Summary1. 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 CL1AE0 and CL2AE0 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 Ko¨ppen-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 CL2AE0 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.

Seed persistence is the survival of seeds in the environment once they have reached maturity. Seed persistence allows a species, population or genotype to survive long after the death of parent plants, thus distributing genetic diversity through time. The ability to predict seed persistence accurately is critical to inform long-term weed management and flora rehabilitation programs, as well as to allow a greater understanding of plant community dynamics. Indeed, each of the 420000 seed-bearing plant species has a unique set of seed characteristics that determine its propensity to develop a persistent soil seed bank. The duration of seed persistence varies among species and populations, and depends on the physical and physiological characteristics of seeds and how they are affected by the biotic and abiotic environment. An integrated understanding of the ecophysiological mechanisms of seed persistence is essential if we are to improve our ability to predict how long seeds can survive in soils, both now and under future climatic conditions. In this review we present an holistic overview of the seed, species, climate, soil, and other site factors that contribute mechanistically to seed persistence, incorporating physiological, biochemical and ecological perspectives. We focus on current knowledge of the seed and species traits that influence seed longevity under ex situ controlled storage conditions, and explore how this inherent longevity is moderated by changeable biotic and abiotic conditions in situ, both before and after seeds are dispersed. We argue that the persistence of a given seed population in any environment depends on its resistance to exiting the seed bank via germination or death, and on its exposure to environmental conditions that are conducive to those fates. By synthesising knowledge of how the environment affects seeds to determine when and how they leave the soil seed bank into a resistance-exposure model, we provide a new framework for developing experimental and modelling approaches to predict how long seeds will persist in a range of environments.

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. MethodsWe 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.