About

Stephanie Pau is an Associate Professor in the Department of Environmental Science, Policy & Management with a joint appointment in the Department of Geography at UC Berkeley. She holds a Ph.D. and M.A. from the University of California, Los Angeles, and a B.A. from the University of California, Santa Barbara. Her research addresses how terrestrial ecosystems are impacted by global change, focusing on understanding interactions between plants and their changing environment. She is particularly interested in the role of biodiversity and plant traits in ecosystem functions and how these vary along environmental gradients. Her work extensively utilizes remote sensing technologies, including satellite, drone, and mounted camera observations, to provide repeated and spatially-extensive measurements of the Earth’s surface. Remote sensing allows her to analyze wavelengths of reflected energy from plants that are beyond visible light, such as near-infrared reflectance, which contains valuable information about plant photosynthetic activity. She combines these remote sensing observations with intensive ground-based measurements to develop relationships between plant reflectance and ecological properties, facilitating the mapping of biodiversity and ecological functions across landscapes.

Research topics

• Environmental science
• Biology
• Ecology
• Atmospheric sciences
• Geology
• Remote sensing
• Geography

Selected publications

• Fire reverses the effect of climate on longleaf pine savanna tree growth

  Fire Ecology · 2026-02-02

  articleOpen accessSenior author

  Abstract Background Fire is a critical ecosystem process for maintaining savannas globally, but how it affects tree growth is complex. Climate, a dominant driver of tree growth, may interact with fire to produce novel climate-growth relationships because of the way climate influences fire intensity and the role it plays in facilitating post-fire tree recovery. Understanding how fire and climate interact to influence tree growth is critical for managing longleaf pine ( Pinus palustris ) savannas, a fire-dependent ecosystem undergoing rapid environmental change. Results We analyzed annual basal area increment (BAI) from 453 longleaf pine tree core samples collected across eight sites spanning three community types in Florida to quantify how monthly and seasonal climatic variables influence tree growth, and how these relationships are modified by fire and fire seasonality. We found that maximum temperature and precipitation during late spring and summer and summer to fall PDSI were key drivers of growth, but their effects varied by site and were frequently altered by the occurrence of fire. Notably, at four of eight sites, fire reversed the direction of climate-growth relationships. Reversals are when a positive climate effect in non-fire years becomes negative in fire years, or vice versa. Reversals most commonly changed the effect of climatic variables on tree growth from positive to negative, though the direction varied across sites and seasons. Fire also strengthened the effect that climate had on growth at four sites. Tree growth was slightly reduced in fire years regardless of the season of fire, but these differences were not statistically significant, suggesting overall resilience of longleaf pine growth to fire occurrence. Conclusions Our findings show that tree growth in frequent-fire systems is shaped by complex interactions between climate and fire, and that fire can mediate or even reverse the effects of climate. Importantly, individual tree responses varied significantly across and within sites, pointing to high intraspecific variability likely driven by individual tree characteristics (size and age) and local site conditions including competition and management legacies. By incorporating individual-level growth data, this study underscores the need for fine-scale, context-specific fire management strategies that account for local climate and ecological variation across the longleaf pine range.

  PublisherDOI

• Scale-dependent responses to environmental fluctuations in tropical tree species’ crown temperatures

  Communications Earth & Environment · 2025-01-18

  articleOpen accessSenior author

  Abstract Tropical forests may be nearing critical temperatures, yet tree species may respond differently. Using high-resolution thermal, hyperspectral, and LiDAR imagery, we mapped 652 crowns of four Hawaiian tree species to study the effects of crown traits and abiotic conditions on species’ temperatures at two scales (whole crown vs. sunlit leaves). We show scale-dependent, species-specific relationships with environmental fluctuations. Net radiation was consistently the dominant determinant of crown temperature deviations from air temperature (Tdiff), while vapor pressure deficit, wind speed, and crown traits (e.g., roughness) varied in importance by species and scale. Species explained 17% and 44% of Tdiff variation at the crown and leaf scales, respectively, after controlling for climatic factors. Findings suggest that leaf temperatures overestimate larger-scale temperature differences, while canopy-scale observations underestimate leaf heat stress. Because leaf and crown traits can have opposing effects on Tdiff, disentangling these can advance our understanding of species’ thermoregulation under climate change.

