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We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon‐chemistry‐climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi‐layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present‐day biomass and carbon uptake in comparison to observations. Plain Language Summary The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new Land Model (LM4.1) as part of its 4th generation coupled model development. This model includes advances from the previous generation and introduces a new vegetation demography model, multi‐layer canopy, plant hydraulics, fire, and land use representation as well as dynamic atmospheric dust coupling. Coupled within an Earth System Model (ESM4.1), LM4.1 features an improved representation of many ecological processes from the previous generation of GFDL ESMs. Key Points A new land model LM4.1 is developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for the next‐generation Earth System Model (ESM) ESM4.1 LM4.1 integrates age‐height structured vegetation dynamics, multi‐layer canopy‐soil‐snow energy exchanges, and prognostic fires and mineral dust ESM4.1/LM4.1 improves patterns of land surface climate and carbon cycle compared to the previous generation GFDL model ESM2G/LM3.0

For the period 1980–89, we estimate a carbon sink in the coterminous United States between 0.30 and 0.58 petagrams of carbon per year (petagrams of carbon = 1015 grams of carbon). The net carbon flux from the atmosphere to the land was higher, 0.37 to 0.71 petagrams of carbon per year, because a net flux of 0.07 to 0.13 petagrams of carbon per year was exported by rivers and commerce and returned to the atmosphere elsewhere. These land-based estimates are larger than those from previous studies (0.08 to 0.35 petagrams of carbon per year) because of the inclusion of additional processes and revised estimates of some component fluxes. Although component estimates are uncertain, about one-half of the total is outside the forest sector. We also estimated the sink using atmospheric models and the atmospheric concentration of carbon dioxide (the tracer-transport inversion method). The range of results from the atmosphere-based inversions contains the land-based estimates. Atmosphere- and land-based estimates are thus consistent, within the large ranges of uncertainty for both methods. Atmosphere-based results for 1980–89 are similar to those for 1985–89 and 1990–94, indicating a relatively stable U.S. sink throughout the period.

Recruitment, the addition of new individuals into a community, is an important factor that can substantially affect community composition and dynamics. We present a method for calibrating spatial models of plant recruitment that does not require identifying the specific parent of each recruit. This method calibrates seedling recruitment functions by comparing tree seedling distributions with adult distributions via a maximum likelihood analysis. The models obtained from this method can then be used to predict the spatial distributions of seedlings from adult distributions. We calibrated recruitment functions for 10 tree species characteristics of transition oak-northern hardwood forests. Significant differences were found in recruit abundances and spatial distributions. Predicted seedling recruitment limitation for test stands varied substantially between species, with little recruitment limitation for some species and strong recruitment limitation for others. Recruitment was limited due to low overall recruit production or to restricted recruit dispersion. When these seedlings recruitment parameters were incorporated into a spatial, individual-based model of forest dynamics, called SORTIE, alterations of recruitment parameters produced substantial changes in species abundance, providing additional support for the potential importance of seedling recruitment processes in community structure and dynamics.

Pacala et al discuss the total costs and benefits brought about by environmental false alarms given by environmentalists. The people relied heavily on environmental science to give warnings on possible disasters in the efforts to prepare for the events. This reliance often results on many warnings that are proven to be unfounded while the other warnings led to actions that prevented or mitigated the predicted dire consequences.

Knowledge of carbon exchange between the atmosphere, land and the oceans is important, given that the terrestrial and marine environments are currently absorbing about half of the carbon dioxide that is emitted by fossil-fuel combustion. This carbon uptake is therefore limiting the extent of atmospheric and climatic change, but its long-term nature remains uncertain. Here we provide an overview of the current state of knowledge of global and regional patterns of carbon exchange by terrestrial ecosystems. Atmospheric carbon dioxide and oxygen data confirm that the terrestrial biosphere was largely neutral with respect to net carbon exchange during the 1980s, but became a net carbon sink in the 1990s. This recent sink can be largely attributed to northern extratropical areas, and is roughly split between North America and Eurasia. Tropical land areas, however, were approximately in balance with respect to carbon exchange, implying a carbon sink that offset emissions due to tropical deforestation. The evolution of the terrestrial carbon sink is largely the result of changes in land use over time, such as regrowth on abandoned agricultural land and fire prevention, in addition to responses to environmental changes, such as longer growing seasons, and fertilization by carbon dioxide and nitrogen. Nevertheless, there remain considerable uncertainties as to the magnitude of the sink in different regions and the contribution of different processes.

