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Warmer conditions over the past two decades have contributed to rapid expansion of bark beetle outbreaks killing millions of trees over a large fraction of western United States (US) forests. These outbreaks reduce plant productivity by killing trees and transfer carbon from live to dead pools where carbon is slowly emitted to the atmosphere via heterotrophic respiration which subsequently feeds back to climate change. Recent studies have begun to examine the local impacts of bark beetle outbreaks in individual stands, but the full regional carbon consequences remain undocumented for the western US. In this study, we quantify the regional carbon impacts of the bark beetle outbreaks taking place in western US forests. The work relies on a combination of postdisturbance forest regrowth trajectories derived from forest inventory data and a process-based carbon cycle model tracking decomposition, as well as aerial detection survey (ADS) data documenting the regional extent and severity of recent outbreaks. We find that biomass killed by bark beetle attacks across beetle-affected areas in western US forests from 2000 to 2009 ranges from 5 to 15 Tg C yr(-1) and caused a reduction of net ecosystem productivity (NEP) of about 6.1-9.3 Tg C y(-1) by 2009. Uncertainties result largely from a lack of detailed surveys of the extent and severity of outbreaks, calling out a need for improved characterization across western US forests. The carbon flux legacy of 2000-2009 outbreaks will continue decades into the future (e.g., 2040-2060) as committed emissions from heterotrophic respiration of beetle-killed biomass are balanced by forest regrowth and accumulation.

Climate change and increased anthropogenic activities are expected to elevate the potential of introducing nonindigenous species (NIS) into the Arctic. Yet, the knowledge base needed to identify gaps and priorities for NIS research and management is limited. Here, we reviewed primary introduction events to each ecoregion of the marine Arctic realm to identify temporal and spatial patterns, likely source regions of NIS, and the putative introduction pathways. We included 54 introduction events representing 34 unique NIS. The rate of NIS discovery ranged from zero to four species per year between 1960 and 2015. The Iceland Shelf had the greatest number of introduction events (n = 14), followed by the Barents Sea (n = 11), and the Norwegian Sea (n = 11). Sixteen of the 54 introduction records had no known origins. The majority of those with known source regions were attributed to the Northeast Atlantic and the Northwest Pacific, 19 and 14 records, respectively. Some introduction events were attributed to multiple possible pathways. For these introductions, vessels transferred the greatest number of aquatic NIS (39%) to the Arctic, followed by natural spread (30%) and aquaculture activities (25%). Similar trends were found for introductions attributed to a single pathway. The phyla Arthropoda and Ochrophyta had the highest number of recorded introduction events, with 19 and 12 records, respectively. Recommendations including vector management, horizon scanning, early detection, rapid response, and a pan‐Arctic biodiversity inventory are considered in this paper. Our study provides a comprehensive record of primary introductions of NIS for marine environments in the circumpolar Arctic and identifies knowledge gaps and opportunities for NIS research and management. Ecosystems worldwide will face dramatic changes in the coming decades due to global change. Our findings contribute to the knowledge base needed to address two aspects of global change—invasive species and climate change., This paper examines the current status and future risks of biological invasions in the marine environments of the Arctic, within the context of climate change, natural resource development, and expanded Arctic shipping. The number of nonindigenous species introductions varied across the region with the greatest number of introductions recorded in the Iceland Shelf, followed by the Barents Sea and the Norwegian Sea. Most introductions were attributed to vessels, natural spread, and aquaculture activities. Vector management, horizon scanning, early detection, rapid response, and a pan‐Arctic biodiversity inventory are recommended to address future invasion risks.

The Global Carbon Project (GCP) has published global carbon budgets annually since 2007 (Canadell et al. [2007], Proc Natl Acad Sci USA, 104, 18866-18870; Raupach et al. [2007], Proc Natl Acad Sci USA, 104, 10288-10293). There are many scientists involved, but the terrestrial fluxes that appear in the budgets are not well understood by ecologists and biogeochemists outside of that community. The purpose of this paper is to make the terrestrial fluxes of carbon in those budgets more accessible to a broader community. The GCP budget is composed of annual perturbations from pre-industrial conditions, driven by addition of carbon to the system from combustion of fossil fuels and by transfers of carbon from land to the atmosphere as a result of land use. The budget includes a term for each of the major fluxes of carbon (fossil fuels, oceans, land) as well as the rate of carbon accumulation in the atmosphere. Land is represented by two terms: one resulting from direct anthropogenic effects (Land Use, Land-Use Change, and Forestry or land management) and one resulting from indirect anthropogenic (e.g., CO2 , climate change) and natural effects. Each of these two net terrestrial fluxes of carbon, in turn, is composed of opposing gross emissions and removals (e.g., deforestation and forest regrowth). Although the GCP budgets have focused on the two net terrestrial fluxes, they have paid little attention to the gross components, which are important for a number of reasons, including understanding the potential for land management to remove CO2 from the atmosphere and understanding the processes responsible for the sink for carbon on land. In contrast to the net fluxes of carbon, which are constrained by the global carbon budget, the gross fluxes are largely unconstrained, suggesting that there is more uncertainty than commonly believed about how terrestrial carbon emissions will respond to future fossil fuel emissions and a changing climate.

