A brighter shade of future climate on Himalayan musk deer Moschus leucogaster.

Himalayan musk deer (Moschus leucogaster) is classified as an endangered species by IUCN with a historically misunderstood distribution due to misidentification with other species of musk deer, Moschus spp. Taking advantage of recent genetic analyses confirming the species of various populations in Nepal and China, we produced an accurate estimate of the species' current and future distribution under multiple climate change scenarios. We collected high-quality occurrence data using systematic surveys of various protected areas of Nepal to train species distribution models. The most influential determinants of the distribution of Himalayan musk deer were precipitation of the driest quarter, temperature seasonality, and annual mean temperature. These variables, and precipitation in particular, determine the vegetation type and structure in the Himalaya, which is strongly correlated with the distribution of Himalayan musk deer. We predicted suitable habitats between the Annapurna and Kanchenjunga region of Nepal Himalaya as well as the adjacent Himalaya in China. Under multiple climate change scenarios, the vast majority (85-89%) of current suitable sites are likely to remain suitable and many new areas of suitable habitat may emerge to the west and north of the current species range in Nepal and China. Two-thirds of current and one-third of future suitable habitats are protected by the extensive network of protected areas in Nepal. The projected large gains in suitable sites may lead to population expansion and conservation gains, only when the threat of overexploitation and population decline is under control.

Convolutional neural network for high-resolution wetland mapping with open data: Variable selection and the challenges of a generalizable model.

Landscape scale wetland conservation requires accurate, up-to-date wetland maps. The most useful approaches to creating such maps are automated, spatially generalizable, temporally repeatable, and can be applied at large spatial scales. However, mapping wetlands with predictive models is challenging due to the highly variable characteristics of wetlands in both space and time. Currently, most approaches are limited by coarse resolution, commercial data, and geographic specificity. Here, we trained a deep learning model and evaluated its ability to automatically map wetlands at landscape scale in a variety of geographies. We trained a U-Net architecture to map wetlands at 1-meter spatial resolution with the following remotely sensed covariates: multispectral data from the National Agriculture Imagery Program and the Sentinel-2 satellite system, and two LiDAR-derived datasets, intensity and geomorphons. The full model mapped wetlands accurately (94 % accuracy, 96.5 % precision, 95.2 % AUC) at 1-meter resolution. Post hoc model evaluation showed that the model correctly predicted wetlands even in areas that had incorrect label/training data, which penalized the recall rate (90.2 %). Applying the model in a new geography resulted in poor performance (precision = ~80 %, recall = 48 %). However, limited retraining in this geography improved model performance substantially, demonstrating an effective means to create a spatially generalizable model. We demonstrate wetlands can be mapped at high-resolution (1 m) using free data and efficient deep-learning models that do not require manual feature engineering. Including LiDAR and geomorphons as input data improved model accuracy by 2 %, and where these data are unavailable a simpler model can efficiently map wetlands. Given the dynamic nature of wetlands and the important ecosystem services they provide, high-resolution mapping can be a game changer in terms of informing restoration and development decisions.

A better index for analysis of co-occurrence and similarity.

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability. We propose an index, α, that is insensitive to prevalences. Published datasets reanalyzed with both α and Jaccard's index (J) yield profoundly different biological inferences. For example, a published analysis using J contradicted predictions of the island biogeography theory finding that community stability increased with increasing physical isolation. Reanalysis of the same dataset with the estimator [Formula: see text] reversed that result and supported theoretical predictions. We found similarly marked effects in reanalyses of antibiotic cross-resistance and human disease biomarkers. Our index α is not merely an improvement; its use changes data interpretation in fundamental ways.

Author Correction: Projected distribution and climate refugia of endangered Kashmir musk deer Moschus cupreus in greater Himalaya, South Asia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Matching expert range maps with species distribution model predictions.

