The Sacramento-San Joaquin Delta in California was drained for agriculture and human settlement over a century ago, resulting in extreme rates of soil subsidence and release of CO2 to the atmosphere from peat oxidation. Because of this century-long ecosystem carbon imbalance where heterotrophic respiration exceeded net primary productivity, most of the land surface in the Delta is now up to 8 meters below sea level. To potentially reverse this trend of chronic carbon loss from Delta ecosystems, land managers have begun converting drained lands back to flooded ecosystems, but at the cost of increased production of CH4, a much more potent greenhouse gas than CO2.

To evaluate the impacts of inundation on the biosphere-atmophere exchange of CO2 and CH4 in the Delta, I first measured and analyzed net fluxes of CO2 and CH4 for two continuous years with the eddy covariance technique in a drained peatland pasture and a recently re-flooded rice paddy. This analysis demonstrated that the drained pasture was a consistent large source of CO2 and small source of CH4, whereas the rice paddy was a mild sink for CO2 and a mild source of CH4. However more importantly, this first analysis revealed nuanced complexities for measuring and interpreting patterns in CO2 and CH4 fluxes through time and space.

CO2 and CH4 fluxes are inextricably linked in flooded ecosystems, as plant carbon serves as the primary substrate for the production of CH4 and wetland plants also provide the primary transport pathway of CH4 flux to the atmosphere. At the spatially homogeneous rice paddy during the summer growing season, I investigated rapid temporal coupling between CO2 and CH4 fluxes. Through wavelet Granger-causality analysis, I demonstrated that daily fluctuations in growing season gross ecosystem productivity (photosynthesis) exert a stronger control than temperature on the diurnal pattern in CH4 flux from rice.

At a spatially heterogeneous restored wetland site, I analyzed the spatial coupling between net CO2 and CH4 fluxes by characterizing two-dimensional patterns of emergent vegetation within eddy covariance flux footprints. I combined net CO2 and CH4 fluxes from three eddy flux towers with high-resolution remote sensing imagery classified for emergent vegetation and an analytical 2-D flux footprint model to assess the impact of vegetation fractal pattern and abundance on the measured flux. Both emergent vegetation abundance and fractal complexity are important metrics for constraining variability within CO2 and CH4 flux in this complex landscape.

Scaling between carbon flux measurements at individual sites and regional scales depends on the connection to remote sensing metrics that can be broadly applied. In the final chapter of this dissertation, I analyzed a long term dataset of hyperspectral ground reflectance measurements collected within the flux tower footprints of three structurally similar yet functionally diverse ecosystems: an annual grassland, a degraded pepperweed pasture, and a rice paddy. The normalized difference vegetation index (NDVI) was highly correlated with landscape-scale photosynthesis across all sites, however this work also revealed new potential spectral indices with high correlation to both net and partitioned CO2 fluxes.

This analysis within this dissertation serves as a framework for considering the impacts of temporal and spatial heterogeneity on measured landscape-scale fluxes of CO2 and CH4. Scaling measurements through time and space is especially critical for interpreting fluxes of trace gases with a high degree of temporal heterogeneity, like CH4 and N2O, from landscapes that have a high degree of spatial heterogeneity, like wetlands. This work articulates a strong mechanistic connection between CO2 and CH4 fluxes in wetland ecosystems, and provides important management considerations for implementing and monitoring inundated land-use conversion as an effective carbon management strategy in the California Delta.

Alfalfa agriculture is a natural laboratory for studying land-atmosphere interactions due to its homogeneity and fluctuating leaf area index from periodic cuttings throughout the year. However, a few problems exist, which are the objectives of this thesis: 1) measuring energy and water fluxes in-situ can be very expensive (~$50K); 2) spatiotemporal heterogeneity could bring in unexpected quantities (e.g., heat and moisture) into the field, distorting the fluxes of energy and water; and 3) long term carbon and water budgets remain largely unknown for irrigated alfalfa in California. In the following, I used a combination of eddy covariance measurements and satellite remote sensing to investigate these problems.

