Evaluate migration at the global, regional, and local scales. The GeoInquiry activity is available here.Educational standards addressed:APHG: II.C. Explain how push and pull factors contribute to migration.APHG: II.C. Analyze the cultural, economic, environmental, and political conse- quences of migration.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

Abstract

The main purpose of the Viet Nam Migration Survey 2004 is to supplement the lack of current understanding about the migration relating to migration decision and results of the different types of moves by migration streams and their relations to regional and national development schemes. Information collected from the survey will form a supplement information source to other available sources (censuses, surveys), and at the same time, provide detailed data on other aspects of the migration process, which are not available at other sources.

The survey focuses on: (a). Model, some streams and types of migration; (b). Cause and results of those moves; (c). Characteristics, including attitude, awareness of the surveyed population in relation to their moves; (d). Characteristics on reproductive health; (e). Those information will be collected for non-migrants in order to find out social-economic differences between migrants and non-migrants. Specifically, the survey aims to the collection of the following data: · Process of migration including decision of move, number of moves, and process of settling their life in and looking jobs; · Socio-economic and demographic factors and facilitating factors of migration; · Consequences of movement of migrants and their family in terms of: - income and employment - living conditions and housing - remittance - access to social and health services - life satisfactions and recreation - adaptation and attitude change · Comparison of situation of migrants and non-migrants in the destination areas · Develop policy recommendations on rural development to prevent out-migration, on regional development to divert migration streams to other regions, on information programs to assist those who wish to move, and on health and social services to assist migrants in their adjustment and integration at destination areas.

Geographic coverage

The survey covered the following areas: Area 1: Hanoi Area 2: Northeast economic zone, including Hai Phong, Hai Duong, and Quang Ninh Area 3: Central Highlands, including Gia Lai, Dak Lak, Dak Nong, and Lam Dong Ho Chi Minh City Industrial zone of Binh Duong, and Dong Nai

Analysis unit

• Household
• Individual

Universe

Household: includes one or more than one persons, having common dwelling and sharing food. Usual residents: are persons who usually live and have food in the household; or persons recently have moved into the household and stayed stably there for one month or more, regardless of the fact that they have or have not been registered by police office.

Definition of migrants:
Including those who are in the age group 15-59 and moved from one district to another within the five years before the survey, and not less than one month. For 3 cities: Hanoi, Hai Phong and Ho Chi Minh, those who moved from one quarter to another within a city are not covered by this definition.

Migration here is the internal migration of the Vietnamese people.

Non-migrants: Including those who are in the age group 15-59 and not determined as migrants.

Kind of data

Sample survey data [ssd]

Sampling procedure

In total, about 10,000 individual interviews will be conducted, including 5,000 migrants and 5,000 non-migrants. All of them will be in the age group 15-59. To ensure a complete obtainment of the above-targeted number of interviews, it is important to have a good preparation, helping in the determination of enumeration areas with highest migration rates.

The extent to which the sample can be generalized is limited. The main objective of the survey was to understand migration and differentials among migration types, and the survey was not intended to provide estimates that were representative of any clearly defined geographical area. For the five main areas including in the sample design, selection of respondents was not undertaken on the basis of equal probability of selection, either between or within the areas. Furthermore, information is not available to construct sampling weights that would adjust for the unequal probability of selection. Therefore the results for each area should not be interpreted as representing the populations of those areas.

To ensure sufficient representation of different types of migrants, defined here in terms of household registration status, the sampling scheme concentrated on those areas that had the highest proportions of temporary migrants. This means that the results are most likely to represent the areas that are the destinations of high numbers of temporary migrants. Because the non-migrant sample was drawn from the same areas as the migrants, the non-migrants do not represent a cross-section of non-migrants. Rather, they represent non-migrants living in areas that attract large numbers of temporary migrants.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey uses 3 kinds of questionnaire: 1) A household questionnaire was administered in each household, which collected various information on household members including - Identification information - Information on each of household members: relationship to household head, sex, age and questions used to identify household with migrants or non-migrants; - Questions for the household as a whole: housing, electricity for lighting, possession of TV, radio, toilet facility, expenses for food, main income source, time to the nearest primary school, secondary school and hospital.

