DOI: 10.5061/dryad.wwpzgmsrd
title: Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate

description: Climate change has well-documented, yet variable, influences on the annual
 movements of migratory birds. The effects of climate change on fall
 migration remains understudied compared to spring, but appears to be less
 consistent among species, regions, and years. Changes in the pattern and
 timing of waterfowl migration in particular may result in cascading
 effects on ecosystem function, and socioeconomic and cultural outcomes. We
 investigated changes in the migration of 15 waterfowl species along a
 major flyway corridor of continental importance in northeastern North
 America using 43 years of community-science data. We built spatially- and
 temporally-explicit hierarchical generative additive models for each
 species and demonstrated that climate, specifically the interaction
 between minimum temperature and precipitation, significantly influences
 migration phenology for most species. Certain species’ migratory movements
 responded to specific temperature thresholds (climate migrants) and others
 reacted more to the interaction of temperature and precipitation (extreme
 event migrants). There are already significant changes in the fall
 migration phenology of common waterfowl species with high ecological and
 economic importance, which may simply increase in the context of a
 changing climate. If not addressed, climate change could induce mismatches
 in management, regulations, and population surveys which would negatively
 impact the hunting industry. Our findings highlight the importance of
 considering species-specific spatiotemporal scales of effect on climate on
 migration and our methods can be widely adapted to quantify and forecast
 climate-driven changes in wildlife migration.
description: <em>Fall migration observation data            
 </em> Étude des populations d'oiseaux du
 Québec (ÉPOQ) is a database of bird observations managed by QuébecOiseaux
 (Larivée, 2001). It is North America’s longest-running, and prior to
 eBird, largest checklist program. Data are collected according to
 guidelines designed to maximize scientific rigor (Dunn et al., 1996). ÉPOQ
 consists of more than six million observations that have been collected
 since the early 1900s by birders from all around Québec, as well as many
 retro-active contributions from other published works that date back to
 the 1700s. Although both professional and amateur birders are able to
 submit their observations to ÉPOQ, 90% of the data is collected by 10% of
 the observers, called ‘expert observers’ (Francoeur, 2012). ÉPOQ is an
 invaluable source of historical bird abundance information in Québec,
 particularly for data compiled from the late 1960s to 2012. After 2012,
 the growing popularity of eBird largely replaced ÉPOQ, and the reduction
 in the number of observations reduced our ability to draw inference for
 multiple species. Unlike standardized bird surveys, ÉPOQ observations are
 mostly opportunistic, variable in effort and there is no explicit
 indication of a species’ absence. However, they are well suited to provide
 information on trajectory and timing trends (Dunn et al.,
 1996).         We compiled all ÉPOQ records from 1970
 to 2012 for waterfowl species from August 15 to January 31 of the
 subsequent year to ensure we captured a buffer period before and after
 fall migration. To minimize potential biases with opportunistic data
 collection, we restricted our analyses to complete checklists (as
 indicated by the observers) that had an effort of ≥ 30 mins and ≤ 8 hrs.
 We restricted the analysis to a focal area within 100 km of the St.
 Lawrence River, starting near Cornwall, ON at the Québec border and
 extending eastward to Riviere-du-Loup, QC. After applying all the filters,
 we identified 15 focal species that had a minimum of 10 years in which a
 species was reported in ≥ 40 checklists per year, which we identified as
 our minimal threshold for inclusion (Table S1, Supplemental Tables) and
 restricted our analyses to these years. To spatially aggregate the ÉPOQ
 records, we georeferenced each observation to one of the 12 economic
 regions in the study area (Fig. 1) and aggregated all observations of
 species/day of year (hereafter DOY) per year for each of the economic
 regions. Because ÉPOQ does not explicitly indicate a species’ absence, we
 assumed the species was absent if not included on a complete checklist. We
 did not include checklists with geese only for duck species, as they
 typically represent observations of large flocks flying overhead, and
 therefore do not necessarily represent effort in suitable waterfowl
 stopover habitat. <em>Climate data and
 spatio-temporal scales</em> To estimate the
 influence of climate to the north of each economic region, which broadly
 reflects conditions from where birds would be migrating from, we
 delineated six spatial scales. Starting at the mid-latitude for each
 economic region and spanning the width of the region, the spatial scales
 moved northward at the following distances: 0- 50 km, 50 – 150 km, 150 –
 250 km, 250 – 400 km, 400 – 600 km, and 600 – 1000 km. The spatial scales
 also widened beyond the width of the region at a logistic growth scale (0,
 4, 45, 118, 496, 800 km) so that the scales would capture possible
 east-west migration of birds as they traveled southwards (Fig. 1). As the
 initial width of the first (and smallest) spatial scale is set to match
