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Climate change will likely shift plant and microbial distributions, creating geographic mismatches between plant hosts and essential microbial symbionts (e.g., ectomycorrhizal fungi, EMF). The loss of historical interactions, or the gain of novel associations, can have important consequences for biodiversity, ecosystem processes, and plant migration potential, yet few analyses exist that measure where mycorrhizal symbioses could be lost or gained across landscapes. Here, we examine climate change impacts on tree-EMF codistributions at the continent scale. We built species distribution models for 400 EMF species and 50 tree species, integrating fungal sequencing data from North American forest ecosystems with tree species occurrence records and long-term forest inventory data. Our results show the following: 1) tree and EMF climate suitability to shift toward higher latitudes; 2) climate shifts increase the size of shared tree-EMF habitat overall, but 35% of tree-EMF pairs are at risk of declining habitat overlap; 3) climate mismatches between trees and EMF are projected to be greater at northern vs. southern boundaries; and 4) tree migration lag is correlated with lower richness of climatically suitable EMF partners. This work represents a concentrated effort to quantify the spatial extent and location of tree-EMF climate envelope mismatches. Our findings also support a biotic mechanism partially explaining the failure of northward tree species migrations with climate change: reduced diversity of co-occurring and climate-compatible EMF symbionts at higher latitudes. We highlight the conservation implications for identifying areas where tree and EMF responses to climate change may be highly divergent., Competing Interests: Competing interests statement:The authors declare no competing interest.

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO 2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO 2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress., Competing Interests: Competing interests statement:The authors declare no competing interest.

Many species are responding to global warming by shifting their distributions upslope to higher elevations, but the observed rates of shifts vary considerably among studies. Here, we test the hypothesis that this variation is in part explained by latitude, with tropical species being particularly responsive to warming temperatures. We analyze two independent empirical datasets-shifts in species' elevational ranges, and changes in composition of forest inventory tree plots. Tropical species are tracking rising temperatures 2.1-2.4 times (range shift dataset) and 10 times (tree plot dataset) better than their temperate counterparts. Models predict that for a 100 m upslope shift in temperature isotherm, species at the equator have shifted their elevational ranges 93-96 m upslope, while species at 45° latitude have shifted only 37-42 m upslope. For tree plots, models predict that a 1°C increase in temperature leads to an increase in community temperature index (CTI), a metric of the average temperature optima of tree species within a plot, of 0.56°C at the equator but no change in CTI at 45° latitude (-0.033°C). This latitudinal gradient in temperature tracking suggests that tropical montane communities may be on an "escalator to extinction" as global temperatures continue to rise., (© 2021 John Wiley & Sons Ltd.)

Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitation changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. We used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. This highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes., (© 2017 John Wiley & Sons Ltd.)

Global changes in climate, atmospheric composition, and pollutants are altering ecosystems and the goods and services they provide. Among approaches for predicting ecosystem responses, long-term observations and manipulative experiments can be powerful approaches for resolving single-factor and interactive effects of global changes on key metrics such as net primary production (NPP). Here we combine both approaches, developing multidimensional response surfaces for NPP based on the longest-running, best-replicated, most-multifactor global-change experiment at the ecosystem scale-a 17-y study of California grassland exposed to full-factorial warming, added precipitation, elevated CO2, and nitrogen deposition. Single-factor and interactive effects were not time-dependent, enabling us to analyze each year as a separate realization of the experiment and extract NPP as a continuous function of global-change factors. We found a ridge-shaped response surface in which NPP is humped (unimodal) in response to temperature and precipitation when CO2 and nitrogen are ambient, with peak NPP rising under elevated CO2 or nitrogen but also shifting to lower temperatures. Our results suggest that future climate change will push this ecosystem away from conditions that maximize NPP, but with large year-to-year variability., Competing Interests: The authors declare no conflict of interest.

The perceived threat of climate change is often evaluated from species distribution models that are fitted to many species independently and then added together. This approach ignores the fact that species are jointly distributed and limit one another. Species respond to the same underlying climatic variables, and the abundance of any one species can be constrained by competition; a large increase in one is inevitably linked to declines of others. Omitting this basic relationship explains why responses modeled independently do not agree with the species richness or basal areas of actual forests. We introduce a joint species distribution modeling approach (JSDM), which is unique in three ways, and apply it to forests of eastern North America. First, it accommodates the joint distribution of species. Second, this joint distribution includes both abundance and presence-absence data. We solve the common issue of large numbers of zeros in abundance data by accommodating zeros in both stem counts and basal area data, i.e., a new approach to zero inflation. Finally, inverse prediction can be applied to the joint distribution of predictions to integrate the role of climate risks across all species and identify geographic areas where communities will change most (in terms of changes in abundance) with climate change. Application to forests in the eastern United States shows that climate can have greatest impact in the Northeast, due to temperature, and in the Upper Midwest, due to temperature and precipitation. Thus, these are the regions experiencing the fastest warming and are also identified as most responsive at this scale.

