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 m2) using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale 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.
Highlights
Predicting the impacts of environmental variation on species is of primary concern in ecology, especially for examining population viability and predicting range shifts within the context of modern climate change and habitat modification (Porter et al 2000, Pearson and Dawson 2003, Thuiller 2004, Guisan and Thuiller 2005, Kearney and Porter 2004)
Microclimate model structure We modelled soil temperatures for the year 2011 for the island of Takapourewa, a 150 ha offshore Nature Reserve located in Cook Strait, New Zealand [approx. 40°40'S 174°00’E] (Fig. 1)
Model selection Neither simulation of evaporative cooling nor inclusion of a 50 mm organic soil cap increased the accuracy of predicted soil temperatures from the baseline set of models
Summary
Predicting the impacts of environmental variation on species is of primary concern in ecology, especially for examining population viability and predicting range shifts within the context of modern climate change and habitat modification (Porter et al 2000, Pearson and Dawson 2003, Thuiller 2004, Guisan and Thuiller 2005, Kearney and Porter 2004). Environmental inputs have been obtained from (1) weather stations, which collect multiple data types (e.g., air temperature, humidity, wind speed, rainfall) long-term at a single point that is geographically near to a population of interest and (2) measurements collected instantaneously or over a pre-determined time period (e.g. with dataloggers) within the known habitat of a population (Porter et al 2002, Kearney and Porter 2004, Austin 2007, Ashcroft and Gollan 2012). Carter et al — Resolution of microclimate model inputs (CRU CL 2.0) include monthly precipitation, mean temperature, relative humidity, sunshine hours, ground frost and 10 m mean monthly wind speed data for the 1961–1990 normal period at a 10' spatial resolution (New et al 2002). A globalextent set of gridded microclimate surfaces ('microclim') is available at a horizontal resolution of approximately 15 km (Kearney et al 2014a)
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