Climatic correlates of tree mortality in water- and energy-limited forests

Determining the role of climate change in India's past forest loss

Tropical forests in India have declined at an alarming rate over the past century, with extensive literature focusing on the high contributions of agricultural expansions to deforestation, while the effects of climate change have largely been overlooked. Climate change effects, such as increasing temperatures, drought and flooding, have already occurred, and are projected to worsen. Climate velocity, a metric that accounts for spatial heterogeneity in climate, can help identify contiguous areas under greater climate stress and potential climate refuges in addition to traditional temporal trends. Here, we examined the relative contribution of climate changes to forest loss in India during the period 2001-2018, at two spatial (regional and national) and two temporal (seasonal and annual) scales. This includes, for the first time, a characterization of climate velocity in the country. Our findings show that annual forest loss increased substantially over the 17-year period examined (2001-2018), with the majority of forest loss occurring in the Northeast region. Decreases in temporal trends of temperature and precipitation were most associated with forest losses, but there was large spatial and seasonal variation in the relationship. In every region except the Northeast, forest losses were correlated with faster velocities of at least one climate variable but overlapping areas of high velocities were rare. Our findings indicate that climate changes have played an important role in India's past forest loss, but likely remain secondary to other factors at present. We stress concern for climates velocities recorded in the country, reaching 97 km year-1 , and highlight that understanding the different regional and seasonal relationships between climatic conditions and forest distributions will be key to effective protection of the country's remaining forests as climate change accelerates.

Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R²) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB’s humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.

Photosynthetic sensitivity to historic meteorological variability for conifers in the eastern Sierra Nevada

Increased climatic variability can impact tree physiological processes beyond what is predicted from changes in mean conditions. We assessed the sensitivity of conifer saplings to spatial and temporal variability in meteorological conditions, taking advantage of the end of California's historic drought and the exceedingly wet winter of 2017. We sought to understand how very dry and very wet conditions constrain photosynthesis and growth in four regionally dominant conifers and whether sensitivity in these processes changes across a 500 m gradient in elevation. All species demonstrated phenotypic plasticity in response to temporal differences in precipitation on both inter-annual and seasonal timescales. Net photosynthesis in Pinus contorta decreased from an early season 2016 average of 12.4 to 6.89 μmol CO₂ m^-2 s^-1 later in the summer, but increased 14.1% between seasons in the wet year. By contrast, elevation had almost no effect on instantaneous photosynthetic gas exchange, CO₂ response curve parameters, or stem water potential in any of the years for any of the species. Effects of the heavy snow year (2017) on needle growth differed between elevations. Pinus contorta showed a 38.9% increase in average needle length at the lower two elevations but a 31.6% decrease at the highest site compared to the height of the drought. Despite these differences, biological variation was dampened compared to the physical variation between years, suggesting these trees can effectively withstand substantial meteorological variability. Our results show that these species demonstrated considerable ability to tolerate and recover from an extreme drought event.

Biophysical Gradients and Performance of Whitebark Pine Plantings in the Greater Yellowstone Ecosystem

Research Highlights: The efficacy of planting for restoration is important for ecosystem managers. Planting efforts represent an opportunity for conserving and managing species during a population crisis. Background and Objectives: Federal agencies have been planting whitebark pine (WBP), an important subalpine species that is late to mature and long-lived, for three decades in the Greater Yellowstone Ecosystem (GYE). These efforts have been met with varying success, and they have not been evaluated beyond the first five years post-planting. Ecosystem managers will continue to plant WBP in the GYE for years to come, and this research helps to inform and identify higher quality habitat during a period of changing climate and high GYE WBP mortality rates. Materials and Methods: We use a combination of field sampling and a water balance model to investigate local biophysical gradients as explanatory variables for WBP performance at twenty-nine GYE planting sites. Results: We found that the WBP growth rate was positively correlated with actual evapotranspiration (AET) and was greatest when cumulative growing season AET was above 350 mm. Growth rate was not strongly affected by competition at the levels found in this study. However, site density change over time was negatively affected by mean growing season temperature and when more than five competitors were present within 3.59 m radius. Conclusions: If they make it to maturity, trees that are planted this season will not begin to produce cones until the latter half of this century. We recommend planting efforts that optimize AET for growth rate objectives, minimize water deficit (WD) that cause stress and mortality, and removing competitors if they exceed five within a short distance of seedlings.

