
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences

38437528
202308433
10.1073/pnas.2308433121
research-articleResearch ArticleecoEcology414
Biological Sciences
Ecology
Decoupling of bird migration from the changing phenology of spring green-up
Robertson Ellen P. robertsonep@gmail.com
a b 1 https://orcid.org/0000-0002-1338-4045

La Sorte Frank A. c d https://orcid.org/0000-0001-8521-2501

Mays Jonathan D. e
Taillie Paul J. f https://orcid.org/0000-0001-7172-3589

Robinson Orin J. g
Ansley Robert J. a https://orcid.org/0000-0002-4448-5623

O’Connell Timothy J. a https://orcid.org/0000-0001-8215-2670

Davis Craig A. a https://orcid.org/0000-0001-7227-7795

Loss Scott R. a https://orcid.org/0000-0002-8753-2995

aDepartment of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078
bSouth Central Climate Adaptation Science Center, Norman, OK 73019
cDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511
dCenter for Biodiversity and Global Change, Yale University, New Haven, CT 06511
eFish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Gainesville, FL 32611
fDepartment of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
gCornell Lab of Ornithology, Cornell University, Ithaca, NY 14850
1To whom correspondence may be addressed. Email: robertsonep@gmail.com.
Edited by Joan Strassmann, Washington University in St. Louis, St. Louis, MO; received May 19, 2023; accepted January 9, 2024

4 3 2024
19 3 2024
4 9 2024
121 12 e230843312119 5 2023
09 1 2024
Copyright © 2024 the Author(s). Published by PNAS.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

Significance

Spring vegetation green-up generates important food resources for many migratory animals, provided the timing of green-up matches the timing of migration. Yet green-up phenology is changing with climate change, and it is unclear for most species whether migrations are flexible to track these changes. We examined changing green-up phenology in relation to the migrations of 150 bird species over 20 y across the Western Hemisphere. Our findings reveal that migrations of most species synchronize more closely with long-term averages of green-up timing than with current green-up conditions. Further, changing green-up strongly influenced phenological mismatches between the timing of bird migration and the timing of green-up, highlighting changing vegetation phenology as a potential growing threat of climate change for migratory animals.

The green-up of vegetation in spring brings a pulse of food resources that many animals track during migration. However, green-up phenology is changing with climate change, posing an immense challenge for species that time their migrations to coincide with these resource pulses. We evaluated changes in green-up phenology from 2002 to 2021 in relation to the migrations of 150 Western-Hemisphere bird species using eBird citizen science data. We found that green-up phenology has changed within bird migration routes, and yet the migrations of most species align more closely with long-term averages of green-up than with current conditions. Changing green-up strongly influenced phenological mismatches, especially for longer-distance migrants. These results reveal that bird migration may have limited flexibility to adjust to changing vegetation phenology and emphasize the mounting challenge migratory animals face in following en route resources in a changing climate.

migration
phenology
climate change
==== Body
pmcGlobal climate change is profoundly affecting many of the world’s species (1–4), and a critical factor influencing these impacts is the ability of species to adjust their behaviors in response to changing environmental conditions (5, 6). Migratory species can travel extraordinary distances between breeding and nonbreeding areas, a life history strategy that has fascinated humans for centuries but that makes their populations particularly vulnerable to climate change (7–9). Although the ability to cover large distances may impart migratory species with some behavioral flexibility to climate change, the far distances traveled may also render them vulnerable to “phenological mismatches” where the timing of a consumer’s resource needs and the timing of resource availability are not aligned (7, 9–13).

A crucial way migratory species may adjust to environmental change is through flexibly synchronizing their migrations over space and time to match resource availability (hereafter “current synchrony”), a strategy that allows alteration of migration route or timing from 1 y to the next based on environmental cues (13–15). Although less-flexible behaviors can also play a role in directing migrations [e.g., photoperiod and endogenous rhythms influencing departure dates (16)], flexible en route decisions affect fitness by influencing refueling rates, fat gain, and occurrence of phenological mismatches (7, 11, 17, 18). Species that use current synchrony to follow optimal conditions along migration routes, and to vary migration behavior from year to year as conditions change, thus may be able to react more nimbly to a changing climate.