Carbon estimates from terrestrial ecosystem models are limited by large uncertainties in the current state of the land surface. Natural and anthropogenic disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect or assess with conventional methods. In this study, we combined two recent advances in remote sensing and ecosystem modeling to improve model carbon stock and flux estimates at a tropical forest study site at La Selva, Costa Rica (10°25' N, 84°00' W). Airborne lidar remote sensing was used to measure spatial heterogeneity in the vertical structure of vegetation. The ecosystem demography model (ED) was used to estimate the consequences of this heterogeneity for regional estimates of carbon stocks and fluxes. Lidar data provided substantial constraints on model estimates of both carbon stocks and net carbon fluxes. Lidar-initialized ED estimates of aboveground biomass were within 1.2% of regression-based approaches, and corresponding model estimates of net carbon fluxes differed substantially from bracketing alternatives. The results of this study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model. Extending these analyses to larger scales will require the development of regional and global lidar data sets, and the continued development and application of height structured ecosystem models.

Spatially density-dependent predation is a leading hypothesis describing mechanisms of population regulation in coral reef fish. However, studies supporting this hypothesis predominantly have been conducted on small, isolated patch reefs. Here, we searched for evidence of spatially density-dependent predation on the continuous reefs of the Netherlands Antilles in a study of a dominant planktivore, the blue chromis (Chromis cyanea). Across space, we quantified both the patterns of loss from site-attached aggregations of C. cyanea through time and the behavioral reaction of predators to these aggregations. Looking across C. cyanea densities, we found that loss from aggregations was not characteristic of direct density dependence, but instead was commonly inversely related to density. Individual C. cyanea in larger aggregations were less likely to be lost from the group than were individuals in smaller aggregations. Thus, the observed density dependence increased spatial heterogeneity of C. cyanea. Predators showed behaviors that were consistent with these demographic patterns. Using remote videography, we quantified predator visitation and strike rates across a range of C. cyanea aggregation sizes. Predators consistently visited and struck at individuals in C. cyanea aggregations in a pattern that was strongly inversely density dependent, suggesting that aggregation is an effective means of minimizing per capita risk of predation for prey reef fish. Differences in spatial distribution of resources for predators (i.e., prey fish) between continuous and patch reef habitats may explain the difference between these results and those of previous studies on patch reefs.

