The global carbon (C) balance is vulnerable to disturbances that alter terrestrial C storage. Disturbances to forests occur along a continuum of severity, from low-intensity disturbance causing the ...mortality or defoliation of only a subset of trees to severe stand-replacing disturbance that kills all trees; yet considerable uncertainty remains in how forest production changes across gradients of disturbance intensity. We used a gradient of tree mortality in an upper Great Lakes forest ecosystem to: (1) quantify how aboveground wood net primary production (ANPP
w
) responds to a range of disturbance severities; and (2) identify mechanisms supporting ANPP
w
resistance or resilience following moderate disturbance. We found that ANPP
w
declined nonlinearly with rising disturbance severity, remaining stable until >60% of the total tree basal area senesced. As upper canopy openness increased from disturbance, greater light availability to the subcanopy enhanced the leaf-level photosynthesis and growth of this formerly light-limited canopy stratum, compensating for upper canopy production losses and a reduction in total leaf area index (LAI). As a result, whole-ecosystem production efficiency (ANPP
w
/LAI) increased with rising disturbance severity, except in plots beyond the disturbance threshold. These findings provide a mechanistic explanation for a nonlinear relationship between ANPP
w
and disturbance severity, in which the physiological and growth enhancement of undisturbed vegetation is proportional to the level of disturbance until a threshold is exceeded. Our results have important ecological and management implications, demonstrating that in some ecosystems moderate levels of disturbance minimally alter forest production.
Abstract
Tidal marshes sequester 11.4–87.0 Tg C yr
−1
globally, but climate change impacts can threaten the carbon capture potential of these ecosystems. Tidal marshes occur across a wide range of ...salinity, with brackish marshes (0.5–18 ppt (parts per thousand)) dominating global tidal marsh extents. A diverse mix of freshwater- and saltwater-tolerant plant and microbial communities has led researchers to predict that carbon cycling in brackish wetlands may be less sensitive to changes in salinity than fresh- or saltwater wetlands. Rush Ranch, a well-monitored brackish tidal wetland of the San Francisco Bay National Estuarine Research Reserve, experiences highly variable annual salinity regimes. Within a five-year period (2014–2018), Rush Ranch experienced particularly extreme drought-induced salinization during the 2014 and 2015 growing seasons. During drought years, tidal channel salinity rose from a 15 year baseline of 4.7 ppt to growing season peaks of 10.3 ppt and 12.5 ppt. Continuous eddy covariance data from 2014 to 2018 demonstrate that during drought summers, gross primary productivity (GPP) decreased by 24%, whereas ecosystem respiration remained similar among all five years. Stepwise linear regression revealed that salinity, not air temperature or tidal height, was the dominant driver of annual GPP. A random forest model trained to predict GPP based on environmental data from low salinity years (i.e. naive to salinization) significantly over predicted GPP in drought years. When growing season salinities were doubled, annual estimates of net ecosystem exchange of CO
2
decreased by up to 30%. These results provide ecosystem-scale evidence that increased salinity influences CO
2
fluxes dominantly through reductions in GPP. This relationship provides a starting point for incorporating the effect of changes in salinity in wetland carbon models, which could improve wetland carbon forecasting and management for climate resilience.
Tidal wetlands play an important role in global carbon cycling by storing carbon in sediment at millennial time scales, transporting dissolved carbon into coastal waters, and contributing ...significantly to global CH4 budgets. However, these ecosystems' greenhouse gas monitoring and predictions are challenging due to spatial heterogeneity and tidal flooding. We utilized eddy covariance and chamber measurements to quantify fluxes of CO2 and CH4 at a restored tidal saltmarsh across spatial and temporal scales. Eddy covariance data revealed that the site was a strong net sink for CO2 (−387 g C‐CO2 m−2 yr−1, SD = 46) and a small net source of CH4 (0.7 g C‐CH4 m−2 yr−1, SD = 0.4). After partitioning net ecosystem exchange of CO2 into gross primary production and ecosystem respiration, we found that high net uptake of CO2 was due to low respiration emissions rather than high photosynthetic rates. We also found that respiration rates varied between land covers with increased respiration in mudflats compared to vegetated areas. Daytime soil chamber measurements revealed that the greatest CO2 emission was from higher elevation mudflat soils (0.5 μmol m−2s−1, SE = 1.3) and CH4 emission was greatest from lower elevation Spartina foliosa soils (1.6 nmol m−2s−1, SD = 8.2). Overall, these results highlight the importance of the relationships between wetland plant community and elevation, and inundation for CO2 and CH4 fluxes. Future research should include the use of high‐resolution imagery, automated chambers, and a focus on quantifying carbon exported in tidal waters.