Though current synchrony can allow species to adapt to a changing climate, some species are less flexible in altering their migrations from year to year and more closely align their movements with long-term averages of environmental conditions than with current conditions (hereafter “climatological synchrony”), rendering them more vulnerable to climate change (15). Climatological synchrony could arise through evolution (e.g., evolution of migration routes to track predictable resources), sociality (e.g., offspring learning routes from parents), or memory (e.g., knowledge of previous routes by long-lived species) (19, 20). For example, blue whales (Balaenoptera musculus) may use memory to track locations across the ocean with historically high decadal averages of chlorophyll, leading to stronger synchrony with long-term chlorophyll averages than with current conditions (15). Climatological synchrony can reduce search effort and be a valuable strategy if conditions remain stable. In contrast, current synchrony often requires searching for resources during migration but also allows greater flexibility if conditions along migration routes change over time, as is occurring with climate and land use change (15, 19, 21). Given these tradeoffs associated with current and climatological synchrony, determining their relative importance is essential for anticipating the ability of migratory species to adjust to environmental change and for understanding the effects of climate change on biodiversity (15, 19).

A critical environmental cue tracked by many migratory species is spring vegetation green-up (22–26). Spring green-up corresponds with a massive pulse of resources that a wide variety of marine and terrestrial species follow and consume while migrating (15, 22, 23, 25, 26). This resource pulse includes the emergence of fresh herbaceous vegetation that feeds and thus stimulates pulses of biomass up through multiple trophic levels (e.g., herbivores up through higher-level consumers) (7, 27, 28). Yet this key resource for migratory species appears to be changing (26, 29, 30). Because green-up is strongly affected by temperature and precipitation, climate change-related alterations in temperature and precipitation regimes are disrupting green-up cues and resources used by migratory animals (26, 30–33).

Studying animals during migration is difficult because distributions of migrating species are dynamic over space and time, leading to data collection challenges (23, 34, 35). However, the emergence of globally coordinated citizen science platforms now allows investigation of animal migration routes and timing across continental scales (18, 23, 36, 37). Here, we examine spatiotemporal dynamics of bird migration and spring green-up by aligning 20 y of remote sensing data on vegetation phenology (38) with bird migration data collected by eBird citizen scientists (36) for 150 species that breed in North America and migrate across the Western Hemisphere.

First, we evaluate the relative importance of current versus climatological synchrony by determining whether migrating birds are more closely synchronized with spring green-up phenology in the current year or with long-term averages of green-up phenology, a comparison fundamental to understanding migratory flexibility with climate change (15, 19). To date, this comparison has been made for only a single species [blue whale (15)], whereas we do so for 150 species and assess differences related to phylogeny, dietary guild, and migration distance. Although the arrival of birds on breeding grounds has been found to coarsely synchronize with current green-up for many migratory bird species (18, 39), the relationship between green-up phenology and the timing of migration for en route migrants has only been assessed for a limited number of species (e.g., refs. 33 and 40). Moreover, climatological synchrony for migrating birds has not been evaluated. Second, we quantify changes in spring green-up phenology over time within migration routes of all 150 species. Shifts in green-up phenology have been documented in North America (29, 30), including in portions of bird breeding ranges over which en route migrants travel (18), but here we quantify how green-up is changing across entire migration routes to better understand changes birds are experiencing while traversing their migration journeys. Third, we evaluate the potential consequences of changing green-up phenology by assessing whether green-up anomalies along migration routes influence phenological mismatches through changing the timing gap between bird migration and green-up (39). Mismatches between green-up and the arrival of breeding birds have been detected (18, 39, 41), but this relationship has not been assessed during migration or in relation to climatological synchrony. The synthesis of all three of these analyses enables a thorough evaluation of the relative roles of current and climatological synchrony, as well as changes in spring green-up across migration routes, in influencing phenological mismatches during migration for Western-Hemisphere bird species.

Results and Discussion

Green-up Synchrony: Current versus Climatological.

