Family trees contain information on individuals such as birth and death places and years, and kinship ties, e.g., parent-child, spouse, and sibling relationships. Such information makes it possible to construct population-scale trees and study population dynamics and migration over many generations and far into the past. Despite the recent advances, existing spatial and temporal abstraction techniques for space-time flow data have limitations due to the lack of knowledge about the effects of temporal partitioning on flow patterns and their visualization. In this study, we extract state-to-state migration patterns over a period between 1789 and 1924 from a set of cleaned, geocoded and connected family trees from Rootsweb.com. We use the child ladder approach, one that captures changes in family locations by comparing birthplaces and birthyears of consecutive siblings. Our study has two major contributions. First, we introduce a methodology to reveal patterns and trends for analyzing and mapping of migration across space and time using a family tree dataset. Specifically, we evaluate a series of temporal partitioning methods to capture how changes in temporal partitioning influence the results of patterns and trends. Second, we visualize longitudinal population mobility in the US using time-series flow maps. This is one of the first studies to uncover dynamic migration patterns on a larger spatial and temporal scale, than the more typical micro studies of individual movement. Our findings are reflective of the migration patterns of European descendants in the U.S., while native Americans, Blacks, Mexican populations are not represented in the data. [KC1]

[KC1]Need to discuss about this more in limitations, and maybe put in in the abstract and/or introduction. Since this is a methodological paper to map migration from trees, I don’t think we need to add this in the title.

This analysis uses location data collected on elk that were fitted with GPS collars in Idaho for 2007 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual elk spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a less than 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers.Teton River Elk Migration StatisticsAnalyzed/Prepared by: Jodi Berg and Scott BergenDecember 2021Spatial MetricsAverage length of Migration: 35.5 milesMaximum Migration Length: 93.3 milesMinimum Migration Length: 2.1 milesTotal Migrations Analyzed: 75Total Number of Individuals: 23Total Number Spring Migrations: 39Total Number Fall Migrations: 36Of 75 individual seasonal migrations, 46 used Brownian bridge movement models with an 8-hour time-lag, 15 used forced motion variance (1400 m) models with a 14-hour time-lag, and 14 used forced motion variance (1400 m) models with a 27-hour time-lag.Temporal Data Extent of Study: March 5, 2018 – February 27, 2020Spring MigrationFall MigrationStart Date AverageMarch 29December 6 Minimum March 5September 28 MaximumMay 28January 8End Date AverageApril 21December 20 MinimumMarch 14October 17 MaximumJune 23April 6Duration Average2635 Minimum12 Maximum101113Migration Use Class StatisticsUse ClassAcres 1 individual500,131 Low (>2 individuals)282,165 Medium (10-20%)145,041 High (>20%)47,997 Stopover23,335

Components of international migratory increase, quarterly: immigrants, emigrants, returning emigrants, net temporary emigrants, net non-permanent residents.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

This map was created as a tool to analyze the growth and distribution of the Hispanic population in a specific Dallas neighborhood during the 1920s to 1940s. Through this map, historical demographic trends are visually represented, offering valuable insights into how the Hispanic community expanded and became more established in this particular area over the course of two decades.By mapping population data from this time period, the map helps contextualize the social, economic, and cultural changes that occurred during this era. The 1920s to 1940s was a time of significant migration, urbanization, and shifting demographics, with many Hispanic families settling in particular neighborhoods as they sought better opportunities in Dallas. This map not only highlights the growth of the Hispanic population but also illustrates the development of community infrastructures, such as schools, businesses, and cultural centers, that supported this population expansion.This map is featured on the Racial Equity Storymap.

Net Migration (absolute value) per Province, and Bi-Directional Net Migration (absolute value) Between Provinces using the 2010 Census. Analysis and map by Fei Carnes. Webmap version of this data: http://worldmap.harvard.edu/maps/chinamap/coW See also the dynamic China Migration web map application for 1995, 2000, 2005, 2010 by Giovanni Zambotti. http://maps.cga.harvard.edu/crossroadsofmigration/china/

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

This dataset includes the data (rds format) used to create the maps (0.1° x 0.1° latitude per longitude resolution) of the sensitivity of the downscaled maps at the same spatial resolution. One map is created for each estimated proportion of individuals on the move.

