High biodiversity of forests is not predicted by traditional models, and evidence for trade‐offs those models require is limited. High‐dimensional regulation (e.g., N factors to regulate N species) has long been recognized as a possible alternative explanation, but it has not be been seriously pursued, because only a few limiting resources are evident for trees, and analysis of multiple interactions is challenging. We develop a hierarchical model that allows us to synthesize data from long‐term, experimental, data sets with processes that control growth, maturation, fecundity, and survival. We allow for uncertainty at all stages and variation among 26 000 individuals and over time, including 268 000 tree years, for dozens of tree species. We estimate population‐level parameters that apply at the species level and the interactions among latent states, i.e., the demographic rates for each individual, every year. The former show that the traditional trade‐offs used to explain diversity are not present. Demographic rates overlap among species, and they do not show trends consistent with maintenance of diversity by simple mechanisms (negative correlations and limiting similarity). However, estimates of latent states at the level of individuals and years demonstrate that species partition environmental variation. Correlations between responses to variation in time are high for individuals of the same species, but not for individuals of different species. We demonstrate that these relationships are pervasive, providing strong evidence that high‐dimensional regulation is critical for biodiversity regulation.

Transit times through hydrologic systems vary in time, but the nature of that variability is not well understood. Transit times variability was investigated in a 1 m3 sloping lysimeter, representing a simplified model of a hillslope receiving periodic rainfall events for 28 days. Tracer tests were conducted using an experimental protocol that allows time‐variable transit time distributions (TTDs) to be calculated from data. Observed TTDs varied with the storage state of the system, and the history of inflows and outflows. We propose that the observed time variability of the TTDs can be decomposed into two parts: “internal” variability associated with changes in the arrangement of, and partitioning between, flow pathways; and “external” variability driven by fluctuations in the flow rate along all flow pathways. These concepts can be defined quantitatively in terms of rank StorAge Selection (rSAS) functions, which is a theory describing lumped transport dynamics. Internal variability is associated with temporal variability in the rSAS function, while external is not. The rSAS function variability was characterized by an “inverse storage effect,” whereby younger water is released in greater proportion under wetter conditions than drier. We hypothesize that this effect is caused by the rapid mobilization of water in the unsaturated zone by the rising water table. Common approximations used to model transport dynamics that neglect internal variability were unable to reproduce the observed breakthrough curves accurately. This suggests that internal variability can play an important role in hydrologic transport dynamics, with implications for field data interpretation and modeling.

Recent field observations indicate that in many forest ecosystems, plants use water that may be isotopically distinct from soil water that ultimately contributes to streamflow. Such an assertion has been met with varied reactions. Of the outstanding questions, we examine whether ecohydrological separation of water between trees and streams results from a separation in time, or in space. Here we present results from a 9‐month drought and rewetting experiment at the 26,700‐m3 mesocosm, Biosphere 2‐Tropical Rainforest biome. We test the null hypothesis that transpiration and groundwater recharge water are sampled from the same soil volume without preference for old nor young water. After a 10‐week drought, we added 66 mm of labeled rainfall with 152‰ δ2H distributed over four events, followed by background rainfall (−60‰ δ2H) distributed over 13 events. Our results show that mean transit times through groundwater recharge and plant transpiration were markedly different: groundwater recharge was 2–7 times faster (~9 days) than transpired water (range 17–62 days). The “age” of transpired water showed strong dependence on species and was linked to the difference between midday leaf water potential and soil matric potential. Moreover, our results show that trees used soil water (89% ±6) and not the “more mobile” (represented by “zero tension” seepage) water (11% ±6). The finding, which rejects our null hypothesis, is novel in that this partitioning is established based on soil water residence times. Our study quantifies mean transit times for transpiration and seepage flows under dynamic conditions.

Distributions of water transit times (TTDs), and related storage‐selection (SAS) distributions, are spatially integrated metrics of hydrological transport within landscapes. Recent works confirm that the form of TTDs and SAS distributions should be considered time variant—possibly depending, in predictable ways, on the dynamic storage of water within the landscape. We report on a 28 day periodic‐steady‐state‐tracer experiment performed on a model hillslope contained within a 1 m3 sloping lysimeter. Using experimental data, we calibrate physically based, spatially distributed flow and transport models, and use the calibrated models to generate time‐variable SAS distributions, which are subsequently compared to those directly observed from the actual experiment. The objective is to use the spatially distributed estimates of storage and flux from the model to characterize how temporal variation in water storage influences temporal variation in flow path configurations, and resulting SAS distributions. The simulated SAS distributions mimicked well the shape of observed distributions, once the model domain reflected the spatial heterogeneity of the lysimeter soil. The spatially distributed flux vectors illustrate how the magnitude and directionality of water flux changes as the water table surface rises and falls, yielding greater contributions of younger water when the water table surface rises nearer to the soil surface. The illustrated mechanism is compliant with conclusions drawn from other recent studies and supports the notion of an inverse‐storage effect, whereby the probability of younger water exiting the system increases with storage. This mechanism may be prevalent in hillslopes and headwater catchments where discharge dynamics are controlled by vertical fluctuations in the water table surface of an unconfined aquifer.