A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data.

Background mortality drivers of European tree species: climate change matters

Increases in tree mortality rates have been highlighted in different biomes over the past decades. However, disentangling the effects of climate change on the temporal increase in tree mortality from those of management and forest dynamics remains a challenge. Using a modelling approach taking tree and stand characteristics into account, we sought to evaluate the impact of climate change on background mortality for the most common European tree species. We focused on background mortality, which is the mortality observed in a stand in the absence of abrupt disturbances, to avoid confusion with mortality events unrelated to long-term changes in temperature and rainfall. We studied 372 974 trees including 7312 dead trees from forest inventory data surveyed across France between 2009 and 2015. Factors related to competition, stand characteristics, management intensity, and site conditions were the expected preponderant drivers of mortality. Taking these main drivers into account, we detected a climate change signal on 45% of the 43 studied species, explaining an average 6% of the total modelled mortality. For 18 out of the 19 species sensitive to climate change, we evidenced greater mortality with increasing temperature or decreasing rainfall. By quantifying the mortality excess linked to the current climate change for European temperate forest tree species, we provide new insights into forest vulnerability that will prove useful for adapting forest management to future conditions.

Fine-scale stand structure mediates drought-induced tree mortality in pinyon-juniper woodlands

Severe drought has resulted in widespread tree die-off events in forests and woodlands globally and is forecast to become more frequent in coming decades. Tree mortality is a complex process influenced by climate, soils, characteristics of individual trees, interactions between trees, and the dynamics of pests and pathogens. The role of stand structure and stand density in mediating the resistance of trees to drought remains poorly understood, especially in semiarid woodlands, which are expected to be highly susceptible to future severe drought. We sampled permanent plots in central Nevada woodlands dominated by single-leaf pinyon pine and Utah juniper before and after a severe multi-year drought (2013-2015) to investigate the importance of climate, tree attributes, and local-neighborhood stand structure on tree mortality and canopy dieback at the level of individual trees and 0.1-ha plots. We observed widespread tree mortality of pinyon at approximately eight times the reported background mortality rate, and substantial canopy dieback in both pinyon and juniper. Both species were more prone to mortality and dieback in hotter, drier sites. Canopy dieback was associated with both long-term average climate and the severity of recent drought, with elevated mortality on sites with higher water deficits, average summer temperatures, and vapor pressure deficits. Soils also played a role in tree dieback, with greater mortality on deeper soils. While mortality was driven largely by climate at coarse scales, fine-scale stand structure interacted with climate to mediate mortality and dieback. Neighborhood statistics showed that trees were susceptible to competitive influence, and pinyon trees were especially sensitive to neighborhood density on drier sites. Mortality and dieback were associated with diverse, co-occurring insect and parasitic plant mortality agents. Canopy dieback prior to the drought was strongly associated with tree mortality during the drought, implying that current widespread defoliation caused by these agents may foreshadow future elevated woodland decline. Fine-scale influences such as stand structure and soil characteristics play a key role in the long-term dynamics of semiarid woodlands, and these factors should be considered in predictive models of forest and woodland susceptibility to drought.

The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Random Forest (RF) algorithm and therefore predict canopy height from other remotely sensed datasets in areas without LiDAR coverage has been one of the most commonly used method in large-scale canopy height mapping. However, how variances in location, vegetation type, and spatial scale of study sites influence the RF modelling results is still a question that needs to be addressed. In this study, we selected 16 study sites (100 km2 each) with full airborne LiDAR coverage across the United States, and used the LiDAR-derived canopy height along with optical imagery, topographic data, and climate surfaces to evaluate the transferability of the RF-based canopy height prediction method. The results show a series of findings from general to complex. The RF model trained at a certain location or vegetation type cannot be transferred to other locations or vegetation types. However, by training the RF algorithm using samples from all sites with various vegetation types, a universal model can be achieved for predicting canopy height at different locations and different vegetation types with self-predicted R2 higher than 0.6 and RMSE lower than 6 m. Moreover, the influence of spatial scales on the RF prediction accuracy is noticeable when spatial extent of the study site is less than 50 km2 or the spatial resolution of the training pixel is finer than 500 m. The canopy height prediction accuracy increases with the spatial extent and the targeted spatial resolution.

