Explore the vast range of titles available.

Ice storms are a type of extreme winter weather event common to north temperate and boreal forests worldwide. Recent climate modelling studies suggest that these storms may become more frequent and severe under a changing climate. Compared to other types of storm events, relatively little is known about the direct and indirect impacts of these storms on forests, as naturally occurring ice storms are inherently difficult to study. Here we describe a novel experimental approach used to create a suite of ice storms in a mature hardwood forest in New Hampshire, USA. The experiment included five ice storm intensities (0, 6.4, 12.7 and 19.1 mm radial ice accretion) applied in a single year, and one ice storm intensity (12.7 mm) applied in two consecutive years. Results demonstrate the feasibility of this approach for creating experimental ice storms, quantify the increase in fine and coarse woody debris mass and nutrients transferred from the forest canopy to the soil under the different icing conditions, and show an increase in the damage to the forest canopy with increasing icing that evolves over time. In this forest, little damage occurred below 6.4 mm radial ice accretion, moderate damage occurred with up to 12.7 mm of accretion, and significant branch breakage and canopy damage occurred with 19.1 mm of ice. The icing in consecutive years demonstrated an interactive effect of ice storm frequency and severity such that some branches damaged in the first year of icing appeared to remain in the canopy and then fall to the ground in the second year of icing. These results have implications for National Weather Service ice storm warning levels, as they provide a quantitative assessment of ice-load related inputs of forest debris that will be useful to municipalities creating response plans for current and future ice storms.

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO 2 and nitrogen across major terrestrial vegetation types of the world. Most experiments manipulated single rather than multiple global change drivers in temperate ecosystems of the USA, Europe and China. The magnitudes of warming and elevated CO 2 treatments were consistent with the ranges of future projections, whereas those of precipitation changes and nitrogen inputs often exceeded the projected ranges. Increases in global change drivers consistently accelerated, but decreased precipitation slowed down carbon-cycle processes. Nonlinear (including synergistic and antagonistic) effects among global change drivers were rare. Belowground carbon allocation responded negatively to increased precipitation and nitrogen addition and positively to decreased precipitation and elevated CO 2 . The sensitivities of carbon variables to multiple global change drivers depended on the background climate and ecosystem condition, suggesting that Earth system models should be evaluated using site-specific conditions for best uses of this large dataset. Together, this synthesis underscores an urgent need to explore the interactions among multiple global change drivers in underrepresented regions such as semi-arid ecosystems, forests in the tropics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback. A synthesis of global change experiments that manipulated temperature, precipitation, carbon dioxide or nitrogen identifies a need to consider site-specific factors and interactions in Earth system models.

Soils are the largest terrestrial pool of mercury (Hg), a neurotoxic pollutant. Pathways of Hg accumulation and loss in forest soils include through-fall, litterfall, soil gas fluxes, and leaching in soil solution, all of which will likely be altered under changing climate. We took advantage of three ongoing climate-change manipulation experiments at the Hubbard Brook Experimental Forest, New Hampshire, USA: a combined growing-season warming and winter freeze-thaw cycle experiment, a throughfall exclusion to mimic drought, and a simulated ice storm experiment to examine the response of the forest Hg cycle to climatic disturbances. Across these three experiments, we compared Hg inputs in throughfall and leaf litterfall and Hg outputs in soil gas fluxes. Soil solution was measured only in the simulated ice storm experiment. We found that northern forest soils retained consistently less Hg by 16–60% in the three climate manipulations compared to the undisturbed controls (~ 7.4 μg Hgm⁻² year⁻¹), although soils across all three experiments still served as a net sink for Hg. Growing-season soil warming and combined soil warming and winter freeze-thaw cycles had little effect on litterfall and throughfall flux, but they increased soil Hg⁰ evasion by 31 and 35%, respectively, relative to the control plots. The drought plots had 5% lower litterfall Hg flux, 50% lower throughfall Hg flux, and 21% lower soil Hg⁰ evasion than the control plots. The simulated ice storm had 23% higher litterfall Hg flux, 1% higher throughfall Hg flux, 37% higher soil Hg⁰ evasion, and 151% higher soil Hg leaching than the control plots. These observations suggest that climate changes such as warmer soils in the growing season or more intense ice storms in winter are likely to exacerbate Hg pollution by releasing Hg sequestered in forest soils via evasion and leaching.

