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This paper documents the biogeochemistry configuration of the Energy Exascale Earth System Model (E3SM), E3SMv1.1-BGC. The model simulates historical carbon cycle dynamics, including carbon losses predicted in response to land use and land cover change, and the responses of the carbon cycle to changes in climate. In addition, we introduce several innovations in the treatment of soil nutrient limitation mechanisms, including explicit dependence on phosphorus availability. The suite of simulations described here includes E3SM contributions to the Coupled Climate-Carbon Cycle Model Intercomparison Project and other projects, as well as simulations to explore the impacts of structural uncertainty in representations of nitrogen and phosphorus limitation. We describe the model spin-up and evaluation procedures, provide an overview of results from the simulation campaign, and highlight key features of the simulations. Cumulative warming over the twentieth century is similar to observations, with a midcentury cold bias offset by stronger warming in recent decades. Ocean biomass production and carbon uptake are underpredicted, likely due to biases in ocean transport leading to widespread anoxia and undersupply of nutrients to surface waters. The inclusion of nutrient limitations in the land biogeochemistry results in weaker carbon fertilization and carbon-climate feedbacks than exhibited by other Earth System Models that exclude those limitations. Finally, we compare with an alternative representation of terrestrial biogeochemistry, which differs in structure and in initialization of soil phosphorus. While both configurations agree well with observational benchmarks, they differ significantly in their distribution of carbon among different pools and in the strength of nutrient limitations.

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

Over the past several decades, the land modeling community has recognized the importance of nutrient regulation on the global terrestrial carbon cycle. Implementations of nutrient limitation in land models are diverse, varying from applying simple empirical down-regulation of potential gross primary productivity under nutrient deficit conditions to more mechanistic treatments. In this study, we introduce a new approach to model multinutrient (nitrogen [N] and phosphorus [P]) limitations in the Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELMv1-ECA). The development is grounded on (1) advances in representing multiple-consumer, multiple-nutrient competition; (2) a generic dynamic allocation scheme based on water, N, P, and light availability; (3) flexible plant CNP stoichiometry; (4) prognostic treatment of N and P constraints on several carbon cycle processes; and (5) global data sets of plant physiological traits. Through benchmarking the model against best knowledge of global plant and soil carbon pools and fluxes, we show that our implementation of nutrient constraints on the present-day carbon cycle is robust at the global scale. Compared with predecessor versions, ELMv1-ECA better predicts global-scale gross primary productivity, ecosystem respiration, leaf area index, vegetation biomass, soil carbon stocks, evapotranspiration, N2O emissions, and NO3- leaching. Factorial experiments indicate that representing the phosphorus cycle improves modeled carbon fluxes, while considering dynamic allocation improves modeled carbon stock density. We also highlight the value of using the International Land Model Benchmarking (ILAMB) package to evaluate and document performance during model development.

Climate change is expected to increase the frequency of flooding events and, thus, the risks of flood-related mortality and infrastructure damage. Global-scale assessments of future flooding from Earth system models based only on precipitation changes neglect important processes that occur within the land surface, particularly plant physiological responses to rising CO2. Higher CO2 can reduce stomatal conductance and transpiration, which may lead to increased soil moisture and runoff in some regions, promoting flooding even without changes in precipitation. Here we assess the relative impacts of plant physiological and radiative greenhouse effects on changes in daily runoff intensity over tropical continents using the Community Earth System Model. We find that extreme percentile rates increase significantly more than mean runoff in response to higher CO2. Plant physiological effects have a small impact on precipitation intensity but are a dominant driver of runoff intensification, contributing to one half of the 99th and one third of the 99.9th percentile runoff intensity changes.

The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

Understanding how anthropogenic CO2 emissions will influence future precipitation is critical for sustainably managing ecosystems, particularly for drought-sensitive tropical forests. Although tropical precipitation change remains uncertain, nearly all models from the Coupled Model Intercomparison Project Phase 5 predict a strengthening zonal precipitation asymmetry by 2100, with relative increases over Asian and African tropical forests and decreases over South American forests. Here we show that the plant physiological response to increasing CO2 is a primary mechanism responsible for this pattern. Applying a simulation design in the Community Earth System Model in which CO2 increases are isolated over individual continents, we demonstrate that different circulation, moisture and stability changes arise over each continent due to declines in stomatal conductance and transpiration. The sum of local atmospheric responses over individual continents explains the pan-tropical precipitation asymmetry. Our analysis suggests that South American forests may be more vulnerable to rising CO2 than Asian or African forests.

