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Land biosphere processes are of central importance to the climate system. Specifically, biological processes interact with the atmosphere through a variety of feedback loops that modulate energy, water and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land-atmosphere interactions. Dynamic Global Vegetation Models (DGVMs) attempt to capture these land surface processes, and are increasingly incorporated into Earth System Models (ESMs), which makes it possible to study the coupled dynamics of the land-biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water and CO2 between the land-ecosystem and the atmosphere. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modeled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types, but the emergent ecosystem composition and structure are consistent between the two versions. We find that the choice of stomatal conductance model has a major impact on the model's predictions. The new LSM implementation described in this work lays the foundation for using the well established LPJ-GUESS DGVM as an alternative LSM in coupled land-biosphere-climate studies, where an accurate representation of ecosystem processes is essential.

Land biosphere processes are of central importance to the climate system. Specifically, biological processes interact with the atmosphere through a variety of feedback loops that modulate energy, water and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land-atmosphere interactions. Dynamic Global Vegetation Models (DGVMs) attempt to capture these land surface processes, and are increasingly incorporated into Earth System Models (ESMs), which makes it possible to study the coupled dynamics of the land-biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water and CO2 between the land-ecosystem and the atmosphere. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modeled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types, but the emergent ecosystem composition and structure are consistent between the two versions. We find that the choice of stomatal conductance model has a major impact on the model's predictions. The new LSM implementation described in this work lays the foundation for using the well established LPJ-GUESS DGVM as an alternative LSM in coupled land-biosphere-climate studies, where an accurate representation of ecosystem processes is essential.

Maps of continental-scale land cover are utilized by a range of diverse users but whilst a range of products exist that describe present and recent land cover in Europe, there are currently no datasets that describe past variations over long time-scales. User groups with an interest in past land cover include the climate modelling community, socio-ecological historians and earth system scientists. Europe is one of the continents with the longest histories of land conversion from forest to farmland, thus understanding land cover change in this area is globally significant. This study applies the pseudobiomization method (PBM) to 982 pollen records from across Europe, taken from the European Pollen Database (EPD) to produce a first synthesis of pan-European land cover change for the period 9000 bp to present, in contiguous 200year time intervals. The PBM transforms pollen proportions from each site to one of eight land cover classes (LCCs) that are directly comparable to the CORINE land cover classification. The proportion of LCCs represented in each time window provides a spatially aggregated record of land cover change for temperate and northern Europe, and for a series of case study regions (western France, the western Alps, and the Czech Republic and Slovakia). At the European scale, the impact of Neolithic food producing economies appear to be detectable from 6000 bp through reduction in broad-leaf forests resulting from human land use activities such as forest clearance. Total forest cover at a pan-European scale moved outside the range of previous background variability from 4000 bp onwards. From 2200 bp land cover change intensified, and the broad pattern of land cover for preindustrial Europe was established by 1000 bp. Recognizing the timing of anthropogenic land cover change in Europe will further the understanding of land cover-climate interactions, and the origins of the modern cultural landscape.

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

Climate and land use change are among the main drivers affecting virtually all species on earth. There were extensive studies projecting impacts of climate and land use changes on habitat loss and fragmentation but few on connectivity loss, and those that investigated connectivity did not disentangle the combined effects between climate change and land use change. This study uses the Himalayan brown bear (Ursus arctos isabellinus) as a case study to illustrate an approach for disentangling the effects of climate change and human land use on population connectivity in the future. First, we assessed the current spatial pattern of population connectivity by simulating cumulative resistant kernels and factorial least‐cost paths with empirical field data. Then, we simulated the changes in connectivity due to future climate change under alternative emission scenarios (RCP 2.6 and RCP 8.5) in mid (2041–2060) and late (2061–2080) 21st century, which served as baseline scenarios of future connectivity. Finally, we estimated the changes in future connectivity due to human land use changes by adding a low and a high human land use activity scenario to the baseline climate change scenarios in our simulations. Alarmingly, all high emission scenarios, with or without human land use change, were projected to result in >99% reduction in current core areas of connectivity by the end of 21st century at 50th percentile threshold or above. This study demonstrates a spatially explicit scenario modeling approach to examine the interplay between future climate change and human land use on species connectivity. Our results suggest that regional land use regulations may be insufficient to conserve connectivity for HBB if nothing is done to reduce climate change at a global scale.

