• Energy Research

AbstractEcotones are transition zones that form, in forests, where distinct forest types meet across a climatic gradient. In mountains, ecotones are compressed and act as potential harbingers of species shifts that accompany climate change. As the climate warms in New England, USA, high‐elevation boreal forests are expected to recede upslope, with northern hardwood species moving up behind. Yet recent empirical studies present conflicting findings on this dynamic, reporting both rapid upward ecotonal shifts and concurrent increases in boreal species within the region. These discrepancies may result from the limited spatial extent of observations. We developed a method to model and map the montane forest ecotone using Landsat imagery to observe change at scales not possible for plot‐based studies, covering mountain peaks over 39 000 km2. Our results show that ecotones shifted downward or stayed stable on most mountains between 1991 and 2010, but also shifted upward in some cases (13–15% slopes). On average, upper ecotone boundaries moved down −1.5 m yr−1 in the Green Mountains, VT, and −1.3 m yr−1 in the White Mountains, NH. These changes agree with remeasured forest inventory data from Hubbard Brook Experimental Forest, NH, and suggest that processes of boreal forest recovery from prior red spruce decline, or human land use and disturbance, may swamp out any signal of climate‐mediated migration in this ecosystem. This approach represents a powerful framework for evaluating similar ecotonal dynamics in other mountainous regions of the globe.

AbstractCold‐air pooling is a global phenomenon that frequently sustains low temperatures in sheltered, low‐lying depressions and valleys and drives other key environmental conditions, such as soil temperature, soil moisture, vapor pressure deficit, frost frequency, and winter dynamics. Local climate patterns in areas prone to cold‐air pooling are partly decoupled from regional climates and thus may be buffered from macroscale climate change. There is compelling evidence from studies across the globe that cold‐air pooling impacts plant communities and species distributions, making these decoupled microclimate areas potentially important microrefugia for species under climate warming. Despite interest in the potential for cold‐air pools to enable species persistence under warming, studies investigating the effects of cold‐air pooling on ecosystem processes are scarce. Because local temperatures and vegetation composition are critical drivers of ecosystem processes like carbon cycling and storage, cold‐air pooling may also act to preserve ecosystem functions. We review research exploring the ecological impacts of cold‐air pooling with a focus on vegetation, and then present a new conceptual framework in which cold‐air pooling creates feedbacks between species and ecosystem properties that generate unique hotspots for carbon accrual in some systems relative to areas more vulnerable to regional climate change impacts. Finally, we describe key steps to motivate future research investigating the potential for cold‐air pools to serve as microrefugia for ecosystem functions under climate change.

Forest biomass growth is almost universally assumed to peak early in stand development, near canopy closure, after which it will plateau or decline. The chronosequence and plot remeasurement approaches used to establish the decline pattern suffer from limitations and coarse temporal detail. We combined annual tree ring measurements and mortality models to address two questions: first, how do assumptions about tree growth and mortality influence reconstructions of biomass growth? Second, under what circumstances does biomass production follow the model that peaks early, then declines? We integrated three stochastic mortality models with a census tree-ring data set from eight temperate forest types to reconstruct stand-level biomass increments (in Minnesota, USA). We compared growth patterns among mortality models, forest types and stands. Timing of peak biomass growth varied significantly among mortality models, peaking 20-30 years earlier when mortality was random with respect to tree growth and size, than when mortality favored slow-growing individuals. Random or u-shaped mortality (highest in small or large trees) produced peak growth 25-30% higher than the surviving tree sample alone. Growth trends for even-aged, monospecific Pinus banksiana or Acer saccharum forests were similar to the early peak and decline expectation. However, we observed continually increasing biomass growth in older, low-productivity forests of Quercus rubra, Fraxinus nigra, and Thuja occidentalis. Tree-ring reconstructions estimated annual changes in live biomass growth and identified more diverse development patterns than previous methods. These detailed, long-term patterns of biomass development are crucial for detecting recent growth responses to global change and modeling future forest dynamics.

AbstractSpruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated.

AbstractChanges in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics—changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non‐linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter‐tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small‐group selection harvests to maintain forest structures characteristic of older, mature stands.

AbstractAs global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2and thereby slow rising CO2concentrations. Forests’ ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree‐ring records. Yet typical tree‐ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals’ size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species likeAcer saccharum,Quercus rubra, andPicea glaucawill vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92–95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone.