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Crop landrace group distribution modellingTo predict the probability of geographic occurrence for each landrace group within each crop, we generated MaxEnt models 56 , 57 using the ‘maxnet’ R package 58 . Group-specific spatial predictors were selected using a combination of the variance inflation factor (VIF) and a principal component *ysis (PCA) to control for excessive model complexity and variable collinearity 59 . We removed variables that did not contribute significantly to the variance in the PCA, defined as contributing less than 15% to the first component, and we further discarded variables with a VIF > 10 (ref. 60 ). The predictors and whether they were selected for the modelling of each landrace group are presented in Supplementary Table 4 .We generated a random sample of pseudo-absences as background points in areas that (1) were within the same ecological land units 61 as the occurrence points, (2) were deemed potentially suitable according to a support vector machine classifier that uses all occurrences and predictor variables and (3) were farther than 5 km from any occurrence 62 . The number of pseudo-absences generated per crop group was ten times its number of unique occurrences.MaxEnt models were fitted through five-fold (K = 5) cross-validation with 80% training and 20% testing. For each fold, we calculated the area under the receiving operating characteristic curve (AUC), sensitivity, specificity and Cohen’s k*a as measures of model performance. To create a single prediction that represents the probability of occurrence for the landrace group, we computed the median across K models. Geographic areas in the form of pixels with probability values above the maximum sum of sensitivity and specificity were treated as the final area of predicted presence 13 .第八期-基于MaxENT模型的不同尺度区域物种适宜区预测及多物种适宜区分析与文章写作指导高级培训班通知各企事业单位：近年来，生态位模型被广泛应用于入侵生物学、保护生物学、生物多样性保护及全球气候变化对物种分布的影响， 谱系生物地理学 及传染病空间传播研究等多个领域。MaxEnt模型基于最大熵原理，即在满足已知约束的条件下，选择熵最大的模型，它利用物种的存在分布点 和环境变量 ，来推算物种的生态需求和模拟物种的潜在分布。MaxEnt由于其简单直接的操作、简洁清晰的图形界面及参数自动配置的功能，被广大研究者特别是初学者所青睐。本次培训创新性增加了不同尺度（大范围、小范围）物种适生区预测建模内容以及多物种适宜区分析与绘图方法等内容，此外还增加了建设项目叠加分析等应用项目中的应用，学员可通过本次课程，掌握基于MAXENT模型的建模整体流程以及参数优化、个性化分析等高级内容，有助于提升论文质量。【培训时间】2024年5月31日-6月2日共计3天（上午9:30-12:00，下午14:00-17:30）【培训方式】线上方式：腾讯会议；开课前会务组会提供房间号及密码。【培训目标】1、掌握MaxEnt软件安装和参数配置。2、掌握MaxEnt建模预测-基础和中阶应用操作。3、掌握MaxEnt模型物种适宜生境分布图制作。4、掌握基于R优化MaxEnt模型参数方法和文章写作制图。5、掌握MaxEnt建模预测-个性化分析。6、掌握不同尺度（小区域、大区域）物种适生区预测方法。7、掌握多物种叠加适宜区预测与制图。8、通过典型案例及实操讲解，使学员能够学会MaxEnt模型建模方法及高级功能，使学员能够在论文写作中掌握使用方法及技巧。【培训对象】各省市、自治区从事自然保护区规划管理、农林、水资源海洋、生态环境等相关领域针对保护地规划、生物多样、生物学、生态学、生态固碳、全球变化、碳水资源研究、生态环境研究与规划的专业技术人员及相关领域专家学者、研究生及生态环境评价的相关工作者等。【课程优势】1、本次课程提供回放，学员参加完课程，后期支持*观看回放。2、课程所用课件、资料全部赠送，并赠送大量相关交叉学科基础资源。3、建立专属*交流群，实时问题反馈解答，导师长期提供课程技术支持。4、参加课程后，即为树谷学院“黄金会员”，享受树谷福利及优惠*。【证书颁发】参加培训的学员可以获得：《MaxEnt模型应用》专业技术培训证书（电子版），网上可查，此证书作为个人学习和继续教育证明、专业技能提升、单位人才聘用的参考依据。查询网址：www.bjsgxxkj.com注：请学员准备证件照电子版（备注姓名+身份证号码）提前发给会务组人员注册证书。【课程大纲】【报名方式】② 联系*人员线下报名并领取课程通知文件。【课程*】胡老师：13261866021（*同步） 肖老师：18910582825（*同步）<树谷课堂-科研领域树人助学基地>
