Global Change Biology最新文献

Soil organic carbon (SOC) can persist from days to millennia but remains vulnerable to carbon (C) loss upon disturbances, depending on environmental conditions and mode of stabilization. Understanding drivers of persistence and vulnerability is crucial to assess soil C sequestration as well as potential SOC losses due to changes in climate and land use. Here, we investigate SOC persistence and vulnerability in five land-use types by combining radiocarbon-derived estimates of SOC age (system age) and age of respired CO₂ (transit time) with indicators of biological (SOC decomposability) and thermal stability (residual oxidisable carbon content, ROC). Based on this, we developed a vulnerability index for SOC and applied it across soil profiles from 19 sites representing temperate and alpine grasslands, forests, croplands, and managed peatlands. Transit times and system ages ranged from 2 years in the organic layer of forests to 5760 years in subsoils of managed peatlands and varied significantly across land-use types and soil depth. Transit times were generally shorter than system ages, indicating that soil-respired CO₂ is dominated by more recent inputs, while bulk SOC contains more persistent C. In forests, temperate grasslands, and croplands, system ages were positively linked to thermal stability and mineral reactivity, indicating higher SOC persistence through organo-mineral stabilization. In contrast, alpine grasslands and managed peatlands showed centennial to millennial system ages despite low thermal stability (< 10%-ROC), reflecting inhibited microbial decomposition due to cold and/or anaerobic conditions in these ecosystems. In combination with high SOC stocks (> 90 kg m^-2 in managed peatlands), this implies a high vulnerability of these soils to environmental disturbances that alleviate these constraints. Our findings demonstrate that combining metrics of biological and thermal stability with radiocarbon data provides a powerful framework to assess SOC vulnerability to disturbances induced by environmental change.

Redox-active organic matter (RAOM) reduction is an important control on carbon cycling in boreal peatlands, suppressing methane production via its energetic favorability. However, the effects of global climate change drivers-notably, warming and elevated atmospheric carbon dioxide (CO₂)-on the relationship between RAOM and production of greenhouse gases remains unknown, constituting an important knowledge gap. Here, we leveraged an experimental boreal peatland in northern Minnesota (USA) that has been subjected to a gradient of warming (+0 to +9°C) and elevated CO₂ (+500 ppm) for almost 10 years. To understand in situ effects of field treatments on RAOM, we equilibrated a homogenized peat substrate and peat from the bog along the depth profile for 1 month in the field. Elevated CO₂ did not have a significant effect on RAOM reduction (p > 0.05) in either peat type. Increased experimental temperatures stimulated RAOM reduction in the homogenized peat substrate (p < 0.05), but there were no effects of warming on RAOM reduction in peat from the bog (p > 0.05). To better understand indirect effects of the treatments, we also measured the potential for RAOM reduction in peat from each treatment under standardized laboratory conditions. The amount of reduced RAOM was variable at 10-20 cm (~15-70 μmoles e^-/g dw peat) and there were no clear patterns of warming or elevated CO₂ effects on RAOM reduction. We compared these findings to measurements conducted in 2016 and found similar microbial processing of the RAOM pool among treatments and a slight decrease in potential RAOM pools over time at three depths (10-20 cm p = 0.60; 75-100 cm and 175-200 cm p < 0.05). Collectively, our findings suggest an unexpected conclusion: peatland RAOM reduction may be resistant to warming and elevated atmospheric CO₂.

Anthropogenic change has resulted in pollinator declines and altered plant-pollinator interactions. This may alter pollination services, reducing the reproductive success of plants. Yet few datasets allow us to track changes in pollination services over time. Herbaria provide a unique opportunity to assess pollination services across broad spatial and temporal scales enabling the examination of associated spatiotemporal anthropogenic drivers of change. We quantified changes in pollination services to the orchid genus Caladenia over the past century, a period of rapid land-use intensification and climate change in Australia. Examining 10,494 Caladenia flowers preserved at the Australian National Herbarium showed a reduction in pollination services totaling > 60% over the whole study period, with rapid declines occurring post 1970. Declines in pollination services occurred across species pollinated by different taxa and with varying threat status. Sexually deceptive species showed more pronounced declines in pollination services than food-deceptive species, whereas no decline was detected in the self-compatible species. Land-use intensity and rising temperatures were significant predictors of changes in pollination service. Our findings provide rare evidence of declines in pollination services and demonstrate the value of herbarium collections in understanding global change.

