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iLAM: Imaging Locomotor Activity Monitor for circadian phenotyping of large‐bodied flying insects iLAM:用于大型飞行昆虫昼夜表型的成像运动活动监测器
IF 6.6 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-19 DOI: 10.1111/2041-210x.14403
Jacob N. Dayton, Avalon C. S. Owens
Historically, most insect chronoecological research has used direct observations, cameras or infrared beam‐based monitors to quantify movement across timed intervals. Although some alternative DIY systems are cheaper than the current standard locomotor activity monitor, these options remain complicated to build and/or computationally intensive. We developed the imaging Locomotor Activity Monitor (iLAM), an affordable (~$75 USD/unit) system for activity quantification. The iLAM utilizes a Raspberry Pi Zero W computer and night‐vision camera inside a flight cage to photograph a population of insects at user‐defined intervals. Open‐source, modular R‐scripts process the images and output a file containing the number, size, coordinate location and timestamp of all movements (blobs) identified between consecutive images. Output can be analysed directly or converted into the standard TriKinetics DAM format. We demonstrated the flexibility and power of the iLAM system by comparing diel and circadian activity of different insect species (fireflies: Photinus marginellus, P. greeni, P. obscurellus), ecotypes (moths: Ostrinia nubilalis) and sexes (moths: O. nubilalis). Data captured by only six iLAMs ($450) identified that peak activity of O. nubilalis females (AZT: 19.2 h) occurs significantly earlier than males (22.0 h). Additionally, male moths from a univoltine population exhibited a significantly shorter endogenous period length (AZT: 21.3 h) than males from a bivoltine genetic background (22.7 h). The iLAM will serve as a valuable tool for researchers seeking to measure locomotor activity across diverse species, sexes and populations in constant and changing environments.
一直以来,大多数昆虫时间生态学研究都使用直接观察、照相机或基于红外光束的监测器来量化定时间隔内的运动。虽然一些 DIY 替代系统比目前的标准运动活动监测器便宜,但这些方案的构建仍然复杂和/或计算密集。我们开发了成像运动活动监测器(iLAM),这是一种经济实惠(约 75 美元/台)的活动量化系统。iLAM 利用飞行笼内的 Raspberry Pi Zero W 计算机和夜视摄像机,以用户定义的时间间隔拍摄昆虫群体。开源的模块化 R 脚本处理图像,并输出一个文件,其中包含连续图像之间识别出的所有运动(blobs)的数量、大小、坐标位置和时间戳。输出结果可以直接进行分析,也可以转换成标准的 TriKinetics DAM 格式。我们通过比较不同昆虫物种(萤火虫:Photinus marginellus、P. greeni、P. obscurellus)、生态型(蛾:Ostrinia nubilalis)和性别(蛾:O. nubilalis)的昼夜活动,展示了 iLAM 系统的灵活性和强大功能。只有六台 iLAM(450 美元)采集到的数据表明,O. nubilalis 雌蛾的活动高峰期(AZT:19.2 小时)明显早于雄蛾(22.0 小时)。此外,来自单基因种群的雄蛾的内源期长度(AZT:21.3 小时)明显短于来自双基因背景的雄蛾(22.7 小时)。iLAM将成为研究人员测量不同物种、性别和种群在恒定和变化环境中的运动活动的重要工具。
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引用次数: 0
A heuristic method to evaluate consequences for flight control and stability induced by attachment of biologging devices to birds and bats 一种启发式方法,用于评估在鸟类和蝙蝠身上安装生物定位装置对飞行控制和稳定性造成的影响
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-11 DOI: 10.1111/2041-210X.14400
Todd E. Katzner, George S. Young

生物定位是野生动物研究的核心,但如何将生物定位装置的影响降至最低仍是个问题。人们很少考虑生物除害装置对动物质心(COM)的影响以及由此造成的飞行控制和稳定性损失。我们运用已确立的航空原理,估算了飞行中的鸟类或蝙蝠在颈部、背部、臀部或尾部放置生物装载装置的典型位置可能对其质心(COM)产生的影响。然后,我们采用航空航天工程学中修改过的阈值来估算 COM 的变化会导致飞行控制和稳定性发生与体能相关的改变的极限值。通用模型说明了影响飞行控制和稳定性的生物锚定装置的位置和质量之间的权衡。七个针对特定物种的例子表明,对不同体型和体质的动物来说,改变COM所造成的后果存在很大差异。将装置放置在尾部总是导致最大的 COM 变化,而放置在背部中央则导致最小的变化。有些人将生物定位装置的重量阈值定为 5%,这在稳定性方面几乎没有误差余地,很容易导致 COM 发生危险的变化。其他人使用的 3% 重量阈值导致的 COM 变化要小得多,但在远离自然 COM 的情况下,仍会影响飞行控制和稳定性。有兴趣最大限度地减少对野生动物健康影响的研究人员在安装生物定位装置时应考虑重量、平衡和 COM。装置离自然 COM 越远,相对于动物的质量就应该越小。
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引用次数: 0
Adaptive sampling in ecology: Key challenges and future opportunities 生态学中的适应性取样:关键挑战与未来机遇
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-06 DOI: 10.1111/2041-210X.14393
Peter A. Henrys, Thomas O. Mondain-Monval, Susan G. Jarvis

