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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
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| 3. |
- Pittman, S. J., et al.
(författare)
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Seascape ecology : identifying research priorities for an emerging ocean sustainability science
- 2021
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Ingår i: Marine Ecology Progress Series. - : INTER-RESEARCH. - 0171-8630 .- 1616-1599. ; 663, s. 1-29
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Tidskriftsartikel (refereegranskat)abstract
- Seascape ecology, the marine-centric counterpart to landscape ecology, is rapidly emerging as an interdisciplinary and spatially explicit ecological science with relevance to marine management, bio-diversity conservation, and restoration. While important progress in this field has been made in the past decade, there has been no coherent prioritisation of key research questions to help set the future research agenda for seascape ecology. We used a 2-stage modified Delphi method to solicit applied research questions from academic experts in seascape ecology and then asked respondents to identify priority questions across 9 interrelated research themes using 2 rounds of selection. We also invited senior management/conservation practitioners to prioritise the same research questions. Analyses highlighted congruence and discrepancies in perceived priorities for applied research. Themes related to both ecological concepts and management practice, and those identified as priorities include seascape change, seascape connectivity, spatial and temporal scale, ecosystem-based management, and emerging technologies and metrics. Highest-priority questions (upper tercile) received 50% agreement between respondent groups, and lowest priorities (lower tercile) received 58% agreement. Across all 3 priority tiers, 36 of the 55 questions were within a +/- 10% band of agreement. We present the most important applied research questions as determined by the proportion of votes received. For each theme, we provide a synthesis of the research challenges and the potential role of seascape ecology. These priority questions and themes serve as a roadmap for advancing applied seascape ecology during, and beyond, the UN Decade of Ocean Science for Sustainable Development (2021-2030).
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| 5. |
- 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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| 6. |
- Solaki, Maria, et al.
(författare)
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Comprehensive variant spectrum of the CNGA3 gene in patients affected by achromatopsia
- 2022
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Ingår i: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 43:7, s. 832-858
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Tidskriftsartikel (refereegranskat)abstract
- Achromatopsia (ACHM) is a congenital cone photoreceptor disorder characterized by impaired color discrimination, low visual acuity, photosensitivity, and nystagmus. To date, six genes have been associated with ACHM (CNGA3, CNGB3, GNAT2, PDE6C, PDE6H, and ATF6), the majority of these being implicated in the cone phototransduction cascade. CNGA3 encodes the CNGA3 subunit of the cyclic nucleotide-gated ion channel in cone photoreceptors and is one of the major disease-associated genes for ACHM. Herein, we provide a comprehensive overview of the CNGA3 variant spectrum in a cohort of 1060 genetically confirmed ACHM patients, 385 (36.3%) of these carrying “likely disease-causing” variants in CNGA3. Compiling our own genetic data with those reported in the literature and in public databases, we further extend the CNGA3 variant spectrum to a total of 316 variants, 244 of which we interpreted as “likely disease-causing” according to ACMG/AMP criteria. We report 48 novel “likely disease-causing” variants, 24 of which are missense substitutions underlining the predominant role of this mutation class in the CNGA3 variant spectrum. In addition, we provide extensive in silico analyses and summarize reported functional data of previously analyzed missense, nonsense and splicing variants to further advance the pathogenicity assessment of the identified variants.
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