Author: P Ofstedal, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): migration, population, mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Analyze migration patterns into and out of their county and explain why these patterns exist.
2. Analyze the age of local in- and out-migrants and explain why these patterns exist.Summary: Students will investigate current in-migration and out-migration patterns in their county through two map websites.

File List hb-migration.r (MD5: 1904c1692a02d984890e4575d0eeb4e6) R script that imports the eBird, map, and equal-area icosahedron data, summarizes the population-level migration patterns, runs the statistical analyses, and outputs figures. migration-fxns.r (MD5: a2ae2a47c066a253f18cad5b13cddcf6) R script that holds the relevant functions for executing the hb-migration.R script. BBL-Appendix.r (MD5: 370c701d6afb07851907922dcab51de4) R script that imports the Breeding Bird Laboratory data and outputs the figures for the Appendix. output-data.zip (MD5: 36e3a92a7d35e84b299d82c8bd746950) Folder containing the partially-processed text files (15 .txt files, 3 per species for centroids, migration dates, and migration speed) for the main analyses and figures in the paper. These text files can be used in part II of hb-migration.r and contain output data on the daily population-level centroids, migration dates, and migration speed. Part I of hb-migration.r relies on raw eBird data, which was queried from the eBird server directly. The raw eBird data can be requested through their online portal after making a user account (http://help.ebird.org/customer/portal/articles/1010524-can-i-download-raw-data-from-ebird-). The equal-area icosahedron maps are available at (http://discreteglobalgrids.org/). The BBL data, used in BBL-Appendix.R, can be requested from the USGS Bird Banding Laboratory (http://www.pwrc.usgs.gov/BBL/homepage/datarequest.cfm). Description The code and data in this supplement allow for the analyses and figures in the paper to be fully replicated using a data set of manipulated communities collected from the literature. Requirements: R 3.x, and the following packages: chron, fields, knitr, gamm4, geosphere, ggplot2, ggmap, maps, maptools, mapdata, mgcv, plyr, raster, reshape2, rgdal, Rmisc, SDMTools, sp, spaa, and files containing functions specific to this code (listed above). The analyses can then be replicated by changing the working directory at the top of the file hb-migration.R to the location on your computer where you have stored the .R and .csv files and running the code. Note that to fully replicate the analyses, the data will need to be requested from the sources listed above. Starting at Part II in hb-migration.R, it should take approximately 30 minutes to run all the code from start to finish. Figures should output as pdfs in your working directory. If you download the raw data and run the analyses starting at Part I, you will need a workstation with large memory to run the analyses in a reasonable amount of time since the raw eBird datafiles are very large. Version Control Repository: The full version control repository for this project (including post- publication improvements) is publicly available https://github.com/sarahsupp/hb-migration. If you would like to use the code in this Supplement for your own analyses it is strongly suggested that you use the equivalent code in the repositories as this is the code that is being actively maintained and developed. Data use: Partially-processed data is provided in this supplement for the purposes of replication. If you wish to use the raw data for additional research, they should be obtained from the original data providers listed above.

This package contains data, filters and visualizations from Nilsson and Dokter et al. (2019).

Files

radar_metadata.csv: Metadata for the 84 European radars considered for this study. Includes radar code (odim_code = country + odim_code_3char and alternative radar code vp_radar), radar site location (location, latitude, longitude), radar site elevation (site_altitude_asl in meters above sea level) and radar altitude range used in this study (min_height_cut_asl and max_height_cut_asl in meters above sea level).

vp.zip: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= odim_code), date and hour directories and follow the ODIM bird profile format specification. Data can be read with the R package bioRad.

vp_processing_settings.yaml: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (include_heights), time periods to exclude (exclude_datetimes) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.

vp_processed_70_radars_20160919_20161009.csv: Processed vp data for 70 radars. Is the result of processing vp.zip with vp_processing_settings.yaml and radar_metadata.csv using vp-processing (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in exclusion_reason). This data file forms the basis for analysis in the study.