The first chapter is on the development of cost-effective measurements for sensible (H) and latent heat fluxes (λE). I deployed the variance-Bowen ratio technique in an irrigated alfalfa field, and measured H and λE using only a sonic anemometer and an air temperature and relative humidity sensor (T-RH). Measured H and λE were validated against eddy covariance measurements, where H showed strong agreement (slope = 0.98, R2 = 0.96, n = 3726), while λE showed good agreement (slope = 0.89, R2 = 0.91, n = 3773). Thermal remote sensing observations from The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and existing tower array showed that the uncertainty in λE was attributed to a product of horizontal heat and moisture advection from upwind fields. Based on our results, the variance-Bowen ratio technique is a robust, inexpensive, yet user-friendly approach to measure sensible and latent heat flux. The utility of this approach could extend to measure horizontal heat advection and provide sensor networks of energy and water fluxes for calibrating/validating remote sensing models.

The second chapter re-evaluated the theoretical limitation of eddy covariance given the spatiotemporal heterogeneity at the site region. Specifically, I focused on the assumed negligible horizontal heat and moisture advection and measured them with tower arrays and profile measurements. Results showed local and non-local processes affected land-atmosphere interactions. Locally, competing process was observed between atmospheric demand and stomatal regulation. As a result of the upwind λE, advection humidified the atmosphere and increased stomatal opening, but λE was suppressed with a lowered atmospheric demand. Non-locally, spectral analysis revealed that low frequency (i.e., large) eddies contributed high heat and moisture advection. Thermal imagery from ECOSTRESS and Landsat 8/9 showed that these large eddies were generated over the upwind surface, and they were independent of the local boundary layer conditions. Hence, λE was enhanced through this non-local transport of heat and moisture. Lastly, by conditionally including the advective fluxes in the turbulence budget, the energy balance closure improved from 89% to 97% (r2 = 0.97, p<0.001) over 37 days.

The third chapter explored how water scarcity affects alfalfa’s ability to consistently provide high yields and serve as a robust carbon sink. Long-term eddy covariance data of energy, water, and carbon fluxes were used over the course of 7 years. In 2022, net ecosystem exchange (-175 g C m-2 y-1) and evaporation (722 mm y-1) suddenly declined, compared to the average value of net ecosystem exchange at -544 g C m-2 y-1 and evaporation at 861 mm y-1. This result showed that water stress greatly impacted carbon and water budgets during the active summer growing season in 2022. Specifically, limited water supply from record-low springtime precipitation and irrigation curtailment impeded crop growth, leading to higher stomatal closure and a consequent decrease in carbon sink strength and evaporation throughout this year.

This thesis addressed key challenges in understanding the land-atmosphere interactions in alfalfa agriculture. The simple and cost-alternative sensors developed in Chapter 1 allow researchers to measure fluxes everywhere, all the time across key ecosystems. Missing fluxes quantified in Chapter 2 highlights the significance of advection in land-atmosphere interactions. Lastly, long-term flux budgets assessed in Chapter 3 underscores the vulnerability of agricultural systems.

The Sacramento–San Joaquin Delta in California was drained for agriculture and human settlement circa 1850, resulting in extreme rates of soil subsidence and CO2 emissions due to peat oxidation. As a result of this prolonged ecosystem carbon imbalance where ecosystem respiration exceeded primary productivity, much of the land surface in the Delta now lies 5 to 8 m below sea level. To help reverse subsidence and convert Delta ecosystems from net carbon sources to carbon sinks, land managers have begun converting drained agricultural lands back to flooded ecosystems including wetlands and irrigated rice paddies. However, this comes at the cost of increased CH4 emissions, a much more potent greenhouse gas than CO2.

To evaluate the impacts of drained to flooded land-use change on the biosphere-atmosphere exchange of CO2 and CH4 in the Delta, I conducted a full year of simultaneous eddy covariance measurements at two conventional drained agricultural peatlands (a pasture and a corn field) and three flooded land-use types (a rice paddy and two restored wetlands). This research showed that the drained sites were large CO2 and greenhouse gas (GHG) sources. However, this study also found that converting drained agricultural peat soils to flooded rice paddies or wetlands can help reduce or reverse soil subsidence and reduce GHG emissions, despite the potential for considerably higher CH4 emissions. In particular, wetlands offer the greatest potential for reversing subsidence since both restored wetlands were large net carbon sinks.

Since natural and managed ecosystems can exhibit large year-to-year variation in CO2 and CH4 exchange, I analyzed 6.5 years of measurements from the irrigated rice paddy to investigate the factors affecting CH4 fluxes across diel to interannual timescales and quantify interannual variability in CO2 and CH4 budgets. Using wavelet analysis, I found that photosynthesis induced the diel pattern in CH4 flux, but soil temperature influenced its amplitude. At the seasonal scale, linear and neural network models indicated that photosynthesis and water levels were the dominant factors regulating daily average CH4 fluxes. However, across years, much of the variability in annual and growing season CH4 sums was driven by soil temperature. Soil temperature also strongly influenced ecosystem respiration, resulting in large interannual variability in the net carbon budget at the paddy. This study emphasizes the need for long-term, continuous measurements particularly under changing climatic conditions.

