An overview of methods used to assess vegetation condition at the scale of the site

Summary   Rapid, on-ground assessments of vegetation condition are frequently used as a basis for landholder education, development applications, distributing incentive funds, prescribing restoration treatments and monitoring change. We provide an overview of methods used to rapidly assess vegetation condition for these purposes. We encourage those developing new approaches to work through the steps we have presented here, namely define management objectives and operational constraints; develop an appropriate conceptual framework for the ecosystems under consideration; select an appropriate suite of indicators; and consider the options available for combining these into an index. We argue that information must be gained from broader scales to make decisions about the condition of individual sites. Remote sensing and spatial modelling might be more appropriate methods than on-ground assessments for obtaining this information. However, we believe that spatial prediction of vegetation condition will only add value to on-ground assessments rather than replace them. This is because the current techniques for spatially predicting vegetation condition cannot capture all of the information in a site assessment or at the required level of accuracy, and maps cannot replace the exchange of information between assessors and land managers that is an important component of on-ground assessment. There is scope for more sophistication in the way on-ground assessments of vegetation condition are undertaken, but the challenge will be to maintain the simplicity that makes rapid on-ground assessment a popular vehicle for informing natural resource management. We encourage greater peer review and publishing in this field to facilitate greater exchange of ideas and experiences.     

Modelling inducible defences in predator–prey interactions: assumptions and dynamical consequences of three distinct approaches

Inducible defences against predation are widespread in the natural world, allowing prey to economise on the costs of defence when predation risk varies over time or is spatially structured. Through interspecific interactions, inducible defences have major impacts on ecological dynamics, particularly predator–prey stability and phase lag. Researchers have developed multiple distinct approaches, each reflecting assumptions appropriate for particular ecological communities. Yet, the impact of inducible defences on ecological dynamics can be highly sensitive to the modelling approach used, making the choice of model a critical decision that affects interpretation of the dynamical consequences of inducible defences. Here, we review three existing approaches to modelling inducible defences: Switching Function, Fitness Gradient and Optimal Trait. We assess when and how the dynamical outcomes of these approaches differ from each other, from classic predator–prey dynamics and from commonly observed eco‐evolutionary dynamics with evolving, but non‐inducible, prey defences. We point out that the Switching Function models tend to stabilise population dynamics, and the Fitness Gradient models should be carefully used, as the difference with evolutionary dynamics is important. We discuss advantages of each approach for applications to ecological systems with particular features, with the goal of providing guidelines for future researchers to build on.     

Coupled land use and ecological models reveal emergence and feedbacks in socio‐ecological systems

Understanding the dynamics of socio‐ecological systems is crucial to the development of environmentally sustainable practices. Models of social or ecological sub‐systems have greatly enhanced such understanding, but at the risk of obscuring important feedbacks and emergent effects. Integrated modelling approaches have the potential to address this shortcoming by explicitly representing linked socio‐ecological dynamics. We developed a socio‐ecological system model by coupling an existing agent‐based model of land‐use dynamics and an individual‐based model of demography and dispersal. A hypothetical case‐study was established to simulate the interaction of crops and their pollinators in a changing agricultural landscape, initialised from a spatially random distribution of natural assets. The bi‐directional coupled model predicted larger changes in crop yield and pollinator populations than a unidirectional uncoupled version. The spatial properties of the system also differed, the coupled version revealing the emergence of spatial land‐use clusters that neither supported nor required pollinators. These findings suggest that important dynamics may be missed by uncoupled modelling approaches, but that these can be captured through the combination of currently‐available, compatible model frameworks. Such model integrations are required to further fundamental understanding of socio‐ecological dynamics and thus improve management of socio‐ecological systems.     

Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon

Following an intense occupation process that was initiated in the 1960s, deforestation rates in the Brazilian Amazon have decreased significantly since 2004, stabilizing around 6000 km² yr^?1 in the last 5 years. A convergence of conditions contributed to this, including the creation of protected areas, the use of effective monitoring systems, and credit restriction mechanisms. Nevertheless, other threats remain, including the rapidly expanding global markets for agricultural commodities, large‐scale transportation and energy infrastructure projects, and weak institutions. We propose three updated qualitative and quantitative land‐use scenarios for the Brazilian Amazon, including a normative ‘Sustainability’ scenario in which we envision major socio‐economic, institutional, and environmental achievements in the region. We developed an innovative spatially explicit modelling approach capable of representing alternative pathways of the clear‐cut deforestation, secondary vegetation dynamics, and the old‐growth forest degradation. We use the computational models to estimate net deforestation‐driven carbon emissions for the different scenarios. The region would become a sink of carbon after 2020 in a scenario of residual deforestation (~1000 km² yr^?1) and a change in the current dynamics of the secondary vegetation – in a forest transition scenario. However, our results also show that the continuation of the current situation of relatively low deforestation rates and short life cycle of the secondary vegetation would maintain the region as a source of CO₂ – even if a large portion of the deforested area is covered by secondary vegetation. In relation to the old‐growth forest degradation process, we estimated average gross emission corresponding to 47% of the clear‐cut deforestation from 2007 to 2013 (using the DEGRAD system data), although the aggregate effects of the postdisturbance regeneration can partially offset these emissions. Both processes (secondary vegetation and forest degradation) need to be better understood as they potentially will play a decisive role in the future regional carbon balance.     

A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa

Abstract The aim of this study was to characterize the short-term land-cover change processes that were detected in Eastern Africa, based on a set of change metrics that allow for the quantification of interannual changes in vegetation productivity, changes in vegetation phenology and a combination of both. We tested to what extent land use, fire activity and livestock grazing modified the vegetation response to short-term rainfall variability in East Africa and how this is reflected in change metrics derived from MODerate Imaging Spectrometer (MODIS) time series of remote sensing data. We used a hierarchical approach to disentangle the contribution of human activities and climate variability to the patterns of short-term vegetation change in East Africa at different levels of organization. Our results clearly show that land use significantly influences the vegetation response to rainfall variability as measured by time series of MODIS data. Areas with different types of land use react in a different way to interannual climate variability, leading to different values of the change indices depending on the land use type. The impact of land use is more reflected in interannual variability of vegetation productivity and overall change in the vegetation, whereas changes in phenology are mainly driven by climate variability and affect most vegetation types in similar ways. Our multilevel approach led to improved models and clearly demonstrated that climate influence plays at a different scale than land use, fire and herbivore grazing. It helped us to understand dynamics within and between biomes in the study area and investigate the relative importance of different factors influencing short-term variability in change indices at different scales.     

Conference and workshop on modelling global land use implications in the environmental assessment of biofuels

Background, Aims and Scope  On 4–5 June 2007, an international conference was held in Copenhagen. It provided an interdisciplinary forum where economists and geographers met with LCA experts to discuss the challenges of modelling the ultimate land use changes caused by an increased demand for biofuels. Main Features  The main feature of the conference was the cross-breeding of experience from the different approaches to land use modelling: The field of LCA could especially benefit from economic modelling in the identification of marginal crop production and the resulting expansion of the global agricultural area. Furthermore, the field of geography offers insights in the complexity behind new land cultivation and practical examples of where this is seen to occur on a regional scale. Results  Results presented at the conference showed that the magnitude and location of land use changes caused by biofuels demand depend on where the demand arises. For instance, mandatory blending in the EU will increase land use both within and outside of Europe, especially in South America. A key learning for the LCA society was that the response to a change in demand for a given crop is not presented by a single crop supplier or a single country, but rather by responses from a variety of suppliers of several different crops in several countries. Discussion  The intensification potential of current and future crop and biomass production was widely discussed. It was generally agreed that some parts of the third world hold large potentials for intensification, which are not realised due to a number of barriers resulting in so-called yield gaps. Conclusions  Modelling the global land use implications of biofuels requires an interdisciplinary approach optimally integrating economic, geographical, biophysical, social and possibly other aspects in the modelling. This interdisciplinary approach is necessary but also difficult due to different perspectives and mindsets in the different disciplines. Recommendations and Perspectives  The concept of a location dependent marginal land use composite should be introduced in LCA of biofuels and it should be acknowledged that the typical LCA assumption of linear substitution is not necessarily valid. Moreover, fertiliser restrictions/accessibility should be included in land use modelling and the relation between crop demand and intensification should be further explored. In addition, environmental impacts of land use intensification should be included in LCA, the powerful concept of land use curves should be further improved, and so should the modelling of diminishing returns in crop production.     

