The effect of reef morphology on coral recruitment at multiple spatial scales.

Coral reefs are in decline worldwide, making it increasingly important to promote coral recruitment in new or degraded habitat. Coral reef morphology-the structural form of reef substrate-affects many aspects of reef function, yet the effect of reef morphology on coral recruitment is not well understood. We used structure-from-motion photogrammetry and airborne remote sensing to measure reef morphology (rugosity, curvature, slope, and fractal dimension) across a broad continuum of spatial scales and evaluated the effect of morphology on coral recruitment in three broadcast-spawning genera. We also measured the effect of other environmental and biotic factors such as fish density, adult coral cover, hydrodynamic larval import, and depth on coral recruitment. All variables combined explained 72% of coral recruitment in the study region. Coarse reef rugosity and curvature mapped at ≥2 m spatial resolution-such as large colonies, knolls, and boulders-were positively correlated with coral recruitment, explaining 22% of variation in recruitment. Morphology mapped at finer scales (≤32 cm resolution) was not significant. Hydrodynamic larval import was also positively related to coral recruitment in Porites and Montipora spp., and grazer fish density was linked to significantly lower recruitment in all genera. In addition, grazer density, reef morphology, and hydrodynamic import had differential effects on coral genera, reflecting genus-specific life history traits, and model performance was lower in gonochoric species. Overall, coral reef morphology is a key indicator of recruitment potential that can be detected by remote sensing, allowing potential larval sinks to be identified and factored into restoration actions.

Mapped coral mortality and refugia in an archipelago-scale marine heat wave.

Corals are a major habitat-building life-form on tropical reefs that support a quarter of all species in the ocean and provide ecosystem services to millions of people. Marine heat waves continue to threaten and shape reef ecosystems by killing individual coral colonies and reducing their diversity. However, marine heat waves are spatially and temporally heterogeneous, and so too are the environmental and biological factors mediating coral resilience during and following thermal events. This combination results in highly variable outcomes at both the coral bleaching and mortality stages of every event. This, in turn, impedes the assessment of changing reef-scale patterns of thermal tolerance or places of resistance known as reef refugia. We developed a large-scale, high-resolution coral mortality monitoring capability based on airborne imaging spectroscopy and applied it to a major marine heat wave in the Hawaiian Islands. While water depth and thermal stress strongly mediated coral mortality, relative coral loss was also inversely correlated with preheat-wave coral cover, suggesting the existence of coral refugia. Subsequent mapping analyses indicated that potential reef refugia underwent up to 40% lower coral mortality compared with neighboring reefs, despite similar thermal stress. A combination of human and environmental factors, particularly coastal development and sedimentation levels, differentiated resilient reefs from other more vulnerable reefs. Our findings highlight the role that coral mortality mapping, rather than bleaching monitoring, can play for targeted conservation that protects more surviving corals in our changing climate.

Large-scale mapping of live corals to guide reef conservation.

Coral is the life-form that underpins the habitat of most tropical reef ecosystems, thereby supporting biological diversity throughout the marine realm. Coral reefs are undergoing rapid change from ocean warming and nearshore human activities, compromising a myriad of services provided to societies including coastal protection, fishing, and cultural practices. In the face of these challenges, large-scale operational mapping of live coral cover within and across reef ecosystems could provide more opportunities to address reef protection, resilience, and restoration at broad management- and policy-relevant scales. We developed an airborne mapping approach combining laser-guided imaging spectroscopy and deep learning models to quantify, at a large archipelago scale, the geographic distribution of live corals to 16-m water depth throughout the main Hawaiian islands. Airborne estimates of live coral cover were highly correlated with field-based estimates of live coral cover (R2 = 0.94). Our maps were used to assess the relative condition of reefs based on live coral, and to identify potential coral refugia in the face of human-driven stressors, including marine heat waves. Geospatial modeling revealed that water depth, wave power, and nearshore development accounted for the majority (>60%) of live coral cover variation, but other human-driven factors were also important. Mapped interisland and intraisland variation in live coral location improves our understanding of reef geography and its human impacts, thereby guiding environmental management for reef resiliency.

Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada.