With a growing interest in including wetlands in carbon markets worldwide due to their ability to accumulate large amounts of carbon, there is a need for models that can accurately and cheaply predict wetland CO2 and CH4 fluxes. In the final chapter of this dissertation, I combined eddy covariance CO2 fluxes measurements, flux footprint analysis, and near-surface (i.e. digital cameras) or satellite remote sensing data to investigate the potential of using the light use efficiency approach to accurately and cost-effectively model photosynthesis in wetland systems. Through this analysis, I showed that digital camera and Landsat imagery can be used to model carbon uptake in wetlands, providing inexpensive means of monitoring carbon cycling in these environments that can be used in carbon markets.

By measuring trace gas exchange across multiple sites for multiple years, this dissertation provides new and important insights on the impacts of land use change in the Delta, improves our understanding of factors influencing CO2 and CH4 fluxes from agricultural and restored wetlands across diel to interannual timescales, and presents cost-effective and accurate ways of estimating photosynthesis in restored wetlands by combining flux measurements with near-surface and satellite remote sensing. This work helps bridge understanding between biometeorology, biogeochemistry and climate policy, and provides valuable information to help inform management decisions regarding carbon and water management of the Delta.

Canopy architecture plays fundamental roles in the land-atmosphere interactions, yet quantification of canopy architecture using optical sensors in an open canopy remains a challenge. Savannas are spatially heterogeneous, open ecosystems, thus efforts to quantify canopy structure with methods developed for homogeneous, closed canopies are prone to failure. I employed a multi-model and multi-instrument approach to quantify leaf area index in an oak savanna ecosystem of California. I found that the effective area index should be calculated by taking the logarithm of average gap fraction. Contrary to boreal and temperate forests, the savanna ecosystem was highly clumped at the ecosystem scale (clumping index=0.49). Thus quantification of clumping effects at the ecosystem scale, which has been overlooked in most leaf area index products, is crucial to obtain the correct leaf area index.

To investigate how evaporation in the annual grassland of the savanna ecosystem is modulated by biological/environmental factors, I investigated the 6 year evaporation data measured with a eddy covariance system. The annual evaporation ranged between 266 mm to 391 mm despite a two-fold range in precipitation. I found that the pronounced energy-limited and water-limited periods occurred within the same year. In the water-limited period, monthly integrated evaporation scaled negatively with solar radiation and was restrained by precipitation. In the energy-limited period, on the other hand, the majority of evaporation scaled positively with solar radiation and was confined by potential evaporation. Evaporation was most sensitive to the availability of soil moisture during the transition to the senescence period rather than the onset of the greenness period, causing annual evaporation to be strongly modulated by the length of growing season.

To bridge canopy structure, function and metabolism, I tested the use of light emitting diodes (LEDs) to monitor the vegetation reflectance in narrow spectral bands. LEDs are appealing because they are inexpensive, small and reliable light sources that used in reverse mode, can measure spectrally selective radiation. To test the efficacy of this approach, I measured the spectral reflectance with LEDs in red and near-infrared wavebands, which are used to calculate the normalized difference vegetation index over the grassland over 3.5 years. The LED-spectrometer captured daily to inter-annual variation of the spectral reflectance at the two bands with reliable and stable performance. The spectral reflectance in the two bands and NDVI proved to be useful to identify the leaf-on and leaf-off dates (mean bias errors of 5.3 and 4.2 days, respectively) and to estimate the canopy photosynthesis (r2=0.91). I suggest that this novel instrument can monitor other structural and functional (e.g. leaf area index, leaf nitrogen) variables by employing the LEDs that have other specific wavelengths bands. Considering that off-the-shelf LEDs cover a wide range of wavebands from the ultraviolet to near-infrared regions, I believe that the research community could explore a range of similar instruments across a range of bands for a variety of ecological applications.

