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This dataset includes tables summarizing image information and bird counts from the aerial digital images taken over open water at Izembek Lagoon in Alaska in fall 2017-2019. These summaries list one record per image and provide the camera parameters, latitude, longitude, altitude, and automated and manual counts representing the total number of birds in each taxon (brant, white-cheeked geese, emperor geese, gulls, and other birds) identified in the image. The original images (.JPG format) and annotations are provided in an accompanying USGS data release (Weiser et al. 2022).
This R code conducts the main changepoint analyses described in the manuscript titled “Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient”, authored by W. E. Thogmartin, J. A. Szymanski, and E. L. Weiser. The code calculates step and segmented changepoints, checks model assumptions for fitted models, calculates the probability of a >=6.05 ha population given a mean expected population of 3.2 ha, calculates the number of additional years required to provide for a statistically significant trend in the event that the current data are not significant, and calculates the probability of continued increase.
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The sampling locations provided here were selected as a two-stage Generalized Random Tessellation Stratified (GRTS) sample (Stevens & Olsen 2004). The first stage of the GRTS draw used a master sample developed by the North American Bat Monitoring Program (Loeb et al. 2015) from a 10 x 10 km grid placed over the conterminous U.S., Canada, and Mexico. Each 10 x 10 km grid cell (hereafter, master cell) was assigned a GRTS rank by NABat. The rank represents the priority order in which master cells should ideally be sampled. For the second stage of the draw, sampling points within a master cell were selected. Each point was defined as a 30 x 30 m cell of the GIS raster that defined monarch-relevant habitat. Sampling...
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The imagery and annotations presented here were generated while testing an aerial photographic survey design to improve repeatability, transparency, and estimation of variance for annual population estimates of geese staging at Izembek Lagoon, Alaska. This dataset includes 1) 131,031 .JPG images captured from a small fixed-wing occupied aircraft, usually at an altitude of about 457 m, over Izembek Lagoon; 2) annotations of objects (geese, ducks, and gulls) assigned by an automated algorithm, and 3) manually corrected annotations that were edited by project staff for only those photos automatically identified as containing geese of any species. The photos were captured at predetermined points to systematically sample...
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This dataset consists of one table with a record (row) for each goose location and columns containing location information and covariates. The dataset was used in an analysis of altitude selection and flight propensity in an accompanying paper (Weiser et al. 2024) and is being provided here to allow replication of that analysis. Goose locations (latitude, longitude, and altitude) were collected with GPS tags and represent three subspecies: Pacific Greater White-fronted Goose, Tule Greater White-fronted Goose, and Lesser Snow Goose. Covariates include weather information from ERA5 (Hersbach et al. 2022). In addition to the "used" locations (altitudes at which birds were recorded), the dataset also includes "available"...
These scripts prepare input files and run a Generalized Random Tessellation Stratified (GRTS) draw to select sampling locations for the Integrated Monarch Monitoring Program in the U.S., Canada, and Mexico.
These scripts perform power analyses for milkweed, monarch eggs, and adult monarchs. Milkweed and eggs are simulated together; adults are simulated separately. Scripts test for either 1) power to detect trends or 2) power to detect differences among land-use sectors in density-when-present, as indicated in the file names. For demonstration purposes, each script runs only a small subset of scenarios and replicates, which finishes in a few minutes; the full set would take days to run unless performed in parallel on a supercomputer. Weiser_MMPower_Code0_PowerAnalysis_functions.r provides source code for functions that are used in the other scripts; scripts 1-4 are independent of one another.
These R scripts prepare the dataset and run the model to evaluate effects of leg flags on nest survival of four species of shorebirds. Flags_Script1_subset_data.r: Subset the dataset and restructure as needed to run the model. Flags_Script2_run_model.r: Run the model in JAGS. The scripts were developed April 2018 in R v. 3.4.0 Patched. The original dataset is publicly available at: https://arcticdata.io/catalog/#view/doi:10.18739/A2CD5M
These R scripts prepare the dataset and run calculations and analyses on patterns in daily predation rate (DPR) of shorebird nests, as part of a comment on a recent paper (V. Kubelka, M. Šálek, P. Tomkovich, Z. Végvári, R. P. Freckleton, T. Székely.
This script simulates data for population surveys (e.g., the density of individuals per some unit of area) and fits a model to the simulated data to determine whether the population trend can be detected. The script tests one replicate of one scenario. This example takes lt 1 sec to run on a modern laptop computer. For each scenario, the process would be repeated over a series of replicates (e.g., 100 or 500), and then the proportion of replicates in which the trend was detected would be tallied. That proportion indicates the statistical power. Additional scenarios are then tested by changing the input parameters provided below.
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The sampling locations provided here were selected as a two-stage Generalized Random Tessellation Stratified (GRTS) sample (Stevens & Olsen 2004). The first stage of the GRTS draw used a master sample developed by the North American Bat Monitoring Program (Loeb et al. 2015) from a 10 x 10 km grid placed over the conterminous U.S., Canada, and Mexico. Each 10 x 10 km grid cell (hereafter, master cell) was assigned a GRTS rank by NABat. The rank represents the priority order in which master cells should ideally be sampled. For the second stage of the draw, sampling points within a master cell were selected. Each point was defined as a 30 x 30 m cell of the GIS raster that defined monarch-relevant habitat. Sampling...


    map background search result map search result map Priority sampling locations for the Integrated Monarch Monitoring Program Priority sampling locations in the U.S., Canada, and Mexico for the Integrated Monarch Monitoring Program Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019 Movement Data for Migrating Geese Over the Northeast Pacific Ocean, 2018-2021 Counts of Birds in Aerial Photos from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019 Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019 Counts of Birds in Aerial Photos from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019 Movement Data for Migrating Geese Over the Northeast Pacific Ocean, 2018-2021 Priority sampling locations for the Integrated Monarch Monitoring Program Priority sampling locations in the U.S., Canada, and Mexico for the Integrated Monarch Monitoring Program