Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data

Monitoring and understanding climate-induced changes in the boreal and arctic vegetation is critical to aid in prognosticating their future. Weused a 33 year (1982-2014) long record of satellite observations to robustly assess changes in metrics of growing season (onset: SOS, end: EOS and length: LOS) and seasonal total gross primary productivity. Particular attention was paid to evaluating the accuracy of these metrics by comparing them to multiple independent direct and indirect growing season and productivity measures. These comparisons reveal that the derived metrics capture the spatio-temporal variations and trends with acceptable significance level (generally p < 0.05). We find that LOS has lengthened by 2.60 d dec(-1) (p < 0.05) due to an earlier onset of SOS (-1.61 d dec(-1), p < 0.05) and a delayed EOS (0.67 d dec(-1), p < 0.1) at the circumpolar scale over the past three decades. Relatively greater rates of changes in growing season were observed in Eurasia (EA) and in boreal regions than in North America (NA) and the arctic regions. However, this tendency of earlier SOS and delayed EOS was prominent only during the earlier part of the data record (1982-1999). During the later part (2000-2014), this tendency was reversed, i.e. delayed SOS and earlier EOS. As for seasonal total productivity, we find that 42.0% of northern vegetation shows a statistically significant (p < 0.1) greening trend over the last three decades. This greening translates to a 20.9% gain in productivity since 1982. In contrast, only 2.5% of northern vegetation shows browning, or a 1.2% loss of productivity. These trends in productivity were continuous through the period of record, unlike changes in growing season metrics. Similarly, we find relatively greater increasing rates of productivity in EA and in arctic regions than in NA and the boreal regions. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation during last three decades

Prediction of the distribution of Arctic‐nesting pink‐footed geese under a warmer climate scenario

Global climate change is expected to shift species ranges polewards, with a risk of range contractions and population declines of especially high-Arctic species. We built species distribution models for Svalbard-nesting pink-footed geese to relate their occurrence to environmental and climatic variables, and used the models to predict their distribution under a warmer climate scenario. The most parsimonious model included mean May temperature, the number of frost-free months and the proportion of moist and wet moss dominated vegetation in the area. The two climate variables are indicators for whether geese can physiologically fulfil the breeding cycle or not and the moss vegetation is an indicator of suitable feeding conditions. Projections of the distribution to warmer climate scenarios propose a large north- and eastward expansion of the potential breeding range on Svalbard even at modest temperature increases (1 and 21C increase in summer temperature, respectively). Contrary to recent suggestions regarding future distributions of Arctic wildlife, we predict that warming may lead to a further growth in population size of, at least some, Arctic breeding geese

An artificial intelligence approach to remotely assess pale lichen biomass

Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for > 20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 x 1 (30 x 30 m) and 3 x 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens.Peer reviewe

Five decades of terrestrial and freshwater research at Ny-Ålesund, Svalbard

For more than five decades, research has been conducted at Ny-Alesund, in Svalbard, Norway, to understand the structure and functioning of High Arctic ecosystems and the profound impacts on them of environmental change. Terrestrial, freshwater, glacial and marine ecosystems are accessible year-round from Ny-Alesund, providing unique opportunities for interdisciplinary observational and experimental studies along physical, chemical, hydrological and climatic gradients. Here, we synthesize terrestrial and freshwater research at Ny-Alesund and review current knowledge of biodiversity patterns, species population dynamics and interactions, ecosystem processes, biogeochemical cycles and anthropogenic impacts. There is now strong evidence of past and ongoing biotic changes caused by climate change, including negative effects on populations of many taxa and impacts of rain-on-snow events across multiple trophic levels. While species-level characteristics and responses are well understood for macro-organisms, major knowledge gaps exist for microbes, invertebrates and ecosystem-level processes. In order to fill current knowledge gaps, we recommend (1) maintaining monitoring efforts, while establishing a longterm ecosystem-based monitoring programme; (2) gaining a mechanistic understanding of environmental change impacts on processes and linkages in food webs; (3) identifying trophic interactions and cascades across ecosystems; and (4) integrating long-term data on microbial, invertebrate and freshwater communities, along with measurements of carbon and nutrient fluxes among soils, atmosphere, freshwaters and the marine environment. The synthesis here shows that the Ny-Alesund study system has the characteristics needed to fill these gaps in knowledge, thereby enhancing our understanding of High-Arctic ecosystems and their responses to environmental variability and change

Five decades of terrestrial and freshwater research at Ny-Ålesund, Svalbard

For more than five decades, research has been conducted at Ny-Ålesund, in Svalbard, Norway, to understand the structure and functioning of High-Arctic ecosystems and the profound impacts on them of environmental change. Terrestrial, freshwater, glacial and marine ecosystems are accessible year-round from Ny-Ålesund, providing unique opportunities for interdisciplinary observational and experimental studies along physical, chemical, hydrological and climatic gradients. Here, we synthesize terrestrial and freshwater research at Ny-Ålesund and review current knowledge of biodiversity patterns, species population dynamics and interactions, ecosystem processes, biogeochemical cycles and anthropogenic impacts. There is now strong evidence of past and ongoing biotic changes caused by climate change, including negative effects on populations of many taxa and impacts of rain-on-snow events across multiple trophic levels. While species-level characteristics and responses are well understood for macro-organisms, major knowledge gaps exist for microbes, invertebrates and ecosystem-level processes. In order to fill current knowledge gaps, we recommend (1) maintaining monitoring efforts, while establishing a long-term ecosystem-based monitoring programme; (2) gaining a mechanistic understanding of environmental change impacts on processes and linkages in food webs; (3) identifying trophic interactions and cascades across ecosystems; and (4) integrating long-term data on microbial, invertebrate and freshwater communities, along with measurements of carbon and nutrient fluxes among soils, atmosphere, freshwaters and the marine environment. The synthesis here shows that the Ny-Ålesund study system has the characteristics needed to fill these gaps in knowledge, thereby enhancing our understanding of High-Arctic ecosystems and their responses to environmental variability and change

Consequences of warming on tundra carbon balance determined by reindeer grazing history

Arctic tundra currently stores half of the global soil carbon (C) stock1. Climate warming in the Arctic may lead to accelerated CO2 release through enhanced decomposition and turn Arctic ecosystems from a net C sink into a net C source, if warming enhances decomposition more than plant photosynthesis2. A large portion of the circumpolar Arctic is grazed by reindeer/caribou, and grazing causes important vegetation shifts in the long-term. Using a unique experimental set-up, where areas experiencing more than 50 years of either light (LG) or heavy (HG) grazing were warmed and/or fertilized, we show that under ambient conditions areas under LG were a 70% stronger C sink than HG areas. Although warming decreased the C sink by 38% under LG, it had no effect under HG. Grazing history will thus be an important determinant in the response of ecosystem C balance to climate warming, which at present is not taken into account in climate change models

State of the Climate in 2018

International audienc