Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species

Abstract

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. Long-term patterns in climate extremes, and how they have shaped contemporary distributions, have rarely been considered or tested. 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. 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. These variables were generated using fine-grain (approx. 250 m), time-series temperature and precipitation data for the hottest, coldest and driest months over 39 years. Extremes provided significant additive improvements in model performance compared to base climate alone and were more consistent than variability across all species. 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. 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. Species occupying hotter and drier locations that are exposed to severe maximum temperature extremes were associated with better predictive performance when modelled using extremes. 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.

Predicting plant species distributions using climate‐based model ensembles with corresponding measures of congruence and uncertainty

“…when compiled as ensembles and compared against separate climate datasets. The improvement is consistent with several recent studies showing how interannual variability in climate can improve SDMs(Karger et al, 2021;Perez-Navarro et al, 2021;Stewart, Elith, et al, 2021;Zimmermann et al, 2009). Importantly, this improvement was also found when using contrasting methodologies to produce temporal variation.…”

“…All species data used in this study were presence and absence records obtained from systematic field studies within forested and woodland ecosystems. Species records were collated from multiple field campaigns to maximize the pool of available data points, including 1804 sites in Victoria (see Stewart, Elith, et al, 2021) and 349 sites in Bhutan (see Choden et al, 2021). The most prevalent canopy and understory plant species in each study region (38 in Victoria and 12 in Bhutan) were selected for modelling.…”

“…Importantly, this improvement was also found when using contrasting methodologies to produce temporal variation. The local interpolations use an anomaly plus climatology technique to generate time-series data(Stewart, Elith, et al, 2021;Stewart & Nitschke, 2017e), whereas CHELSA uses statistical downscaling techniques applied to 3-hourly ERA5 reanalysis data.Although the monthly mean surfaces are likely less prone to error accumulation as they are often interpolated directly, the ability to characterize interannual variability can improve predictive performance while allowing for more flexibility in the design and selection of climate predictors. Even though the time-series models often performed better than the monthly mean alternatives, further improvements were achieved using ensembles.F I G U R E 4 Spatial distribution of the ensemble mean (a), ensemble standard deviation (b), threshold agreement index (TAI; c) and threshold-scaled standard deviation (TSD; d) from distribution models fitted for Eucalyptus obliqua using 10 alternative sets of climate variables across Victoria, southeast AustraliaThe proposed TAI and TSD metrics characterize congruence and variability across any number of predictions, supporting the ensemble mean by ensuring that information describing the contributing models is retained.…”

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

“…glauca distributions are best predicted by the climate parameters yearly minimum and maximum temperatures, temperature range, temperature continentality, and spring precipitation. This suggests the importance of considering climate parameters beyond annual means, such as seasonal precipitation and climatic extremes (Stewart et al, 2021; Zimmermann et al, 2009). This is reflected in other Arctic studies, which have found plant responses to both climatic extremes and seasonal variables (Berner et al, 2020; Niittynen et al, 2020; von Oppen et al, 2021).…”

Measuring Metrics of Climate Change and Its Implication on the Endangered Mammal Conservation in the Leuser Ecosystem

“…On the other hand, wet and cold aspects of the climate extreme were retrieved from the 95th percentile of precipitation and fifth percentile of temperature, respectively. Several recent studies also used the monthly climatic data to capture the climate extremes (Albright et al, 2011;Suggitt et al, 2011;Stewart et al, 2021).…”