The lichen Allocetraria madreporiformis in high-arctic steppes on Svalbard: a result of out-of-Tibet migration?

Published version available at: http://nhm2.uio.no/lichens/nordiclichensociety/Allocetraria madreporiformis is a small, finger-like, fruticose lichen with isolated occurrences in the inner fiord section of the long, straight fiord Wijdefjorden in Svalbard. Several new localities are added and mapped here, and we show that the species is confined to exclusive high-arctic steppe habitats on finetextured, moderately alkaline soil, exposed to wind erosion and aeolian transport of silt and sand. It avoids the most saline steppes and adjacent tundra areas, as indicated by numerous pH samples of mineral soils from sites with and without occurrences of A. madreporiformis. In this open habitat, all otherwise common arcticalpine fruticose lichen species were absent or extremely rare, and a cryptogamic cover was very depauperate. On Svalbard, this species is an exclusive character species of the steppe areas in Inner Wijdefjorden National Park. The genus Allocetraria is strongly centred in the Sino-Himalayan area. It is discussed here that it probably evolved as a response to the very extensive new habitats formed during a series of Qinghai-Tibetan Plateau uplift and orogeny events taking place 25–1.6 Ma. This and other aspects affecting current classification alternatives of cetrarioid lichens are also discussed. The habitat preferences of A. madreporiformis appear to have been largely defined by the conditions of its probable area of origin in steppe-like habitats of the northern part of the Qinghai-Tibetan Plateau

Finnmarksvidda – kartlegging og overvåking av reinbeiter – status 2013

Reindriftsforvaltningen i Alta startet i 1998 opp et program for overvåking av vår-/høst- og vinterbeitene i Indre Finnmark. Hensikten med dette programmet var å framskaffe dokumentasjon i om endringer beiteforholdene for reinsdyr i området. Dokumentasjonen i prosjektet er gitt gjennom feltregistreringer og ved studier av satellittbilder. Ved oppstart av programmet ble det lagt ut i alt 66 studiefelt med 324 registreringsruter. Det har tidligere vært gjort to ”omdrev” med hensyn på innsamling av data i programmet – et i 2005/2006 et i 2009/2010. NINA har hatt ansvaret for innsamling av bakkedata, mens Norut IT har ansvaret for satellittdata-delen av programmet. Denne rapporten presenterer tredje ”omdrev” i programmet. Rapporten oppsummerer status for vegetasjons- og beiteforhold for vinterbeitene i Indre Finnmark basert på data fra 2013. Det er gjort en bearbeiding av tre Landsat 8 OLI scener fra 2013. Bearbeidingen av tilgjengelige satellittscener er gjort etter samme metodikk som i første ”omdrev” av programmet. Feltdata ble innsamlet sommeren 2013. Resultatet er oppsummert i form av arealtabeller og som vegetasjonskart. Videre er arealtall fra 2013, sammenlignet med tilsvarende data fra 1996, 2000, 2006 og 2009. Lav er en viktig del av vinterføden for reinsdyr. Lavrik vegtasjon i vinterbeiteområdet i Indre Finnmark utgjør i dag et areal på 344,0 kvadratkilometer noe som utgjør 4,0 % av totalarealet. I 1987 utgjorde lavdekket 19,0 % av total arealet. I 1996 var dette tallet redusert til 8,4 prosent og videre til 5,6 % i år 2000. I 2006 ble det registrert en økning til 6,7 % med en ny nedgang i 2009 til 6,1 %. Dagens arealtall for lavdekke er det laveste som målt for Indre Finnmark siden «Overvåkingsprogrammet for Indre Finnmark» startet opp.publishedVersio

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning : Average climate versus extremes

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.Peer reviewe

High tolerance of a high-arctic willow and graminoid to simulated ice encasement

Source at http://www.borenv.net/BER/ber231-6.htm.Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential, major threat to snow-dominated ecosystems. While impacts of ground-ice on arctic wildlife are well explored, the impacts on tundra vegetation is far from understood. We therefore tested the vulnerability of two high-arctic plants, the prostrate shrub Salix polaris and the graminoid Luzula confusa, to ice encasement for 60 days under full environmental control. Both species were tolerant, showing only minor negative responses to the treatment. Subsequent exposure to simulated late spring frost increased the amount of damaged tissue, particularly in S. polaris, compared to the pre-frost situation. Wilting shoot tips of S. polaris increased nearly tenfold, while the proportion of wilted leaves of L. confusa increased by 15%. During recovery, damaged plants of S. polaris responded by extensive compensatory growth of new leaves that were much smaller than leaves of non-damaged shoots. The results suggest that S. polaris and L. confusa are rather tolerant to arctic winter-spring climate change, and this may be part of the reason for their wide distribution range and abundance in the Arctic

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Open Access Journal (SHERPA RoMEO Green) DOI: 10.1007/s13280-016-0770-0Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance. Chlorophyll fluorescenceSolar Induced Fluorescence (SIF)ReflectancePhotosynthetic functionMERIS terrestrial chlorophyll index (MTCI)High-Arctic tundrapublishedVersio

Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub Salix polaris, the herb Bistorta vivipara, and the prostrate semievergreen shrub Dryas octopetala. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that S. polaris and B. vivipara were in good health, while the health status of D. octopetala was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas

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