To determine whether bird migrations are more closely synchronized with current or long-term averages of green-up, we first gridded the Western Hemisphere into 10 × 10 km pixels (i.e., the same spatial resolution as ref. 22) (SI Appendix, Fig. S1) and then identified the temporal midpoint of spring migration for each species-pixel-year. We calculated this migration midpoint as the mean day of all observations during spring migration for each species-pixel-year, which we used to represent dates of migration passage through the pixels (similar to refs. 42–44). Second, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) Global Vegetation Phenology product (MCD12Q2) to obtain the mid-green-up day for each pixel-year, which reflects the day of year at which the Enhanced Vegetation Index’s (EVI) amplitude was estimated to be half its maximum (18, 38, 39, 41), and which we used as the current green-up value. This mid-green-up metric has been used previously as a proxy for food availability for migratory birds because green-up timing correlates with the emergence of insects that are a primary food source for migrants (18, 39, 45–48). Third, we calculated the long-term average mid-green-up day from 2002 to 2021 for each pixel, which we used as the climatological green-up value (15). Fourth, we fit generalized linear mixed models (GLMMs) that estimated the strength of green-up synchrony—defined as the slope (β) relating the migration midpoint day and mid-green-up day (current or climatological), where a significant positive value suggests at least some synchrony with green-up and a larger value closer to 1 indicates stronger synchrony (18)—to determine the relative strengths of current versus climatological synchrony for each species while controlling for spatial and temporal trends (latitude, longitude, year) that influence migration phenology. Last, we fit a phylogenetic mixed model to examine differences in the strength of green-up synchrony (β) by species traits (migratory distance, dietary guild) while controlling for phylogenetic dependency in the dataset (49).

The majority of bird species (72.67%, n = 109) had spring migration passage dates that were at least coarsely synchronous with mid-green-up (i.e., significant positive β for current, climatological, or both), and nearly all of these species (94.50%; n = 103) were more strongly synchronous with long-term average conditions than with conditions currently on the ground (i.e., climatological β was larger than current β; and because all values were <1, this also meant these larger values were closer to 1) (Fig. 1A). This difference between current and climatological synchrony was significant (i.e., contrast P < 0.05) for 71 species, and migrations for nearly all of these species (95.77%; n = 68) were more strongly synchronous (i.e., larger β) with long-term average conditions than with conditions currently on the ground (Fig. 1B). This predominance of climatological synchrony was widespread across the phylogenetic tree, dietary guilds, and migratory distance classes (Fig. 2 A−C). Nonetheless, green-up synchrony (both current and climatological) was stronger for herbivores, which are more directly reliant on vegetation, and weaker for longer-distance migrants that may rely more on endogenous mechanisms rather than external cues like green-up (50, 51) (Fig. 2 B and C). These results signify that migration phenologies across bird groups in the Western Hemisphere are more closely aligned with long-term averages of green-up than with current green-up, and the predominance of climatological synchrony suggests that migratory birds may have limited flexibility to shift their migration timing and routes as green-up phenology changes with climate change.

Fig. 1. (A and B) The majority of the 150 Western Hemisphere migratory bird species evaluated have migrations that are more closely synchronized with long-term averages of spring green-up phenology (climatological synchrony) (orange) than with conditions currently on the ground (current synchrony) (blue). (A) Species are sorted by phylogeny on the y-axis with brackets showing bird taxonomic families. Points represent the strength of green-up synchrony which we define as the slope (β) for migration midpoint day ~ mid-green-up day (±95% CI) from a GLMM model for each species that also included spatiotemporal covariates (colors are bolded for significant green-up synchrony with β > 0 and P < 0.05 for current, climatological, or both). (B) Dashes indicate species with a significant difference between current and climatological synchrony (contrast P < 0.05), and color of dashes indicates whether current (blue) or climatological (orange) synchrony was stronger. “Current” green-up is the mid-green-up day for the current year (within 2002 to 2021), and “climatological” green-up is the long-term average of mid-green-up days over the years 2002 to 2021. Photographs taken by Jonathan D. Mays.

