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Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution). Plain Language Summary: Soil moisture (SM) widely varies in space and time. This variability critically influences freshwater availability, agriculture, ecosystem dynamics, climate and land‐atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Limited SM observational data constrained our understanding of this variability and its impact on the Earth system. Here, we present the first continental assessment of how SM varies at the local scales using SMAP‐HydroBlocks – the first 30‐m surface SM data set over the United States. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States, mainly driven by local spatial variations in soil properties and less so by vegetation and topography. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non‐linear SM‐dependent hydrological, ecological, and biogeochemical processes using coarse‐scale models and satellite estimates. By mapping the SM spatial variability locally and its scaling behavior, we provide a pathway toward understanding SM‐dependent hydrological, biogeochemical, and ecological processes at local (and so far unresolved) spatial scales. Key Points: 30‐m soil moisture (SM) data shows striking and complex spatial variability driven mainly by climate and local variations in soil propertiesThis variability yields a remarkable and unique multi‐scale behavior at each location that cannot be generalized across the diverse USUp to 80% of SM information is lost at the 1‐km scale, with complete loss at the scale of state‐of‐the‐art SM observation/monitoring systems [ABSTRACT FROM AUTHOR]

• DayCent now includes microbial-explicit decomposition and soil carbon stabilization. • Refined perennial plant traits better represent litter chemistry. • Microbe model projects less soil carbon under future climate than first-order model. Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation. [ABSTRACT FROM AUTHOR]

Timing of reproduction may be of crucial importance for fitness, particularly in environments that vary seasonally in food availability or predation risk. However, for animals with spatially separated feeding and breeding habitats, optimal reproductive timing may differ between parents and their offspring, leading to parent-offspring conflict. We assume that offspring have highest survival and fitness if they are spawned around a fixed date, and use state-dependent life-history theory to explore whether variation in conditions affecting only parents (food availability and survival) may influence optimal timing of reproduction. We apply the model to Pacific herring (Clupea palasii) in Puget Sound, USA, where 20 subpopulations spawn at different times of the year. Our model suggests that relatively small differences in adult food availability can lead to altered prioritization in the trade-off between maternal fecundity and what from the offspring's perspective is the best time to be spawned. Our model also shows that observed among-population variability in reproductive timing may result from adults using different feeding grounds with divergent food dynamics, or from individual variation in condition caused by stochasticity at a single feeding ground. Identifying drivers of reproductive timing may improve predictions of recruitment, population dynamics, and responses to environmental change. [ABSTRACT FROM AUTHOR]

• Establishes consistent, generalizable standards for calibrating and validating LANDIS-II. • Initialization methods based on publicly available, full coverage datasets. • LANDIS-II can be calibrated to reliably simulate wildfire and biomass dynamics. • This study lowers the barrier to adapting forest landscape models to new landscapes. • Aids independent assessment of the credibility of existing forest landscape models. Process-based Forest Landscape Models (FLMs) rely on first principles to simulate ecological patterns and processes, making them uniquely powerful for forecasting ecological dynamics under unprecedented climatic and disturbance regimes. Persistent challenges with any ecological forecasting model are calibration ("tuning" the model) and validation ("proofing" the model). As no actual future data exist from which to conduct a formal model validation, model credibility is established through numerous tests against empirical datasets and comparisons with other types of models. The purpose of this study was to establish more consistent and generalizable standards for calibrating and validating LANDIS-II, a widely used, open-source FLM. We reviewed methods gleaned from a wide variety of previous FLM studies and advance some new techniques for evaluating the credibility of the model outputs. We used publicly available data with full coverage for the United States (US) so that our methods will be generalizable to other landscapes in the US, and we developed an ecologically meaningful set of validation metrics for evaluating the credibility of new applications. We found that LANDIS-II could be calibrated to reliably simulate empirical vegetation-disturbance-climate dynamics in diverse, mountainous terrain and fire-prone landscapes of the eastern Cascade Mountains. We performed an inter-model validation between LANDIS-II and the Forest Vegetation Simulator (FVS), demonstrating consistent projections of biomass dynamics for all tree-dominated ecoregions in the study domain. Similarly, simulated fires reliably approximated the empirical fire event size and severity patch size distributions based on observed fire activity from 1984 to 2019. By establishing rigorous, transparent, and repeatable standards for calibrating and validating FLM dynamics, we sought to remove some of the barriers to adapting LANDIS-II to new landscapes and climates, facilitate further validation of existing models, and aid independent assessment of the credibility of forest landscape models. [ABSTRACT FROM AUTHOR]

From the planning days preceding the establishment of the US Global Ocean Ecosystem Dynamics (GLOBEC) program, modeling was recognized as one of the program's pillars. In particular, predictions of future ecosystem states in an evolving climate system required new interdisciplinary approaches that brought together physicists, biologists, modelers, and observational scientists. The GLOBEC program coincided with, took advantage of, and contributed to significant advances in ocean modeling capabilities. During the GLOBEC years, computer power increased substantially to the point where coupled physical-biological models, at resolutions where important interactions are resolved, became feasible. Ocean models were maturing so that complex coastal processes were explicitly represented, and advances in different ways of modeling the biosphere, from Lagrangian individuals to Eulerian community-based, multitrophic models, were emerging. The US GLOBEC program addressed the question: How can we use all these developments to help us understand how ecosystems will respond to climate change? his paper includes a review of state-of-the-science modeling at the onset of the GLOBEC program and highlights the evolution of physical and biological models used for the program' s target regions and species throughout the GLOBEC years, 1992-2012. [ABSTRACT FROM AUTHOR]

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980–2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products. [ABSTRACT FROM AUTHOR]