes. In: Longmore Red. Atlas of Australian Elapid Snakes. Australian Government Publishing Service, Canberra,4-15.
[38]	Paine RT (1995). A conversation on refining the concept of keystone species. Conservation Biology, 9,962-964.
[39]	Patterson BD (1999). Contingency and determinism in mammalian biogeography: the role of history. Journal of Mammalogy, 80,345-360.
[40]	Peterson AT, Cohoon KP (1999). Sensitivity of distributional prediction algorithms to geographic data completeness. Ecological Modelling, 117,159-164.
[41]	Peterson AT, Pages M, Kluza DA (2003). Predicting the potential invasive distributions of four alien plant species in North America. Weed Science, 51,863-868.
[42]	Peterson AT, Soberon J, Sanchez-Cordero V (1999). Conservatism of ecological niches in evolutionary time. Science, 285,1265-1267. DOI URL PMID
[43]	Peterson AT, Vieglais DA (2001). Predicting species invasions using ecological niche modeling: new approaches from bioinformatics atack a pressing problem. BioScience, 51,363-371.
[44]	Power ME, Mills LS (1995). The keystone cops meet in Hilo. Trends in Ecology & Evolution, 10,182-184. DOI URL PMID
[45]	Power ME, Tilman D, Estes JA, Menge BA, Bond WJ, Mills LS, Daily G, Castilla JC, Lubchenco J, Paine RT (1996). Changes in the quest for keystones. BioScience, 46,609-620.
[46]	Raven PH, Wilson EO (1992). A fifty-year plan for biodiversity surveys. Science, 258,1099-1100. DOI URL PMID
[47]	Saunders DA, Hobbs RJ, Margules CR (1991). Biological consequences of ecosystem fragmentation: a review. Conservation Biology, 5,18-32.
[48]	Scachetti-Pereira R (2001). Desktop GARP. www.lifemapper.org/desktopgarp. Cited 10 Feb. 2006.
[49]	Skidmore AK, Gauld A, Walker P (1996). Classification of kangaroo distribution using three GIS models. International Journal of Information Systems, 10,441-454.
[50]	SPSS (2004). SPSS for Windows, Version 13. 0, Chicago.
[51]	Stockman AK, Beamer DA, Bond JE (2006). An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Diversity & Distributions, 12,81-89.
[52]	Stockwell DRB, Peters DP (1999). THe GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Information Systems, 13,143-158.
[53]	Stockwell DRB, Peterson AT (2002). Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148,1-13.
[54]	Swenson NG (2005). Gis-based niche models reveal unifying climatic mechanisms that maintain the location of avian hybrid zones in a North American sutrue zone. Journal of Evolutionary Biology, 19,717-725. URL PMID
[55]	Swets JA (1988). Measuring the accuracy of diagnostic systems. Science, 240,1285-1293. DOI URL PMID
[56]	Underwood EC, Klinger R, Moore PE (2004). Predicting patterns of non-native plant invasions in Yosemite National Park, California, USA. Diversity & Distributions, 10,447-459.
[57]	Voss RS, Emmons LH (1996). Mammalian diversity in neotropical lowland rainforests: a preliminary assessment. Bulletin of the American Museum of Natural History, 230,1-115.
[58]	Whitmore TC (1998). An Introduction to Tropical Rain Forest 2nd edn. Oxford University Press, Oxford.
[59]	Wiley EO, McNyset KM, Peterson AT, Robins CR, Stewart AM (2003). Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography, 16,120-127.
[60]	Willmott CJ, Matsuura K (1999). Smart interpolation of annually averaged air temperature in the United States. Journal of Applied Meteorology, 34,2577-2586.
[61]	Yan H (阎洪) (2003). Spline interpolation of spatial-temporal climate data for China. Geography and Geo-Information Science (地理与地理信息科学), 19,27-31. (in Chinese with English abstract)
[62]	Yan H (阎洪) (2004). Modeling spatial distribution of climate in China using thin plate smoothing spline interpolation. Scientia Geographica Sinica (地理科学), 24,163-169. (in Chinese with English abstract)
[63]	Zang RG (臧润国), An SQ (安树青), Tao JP (陶建平), Jiang YX (蒋有绪), Wang BX (王伯荪) (2004). Biodiversity and Mechanism of Maintenance of the Tropical Forest in Hainan Island (海南岛热带林生物多样性维持机制). Science Press, Beijing,1-169. (in Chinese)
[64]	Zang RG (臧润国), Cheng KW (成克武), Li JQ (李俊清), Zhang WY (张炜银), Chen XF (陈雪峰), Tao JP (陶建平) (2005). Conservation and Restoration of Biodiversity in Natural Forest (天然林生物多样性保育与恢复). China Science and Technology Press, Beijing,4-6. (in Chinese)

