![]() At a broad temporal scale, climate extreme driven demographic events can expose maladaptation of species and genotypes to average climate conditions. ![]() 2009) or photosynthetic function (Cavanaugh et al. Mortality of adult plants under extremes are driven by physiological thresholds that prevent maintenance of water relations (Adams et al. 2012), particularly at their range margins (Zimmermann et al. 2007) and changes in recruitment ability due to these variations can lead to changes in landscape-scale distributions of plants (Mok et al. Seasonal and annual variations in temperature and soil moisture are important determinants of recruitment success in plants (Ibáñez et al. ( 2016) estimated that in southeast Australia, short-duration extremes in water deficit every 1–2 years can lead to recruitment failures while more intense but rarer (every seven years) events could lead to tree dieback and mortality. Weather and climate extremes can initiate demographic events that are not in equilibrium with long-term average conditions, leading to mortality and dieback in plant communities (Law et al. Changes to the frequency and magnitude of extremes, and variability in climatic conditions more generally, are expected to promote more drastic shifts in species distributions than those expected by changes in mean climate (Adams et al. 2014) while increases in hot and dry conditions are contributing to plant dieback at regional and global scales (Mitchell et al. Observed declines in frost events have facilitated the expansion of frost sensitive species to cooler climes (Cavanaugh et al. 2014) and limit the poleward distribution of plants (Cavanaugh et al. frost events) can cause dieback (Cavanaugh et al. 2015) has highlighted how extreme weather events can shape the abundance and distribution of plant communities (Allen and Breshears 1998, Breshears et al. The global increase in heatwave and drought related mortality events since the start of the century (Allen et al. Understanding how plant species have historically responded to climate extremes may provide valuable insights into our understanding of contemporary distributions and help to make more accurate predictions under a changing climate.Įxtreme weather events are short in duration but can have significant impacts upon plant demography (Walter et al. Species occupying hotter and drier locations that are exposed to severe maximum temperature extremes were associated with better predictive performance when modelled using extremes. These differences in spatial patterns were most pronounced at the predicted range margins, and reflect the influence of coastal proximity, continentality, topography and orographic barriers on climate extremes. Models that included extremes frequently showed notably different mapped predictions relative to those using base climate alone, despite often small differences in statistical performance as measured as a summary across sites. ![]() Extremes provided significant additive improvements in model performance compared to base climate alone and were more consistent than variability across all species. 250 m), time-series temperature and precipitation data for the hottest, coldest and driest months over 39 years. These variables were generated using fine-grain (approx. ![]() We evaluated the response of 37 plant species to base climate (long-term means, equivalent to BIOCLIM variables), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. BIOCLIM variables that are commonly used in correlative species distribution modelling studies cannot be used to quantify climate extremes, as they are generated using long-term averages and therefore do not describe year-to-year, temporal variability. Long-term patterns in climate extremes, and how they have shaped contemporary distributions, have rarely been considered or tested. Extreme weather can have significant impacts on plant species demography however, most studies have focused on responses to a single or small number of extreme events. ![]()
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