Acoustic monitoring reveals a diel rhythm of an arctic seabird colony (little auk, Alle alle).

The child-like question of why birds sing in the morning is difficult to answer, especially in polar regions. There, in summer animals live without the time constraints of daylight, and little is known about the rhythmicity of their routines. Moreover, in situ monitoring of animal behavior in remote areas is challenging and rare. Here, we use audio data from Greenland to show that a colony of a key Arctic-breeding seabird, the little auk (Alle alle), erupts with acoustic excitement at night in August, under the midnight sun. We demonstrate that the acoustic activity cycle is consistent with previous direct observations of the feeding and attendance patterns of the little auk. We interpret this pattern as reflecting their foraging activities, but further investigation on fledging and predators is needed. The study demonstrates that acoustic monitoring is a promising alternative to otherwise demanding manual observations of bird colonies in remote Arctic areas.

Quantifying mangrove carbon assimilation rates using UAV imagery.

Mangrove forests are recognized as one of the most effective ecosystems for storing carbon. In drylands, mangroves operate at the extremes of environmental gradients and, in many instances, offer one of the few opportunities for vegetation-based sequestering of carbon. Developing accurate and reproducible methods to map carbon assimilation in mangroves not only serves to inform efforts related to natural capital accounting, but can help to motivate their protection and preservation. Remote sensing offers a means to retrieve numerous vegetation traits, many of which can be related to plant biophysical or biochemical responses. The leaf area index (LAI) is routinely employed as a biophysical indicator of health and condition. Here, we apply a linear regression model to UAV-derived multispectral data to retrieve LAI across three mangrove sites located along the coastline of the Red Sea, with estimates producing an R2 of 0.72 when compared against ground-sampled LiCOR LAI-2200C LAI data. To explore the potential of monitoring carbon assimilation within these mangrove stands, the UAV-derived LAI estimates were combined with field-measured net photosynthesis rates from a LiCOR 6400/XT, providing a first estimate of carbon assimilation in dryland mangrove systems of approximately 3000 ton C km-2 yr-1. Overall, these results advance our understanding of carbon assimilation in dryland mangroves and provide a mechanism to quantify the carbon mitigation potential of mangrove reforestation efforts.

EOS - Environment & Oil Spill Response. An analytic tool for environmental assessments to support oil spill response planning: Framework, principles, and proof-of-concept by an Arctic example.

The Environment & Oil Spill Response tool (EOS), supports oil spill response planning and decision making. This tool is developed on a research basis, and is an index based, generic and open-source analytic tool, which environmentally can optimise the choice of oil spill response methods for a given spill situation and for a given sea area with respect to environment and nature. The tool is not linked to a particular oil spill simulation model, although it is recommended using oil spill simulation models to have detailed data available for the analysis. The EOS tool consists of an Excel workbook with formulas for calculations and scores followed by screening through decision trees. As case for the EOS tool proof-of-concept, the area of Store Hellefiskebanke, West Greenland, is used. The tool can be downloaded from the Aarhus University home page as a free-of-charge application and is accompanied by a handbook for guidance.

Foraging range scales with colony size in high-latitude seabirds.

Density-dependent prey depletion around breeding colonies has long been considered an important factor controlling the population dynamics of colonial animals.1-4 Ashmole proposed that as seabird colony size increases, intraspecific competition leads to declines in reproductive success, as breeding adults must spend more time and energy to find prey farther from the colony.1 Seabird colony size often varies over several orders of magnitude within the same species and can include millions of individuals per colony.5,6 As such, colony size likely plays an important role in determining the individual behavior of its members and how the colony interacts with the surrounding environment.6 Using tracking data from murres (Uria spp.), the world's most densely breeding seabirds, we show that the distribution of foraging-trip distances scales to colony size0.33 during the chick-rearing stage, consistent with Ashmole's halo theory.1,2 This pattern occurred across colonies varying in size over three orders of magnitude and distributed throughout the North Atlantic region. The strong relationship between colony size and foraging range means that the foraging areas of some colonial species can be estimated from colony sizes, which is more practical to measure over a large geographic scale. Two-thirds of the North Atlantic murre population breed at the 16 largest colonies; by extrapolating the predicted foraging ranges to sites without tracking data, we show that only two of these large colonies have significant coverage as marine protected areas. Our results are an important example of how theoretical models, in this case, Ashmole's version of central-place-foraging theory, can be applied to inform conservation and management in colonial breeding species.

