Truncation of thermal tolerance niches among Australian plants

Aim: Despite recognition that realized distributions inherently underestimate species' physiological tolerances, we are yet to identify the extent of these differences within diverse taxonomic groups. The degree to which species could tolerate environmental conditions outside their observed distributions may have a significant impact on the perceived extinction risk in ecological models. More information on this potential error is required to improve our confidence in management strategies. Location: Australia. Time Period: 1983–2012. Major Taxa Studied: Plants. Methods: To quantify the scale and spatial patterns of this disparity, we estimated the existing tolerance to thermal extremes of 7,124 Australian plants, more than one-third of the native continental flora, using data from cultivated records at 128 botanical gardens and nurseries. Hierarchical Bayesian beta regression was used to assess whether factors such as realized niches, traits or phylogeny could predict the incidence or magnitude of niche truncation (underestimation of thermal tolerances), while controlling for sources of collection bias. Results: Approximately half of the cultivated species analysed could tolerate temperature extremes beyond those experienced in their native range. Niche truncation was predictable from the breadth and extremes of their realized niches and by traits such as plant growth form. Phylogenetic relationships with niche truncation were weak and appeared more suited to predicting thermal tolerances directly. Main conclusions: This study highlights a widespread disparity between realized and potential thermal limits that may have significant implications for species' capacity to persist in situ with a changing climate. Identifying whether thermal niche truncation is the result of biotic interactions, dispersal constraints or other environmental factors could provide significant insight into community assembly at macroecological scales. Estimating niche truncation may help to explain why certain ecological communities are more resilient to change and may potentially improve the reliability of model projections under climate change.