Alaska Landbird Monitoring Survey Dataset

Dates

Publication Date: 2024-04-26
Start Date: 2002
End Date: 2022

Citation

Handel C.M., Pajot, L.M., Matsuoka, S.M., 2024, Alaska Landbird Monitoring Survey dataset: U.S. Geological Survey data release, https://doi.org/10.5066/P9SCO7AN.

Summary

Boreal Partners in Flight developed the Alaska Landbird Monitoring Survey (ALMS) primarily to monitor breeding populations of landbirds in the vast off-road areas of Alaska in conjunction with data collected from the roadside North American Breeding Bird Survey (BBS). ALMS is a collaborative program in which agencies and other entities conduct standardized surveys of breeding birds and their habitats on the lands they manage and then provide the data to the U.S. Geological Survey Alaska Science Center for analysis and archiving. The ALMS monitoring program comprises a set of survey blocks that have been selected in a stratified-random design primarily across state and Federal natural resource lands in Alaska with the principal goal [...]

Summary

Boreal Partners in Flight developed the Alaska Landbird Monitoring Survey (ALMS) primarily to monitor breeding populations of landbirds in the vast off-road areas of Alaska in conjunction with data collected from the roadside North American Breeding Bird Survey (BBS). ALMS is a collaborative program in which agencies and other entities conduct standardized surveys of breeding birds and their habitats on the lands they manage and then provide the data to the U.S. Geological Survey Alaska Science Center for analysis and archiving. The ALMS monitoring program comprises a set of survey blocks that have been selected in a stratified-random design primarily across state and Federal natural resource lands in Alaska with the principal goal of assessing regional and statewide population trends of birds during the breeding season (Handel and Sauer 2017). Each block has a mini-grid of 15−25 points that are typically surveyed biennially, with half of the blocks surveyed in alternating years. This sampling frame has been augmented with a set of 34 transects or mini-grids, each comprising 12‒28 points, that had previously been established for monitoring on some of these public resource lands and that were suitable for including in the sampling frame for trend estimates. This dataset has been further augmented with data from other non-random locations within Alaska that were collected following the same survey protocol but used for other purposes, such as inventories and specialized studies. At each point within all sampling units, a 10-min point count is conducted by a highly skilled observer, who identifies and records all birds detected. The distance from the observer to the bird and the time at which each bird is observed are recorded in prescribed distance and time intervals for each observation so that densities can be estimated by correcting for detection probability. Observers collect corresponding habitat data during the first visit and at subsequent 10-year intervals or whenever a disturbance (e.g., fire, wind) has caused a significant change. Habitat data will be available in a separate USGS data release.

Contacts

Point of Contact :: Alaska Science Center
Originator :: Colleen M Handel, Pajot, Lisa M., Steven M Matsuoka
Metadata Contact :: Alaska Science Center
Publisher :: Alaska Science Center
SDC Data Owner :: Alaska Science Center
USGS Mission Area :: Ecosystems
Distributor :: U.S. Geological Survey - ScienceBase

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AlaskaLandbirdMonitoringSurvey_metadata.xml Original FGDC Metadata	View	92.48 KB	application/fgdc+xml
AlaskaLandbirdMonitoringSurvey_bird_detections.csv		13.23 MB	text/csv
AlaskaLandbirdMonitoringSurvey_bird_taxonomy.csv		19.97 KB	text/csv
AlaskaLandbirdMonitoringSurvey_contributors.csv		12.45 KB	text/csv
AlaskaLandbirdMonitoringSurvey_location_info.csv		210.17 KB	text/csv
AlaskaLandbirdMonitoringSurvey_weather_conditions.csv		139.1 KB	text/csv
AlaskaLandbirdMonitoringSurvey_metadata.html		201.15 KB	text/html