Plain Language Summary
At the ecosystem level, a restored tidal salt marsh in the South San Francisco Bay California took in more carbon dioxide (CO2) from the atmosphere through photosynthetic activity than it emitted through respiration, and it emitted very small amounts of methane (CH4). This site appears to be a stronger sink for CO2 compared to other tidal marsh sites due to the very low rate of CO2 being lost through respiration to the atmosphere, rather than strong photosynthetic rates. We also found that ecosystem level CO2 emissions and the responses to temperature and light varied based on land cover type. By measuring soil surface emissions from each of the main land cover types of pickleweed, cordgrass, and mudflats we found that on average soils with lower elevation where cordgrass grows were stronger sources of CH4 while mudflat soils with greater elevation were stronger sources of CO2.
Key Points
Soil chamber measurements were able to detect significant differences in CO2 and CH4 fluxes between land cover types
Vegetation and microtopography are drivers of the spatially heterogeneous CO2 and CH4 emissions within the wetland
At the ecosystem level, high net uptake of CO2 was the result of low respiration emissions, suggesting lateral transport of dissolved CO2
•Large-scale eddy-covariance flux datasets need to be used with footprint-awareness•Using a fixed-extent target area across sites can bias model-data integration•Most sites do not represent the ...dominant land-cover type at a larger spatial extent•A representativeness index provides general guidance for site selection and data use
Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
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Extreme drought is increasing in frequency and intensity in many regions globally, with uncertain consequences for the resistance and resilience of ecosystem functions, including primary production. ...Primary production resistance, the capacity to withstand change during extreme drought, and resilience, the degree to which production recovers, vary among and within ecosystem types, obscuring generalized patterns of ecological stability. Theory and many observations suggest forest production is more resistant but less resilient than grassland production to extreme drought; however, studies of production sensitivity to precipitation variability indicate that the processes controlling resistance and resilience may be influenced more by mean annual precipitation (MAP) than ecosystem type. Here, we conducted a global meta-analysis to investigate primary production resistance and resilience to extreme drought in 64 forests and grasslands across a broad MAP gradient. We found resistance to extreme drought was predicted by MAP; however, grasslands (positive) and forests (negative) exhibited opposing resilience relationships with MAP. Our findings indicate that common plant physiological mechanisms may determine grassland and forest resistance to extreme drought, whereas differences among plant residents in turnover time, plant architecture, and drought adaptive strategies likely underlie divergent resilience patterns. The low resistance and resilience of dry grasslands suggests that these ecosystems are the most vulnerable to extreme drought – a vulnerability that is expected to compound as extreme drought frequency increases in the future.
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•Whether forests and grasslands respond similarly to extreme drought is unknown.•Meta-analysis compared forest and grassland production resistance and resilience.•Resistance followed a common continuum of mean annual precipitation (MAP).•Grassland resilience increased, forest resilience decreased, with increasing MAP.•Dry grasslands are most vulnerable; dry forest response requires more research.
•We evaluate methane flux gap-filling methods across 17 boreal-to-tropical wetlands•New methods for generating realistic artificial gaps and uncertainties are proposed•Decision tree algorithms ...perform slightly better than neural networks on average•Soil temperature and generic seasonality are the most important predictors•Open-source code is released for gap-filling steps and uncertainty evaluation
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
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•Subcanopy contributed substantially to forest growth response to moderate disturbance.•Variable subcanopy response was mediated by composition not disturbance severity.•Moderate severity disturbance ...promoted growth of mid-tolerant northern red oak.