2) A Migrant questionnaire includes: - Part 1: Used to collect information on characteristics of the respondent, such as age, sex, marital status, education level and access to mass media; - Part 2: Used to collect information on the migration history, such as place of birth, place of residence at age 15, number of moves, and access to urban centre; - Part 3: Used to collect information on last move, such as the place of residence before the move, reasons of move, decision making process to move, persons accompanying migrant, assistance received, knowledge and utilization of job introduction agency, time for looking for work, difficulties faced, residence registration, and remittances; - Part 4: Used to collect information on current activity and living conditions, such as: activity status, occupation, industry, time of work, income, expenses, savings, access and use of health services, access to education of children, participation in mass organization activity, and security; - Part 5: Used to collect information on health, health care, cigarette smoking and alcohol drinking; - Part 6: Used to collect information on HIV/AIDS, sexual transmitted infections and family planning. 3) A Non - migrant questionnaire: - Except for the exclusion of Part 3, the content of the Form C questionnaire was similar to that of Form B.

Cleaning operations

The data-entry programme was developed in CSPRO25 to ensure proper validation of the entered data. The program provided value and range checks of the variables, skip patterns, and relationship checks among designated variables. During data entry, the programme prompted data entry personnel to check the entered value against the value in the questionnaire when inconsistencies were located. Two kinds of prompts were programmed: i). A warning prompt that meant that if the entered value was confirmed the warning could be ignored; ii). A confirmatory prompt that would not allow data to be entered. In the case of a confirmatory prompt, data entry personnel were required to discuss the problem with staff of the Department of Population and Labour Statistics before the prompt could be over-ridden. The migrant household and non-migrant household used the same data-entry programme and have the same data structure.

The data-entry management programme was written in Visual FoxPro. It ensured that data entry and editing worked smoothly and efficiently. This programme was used to monitor survey units and quality of questionnaires; to manage the user data-entry programme; to provide authorization for use of data; to protect against duplication or missing questionnaires in comparison with the selected sample; and to manage data in the LAN environment. It is the interface between the system, data entry personnel and users.

This activity uses Map Viewer and is designed for intermediate users. We recommend MapMaker when getting started with maps in the classroom - see this StoryMap for the same activity in MapMaker.ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.AP skills & objectives (CED)Skill 2.B: Explain spatial relationships in a specified context or region of the world, using geographic concepts, processes, models, or theories.IMP-2.A: Explain factors that account for contemporary and historical trends in population growth and decline.IMP-2.C: Explain how different causal factors encourage migration.Learning outcomesStudents will identify and explain why some regions of the world experience high and low net migration rates.Students will analyze the relationship between Lampedusa, Italy’s relative location and refugee migration.More activitiesAll Human Geography GeoInquiriesAll GeoInquiries

Annual migratory movements can range from a few tens to thousands of kilometers, creating unique energetic requirements for each specific species and journey. Even within the same species, migration costs can vary largely because of flexible, opportunistic life history strategies. We uncover the large extent of variation in the lifetime migratory decisions of young white storks originating from eight populations. Not only did juvenile storks differ in their geographically distinct wintering locations, their diverse migration patterns also affected the amount of energy individuals invested for locomotion during the first months of their life. Overwintering in areas with higher human population reduced the stork’s overall energy expenditure because of shorter daily foraging trips, closer wintering grounds, or a complete suppression of migration. Because migrants can change ecological processes in several distinct communities simultaneously, understanding their life history decisions helps not only to protect migratory species but also to conserve stable ecosystems. Flack A, Fiedler W, Blas J, Pokrovski I, Kaatz M, Mitropolsky M, Aghababyan K, Fakriadis Y, Makrigianni E, Jerzak L, Azafzaf H, Feltrup-Azafzaf C, Rotics S, Mokotjomela TM, Nathan R, Wikelski M, 2016, Costs of migratory decisions: a comparison across eight white stork populations. Science Advances 2(1): e1500931. doi:10.1126/sciadv.1500931