©2015. American Geophysical Union. Improved constraints on carbon cycle responses to climate change are needed to inform mitigation policy, yet our understanding of how these responses may evolve after 2100 remains highly uncertain. Using the Community Earth System Model (v1.0), we quantified climate-carbon feedbacks from 1850 to 2300 for the Representative Concentration Pathway 8.5 and its extension. In three simulations, land and ocean biogeochemical processes experienced the same trajectory of increasing atmospheric CO2. Each simulation had a different degree of radiative coupling for CO2 and other greenhouse gases and aerosols, enabling diagnosis of feedbacks. In a fully coupled simulation, global mean surface air temperature increased by 9.3K from 1850 to 2300, with 4.4K of this warming occurring after 2100. Excluding CO2, warming from other greenhouse gases and aerosols was 1.6K by 2300, near a 2K target needed to avoid dangerous anthropogenic interference with the climate system. Ocean contributions to the climate-carbon feedback increased considerably over time and exceeded contributions from land after 2100. The sensitivity of ocean carbon to climate change was found to be proportional to changes in ocean heat content, as a consequence of this heat modifying transport pathways for anthropogenic CO2 inflow and solubility of dissolved inorganic carbon. By 2300, climate change reduced cumulative ocean uptake by 330PgC, from 1410PgC to 1080PgC. Land fluxes similarly diverged over time, with climate change reducing stocks by 232PgC. Regional influence of climate change on carbon stocks was largest in the North Atlantic Ocean and tropical forests of South America. Our analysis suggests that after 2100, oceans may become as important as terrestrial ecosystems in regulating the magnitude of the climate-carbon feedback.

Version 1 of the Community Earth SystemModel, in the configurationwhere its full carbon cycle is enabled, is introduced and documented. In this configuration, the terrestrial biogeochemical model, which includes carbon-nitrogen dynamics and is present in earlier model versions, is coupled to an ocean biogeochemical model and atmospheric CO2 tracers. The authors provide a description of the model, detail how preindustrial-control and twentieth-century experiments were initialized and forced, and examine the behavior of the carbon cycle in those experiments. They examine how sea- and land-to-air CO2 fluxes contribute to the increase of atmospheric CO2 in the twentieth century, analyze how atmospheric CO2 and its surface fluxes vary on interannual time scales, including how they respond to ENSO, and describe the seasonal cycle of atmospheric CO2 and its surface fluxes. While the model broadly reproduces observed aspects of the carbon cycle, there are several notable biases, including having too large of an increase in atmospheric CO2 over the twentieth century and too small of a seasonal cycle of atmospheric CO2 in the Northern Hemisphere. The biases are related to a weak response of the carbon cycle to climatic variations on interannual and seasonal time scales and to twentieth-century anthropogenic forcings, including rising CO2, land-use change, and atmospheric deposition of nitrogen.

With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon-LAnd Model Intercomparison Project (C-LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) -Carnegie-Ames-Stanford Approach′ (CASA′) and carbon-nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1-3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4PgCyr-1 for CASA′ vs. 1.2PgCyr-1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988-2004 based on comparison with atmospheric inversion results from TRANSCOM (r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C-LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community-wide platform for model-data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C-LAMP are publicly available on the Earth System Grid. © 2009 Blackwell Publishing Ltd.

This paper describes the Carbon-Land Model Intercomparison Project (C-LAMP) being carried out through a collaboration between the Community Climate System Model (CCSM) Biogeochemistry Working Group, a DOE SciDAC-2 project, and the DOE Program for Climate Model Diagnosis and Intercomparison (PCMDI). The goal of the project is to intercompare terrestrial biogeochemistry models running within the CCSM framework to determine the best set of processes to include in future versions of CCSM. As a part of the project, observational datasets are being collected and used to score the scientific performance of these models following a well-defined set of metrics. In addition, metadata standards for terrestrial biosphere models are being developed to support archival and distribution of the C-LAMP model output via the Earth System Grid (ESG). Progress toward completion of this project and preliminary results from the first set of experiments are reported. © 2007 IOP Publishing Ltd.

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N-65°N, during 1971-2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0-10 cm SSI during August-November, and 0-100 cm during September-April. We further develop and apply an emergent constraint method on the future SSI's signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0-10 cm than 0-100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management., (© 2022. UT-Battelle, LLC.)

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth's surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth's albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth's climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth's surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Background: Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink and its involving processes by natural ecosystems remain difficult to quantify., Methods: Using a transient traceability framework, we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under RCP8.5., Results: The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total, among which, Russia, Canada, USA, China, and Brazil sequester the most. This C sink consists of four components: production-driven change, turnover-driven change, change in instantaneous C storage potential, and interaction between production-driven change and turnover-driven change. The four components account for 49.5%, 28.1%, 14.5%, and 7.9% of the land C sink, respectively., Conclusion: The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions, especially for countries where net primary production (NPP) likely increases substantially and inherent residence time elongates., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2021.)

Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil properties and environmental factors with spatial autocorrelation between soil observations. In this study, we compared four machine learning approaches (gradient boosting machine, multinarrative adaptive regression spline, random forest, and support vector machine) with regression kriging to predict the spatial variation of surface (0-30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. We combined 2,374 soil profile observations (calibration datasets) with georeferenced datasets of environmental factors (climate, topography, land cover, bedrock geology, and soil types) to predict the spatial variation of surface SOC stocks. We evaluated the prediction accuracy at randomly selected sites (validation datasets) across the study area. We found that different techniques inferred different numbers of environmental factors and their relative importance for prediction of SOC stocks. Regression kriging produced lower prediction errors in comparison to multinarrative adaptive regression spline and support vector machine, and comparable prediction accuracy to gradient boosting machine and random forest. However, the ensemble median prediction of SOC stocks obtained from all four machine learning techniques showed highest prediction accuracy. Although the use of different approaches in spatial prediction of soil properties will depend on the availability of soil and environmental datasets and computational resources, we conclude that the ensemble median prediction obtained from multiple machine learning approaches provides greater spatial details and produces the highest prediction accuracy. Thus an ensemble prediction approach can be a better choice than any single prediction technique for predicting the spatial variation of SOC stocks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Mishra, Gautam, Riley and Hoffman.)

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from -20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

Rising atmospheric CO2 will make Earth warmer, and many studies have inferred that this warming will cause droughts to become more widespread and severe. However, rising atmospheric CO2 also modifies stomatal conductance and plant water use, processes that are often are overlooked in impact analysis. We find that plant physiological responses to CO2 reduce predictions of future drought stress, and that this reduction is captured by using plant-centric rather than atmosphere-centric metrics from Earth system models (ESMs). The atmosphere-centric Palmer Drought Severity Index predicts future increases in drought stress for more than 70% of global land area. This area drops to 37% with the use of precipitation minus evapotranspiration (P-E), a measure that represents the water flux available to downstream ecosystems and humans. The two metrics yield consistent estimates of increasing stress in regions where precipitation decreases are more robust (southern North America, northeastern South America, and southern Europe). The metrics produce diverging estimates elsewhere, with P-E predicting decreasing stress across temperate Asia and central Africa. The differing sensitivity of drought metrics to radiative and physiological aspects of increasing CO2 partly explains the divergent estimates of future drought reported in recent studies. Further, use of ESM output in offline models may double-count plant feedbacks on relative humidity and other surface variables, leading to overestimates of future stress. The use of drought metrics that account for the response of plant transpiration to changing CO2, including direct use of P-E and soil moisture from ESMs, is needed to reduce uncertainties in future assessment., Competing Interests: The authors declare no conflict of interest.

In C3 plants, CO2 concentrations drop considerably along mesophyll diffusion pathways from substomatal cavities to chloroplasts where CO2 assimilation occurs. Global carbon cycle models have not explicitly represented this internal drawdown and therefore overestimate CO2 available for carboxylation and underestimate photosynthetic responsiveness to atmospheric CO2. An explicit consideration of mesophyll diffusion increases the modeled cumulative CO2 fertilization effect (CFE) for global gross primary production (GPP) from 915 to 1,057 PgC for the period of 1901-2010. This increase represents a 16% correction, which is large enough to explain the persistent overestimation of growth rates of historical atmospheric CO2 by Earth system models. Without this correction, the CFE for global GPP is underestimated by 0.05 PgC/y/ppm. This finding implies that the contemporary terrestrial biosphere is more CO2 limited than previously thought.

Although temperature is an important driver of seasonal changes in photosynthetic physiology, photoperiod also regulates leaf activity. Climate change will extend growing seasons if temperature cues predominate, but photoperiod-controlled species will show limited responsiveness to warming. We show that photoperiod explains more seasonal variation in photosynthetic activity across 23 tree species than temperature. Although leaves remain green, photosynthetic capacity peaks just after summer solstice and declines with decreasing photoperiod, before air temperatures peak. In support of these findings, saplings grown at constant temperature but exposed to an extended photoperiod maintained high photosynthetic capacity, but photosynthetic activity declined in saplings experiencing a naturally shortening photoperiod; leaves remained equally green in both treatments. Incorporating a photoperiodic correction of photosynthetic physiology into a global-scale terrestrial carbon-cycle model significantly improves predictions of seasonal atmospheric CO(2) cycling, demonstrating the benefit of such a function in coupled climate system models. Accounting for photoperiod-induced seasonality in photosynthetic parameters reduces modeled global gross primary production 2.5% (∼4 PgC y(-1)), resulting in a >3% (∼2 PgC y(-1)) decrease of net primary production. Such a correction is also needed in models estimating current carbon uptake based on remotely sensed greenness. Photoperiod-associated declines in photosynthetic capacity could limit autumn carbon gain in forests, even if warming delays leaf senescence.

