Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data

The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400-2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values (r (2) = 0.61-0.88, RMSEmean = 12%-64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.Peer reviewe

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families

Global shift in a key plant trait indicates a change in biosphere function

In the face of climate change, understanding the dynamic responses of vegetation is crucial for predicting shifts in biosphere functioning. Plant functional traits, particularly leaf mass per area (LMA), are critical links between plant metabolism, vegetation responses to climate change, and the broader exchanges of energy and matter within the biosphere. Despite their importance, a comprehensive, predictive understanding of traits and biosphere changes is hampered by spatial and temporal gaps in trait observations. Here, we introduce a novel remote sensing method for the global, continuous mapping of LMA and its historical shifts. Consistent with ecological theory predicting a widespread decrease in LMA with global warming, our findings reveal a global LMA reduction of 6.5-7.6 % between 1985 and 2019, primarily due to increasing temperatures. This decrease varies among biomes, with evergreen conifer and tropical forests showing the most significant declines. Due to LMA connections with carbon metabolism in ecosystems, a global decrease in LMA points to a quickening of the carbon cycle, including largely unexplored contributions to increased global photosynthesis in recent decades. Collectively, these results signal an ongoing widespread and profound transformation in the functioning of the biosphere resulting from climate-related changes in vegetation and its traits

Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data

The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values (r2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic

Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard.

Acknowledgements: This research was conducted at the University Centre in Svalbard (UNIS), which provided background knowledge of the study sites and systems, accommodation, lab space, and logistical support for lab and field work during the PFTC4 course. Funding provided by the Norwegian Center for International Cooperation in Education (SIU) and the Research Council of Norway (grants 2013/10074, HNP2015/10037, INTPART 274831) made it possible to conduct this field course at Svalbard with 21 students from 12 nationalities and 4 continents as participants and co-authors to this data paper. The ITEX experiment and field site was funded by UNIS and the University of Iceland Research Funds (grants to ISJ) and the Research Council of Norway (grant 246080/E10). We thank Pernille Bronken Eidesen for introducing us to the local study systems and invaluable assistance with taxonomic identifications, Geir Wing Gabrielsen for background information on the seabird nutrient input gradient below the little auk colony in Bjørndalen, and Christine Schirmer and her team of internship students at the University of Arizona for assistance with stoichiometric and isotope analysis.The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions

Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard.

The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions

Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard

Abstract The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions

Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard

Acknowledgements: This research was conducted at the University Centre in Svalbard (UNIS), which provided background knowledge of the study sites and systems, accommodation, lab space, and logistical support for lab and field work during the PFTC4 course. Funding provided by the Norwegian Center for International Cooperation in Education (SIU) and the Research Council of Norway (grants 2013/10074, HNP2015/10037, INTPART 274831) made it possible to conduct this field course at Svalbard with 21 students from 12 nationalities and 4 continents as participants and co-authors to this data paper. The ITEX experiment and field site was funded by UNIS and the University of Iceland Research Funds (grants to ISJ) and the Research Council of Norway (grant 246080/E10). We thank Pernille Bronken Eidesen for introducing us to the local study systems and invaluable assistance with taxonomic identifications, Geir Wing Gabrielsen for background information on the seabird nutrient input gradient below the little auk colony in Bjørndalen, and Christine Schirmer and her team of internship students at the University of Arizona for assistance with stoichiometric and isotope analysis.The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions

Further Evidence for the Absence of Polyproline II Stretch in the XAO Peptide

It has been suggested that the alanine-based peptide with sequence Ac-XX-[A](7)-OO-NH(2), termed XAO where X denotes diaminobutyric acid and O denotes ornithine, exists in a predominantly polyproline-helix (P(II)) conformation in aqueous solution. In our recent work, we demonstrated that this “polyproline conformation” should be regarded as a set of local conformational states rather than as the overall conformation of the molecule. In this work, we present further evidence to support this statement. Differential scanning calorimetry measurements showed only a very small peak in the heat capacity of an aqueous solution of XAO at 57°C, whereas the suggested transition to the P(II) structure should occur at ∼30°C. We also demonstrate that the temperature dependence of the (3)J(HNHα) coupling constants of the alanine residues can be explained qualitatively in terms of Boltzmann averaging over all local conformational states; therefore, this temperature dependence proves that a conformational transition does not occur. Canonical MD simulations with the solvent represented by the generalized Born model, and with time-averaged NMR-derived restraints, demonstrate the presence of an ensemble of structures with a substantial amount of local P(II) conformational states but not with an overall P(II) conformation

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

International audienceAlpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families. © The Author(s) 2024