Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate
Data files
Jan 18, 2024 version files 620.28 MB
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Fall_DOY_EPOQ_with_pentads.csv
620.27 MB
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README.md
11.74 KB
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.
README: 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.
- Author List: Barbara Frei, Amelia R. Cox, Ana Morales, and Christian Roy
- Date of data collection (single date, range, approximate date): 1970-2012
- Geographic location of data collection: Quebec, Canada
- Information about funding sources that supported the collection of the data: Environment and Climate Change Canada
SHARING/ACCESS INFORMATION
- Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain
- 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.
- Links to other publicly accessible locations of the data: None
- Links/relationships to ancillary data sets: None
- Was data derived from another source? No A. If yes, list source(s): NA
- 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. https://doi.org/10.5061/dryad.wwpzgmsrd
- 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
- 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.
- Relationship between code files, if important: None.
DATA OVERVIEW
- File list for data
A) Fall DOY EPOQ with pendads.csv
- Relationship between files, if important: None
- Additional related data collected that was not included in the current data package: None
- 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
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DATA-SPECIFIC INFORMATION FOR: Fall DOY EPOQ with pendads.csv
- Number of variables: 72
- Number of cases/rows: 548,744
- 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)
- Missing data codes: NA
- Specialized formats or other abbreviations used: None
Methods
Fall migration observation data
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.
Climate data and spatio-temporal scales
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.