Multivariate clustering based on fine spatial resolution maps of elevation, temperature, precipitation, soil characteristics, and solar inputs has been used at several specified levels of division to produce a spectrum of quantitative ecoregion maps for the conterminous United States. The coarse ecoregion divisions accurately capture intuitively-understood regional environmental differences, whereas the finer divisions highlight local condition gradients, ecotones, and clines. Such statistically generated ecoregions can be produced based on user-selected continuous variables, allowing customized regions to be delineated for any specific problem. By creating an objective ecoregion classification, the ecoregion concept is removed from the limitations of human subjectivity, making possible a new array of ecologically useful derivative products. A red-green-blue visualization based on principal components analysis of ecoregion centroids indicates with color the relative combination of environmental conditions found within each ecoregion. Multiple geographic areas can be classified into a single common set of quantitative ecoregions to provide a basis for comparison, or maps of a single area through time can be classified to portray climatic or environmental changes geographically in terms of current conditions. Quantified representativeness can characterize borders between ecoregions as gradual, sharp, or of changing character along their length. Similarity of any ecoregion to all other ecoregions can be quantified and displayed as a "representativeness" map. The representativeness of an existing spatial array of sample locations or study sites can be mapped relative to a set of quantitative ecoregions, suggesting locations for additional samples or sites. In addition, the shape of Hutchinsonian niches in environment space can be defined if a multivariate range map of species occurrence is available.

During eye development in Drosophila, cell cycle progression is coordinated with differentiation. Prior to differentiation, cells arrest in G1 phase anterior to and within the morphogenetic furrow. We show that Decapentaplegic (Dpp), a TGF-&bgr; family member, is required to establish this G1 arrest, since Dpp-unresponsive cells located in the anterior half of the morphogenetic furrow show ectopic S phases and ectopic expression of the cell cycle regulators Cyclins A, E and B. Conversely, ubiquitous over-expression of Dpp in the eye imaginal disc transiently inhibits S phase without affecting Cyclin E or Cyclin A abundance. This Dpp-mediated inhibition of S phase occurs independently of the Cyclin A inhibitor Roughex and of the expression of Dacapo, a Cyclin E-Cdk2 inhibitor. Furthermore, Dpp-signaling genes interact genetically with a hypomorphic cyclin E allele. Taken together our results suggest that Dpp acts to induce G1 arrest in the anterior part of the morphogenetic furrow by a novel inhibitory mechanism. In addition, our results provide evidence for a Dpp-independent mechanism that acts in the posterior part of the morphogenetic furrow to maintain G1 arrest.

From December 1985 through April 1988, 11 patients (three female and eight males), 33.1 +/- 3.9 (+/- SEM) years of age (range, 15 to 50 years), underwent heart transplantation preceded by the use of mechanical circulatory support. The causes of cardiac failure were ischemic (four), viral cardiomyopathy (three), idiopathic cardiomyopathy (two), congenital heart disease (one), and valvular heart disease (one). All patients were preterminal. Mechanical circulatory support consisted of intra-aortic balloon pump (eight), the total artificial heart (seven), biventricular assist (three), and left ventricular assist (two). Seven patients had more than one form of support. The duration of mechanical circulatory support was 12.2 +/- 4.1 days (range, 1 to 44 days). Once listed for transplantation, patients waited for 8.1 +/- 2.4 days for a donor. Seven patients received OKT3 monoclonal antibody as prophylaxis, in addition to triple-drug immunosuppression. There were four rejection episodes and 12 serious infections. In addition, eight patients suffered a major posttransplant complication of a distant organ system: central nervous system (three), renal (two), and respiratory (three). Post transplant hospitalization was 48 +/- 22 days (range, 15 to 248 days). Two patients (18%) died after transplant, one of severe acute rejection (29 days) and one of multisystem failure (248 days). All patients surviving transplant hospitalization are alive 6 to 34 months after the transplant procedure. Eight patients are in functional class I and 1 patient class II. This patient attends school full-time but has a premechanical support system neurologic defect. In follow-up of 163 patient-months except for yearly catheterization, these nine patients required only 5 hospital days. Although the use of the mechanical circulatory support as a bridge to transplantation can result in a prolonged, complicated hospitalization after transplant, the long-term results are gratifying.