Fig. 2. (A–C) Migrations of most bird species are more synchronous with long-term averages of spring green-up phenology (climatological synchrony) than with conditions currently on the ground (current synchrony), and this pattern is widespread across (A) the phylogenetic tree, (B) migratory distance classes, and (C) dietary guilds. (A) Colored circles represent 150 species on the avian phylogenetic tree, and colors indicate whether a species’ migration timing is more synchronous (i.e., larger β) with climatological green-up (orange) or current green-up (blue), or if the species is not significantly synchronous with green-up (P > 0.05 for both current and climatological synchrony) (gray). Circles have a bolded border if the contrast between current versus climatological synchrony is significant (i.e., contrast P < 0.05). Current green-up is the mid-green-up day for the current year (within 2002 to 2021), and climatological green-up is the mean of mid-green-up days over the years 2002 to 2021. (B and C) Points represent the strength of green-up synchrony for each trait category (migratory distance, dietary guild) which is the slope (β) for migration midpoint day ~ mid-green-up day (±95% CI) from a phylogenetic mixed model that controls for phylogeny.

Changes in Green-up Phenology along Migration Routes.

To assess whether changes in green-up phenology have already occurred across avian migration routes, we determined spring green-up anomalies from long-term averages (current mid-green-up day minus long-term average mid-green-up day from 2002 to 2021) for each year from 2002 to 2021 in each 10 × 10 km pixel used by at least one of the 150 bird species. Using generalized linear models (GLMs) that controlled for spatial trends in green-up anomalies, we found green-up anomalies became more negative over time (i.e., earlier green-up over time) within these species’ migration routes (β = −0.07, 95% CI: −0.09, −0.05) (Fig. 3 A and B). These results are consistent with studies showing earlier spring green-up in recent decades across North America (29, 30), including in portions of bird breeding ranges over which en route migrants travel (18). Most land cover types had significant trends toward earlier green-up, including Mixed Forest, Evergreen Needleleaf Forest, Evergreen Broadleaf Forest, Closed Shrubland, Woody Savannah, Grassland, and Urban and Built-up Land. Two land cover types had nonsignificant trends toward earlier green-up, including Deciduous Broadleaf Forest and Deciduous Needleleaf Forest; only two land cover types, Savannah and Open Shrubland, had a trend toward later green-up (SI Appendix, Table S1).

Fig. 3. (A–D) Spring green-up phenology is changing across bird migration routes in the Western Hemisphere and is influencing phenological mismatches for migratory birds, particularly longer-distance migrants. (A and B) Spring green-up phenology is shifting farther away from long-term averages over time where anomalies (current mid-green-up day minus long-term average mid-green-up day) are becoming more negative as green-up occurs earlier over time. (A) Photo showing recently emerged leaves in spring as a visual example of spring green-up phenology measured using the EVI within the MODIS Global Vegetation Phenology product. (B) Estimated spring green-up day anomaly (±95% CI) over time from 2002 to 2021 (C) Across all species, changes in green-up contribute more to phenological mismatches than do changes in migration; mid-green-up day explains ~ 9 times more variance in phenological intervals (phenological interval = migration midpoint day minus mid-green-up day) (45%; part R2 = 0.45) than does migration midpoint day (5%; part R2 = 0.05). (D) Even though phenological intervals were more strongly influenced by green-up day than by migration day for all migratory distance classes, species that migrate farther distances have greater estimated sensitivities of phenological intervals (±95% CI) to changing green-up (i.e., larger part R2s for mid-green-up day) (green ribbon) and weaker estimated sensitivities to changing migration day (i.e., smaller part R2s for migration midpoint day) (gray ribbon) compared to respective sensitivities for shorter-distance migrants. Photograph taken by Jonathan D. Mays.

Phenological Mismatches.

