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Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.

The rapid and accurate assessment of above ground biomass (AGB) of woody vegetation is a critical component of climate mitigation strategies, land management practices and process-based models of ecosystem function. This is especially true of semi-arid ecosystems, where the high variability in precipitation and disturbance regimes can have dramatic impacts on the global carbon budget by rapidly transitioning AGB between live and dead pools. Measuring regional AGB requires scaling ground-based measurements using remote sensing, an inherently challenging task in the sparsely-vegetated, spatially-heterogeneous landscapes characteristic of semi-arid regions. Here, we test the ability of canopy segmentation and statistic generation based on aerial LiDAR (light detection and ranging)-derived 3D point clouds to derive AGB in clumps of vegetation in a juniper savanna in central New Mexico. We show that single crown segmentation, often an error-prone and challenging task, is not required to produce accurate estimates of AGB. We leveraged the relationship between the volume of the segmented vegetation clumps and the equivalent stem diameter of the corresponding trees (R2 = 0.83, p < 0.001) to drive the allometry for J. monosperma on a per segment basis. Further, we showed that making use of the full 3D point cloud from LiDAR for the generation of canopy object statistics improved that relationship by including canopy segment point density as a covariate (R2 = 0.91). This work suggests the potential for LiDAR-derived estimates of AGB in spatially-heterogeneous and highly-clumped ecosystems.

In the southwestern USA, increases in size, frequency, and severity of wildfire are driving the conversion of forests to shrub‐dominated ecosystems. Increases in drought extent and severity, coupled with the way that shrub‐dominated systems are perpetuated by high‐severity fire, predisposes these post‐disturbance landscapes to remain in a non‐forest condition. Consequently, understanding the distribution of aboveground biomass in post‐disturbance, shrub‐dominated ecosystems is central to constraining the uncertainty surrounding how these ecosystems interact with light and water to sequester carbon. Here we present allometric regressions for Quercus gambelii (Gambel oak) and Robinia neomexicana (New Mexico locust), two species that dominate post‐fire landscapes in the southwestern USA. Our allometric regressions are designed to be driven by either field plot or high‐resolution remote sensing data, using either shrub area or shrub volume to estimate biomass.

Remote sensing is a key technology that enables us to scale up our empirical, in situ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in piñon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against in situ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R2adj of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance.

Fire exclusion in historically frequent-fire forests of the southwestern United States has altered forest structure and increased the probability of highseverity fire. Warmer and drier conditions, coupled with dispersal distance limitations, are impeding tree seedling establishment and survival following high-severity fire. High-severity patches are commonly dominated by non-forest vegetation, a state that can be reinforced by subsequent fire events. We sought to determine the influence of fire probability on post-fire vegetation development in a severely burned landscape in New Mexico, USA. We used LANDIS-II to simulate three fire probability scenarios—historical fire probability, contemporary fire probability, and the mean of the two—with contemporary climate. As fire probability increased, the mean size of the largest fires and the mean landscape fire severity increased. These changes in fire characteristics resulted in decreased total aboveground biomass and photosynthetic capacity on the landscape after 50 years. Additionally, the distribution of individual species biomass shifted, with early successional species, especially those that resprout after fire, increasing as a fraction of total biomass with increasing fire occurrence. Counter to empirical data, our simulations did not show a conifer establishment limitation, suggesting a source of uncertainty that will need to be addressed to improve projections of forest dynamics under future climate. Even without limited conifer regeneration, continued increases in fire frequency are likely to favor resprouting species and result in a loss of forest biomass and ecosystem productivity in this southwestern forest landscape.

Over the twenty-first century, the combined effects of increased fire activity and climate change are expected to alter forest composition and structure in many ecosystems by changing postfire successional trajectories and recovery. Southwestern US mountain ecosystems contain a variety of vegetation communities organized along an elevation gradient that will respond uniquely to changes in climate and fire regime. Moreover, the twentieth-century fire exclusion has altered forest structure and fuel loads compared to their natural states (i.e., without fire suppression). Consequently, uncertainties persist about future vegetation shifts along the elevation gradient. In this study, we simulated future vegetation dynamics along an elevation gradient in the southwestern US comprising pinyon-juniper woodlands, ponderosa pine forests, and mixed-conifer forests for the period 2000–2099, to quantify the effects of future climate conditions and projected wildfires on species productivity and distribution. While we expected to find larger changes in aboveground biomass, species diversity and species-specific abundance at low elevation due to warmer and drier conditions, the largest changes occurred at high elevation in mixed-conifer forests and were caused by wildfire. The largest increase in high-severity and large fires were recorded in this vegetation type, leading to high mortality of the dominant species, Picea engelmannii and Abies lasiocarpa, which are not adapted to fire. The decline of these two species reduced biomass productivity at high elevation. In ponderosa pine forests and pinyon-juniper woodlands, fewer vegetation changes occurred due to higher abundance of well-adapted species to fire and the lower fuel loads mitigating projected fire activity, respectively. Thus, future research should prioritize understanding of the processes involved in future vegetation shifts in mixed-conifer forests in order to mitigate both loss of diversity specific to high-elevation forests and the decrease in biomass productivity, and thus carbon storage capacity, of these ecosystems due to wildfires.

