Climate is the primary factor constraining the potential distribution of many organisms (Andrewartha & Birch 1984; Woodward & Long 1988). As such, climatic data sets have long underpinned efforts to describe species’ native distribution (Fitzpatrick & Nix 1970) for addressing conservation-based questions (e.g. Graham et al. 2004; Chefaoui, Hortal, & Lobo 2005; Guisan & Thüiller 2005). Climatic variables have also been used to model the potential distribution of invasive alien species to better understand weed and pest risk (Baker et al. 2000; Kriticos & Randall 2001; Vaclavik & Meentemeyer 2009; Venette et al. 2010) and to aid in biological control programmes (Julien, Skarratt, & Maywald 1995; Robertson, Kriticos, & Zachariades 2008). To model the potential distribution of species over multiple continents, a globally conformal data set is a critical requirement.
The earliest computer-based potential distribution models relied upon point location data sets (Sutherst & Maywald 1985) or spatially gridded climatologies over a limited spatial region (e.g. Booth et al. 1987). Following the development of sophisticated spatial interpolation routines such as thin-plate splines (Hutchinson & Gessler 1994) and more powerful personal computers, bioclimatic modellers rapidly adopted spatially gridded global climatologies. Subsequently, the globally conformal CRU CL1·0 (New, Hulme, & Jones 1999) and WorldClim (Hijmans et al. 2005) data sets (Table 1) have become popular among correlative and mechanistic niche modellers alike. Despite concerns that 30′ might represent the spatial resolution limit at which climate data might be reliably estimated (New, Hulme, & Jones 1999; Daly 2006), there has been a strong drive to develop finer scale data sets (New et al. 2002; Hijmans et al. 2005).
|Data set||Nominal time period||Spatial resolution||Primary variables (monthly averages)*||Source and description|
|WorldClim||1950–2000||30″, 2·5′, 5′ and 10′||prec, tmin, tmax, tmp||Hijmans et al. (2005)|
|CRU CL1·0||1990–1961||30′||prec, wet, tmp, tmin†, tmax†, dtr, vap, spc‡, cld, wnd, frs§, rad†||New, Hulme, & Jones (1999)|
|CRU CL2·0||1990–1961||10′||prec, wet, tmp, dtr, rh, ssh, frs, wnd||New et al. (2002)|
The WorldClim data set is valued for its fine (10′) or very fine (30″) spatial resolution. The CRU CL1·0 data set (30′ resolution) is attractive because when compared with station data it contains few apparent anomalies (D. J. Kriticos, pers. obs.) and it includes vapour pressure and other variables that can be used to estimate potential evapotranspiration to drive soil moisture submodels. In choosing between these data sets, modellers have to assess whether their desire for finer spatial resolution outweighs their need for variables relating to atmospheric or soil moisture balance. The WorldClim data set includes the 19 core Bioclim variables (Busby 1991; described in full in Table S1 Supporting information) derived from monthly averages of daily minimum and maximum temperatures and monthly total precipitation. These variables appear to satisfy the requirements of most regression-based species distribution modelling efforts.
However, more process-oriented modelling approaches such as CLIMEX (Sutherst & Maywald 1985; Sutherst, Maywald, & Kriticos 2007), infection models (Magarey, Sutton, & Thayer 2005) and mechanistic ecophysiological models (Kearney et al. 2008) rely upon estimates of atmospheric moisture as an index of the saturation pressure deficit and hence the drying potential of the atmosphere. Atmospheric moisture affects phenomena such as the soil moisture balance that is critical for plant growth and survival, dew point duration, which can affect plant pathogen infection rates, and desiccation potential for amphibians. The extended list of 35 Bioclim variables (Table S1; Hutchinson et al. 2009) includes derived variables that rely upon potential evapotranspiration, indicating that some species distribution models developed using Bioclim also required indications of atmospheric moisture characteristics to model the species ranges. While the CRU CL2·0 data set (New et al. 2002) is available at 10′ resolution and includes relative humidity data, it contains many anomalies in the rainfall data, which do not correspond satisfactorily with station data or plausible extrapolations (D. J. Kriticos, pers. obs.). Moreover, the CRU CL 2·0 data set does not include future climate scenario layers, which are becoming an increasingly important component of bioclimatic modelling in an era of rapid global change.
In an effort to understand the climatic component of global change, climate classification systems, such as Köppen–Geiger (Köppen 1936) and Klimadiagramms (von Walter & Leith 1960), are experiencing a resurgence in popularity as modellers seek to understand bioclimatic patterns in global climates. For example, there is a growing appreciation of the sensitivity of correlative species distribution models such as MaxEnt, ecological niche factor analysis (ENFA) and logistic regression to the manner in which the pseudo-absence or background data are specified (Chefaoui & Lobo 2008; VanDerWal et al. 2009). A recent novel approach has been to use the Köppen–Geiger zonation to specify a background or pseudo-absence data around the known presence points for these models (Webber et al. 2011).
Mechanistic modelling approaches can be costly and time consuming, which is a strong incentive to avoid developing an elaborate model for pest risk assessments (PRA) if it is unnecessary. Climatic zonations can be used as a first approximation to assess the climatic similarity between the region the pest occupies, or is creating economic damage, to the PRA area. While the Köppen–Geiger climate classification has been recently updated and digitized for GIS applications, it is only available globally at a 30′ spatial resolution based on a gridded climatology (Kottek et al. 2006), or at a finer resolution, but relying on climatologies that were interpolated without the benefit of altitude as a temperature covariate (Peel, Finlayson, & McMahon 2007).
The purpose of this paper is to describe the development and testing of a fine-scale global data set uniquely tailored for use in species bioclimatic modelling, including correlative and process-based mechanistic models. The 10′ gridded data set includes a hybrid historical data set, a set of future climate scenarios from two global climate models (GCMs) for a range of time periods in the twenty-first century and the Köppen–Geiger climate classification constructed using the same climatologies developed elsewhere. We demonstrate the use of the climatologies with a correlative model for a rare and threatened species and two previously published mechanistic models for invasive species. The combined data set has been named CliMond, derived from the French climat mondial, meaning ‘world climate’, recognizing the global coverage of the data set.