Evaluating the impact of biodiversity offsetting on native vegetation

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade-offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria's Native Vegetation Framework (2002–2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near-complete baseline woody vegetation cover (“avoided loss”, 2702 ha) and with incomplete cover (“regeneration”, 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self-selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%–3.6%/year more than non-offset sites (138–180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%–1.9%/year more than non-offset sites; 19–97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of ‘net gain’ (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self-selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.     

How good are Bayesian belief networks for environmental management? A test with data from an agricultural river catchment

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Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints

Assumptions of steady‐state conditions in biogeochemical modelling are often invoked because knowledge on the development status of the modelling domain is generally unavailable. Here, we investigate the role of vegetation pool sizes on nonequilibrium conditions through model‐data integration approaches for a set of sites using eddy covariance CO₂ flux data. The study is based on the Carnegie–Ames–Stanford Approach (CASA) model, modified (CASA_G) in order to evaluate the sensitivity of simulated net ecosystem production (NEP) fluxes to vegetation pool sizes. The experimental design is based on the inverse model optimization of different parameter vectors performed at the measurement site level. Each parameter vector prescribes different simulation dynamics that embody different model structural assumptions concerning (non)steady‐state conditions in vegetation and soil carbon pools. We further explore the potential of assimilating biometric constraints through the cost function for sites where in situ information on aboveground biomass or wood pools is available. The integration of biometric data yields marked improvements in the simulation of vegetation C pools compared to single constraints with eddy flux data. Overall, it is necessary to relax both vegetation and soil carbon pools for consistency with the observed data streams. Multiple constraints approaches also leads to variable model performance among the different experimental setups and model structures. We identify and assess the limitations of various model structures and the role of multiple constraints approaches for tackling issues of equifinality. These studies emphasize the need for establishing consistent data sets of fluxes and biometric data for successful model‐data fusion.     

Ecological hierarchies and self-organisation – Pattern analysis,modelling and process integration across scales

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an ‘across-scale-approach’, closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems.     

The use of adjacency analysis for quantifying landscape changes

Abstract

The analysis of landscape changes in space and time plays an important role in landscape ecology. Analyzing landscape dynamics through time may be crucial for identifying historical and current processes that shape the actual landscapes and for developing predictive landscape models for ecosystem management and conservation. In this view, the propensity of land cover patches to change is at least partially related to the nature of their contact types. The interactions of a given patch with adjacent land cover types affect both land use exploitation by humans and vegetation dynamics. The aim of this paper is to use patch boundary dynamics for describing the landscape changes that occurred in the Lepini Mountains (central Italy) during 1954 – 2000. Results show an increase in landscape complexity in the Mediterranean land units and a corresponding decrease in landscape complexity in the Temperate land units. This differential trend is due to a complex, human-driven temporal dynamics of Mediterranean ecosystems that generates heterogeneity as opposed to a diffuse landscape abandonment in the Temperate region that leads to a more homogeneous boundary structure.     

CLUENaban—A land use change model combining social factors with physical landscape factors for a mountainous area in Southwest China

Land use modelling has used rural areas as object for a long time. Modellers start in general with physical landscape factors to determine the spatial distribution of land use, especially agricultural crops. An important factor defining rural areas is the dominance of livelihoods/economies based on agriculture combined with a strong relationship to tradition, high value of family ties, scales of social aggregation, and a framework given by nature. The inter-linkages among economic, social and environmental components determine both the complexity and dynamics of rural development. Both modellers and social scientists seek a better understanding of these complex rural systems in terms of their development paths, the underlying forces and the interactions between physical land use and rural society. In this paper, we use a combination of a land use change model and information about social relationship by referring both to the common spatial unit of villages for a rural region in south west China. We want to show how such a common unit can help to include social science information into a physically oriented land use model. The inclusion of social data results in a substantial improvement of the modelling results. In the model run we could show that land use patterns in the study area were closely related to socio-economic characteristics and governmental rules. In China, collective land and state land are distinguished. This land designation turned out to be the most important factor for the land use types rainfed, irrigated land, and rubber. All land use types were influenced by the ethnicity of village inhabitants because they had different land use traditions.     

Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long‐term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, ≥ 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome‐specific calibrations. Active fire locations are provided in near‐real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long‐term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter‐operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.     