The regular monitoring of evaporation from satellites has been limited because of discontinuous temporal coverage. Here, I found a strong linear relationship between mean hourly λE (i.e., 1000-1100hh; 1100-1200hh; 1200-1300hh; 1300-1400hh) and 8-day means of λE at 26 eddy covariance flux towers across seven plant functional types from boreal to tropical climatic zones. Hourly time steps of evaporation were selected to correspond with potential overpass times of the MODIS Terra and Aqua satellites. The mean slope of the linear relationship between mean hourly means of evaporation and 8-day, 24-h evaporation means showed no significant differences among sites and for each of the four mid-day hours. The results suggest a factor of 0.370 (95% CI: 0.354, 0.385) can be used to temporally upscale instantaneous evaporation measurements averaged over 8-day periods to an 8-day mean evaporation.

The biosphere removes nearly a quarter of anthropogenic greenhouse gas emissions each year through biogeochemical processes. These fluxes, along with biophysical exchanges of energy and water, play an important role in local to global climate dynamics and human well-being. With an urgent need to decrease the amount of greenhouse gases in the atmosphere to avoid runaway climate change, much recent work has focused on identifying and quantifying the role that terrestrial ecosystems could play in mitigating climate change. Restoration of coastal and deltaic wetlands, with their often carbon-rich organic soils and high productivity, presents an attractive but largely untested land-based climate mitigation strategy.

The benefits associated with wetland restoration stem from two key areas. First, drained agricultural peat soils can be large greenhouse gas sources. Second, the slow decomposition rates of inundated wetland soil organic matter along with high productivity leads to soil carbon accumulation and protection. While these tenets are generally widely appreciated, they have rarely been tested and measured at the ecosystem scale, over multiple years.

In this dissertation work, I studied the coupled biogeochemical and biophysical impacts of a long-term wetland restoration ecological experiment in the Sacramento-San Joaquin River Delta in California, USA. By continuously measuring greenhouse gas and energy fluxes at the ecosystem scale over a variety of land cover types, including four restored wetlands of various ages and structures, I was able to characterize the carbon, greenhouse gas, and biophysical impacts of degraded peat soil restoration to freshwater deltaic wetlands.

I find that these restored freshwater deltaic wetlands are highly productive, sequestering carbon in the soil as productivity outpaces ecosystem respiration. This productivity comes at the cost of substantial methane emissions, however, making these wetlands greenhouse gas neutral to sources over a century. Despite this fact, transitions from high-emission degraded peat soil agricultural land uses to restored deltaic wetlands often reduce greenhouse gas emissions overall. Furthermore, I analyze how the biophysical impacts of this restoration activity – the changes to the way the ecosystems exchange heat and water – affect the surface temperature and boundary layer. I find that along with potential biogeochemical benefits, restored deltaic wetlands have an evaporative cooling effect due to rougher, wetter canopies.

These findings shed light on the viability of freshwater wetland restoration as a land-based climate mitigation solution. Restored wetlands should be part of a climate solution, but aren’t a ‘quick fix’. Ecosystem restoration is a dynamic process, with interannual variability, succession, and disturbance influencing the long-term performance of these ecosystems. Despite the incurred methane emissions in our restored wetlands, the flooded conditions effectively inhibit heterotrophic soil respiration and thus sequester carbon and create soil, refilling the deeply subsided ‘islands’ that have formed over the past century and a half. Other positive co-benefits, like local to regional biophysical cooling, along with habitat enhancement, must be considered to understand the full potential of restored wetlands as a part of the climate solution.

Quercus douglasii, blue oak, is endemic to the California mediterranean-type climate region characterized by annual summer drought. Because blue oak is winter deciduous, an understanding of the strategy for surviving under current climate conditions is vital to improving the predictions for survival and adaptation to predicted climate change. This is especially important because the region may experience strengthened climate-change type drought, a decrease in precipitation accompanied by higher temperatures. As such, this dissertation was focused on studying the ecophysiology of Quercus douglasii by intense measurements of the seasonal trends in leaf structure and function, photosynthetic functional response to three environmental variables (PAR, CO₂, and temperature), leaf water potentials, and the limitation imposed on photosynthetic carbon fixation by stomatal and internal (mesophyll) conductance. The study was realized in situ in a grazed oak-grass savanna near Ione, CA, USA, in multiple years and on multiple trees of different water use strategies (i.e. both isohydric and anisohydric).

