Muséum National d'Histoire Naturelle, Centre d'Ecologie et des Sciences de la Conservation, Paris, France.
Ligue Pour la Protection des Oiseaux, Rochefort, France.
PeerJ. 2024 Aug 27;12:e17889. doi: 10.7717/peerj.17889. eCollection 2024.
Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (), Eurasian Hoopoe () or the Eurasian Blackbird () had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.
通过使用基本生物多样性变量，可以在大规模和长期的生物多样性监测中提高效率，其中物种种群大小为保护计划提供了关键数据。实际种群大小的相关估计和评估对物种保护至关重要，特别是在当前全球生物多样性丧失的背景下。然而，种群大小的知识差异很大，这取决于物种的保护状况和分布范围。虽然最受威胁或分布范围有限的物种通常受益于详尽的计数和调查，但监测常见和广泛分布的物种的种群大小往往被忽视，或者更难以实现。在这种情况下，公民科学（CS）是通过各种志愿者长期监测常见物种的有力工具，可以长期在大空间尺度上获取数据。尽管这种采样努力大大增加，但可检测性问题意味着，即使是常见物种，在合适的地点也可能未被发现。使用结构化的 CS 方案，包括重复访问，可以对检测过程进行建模，从而可靠地推断种群大小估计。在这里，我们依赖于一个大型的法国结构化 CS 方案（EPOC-ODF），该方案包括 2021-2023 年繁殖季节在 3873 个地点收集的 27156 个完整清单，使用分层距离抽样（HDS）来估计 63 种常见鸟类的种群大小。这些种群大小估计值与之前基于专家的法国繁殖鸟类图集估计值进行了比较，后者没有考虑到可检测性问题。我们发现，对于 65%的物种，前者的种群大小估计值高于后者基于 HDS 的估计值。这种较低估计值的普遍性很可能是由于以前的图集使用的半定量专家评估推断出的更保守的估计值。我们还发现，像普通杜鹃（）、欧亚戴胜（）或欧亚八哥（）这样有远程歌曲的物种，在以前的图集中的估计种群大小反而高于我们的 HDS 模型。我们的研究强调了需要依靠可靠的统计方法来确保进行可靠的生态推断，并适当估计不确定性，同时提倡在长期生物多样性监测中更多地依赖结构化 CS。
