Niche Shifts From Trees to Fecundity to Recruitment That Determine Species Response to Climate Change

Abstract

Anticipating the next generation of forests requires understanding of recruitment responses to habitat change. Tree distribution and abundance depend not only on climate, but also on habitat variables, such as soils and drainage, and on competition beneath a shaded canopy. Recent analyses show that North American tree species are migrating in response to climate change, which is exposing each population to novel climate-habitat interactions (CHI). Because CHI have not been estimated for either adult trees or regeneration (recruits per year per adult basal area), we cannot evaluate migration potential into the future. Using the Masting Inference and Forecasting (MASTIF) network of tree fecundity and new continent-wide observations of tree recruitment, we quantify impacts for redistribution across life stages from adults to fecundity to recruitment. We jointly modeled response of adult abundance and recruitment rate to climate/habitat conditions, combined with fecundity sensitivity, to evaluate if shifting CHI explain community reorganization. To compare climate effects with tree fecundity, which is estimated from trees and thus is "conditional" on tree presence, we demonstrate how to quantify this conditional status for regeneration. We found that fecundity was regulated by temperature to a greater degree than other stages, yet exhibited limited responses to moisture deficit. Recruitment rate expressed strong sensitivities to CHI, more like adults than fecundity, but still with substantial differences. Communities reorganized from adults to fecundity, but there was a re-coalescence of groups as seedling recruitment partially reverted to community structure similar to that of adults. Results provide the first estimates of continent-wide community sensitivity and their implications for reorganization across three life-history stages under climate change.

Linking seed size and number to trait syndromes in trees

“…Anticipating individual and combined effects of global change requires an understanding of the vulnerability not only of mature trees but also of seed number and recruitment (Bogdziewicz, 2022; Clark et al, 2021; Hanbury‐Brown et al, 2022; Ohse et al, 2023; Qiu, Sharma, et al, 2021; Sharma et al, 2022). One major challenge that exists in ecology more generally (Nuñez et al, 2021) is to increase the data coverage to underrepresented regions, such as Africa or Southeast Asia in our case.…”

Learning from monitoring networks: Few-large vs. many-small plots and multi-scale analysis

“…To demonstrate aggregation effects in a real-world example, we modeled basal area (continuous) on FIA phase-2 plots that were sampled after 1995 in the eastern US (Gray et al, 2012). Following Qiu et al (2021), we used all four sub-plots for each FIA plot site. However, we note that an application can also be restricted to only the central sub-plot, which has been shown to have a limited impact on residual variation as compared to compositing all four subplots (Gray et al, 2012;McRoberts et al, 2018).…”

“…We excluded non-response plots but included mixed condition plots, which skews our sample away from private property and potentially includes plots that are particularly noisy due to intra-site condition variation. Following Qiu et al (2021), covariates included percent clay and cation exchange capacity (CEC) in the upper 30 cm of soil (Hengl et al, 2017), mean annual temperature ( • C), and moisture deficit (mm; Abatzoglou et al (2018)), and stand age (Burrill et al, 2021). Spatial correlation persists over a large spatial range for temperature and moisture deficit, but not for soil variables and stand age (Supplementary Figure 2A).…”

“…This k-means clustering was modified to only allow plot clusters approximately equal to the target aggregate size but not much larger or smaller (so for J = 10, 60, 125, 200, the ranges were 10-15, 60-65, 125-135, and 200-210, respectively). Following Qiu et al (2021), this was achieved by first using a typical k-means clustering algorithm, retaining all the clusters within the initial cluster size tolerance, and then re-running the clustering with the remaining plots until they matched the required cluster size tolerance. Because of the tolerance, the resulting sizes of each aggregated cluster j may slightly differ (i.e., J j not necessarily equal to J.…”

Impact of Silica Addition on Alleviating Cadmium Stress: Case Studies of Three Afforestation Tree Species Seedlings in Southern China

“…The negative effects of Cd include the decline of biomass production and soluble protein content, the disturbance of metabolism, the retardation of enzymatic activities and the excessive production of reactive oxygen species (ROS), thereby stunting plant growth [2][3][4][5]. Additionally, the presence of Cd may undermine the microorganisms in soil, which may result in a decline in soil organic matter decomposition, and thus lead Forests 2022, 13, 1641 2 of 16 to decrease in soil nutrients, while soil nutrients are generally believed to have strong effects on seed yield [6,7], the recruitment of plant communities [8,9] and some other crucial functions for plants. Therefore, it is vital to enhance the capacity of defenses against the adverse impacts induced by Cd stress in plants.…”