高寒内陆河流域植被覆盖增加对地下水补给的影响

地下水是干旱区内陆河流域的主要基础性资源,对流域生态安全、可持续发展等具有重要意义。干旱/半干旱区的地下水补给比湿润地区更易受到地表覆盖条件的影响。为揭示干旱区内陆河流域植被覆盖增加对地下水补给的影响,以巴音河中下游为例,针对土壤和水评价工具(SWAT)模型未有效考虑降水、地形等因素对植被覆盖影响的缺陷,改进SWAT模型,采用全球地表卫星叶面积指数(GLASS LAI)数据代替其LAI计算模块,再结合SWAT土地利用更新模块,准确刻画区域植被覆盖变化。将改进后的SWAT模型与模块化有限拆分地下水流耦合(MODFLOW)模型耦合,准确模拟并分析植被覆盖增加对流域地下水补给的影响。结果表明：基于植被动态变化的土壤和水评价工具与模块化有限拆分地下水流耦合模型(DVSWAT-MODFLOW)模型的月蒸散发及月地下水位模拟效果较好;巴音河中下游2019年林地及草地面积以及LAI较2001年明显增加;2019年植被覆盖情况对应的年际及月际尺度地下水补给量较2001年分别减少了6.1—26.52 mm以及0—15.03 mm;植被覆盖增加对年际尺度地下水补给量的影响强弱在一定程度上取决于年降水量,对...     

A test of the optimality approach to modelling canopy properties and CO2 uptake by natural vegetation

Photosynthesis provides plants with their main building material, carbohydrates, and with the energy necessary to thrive and prosper in their environment. We expect, therefore, that natural vegetation would evolve optimally to maximize its net carbon profit (NCP), the difference between carbon acquired by photosynthesis and carbon spent on maintenance of the organs involved in its uptake. We modelled N(CP) for an optimal vegetation for a site in the wet-dry tropics of north Australia based on this hypothesis and on an ecophysiological gas exchange and photosynthesis model, and compared the modelled CO2 fluxes and canopy properties with observations from the site. The comparison gives insights into theoretical and real controls on gas exchange and canopy structure, and supports the optimality approach for the modelling of gas exchange of natural vegetation. The main advantage of the optimality approach we adopt is that no assumptions about the particular vegetation of a site are required, making it a very powerful tool for predicting vegetation response to long-term climate or land use change.     

Increase in nonnative understorey vegetation cover after nonnative conifer removal and passive restoration

Nonnative conifers are widespread in the southern hemisphere, where their use as plantation species has led to adverse ecosystem impacts sometimes intensified by invasion. Mechanical removal is a common strategy used to reduce or eliminate the negative impacts of nonnative conifers, and encourage native regeneration. However, a variety of factors may preclude active ecological restoration following removal. As a result, passive restoration – unassisted natural vegetation regeneration – is common following conifer removal. We asked, ‘what is the response of understorey cover to removal of nonnative conifer stands followed by passive restoration?' We sampled understorey cover in three site types: two‐ to ten‐year‐old clearcuts, native forest and current plantations. We then grouped understorey species by origin (native/nonnative) and growth form, and compared proportion and per cent cover of these groups as well as of bare ground and litter between the three site types. For clearcuts, we also analysed the effect of time since clearcut on the studied variables. We found that clearcuts had a significantly higher average proportion of nonnative understorey vegetation cover than native forest sites, where nonnative vegetation was nearly absent. The understorey of clearcut sites also averaged more overall vegetation cover and more nonnative vegetation cover (in particular nonnative shrubs and herbaceous species) than either plantation or native forest sites. Notably, 99% of nonnative shrub cover in clearcuts was the invasive nonnative species Scotch broom (Cytisus scoparius). After ten years of passive recovery since clearcutting, the proportion of understorey vegetation cover that is native has not increased and remains far below the proportion observed in native forest sites. Reduced natural regeneration capacity of the native ecosystem, presence of invasive species in the surrounding landscape and land‐use legacies from plantation forestry may inhibit native vegetation recovery and benefit opportunistic invasives, limiting the effectiveness of passive restoration in this context. Abstract in Spanish is available with online material.     