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks.

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

Evaluations of the local effects of global change are often confounded by the interactions of natural and anthropogenic factors that overshadow the effects of climate changes on ecosystems. Long-term watershed and natural elevation gradient studies at the Hubbard Brook Experimental Forest and in the surrounding region show surprising results demonstrating the effects of climate change on hydrologic variables (e.g., evapotranspiration, streamflow, soil moisture); the importance of changes in phenology on water, carbon, and nitrogen fluxes during critical seasonal transition periods; winter climate change effects on plant and animal community composition and ecosystem services; and the effects of anthropogenic disturbances and land-use history on plant community composition. These studies highlight the value of long-term integrated research for assessments of the subtle effects of changing climate on complex ecosystems.

Many global change drivers chronically alter resource availability in terrestrial ecosystems. Such resource alterations are known to affect aboveground net primary production (ANPP) in the short term; however, it is unknown if patterns of response change through time. We examined the magnitude, direction, and pattern of ANPP responses to a wide range of global change drivers by compiling 73 datasets from long-term (>5 years) experiments that varied by ecosystem type, length of manipulation, and the type of manipulation. Chronic resource alterations resulted in a significant change in ANPP irrespective of ecosystem type, the length of the experiment, and the resource manipulated. However, the pattern of ecosystem response over time varied with ecosystem type and manipulation length. Continuous directional responses were the most common pattern observed in herbaceous-dominated ecosystems. Continuous directional responses also were frequently observed in longer-term experiments (>11 years) and were, in some cases, accompanied by large shifts in community composition. In contrast, stepped responses were common in forests and other ecosystems (salt marshes and dry valleys) and with nutrient manipulations. Our results suggest that the response of ANPP to chronic resource manipulations can be quite variable; however, responses persist once they occur, as few transient responses were observed. Shifts in plant community composition over time could be important determinants of patterns of terrestrial ecosystem sensitivity, but comparative, long-term studies are required to understand how and why ecosystems differ in their sensitivity to chronic resource alterations.

Ice storms are important but understudied disturbances that influence forest structure and function. In 1998, an ice storm damaged forest canopies and led to increased hydrologic losses of nitrogen (N) from the northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF), a Long-Term Ecological Research (LTER) site in New Hampshire, USA. To evaluate the mechanisms underlying this response, we experimentally simulated ice storms with different frequencies and severities at the small plot scale. We took measurements of plant and soil variables before (2015) and after (2016, 2017) treatments using the same methods used in 1998 with a focus on hydrologic and gaseous losses of reactive N, as well as rates of soil N cycle processes. Nitrogen cycle responses to the treatments were insignificant and less marked than the responses to the 1998 natural ice storm. Pools and leaching of inorganic N, net and gross mineralization and nitrification and denitrification rates, and soil to atmosphere fluxes of nitrous oxide (N₂O) were unaffected by the treatments, in contrast to the 1998 storm which caused marked increases in leaching and watershed export of inorganic N. The difference in response may be a manifestation of N oligotrophication that has occurred at the HBEF over the past 30 years. Results suggest that ecosystem response to disturbances, such as ice storms, is changing due to aspects of global environmental change, challenging our ability to understand and predict the effects of these events on ecosystem structure, function, and services.

Despite a growing recognition that the solutions to current environmental problems will be developed through collaborations between scientists and stakeholders, substantial challenges stifle such cooperation and slow the transfer of knowledge. Challenges occur at several levels, including individual, disciplinary, and institutional. All of these have implications for scholars working at academic and research institutions. Fortunately, creative ideas and tested models exist that provide opportunities for conversation and serious consideration about how such institutions can facilitate the dialogue between scientists and society.