domain: sde sdv

text:
Sarah C Davidson

Gil Bohrer
email: bohrer.17@osu.edu

Eliezer Gurarie

Scott Lapoint

Peter J Mahoney

Natalie T Boelman

Jan U H Eitel

Laura R Prugh

Lee A Vierling

Jyoti Jennewein

Emma Grier

Ophélie Couriot

Allicia P Kelly

Arjan J H Meddens

Ruth Y Oliver

Roland Kays

Martin Wikelski

Tomas Aarvak

Joshua T Ackerman

José A Alves

Erin Bayne

Bryan Bedrosian

Jerrold L Belant

Andrew M Berdahl

Alicia M Berlin

Dominique Berteaux

Joël Bêty

Dmitrijs Boiko

Travis L Booms

Bridget L Borg

Stan Boutin

W Sean Boyd

Kane Brides

Stephen Brown

Victor N Bulyuk

Kurt K Burnham

David Cabot

Michael Casazza

Katherine Christie

Erica H Craig

Shanti E Davis

Tracy Davison

Dominic Demma

Christopher R Desorbo

Andrew Dixon

Robert Domenech

Götz Eichhorn

Kyle Elliott

Joseph R Evenson

Klaus- Michael Exo

Steven H Ferguson

Wolfgang Fiedler

Aaron Fisk

Jérôme Fort

Alastair Franke

Mark R Fuller

Stefan Garthe

Gilles Gauthier

Grant Gilchrist

Petr Glazov

Carrie E Gray

David Grémillet

Larry Griffin

Mike Hallworth

Autumn-Lynn Harrison

Holly L Hennin

J Mark Hipfner

James Hodson

James A Johnson

Kyle Joly

Kimberly Jones

Todd E Katzner

Jeff W Kidd

Elly C Knight

Michael N Kochert

Andrea Kölzsch

Helmut Kruckenberg

Benjamin J Lagassé

Sandra Lai

Jean-François Lamarre

Richard B Lanctot

Nicholas C Larter

A David

M Latham

Christopher J Latty

James P Lawler

Don-Jean Léandri-Breton

Hansoo Lee

Stephen B Lewis

Oliver P Love

Jesper Madsen

Mark Maftei

Mark L Mallory

Buck Mangipane

Mikhail Y Markovets

Peter P Marra

Rebecca Mcguire

Carol L Mcintyre

Emily A Mckinnon

Tricia A Miller

Sander Moonen

Tong Mu

Gerhard J D M Müskens

Janet Ng

Kerry L Nicholson

Ingar Jostein Øien

Cory Overton

Patricia A Owen

Allison Patterson

Aevar Petersen

Ivan Pokrovsky

Luke L Powell

Rui Prieto

Petra Quillfeldt

Jennie Rausch

Kelsey Russell

Sarah T Saalfeld

Hans Schekkerman

Joel A Schmutz

Philipp Schwemmer

Dale R Seip

Adam Shreading

Mónica A Silva

Bryan W Smith

Fletcher Smith

Jeff P Smith

Katherine R S Snell

Aleksandr Sokolov

Vasiliy Sokolov

Diana V Solovyeva

Mathew S Sorum

Grigori Tertitski

J F Therrien

Kasper Thorup

T Lee Tibbitts

Ingrid Tulp

Brian D Uher-Koch

Rob S A Van Bemmelen

Steven Van Wilgenburg

Andrew L Von Duyke

Jesse L Watson

Bryan D Watts

Judy A Williams

Matthew T Wilson

Jay Wright

Michael A Yates

David J Yurkowski

Mark Hebblewhite

Ecological insights from three decades of animal movement tracking across a changing Arctic

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a unique window into these changes. Although substantial animal tracking data from the Arctic and Subarctic exist, most are difficult to discover and access. Here we utilize the new Arctic Animal Movement Archive (AAMA), a growing collection of 201 standardized terrestrial and marine animal tracking studies from 1991-present.

The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

One Sentence Summary: The Arctic Animal Movement Archive facilitates long-term and large-scale ecological studies of the Arctic and reveals the timing and extent of changes to animal behavior patterns.

Main Text

The Arctic and adjacent regions are experiencing the most rapid climate and environmental changes on Earth, caused primarily by anthropogenic greenhouse gas emissions [START_REF] Hebblewhite | Climate change 2014 -synthesis report, contribution of working groups I, II and III to the fifth assessment report of the IPCC[END_REF]. Striking trends include warming winter temperatures, ice loss, and earlier spring snowmelt. These changes profoundly affect conditions experienced by animals, including food availability, interspecific competition, predation and increased human disturbances [START_REF] Gilg | Climate change and the ecology and evolution of Arctic vertebrates[END_REF]. Impacts of climate change on Arctic vertebrates include rapid poleward range shifts [START_REF] Fossheim | Recent warming leads to a rapid borealization of fish communities in the Arctic[END_REF][START_REF] Chen | Rapid range shifts of species associated with high levels of climate warming[END_REF], phenological trophic mismatches [START_REF] Saalfeld | Multispecies comparisons of adaptability to climate change: A role for life-history characteristics?[END_REF], and changes in migration [START_REF] Ward | Multi-decadal trends in spring arrival of avian migrants to the central Arctic coast of Alaska: effects of environmental and ecological factors[END_REF], foraging, and predator-prey dynamics [START_REF] Rockwell | Trophic matches and mismatches: can polar bears reduce the abundance of nesting snow geese in western Hudson Bay?[END_REF]. Because rapid environmental change in the Arctic challenges the ability of the region's fauna to adapt, a primary response will likely occur through phenotypic plasticity in the patterns, locations, and timing of their movements [START_REF] Gilg | Climate change and the ecology and evolution of Arctic vertebrates[END_REF]. Documenting and understanding these changes requires multidecadal, pan-Arctic data at multiple trophic levels.