Migration to and from the UK after Brexit was a thirty-nine months project (Jan 2021 – March 2024) funded by the Economic and Social Research Council (ESRC) through their Governance After Brexit Scheme [‘Rebordering Britain and Britons after Brexit’ (MIGZEN), Grant Number: ES/V004530/1] and led by researchers at the University of Birmingham (Lead Research Organisation) and Lancaster University. Brexit brought public and political attention to longstanding concerns within migration and citizenship scholarship, throwing questions of citizenship, migration and belonging into sharp relief; it also affected people's sense of belonging, mobility and settlement plans, as Britons in the EU and EU citizens and non-EU Third Country Nationals (TCN) in the UK found the status and the terms of their residence challenged, their claims to belonging, and access to rights questioned, their settlement plans in jeopardy.

With the end of the Brexit transition period came significant changes in the composition of migration flows to and from the UK, which were further compounded by the geopolitical effects and implications of the tense relationship between China and Hong Kong, and the Russian invasion of Ukraine in 2022.

A collaborative, mixed-method research project involving academics, policy makers, civil society and migrant-led organisations, the project therefore explored the long-term impacts of Brexit and Britain’s shifting position on the world stage on migration to and from the UK, and on migrants’ experiences of these. Through this research, we sought to inform migration policy and debate by providing evidence of the everyday challenges brought by Brexit on individuals and their families living within and across the UK borders.

The project consisted of three phases as follows: - Phase 1: Survey (‘Migration and Citizenship after Brexit’): conducted in the UK and the EU between 13 December 2021 and 16 January 2022. - Phase 2: People’s Panel: conducted in the UK and the EU between May and December 2022. - Phase 3: Interviews with repatriating British citizens: conducted in the UK between May and September 2022; Interviews with Ukrainians, family and highly skilled migrants in the UK: conducted in the UK between September 2022 and February 2023; and Interviews with British emigrants: conducted in the UK between April and August 2023.

The Brexit negotiations have brought public and political attention to longstanding concerns within migration and citizenship scholarship, throwing questions of citizenship, migration and belonging into sharp relief. It is also clear that Brexit has affected people's sense of belonging, mobility and settlement plans. In the wake of Brexit, Britons in the EU and EU citizens and non-EU Third Country Nationals (TCN) in the UK are finding the status and the terms of their residence challenged, their claims to belonging, and access to rights questioned, their settlement plans in jeopardy.

Rebordering Britain and Britons after Brexit (MIGZEN) turns its attention towards these emerging issues. Through a collaborative project involving academics, policy makers, civil society and migrant-led organisations it aims to produce new knowledge about migration between the UK and EU, and how the changing legal and political relationship between the UK and EU in consequence of Brexit shapes migration and migrant experience - including settlement, questions of identity, citizenship and belonging.

It takes a unique approach to understanding the story of migration between Britain and Europe that foregrounds both immigration and emigration from Britain, and adopts an inclusive understanding of who is a migrant to examine different forms of mobility, including third country nationals and those previously entitled to freedom of movement, namely UK nationals moving within the EU, and EU citizens moving to the UK. It offers a critical analysis of the relationship between migration and migration governance in the UK that situates it in the context of the current geopolitical repositioning of the country. By foregrounding the nexus between migration and citizenship, MIGZEN offers in-depth insights into the changing relationship between the UK and European Union through a focus on migration and its governance.

The project develops an ambitious and innovative programme of work on the impact of Brexit on migration to and from the United Kingdom at a range of scales: (a) policies and legal structures; (b) flows and routes; (c) migration strategies and settlement experiences where it addresses the following research questions:

• How, and in what ways, have volume, geography and direction of migration flows between the UK and EU changed since the Brexit Referendum? And how does this relate to global migrations to and from the UK?
• In what ways do settled populations - UK nationals resident in EU member states before Brexit and EU citizens living in the UK - assess their mobile and residential futures in light...

