Opportunities and challenges for herbaria in studying the spatial variation in plant functional diversity

Abstract

Herbaria are renowned as collections of specimens for research in plant taxonomy, plant identification and more recently in plant phylogenetics. The production of Floras and monographs in herbaria is fundamental to the understanding of plant taxonomy and plant biogeography. Herbaria have played an important role in providing the raw geographic data behind plant species distributions which form the basis of the most commonly used biodiversity metric: species richness. Less well recognised is the potential for using comprehensive species checklists, produced by herbaria, as a sampling frame when projecting biodiversity metrics. Functional diversity metrics derived from plant trait values are growing in importance in biodiversity monitoring; however, it is unclear whether the trait-based functional attributes are responsive to changes in species richness in all geographic areas. Modelling of the spatial distribution of trait values is one way to investigate the limits of biodiversity monitoring reliant on trait values. The research outputs of herbaria are arguably an untapped resource of such trait data. Greater digitisation of published Flora treatments as well as continuing digitisation of herbarium specimens is increasingly making these resources more available. With appropriate methods to ameliorate known biases in the species locality and plant trait data held by herbaria, these institutions can play an important role in building spatial models of plant trait distributions. Such models help to establish the relationships between species richness and plant functional diversity metrics in different biomes required for trait-led biodiversity monitoring. Here we present a six-step method to allow data held by herbaria to be used to establish a spatial model of functional diversity metrics at a continental scale.

Trait hypervolumes based on natural history collections can detect ecological strategies that are distinct to biogeographic regions

“…Under this approach, each sampled species is ascribed one value of each trait considered, namely the maximum value for the species (Supplementary Material S1.3). Maximum values were used for each species because mean values could not be calculated from the information in the taxonomic literature and minimum values can record dimensions of immature organs—see Harris et al (2021) for further discussion. For the number of seeds per fruit, taxonomic literature typically gives rounded values.…”

“…We propose biogeographic regions based on the spatial projections of multiple plant trait values. As described in Harris et al (2021), we advocate combining two forms of information held by natural history collections to better understand the spatial variation in trait values, namely specimen localities and trait values compiled in taxonomic publications (see Supplementary Material S2). We suggest using a sample of species for such analyses that is drawn randomly from a wider taxonomic checklist for the region (see Supplementary Material S1.1)-in this case, c. 1% of African (including Madagascar) angiosperm flora (484 species).…”

“…We extracted data on four species-level, numeric plant traits from taxonomic literature derived from natural history collections-plant height, leaf area, seed volume and number of seeds per fruit. These traits allow every species to be positioned within a multidimensional trait-space reflecting two major plant functional axes-refer to Harris et al, 2021, for a detailed list of sources. One of these axes is mainly related to plant species' competitive ability to capture light (plant height, leaf area; Grime, 1977;Westoby, 1998) and the latter axis is related to establishment and dispersal, and is recognized as the seed packing trade-off (seed volume, number of seeds per fruit; Bentos et al, 2014;Ibarra-Manriquez & Oyama, 1992).…”

“…Note that, for simplicity, this schematic presents a twodimensional trait-space (using plant height and seed volume as exemplar traits): (a) hypervolume boundary established in trait-space, (b) segments of trait space established using hierarchical clustering of species-level trait values, (c) pixel-based hypervolumes intersected with trait-space segments, allowing (d) the spatial extent of each trait-space segment to be mapped, and (e) the spatial intersections of traitspace segments to be mapped. dimensions of immature organs-see Harris et al (2021) for further discussion. For the number of seeds per fruit, taxonomic literature typically gives rounded values.…”

“…Discrepancies between different model runs allowed variation in modelling uncertainty to be spatially projected using committee average values (see Supplementary Material S1.6, Figure S1). We aggregated modelled ensemble species distributions from Harris et al (2021) at a spatial resolution of 2.5° × 2.5° pixels (see Supplementary Material S1.5). This spatial resolution was chosen for coverage of the whole continent to ensure sufficient sampled species richness in each pixel with which to estimate trait hypervolumes (for the four traits considered) for the sampled species in each pixel.…”