基于MaxEnt模型的毛红椿适生区预测

[目的]了解环境因子对毛红椿适生区分布的影响,为其资源保护、引种及其人工林的发展提供参考。[方法]基于MaxEnt模型能利用现存不完整、小样本、离散型分布数据构建物种适生区预测模型,用受试者工作曲线线下面积(AUC)检验预测模型的精度,面积越大精度越高等优点,本研究应用毛红椿在云南的分布数据及1个地型因子和6个气候因子,来构建其适生区分布模型。[结果]毛红椿适生区分布MaxEnt模型平均训练AUC和平均测试AUC分别为0.891、0.885,说明对毛红椿适生区的预测是可靠的;降水量变异系数和最干季度降水量是决定毛红椿适生区分布的主要因子,年均气温变化范围、最冷季度平均气温、最湿季度降水量、最冷季度降水量是次要因子。在当代和未来(2050S、2070S)气候变暖条件下(RCP2.6情景),云南省和全国适生区面积计算结果直观、定量的反应了全球变暖对毛红椿适生区变迁的影响。[结论]预测云南省及全国的毛红椿适生区随全球变暖而小幅萎缩。     

基于生态位模型的森林害虫潜在分布区预测研究进展——以蒙古高原雅氏落叶松尺蠖为例

指出了生态位模型是使用物种分布和系列环境变量数据,借助于某一算法对物种的生态需求进行分析,其运算结果在不同时空中的表达即为物种的实际和潜在分布区。近年来,该种模型在物种潜在分布区、动植物入侵风险、生物多样性保护、全球气候变化与物种分布等研究中得到广泛应用。从雅氏落叶松尺蠖的分布现状、生态位模型构建的基本原理应用等方面探讨了生态位模型在雅氏落叶松尺蠖潜在分布预测中的应用。生态位模型利用物种的分布点数据及与之相关的环境变量来模拟物种潜在分布,预测结果取决于环境变量的类别、分辨率等因素。MaxEnt模型基于有限的分布信息进行潜在分布区预测,且能在预测时充分考虑已知信息,而对未知信息不做过多揣测,根据客观的环境变量特征得出约束条件,探寻此约束条件下,该模型能够根据物种"出现点"的最大熵的可能分布。该种模型在蒙古高原雅氏落叶松尺蠖潜在分布预测研究中极具应用潜力。     

寄主植物与环境因子相互作用对达摩麝凤蝶潜在分布的影响

达摩麝凤蝶是一种亟待保护的珍惜濒危物种,然而目前我们对其自然资源的分布状况了解十分匮乏,只有零星的野外数据,这对于达摩麝凤蝶的保护工作十分不利。因此,通过物种分布模型来预测其潜在适生区是掌握其自然资源分布的有效手段。同时,达摩麝凤蝶与贯叶马兜铃(濒危物种)之间存在特异性寄生的关系。这种强种间相互作用在以往的物种分布模型研究中往往被忽视,但近来一些研究表明,种间关系在对有强烈相互作用的物种进行分布预测时可能是非常重要的影响因子。因此种间关系在达摩麝凤蝶的适生区预测中可能是不可忽略的因子。本文使用最大熵模型(MaxEnt),设置了Ma(仅考虑非生物因子)、Mb(仅考虑种间关系)、Mc(寄主植物的潜在分布)和Md(同时考虑非生物因子和种间关系)4个模型对达摩麝凤蝶的适生区进行了预测,并对Md模型使用通径分析量化非生物因子和种间关系因子对达摩麝凤蝶潜在适生区的影响。结果显示,各模型的预测精度都较高(AUC>0.9),考虑了种间关系的2个模型拟合结果表现较好(AIC:Ma>Mc>Md>Mb)。加入种间关系后得到的分布区面积明显缩小(高适宜区面积:Ma>Mc>Mb>Md)。刀切法结果表明,种间关系在预测中的贡献率最高。通径分析结果表明Mc模型(即种间关系)对Md模型预测的直接通径系数(0.578)和决策系数(R 2=0.546)均为最高。综上所述,种间关系是达摩麝凤蝶潜在分布最主要的影响因子,其在达摩麝凤蝶的适生区预测中有不可忽视的影响。     