Related External Resources

Type: Related Primary Publication

Sólymos, P., Matsuoka, S.M., Bayne, E.M., Lele, S.R., Fontaine, P., Cumming, S.G., Stralberg, D., Schmiegelow, F.K.A., Song, S.J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution 4(11):1047–1058.	https://doi.org/10.1111/2041-210X.12106
Matsuoka, S.M., Mahon, C.L., Handel, C.M., Sólymos, P., Bayne, E.M., Fontaine, P.C., Ralph, C.J., 2014. Reviving common standards in point-count surveys for broad inference across studies. The Condor: Ornithological Applications 116(1):599–608.	https://doi.org/10.1650/CONDOR-14-108.1
Stralberg, D., Bayne, E.M., Cumming, S.G., Sólymos, P., Song, S.J., Schmiegelow, F.K.A., 2015. Conservation of future boreal forest bird communities considering lags in vegetation response to climate change: a modified refugia approach. Diversity and Distributions 21(9):1112–1128.	https://doi.org/10.1111/ddi.12356
Stralberg, D., Matsuoka, S.M., Hamann, A., Bayne, E.M., Sólymos, P., Schmiegelow, F.K.A., Wang, X, Cumming, S.G., Song, S.J., 2015. Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25(1):52–69.	https://doi.org/10.1890/13-2289.1
Sólymos, P., 2016. QPAD version 3 documentation. Technical Report, Boreal Avian Modelling Project.	https://doi.org/10.5281/zenodo.3251110
Handel, C.M., Sauer, J.R., 2017. Combined analysis of roadside and off-road breeding bird survey data to assess population change in Alaska. The Condor 119(3):557–575.	https://doi.org/10.1650/CONDOR-17-67.1
Stralberg, D., Matsuoka, S.M., Handel, C.M., Bayne, E.M., Schmiegelow, F.K.A, Hamman, A., 2017. Biogeography of boreal passerine range dynamics in western North America: past, present, and future. Ecography 40(9):1050–1066.	https://doi.org/10.1111/ecog.02393
Sólymos, P., Matsuoka, S.M., Cumming, S.G., Stralberg, D., Fontaine, P., Schmiegelow, F.K.A., Song, S.J., Bayne, E.M., 2018a. Evaluating time-removal models for estimating availability of boreal forest birds during point-count surveys: sample size requirements and model complexity. The Condor: Ornithological Applications 120(4):765–786.	https://doi.org/10.1650/CONDOR-18-32.1
Sólymos, P., Matsuoka, S.M., Stralberg, D., Barker, N., Bayne, E.M., 2018b. Phylogeny and species traits predict songbird detectability. Ecography 41(10):1595–1603.	https://doi.org/10.1111/ecog.03415
Bateman, B.L., Taylor, L., Wilsey, C., Wu, J., LeBaron, G.S., Langham, G., 2020. Risk to North American birds from climate change-related threats. Conservation Science and Practice 2(8):e243.	https://doi.org/10.1111/csp2.243
Bateman, B.L., Wilsey, C., Taylor, L., Wu, J., LeBaron, G.S., Langham, G., 2020. North American birds require mitigation and adaptation to reduce vulnerability to climate change. Conservation Science and Practice 2(8):e242.	https://doi.org/10.1111/csp2.242
Wu, J. X., Bateman, B.L., Heglund, P.J., Taylor, L., Allstadt, A.J., Granfors, D., Westerkam, H., Michel, N.L., Wilsey, C.B., 2022. U.S. National Wildlife Refuge System likely to see regional and seasonal species turnover in bird assemblages under a 2°C warming scenario, Ornithological Applications 124(3):duac016.	https://doi.org/10.1093/ornithapp/duac016
Edwards, B.P.M., Smith, A.C., Docherty, T.D.S., Gahbauer, M.A., Gillespie, C.R., Grinde, A.R., Harmer, T., Iles, D., Matsuoka, S.M., Michel, N.L., Murray, A., Niemi, G., Pasher, J., Pavlacky, D., Robinson, B., Ryder, R., Sólymos, P., Stralberg, D., Zlonis, D.J., 2023. Point count offsets for estimating population sizes of North American landbirds. Ibis 165(2):482–503.	https://doi.org/10.1111/ibi.13169

Purpose

The primary objective of ALMS is to monitor long-term population trends of landbirds and other species that can be monitored by diurnal point counts during the breeding season, including many shorebirds and aquatic birds. A secondary objective is to estimate landbird densities by habitat, which can be used to model avian distribution and abundance across Alaska.

Alaska Landbird Monitoring Survey Dataset

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