Summary: 1. Gridded climatologies have become an indispensable component of bioclimatic modelling, with a range of applications spanning conservation and pest management. Such globally conformal data sets of historical and future scenario climate surfaces are required to model species potential ranges under current and future climate scenarios.2. We developed a set of interpolated climate surfaces at 10′ and 30′ resolution for global land areas excluding Antarctica. Input data for the baseline climatology were gathered from the WorldClim and CRU CL1·0 and CL2·0 data sets. A set of future climate scenarios were generated at 10′ resolution. For each of the historical and future scenario data sets, the full set of 35 Bioclim variables was generated. Climate variables (including relative humidity at 0900 and 1500 hours) were also generated in CLIMEX format. The Köppen–Geiger climate classification scheme was applied to the 10′ hybrid climatology as a tool for visualizing climatic patterns and as an aid for specifying absence or background data for correlative modelling applications.3. We tested the data set using a correlative model (MaxEnt) addressing conservation biology concerns for a rare Australian shrub, and a mechanistic niche model (CLIMEX) to map climate suitability for two invasive species. In all cases, the underlying climatology appeared to behave in a robust manner.4. This global climate data set has the advantage over the WorldClim data set of including humidity data and an additional 16 Bioclim variables. Compared with the CRU CL2·0 data set, the hybrid 10′ data set includes improved precipitation estimates as well as projected climate for two global climate models running relevant greenhouse gas emission scenarios.5. For many bioclimatic modelling purposes, there is an operational attraction to having a globally conformal historical climatology and future climate scenarios for the assessments of potential climate change impacts. Our data set is known as ‘CliMond’ and is available for free download from http://www.climond.org.

1 Acacia nilotica is a spinescent woody legume that has become highly invasive in several parts of the world, including Australia where it has been declared a weed of national significance. Under...

The oriental fruit fly,Bactrocera dorsalis(Hendel), is a major pest throughout South East Asia and in a number of Pacific Islands. As a result of their widespread distribution, pest status, invasive ability and potential impact on market access,B. dorsalisand many other fruit fly species are considered major threats to many countries. CLIMEX™ was used to model the potential global distribution ofB. dorsalisunder current and future climate scenarios. Under current climatic conditions, its projected potential distribution includes much of the tropics and subtropics and extends into warm temperate areas such as southern Mediterranean Europe. The model projects optimal climatic conditions forB. dorsalisin the south-eastern USA, where the principle range-limiting factor is likely to be cold stress. As a result of climate change, the potential global range forB. dorsalisis projected to extend further polewards as cold stress boundaries recede. However, the potential range contracts in areas where precipitation is projected to decrease substantially. The significant increases in the potential distribution ofB. dorsalisprojected under the climate change scenarios suggest that the World Trade Organization should allow biosecurity authorities to consider the effects of climate change when undertaking pest risk assessments. One of the most significant areas of uncertainty in climate change concerns the greenhouse gas emissions scenarios. Results are provided that span the range of standard Intergovernmental Panel on Climate Change scenarios. The impact on the projected distribution ofB. dorsalisis striking, but affects the relative abundance of the fly within the total suitable range more than the total area of climatically suitable habitat.

Aim  Investigate the relative abilities of different bioclimatic models and data sets to project species ranges in novel environments utilizing the natural experiment in biogeography provided by Australian Acacia species.Location  Australia, South Africa.Methods  We built bioclimatic models for Acacia cyclops and Acacia pycnantha using two discriminatory correlative models (MaxEnt and Boosted Regression Trees) and a mechanistic niche model (CLIMEX). We fitted models using two training data sets: native‐range data only (‘restricted’) and all available global data excluding South Africa (‘full’). We compared the ability of these techniques to project suitable climate for independent records of the species in South Africa. In addition, we assessed the global potential distributions of the species to projected climate change.Results  All model projections assessed against their training data, the South African data and globally were statistically significant. In South Africa and globally, the additional information contained in the full data set generally improved model sensitivity, but at the expense of increased modelled prevalence, particularly in extrapolation areas for the correlative models. All models projected some climatically suitable areas in South Africa not currently occupied by the species. At the global scale, widespread and biologically unrealistic projections by the correlative models were explained by open‐ended response curves, a problem which was not always addressed by broader background climate space or by the extra information in the full data set. In contrast, the global projections for CLIMEX were more conservative. Projections into 2070 indicated a polewards shift in climate suitability and a decrease in model interpolation area.Main conclusions  Our results highlight the importance of carefully interpreting model projections in novel climates, particularly for correlative models. Much work is required to ensure bioclimatic models performed in a robust and ecologically plausible manner in novel climates. We explore reasons for variations between models and suggest methods and techniques for future improvements.