Global Change Biology最新文献

The role of fertilization diversity (the variety of nutrient types added) in modulating the effects of extreme drought on multi-trophic communities and their functions remains a critical unknown in ecology. We conducted a multi-nutrient factorial experiment (control, N, NP, and NPK additions) under simulated extreme drought in an alpine meadow on the Qinghai-Tibetan Plateau. We assessed the responses of soil abiotic properties, plant communities, soil microbial (bacteria and fungi) and nematode communities, and ecosystem multifunctionality (EMF). Our results reveal that fertilization diversity induces a trophic asymmetry in ecosystem responses to drought: increasing fertilization diversity (and total nutrient load) significantly altered the response of the plant community to drought by promoting the competitive dominance of drought-tolerant grasses, which in turn modulated ecosystem function of aboveground net primary productivity. Conversely, fertilization diversity amplified the negative effects of drought on soil bacterial and fungal biomass, likely due to exacerbating microbial carbon limitation. For soil nematodes, fertilization diversity modified the response of community composition to drought and simplified nematode communities. Consequently, despite these compensatory and contrasting responses across trophic levels, fertilization diversity led to a net decline in EMF under drought. This net loss occurred because the positive contributions from the plant pathway were decisively outweighed by the substantial suppression of microbial processes, revealing a fundamental decoupling between above- and below-ground components. Our findings demonstrate that the balance and diversity of nutrient inputs, rather than nitrogen dose alone, is a critical factor modulating ecosystem responses to drought, creating contrasting responses across trophic levels that necessitate a holistic, multi-trophic perspective for effective ecosystem management under global change.

In 2023, more than half of olive harvests (Olea europaea) across Spain, Greece, and Türkiye were lost to drought. The same year late freeze destroyed 90% of the peach crop (Prunus persica) on the Georgia Piedmont and the apple crop (Malus domestica) in central New York, Vermont, and southern Quebec. Climate extremes now rank with the costliest threats to agriculture, but their role in forest recovery from diebacks that are happening globally is unknown for lack of tree fecundity estimates in forests. Tolerance of climate extremes could depend on past exposure but constrained by phylogenetic conservatism. We report a continental scale analysis of climate extremes and forest fecundity across North America and Europe showing that responses to late freeze and drought are happening now. Species differences are not explained by the traits typically included in ecological studies and they are weakly associated with phylogeny. Late freeze, that is, freezing temperatures that follow the onset of flower development in spring, is shown to be “normal” in North America, but not Europe, potentially explaining failed seed production due to delayed onset and the resultant shorter growing period by North American transplants dating back at least to the 18th century. Drought has thus far had the greatest impacts in dry forested regions, but here too, species differences are not explained by traditional trait values. If responses have been buffered from drought and late freeze by past exposure, acclimation and local adaptation prove inadequate as extremes intensify.

Understanding the apparent temperature dependence of wetland methane emissions (E_M) is critical for predicting climate-carbon feedbacks, yet current estimates remain constrained by observational limitations and methodological inconsistencies. The inherent biogeographic heterogeneity of wetland ecosystems combined with sparse, unevenly distributed flux measurements introduces substantial uncertainty in characterizing spatial patterns of E_M. This knowledge gap impedes accurate projections of wetland methane contributions under climate warming scenarios. Here, we develop a framework that integrates mixed-effects models with artificial intelligence techniques to resolve scale-dependent patterns in methane emission thermodynamics across global wetlands. Our unified framework demonstrates that only 73.6% (5th–95th quantiles: 71.8%–75.4%) of the global wetland area conforms to classical Arrhenius-type temperature dependence. This framework can predict 69.5% (67.9%–71.1%) of the global wetlands with high confidence using the Mahalanobis distance and area of applicability tests. We quantify the weighted mean E_M across high-confidence predictable areas of 0.694 eV, with latitudinal differentiation: tropical (0.634 eV), temperate (0.678 eV), and boreal (0.745 eV) wetlands exhibit progressively stronger temperature responses. Ignoring these biogeographic variations could result in underestimation of projected end-century methane emissions by 4.2%–13.3% across selected socioeconomic pathway scenarios. Our study refined the temperature sensitivity parameter in coupled climate-carbon cycle models, thereby enhancing predictive accuracy of future global warming trends and informing strategic responses to climate change mitigation.

