National Ecological Observatory Network

, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and informing resource management through data‐intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large‐scale efforts that have had mixed success in the individual‐driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high‐value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Citizen science creates a nexus between science and education that, when coupled with emerging technologies, expands the frontiers of ecological research and public engagement. Using representative technologies and other examples, we examine the future of citizen science in terms of its research processes, program and participant cultures, and scientific communities. Future citizen‐science projects will likely be influenced by sociocultural issues related to new technologies and will continue to face practical programmatic challenges. We foresee networked, open science and the use of online computer/video gaming as important tools to engage non‐traditional audiences, and offer recommendations to help prepare project managers for impending challenges. A more formalized citizen‐science enterprise, complete with networked organizations, associations, journals, and cyberinfrastructure, will advance scientific research, including ecology, and further public education.

Global biodiversity is in decline. This is of concern for aesthetic and ethical reasons, but possibly also for practical reasons, as suggested by experimental studies, mostly with plants, showing that biodiversity reductions in small study plots can lead to compromised ecosystem function. However, inferring that ecosystem functions will decline due to biodiversity loss in the real world rests on the untested assumption that such loss is actually occurring at these small scales in nature. Using a global database of 168 published studies and >16,000 nonexperimental, local-scale vegetation plots, we show that mean temporal change in species diversity over periods of 5-261 y is not different from zero, with increases at least as likely as declines over time. Sites influenced primarily by plant species' invasions showed a tendency for declines in species richness, whereas sites undergoing postdisturbance succession showed increases in richness over time. Other distinctions among studies had little influence on temporal richness trends. Although maximizing diversity is likely important for maintaining ecosystem function in intensely managed systems such as restored grasslands or tree plantations, the clear lack of any general tendency for plant biodiversity to decline at small scales in nature directly contradicts the key assumption linking experimental results to ecosystem function as a motivation for biodiversity conservation in nature. How often real world changes in the diversity and composition of plant communities at the local scale cause ecosystem function to deteriorate, or actually to improve, remains unknown and is in critical need of further study.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Biodiversity and carbon (C) cycling have been the focus of much research in recent decades, partly because both change as a result of anthropogenic activities that are likely to continue. Soils are extremely species‐rich and store approximately 80% of global terrestrial C. Soil organisms play a key role in C dynamics and a loss of species through global changes could influence global C dynamics. Here, we synthesize findings from published studies that have manipulated soil species richness and measured the response in terms of ecosystem functions related to C cycling (such as decomposition, respiration and the abundance or biomass of decomposer biota) to evaluate the impact of biodiversity loss on C dynamics. We grouped studies where one or more biotic groups had been manipulated to include a richness of ≤10 species or >10 species in order to reflect ‘low’ and ‘high’ extents of diversity manipulations. There was a positive relationship between species richness and C cycling in 77–100% of low‐diversity experiments, even when the richness of just one biotic group was manipulated, whereas positive relationships occurred less frequently in studies with greater richness (35–64%). Moreover, when positive relationships were observed, these often indicated functional redundancy at low extents of diversity or that community composition had a stronger influence on C cycling than did species richness. Initial reductions in soil species richness resulting from global changes are unlikely to alter C dynamics significantly unless particularly influential species are lost. However, changes in community composition, and the loss of species with an ability to facilitate specialized soil processes related to C cycling, as a result of global changes, may have larger impacts on C dynamics.

Abstract Aim To examine the contribution of large‐diameter trees to biomass, stand structure, and species richness across forest biomes. Location Global. Time period Early 21st century. Major taxa studied Woody plants. Methods We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees ≥ 1 cm diameter at breast height (DBH), all trees ≥ 60 cm DBH, and those rank‐ordered largest trees that cumulatively comprise 50% of forest biomass. Results Averaged across these 48 forest plots, the largest 1% of trees ≥ 1 cm DBH comprised 50% of aboveground live biomass, with hectare‐scale standard deviation of 26%. Trees ≥ 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass ( r 2 = .62, p < .001). Large‐diameter trees in high biomass forests represented far fewer species relative to overall forest richness ( r 2 = .45, p < .001). Forests with more diverse large‐diameter tree communities were comprised of smaller trees ( r 2 = .33, p < .001). Lower large‐diameter richness was associated with large‐diameter trees being individuals of more common species ( r 2 = .17, p = .002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude ( r 2 = .46, p < .001), as did forest density ( r 2 = .31, p < .001). Forest structural complexity increased with increasing absolute latitude ( r 2 = .26, p < .001). Main conclusions Because large‐diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large‐diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well‐established theory and concepts from other subdisciplines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents.

