Geographical variation in the population dynamics of Thecodiplosis japonensis: causes and effects on spatial synchrony

被引:8
作者
Choi, Won Il [3 ]
Ryoo, Mun Il [2 ]
Chung, Yeong-Jin [3 ]
Park, Young-Seuk [1 ]
机构
[1] Kyung Hee Univ, Dept Biol, Seoul 130701, South Korea
[2] Korea Univ, Div Environm Sci & Ecol Engn, Seoul 136713, South Korea
[3] Korea Forest Res Inst, Div Forest Insect Pests & Dis, Seoul 130712, South Korea
关键词
Autocorrelation function; Invasive species; Nonparametric spatial covariance functions; Pine needle gall midge; Self-organizing map; Spatial synchrony; NEEDLE GALL MIDGE; INOUYE DIPTERA; CECIDOMYIIDAE; OUTBREAKS; PATTERNS; UCHIDA; CYCLES;
D O I
10.1007/s10144-011-0263-8
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Geographical variation in population dynamics of a species offers an opportunity to understand the factors determining observed patterns of spatial dynamics. We evaluated the spatial variation in the population dynamics of the pine needle gall midge (PNGM), Thecodiplosis japonensis, which is a severe insect pest in pine forests in Korea, and studied the influences of weather factors that could affect its population dynamics. Results revealed that PNGM population dynamics were classified into five clusters based on the analysis of autocorrelation function and self-organizing map, which is an artificial neural network. We also quantified spatial synchrony in the population dynamics of PNGM using the nonparametric covariance function. Variation in spatial synchrony was strongly related to differences in maximum temperature and precipitation in Random Forest analysis, suggesting that the synchrony in PNGM population dynamics is largely the result of the Moran effect. In addition, spatial differences in population dynamics could be influenced by transient process of synchronization following invasion. Finally, the present results indicate that differences in population dynamics can be induced by interactions among several factors such as maximum temperature, precipitation, and invasion history of species.
引用
收藏
页码:429 / 439
页数:11
相关论文
共 46 条
[1]  
Alhoniemi E., 2000, SOM Toolbox
[2]  
[Anonymous], 2004, SYSTAT 11
[3]  
[Anonymous], 2001, MATLAB VERS 6 1
[4]   Spatial population dynamics: analyzing patterns and processes of population synchrony [J].
Bjornstad, ON ;
Ims, RA ;
Lambin , X .
TRENDS IN ECOLOGY & EVOLUTION, 1999, 14 (11) :427-432
[5]   Transient synchronization following invasion: revisiting Moran's model and a case study [J].
Bjornstad, Ottar N. ;
Liebhold, Andrew M. ;
Johnson, Derek M. .
POPULATION ECOLOGY, 2008, 50 (04) :379-389
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]  
Buonaccorsi JP, 2001, ECOLOGY, V82, P1668, DOI 10.2307/2679809
[8]  
Calle M Luz, 2011, Brief Bioinform, V12, P86, DOI 10.1093/bib/bbq011
[9]   Patternizing communities by using an artificial neural network [J].
Chon, TS ;
Park, YS ;
Moon, KH ;
Cha, EY .
ECOLOGICAL MODELLING, 1996, 90 (01) :69-78
[10]   Use of an artificial neural network to predict population dynamics of the forest-pest pine needle gall midge (Diptera: Cecidomyiida) [J].
Chon, TS ;
Park, YS ;
Kim, JM ;
Lee, BY ;
Chung, YJ ;
Kim, Y .
ENVIRONMENTAL ENTOMOLOGY, 2000, 29 (06) :1208-1215