The recent global increase in forest mortality episodes could not have been predicted from current vegetation models that are calibrated to regional climate data. Physiological studies show that mortality results from interactions between climate and competition at the individual scale. Models of forest response to climate do not include interactions because they are hard to estimate and require long-term observations on individual trees obtained at frequent (annual) intervals. Interactions involve multiple tree responses that can only be quantified if these responses are estimated as a joint distribution. A new approach provides estimates of climate–competition interactions in two critical ways, (i) among individuals, as a joint distribution of responses to combinations of inputs, such as resources and climate, and (ii) within individuals, due to allocation requirements that control outputs, such as demographic rates. Application to 20 years of data from climate and competition gradients shows that interactions control forest responses, and their omission from models leads to inaccurate predictions. Species most vulnerable to increasing aridity are not those that show the largest growth response to precipitation, but rather depend on interactions with the local resource environment. This first assessment of regional species vulnerability that is based on the scale at which climate operates, individual trees competing for carbon and water, supports predictions of potential savannification in the southeastern US.

Tree species are predicted to track future climate by shifting their geographic distributions, but climate-mediated migrations are not apparent in a recent continental-scale analysis. To better understand the mechanisms of a possible migration lag, we analyzed relative recruitment patterns by comparing juvenile and adult tree abundances in climate space. One would expect relative recruitment to be higher in cold and dry climates as a result of tree migration with juveniles located further poleward than adults. Alternatively, relative recruitment could be higher in warm and wet climates as a result of higher tree population turnover with increased temperature and precipitation. Using the USDA Forest Service's Forest Inventory and Analysis data at regional scales, we jointly modeled juvenile and adult abundance distributions for 65 tree species in climate space of the eastern United States. We directly compared the optimal climate conditions for juveniles and adults, identified the climates where each species has high relative recruitment, and synthesized relative recruitment patterns across species. Results suggest that for 77% and 83% of the tree species, juveniles have higher optimal temperature and optimal precipitation, respectively, than adults. Across species, the relative recruitment pattern is dominated by relatively more abundant juveniles than adults in warm and wet climates. These different abundance-climate responses through life history are consistent with faster population turnover and inconsistent with the geographic trend of large-scale tree migration. Taken together, this juvenile-adult analysis suggests that tree species might respond to climate change by having faster turnover as dynamics accelerate with longer growing seasons and higher temperatures, before there is evidence of poleward migration at biogeographic scales.

Anticipating how biodiversity will respond to climate change is challenged by the fact that climate variables affect individuals in competition with others, but interest lies at the scale of species and landscapes. By omitting the individual scale, models cannot accommodate the processes that determine future biodiversity. We demonstrate how individual-scale inference can be applied to the problem of anticipating vulnerability of species to climate. The approach places climate vulnerability in the context of competition for light and soil moisture. Sensitivities to climate and competition interactions aggregated from the individual tree scale provide estimates of which species are vulnerable to which variables in different habitats. Vulnerability is explored in terms of specific demographic responses (growth, fecundity and survival) and in terms of the synthetic response (the combination of demographic rates), termed climate tracking. These indices quantify risks for individuals in the context of their competitive environments. However, by aggregating in specific ways (over individuals, years, and other input variables), we provide ways to summarize and rank species in terms of their risks from climate change.

Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.

Because it can lead to retaliatory killing, livestock depredation by large carnivores is among the foremost threats to carnivore conservation, and it severely impacts human well‐being worldwide. Ongoing climate change can amplify these human–wildlife conflicts, but such issues are largely unexplored, though are becoming increasingly recognized. Here, we assessed how the availability of primary resources and wild prey interact to shape large carnivore selection for livestock rather than wild prey (i.e., via prey switching or apparent competition). Specifically, we combined remotely sensed estimates of primary resources (i.e., water availability and primary productivity), wild prey movement, and 7 years (2015–2021) of reports for livestock depredation by African lions (Panthera leo) in the Makgadikgadi Pans ecosystem, Botswana. Although livestock depredation did not vary between wet versus dry seasons, analyses at finer temporal scales revealed higher incidences of livestock depredation when primary production, water availability, and wild prey availability were lower, though the effects of wild prey availability were mediated by water availability. Increased precipitation also amplified livestock depredation events despite having no influence on wild prey availability. Our results suggest that livestock depredation is influenced by the diverse responses of livestock, wild prey, and lions to primary resource availability, a driver that is largely overlooked or oversimplified in studies of human–carnivore conflict. Our findings provide insight into tailoring potential conflict mitigation strategies to fine‐scale changes in resource conditions to efficiently reduce conflict and support human livelihoods. [ABSTRACT FROM AUTHOR]