Despite the widespread need to predict and assess the effects of landscape change on biodiversity, the array of tools available for this purpose is still limited. Species’ patterns and human activities such as land use respond to the environment on their own suite of scales in space and time so that their interactions are overlapping but complex. It is difficult, therefore, to relate biodiversity to patterns described solely by metric assessments of spatial heterogeneity. In this methodological paper, we therefore propose consideration of two measures of landscape organisation which focus on the relationships between different properties of the landscape system (e.g., soil type distribution, land use distribution), rather than on their description alone. Alpha organisation measures the degree to which the distribution of features such as land use deviate from a random distribution, measured here as fractal dimension from the semivariogram of a variable describing agricultural intensity. Beta organisation measures the degree of deviation by which the spatial distribution of one property (e.g., human land use) is independent of the distribution of another (e.g., soil type) and was derived from relative mutual information (= redundancy) between the ‘agricultural land use’ and ‘soil types’. These measures are illustrated in a rural landscape of the lower Seine valley, at two scales of observation, and at two dates (1963 and 1999) separated by substantial agricultural change due the European Common Agricultural Policy (= CAP). The results show that analysis of patterns of agricultural activity across a range of spatial scales (α organisation), or across the pattern of spatial variation in soil types (β organisation) reveal how the agricultural actors respond to environmental constraints at different scales. This organisation concept relates to the metastability of landscape systems, and suggest possible correlation between high values of landscape organisation and high levels in biodiversity.

Abstract. The response of crops to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of ∼ 9.0–20.1 and ∼ 9.4–22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was ∼ 8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements, which themselves showed an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to unrealistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

Coastal urbanization and the integrity of estuarine waterbird communities: Threshold responses and the importance of scale

Intense urban and agricultural development alters habitats, increases fragmentation, and may decouple trophic interactions if plants or animals cannot disperse to needed resources. Specialist insects represent a substantial proportion of global biodiversity and their fidelity to discrete microhabitats provides a powerful framework for investigating organismal responses to human land use. We sampled site occupancy and densities for two plant-herbivore-parasitoid systems from 250 sites across a 360 km2 urban/agricultural landscape to ask whether and how human development decouples interactions between trophic levels. We compared patterns of site occupancy, host plant density, herbivory and parasitism rates of insects at two trophic levels with respect to landcover at multiple spatial scales. Geospatial analyses were used to identify landcover characters predictive of insect distributions. We found that herbivorous insect densities were decoupled from host tree densities in urban landcover types at several spatial scales. This effect was amplified for the third trophic level in one of the two insect systems: despite being abundant regionally, a parasitoid species was absent from all urban/suburban landcover even where its herbivore host was common. Our results indicate that human land use patterns limit distributions of specialist insects. Dispersal constraints associated with urban built development are specifically implicated as a limiting factor.

Drylands play a critical role in the global carbon cycle. Recent studies have documented widespread dryland greening largely attributed to CO 2 fertilization, yet the role of human activities remained unclear. Here, we used satellite gross primary productivity (GPP) and state-of-the-art dynamic global vegetation models to quantify the global dryland productivity trend from 2001 to 2024 and determine the contributions of climatic and anthropogenic factors. Our results revealed that 29.20% of global drylands experienced significant greening; ( P value < 0.1) only 4.91% showed a significant browning trend. The robustness of the satellite GPP trend was corroborated by multiple lines of evidence from leaf area index, enhanced vegetation index, and machine-learning-based upscaling of in situ flux tower GPP (FLUXCOM-X). From 2001 to 2024, the global dryland GPP increased by 1,899 Tg C, with Asia contributing the largest share (983 Tg C). Partial least squares regression analysis revealed that human activities (effect size = 0.68) played a more important role than the CO 2 fertilization effect (0.32), with climate factors having only a minor role (0.03). Dynamic global vegetation models from the latest TRENDY v12 project substantially underestimated global dryland greening (multimodel ensemble mean GPP slope = 0.18 ± 2.45 g C m −2 y −1 versus mean satellite GPP slope = 7.66 ± 4.92 g C m −2 y −1 ) and overemphasized the role of CO 2 fertilization effect and climate. Our results therefore not only advanced our mechanic understanding of global dryland greening but also highlighted the urgent need for refined representation of human land use activities in Earth system models to accurately predict dryland ecosystem dynamics amid global warming and escalating freshwater demand.

