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- Criado, M. G., et al.
(author)
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Plant traits poorly predict winner and loser shrub species in a warming tundra biome
- 2023
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In: Nature Communications. - 2041-1723. ; 14:1
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Journal article (peer-reviewed)abstract
- Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces. Functional trait data could guide predictions of species responses to environmental change. Here, the authors show that winner and loser shrub species in the warming tundra biome overlap in trait space and may therefore be difficult to predict based on commonly measured traits.
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- Posti, J. P., et al.
(author)
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Admission Levels of Interleukin 10 and Amyloid beta 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury
- 2020
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In: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 11
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Journal article (peer-reviewed)abstract
- Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI). Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI. Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of beta-amyloid isoforms 1-40 (A beta 40) and 1-42 (A beta 42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale-Extended 5-8, n = 49) and unfavorable (Glasgow Outcome Scale-Extended 1-4, n = 33) groups. The outcome was assessed 6-12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90-100%. Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9-100) and specificity of 22.4% (95% CI: 10.2-32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1-4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were A beta 40, A beta 42, and neurofilament light. The optimal panel included IL-10, A beta 40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7-6.2) with a sensitivity of 90.9% (95% CI: 81.8-100) and specificity of 59.2% (95% CI: 40.8-69.4). Conclusion: Admission plasma levels of IL-10 and A beta 40 significantly improve the prognostication ability of the HCTS after TBI.
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- Berner, Logan T., et al.
(author)
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The Arctic plant aboveground biomass synthesis dataset
- 2024
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In: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
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Journal article (peer-reviewed)abstract
- Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.
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- Björkman, Anne, 1981, et al.
(author)
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Tundra Trait Team: A database of plant traits spanning the tundra biome
- 2018
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In: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 27:12, s. 1402-1411
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Journal article (peer-reviewed)abstract
- © 2018 The Authors Global Ecology and Biogeography Published by John Wiley & Sons Ltd Motivation: The Tundra Trait Team (TTT) database includes field-based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade-offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained: The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (>1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain: Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub-Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain: All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement: Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format: csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.
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- Graco-Roza, Caio, et al.
(author)
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Distance decay 2.0 – A global synthesis of taxonomic and functional turnover in ecological communities
- 2022
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In: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 31:7, s. 1399-1421
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Journal article (peer-reviewed)abstract
- Aim: Understanding the variation in community composition and species abundances (i.e., beta-diversity) is at the heart of community ecology. A common approach to examine beta-diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments.Location: Global.Time period: 1990 to present.Major taxa studied: From diatoms to mammals.Method: We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features.Results: Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid-latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances.Main conclusions: In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal-related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost-effective option for investigating community changes in heterogeneous environments.
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- Lembrechts, Jonas J., et al.
(author)
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Global maps of soil temperature
- 2022
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:9, s. 3110-3144
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Journal article (peer-reviewed)abstract
- Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean=3.0±2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6±2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7±2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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- Lembrechts, Jonas J., et al.
(author)
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SoilTemp : A global database of near-surface temperature
- 2020
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 26:11, s. 6616-6629
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Journal article (peer-reviewed)abstract
- Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
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- Picetti, Edoardo, et al.
(author)
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Early management of adult traumatic spinal cord injury in patients with polytrauma : a consensus and clinical recommendations jointly developed by the World Society of Emergency Surgery (WSES) & the European Association of Neurosurgical Societies (EANS)
- 2024
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In: World Journal of Emergency Surgery. - : BioMed Central (BMC). - 1749-7922. ; 19
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Journal article (peer-reviewed)abstract
- Background: The early management of polytrauma patients with traumatic spinal cord injury (tSCI) is a major challenge. Sparse data is available to provide optimal care in this scenario and worldwide variability in clinical practice has been documented in recent studies.Methods: A multidisciplinary consensus panel of physicians selected for their established clinical and scientific expertise in the acute management of tSCI polytrauma patients with different specializations was established. The World Society of Emergency Surgery (WSES) and the European Association of Neurosurgical Societies (EANS) endorsed the consensus, and a modified Delphi approach was adopted.Results: A total of 17 statements were proposed and discussed. A consensus was reached generating 17 recommendations (16 strong and 1 weak).Conclusions: This consensus provides practical recommendations to support a clinician's decision making in the management of tSCI polytrauma patients.
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- Raj, R, et al.
(author)
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Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm
- 2022
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In: NPJ digital medicine. - : Springer Science and Business Media LLC. - 2398-6352. ; 5:1, s. 96-
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Journal article (peer-reviewed)abstract
- Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.
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