We demonstrate the ecological utility of the Arctic Animal Movement Archive (AAMA), an active, collaborative collection of animal tracking datasets (S.M). Marine ecology archives, such as IOOS-ATN, IMOS, OBIS-SEAMAP, and [START_REF] Ropert-Coudert | The retrospective analysis of Antarctic tracking data project[END_REF], provide insight regarding space use, movement and connectivity (e.g., [START_REF] Hindell | Tracking of marine predators to protect Southern Ocean ecosystems[END_REF][START_REF] Hays | Translating marine animal tracking data into conservation policy and management[END_REF][START_REF] Brodie | Continental-scale animal tracking reveals functional movement classes across marine taxa[END_REF]. Terrestrial animal movement archives are rare and tend to have a regional or taxonomic focus (e.g., [START_REF] Cagnacci | Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors[END_REF]. AAMA is the first Arctic-focused archive with both terrestrial and marine data and is hosted on the global Movebank database. The geographic scope of the AAMA (Fig. 1) includes the Arctic, Arctic-marine, and Subarctic 'boreal forests/taiga' regions defined by ( 13) and (14) (S.M.). The dataset contains over 15,000,000 occurrences of 8,000 individuals representing 96 species, from 1991-present (Fig.

S.1-S.2, Tables S.1-S.4). Combining data from multiple AAMA studies, we show evidence of (1) climate drivers of golden eagle migration phenology, (2) climate adaptation of parturition by caribou, and (3) consequences of increased temperature and precipitation on movements of mammalian predators and herbivores. Behavioral flexibility enables migrants to optimize energy expenditure during migration and adjust arrival at summering grounds [START_REF] Winkler | Cues, strategies, and outcomes: how migrating vertebrates track environmental change[END_REF][START_REF] Miller | Limitations and mechanisms influencing the migratory performance of soaring birds[END_REF]. We used tracking data from 103 individuals during 1993-2017 (S.M., Table S.5) to examine arrival timing to breeding grounds of northward migrating golden eagles ('summering'), modeling it with predictors for age, sex, summering onset latitude, year, and preceding-winter mean Pacific Decadal Oscillation index (PDO).

Mean summering date changed slowly over 25 years (-0.5 days/year). The long-term trend differed among age classes, with adults arriving earliest, followed by sub-adults and then juveniles, and was influenced by winter climate (PDO, Fig. 2; Tables S. [START_REF] Ropert-Coudert | The retrospective analysis of Antarctic tracking data project[END_REF]-S.9). Eagles of all age classes began summering later at northern latitudes (1.08 days/degree). The significant interaction of year and previous "warm-phase" PDO explains earlier summering dates for subadults and juveniles, highlighting their known responsiveness to environmental conditions [START_REF] Miller | Limitations and mechanisms influencing the migratory performance of soaring birds[END_REF]. These warm-phase winters cause warmer and drier climate with reduced snowpack and an earlier snow-free date. Earlier adult arrival to summering grounds should result from selection and competition for territories, yet local climatic variables affect eagle condition prior to, and energy expenditure during, northward migration [START_REF] Miller | Limitations and mechanisms influencing the migratory performance of soaring birds[END_REF]. For sub-adults sampled after 2011, the direct effect of PDO is significant, whereas in the full dataset the direct effect of year replaces PDO (Fig. 2). This period-related difference in inference of climatic drivers highlights the importance of compiling long-term, multi-generational observations. Given the importance of the winter PDO and known impacts of global climate change, golden eagles could face age-specific challenges during migration and at their warming Arctic summering grounds.

The timing of parturition is a key to the demography of wildlife populations, and can be an adaptive response to climate shifts [START_REF] Bonnet | The role of selection and evolution in changing parturition date in a red deer population[END_REF]. For many mammals, the period from late pregnancy through weaning has the highest energetic demands, and thus is timed to occur when vegetation productivity is highest [START_REF] Stoner | Ungulate reproductive parameters track Satellite observations of plant phenology across latitude and climatological regimes[END_REF]. Caribou occur in five different ecotypes (Fig. 3) across Boreal and Arctic North America, and are facing global declines [START_REF] Vors | Global declines of caribou and reindeer[END_REF]. Using data from 928 individuals during 2000-2017 in northern Canada, we used characteristic patterns of low movement during the calving season to estimate 1647 parturition dates in five populations of barren-ground, boreal, and mountain caribou (S.M., Table S.6). We found differences in parturition timing and trends among five populations. The southern and northern-boreal populations calved earliest, followed by northern and southern mountain populations (Table S.10). Barren-ground caribou calved later despite occupying a similar latitudinal range as the northern-boreal caribou (Fig. 3). Most importantly, only northern boreal caribou exhibited significant trends towards earlier parturition (0.4-1.1 days/year, Table S.10). This is the first continental-scale retrospective evidence of potential adaptive responses to climate trends by caribou. Animals conserve energy by modifying their behavior in response to weather conditions, with important implications for individual fitness and species resilience under climate change [START_REF] Clarke | Why does metabolism scale with temperature?[END_REF]. We tested for effects of temperature and precipitation on seasonal movement rates (m/min) using records from 1,720 individuals of two herbivore and three predator species (black bear, grizzly bear, caribou, moose, and wolf) during 1998-2019 (S.M., Table S.7). We predicted that winter movement rates would decline relative to summer, when energetic costs of selfmaintenance would be highest. Rate would also decline within seasons, during weather conditions that increase the energetic cost of movement (snow that increases energy requirements for movement, higher ambient temperatures during the summer that accelerate metabolism).

