Search for: All records

The pace and trajectory of ecosystem development are governed by the availability and cycling of limiting nutrients, and anthropogenic disturbances such as acid rain and deforestation alter these trajectories by removing substantial quantities of nutrients via titration or harvest. Here, we use six decades of continuous chemical and hydrologic data from three adjacent headwater catchments in the Hubbard Brook Experimental Forest, New Hampshire—one deforested (W5), one CaSiO₃-enriched (W1), and one reference (W6)—to quantify long-term nutrient and mineral fluxes. Acid deposition since 1900 drove pronounced depletion and export of base cations, particularly calcium, across all watersheds. Experimental deforestation of W5 intensified loss of biomass and nutrient cations and triggered sustained increases in streamwater pH, Ca²⁺, and SiO₂exports over nearly four decades, greatly exceeding the effects of direct CaSiO₃enrichment in both duration and magnitude. We detect no long-term changes in water yield or water flow paths in the experimental watersheds, and we attribute this multidecadal increase in weathering rates following deforestation to biological responses to severe nutrient limitation. Our evidence suggests that in the regrowing forest, plants are investing photosynthate into belowground processes that amplify mineral weathering to access phosphorus and micronutrients, consequently elevating the export of less limiting elements present in silicate parent material. Throughout decades of forest regrowth, enhanced biotic weathering has continued to deplete the acid buffering capacity of the terrestrial ecosystem while the export of weathering products has elevated the pH of the receiving stream. 

This dataset comprises daily images positioned to view streams above the weirs at Hubbard Brook for watersheds 1, 2, 3, 4, 5, 6, and 9. Cameras are programmed to take one image per day at ~12:00 pm ET. Each file is timestamped with the image metadata, but also within the file name, and structured to enable temporal trend analysis for end-users. The cameras used are BUSHNELL model number 119R3, and data are collected on SIM cards and manually downloaded every six months. Data gaps are minimal and generally associated with battery failures. These data are designed to capture stream dynamics over time for the purpose of visual pattern analysis, environmental monitoring, and machine learning applications. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Stream metabolism, encompassing gross primary production and ecosystem respiration, reflects the fundamental energetic dynamics of freshwater ecosystems. These processes regulate the concentrations of dissolved gases like oxygen and carbon dioxide, which in turn shape aquatic food webs and ecosystem responses to stressors such as floods, drought, and nutrient loading. Historically difficult to quantify, stream metabolism is now measurable at high temporal resolution thanks to advances in sensor technology and modeling. The StreamPULSE dataset includes high-frequency sensor data, metadata, and modeled estimates of ecosystem metabolism. This living dataset contributes to a growing body of open-access data characterizing the metabolic pulse of stream ecosystems worldwide. To contribute to StreamPULSE, visit data.streampulse.org. All data contributed to StreamPULSE become public after an optional embargo period. Use this publication to access annual data releases, or use data.streampulse.org to download new data as they become available. 

Stream bryophytes (mosses and liverworts) are widely recognized as important macroinvertebrate habitats, but their overall role in the stream ecosystem, particularly in nutrient cycling, remains understudied. Hubbard Brook Experimental Forest in New Hampshire, USA, contains some of the most extensively researched streams in the world, yet few studies mention their bryophytes. Perhaps this is because early estimates place bryophyte coverage in these streams at an insignificant 2%. However, data from 2019 show that contemporary coverage ranges from 4 to 40% among streams. To investigate how stream bryophyte cover may be changing over time and influencing stream nutrient stocks, we conducted field surveys, measured the mass of organic and inorganic bryophyte contents, and quantified nutrient uptake with bottle incubations of bryophyte mats. This study marks a novel attempt to map stream bryophyte coverage with estimates of C, P, and N stocks and fluxes. From our 2022 field surveys, we found that median bryophyte coverage varied across streams in the same catchment (0–41.4%) and shifted from just 3 y prior. We estimate that these bryophyte mats stored between 14 and 414 g of organic matter per m2 of stream in the form of live biomass and captured particulates. Within 12 h of light incubation, 35 out of 36 bryophyte clump samples sorbed peak historical water-column concentrations of PO43– as measured in the Hubbard Brook stream chemistry record. In Bear Brook, our scaled estimate of bryophyte mat NO3– uptake (2.3 g N/y) constitutes a substantial portion of previously estimated whole-stream NO3– uptake (12 g N/y). Cumulatively, our data demonstrate that bryophytes and their associated mineral substrates and biota—known as the bryosphere—are crucial in facilitating headwater stream nutrient cycling. These bryospheres may contribute significantly to interannual variability in stream nutrient concentrations within nutrient-poor streams, especially in climate-sensitive regions. 

