Biome‐specific climatic space defined by temperature and precipitation predictability

Abstract

Aim Global biomes are often classified by mean annual temperature and precipitation, but there is significant overlap between biomes, making it difficult to interpret the role of climate in the distribution of biomes globally. Climate predictability (including long‐term reliability of both seasonality and inter‐annual variability) varies considerably between biomes and regulates biodiversity distribution, adaptation and evolution, but its global pattern has rarely been investigated. The aim of this study was to characterize climatic space quantitatively for major biomes of the world using temperature and precipitation predictability, and to interpret its biological implications under future climate change. Location Global. Time Period 1901–2012. Methods We calculated global gridded temperature and precipitation predictability based on an information theory approach, and compared climatic spaces defined by these measures within and across biomes. Results We show that temperature predictability has a clear latitudinal gradient, whereas precipitation predictability is geographically variable. We further show that temperature and precipitation predictability form distinct climatic spaces for major biomes across the globe, and importantly, temperature and precipitation predictability can robustly distinguish biomes that are overlapping in mean annual climate statistics. Main conclusions Climatic space created by temperature and precipitation predictability supplements the traditional biome‐specific climatic spaces created by annual mean temperature and total precipitation. Quantifying measures of climate predictability helps us to understand adaptation strategies adopted by local organisms within and across biomes. Our results show that quantifying climate predictability is a simple and effective way to delineate more robustly biome climate space and its influences on macroecology and evolutionary biology, which in turn could underpin conservation efforts under future climate change, especially when prevailing climates are comparable in terms of magnitude.

Global diversity patterns are modulated by temporal fluctuations in primary productivity

“…Moreover, most studies on the role of environmental variation in determining species richness have explored only the effect of seasonality (Dalby, McGill, Fox, & Svenning, 2014;Gouveia, Hortal, Cassemiro, Rangel, & Diniz-Filho, 2013;Hurlbert & Haskell, 2003) and not the unpredictable component of environmental variation (but see Letten et al, 2013). In this respect, some studies have explored variation in temperature and precipitation (Jiang et al, 2017;Letten et al, 2013;Tonkin et al, 2017). However, although the observed patterns are illuminating (see Jiang et al, 2017), and temperature and precipitation surely affect species distribution and richness, these variables represent only the ultimate drivers of diversity patterns, acting through their effects on biological rates (Brown, Gillooly, Allen, Savage, & West, 2004) or resource levels (Storch, 2012).…”

Climate and geographic adaptation drive latitudinal clines in biomass of a widespread saltmarsh plant in its native and introduced ranges

“…Recent increase in global temperature is predicted to have widespread consequences for the distribution and abundance of organisms (Parmesan ; Jiang et al ). As temperatures increase, areas of suitable climate will shift poleward and upward in elevation for many species (Burrows et al ).…”

Dynamic management needs for long‐lived, sporadically recruiting plant species in human‐dominated landscapes

“…The gray line illustrates the global mean of temporal change in climatic predictability with the dashed lines indicating the 5% and 95% percentiles. Climatic predictability as a combination of climatic constancy (variability) and periodicity was calculated according to (Jiang, Felzer, Nielsen, & Medlyn, ) by using the R package hydrostats (v.0.2.4; Bond, ) with climate data binned in n = 11 classes for analyses (cf. Beissinger & Gibbs, ).…”