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Decomposition of dead organic matter is fundamental to carbon (C) and nutrient cycling in terrestrial ecosystems, influencing C fluxes from the biosphere to the atmosphere. Theory predicts and evidence strongly supports that the availability of nitrogen (N) limits litter decomposition. Positive relationships between substrate N concentrations and decomposition have been embedded into ecosystem models. This decomposition paradigm, however, relies on data mostly from short-term studies analyzing controls on early-stage decomposition. We present evidence from three independent long-term decomposition investigations demonstrating that the positive N-decomposition relationship is reversed and becomes negative during later stages of decomposition. First, in a 10-y decomposition experiment across 62 woody species in a temperate forest, leaf litter with higher N concentrations exhibited faster initial decomposition rates but ended up a larger recalcitrant fraction decomposing at a near-zero rate. Second, in a 5-y N-enrichment experiment of two tree species, leaves with experimentally enriched N concentrations had faster decomposition initial rates but ultimately accumulated large slowly decomposing fractions. Measures of amino sugars on harvested litter in two experiments indicated that greater accumulation of microbial residues in N-rich substrates likely contributed to larger slowly decomposing fractions. Finally, a database of 437 measurements from 120 species in 45 boreal and temperate forest sites confirmed that higher N concentrations were associated with a larger slowly decomposing fraction. These results challenge the current treatment of interactions between N and decomposition in many ecosystems and Earth system models and suggest that even the best-supported short-term controls of biogeochemical processes might not predict long-term controls. [ABSTRACT FROM AUTHOR]

Cold‐air pooling is an important topoclimatic process that creates temperature inversions with the coldest air at the lowest elevations. Incomplete understanding of sub‐canopy spatiotemporal cold‐air pooling dynamics and associated ecological impacts hinders predictions and conservation actions related to climate change and cold‐dependent species and functions. To determine if and how cold‐air pooling influences forest composition, we characterized the frequency, strength, and temporal dynamics of cold‐air pooling in the sub‐canopy at local to regional scales in New England, USA. We established a network of 48 plots along elevational transects and continuously measured sub‐canopy air temperatures for 6–10 months (depending on site). We then estimated overstory and understory community temperature preferences by surveying tree composition in each plot and combining these data with known species temperature preferences. We found that cold‐air pooling was frequent (19–43% seasonal occurrences) and that sites with the most frequent inversions displayed inverted forest composition patterns across slopes with more cold‐adapted species, namely conifers, at low instead of high elevations. We also observed both local and regional variability in cold‐air pooling dynamics, revealing that while cold‐air pooling is common, it is also spatially complex. Our study, which uniquely focused on broad spatial and temporal scales, has revealed some rarely reported cold‐air pooling dynamics. For instance, we discovered frequent and strong temperature inversions that occurred across seasons and in some locations were most frequent during the daytime, likely affecting forest composition. Together, our results show that cold‐air pooling is a fundamental ecological process that requires integration into modeling efforts predicting future forest vegetation patterns under climate change, as well as greater consideration for conservation strategies identifying potential climate refugia for cold‐adapted species. [ABSTRACT FROM AUTHOR]

Extreme weather events, such as ice storms, are increasing and have potentially large impacts on forests, including belowground structures such as fine roots and mycorrhizal fungi. Many forest trees rely on the mutualistic relationship between mycorrhizal fungi and plants; a relationship that, when disrupted, can negatively impact tree net primary productivity. We took advantage of a large‐scale ice storm manipulation in the northeastern United States to test the hypothesis that increasing ice storm intensity and frequency would reduce ectomycorrhizal fungal root tips per unit root length and arbuscular mycorrhizal fungal structures per unit root length, hereafter colonization. We found that ice storm intensity reduced spring ectomycorrhizal fungal and arbuscular mycorrhizal fungal colonization. However, these patterns changed in the fall, where ice storm intensity still reduced ectomycorrhizal fungal root tips, but arbuscular mycorrhizal fungal colonization was higher in ice storm treatments than controls. The amount of ectomycorrhizal fungal root tips and arbuscular mycorrhizal fungal colonization differed seasonally: ectomycorrhizal fungal root tips were 1.7× higher in the spring than in the fall, while arbuscular mycorrhizal fungal colonization was 3× higher in the fall than in the spring. Our results indicate that mycorrhizal fungal colonization responses to ice storm severity vary temporally and by mycorrhizal fungal type. Further, arbuscular mycorrhizal fungi may recover from ice storms relatively quickly, potentially aiding forests in their recovery, whereas ice storms may have a long lasting impact on ectomycorrhizal fungi. [ABSTRACT FROM AUTHOR]