Mangrove distribution and afforestation potential in the Red Sea.

Mangrove ecosystems represent one of the most effective natural environments for fixing and storing carbon (C). Mangroves also offer significant co-benefits, serving as nurseries for marine species, providing nutrients and food to support marine ecosystems, and stabilizing coastlines from erosion and extreme events. Given these considerations, mangrove afforestation and associated C sequestration has gained considerable attention as a nature-based solution to climate adaptation (e.g., protect against more frequent storm surges) and mitigation (e.g. offsetting other C-producing activities). To advance our understanding and description of these important ecosystems, we leverage Landsat-8 and Sentinel-2 satellite data to provide a current assessment of mangrove extent within the Red Sea region and also explore the effect of spatial resolution on mapping accuracy. We establish that Sentinel-2 provides a more precise spatial record of extent and subsequently use these data together with a maximum entropy (MaxEnt) modeling approach to: i) map the distribution of Red Sea mangrove systems, and ii) identify potential areas for future afforestation. From these current and potential mangrove distribution maps, we then estimate the carbon sequestration rate for the Red Sea (as well as for each bordering country) using a meta-analysis of sequestration values surveyed from the available literature. For the mangrove classification, we obtained mapping accuracies of 98 %, with a total Red Sea mangrove extent estimated at approximately 175 km2. Based on the MaxEnt approach, which used soil physical and environmental variables to identify the key factors limiting mangrove growth and distribution, an area of nearly 410 km2 was identified for potential mangrove afforestation expansion. The factors constraining the potential distribution of mangroves were related to soil physical properties, likely reflecting the low sediment load and limited nutrient input of the Red Sea. The current rate of carbon sequestration was calculated as 1034.09 ± 180.53 Mg C yr-1, and the potential sequestration rate as 2424.49 ± 423.26 Mg C yr-1. While our results confirm the maintenance of a positive trend in mangrove growth over the last few decades, they also provide the upper bounds on above ground carbon sequestration potential for the Red Sea mangroves.

Monitoring coastal water flow dynamics using sub-daily high-resolution SkySat satellite and UAV-based imagery.

Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73-16.05%) and commission errors (0.32-2.77%) and model dye concentration (coefficient of determination = 0.73; root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems.

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals.

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles.

Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serve as ideal platforms from which to capture such dynamic responses. Here, we provide a UAV-based approach for tracing coastal water flows using object-based detection of dye plume extent coupled with a regression approach for mapping dye concentration. From hovering UAV images and nine subsequent flight surveys covering the duration of an ebbing tide in the Red Sea, our results show that dye plume extent can be mapped with low omission and commission errors when assessed against manual delineations. Our results also demonstrated that the interaction term of two UAV-derived indices may be employed to accurately map dye concentration (coefficient of determination = 0.96, root mean square error = 7.78 ppb), providing insights into vertical and horizontal transportation and dilution of materials in the water column. We showcase the capabilities of high-frequency UAV-derived data and demonstrate how field-based dye concentration measurements can be integrated with UAV data for future studies of coastal water flow dynamics.

Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle.

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green-red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

Quinoa Phenotyping Methodologies: An International Consensus.

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

North Atlantic winter cyclones starve seabirds.

Each winter, the North Atlantic Ocean is the stage for numerous cyclones, the most severe ones leading to seabird mass-mortality events called "winter wrecks."1-3 During these, thousands of emaciated seabird carcasses are washed ashore along European and North American coasts. Winter cyclones can therefore shape seabird population dynamics4,5 by affecting survival rates as well as the body condition of surviving individuals and thus their future reproduction. However, most often the geographic origins of impacted seabirds and the causes of their deaths remain unclear.6 We performed the first ocean-basin scale assessment of cyclone exposure in a seabird community by coupling winter tracking data for ∼1,500 individuals of five key North Atlantic seabird species (Alle alle, Fratercula arctica, Uria aalge, Uria lomvia, and Rissa tridactyla) and cyclone locations. We then explored the energetic consequences of different cyclonic conditions using a mechanistic bioenergetics model7 and tested the hypothesis that cyclones dramatically increase seabird energy requirements. We demonstrated that cyclones of high intensity impacted birds from all studied species and breeding colonies during winter but especially those aggregating in the Labrador Sea, the Davis Strait, the surroundings of Iceland, and the Barents Sea. Our broad-scale analyses suggested that cyclonic conditions do not increase seabird energy requirements, implying that they die because of the unavailability of their prey and/or their inability to feed during cyclones. Our study provides essential information on seabird cyclone exposure in a context of marked cyclone regime changes due to global warming.8.