 with the width of the economic region, which differ in size, there is some
 variation in spatial scales for each economic region. This variation is
 small compared to the size differences between the scales (Fig. S1), with
 the largest difference occurring at the largest scales which,
 proportionally, would be the smallest comparative difference across the
 economic regions. Expert waterfowl researchers and biologists across the
 study region guided the decision for the spatial scale sizes and the
 funnel-like shape of the scales. Natural Resources
 Canada provided spatiotemporal data of minimum temperature and
 precipitation from interpolated weather station data (McKenney et al.,
 2011). This included 5-day averages (pentads) for minimum temperature and
 precipitation. The only other variable available from this dataset
 (maximum temperature) was not used in the analysis due to its correlations
 with minimum temperature, and the consensus among waterfowl experts was
 that minimum temperature during the fall was the most ecologically
 relevant variable. We extracted minimum temperature and precipitation at
 six spatial scales for each of the 12 economic regions (Fig.1).  To assess
 how climatic conditions had changed during migration, we identified the
 time-period during which 90% of the EPOQ observations were made and
 calculated the seasonal (fall migration period) mean minimum temperature
 and mean precipitation during this time-period for each spatial scale. We
 then used a linear model (LM) to test seasonal trends in minimum
 temperature and precipitation with effects for year, spatial scale, and
 their interaction that differed across spatial scales. 
description: # Community-science reveals delayed fall migration of waterfowl and
 spatiotemporal effects of a changing climate Abstract Climate change has
 well-documented, yet variable, influences on the annual movements of
 migratory birds. The effects of climate change on fall migration remains
 understudied compared to spring, but appears to be less consistent among
 species, regions, and years. Changes in the pattern and timing of
 waterfowl migration in particular may result in cascading effects on
 ecosystem function, and socioeconomic and cultural outcomes. We
 investigated changes in the migration of 15 waterfowl species along a
 major flyway corridor of continental importance in northeastern North
 America using 43 years of community-science data. We built spatially- and
 temporally-explicit hierarchical generative additive models for each
 species and demonstrated that climate, specifically the interaction
 between minimum temperature and precipitation, significantly influences
 migration phenology for most species. Certain species’ migratory movements
 responded to specific temperature thresholds (climate migrants) and others
 reacted more to the interaction of temperature and precipitation (extreme
 event migrants). There are already significant changes in the fall
 migration phenology of common waterfowl species with high ecological and
 economic importance, which may simply increase in the context of a
 changing climate. If not addressed, climate change could induce mismatches
 in management, regulations, and population surveys which would negatively
 impact the hunting industry. Our findings highlight the importance of
 considering species-specific spatiotemporal scales of effect on climate on
 migration and our methods can be widely adapted to quantify and forecast
 climate-driven changes in wildlife migration. 1. Author List: Barbara
 Frei, Amelia R. Cox, Ana Morales, and Christian Roy 2. Date of data
 collection (single date, range, approximate date): 1970-2012 3. Geographic
 location of data collection: Quebec, Canada 4. Information about funding
 sources that supported the collection of the data: Environment and Climate
 Change Canada ## SHARING/ACCESS INFORMATION 1. Licenses/restrictions
 placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 2. Links to
 publications that cite or use the data: Barbara Frei, Amelia R. Cox, Ana
 Morales, and Christian Roy 2024. Community-science reveals delayed fall
 migration of waterfowl and spatiotemporal effects of a changing climate.
 Journal of Animal Ecology. 1. Links to other publicly accessible locations
 of the data: None 2. Links/relationships to ancillary data sets: None 3.
 Was data derived from another source? No A. If yes, list source(s): NA 4.
 Recommended citation for this dataset: Barbara Frei, Amelia R. Cox, Ana
 Morales, and Christian Roy. 2024. Data from: Community-science reveals
 delayed fall migration of waterfowl and spatiotemporal effects of a
 changing climate. Journal of Animal Ecology. Dryad Digital Repository.
 1. Description of data
 source Raw bird observation data is hosted by QuébecOiseaux and can only
 be requested directly. Raw climatic data is hosted by Natural Resources
 Canada and can only be requested directly. To run the climate models, rank
 models, and perform all other analyses and create the figures we have
 included the transformed and joined data file "Fall DOY EPOQ with
 pendads.csv" ## CODE OVERVIEW 1. File list for code All analysis was
 conducted in R version 4.2.3 (2023-03-15). A) JAE_code.R : This code runs
 24 GAM models for each species, ranks the models, creates the predicted
 abundance plots, extracts the peak migration and passage period for each
 species, runs LM to identify changes in peak migration data and passage
 period length over time, and creates plots to visualize the findings. 1.
