Strong Discrepancies between Local Temperature Mapping and Interpolated Climatic Grids in Tropical Mountainous Agricultural Landscapes

Bridging the gap between the predictions of coarse-scale climate models and the fine-scale climatic reality of species is a key issue of climate change biology research. While it is now well known that most organisms do not experience the climatic conditions recorded at weather stations, there is little information on the discrepancies between microclimates and global interpolated temperatures used in species distribution models, and their consequences for organisms’ performance. To address this issue, we examined the fine-scale spatiotemporal heterogeneity in air, crop canopy and soil temperatures of agricultural landscapes in the Ecuadorian Andes and compared them to predictions of global interpolated climatic grids. Temperature time-series were measured in air, canopy and soil for 108 localities at three altitudes and analysed using Fourier transform. Discrepancies between local temperatures vs. global interpolated grids and their implications for pest performance were then mapped and analysed using GIS statistical toolbox. Our results showed that global interpolated predictions over-estimate by 77.5±10% and under-estimate by 82.1±12% local minimum and maximum air temperatures recorded in the studied grid. Additional modifications of local air temperatures were due to the thermal buffering of plant canopies (from −2.7°K during daytime to 1.3°K during night-time) and soils (from −4.9°K during daytime to 6.7°K during night-time) with a significant effect of crop phenology on the buffer effect. This discrepancies between interpolated and local temperatures strongly affected predictions of the performance of an ectothermic crop pest as interpolated temperatures predicted pest growth rates 2.3–4.3 times lower than those predicted by local temperatures. This study provides quantitative information on the limitation of coarse-scale climate data to capture the reality of the climatic environment experienced by living organisms. In highly heterogeneous region such as tropical mountains, caution should therefore be taken when using global models to infer local-scale biological processes.

弥合粗尺度气候模型（coarse-scale climate models）的预测结果与物种所经历的精细尺度气候实况之间的差距，是气候变化生物学研究的核心议题之一。尽管目前学界已达成共识：绝大多数生物并不会直接接触气象站记录的气候条件，但关于微气候（microclimate）与物种分布模型（species distribution models）中所采用的全球插值气温之间的偏差，及其对生物生存表现的影响，相关研究仍较为匮乏。为解决这一问题，本研究针对厄瓜多尔安第斯山区农业景观中的空气、作物冠层及土壤温度开展了精细尺度时空异质性调查，并将实测数据与全球插值气候格点（global interpolated climatic grids）的预测结果进行对比。研究人员在三个海拔梯度的108个采样点位，分别测定了空气、冠层与土壤的温度时序数据，并采用傅里叶变换（Fourier transform）开展分析。随后，本研究借助地理信息系统（Geographic Information System, GIS）统计工具箱，对当地气温与全球插值格点之间的偏差，以及该偏差对害虫生存表现的影响进行了可视化制图与分析。研究结果显示，全球插值模型对本研究样地内记录到的当地最低与最高气温的预测分别高估了77.5±10%、低估了82.1±12%。当地空气温度的额外波动，源于植物冠层与土壤的热缓冲效应：冠层的热缓冲范围为日间-2.7开尔文至夜间+1.3开尔文，土壤则为日间-4.9开尔文至夜间+6.7开尔文；作物物候期对该缓冲效应存在显著影响。插值气温与当地实测气温之间的偏差，对变温作物害虫（ectothermic crop pest）的生存表现预测产生了显著影响：插值气温预测的害虫生长速率，较当地实测气温的预测结果低2.3~4.3倍。本研究定量揭示了粗尺度气候数据在还原生物实际所处的气候环境方面存在的局限性。在热带山地这类高度异质性的区域中，利用全球气候模型推演局地尺度生物过程时，需格外谨慎。