Warmer winters with less snowfall are increasing the frequency of soil freeze–thaw cycles across temperate regions. Soil microbial responses to freeze–thaw cycles vary and some of this variation may be explained by microbial conditioning to prior winter conditions, yet such linkages remain largely unexplored. We investigated how differences in temperature history influenced microbial community composition and activity in response to freeze–thaw cycles.We collected soil microbial communities that developed under colder (high elevation) and warmer (low elevation) temperature regimes in spruce‐fir forests, then added each of these soil microbial communities to a sterile bulk‐soil in a laboratory microcosm experiment. The inoculated high‐elevation cold and low‐elevation warm microcosms were subjected to diurnal freeze–thaw cycles or constant above‐freezing temperature for 9 days. Then, all microcosms were subjected to a 7‐day above‐freezing recovery period.Overall, we found that the high‐elevation cold community had, relative to the low‐elevation warm community, a smaller reduction in microbial respiration (CO2 flux) during freeze–thaw cycles. Further, the high‐elevation cold community, on average, experienced lower freeze–thaw‐induced bacterial mortality than the warm community and may have partly acclimated to freeze–thaw cycles via increased lipid membrane fluidity. Respiration of both microbial communities quickly recovered following the end of the freeze–thaw treatment period and there were no changes in soil extractable carbon or nitrogen.Our results provide evidence that past soil temperature conditions may influence the responses of soil microbial communities to freeze–thaw cycles. The microbial community that developed under a colder temperature regime was more tolerant of freeze–thaw cycles than the community that developed under a warmer temperature regime, although both communities displayed some level of resilience. Taken together, our data suggest that microbial communities conditioned to less extreme winter soil temperatures may be most vulnerable to rapid changes in freeze–thaw regimes as winters warm, but they also may be able to quickly recover if mortality is low. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Evidence is mounting that extinctions are altering key processes important to the productivity and sustainability of Earth's ecosystems (1-4). Further species loss will accelerate change in ecosystem processes (5-8), but it is unclear how these effects compare to the direct effects of other forms of environmental change that are both driving diversity loss and altering ecosystem function. Here we use a suite of meta-analyses of published data to show that the effects of species loss on productivity and decomposition--two processes important in all ecosystems--are of comparable magnitude to the effects of many other global environmental changes. In experiments, intermediate levels of species loss (21-40%) reduced plant production by 5-10%, comparable to previously documented effects of ultraviolet radiation and climate warming. Higher levels of extinction (41-60%) had effects rivalling those of ozone, acidification, elevated C[O.sub.2] and nutrient pollution. At intermediate levels, species loss generally had equal or greater effects on decomposition than did elevated C[O.sub.2] and nitrogen addition. The identity of species lost also had a large effect on changes in productivity and decomposition, generating a wide range of plausible outcomes for extinction. Despite the need for more studies on interactive effects of diversity loss and environmental changes, our analyses clearly show that the ecosystem consequences of local species loss are as quantitatively significant as the direct effects of several global change stressors that have mobilized major international concern and remediation efforts (9)., A variety of global changes are driving rates of species extinction that greatly outpace background rates in the fossil record (10,11). If these trends continue, projections suggest that within (240) [...]