One of the great realizations of the past half-century in both biological and Earth sciences is that, throughout geologic time, life has been shaping the Earth's surface and regulating the chemistry of its oceans and atmosphere (eg Berkner and Marshall 1964). In the present Anthropocene Era (Crutzen and Steffen 2003; Ruddiman 2003), humanity is directly shaping the biosphere and physical environment, triggering potentially devastating and currently unpredictable consequences (Doney and Schimel 2007). While subtle interactions between the Earth's orbit, ocean circulation, and the biosphere have dominated climate feedbacks for eons, now human perturbations to the cycles of CO2, other trace gases, and aerosols regulate the pace of climate change. Accompanying the biogeochemical perturbations are the vast changes resulting from biodiversity loss and a profound rearrangement of the biosphere due to species movements and invasions. Scientists and managers of biological resources require a stronger basis for forecasting the consequences of such changes. In this Special Issue of Frontiers, the scientific community confronts the challenge of research and environmental management in a human-dominated, increasingly connected world (Peters et al. p 229). Carbon dioxide, a key driver of climate change produced by a host of local and small-scale processes (eg clearing of forests, extraction and use of fossil fuels), affects the global energy balance (Marshall et al. p 273). Invasive species, though small from a large-scale perspective, nonetheless modify the continental biosphere (Crowl et al. p 238). Aquatic systems are tightly coupled to both terrestrial systems and the marine environment (Hopkinson et al. p 255). Flowing water not only intrinsically creates a highly connected system, but acts a transducer of climate, land-use, and invasive species effects, spreading their impacts from terrestrial and upstream centers of action downstream and into distant systems (Williamson et al. p 247). Human activities such as urbanization create new connections; materials, organisms, and energy flow into cities from globally distributed sources and waste products are exported back into the environment (Grimm et al. p 264). How will the ecosystems (of the US) and their components respond to changes in natural- and human-induced forcings, such as climate, land use, and invasive species, across a range of spatial and temporal scales? What is the pace and pattern of the responses? How do the internal responses and feedbacks of bio-geochemistry, biodiversity, hydroecology, and biotic structure and function interact with changes in climate, land use, and invasive species? How do these feedbacks vary with ecological context and spatial and temporal scales? NEON will enable us to answer these questions by providing data and other facilities to support the development of ecological forecasting at continental scales. Required data range spatially from the genome to the continental scale, and temporally from seconds to decades. Control of transport in, and the chemistry of, the atmosphere, modulation of the physics of land surfaces, and influence over water supply and quality emerge from the aggregated behavior of almost innumerable organisms (Hopkinson et al. p 255). The disparity between the scale of organisms and the scales of their effects on the global environment represents an important problem for large-scale ecological research (Hargrove and Pickering 1992). While the consequences of life for the environment occur on the largest spatial and longest temporal scales, biological processes must be understood by documenting the responses of organisms, communities, populations, and other small-scale phenomena. To bridge this diversity of scales, NEON will approach such questions through an analysis of processes, interactions, and responses, including those mediated by transport and connectivity (Figure 1). Most environmental monitoring networks focus either on processes or responses and do not link these with key interactions and feedbacks. NEON addresses the multi-scaled nature of the biosphere. The fundamental NEON observations (the Fundamental Sentinel Unit, focused on sentinel organisms, and the Fundamental Instrument Unit, focused on airsheds and watersheds) start at the scales of organisms, populations, and communities of organisms and directly observe biological processes (Figure 2). NEON differs from other environmental monitoring networks because, by design, it integrates processes, interactions, and responses. A Stommel diagram of temporal and spatial scales for the components of the observational design of NEON. A finite budget limits the number and the spatial extent of the fundamental observations; therefore, NEON uses a parsimonious continental strategy for placement of the observational units. The observations must systematically sample the US in a system design that objectively represents environmental variability. Existing maps spatially divide the US into ecological regions (Bailey 1983; Omernik 1987). In contrast to these earlier maps, NEON domains are based on a new, statistically rigorous analysis using national datasets for ecoclimatic variables. The statistical design is based upon algorithms for multivariate geographic clustering (MGC; Hargrove and Hoffman 1999 Hargrove and Hoffman 2004; WebPanel 1). The optimized outcome of the geographical analysis results in 20 domains (Figure 3). NEON domain boundaries for the conterminous US (in red) determined using the