skip to main content

Title: Is there tree senescence? The fecundity evidence

Despite its importance for forest regeneration, food webs, and human economies, changes in tree fecundity with tree size and age remain largely unknown. The allometric increase with tree diameter assumed in ecological models would substantially overestimate seed contributions from large trees if fecundity eventually declines with size. Current estimates are dominated by overrepresentation of small trees in regression models. We combined global fecundity data, including a substantial representation of large trees. We compared size–fecundity relationships against traditional allometric scaling with diameter and two models based on crown architecture. All allometric models fail to describe the declining rate of increase in fecundity with diameter found for 80% of 597 species in our analysis. The strong evidence of declining fecundity, beyond what can be explained by crown architectural change, is consistent with physiological decline. A downward revision of projected fecundity of large trees can improve the next generation of forest dynamic models.

; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; « less
Award ID(s):
1655896 1745496 1754668 1754647 2025755
Publication Date:
Journal Name:
Proceedings of the National Academy of Sciences
Page Range or eLocation-ID:
Article No. e2106130118
Proceedings of the National Academy of Sciences
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Pinus edulis Engelm. is a short-stature, drought-tolerant tree species that is abundant in piñon-juniper woodlands throughout semiarid ecosystems of the American Southwest. P. edulis is a model species among ecophysiological disciplines, with considerable research focus given to hydraulic functioning and carbon partitioning relating to mechanisms of tree mortality. Many ecological studies require robust estimates of tree structural traits such as biomass, active sapwood area, and leaf area. We harvested twenty trees from Central New Mexico ranging in size from 1.3 to 22.7 cm root crown diameter (RCD) to derive allometric relationships from measurements of RCD, maximum height, canopy area (CA), aboveground biomass (AGB), sapwood area (AS), and leaf area (AL). Total foliar mass was measured from a subset of individuals and scaled to AL from estimates of leaf mass per area. We report a strong nonlinear relationship to AGB as a function of both RCD and height, whereas CA scaled linearly. Total AS expressed a power relationship with RCD. Both AS and CA exhibited strong linear relationships with AL (R2 = 0.99), whereas RCD increased nonlinearly with AL. We improve on current models by expanding the size range of sampled trees and supplement the existing literature for this species.more »Study Implications: Land managers need to better understand carbon and water dynamics in changing ecosystems to understand how those ecosystems can be sustainably used now and in the future. This study of two-needle pinon (Pinus edulis Engelm.) trees in New Mexico, USA, uses observations from unoccupied aerial vehicles, field measurements, and harvesting followed by laboratory analysis to develop allometric models for this widespread species. These models can be used to understand plant traits such biomass partitioning and sap flow, which in turn will help scientists and land managers better understand the ecosystem services provided by pinon pine across North America.« less
  2. The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factorsmore »varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.« less
  3. Bark beetles naturally inhabit forests and can cause large-scale tree mortality when they reach epidemic population numbers. A recent epidemic (1990s–2010s), primarily driven by mountain pine beetles ( Dendroctonus ponderosae ), was a leading mortality agent in western United States forests. Predictive models of beetle populations and their impact on forests largely depend on host related parameters, such as stand age, basal area, and density. We hypothesized that bark beetle attack patterns are also dependent on inferred beetle population densities: large epidemic populations of beetles will preferentially attack large-diameter trees, and successfully kill them with overwhelming numbers. Conversely, small endemic beetle populations will opportunistically attack stressed and small trees. We tested this hypothesis using 12 years of repeated field observations of three dominant forest species (lodgepole pine Pinus contorta , Engelmann spruce Picea engelmannii , and subalpine fir Abies lasiocarpa ) in subalpine forests of southeastern Wyoming paired with a Bayesian modeling approach. The models provide probabilistic predictions of beetle attack patterns that are free of assumptions required by frequentist models that are often violated in these data sets. Furthermore, we assessed seedling/sapling regeneration in response to overstory mortality and hypothesized that higher seedling/sapling establishment occurs in areas with highestmore »overstory mortality because resources are freed from competing trees. Our results indicate that large-diameter trees were more likely to be attacked and killed by bark beetles than small-diameter trees during epidemic years for all species, but there was no shift toward preferentially attacking small-diameter trees in post-epidemic years. However, probabilities of bark beetle attack and mortality increased for small diameter lodgepole pine and Engelmann spruce trees in post-epidemic years compared to epidemic years. We also show an increase in overall understory growth (graminoids, forbs, and shrubs) and seedling/sapling establishment in response to beetle-caused overstory mortality, especially in lodgepole pine dominated stands. Our observations provide evidence of the trajectories of attack and mortality as well as early forest regrowth of three common tree species during the transition from epidemic to post-epidemic stages of bark beetle populations in the field.« less
  4. Abstract
    Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred metersMore>>
  5. Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based onmore »internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.« less