Super oil and gas basins provide the energy foundation for social progress and human development. In the context of climate change and carbon peak and carbon neutrality goals, constructing an integrated energy and carbon neutrality system that balances energy production and carbon reduction becomes crucial for the transformation of such basins. Under the framework of a green and intelligent energy system primarily based on "four news", new energy, new electricity, new energy storage, and new intelligence, integrating a "super energy system" composed of a huge amount of underground resources of coal, oil, gas and heat highly overlapping with abundant wind and solar energy resources above ground, and a regional intelligent energy consumption system with coordinated development and utilization of fossil energy and new energy, with a carbon neutrality system centered around carbon cycling is essential. This paper aims to select the traditional oil and gas basins as "super energy basins" with the conditions to build world-class energy production and demonstration bases for carbon neutrality. The Ordos Basin has unique regional advantages, including abundant fossil fuel and new energy resources, as well as matching CO2 sources and sinks, position it as a carbon neutrality "super energy basin" which explores the path of transformation of traditional oil and gas basins. Under the integrated development concept and mode of "coal + oil + gas + new energy + carbon capture, utilization and storage (CCUS)/carbon capture and storage (CCS)", the carbon neutrality in super energy basin is basically achieved, which enhance energy supply and contribute to the carbon peak and carbon neutrality goals, establish a modern energy industry and promote regional green and sustainable development. The pioneering construction of the world-class carbon neutrality "super energy system" demonstration basin in China represented by the Ordos Basin will reshape the new concept and new mode of exploration and development of super energy basins, which is of great significance to the global energy revolution under carbon neutrality. [ABSTRACT FROM AUTHOR]

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season. [ABSTRACT FROM AUTHOR]

Mountain treelines are thought to be sensitive to climate change. However, how climate impacts mountain treelines is not yet fully understood as treelines may also be affected by other human activities. Here, we focus on "closed‐loop" mountain treelines (CLMT) that completely encircle a mountain and are less likely to have been influenced by human land‐use change. We detect a total length of ~916,425 km of CLMT across 243 mountain ranges globally and reveal a bimodal latitudinal distribution of treeline elevations with higher treeline elevations occurring at greater distances from the coast. Spatially, we find that temperature is the main climatic driver of treeline elevation in boreal and tropical regions, whereas precipitation drives CLMT position in temperate zones. Temporally, we show that 70% of CLMT have moved upward, with a mean shift rate of 1.2 m/year over the first decade of the 21st century. CLMT are shifting fastest in the tropics (mean of 3.1 m/year), but with greater variability. Our work provides a new mountain treeline database that isolates climate impacts from other anthropogenic pressures, and has important implications for biodiversity, natural resources, and ecosystem adaptation in a changing climate. [ABSTRACT FROM AUTHOR]

The Hanjiang River Basin (HJRB) encompasses the Danjiangkou Reservoir, a critical water source for the South-to-North Water Transfer project, the world's largest such endeavor. Recent studies have highlighted that increased vegetation growth in the HJRB has led to reduced water availability in the region. To investigate the seasonal dynamics and spatial patterns of vegetation and their association with the local climate, we employed Gross Primary Productivity (GPP), a pivotal component of terrestrial carbon-water cycling, derived from the MODIS MOD17A2HGF dataset at a 500 m resolution. We combined this dataset with station meteorological data and the Standardized Precipitation Evapotranspiration Index (SPEI) to explore the complex relationship between vegetation productivity, climate fluctuations, and hydrothermal changes in the HJRB from 2000 to 2020. Our findings reveal that the rising trend in vegetation productivity in the HJRB is primarily attributable to climate warming. Different types of vegetation in the upstream and downstream areas exhibit varying water requirements. While the region has not experienced prolonged widespread drought conditions thanks to its excellent water conservation capabilities, there remains a certain level of drought risk in the downstream area as the climate continues to warm. Moreover, variables such as wind speed and sunshine duration significantly impact the hydrothermal conditions within the river basin, consequently influencing vegetation productivity. This study elucidates the mechanisms through which climate change affects vegetation productivity in the HJRB. Despite afforestation efforts in the upstream region and climate warming leading to increased greening, there may be implications for the water retention function of the HJRB. This understanding is crucial for water resource management and ecosystem sustainability in the HJRB. [ABSTRACT FROM AUTHOR]