功能群 Functional group	潜在最大树高 Potential maximal tree height (m)	物种个数 Species richness	相对多度 Relative abundance (%)
先锋灌木Pioneer shrub (F1)	2~5	18	2.20
先锋次林层乔木Pioneer subcanopy tree (F2)	5~15	33	8.51
先锋主林层乔木Pioneer canopy tree (F3)	15~30	8	1.78
先锋超冠层乔木Pioneer emergent tree (F4)	>30	2	0.43
顶极灌木Climax shrub (F5)	2~5	69	19.73
顶极次林层乔木Climax subcanopy tree (F6)	5~15	276	35.16
顶极主林层乔木Climax canopy tree (F7)	15~30	157	29.82
顶极超冠层乔木Climax emergent tree (F8)	>30	16	2.38

功能群 Functional group	潜在最大树高 Potential maximal tree height (m)	物种个数 Species richness	相对多度 Relative abundance (%)
先锋灌木Pioneer shrub (F1)	2~5	18	2.20
先锋次林层乔木Pioneer subcanopy tree (F2)	5~15	33	8.51
先锋主林层乔木Pioneer canopy tree (F3)	15~30	8	1.78
先锋超冠层乔木Pioneer emergent tree (F4)	>30	2	0.43
顶极灌木Climax shrub (F5)	2~5	69	19.73
顶极次林层乔木Climax subcanopy tree (F6)	5~15	276	35.16
顶极主林层乔木Climax canopy tree (F7)	15~30	157	29.82
顶极超冠层乔木Climax emergent tree (F8)	>30	16	2.38

功能群 Functional group	潜在关键种 Potential keystone species	DI_ij (×100)	N_ij	N_j
F1	1.毛稔Melastoma sanquiueum	64.85	832	1 283
	2.紫毛野牡丹Melastoma penicillatum	21.43	275
	3.野牡丹Melastoma candidum	4.99	64
F2	4.银柴Aporosa chinensis	19.81	982	4 956
	5.猪肚木Canthium horridum	13.94	691
	6.黄牛木Cratoxylum ligustrinum	11.86	588
F3	7.海南杨桐Adinandra hainanensis	35.78	370	1 034
	8.翻白叶Pterospermum heterophyllum	29.69	307
	9.拟赤杨Alniphyllum fortunei	16.15	167
F4	10.枫香Liquidambar formosana	99.60	248	249
	11.西南桦Betula alnoides	0.40	1
F5	12.九节Psychotria rubra	51.95	5 967	11 486
	13.柏拉木Blastus cochinchinensis	11.04	1 268
	14.粗叶木Lasianthus chinensis	8.10	930
F6	15.高脚罗伞Ardisia quinquegona	10.11	2 070	20 469
	16.粗毛野桐Mallotus hookerianus	6.06	1 241
	17.烟斗稠Lithocarpus corneus	4.33	886
F7	18.海南椎Castanopsis hainanensis	4.67	811	17 360
	19.中华厚壳桂Cryptocarya chinensis	4.03	699
	20.黄叶树Xanthophyllum hainanense	3.92	681
F8	21.南亚松Pinus merkusii	36.38	505	1 388
	22.多腺水翁Cleistocalyx conspersipunctatus	13.40	186
	23.野荔枝Litchi chinensis	10.09	140