基于生态位模型的松材线虫潜在生境预测方法研究

以江苏省松材线虫（Bursaphelenchus xylophilus）定点发生数据和31个环境变量为主要信息源,采用分类与回归树模型（CART）、基于规则的遗传算法（GARP）、最大熵法（Maxent）、逻辑斯蒂回归（LR）4种模型建立松材线虫在江苏省的潜在生境预测模型,从接受者曲线下面积（AUC）、Pearson相关系数、Kappa值3个方面来检验模型预测精度,分析松材线虫的空间分布规律及其环境影响因素.研究结果表明：在3个评价指标中,CART模型的预测精度较低,其它3个模型的预测精度均达到优良水平,其中Maxent在物种现实生境模拟、主要生态环境因子筛选、环境因子对物种生境影响的定量描述方面都表现出优越的性能.GARP模型对松材线虫潜在生境的预测方面表现出优越的性能.海拔、年降水量、降水的季节性变化、温度的年变化范围是影响松材线虫空间分布的主要环境因子.     

气候变化下濒危树种华榛的潜在适生区预测

[目的]探究华榛(Corylus chinensis?Franch.)地理分布格局随气候的变化趋势,划分不同等级的华榛适生区,为华榛的保护利用提供科学依据.[方法]本研究基于华榛83条现代分布记录信息和筛选后的18个环境变量数据,采用物种分布模型中的最大熵模型(MaxEnt)模拟华榛末次间冰期以来6个时期的潜在分布区....     

MaxEnt模型综合应用研究进展分析

指出了最大熵模型(MaxEnt)是以生态位理论为根基广泛应用于各个研究领域受到学者们的青睐和高度认可,为实验者的探究打开崭新的一面,介绍了Maxent模型的基本原理并对物种潜在分布的生境适宜性评价、濒危动植物保护、物种入侵、病虫害防治、在气候变化下药用植物的潜在适生区分布等领域阐述MaxEnt模型应用,对加强物种的保护与管理提供科学依据。     

基于MaxEnt模型预测砂生槐在西藏的潜在分布区

砂生槐具有极强的抗旱、耐瘠薄、防风固沙等生态适应性和水土保持功能，是雅江流域干旱、半干旱河谷的主要建群灌丛植物，也是防沙治沙与生态建设的先锋树种。使用MaxEnt生态位模型软件和ArcGIS软件预测砂生槐在西藏的潜在分布范围，用ROC曲线检测预测精度，用刀切法筛选主要环境变量，通过主要环境变量的响应曲线分析适宜砂生槐的生态环境条件，并在此基础上划分适宜栽培区。结果表明：采用MaxEnt模型预测砂生槐潜在适宜分布区准确度极高，预测模型的训练数据集和测试数据集的AUC值分别是0.980和0.977;采用MaxEnt模型预测的砂生槐适宜分布区涵盖西藏辖区内的那曲、拉萨、日喀则、山南、林芝5个地市，高度适宜区集中分布在西藏雅鲁藏布江中游的山坡、河谷、沙滩，主要涉及拉孜县、城关区、贡嘎县、扎囊县、乃东区、桑日县、朗县、隆子县8个县(区)的中部，桑珠孜区、白朗县和江孜县3个县(区)的北部，林周县的南部、达孜区的西北部等区域，预测结果与现地调查结果高度契合；降水因子和气温因子对砂生槐分布的影响很大。     

基于生态位模型的石蒜适生区预测

运用预设预测规则的遗传算法(GARP)和最大熵(MaxEnt)两种生态位预测模型,以及受试者工作曲线(ROC)分析方法,预测石蒜属石蒜潜在适生区.结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.988,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于石蒜的适生区预测.对环境变量进行刀切法表明,在所有环境变量中,最冷季度平均温度对于石蒜分布的影响(贡献)最大,其次是年均温、最冷月的最低温度和最暖季度降水量,而海拔、降水量变化方差对石蒜分布的影响比较小.预测结果显示,石蒜在世界范围内主要分布在亚洲东部以及亚洲中部一小部分,另外北美洲的东部,欧洲南部一小部分地区也适合其生长.在中国范围内主要分布在云南、贵州、福建、江苏、浙江、安徽、江西、重庆、湖北、湖南等省,以及山东、河南、陕西、甘肃等省南部;四川东部和广东、广西(除了南部沿海地区)均预测为适生区,海南、台湾、西藏部分地区也是适生区.     