All species exhibited lower movement rates during winter relative to summer (Fig. 4). As temperatures increased in summer, wolves and black bears slowed their movement rates, while moose increased their movement rates. In winter, only barren-ground caribou increased movement rates as temperature increased. Snow impeded wolves, boreal caribou, and moose, whereas all species were generally insensitive to summer precipitation. These patterns may reflect asynchronous responses to climate change within and across trophic levels. Climatedriven variation in animal activity is likely to affect species interactions, altering energy expenditure, encounter rates, and foraging success with demographic implications for both predators and prey.

As we demonstrate, the AAMA provides a solution to Arctic data collection and sharing challenges. It serves as a critical baseline and resource to identify early signals of local or largescale changes in animal distribution, movement responses, and adaptive traits. Continued shifts in phenology in the Arctic pose challenges to migratory species that encounter changing seasonal fluctuations along migration routes, and at Arctic summering and southern wintering grounds [START_REF] Gill | Why is timing of bird migration advancing when individuals are not?[END_REF]. Key drivers of population responses, such as migration, parturition, and foraging movement, are undergoing rapid changes, suggesting that climate change is affecting animals in ways that will shape the future of the Arctic.

Materials and Methods:

Arctic Animal Movement Archive (AAMA)

The AAMA enables the discovery and use of Arctic and Subarctic animal movement data. The AAMA is hosted on Movebank (movebank.org), a global research platform for animal tracking and bio-logging data. We defined the geographic scope of the AAMA to include marine and terrestrial Arctic areas defined by the Arctic Monitoring and Assessment Programme (13) (shapefile available from https://www.amap.no/about/geographical-coverage) and Subarctic land regions represented as 'boreal forests/taiga' as classified by the Nature Conservancy (14) (shapefile available from http://maps.tnc.org/files/metadata/TerrEcos.xml). The AAMA boundary and the observation density within it are shown in Fig. 1. We calculated the density of animal occurrences shown in Fig. 1 as location counts within 10-km hexagonal grid cells (spacing 9.68 km, area 95.98 km 2 ) in equal area projection in R using the dggridR package (22).

The AAMA is a living archive. Many ongoing studies continuously add data, and new participants and studies can join. Data in the AAMA are organized into studies, with each study (i.e. dataset) managed by one or more data owners. Studies participating in the AAMA as of November 2019 are listed in Table S.1. Participating studies include data within the geographic area described above and can include additional data from other regions, thus supporting inclusion of species that spend substantial portions of their annual life cycle in temperate or tropical regions, and future inclusion of species that enter the region due to northward range shifts. The AAMA preserves the data, harmonizes it, and makes it discoverable by publishing the metadata. All data are curated to ensure they are accurately stored and include sufficient descriptive detail. AAMA Participants retain ownership of their data, with the option to allow public download or limit access to approved users. Study-level information including species, tagging method, number of animals and locations, time period covered, project description and available citations, and owner contact information are publicly available, searchable online, and by API. In all cases, data owners can be contacted directly to discuss data access and proposed uses. Location and other sensor data from the majority of participating studies are available for public download from the AAMA (currently 56% of the data), and many represent datasets formally published through the Movebank Data Repository. The AAMA offers assistance for potential users in negotiating data sharing and maintains current contact information for study owners. Reasons for access-controlled sharing include (i) data are still being actively collected and are not yet quality controlled, (ii) public knowledge of animal locations could compromise conservation efforts, and (iii) legal or contractual restrictions prohibit uncontrolled public data access. The AAMA's support for public and controlled-access sharing provides a solution that preserves and allows collaboration using data that would otherwise remain undiscoverable, and in many cases would eventually be lost (e.g., after the data owner retires). All data in the AAMA were collected using appropriate animal care permissions (Table S.3). Funding sources for data collection are provided in Table S.4. Current information about participating studies, how to download or request data, and how to join the archive and provide new datasets is available at the AAMA landing page (https://www.movebank.org/cms/movebankcontent/arctic-animal-movement-archive).

The organization of studies in the AAMA on the Movebank platform represents significant efforts to document and organize data using a consistent format, vocabulary, and set of quality-control procedures. This reduces the time needed to prepare data for sharing and integration in combined analyses, and provides a single access point, thus minimizing logistical efforts needed for construction of data ensembles to address large-scope research questions in meaningful meta-analyses. Furthermore, automated data feeds and management tools promote further participation by allowing data owners to efficiently add and maintain studies over time. We solicit additional participation in the AAMA, both by data owners and those conducting new analyses. As the archive and period of record grow, it will allow increasingly rigorous testing of hypotheses about human-driven, climate-induced, or other natural effects on animal movement, distribution and behavior.

Case Study 1. Long-term data reveal influence of decadal climate patterns on summering behavior in a migratory raptor

We used 569,720 location estimates collected from 146 golden eagles (Aquila chrysaetos) in 12 AAMA studies across western North America (Table S.5). Records were imported into R via the move package (23). We assigned all individuals to one of three age categories: juvenile (survived up to the onset of its 2 nd southbound migration), sub-adult (surviving up to the onset of its 4 th southbound migration), or adult (surviving past the onset of its 4 th southbound migration) [START_REF] Miller | Limitations and mechanisms influencing the migratory performance of soaring birds[END_REF][START_REF] Bloom | Molt and sequence of plumages of golden eagles and a technique for in-hand ageing[END_REF]. We incorporated into analyses instances where individuals were observed during an age-class transition. Breeding status was not considered in analyses because it was not determined for all individuals. We identified the onset of golden eagle summering behavior via a mechanistic range shift analysis (MRSA ( 25)) in R with the marcher package (26). The model was fit with the maximum likelihood method, and we used the Ornstein-Uhlenbeck model option to estimate ranging behavior. We modified the MRSA slightly by forcing the model to fail if a data gap of >30 days occurred. We inspected start dates and locations to remove spurious model results due to tag deployments that ended during spring migration or began during summer. This process yielded 179 summering onset estimates with modelled start and end dates made by 103 eagles (Table S.5).