Vulnerability of the North Water ecosystem to climate change.

High Arctic ecosystems and Indigenous livelihoods are tightly linked and exposed to climate change, yet assessing their sensitivity requires a long-term perspective. Here, we assess the vulnerability of the North Water polynya, a unique seaice ecosystem that sustains the world's northernmost Inuit communities and several keystone Arctic species. We reconstruct mid-to-late Holocene changes in sea ice, marine primary production, and little auk colony dynamics through multi-proxy analysis of marine and lake sediment cores. Our results suggest a productive ecosystem by 4400-4200 cal yrs b2k coincident with the arrival of the first humans in Greenland. Climate forcing during the late Holocene, leading to periods of polynya instability and marine productivity decline, is strikingly coeval with the human abandonment of Greenland from c. 2200-1200 cal yrs b2k. Our long-term perspective highlights the future decline of the North Water ecosystem, due to climate warming and changing sea-ice conditions, as an important climate change risk.

General practitioners' deprescribing decisions in older adults with polypharmacy: a case vignette study in 31 countries.

BACKGROUND: General practitioners (GPs) should regularly review patients' medications and, if necessary, deprescribe, as inappropriate polypharmacy may harm patients' health. However, deprescribing can be challenging for physicians. This study investigates GPs' deprescribing decisions in 31 countries. METHODS: In this case vignette study, GPs were invited to participate in an online survey containing three clinical cases of oldest-old multimorbid patients with potentially inappropriate polypharmacy. Patients differed in terms of dependency in activities of daily living (ADL) and were presented with and without history of cardiovascular disease (CVD). For each case, we asked GPs if they would deprescribe in their usual practice. We calculated proportions of GPs who reported they would deprescribe and performed a multilevel logistic regression to examine the association between history of CVD and level of dependency on GPs' deprescribing decisions. RESULTS: Of 3,175 invited GPs, 54% responded (N = 1,706). The mean age was 50 years and 60% of respondents were female. Despite differences across GP characteristics, such as age (with older GPs being more likely to take deprescribing decisions), and across countries, overall more than 80% of GPs reported they would deprescribe the dosage of at least one medication in oldest-old patients (> 80 years) with polypharmacy irrespective of history of CVD. The odds of deprescribing was higher in patients with a higher level of dependency in ADL (OR =1.5, 95%CI 1.25 to 1.80) and absence of CVD (OR =3.04, 95%CI 2.58 to 3.57). INTERPRETATION: The majority of GPs in this study were willing to deprescribe one or more medications in oldest-old multimorbid patients with polypharmacy. Willingness was higher in patients with increased dependency in ADL and lower in patients with CVD.

Meeting Paris agreement objectives will temper seabird winter distribution shifts in the North Atlantic Ocean.

We explored the implications of reaching the Paris Agreement Objective of limiting global warming to <2°C for the future winter distribution of the North Atlantic seabird community. We predicted and quantified current and future winter habitats of five North Atlantic Ocean seabird species (Alle alle, Fratercula arctica, Uria aalge, Uria lomvia and Rissa tridactyla) using tracking data for ~1500 individuals through resource selection functions based on mechanistic modeling of seabird energy requirements, and a dynamic bioclimate envelope model of seabird prey. Future winter distributions were predicted to shift with climate change, especially when global warming exceed 2°C under a "no mitigation" scenario, modifying seabird wintering hotspots in the North Atlantic Ocean. Our findings suggest that meeting Paris agreement objectives will limit changes in seabird selected habitat location and size in the North Atlantic Ocean during the 21st century. We thereby provide key information for the design of adaptive marine-protected areas in a changing ocean.