Questions Woody encroachment into grasslands is a worldwide phenomenon partially influenced by climate change, including extreme weather events. Larrea tridentata is a common shrub throughout the warm deserts of North America that has encroached into grasslands over the past 150 years. Physiological measurements suggest that the northern distribution of L. tridentata is limited by cold temperatures; thus extreme winter events may slow or reverse shrub expansion. We tested this limitation by measuring the response of individual L. tridentata shrubs to an extreme winter cold (−31°C) event to assess shrub mortality and rate of recovery of surviving shrubs. Location Sevilleta National Wildlife Refuge, Socorro County, New Mexico, USA. Methods Canopy dieback and recovery following an extreme cold event were measured for 869 permanently marked individual L. tridentata shrubs in grass–shrub ecotone and shrubland sites. Individual shrubs were monitored for amount of canopy dieback, rate of recovery, and seed set for three growing seasons after the freeze event. Results Shrubs rapidly suffered a nearly complete loss of canopy leaf area across all sites. Although canopy loss was high, mortality was low and 99% of shrubs resprouted during the first growing season after the freeze event. Regrowth rates were similar within ecotone and shrubland sites, even when damage by frost was larger in the latter. After three years of recovery, L. tridentata canopies had regrown on average 23–83% of the original pre‐freeze canopy sizes across the sites. Conclusions We conclude that isolated extreme cold events may temporarily decrease shrubland biomass but they do not slow or reverse shrub expansion. These events are less likely to occur in the future as regional temperatures increase under climate change. A single extreme cold event did not slow or reverse shrub encroachment of a cold‐intolerant desert shrub (Larrea tridentata). Although extreme cold temperatures caused extensive canopy mortality across Chihuahuan Desert shrublands, damage was less for individuals in the shrubland–grassland ecotone and nearly all (99%) shrubs regrew.

Remote sensing is a key technology that enables us to scale up our empirical, in situ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in piñon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against in situ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R2adj of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance.

The Southwestern US is projected to continue to experience a significant warming trend, with increased variability in the timing and magnitude of rainfall events. The effects of theses changes in climate are already manifesting in the form of expansive, prolonged 'megadroughts', which have resulted in the widespread mortality of woody vegetation across the region.Therefore the need to monitor and model forest mortality and carbon dynamics at the landscape and regional scale is an essential component of regional and global climate mitigation strategies, and critical if we are to understand how the imminent state transitions taking place in forests globally will affect climate forcing and feedbacks. Remote sensing offers the only solution to multitemporal regional observation, yet many challenges exist with employing modern remote sensing solutions in highly stressed vegetation characteristic of semi-arid biomes, making one of the most expansive biomes on the globe also one of the most difficult to accurately monitor and model. The goal of this research was to investigate how changes in the structure of semi-arid woodlands following forest mortality impacts ecosystem function, and address this question in the context of remote sensing data sets, thereby contributing to the remote sensing community's ability to interact with these challenging ecosystems. We first focused on pinus edulis and juniperous monosperma (pinon-juniper) woodlands, as they comprise a model semi-arid biome. We tested the ability of high resolution remote sensing data to mechanistically describe the patterns in overstory mortality and understory green-up, and were able to observe the heterogeneous response of the understory as a function of cover type. We also investigated the relationship between changes in soil water content and the greenness of the canopy, noting that in these stress ecosystems there is often a decoupling of the canopy as measured remotely (e.g., via vegetation indices, VI), and photoysnthesis, potentially presenting a significant source of error in existing light use efficiency models of carbon uptake. Our analysis also suggested that leveraging remeote sensing data which measures in the red-edge portion of reflected light can provide increased sensitivity to the low leaf area, ephemeral pulses of greenup that we identified post canopy mortality. Given these findings, we developed a hierarchy of simple linear models to test the ability of a moisture sensitive VI, and a red-edge leveraging VI, to predict carbon uptake. We determined that the red-edge VI and the moisture sensitive VI both constrained uncertainty associated with carbon uptake, but that the variability in satellite view angle from scene to scene can impose a significant amount of noise in sparse canopy ecosystems. Finally, given the extent and prevalence of j. monosperma across the region, and its complex growth morphology, we tested the ability of aerial lidar to quantify the biomass of juniperous ecosystems. In this simplified case study, we developed a methodology to relate the volume of canopy objects to the equivalent stem area at the root crown. By working in a single species ecosystem, we circumvented many challenges associated with driving allometries remotely, but also present a workflow that we intend adapted to more complex systems, namely pinon-juniper woodlands. Together, this work describes and addresses existing challenges with respect to remote sensing of semi-arid vegetation, and provides a body of research that can mitigate the difficulties associated with monitoring mortality / recovery dynamics, predicting canopy funciton, and determining ecosystem state parameters in these complex, sensitive biomes.