procedure described in WebPanel 1. Locations of candidate core sites (Table 2) are represented by red symbols. The shading from white (well-represented) to black indicates the quality of representation for a given area, based on the set of candidate core sites. Relocatable sites will be moved on a 3- to 5-year rotation. Candidate core wildland sites have been specifically selected to be as representative as possible of the ecoclimatic variability in each domain (Table 1; WebTable 1). Nonetheless, one may question whether 20 sites can adequately address the ecoclimatic variability in a large, diverse continental area. The shading in Figure 3 represents the degree to which the ecoclimatic characteristics of the candidate core wildland sites represents environments in the conterminous US. Inspection of the figure shows that the Eastern portion of the country is generally well-represented, although southern Florida and the Gulf Coast are somewhat less well covered than the majority of the East. Representation in these areas would probably increase if the NEON Core site for the Atlantic Neotropical domain had been included in the analysis. In the West, representation is more heterogeneous, particularly in the desert Southwest and in the Rocky mountains. This is because of the high degree of linked climatic and biological variation related to complex mountainous terrain. The observatory design, including both permanent core sites and relocatable sites, allows for planned contrasts within domains (eg mature versus young forest, urban versus wildland) and comparisons across domains (eg urban–rural in the Northeast and Southwest, nitrogen deposition effects in forests from the Southeast to the Northeast), using a core-and-constellation strategy. Mobile systems for short deployments (weeks to months) supplement the core and relocatable sites to explore details within these sites and to study discrete events and variability in the domains. Currently, there is approximately one planned mobile system per domain. These systems may be assigned to network tasks or to calls from individuals or groups of investigators. The design is based on rigorous scientific priorities and scaled to maintain budget discipline. Present scientific questions guide the first cycle of deployment; additional questions will be implemented as the network matures. While the set of candidate core sites provides a reasonable, static representation of the ecoclimatic variability for the continental region, scaling from point observations to the continent remains challenging. Each NEON domain observatory physically occupies a relatively small area and trades breadth of coverage for depth of insight. Modern, high-resolution, airborne remote sensing allows us to add a second strategy; the combination of imaging spectrometry (which can retrieve the chemical composition and, often, species composition of vegetation) with imaging lidar (light detection and ranging, which retrieves three-dimensional structural properties of vegetation) will provide regional coverage of key ecosystem properties. Imaging each NEON site regularly with 1.5-m resolution coverage, but expanding the scale to hundreds of square kilometers, provides a context for each site that allows the local observations of processes and responses to climate to be extended in space and generalized. NEON data products will integrate the local and regional measurements to quantify how processes are responding to climate, land use, and species changes across each NEON domain. The combined site data and airborne remote sensing data extend NEON observations of ecological processes and responses to scales large enough to correspond to space-borne remote sensing and other geographic data collected operationally (Figure 2). The NEON information system is structured with time–space coordinates that allow a natural merger between NEON's local and regional observations and national-scale satellite observations, to systematically link detailed ecological observations with global surveillance. The NEON observing strategy provides strategic, critical biological and physical observations, distributed over the landscape via a statistical observing design, so that, together, the observatories constitute a single, virtual instrument sampling the entire US. This virtual instrument can not only determine average changes over the whole country (through its sampling, scaling, and observing design) but, like a telescope, can observe the critical texture within the country and distinguish among regions with different drivers of change, or different responses to change, as well as sampling vectors for transport of materials, organisms, and energy. NEON strategically addresses gaps in the scales of our current observing systems by recognizing that biology is both a global and a highly local phenomenon, and reconciling the scale-observing requirements of these two aspects of life. While the NEON design cannot address all of the questions raised in this Special Issue (Peters et al. p 229), as a research platform, it will be the backbone of evolving efforts to observe, understand, and forecast environmental change in the Anthropocene Era. We thank the participants in the Sioux Falls workshop and J MacMahon, P Duffy, T Hobbs, B Wee, D Johnson, K Remington, D Greenlee, H Loescher, A Marshall, B Hayden, D Urban, and J Franklin for their contributions to the NEON design. We thank S Aulenbach for his assistance with graphics. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.