Instantaneous gas-exchange and leaf-fluorescence was measured with the Li-Cor 6400-40 in order to estimate photosynthetic capacity via photosynthetic response curves to CO₂, mitochondrial respiration in the dark during the day (R_d) via photosynthetic light response curves, as well as instantaneous maximum rates of photosynthesis, stomatal conductance, and transpiration. Additionally, by combining gas-exchange and fluorescence measurements, I calculated internal conductance (the conductance of CO₂ from the intercellular airspaces to the site of carboxylation, also referred to as mesophyll conductance) employing the `variable J' method. In order to estimate the relative contribution of electron transport to each photosynthesis and alternative electron sinks, I conducted photosynthetic response curves under 2% O₂. I also developed and utilized a novel method of performing photosynthetic response curves to leaf temperature in in situ. Finally, I routinely measured leaf water potential in the mid-day and pre-dawn, leaf mass, area, nitrogen content, chlorophyll content, and leaf absorptance.

The results from this study indicate that the majority of photosynthetic carbon fixation occurs during the spring when photosynthetic capacity of blue oaks is exceptionally high (V_cmax = 118 μmol m^-2 s^-1) and prior to the extreme drop of mid-day leaf water potential brought on by the onset of the summer drought. During this peak period, stomatal and internal conductances also peaked and the negative response of photosynthesis to increased leaf temperatures was minimal especially in the isohydric individual. During the summer drought when trees increasingly rely on the water table, mid-day leaf water potentials reached -5.1 MPa. While values of photosynthesis and conductance were low in all individuals, the isohydric individual displayed less suppression. In the anisohydric species, values approached zero toward day of year 200 and photosynthesis decreased nearly linearly with an increase in temperature and leaf-to-air vapor pressure difference, approaching 0 at 40°C. In all individuals in all years, R_d normalized to 25°C decreased exponentially from an initial value around 3 μmol m^-2 s^-1 throughout the season.

While photosynthetic activity, transpiration, and stomatal and internal conductances decreased throughout the summer drought, leaf absorptance and chlorophyll content tended to reach a plateau rather than a peak. Additionally, except in 2007 when the drought was exceptionally strong, leaf nitrogen on an area basis did not continually decrease. Similarly, temperature-normalized rates of electron transport tended to remain constant throughout the summer drought despite decreased photosynthetic demand for electron transport. Additionally, the ratio of electron transport to photosynthesis increased exponentially with temperature. This information indicated that despite reduction of photosynthesis during the summer drought, the leaves did not translocate resources, but rather acted to conserve water through minimized stomatal conductance and maintained the ability to process the high levels of incoming radiation at the site. Additionally, as temperature increased and PAR exceeded demand, alternative electron sinks were increasingly utilized to avoid photo-oxidation of the leaf. The presence of alternative electron sinks was emphasized by the offset of the linear regression between electron transport required for photosynthetic activity (J_a) and the actual electron transport rate measured by chlorophyll fluorescence (J_f), with J_f having a value of 30 μmol m^-2 s^-1 in the absence of photosynthesis and photorespiration.

This research has provided an increased understanding of the strategy for survival of blue oak during summer drought by demonstrating the strong peak of photosynthetic activity during the spring, the maintained capacity to process radiation beyond that required for photosynthesis, and the presence of alternative electron sinks to avoid photo-oxidation of the leaf especially during the drought. Additionally, it describes a protocol developed to perform photosynthetic temperature response curves in situ in drought conditions which can be easily implemented at other field sites. Finally, the seasonal trend of internal conductance and limitations in measuring internal conductance under extreme drought, are described. The information provided is valuable to ecosystem modelers because it improves estimates of photosynthetic capacity often used in models through the incorporation of internal conductance. Finally, the understanding of the seasonal plasticity in photosynthetic response to environmental conditions and capacity for tolerating high temperatures and incoming PAR can be utilized to improve predictions of the capacity of blue oaks to adapt and survive under predicted future climate scenarios.

ABSTRACT

Complexity in Climatic Controls on Plant Species Distribution: Satellite Data Reveal Unique Climate for Giant Sequoia in the California Sierra Nevada