A paper published in Global Change Biology in 2006 revealed that phenological responses in 1971-2000 matched the warming pattern in Europe, but a lack of chilling and adaptation in farming may have reversed these findings. Therefore, for 1951-2018 in a corresponding data set, we determined changes as linear trends and analysed their variation by plant traits/groups, across season and time as well as their attribution to warming following IPCC methodology. Although spring and summer phases in wild plants advanced less (maximum advances in 1978-2007), more (~90%) and more significant (~60%) negative trends were present, being stronger in early spring, at higher elevations, but smaller for nonwoody insect-pollinated species. These trends were strongly attributable to winter and spring warming. Findings for crop spring phases were similar, but were less pronounced. There were clearer and attributable signs for a delayed senescence in response to winter and spring warming. These changes resulted in a longer growing season, but a constant generative period in wild plants and a shortened one in agricultural crops. Phenology determined by farmers' decisions differed noticeably from the purely climatic driven phases with smaller percentages of advancing (~75%) trends, but farmers' spring activities were the only group with reinforced advancement, suggesting adaptation. Trends in farmers' spring and summer activities were very likely/likely associated with the warming pattern. In contrast, the advance in autumn farming phases was significantly associated with below average summer warming. Thus, under ongoing climate change with decreased chilling the advancing phenology in spring and summer is still attributable to warming; even the farmers' activities in these seasons mirror, to a lesser extent, the warming. Our findings point to adaptation to climate change in agriculture and reveal diverse implications for terrestrial ecosystems; the strong attribution supports the necessary mediation of warming impacts to the general public.

There is growing international interest in better managing soils to increase soil organic carbon (SOC) content to contribute to climate change mitigation, to enhance resilience to climate change and to underpin food security, through initiatives such as international ‘4p1000’ initiative and the FAO's Global assessment of SOC sequestration potential (GSOCseq) programme. Since SOC content of soils cannot be easily measured, a key barrier to implementing programmes to increase SOC at large scale, is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms, both for national reporting and for emissions trading. Without such platforms, investments could be considered risky. In this paper, we review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. We describe how repeat soil surveys are used to estimate changes in SOC over time, and how long‐term experiments and space‐for‐time substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. We briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for SOC change already in use in various countries/regions. In the final section, we bring together the various components described in this review, to describe a new vision for a global framework for MRV of SOC change, to support national and international initiatives seeking to effect change in the way we manage our soils., Since soil organic carbon (SOC) changes are difficult to measure, a key barrier to implementing programmes to increase SOC is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms. We review methods for measuring SOC change directly in soils, examine novel developments for quantifying SOC change and describe how surveys, long‐term experiments and chronosequences can be used for testing models and as benchmark sites in global frameworks to estimate SOC change. We review MRV platforms for soil organic carbon change already in use and describe a new vision for a global framework for MRV of SOC change.

Climate change is outpacing the ability of species and populations to naturally adapt warranting active interventions to boost climate resilience. In their review paper, Gaitán-Espitia and Hobday discuss how such interventions may, however, counter natural evolutionary processes and adaptive capacity if not underpinned by background knowledge from genes through to ecosystems. They present a robust decision framework to guide implementation of climate adaptation interventions to avoid unintended evolutionary outcomes.

