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1. pnetr : An R package for the PnET family of forest ecosystem models

   https://doi.org/10.1111/2041-210X.70076  

   Gao, Xiaojie; Zhou, Zaixing; Ollinger, Scott_V; Matthes, Jaclyn_Hatala; Jiao, Wenzhe; Thompson, Jonathan_R (June 2025, Methods in Ecology and Evolution)

   Abstract Ecosystem models offer a rigorous way to formalize scientific theories and are critical to evaluating complex interactions among ecological and biogeochemical processes. In addition to simulation and prediction, ecosystem models are a valuable tool for testing hypotheses about mechanisms and empirical findings because they reveal critical internal processes that are difficult to observe directly.However, many ecosystem models are difficult to manage and apply by scientists because of complex model structures, lack of consistent documentation, and low‐level programming implementation.Here, we present the ‘pnetr’ R package, which is designed to provide an easy‐to‐manage ecosystem modelling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET (net photosynthesis, evapotranspiration) ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon and nitrogen. We chose the R programming language because it is familiar to many ecologists and has abundant statistical modelling resources. We showcase examples of model simulations and test the effects of phenology on carbon assimilation and wood production using data measured by the Environmental Measurement Station (EMS) eddy‐covariance flux tower at Harvard Forest, MA.We hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modelling and ecological forecasting. 

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2. Uncovering the role of solar radiation and water stress factors in constraining decadal intra‐site spring phenology variability in diverse ecosystems across the Northern Hemisphere

   https://doi.org/10.1111/nph.70104  

   Gu, Yating; Meng, Lin; Wang, Yantian; Wu, Zherong; Pan, Yuhao; Zhao, Yingyi; Detto, Matteo; Wu, Jin (June 2025, New phytologist)

   Summary The spring phenology has advanced significantly over recent decades with climate change, impacting large‐scale biogeochemical cycles, climate feedback, and other essential ecosystem processes. Although numerous prognostic models have been developed for spring phenology, regional analyses of the optimality (OPT) strategy model that incorporate environmental variables beyond temperature and photoperiod remain lacking.We investigated the roles of solar radiation (SR) and three water stress factors (precipitation (P), soil moisture, and vapor pressure deficit (VPD)) on spring phenology from 1982 to 2015 using the OPT model with Global Inventory Modeling and Mapping Studies NDVI3g dataset and environmental data from TerraClimate, CRU_TS, and Global Land Data Assimilation System across the Northern Hemisphere (> 30°N).Our results show that SR and water stress factors significantly impacted intrasite decadal spring phenology variability, with water stress factors dominant in grassland ecosystems while SR dominated in the rest of the ecosystem types. Enhanced models incorporating SR (OPT‐S) and VPD (OPT‐VPD) outperformed the original OPT model, likely due to improved representation of the adaptive strategy of spring phenology to optimize photosynthetic carbon gain while minimizing frost risk.Our research enhances the understanding of the key environmental drivers influencing decadal spring phenology variation in the Northern Hemisphere and contributes to more accurate forecasts of ecological responses to global environmental change. 

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3. Fire‐regime variability and ecosystem resilience over four millennia in a Rocky Mountain subalpine watershed

   https://doi.org/10.1111/1365-2745.14201  

   Clark‐Wolf, Kyra D.; Higuera, Philip E.; McLauchlan, Kendra K.; Shuman, Bryan N.; Parish, Meredith C. (October 2023, Journal of Ecology)