Catchment nutrient export, especially during high flow events, can influence ecological processes in receiving waters by altering nitrogen (N) and phosphorus (P) concentrations and relative amounts (stoichiometry). Event‐scale N and P export dynamics may be significantly altered by land use/land cover (LULC) and season. Consequently, to manage water resources, it is important to understand how LULC and season interact to influence event N and P export. In situ, high‐frequency spectrophotometers allowed us to continuously and concurrently monitor nitrate (NO3−) and soluble reactive P (SRP) concentrations and therefore examine event‐scale NO3− and SRP export dynamics. Here we analyzed event NO3− and SRP concentration‐discharge hysteresis patterns and yields for >400 events to evaluate how LULC and seasonality influence event NO3− and SRP export dynamics in three low‐order watersheds with different primary LULCs (agricultural, forested, and urban). Differences among event NO3− and SRP hysteresis patterns suggest these nutrients have different source areas and dominant transport pathways that were impacted by both LULC and seasonality. Unexpectedly, we observed similar seasonal patterns in event NO3−:SRP stoichiometry among LULCs, with the most N‐enriched events occurring in spring, and event stoichiometry approaching Redfield N:P ratios in the fall. However, seasonal stoichiometry patterns were driven by unique seasonal NO3− and SRP export patterns at each site. Overall these findings suggest LULC and seasonality interact to alter the timing and magnitude of event NO3− and SRP exports, leading to seasonal patterns in event NO3− to SRP stoichiometry that may influence ecological processes, such as productivity, in receiving waters. Plain Language Summary: High flow events transport relatively large quantities of nitrogen (N) and phosphorus (P) to streams and downstream waterbodies where they may stimulate algal blooms and degrade water quality. We evaluated how land uses and seasons alter event nutrient transport. We monitored >400 events with sensors in streams with contrasting land uses. Event N and P concentration patterns differed from each other suggesting dissolved N and P were transported from different locations in the landscape. Further, the agricultural and urban streams received more dissolved N and P than the forested stream. This likely results from fertilizer applications in excess of crop (agricultural and lawn grass) needs and landscape modifications, such as drainage systems and impervious surfaces, that limit soils and vegetation from removing nutrients from runoff. Lastly, season influenced the ratio of dissolved N to P delivery, with spring events transporting the most N relative to P and fall events transporting the least. Overall, land use and season uniquely influenced event nutrient transport. Management strategies to reduce algal blooms in downstream waterbodies must consider interactions among land use, nutrient type, and season. However, ratios of N to P may change seasonally but independently of land use, which could simplify management approaches. Key Points: High‐frequency sensors revealed C‐Q patterns that provide valuable information on source areas and transport pathways for nitrate and SRPSource and transport pathways differed for nitrate and SRP and were influenced by land use/land cover and seasonal dynamicsSeasonal patterns in event nitrate to SRP ratios were similar among sites, but were driven by site‐specific nitrate and SRP export dynamics [ABSTRACT FROM AUTHOR]

Climate and agricultural management are strong drivers of greenhouse gas (GHG) emissions, but little is known about potential interactions among these drivers. Climate change will likely increase the frequency of wintertime thaws in northern agricultural systems, which have been shown to induce large pulses of carbon dioxide (CO2) and nitrous oxide (N2O). We tested the hypothesis that different manure application practices would interact with thaw events to produce GHG emission pulses of different sizes. Specifically, we expected manure injection would increase CO2 and N2O emissions relative to other manure application methods by enhancing subsurface microbial substrate availability. We conducted a laboratory incubation study with frozen, intact soil cores from a continuous corn (Zea mays L.) system under three manure application methods: broadcast, broadcast + plow, and injection. Cores were subjected to three temperature treatments over 8 d: frozen (-7°C), freeze-thaw (alternating -7 and 5°C), and thaw (5°C). In the freeze-thaw and thaw treatments, cumulative N2O emissions were 2 to 20 times greater in injected versus broadcast treatments (6.5 mg N2O-N m-2 averaged across broadcast treatments); cumulative CO2 emissions were up to two times higher in injected versus broadcast treatments (1017 mg CO2-C m-2 averaged across broadcast treatments). Our results suggest that the impacts of manure application choices extend beyond the growing season to increase N2O and CO2 emissions during wintertime thaws, potentially interacting with a warming climate to increase GHG emissions. [ABSTRACT FROM AUTHOR]