Given these changes in green-up timing and the predominance of climatological synchrony with green-up among migratory birds, we evaluated if green-up anomalies are altering the width of the phenological interval in these species. The phenological interval (migration midpoint day minus mid-green-up day) measures the time gap between migration and green-up and thereby provides insight into phenological mismatches (39). We use the phrase phenological mismatch to indicate asynchrony in the timing of two events (such as migration and green-up), regardless of the impacts of asynchrony on fitness [a similar definition to refs. 10, and 52, although we recognize that this term has also been used to refer to asynchrony that specifically leads to fitness impacts (53)]. We calculated the phenological interval for each species-pixel-year combination. Phenological intervals by definition are influenced by both green-up day and migration day, yet these underlying factors can contribute differentially to variation in the phenological interval. We therefore determined the proportion of variance in the phenological interval explained by migration day and green-up day by fitting GLMMs for all species combined—with species as a random effect, phenological interval as the response variable, and explanatory variables including latitude, longitude, year, dietary guild, migratory distance, and either migration midpoint day or mid-green-up day—and by comparing values of the part R2 for migration day and green-up day (54). Then, we fit these models separately for each species, which allowed us to determine each species’ sensitivity to green-up day and migration day (i.e., part R2 for green-up day and migration day, respectively). Finally, we assessed how these sensitivities varied in relation to species’ traits by including part R2 for green-up day or migration day as the response variable and species’ trait (migratory distance or dietary guild) as the explanatory variable.

Green-up anomalies strongly influenced phenological intervals during migration for these bird species. Across all species, mid-green-up day explained 45% of variation in the phenological interval (green-up day part R2 = 0.45, 95% CI: 0.44, 0.48), which was ~ 9 times more variance than explained by migration midpoint day (5%, part R2 = 0.05, 95% CI: 0.03, 0.06) (Fig. 3C). This result indicates that changing green-up is more strongly driving phenological mismatches between green-up and migration for migratory birds than are changes in migration timing. Indeed, in considering species separately, mid-green-up day more strongly influenced phenological intervals than did migration midpoint day for 99% of species (n = 148). However, even though phenological intervals were more strongly influenced by green-up day than by migration day for all migratory distance classes and dietary guilds, the effect of green-up timing was smaller and the effect of migration timing was larger for shorter-distance migrants and herbivores than for longer-distance migrants and other dietary guilds (i.e., smaller green-up day part R2 values and larger migration day part R2 values for the former trait groups compared to respective values for the latter groups) (Fig. 3D and SI Appendix, Figs. S2 and S3).

These results support that migratory bird species that do not synchronize as closely with green-up (e.g., longer-distance migrants) may be at greater risk of phenological mismatches with changing climate, while species that more closely synchronize with changing green-up (e.g., shorter-distance migrants and herbivores) may, to a small degree, offset consequences of changing green-up through more flexible migration behavior. However, even these species that are most synchronous with green-up (e.g., shorter-distance migrants and herbivores) are still at risk of mismatches with changing climate because their phenological intervals are still more strongly influenced by green-up day than by migration day (Fig. 3D and SI Appendix, Figs. S2 and S3).

Implications for Understanding the Effects of Climate Change on Migratory Species.

Our finding that bird migrations are more closely synchronized with long-term averages of spring green-up phenology than with current green-up, while green-up phenology shifts earlier in time across migration routes with climate change, suggests that the migrations of most bird species may not be keeping pace with changing green-up. Further, we show strong impacts of changing green-up phenology on phenological intervals in these migratory species. More detailed information about food availability, foraging, and demographic impacts is needed to further clarify implications of these phenological mismatches [e.g., see ref. 41 for evidence of demographic consequences of changing green-up during the breeding season], including whether birds are already missing optimal time windows to forage on the fleeting resources provided by spring green-up during migration [e.g., freshly unfurled leaves and insects that emerge with leaf out (45–48)]. Because this study and other similar studies on bird migration and green-up (e.g., refs. 18, 39, and 41) are only correlational, there remains a need for further research that identifies causal factors influencing foraging during migration, including the potential effects of green-up timing. Further evaluation of how migration is influenced by endogenous factors (e.g., inherited genetic programs) and exogenous factors other than green-up (e.g., weather conditions experienced en route) (6, 55) is also needed to clarify whether the widespread climatological synchrony we documented arises primarily due to exogenous or endogenous control for en route timing. Nonetheless, we show that green-up phenology is changing across routes of 150 migratory bird species and that these changes, in combination with the predominance of climatological synchrony, are influencing phenological intervals between migration and vegetation green-up and its associated pulse of resources.

