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1.
Aim The spatial extent of western Canada’s current epidemic of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae), is increasing. The roles of the various dispersal processes acting as drivers of range expansion are poorly understood for most species. The aim of this paper is to characterize the movement patterns of the mountain pine beetle in areas where range expansion is occurring, in order to describe the fine‐scale spatial dynamics of processes associated with mountain pine beetle range expansion. Location Three regions of Canada’s Rocky Mountains: Kicking Horse Pass, Yellowhead Pass and Pine Pass. Methods Data on locations of mountain pine beetle‐attacked trees of predominantly lodgepole pine (Pinus contorta var. latifolia) were obtained from annual fixed‐wing aircraft surveys of forest health and helicopter‐based GPS surveys of mountain pine beetle‐damaged areas in British Columbia and Alberta. The annual (1999–2005) spatial extents of outbreak ranges were delineated from these data. Spatial analysis was conducted using the spatial–temporal analysis of moving polygons (STAMP), a recently developed pattern‐based approach. Results We found that distant dispersal patterns (spot infestations) were most often associated with marginal increases in the areal size of mountain pine beetle range polygons. When the mountain pine beetle range size increased rapidly relative to the years examined, local dispersal patterns (adjacent infestation) were more common. In Pine Pass, long‐range dispersal (> 2 km) markedly extended the north‐east border of the mountain pine beetle range. In Yellowhead Pass and Kicking Horse Pass, the extension of the range occurred incrementally via ground‐based spread. Main conclusions Dispersal of mountain pine beetle varies with geography as well as with host and beetle population dynamics. Although colonization is mediated by habitat connectivity, during periods of low overall habitat expansion, dispersal to new distant locations is common, whereas during periods of rapid invasion, locally connected spread is the dominant mode of dispersal. The propensity for long‐range transport to establish new beetle populations, and thus to be considered a driver of range expansion, is likely to be determined by regional weather patterns, and influenced by local topography. We conclude that STAMP appears to be a useful approach for examining changes in biogeograpical ranges, with the potential to reveal both fine‐ and large‐scale patterns.  相似文献   

2.
We isolated 16 polymorphic microsatellite loci in the mountain pine beetle (Dendroctonus ponderosae Hopkins) and developed conditions for amplifying these markers in four multiplex reactions. Three to 14 alleles were detected per locus across two sampled populations. Observed and expected heterozygosities ranged from 0.000 to 0.902 and from 0.100 to 0.830, respectively. Three loci deviated from Hardy-Weinberg equilibrium in one sampled population. One of these loci may be sex linked. These markers will be useful in the study of population structure in this important pest species.  相似文献   

3.
Environmental change has a wide range of ecological consequences, including species extinction and range expansion. Many studies have shown that insect species respond rapidly to climatic change. A mountain pine beetle epidemic of record size in North America has led to unprecedented mortality of lodgepole pine, and a significant range expansion to the northeast of its historic range. Our goal was to determine the spatial genetic variation found among outbreak population from which genetic structure, and dispersal patterns may be inferred. Beetles from 49 sampling locations throughout the outbreak area in western Canada were analysed at 13 microsatellite loci. We found significant north-south population structure as evidenced by: (i) Bayesian-based analyses, (ii) north-south genetic relationships and diversity gradients; and (iii) a lack of isolation-by-distance in the northernmost cluster. The north-south structure is proposed to have arisen from the processes of postglacial colonization as well as recent climate-driven changes in population dynamics. Our data support the hypothesis of multiple sources of origin for the outbreak and point to the need for population specific information to improve our understanding and management of outbreaks. The recent range expansion across the Rocky Mountains into the jack/lodgepole hybrid and pure jack pine zones of northern Alberta is consistent with a northern British Columbia origin. We detected no loss of genetic variability in these populations, indicating that the evolutionary potential of mountain pine beetle to adapt has not been reduced by founder events. This study illustrates a rapid range-wide response to the removal of climatic constraints, and the potential for range expansion of a regional population.  相似文献   

