Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?

Abstract

The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island, generated at a very high spatial resolution (<1 m 2 ) using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continentalscale microclimate layers (within about 2-3°C of observed values) required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles), while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.

Modelled incubation conditions indicate wider potential distributions based on thermal requirements for an oviparous lizard

“…| 1879 be used to define areas where a higher resolution surface should be generated to confirm model predictions and help inform management decisions (Carter et al, 2015). Given the limited data available for O. suteri (i.e.…”

Geostatistical interpolation can reliably extend coverage of a very high‐resolution model of temperature‐dependent sex determination

“…We modelled substrate temperatures for Takapourewa (also referred to as Stephens Island), New Zealand [40°40 0 00″ S 174°00 0 00″ E], a 150-ha offshore island that supports a population of 30,000+ tuatara (Newman, 1987). We used a localized version of the NicheMapR mechanistic microclimate model, which solves heat-mass balance equations for up to ten soil depths (Carter et al, 2015;Kearney & Porter, 2016) (Figure 1). We parameterized the model using local, daily climate data downloaded from the NIWA CliFlo database (see Data accessibility) (Station nos 26169, 12430; http://www.cliflo.…”

“…niwa.co.nz), generating hourly soil temperatures for three climate scenarios: (1) a 'current climate' scenario, using in situ weather station data from the year 2000; (2) a 'minimum warming' scenario predicted for the next century, in which minimum and maximum air temperatures were increased by the predicted range of 0.3-0.9°C, with an increase of 0.3°C in the meteorological austral spring, a 0.6°C increase in autumn and winter, and an increase of 0.9°C in summer; and (3) a 'maximum warming' scenario, in which minimum and maximum temperatures were increased by the predicted range of 4.8-5.6°C, with an increase of 4.8°C in the austral spring, a 5.0°C increase in autumn and winter, and a 5.6°C increase in summer over the next 100 years (Ministry for the Environment (MFE) (New Zealand), 2008). Available shade was simulated as 95% for forested microsites and 0% otherwise, with sites classed as 'forest' or 'nonforest' according to a spatially explicit vegetation map (Carter et al, 2015). The model under-predicted substrate temperatures, compared with measured values (Carter et al, 2015), so modelled soil F I G U R E 1 Flow chart summarizing the procedure for predicting offspring sex ratios for tuatara, using modelled hourly soil temperatures.…”

“…Available shade was simulated as 95% for forested microsites and 0% otherwise, with sites classed as 'forest' or 'nonforest' according to a spatially explicit vegetation map (Carter et al, 2015). The model under-predicted substrate temperatures, compared with measured values (Carter et al, 2015), so modelled soil F I G U R E 1 Flow chart summarizing the procedure for predicting offspring sex ratios for tuatara, using modelled hourly soil temperatures. Semi-enclosed rectangles designate model inputs, enclosed rectangles are model algorithms, parallelograms are outputs, and the diamonds are optional modifications.…”

“…For example, examining species' responses to environmental extremes (Kearney, Matzelle, & Helmuth, 2012) or at range edges requires finer-resolution predictions of microclimate conditions than can be attained by simply modelling distributional limits within available environments. In addition, the coarser the spatial resolution of a predictive surface, the more within-pixel variation is masked and, as a result, model uncertainty increased (Carter et al, 2015). For example, if a population is bounded within an area of 1 km 2 , a model built using 1-km 2 -gridded climate variables (very high resolution on a continental scale) could not capture effects of environmental heterogeneity on that population.…”