基于MaxEnt模型新疆枣潜在适生区预测

【目的】研究全球气候变化对新疆枣潜在分布的影响,划分新疆枣不同等级的适生区,为新疆枣产业的持续稳定发展提供参考。【方法】基于新疆枣地理分布的调查数据和2种气候情景(RCP4.5和RCP8.5),利用GIS技术和MaxEnt生态位模型相结合的方法,在全球气候变化背景下,对新疆枣的当前及未来(2050和2070年)潜在适生区分布进行预测。【结果】在当前气候条件下,新疆枣适生区主要分布在南疆和东疆地区。其中适生区总面积达到11.3×10~4 km^2,占新疆土地总面积的6.8%。采用受试者工作特征曲线(ROC曲线)对MaxEnt模型预测结果进行评价,结果显示训练数据集和测试数据集的曲线下的面积值(AUC值)分别为0.988和0.978,说明模型预测结果较为理想。刀切法分析结果显示,影响新疆枣当前分布的气候因子主要为最热月最高温度、最冷月最低温度、最暖季度均温、最冷季度均温、6月最高气温、7月最高气温、8月最高气温、12月最低气温、1月最低气温和2月最低气温。在未来气候条件下,新疆枣适生区面积有着一定的增加,但适生区的区域变化较小。【结论】Maxent模型预测结果与新疆枣的实际分布重合度较高。低温是影响新疆枣潜在适生区分布的重要因素。在全球气候变暖的趋势下,新疆枣整个潜在适生区面积呈现增加的特点且有向高纬度区域迁移的趋势,北疆地区开始出现较少部分的低适生区。     

基于最大熵模型(MaxEnt)预测北沙柳潜在地理分布

为确定北沙柳在我国的适生情况,本研究将北沙柳地理分布信息与6个气候因子基于最大熵模型(MaxEnt)对其潜在分布进行模拟预测。AUC值大于0.9,表明预测结果良好;影响北沙柳分布主导环境因子有年平均温度、温度季节变化、海拔、最湿月降水量、昼夜温差与年温差比值、最干月降水量、昼夜温差月均值;计算出北沙柳在我国的存在概率,DIVA-GIS自动划分为4个等级,分别为不适应生境(p0.07)、低度适应生境(0.07≤p0.24)、中度适应生境(0.24≤p0.49)、高度适应生境(0.49≤p0.95);当前时期到未来(2050年)时期4个情景(RCP 2.6、RCP 4.5、RCP 6.0、RCP 8.5)下的适生区面积均有所增加。模型预测结果显示,北沙柳适生区主要集中在鄂尔多斯市和榆林市交汇处、中卫市与阿拉善左旗交汇处。结合北沙柳的实际地理分布信息与基于MaxEnt模型预测的当前时期的潜在分布基本吻合,表明MaxEnt模型对北沙柳的潜在生境概率预测可靠,得出的结果对北沙柳的推广与引种具有一定参考价值。     

运用机载LiDAR数据对橡胶林地上生物量估测的采样尺度效应分析

气候变化下,森林生物量遥感监测是当前研究的热点,机载LiDAR作为重要的遥感信息源,其采样大小对生物量估测精度有着一定的影响。以机载LiDAR数据为信息源,以44块30m×30m的方形橡胶林实测样地数据为基础,对机载激光雷达数据进行不同尺寸采样(共21个采样尺寸,边长从10m至30m,间隔为1m),提取不同采样尺寸下的激光雷达参数,并与橡胶林地上生物量建立PLSR模型,就机载激光雷达采样大小对橡胶林地上生物量估测精度的影响进行研究。研究表明:当采样尺寸小于18m时,估测精度随着采样尺寸的增大而增大;而当采样尺寸大于18m时,估测精度随着采样尺寸的增大而减小,进而趋于平缓。结果虽然呈现出一定的规律性,但是差异并不是很明显。当采样尺寸为18m时估测效果最佳,模型决定系数(R^2)为0.718,均方根误差(RMSE)为17.830 t/hm^2;交叉验证精度P和RMSEcv分别为82.741%和18.874t/hm^2。相较于实际样地(30m)尺寸下的估测结果,18m采样尺寸下的R^2提高了1.989%,RMSEcv降低了2.611%。因此,生物量的估测精度受机载激光雷达数据采样尺寸大小的影响,在生物量估测过程中需结合研究对象和研究区的实际情况对采样尺寸进行选择,从而提高生物量估测精度。     