A study of one of the world's driest forests elucidates the climatic effects of drylands.

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field‐collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time‐lapse cameras – “phenocams” – can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental‐scale ecological research networks, such as the US National Ecological Observatory Network ( NEON ) and the European Union's Integrated Carbon Observation System ( ICOS ), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground‐truthing. Open‐source tools for analysis, visualization, and collaboration will make phenocam data more widely usable.

The National Ecological Observatory Network (NEON) is an ecological observation platform for discovering, understanding and forecasting the impacts of climate change, land use change, and invasive species on continental-scale ecology. NEON will operate for 30 years and gather long-term data on ecological response changes and on feedbacks with the geosphere, hydrosphere, and atmosphere. Local ecological measurements at sites distributed within 20 ecoclimatic domains across the contiguous United States, Alaska, Hawaii, and Puerto Rico will be coordinated with high resolution, regional airborne remote sensing observations. The Airborne Observation Platform (AOP) is an aircraft platform carrying remote sensing instrumentation designed to achieve sub-meter to meter scale ground resolution, bridging scales from organisms and individual stands to satellite-based remote sensing. AOP instrumentation consists of a VIS/SWIR imaging spectrometer, a scanning small-footprint waveform LiDAR for 3-D canopy structure measurements and a high resolution airborne digital camera. AOP data will be openly available to scientists and will provide quantitative information on land use change and changes in ecological structure and chemistry including the presence and effects of invasive species. AOP science objectives, key mission requirements, and development status are presented including an overview of near-term risk-reduction and prototyping activities.

Inference about future climate change impacts typically relies on one of three approaches: manipulative experiments, historical comparisons (broadly defined to include monitoring the response to ambient climate fluctuations using repeat sampling of plots, dendroecology, and paleoecology techniques), and space-for-time substitutions derived from sampling along environmental gradients. Potential limitations of all three approaches are recognized. Here we address the congruence among these three main approaches by comparing the degree to which tundra plant community composition changes (i) in response to in situ experimental warming, (ii) with interannual variability in summer temperature within sites, and (iii) over spatial gradients in summer temperature. We analyzed changes in plant community composition from repeat sampling (85 plant communities in 28 regions) and experimental warming studies (28 experiments in 14 regions) throughout arctic and alpine North America and Europe. Increases in the relative abundance of species with a warmer thermal niche were observed in response to warmer summer temperatures using all three methods; however, effect sizes were greater over broad-scale spatial gradients relative to either temporal variability in summer temperature within a site or summer temperature increases induced by experimental warming. The effect sizes for change over time within a site and with experimental warming were nearly identical. These results support the view that inferences based on space-for-time substitution overestimate the magnitude of responses to contemporary climate warming, because spatial gradients reflect long-term processes. In contrast, in situ experimental warming and monitoring approaches yield consistent estimates of the magnitude of response of plant communities to climate warming.

Warmer temperatures are accelerating the phenology of organisms around the world. Temperature sensitivity of phenology might be greater in colder, higher latitude sites than in warmer regions, in part because small changes in temperature constitute greater relative changes in thermal balance at colder sites. To test this hypothesis, we examined up to 20 years of phenology data for 47 tundra plant species at 18 high-latitude sites along a climatic gradient. Across all species, the timing of leaf emergence and flowering was more sensitive to a given increase in summer temperature at colder than warmer high-latitude locations. A similar pattern was seen over time for the flowering phenology of a widespread species, Cassiope tetragona. These are among the first results highlighting differential phenological responses of plants across a climatic gradient and suggest the possibility of convergence in flowering times and therefore an increase in gene flow across latitudes as the climate warms.

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Abstract The James Webb Space Telescope (JWST) Early Release Observations (EROs) is a set of public outreach products created to mark the end of commissioning and the beginning of science operations for JWST. Colloquially known as the “Webb First Images and Spectra,” these products were intended to demonstrate to the worldwide public that JWST is ready for science, and is capable of producing spectacular results. The package was released on 2022 July 12 and included images and spectra of the galaxy cluster SMACS J0723.3-7327 and distant lensed galaxies, the interacting galaxy group Stephan’s Quintet, NGC 3324 in the Carina star-forming complex, the Southern Ring planetary nebula NGC 3132, and the transiting hot Jupiter WASP-96b. This paper describes the ERO technical design, observations, and scientific processing of data underlying the colorful outreach products.

[This corrects the article DOI: 10.1093/biosci/biz140.].