Global change ecology demands predictive models that reconcile data-driven learning with mechanistic theory to address complex, interconnected ecosystem challenges. Traditional process-based approaches struggle with spatiotemporal parameterization, while purely data-driven machine learning approaches suffer from extrapolation, interpretability, and physical consistency. Knowledge-guided machine learning (KGML) bridges this divide by systematically integrating ecological principles (e.g., physical first principles, stoichiometry, process understanding, disturbance regimes) into how models are designed, trained, and adjusted to generalize across different ecosystems. The emerging KGML paradigm offers tremendous opportunities to advance the research of global change ecology. This review synthesizes KGML's transformative potential, showcasing its capacity to enhance the prediction of carbon-water-nutrient cycles and other ecological processes and lay groundwork for ecological foundation models. Emerging applications in decision support and symbolic regression further illustrate its role in deriving actionable insights and novel theoretical hypotheses. Future directions emphasize adaptive integration of data and knowledge, uncertainty quantification, causal embedding in foundation models, and interdisciplinary collaboration to align KGML innovations with sustainability goals. By uniting ecological theory with AI advances, KGML offers a robust pathway to encompass ecosystem responses to global change, fostering scientific discovery and actionable solutions.

Human agricultural activities have exacerbated multiple types of natural resource depletion and environmental impacts through complex interactions with land, water, carbon, and nutrient cycles, which can be measured as corresponding environmental footprints (EFs). However, the spatiotemporal trade-offs and synergies among multiple EFs in agricultural systems remain under-quantified, hindering effective mitigation strategies. Here, we propose an assessment framework of spatiotemporal trade-offs and synergies among multiple EFs of crop production, with a case study on blue water, green water, land, carbon, nitrogen, and phosphorus footprints for wheat, maize, rice, and soybean production across 31 Chinese provinces over 2000–2018. In total, 3630 pairwise EFs were analyzed. Results show that, although the EFs of unit mass crop production generally declined across provinces, national total EFs increased, with land, carbon, and phosphorus footprints rising by 16%, 17%, and 23%, respectively, during the study period. Synergistic interactions among EFs prevailed, comprising 50% positive and 32% negative synergies. The spatial distribution of trade-offs and synergies varies by crop and region. Land use intensity is the main factor limiting the positive EF synergies.

Tropical forests contribute disproportionately to global carbon cycling, yet their resilience under climate warming remains uncertain, partly due to limited understanding of leaf-level temperature responses of photosynthesis. In particular, the role of fine-scale canopy microclimate in shaping photosynthetic temperature responses in tropical trees has been overlooked. We quantified vertical microclimate variation and measured leaf-level photosynthetic temperature responses in 13 coexisting evergreen tree species spanning the full canopy profile in a lowland Congo Basin forest. Leaf gas exchange measurements were integrated with structural leaf traits and high-resolution microclimate profiles to assess how temperature conditions and ecological strategies shape photosynthetic responses. Photosynthetic traits, including the light-saturated photosynthetic rate at the temperature optimum and stomatal conductance at the temperature optimum, increased with canopy height, with pioneer species showing steeper increases than non-pioneers. The temperature optimum of photosynthesis (T_opt) was positively related to both mean and maximum leaf temperature (T_leaf), driven mainly by interspecific differences rather than intraspecific plasticity. This suggests that T_opt reflects species-level adaptation to the temperature conditions of their canopy niche rather than leaf-level adjustment to local microclimate. Stomatal conductance influenced T_leaf via transpiration and thereby contributed to shaping T_opt. Leaves experiencing larger temperature fluctuations showed reduced sensitivity, reflected in a broader photosynthetic temperature-response width (Ω). Ω was also positively associated with structural traits such as leaf mass per area and leaf dry matter content, both within and among species, indicating that greater structural investment helps sustain higher photosynthetic rates across wider temperature ranges and enhances tolerance to temperature variability. By linking canopy microclimate, physiological traits, and structural characteristics, our findings demonstrate how vertical microclimatic gradients and functional diversity jointly determine photosynthetic temperature responses in tropical forest trees. Incorporating leaf-level temperature regimes, stomatal regulation, and trait variation into vegetation models could improve predictions of tropical forest carbon dynamics under climate change.