思茅区森林生物量分层抽样调查设计

基于普洱市思茅区森林资源二类调查数据,以森林起源、地类、龄组、优势树种作为分层变量,在95%的可靠性下,设置95%,90%和85%的抽样设计精度分层抽样进行森林生物量估测,将抽样调查结果与系统抽样方法进行比较。结果表明:1)分层抽样具有样本数量少、抽样精度高的明显优势,在普洱市思茅区,按85%的设计精度,按优势树种的分层抽样比系统抽样的效率约提高了52%;2)采用单变量进行分层抽样,优势树种具有显著优势。按85%的设计精度,在样本数量为44个的前提下,其实际抽样精度达到87.82%。     

Change in current and future geographic distributions of Ulmus lamellosa in China

Prediction of potential geographic distributions is important for species protection and habitat restoration.Ulmus lamellosa is an endangered and endemic species in China for which conservation efforts are required.The maximum entropy(MaxEnt) model was used to predict the current and future geographic distribution(from 2030 to 2070) of U.lamellosa in China and discuss the reasons for changes in climatic suitability.The MaxEnt model provided a good fit to our data as confirmed by an AUC value of 0.948.The suitable areas for U.lamellosa were primarily projected in the northern part of China from 2030 to2070, especially in Liaoning province.The variables"temperature seasonality", "precipitation of wettest month" and "precipitation of warmest quarter" were the most influential climatic variables in limiting the distribution of U.lamellosa.Our results clearly predict the future impacts of climate change on the geographic distribution of U.lamellosa and this can help prioritize design of localized conservation strategies in China.     

Estimators of Tree Species Richness: An Assessment for Central and Eastern Canada

Abstract

A total of 11 sample-based estimators of tree species richness (S) are evaluated in terms of accuracy and precision in a Monte Carlo simulated simple random sampling from 39,779 forest inventory plots with 7.8 million trees belonging to 85 species. The plots represent a 108 million hectare forested region in central and eastern Canada. Sample sizes varied from 50 to 800. A weighted index combining estimates of accuracy and precision identified Chao's first estimator (CHAO1) as overall best with an estimator based on the assumption of a gamma mixed Poisson distribution of species occurrence as a close runner-up. The observed sample species richness was almost always the most negatively biased estimate. A sample size of 400-700 conventional fixed area forest inventory plots are needed to produce results with bias <20%.     

珍稀植物楠木的地理分布及潜在分布区的预测

以植物地理分布与气候变化间的关系为研究基础,通过MaxEnt模型,应用贡献率、置换重要值以及Jackknife检验、主成分分析,模拟研究影响楠木Phoebe zhennan地理分布的主要因子,分析楠木在当前的地理分布格局,并预测未来楠木在中国的潜在分布格局。结果表明,采用MaxEnt模型预测其潜在适宜分布区准确度极高,楠木在ROC曲线下的AUC值达0.995;当前(2019)适生区预测表明,四川东南部、重庆和贵州是其分布最集中的地方,其次云南、广西、广东、福建、浙江、江西、湖北也有零散分布;随着未来(2050年)气温与降雨量的变化,江苏将有较大范围适合楠木分布,但是总的适生区面积比当前减少,例如贵州西南部的适生区面积将变小,福建、江西中度适生分布面积将变大,其余地区的适生区和当前持平。本研究结果表明,影响楠木分布的主要气候因子是温度和降雨量,可为楠木资源的科学保护和合理利用提供理论依据。     

