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- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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- Duncanson, Laura, et al.
(författare)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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- Jonauskaite, D., et al.
(författare)
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Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity
- 2020
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Ingår i: Psychological Science. - : SAGE Publications Inc.. - 0956-7976 .- 1467-9280. ; 31:10, s. 1245-1260
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Tidskriftsartikel (refereegranskat)abstract
- Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r =.88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design. © The Author(s) 2020.
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| 7. |
- Diaz-Sanchez, A. A., et al.
(författare)
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Molecular detection and identification of spotted fever group rickettsiae in ticks collected from horses in Cuba
- 2021
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Ingår i: Medical and Veterinary Entomology. - : John Wiley & Sons. - 0269-283X .- 1365-2915. ; 35:2, s. 207-212
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Tidskriftsartikel (refereegranskat)abstract
- Spotted fever group (SFG) rickettsiae are obligatory intracellular bacteria that cause disease in humans and other animals. Ixodid ticks are the principal vectors of SFG rickettsiae. The present study aimed to determine the prevalence and species identity of SFG rickettsiae in ticks and horses from urban and rural areas of western Cuba using PCR assays. Tick samples, collected from 79 horses, consisted of 14 Amblyomma mixtum adults, 111 Dermacentor nitens adults and 19 pools of D. nitens nymphs (2-5 individuals/pool). The PCR results revealed the presence of Rickettsia spp. in 64% of the A. mixtum adults, 16% of the D. nitens adults, and 11% of the pooled samples of D. nitens nymphs. In contrast, Rickettsia spp. was not detected in any of the 200 horse blood samples included in this study. DNA sequence data of the rickettsial 17 kDa antigen gene showed that Rickettsia amblyommatis was present in A. mixtum; and Rickettsia felis in D. nitens. This is the first report of R. felis in D. nitens in Cuba. The present study extends our knowledge of the potential vector spectrum and distribution of SFG rickettsiae pathogens in western Cuba.
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