传统的生态监测采用的是固定设计,在调查期间不会发生变化。适应性取样是指根据已收集的数据设计取样方法,并在研究过程中不断变化,这种方法可以提供更优化、更灵活的调查设计,但在生态学中很少使用。我们旨在为生态学家介绍适应性取样。我们回顾了以往的文献,重点介绍了经验性自适应方法(如自适应聚类取样)和基于模型的新型自适应方法。为了使适应性取样过程概念化,我们确定了四个关键阶段:选择数据、定义标准、选择新的取样场合和取样活动。我们将依次讨论每个阶段,并重点讨论生态学家在成功实施适应性取样策略时需要考虑的决策。我们提供了一个自适应取样示例的完整演练,并提供了代码来演示每个步骤。自适应取样对生态学家来说具有潜在优势,但迄今为止,其应用还很有限。我们回顾了采用该方法所面临的主要挑战和障碍,并提出了可能的前进方向。我们希望我们的论文既能提高人们对适应性取样方法的认识,又能为考虑采用适应性调查设计的生态学家提供有用的资源。
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引用次数: 0
Sap Flow Analyzer: A tool to standardize sap flow estimation and scaling to whole-tree water use using the HFD method 树液流量分析仪:使用高频分解法对树液流量估算进行标准化并扩展到全树用水的工具
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-06 DOI: 10.1111/2041-210X.14392
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

树液流测量是了解树木用水情况的基础,有助于预测气候变化对森林功能的影响。从此类测量中获取知识需要经验校准和升级方法,以便将测温记录转化为树木的用水量。在此,我们开发了一款用户友好型开源应用程序--树液流分析仪(SFA),它可以通过热场变形(HFD)仪器估算树液流速和树木用水量。SFA 包含四个关键功能,以确保最大限度地提高树液流量估算的准确性和可重复性:通过诊断图直观地评估数据模式;通过回归模型提高 K(HFD 的主要参数)估算的准确性;通过三种方法将树液流量提升到整棵树的用水量;以及输入参数不确定性的可视化。13 位参与者获得了三个原始数据集,并使用 SFA 用户指南分配了数据处理任务,从估计边材深度到将树液流速放大到全树用水量,以评估 SFA 的可重复性和适用性。参与者的结果相当一致,与他们使用 SFA、R 或 HFD 方法的背景无关。结果表明,高流速的变异性较低(SD:平均 1% 对 10% )。K 值估计值和边材深度差异是主要的变异性来源,而这又主要是由用户选择的缩放方法造成的。SFA 提供了一种简单的方法来可视化和处理来自高频分解测量的树液流和树木用水数据。它是首个面向高频分解用户的免费开放软件工具。跟踪分析步骤的功能可确保数据处理的可重复性、透明度和一致性。开发 SFA 和掩蔽试验等工具对于更精确的工作流程以及提高 HFD 树液流数据集的质量和可比性至关重要。
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引用次数: 0
Non-linear phylogenetic regression using regularised kernels 使用正则化核的非线性系统发育回归
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-05 DOI: 10.1111/2041-210X.14385
Ulises Rosas-Puchuri, Aintzane Santaquiteria, Sina Khanmohammadi, Claudia Solís-Lemus, Ricardo Betancur-R

系统发育回归是广义最小二乘法(GLS)的一种,它根据物种之间的进化关系(即系统发育关系)纳入一个建模协方差矩阵。虽然这种方法通过系统发育方差分析等系统发育比较方法广泛用于假设检验,但其考虑非线性关系的能力却很少受到关注。为了解决这个问题,我们在这里实施了一种系统发育核岭上回归(phyloKRR)方法,该方法在高维特征空间中利用 GLS,采用系统发育加权数据的线性组合来考虑非线性关系。我们使用径向基函数和线性核函数分析了两个生物数据集。第一个数据集包含形态计量数据,第二个数据集包含离散性状数据和作为响应变量的多样化率。模型的超参数调整是通过训练集的交叉验证轮来实现的。在测试的生物数据集中,当数据不表现出线性关系时,phyloKRR 与基于线性回归的模型相比,误差率(以 RMSE 度量)降低了约 20%。在模拟数据集中,错误率随着非线性程度的增加几乎呈指数下降。这些结果表明,在系统发育回归分析中引入核,为补充系统发育比较方法提供了一种新颖而有前途的工具。我们已将该方法集成到名为 phyloKRR 的 Python 软件包中,该软件包可在以下网址免费获取:https://github.com/ulises-rosas/phylokrr。
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引用次数: 0
Why shouldn't I collect more data? Reconciling disagreements between intuition and value of information analyses 为什么我不应该收集更多数据?协调直觉与信息价值分析之间的分歧
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-05 DOI: 10.1111/2041-210X.14391
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。
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引用次数: 0
Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU) 利用多通道声学自主记录装置(MAARU)进行空间生态系统监测
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-05 DOI: 10.1111/2041-210X.14390
Becky E. Heath, Reiji Suzuki, Neel Patrick Le Penru, James Skinner, C. David L. Orme, Robert M. Ewers, Sarab Singh Sethi, Lorenzo Picinali

