Comparing temperature data sources for use in species distribution models: From in‐situ logging to remote sensing

Abstract

Aim Although species distribution models (SDMs) traditionally link species occurrences to free‐air temperature data at coarse spatio‐temporal resolution, the distribution of organisms might instead be driven by temperatures more proximal to their habitats. Several solutions are currently available, such as downscaled or interpolated coarse‐grained free‐air temperatures, satellite‐measured land surface temperatures (LST) or in‐situ‐measured soil temperatures. A comprehensive comparison of temperature data sources and their performance in SDMs is, however, currently lacking. Location Northern Scandinavia. Time period 1970–2017. Major taxa studied Higher plants. Methods We evaluated different sources of temperature data (WorldClim, CHELSA, MODIS, E‐OBS, topoclimate and soil temperature from miniature data loggers), differing in spatial resolution (from 1″ to 0.1°), measurement focus (free‐air, ground‐surface or soil temperature) and temporal extent (year‐long versus long‐term averages), and used them to fit SDMs for 50 plant species with different growth forms in a high‐latitudinal mountain region. Results Differences between these temperature data sources originating from measurement focus and temporal extent overshadow the effects of temporal climatic differences and spatio‐temporal resolution, with elevational lapse rates ranging from −0.6°C per 100 m for long‐term free‐air temperature data to −0.2°C per 100 m for in‐situ soil temperatures. Most importantly, we found that the performance of the temperature data in SDMs depended on the growth forms of species. The use of in‐situ soil temperatures improved the explanatory power of our SDMs (R2 on average +16%), especially for forbs and graminoids (R2 +24 and +21% on average, respectively) compared with the other data sources. Main conclusions We suggest that future studies using SDMs should use the temperature dataset that best reflects the ecology of the species, rather than automatically using coarse‐grained data from WorldClim or CHELSA.

Predicting future climate at high spatial and temporal resolution

“…Climate variables derived using coarse‐resolution data may thus bear little resemblance to conditions experienced by organisms, which at worst may yield highly erroneous predictions, and at best will greatly increase uncertainty (e.g. Lembrechts, Lenoir, et al, ; Suggitt et al, ).…”

“…This mismatch greatly hinders scientific understanding of the mechanisms explaining how organisms interact with their environment (Kearney & Porter, ) and hampers efforts to address the applied challenges such as predicting the ecological consequences of climate change (Potter et al, ; Suggitt et al, ). Spatial variability in microclimate greatly exceeds the magnitude of climate change expected in the upcoming century, and ignoring this variability leads to erroneous predictions of climate change impacts on species distributions (Gillingham, Huntley, Kunin, & Thomas, ; Lembrechts, Lenoir, et al, ; Lenoir, Hattab, & Pierre, ), population dynamics (Bennie et al, ) and behaviour (Blackshaw & Blackshaw, ). Failing to account for temporal variability also hinders quantification of exposure to extreme conditions (Parmesan, Root, & Willig, ).…”

“…Techniques range from interpolation of in‐situ measurements and statistical downscaling through mechanistic models of physical processes underpinning local climatic variation (Bramer et al, ; Lembrechts, Nijs, & Lenoir, ). Interpolation methods, while good at capturing temporal variation may fail to capture heterogeneity in microclimate where networks of measurements are sparse (Lembrechts, Lenoir, et al, ). Statistical approaches (e.g.…”

Microclimate buffering and thermal tolerance across elevations in a tropical butterfly

“…In large part, these disparities can be attributed to the fact that coarse-gridded temperature surfaces, such as WorldClim2, are interpolated from weather stations that are located in open habitats (De Frenne et al, 2019;Frenne and Verheyen, 2016). Several recent studies have reported similarly striking differences (Jucker et al, 2018;Lembrechts et al, 2019;Potter et al, 2013;Storlie et al, 2014). Near-surface temperatures can only be accurately measured with in-situ loggers or with emerging remote sensing technologies, such as airborne laser scanning (Jucker et al, 2018).…”

How butterflies keep their cool: Physical and ecological traits influence thermoregulatory ability and population trends

“…It is also microclimatic temperatures that individual organisms experience on a day-to-day basis and which can affect the distribution and abundance of species at the local scale. Therefore, information on how individuals respond to finescale microclimatic temperature variation over short time-periods (Bladon et al, 2019;Ide, 2002;Kelly, Godley, & Furness, 2004) may need to be incorporated into models to accurately predict species' responses to climate change (Kearney, Shine, & Porter, 2009;Lembrechts et al, 2019). For example, it is likely that a diversity of K E Y W O R D S behavioural thermoregulation, butterflies, climate change, generalist, microclimate, population trends, specialist, temperature microclimates at the local scale could protect species from wider temperature change, by providing pockets of favourable microclimate for temperature-sensitive species (Thomas & Simcox, 2005).…”