The changes in spring green-up phenology that we show to be widely occurring across avian migration routes in the Western Hemisphere are projected to become more substantial with future climate change (29, 30, 33). Further, these changes and the potential decoupling of bird migrations from vegetation phenology will be even more severe if Intergovernmental Panel on Climate Change recommendations to rapidly reduce global CO2 emissions and net non-CO2 radiative forcing are not implemented and global temperatures and frequency and extremity of weather events increase even further beyond long-term averages (55). Our results suggest that longer-distance migrants may be especially at risk of phenological mismatches associated with changing green-up phenology. Migratory species, including numerous long-distance migrants, are already in steep decline worldwide and further losses will threaten not only species persistence and diversity but also the ecosystem functions and services migrating animals provide (8, 56, 57).

Materials and Methods

Compilation and Filtering of Bird Migration Data.

We downloaded data from eBird (58) on 31 July 2022, and used the supercomputer at Oklahoma State University for data processing. The eBird dataset is collected by citizen scientists that compile bird observations by sight or sound into checklists that provide the option to record information on survey effort (e.g., distance traveled, duration, number of observers). We filtered eBird datasets based on Cornell Lab of Ornithology recommendations (59). Specifically, we selected “complete” checklists where the participant reported all birds they were able to detect and identify. We included data from 2002 to 2021 to correspond with the years that both vegetation green-up data and eBird data were available. We selected checklists that followed the “Stationary” and “Traveling” sampling protocols. Last, we selected checklists for which the distance traveled was 0 to 5 km, the duration was 0 to 300 min, and the number of observers was 10 or fewer (59).

For analysis, we selected 150 Western-Hemisphere migratory bird species with breeding range centers located north of the tropics (24°N latitude) and with breeding and nonbreeding ranges that intersected <50%, based on NatureServe range maps (22, 60). We excluded species that primarily use marine environments and that lacked eBird occurrence information for a significant portion of their annual cycle (22). We classified each of the resulting species into dietary guilds using the dominant category from EltonTraits (22, 61); migratory distance classes using geographical centers of wintering and breeding ranges based on Nature Serve range maps (22); and taxonomic families using the BirdTree.org taxonomy (62).

Calculation of Migration Timing.

We gridded the Western Hemisphere into 10 × 10 km pixels (following ref. 22, a study that also used this resolution for aggregating eBird data at continental scales) using Albers Equal Area Conic projection (SI Appendix, Fig. S1). We then used eBird data to determine the timing of migration (hereafter “migration midpoint day”) for each species-pixel-year combination. We did so by first using the “ebirdst” package (63) to obtain species-specific start and end dates for spring migration that have passed expert review, and we filtered eBird data for each species to fall within these species-specific spring migration date windows. Next, we calculated the average day of year among all checklists for which the species was observed during spring migration for each pixel and year. We used these mean day-of-year values as the migration midpoint day, similar to mean spring passage dates that have been used in previous migration studies to capture the peak of migration (e.g., refs. 42–44). We excluded species-pixel-year combinations that had less than 20 eBird observations available for calculating migration midpoint day to ensure that the calculation for each migration midpoint day was based on at least 20 checklists. Note that previous studies have used even fewer checklists to aggregate eBird data [e.g., (64)]; however, to evaluate the robustness of our results relative to this data availability threshold, we also reran our models using 30 and 50 checklist thresholds, and we confirmed that results were similar. We also removed species with less than 250 rows (i.e., pixel-year combinations) of data (80 out of 230 species) to ensure that we had sufficient data for downstream analyses.

We note that the above approach to calculate migration timing is different than previous studies that assessed arrival phenology of local breeders on their breeding grounds (18, 39, 65). Here, we focus on migration midpoint days across migration routes during spring migration rather than days of arrival on breeding grounds. Consequently, instead of estimating the half-maximum value of modeled dates, as has been done to assess arrival dates of local breeders (e.g., refs. 18, 39, 65 and as detailed in the published peer review of ref. 18), we used the mean value of checklist observation dates during the migration season to capture the midpoint of migration timing, similar to prior studies on migration passage dates (42–44). We used this approach because we expected that migration passage dates would be adequately reflected by the mean of observed dates during spring migration (following refs. 42–44), although we acknowledge that this approach excludes some of the uncertainty that arises from taking the mean of checklists per species-pixel-year (also a limitation of refs. 42–44).

Calculation of Vegetation Green-up Timing.