Species' range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model. This method rewards both the avoidance of unsuitable sites and the inclusion of suitable sites in the expert map. We obtained expert maps of 330 butterfly species from each of 2 widely used North American sources (Glassberg [1999, 2001] and Scott [1986]) and computed species-wise expert scores for each. Overall, the Glassberg maps secured higher expert scores than Scott (0.61 and 0.41, respectively) due to the specific rules (e.g., Glassberg only included regions where the species was known to reproduce whereas Scott included all areas a species expanded to each year) they used to include or exclude areas from ranges. The predictive performance of expert maps was almost always hampered by the inclusion of unsuitable sites, rather than by exclusion of suitable sites (deviance outside of expert maps was extremely low). Map topology was the primary predictor of expert performance rather than any factor related to species characteristics such as mobility. Given the heterogeneity and discontinuity of suitable landscapes, expert maps drawn with more detail are more likely to agree with species distribution models and thus minimize both commission and omission errors.

Concordancia entre los Mapas de Extensión Realizados por Expertos y las Predicciones de los Modelos de Distribución de Especies Resumen Los mapas de extensión de especies basados en la opinión de expertos son un recurso de suma importancia para la planeación de la conservación. Los mapas realizados por expertos generalmente van acompañados de las descripciones de las especies que detallan el origen de la heterogeneidad interna de la distribución, como las asociaciones entre hábitats, pero rara vez se consideran estas descripciones cuando se usan los mapas de expertos para un análisis. Desarrollamos una medida cuantitativa (puntaje de expertos) para evaluar la concordancia entre un mapa realizado por expertos y una superficie probable de hábitat obtenida a partir del modelo de distribución de especies (SDM). Este método recompensa tanto a la evasión de sitios inadecuados como a la inclusión de sitios adecuados en el mapa realizado por expertos. Obtuvimos los mapas realizados por expertos para 330 especies de mariposas a partir de dos fuentes norteamericanas usadas ampliamente (Glassberg [1999, 2001] y Scott [1986]) y calculamos los puntajes de expertos, hablando de cada especie, para cada mapa. En general, los mapas de Glassberg aseguraron puntajes de expertos más altos que los de Scott (0.61 y 0.41 respectivamente) debido a las reglas específicas (p. ej., Glassberg sólo incluyó las regiones en donde es sabido que la especie se reproduce, mientras que Scott incluyó todas las áreas a las que la especie se expandió cada año) que cada una usa para incluir o excluir áreas de las distribuciones. El desempeño pronosticado de los mapas realizados por expertos casi siempre se vio afectado por la inclusión de los sitios inadecuados, en lugar de estar afectado por la exclusión de sitios adecuados (la desviación fuera de los mapas realizados por expertos fue extremadamente baja). La topología del mapa fue el indicador primario del desempeño de los expertos en lugar de cualquier factor relacionado con las características de la especie, como la movilidad. Dada la heterogeneidad y la discontinuidad de los paisajes adecuados, los mapas realizados por expertos dibujados con mayor detalle tienen una mayor probabilidad de concordar con los SMD y por lo tanto minimizar los errores de comisión y de omisión.

Contrasting responses to climate change at Himalayan treelines revealed by population demographics of two dominant species.

Alpine treelines are expected to shift upward due to recent climate change. However, interpretation of changes in montane systems has been problematic because effects of climate change are frequently confounded with those of land use changes. The eastern Himalaya, particularly Langtang National Park, Central Nepal, has been relatively undisturbed for centuries and thus presents an opportunity for studying climate change impacts on alpine treeline uncontaminated by potential confounding factors.We studied two dominant species, Abies spectabilis (AS) and Rhododendron campanulatum (RC), above and below the treeline on two mountains. We constructed 13 transects, each spanning up to 400 m in elevation, in which we recorded height and state (dead or alive) of all trees, as well as slope, aspect, canopy density, and measures of anthropogenic and animal disturbance.All size classes of RC plants had lower mortality above treeline than below it, and young RC plants (<2 m tall) were at higher density above treeline than below. AS shows little evidence of a position change from the historic treeline, with a sudden extreme drop in density above treeline compared to below. Recruitment, as measured by size-class distribution, was greater above treeline than below for both species but AS is confined to ~25 m above treeline whereas RC is luxuriantly growing up to 200 m above treeline. Synthesis. Evidence suggests that the elevational limits of RC have shifted upward both because (a) young plants above treeline benefited from facilitation of recruitment by surrounding vegetation, allowing upward expansion of recruitment, and (b) temperature amelioration to mature plants increased adult survival. We predict that the current pure stand of RC growing above treeline will be colonized by AS that will, in turn, outshade and eventually relegate RC to be a minor component of the community, as is the current situation below the treeline.

