Regional uniqueness of tree species composition and response to forest loss and climate change

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 31, 2024

Abstract The conservation and restoration of forest ecosystems require detailed knowledge the native plant compositions. Here, we map global tree composition assess impacts historical cover loss climate change on trees. occupancy 10,590 species reveals complex taxonomic phylogenetic gradients determining a local signature lineage assembly. Species analyses indicate that has significantly restricted potential suitable range in all biomes. Nevertheless, tropical moist boreal biomes display lowest level restriction harbor extremely large ranged species, albeit with stark contrast richness composition. Climate simulations are projected to differ their response change, highest predicted dry Mediterranean ecoregions. Our findings highlight need for preserving remaining while regenerating degraded forests way provides resilience against change.

Language: Английский

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Climate Change Reduces and Shifts Suitable Habitats of Uapaca Kirkiana Müll. Arg. To Higher Altitudes in Malawi

Published: Jan. 1, 2025

Language: Английский

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Influence of data and methods on high-resolution imagery-based tree species recognition considering phenology: The case of temperate forests

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 323, P. 114654 - 114654

Published: April 4, 2025

Language: Английский

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Patterns and Drivers of Phylogenetic Beta Diversity in the Forests and Savannas of Africa

Journal of Biogeography, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

ABSTRACT Aim Studying beta diversity, or the variation in species composition among communities, can give insights into plant community assembly over space and time. If different biomes show contrasting large‐scale beta‐diversity patterns, this indicate divergent evolutionary histories ecological processes that then drive turnover communities. Here, we examine phylogenetic patterns across Africa forest savanna assemblages, two most widespread tropical on continent. We hypothesise savannas will lower diversity due to their younger history. Location Tropical Africa. Taxon Woody angiosperms. Methods gathered 301,159 occurrences of woody angiosperms representing 1883 1302 species. compared levels between analysed spatial using 1° grid cells modelled relationship with climate, disturbance geographical distance. Results found greater relative regional whereas assemblages local diversity. The distribution showed strong East–West for both forests savannas, aligned a major floristic discontinuity associated Albertine rift. Our results also highlighted West as showing high amount compositional change biomes, arranged along an aridity gradient. Variance partitioning predictors linked precipitation were main drivers but importance individual was each biome. Main Conclusions Contrary our expectations, may have deeper richer history than suggested by previous studies regions conservation value. Finally, demonstrate environmental filtering is dominant force influencing these important at continental scale.

Language: Английский

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Modeling Future Tree Species Distributions Under Climate Change to Guide Restoration Planning: Application to the Brazilian Amazon

Published: Jan. 1, 2025

Language: Английский

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Tree diversity, population structure, biomass accumulation, and carbon stock dynamics in tropical dry deciduous forests of Eastern India

BMC Ecology and Evolution, Journal Year: 2025, Volume and Issue: 25(1)

Published: May 16, 2025

Tropical dry deciduous forests are crucial for biodiversity conservation and carbon storage but increasingly threatened by human activities climate change. This Study evaluates tree diversity, population structure, biomass stock across five forest ranges of eastern India. A stratified random sampling approach was implemented using a 5 km × grid vegetational attribute studies. Tree diversity assessed within 0.1 ha (31.62 m 31.62 m) plots, while estimation focused on trees with ≥ 10 cm. girth at breast height. Population structure were analyzed six defined classes, employing standardized protocols to ensure accurate estimation. total 80 species belonging 68 genera 33 families recorded, Fabaceae emerging as the dominant family. Significant variation in richness (32-52 species), density (804-1332 trees/ha), basal area (18.28-24.92 m²/ha) observed ranges. Kolabira range (3.45) Bagdihi (3.37) exhibited highest indices, highlighting their ecological significance sequestration potential. Mid-sized (32-101 cm) contributed most accumulation, lower densities other size classes suggest selective exploitation. Total Belpahar (129.63 Mg/ha) lowest Jharsuguda (86.73 Mg/ha), corresponding 58.47 MgC/ha 40.76 MgC/ha, respectively, emphasizing spatial variations these forests. The findings highlight tropical underscore urgent need strategies safeguard enhance storage. In parallel, study offers valuable scientific foundation advancing management practices shaping policies address loss challenges this vital region

Language: Английский

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Climate change reduces and shifts suitable habitats of Uapaca kirkiana Müll. Arg. to higher altitudes in Malawi

Trees Forests and People, Journal Year: 2025, Volume and Issue: unknown, P. 100884 - 100884

Published: May 1, 2025

Language: Английский

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Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3313 - 3313

Published: Sept. 6, 2024

Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global distribution. Many questions remain unanswered about the best practices creating global, general model. This study aims to address three key issues We assessed effectiveness of data-labeling methods create training data, data-splitting training/validation/testing, and machine-learning deep-learning (including semi-supervised deep-learning) models at National Ecological Observatory Network (NEON) Sites. Our analysis revealed that existing method field vegetation structure survey performed reasonably well. The random technique was most efficient both intra-site inter-site classifications overcome impact spatial autocorrelation avoid potential locally overfit Deep learning consistently outperformed forest classification; supervised displayed promising results taxa-classification work has demonstrated possibility tree-classification can identify from outside their area deep may potentially utilize untapped terabytes unlabeled imagery.

Language: Английский

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Tree Growth in Relation to Climate Change: Understanding the Impact on Species Worldwide

Forests, Journal Year: 2024, Volume and Issue: 15(9), P. 1601 - 1601

Published: Sept. 11, 2024

Climate change is one of the most important environmental issues our time, which has profound effects on ecosystems all over world [...]

Language: Английский

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FO-Net: An advanced deep learning network for individual tree identification using UAV high-resolution images

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 220, P. 323 - 338

Published: Dec. 28, 2024

Language: Английский

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