Process‐based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass‐dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3grasses and (sub)tropical C4grasses. Incorporation of species‐level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance‐related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage‐based functional types (LFTs), situated between species‐level trait data and PFT‐level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.
Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere–atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of “Plant Functional Types” (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution‐based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages (“Lineage Functional Types”) will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next‐generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.
more » « less- NSF-PAR ID:
- 10364074
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 28
- Issue:
- 8
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 2541-2554
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Summary -
more » « less
Abstract Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non‐linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs.
-
more » « less
Summary A minuscule fraction of the Earth's paleobiological diversity is preserved in the geological record as fossils. What plant remnants have withstood taphonomic filtering, fragmentation, and alteration in their journey to become part of the fossil record provide unique information on how plants functioned in paleo‐ecosystems through their traits. Plant traits are measurable morphological, anatomical, physiological, biochemical, or phenological characteristics that potentially affect their environment and fitness. Here, we review the rich literature of paleobotany, through the lens of contemporary trait‐based ecology, to evaluate which well‐established extant plant traits hold the greatest promise for application to fossils. In particular, we focus on fossil plant functional traits, those measurable properties of leaf, stem, reproductive, or whole plant fossils that offer insights into the functioning of the plant when alive. The limitations of a trait‐based approach in paleobotany are considerable. However, in our critical assessment of over 30 extant traits we present an initial, semi‐quantitative ranking of 26 paleo‐functional traits based on taphonomic and methodological criteria on the potential of those traits to impact Earth system processes, and for that impact to be quantifiable. We demonstrate how valuable inferences on paleo‐ecosystem processes (pollination biology, herbivory), past nutrient cycles, paleobiogeography, paleo‐demography (life history), and Earth system history can be derived through the application of paleo‐functional traits to fossil plants.
-
Reconstructions of eastern African vegetation and climate are critical for understanding primate and large mammal evolution in the Neogene. Insight into past ecological conditions can be gleaned from lipid biomarkers preserved in sedimentary archives, providing evidence for the role of habitats (e.g. open vs. closed vegetation) on evolutionary trait selection. A common paleoecological proxy is the 𝛿¹³C of n-alkanes, which integrates the distinct isotopic signatures of C3 and C4 vegetation. In typical modern tropical ecosystems, “woody” vegetation uses C3 photosynthesis while “grassy” vegetation uses C4 photosynthesis. Under these conditions, mixing models can then estimate the fraction of woody cover of a landscape. While the use of photosynthetic pathways to infer plant functional type (PFT) is powerful, this paradigm does not hold prior to the rise of C4 grasses at 10 Ma, leaving a gap in understanding of ecosystem structure in the early-mid Miocene. To address this issue, we investigate whether n-alkane chain length distributions (rather than 𝛿¹³C) hold information about plant functional type independent of photosynthetic pathway. Here, we present n-alkane chain length data from over 800 modern plant samples, representing a variety of different photosynthetic pathways, growth forms, habitats, and locations. This dataset comprises a significant literature review component, as well as over 400 new distributions generated in this study. We build upon our previous work using PCA and turn to non-linear methods – including both supervised neural network classifiers and unsupervised dimensionality reduction – to determine the potential of n-alkane distributions for PFT identification. Successful differentiation between woody and grassy PFTs using modern plant n-alkane chain lengths will provide a foundation for applying this tool to the geologic record. Our method will compliment well-established isotopic measurement practices while offering the novel ability to reconstruct vegetation structure in pure C3 ecosystems. This represents a particularly powerful tool for understanding ecological history prior to the rise of C4 grasses.more » « less
-
Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers.more » « less