We used linear mixed-effects models in R via the lme4 package (version 1.1-17) [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF], fit by restricted maximum likelihood to assess the significance of year (fixed, continuous, scaled), eagle sex and age during migration (both fixed, categorical), latitude (fixed, continuous), and PDO (fixed, continuous) in explaining golden eagle summering onset date. For adults and subadults, we included eagle individuals as a random term to account for repeated measures for some individuals (n = 49), but not for juveniles, due to a lack of repeated measures. We meancentered year and included an interaction between year and PDO. We averaged the monthly mean PDO [START_REF] Mantua | A Pacific interdecadal climate oscillation with impacts on salmon production[END_REF][START_REF] Litzow | The changing physical and ecological meanings of North Pacific Ocean climate indices[END_REF] of the preceding winter (November to March) to explore the influence of this large-scale climate indicator on golden eagle behavioral phenology. Multi-collinearity between predictor variables did not exceed recommended varianceinflation levels [START_REF] Dormann | Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[END_REF]. We performed model selection in R using the dredge function of the MuMIn package (31) and report results from the conditional averaged models. We first ran a model including eagles of all age classes to explore differences in age classes. We then analyzed each age class separately to facilitate interpretation. Because few summering onsets before 2011 were available for adults and sub-adults, we also compared model-averaged results for the full sets of adult and subadult observations (n = 89, n = 72, respectively) to those for the post-2011 adult and subadult observations (n = 83, n = 67, respectively).

Case Study 2. Large-scale geographic differences in parturition timing of caribou

We used caribou movement data in 13 AAMA studies (Table S.6). Among the caribou in our study, woodland caribou (Rangifer tarandus caribou) are considered a separate subspecies from barren-ground caribou (R. t. groenlandicus), which are known for their long-distance calving migrations. Woodland caribou are further classified as belonging to different ecotypes, including boreal and mountain, which differ by habitat and behavioral phenotype. Following exploratory analysis, we further partitioned woodland caribou by geographic area into northern and southern mountain and boreal caribou. Barren-ground caribou data were partitioned into their largely coherent reproductive subpopulations (herds).

Parturition dates were estimated by adapting methods described in [START_REF] Demars | Inferring parturition and neonate survival from movement patterns of female ungulates: a case study using woodland caribou[END_REF] for each of the two subspecies. For R. t. caribou, we analyzed movements between April 28 and June 30 and found times when movement activity decreased suddenly and persistently below 5 threshold speeds [START_REF] Hays | Translating marine animal tracking data into conservation policy and management[END_REF][START_REF] Winkler | Cues, strategies, and outcomes: how migrating vertebrates track environmental change[END_REF][START_REF] Clarke | Why does metabolism scale with temperature?[END_REF][START_REF] Gurarie | A framework for modelling range shifts and migrations: asking when, whither, whether and will it return[END_REF], and 30 m/h), and conservatively retained those parturitions where three of five thresholds agreed within a day of each other. For barren-ground caribou, which calve significantly later and shortly after completing a long-distance migration, we adapted the individual-based method [START_REF] Demars | Inferring parturition and neonate survival from movement patterns of female ungulates: a case study using woodland caribou[END_REF], which fits a non-calving model, in which movement rates remain constant throughout the study period, and a calving model, where there is a sudden drop in movement rate followed by a progressive increase. The models were fit using maximum likelihood, and AIC was used as a criterion to determine which model was selected. Tests for the effects of latitude, longitude, elevation, and year on parturition dates were conducted using a combination of linear models, generalized additive models, and mixed effects models, with individuals as a random effect. The R. t. groenlandicus (barren-ground) population is subdivided into five subpopulations (herds), which were also modeled as a random effect. Results are summarized in Table S.10.

Case Study 3: Temperature and precipitation response in movement rates of terrestrial mammals

We compiled movement paths for 1,720 individuals representing five mammalian species from 22 AAMA studies: black bear, Ursus americanus; grizzly bear, U. arctos; caribou, R. tarandus ssp.; moose, Alces alces; and wolf, Canis lupus (Table S.7). We calculated step lengths (m) and location sampling interval (min) along each movement time series, excluding locations with sampling intervals longer than 24 hours. We annotated movement data with daily maximum temperatures (°C) and summertime precipitation or winter snow water equivalent (SWE) estimates using the Daymet data product (33). To match the temporal scale of Daymet, we aggregated movements by estimating daily mean step lengths and sampling intervals for each day within an individual's time series. In order to reduce the influence of seasonally dependent behaviors (e.g., migration or parturition), we partitioned data into January-only (peak winter) and July-only (peak summer) datasets. As in case study 2, we treated the two subspecies of caribou (R. t. groenlandicus and R. t. caribou) as distinct groups within our analysis due to anticipated differences in daily movement rates. We also excluded bears from the winter analyses due to lack of data; however, the very nature of their hibernation represents a reduced overwinter movement rate.. We assessed the influence of daily maximum temperature and cumulative precipitation on daily movement rates (m•min -1 ) using a generalized linear mixed model (GLMM) framework with the lme4 R package [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]. Because movement rates are positive continuous, we used discretized step lengths (rounded to the nearest meter) as the response and location sampling interval as an offset in a GLMM with negative binomial errors. Prior to modeling, we standardized daily temperature and precipitation estimates by subtracting means and dividing by one standard deviation, each estimated at the study level by season in an effort to account for regional differences in exposure to weather conditions. We evaluated two models for each species represented by an interaction between season (January or July) and a single climate metric: season × temperature and season × precipitation. However, we used SWE (kg•m 2 ) as a measure of snow cover during the winter and daily total precipitation (mm) during the summer. We included random intercepts for individual nested within study crossed within year to account for variation in individual movement rates and sample sizes within and across studies. As there was a single study for bears, we included random intercepts for individual and year only. Variable effectiveness was determined by coefficient confidence interval overlap with zero, where an overlap indicating the effect is neutral (i.e., no change).

