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Arthropods are active during the winter in temperate regions. Many use the seasonal snowpack as a buffer against harsh ambient conditions and are active in a refugium known as the subnivium. While the use of the subnivium by arthropods is well established, far less is known about subnivium community composition, abundance, biomass, and diversity and how these characteristics compare with the community in the summer. Understanding subnivium communities is especially important given the observed and anticipated changes in snowpack depth and duration due to the changing climate. We compared subnivium arthropod communities with those active during the summer using pitfall trapping in northern New Hampshire. We found that compositions of ground-active arthropod communities in the subnivium differed from those in the summer. The subnivium arthropod community featured moderate levels of richness and other measures of diversity that tended to be lower than the summer community. More strikingly, the subnivium community was much lower in overall abundance and biomass. Interestingly, some arthropods were dominant in the subnivium but either rare or absent in summer collections. These putative “subnivium specialists” included the spider Cicurina brevis (Emerton 1890) (Araneae: Hahniidae) and 3 rove beetles (Coleoptera: Staphylinidae): Arpedium cribratum Fauvel, 1878, Lesteva pallipes LeConte, 1863, and Porrhodites inflatus (Hatch, 1957). This study provides a detailed account of the subnivium arthropod community, establishes baseline information on arthropod communities in temperate forests of northeastern North America, and explores the idea of subnivium specialist taxa that are highly active in winter and might be especially vulnerable to climate change.

 

Resilience is the ability of ecosystems to maintain function while experiencing perturbation. Globally, forests are experiencing disturbances of unprecedented quantity, type, and magnitude that may diminish resilience. Early warning signals are statistical properties of data whose increase over time may provide insights into decreasing resilience, but there have been few applications to forests. We quantified four early warning signals (standard deviation, lag-1 autocorrelation, skewness, and kurtosis) across detrended time series of multiple ecosystem state variables at the Hubbard Brook Experimental Forest, New Hampshire, USA and analyzed how these signals have changed over time. Variables were collected over periods from 25 to 55 years from both experimentally manipulated and reference areas and were aggregated to annual timesteps for analysis. Long-term (>50 year) increases in early warning signals of stream calcium, a key biogeochemical variable at the site, illustrated declining resilience after decades of acid deposition, but only in watersheds that had previously been harvested. Trends in early warning signals of stream nitrate, a critical nutrient and water pollutant, likewise exhibited symptoms of declining resilience but in all watersheds. Temporal trends in early warning signals of some of groups of trees, insects, and birds also indicated changing resilience, but this pattern differed among, and even within, groups. Overall, ∼60% of early warning signals analyzed indicated decreasing resilience. Most of these signals occurred in skewness and kurtosis, suggesting ‘flickering’ behavior that aligns with emerging evidence of the forest transitioning into an oligotrophic condition. The other ∼40% of early warning signals indicated increasing or unchanging resilience. Interpretation of early warning signals in the context of system specific knowledge is therefore essential. They can be useful indicators for some key ecosystem variables; however, uncertainties in other variables highlight the need for further development of these tools in well-studied, long-term research sites.

 

Abstract. Understanding the sources and sinks of methane (CH4)is critical to both predicting and mitigating future climate change. Thereare large uncertainties in the global budget of atmospheric CH4, butnatural emissions are estimated to be of a similar magnitude toanthropogenic emissions. To understand CH4 flux from biogenic sourcesin the United States (US) of America, a multi-scale CH4 observationnetwork focused on CH4 flux rates, processes, and scaling methods isrequired. This can be achieved with a network of ground-based observationsthat are distributed based on climatic regions and land cover. To determinethe gaps in physical infrastructure for developing this network, we need tounderstand the landscape representativeness of the current infrastructure.We focus here on eddy covariance (EC) flux towers because they are essentialfor a bottom-up framework that bridges the gap between point-based chambermeasurements and airborne or satellite platforms that inform policydecisions and global climate agreements. Using dissimilarity,multidimensional scaling, and cluster analysis, the US was divided into 10clusters distributed across temperature and precipitation gradients. Weevaluated dissimilarity within each cluster for research sites with activeCH4 EC towers to identify gaps in existing infrastructure that limitour ability to constrain the contribution of US biogenic CH4 emissionsto the global budget. Through our analysis using climate, land cover, andlocation variables, we identified priority areas for research infrastructureto provide a more complete understanding of the CH4 flux potential ofecosystem types across the US. Clusters corresponding to Alaska and theRocky Mountains, which are inherently difficult to capture, are the mostpoorly represented, and all clusters require a greater representation ofvegetation types. 

Northern temperate ecosystems are experiencing warmer and more variable winters, trends that are expected to continue into the foreseeable future. Despite this, most studies have focused on climate change impacts during the growing season, particularly when comparing responses across different vegetation cover types. Here we examined how a perennial grassland and adjacent mixed forest ecosystem in New Hampshire, United States, responded to a period of highly variable winters from 2014 through 2017 that included the warmest winter on record to date. In the grassland, record‐breaking temperatures in the winter of 2015/2016 led to a February onset of plant growth and the ecosystem became a sustained carbon sink well before winter ended, taking up roughly 90 g/m²more carbon during the winter to spring transition than in other recorded years. The forest was an unusually large carbon source during the same period. While forest photosynthesis was restricted by leaf‐out phenology, warm winter temperatures caused large pulses of ecosystem respiration that released nearly 230 g C/m²from February through April, more than double the carbon losses during that period in cooler years. These findings suggest that, as winters continue to warm, increases in ecosystem respiration outside the growing season could outpace increases in carbon uptake during a longer growing season, particularly in forests that depend on leaf‐out timing to initiate carbon uptake. In ecosystems with a perennial leaf habit, warming winter temperatures are more likely to increase ecosystem carbon uptake through extension of the active growing season. Our results highlight the importance of understanding relationships among antecedent winter conditions and carbon exchange across land‐cover types to understand how landscape carbon exchange will change under projected climate warming.

 

Climate change is altering the timing and duration of the vernal window, a period that marks the end of winter and the start of the growing season when rapid transitions in ecosystem energy, water, nutrient, and carbon dynamics take place. Research on this period typically captures only a portion of the ecosystem in transition and focuses largely on the dates by which the system wakes up. Previous work has not addressed lags between transitions that represent delays in energy, water, nutrient, and carbon flows. The objectives of this study were to establish the sequence of physical and biogeochemical transitions and lags during the vernal window period and to understand how climate change may alter them. We synthesized observations from a statewide sensor network in New Hampshire,USA, that concurrently monitored climate, snow, soils, and streams over a three‐year period and supplemented these observations with climate reanalysis data, snow data assimilation model output, and satellite spectral data. We found that some of the transitions that occurred within the vernal window were sequential, with air temperatures warming prior to snow melt, which preceded forest canopy closure. Other transitions were simultaneous with one another and had zero‐length lags, such as snowpack disappearance, rapid soil warming, and peak stream discharge. We modeled lags as a function of both winter coldness and snow depth, both of which are expected to decline with climate change. Warmer winters with less snow resulted in longer lags and a more protracted vernal window. This lengthening of individual lags and of the entire vernal window carries important consequences for the thermodynamics and biogeochemistry of ecosystems, both during the winter‐to‐spring transition and throughout the rest of the year.