by

Eric Kindseth Waller

Doctor of Philosophy in Environmental Science, Policy, and Management

University of California, Berkeley

Professor Dennis D. Baldocchi, Chair

A better understanding of the environmental controls on current plant species distribution is essential if the impacts of such diverse challenges as invasive species, changing fire regimes, and global climate change are to be predicted and important diversity conserved. Climate, soil, hydrology, various biotic factors (e.g., competition, disease, dispersal), fire, history, and chance can all play a role, but disentangling these factors is a daunting task. Increasingly sophisticated statistical models relying on existing distributions and mapped climatic variables, among others, have been developed to try to answer these questions. Any failure to explain pattern with existing mapped climatic variables is often taken as a referendum on climate as a whole, rather than on the limitations of the particular maps or models. Every location has a unique and constantly changing climate (challenging our definition of climate), so that any distribution could be explained by some aspect of climate. This is not a rationale for doing so - such models would certainly be overly fit to the given conditions and not generalizable. It is an argument for a more complete evaluation of possible climatic controls and the interaction of climate with other important variables (e.g., soil). A lack of adequate maps for various climate and climate-derived variables plays a large role in the failure of modelers to sufficiently address climatic complexity. In particular, site water balance, radiation, humidity, wind, and temporal variability in all of these factors may be poorly understood factors in controlling distributions. Weakness in the mapping of these variables is well recognized in the water balance modeling field, but is less emphasized in the species distribution modeling field, despite the fact that variables that affect the water balance are also likely to play a major role in species distribution. In this dissertation, I 1) improve the mapping of cloud cover from satellite imagery, in order to generate accurate monthly cloud frequency variables; 2) use these cloud frequency variables to demonstrate the possible importance of cloud cover, a previously overlooked climate variable, to the distribution of a particularly charismatic plant species, giant sequoia; and 3) further investigate the climate associated with the frequent cloud cover in the vicinity of giant sequoia, to identify other climatic factors that may also be important to the tree's distribution.

Chapter 1 of this dissertation reviews some of the major flaws in species distribution modeling (with existing climate data) and addresses concerns that climate may therefore not be predictive of, or even relevant to, species distributions. Despite problems with climate-based models, climate and climate-derived variables still have substantial merit for explaining species distribution patterns. Additional generation of relevant climate variables and improvements in other climate and climate-derived variables are still needed to demonstrate this more effectively. Satellite data have a long history of being used for vegetation mapping and even species distribution mapping. They have great potential for being used for additional climatic information, and for improved mapping of other climate and climate-derived variables.

Improving the characterization of cloud cover frequency with satellite data is one way in which the mapping of important climate and climate-derived variables (e.g., water balance) can be improved. An important input to water balance models, solar radiation maps could be vastly improved with a better mapping of spatial and temporal patterns in cloud cover. Chapter 2 of this dissertation describes the generation of custom daily cloud cover maps from Advanced Very High Resolution Radiometer (AVHRR) satellite data from 1981-1999 at ~5 km resolution and Moderate Resolution Imagine Spectroradiomter (MODIS) satellite reflectance data at ~500 meter resolution for much of the western U.S., from 2000 to 2012. Intensive comparisons of reflectance spectra from a variety of cloud and snow-covered scenes from the southwestern United States allowed the generation of new rules for the classification of clouds and snow in both the AVHRR and MODIS data. The resulting products avoid many of the problems that plague other cloud mapping efforts, such as the tendency for snow cover and bright desert soils to be mapped as cloud. This consistency in classification across cover types is critically important for any distribution modeling of a plant species that might be dependent on cloud cover.

In Chapter 3, monthly cloud frequencies derived from the daily classifications were used directly in species distribution models for giant sequoia (Sequoiadendron giganteum (Lindley) Buchholz) and were found to be the strongest predictors of giant sequoia distribution. A high frequency of cloud cover, especially in the spring, differentiated the climate of the west slope of the southern Sierra Nevada, where giant sequoia are prolific, from central and northern parts of the range, where the tree is rare and generally absent. Other mapped cloud products, contaminated by confusion with high elevation snow, would likely not have found this important result. The result illustrates the importance of accuracy in mapping as well as the importance of previously overlooked aspects of climate for species distribution modeling. But it also raises new questions about why the clouds form where they do and whether they might be associated with other aspects of climate important to giant sequoia distribution. What are the exact climatic mechanisms governing the distribution? Detailed aspects of the local climate warranted more investigation.

Chapter 4 investigates the climate associated with the frequent cloud formation over the western slopes of the southern Sierra Nevada: the "sequoia belt". This region is climatically distinct in a number of ways, all of which could be factors in influencing the distribution of giant sequoia and other species. Satellite and micrometeorological flux tower data reveal characteristics of the sequoia belt that were not evident with surface climate measurements and maps derived from them. Results have implications for species distributions everywhere, but especially in rugged mountains, where climates are complex and poorly mapped.

Chapter 5 summarizes some of the main conclusions from the work and suggests directions for related future research.