   Abstract Wildfires strongly influence forest ecosystem processes, including carbon and nutrient cycling, and vegetation dynamics. As fire activity increases under changing climate conditions, the ecological and biogeochemical resilience of many forest ecosystems remains unknown.To investigate the resilience of forest ecosystems to changing climate and wildfire activity over decades to millennia, we developed a 4800‐year high‐resolution lake‐sediment record from Silver Lake, Montana, USA (47.360° N, 115.566° W). Charcoal particles, pollen grains, element concentrations and stable isotopes of C and N serve as proxies of past changes in fire, vegetation and ecosystem processes such as nitrogen cycling and soil erosion, within a small subalpine forest watershed. A published lake‐level history from Silver Lake provides a local record of palaeohydrology.A trend towards increased effective moisture over the late Holocene coincided with a distinct shift in the pollen assemblage c. 1900 yr BP, resulting from increased subalpine conifer abundance. Fire activity, inferred from peaks in macroscopic charcoal, decreased significantly after 1900 yr BP, from one fire event every 126 yr (83–184 yr, 95% CI) from 4800 to 1900 yr BP, to one event every 223 yr (175–280 yr) from 1900 yr BP to present.Across the record, individual fire events were followed by two distinct decadal‐scale biogeochemical responses, reflecting differences in ecosystem impacts of fires on watershed processes. These distinct biogeochemical responses were interpreted as reflecting fire severity, highlighting (i) erosion, likely from large or high‐severity fires, and (ii) nutrient transfers and enhanced within‐lake productivity, likely from lower severity or patchier fires. Biogeochemical and vegetation proxies returned to pre‐fire values within decades regardless of the nature of fire effects.Synthesis. Palaeorecords of fire and ecosystem responses provide a novel view revealing past variability in fire effects, analogous to spatial variability in fire severity observed within contemporary wildfires. Overall, the palaeorecord highlights ecosystem resilience to fire across long‐term variability in climate and fire activity. Higher fire frequencies in past millennia relative to the 20th and 21st century suggest that northern Rocky Mountain subalpine ecosystems could remain resilient to future increases in fire activity, provided continued ecosystem recovery within decades. 

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4. neonSoilFlux: An R package for continuous sensor‐based estimation of soil CO₂ fluxes

   https://doi.org/10.1111/2041-210x.70216  

   Zobitz, John; Ayres, Edward; Werbin, Zoey; Abdi, Ridwan; Ashburner‐Wright, Natalie; Brown, Lillian; Frink‐Sobierajski, Ryan; Lee, Lajntxiag; Mehmeti, Dijonë; Tran, Christina; et al (December 2025, Methods in Ecology and Evolution)

   Abstract Accurate quantification of soil carbon fluxes is essential to reduce uncertainty in estimates of the terrestrial carbon sink. However, these fluxes vary over time and across ecosystem types and so, it can be difficult to estimate them accurately across large scales. The flux‐gradient method estimates soil carbon fluxes using co‐located measurements of soil CO₂concentration, soil temperature, soil moisture and other soil properties. The National Ecological Observatory Network (NEON) provides such data across 20 ecoclimatic domains spanning the continental U.S., Puerto Rico, Alaska and Hawai‘i.We present an R software package (neonSoilFlux) that acquires soil environmental data to compute half‐hourly soil carbon fluxes for each soil replicate plot at a given terrestrial NEON site. To assess the computed fluxes, we visited six focal NEON sites and measured soil carbon fluxes using a closed‐dynamic chamber approach.Outputs from theneonSoilFluxshowed agreement with measured fluxes (R²between measured andneonSoilFluxoutputs ranging from 0.12 to 0.77 depending on calculation method used); measured outputs generally fell within the range of calculated uncertainties from the gradient method. Calculated fluxes fromneonSoilFluxaggregated to the daily scale exhibited expected site‐specific seasonal patterns.While the flux‐gradient method is broadly effective, its accuracy is highly sensitive to site‐specific inputs, including the extent to which gap‐filing techniques are used to interpolate missing sensor data and to estimates of soil diffusivity and moisture content. Future refinement and validation ofneonSoilFluxoutputs can contribute to existing databases of soil carbon flux measurements, providing near real‐time estimates of a critical component of the terrestrial carbon cycle. 

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5. TLSL e AF: automatic leaf angle estimates from single‐scan terrestrial laser scanning

   https://doi.org/10.1111/nph.17548  

   Stovall, Atticus E. L.; Masters, Benjamin; Fatoyinbo, Lola; Yang, Xi (July 2021, New Phytologist)

   Summary Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global‐scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open‐source method of deriving LADs from terrestrial laser scanning.TLSLeAF produces canopy‐scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community.TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer‐grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate toc. 25% andc. 11% increases in canopy net photosynthesis (c. 25%) and solar‐induced chlorophyll fluorescence (c. 11%).TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote‐sensing experts, allowing greater accessibility for addressing ecological hypotheses and large‐scale ecosystem modeling efforts. 

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