A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments.

Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.

Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species (Solanum pimpinellifolium) through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red-green-blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.

Radiometric Assessment of a UAV-Based Push-Broom Hyperspectral Camera.

The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially; (2) evaluate spectral fidelity; and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time (<2%) across spatial pixels and spectral bands for >99.5% of pixel-waveband combinations. Using a mercury/argon (Hg/Ar) lamp, the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.

Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress.

With salt stress presenting a major threat to global food production, attention has turned to the identification and breeding of crop cultivars with improved salt tolerance. For instance, some accessions of wild species with higher salt tolerance than commercial varieties are being investigated for their potential to expand food production into marginal areas or to use brackish waters for irrigation. However, assessment of individual plant responses to salt stress in field trials is time-consuming, limiting, for example, longitudinal assessment of large numbers of plants. Developments in Unmanned Aerial Vehicle (UAV) sensing technologies provide a means for extensive, repeated and consistent phenotyping and have significant advantages over standard approaches. In this study, 199 accessions of the wild tomato species, Solanum pimpinellifolium, were evaluated through a field assessment of 600 control and 600 salt-treated plants. UAV imagery was used to: (1) delineate tomato plants from a time-series of eight RGB and two multi-spectral datasets, using an automated object-based image analysis approach; (2) assess four traits, i.e., plant area, growth rates, condition and Plant Projective Cover (PPC) over the growing season; and (3) use the mapped traits to identify the best-performing accessions in terms of yield and salt tolerance. For the first five campaigns, >99% of all tomato plants were automatically detected. The omission rate increased to 2-5% for the last three campaigns because of the presence of dead and senescent plants. Salt-treated plants exhibited a significantly smaller plant area (average control and salt-treated plant areas of 0.55 and 0.29 m2, respectively), maximum growth rate (daily maximum growth rate of control and salt-treated plant of 0.034 and 0.013 m2, respectively) and PPC (5-16% difference) relative to control plants. Using mapped plant condition, area, growth rate and PPC, we show that it was possible to identify eight out of the top 10 highest yielding accessions and that only five accessions produced high yield under both treatments. Apart from showcasing multi-temporal UAV-based phenotyping capabilities for the assessment of plant performance, this research has implications for agronomic studies of plant salt tolerance and for optimizing agricultural production under saline conditions.

Present and past dynamics of Inughuit resource spaces.

Information from a collaborative GPS tracking project, Piniariarneq, involving 17 occupational hunters from Qaanaaq and Savissivik, Northwest Greenland, is used to explore the resource spaces of hunters in Avanersuaq today. By comparison with historical records from the time of the Thule Trading Station and the decades following its closure, we reveal a marked variability in resource spaces over time. It is argued that the dynamics of resources and resource spaces in Thule are not underlain by animal distribution and migration patterns, or changes in weather and sea ice conditions alone; but also by economic opportunities, human mobility, settlement patterns, particular historical events and trajectories, and not least by economic and political interests developed outside the region.

The history of seabird colonies and the North Water ecosystem: Contributions from palaeoecological and archaeological evidence.

The North Water (NOW) polynya is one of the most productive marine areas of the Arctic and an important breeding area for millions of seabirds. There is, however, little information on the dynamics of the polynya or the bird populations over the long term. Here, we used sediment archives from a lake and peat deposits along the Greenland coast of the NOW polynya to track long-term patterns in the dynamics of the seabird populations. Radiocarbon dates show that the thick-billed murre (Uria lomvia) and the common eider (Somateria mollissima) have been present for at least 5500 cal. years. The first recorded arrival of the little auk (Alle alle) was around 4400 cal. years BP at Annikitsoq, with arrival at Qeqertaq (Salve Ø) colony dated to 3600 cal. years BP. Concentrations of cadmium and phosphorus (both abundant in little auk guano) in the lake and peat cores suggest that there was a period of large variation in bird numbers between 2500 and 1500 cal. years BP. The little auk arrival times show a strong accord with past periods of colder climate and with some aspects of human settlement in the area.