We used Google Earth Engine (66) to obtain images from the MODIS Global Vegetation Phenology product (MCD12Q2 V6 Land Cover Dynamics) (38) that provided information on the timing of vegetation phenology based on the EVI from 2002 to 2021 (18, 39). From these images, we estimated the timing of spring green-up by obtaining the day of year that the green-up midpoint of the first growing cycle occurred (hereafter “mid-green-up day”) (18), a metric representing the day of year at which the EVI’s amplitude was estimated to be half its maximum (38) and that has been used to reflect the arrival of spring and availability of key resources for migratory birds (18, 39). We generated a mid-green-up estimate for each 10 × 10 km pixel each year, and we retained only high-quality pixels (i.e., pixels for which both overall quality and quality of the green-up midpoint were ranked “Best” or “Good”). We considered both current and long-term averages (climatological) of mid-green-up for our analyses. For current mid-green-up, we used the mid-green-up day per pixel for the same year that corresponded with the eBird data. For climatological mid-green-up, we used the mean of mid-green-up days over the years 2002 to 2021 per pixel. We also used Google Earth Engine to obtain the MODIS Landcover (MCD12Q1.006) product that provided information on land cover from 2002 to 2021 (67) in the same resolution as the Vegetation Phenology product (500 m), and we used the Landcover product to exclude areas from the Vegetation Phenology product classified as croplands, waterbodies, or nonvegetated lands according to the Land Cover Type 2 (Annual University of Maryland) classification. We reprojected the Vegetation Phenology images to Albers Equal Area Conic projection and then resampled from 500m to 10km spatial resolution using bilinear interpolation to match the projection and resolution of the eBird data.

Evaluation of Green-up Synchrony for Migratory Birds.

To evaluate the relative importance of current versus climatological green-up synchrony, we fit separate Gaussian GLMMs for each species with migration midpoint day as the response variable and explanatory variables including mid-green-up day, mid-green-up day type (indicator variable for whether mid-green-up day is current or climatological), and their interaction. We also included latitude and longitude as explanatory variables and a random intercept for year to account for spatial and temporal trends in migration midpoint day. The incorporation of latitude in the model was particularly important as it allowed us to assess whether phenological synchrony between green-up and migration existed above-and-beyond associations that would emerge solely due to the latitudinal progression (e.g., northward in spring) of bird migrations. We used the “lme4” (68) package in R (69) and coded this model separately for each species as: model = lmer (migration midpoint dayj,k ~ mid-green-up dayj,k * typej,k + latitudej + longitudej + (1|yeark) where indices j = pixel, and k = year (Fig S4). Our full dataset for phenological synchrony included data for 150 species and N = 474,421 species-pixel-years (with data for individual species ranging from N = 262 to N = 24,088 pixel-years with a mean of N = 3,163 pixel-years). All species-pixel-years for which we had data for both migration midpoint day and mid-green-up day were included in this model.

We identified significant green-up synchrony when the slope (β) relating migration midpoint day and mid-green-up day was positive and significant (P < 0.05) and we defined stronger green-up synchrony as occurring when this term was larger (because all values were <1, this also meant larger values were closer to 1) (18). We then summarized the proportion of species with significant green-up synchrony (i.e., with β > 0 and P < 0.05 for current, climatological, or both) and the proportion of these species for which climatological synchrony was stronger than current synchrony (i.e., the climatological β was larger and closer to 1 than current β). To visualize differences among species in their strengths of green-up synchrony, we plotted estimated slopes (β for migration midpoint day ~ mid-green-up day) (±95% CIs) for each species for both climatological and current green-up synchrony, and we denoted whether the contrast between climatological and current green-up synchrony was significant (contrast P < 0.05).

Evaluation of the Effect of Species Traits on Green-up Synchrony.