多麦克风录音为录制的音频添加了空间信息,在生态系统监测中得到了新的应用。具体来说,在空间中放置声音可以提高动物计数的准确性,定位伐木和偷猎等非法活动,跟踪动物以监测其行为和栖息地的使用情况,并允许 "波束成形 "以放大来自目标方向的声音,从而进行下游分类。研究表明,空间声学具有许多优势,但由于设备通常昂贵、复杂、难以使用或只适合短期部署,因此其应用仍然有限。为了提高吸收率和可用性,我们提出了一种低成本、开源的六通道记录仪,完全采用市场上可买到的部件,可集成到太阳能供电的在线系统中。MAARU(多通道声学自主记录单元)在长期自主、被动和/或短期部署中作为一个独立节点工作。在此,我们将介绍 MAARU 的硬件和软件,并展示实验室和现场测试的结果,以调查该设备的耐用性和可用性。MAARU 可记录多声道音频,其成本和功耗要求与同类全向录音机相似。我们已经在英国和巴西部署了 MAARU 设备,结果表明 MAARU 能够准确定位 6 kHz 以下的纯音和 8 m 远的鸟叫声(±10° 范围内,分别为信号的 100% 和 >60%)。更大音量的鸟叫声的探测半径可能更远。我们还展示了波束成形如何与 MAARU 配合使用,以提高物种识别置信度得分。对于那些希望使用单个设备准确、轻松地探索空间声学的人来说,MAARU 是一种方便、低成本的选择,而且无需与建立阵列相关的高昂费用和复杂的处理过程。最终,MAARU 提供的多通道记录增加了方向性元素,可以增强对声学环境的记录,进一步推动空间声学在更广泛领域的应用。
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引用次数: 0
Clustering and unconstrained ordination with Dirichlet process mixture models 使用 Dirichlet 过程混合物模型进行聚类和无约束排序
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-02 DOI: 10.1111/2041-210X.14389
Christian Stratton, Andrew Hoegh, Thomas J. Rodhouse, Jennifer L. Green, Katharine M. Banner, Kathryn M. Irvine

评估不同采样地点物种组成或丰度的相似性是多物种监测计划的共同目标。现有的顺序排列技术通过将高维群落数据投射到代表物种组成的低维潜在生态梯度中,为根据物种组成对采样地点进行聚类提供了一个框架。然而,这些技术需要指定潜空间中存在的不同生态群落的数量,而这很难事先确定。我们开发了一种能够同时进行聚类和排序的排序模型,可以估算出潜在生态梯度中存在的聚类数量。该模型从 Dirichlet 过程混合物模型中提取每个样本位置的潜在坐标,为研究人员提供了关于潜在生态梯度中存在的聚类数量的概率声明。我们通过模拟将该模型与现有的同时聚类和排序方法进行了比较,并将其应用于两个经验数据集;附录中提供了拟合拟议模型的 JAGS 代码。第一个数据集涉及法国东部杜布河鱼类的存在-消失记录,第二个数据集描述了美国爱达荷州月球环形山国家纪念碑和保护区(CRMO)植物物种的存在-消失记录。这两项分析的结果与每个地点现有的生态梯度一致。Dirichlet 过程排序模型的开发为野生动物管理者提供了以数据为依据的推断,即在监测地点存在的不同群落的数量,从而能够为保护管理提供更具成本效益的监测和可靠的决策。
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引用次数: 0
Benchmarking imputation methods for categorical biological data 为分类生物数据的估算方法设定基准
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-07-24 DOI: 10.1111/2041-210X.14339
Matthieu Gendre, Torsten Hauffe, Catalina Pimiento, Daniele Silvestro

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。
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引用次数: 0
A method for characterizing disease emergence curves from paired pathogen detection and serology data 根据病原体检测和血清学配对数据确定疾病出现曲线特征的方法
IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-07-24 DOI: 10.1111/2041-210X.14370
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。
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引用次数: 0
期刊
Methods in Ecology and Evolution
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