The impact of climate change on river systems is a multifaceted threat to the environment, affecting various aspects of ecosystems. The Upper Benue River Basin (UBRB) in Nigeria is an area of concern, as river flow and water levels are crucial for irrigation and transportation. In this study, we investigate the impact of climate change on the hydrology of the UBRB using data on rainfall, temperature, relative humidity, wind speed, river discharge, and water level. Trend, correlation, and stepwise regression analyses were conducted using Excel and SPSS 20 to analyze the data. The results indicate that the UBRB is experiencing climate change, as evidenced by annual decreases in rainfall and relative humidity and increases in maximum and minimum temperatures. Specifically, mean annual rainfall and relative humidity exhibit a negative trend, while the maximum and minimum temperature exhibit a positive trend. Furthermore, we found that rainfall and relative humidity have a significant positive relationship with river discharge and level (p < 0.01), whereas maximum temperature and wind speed have a significant negative relationship with water discharge and level. We also identified wind speed and rainfall as the critical climatic indices influencing river discharge, accounting for 21.7% of the variation in river discharge within the basin (R2 = 21.7). Based on these findings, we conclude that increases in rainfall and relative humidity will lead to significant increases in river discharge and level, while increases in wind speed and maximum temperature will decrease river discharge and level. Moreover, wind speed and rainfall are the critical climatic indices influencing river discharge, whereas relative humidity, wind speed, and rainfall are the critical climatic indices influencing water level. Thus, we recommend constructing more reservoirs (dams) to mitigate the negative trend in rainfall and encourage climate change control, such as afforestation among the population of the region. These findings have important implications for understanding the impact of climate change on river systems and developing effective strategies to mitigate its effects. [ABSTRACT FROM AUTHOR]

Aim: Efforts to predict the responses of soil fungal communities to climate change are hindered by limited information on how fungal niches are distributed across environmental hyperspace. We predict the climate sensitivity of North American soil fungal assemblage composition by modelling the ecological niches of several thousand fungal species. Location: One hundred and thirteen sites in the United States and Canada spanning all biomes except tropical rain forest. Major Taxa Studied: Fungi. Time Period: 2011–2018. Methods: We combine internal transcribed spacer (ITS) sequences from two continental‐scale sampling networks in North America and cluster them into operational taxonomic units (OTUs) at 97% similarity. Using climate and soil data, we fit ecological niche models (ENMs) based on logistic ridge regression for all OTUs present in at least 10 sites (n = 8597). To describe the compositional turnover of soil fungal assemblages over climatic gradients, we introduce a novel niche‐based metric of climate sensitivity, the Sørensen climate sensitivity index. Finally, we map climate sensitivity across North America. Results: ENMs have a mean out‐of‐sample predictive accuracy of 73.8%, with temperature variables being strong predictors of fungal distributions. Soil fungal climate niches clump together across environmental space, which suggests common physiological limits and predicts abrupt changes in composition with respect to changes in climate. Soil fungi in North American climates are more likely to be limited by cold and dry conditions than by warm and wet conditions, and ectomycorrhizal fungi generally tolerate colder temperatures than saprotrophic fungi. Sørensen climate sensitivity exhibits a multimodal distribution across environmental space, with a peak in climates corresponding to boreal forests. Main Conclusions: The boreal forest occupies an especially precarious region of environmental space for the composition of soil fungal assemblages in North America, as even small degrees of warming could trigger large compositional changes characterized mainly by an influx of warm‐adapted species. [ABSTRACT FROM AUTHOR]

