Kooijman Sebastiaan A L M, Lika Konstadia, Augustine Starrlight, Marn Nina
Department of Theoretical Biology, VU University Amsterdam, de Boelelaan 1087, 1081 HV Amsterdam, The Netherlands.
Department of Biology, University of Crete, 70013, Heraklion, Greece.
Conserv Physiol. 2021 Dec 15;9(1):coab086. doi: 10.1093/conphys/coab086. eCollection 2021.
The method of multidimensional scaling (MDS) has long existed, but could only recently be applied to animal traits in the context of dynamic energy budget (DEB) theory. The application became possible because of the following: (i) the Add-my-Pet (AmP) collection of DEB parameters and traits (approximately 280) recently reached 3000 animal species with 45000 data sets of measurements; (ii) we found a natural distance measure for species based on their traits as a side result of our research on parameter estimation in DEB context; and (iii) we developed plotting code for visualization that allows labelling of taxonomic relationships. Traits, here defined as DEB parameters or any function of these parameters, have different dimensions, which hamper application of many popular distance measures since they (implicitly) assume that all traits have the same dimensions. The AmP collection follows the workflow that measured data determine parameters and parameters determine trait values. In this way we could fill up the species traits table completely, which we could not do by using measured values only, as data availability varies considerably between species and is typically poor. The goodness of fit of predictions for all data sets is generally excellent. This paper discusses links between the MDS method and parameter estimation and illustrates the application of MDS for the AmP collection to five taxa, three ectothermic and two endothermic, which we consider to be 'complete', in the sense that we expect that it will be difficult to find more species with data in the open literature. This application of MDS shows links between traits and taxonomy that supplements our efforts to find patterns in the co-variation of parameter values. Knowledge about metabolic performance is key to conservation biology, sustainable management and environmental risk assessment, which are seen as interlinked fields.
多维尺度分析(MDS)方法早已存在,但直到最近才能够在动态能量收支(DEB)理论的背景下应用于动物性状。这种应用之所以成为可能,原因如下:(i)最近,DEB参数和性状的“添加我的宠物”(AmP)数据集(约280个)已涵盖3000个动物物种,包含45000个测量数据集;(ii)作为我们在DEB背景下进行参数估计研究的一个附带结果,我们发现了一种基于物种性状的自然距离度量;(iii)我们开发了用于可视化的绘图代码,可对分类关系进行标注。这里将性状定义为DEB参数或这些参数的任何函数,它们具有不同的维度,这妨碍了许多常用距离度量的应用,因为这些距离度量(隐含地)假设所有性状具有相同的维度。AmP数据集遵循这样的工作流程:测量数据确定参数,参数确定性状值。通过这种方式,我们能够完整地填充物种性状表,而仅使用测量值则无法做到这一点,因为不同物种的数据可用性差异很大,而且通常很匮乏。所有数据集预测的拟合优度总体上都非常好。本文讨论了MDS方法与参数估计之间的联系,并说明了MDS在AmP数据集对五个分类单元(三个变温动物和两个恒温动物)的应用,我们认为这五个分类单元是“完整的”,因为我们预计在公开文献中很难找到更多有数据的物种。MDS的这种应用展示了性状与分类学之间的联系,补充了我们在寻找参数值协变模式方面所做的努力。关于代谢性能的知识是保护生物学、可持续管理和环境风险评估的关键,这些领域被视为相互关联的领域。