Yue Wang, Yuhao Zhao, Morgan W. Tingley, Xingfeng Si
<p>Ecologists have recognized the “problem” of imperfect detection for decades, a pervasive phenomenon in which species frequently go undetected during field surveys, yet predominantly treated it as statistical noise or analytical bias to be corrected. Many methods have been developed to estimate detection probabilities, refine statistical frameworks, and compare modeling approaches (MacKenzie et al. <span>2017</span>). This method-centric perspective is valuable for enriching analytical frameworks, but it overlooks a more fundamental understanding: imperfect detection is not merely a statistical problem but an intrinsic phenomenon that shapes our interpretation of ecological patterns and processes. When ignored, it can distort species-environment relationships, misrepresent community dynamics, or lead to biased inferences about biodiversity change, particularly in ecosystems with numerous rare species or in those responding rapidly to global change. While much previous work has addressed <i>how</i> to correct for detection bias, less attention has been paid to <i>why</i> imperfect detection matters ecologically and <i>how</i> it can affect our conclusions. This conceptual gap has treated imperfect detection as a marginal technical problem, rather than recognizing it as a fundamental component of reliable ecological inference.</p><p>In this context, the study by Miller-ter Kuile et al. (<span>2025</span>) provides a critical advance. It shifts the perspective, framing imperfect detection not merely as a statistical problem to be corrected, but as an ecological variable that can directly alter the observed relationships between biodiversity and global change drivers. Using multi-species occupancy and abundance models to correct detection error for multiple taxa, they examined how ignoring imperfect detection changes the estimates of taxonomic and functional alpha and beta diversity and alters inferred responses to temperature and precipitation. These results demonstrate that ignoring imperfect detection can bias the inferred direction, magnitude, and timescale of the effects of global change drivers on biodiversity. This represents a conceptual shift from purely methodological correction toward a deeper ecological understanding of systems.</p><p>A main strength of the study is its strong empirical generality. By integrating data across multiple taxonomic groups (birds, grasshoppers, and even plants), data structures (occurrence and abundance), and biodiversity dimensions (taxonomic and functional alpha and beta diversity), Miller-ter Kuile et al. (<span>2025</span>) demonstrate that the ecological consequences of imperfect detection are consistent and pervasive. For example, accounting for imperfect detection in bird communities increased estimates of functional alpha diversity and revealed short-term precipitation effects and stronger seasonal temperature influences—patterns that were masked otherwise. In temporal monitoring of plant communities,