4.
Aim Our aim is to examine the historical breach of the geoclimatic barrier of the Rocky Mountains by the mountain pine beetle (Dendroctonus ponderosae Hopkins). This recent range expansion from west of the North American continental divide into the eastern boreal forest threatens to provide a conduit to naïve pine hosts in eastern North America. We examine the initial expansion events and determine potential mechanism(s) of spread by comparing spread patterns in consecutive years to various dispersal hypotheses such as: (1) meso‐scale atmospheric dispersal of insects from source populations south‐west of the Rocky Mountains in British Columbia (i.e. their historical range), (2) anthropogenic transport of infested plant material, and (3) spread of insect populations across adjacent stands via corridors of suitable habitat. Location British Columbia, Canada. Methods We explore potential mechanism(s) of invasion of the mountain pine beetle using spatial point process models for the initial 3 years of landscape‐level data collection, 2004–2006. Specifically, we examine observed patterns of infestation relative to covariates reflecting various dispersal hypotheses. We select the most parsimonious models for each of the initial 3 years of invasion using information criteria statistics. Results The initial range expansion and invasion of the beetle was characterized by aerial deposition along a strong north‐west to south‐east gradient, with additional aerial deposition and localized dispersal from persisting populations in following years. Main conclusions Following deposition of a wave front of mountain pine beetles parallel to the Rocky Mountains via meso‐scale atmospheric dispersal, the areas of highest intensity of infestations advanced up to 25 km north‐east towards jack pine (Pinus banksiana) habitat in a single year. There appeared to be no association between putative anthropogenic movement of infested materials and initial range expansion of the mountain pine beetle across the continental divide.  相似文献   

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The circular economy (CE) is attracting increasing interest, as it can bring environmental, social, and economic benefits. However, policymakers and scholars appear to concentrate more on the production side of CE, while consumption, and particularly policies that affect consumption have received less attention and their effect is ambiguous. This paper investigates the effect of CE consumption policies on circular economy business models (CEBMs) in firms, but also examines the interplay this type of policies have with CE production policies to have a broader picture of the circular economy policy framework and the relevance of each type of policy on firms. While previous studies assume rational and passive consumer behavior, this paper borrows from a natural resource-based view and stakeholder theory, arguing that consumers have a proactive attitude toward the consumption of environmentally friendly products. Moreover, we use institutional theory as an analytical framework for modeling the effects of a particular policy framework on the CEBM. Our analysis combines classical econometric methods with machine learning approaches, employing data from the EU. The results show that CE policies aimed at promoting consumption have a direct and positive effect on CEBMs. This paper also confirms that a wide portfolio of CE policies on production and consumption has a greater effect on the development of CEBMs, due to the complementarity of CE consumption and production policies. Moreover, we show that in interaction with CE production policies, CE policies on consumption have an even greater effect on CEBMs in firms than would have been anticipated.  相似文献   

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A variety of water quality indices have been used to assess the state of waterbodies all over the world. In calculating a Water Quality Index (WQI), traditional methods require the evaluation of many water quality parameters, making them costly and time-consuming. In recent years, machine learning (ML) algorithms have emerged as an effective tool to solve many environmental problems, including water quality management. In this study, we investigate the performance of the ML-based method in calculating the WQI. We apply several feature selection techniques to select the key parameters fed the ML models. Experiments are carried out to evaluate the WQI based on a dataset collected from 2007 to 2020 of An Kim Hai system, one of the most important irrigation systems in the north of Vietnam. The obtained results show that the application of selection methods allows reducing significantly the number of water quality parameters fed the ML models without losing their accuracy. In particular, by using the embedded method, we find out four important parameters, including Coliform, DO, Turbidity, and TSS, that have the greatest impact on water quality. Based on these parameters, the Random Forest model provides the best accuracy in predicting the WQI values from the An Kim Hai system with a Similarity of 0.94. The combination of feature selection and ML methods is then considered an effective alternative for calculating the WQI, leading to a desirable performance and a reduction of input parameters. This makes water quality monitoring less costly, substantial effort, and time.  相似文献   