Due to their position at the land-sea interface, barrier islands are vulnerable to both oceanic and atmospheric climate change-related drivers. In response to relative sea-level rise, barrier islands tend to migrate landward via overwash processes which deposit sediment onto the backbarrier marsh, thus maintaining elevation above sea level. In this paper, we assess the importance of interior upland vegetation and sediment transport (from upland to marsh) on the movement of the marsh-upland boundary in a transgressive barrier system along the mid-Atlantic Coast. We hypothesize that recent woody expansion is altering the rate of marsh to upland conversion. Using Landsat imagery over a 32 year time period (1984-2016), we quantify transitions between land cover (bare, grassland, woody vegetation, and marsh) and the marsh-upland boundary. We find that the Virginia Barrier Islands have both gains and losses in backbarrier marsh and upland, with 19% net loss from the system during the timeframe of the study and increased variance in marsh to upland conversion. This is consistent with recent work indicating a shift toward increasing rates of landward barrier island migration. Despite a net loss of upland area, macroclimatic winter warming resulted in 41% increase in woody vegetation in protected, low-elevation areas, introducing new ecological scenarios that increase resistance to sediment movement from upland to marsh. Our analysis demonstrates how the interplay between elevation and interior island vegetative cover influences landward migration of the boundary between upland and marsh (a previously underappreciated indicator that an island is migrating), and thus, the importance of including ecological processes in the island interior into coastal modeling of barrier island migration and sediment movement across the barrier landscape.

Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall-driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green-up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to-date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green-up over Africa than previously reported, with prerain green-up being the norm rather than the exception. We also show the relative sparsity of postrain green-up, confined largely to the Sudano-Sahel region. While the prerain green-up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling.

The accelerating rate of global change has focused attention on the cumulative impacts of novel and extreme environmental changes (i.e. stressors), especially in marine ecosystems. As integrators of local catchment and regional processes, freshwater ecosystems are also ranked highly sensitive to the net effects of multiple stressors, yet there has not been a large-scale quantitative synthesis. We analysed data from 88 papers including 286 responses of freshwater ecosystems to paired stressors and discovered that overall, their cumulative mean effect size was less than the sum of their single effects (i.e. an antagonistic interaction). Net effects of dual stressors on diversity and functional performance response metrics were additive and antagonistic, respectively. Across individual studies, a simple vote-counting method revealed that the net effects of stressor pairs were frequently more antagonistic (41%) than synergistic (28%), additive (16%) or reversed (15%). Here, we define a reversal as occurring when the net impact of two stressors is in the opposite direction (negative or positive) from that of the sum of their single effects. While warming paired with nutrification resulted in additive net effects, the overall mean net effect of warming combined with a second stressor was antagonistic. Most importantly, the mean net effects across all stressor pairs and response metrics were consistently antagonistic or additive, contrasting the greater prevalence of reported synergies in marine systems. Here, a possible explanation for more antagonistic responses by freshwater biota to stressors is that the inherent greater environmental variability of smaller aquatic ecosystems fosters greater potential for acclimation and co-adaptation to multiple stressors.

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria., Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. In this paper we show that under climate change alone, population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. When applying the International Union for Conservation of Nature’s Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria.

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool-dominant transfers, and the first-order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.

Food security and agriculture productivity assessments in sub-Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale-compatible climate data for the 1962-2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates.

Globally, spatial distributions of fish stocks are shifting but although the role of climate change in range shifts is increasingly appreciated, little remains known of the likely additional impact that high levels of fishing pressure might have on distribution. For North Sea cod, we show for the first time and in great spatial detail how the stock has shifted its distribution over the past 100 years. We digitized extensive historical fisheries data from paper charts in UK government archives and combined these with contemporary data to a time-series spanning 1913-2012 (excluding both World Wars). New analysis of old data revealed that the current distribution pattern of cod - mostly in the deeper, northern- and north-easternmost parts of the North Sea - is almost opposite to that during most of the Twentieth Century - mainly concentrated in the west, off England and Scotland. Statistical analysis revealed that the deepening, northward shift is likely attributable to warming; however, the eastward shift is best explained by fishing pressure, suggestive of significant depletion of the stock from its previous stronghold, off the coasts of England and Scotland. These spatial patterns were confirmed for the most recent 3 1/2 decades by data from fisheries-independent surveys, which go back to the 1970s. Our results demonstrate the fundamental importance of both climate change and fishing pressure for our understanding of changing distributions of commercially exploited fish.