Headers are:

radar_id: odim_code of the radar

datetime: timestamp

HGHT: lower altitude of altitude bin (m above sea level)

u: bird ground speed towards east (m/s)

v: bird ground speed towards north (m/s)

dens: bird density (birds/km3)

dd: bird flight direction (degrees from north)

ff: bird ground speed (m/s)

DBZH: reflectivity factor (dBZ) in horizontal polarisation

mtr: migration traffic rate (birds/km/h)

day_night: timestamp occurs during day or night (based on sunrise/sunset)

date_of_sunset: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date

exclusion_reason: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have NA values for u, v, dens, dd, ff, DBZH, and mtr.

vp_flowviz.csv: Input data for visualizations. Is the result of processing vp_processed_70_radars_20160919_20161009.csv using vp-to-flowviz.Rmd in vp-processing (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (altitude_band = 1) and above (altitude_band = 2). Only altitude band 1 is used in visualizations.

flowviz.mov: Screencast of vp_flowviz.csv visualized with Bird migration flow visualization v2 (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.

cartoviz.mov: Screencast of vp_flowviz.csv visualized as an interactive map with CARTO. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed.

This dataset includes the maps (0.1° x 0.1° latitude per longitude resolution) obtained by averaging the downscaled maps at the same spatial resolution. One map is created for each estimated proportion of individuals on the move by averaging the corresponding downscaled maps.

Migration is fundamental in the life of many birds and entails significant energetic and time investments. Given the importance of arrival time in the breeding area and the relatively short period available to reproduce (particularly at high latitudes), it is expected that birds reduce spring migration duration to a greater extent than autumn migration, assuming that pressure to arrive into the wintering area might be relaxed. This has previously been shown for several avian groups, but recent evidence from four tracked Icelandic Whimbrels (Numenius phaeopus islandicus), a long distance migratory wader, suggests that this subspecies tends to migrate faster in autumn than in spring. Here, we (1) investigate differences in seasonal migration duration, migration speed and ground speed of Whimbrels using 56 migrations from 19 individuals tracked with geolocators and (2) map the migration routes, wintering and stopover areas for this population. Tracking methods only provide temporal information on the migration period between departure and arrival. However, migration starts with the fuelling that takes place ahead of departure. Here we estimate the period of first fuelling using published fuel deposition rates and thus explore migration speed using tracking data. We found that migration duration was shorter in autumn than in spring. Migration speed was higher in autumn, with all individuals undertaking a direct flight to the wintering areas, while in spring most made a stopover. Wind patterns could drive Whimbrels to stop in spring, but be more favourable during autumn migration and allow a direct flight. Additionally, the stopover might allow the appraisal of weather conditions closer to the breeding areas and/or improve body condition in order to arrive at the breeding sites with reserves.

The project leads for the collection of this data were Julie Garcia and Evan King. Mule deer (47 adult females) from the Manache herd were captured and equipped with Lotek LiteTrack Iridium collars, transmitting data from 2020-2022. GPS fixes were set for 2-hour intervals. The Manache herd migrates from winter ranges just west of Route 395 on the steep slopes and valleys of the Sierra Nevada range near Dunmovin and Haiwee eastward to some of the higher altitude terrain in the continental USA in Inyo and Sequoia National Forests. Due to a high percentage of poor fixes, likely due to highly variable topographic terrain, between 2-18% of GPS locations per deer, or 5.78% of the entire dataset, were fixed in 2-dimensional space and removed to ensure locational accuracy.The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors and stopovers. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 38 migrating deer, including 114 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 9.34 days and 13.65 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Separate models using Brownian bridge movement models (BMMM) and fixed motion variances (FMV) of 1000 were produced per migration sequence. Due to high variances, Only FMV models were retained. Corridors were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 40 individual deer and 88 wintering sequences using an FMV of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 4 deer (10% of the sample), and greater than or equal to 8 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

