{"title":"Sap Flow Analyzer: A tool to standardize sap flow estimation and scaling to whole-tree water use using the HFD method","authors":"Marie-Christin Wimmler, Nadezhda Nadezhdina, Hannah Bowen, Susana Alvarado-Barrientos, Teresa David, Gabriela Fontenla-Razzetto, Britt Kniesel, Holger Lange, Roman Mathias Link, Yang Liu, Jorge López-Portillo, Clara Pinto, Junbin Zhao, Alejandra G. Vovides","doi":"10.1111/2041-210X.14392","DOIUrl":"10.1111/2041-210X.14392","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1532-1539"},"PeriodicalIF":6.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU)","authors":"Becky E. Heath, Reiji Suzuki, Neel Patrick Le Penru, James Skinner, C. David L. Orme, Robert M. Ewers, Sarab Singh Sethi, Lorenzo Picinali","doi":"10.1111/2041-210X.14390","DOIUrl":"10.1111/2041-210X.14390","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1568-1579"},"PeriodicalIF":6.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew H. Holden, Morenikeji D. Akinlotan, Allison D. Binley, Frankie H. T. Cho, Kate J. Helmstedt, Iadine Chadès
信息价值（VoI）分析是一种量化额外信息如何改善管理决策的方法，其应用范围从自然保护到渔业。然而，VoI 研究经常表明，收集更多数据并不会显著改善管理结果。这往往与生态学家和管理者的直觉相矛盾，他们通常认为新信息对管理至关重要。认为 VoI 是黑箱方法的看法加剧了这种不一致。对 VoI 通常低于生态学家预期的原因缺乏了解，这阻碍了实地应用。目前迫切需要确定促使 VoI 方法产生低值的因素。在此，我们采用严谨的方法来深入探讨 VoI 值通常较低的原因。我们首先推导出一个具有两个不确定状态、两个行动和四个管理结果的 VoI 问题的分析解决方案和上限。我们展示了 VoI 如何随在每种状态下实施行动的收益（即效用）以及系统处于每种状态的概率而变化。我们将我们的公式应用于一个已发表的青蛙种群管理案例研究，并将结果扩展到 1000 万个随机生成的大型问题。在我们的双行动双状态模拟中，有一半的时间 VoI 为零，这相当于在所有状态下有一个行动是最佳或等同最佳的。即使 VoI 值为正，通常也很低。然而，平均而言，VoI 会随着状态和行动数量的增加而增加。在 VoI 为正值的情况下，我们的 VoI 分析表达式表明，VoI 的特征是状态概率和效用差距，即在每个状态下部署每个行动的效用差异。我们推导出的边界表明，在所有双行动双状态系统中，VoI 不可能大于最大效用差距的一半。我们的分析简单而有力，为我们提供了对驱动 VoI 分析的重要因素的宝贵见解。我们的工作为在更复杂的环境中提高 VoI 分析的可解释性提供了重要的垫脚石，最终使管理者能够利用 VoI 帮助他们做出明智的决策。
{"title":"Why shouldn't I collect more data? Reconciling disagreements between intuition and value of information analyses","authors":"Matthew H. Holden, Morenikeji D. Akinlotan, Allison D. Binley, Frankie H. T. Cho, Kate J. Helmstedt, Iadine Chadès","doi":"10.1111/2041-210X.14391","DOIUrl":"10.1111/2041-210X.14391","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1580-1592"},"PeriodicalIF":6.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clustering and unconstrained ordination with Dirichlet process mixture models","authors":"Christian Stratton, Andrew Hoegh, Thomas J. Rodhouse, Jennifer L. Green, Katharine M. Banner, Kathryn M. Irvine","doi":"10.1111/2041-210X.14389","DOIUrl":"10.1111/2041-210X.14389","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1720-1732"},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
性状数据集是大量生态学和进化论研究的基础，可用于推断祖先形态、量化物种灭绝风险或评估生物群落的功能多样性。然而，这些数据集常常受到数据缺失的困扰，例如，由于取样不完整、数据有限和资源可用性等原因。目前有几种预测缺失值的估算方法，最近的研究探讨了这些方法在生物数据集连续性特征方面的性能。然而，人们对这些方法用于分类性状的准确性知之甚少。在这里，我们结合系统发育比较方法、机器学习和深度学习模型，探讨了不同估算方法在分类生物性状上的性能。为此，我们开发了一个开源 R 软件包，用于对性状数据进行估算，同时整合了一个模拟框架，以评估它们在合成数据集上的性能。我们在不同的缺失率、机制、偏差和进化模型下运行了一系列模拟。我们提出了系统发育比较方法和机器学习估算之间的整合方法，以及一种组合方法，其中将选定的估算方法结合在一起。我们的模拟结果表明，这种方法能提供最稳健、最准确的预测。我们将我们的估算管道应用于 1015 种鳞鳃类物种（即鲨鱼、鳐鱼和鳐）的不完整性状数据集，发现基于专家对缺失性状的评估，估算预测的准确率很高。总之，我们的 R 软件包有助于比较多种估算方法，并对缺失性状值进行稳健预测。我们的研究凸显了将系统进化模型与机器学习推断结合起来以扩充不完整生物数据集的好处。
