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355 results for “bryophyte”
Bryophyte Species at Harvard Forest 1994
This dataset contains a list of bryophyte species found in 105 11.25 x 11.25 m plots on the Prospect Hill Tract, Harvard Forest, Petersham, Massachusetts in 1994.
Arctic-boreal bryophyte dynamics since the last glacial from ancient DNA metabarcoding
<p>A total of 26 lake-sediment cores collected from 26 study sites spanning the glacial and interglacial transition are used in this study. These sites are distributed across Siberia, Beringia, and Alaska regions, with a gradient of vegetation types dominated by tundra in the northern region and transitioning to boreal forest in the southern extents. DNA samples from the sediment core were analysed with a standard sedimentary ancient DNA metabarcoding pipeline (see additional description), which resulted in a raw dataset of all DNA plant sequences, which were then filtered for Bryophytes (Bryophyte DNA dataset). The Bryophyte DNA dataset contains 120 unique ASV. Samples in the Bryophyte DNA dataset are then grouped into 1000-year time slices and are subsequently resampled to a base count of 500 read counts for each time slice. After that, a Bryophyte trait datastet is assigned to the Bryophyte DNA dataset. </p> <p> </p> <h3>Input files</h3> <ul> <li><strong>Excel file with all data used in the R-Script:</strong> "Bryophytes_data.xlsx"</li> <li><strong>WorldClim 2.0 dataset with mean temperatures of Warmest Quarter</strong> (https://www.worldclim.org/; Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086">International Journal of Climatology 37 (12): 4302-4315</a>): "wc2.1_30s_bio_10.tif"</li> </ul> <h3>R script</h3> <ul> <li><strong>R-Script:</strong> "2025-01-14_R-Script_ arctic_boreal_bryophyte_dynamics_DNA_metabarcoding.R"</li> </ul> <h3>R outputs</h3> <ul> <li><strong>resampled Bryophyte metabarcoding percentage dataset with ASV:</strong> "2025-01-14_bryophyta_resampled_percentages_mean_100runs_sequences.csv"</li> <li><strong>resampled Bryophyte metabarcoding percentage dataset with unique scientific names: </strong>"2025-01-14_bryophyta_resampled_percentages_mean_100runs_scientific_names.csv"</li> <li><strong>GBIF taxa occurrences with WorldClim temperature data: </strong>"2025-01-14_gbif_taxa_occurrences_seqtypes_climate.csv"</li> </ul> <p> </p>
Species richness of vascular plants and bryophytes in nine grassland sites (Europe and California collected in 2013-2016)
We sampled vascular plants (VP) and bryophytes (non-vascular plant; NVP) 1×1 m experimental plots in nine sites belonging to the Nutrient Network. Three sites were in California, two in Finland and UK and one in Germany and Switzerland. The data were collected to compare the responses of NVPs and VPs to nutrient addition and grazing exclusion treatments. The NVP and VP cover sampling was conducted in March-August 2016, except for heron.uk and rook.uk, which had been sampled for VPs in 2013. NVPs were mostly identified to species, but in absence of necessary diagnostic characters (capsules, other reproductive organs, distinctive gametophytic features), some specimens were identified at morphospecies group, subgenus, or genus level. We calculated three plant diversity indices for NVPs, VPs and total (NVPs and VPs combined) in each plot. First, species richness (S) is the number of species per 1 m2 for NVPs and VPs. For plots having no NVPs, NVP richness is zero. Second, for plots having at least one NVP, we calculated Inverse Simpson’s index of diversity (referred to as species diversity), which is equivalent to the Probability of Interspecific Encounter or Effective Number of Species (ENSPIE). Third, we calculated Simpson’s evenness (E = ENSPIE/S; referred to as evenness), which was expected to reflect changes in species’ dominance. We also sampled aboveground plant biomass at peak biomass of vascular plants (in May- August, depending on local site level characteristics) by clipping at ground level and removing all aboveground vegetation (live and dead) from two 0.1 × 1 m strips, sorting the current year’s VP and NVP biomass from the previous year’s biomass (dead litter), drying the biomass to a constant mass at 60 °C, and weighing it to the nearest 0.01 g. Except for two sites (heron.uk and rook.uk), we also measured photosynthetically active radiation (PAR) at the ground surface and above grassland canopy at time of peak biomass and calculated the proportion of tra
Bryophyte Cover Summary Data for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the summarized bryophyte (percent cover) data obtained from point frame measurements, as used in Jean et al. 2017 Canadian Journal of Forest Research. ?Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Data from nitrogen isotopic analyses used to calculate biological nitrogen fixation (BNF) rates and field measurements from lichen, bryophyte, litter, and soil samples in MAT2006 plots, Arctic LTER, Toolik Field Station, Alaska, summers 2022-2023.
