Linear disturbances shift boreal peatland plant communities toward earlier peak greenness

Davidson, Scott J.; Goud, Ellie M.; Malhotra, Avni; Estey, Claire O.; Korsah, Percy; Strack, Maria

doi:10.1002/essoar.10506838.1

“…Although total leaf area and height are not considered in growth analysis, LES or CSR theories, height is a key component of plant size and has been used to differentiate among plant functional diversity and ecological strategies at local 36 and global scales 5 . Importantly, LA and height can be remotely sensed, which is particularly useful where it is not feasible to directly measure traits for all individuals (e.g., forest canopies) or for repeated measurements that require non-destructive sampling 35,37 .…”

Section: Discussionmentioning

confidence: 99%

A direct comparison of ecological theories for predicting the relationship between plant traits and growth

Goud

¹

,

Agrawal

²

,

Sparks

³

2023

Ecology

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Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime's CSR triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to biomass investments, leaf structure or gas exchange, respectively. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and ecophysiological traits, growth rate varied 50-fold and was best predicted by growth analysis and CSR traits, as well as total leaf area and plant height. Despite two LES traits correlating with growth, they contradicted predictions and leaf traits did not scale with root and stem characteristics. Thus, although combining leaf traits and whole-plant allocation best predicts growth, when destructive measures are not feasible, we suggest total leaf area and plant height, or easy-to-measure traits associated with the CSR classification.Predicting variation in plant growth is a long-standing problem in ecology. Because plants largely determine ecosystem productivity, estimating current and future plant growth is increasingly relevant as global change drivers impact ecosystem services 1,2 . As it is typically impractical to measure the total vegetative biomass of a community or ecosystem, an emerging method is to apply plant traits to predict growth rate. These trait-based approaches take advantage of a large body of literature that analyzes co-variation and trade-offs among plant traits [3][4][5][6] . Given that morphological and physiological characters are central to resource acquisition and allocation, they are likely to shape plant productivity in predictable ways. Three classic approaches have attempted to distill plant diversity into cohesive strategies and estimate growth based on defining characteristics: growth analysis, Grime's CSR triangle, and the leaf economics spectrum (Table 1). In growth analysis, growth rate is predicted by the relative allocation of biomass among roots, stems, and leaves 3,7 . Faster growing plants are expected to invest more in leaves relative to stems and roots. Due to the importance of leaf investment, growth rates are additionally dependent on specific leaf area (SLA), the ratio of leaf area to dry mass. Grime's CSR (competition-stress tolerant-ruderal) framework predicts that these three plant strategies have repeatedly evolved in response to combinations of stress and disturbance 8 . Until recently, the CSR framework was conceptual rather than empirically traitbased. However, Pierce et al. 2016 showed that three leaf traits were predictive of the scheme: average leaf surface area (individual leaf size, LS), SLA, and leaf dry matter content (LD). In this context, the C-strategy is defined by large LS and intermediate LD and SLA. The S-strategy has small LS and SLA with large LD, and R-strategy has small LS, small LD and large SLA 9 . The most commonly ap...

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A temporal snapshot of ecosystem functionality during the initial stages of reclamation of an upland-fen complex

Popović

¹

,

Petrone

²

,

Green

³

et al. 2022

Journal of Hydrology: Regional Studies

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A direct comparison of ecological theories for predicting the relationship between plant traits and growth

Goud

¹

,

Agrawal

²

,

Sparks

³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime’s CSR triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to biomass investments, leaf structure or gas exchange, respectively. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and ecophysiological traits, growth rate varied 50-fold and was best predicted by growth analysis and CSR traits, as well as total leaf area and plant height. Despite two LES traits correlating with growth, they contradicted predictions and leaf traits did not scale with root and stem characteristics. Thus, although combining leaf traits and whole-plant allocation best predicts growth, when destructive measures are not feasible, we suggest total leaf area and plant height, or easy-to-measure traits associated with the CSR classification.

show abstract

Linear disturbances shift boreal peatland plant communities toward earlier peak greenness

Cited by 3 publications

References 10 publications

A direct comparison of ecological theories for predicting the relationship between plant traits and growth

A direct comparison of ecological theories for predicting the relationship between plant traits and growth

A temporal snapshot of ecosystem functionality during the initial stages of reclamation of an upland-fen complex

A direct comparison of ecological theories for predicting the relationship between plant traits and growth

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