Table S1.

Studies participating in the AAMA as of November 2019. 'Study name' and 'Study ID' refer to the dataset as stored in a study on Movebank. 'Species' and 'Years' are based on locations within the AAMA boundary. 'Animals' shows the number of individuals with locations within the AAMA boundary, followed in parentheses, by the total number of animal individuals that 5 were tracked in the study, regardless of location. Superscript 'CSx' in 'Study name' indicates studies used in the case studies where x is the case study number (Tables S.5-S.7). 'Data ref' lists the citation for published study datasets. Current information about participating studies, and information about how to join, is available at the AAMA landing page.

Fig. 1 .

 1 Fig. 1. Map of the AAMA boundary and data. Density of animal locations at logarithmic scale characterizes data availability, not animal density or utilization.

Fig. 2 .

 2 Fig. 2. Changes in the onset date of golden eagles' summering. Coefficient estimates (±95% CI) reflecting age-specific changes in response to year, previous winter PDO, sex (reference:

Fig. 3 .

 3 Fig. 3. Climate-change adaptation of parturition times (PT) of caribou. Top (map): PT by population. Bottom left: PT trends by population, including five BG subpopulations. Bottom right: PT dates by elevation.

Fig. 4 .

 4 Fig. 4. Changes in species-specific movement rates in response to daily maximum temperature, summer precipitation and winter snow-water equivalent (SWE). Odds ratios

and RYO assisted with data interpretation. Other authors (listed alphabetically) contributed data to AAMA. All authors contributed to the writing of the manuscript. Competing interests: Authors declare no competing interests. Data and materials availability: Data supporting the analysis in

  Table S.3). Funding: NASA ABoVE NNX15AT91A, NNX15AW71A, NNX15AV92A, NNX15AT89A, NNX15AU20A; NSF 1564380, 1823498, 1560727, DMS-1853465, IIBR-1915347. Funding for studies participating in the AAMA are listed in Table S.4. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US or Canada State, Province, Territory, or Federal Governments. Author contributions: SCD and GB conceived and drafted the manuscript. NB, JE, MH, LRP, LV managed AAMA data-sharing agreements. SCD organized and curated the AAMA on Movebank, with assistance from MW. RK produced Fig. S.2. SL, EG and PJM developed the case studies. MH, LP, JJ, EG, OC, APK, AJHM,

	Supplementary Materials for
	this
	manuscript are available through Dryad (link will be provided when paper is accepted). Links to
	studies participating in the AAMA (Table S.1) and instructions for data access are at
	https://www.movebank.org/cms/movebank-content/arctic-animal-movement-archive (S.M.)
	Supplementary Materials:
	Methods
	Figures S.1-S.2
	Tables S.1-S.10
	References (22-63)

Ecological insights from three decades of animal movement tracking across a changing Arctic