This dataset contains nitrogen (N) fixation and isotope data from experimental samples collected at Toolik Lake, Alaska during the 2022 and 2023 growing seasons. Sampling was conducted across multiple block treatments to capture spatial variability and included four substrate types: lichen, moss, litter, and soil. Within each plot, substrates were collected systematically along transects to ensure representative sampling, with lichen samples collected opportunistically due to lower abundance. Samples were incubated in the field under ambient conditions using 15N₂ to measure biological nitrogen fixation (BNF). In 2023, a short-term wetting experiment was conducted to assess the influence of moisture on BNF rates, with subsamples exposed to controlled additions of water. Across both years, data include isotope ratios, incubation conditions, moisture, fresh and dry biomass, and treatment assignments. The dataset provides information on BNF across substrate types, moisture regimes, and fertilization treatments in Arctic tundra. These data support investigation of N cycling processes, the influence of moisture and fertilization on fixation rates, and variability across vegetation types. The dataset is complete for the two field seasons (2022 and 2023) and includes sample- and block-level metadata necessary for reuse in ecological and biogeochemical research.
Hubbard Brook Experimental Forest: Data for Stream bryophytes promote cryptic productivity, 2018-2021
This is the data and code associated with "Stream bryophytes promote 'cryptic' productivity in highly oligotrophic headwaters. Recent observations document increased abundance of algae in the headwater streams of Hubbard Brook Experimental Forest (HBEF). It is possible that this 'greening up' of HBEF streams may be due to climate change with rising temperatures, altered terrestrial phenology, and shifting hydrologic regimes. Alternatively, stream 'greening' could be due to the slow recovery of stream chemistry from decades of acid rain, which have led to rising stream water pH, declining concentrations of toxic Al3+, and extremely low solute concentrations. Three years of weekly algal measurements on contrasting substrates, 6 nutrient enrichment experiments reveal important new insights about the interactions between these two groups of autotrophs. We predicted that light availability, hydrologic disturbance and nutrient limitation were all important determinants of algal biomass in streams. To evaluate the relative strength and hierarchy of these limiting factors, we used nutrient diffusing substrates to investigate the role of nutrients for algae and compared algal accrual rate on artificial rock vs. moss substrates in stream channels vs. weir ponds to assess the role of hydrologic disturbance and scour. Our surveys and experiments spanned across seasons and local light regimes. Algal biomass was substantially higher in protected weir ponds than in stream channels, and in both habitats, algal biomass was substantially higher on artificial moss substrates than on tiles. Taken together, these results suggest that moss can provide physical protection from flood scour. Algal biomass instream on both substrate types was higher in high light seasons (pre-leaf out) and well-lit habitats indicating strong light limitation. Results from a series of 6 nutrient diffusing substrate experiments over the course of 2 years provided little evidence of nutrient limitation instream
Hubbard Brook Experimental Forest: 2019 and 2022 Stream Bryophyte Abundance
Stream bryophytes (mosses and liverworts) are widely recognized as important macroinvertebrate habitats, but their overall role in the stream ecosystem, particularly in nutrient cycling, remains understudied. Hubbard Brook Experimental Forest contains some of the most extensively researched streams in the world, yet few studies mention their bryophytes. Perhaps this is because early estimates place stream bryophyte coverage at an insignificant 2%. However, data from 2019 show that contemporary coverage ranges from 4%–40% among streams. To investigate how stream bryophyte cover may be changing over time and influencing stream nutrient stocks, we conducted field surveys, measured organic and inorganic mass contents of bryophytes, and quantified nutrient uptake with bottle incubations of bryophyte mats. This study marks a novel attempt to map stream bryophyte coverage with estimates of carbon, phosphorus, and nitrogen stocks and fluxes. From our 2022 field surveys, we found that median bryophyte coverage can vary greatly across streams in the same catchment (0%–41.4%) and can also shift from just three years prior. We estimate that these bryophyte mats store between 14–414 g of organic matter per m2 of stream in the form of live biomass and captured particulates. Out of 36 bryophyte clump samples, 35 sorbed peak historical water column concentrations of PO43- measured within the Hubbard Brook stream chemistry record within 12 hours of light incubation. In Bear Brook, our scaled estimate of bryophyte mat nitrate uptake (2.3 g N y-1) constitutes a substantial portion of previously estimated whole-stream nitrate uptake (12 g N y-1). Cumulatively, our data demonstrates that bryophytes and their associated mineral substrates and biota—known as the bryosphere—are crucial in facilitating headwater stream nutrient cycling. These bryospheres may contribute significantly to interannual variability in stream nutrient concentrations within nutrient-poor streams, especially in clim
Riparian bryophyte list of the Andrews Experimental Forest, 1994/1995
The following bryophyte species list compiles habitat information based on the quantitative data collected from 360 samples (2 x 4 m quadrats) distributed among 42 sites within the Andrews Forest. The sites range from 420 m to 1250 m asl stream orders 1 to 5. The list comprises 131 taxa, 84 mosses and 47 hepatics. Many of the species were however infrequent and thus detailed and objective accounts of their habitat demands are difficult to provide. The data is specific for the studied sites but may serve as an indication of the general habitat demands of the species within the western Cascades and to some extent for riparian zones in the Coastal Mountain Range. Voucher specimens have been deposited at the herbaria Oregon State University and UME, Sweden.