Climate change and habitat loss are both key threatening processes driving the global loss in biodiversity. Yet little is known about their synergistic effects on biological populations due to the complexity underlying both processes. If the combined effects of habitat loss and climate change are greater than the effects of each threat individually, current conservation management strategies may be inefficient and at worst ineffective. Therefore, there is a pressing need to identify whether interacting effects between climate change and habitat loss exist and, if so, quantify the magnitude of their impact. In this article, we present a meta-analysis of studies that quantify the effect of habitat loss on biological populations and examine whether the magnitude of these effects depends on current climatic conditions and historical rates of climate change. We examined 1319 papers on habitat loss and fragmentation, identified from the past 20 years, representing a range of taxa, landscapes, land-uses, geographic locations and climatic conditions. We find that current climate and climate change are important factors determining the negative effects of habitat loss on species density and/or diversity. The most important determinant of habitat loss and fragmentation effects, averaged across species and geographic regions, was current maximum temperature, with mean precipitation change over the last 100 years of secondary importance. Habitat loss and fragmentation effects were greatest in areas with high maximum temperatures. Conversely, they were lowest in areas where average rainfall has increased over time. To our knowledge, this is the first study to conduct a global terrestrial analysis of existing data to quantify and test for interacting effects between current climate, climatic change and habitat loss on biological populations. Understanding the synergistic effects between climate change and other threatening processes has critical implications for our ability to support and incorporate climate change adaptation measures into policy development and management response.

Global environmental change, related to climate change and the deposition of airborne N-containing contaminants, has already resulted in shifts in plant community composition among plant functional types in Arctic and temperate alpine regions. In this paper, we review how key ecosystem processes will be altered by these transformations, the complex biological cascades and feedbacks that might result, and some of the potential broader consequences for the earth system. Firstly, we consider how patterns of growth and allocation, and nutrient uptake, will be altered by the shifts in plant dominance. The ways in which these changes may disproportionately affect the consumer communities, and rates of decomposition, are then discussed. We show that the occurrence of a broad spectrum of plant growth forms in these regions (from cryptogams to deciduous and evergreen dwarf shrubs, graminoids and forbs), together with hypothesized low functional redundancy, will mean that shifts in plant dominance result in a complex series of biotic cascades, couplings and feedbacks which are supplemental to the direct responses of ecosystem components to the primary global change drivers. The nature of these complex interactions is highlighted using the example of the climate-driven increase in shrub cover in low-Arctic tundra, and the contrasting transformations in plant functional composition in mid-latitude alpine systems. Finally, the potential effects of the transformations on ecosystem properties and processes that link with the earth system are reviewed. We conclude that the effects of global change on these ecosystems, and potential climate-change feedbacks, cannot be predicted from simple empirical relationships between processes and driving variables. Rather, the effects of changes in species distributions and dominances on key ecosystem processes and properties must also be considered, based upon best estimates of the trajectories of key transformations, their magnitude and rates of change. © Journal compilation © 2009 Blackwell Publishing.

Lowland boreal forest ecosystems in Alaska are dominated by wetlands comprised of a complex mosaic of fens, collapse-scar bogs, low shrub/scrub, and forests growing on elevated ice-rich permafrost soils. Thermokarst has affected the lowlands of the Tanana Flats in central Alaska for centuries, as thawing permafrost collapses forests that transition to wetlands. Located within the discontinuous permafrost zone, this region has significantly warmed over the past half-century, and much of these carbon-rich permafrost soils are now within ~0.5 °C of thawing. Increased permafrost thaw in lowland boreal forests in response to warming may have consequences for the climate system. This study evaluates the trajectories and potential drivers of 60 years of forest change in a landscape subjected to permafrost thaw in unburned dominant forest types (paper birch and black spruce) associated with location on elevated permafrost plateau and across multiple time periods (1949, 1978, 1986, 1998, and 2009) using historical and contemporary aerial and satellite images for change detection. We developed (i) a deterministic statistical model to evaluate the potential climatic controls on forest change using gradient boosting and regression tree analysis, and (ii) a 30 × 30 m land cover map of the Tanana Flats to estimate the potential landscape-level losses of forest area due to thermokarst from 1949 to 2009. Over the 60-year period, we observed a nonlinear loss of birch forests and a relatively continuous gain of spruce forest associated with thermokarst and forest succession, while gradient boosting/regression tree models identify precipitation and forest fragmentation as the primary factors controlling birch and spruce forest change, respectively. Between 1950 and 2009, landscape-level analysis estimates a transition of ~15 km² or ~7% of birch forests to wetlands, where the greatest change followed warm periods. This work highlights that the vulnerability and resilience of lowland ice-rich permafrost ecosystems to climate changes depend on forest type.