Projected distribution and climate refugia of endangered Kashmir musk deer Moschus cupreus in greater Himalaya, South Asia.

Kashmir musk deer Moschus cupreus (KMD) are the least studied species of musk deer. We compiled genetically validated occurrence records of KMD to construct species distribution models using Maximum Entropy. We show that the distribution of KMD is limited between central Nepal on the east and north-east Afghanistan on the west and is primarily determined by precipitation of driest quarter, annual mean temperature, water vapor, and precipitation during the coldest quarter. Precipitation being the most influential determinant of distribution suggests the importance of pre-monsoon moisture for growth of the dominant vegetation, Himalayan birch Betula utilis and Himalayan fir Abies spectabilis, in KMD's preferred forests. All four Representative Concentration Pathway Scenarios result an expansion of suitable habitat in Uttarakhand, India, west Nepal and their associated areas in China in 2050s and 2070s but a dramatic loss of suitable habitat elsewhere (Kashmir region and Pakistan-Afghanistan border). About 1/4th of the current habitat will remain as climate refugia in future. Since the existing network of protected areas will only include a tiny fraction (4%) of the climatic refugia of KMD, the fate of the species will be determined by the interplay of more urgent short-term forces of poaching and habitat degradation and long-term forces of climate change.

Detecting interaction networks in the human microbiome with conditional Granger causality.

Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction; we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.

Ecological correlates of Himalayan musk deer Moschus leucogaster.

Himalayan musk deer (Moschus leucogaster; hereafter musk deer) are endangered as a result of poaching and habitat loss. The species is nocturnal, crepuscular, and elusive, making direct observation of habitat use and behavior difficult. However, musk deer establish and repeatedly use the same latrines for defecation. To quantify musk deer habitat correlates, we used observational spatial data based on presence-absence of musk deer latrines, as well as a range of fine spatial-scale ecological covariates. To determine presence-absence of musk deer, we exhaustively searched randomly selected forest trails using a 20-m belt transect in different study sites within the Neshyang Valley in the Annapurna Conservation Area. In a subsequent way, study sites were classified as habitat or nonhabitat for musk deer. A total of 252 plots, 20 × 20 m, were systematically established every 100 m along 51 transects (each ~0.5 km long) laid out at different elevations to record a range of ecological habitat variables. We used mixed-effect models and principal component analysis to characterize relationships between deer presence-absence data and habitat variables. We confirmed musk deer use latrines in forests located at higher elevations (3,200-4,200 m) throughout multiple seasons and years. Himalayan birch (Betula utilis) dominated forest, mixed Himalayan fir (Abies spectabilis), and birch forest were preferred over pure Himalayan fir and blue pine (Pinus wallichiana) forest. Greater crown cover and shrub diversity were associated with the presence of musk deer whereas tree height, diameter, and diversity were weakly correlated. Topographical attributes including aspect, elevation, distance to water source, and slope were also discriminated by musk deer. Over- and understory forest management can be used to protect forests likely to have musk deer as predicted by the models to ensure long-term conservation of this rare deer.

Foraging and inter-individual distances of bearded capuchin monkeys.

Extractive foraging is a skill young capuchin monkeys learn over time. A key unknown is whether unskilled individuals occupy spatial positions that increase their opportunities to learn. We observed the spatial positions of individuals in a group of capuchin monkeys in Northeastern Brazil. To improve our understanding of the relationship between learning by young capuchin monkeys and inter-individual distance, we investigated the associations between the proximity of individuals and their age, activity, and proficiency at extractive foraging. To do this, we used one form of extractive foraging, opening palm nuts, as an index of proficiency at all types of extractive foraging. Our results indicate that, in the subset of the data where dyads consisted of one proficient individual and a partner with any level of proficiency, the distance between individuals was predicted by their foraging activity (i.e., extractive foraging, other foraging, or not foraging). In those dyads, the proficiency of the partner did not significantly improve prediction of inter-individual distances, indicating that spatial proximity of proficient individuals to others does not function primarily to increase opportunities for unskilled individuals to observe extractive foraging. Dyads in which both individuals were engaged in similar foraging activities (e.g., both "extractive foraging") exhibited the shortest inter-individual distances. Proximity between individuals engaged in similar foraging activities may result from the spatial distribution of resources or from social learning mechanisms, such as local or stimulus enhancement.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

A Stoichioproteomic Analysis of Samples from the Human Microbiome Project.