1. Network theory offers new perspective on movement data by evaluating the relationships between animal movements (links) and detection locations (nodes) in spatially complex systems, including human-altered landscapes.

2. We applied network analyses to intra- and interspecific movement patterns in the migration behaviour and dam passage success of two anadromous fish species, Pacific lamprey Entosphenus tridentatus Gairdner and Chinook salmon Oncorhynchus tshawytscha Walbaum, when moving through a large multi-fishway hydroelectric project (Bonneville Dam, USA).

3. Network analyses revealed greater variation in movement for Pacific lamprey compared with Chinook salmon. Salmon that passed the dam had networks consisting of more direct passage routes with fewer overall movements compared with lamprey that passed the dam. Lamprey that did not pass the dam exhibited a wide range of behaviours, from approaching only one fishway site to testing all possible passage routes. Accounting for the time spent in the network improved the ability to detect biological differences in network structure for lamprey that did and did not pass the dam.

4. The movement patterns likely resulted from different behavioural responses to complex environmental and internal factors affecting a philopatric species (Chinook salmon) versus a non-philopatric species (Pacific lamprey) when moving through an engineered environment designed primarily for salmon.

5. Synthesis and applications. Our case study highlights the potential for network analyses to link questions of basic movement ecology with monitoring of movement and behaviour in human-altered landscapes. Network analyses can thus serve as a valuable tool for describing movement and behaviour in the face of environmental change and assessing the effectiveness of mitigation efforts at spatially complex obstacles to animal movement.

A figure showing the total proportion of time spent diving to >10 m in each hour and the mean dive depth for three latitudinal bands.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 6.5-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

Timeseries of structure and development of the former German Democratic Republic’s population.

The aim of this data-collection is to inform about the population’s structure and development in the former GDR, including East-Berlin, from 1946 to 1989.

Basis of the compilation is the published statistical population overview of the German Federal Statistical Office (Statistisches Bundesamt (hrsg.): Sonderreihe mit Beiträgen für das Gebiet der ehemaligen DDR. Heft 3: Bevölkerungsstatistische Übersichten 1946 bis 1989. Wiesbaden, 1993), completed by census data and scientific publications.

The survey contains details on population and populationstructure (population-size, -growth, density, agegroups, etc.), on natural population movement (birth, decease, marriages, divorces), on spatial population movement (internal migration, migration beyond the borders of the former GDR), and on households.

The datacompilation covers the following topics:

A) population B) natural population movement C) households D) migration

Topics:

Data-Tables in the download-system HISTAT (Thema: Bevölkerung)

A. Bevölkerungsstand:

A01 Bevölkerungsstand und Bevölkerungsentwicklung (1939-1989) A02 Bevölkerung nach Altersgruppen 1946-1989 A03 Männliche Bevölkerung nach Altersgruppen 1946-1989 A04 Weibliche Bevölkerung nach Altersgruppen 1946-1989 A05. Bevölkerungsgröße, Bevölkerungswachstum, Bevölkerungsdichte und Sexualproportion 1950- 1992 A06. Bevölkerung insgesamt, männlich und weiblich nach Ländern 1950-1998 A07. Fläche, Bevölkerung am Ort der Hauptwohnung und Bevölkerungsdichte für 1950, 1964, 1971, 1981 A08. Bevölkerung am Ort der Hauptwohnung nach Altersgruppen und Geschlecht 1950-1981 A09. Bevölkerung am Ort der Hauptwohnung nach Altersgruppen und Geschlecht 1950-1981 A10. Bevölkerung ab 18 Jahre am Ort der Hauptwohnung nach Familienstand und Geschlecht 1950-1981 A11. Fläche und Bevölkerung nach Bezirken 1950-1989 A12. Bevölkerung nach Altersgruppen und Geschlecht für die neuen Länder und Berlin Ost 1950-1990 A13 Bevölkerung nach Gemeindegrößenklassen (in 1000) 1950-1989