The recent quantification of changes in time series of phenology data with Bayesian methods has provided compelling evidence for changes during the last 20 years. In this paper we correlate the phenological observations with spring temperature time series. We provide quantitative answers to the question whether changes in temperature and phenological time series should be regarded as coherent or independent. For the three considered species snowdrops, cherry and lime tree we find factors of 1.05, 2.19 and 3.26, respectively, in favor of coherence. The functional behavior and the trend in the temperature time series are presented. They amount to 0.151Cyr � 1 for the January‐ March average, 0.091Cyr � 1 for February‐April and 0.11Cyr � 1 for March‐May in 2002. In addition, we compare blossom trends for the coherent and independent hypotheses and find that the transition from trend values slightly positive before 1970 to strongly negative at present becomes sharper as the temperature data are included in the analysis.

The need for anticipating and mitigating the potential effects of climate change on biodiversity has triggered the development of predictive tools to provide quantitative scenarios to guide decision making (Pereira et al., 2010). Notably, the recently established ‘Intergovernmental Platform on Biodiversity and Ecosystem Services’ (IPBES) will rely on these predictive tools. Between 1990 and 2000, statistical models that relate species occurrences to climatic variables were developed to predict species ranges and to forecast potential changes under the strong assumption that the detected relationships between the species of interest and the selected climatic variables will hold into the future (reviewed in Guisan & Thuiller, 2005). Since then, a variety of statistical approaches have been proposed and compared (see Thuiller, same issue). In the early 2000s, with the rise of statistical software such as Splus, SAS or later R, several algorithms became available to ecologists to propose species range change scenarios based on the IPCC climate models and SRES scenarios (Nakicenovic & Swart, 2000). Generalised linear models, generalised additive models, regression trees, genetic algorithms or artificial neural networks were used increasingly to assess the potential impacts of climate change on biodiversity. During this period I stumbled upon the issue that although different algorithms are likely to give the same answer under current (calibration) conditions, they tended to drastically diverge when they were used to predict species ranges under future climate. This was published with Global Change Biology under this paper “Patterns and uncertainties of species’ range shifts under climate change” (Thuiller, 2004). Although ten years later I find this result relatively obvious, at the time I was amazed that seemingly subtle differences between algorithms could actually lead to sharp divergences when projecting into the future. A species could indeed be predicted committed to extinction with one algorithm, and under range expansion with another. Since then, substantial progress has been made toward understanding why such strong discrepancies could occur (e.g. collinearity, complex interactions between variables or incomplete climatic niche estimations) and several analyses have confirmed this result (Lawler et al., 2006, Pearson et al., 2006). How to deal with variability from different initial conditions, algorithms, parameterisation and bounding conditions is still an open question in ecology and other fields of science (Araujo & New, 2007). As reviewed in Araujo and New (2007), an ensemble of forecasts is one of the most accepted ways to account for projection variability since it relies on multiple projections across sets of initial conditions, algorithms (e.g. generalised linear models or boosted regression trees), parameters (e.g. quadratic vs polynomial terms, number of regression trees in a random forest) and bounding conditions (e.g. different climate models). Although ensemble forecasting was a relatively well-accepted approach in other fields such as economics or climatology, it was relatively unknown in ecology in the early 2000s and did not emerge as a plausible alternative to single initial data-algorithms until 2004 (Thuiller, 2004) and 2007 (Araujo & New, 2007). The major advantage of combining a set of forecasts is to give a probability distribution per pixel as opposed to a single crude value. This allows for extraction of average predictions as well as confidence intervals given varying input data, algorithms, parameterisation and bounding conditions. Still, although such an approach is now a common practice (Marmion et al., 2009), ensemble forecasts are often used as a single forecast by extracting an average or a weighted average based on different evaluation techniques, without considering the variability behind those averages and without considering which metric to use for scoring the different projections. If forecasts have to be used in conservation planning or to be used guiding tools for decision-making, they should present not only the main trend but also the variability around this trend.