功能群 Functional group	潜在关键种 Potential keystone species	DI_ij (×100)	N_ij	N_j
F1	1.毛稔Melastoma sanquiueum	64.85	832	1 283
	2.紫毛野牡丹Melastoma penicillatum	21.43	275
	3.野牡丹Melastoma candidum	4.99	64
F2	4.银柴Aporosa chinensis	19.81	982	4 956
	5.猪肚木Canthium horridum	13.94	691
	6.黄牛木Cratoxylum ligustrinum	11.86	588
F3	7.海南杨桐Adinandra hainanensis	35.78	370	1 034
	8.翻白叶Pterospermum heterophyllum	29.69	307
	9.拟赤杨Alniphyllum fortunei	16.15	167
F4	10.枫香Liquidambar formosana	99.60	248	249
	11.西南桦Betula alnoides	0.40	1
F5	12.九节Psychotria rubra	51.95	5 967	11 486
	13.柏拉木Blastus cochinchinensis	11.04	1 268
	14.粗叶木Lasianthus chinensis	8.10	930
F6	15.高脚罗伞Ardisia quinquegona	10.11	2 070	20 469
	16.粗毛野桐Mallotus hookerianus	6.06	1 241
	17.烟斗稠Lithocarpus corneus	4.33	886
F7	18.海南椎Castanopsis hainanensis	4.67	811	17 360
	19.中华厚壳桂Cryptocarya chinensis	4.03	699
	20.黄叶树Xanthophyllum hainanense	3.92	681
F8	21.南亚松Pinus merkusii	36.38	505	1 388
	22.多腺水翁Cleistocalyx conspersipunctatus	13.40	186
	23.野荔枝Litchi chinensis	10.09	140

环境因子图层 Environmental layer	外部缺省误差Omission error (Ext)
环境因子图层 Environmental layer	1	4	7	10	12	15	18	21
平面曲率Plan curvature	(0.242)	(0.389)	(0.491)	(0.423)	(0.464)	(0.457)	(0.469)	(0.477)
坡向Aspect (°)	(-0.282^*)	(0.269)	(-0.215^*)	(0.304)	(-0.321^*)	(-0.201^*)	(-0.187^*)	(0.237)
海拔Altitude (m)	0.036	(-0.202^*)	-0.101^*	(-0.200^*)	(-0.209^*)	(-0.198^*)	(-0.202^*)	(-0.078^*)
剖面曲率Profile curvature	(0.213)	(0.281)	(-0.151^*)	0.137	(0.270)	(0.230)	(0.242)	(0.250)
坡度Slope (°)	0.127	(0.309)	0.014	(0.298)	0.123	(0.171)	(0.248)	(0.241)
年均降雨量Annual mean precipitation (mm)	(-0.248^*)	-0.121^*	-0.024^*	(-0.221^*)	0.076	-0.062^*	-0.030^*	-0.101^*
年均相对湿度Annual mean relative humidity (%)	0.072	(-0.242^*)	0.041	-0.127^*	-0.117^*	0.025	-0.154^*	(-0.302^*)
最低温度Minimum temperature (℃)	-0.123^*	-0.122^*	0.064	-0.108^*	-0.077^*	-0.041^*	-0.069^*	(-0.295^*)
平均温度Mean temperature (℃)	0.020	(-0.267^*)	-0.138^*	(-0.203^*)	-0.033^*	(-0.225^*)	-0.155^*	(-0.173^*)
最高温度Maximum temperature (℃)	-0.058^*	(-0.296^*)	0.019	(-0.303^*)	(-0.176^*)	-0.157^*	-0.163^*	(-0.255^*)