A spatial and mechanistic model is developed for the dynamics of transition oak-northern hardwoods forests in northeastern North America. The purpose of the model is to extrapolate from measurable fine-scale and short-term interactions among individual trees to large-scale and long-term dynamics of forest communities. Field methods, statistical estimators, and model structure were designed simultaneously to ensure that parameters could be estimated from data collected in the field. This paper documents eight aspects of a three-year study to calibrate, test, and analyze the model for the nine dominant and subdominant tree species in transition oak-northern hardwoods forests: 1) Design and structure of the model. The model makes population dynamic forecasts by predicting the fate of every individual tree throughout its life. Species-specific functions predict each tree's dispersal, establishment, growth, mortality, and fecundity. Trees occupy unique spatial positions, and individual performance is affected by the local availability of resources. Competition is mechanistic; resources available to each tree are reduced by neighbors. Although the model was developed to include light, water, and nitrogen, the version described here includes only competition for light (shading and light-dependent performance) because the field data provide little evidence of competition for nitrogen and water over the range of sites examined. 2) Estimates of the model's parameters for each species. The estimates reveal a variety of \"strategic trade-offs\" among the species. For example, species that grow quickly under high light tend to cast relatively little shade, have low survivorship under low light, and have high dispersal. In contrast, species that grow slowly under high light tend to cast relatively dark shade, and to have high survivorship under low light and low dispersal. These trade-offs define one of two dominant \"axes\" of strategic variation. 3) Community level predictions of the model. The model predicts succession from early dominance by species such as Quercus rubra and Prunus serotina, to late dominance by Fagus grandifolia and Tsuga canadensis, with Betula alleganiensis present as a gap phase species in old-growth stands. The model also predicts that old-growth communities will have intraspecifically clumped and interspecifically segregated spatial distributions. 4) An error analysis that identifies community level predictions that are robust given the level of sampling uncertainty in the study. This analysis translates the statistical uncertainty associated with each parameter estimate into statistical uncertainty in the model's predictions. The robust predictions include those mentioned in aspect (3) above. 5) Sensitivity of the model to changes in initial conditions and to changes in the three parameters not included in the error analysis. For example, the model predicts that initial abundances continue to affect community composition well into succession (> 300 yr for some species). 6) Tests of the system- and community-level predictions of the model against independent data gleaned from other studies. These tests support the predictions found to be robust in the error analysis, including those predictions mentioned in aspect (3) above. 7) Modeling experiments that determine which aspects of individual performance and inter-neighbor competition are responsible for each of the robust predictions identified in aspect (4) above and tested in aspect (6) above. This analysis reveals a wide variety of causal relationships, with most parameters contributing to at least one community level phenomenon. 8) An explanation of the diversity of individual level causes identified in aspect (7). The two \"axes\" describing most of the strategic variation among the species (see [2]), provide a simple explanation of community level pattern in terms of individual level processes.

With a view toward understanding species-specific differences in juvenile tree mortality and the community-level implications of these differences, we characterized juvenile survivorship of 10 dominant tree species of oak transition-northern hardwood forests using species-specific mathematical models. The mortality models predict a sapling's probability of dying as a function of its recent growth history. These models and species-specific growth functions (published elsewhere), characterize a species' shade tolerance. Combined growth and mortality models express a sapling's probability of mortality as a function of light availability. We describe the statistical bases and the field methods used to calibrate the mortality models. We examined inter- and intraspecific variation in juvenile mortality across three sites: Great Mountain Forest (low pH, nutrient poor soils) in northwestern Connecticut, a calcareous bedrock region (neutral pH, nutrient rich soils) also in northwestern Connecticut, and a site in central-western Michigan (low pH, nutrient poor soils). Interspecific differences in juvenile mortality have profound effects on community dynamics and composition; the importance of these effects is demonstrated through a spatially explicit simulator of forest dynamics (SORTIE). The 10 species we examined occupy a continuum of survivorship levels at 1% of full sun. There was surprisingly little intraspecific variation in mortality functions for sugar maple, American beech, eastern hemlock, and white ash between the Great Mountain and Michigan sites. However, there was a striking increase in survivorship for sugar maple in the calcareous site. Differences in survivorship among the sites are correlated with soil pH and presumably nutrient availability. Growth rates in high-light and low-light survivorship are inversely correlated across species; as level of shade tolerance increases, a species grows more slowly in high light and exhibits increased survivorship under low light. Our results indicate that interspecific differences in sapling mortality are critical components of forest community dynamics.

Spatially explicit models for animal populations (SEPMs) necessarily embody assumptions about plant community structure and dynamics. This paper explores the advantages and limitations of directly linking animal SEPMs with models for vegetation dynamics. Such linkages may often be unnecessary. For instance, in research focussed on questions with short time horizons, the spatial patterning of vegetation can be reasonably approximated as a fixed landscape templet for animal population dynamics. But if one needs to consider longer time scales (e.g., decades to centuries), landscapes will be dynamic. Models of vegetation dynamics provide useful tools for predicting landscape dynamics. We outline the sorts of output from vegetation models that might be useful in animal SEPMs. We discuss as a concrete example recent forest simulators, which predict with reasonable accuracy some variables (e.g., tree species composition), but which, to date, are quite poor for others (e.g., seed production). Moreover, because vegetation models target a restricted range of temporal and spatial scales, they may be more useful for certain consumer groups than for others. Despite these cautionary observations, we believe that the time is ripe for fruitful linkages between models of vegetation dynamics and animal SEPMs.