几十年来,生态学家已经认识到不完善检测的“问题”,这是一个普遍现象,在实地调查中,物种经常未被发现,但主要是将其视为需要纠正的统计噪声或分析偏差。已经开发了许多方法来估计检测概率、改进统计框架和比较建模方法(MacKenzie et al. 2017)。这种以方法为中心的观点对于丰富分析框架是有价值的,但它忽略了一个更基本的理解:不完美的检测不仅仅是一个统计问题,而且是一种内在现象,它塑造了我们对生态模式和过程的解释。如果被忽视,它可能扭曲物种与环境的关系,歪曲群落动态,或导致对生物多样性变化的偏见推断,特别是在拥有众多稀有物种的生态系统或对全球变化反应迅速的生态系统中。虽然以前的很多工作都解决了如何纠正检测偏差,但很少有人关注为什么不完美的检测在生态上很重要,以及它如何影响我们的结论。这种概念上的差距将不完美检测视为一个边缘技术问题,而不是将其视为可靠生态推断的基本组成部分。在此背景下,Miller-ter Kuile等人(2025)的研究提供了一个关键的进步。它改变了视角,将不完善的检测不仅作为一个需要纠正的统计问题,而且作为一个生态变量,可以直接改变观察到的生物多样性与全球变化驱动因素之间的关系。他们利用多物种占用和丰度模型来修正多分类群的检测误差,研究了忽略不完善的检测如何改变分类和功能α和β多样性的估计,以及改变对温度和降水的推断响应。这些结果表明,忽略不完善的检测会使推断出的全球变化驱动因素对生物多样性影响的方向、幅度和时间尺度产生偏差。这代表了一种概念上的转变,从纯粹的方法修正到对系统的更深层次的生态理解。这项研究的一个主要优点是它具有很强的经验普遍性。Miller-ter Kuile等人(2025)通过整合多个分类类群(鸟类、蚱蜢甚至植物)、数据结构(发生率和丰度)和生物多样性维度(分类和功能α和β多样性)的数据,证明了不完善检测的生态后果是一致的和普遍的。例如,考虑到鸟类群落中不完善的探测,增加了对功能性α多样性的估计,并揭示了短期降水效应和更强的季节性温度影响——这些模式被掩盖了。在植物群落的时间监测中,考虑物种可探测性揭示了更大的物种损失,并确定降水和蒸汽压赤字是关键驱动因素,具有强烈的季节信号和多季节“记忆”效应,这些效应以前未被发现。相反,考虑到蚱蜢群落的检测误差,减少了基于丰度的群落变化的估计,从而削弱了气候驱动因素和气候塑造群落动态的季节性途径的明显影响。综上所述,这些发现传达了一个明确的信息:不完善的检测问题可以从根本上改变对生物多样性如何响应全球变化驱动因素的生态学解释。Miller-ter Kuile等人(2025)的这项工作进一步强调了稀有物种在揭示气候变化下不完善检测的生态后果方面的重要性。稀有物种往往功能独特,对全球变化高度敏感,也是最常未被发现的物种。重要的是,即使在长期的、多季节的数据集中,不完美的探测仍然存在,并且可以显著地改变推断的气候响应。Miller-ter Kuile等人(2025)通过明确考虑鸟类、植物和昆虫中稀有物种的检测误差,证明忽略不完善的检测不仅会低估甚至有时会逆转温度和降水对群落动态的影响。稀有物种,虽然只占群落的一小部分,但作为一个特别说明的例子,说明检测错误如何不成比例地影响推断的气候响应、群落结构和生物多样性模式。这一见解对生态推理和应用保护科学都有直接的影响。虽然以前的研究已经认识到检测误差会影响生态推断,但大部分工作仅限于单一分类群或单一指标(Tingley和Beissinger 2013; Wang et al. 2025)。Miller-ter Kuile等人提供的广泛的跨系统合成。 (2025)超越了这些有价值但更有限的贡献。作者提供了明确的证据,不完善的检测是生态推断和结论的关键决定因素,而不是外围的方法论问题。这项研究的实际意义同样重要。结合检测误差可以通过优化重复调查的次数、采样时间和地点选择,直接改善长期监测方案,从而提高效率和代表性(ksamry and Royle 2008)。对于以机制为中心的生态学研究,它可以揭示物种对环境变化的真实反应,而不是被误导的信号所偏见的推断。在保护中,人们可以更可靠地评估保护区内的物种丰富度和种群趋势,为稀有或功能重要物种的优先级提供信息,并改进对恢复成功的评估,从而降低基于扭曲数据的决策风险(Bennett et al. 2024)。通过这种方式,Miller-ter Kuile等人(2025)将概念洞察力与生态应用联系起来,表明考虑不完美检测对于理解真实的生态动态和指导有效的生物多样性保护至关重要。尽管有其优势,该研究仍为未来的工作留下了很大的空间,特别是与方法进步的结合。目前的框架侧重于与环境变量的关系,并假设物种之间的相互作用是弱的或随机的。然而,物种之间的相互作用(例如竞争和捕食)可以改变物种的分布、活动周期或行为模式,所有这些都可能直接影响物种的可探测性。将物种相互作用纳入多物种模型——要么直接通过明确的相互作用条款(Rota等人,2016),要么间接通过共享的潜在结构或相关框架(Dorazio等人,2025)——可以更机械地理解全球变化下不完美检测与群落组装和时间动态之间的关系。此外,虽然作者通过使用beta回归的后续分析有效地证明了传播后验群落结构不确定性的重要性,但这里使用的方法可能会扩展到适用于不同随机多样性指数的其他分布(例如,物种丰富度的负二项分布或Shannon多样性的伽马分布)。当将校正后的生物多样性估计值与环境驱动因素联系起来时,这些扩展将增强推理的稳健性。然而,对鸟类、植物和蚱蜢群落提供的经验证据强调了检测偏差对测量生物多样性变化的广泛影响。未来的研究可以研究检测异质性的生态机制,包括物种特征,如体型、发声频率、生态位和/或系统发育影响(例如Si et al. 2018),从而将框架扩展到其他生物或非生物驱动因素。该框架是灵活的,也可以适应由环境DNA (eDNA)采样的新兴生态数据。未来的发展可以超越假阴性,也可以解释假阳性,这在自动传感器或公民科学的数据中很常见(Guillera-Arroita et al. 2017)。总的来说,这项工作擅长于展示一个明确的概念信息:不完美的检测应该被视为生态过程的固有组成部分,而不是作为“讨厌的”技术细节。Miller-ter Kuile等人(2025)通过展示多分类群、多度量、跨系统的证据,展示了在全球变化时代,检测偏差如何从根本上改变我们对群落动态和生物多样性变化的理解。因此,将缺陷检测整合到研究设计、生态机制推断和保护规划中,不仅是一个方法论问题,而且是一个基本的生态需求。这项研究强调了一个基本的观念转变:不完善的检测不仅仅是统计噪声——它是生态信号的一部分,对生态研究和保护实践都具有持久的价值。王岳:写作——原稿,构思。赵宇浩:写作——审编、构思。摩根·廷利:写作——评论和编辑,概念化。兴风司:构思、撰写、审编、监督。作者声明无利益冲突。本文是Miller-ter Kuile等人的特邀评论,https://doi.org/10.1111/gcb.70362.The支持本研究结果的数据可从通讯作者处索取。由于隐私或道德限制,这些数据不会公开。
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