North American temperate forests have functioned as a terrestrial carbon (C) sink for more than a century, but the future of this sink is highly uncertain as disturbance frequency increases and regrown forests approach maturity. The subcanopy is integral to the functional recovery of forests, supporting short-term resilience of primary production and longer-term shifts in tree species composition and diversity. However, the factors that contribute to variation in forest subcanopy response to disturbance are not well understood. In this study, we investigated subcanopy shifts in aboveground wood net primary productivity (ANPPw) and composition following experimental moderate severity disturbance emulating natural canopy mortality from age-related senescence. We assessed the importance of variation in disturbance severity, site fertility, and community composition on subcanopy disturbance response and contribution to total (canopy and subcanopy) ANPPw response. We also assessed the effect of the moderate severity disturbance on species composition and diversity, and competitive patterns within the subcanopy layer. Subcanopy aboveground biomass and ANPPw increased substantially relative to pre-disturbance levels by a factor of 1.4 and 22.7, respectively. The subcanopy (stems <8cm DBH) made up a large component of overall (canopy plus subcanopy) post disturbance ANPPw (16.2%) and disturbance response (post-disturbance ANPPw/pre-disturbance ANPPw; 54.1%). Subcanopy ANPPw, subcanopy post-disturbance ANPPw response, and subcanopy contribution to total post-disturbance ANPPw response were all most strongly predicted by subcanopy community composition in combination with canopy composition and site fertility. Variation in disturbance severity was not a strong predictor of subcanopy ANPPw response to disturbance. Subcanopy compositional trends and growth patterns both indicate likely increased heterogeneity in canopy composition (greater β diversity) and a potential shift toward greater dominance by mid-tolerant Quercus rubra (northern red oak). Our results illustrate the importance of the subcanopy in the response of forest productivity to moderate severity disturbance and illustrate that composition of the subcanopy layer exerts a strong influence on the growth response both of the subcanopy and the forest as a whole. Our findings highlight the unique role of moderate severity disturbance, relative to more severe disturbances, in promoting biological and structural heterogeneity in forest ecosystems and favoring underrepresented mid-tolerant species.
Vegetation canopy structure is a fundamental characteristic of terrestrial ecosystems that defines vegetation types and drives ecosystem functioning. We use the multivariate structural trait ...composition of vegetation canopies to classify ecosystems within a global canopy structure spectrum. Across the temperate forest sub‐set of this spectrum, we assess gradients in canopy structural traits, characterise canopy structural types (CST) and evaluate drivers and functional consequences of canopy structural variation. We derive CSTs from multivariate canopy structure data, illustrating variation along three primary structural axes and resolution into six largely distinct and functionally relevant CSTs. Our results illustrate that within‐ecosystem successional processes and disturbance legacies can produce variation in canopy structure similar to that associated with sub‐continental variation in forest types and eco‐climatic zones. The potential to classify ecosystems into CSTs based on suites of structural traits represents an important advance in understanding and modelling structure–function relationships in vegetated ecosystems.
Many secondary deciduous forests of eastern North America are approaching a transition in which mature early-successional trees are declining, resulting in an uncertain future for this century-long ...carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem girdling-induced mortality of >6,700 early-successional Populus spp. (aspen) and Betula papyrifera (paper birch). Meteorological flux tower-based C cycling observations from the 33-ha treatment forest have been paired with those from a nearby unmanipulated forest since 2008. Following over a decade of observations, we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid-late-successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather with a rapid 1-yr recovery of total leaf area index as mid-late-successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid-late-successional species dominance improved carbon-use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid-late-successional species in the treatment forest, suggest changes in aboveground plant respiration and growth were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come.
Climate change is a world‐wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for ...nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil–plant–atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high‐quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis and upscaling. Many of these challenges relate to a lack of an established ‘best practice’ for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change.
To overcome these challenges, we collected best‐practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re‐use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re‐use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second‐order research outputs and create opportunities for collaboration across scientific communities. Ultimately, this should significantly improve the quality and impact of the science, which is required to fulfil society's needs in a changing world.