9.
We compared the ability of three machine learning algorithms (linear discriminant analysis, decision tree, and support vector machines) to automate the classification of calls of nine frogs and three bird species. In addition, we tested two ways of characterizing each call to train/test the system. Calls were characterized with four standard call variables (minimum and maximum frequencies, call duration and maximum power) or eleven variables that included three standard call variables (minimum and maximum frequencies, call duration) and a coarse representation of call structure (frequency of maximum power in eight segments of the call). A total of 10,061 isolated calls were used to train/test the system. The average true positive rates for the three methods were: 94.95% for support vector machine (0.94% average false positive rate), 89.20% for decision tree (1.25% average false positive rate) and 71.45% for linear discriminant analysis (1.98% average false positive rate). There was no statistical difference in classification accuracy based on 4 or 11 call variables, but this efficient data reduction technique in conjunction with the high classification accuracy of the SVM is a promising combination for automated species identification by sound. By combining automated digital recording systems with our automated classification technique, we can greatly increase the temporal and spatial coverage of biodiversity data collection.  相似文献   

10.
BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.  相似文献   

11.
EDock‐ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock‐ML takes a bottom‐up approach in which machine‐learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine‐learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324–2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock‐ML can be accessed freely at edock‐ml.umsl.edu  相似文献   

12.
Genetic surveys of the population structure of species can be used as resources for exploring their genomic architecture. By adjusting filtering assumptions, genome‐wide single‐nucleotide polymorphism (SNP) datasets can be reused to give new insights into the genetic basis of divergence and speciation without targeted resampling of specimens. Filtering only for missing data and minor allele frequency, we used a combination of principal components analysis and linkage disequilibrium network analysis to distinguish three cohorts of variable SNPs in the mountain pine beetle in western Canada, including one that was sex‐linked and one that was geographically associated. These marker cohorts indicate genomically localized differentiation, and their detection demonstrates an accessible and intuitive method for discovering potential islands of genomic divergence without a priori knowledge of a species’ genomic architecture. Thus, this method has utility for directly addressing the genomic architecture of species and generating new hypotheses for functional research.  相似文献   

13.
草地地上生物量(Aboveground Biomass,AGB)是指导畜牧业生产管理的重要指标,是草畜平衡综合分析的基础。目前,有关祁连山草地AGB反演的研究较少,且多源数据间的尺度差异问题并未得到很好的解决。为了解祁连山草地AGB的空间分布状况,利用Sentinel-2多光谱数据、无人机(Unmanned Aerial Vehicle,UAV)数据以及2021年植被生长期实测草地AGB数据实现了空天地一体化监测,通过决策树回归(Decision Tree Regression,DTR)、随机森林回归(Random Forest Regression,RFR)、梯度提升决策回归树(Gradient Boosting Regression Tree,GBRT)以及极致梯度提升(eXtreme Gradient Boosting,XGBoost)共4种算法反演草地AGB的适用性分析,利用最优模型反演了祁连山草地的AGB空间分布状况。结果表明:研究区内多种植被指数所表现出的特性有所差异。祁连山地区AGB在空间分布上呈现出由西北向东南递增的趋势,平均AGB为925.43kg/hm2。6种植被指数与实测AGB之间均表现为显著正相关,适合作为祁连山草地AGB遥感反演的指标;XGBoost模型较其它模型具有最高的R2值(0.78)和精度(74.75%)、最低的均方根误差(RMSE,99.74 kg/hm2)和平均绝对误差(MAE,71.60 kg/hm2),模型反演效果最好;UAV数据能够提供更加详细的空间细节特征,减小Sentinel-2数据和实地采样数据间的尺度差异;因此,基于6种植被指数与祁连山草地AGB间的相关性,构建XGBoost模型反演研究区草地AGB空间分布状况是具有实践意义的。研究结果将为指导祁连山草地畜牧业的发展和维护草地生态系统的平衡提供一定的参考价值与数据支撑。  相似文献   

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