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Pacala, S. W.

Publications and source records attributed to Pacala, S. W..

2 recordsLinked to original sources

Competition for water and species coexistence in phenologically structured annual plant communities

Both competition for water and phenological variation are important determinants of plant community structure, but ecologists lack a synthetic theory for how they affect coexistence outcomes. We developed an analytically tractable model of water competition for Mediterranean annual communities and demonstrate that variation in phenology alone can maintain high diversity in spatially homogenous assemblages of water-limited plants. We modeled a system where all water arrives early in the season and species vary in their ability to grow under drying conditions. As a consequence, species differ in growing season length, and compete by shortening the growing season of their competitors. This model replicates and offers mechanistic explanations for qualitative patterns observed in prior empirical studies of how phenology influences coexistence among Mediterranean annuals. Additionally, we found that a decreasing, concave-up tradeoff between growth rate and access to water can theoretically maintain infinite diversity under simple but realistic assumptions. High diversity is possible because: 1) later plants escape competition after their earlier-season competitors have gone to seed and 2) early-season species are more than compensated for their shortened growing season by a growth-rate advantage. Together, these mechanisms provide an explanation for how annual plant species might coexist when competing only for water.

ecology↗

Why can we detect lianas from space?

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing offers a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global specificity of spectral signals from liana-infested tree crowns and forest stands, examining the underlying mechanisms. We compiled a database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signature at canopy and stand scales across sites. This signature mainly arises at the canopy level due to direct effects of leaf angles, resulting in a larger apparent leaf area, and indirect effects from increased light scattering in the NIR and SWIR regions, linked to lianas less costly leaf construction compared to trees. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits identified are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life-history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas adaptive similarities across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. Open data statementOf the 17 datasets used, 10 are published and publicly accessible, with links provided in this submission (Appendix S1: Section S1). Upon acceptance, remaining seven datasets will be provided via Smithsonians Dspace. The open-source model code is available as R-package ccrtm (https://cran.r-project.org/web/packages/ccrtm/index.html) and on github (https://github.com/MarcoDVisser/ccrtm). Code will be archived in Zenodo should the manuscript be accepted for publication

plant biology↗