What mediates tree mortality during drought in the southern Sierra Nevada?

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains, (2) to assess where mortality events have a greater probability of occurring, and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest- and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data

A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer’s accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.

Water-use efficiency of an old-growth forest in lower subtropical China

Carbon and water fluxes are key properties of ecosystem processes and functions. A better understanding of their temporal dynamics and coupling mechanism between these fluxes will help us improve ecosystem management for mitigation as well as adaption to future climatic change. From 2003 to 2009, carbon and water flux data were obtained by the eddy covariance method over an old-growth forest in the lower subtropical China. The 7 years of observational data indicated that the water-use efficiency (WUE) of the old-growth forest exhibited weak inter-annual variability. The mean annual WUE ranged from 1.70 to 1.98 g C kg-1 H2O. An analysis of the effects of environmental variables on the monthly gross primary productivity (GPP) and evapotranspiration (ET) indicated that solar radiation, air temperature, precipitation and vapor pressure deficit (VPD) produced similar effects on the monthly GPP and ET, which suggests that photosynthesis and ET were similarly driven by the climatic variables. At the monthly scale, the WUE decreased significantly as the precipitation and soil moisture content increased. However, a significant correlation was not detected between the WUE and the VPD at the monthly scale. Moisture conditions tend to be major drivers of the ecosystem WUE.

Towards a common methodology for developing logistic tree mortality models based on ring-width data

Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth-mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth-mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth-mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth-mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth-mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth-mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.

Effects of topoclimatic complexity on the composition of woody plant communities

Topography can create substantial environmental variation at fine spatial scales. Shaped by slope, aspect, hill-position and elevation, topoclimate heterogeneity may increase ecological diversity, and act as a spatial buffer for vegetation responding to climate change. Strong links have been observed between climate heterogeneity and species diversity at broader scales, but the importance of topoclimate for woody vegetation across small spatial extents merits closer examination. We established woody vegetation monitoring plots in mixed evergreen-deciduous woodlands that spanned topoclimate gradients of a topographically heterogeneous landscape in northern California. We investigated the association between the structure of adult and regenerating size classes of woody vegetation and multidimensional topoclimate at a fine scale. We found a significant effect of topoclimate on both single-species distributions and community composition. Effects of topoclimate were evident in the regenerating size class for all dominant species (four Quercus spp., Umbellularia californica and Pseudotsuga menziesii) but only in two dominant species (Quercus agrifolia and Quercus garryana) for the adult size class. Adult abundance was correlated with water balance parameters (e.g. climatic water deficit) and recruit abundance was correlated with an interaction between the topoclimate parameters and conspecific adult abundance (likely reflecting local seed dispersal). However, in all cases, the topoclimate signal was weak. The magnitude of environmental variation across our study site may be small relative to the tolerance of long-lived woody species. Dispersal limitations, management practices and patchy disturbance regimes also may interact with topoclimate, weakening its influence on woody vegetation distributions. Our study supports the biological relevance of multidimensional topoclimate for mixed woodland communities, but highlights that this relationship might be mediated by interacting factors at local scales.

Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America

Using an extensive network of occurrence records for 293 plant species collected over the past 40 years across a climatically diverse geographic section of western North America, we find that plant species distributions were just as likely to shift upwards (i.e., towards higher elevations) as downward (i.e., towards lower elevations)–despite consistent warming across the study area. Although there was no clear directional response to climate warming across the entire study area, there was significant region- to region- variation in responses (i.e. from as many as 73% to as few as 32% of species shifting upward). To understand the factors that might be controlling region-specific distributional shifts of plant species, we explored the relationship between the direction of change in distribution limits and the nature of recent climate change. We found that the direction that distribution limits shifted was explained by an interaction between the rate of change in local summer temperatures and seasonal precipitation. Specifically, species were more likely to shift upward at their upper elevational limit when minimum temperatures increased and snowfall was unchanging or declined at slower rates (<0.5 mm/year). This suggests that both low temperature and water availability limit upward shifts at upper elevation limits. By contrast, species were more likely to shift upwards at their lower elevation limit when maximum temperatures increased, but also shifted upwards under conditions of cooling temperatures when precipitation decreased. This suggests increased water stress may drive upward shifts at lower elevation limits. Our results suggest that species’ elevational distribution shifts are not predictable by climate warming alone but depend on the interaction between seasonal temperature and precipitation change.

Extended density-dependent mortality in mature conifer forests: causes and implications for ecosystem management

Understanding processes driving mortality in forests is important for comprehension of natural stand dynamics and for informing natural disturbance-based ecosystem management. There has been considerable study of mortality in forests during the self-thinning phase but we know much less about processes driving mortality in stands at later successional stages. We addressed this through study of five 1-ha spatially explicit permanent plots in mature (111-186 yr old in 2012) Pinus contorta stands in the Canadian Rocky Mountains using data from repeated measurements over a 45-yr period, dendrochronological information, and point pattern analysis. We tested the hypothesis that these stands had completed the self-thinning/density-dependent mortality stage of succession. Contrary to our expectations, the self-thinning phase can persist for more than 140 yr following stand establishment. Our findings suggest this was attributable to prolonged post-fire establishment periods due to surface fires in three of the plots while in the other two plots moist conditions and slow growth most likely delayed the onset of competition. Several pieces of evidence indicated the importance of density-dependent mortality in these stands over the study period: (1) The diameter distribution of individuals changed from initially right-skewed toward normality as a result of mortality of smaller-diameter stems. (2) Individuals of lower canopy positions were proportionally more affected by mortality. (3) When compared to the pre-mortality pattern, surviving stems in all stands had an increasingly uniform spatial distribution. In two of the plots, recent windthrow and/or ingrowth initially hindered our ability to detect density-dependent mortality but our dendrochronological sampling and permanent plot data allowed us to untangle the different processes at play; in doing so we demonstrate for the first time how density-independent processes can mask underlying density-dependent mortality processes in older stands. Mortality of larger dominant canopy trees increased over the study period and mortality of dominant stems was a random process in all stands suggesting these stands were approaching the end of the self-thinning stage and that density-independent processes might soon become more important. Our results provide an improved understanding of mortality processes that can be applied to natural disturbance-based ecosystem management.

Global change and terrestrial plant community dynamics

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.

Senescence: Is It Universal or Not?

Both demographic and physiological senescence have been demonstrated to occur in various organisms. However, indeterminate growers, such as some animals and most perennial plants, seem to escape the wear and tear of aging. Indeed, most angiosperms show no signs of senescence, and both negligible and negative senescence (improved physiological performance with aging) have been reported in perennial plants growing in their natural habitat. In this opinion article, I review recent developments in the study of senescence in perennial plants and propose that continuous growth prevents senescence. I also address the question whether senescence is a universal process.

Linking nonstructural carbohydrate dynamics to gas exchange and leaf hydraulic behavior in Pinus edulis and Juniperus monosperma

Leaf hydraulics, gas exchange and carbon storage in Pinus edulis and Juniperus monosperma, two tree species on opposite ends of the isohydry-anisohydry spectrum, were analyzed to examine relationships between hydraulic function and carbohydrate dynamics. Leaf hydraulic vulnerability, leaf water potential (Ψl ), leaf hydraulic conductance (Kleaf ), photosynthesis (A), stomatal conductance (gs) and nonstructural carbohydrate (NSC) content were analyzed throughout the growing season. Leaf hydraulic vulnerability was significantly lower in the relatively anisohydric J. monosperma than in the more isohydric P. edulis. In P. edulis, Ψl dropped and stayed below 50% loss of leaf hydraulic conductance (P₅₀) early in the day during May, August and around midday in September, leading to sustained reductions in Kleaf . In J. monosperma, Ψl dropped below P₅₀ only during August, resulting in the maintenance of Kleaf during much of the growing season. Mean A and gs during September were significantly lower in P. edulis than in J. monosperma. Foliar total NSC was two to three times greater in J. monosperma than in P. edulis in June, August and September. Consistently lower levels of total NSC in P. edulis suggest that its isohydric strategy pushes it towards the exhaustion of carbon reserves during much of the growing season.