	Authors: Sarah C. Davidson, Gil Bohrer * , Eliezer Gurarie, Scott LaPoint, Peter J. Mahoney, Natalie T. Boelman,
	Jan U.H. Eitel, Laura R. Prugh, Lee A. Vierling, Jyoti Jennewein, Emma Grier, Ophélie Couriot, Allicia P. Kelly,
	Arjan J.H. Meddens, Ruth Y. Oliver, Roland Kays, Martin Wikelski, Tomas Aarvak, Joshua T. Ackerman, José A.
	Alves, Erin Bayne, Bryan Bedrosian, Jerrold L. Belant, Andrew M. Berdahl, Alicia M. Berlin, Dominique Berteaux,
	Joël Bêty, Dmitrijs Boiko, Travis L. Booms, Bridget L. Borg, Stan Boutin, W. Sean Boyd, Kane Brides, Stephen
	Brown, Victor N. Bulyuk, Kurt K. Burnham, David Cabot, Michael Casazza, Katherine Christie, Erica H. Craig,
	Shanti E. Davis, Tracy Davison, Dominic Demma, Christopher R. DeSorbo, Andrew Dixon, Robert Domenech,
	Götz Eichhorn, Kyle Elliott, Joseph R. Evenson, Klaus-Michael Exo, Steven H. Ferguson, Wolfgang Fiedler, Aaron
	Fisk, Jérôme Fort, Alastair Franke, Mark R. Fuller, Stefan Garthe, Gilles Gauthier, Grant Gilchrist, Petr Glazov,
	Carrie E. Gray, David Grémillet, Larry Griffin, Mike Hallworth, Autumn-Lynn Harrison, Holly L. Hennin, J. Mark
	Hipfner, James Hodson, James A. Johnson, Kyle Joly, Kimberly Jones, Todd E. Katzner, Jeff W. Kidd, Elly C.
	Knight, Michael N. Kochert, Andrea Kölzsch, Helmut Kruckenberg, Benjamin J. Lagassé, Sandra Lai, Jean-
	François Lamarre, Richard B. Lanctot, Nicholas C. Larter, A. David M. Latham, Christopher J. Latty, James P.
	Lawler, Don-Jean Léandri-Breton, Hansoo Lee, Stephen B. Lewis, Oliver P. Love, Jesper Madsen, Mark Maftei,
	Mark L. Mallory, Buck Mangipane, Mikhail Y. Markovets, Peter P. Marra, Rebecca McGuire, Carol L. McIntyre,
	Emily A. McKinnon, Tricia A. Miller, Sander Moonen, Tong Mu, Gerhard J.D.M. Müskens, Janet Ng, Kerry L.
	Nicholson, Ingar Jostein Øien, Cory Overton, Patricia A. Owen, Allison Patterson, Aevar Petersen, Ivan Pokrovsky,
	Luke L. Powell, Rui Prieto, Petra Quillfeldt, Jennie Rausch, Kelsey Russell, Sarah T. Saalfeld, Hans Schekkerman,
	Joel A. Schmutz, Philipp Schwemmer, Dale R. Seip, Adam Shreading, Mónica A. Silva, Bryan W. Smith, Fletcher
	Smith, Jeff P. Smith, Katherine R.S. Snell, Aleksandr Sokolov, Vasiliy Sokolov, Diana V Solovyeva, Mathew S.
	Sorum, Grigori Tertitski, J.F. Therrien, Kasper Thorup, T. Lee Tibbitts, Ingrid Tulp, Brian D. Uher-Koch, Rob S.A.
	van Bemmelen, Steven Van Wilgenburg, Andrew L. Von Duyke, Jesse L. Watson, Bryan D. Watts, Judy A.
	Williams, Matthew T. Wilson, Jay Wright, Michael A. Yates, David J.
	Hebblewhite
	Correspondence to: bohrer.17@osu.edu
	This PDF file includes
	Materials and Methods
	Figs. S.1 to S.2
	Tables S.1 to S.10

Table S2 .

 S2 Species-level and species group summary of data from studies participating in the AAMA as of November 2019.

	Gavia stellata	Seabirds		2 Argos Doppler
	Haematopus ostralegus	Shorebirds		2 GPS
	Haliaeetus leucocephalus Raptors		6 GPS, Argos Doppler
	Species Histrionicus histrionicus	Species group Seabirds	Studies	Location 1 Argos Doppler
	Larus argentatus	Shorebirds		sensors 2 GPS, Argos Doppler
	Larus fuscus	Seabirds		1 GPS
	Alces alces Larus hyperboreus	Terrestrial mammals Shorebirds		4 GPS 2 GPS, Argos Doppler
	Alle alle Larus marinus	Seabirds Seabirds		1 Geolocator 1 GPS
	Anas acuta Larus thayeri	Waterbirds Shorebirds		1 GPS 1 Argos Doppler
	Anas Americana Larus vegae	Waterbirds Shorebirds		1 GPS 2 GPS
	Anas clypeata Lepus arcticus	Waterbirds Terrestrial mammals		1 GPS 1 Argos Doppler
	Anas discors Limosa haemastica	Waterbirds Shorebirds		1 GPS 1 Argos Doppler
	Anser albifrons Limosa lapponica	Waterbirds Shorebirds		10 GPS, Argos Doppler 1 Argos Doppler
	Anser brachyrhynchus Melanitta fusca	Waterbirds Seabirds		1 GPS, Argos Doppler 1 Argos Doppler
	Anser erythropus Melanitta nigra	Waterbirds Seabirds		1 GPS 1 Argos Doppler
	Anser fabalis Melanitta perspicillata	Waterbirds Seabirds		2 GPS 3 Argos Doppler
	Aquila chrysaetos Numenius arquata	Raptors Shorebirds		18 GPS, Argos Doppler 1 GPS
	Asio flammeus Numenius phaeopus	Raptors Shorebirds		2 Argos Doppler 3 Argos Doppler
	Balaena mysticetus Oporornis agilis	Marine mammals Passerines+cuckoo		3 Argos Doppler 1 Geolocator
	Balaenoptera borealis Ovis dalli	Marine mammals Terrestrial mammals		1 Argos Doppler 4 GPS, Radio transmitter
	Balaenoptera physalus Phalaropus lobatus	Marine mammals Seabirds		1 Argos Doppler 3 Geolocator
	Branta bernicla Phoca largha	Waterbirds Marine mammals		5 GPS, Argos Doppler 1 Argos Doppler
	Branta canadensis Plectrophenax nivalis	Waterbirds Passerines+cuckoo		1 Argos Doppler 2 Geolocator
	Branta hutchinsii Pluvialis dominica	Waterbirds Shorebirds		1 GPS 1 GPS
	Branta leucopsis Pluvialis squatarola	Waterbirds Shorebirds		8 GPS, Argos Doppler 3 Argos Doppler
	Bubo scandiacus Pusa hispida	Raptors Marine mammals		3 Argos Doppler 4 Argos Doppler
	Bucephala islandica Rangifer tarandus	Seabirds Terrestrial mammals		1 Argos Doppler 17 GPS, Argos Doppler, Radio transmitter
	Buteo lagopus Rissa tridactyla	Raptors Seabirds		3 GPS, Argos Doppler, Radio transmitter 2 GPS
	Buteo platypterus Somateria fischeri	Raptors Seabirds		1 Argos Doppler 1 Argos Doppler
	Calidris alpina Somateria mollissima	Shorebirds Seabirds		2 GPS, Geolocator 5 GPS, Argos Doppler
	Calidris melanotos Somateria spectabilis	Shorebirds Seabirds		1 GPS 3 Argos Doppler
	Calidris pusilla Stercorarius longicaudus	Shorebirds Seabirds		1 GPS 2 Argos Doppler, Geolocator
	Canis lupus Stercorarius parasiticus	Terrestrial mammals Seabirds		7 GPS, Radio transmitter 4 GPS, Argos Doppler, Geolocator
	Charadrius hiaticula Stercorarius pomarinus	Shorebirds Seabirds		1 Geolocator 1 Argos Doppler
	Chen caerulescens Sterna paradisaea	Waterbirds Seabirds		3 GPS, Argos Doppler 1 Geolocator
	Chen rossii Tringa flavipes	Waterbirds Shorebirds		1 GPS 1 GPS, Argos Doppler
	Chordeiles minor Turdus migratorius	Raptors Passerines+cuckoo		1 GPS 1 GPS, Argos Doppler
	Circus cyaneus Uria aalge	Raptors Seabirds		2 GPS 1 Geolocator
	Clangula hyemalis Uria lomvia	Seabirds Seabirds		5 Argos Doppler, Geolocator 13 GPS, Geolocator
	Cuculus canorus Ursus americanus	Passerines+cuckoo Terrestrial mammals		2 GPS, Argos Doppler 1 GPS
	Cygnus cygnus Ursus arctos	Waterbirds Terrestrial mammals		5 GPS, Argos Doppler 3 GPS, Radio transmitter
	Erignathus barbatus Vulpes lagopus	Marine mammals Terrestrial mammals		1 Argos Doppler 1 Argos Doppler
	Euphagus carolinus Xema sabini	Passerines+cuckoo Seabirds		1 GPS 2 Argos Doppler, Geolocator
	Falco columbarius	Raptors		1 Argos Doppler
	Falco peregrinus	Raptors		8 GPS, Argos Doppler
	Falco rusticolus	Raptors		5 Argos Doppler
	Fratercula arctica	Seabirds		1 Geolocator
	Gavia adamsii	Seabirds		1 Argos Doppler
	Gavia pacifica	Seabirds		1 Argos Doppler

