Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

Abstract

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model

“…Notably, the model does not simulate the development of peatlands or the process of paludification, and it does not include a complete module of biogeochemical cycling in soils that would emulate processes leading to acidification, for instance. As proposed by Beauregard and de Blois (2014), however, some edaphic variables may be indicative of certain soil processes at the stand level. In this study, correlations between the abundance of specific tree genera and soil clay content led to the implementation of a simple scheme to limit tree establishment in LPJ-LMfire (Fig.…”

“…Establishment and growth of boreal tree species are influenced by a wide range of soil properties that are related to soil nutrient availability, which include pH, parent material, soil particle size, and water content, among others (Girardin et al, 2001;Beauregard and de Blois, 2014;Gewehr et al, 2014). Not all ecosystem processes linking these properties to tree establishment are simulated in the current version of LPJ-LMfire.…”

The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model

“…Notably, the model does not simulate the development of peatlands or the process of paludification, and does not include a complete module of biogeochemical cycling in soils that 15 would emulate processes leading to acidification, for instance. As has been proposed by Beauregard and de Blois (2014), however, some edaphic variables may be indicative of certain soil processes at the stand level. In this study, correlations between the abundance of specific tree genera and soil clay content led to the implementation of a simple scheme to limit tree establishment in LPJ-LMfire (supplement S3 Fig.…”

“…Establishment and growth of boreal tree species are influenced by a wide range of soil properties that were related to soil nutrient availability, and which included pH, parent material, soil particle size, and water content, among others (Girardin et al, 2001;Beauregard and de Blois, 2014;Gewehr et al, 2014). Not all ecosystem processes linking these properties to tree establishment are simulated in the current version of LPJ-LMfire.…”

Right here, right now: Populations of Actinotus helianthi differ in their early performance traits and interactions

“…It is well established that environmental drivers of population differences will vary across spatial scales (Ara ujo & Pearson 2005). At a broad geographical scale, populations might differ due to climates or substrates (Aitken et al 2008;Beauregard & de Blois 2014), but they may also be locally adapted at the population scale with no single gradient or spatial trend that can be associated with any variation in traits (Thuiller et al 2004;Byars et al 2007). This variation is known as plasticity and is the capacity of plants among populations with variable prevailing environments to express a phenotype that is the best match to the local conditions (Qaderi & Cavers 2002;Byars et al 2007).…”