Severe droughts are predicted to become more frequent in the future, and the consequences of such droughts on forests can be dramatic, resulting in massive tree mortality, rapid change in forest structure and composition, and substantially increased risk of catastrophic fire. Forest managers have tools at their disposal to try to mitigate these effects but are often faced with limited resources, forcing them to make choices about which parts of the landscape to target for treatment. Such planning can greatly benefit from landscape vulnerability assessments, but many existing vulnerability analyses are unvalidated and not grounded in robust empirical datasets. We combined robust sets of ground-based plot and remote sensing data, collected during the 2012-2016 California drought, to develop rigorously validated tools for assessing forest vulnerability to drought-related canopy tree mortality for the mixed conifer forests of the Sequoia and Kings Canyon national parks and potentially for mixed conifer forests in the Sierra Nevada as a whole. Validation was carried out using a large external dataset. The best models included normalized difference vegetation index (NDVI), elevation, and species identity. Models indicated that tree survival probability decreased with greenness (as measured by NDVI) and elevation, particularly if trees were growing slowly. Overall, models showed good calibration and validation, especially for Abies concolor, which comprise a large majority of the trees in many mixed conifer forests in the Sierra Nevada. Our models tended to overestimate mortality risk for Calocedrus decurrens and underestimate risk for pine species, in the latter case probably due to pine bark beetle outbreak dynamics. Validation results indicated dangers of overfitting, as well as showing that the inclusion of trees already under attack by bark beetles at the time of sampling can give false confidence in model strength, while also biasing predictions. These vulnerability tools should be useful to forest managers trying to assess which parts of their landscape were vulnerable during the 2012-2016 drought, and, with additional validation, may prove useful for ongoing assessments and predictions of future forest vulnerability.

Early detection of a tree pathogen using airborne remote sensing.

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ohi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ohi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ohi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ohi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.

Mapping the vulnerability of giant sequoias after extreme drought in California using remote sensing.

Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.

Leaf reflectance spectra capture the evolutionary history of seed plants.

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Large-scale climatic and geophysical controls on the leaf economics spectrum.

Leaf economics spectrum (LES) theory suggests a universal trade-off between resource acquisition and storage strategies in plants, expressed in relationships between foliar nitrogen (N) and phosphorus (P), leaf mass per area (LMA), and photosynthesis. However, how environmental conditions mediate LES trait interrelationships, particularly at large biospheric scales, remains unknown because of a lack of spatially explicit data, which ultimately limits our understanding of ecosystem processes, such as primary productivity and biogeochemical cycles. We used airborne imaging spectroscopy and geospatial modeling to generate, to our knowledge, the first biospheric maps of LES traits, here centered on 76 million ha of Andean and Amazonian forest, to assess climatic and geophysical determinants of LES traits and their interrelationships. Elevation and substrate were codominant drivers of leaf trait distributions. Multiple additional climatic and geophysical factors were secondary determinants of plant traits. Anticorrelations between N and LMA followed general LES theory, but topo-edaphic conditions strongly mediated and, at times, eliminated this classic relationship. We found no evidence for simple P-LMA or N-P trade-offs in forest canopies; rather, we mapped a continuum of N-P-LMA interactions that are sensitive to elevation and temperature. Our results reveal nested climatic and geophysical filtering of LES traits and their interrelationships, with important implications for predictions of forest productivity and acclimation to rapid climate change.

Progressive forest canopy water loss during the 2012-2015 California drought.

The 2012-2015 drought has left California with severely reduced snowpack, soil moisture, ground water, and reservoir stocks, but the impact of this estimated millennial-scale event on forest health is unknown. We used airborne laser-guided spectroscopy and satellite-based models to assess losses in canopy water content of California's forests between 2011 and 2015. Approximately 10.6 million ha of forest containing up to 888 million large trees experienced measurable loss in canopy water content during this drought period. Severe canopy water losses of greater than 30% occurred over 1 million ha, affecting up to 58 million large trees. Our measurements exclude forests affected by fire between 2011 and 2015. If drought conditions continue or reoccur, even with temporary reprieves such as El Niño, we predict substantial future forest change.

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient.

One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.

Scale dependence of canopy trait distributions along a tropical forest elevation gradient.

Average responses of forest foliar traits to elevation are well understood, but far less is known about trait distributional responses to elevation at multiple ecological scales. This limits our understanding of the ecological scales at which trait variation occurs in response to environmental drivers and change. We analyzed and compared multiple canopy foliar trait distributions using field sampling and airborne imaging spectroscopy along an Andes-to-Amazon elevation gradient. Field-estimated traits were generated from three community-weighting methods, and remotely sensed estimates of traits were made at three scales defined by sampling grain size and ecological extent. Field and remote sensing approaches revealed increases in average leaf mass per unit area (LMA), water, nonstructural carbohydrates (NSCs) and polyphenols with increasing elevation. Foliar nutrients and photosynthetic pigments displayed little to no elevation trend. Sample weighting approaches had little impact on field-estimated trait responses to elevation. Plot representativeness of trait distributions at landscape scales decreased with increasing elevation. Remote sensing indicated elevation-dependent increases in trait variance and distributional skew. Multiscale invariance of LMA, leaf water and NSC mark these traits as candidates for tracking forest responses to changing climate. Trait-based ecological studies can be greatly enhanced with multiscale studies made possible by imaging spectroscopy.

Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2  = 0.07-0.73; %RMSE = 7-38) and multiple (R2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

Variation in leaf wettability traits along a tropical montane elevation gradient.

Leaf wetting is often considered to have negative effects on plant function, such that wet environments may select for leaves with certain leaf surface, morphological, and architectural traits that reduce leaf wettability. However, there is growing recognition that leaf wetting can have positive effects. We measured variation in two traits, leaf drip tips and leaf water repellency, in a series of nine tropical forest communities occurring along a 3300-m elevation gradient in southern Peru. To extend this climatic gradient, we also assembled published leaf water repellency values from 17 additional sites. We then tested hypotheses for how these traits should vary as a function of climate. Contrary to expectations, we found that the proportion of species with drip tips did not increase with increasing precipitation. Instead, drip tips increased with increasing temperature. Moreover, leaf water repellency was very low in our sites and the global analysis indicated high repellency only in sites with low precipitation and temperatures. Our findings suggest that drip tips and repellency may not solely reflect the negative effects of wetting on plant function. Understanding the drivers of leaf wettability traits can provide insight into the effects of leaf wetting on plant, community, and ecosystem function.

Leaf-level photosynthetic capacity in lowland Amazonian and high-elevation Andean tropical moist forests of Peru.

We examined whether variations in photosynthetic capacity are linked to variations in the environment and/or associated leaf traits for tropical moist forests (TMFs) in the Andes/western Amazon regions of Peru. We compared photosynthetic capacity (maximal rate of carboxylation of Rubisco (Vcmax ), and the maximum rate of electron transport (Jmax )), leaf mass, nitrogen (N) and phosphorus (P) per unit leaf area (Ma , Na and Pa , respectively), and chlorophyll from 210 species at 18 field sites along a 3300-m elevation gradient. Western blots were used to quantify the abundance of the CO2 -fixing enzyme Rubisco. Area- and N-based rates of photosynthetic capacity at 25°C were higher in upland than lowland TMFs, underpinned by greater investment of N in photosynthesis in high-elevation trees. Soil [P] and leaf Pa were key explanatory factors for models of area-based Vcmax and Jmax but did not account for variations in photosynthetic N-use efficiency. At any given Na and Pa , the fraction of N allocated to photosynthesis was higher in upland than lowland species. For a small subset of lowland TMF trees examined, a substantial fraction of Rubisco was inactive. These results highlight the importance of soil- and leaf-P in defining the photosynthetic capacity of TMFs, with variations in N allocation and Rubisco activation state further influencing photosynthetic rates and N-use efficiency of these critically important forests.

Amazonian landscapes and the bias in field studies of forest structure and biomass.

Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.

Amazonian functional diversity from forest canopy chemical assembly.

Patterns of tropical forest functional diversity express processes of ecological assembly at multiple geographic scales and aid in predicting ecological responses to environmental change. Tree canopy chemistry underpins forest functional diversity, but the interactive role of phylogeny and environment in determining the chemical traits of tropical trees is poorly known. Collecting and analyzing foliage in 2,420 canopy tree species across 19 forests in the western Amazon, we discovered (i) systematic, community-scale shifts in average canopy chemical traits along gradients of elevation and soil fertility; (ii) strong phylogenetic partitioning of structural and defense chemicals within communities independent of variation in environmental conditions; and (iii) strong environmental control on foliar phosphorus and calcium, the two rock-derived elements limiting CO2 uptake in tropical forests. These findings indicate that the chemical diversity of western Amazonian forests occurs in a regionally nested mosaic driven by long-term chemical trait adjustment of communities to large-scale environmental filters, particularly soils and climate, and is supported by phylogenetic divergence of traits essential to foliar survival under varying environmental conditions. Geographically nested patterns of forest canopy chemical traits will play a role in determining the response and functional rearrangement of western Amazonian ecosystems to changing land use and climate.

Targeted carbon conservation at national scales with high-resolution monitoring.

Terrestrial carbon conservation can provide critical environmental, social, and climate benefits. Yet, the geographically complex mosaic of threats to, and opportunities for, conserving carbon in landscapes remain largely unresolved at national scales. Using a new high-resolution carbon mapping approach applied to Perú, a megadiverse country undergoing rapid land use change, we found that at least 0.8 Pg of aboveground carbon stocks are at imminent risk of emission from land use activities. Map-based information on the natural controls over carbon density, as well as current ecosystem threats and protections, revealed three biogeographically explicit strategies that fully offset forthcoming land-use emissions. High-resolution carbon mapping affords targeted interventions to reduce greenhouse gas emissions in rapidly developing tropical nations.

Convergent elevation trends in canopy chemical traits of tropical forests.