B. Natürliche Bevölkerungsbewegung

B01 Natürliche Bevölkerungsbewegung 1946-1995 B02a Eheschließungen, durschnittliches Heiratsalter, Ehescheidungen 1946-1989 B02b Eheschließungen nach Familienstand der Partner vor Eheschließung 1946-1989 B03 Eheschließende, Ersteheschließende und Wiederverheiratete (insgesamt) 1946-1989 B04 Eheschließende nach Ersteheschließenden und Wiederverheirateten (je 100 Eheschließende) 1946-1989 B05 Eheschließende nach Familienstand vor der Eheschließung (insgesamt) 1946-1989 B06 Eheschließende nach Familienstand vor der Eheschließung (je 100 Eheschließende) 1946-1989 B07 Zusammengefasste Geburtenziffer nach Altersgruppen 1952-1989 B08 Das Reproduktionsniveau der Bevölkerung 1946-1989 B09 Durchschnittliche Lebenserwartung Neugeborener in Jahren 1946-1989 B10a Geborene, Lebendgeborene und Totgeborene nach Legitimität 1952-1989 B10b Lebend- und Totgeborene nach Geschlecht 1950-1989 B11 Zusammengefaßte Geburtenziffer nach Gemeindegrößenklassen (1965-1989) B12 Altersgruppenspezifische Sterbeziffern nach Geschlecht ( standardisiert) 1964-1989 B13a Gestorbene insgesamt und gestorbene Säuglinge nach Geschlecht (1946-1989) B13b Gestorbene nach ausgewählten Todesursachen und nach Geschlecht 1947-1989 B13c Gestorbene nach ausgewählten Krankheiten als Todesursachen und nach Geschlecht 1947-1989 B14 Gestorbene infolge Suizid- DDR 1947-1989 B15 Gestorbene infolge Suizid- BRD B16 Gestorbene infolge Mord und Totschlag- DDR 1949-1989 B17 Gestorbene infolge Mord und Totschlag- BRD / Bundesrepublik Deutschland (1961-1989) B18 Die Entwicklung der Fruchtbarkeitsziffern in den beiden Teilen Deutschlands (1946/50-1995)

C. Haushalte

C01 Privathaushalte nach Haushaltsgröße 1950-1981 C02 Personen in Privathaushalten und Gemeinschaftseinrichtungen 1950-1981 C03 Mehrpersonenhaushalte nach im Haushalt lebenden Kindern unter 17 Jahren 1950-1981 C04 Privathaushalte nach Haushaltsgroesse und nach Altersgruppen des Haushaltsvorstandes 1950 bis 1981 C05 Privathaushalte nach Haushaltsgroesse und nach Altersgruppen des maennlichen Haushaltsvorstandes 1950 bis 1981

D. Wanderung

D01 Wanderung über die Grenzen der DDR 1951-1989 D02 Wanderung über die Grenzen der DDR nach Altersgruppen 1965-1989 D03 Binnenwanderungsgewinn bzw.- verlust (-) nach Gemeindegrößenklassen 1970-1989 D04 Saldo aus zu- und Fortzügen (-) über die Grenzen der ehemaligen DDR nach Gemeindegrößeklassen 1965-1989 D05 Binnenwanderung über die Gemeinde- bzw. Kreisgrenzen 1953-1989

Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world’s most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone’s bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.