Prolonged experimental drought reduces plant hydraulic conductance and transpiration and increases mortality in a piñon-juniper woodland

Plant hydraulic conductance (k_s) is a critical control on whole-plant water use and carbon uptake and, during drought, influences whether plants survive or die. To assess long-term physiological and hydraulic responses of mature trees to water availability, we manipulated ecosystem-scale water availability from 2007 to 2013 in a piñon pine (Pinus edulis) and juniper (Juniperus monosperma) woodland. We examined the relationship between k_s and subsequent mortality using more than 5 years of physiological observations, and the subsequent impact of reduced hydraulic function and mortality on total woody canopy transpiration (E_C) and conductance (G_C). For both species, we observed significant reductions in plant transpiration (E) and k_s under experimentally imposed drought. Conversely, supplemental water additions increased E and k_s in both species. Interestingly, both species exhibited similar declines in k_s under the imposed drought conditions, despite their differing stomatal responses and mortality patterns during drought. Reduced whole-plant k_s also reduced carbon assimilation in both species, as leaf-level stomatal conductance (g_s) and net photosynthesis (A_n) declined strongly with decreasing k_s. Finally, we observed that chronically low whole-plant k_s was associated with greater canopy dieback and mortality for both piñon and juniper and that subsequent reductions in woody canopy biomass due to mortality had a significant impact on both daily and annual canopy E_C and G_C. Our data indicate that significant reductions in k_s precede drought-related tree mortality events in this system, and the consequence is a significant reduction in canopy gas exchange and carbon fixation. Our results suggest that reductions in productivity and woody plant cover in piñon–juniper woodlands can be expected due to reduced plant hydraulic conductance and increased mortality of both piñon pine and juniper under anticipated future conditions of more frequent and persistent regional drought in the southwestern United States.

Twentieth-century shifts in forest structure in California: Denser forests, smaller trees, and increased dominance of oaks

We document changes in forest structure between historical (1930s) and contemporary (2000s) surveys of California vegetation through comparisons of tree abundance and size across the state and within several ecoregions. Across California, tree density in forested regions increased by 30% between the two time periods, whereas forest biomass in the same regions declined, as indicated by a 19% reduction in basal area. These changes reflect a demographic shift in forest structure: larger trees (>61 cm diameter at breast height) have declined, whereas smaller trees (<30 cm) have increased. Large tree declines were found in all surveyed regions of California, whereas small tree increases were found in every region except the south and central coast. Large tree declines were more severe in areas experiencing greater increases in climatic water deficit since the 1930s, based on a hydrologic model of water balance for historical climates through the 20th century. Forest composition in California in the last century has also shifted toward increased dominance by oaks relative to pines, a pattern consistent with warming and increased water stress, and also with paleohistoric shifts in vegetation in California over the last 150,000 y.

Spatially nonrandom tree mortality and ingrowth maintain equilibrium pattern in an old-growth Pseudotsuga-Tsuga forest