Green stormwater infrastructure such as bioretention is commonly implemented in urban areas for stormwater quality improvements. Although bioretention systems' soil media and vegetation have the potential to increase carbon (C) and nitrogen (N) storage for climate change mitigation, this storage potential has not been rigorously studied, and any analysis of it must consider the question of whether bioretention emits greenhouse gases to the atmosphere. We monitored eight roadside bioretention cells for CO2-C and N2O-N fluxes during two growing seasons (May through October) in Vermont, USA. C and N stocks in the soil media layers, microbes, and aboveground vegetation were also quantified to determine the overall C and N balance. Our bioretention cells contained three different treatments: plant species mix (high diversity versus low diversity), soil media (presence or absence of P-sorbent filter layer), and hydrologic (enhanced rainfall and runoff in some cells). CO2-C and N2O-N fluxes from all cells averaged 194 mg m-2 h-1 (range: 37 to 374 mgm-2 h-1) and 10 μgm-2 h-1 (range: -1100 to 330 μgm-2 h-1), respectively. There were no treatment-induced changes on gas fluxes. CO2-C fluxes were highly significantly correlated with soil temperature (R² = 0.68, p < 0.0001), while N2O-N fluxes were weakly correlated with temperature (R² = 0.017, p = 0.04). Bioretention soil media contained the largest pool of total C and N (17,122 g and 1236 g, respectively) when compared with vegetation and microbial pools. Microbial biomass C made up 14% (1936 g) of the total soil C in the upper 30 cm media layer. The total C and N sequestered by bioretention plants were 13,020 g and 320 g, respectively. After accounting for C and N losses via gas fluxes, the bioretention appeared to be a net sink for those nutrients. We also compared our bioretention gas fluxes to those from a variety of natural (i.e., grasslands and forests) and artificial (i.e., fertilized and irrigated or engineered) land-use types. We found bioretention fluxes to be in the mid-range among these land-use types, mostly likely due to organic matter (OM) influences on decomposition being similar to processes in natural systems. [ABSTRACT FROM AUTHOR]

Traditional models of decomposition fail to capture litter mass loss patterns in dryland systems. This shortcoming has stimulated research into alternative drivers of decomposition, including photodegradation. Here, we use aboveground litter decomposition data for dryland (arid) sites from the Long‐term Intersite Decomposition Experiment Team data set to test hypotheses (models) about the mechanisms and impacts of photodegradation. Incorporating photodegradation into a traditional biotic decomposition model substantially improved model predictions for mass loss at these dryland sites, especially after four years. The best model accounted for the effects of solar radiation via photodegradation loss from the intermediate cellulosic and lignin pools and direct inhibition of microbial decomposition. Despite the concurrent impacts of photodegradation and inhibition on mass loss, the best photodegradation model increased mass loss by an average of 12% per year compared to the biotic‐only decomposition model. The best model also allowed soil infiltration into litterbags to reduce photodegradation and inhibition of microbial decomposition by shading litter from solar radiation. Our modeling results did not entirely support the popular hypothesis that initial lignin content increases the effects of photodegradation on litter mass loss; surprisingly, higher initial lignin content decreased the rate of cellulosic photodegradation. Importantly, our results suggest that mass loss rates due to photodegradation may be comparable to biotic decomposition rates: Mass loss due to photodegradation alone resulted in litter mass losses of 6–15% per year, while mass loss due to biotic decomposition ranged from 20% per year during early‐stage decomposition to 3% per year during late‐stage decomposition. Overall, failing to account for the impacts of solar radiation on litter mass loss under‐predicted long‐term litter mass loss by approximately 26%. Thus, not including photodegradation in dryland decomposition models likely results in large underestimations of carbon loss from dryland systems. [ABSTRACT FROM AUTHOR]

