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592 results for “forest distributions”
Figure 3. Likelihood trees generated from the X in Forest monkeys and Pleistocene refugia: a phylogeographic window onto the disjunct distribution of the Chlorocebus lhoesti species group
Figure 3. Likelihood trees generated from the X- and Y-chromosomal datasets. Bootstrap values of 50 and above (100 replicates, 'fast' stepwise addition) are included throughout the trees. The dashed ovals indicate the consistent recovery of a lhoesti group monophyly. One Chlorocebus solatus sample (CS026) is derived from a female (XX), and therefore is not represented in the Y-chromosomal tree.
Figure 1 in Forest monkeys and Pleistocene refugia: a phylogeographic window onto the disjunct distribution of the Chlorocebus lhoesti species group
Figure 1. Present ranges of the lhoesti group relative to two putative Pleistocene refugia and the Congo River Basin. Species distributions follow Kingdon (1997), and refuges follow Grubb (2001). Harrison (1988) hypothesized that the evolutionary dispersal of the lhoesti group followed an eastward path around the Congo Basin, along either its northern or southern rim. In contrast, Kaplin (2002) suggested that the lhoesti group ancestor may have spread through the basin, with its present distribution being the result of a vicariant event.
Dataset for the paper "Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)"
<p>Dataset associated with the manuscript “Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)” submitted to the journal Data.</p>
Plant Community Formation and Species Distribution Patterns in relation to environmental variables in Endiras Natural Forest, Fogera District, South Gondar Zone, Ethiopia
<p>We need to deposit the data for the manuscript entitled plant community formation and species distribution patterns in relation to environmental variables in Endiras forest, Fogera District, South Gondar Zone, Ethiopia so as to be cited easily </p>
Data from: To the top or into the dark? Relationships between elevational and canopy cover distribution shifts in mountain forests
Open the record for dataset details and reuse information.
Spatial variation in current and historical management of Arabica coffee across forests in its indigenous distribution
Open the record for dataset details and reuse information.
Data from: What controls forest litter decomposition? A coordinated distributed teabag experiment across ten mountains
Open the record for dataset details and reuse information.
Annual tree growth for Red and Sugar Maples in six forest plots distributed around Ann Arbor, MI and around the University of Michigan Biological Station (UMBS), 1999 to 2022
The dataset contains the annual growth, in mm, of over 200 red and sugar maple trees. All trees are located in established study sites in the vicinity of Ann Arbor or UMBS.
Far northeastern Siberia boreal forest data: Larch tree snag (dead tree) size distribution across a density gradient
This dataset includes basal diameter (bd) or diameter at breast height (DBH) of dead larch trees (snags) within four stands near Cherskii, Siberia. Data have not been published.
Far northeastern Siberia boreal forest data: Larch tree size distribution across a density gradient
This dataset includes basal diameter (bd) or diameter at breast height (DBH) of larch trees within four stands near Cherskii, Siberia. Data have not been published.
Trait coordination and environmental filters shape functional trait distributions of forest understory herbs
We test the importance of environmental filtering and trait covariance for structuring the functional traits of understory herbaceous communities.
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Species Abundance Distributions (SADs) for local tree communities in 1-ha forest plots on 20 tropical islands in the Indo-Pacific region
<p>Species abundance distributions (SADs) characterise the distribution of individuals among species. This dataset was used to investigate the relative importance of disturbance regime (tropical cyclone regime) and island geography (the area and isolation of islands) on the shape of SADs.</p>
Data from: Environmental controls on canopy foliar N distributions in a neotropical lowland forest
Distributions of foliar nutrients across forest canopies can give insight into their plant functional diversity and improve our understanding of biogeochemical cycling. We used airborne remote sensing and Partial Least Squares Regression (PLSR) to quantify canopy foliar nitrogen (N) across ~164 km2 of wet lowland tropical forest in the Osa Peninsula, Costa Rica. We determined the relative influence of climate and topography on the observed patterns of canopy foliar N using a gradient boosting model (GBM) technique. At a local scale, where climate and substrate where constant, we explored the influence of slope position on canopy N by quantifying canopy N on remnant terraces, their adjacent slopes and knife edged ridges. In addition, we climbed and sampled 540 trees and analyzed foliar N in order to quantify the role of species identity (phylogeny) and environmental factors in predicting canopy N. Observed canopy N heterogeneity reflected environmental factors working at multiple spatial scales. Across the larger landscape, elevation and precipitation had the highest relative influence on predicting canopy foliar N (30 and 24%), followed by soils (15%), site exposure (9%), compound topographic index (8%), substrate (6%), and landscape dissection (6%). Phylogeny explained ~75% of the variation in the filed collected foliar N data, suggesting that phylogeny largely underpins the response to the environmental factors. Taken together, these data suggest that a large fraction of the variance in canopy N across the landscape is proximately driven by species composition, though ultimately this is likely a response to abiotic factors such as climate and topography. Future work should focus on the mechanisms and feedbacks involved, and how shifts in climate may translate to changes in forest function.