A method of determining rooting depth from a terrestrial biosphere model and its impacts on the global water and carbon cycle

We outline a method of inferring rooting depth from a Terrestrial Biosphere Model by maximizing the benefit of the vegetation within the model. This corresponds to the evolutionary principle that vegetation has adapted to make best use of its local environment. We demonstrate this method with a simple coupled biosphere/soil hydrology model and find that deep rooted vegetation is predicted in most parts of the tropics. Even with a simple model like the one we use, it is possible to reproduce biome averages of observations fairly well. By using the optimized rooting depths global Annual Net Primary Production (and transpiration) increases substantially compared to a standard rooting depth of one meter, especially in tropical regions that have a dry season. The decreased river discharge due to the enhanced evaporation complies better with observations. We also found that the optimization process is primarily driven by the water deficit/surplus during the dry/wet season for humid and arid regions, respectively. Climate variability further enhances rooting depth estimates. In a sensitivity analysis where we simulate changes in the water use efficiency of the vegetation we find that vegetation with an optimized rooting depth is less vulnerable to variations in the forcing. We see the main application of this method in the modelling communities of land surface schemes of General Circulation Models and of global Terrestrial Biosphere Models. We conclude that in these models, the increased soil water storage is likely to have a significant impact on the simulated climate and the carbon budget, respectively. Also, effects of land use change like tropical deforestation are likely to be larger than previously thought.     

A Robust Technique for Mapping Vegetation Condition Across a Major River System

Ecologists need to develop tools that allow characterization of vegetation condition over scales that are pertinent to species’ persistence and appropriate for management actions. Our study shows that stand condition can be mapped accurately over the floodplain of a major river system (ca 100,000 ha of forest over 1600 km of river)—the Murray River in southeastern Australia. It demonstrates the value of using quantitative ground surveys in conjunction with remotely sensed data to model vegetation condition over very large spatial domains. A comparison of four modelling methods found that stand condition was best modelled using the multivariate adaptive regression spline (MARS) method (R ² = 0.85), although there was little difference among the methods (R ² = 0.77–0.85). However, a subsequent validation survey of condition at new locations showed that use of artificial neural networks had substantially higher predictive power (R ² = 0.78) than the MARS model (R ² = 0.28). This discrepancy demonstrates the value of using several modelling approaches to determine relationships among vegetation responses and environmental variables, and stresses the importance of validating ecological models with predictive surveys conducted after model building. The artificial neural network was used to produce a stand condition map for the whole floodplain, which predicted that only 30% of the area containing Eucalyptus camaldulensis stands is currently in good condition. There is a downstream decline in stand condition, which is related to more extreme declines in flooding, due to water harvesting, and drier climate found in the Lower Murray region. Rigorous surveying and modelling approaches, such as those used here, are necessary if vegetation health is to be effectively monitored and managed. Author Contribution: SCC wrote the paper and was involved in all parts of the research; RM conceived and designed the study, and contributed to analysis and writing; JR designed the study, and contributed to writing; PJB designed the study and contributed to writing; MW contributed to design and modelling; JRT contributed to modelling; PG contributed new methods and modelling.     

Projected carbon stocks in the conterminous USA with land use and variable fire regimes

The dynamic global vegetation model (DGVM) MC2 was run over the conterminous USA at 30 arc sec (~800 m) to simulate the impacts of nine climate futures generated by 3GCMs (CSIRO, MIROC and CGCM3) using 3 emission scenarios (A2, A1B and B1) in the context of the LandCarbon national carbon sequestration assessment. It first simulated potential vegetation dynamics from coast to coast assuming no human impacts and naturally occurring wildfires. A moderate effect of increased atmospheric CO₂ on water use efficiency and growth enhanced carbon sequestration but did not greatly influence woody encroachment. The wildfires maintained prairie‐forest ecotones in the Great Plains. With simulated fire suppression, the number and impacts of wildfires was reduced as only catastrophic fires were allowed to escape. This greatly increased the expansion of forests and woodlands across the western USA and some of the ecotones disappeared. However, when fires did occur, their impacts (both extent and biomass consumed) were very large. We also evaluated the relative influence of human land use including forest and crop harvest by running the DGVM with land use (and fire suppression) and simple land management rules. From 2041 through 2060, carbon stocks (live biomass, soil and dead biomass) of US terrestrial ecosystems varied between 155 and 162 Pg C across the three emission scenarios when potential natural vegetation was simulated. With land use, periodic harvest of croplands and timberlands as well as the prevention of woody expansion across the West reduced carbon stocks to a range of 122–126 Pg C, while effective fire suppression reduced fire emissions by about 50%. Despite the simplicity of our approach, the differences between the size of the carbon stocks confirm other reports of the importance of land use on the carbon cycle over climate change.