Department of Zoology, The University of Melbourne, Victoria 3010, Australia.
J Exp Biol. 2012 Mar 15;215(Pt 6):922-33. doi: 10.1242/jeb.059634.
The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.
新兴的机制生态位模型领域旨在将生物的功能特征与其环境联系起来,以预测其生存、繁殖、分布和丰度。这种方法通过在生理和生态对日益可用的空间环境数据的反应之间提供功能联系,有可能增加我们对环境变化对个体、种群和群落的影响的理解。相比之下,这种机制模型本质上更需要数据密集型,而更广泛应用的相关方法则可以提供更具空间和时间明确的预测,这通常是决策者所需要的。在这种情况下,一个尚未得到充分探讨的问题是,这些模型所需的输入数据的时间分辨率的适当水平,特别是通过使用时间平均数据可能会导致预测中的误差。在这里,我们回顾了热传递和代谢理论中的生物力学原理如何被当前用于机制生态位模型的基础,并考虑了环境数据的不同时间分辨率对行为性体温调节陆生蜥蜴生态位建模的影响。我们表明,细时间分辨率(每日)数据对于对生存、生长和繁殖的气候影响进行无偏推断至关重要。对于行为缓冲能力较弱的物种,由于行为或栖息地限制以及检测时间趋势,情况更是如此。然而,在广泛的空间尺度上,对于行为性体温调节物种的分布和丰度限制的气候约束的机制研究来说,较粗分辨率的数据(长期每月平均值)可能是合适的。