Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8
datasets available to search
ShareScore release 0.7.1
Dataset results
8 results for “lichen conservation”
Data from: Low-productivity boreal forests have high conservation value for lichens
1. Land set aside for preservation of biodiversity often has low productivity. As biodiversity generally increases with productivity, due to higher or more diverse availability of resources, this implies that some of the biodiversity may be left unprotected. Due to a lack of knowledge on the species diversity and conservation value of low-productivity habitats, the consequences of the biased allocation of low-productivity land for set-asides are unknown. 2. We examined the conservation value of boreal low-productivity forests (potential tree growth < 1 m3 ha-1 year-1) by comparing assemblages of tree- and deadwood-dwelling lichens and forest stand structure between productive and low-productivity forest stands. We surveyed 84 Scots pine-dominated stands in three regions in Sweden, each including four stand types: two productive (managed and unmanaged) and two low-productivity stands (on mires and on thin, rocky soils). 3. Lichen species richness was highest in low-productivity stands on thin soil, which had similar amounts and diversity of resources (living trees and dead wood) to productive unmanaged stands. Stands in low-productivity mires, which had low abundance of living trees and dead wood, hosted the lowest lichen richness. Lichen species composition differed among stand types, but none of them hosted unique species. The differences in both species richness and composition were more pronounced in northern than in southern Sweden, likely due to shorter history of intensive forestry. 4. Synthesis and applications: Boreal low-productivity forests can have as high conservation value as productive forests, which should be reflected in conservation strategies. However, their value is far from uniform, and conservation planning should acknowledge this variation and not treat all low-productivity forests as a uniform group. Some types of low-productivity forest (e.g. on thin soil) are more valuable than others (e.g. on mires), and should thus be prioritized in conservation. It is also important to consider the landscape context: low-productivity forests may have higher value in landscapes where high-productivity forests are highly influenced by forestry. Finally, although low-productivity forests can be valuable for some taxa, productive forests may still be important for other taxa.
Data from: Low-productivity boreal forests have high conservation value for lichens
Open the record for dataset details and reuse information.
FIGURE 5. A in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 5. A typical view of the forest, 12 months after the clearfell, burn and sow treatment has been applied (Photo: J.Jarman).
FIGURE 1. A in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 1. A forest edge, showing the typical layered structure of the vegetation. Eucalypts up to about 50 m tall form a canopy over a dense layer of secondary trees about 18–25 m tall. A layer, 1–3 m tall and dominated by the large rosette sedge Gahnia grandis and a vigorous scrambling shrub Bauera rubioides, is present in the understorey (Photo: J.Jarman).
FIGURE 2 in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 2. The forest interior in a poorly drained site, where the flakey-barked tree Melaleuca squarrosa dominates the low tree layer. An opening in the Gahnia-dominated layer provides an opportunity for shade-loving cryptogams to colonise the logs and ground surface (Photo: J.Jarman).
FIGURE 6. A in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 6. A comparison of Figures 5 and 6, taken from the same place, demonstrates the dynamic nature of the vegetation in the early years after harvesting. Five to six years after the clearfell, burn and sow treatment, the large stump in Figure 5 is completely obscured by the vigorous growth of young eucalypts and Gahnia (Photo: J.Jarman).
FIGURE 4 in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 4. The forest interior, with scattered large fibrous-barked eucalypts interspersed among the smaller understorey trees (Photo: J.Jarman).
FIGURE 3 in Lichens and bryophytes in Tasmanian wet eucalypt forest: floristics, conservation and ecology
FIGURE 3. The forest interior at a site with moderate drainage, where the smooth-barked tree Nematolepis squamea is common and the shrub Bauera dominates the understorey. A large rock in the background provides a substrate for cryptogams raised above the dense shrubbery (Photo: J.Jarman).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.