Geostatistical interpolation can reliably extend coverage of a very high‐resolution model of temperature‐dependent sex determination. (19th December 2017)

• Record Type: Journal Article
• Authors: Carter, Anna L.; Kearney, Michael R.; Hartley, Stephen; Porter, Warren P.; Nelson, Nicola J.
• Is Part Of: Journal of biogeography. Volume 45:Number 3(2018:Mar.)
• Page Start: 652
• Page End: 663
• Publication Date: 2017-12-19
• Subjects: Dallwitz–Higgins development rate -- mechanistic microclimate model -- ordinary kriging -- species distributions -- temperature‐dependent sex determination -- tuatara (Sphenodon punctatus); Biogeography -- Periodicals; 578.09
• DOI: 10.1111/jbi.13152
• Languages: English
• ISSNs: 0305-0270

Abstract

Abstract: Aim: Recognition that statistical models do not always reliably predict habitat suitability under future climate scenarios is leading increasingly to explicit incorporation of the physiological constraints that underlie species' distributions into spatially explicit predictions. However, computational intensity constrains the use of high‐resolution, process‐explicit models. We examined whether geostatistical analysis can effectively interpolate a biophysical model, reducing the computational investment typically required for using mechanistic methods to inform physiological predictions. Location: New Zealand [40°40′00″ S 174°00′00″ E]. Methods: We used a spatially explicit, mechanistic microclimate model to predict hourly temperatures at five soil depths under two scenarios of climate warming. Using the predicted soil temperatures as input to a biophysical model of temperature‐dependent embryonic development, we estimated incubation temperatures and corresponding hatchling sex ratios for tuatara, a reptile with temperature‐dependent sex determination, at a submetre horizontal spatial resolution. We then applied ordinary kriging, a robust method of geostatistical interpolation, to estimate predictions throughout the full extent of our study location, an additional 480, 000+ microsites, and validated the interpolation against an independent set of predictions. Results: Ordinary kriging accurately predicted spatial variability in incubation temperatures. Mean predictions were similar between methods, and error in the geospatial model generally decreased with increasing soil depth. Error was higher for the geospatial model of the 'maximum warming', compared with the 'minimum warming', scenario of climate change. Main conclusions: Our results show that ordinary kriging can be a reliable method for interpolating variability in high‐resolution predictions. However, the effects of error on the accuracy of interpolated predictions will become more severe as values approach a physiological threshold, such as the minimum and maximum incubation temperatures that result in extreme sex ratio bias. For distribution models, the widths of geographic areas predicted to be suitable for, in this case, maintaining balanced sex ratios, compared to those predicted to be unsuitable, may be narrower than in reality.