 Relationship between code files, if important: None. ## DATA OVERVIEW 1.
 File list for data A) Fall DOY EPOQ with pendads.csv 1. Relationship
 between files, if important: None 2. Additional related data collected
 that was not included in the current data package: None 3. Are there
 multiple versions of the dataset? No A. If yes, name of file(s) that was
 updated: NA i. Why was the file updated? NA ii. When was the file updated?
 NA
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: Fall DOY EPOQ with pendads.csv 1. Number of
 variables: 72 2. Number of cases/rows: 548,744 3. Variable List: * Species
 - Four letter species code (ABDU - American Black Duck, AMWI - American
 Wigeon, BAGO - Barrow's Goldeneye, BWTE - Blue-winged Tea, CANG -
 Canada Goose, COEI - Common Eider, COGO - Common Goldeneye, GWTE -
 Green-winged Teal, HOME - Hooded Merganser, LTDU - Long-tailed Duck, MALL
 - Mallard, NOPI - Northern Pintail, RNDU - Ring-necked Duck, SNGO - Snow
 Goose, SUSC - Surf Scoter) * Year - Year of observation * DOY - Day of
 Year (1-365) * Period - This variable is similar to year but included the
 January dates from the following year in the same period (e.g., January
 dates from 1971 were coded as Period 1970). * DOY2 - Day of Year (1-396).
 Note that as the data range was from August - January of the following
 year, the DOY included the January dates of the following year that were
 still considered to be in the same period (see period description above).
 * Zone - The economic zone ID of Quebec, Canada * Count - The count of
 individuals for the species in the observation (abundance) * Effort -
 Effort recorded by the individual for the checklist (minutes) * Pentad -
 The pentad number (5-day period) from which the climatic data was
 extracted from which matches the DOY of the bird observation * Pentad2 -
 The pentad number (5-day period) from which the climatic data was
 extracted from which matches the DOY2 of the bird observation * Pentad_ID
 - The unique ID of the year, economic scale, and pentad raster from which
 the climate data was extracted from * log_Effort - The logged value of the
 Effort (min) * MINT_present_s1 - Minimum temperature for the present
 period for spatial scale 1 (Celsius) * MINT_lag1_s1 - Minimum temperature
 for time lag of 5 days for spatial scale 1 (Celsius) * MINT_lag2_s1 -
 Minimum temperature for time lag of 10 days for spatial scale 1 (Celsius)
 * MINT_lag3_s1 - Minimum temperature for time lag of 15 days for spatial
 scale 1 (Celsius) * MINT_lag4_s1 - Minimum temperature for time lag of 20
 days for spatial scale 1 (Celsius) * MINT_present_s2 - Minimum temperature
 for the present period for spatial scale 2 (Celsius) * MINT_lag1_s2 -
 Minimum temperature for time lag of 5 days for spatial scale 2 (Celsius) *
 MINT_lag2_s2 - Minimum temperature for time lag of 10 days for spatial
 scale 2 (Celsius) * MINT_lag3_s2 - Minimum temperature for time lag of 15
 days for spatial scale 2 (Celsius) * MINT_lag4_s2 - Minimum temperature
 for time lag of 20 days for spatial scale 2 (Celsius) * MINT_present_s3 -
 Minimum temperature for the present period for spatial scale 3 (Celsius) *
 MINT_lag1_s3 - Minimum temperature for time lag of 5 days for spatial
 scale 3 (Celsius) * MINT_lag2_s3 - Minimum temperature for time lag of 10
 days for spatial scale 3 (Celsius) * MINT_lag3_s3 - Minimum temperature
 for time lag of 15 days for spatial scale 3 (Celsius) * MINT_lag4_s3 -
 Minimum temperature for time lag of 20 days for spatial scale 3 (Celsius)
 * MINT_present_s4 - Minimum temperature for the present period for spatial
 scale 4 (Celsius) * MINT_lag1_s4 - Minimum temperature for time lag of 5
 days for spatial scale 4 (Celsius) * MINT_lag2_s4 - Minimum temperature
 for time lag of 10 days for spatial scale 4 (Celsius) * MINT_lag3_s4 -