Recent studies have found that solar ultraviolet ( UV) radiation significantly shifts the mass loss and nitrogen dynamics of plant litter decomposition in semi-arid and arid ecosystems. In this study, we examined the role of photodegradation in litter decomposition by using the DayCent- UV biogeochemical model. DayCent- UV incorporated the following mechanisms related to UV radiation: (1) direct photolysis, (2) facilitation of microbial decomposition via production of labile materials, and (3) microbial inhibition effects. We also allowed maximum photodecay rate of the structural litter pool to vary with litter's initial lignin fraction in the model. We calibrated DayCent- UV with observed ecosystem variables (e.g., volumetric soil water content, live biomass, actual evapotranspiration, and net ecosystem exchange), and validated the optimized model with Long-Term Intersite Decomposition Experiment ( LIDET) observations of remaining carbon and nitrogen at three semi-arid sites in Western United States. DayCent- UV better simulated the observed linear carbon loss patterns and the persistent net nitrogen mineralization in the 10-year LIDET experiment at the three sites than the model without UV decomposition. In the DayCent- UV equilibrium model runs, UV decomposition increased aboveground and belowground plant production, surface net nitrogen mineralization, and surface litter nitrogen pool, but decreased surface litter carbon, soil net nitrogen mineralization, and mineral soil carbon and nitrogen. In addition, UV decomposition had minimal impacts on trace gas emissions and biotic decomposition rates. The model results suggest that the most important ecological impact of photodecay of surface litter in dry grasslands is to increase N mineralization from the surface litter (25%), and decay rates of the surface litter (15%) and decrease the organic soil carbon and nitrogen (5%). [ABSTRACT FROM AUTHOR]

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AICc), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates as nonlinear beta regression. Our simulation results also suggest that k estimates will be most accurate when study length captures mid to late stage decomposition (50-80% mass loss) and the number of measurements through time is ≥5. Regression method and data transformation choices had the smallest impact on k estimates during mid and late stage decomposition. Estimates of k were more variable among methods and generally less accurate during early and end stage decomposition. With real data, neither model was predominately best; in most cases the models were indistinguishable based on AICc, and gave similar k estimates. However, when decomposition rates were high, normal and beta model k estimates often diverged substantially. Therefore, we recommend a pragmatic approach where both models are compared and the best is selected for a given data set. Alternatively, both models may be used via model averaging to develop weighted parameter estimates. We provide code to perform nonlinear beta regression with freely available software. [ABSTRACT FROM AUTHOR]

Predicting if ecosystems will mitigate or exacerbate rising CO2 requires understanding how elevated CO2 will interact with coincident changes in diversity and nitrogen (N) availability to affect ecosystem carbon (C) storage. Yet achieving such understanding has been hampered by the difficulty of quantifying belowground C pools and fluxes. Thus, we used mass balance calculations to quantify the effects of diversity, CO2, and N on both the total amount of C allocated belowground by plants (total belowground C allocation, TBCA) and ecosystem C storage in a periodically burned, 8-year Minnesota grassland biodiversity, CO2, and N experiment (BioCON). Annual TBCA increased in response to elevated CO2, enriched N, and increasing diversity. TBCA was positively related to standing root biomass. After removing the influence of root biomass, the effect of elevated CO2 remained positive, suggesting additional drivers of TBCA apart from those that maintain high root biomass. Removing root biomass effects resulted in the effects of N and diversity becoming neutral or negative (depending on year), suggesting that the positive effects of diversity and N on TBCA were related to treatment-driven differences in root biomass. Greater litter production in high diversity, elevated CO2, and enhanced N treatments increased annual ecosystem C loss in fire years and C gain in non-fire years, resulting in overall neutral C storage rates. Our results suggest that frequently burned grasslands are unlikely to exhibit enhanced C sequestration with increasing atmospheric CO2 levels or N deposition. [ABSTRACT FROM AUTHOR]

As atmospheric CO2 increases, ecosystem carbon sequestration will largely depend on how global changes in climate will alter the balance between net primary production and decomposition. The response of primary production to climatic change has been examined using well-validated mechanistic models, but the same is not true for decomposition, a primary source of atmospheric CO2. We used the Long-term Intersite Decomposition Experiment Team (LIDET) dataset and model-selection techniques to choose and parameterize a model that describes global patterns of litter decomposition. Mass loss was best represented by a three-pool negative exponential model, with a rapidly decomposing labile pool, an intermediate pool representing cellulose, and a recalcitrant pool. The initial litter lignin/nitrogen ratio defined the size of labile and intermediate pools. Lignin content determined the size of the recalcitrant pool. The decomposition rate of all pools was modified by climate, but the intermediate pool's decomposition rate was also controlled by relative amounts of litter cellulose and lignin (indicative of lignin-encrusted cellulose). The effect of climate on decomposition was best represented by a composite variable that multiplied a water-stress function by the Lloyd and Taylor variable Q10 temperature function. Although our model explained nearly 70% of the variation in LIDET data, we observed systematic deviations from model predictions. Below- and aboveground material decomposed at notably different rates, depending on the decomposition stage. Decomposition in certain ecosystem-specific environmental conditions was not well represented by our model; this included roots in very wet and cold soils, and aboveground litter in N-rich and arid sites. Despite these limitations, our model may still be extremely useful for global modeling efforts, because it accurately ( R2=0.6804) described general patterns of long-term global decomposition for a wide array of litter types, using relatively minimal climatic and litter quality data. [ABSTRACT FROM AUTHOR]