The functional biogeography of tropical forests is expressed in foliar chemicals that are key physiologically based predictors of plant adaptation to changing environmental conditions including climate. However, understanding the degree to which environmental filters sort the canopy chemical characteristics of forest canopies remains a challenge. Here, we report on the elevation and soil-type dependence of forest canopy chemistry among 75 compositionally and environmentally distinct forests in nine regions, with a total of 7819 individual trees representing 3246 species collected, identified and assayed for foliar traits. We assessed whether there are consistent relationships between canopy chemical traits and both elevation and soil type, and evaluated the general role of phylogeny in mediating patterns of canopy traits within and across communities. Chemical trait variation and partitioning suggested a general model based on four interconnected findings. First, geographic variation at the soil-Order level, expressing broad changes in fertility, underpins major shifts in foliar phosphorus (P) and calcium (Ca). Second, elevation-dependent shifts in average community leaf dry mass per area (LMA), chlorophyll, and carbon allocation (including nonstructural carbohydrates) are most strongly correlated with changes in foliar Ca. Third, chemical diversity within communities is driven by differences between species rather than by plasticity within species. Finally, elevation- and soil-dependent changes in N, LMA and leaf carbon allocation are mediated by canopy compositional turnover, whereas foliar P and Ca are driven more by changes in site conditions than by phylogeny. Our findings have broad implications for understanding the global ecology of humid tropical forests, and their functional responses to changing climate.

Hemiparasite--host plant interactions in a fragmented landscape assessed via imaging spectroscopy and LiDAR.

Species interactions are susceptible to anthropogenic changes in ecosystems, but this has been poorly investigated in a spatially explicit manner in the case of plant parasitism, such as the omnipresent hemiparasitic mistletoe-host plant interactions. Analyzing such interactions at a large spatial scale may advance our understanding of parasitism patterns over complex landscapes. Combining high-resolution airborne imaging spectroscopy and LiDAR, we studied hemiparasite incidence within and among tree host stands to examine the prevalence and spatial distribution of hemiparasite load in ecosystems. Specifically, we aimed to assess: (1) detection accuracy of mistletoes on their oak hosts; (2) hemiparasitism prevalence within host tree canopies depending on tree height, and (3) spatial variation in hemiparasitism across fragmented woodlands, in a low-diversity mediterranean oak woodland in California, USA. We identified mistletoe infestations with 55-96% accuracy, and detected significant differences in remote-sensed spectra between oak trees with and without mistletoe infestation. We also found that host canopy height had little influence on infestation degree, whereas landscape-level variation showed consistent; non-random patterns: isolated host trees had twice the infestation load than did trees located at the core of forest fragments. Overall, we found that canopy exposure (i.e., lower canopy density or proximity to forest edge) is more important than canopy height for mistletoe infestation, and that by changing landscape structure, parasitic prevalence increased with woodland fragmentation. We conclude that reducing fragmentation in oak woodlands will minimize anthropogenic impact on mistletoe infestation at the landscape level. We argue that advanced remote sensing technology can provide baselines to quantitatively analyze and monitor parasite-host trajectories in light of global environmental change, and that this is a promising approach to be further tested in other temperate and tropical forests.

Environmental controls on canopy foliar nitrogen distributions in a Neotropical lowland forest.

Distributions of foliar nutrients across forest canopies can give insight into their plant functional diversity and improve our understanding of biogeochemical cycling. We used airborne remote sensing and partial least squares regression to quantify canopy foliar nitrogen (foliar N) across ~164 km2 of wet lowland tropical forest in the Osa Peninsula, Costa Rica. We determined the relative influence of climate and topography on the observed patterns of foliar N using a gradient boosting model technique. At a local scale, where climate and substrate were constant, we explored the influence of slope position on foliar N by quantifying foliar N on remnant terraces, their adjacent slopes, and knife-edged ridges. In addition, we climbed and sampled 540 trees and analyzed foliar N in order to quantify the role of species identity (phylogeny) and environmental factors in predicting foliar N. Observed foliar N heterogeneity reflected environmental factors working at multiple spatial scales. Across the larger landscape, elevation and precipitation had the highest relative influence on predicting foliar N (30% and 24%), followed by soils (15%), site exposure (9%), compound topographic index (8%), substrate (6%), and landscape dissection (6%). Phylogeny explained ~75% of the variation in the field collected foliar N data, suggesting that phylogeny largely underpins the response to the environmental factors. Taken together, these data suggest that a large fraction of the variance in foliar N across the landscape is proximately driven by species composition, though ultimately this is likely a response to abiotic factors such as climate and topography. Future work should focus on the mechanisms and feedbacks involved, and how shifts in climate may translate to changes in forest function.