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Identifying which aspects of global environmental change are driving observed ecosystem process responses is a great challenge. Here, we address how long-term (10-25 year) alterations in soil moisture, and nitrogen (N) oligotrophication (i.e. decreases in soil N availability relative to plant demand), alter the production of plant-available N via net mineralization and nitrification in a northern hardwood forest. Our objectives were to determine whether soil moisture has changed over the past decade and whether N cycle processes have become less sensitive to soil moisture over time due to N oligotrophication. We used long-term data sets from several related studies to show: (i) increasing winter soil temperatures and declining summer soil moisture from late 2010 into 2024; (ii) reductions in sensitivity of N cycling rates to soil moisture, and (iii) declining moisture-adjusted N cycle processes (the ratio of rate of N process:soil moisture) over time in both summer and winter. These changes suggest continued reductions in N availability to plants in these forests, with potential effects on forest productivity and response to disturbance. 

The 2024 Critical Review examines the topic of atmospheric reduced nitrogen: sources, transformations, effects, and management. The full-length review appears in the June 2024 issue of the Journal of the Air & Waste Management Association (JA&WMA). 

Critical loads (CLs) are frequently used to quantify terrestrial ecosystem impacts from nitrogen (N) deposition using ecological responses such as the growth and mortality of tree species. Typically, CLs are reported as a single value, with uncertainty, for an indicator across a species' entire range. Mediating factors such as climate and soil conditions can influence species' sensitivity to N, but the magnitudes of these effects are rarely calculated explicitly. Here, we quantify the spatial variability and estimation error in N CLs for the growth and survival of 10 different tree species while accounting for key environmental factors that mediate species sensitivity to N (e.g., soil characteristics). We used a bootstrapped machine learning approach to determine the level of N deposition at which a 1% decrease occurs in growth rate or survival probability at forest plot locations across the United States. We found minimal differences (<5 kg N ha⁻¹ year⁻¹) when comparing a single species' CLs across climatic regimes but found considerable variability in species' local N CLs (>8.5 kg N ha⁻¹ year⁻¹) within these regimes. We also evaluated the most important factors for predicting tree growth rates and mortality and found that climate, competition, and air pollution generally have the greatest influence on growth rates and survival probability. Lastly, we developed a new probability of exceedance metric for each species and found high likelihoods of exceedance across large portions (46%) of some species' ranges. Our analysis demonstrates that machine learning approaches provide a unique capability to: (1) quantify mediating factor influences on N sensitivity of trees, (2) estimate the error in local N CL estimates, and (3) generate localized N CLs with probabilities of exceedance for tree species. 

Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO2) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event. 

Abstract Sulfur, as an essential nutrient for plant growth, has increasingly been used in fertiliser applications for many crops. This increase is coincident with declines in atmospheric sulfur deposition in response to air quality improvements in the United States and Europe. Here, we evaluate trends in sulfur fertiliser sales by mass, as a proxy for fertiliser applications, and estimate total atmospheric sulfur deposition across the Midwestern United States. Crop acreage, yield and sulfur fertiliser application substantially increased between 1985 and 2015, coincident with declines in atmospheric sulfur deposition. The increase in sulfur fertiliser has outpaced the relative rate of change in other major nutrient fertilisers including nitrogen, phosphorus and potassium, by approximately 7-fold prior to 2009, and 29-fold after 2009. We suggest that there is a critical need to develop sulfur management tools that optimize fertiliser applications to maintain crop yields while minimizing the consequences of excess sulfur in the environment. 

abstract In this article marking the 40th anniversary of the US National Science Foundation's Long Term Ecological Research (LTER) Network, we describe how a long-term ecological research perspective facilitates insights into an ecosystem's response to climate change. At all 28 LTER sites, from the Arctic to Antarctica, air temperature and moisture variability have increased since 1930, with increased disturbance frequency and severity and unprecedented disturbance types. LTER research documents the responses to these changes, including altered primary production, enhanced cycling of organic and inorganic matter, and changes in populations and communities. Although some responses are shared among diverse ecosystems, most are unique, involving region-specific drivers of change, interactions among multiple climate change drivers, and interactions with other human activities. Ecosystem responses to climate change are just beginning to emerge, and as climate change accelerates, long-term ecological research is crucial to understand, mitigate, and adapt to ecosystem responses to climate change.