Regression tree analysis for the determinants of first detection location of invasive alien species.

<p>A: using all explanatory variables; B: using explanatory variables except those classified into “IP” category (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031734#pone-0031734-t001" target="_blank">Table 1</a>). Each node of the tree is described by the splitting variable, its splitting criteria, percentage of variance the splitter explains, mean ± standard deviation for the number of first detection locations of invasive alien species, and the number of sample (i.e. species) at that node in brackets. (<i>Inset</i>) Cross-validation processes for selection of the best regression trees. Line shows a single representative 10-fold cross-validation of the most frequent (modal) best trees with standard error (SE) estimates of each tree size. Bar charts are the numbers of the optimal trees of each size (frequency of tree) selected from a series of 50 cross-validations based on the minimum cost tree, which minimizes the cross-validated relative error (white, SE rule 0), and 50 cross-validations based on the one-SE rule (gray, SE rule 1), which minimizes the cross-validated relative error within one SE of the minimum. The most frequent trees have four terminal nodes. See the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031734#pone-0031734-g001" target="_blank">Fig. 1</a> for province codes.</p

Distribution of first detection locations of invasive alien species in mainland China.

<p>Provincial administrative units in mainland China were separated into three groups according to their geographic position: coastal region in blue ( = provinces with sea coasts except Beijing), border region in grey ( = provinces continuous to other countries) and midland region in white ( = provinces without sea coasts or borders on other countries). Bars in red are the number of first detection locations in each province. Bars in yellow and green (for the average GDP and import value of commodities from 1986 to 2007, respectively) are standardized with same height in Guangdong province which has the highest GDP and the highest number of first detection locations. AH, BJ, CQ, FJ, GS, GD, GX, GZ, HeB, HeN, HLJ, HN, HuB, HuN, JL, JS, JX, NMG, NX, QH, SD, SaX, SaaX, SC, SH, TJ, XJ, XZ, YN and ZJ are provinces codes, standing for Anhui, Beijing, Chongqing, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hebei, Henan, Heilongjiang, Hainan, Hubei, Hunan, Jilin, Jiangsu, Jiangxi, Inner Mongolia, Ningxia, Qinghai, Shandong, Shanxi, Shaanxi, Sichuan, Shanghai, Tianjin, Xinjiang, Tibet, Yunnan and Zhejiang, respectively.</p

List of explanatory variables in China by province.

a<p>DI: Disturbance; EB: Ecological/bio-geographical variance; IP: Introduction pressure; SE: Search and recording effort; SI: Spread by unintentional introduction.</p>b<p>Data of variables except EN, AP, WP, LP, NC and NP were collected from National Bureau of Statistics of China (1986–2007) China statistical yearbook. The mean values of these variables were used for data analysis. Endemism score (EN) means the total values of endemism of species including plants, mammals and birds in each province, collected from McBeath G.A & Leng T.K. (2006) Governance of Biodiversity Conservation in China and Taiwan. Information about AP, WP, LP, NC and NP was collected from China Association of Port-of-Entry (2003) Practical Manual of Ports of Entry in China.</p>c<p>EEIQ: Entry-Exit Inspection and Quarantine.</p>d<p>Scientific research refers to state-owned research and development institutions above county level in the field of natural sciences and technology.</p

Will Climate Change Affect Outbreak Patterns of Planthoppers in Bangladesh?

<div><p>Recently, planthoppers outbreaks have intensified across Asia resulting in heavy rice yield losses. The problem has been widely reported as being induced by insecticides while other factors such as global warming that could be potential drivers have been neglected. Here, we speculate that global warming may increase outbreak risk of brown planthopper (<i>Nilaparvata lugens</i> Stål.). We present data that demonstrate the relationship between climate variables (air temperature and precipitation) and the abundance of brown planthopper (BPH) during 1998–2007. Data show that BPH has become significantly more abundant in April over the 10-year period, but our data do not indicate that this is due to a change in climate, as no significant time trends in temperature and precipitation could be demonstrated. The abundance of BPH varied considerably between months within a year which is attributed to seasonal factors, including the availability of suitable host plants. On the other hand, the variation within months is attributed to fluctuations in monthly temperature and precipitation among years. The effects of these weather variables on BPH abundance were analyzed statistically by a general linear model. The statistical model shows that the expected effect of increasing temperatures is ambiguous and interacts with the amount of rainfall. According to the model, months or areas characterized by a climate that is either cold and dry or hot and wet are likely to experience higher levels of BPH due to climate change, whereas other combinations of temperature and rainfall may reduce the abundance of BPH. The analysis indicates that global warming may have contributed to the recent outbreaks of BPH in some rice growing areas of Asia, and that the severity of such outbreaks is likely to increase if climate change exaggerates. Our study highlights the need to consider climate change when designing strategies to manage planthoppers outbreaks.</p></div

The predicted relative changes in daily catches of BPH under the various scenarios.

<p>The predicted relative changes in daily catches of BPH under the various scenarios.</p

The effects of minimum temperature and rainfall on the predicted abundance of BPH (log(<i>N</i>+1)) (see Table 1 for further explanation).

<p>The effects of minimum temperature and rainfall on the predicted abundance of BPH (log(<i>N</i>+1)) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091678#pone-0091678-t001" target="_blank">Table 1</a> for further explanation).</p

The predicted daily catches of adult BPH (±SD) if minimum temperature increases with either 0°C, 1°C or 2°C, and daily precipitation either decreases or increases with 10%.

<p>The predicted daily catches of adult BPH (±SD) if minimum temperature increases with either 0°C, 1°C or 2°C, and daily precipitation either decreases or increases with 10%.</p

The predicted mean daily catches of BPH averaged over a year if daily minimum temperature changes with either 0°C, +1°C or +2°C, and if daily rainfall changes with either 0%,

<p>−<b>10% or +10%.</b></p

Log (variance) plotted against log(average) for (a) daily rainfall and (b) daily catches of BPH adults.

<p>Each dot represents a month. The straight line for daily rainfall is described by <i>y = </i>1.3068<i>x</i>+0.1252 (<i>R</i>² = 0.9587) and for daily catches of BPH by <i>y = </i>2.0512<i>x</i>–0.2258 (<i>R</i>² = 0.9793).</p

Monthly averages (±SD) of (A) Minimum and maximum temperatures; (B) daily rainfall; and (C) daily catches of Brown Planthoppers (BPH).

<p>Data from Bangladesh Rice Research Institute (BRRI).</p