As a wetland of international importance, the ecological function zone of the Sanjiang Plain (EFZSP) plays an important role in maintaining waterfowl habitat. With the increasing disturbance of human beings, land use and climate changes, habitat suitability for waterfowls (HSW) has drawn greater attention of ecologists. In this study, Landsat TM/ETM+/OLI and HJ-1B images in 1990, 2000, 2010 and 2015 were used to obtain land cover classification by object-based image analysis, and the entropy and analytic hierarchy process methods were adopted to determine the factors and their weights, the factors selected in this study included water condition (lake and river density), disturbance factor (residence and road density), shelter condition (land cover type and slope), and food abundance (NDVI). Finally, the evaluation results of habitat suitability of year 1990, 2000, 2010, and 2015 were achieved based on the habitat suitability index (HSI) system, and the spatio-temporal dynamics and driving forces causing the changes were analyzed. The results showed that the excellent level of HSW mainly distributed in abundant water areas, especially along the riparian zone of the Heilongjiang River, Naoli River, Ussuri River, Muling River and Xingkai Lake. Its area decreased by 3.2% from 1990 to 2015 because of wetland reclamation. The good level of HSW concentrated in Raohe County over the past 25 years, the condition of Hulin County and Fuyuan County were improved to a good level from 2010 to 2015 due to the substantial increase of paddy field areas. The fair level of HSW distributed dispersedly, and the areas first increased from 1990 to 2000 and then decreased during 2000-2010 and 2010-2015 periods. The areas of poor le-vel increased by 6.7% from 1990 to 2000 and decreased by 3.1% from 2000 to 2015. The change of HSW level in this study area was dominated by land cover change, and both of the increasing population and economy and the drying and worming climate resulted in the decline of the HSW level, whereas the establishment of nature reserves is crucial to protect habitat for waterfowls.

Research that connects the effects of urbanization on biodiversity and ecosystem services is lacking. Ants perform multifarious ecological functions that stabilize ecosystems and contribute to a number of ecosystem services. We studied responses of ant communities to urbanization in the Lake Tahoe basin by sampling sites along a gradient of urban land development. We sampled ant communities, measured vegetation characteristics, quantified human activities, and evaluated ant-community responses by grouping ants into service-providing units (SPUs), defined as a group of organisms and their populations that perform specific ecosystem services, to provide an understanding of urbanization impacts on biodiversity and their delivery of ecosystem services. Species richness and abundance peaked at intermediate levels of urban development, as did the richness of 3 types of ant SPUs (aerators, decomposers, and compilers). With increasing land development aerator and decomposer ants significantly declined in abundance, whereas compiler ants significantly increased in abundance. Competing models demonstrated that precipitation was frequently among the strongest influences on ant community structure; however, urban development and human activities also had a strong, negative influence on ants, appearing in most models with DeltaAIC(c) < 2 for species richness and abundance patterns of SPUs and generalists. Response diversity was observed within SPUs, which suggests that the corresponding ecosystem services were maintained until development reached 30-40%. Our data provide evidence that ecosystem functions, such as water infiltration and soil productivity, may be diminished at sites subject to greater levels of urbanization and that conserving ant communities and the ecosystem services they provide could be an important target in land-use planning and conservation efforts.

Dynamic global vegetation models (DGVMs) are essential for quantification of the response of land carbon storage to changes in atmospheric chemistry, climate, and land cover. While DGVMs are often evaluated concerning carbon responses to changes in CO2 and climate, local responses to changes in land cover have received less attention. This is of concern as DGVMs are needed to project the long-term consequence of afforestation or deforestation on the land carbon balance under climate. Here, we present an assessment of the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model, one of the state-of-the-art DGVMs, aiming to evaluate the simulated growth in forest biomass carbon stocks across the European Union against comprehensive observational data.We conduct a model-data comparison of biomass growth using databases that contain paired observations of above-ground biomass (AGB) and plant age, categorized across various age groups spanning from very young (0-19 years) to old (&gt;99 years) forests for boreal and temperate forests. The biomass dataset encompasses a harmonized collection from multiple open forest inventory databases, comprising 603 sites across Europe for six plant functional types (PFTs). On average, the stands are approximately 58 years old, with a mean AGB of 6.4 kgC.m-2. The findings indicate that simulations replicate the observed trend: AGB increases rapidly in young stands (60 years old). However, the observed AGBs exhibit broader ranges and have more extremes than the simulated values. This is expected as observations refer to individual species, while our simulations are on the level of PFTs, which are an assemblage of species. Moreover, the comparisons reveal that the model underestimates AGB for temperate needleleaf evergreen forests, with a median deviation of approximately 60% from observed values. We propose a recalibration of the maximal rate of carboxylation and gross primary production fraction lost as growth respiration to reduce this deviation to less than 10%.Our study highlights the potential of using observational biomass data to assess and calibrate DGVMs. This approach significantly enhances the ability of DGVMs to accurately reproduce the short-term land carbon sink response to reforestation. We provide a protocol that can easily be adapted to evaluate and recalibrate other DGVMs for the same purpose.

