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.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “avian point count surveys”
Avian community survey by point count method in a leafy forest of the Station de Biologie des Laurentides, Université de Montréal
<p>Here we provided a dataset of birds’ communities census in the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Census was performed as part of the BIO2854 field course. BIO2854 is an undergraduate course in terrestrial animal ecology in biological science at Université de Montréal. Highly qualified teaching assistant and students surveyed forest birds over 4 days in May-June 2011 to 2019 by acoustic point-count method. Abundance counts were made based on bird vital domain available in literature and the assumption that each recorded male was associated with one female from the same species. Data are made available to consult for future research and teaching purpose.</p>
Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents
<p>Accurate biodiversity and population monitoring is a requirement for effective conservation decision-making. Survey method bias is therefore a concern, particularly when research programs face logistical and cost limitations.</p> <p>We employed point counts (PCs) and autonomous recording units (ARUs) to survey avian biodiversity within comparable, high elevation, temperate mountain habitats at opposite ends of the Americas: 9 mountains in British Columbia (BC), Canada and 10 in southern Chile. We compared detected species richness against multi-year species inventories and examined method-specific detection probability by family. By incorporating time costs, we assessed the performance and efficiency of single vs. combined methods.</p> <p>Species accumulation curves indicate ARUs can capture ~93% of species present in BC but only ~58% in Chile, despite Chilean mountain communities being less diverse. The avian community, rather than landscape composition, appears to drive this dramatic difference. Chilean communities contain less-vocal species, which ARUs missed. Further, 6/13 families in BC were better detected by ARUs while 11/11 families in Chile were better detected by PCs. Where survey conditions differentially impacted method performance, PCs mostly varied over the morning and with canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a single year of monitoring, neither method alone was predicted to capture the full avian community, with the exception of ARUs in the alpine and subalpine of BC. PCs contributed little to detected diversity in BC, but including this method resulted in negligible increases in total time costs. Combining PCs with ARUs in Chile significantly increased species detections, again, for little cost.</p> <p>Combined methods were among the most efficient and accurate approaches to capturing diversity. We recommend conducting point counts while ARUs are being deployed and retrieved in order to capture additional diversity with minimal additional effort and to flag methodological biases using a comparative framework.</p>
Avian Point Count Surveys from Yáláƛi (Goose Island) Archipelago, British Columbia, Canada in 2011
<p>This file contains some data used in the article, "<em>Comparing historical and contemporary observations of avian fauna across 75 years on the Yáláƛi (Goose Island) Archipelago, British Columbia, Canada</em>" by Obrist et al. currently in peer-review as a Nature Note at Ecology and Evolution. A DOI to the article will be added upon publication. Specifically, these data refer to the <u>2011 Surveys by Simon Fraser University (SFU) Researchers. </u></p> <ul> <li>Avian point count surveys were conducted in 2011 at 50 locations across the six main islands of Yáláƛi, the Goose Island Archipelago (i.e., Goose, Gosling, Gull, Duck, Snipe, and Swan Island).</li> <li>Locations were chosen to replicate surveys within the four main habitat types outlined by Charles J. Guiguet (1953) (i.e., climax forest, ecotone, muskeg, meadow).</li> <li>Points were surveyed three times during the breeding season, between May 12th and June 2nd, 2011.</li> <li>The date, survey number, start time, point ID, temperature, and weather were recorded for each point count.</li> <li>A single observer recorded all birds seen and heard within a 100 m radius of each point for 10 minutes, separating all observations into minute-long intervals.</li> <li>The distance between the observer (point) and bird was estimated for each observation.</li> <li>Point counts were conducted within five hours of sunrise.</li> <li>Protocols based on Huff et al. (2000) </li> </ul> <p>Please see the article at DOI for a more comprehensive description of the data collection and processing. </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><span><strong>File: GooseIslandBirds_Data.xlsx</strong></span></p> <p>This excel file contains the following sheets: </p> <p>1.) Relative Abundance Comparisons: The comparisons of totals between 2011 survey data and data collected by Charles Guiguet (1953). </p> <p>2.) Point Count Summaries: Shows the way the 2011 totals in Sheet 1 (Relative Abundance Comparisons) was calculated, showing the total abundance for each species for each of the 3 visits, and the total from the Casual Observations sheet. </p> <p>3.) Raw Point Counts</p> <p>4.) Casual Observations <strong>(Caution: observations beyond line 457 are considered duplicates of individuals already accounted for on point counts).</strong></p> <p>5.) Locations</p> <p>6.) Metadata <strong>(READ FIRST)</strong></p>
Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents
Open the record for dataset details and reuse information.
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.