Vertical distribution of epiphytic lichens on Quercus laurina Humb. & Bonpl. in a remnant of cloud forest in the state of Veracruz, México
<p>It is considered that in the tropics, lichen richness and cover tend to increase from the trunk base to the top of the crown of trees. In this study we calculate total beta diversity of the lichen community along a vertical gradient on Quercus laurina. By comparing the richness and cover of the lichens by zone, we will be able to prove that the foliose and fruticose forms will be the minor component of the total lichen species richness; but with the highest cover with respect to the crustose lichens. Five zones were identified in each phorophyte (n=15) with a diameter at breast height > 40 cm. A total of 92 species were identified, of these, 38% were found only in a single zone, 51% were shared between the different zones, 11% occurred across all zones. Species richness and cover increased from the lowest to the highest zones of phorophytes. Dissimilarity in species composition between the zones, can be explained by species replacement. The Indicator Species Analysis revealed that only a few species such as Hypotrachyna vexans, H. cf. sublaevigata and Ramalina cf. sinaloensis preferred a particular zone. The results obtained to date show that the lichen community associated with Quercus laurina phorophytes is highly diverse. There is a high replacement of species across the different zones. Our results suggest that species richness and cover of the corticolous lichen community are related to the zone and the diverse growth forms.</p>
Data from: Microclimate predicts within-season distribution dynamics of montane forest birds
Aim Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial scales. However, in montane forest landscapes, high degrees of microclimate variability could influence occupancy dynamics and distributions of forest species. Using high-resolution bird survey and under-canopy air temperature data, we tested the hypothesis that the high vagility of most forest bird species combined with the heterogeneous thermal regime of mountain landscapes would enable them to adjust initial settlement decisions to track their thermal niches. Location Western Cascade Mountains, Oregon, USA. Methods We used dynamic occupancy models to test the degree to which microclimate affects the distribution patterns of forest birds in a heterogeneous mountain environment. In all models we statistically accounted for vegetation structure, vegetation composition and potential biases due to imperfect detection of birds. We generated spatial predictions of forest bird distributions in relation to microclimate and vegetation structure. Results Fine-scale temperature metrics were strong predictors of bird distributions; effects of temperature on within-season occupancy dynamics were as large or larger (1–1.7 times) than vegetation effects. Most species (86.7%) exhibited apparent within-season occupancy dynamics. However, species were almost as likely to be warm associated (i.e., apparent settlement at warmer sites and/or vacancy at cooler sites; 53.3% of species) as cool associated (i.e., apparent settlement at cooler sites and/or vacancy at warmer sites; 46.7% of species), suggesting that microclimate preferences are species specific. Main conclusions High-resolution temperature data increase the quality of predictions about avian distribution dynamics and should be included in efforts to project future distributions. We hypothesize that microclimate-associated distribution patterns may reflect species' potential for behavioural buffering from climate change in montane forest environments.
Patterns in the genetic structure of 49 lowland rain forest tree species co-distributed on opposite sides of the Northern Andes
<p>The Andes are a major dispersal barrier for lowland rain forest plants and animals, yet hundreds of lowland tree species are distributed on both sides of the Northern Andes, raising questions about how the Andes influenced their biogeographic histories and population genetic structure. To explore these questions, we generated standardized datasets of thousands of SNPs from paired populations of 49 tree species co-distributed in rain forest tree communities located in Panama and Amazonian Ecuador and calculated genetic diversity (<em>π</em>) and absolute genetic divergence (<em>d</em><sub>XY</sub>) within and between populations, respectively. We predicted (1) higher genetic diversity in the ancestral source region (east or west of the Andes) for each taxon, and (2) correlation of genetic statistics with species attributes, including elevational range and life-history strategy. We found that genetic diversity was higher in putative ancestral source regions, possibly reflecting founder events during colonization. We found little support for a relationship between genetic divergence and species attributes except that species with higher elevational range limits exhibited higher <em>d</em><sub>XY</sub>, implying older divergence times. One possible explanation for this pattern is that dispersal through mountain passes declined in importance relative to dispersal via alternative lowland routes as the Andes experienced uplift. We found no difference in mean genetic diversity between populations in Central America and the Amazon. Overall, our results suggest that dispersal across the Andes has left enduring signatures in the genetic structure of widespread rain forest trees. We outline additional hypotheses to be tested with species-specific case studies.</p>
Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"
<h1>1. Selection of European forest bird species and classification of their biome preferences</h1> <p>We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1.</p> <h1>2. Changes in abundance and distribution of European forest bird species</h1> <p>We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1).</p> <p>We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8).</p> <p>To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023).</p> <h1>3. Trait and specialisation data for European forest bird species</h1> <p>We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022).</p> <p>Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016).</p> <p>Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167.</p> <h1>4. Data analysis</h1> <p>Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2).</p> <p>To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013).</p> <p>Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.</p>
Figure 4 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 4. The fit of the count part of the "hurdle" model to the relationship between galling species richness and plant genus species richness.
Figure 2 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 2. Boxplots with jittered illustrating the galling species richness between year season, with jitered raw values strung vertical corresponding to plots (a, b, c), numbers the plots (N) and sites (n), and mean ± SD bars.
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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.