Ecological stoichiometry (ES) uses organism-specific elemental content to explain differences in species life histories, species interactions, community organization, environmental constraints and even ecosystem function. Although ES has been successfully applied to a range of different organisms, most emphasis on microbial ecological stoichiometry focuses on lake, ocean, and soil communities. With the recent advances in human microbiome research, however, large amounts of data are being generated that describe differences in community composition across body sites and individuals. We suggest that ES may provide a framework for beginning to understand the structure, organization, and function of human microbial communities, including why certain organisms exist at certain locations, and how they interact with both the other microbes in their environment and their human host. As a first step, we undertake a stoichioproteomic analysis of microbial communities from different body sites. Specifically, we compare and contrast the elemental composition of microbial protein samples using annotated sequencing data from 690 gut, vaginal, oral, nares, and skin samples currently available through the Human Microbiome Project. Our results suggest significant differences in both the median and variance of the carbon, oxygen, nitrogen, and sulfur contents of microbial protein samples from different locations. For example, whereas proteins from vaginal sites are high in carbon, proteins from skin and nasal sites are high in nitrogen and oxygen. Meanwhile, proteins from stool (the gut) are particularly high in sulfur content. We interpret these differences in terms of the local environments at different human body sites, including atmospheric exposure and food intake rates.

Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers.

The reliable detection and attribution of changes in vegetation greenness is a prerequisite for the development of strategies for the sustainable management of ecosystems. We conducted a robust trend analysis on remote sensing derived vegetation index time-series matrices to detect significant changes in inter-annual vegetation productivity (greening versus browning) for the entire Himalaya, a biodiverse and ecologically sensitive yet understudied region. The spatial variability in trend was assessed considering elevation, 12 dominant land cover/use types and 10 ecoregions. To assess trend causation, at local scale, we compared multi-temporal imagery, and at regional scale, referenced ecological theories of mountain vegetation dynamics and ancillary literature. Overall, 17.56% of Himalayan vegetation (71,162km2) exhibited significant trend (p<0.01) and majority (94%) showed positive trend (greening). Trend distribution showed strong elevational and ecoregion dependence as greening was most dominant at lower and middle elevations whereas majority of the browning occurred at higher elevation (>3800m), with eastern high Himalaya browning more dominantly than western high Himalaya. Land cover/use based categorization confirmed dominant greening of rainfed and irrigated agricultural areas, though cropped areas in western Himalaya contained higher proportion of greening areas. While rising atmospheric CO2 concentration and nitrogen deposition are the most likely climatic causes of detected greening, success of sustainable forestry practices (community forestry in Nepal) along with increasing agricultural fertilization and irrigation facilities could be possible human drivers. Comparison of multi-temporal imagery enabled direct attribution of some browning areas to anthropogenic land change (dam, airport and tunnel construction). Our satellite detected browning of high altitude vegetation in eastern Himalaya confirm the findings of recent dendrochronology based studies which possibly resulted from reduced pre-monsoon moisture availability in recent decades. These results have significant implications for environmental management in the context of climate change and ecosystem dynamics in the Himalaya.

Timing Effects of Heat-Stress on Plant Ecophysiological Characteristics and Growth.