Table S . 3 .

 S3 Light-bellied brent goose (Canadian High Arctic) Kendrew Colhoun British Trust for Ornithology Licence A/5436 Long-tailed ducks ARGOS 2019 No permissions were required according to the §44 and §6 of the Federal Law of the Russian Federation No. 52 from 24.04.1995 (last update 18.02.2020) "On Wildlife". St Petersburg State University Ethic Committee Permission 131-03-4. Long-tailed ducks GLS 2018 No permissions were required according to the §44 and §6 of the Federal Law of the Russian Federation No. 52 from 24.04.1995 (last update 18.02.2020) "On Wildlife". Long-tailed Jaeger Alaska US Federal Bird Banding Permit 09700; Alaska scientific permits 15-33, 17-162, 18-156, 19-157; National Zoological Park ACUC 15-33, 18-06; National Park Service AKR_DENA_Harrison_Jaegers_2018; Environment and Climate Change Canada Scientific Permit 10565N; Government of Nunavut wildlife research permits WL2018-052, WL2019-041; Environment Canada Western and Northern ACC approvals EC-PN-13-006, 18JR01, 19JR01; Environment Canada scientific permits 10565J, 10565M, 10565N; Environment Canada Wildlife and Landscape Science Directorate and Canadian Wildlife Service ACC Approval EC-PN-14-006 Long-tailed Skua, van Bemmelen, Sweden Jordbruks verket permits M160-11, M470-12, M472-12 Lower 48 GOEA Migration US Federal Bird Banding permits 23353, 24140 LTDU US Federal Bird Banding permits 23769, 22636; US Fish and Wildlife Service Permit MB48614B; annual state permits for Delaware, Maryland, North Carolina, Virginia; US Geological Survey Patuxent Wildlife Research Center IACUC Protocol 2010-10 Migration of Sabine's gulls from the Canadian High Arctic CWS Animal Care EC-PN-11-020; CWS Scientific Permit NUN-SCI-09-01; Government of Nunavut Wildlife Research Licence WL 2010-042; Nunavut Water Board Licence 3BC-TER0811; Indian and Northern Affairs Land Use Reserve 068H16001; CWS Banding Permit 10694 Migration Patterns of Pacific Sea Ducks Simon Fraser University permits 781B-06, 999B-06 Migration timing in barnacle geese (Barents Sea) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014) Lower Saxony State Office for Consumer Protection and Food Safety (LAVES) Permission 33-42502-04-10/0307; Institute of Avian Research, Wilhelmshaven, Germany; National Park Administration of Lower Saxony, Wilhelmshaven, Germany Migration timing in barnacle geese (Greenland) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)

		British Trust for Ornithology Licence A/5436 and
		Endorsement 06-01w
	Migration timing in barnacle geese (Svalbard)	British Trust for Ornithology Licence A/5436 and
	(data from Kölzsch et al. and Shariatinajafabadi et	Endorsement 06-01w
	al. 2014)
	Migration timing in white-fronted geese (data from	Russian Academy of Sciences Institute of Geography (no
	Kölzsch et al. 2016)	approval needed); Wageningen Environmental Research (no
		approval needed)
	Mountain caribou in British Columbia	British Columbia Wildlife Act Permits FJ08-59586, FJ07-
		40107 and FJ17-282173

Table S .4.