Methods Details of how these data were collected can be found in the Methods and Supplementary Materials of Geremia et al. 2019, Migrating bison engineer the green wave (Proceedings of the National Academy of Science). A brief summary of each dataset follows:

bisonsurfdata.csv - Data to replicate analysis of green-wave surfing in bison. See Methods and Text S1 in Supplementary text for details. Columns: id = animal id; year = year of day of animal location; jul = julian date of animal location; maxIRGdate = julian date of max IRG for the location.

fecaldata.csv - Data to replicate analysis of bison diet quality over time. See Methods for details. Columns: year = year fecal sample was collected; julianday = julian day fecal sample was collected; CP = crude protien of sample; DOM = digestible organic matter of sample.

leafdata.csv - Data to replicate anlaysis of plant-forage quality as it relates to days from peak IRG. See Methods and Text S1 for details. Columns: year = year plant tissue sample was collected; julianday = julian day plant tissue sample was collected; leafN = N of sample; leafC = C of sample; NETDFPIRG = absolute value of the number of days between the julian date the sample was collected and the julian day of peak IRG for the pixel where the sample was collected.

functionalNDVIdata.csv - Data to replicate functional NDVI analysis. See Methods and Text S3, S4, S5, and S6 for details. Columns: site = name of grazing experiment site; year = year of NDVI data; bisonuseindex = grazing intensity index; swe = Snow Water Equivelant value; precip = precipitation value; temp = temperature value; slope = slope of site in degrees; aspect = aspect of site in degrees; elev = elevation of site; columns 10 through 42 = NDVI values for julian dates 57 through 313 at 8 day intervals.

grazingexperimentdata.csv - Data to replicate grazing experiment analysis. See Methods and Text S3 and S5 for details. Columns: siteyrid = site and year combined; site = name of grazing experiment site; year = year of data collection; julianday = julian day of data collection; plottype = type of plot, control (for plots within fenced exclosure) or experimental (plots outside in grazed areas); plotnumber = both control and experimental plots were replicated with up to 6 replicates each; shootbiomass = shoot biomass of sample; leafN = N of sample; leafC = C of sample; SiteAnnualgrazingintensity = grazing intensity index for the year. Note that site grazing intensity can be slightly less than 0 due to sampling variation. See Text S3 in regards to "adding positive and negative increments."

grazingintensitydata.csv - Data to to build the linear relationship between field measured grazing intensity and landscape modeled grazing intensity. See Text S3 and S5 for details. Columns: siteyrid = site and year combined; site = name of grazing experiment site; year = year; grazingintensity = is field measured grazing intensity using the plot data; bisonuseindex = the averaged value for bison use from scaled Brownian Bridge Movement Models for the larger areas around each grazing experiment site.

fullspringbisonsurfdata.csv – Additional data to replicate analysis of green-wave surfing in bison described in Geremia et al. 2020 Response to Craine: Bison redefine what it means to move to find food. Columns are the same as described in bisonsurfdata.csv. Data file includes locations as described in the response letter.

Run analyses.R – Native R code to replicate analyses in Geremia et al. 2019 and Geremia et al. 2020. Run by downloading and unzipping files to a user defined working directory and specifying the working directory in the code header.

Hummingbirds and other lightweight bird species are challenging to track because they have limited capacity to carry devices for data-logging. We present a simple and customizable three-loop 'backpack' harness for studying hummingbird migration and movement, with step-by-step instructions for harness construction and attachment. The harness has negligible weight and cost (< $0.50 USD/each), is easy for a single person to make and apply in the field, and it requires no complicated setup or equipment. We have field-tested this harness on 74 Giant Hummingbirds (Patagona gigas) with three different types of tracking devices (geolocators, GPS tags, and satellite transmitters) in Chile and Peru from 2017–2020. Based on recaptures to date, we report that harnesses last at least two years, even under high UV-light conditions. We found no evidence of adverse effects of the harnesses on birds after one to two years and apparent survival of geolocator-tracked Giant Hummingbirds was in line with published estimates for other hummingbird species. This harness method is a practical and effective option for mounting ultra-light devices on hummingbirds, and it can be readily modified for other species with short legs, prominent sternal keels, and long wings.

A figure showing the daily speed of travel, mean duration of dives, the latitude, and the mean depth of dives for turtle C.

Table of data for modeled tributaries, including distance from mouth and relative proportion of smolts originating from each.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 3.0-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

Details and model validation of generalized additive mixed models.