Grassland ecosystems provide essential services to society. To maintain ecosystem functions and services of grasslands under changing environments, it is critical to understand how grasslands respond and feedback to climate change. Here, we present results from a long‐term (16 years) warming and clipping (to mimic hay harvesting or grazing) experiment conducted in a grassland ecosystem dominated by C4 grasses in the Great Plains, USA. We analyzed responses of net primary production (NPP), aboveground NPP (ANPP), and belowground NPP (BNPP) to the expanded ranges of climate conditions observed in the experiment. NPP, ANPP, and BNPP all responded to the climatic variables nonlinearly, with higher degrees of nonlinearity for BNPP than that for ANPP. BNPP peaked at the optimum air temperature of 17.5–18.5°C while ANPP peaked at the optimum air temperature of 18.5–20.0°C. The optimum air temperatures for both ANPP and BNPP were higher than mean annual temperature (16.3°C) at the site. The optimum precipitation for BNPP was intermediate levels (775–1250 mm) while ANPP was maximized in wetter conditions (1250–1605 mm) compared with BNPP. Clipping significantly reduced ANPP and shifted the optimum conditions for BNPP to warmer and wetter conditions. In summary, C4‐grass dominant ecosystems have the potential to increase NPP in future warmer and wetter conditions, and clipping may amplify this positive effect in this grassland ecosystem. [ABSTRACT FROM AUTHOR]

Coastal tidal wetlands produce and accumulate significant amounts of organic carbon (C) that help to mitigate climate change. However, previous data limitations have prevented a robust evaluation of the global rates and mechanisms driving C accumulation. Here, we go beyond recent soil C stock estimates to reveal global tidal wetland C accumulation and predict changes under relative sea level rise, temperature and precipitation. We use data from literature study sites and our new observations spanning wide latitudinal gradients and 20 countries. Globally, tidal wetlands accumulate 53.65 (95%CI: 48.52–59.01) Tg C yr−1, which is ∼30% of the organic C buried on the ocean floor. Modeling based on current climatic drivers and under projected emissions scenarios revealed a net increase in the global C accumulation by 2100. This rapid increase is driven by sea level rise in tidal marshes, and higher temperature and precipitation in mangroves. Countries with large areas of coastal wetlands, like Indonesia and Mexico, are more susceptible to tidal wetland C losses under climate change, while regions such as Australia, Brazil, the USA and China will experience a significant C accumulation increase under all projected scenarios. [ABSTRACT FROM AUTHOR]

Summary: How much carbon a forest ecosystem can sequester is determined by both postdisturbance regrowth and environmentally modified growth. Disturbance causes sharp declines in the short term and is followed by regrowth in the long term. Environmental change may alter carbon accumulation through increasing CO2, nitrogen deposition and climate change. Regrowth and modified growth occur simultaneously, yet they are usually studied separately and assessed using an additive approach. Alternatively, an interactive approach using hierarchical models can address their concurrent nature and evaluate their joint effects. Hierarchical models are informed by forest age data, which have recently become available at global scales. The age‐based hierarchical framework provides a coherent and feasible way to integrate regrowth and modified growth in understanding forest dynamics. [ABSTRACT FROM AUTHOR]

Aim: Ectomycorrhizal fungi (ECMF) are partners in a globally distributed tree symbiosis implicated in most major ecosystem functions. However, resilience of ECMF to future climates is uncertain. We forecast these changes over the extent of North American Pinaceae forests. Location: About 68 sites from North American Pinaceae forests ranging from Florida to Ontario in the east and southern California to Alaska in the west. Taxon: Ectomycorrhizal fungi (Asco‐ and Basidiomycetes). Methods: We characterized ECMF communities at each site using molecular methods and modelled climatic drivers of diversity and community composition with general additive, generalized dissimilarity models and Threshold Indicator Taxa ANalysis (TITAN). Next, we projected our models across the extent of North American Pinaceae forests and forecast ECMF responses to climate changes in these forests over the next 50 years. Results: We predict median declines in ECMF species richness as high as 26% in Pinaceae forests throughout a climate zone comprising more than 3.5 million square kilometres of North America (an area twice that of Alaska state). Mitigation of greenhouse gas emissions can reduce these declines, but not prevent them. The existence of multiple diversity optima along climate gradients suggest regionally divergent trajectories for North American ECMF, which is corroborated by corresponding ECMF community thresholds identified in TITAN models. Warming of forests along the boreal–temperate ecotone results in projected ECMF species loss and declines in the relative abundance of long‐distance foraging ECMF species, whereas warming of eastern temperate forests has the opposite effect. Main Conclusions: Our results reveal potentially unavoidable ECMF species‐losses over the next 50 years, which is likely to have profound (if yet unclear) effects on ECMF‐associated biogeochemical cycles. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) has been widely used in recent decades to monitor vegetation phenology. However, interference from snow cover introduces a high degree of uncertainty in interpreting NDVI fluctuation, because snow melting increases NDVI value in a manner similar to vegetation growth, leading to false detection. In this study, we present a novel methodology to smooth out data noise caused by snow in the third generation NDVI dataset from Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g). This method is developed to replace small values with a pixel-specific snow-free background NDVI estimate, based on the assumption that the existence of snow decrease NDVI value and the patterns of NDVI fluctuation after snow melting and that after initiation of vegetation growth are different. Using the daily gross primary production (GPP) data of 111 site-years from FLUXNET in nine North American sites and the GIMMS NDVI3g dataset, we found that the green-up onset day (GUD) derived from raw NDVI is 42.2 days earlier than that of GPP, on average. This difference decreases to 4.7 days when applying the newly developed method. Additionally, the root mean square error and Spearman's correlation coefficient between NDVI-derived GUD and GPP-derived GUD are improved from 46.8 to 12.8 days and 0.22 to 0.64, respectively. Our results indicate that this method could effectively improve the ability to monitor the vegetation phenology by NDVI time series in areas with seasonal snow cover. [ABSTRACT FROM PUBLISHER]