Adaptive cluster sampling for estimation of deforestation rates

National estimates of deforestation rates may be based on a survey. Precise estimation requires an efficient design. When deforestation rates are low (<1%) large sample sizes are required with traditional sampling designs to meet a precision target. This study explores the efficiency of adaptive cluster sampling (ACS) for this estimation problem. The efficiency is assessed by simulated ACS sampling from 18,200 × 200 km populations with 78–10,742 deforestation polygons (DFP) of different shape and size and average 10-year deforestation rates between 0.2% and 1.0%. Each population is composed of four million square 1 ha population units (PU) in a regular grid. Relative root mean square errors (RMSE) of ACS were, depending on sample size, 30–50% lower than comparable errors with simple random sampling (SRS) designs. ACS achieves this advantage by adaptively adding PUs to an initial SRS sample of size n. Realized ACS sample sizes were, on average, twice the nominal size (n). Three measures of ACS efficiency indicated that the costs of adaptively increasing the sample size are critical for the effectiveness of ACS. Population effects were manifest in all estimators. Estimates of the abundance, size, and shape of DFPs will allow a prediction of these effects. Populations dominated by a few large DFPs were clearly unsuited for ACS. The performance of ACS relative to that of SRS was similar across plot sizes of 1, 10, and 40 ha. The general conclusion of this study is that the lower RMSE of ACS remains attractive when the average cost of adaptively adding a PU to the initial sample is low relative to the average cost of sampling a PU at random.     

林分空间结构参数——角尺度的研究进展

角尺度作为测度林木空间分布格局的林分空间结构参数之一,具有检验能力强,调查简单易行等优点,现已得到了广泛应用。目前,有很多研究对这一参数应用不当并因此得出不合理的结论,究其原因是对角尺度理论基础缺乏了解。本文从角尺度概念的提出、相邻木个数的选择、标准角和随机分布阈值的确定、角尺度的应用等几个方面作详细阐述与总结,并指出目前的研究方向为空间结构单元大小对角尺度分析的影响及如何利用点抽样调查数据对种群分布格局进行检验等。     

Spatial distribution and impacts of climate change on Milicia excelsa in Benin,West Africa

African teak(Milicia excelsa(Welw.) C.C.Berg) is an endangered multi-use species.Understanding the impact of climate change on the distribution of this species may improve the ability to anticipate or recognize its decline or expansion and to take appropriate conservation measures if necessary.Ecological niche modeling was projected in geographical space to study the current and future distribution of M.excelsa in Benin.MaxEnt was used to estimate the potential geographic distribution of the species under two Representative Concentration Pathways(RCP).Miroc 5 summaries and two RCP 4.5 and RCP 8.5 scenarios were used as predictor variables for projections of the geographic potential of this species.The performance of the model was assessed by the area under the curve(AUC),true skill statistics(TSS) and partial receiver operating characteristics(Partial ROC).From the results,M.excelsa was more a secondary species in the Guinean climatic zone and part of the Sudanian-Guinean and Sudanian climatic zone.The projections show a significant decrease in suitable habitats for the species from the two RCP scenarios.Only a part of the Guinean climatic zone remained suitable and few protected areas will conserve in situ M.excelsa.For the sustainable conservation of M.excelsa,it is essential to strengthen the protection of sacred forests located in the Guinean climatic zone.     

Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA

Predicting the potential distribution of invasive plants within a specific region is pivotal to planning effective management but is challenged by attempting to model expanding populations that are rarely at equilibrium with their environment. We adopt an ensemble modelling approach to assess the potential distribution of Japanese honeysuckle (Lonicera japonica), a vine invasive to forests of the Cumberland Plateau and Mountain Region in the southeast of USA. The influence of disturbance, spatial and temporal heterogeneity and other landscape characteristics were assessed by creating regional level models based on occurrence records from the United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) database. Logistic regression and maximum entropy (MaxEnt) models were assessed independently and evaluated as predictive tools to test the value of presence/absence and presence only data in predicting species distributions. Ensemble models were also developed that combined the predictions of the two modelling approaches to obtain a more robust prediction. While logistic and MaxEnt models were similar in their predictive ability and dominant input variables, the ensemble approach derived the best fitting model overall. The regional distribution of Japanese honeysuckle was influenced greatly by environmental conditions such as elevation, slope, and temperature with anthropogenic activity having significant, though lesser, influence. The ensemble models predict that Japanese honeysuckle has nearly reached its potential distribution. However, given the critical role of minimum temperature on Japanese honeysuckle distribution, future occupancy at higher elevations is likely to increase since January temperatures for this region are predicted to rise by 1-4 °C over the next 100 years. The models also give some indication of the likely effect of land cover change on its distribution. Japanese honeysuckle tended to be associated with a high component of farming or low component of forest within the local neighbourhood. This suggests disturbed forest and/or high fragmentation has a higher invasion potential and given past trends and expected continued population growth this disturbance and fragmentation will only increase. The models can be integrated into forest management decision support systems and assist in the development of long term management plans, integrating the impact of potential climate and land cover change scenarios.