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AbstractPlant species exert a dominant control over the nitrogen (N) cycle of natural and managed grasslands. Although in intensively managed systems that receive large external N inputs the emission of the potent greenhouse gas nitrous oxide (N2O) is a crucial component of this cycle, a mechanistic relationship between plant species and N2O emissions has not yet been established. Here we use a plant functional trait approach to study the relation between plant species strategies and N2O emissions from soils. Compared to species with conservative strategies, species with acquisitive strategies have higher N uptake when there is ample N in the soil, but also trigger N mineralization when soil N is limiting. Therefore, we hypothesized that (1) compared to conservative species, species with acquisitive traits reduce N2O emissions after a high N addition; and (2) species with conservative traits have lower N2O emissions than acquisitive plants if there is no high N addition. This was tested in a greenhouse experiment using monocultures of six grass species with differing above‐ and below‐ground traits, growing across a gradient of soil N availability. We found that acquisitive species reduced N2O emissions at all levels of N availability, produced higher biomass and showed larger N uptake. As such, acquisitive species had 87% lower N2O emissions per unit of N uptake than conservative species (p < .05). Structural equation modelling revealed that specific leaf area and root length density were key traits regulating the effects of plants on N2O emission and biomass productivity. These results provide the first framework to understand the mechanisms through which plants modulate N2O emissions, pointing the way to develop productive grasslands that contribute optimally to climate change mitigation.

AbstractGlobal biodiversity is eroding due to anthropogenic causes, such as climate change, habitat loss, and trophic simplification of biological communities. Most studies address only isolated causes within a single group of organisms; however, biological groups of different trophic levels may respond in particular ways to different environmental impacts. Our study used natural microcosms to investigate the predicted individual and interactive effects of warming, changes in top predator diversity, and habitat size on the alpha and beta diversity of macrofauna, microfauna, and bacteria. Alpha diversity (i.e., richness within each bromeliad) generally explained a larger proportion of the gamma diversity (partitioned in alpha and beta diversity). Overall, dissimilarity between communities occurred due to species turnover and not species loss (nestedness). Nevertheless, the three biological groups responded differently to each environmental stressor. Microfauna were the most sensitive group, with alpha and beta diversity being affected by environmental changes (warming and habitat size) and trophic structure (diversity of top predators). Macrofauna alpha and beta diversity was sensitive to changes in predator diversity and habitat size, but not warming. In contrast, the bacterial community was not influenced by the treatments. The community of each biological group was not mutually concordant with the environmental and trophic changes. Our results demonstrate that distinct anthropogenic impacts differentially affect the components of macro and microorganism diversity through direct and indirect effects (i.e., bottom‐up and top‐down effects). Therefore, a multitrophic and multispecies approach is necessary to assess the effects of different anthropogenic impacts on biodiversity.

AbstractRadial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non‐stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non‐stationarity. To systematically assess potential drivers of non‐stationarity, we compiled tree‐ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non‐linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate–growth models were tested in independent verification periods to quantify their non‐stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non‐stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non‐stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non‐stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non‐stationarity compared with monthly‐resolved non‐linear models. We conclude that non‐stationarity in climate–growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non‐stationarity, we recommend that temporal non‐stationarity rather than stationarity should be considered as the baseline model of climate–growth response for temperate forests.

AbstractOngoing rapid climate change is predicted to cause local extinction of plant species in mountain regions. However, some plant species could have persisted during Quaternary climate oscillations without shifting their range, despite the limited evidence from fossils. Here, we tested two candidate mechanisms of persistence by comparing the macrorefugia and microrefugia (MR) hypotheses. We used the rare and endemic Saxifraga florulenta as a model taxon and combined ensembles of species distribution models (SDMs) with a high‐resolution paleoclimatic and topographic dataset to reconstruct its potential current and past distribution since the last glacial maximum. To test the macrorefugia hypothesis, we verified whether the species could have persisted in or shifted to geographic areas defined by its realized niche. We then identified potential MR based on climatic and topographic properties of the landscape and applied refined scenarios of MR dynamics and functions over time. Last, we quantified the number of known occurrences that could be explained by either the macrorefugia or MR model. A consensus of two or three SDM techniques predicted absence between 14–10, 3–4 and 1 ka bp, which did not support the macrorefugia model. In contrast, we showed that S. florulenta could have contracted into MR during periods of absence predicted by the SDMs and later re‐colonized suitable areas according to the macrorefugia model. Assuming a limited and realistic seed dispersal distance for our species, we explained a large number of the current occurrences (61–96%). Additionally, we showed that MR could have facilitated range expansions or shifts of S. florulenta. Finally, we found that the most recent and the most stable MR were the ones closest to current occurrences. Hence, we propose a novel paradigm to explain plant persistence by highlighting the importance of supporting functions of MR when forecasting the fate of plant species under climate change.

AbstractThe recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

AbstractHistoric land‐cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land‐cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land‐cover/use reconstructions underestimate land‐cover/use changes in Europe for the 1900–2010 period by accounting for net changes only. We empirically analyzed available historic land‐change data, quantified their uncertainty, corrected for spatial‐temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land‐change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr−1) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land‐change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization.

Global climate models suggest enhanced warming of the tropical mid and upper troposphere, with larger temperature rise rates at higher elevations. Changes in fire activity are amongst the most significant ecological consequences of rising temperatures and changing hydrological properties in mountainous ecosystems, and there is a global evidence of increased fire activity with elevation. Whilst fire research has become popular in the tropical lowlands, much less is known of the tropical high Andean region (>2000 masl, from Colombia to Bolivia). This study examines fire trends in the high Andes for three ecosystems, the Puna, the Paramo and the Yungas, for the period 1982-2006. We pose three questions: (i) is there an increased fire response with elevation? (ii) does the El Niño- Southern Oscillation control fire activity in this region? (iii) are the observed fire trends human driven (e.g., human practices and their effects on fuel build-up) or climate driven? We did not find evidence of increased fire activity with elevation but, instead, a quasicyclic and synchronous fire response in Ecuador, Peru and Bolivia, suggesting the influence of high-frequency climate forcing on fire responses on a subcontinental scale, in the high Andes. ENSO variability did not show a significant relation to fire activity for these three countries, partly because ENSO variability did not significantly relate to precipitation extremes, although it strongly did to temperature extremes. Whilst ENSO did not individually lead the observed regional fire trends, our results suggest a climate influence on fire activity, mainly through a sawtooth pattern of precipitation (increased rainfall before fire-peak seasons (t-1) followed by drought spells and unusual low temperatures (t0), which is particularly common where fire is carried by low fuel loads (e.g., grasslands and fine fuel). This climatic sawtooth appeared as the main driver of fire trends, above local human influences and fuel build-up cyclicity.