Farmers and policy makers pursue management practices that enhance water quality, increase landscape flood resiliency, and mitigate agriculture's contribution to climate change, all while remaining economically viable. This study presents a holistic assessment of how two practices influence the supply of these ecosystem services—the use of an aerator prior to manure application in haylands, and the stacked use of manure injection, cover crops, and reduced tillage in corn silage production. Field data are contextualized by semi-structured interviews that identify influences on adoption. Causal loop diagrams then illustrate feedbacks from ecosystem services onto decision making. In our study, unseen nutrient pathways are the least understood, but potentially the most important in determining the impact of a practice on ecosystem services supply. Subsurface runoff accounted for 64% to 92% of measured hydrologic phosphorus export. Average soil surface greenhouse gas flux constituted 38% to 73% of all contributions to the equivalent CO2 footprint of practices, sometimes outweighing carbon sequestration. Farmers identified interest in better understanding unseen nutrient pathways, expressed intrinsic stewardship motivations, but highlighted financial considerations as dominating decision making. Our analysis elevates the importance of financial supports for conservation, and the need for comprehensive understandings of agroecosystem performance that include hard-to-measure pathways. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil organic carbon (SOC) stocks predicted using machine learning algorithms. • Spatial resolutions of environmental variables examined on national and global data. • Source of data (national or global) defines the importance of environmental variables. • Independent reference data showed a significant decrease in prediction accuracy. • DSM should be re-evaluated by local data when spatial data differ from the original data. We evaluated how the spatial resolution of environmental variables (n = 47) altered their ability to predict soil organic carbon (SOC) stocks (0–30 cm depth) using training data from Gridded Soil Survey Geographic-gSSURGO and SoilGrids databases. Training and validation subsamples (1,629) were selected using a conditioned Latin hypercube sampling (cLHS) design based on environmental variables in Vermont, U.S. The predictive relationships between environmental variables and SOC stock (t C ha−1) were developed using machine learning algorithms. The algorithms were trained (70 %) and evaluated (30 %) using a random subset of database subsamples, respectively, with an additional evaluation step using local, independent SOC reference data (n = 272). The Random Forest (RF) algorithm outperformed other algorithms at all spatial resolutions in estimating SOC stocks. As spatial resolution increased, model performance with the gSSURGO database increased (R2 = 0.33–0.62 and RMSE = 42.42–34.92), while no such trend was observed for the SoilGrids database. The best SOC stock model prediction using the SoilGrids database was achieved with a 10 m resolution (R2 = 0.54 and RMSE = 4.67). Evaluation of modeled results using the external, or independent, reference data showed a significant decrease compared to the internal validation in prediction accuracy (R2 = 0.11–0.14 for gSSURGO and, R2 = −0.19 for SoilGrids). The gSSURGO database showed that soil maps (including suborders, drainage classes, temperature, and moisture) and geology/landform maps had a greater influence than other environmental variables at all spatial resolution scales. In contrast, climatic- and DEM-related variables were more significant for the SoilGrids database. Our study suggested that the origin of the SOC stock database and the sampling scheme largely affects the importance of environmental variables assigned in the machine learning algorithm. Our results confirmed that the variable and data sources, model type, and combination of environmental variables significantly influenced prediction accuracy. In conclusion, DSM products should be re-evaluated with local references when used for spatial extents that are different from those for which they were initially designed. [ABSTRACT FROM AUTHOR]