Tree species are expected to track warming climate by shifting their ranges to higher latitudes or elevations, but current evidence of latitudinal range shifts for suites of species is largely indirect. In response to global warming, offspring of trees are predicted to have ranges extend beyond adults at leading edges and the opposite relationship at trailing edges. Large-scale forest inventory data provide an opportunity to compare present latitudes of seedlings and adult trees at their range limits. Using the USDA Forest Service's Forest Inventory and Analysis data, we directly compared seedling and tree 5th and 95th percentile latitudes for 92 species in 30 longitudinal bands for 43 334 plots across the eastern United States. We further compared these latitudes with 20th century temperature and precipitation change and functional traits, including seed size and seed spread rate. Results suggest that 58.7% of the tree species examined show the pattern expected for a population undergoing range contraction, rather than expansion, at both northern and southern boundaries. Fewer species show a pattern consistent with a northward shift (20.7%) and fewer still with a southward shift (16.3%). Only 4.3% are consistent with expansion at both range limits. When compared with the 20th century climate changes that have occurred at the range boundaries themselves, there is no consistent evidence that population spread is greatest in areas where climate has changed most; nor are patterns related to seed size or dispersal characteristics. The fact that the majority of seedling extreme latitudes are less than those for adult trees may emphasize the lack of evidence for climate-mediated migration, and should increase concerns for the risks posed by climate change. [ABSTRACT FROM AUTHOR]

• We held a community forecast challenge to predict daily plant greenness through which we submitted forecasts from 18 models. • Forecast skill was highest when forecasting earlier in the greenup period compared to the end. • Forecasts were worst when submitted right before budburst. • Historical means of greenness on each day of year were difficult to outperform. Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. [ABSTRACT FROM AUTHOR]

Vegetative spring green-up date (GUD), an indicator of plants' sensitivity to climate change, exerts an important influence on biogeochemical cycles. Conventionally, large-scale monitoring of spring phenology is primarily detected by satellite-based vegetation indices (VIs), e.g. the Normalized Difference Vegetation Index (NDVI). However, these indices have long been criticized, as the derived GUD can be biased by snowmelt. To minimize the snowmelt effect in monitoring spring phenology, we developed a new index, Normalized Difference Phenology Index (NDPI), which is a 3-band VI, designed to best contrast vegetation from the background (i.e. soil and snow in this study) as well as to minimize the difference among the backgrounds. We examined the rigorousness of NDPI in three ways. First, we conducted mathematical simulations to show that NDPI is mathematically robust and performs superior to NDVI for differentiating vegetation from the background, theoretically justifying NDPI for spring phenology monitoring. Second, we applied NDPI using MODIS land surface reflectance products to real vegetative ecosystems of three in-situ PhenoCam sites. Our results show that, despite large snow cover in the winter and snowmelt process in the spring, the temporal trajectories of NDPI closely track the vegetation green-up events. Finally, we applied NDPI to 11 eddy-covariance tower sites, spanning large gradients in latitude and vegetation types in deciduous ecosystems, using the same MODIS products. Our results suggest that the GUD derived by using NDPI is consistent with daily gross primary production (GPP) derived GUD, with R (Spearman's correlation) = 0.93, Bias = 2.90 days, and RMSE (the root mean square error) = 7.75 days, which outcompetes the snow removed NDVI approach, with R = 0.90, Bias = 7.34 days, and RMSE = 10.91 days. We concluded that our newly-developed NDPI is robust to snowmelt effect and is a reliable approach for monitoring spring green-up in deciduous ecosystems. [ABSTRACT FROM AUTHOR]