Data: Western United States

Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient: changepoint analysis R code

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.

Priority sampling locations for the Integrated Monarch Monitoring Program

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...

Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Canada, Long-term monitoring, Mexico, Monarch Butterfly, Sampling design, All tags... Status and Trends, USGS Science Data Catalog (SDC), United States, biota, Fewer tags

Movement Data for Migrating Geese Over the Northeast Pacific Ocean, 2018-2021

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"...

Categories: Data; Tags: Alaska, Animal behavior, Animal tracking, Animals/Vertebrates, Anser albifrons elgasi, All tags... Anser albifrons sponsa, Anser caerulescens caerulescens, Biota, Birds, British Columbia, California, Ducks/Geese/Swans, Environment, GPS, Geospatial datasets, Greater White-fronted Goose, Human impacts, Human wildlife interactions, Marine ecosystems, Migration (organisms), Migratory birds, Migratory rates/routes, Oregon, Pacific Ocean, Pelagic habitat, Seasonal movement, Snow Goose, USGS Science Data Catalog (SDC), Washington, Waterfowl, Fewer tags

Generalized Random Tessellation Stratified (GRTS) for Integrated Monarch Monitoring Program

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.

Data: Canada

Power analysis for monarchs and milkweed code

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.

R Code: Effects of leg flags on nest survival of Arctic-breeding shorebirds

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

Data: Mexico

R code: R_nest_predetation

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.

Data: Eastern United States

R code: Simulation-based power analysis code

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.

Priority sampling locations in the U.S., Canada, and Mexico for the Integrated Monarch Monitoring Program

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...

Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Canada, Long-term monitoring, Mexico, Monarch Butterfly, Sampling design, All tags... Status and Trends, United States, biota, Fewer tags