The project leads for the collection of most of this data were Heiko Wittmer, Christopher Wilmers, Bogdan Cristescu, Pete Figura, David Casady, and Julie Garcia. Mule deer (82 adult females) from the Siskiyou herd were captured and equipped with GPS collars (Survey Globalstar, Vectronic Aerospace, Germany; Vertex Plus Iridium, Vectronic Aerospace, Germany), transmitting data from 2015-2020. The Siskiyou herd migrates from winter ranges primarily north and east of Mount Shasta (i.e., Shasta Valley, Red Rock Valley, Sheep Camp Butte, Sardine Flat, Long Prairie, and Little Hot Spring Valley) to sprawling summer ranges scattered between Mount Shasta in the west and the Burnt Lava Flow Geological Area to the east. A small percentage of the herd were residents. GPS locations were fixed between 1-2 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 67 migrating deer, including 167 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 12.09 days and 41.33 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to often produced BBMM variance rates greater than 8000, separate models using BBMMs and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (62 percent of sequences selected with BMMM). Winter range analyses were based on data from 66 individual deer and 111 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 4 deer (10 percent of the sample), and greater than or equal to 7 deer (20 percent of the sample) representing migration corridors, medium use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

This dataset includes the maps (0.1° x 0.1° latitude per longitude resolution) of the sensitivity of the values of the downscaled maps at the same spatial resolution. One map is created for each estimated proportion of individuals on the move.

Data, code and materials from a discrete experiment to test the validity of an Bayesian areal wombling algorithm for predicting social boundaries. The experiment was conducted as a part of project ‘Life at the Frontier: Researching the Impact of Social Frontiers on the Social Mobility and Integration of Migrants’ (2020-2023; NordForsk/ESRC, project no 95193), and experiment data was collected in Rotherham (UK).About the experimentEach border on a map is assigned a boundary value based on how dissimilar the adjacent neighbourhoods are (higher = more dissimilar = more likely to be a social boundary).The experiment was carried out as follows:- We created three maps of the same area with different boundaries using the Bayesian areal wombling approach.- Map A contained the boundaries with the highest boundary values, whilst map C had the lowest boundary values. Map B contained boundaries that were in between.- During an interview, participants were then shown pairs of maps and asked which map in each pair best corresponds to local community boundaries.- The sequence and order of the maps shown were randomised.- Assuming that residents and experts can recognise (but not necessarily recall) social boundaries, we conjecture that participants would choose the map containing borders with higher boundary values.Hypothesis: We hypothesise that participants will agree with the predictions of the areal wombling algorithm and choose boundaries with higher boundary values.Null hypothesis: Participants are not more or less likely to choose boundaries with higher boundary values.Aside from testing a hypothesis, another motivation behind the study is to explore the feasibility of the method for future replications and follow-up research.More informationThis study was approved by the University of Sheffield ethics committee (application number 042378).Please read the README file for a more detailed description of the content of this repository.

Background and goalThe study is conducted to facilitate conservation of migratory wader species along the East Asian-Australasian Flyway, particularly to 1) Identify hotspots of wader species richness along the flyway and effectively map how these might change between breeding, non-breeding and migratory phases; 2) Determine if the existing network of protected areas (PA) is sufficient to effectively conserve wader biodiversity hotspots along the EAAF; 3) Assess how species distribution models can provide complementary distribution estimates to existing BirdLife range maps.MethodsWe use a species distribution modelling (SDM) approach (MaxEnt) to develop temporally explicit individual range maps of 57 migratory wader species across their annual cycle, including breeding, non-breeding and migratory phases, which in turn provide the first biodiversity hotspot map of migratory waders along the EAAF for each of these phases. We assess the protected area coverage during each migration period, and analyse the dominant environmental drivers of distributions for each period. Additionally, we compare model hotspots to those existing range maps of the same species obtained from the BirdLife Internationals’ database.ResultsOur model results indicate an overall higher and a spatially different species richness pattern compared to that derived from a wader biodiversity hotspot map based on BirdLife range maps. Field observation records from the eBird database for our 57 study species confirm many of the hotspots revealed by model outputs (especially within the Yellow Sea coastal region), suggesting that current richness of the EAAF may have been underestimated and certain hotspots overlooked. Less than 10% of the terrestrial zones area (inland and coastal) which support waders are protected and, only 5% of areas with the highest 10% species richness is protected.Main conclusionsThe study results suggest the need for new areas for migratory wader research and conservation priorities including Yellow Sea region and Russian far-East. It also suggests a need to increase the coverage and percentage of current PA network to achieve Aichi Target 11 for Flyway countries, including giving stronger consideration to the temporal dynamics of wader migration.