The use of high-resolution gridded climate data in the development of chironomid-based inference models from remote areas

Abstract

The development of chironomid-based air temperature inference models in high latitude regions often relies on limited spatial coverage of meteorological data and/or on punctual measurements of water temperature at the time of sampling. The use of simple linear regression to relate air temperature and latitude was until recently the best method to characterize the air temperature gradient along a latitudinal gradient. However, recent studies have used high-resolution gridded climate data to develop new chironomid-based air temperature inference models. This innovative approach has, however, never been further analyzed to test its reliability. This study presents a method using ArcGIS^® to extract air temperatures from a high-resolution global gridded climate data set (New et al. 2002) and to incorporate these new data in a variety of chironomid-based air temperature inference models to test their performance. Results suggest that this method is reliable and produces better estimates of air temperature and will be helpful in the development of further quantitative air temperature inference models in remote areas.