检测化石记录中的真实与伪缺失:占用模型在保护古生物学中的应用

C. Dean, P. Mannion
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摘要

古生物学数据提供了一个独特的途径来评估气候、栖息地和生态系统变化在更长的时间尺度上的影响,而不是在生态学和保护学中通常检查的,提供了关于灭绝动态的关键数据,可以帮助了解当前生物多样性危机的背景。然而,化石记录受到各种因素的影响。特别是,在试图识别空间模式时,数据缺失的问题引起了真正的关注。化石的缺失是真正的缺失还是不完美的检测(即伪缺失)?如果不能量化、辨别和减轻数据缺失的主要驱动因素和影响,将对重建过去多样性动态的任何尝试产生重大影响,限制古生物学数据在解决与当今生物多样性有关的问题方面的适用性。占用模型是一种广泛应用于生态学和自然保护领域的技术,它提供了一种新的方法来评估时空偏差对化石记录的影响。通过区分真缺席(分类群真正缺席)和假缺席(分类群存在,但未观察到),占用模型为占用和检测产生独立且同时的概率估计。在这里,我们展示了如何在使用R包“未标记”运行的单季节模型和使用贝叶斯框架的动态占用模型中,适应古生物事件与相关的现代和古协变量数据一起使用。此外,我们还测试了不同空间尺度以及不均匀的重复访问次数对模型结果的影响,并为打算运行这些模型的保护古生物学家提供建议。最后,我们概述了在保护古生物学中应用占用模型的其他好处。
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Detecting Genuine Versus Pseudo-Absence in The Fossil Record: Applications of Occupancy Modelling For Conservation Palaeobiology
Palaeontological data provide a unique avenue to evaluate the impact of climatic, habitat and ecosystem change over longer temporal scales than typically examined in ecology and conservation, contributing critical data on extinction dynamics that can help contextualize the current biodiversity crisis. However, the fossil record is biased by a variety of factors. In particular, the issue of data absence causes a genuine concern when attempting to discern spatial patterns. Does the lack of a fossil occurrence indicate genuine absence or imperfect detection (i.e., pseudo-absence)? Failing to quantify, discern and mitigate both the main drivers and impacts of data absence will have major implications for any attempt to reconstruct past diversity dynamics, limiting the applicability of paleontological data for addressing questions pertaining to present-day biodiversity. Occupancy modelling, a technique commonly applied in the fields of ecology and conservation, provides a novel way to evaluate the impact of both spatial and temporal biases on the fossil record. By distinguishing between true (taxon genuinely absent) and false (taxon present, but not observed) absences, occupancy modelling produces independent and simultaneous probability estimates for both occupancy and detection. Here, we show how paleontological occurrences can be adapted for use alongside relevant modern and paleo covariate data in both single season models run using the R package ‘unmarked’ and dynamic occupancy models using a Bayesian framework. We additionally test the impact of varying spatial scale, as well as uneven numbers of repeated site visits, on model outcomes, and provide recommendations for conservation paleobiologists intending to run these models. Finally, we outline additional benefits of applying occupancy modelling within conservation paleobiology.
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