{"title":"Benchmarking imputation methods for categorical biological data","authors":"Matthieu Gendre, Torsten Hauffe, Catalina Pimiento, Daniele Silvestro","doi":"10.1111/2041-210X.14339","DOIUrl":"10.1111/2041-210X.14339","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1624-1638"},"PeriodicalIF":6.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joshua Hewitt, Grete Wilson-Henjum, Derek T. Collins, Jourdan M. Ringenberg, Christopher A. Quintanal, Robert Pleszewski, Jeffrey C. Chandler, Thomas J. DeLiberto, Kim M. Pepin
野生动物疾病监测计划和研究跟踪感染情况，并确定野生种群、人类和农业的风险因素。通常情况下，会从个体身上采集多种类型的样本，以提供有关动物感染历史的更完整信息。通过流行病学过程模型联合分析多种数据流以研究疾病出现和感染驱动因素的方法仍未得到充分发展。联合分析方法可以更全面地分析所有可用数据，更精确地量化流行过程、疫情爆发状况和风险。我们提出了一种配对数据建模方法，可对来自个体的多个样本进行分析。我们使用 "表征图"，通过分层统计观测模型将配对数据与流行病学过程联系起来。我们的方法可以提供贝叶斯和频数估计的流行病学参数和状态。我们的方法还可以结合检验灵敏度和特异性，并提出了模型拟合诊断方法。我们通过使用成对病原体和抗体检测试验来估算广泛应用的易感、感染、恢复（SIR）模型的参数和感染轨迹的需要来激发我们的方法。我们提供了将特征描述图与任意过程模型和数据集联系起来的通用公式，以及能更好地适应配对数据的扩展 SIR 模型。我们通过模拟发现，与非配对数据相比，配对数据能更有效地估算 SIR 参数，所需的个体样本也要少 5 到 10 倍。我们使用我们的方法研究了美国三个县的野生白尾鹿（Odocoileus virginianus）的 SARS-CoV-2 感染情况。平均感染时间的估计值与圈养动物的研究结果相吻合。据估计，三个县受感染鹿的平均累计比例为 73%，基本繁殖数 (R0) 为 1.88。野生动物疾病监测计划和调查研究可以使用我们的方法来联合分析配对数据，以估计流行病学过程参数并追踪疫情。在取样有限的情况下，配对数据分析可以提高精确度和准确性。我们的方法使用了一般统计理论，使应用范围超越了我们考虑的 SIR 模型，并扩展到更复杂的配对数据实例。这些方法还可以嵌入到更大的分层模型中，以提供景观尺度的风险评估并确定感染的驱动因素。
{"title":"A method for characterizing disease emergence curves from paired pathogen detection and serology data","authors":"Joshua Hewitt, Grete Wilson-Henjum, Derek T. Collins, Jourdan M. Ringenberg, Christopher A. Quintanal, Robert Pleszewski, Jeffrey C. Chandler, Thomas J. DeLiberto, Kim M. Pepin","doi":"10.1111/2041-210X.14370","DOIUrl":"10.1111/2041-210X.14370","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1677-1690"},"PeriodicalIF":6.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identifying important species in meta-communities","authors":"Guillaume Rollin, Susanne Kortsch, José Lages, Benoit Gauzens","doi":"10.1111/2041-210X.14384","DOIUrl":"10.1111/2041-210X.14384","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1691-1703"},"PeriodicalIF":6.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Higher-order dissimilarity in biodiversity: Identifying dissimilarities of spatial or temporal dissimilarity structures","authors":"Ryosuke Nakadai, Keita Fukasawa, Taku Kadoya, Fumiko Ishihama","doi":"10.1111/2041-210X.14363","DOIUrl":"10.1111/2041-210X.14363","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1497-1506"},"PeriodicalIF":6.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Monte Carlo resampling framework for implementing goodness-of-fit tests in spatial capture-recapture models","authors":"Yan Ru Choo, Chris Sutherland, Alison Johnston","doi":"10.1111/2041-210X.14386","DOIUrl":"10.1111/2041-210X.14386","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 9","pages":"1653-1666"},"PeriodicalIF":6.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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