Cover of ground lichens, bryophytes, and cyanobacteria: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
FIG. 7 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 7. — Dendrogram of floristic similarity of the bryophyte flora of mangroves on the Northern and Southeastern coast of Brazil.
FIG. 5. — A in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 5. — A, mean richness; B, density of bryophytes in the sampled mangroves per light tolerance guilds; C, interaction plot between sampled zones and light tolerance guilds on mean richness of bryophytes; D, interaction plot between sampled zones and light tolerance guilds on mean density of bryophytes.
FIG. 4 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 4. — Violin plot with included boxplot: A, species richness; B, species density. Alpha-diversity indices: C, Shannon Index (H'); D, Pielou's Evenness (J').
FIG. 3 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 3. — Accumulation curves based on the abundance of individuals in the fringe and inland zones of the mangroves of Salvaterra, Pará, Brazil: A, species richness (q = 0); B, Shannon diversity (q = 1). The fringe zone is shown in red color and the inland zone in blue color. Continuous line represents interpolation and dotted line represents extrapolation.
FIG. 2 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 2. — Mangroves on the east coast of the municipality of Salvaterra, Marajó Island, Pará: A, B, mangrove in inland zone; C, D, fringe zone.
FIG. 1 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil
FIG. 1. — Location map of collection points in Marajó Island, Pará, Brazilian Amazon:A, localization of Marajó Island in Pará, Brazil, South America (red rectangle); B, localization of Salvaterra in Marajó Island; C, localization of sampling points on the east coast of the Salvaterra, with 1 km between the fringe zone and the inland zone in each area (map prepared by P.W.P. Gomes).
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
FIG. 1 in Epiphyllous bryophyte diversity in lowland rain forest and lowland cloud forest of French Guiana
FIG. 1. ― Species accumulation curves and estimated total number of species (*) of epiphyllous bryophytes in the understory of lowland cloud forest (LCF) and lowland rain forest (LRF) at Nouragues, French Guiana.
FIG. 6 in Exploring the diversity of bryophytes in different forests in the eastern Amazonia
FIG. 6. — Classification of bryophyte species in different substrates in Abaetetuba, Lower Tocantins:A, substrate preference of bryophytes in Abaetetuba;B, occurrence of two species in four types of substrate; C, exclusive and shared species among substrates in environments. Abbreviations: Co, corticolous; Ep, epiphyllous; Ex, epixylic; Ru, rupicolous; Te, terricolous; Tm, termite mound.
FIG. 5. — A-D, Cololejeunea setiloba A in Exploring the diversity of bryophytes in different forests in the eastern Amazonia
FIG. 5. — A-D, Cololejeunea setiloba A.Evans (from A. K. Sousa-Pereira 608): A, sector of a stem, ventral view;B, C, leaves,ventral view; D, lobules.E-J, Leptolejeunea radicosa (Mont.) Grolle (from A. K. Sousa-Pereira 202): E, F, leaves, ventral view; G, I, underleaves; H, leaf, dorsal view; J, sector of a stem. Scale bars: A, 250 µm; B, C, E, J, 100 µm; D, F-I, 50 µm.
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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.