Current estimates of land cover and land use are important aspects in understanding the flux of material and energy from terrestrial ecosystems into hydrological and atmospheric components of the earth system. These land use characteristics define the various dimensions of human impacts on ecosystem processes and resource use. The recent availability of global remote sensing data and agricultural census data from various regions of the world, including portions of Asia such as China and Mongolia, now makes it feasible to evaluate differences in the physical environment and land use patterns affecting land cover and land use changes. In addition, recent research involving transects studies along gradients of climate, soil, and land use intensity has been carried out during the past decade to allow more careful regional comparisons. Using the analysis of the Mongolian-North East China Transect (NECT), we can estimate the effect of land cover differences on ecosystem dynamics. The 30 m-Thematic Mapper data provides a way to identify fine scale features of the regional landscape patterns which are not captured by the 1-km AVHRR data. The focus of this paper is to evaluate the scale of land use and cover changes in the Asian region and the biogeochemical implications of these changes. The estimates of land cover and land use can vary depending on the spatial scale of the features represented across the land surface. Both physical and social factors influence the spatial extent these features represent. Therefore, the scale of land use intensification can affect the accuracy of land use area estimations. In particular, those regions of the world where land use practices are implemented via small individual holdings may be underestimated when using coarse scale remote sensing data such as AVHRR, where as the Thematic Mapper satellite data at 30 to 50 m resolution can provide a better estimate of land cover and land use area. The accuracy of the extrapolated fluxes of ecosystem dynamics and biogeochemical cycles are directly proportional to the accuracy associated with the land estimates. We will evaluate the land cover estimates in this region of Asia using these data sources. This region encompasses a broad set of precipitation and temperature regimes and includes characteristics of regional land cover and land use related to grazing systems and grain cultivation.

Fire is a complex process involving interactions and feedbacks between biological, socioeconomic, and physical drivers across multiple spatial and temporal scales. This complexity limits our ability to incorporate fire into Earth system models and project future fire activity under climate change. Conceptual, empirical, and process models have identified the mechanisms and processes driving fire regimes, and provide a useful basis to consider future fire activity. However, these models generally deal with only one component of fire regimes, fire frequency, and do not incorporate feedbacks between fire, vegetation, and climate. They are thus unable to predict the location, severity or timing of fires, the socioecological impacts of fire regime change, or potential non‐linear responses such as biome shifts into alternative stable states. Dynamic modeling experiments may facilitate more thorough investigations of fire–vegetation–climate feedbacks and interactions, but their success will depend on the development of dynamic global vegetation models (DGVMs) that more accurately represent biological drivers. This requires improvements in the representation of current vegetation, plant responses to fire, ecological dynamics, and land management to capture the mechanisms behind fire frequency, intensity, and timing. DGVMs with fire modules are promising tools to develop a globally consistent analysis of fire activity, but projecting future fire activity will ultimately require a transdisciplinary synthesis of the biological, atmospheric, and socioeconomic drivers of fire. This is an important goal because fire causes substantial economic disruption and contributes to future climate change through its influence on albedo and the capacity of the biosphere to store carbon. WIREs Clim Change 2016, 7:910–931. doi: 10.1002/wcc.428This article is categorized under: Climate Models and Modeling &gt; Model Components Assessing Impacts of Climate Change &gt; Evaluating Future Impacts of Climate Change Climate, Ecology, and Conservation &gt; Observed Ecological Changes