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430 results for “forest structure”
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Silvicultural regime shapes understory functional structure in European forests
<p>This is the dataset used for the article "Silvicultural regime shapes understory functional structure in European forests" by Francesco Chianucci, Francesca Napoleone et al., which has been accepted in Journal of Applied Ecology.</p> <p>Attached is also an R code to illustrate the statistical analyses performed in the study.</p>
Figure 2 in Genetic structure of Trypanosoma congolense "forest type" circulating in domestic animals and tsetse flies in the South-West region of Cameroon
Figure 2. NJ Tree based on Cavalli-Sforza and Edwards chord distance matrix of T. congolense "forest type" circulating in tsetse flies and domestic animals of Fontem.
Fig. 9 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 9. Influence of monthly average temperatures on date of flowering onset: Helleborus caucasica (1), Anemonoides nemorosa (2), Ficaria verna (3), Corydalis solida (4), Gymnospermum odessanum (5), C. marshalliana (6), Viola odorata (7), Anemonoides ranunculoides (8). See explanations in the text.
Fig. 12 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 12. Dependence of variability of the date (expressed by coefficient of variation) of flowering onset onto its average value (a) and amplitude of variations of monthly average temperatures (b) for 7 years of observations.
Fig. 4 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 4. The age structure of populations in artificial plant community: a – association of Glechoma hederaceae L. + Pulmonaria obscura (1) + Viola odorata + Lysimachia nummularia L.; association Pulmonaria obscura + Viola odorata + Viola alba + Primula veris (2); b – association of Hepatica nobilis (3) + Anemonoides blanda (4) + Viola odorata + Ficaria verna (5).
Fig. 5 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 5. The age structure of populations of spring ephemeroids in 2011–2012; a – Corydalis solida, b – Anemonoides ranunculoides, c – A. blanda.
Fig. 11 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 11. Dependence of flowering phenophase-starting date onto precipitation in forest herbaceous perennials. a: Anemonoides nemorosa (1), Corydalis marshalliana (2), Anemonoides ranunculoides (3), Gymnospermum odessanum (4), Viola odorata (5), Anemonoides blanda (6). b: Anemona sylvestris (7), November precipitation; Hepatica nobilis (8), July precipitation; Hepatica nobilis (9), January precipitation; Anemona sylvestris previous year July precipitation (10)".
Fig. 3 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 3. The age structure of populations in artificial plant community: a – association of Ficaria verna (1) + Corydalis solida (2) + Viola odorata + Anemonoides blanda (3) + Anemonoides ranunculoides, b – Corydalis paczoskii (5)" - "Corydalis paczoskii (4)"; "Gymnospermium odessanum (6)" – "Gymnospermium odessanum (5)".
Fig. 6 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 6. The age structure of populations in natural plant community: a – association of Corydalis solida (1) + Anemonoides ranunculoides (2) + Ficaria verna (3) + Corydalis marschalliana (4); b –Association Ficaria verna (5) + Corydalis solida (6) + Viola odorata + Fragaria vesca.
Fig. 7 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 7. The age structure of populations of spring ephemeroids under artificial plant community conditions (1) and natural ravine forest (2): a – Corydalis solida, b – Ficaria verna, c – Anemonoides ranunculoides.
Fig. 1 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 1. The age structure of populations in artificial plant community: a – association of Ficaria verna (1) + Anemonoides blanda (2) + Corydalis solida (3), b – "Ficaria verna (3)" to "Ficaria verna (4)"; "Anemonoides blanda (4)" - "Anemonoides blanda (5)"; "Corydalis solida (5)" – "Corydalis solida (6)".
Fig. 10 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 10. Influence of January-April total precipitation value on the date of Primula veris budding onset.
Fig. 2 in Phenology And Population Structure Of Forest Herbaceous Species In Artificial And Natural Communities In The Steppe Zone Of Ukraine
Fig. 2. The age structure of populations in artificial plant community: a - association Anemonoides ranunculoides (1) + Corydalis solida (2) + Corydalis marschalliana, b - association Ficaria verna (3) + Anemonoides ranunculoides (4) + Anemonoides nemorosa, (5) + Corydalis solida (6).
Figure 5 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 5. CCA plot Analysis of illustrating the influence of elevation on spreading pattern of plant communities in Lalkoo valley Swat.
Figure 4 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 4. Results of CCA joint biplot showing results for eleven plant communities' correlation with environmental variable. BAB-I: Berberis- Abies- Bergenia; PIP-II: Picea-Indigofera- Poa; APP-III: Abies- Parrotiopsis- Poa,QVP-IV:Quercus-Viburnum-Poa,PSP-V:PiceaSalix-Primula,AVP-VI:Abies-Viburnum -Poa; VTP-VII: ViburnumTaxus-Poa; PVL-VIII: Pinus-Viburnum-Lithospermum; ABC-IX: Abies-Berberis-Carex; PVP-X: Pinus-Viburnum-Poa; and PPP-XI: Parrotiopsis-Picea-Poa represents community types.
Figure 2 in Fire effects on Atlantic Forest sites from a composition, structure and functional perspective
Figure 2. Average of species richness (A), basal area (B), Shannon index (C), tree density (D), CWM Height (E), CWM Leaf length (F), CWM wood density (G), CWM Leaf deciduousness (H), CWM dispersal mode (I), CWM shade tolerance (J) for tree species inventoried in burned and unburned sites in Paraíba do Sul river basin, Southeast Atlantic Forest biome, Brazil.Same letters represent no statistical difference.
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)
Figure 6 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses
Figure 6. Results of the Bayesian Analysis of Population Structure (BAPS) of the Necromys lasiurus Cytochrome b sequences compiled in the present study, showing the four genetic clades, which are color-coded. The vertical black lines separate the sample groups. Insert map shows the Brazilian biomes.
Figure 7 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses
Figure 7. Mismatch distribution of the Necromys lasiurus samples from the Rio de Janeiro state, Brazil. The observed frequencies are shown in red, and the expected frequencies, in green.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.