  PublisherOA PDFDOI

• Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

  Ecosphere · 2025-04-01 · 4 citations

  articleOpen access1st authorCorresponding

  Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.

  PublisherOA PDFDOI

• Sub-pixel detection of dominant grass evolutionary lineages at four sites across the Great Plains, U.S. using hyperspectral data

  International Journal of Remote Sensing · 2025-11-26

  articleSenior author
  PublisherDOI

• Sensing Potential, Scientists Refine Thermal Imaging of Ecosystems

  Eos · 2025-02-07 · 1 citations

  articleOpen access

  At a recent “bake-off,” researchers judged thermal infrared cameras and developed guidelines for their consistent use in studying vegetation temperatures, which illuminate vital ecosystem processes.

  PublisherDOI

• Variation in the strength and stationarity of southern longleaf pine seasonwood climate-growth relationships

  Dendrochronologia · 2024-05-09 · 6 citations

  article
  PublisherDOI

• Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

  Journal of Geophysical Research Biogeosciences · 2024-02-01 · 3 citations

  articleOpen accessCorresponding

  Abstract Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C 4 ), Chloridoideae (C 4 ), and Pooideae (C 3 ). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.

  PublisherOA PDFDOI

• Longleaf pine savannas reveal biases in current understanding of savanna biogeography

  Global Ecology and Biogeography · 2023-09-05 · 9 citations

  articleOpen access1st authorCorresponding

  Abstract Biased understanding of savanna biogeography Grasslands and savannas exist across a wide range of climates. Mesic savannas, with highly variable tree densities, are particularly misunderstood and understudied in comparison to arid and semi‐arid savannas. North America contains historically extensive mesic savannas dominated by longleaf pine. Longleaf pine savannas may have once been the largest savanna type on North America, yet these ecosystems have been overlooked in global syntheses. Excluding these “Forgotten Ecosystems” from global syntheses biases our understanding of savanna biogeography and distribution. Evolutionary history and distinct climate of longleaf savannas We assessed the evolutionary history and biogeography of longleaf pine savannas. We then harmonize plot data from longleaf savannas with plot data from valuable existing global synthesis of savannas on other continents. We show that longleaf pine savannas occur in a strikingly distinct climate space compared to savannas on Africa, Australia, and South America, and are unique in having wide ranging tree basal areas. Future directions Grass‐dominated ecosystems are increasingly recognized as being ancient and biologically diverse, yet threatened and undervalued. A new synthesis of savanna ecosystems considering their full range of distributions is needed to understand their ecology and conservation status. Interestingly, the closest analogues to North American savannas and their relatives in Mesoamerica and the Caribbean may be Asian savannas, which also contain mesic fire‐driven pine savannas and have been similarly neglected in existing global syntheses.

  PublisherOA PDFDOI

• Comparing Phenology of a Temperate Deciduous Forest Captured by Solar-Induced Fluorescence and Vegetation Indices

  Remote Sensing · 2023-10-25 · 4 citations

  articleOpen accessSenior author

  A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIFy), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIFy. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses.

  PublisherOA PDFDOI

• Reply to Garen et al.: Within-canopy temperature data also do not support limited homeothermy

  Proceedings of the National Academy of Sciences · 2023-04-03 · 3 citations

  letterOpen access

  Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans the biological, physical, and social sciences.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: MRA: A lineage-based framework to advance grassland macroecology and Earth System Modeling

  NSF · $442k · 2020–2024

Frequent coauthors

• Christopher J. Still

  Oregon State University

  30 shared

• Fabrizio De Benedetti

  Istituti di Ricovero e Cura a Carattere Scientifico

  22 shared

• Benjamin I. Cook

  Lamont-Doherty Earth Observatory

  20 shared

• Daniel M. Griffith

  Oregon State University

  19 shared

• Antonella Insalaco

  Istituti di Ricovero e Cura a Carattere Scientifico

  18 shared

• Elizabeth M. Wolkovich

  University of British Columbia

  17 shared

• Matteo Detto

  Princeton University

  17 shared

• Marco Gattorno

  Istituto Giannina Gaslini

  16 shared