 S

	latitude	0.6672	0.6334	46	1.032	0.302
	wPDO	-8.5069	2.6366	33	3.162	0.002
	sex:male	-0.1708	5.0829	43	0.033	0.974
	year:wPDO	-9.4866	2.4671	51	3.768	<0.001
	Juvenile golden eagles (n = 18):
	Full model: df =6, BIC = 142.8, AICc = 145.0, R 2 = 0.50, weight = 0.057
	year	1.0892	4.4923	NA	0.230	0.818
	latitude	0.9474	0.7820	NA	1.121	0.262
	wPDO	1.4309	2.2793	NA	0.579	0.563
	year:wPDO	-6.4330	2.3073	NA	2.546	0.011
	Natural Environment Research Council
	Grant NE/M012549/1
	Natural History Museum of Latvia
	Natural Resources Canada Polar Continental Shelf Program
	Natural Sciences & Engineering Research Council of Canada (NSERC)
	Discovery Grants
	Discovery, Fisheries, & Oceans Canada
	Integrated Landscape Management Chair
	Northern Internship Program & Discovery Grant
	Ocean Tracking Network
	Research Tools & Instruments Grants
	Nature Conservation & Nuclear Safety

Acknowledgments:

The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the US Fish and Wildlife Service, NSF or NASA. This manuscript has been peer reviewed and approved for publication consistent with USGS Fundamental Science Practices. All tagging studies were performed in accordance with Acknowledgements and funding for studies participating in the AAMA. Organization funding might extend beyond the subdivisions, grants, and programs listed. Netherlands Institute of Ecology, Department of Animal Ecology (NIOO-KNAW) Netherlands Organisation for Scientific Research (NWO) Polar Programme New Brunswick Wildlife Trust Fund New Mexico Department of Game & Fish New York State Department of Environmental Conservation Niedersächsische Wattenmeerstiftung Project 18/19 Norcross Wildlife Foundation North Carolina Museum of Natural Sciences North Carolina State University, College of Natural Resources North Slope Borough Department of Wildlife Management Shell Baseline Studies Program North Star Science and Technoloy Northern Arizona University Norwegian Birds Protection Fund Norwegian Research Council (MIGRAPOP) Novartis Pesticide Division Nuttall Ornithological Society Offield Family Foundation Oregon Department of Fish & Wildlife Ornitela UAB Outer Continental Shelf Program Overlook Foundation Owl Research Institute, Charlo, Montana Parks Canada, Government of Canada Patagonia Foundation Petroleum Technology Alliance Canada Princeton University, Department of Ecology and Evolutionary Biology Private donors to author KRSS; College of William & Mary, Center for Conservation Biology; Raptor View Research Institute Proconvergencia Açores/EU Program Programa Operacional Potencial Humano (POPH) Acores-01-0145-FEDER-000057 Raptor View Research Institute Reneco International Wildlife Consultants Robert B. Berry Royal Caribbean Cruiseline, Inc. Royal Society for the Protection of Birds Russian Academy of Sciences Ecological Research Station of the Institute of Plant and Animal Ecology Institute of Biological Problems of the North FEB Institute of Geography Laboratory of Ornithology, Institute of Biological Problems of the North Plant and Animal Ecology UD RAS University of Alaska Anchorage University of Alberta Department of Biological Sciences Faculty of Science University of Aveiro Department of Biology Centre for Environmental and Marine Studies (CESAM) University of California-Davis University of Cape Town Percy Fitzpatrick Institute of African Ornithology, NRF-DST Centre of Excellence University of Colorado Department of Integrative Biology University of Copenhagen Center for Macroecology, Evolution and Climate GLOBE Institute University of Glasgow University of Iceland South Iceland Research Centre University of Idaho Department of Natural Resources and Society University of Konstanz Centre for the Advanced Study of Collective Behaviour Department of Biology University of Latvia Institute of Biology University of Maine School of Biology and Ecology University of Manitoba University of Massachusetts Amherst Northeast Climate Adaptation Science Center University of Montana W.A. Franke College of Forestry and Conservation, Department of Ecosystem and Conservation Sciences, Wildlife Biology Program University of Regina University of South Dakota University of the Azores Institute of Marine Research (IMAR) Marine and Environmental Sciences Centre (MARE) Okeanos R&D Centre University of Washington College of the Environment School of Aquatic & Fishery Sciences University of Windsor Department of Biological Sciences Great Lakes Institute for Environmental Research, School of the Environment US Bureau of Ocean Energy Management (formerly, Minerals Management Service)

ACUC = Animal Care and Use Committee, AWC = Animal Welfare Committee, IACUC = Institutional Animal Care and Use Committee, WCC = Wildlife Care Committee. ADFG Fortymile River Brown Bears Alaska Department of Fish and Game Protocol 08-08 ABoVE: ADFG Mulchatna Caribou Alaska Department of Fish and Game Division of Wildlife Conservation 2013-10, 2015-33 ABoVE: BC Atlin Caribou British Columbia Provincial authority under the BC Wildlife Act ABoVE: Boelman Alberta American Robins Alberta Environment

 S.1) colored by species group (Table S.2). Note logarithmic scale on yaxis. 5

Figure S.2.

The number of locations per year per species group for data participating the AAMA as of November 2019. The number of locations includes only those with locations within the AAMA boundary. See Table S Population-level summary statistics for caribou parturition analysis (case study 2). The trend for mountain caribou was modeled by generalized additive models (GAMs) that include linear latitude and longitude effects and a spline on elevation. The woodland caribou trend models only include latitude and longitude effects. The barren-ground trend model is a linear mixed effects models with random intercepts and slopes (year effect) on herd (sub-5 population) as a random effect. The r 2 values are the conditional as opposed to marginal r 2 following (63). The pvalue and trend are on the collective main effect and do not account for variation among herds.