RESUMEN
El cambio climático es una amenaza global para la biodiversidad, la estructura y funcionamiento de los ecosistemas y la habilidad de éstos para generar servicios ecosistémicos. Para poder comprender las causas y los mecanismos subyacentes, y poder predecir las consecuencias del cambio climático tanto para la naturaleza como para los seres humanos, debemos entender la magnitud y dirección de estos cambios a través del continuo suelo‐planta‐atmósfera. El creciente número de estudios sobre cambio climático brinda nuevas oportunidades para poder generalizar de forma más robusta y entender mejor los procesos implicados. Sin embargo, todavía hay grandes obstáculos en cuanto a la disponibilidad de datos y cómo de compatibles son los distintos estudios, que ponen en riesgo las oportunidades para reutilizar y sintetizar datos y comparar a distintas escalas. Estos obstáculos limitan nuesta habilidad para comprender los complejos procesos y mecanismos relacionados con el cambio climático en ecosistemas terrestres.
Para superar estos obstáculos, hemos recopilado recomendaciones metodológicas basadas en las mejores prácticas propuestas por las principales redes de investigación en ecología, avalados por 115 expertos de un amplio rango de disciplinas científicas. Nuestro manual contiene recomendaciones para la selección de variables respuesta para diferentes propósitos, y protocolos para realizar medidas estandarizadas de 66 posibles variables respuesta, así como sugerencias para la gestión de los datos obtenidos. Recomendamos específicamente un mínimo de variables que deben medirse en todos los estudios sobre cambio climático para permitir la reutilización y síntesis de datos. Además, sugerimos una serie de variables adicionales que pueden ser relevantes para distintos tipos de síntesis y para la comparación a distintas escalas. El objetivo de este esfuerzo comunitario es concienciar sobre la importancia de la aplicación de métodos estandarizados para facilitar la reutilización, disponibilidad, compatibilidad y transparencia de los datos. Mejorar las prácticas de investigación aumentará la eficiencia de proyectos de investigación individuales, facilitará resultados de investigación de segundo orden y creará oportunidades para la colaboración entre comunidades científicas. Por último, estas prácticas mejorarán considerablemente la calidad y el impacto de la ciencia, que se requiere para satisfacer las necesidades de la sociedad en un mundo cambiante.
摘要
气候变化正在对全球生物多样性, 生态系统结构, 功能和服务造成严重的威胁。为了理解这一过程的潜在驱动和机制, 以及预测这一过程对自然和人类社会的影响, 我们迫切的需要更好的了解气候变化对土壤‐植物‐大气系统影响的方向和强度。不断增加的相关方向的研究正在使对该研究领域的数据进行广泛而有意义的概括和总结成为可能, 从而进一步提高我们对这一重要过程的理解。然而, 显著的挑战依然存在, 特别是不同的生态研究产生的数据的可获得性和兼容性有着巨大差异, 这在一定程度上限制了研究数据的再度利用, 概括总结以及进一步推广应用。这个挑战与我们在观测气候变化和生态系统的关键影响及响应时缺乏良好的“最佳实践”有关。这个问题在很大程度上限制了我们对气候变化背景下陆地生态系统的复杂过程和机制的理解。
为了克服这些挑战, 我们收集了基于115个不同陆地生态研究领域的专家总结的主要生态研究网络和试验中的“最佳实践”方法。我们建立的“最佳实践”手册包括了对不同研究目的下选择测量的生态响应变量的建议, 测量66个生态响应变量的标准化流程, 以及如何进行数据管理的建议。特别地, 为了达到研究数据的可再度利用和融合, 我们推荐了一个在所有相关陆地生态研究中必须收集的最小数据集。同时针对不同类型的数据融合和推广应用, 我们给出了额外的关键数据和变量的收集建议。我们的工作希望能够有助于提高该研究领域的研究者对数据标准化的重要性和广泛应用的认识, 从而促进数据的再度利用, 数据的可获得性, 兼容性和透明度。我们期望改进的研究实践将能够增加每一个研究项目的投资回报率, 促进科研数据的二度产出, 以及创造更多的跨研究领域, 跨学科的合作机会。并最终, 显著提高我们的科学研究的质量和影响力, 以满足全球变化下社会对相关科学成果的需求。
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