An operational definition of the biome for global change research

Abstract

Biomes are constructs for organising knowledge on the structure and functioning of the world's ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and globalchange research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example, we map gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.

Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies

“…Projected biome changes towards woody vegetation under eCO 2 are consistent with results from previous studies on regional (Doherty et al., 2010) and continental scale (Conradi et al, 2020; Gonzalez et al., 2010; Higgins & Scheiter, 2012; Niang et al., 2014; Scholze et al., 2006; Sitch et al., 2008). The transition of grassland and savanna biomes, that is, C 4 ‐dominated biomes, to more woody biomes, that is, C 3 ‐dominated biomes, in eCO 2 simulations corroborates findings that savannas and grasslands are particularly vulnerable to biome changes under eCO 2 (Higgins & Scheiter, 2012; Osborne et al., 2018; Scheiter et al., 2018).…”

“…Explanations for differences in biome change projections between different studies include the use of different modelling approaches (e.g. species distribution models, Conradi et al., 2020), different climate data sets (e.g. GCM data or interpolated data such as ISIMIP instead of RCM data), different DGVMs (Doherty et al., 2010; Gonzalez et al., 2010) and that precipitation changes until 2100 were not accounted for (Higgins & Scheiter, 2012).…”

Climate change promotes transitions to tall evergreen vegetation in tropical Asia

“…Such changes might not necessarily modify the biome state but nonetheless influence the vegetation state and ecosystem functions. Keeping track of simultaneous changes in multiple state variables and interpreting the implications for ecosystem functioning might be more difficult than using a biome approach (Conradi et al., 2020). There is no single consensus biome classification scheme that adequately covers all biome types globally (Moncrieff, Bond, et al, 2016; Moncrieff, Scheiter, et al, 2016; Mucina, 2019), and that can be applied in modelling studies, remote sensing and other observational studies.…”

“…Relying on a single set of climate forcing data may constrain the range of possible vegetation states and lead to inappropriate management decisions. Utilization of regionally adapted vegetation models (Moncrieff, Bond, & Higgins, 2016;Moncrieff, Scheiter, Langan, Trabucco, & Higgins, 2016) (Higgins et al, 2016;Moncrieff, Bond, et al, 2016;Moncrieff, Scheiter, et al, 2016 the implications for ecosystem functioning might be more difficult than using a biome approach (Conradi et al, 2020). There is no single consensus biome classification scheme that adequately covers all biome types globally (Moncrieff, Bond, et al, 2016;Moncrieff, Scheiter, et al, 2016;Mucina, 2019), and that can be applied in modelling studies, remote sensing and other observational studies.…”

Predictive ability of a process‐based versus a correlative species distribution model

“…For the TTR‐SDM, we use two variations. The first is exactly as described by Higgins et al (2012); the second involves the replacement of the net photosynthesis function (see equation 14 in Higgins et al, 2012) with a Farquhar style photosynthesis function (see Conradi et al, 2020 for a case study that uses this model variant). This means that the parameters associated with this function (labeled β 1 − β 8 in Higgins et al, 2012) are no longer estimated from the distribution data.…”