International audience; Antarctic and Southern Ocean (ASO) marine ecosystems have been changing for at least the last 30 years, including in response to increasing ocean temperatures and changes in the extent and seasonality of sea-ice; the magnitude and direction of these changes differ between regions around Antarctica that could see populations of the same species changing differently in different regions. This paper reviews current and expected changes in ASO physical habitats in response to climate change. It then reviews how these changes may impact the autecology of marine biota of this polar region: microbes, zooplankton, salps, Antarctic krill, fish, cephalopods, marine mammals, seabirds, and benthos. The general prognosis for ASO marine habitats is for an overall warming and freshening, strengthening of westerly winds, with a potential pole-ward movement of those winds and the frontal systems, and an increase in ocean eddy activity. Many habitat parameters will have regionally specific changes, particularly relating to sea-ice characteristics and seasonal dynamics. Lower trophic levels are expected to move south as the ocean conditions in which they are currently found move pole-ward. For Antarctic krill and finfish, the latitudinal breadth of their range will depend on their tolerance of warming oceans and changes to productivity. Ocean acidification is a concern not only for calcifying organisms, but also for crustaceans such as Antarctic krill; it is also likely to be the most important change in benthic habitats over the coming century. For marine mammals and birds, the expected changes primarily relate to their flexibility in moving to alternative locations for food and the energetic cost of longer or more complex foraging trips for those that are bound to breeding colonies. Few species are sufficiently well studied to make comprehensive species-specific vulnerability assessments possible. Priorities for future work are discussed. This article is protected by copyright. All rights reserved.

A future higher risk of severe flooding of streams and rivers has been projected to change riparian plant community composition and species richness, but the extent and direction of the expected change remain uncertain. We conducted a meta-analysis to synthesize globally available experimental evidence and assess the effects of increased flooding on (1) riparian adult plant and seedling survival, (2) riparian plant biomass and (3) riparian plant species composition and richness. We evaluated which plant traits are of key importance for the response of riparian plant species to flooding. We identified and analysed 53 papers from ISI Web of Knowledge which presented quantitative experimental results on flooding treatments and corresponding control situations. Our meta-analysis demonstrated how longer duration of flooding, greater depth of flooding and, particularly, their combination reduce seedling survival of most riparian species. Plant height above water level, ability to elongate shoots and plasticity in root porosity were decisive for adult plant survival and growth during longer periods of flooding. Both ‘quiescence’ and ‘escape’ proved to be successful strategies promoting riparian plant survival, which was reflected in the wide variation in survival (full range between 0 and 100%) under fully submerged conditions, while plants that protrude above the water level (>20 cm) almost all survive. Our survey confirmed that the projected increase in the duration and depth of flooding periods is sufficient to result in species shifts. These shifts may lead to increased or decreased riparian species richness depending on the nutrient, climatic and hydrological status of the catchment. Species richness was generally reduced at flooded sites in nutrient-rich catchments and sites that previously experienced relatively stable hydrographs (e.g. rain-fed lowland streams). Species richness usually increased at sites in desert and semi-arid climate regions (e.g. intermittent streams).

Dairy systems in Europe contribute to the emissions of the greenhouse gases (GHGs) nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2). In this paper, the effects of improved nitrogen (N) management on GHG emissions from Dutch dairy farms are determined. The GHG emissions are calculated using the panel on climate change (IPCC) methodology for the Netherlands, an updated and refined IPCC methodology, and a full accounting approach. The changes in dairy farming over the last 20 years, and the consequences for N management are described using detailed farm-level data, collected in 1985, 1997 and 2002. The selected years represent distinct stages in the implementation of N policies. The changes in N management have reduced the GHG emissions. A reduction of the N surplus per kilogram milk with 1 g N reduced the GHG emissions per kilogram milk with approximately 29 g CO2-equivalents. The reduction of the N surpluses was mainly brought about by reduced fertilizer use and reduced grazing time. The use of updated and refined emission factors resulted in higher CH4 emissions and lower N2O emissions. On average, the overall emission was 36% higher with the refined method. Full accounting, including all direct and indirect emissions of CH4, N2O and CO2, increased the emission with 36% compared with the refined IPCC methodology. We conclude that the N surplus at farm level is a useful indicator of GHG emissions. A full accounting system as presented in this study may effectively enable farmers to address the issue of emissions of GHGs in their operational management decisions. Both approaches serve their own specific objectives: full accounting at the farm level to explore mitigation options, and the IPCC methods to report changes in GHG emissions at the national level

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NNC‐part). NNC‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R 2 > .94) and RECO (R 2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods., In this paper, we present an innovative neural network (NNC‐part) for partitioning net ecosystem exchange of CO2, measured by eddy covariance over land ecosystems, into ecosystem respiration (RECO) and gross primary production (GPP). NNC‐part has a hybrid nature for assimilating the light‐use efficiency (LUE) concept. The GPP and RECO produced by NNC‐part resulted consistent with estimates provided in FLUXNET2015 and patterns of fluxes very realistic, being the method able to reproduce: the GPP and RECO response to meteorological forces; hysteresis in the diel cycle of fluxes; effect of diffuse to direct radiation ratio on LUE, respiration pulse.