 Minimum temperature for time lag of 15 days for spatial scale 4 (Celsius)
 * MINT_lag4_s4 - Minimum temperature for time lag of 20 days for spatial
 scale 4 (Celsius) * MINT_present_s5 - Minimum temperature for the present
 period for spatial scale 5 (Celsius) * MINT_lag1_s5 - Minimum temperature
 for time lag of 5 days for spatial scale 5 (Celsius) * MINT_lag2_s5 -
 Minimum temperature for time lag of 10 days for spatial scale 5 (Celsius)
 * MINT_lag3_s5 - Minimum temperature for time lag of 15 days for spatial
 scale 5 (Celsius) * MINT_lag4_s5 - Minimum temperature for time lag of 20
 days for spatial scale 5 (Celsius) * MINT_present_s6 - Minimum temperature
 for the present period for spatial scale 6 (Celsius) * MINT_lag1_s6 -
 Minimum temperature for time lag of 5 days for spatial scale 6 (Celsius) *
 MINT_lag2_s6 - Minimum temperature for time lag of 10 days for spatial
 scale 6 (Celsius) * MINT_lag3_s6 - Minimum temperature for time lag of 15
 days for spatial scale 6 (Celsius) * MINT_lag4_s6 - Minimum temperature
 for time lag of 20 days for spatial scale 6 (Celsius) * logPCP_present_s1
 - Logged precipitation for the present period for spatial scale 1
 (Celsius) * logPCP_lag1_s1 - Logged precipitation for time lag of 5 days
 for spatial scale 1 (mm) * logPCP_lag2_s1 - Logged precipitation for time
 lag of 10 days for spatial scale 1 (mm) * logPCP_lag3_s1 - Logged
 precipitation for time lag of 15 days for spatial scale 1 (mm) *
 logPCP_lag4_s1 - Logged precipitation for time lag of 20 days for spatial
 scale 1 (mm) * logPCP_present_s2 - Logged precipitation for the present
 period for spatial scale 2 (mm) * logPCP_lag1_s2 - Logged precipitation
 for time lag of 5 days for spatial scale 2 (mm) * logPCP_lag2_s2 - Logged
 precipitation for time lag of 10 days for spatial scale 2 (mm) *
 logPCP_lag3_s2 - Logged precipitation for time lag of 15 days for spatial
 scale 2 (mm) * logPCP_lag4_s2 - Logged precipitation for time lag of 20
 days for spatial scale 2 (mm) * logPCP_present_s3 - Logged precipitation
 for the present period for spatial scale 3 (mm) * logPCP_lag1_s3 - Logged
 precipitation for time lag of 5 days for spatial scale 3 (mm) *
 logPCP_lag2_s3 - Logged precipitation for time lag of 10 days for spatial
 scale 3 (mm) * logPCP_lag3_s3 - Logged precipitation for time lag of 15
 days for spatial scale 3 (mm) * logPCP_lag4_s3 - Logged precipitation for
 time lag of 20 days for spatial scale 3 (mm) * logPCP_present_s4 - Logged
 precipitation for the present period for spatial scale 4 (mm) *
 logPCP_lag1_s4 - Logged precipitation for time lag of 5 days for spatial
 scale 4 (mm) * logPCP_lag2_s4 - Logged precipitation for time lag of 10
 days for spatial scale 4 (mm) * logPCP_lag3_s4 - Logged precipitation for
 time lag of 15 days for spatial scale 4 (mm) * logPCP_lag4_s4 - Logged
 precipitation for time lag of 20 days for spatial scale 4 (mm) *
 logPCP_present_s5 - Logged precipitation for the present period for
 spatial scale 5 (mm) * logPCP_lag1_s5 - Logged precipitation for time lag
 of 5 days for spatial scale 5 (mm) * logPCP_lag2_s5 - Logged precipitation
 for time lag of 10 days for spatial scale 5 (mm) * logPCP_lag3_s5 - Logged
 precipitation for time lag of 15 days for spatial scale 5 (mm) *
 logPCP_lag4_s5 - Logged precipitation for time lag of 20 days for spatial
 scale 5 (mm) * logPCP_present_s6 - Logged precipitation for the present
 period for spatial scale 6 (mm) * logPCP_lag1_s6 - Logged precipitation
 for time lag of 5 days for spatial scale 6 (mm) * logPCP_lag2_s6 - Logged
 precipitation for time lag of 10 days for spatial scale 6 (mm) *
 logPCP_lag3_s6 - Logged precipitation for time lag of 15 days for spatial
 scale 6 (mm) * logPCP_lag4_s6 - Logged precipitation for time lag of 20
 days for spatial scale 6 (mm) 1. Missing data codes: NA 2. Specialized
 formats or other abbreviations used: None