Mortality processes in old-growth forests are generally assumed to be driven by gap-scale disturbance, with only a limited role ascribed to density-dependent mortality, but these assumptions are rarely tested with data sets incorporating repeated measurements. Using a 12-ha spatially explicit plot censused 13 years apart in an approximately 500-year-old Pseudotsuga-Tsuga forest, we demonstrate significant density-dependent mortality and spatially aggregated tree recruitment. However, the combined effect of these strongly nonrandom demographic processes was to maintain tree patterns in a state of dynamic equilibrium. Density-dependent mortality was most pronounced for the dominant late-successional species, Tsuga heterophylla. The long-lived, early-seral Pseudotsuga menziesii experienced an annual stem mortality rate of 0.84% and no new recruitment. Late-seral species Tsuga and Abies amabilis had nearly balanced demographic rates of ingrowth and mortality. The 2.34% mortality rate for Taxus brevifolia was higher than expected, notably less than ingrowth, and strongly affected by proximity to Tsuga. Large-diameter Tsuga structured both the regenerating conspecific and heterospecific cohorts with recruitment of Tsuga and Abies unlikely in neighborhoods crowded with large-diameter competitors (P < 0.001). Density-dependent competitive interactions strongly shape forest communities even five centuries after stand initiation, underscoring the dynamic nature of even equilibrial old-growth forests.

Beyond a warming fingerprint: individualistic biogeographic responses to heterogeneous climate change in California

Understanding recent biogeographic responses to climate change is fundamental for improving our predictions of likely future responses and guiding conservation planning at both local and global scales. Studies of observed biogeographic responses to 20th century climate change have principally examined effects related to ubiquitous increases in temperature - collectively termed a warming fingerprint. Although the importance of changes in other aspects of climate - particularly precipitation and water availability - is widely acknowledged from a theoretical standpoint and supported by paleontological evidence, we lack a practical understanding of how these changes interact with temperature to drive biogeographic responses. Further complicating matters, differences in life history and ecological attributes may lead species to respond differently to the same changes in climate. Here, we examine whether recent biogeographic patterns across California are consistent with a warming fingerprint. We describe how various components of climate have changed regionally in California during the 20th century and review empirical evidence of biogeographic responses to these changes, particularly elevational range shifts. Many responses to climate change do not appear to be consistent with a warming fingerprint, with downslope shifts in elevation being as common as upslope shifts across a number of taxa and many demographic and community responses being inconsistent with upslope shifts. We identify a number of potential direct and indirect mechanisms for these responses, including the influence of aspects of climate change other than temperature (e.g., the shifting seasonal balance of energy and water availability), differences in each taxon's sensitivity to climate change, trophic interactions, and land-use change. Finally, we highlight the need to move beyond a warming fingerprint in studies of biogeographic responses by considering a more multifaceted view of climate, emphasizing local-scale effects, and including a priori knowledge of relevant natural history for the taxa and regions under study.

The importance of large-diameter trees to forest structural heterogeneity

Large-diameter trees dominate the structure, dynamics and function of many temperate and tropical forests. However, their attendant contributions to forest heterogeneity are rarely addressed. We established the Wind River Forest Dynamics Plot, a 25.6 ha permanent plot within which we tagged and mapped all 30,973 woody stems ≥1 cm dbh, all 1,966 snags ≥10 cm dbh, and all shrub patches ≥2 m². Basal area of the 26 woody species was 62.18 m²/ha, of which 61.60 m²/ha was trees and 0.58 m²/ha was tall shrubs. Large-diameter trees (≥100 cm dbh) comprised 1.5% of stems, 31.8% of basal area, and 17.6% of the heterogeneity of basal area, with basal area dominated by Tsuga heterophylla and Pseudotsuga menziesii. Small-diameter subpopulations of Pseudotsuga menziesii, Tsuga heterophylla and Thuja plicata, as well as all tree species combined, exhibited significant aggregation relative to the null model of complete spatial randomness (CSR) up to 9 m (P≤0.001). Patterns of large-diameter trees were either not different from CSR (Tsuga heterophylla), or exhibited slight aggregation (Pseudotsuga menziesii and Thuja plicata). Significant spatial repulsion between large-diameter and small-diameter Tsuga heterophylla suggests that large-diameter Tsuga heterophylla function as organizers of tree demography over decadal timescales through competitive interactions. Comparison among two forest dynamics plots suggests that forest structural diversity responds to intermediate-scale environmental heterogeneity and disturbances, similar to hypotheses about patterns of species richness, and richness- ecosystem function. Large mapped plots with detailed within-plot environmental spatial covariates will be required to test these hypotheses.