Abstract. Quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalies related to sensor drift, gauge movement, and incomplete rating curves. Here, we investigate the potential to estimate continuous discharge when discrete streamflow measurements are available at the site of interest. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Reconstructions achieved median efficiencies of 0.83 (Nash–Sutcliffe, or NSE) and 0.81 (Kling–Gupta, or KGE) across all sites and improved KGE at 11 sites versus published data, with linear regression generally outperforming deep learning approaches due to the use of target site data for model fitting rather than evaluation only. Estimates from this analysis inform ∼199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON's stream data products. We provide 5 min composite discharge series for each site that combine the best estimates across modeling approaches and NEON's published data. The success of this effort demonstrates the potential to establish “virtual gauges”, sites at which continuous streamflow can be accurately estimated from discrete measurements, by transferring information from nearby donor gauges and/or large collections of training data. 

This is the data and code associated with "Stream bryophytes promote 'cryptic' productivity in highly oligotrophic headwaters. Recent observations document increased abundance of algae in the headwater streams of Hubbard Brook Experimental Forest (HBEF). It is possible that this 'greening up' of HBEF streams may be due to climate change with rising temperatures, altered terrestrial phenology, and shifting hydrologic regimes. Alternatively, stream 'greening' could be due to the slow recovery of stream chemistry from decades of acid rain, which have led to rising stream water pH, declining concentrations of toxic Al3+, and extremely low solute concentrations. Three years of weekly algal measurements on contrasting substrates, 6 nutrient enrichment experiments reveal important new insights about the interactions between these two groups of autotrophs. We predicted that light availability, hydrologic disturbance and nutrient limitation were all important determinants of algal biomass in streams. To evaluate the relative strength and hierarchy of these limiting factors, we used nutrient diffusing substrates to investigate the role of nutrients for algae and compared algal accrual rate on artificial rock vs. moss substrates in stream channels vs. weir ponds to assess the role of hydrologic disturbance and scour. Our surveys and experiments spanned across seasons and local light regimes. Algal biomass was substantially higher in protected weir ponds than in stream channels, and in both habitats, algal biomass was substantially higher on artificial moss substrates than on tiles. Taken together, these results suggest that moss can provide physical protection from flood scour. Algal biomass instream on both substrate types was higher in high light seasons (pre-leaf out) and well-lit habitats indicating strong light limitation. Results from a series of 6 nutrient diffusing substrate experiments over the course of 2 years provided little evidence of nutrient limitation instream. The most striking finding of our investigation is the previously unsuspected role of stream bryophytes in providing critical refugia for algae in these steep, heavily shaded and oligotrophic headwaters. Shifts in stream productivity over time are likely to be closely tied to changes in bryophyte cover. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Abstract. Streamflow, or discharge, is an essential measure in the study of rivers and streams. However, quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Here, we investigate the potential for both simple and complex models to accurately estimate continuous discharge (at least daily estimates), using only discrete manual measurements of streamflow. We were inspired to do this work because some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalous data due to sensor drift, gauge movement, and incomplete rating curves. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges over this period via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Top reconstructions achieved median efficiencies of 0.83 (Nash-Sutcliffe, or NSE) and 0.81 (Kling-Gupta, or KGE) across all sites, and improved KGE at 11 sites versus published data. Estimates from this analysis inform ~199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON’s stream data products. We provide 5-minute composite discharge series for each site that combine the best estimates across modeling approaches and NEON’s published data. 

In summer 2015 and spring 2016, researchers collected soils from the CaSiO3-enriched watershed at Hubbard Brook (W1) and from a nearby site west of the reference watershed at Hubbard Brook (W6). These soils were sampled throughout the hardwood zone of each watershed, and the sampling scheme explicitly examined pit-and-mound microtopographic gradients. These soils were analyzed for acid-base properties, net and gross N cycling rates, microbial biomass, and C cycling rates. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station. 

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.