Heat-waves with higher intensity and frequency and longer durations are expected in the future due to global warming, which could have dramatic impacts in agriculture, economy and ecology. This field study examined how plant responded to heat-stress (HS) treatment at different timing in naturally occurring vegetation. HS treatment (5 days at 40.5°C) were applied to 12 1 m2 plots in restored prairie vegetation dominated by a warm-season C4 grass, Andropogon gerardii, and a warm-season C3 forb, Solidago canadensis, at different growing stages. During and after each heat stress (HS) treatment, temperature were monitored for air, canopy, and soil; net CO2 assimilation (Anet), quantum yield of photosystem II (ΦPSII), stomatal conductance (gs), and internal CO2 level (Ci), specific leaf area (SLA), and chlorophyll content of the dominant species were measured. One week after the last HS treatment, all plots were harvested and the biomass of above-ground tissue and flower weight of the two dominant species were determined. HS decreased physiological performance and growth for both species, with S. canadensis being affected more than A. gerardii, indicated by negative HS effect on both physiological and growth responses for S. canadensis. There were significant timing effect of HS on the two species, with greater reductions in the net photosynthetic rate and productivity occurred when HS was applied at later-growing season. The reduction in aboveground productivity in S. canadensis but not A. gerardii could have important implications for plant community structure by increasing the competitive advantage of A. gerardii in this grassland. The present experiment showed that HS, though ephemeral, may promote long-term effects on plant community structure, vegetation dynamics, biodiversity, and ecosystem functioning of terrestrial biomes when more frequent and severe HS occur in the future.

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling.

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.

Impact of a short-term heat event on C and N relations in shoots vs. roots of the stress-tolerant C4 grass, Andropogon gerardii.

Global warming will increase heat waves, but effects of abrupt heat stress on shoot-root interactions have rarely been studied in heat-tolerant species, and abrupt heat-stress effects on root N uptake and shoot C flux to roots and soil remains uncertain. We investigated effects of a high-temperature event on shoot vs. root growth and function, including transfer of shoot C to roots and soil and uptake and translocation of soil N by roots in the warm-season drought-tolerant C4 prairie grass, Andropogon gerardii. We heated plants in the lab and field (lab=5.5days at daytime of 30+5 or 10°C; field=5days at ambient (up to 32°C daytime) vs. ambient +10°C). Heating had small or no effects on photosynthesis, stomatal conductance, leaf water potential, and shoot mass, but increased root mass and decreased root respiration and exudation per g. (13)C-labeling indicated that heating increased transfer of recently-fixed C from shoot to roots and soil (the latter likely via increased fine-root turnover). Heating decreased efficiency of N uptake by roots (uptake/g root), but did not affect total N uptake or the transfer of labeled soil (15)N to shoots. Though heating increased soil temperature in the lab, it did not do so in the field (10cm depth); yet results were similar for lab and field. Hence, acute heating affected roots more than shoots in this stress-tolerant species, increasing root mass and C loss to soil, but decreasing function per g root, and some of these effects were likely independent of direct effects from soil heating.

Effects of N on plant response to heat-wave: a field study with prairie vegetation.

More intense, more frequent, and longer heat-waves are expected in the future due to global warming, which could have dramatic ecological impacts. Increasing nitrogen (N) availability and its dynamics will likely impact plant responses to heat stress and carbon (C) sequestration in terrestrial ecosystems. This field study examined the effects of N availability on plant response to heat-stress (HS) treatment in naturally-occurring vegetation. HS (5 d at ambient or 40.5 degrees C) and N treatments (+/-N) were applied to 16 1 m(2) plots in restored prairie vegetation dominated by Andropogon gerardii (warm-season C4 grass) and Solidago canadensis (warm-season C3 forb). Before, during, and after HS, air, canopy, and soil temperature were monitored; net CO2 assimilation (P(n)), quantum yield of photosystem II (Phi(PSII)), stomatal conductance (g(s)), and leaf water potential (Psi(w)) of the dominant species and soil respiration (R(soil)) of each plot were measured daily during HS. One week after HS, plots were harvested, and C% and N% were determined for rhizosphere and bulk soil, and above-ground tissue (green/senescent leaf, stem, and flower). Photosynthetic N-use efficiency (PNUE) and N resorption rate (NRR) were calculated. HS decreased P(n), g(s), Psi(w), and PNUE for both species, and +N treatment generally increased these variables (+/-HS), but often slowed their post-HS recovery. Aboveground biomass tended to decrease with HS in both species (and for green leaf mass in S. canadensis), but decrease with +N for A. gerardii and increase with +N for S. canadensis. For A. gerardii, HS tended to decrease N% in green tissues with +N, whereas in S. canadensis, HS increased N% in green leaves. Added N decreased NRR for A. gerardii and HS increased NRR for S. canadensis. These results suggest that heat waves, though transient, could have significant effects on plants, communities, and ecosystem N cycling, and N can influence the effect of heat waves.