Plant functional traits moderate ecosystem responses to climate and exchanges of water and carbon between the land surface and the atmosphere. However, the extent to which diversity in functional traits influences global carbon and hydrological cycles is a major unknown. The scaling gap between site-level analyses and global biogeochemical cycling makes it difficult to develop informed protocols for representing physiologically diverse organismal responses in a parsimonious manner suitable for land surface models used in Earth system model projections. Here, we used a perturbed parameter ensemble with the Community Land Model (CLM5) that varied hydraulic, carbon economy, and stomatal parameters across 500 global simulations of the land surface. Parameters were perturbed independently for each plant functional type (PFT), resulting in variation across ensemble members in trait means and ranges for PFTs co-occurring in the same land surface grid cell, while preserving the same number of PFTs. We calculated metrics of ecosystem drought sensitivity and used gaussian process emulators to quantify the relative importance of stomatal, carbon economy, and hydraulic trait diversity in moderating carbon and water fluxes. We found that the type of trait regulating vegetation productivity, drought sensitivity, and stress varies with resource limitation globally. Hydraulic trait diversity showed widespread importance in regulating water and carbon exchange during drought in regions where model structure permits multiple interacting PFTs. Interestingly, increasing functional diversity tended to increase the sensitivity of ecosystem carbon fluxes to drought, contrary to expectations from ecological theory. However, we show this finding is a numerical consequence of sampling across nonlinear functions and is not behavior emergent in the interaction between different PFTs.

Accurately predicting future events under novel environmental conditions is a central challenge in modeling, especially when no validation data are available. While model transferability is often discussed through the concept of a "forecast horizon," we expand this framework by introducing the concept of "validity domains." These consider not only the extrapolation distance from the calibration data but also the absolute position of calibration and application conditions along an environmental gradient. Using phenological observations from Japanese Yoshino cherry (Prunus × yedoensis) across a climate gradient in Japan, we calibrated process-based and machine learning models for each of 48 locations and validated them with data from all other locations. Interpolating model performance metrics yielded a continuous synthetic surface of predictive accuracy across the full observed temperature range, from which we delineated model-specific validity domains and assessed how transferability depends on both model type and calibration environment. Our findings show that process-based models retain broader validity when calibrated in colder environments but degrade in warmer settings. In contrast, machine learning models exhibit narrower but more consistent validity across the gradient. These systematic differences reveal that the location of calibration and the structure of the model fundamentally shape its reliability under new conditions. By identifying where prediction errors remain below a context-specific validity threshold, our approach provides a robust framework for assessing model applicability under shifting climate conditions. Mapping validity domains offers practical guidance for model selection and allows quantifying how far models can be pushed before their predictions become unreliable.

The sustainability of cropping systems is linked to their circularity, which is their ability to close resource cycles such as carbon and nitrogen through strategies for managing crop residues, byproducts, and other inputs. Here, we investigate three crop rotations—business-as-usual (BAU), vegan, and integrated crop-livestock systems (ICLS)—varying in livestock integration, crop residue fate, and human diet sustained. Under ten climate change scenarios, we compare their impacts on multiple ecosystem services during 24 years over 541,800 ha in Belgium using a validated crop model. All three circularity scenarios are found to be net greenhouse gas (GHG) emitters, with increasing intensity under climate change. The BAU system, favoring cash crops such as sugarbeet or potato, demonstrates the highest productivity, which, however, is highly variable across years and comes with greater environmental impacts such as GHG emissions (+45% and +23% compared to ICLS and Vegan in average—i.e., across all sites and climate scenarios). The Vegan system has lower carbon sequestration than the ICLS due to the lack of pasture and livestock, which, however, is partly offset by the regular incorporation of crop residues into the soil. Finally, ICLS, which include temporary pastures and sheep, demonstrate intermediate productivity levels compared to the other systems. However, they offer the greatest stability and resistance to extreme weather (+43% and +86% for stability compared to BAU and Vegan, in average), with better environmental performance. Therefore, our study reveals the benefits of crop-livestock systems in terms of climate change adaptation, through stability and resistance to extreme climate events, and mitigation, through soil carbon sequestration and reduced greenhouse gas emissions and nitrate leaching. Moreover, our findings highlight the critical links between farm-level circularity, soil-crop feedbacks, human diet, and climate change.