We evaluated how two sets of traits, dietary guild (carnivore, granivore, herbivore, herbivore–granivore, insectivore, nectarivore, omnivore) and migratory distance (divided into 1,000 km bins), influenced green-up synchrony while controlling for phylogenetic relationships among species. We focus on these traits because they have previously been found to explain variation in bird migration relative to green-up (e.g., refs. 22 and 64). However, we acknowledge that evaluation of other species’ traits (e.g., breeding and wintering latitude, species’ temperature index, body size) would also be a useful avenue for future research. To obtain phylogenetic information, we downloaded 100 phylogenetic trees from BirdTree.org (62) derived using the Ericson backbone (70). After compiling into a single consensus tree using majority rule, we used GLMMs with a Markov chain Monte Carlo sampler implemented using the “MCMCglmm” R package (71) to fit phylogenetic mixed models (separately for climatological and current synchrony) (N = 474,421 species-pixel-years) that included phylogeny as a random effect to control for phylogenetic dependency in the dataset (49). The response variable in our MCMCglmm models was migration midpoint day and predictor variables included mid-green-up day (either current or climatological), species trait (either dietary guild or migratory distance), and the interaction between these variables. We also included latitude, longitude, and year as explanatory variables. Priors for both the random effect and residual variance corresponded to an inverse-Gamma distribution with shape and scale parameters equal to 0.01 (49). We ran 100,000 iterations with a burn-in period of 20,000 and a thinning interval of 10 for each model. We contrasted the strength of green-up synchrony among the different trait categories (dietary guild, migratory distance) by comparing estimated slopes (β for migration midpoint day ~ mid-green-up day) among categories. To visualize differences in green-up synchrony among trait categories, we plotted estimated slopes (±95% CIs) for each category. To also visualize green-up synchrony across bird phylogenies, we plotted the phylogenetic tree and colored it by whether species were more closely synchronized with current or climatological green-up or whether there was no significant green-up synchrony.

Evaluation of Green-up Changes over Time.

To examine how green-up has changed over time (2002 to 2021) across spring migration routes of these 150 species, we restricted analyses to pixels that were included in the data used for our synchrony models. We then determined spring green-up anomalies from long-term averages (current mid-green-up day in a pixel minus the long-term average mid-green-up day over 2002 to 2021 in the pixel) for each pixel-year combination. To evaluate how green-up is changing over time, we used Gaussian GLMs (N = 202,624 pixel-years) with green-up anomaly as the response variable and year as an explanatory variable. We also included latitude and longitude to account for spatial trends in green-up anomalies. We coded this model in R as: model = lm (green-up anomalyj,k ~ yeark + latitudej + longitudej) where indices j = pixel, and k = year. To examine potential differences in trends by landcover type (Deciduous Broadleaf Forest, Mixed Forest, Evergreen Needleleaf Forest, Evergreen Broadleaf Forest, Closed Shrubland, Woody Savannah, Grassland, Urban and Built-up Land, Deciduous Needleleaf, Savannah, Open Shrubland), we ran the model with an interaction between year and landcover: model = lm (green-up anomalyj,k ~ yeark * landcoverj,k + latitudej + longitudej) and obtained β estimates for each landcover type (indices j = pixel, and k = year).

Evaluation of Phenological Mismatches.

The phenological interval (i.e., migration midpoint day minus mid-green-up day) measures the time gap between migration and green-up and thereby provides information on phenological mismatches (39). We use the phrase phenological mismatch to indicate asynchrony in the timing of two events (such as migration and green-up), regardless of the impacts of asynchrony on fitness [a similar definition to (10, 52), although we recognize that this term has also been used to refer to asynchrony that specifically leads to fitness impacts (53)]. We determined the phenological interval for each species-pixel-year combination. A species’ average phenological interval can be either positive or negative depending on whether a species tends to arrive before or after green-up (39). While some species may precede mid-green-up (e.g., species tracking freshly emerging vegetation), others may follow after mid-green-up (e.g., species tracking emergence of insects or higher trophic-level prey after vegetation has emerged); therefore, the phenological interval, including whether it is negative or positive, may vary among species. To account for this among-species variation in the phenological interval, we either modeled species separately or included a random intercept for species in our model. Similar to ref. 39, we do not presume that a particular interval width has positive or negative effects on a species’ fitness. Instead, we use the interval as an index of phenology, where a changing interval width indicates increasing asynchrony between migration and green-up (39). Phenological intervals by definition are influenced by both green-up day and migration day. However, each of these underlying factors can contribute differentially to variation in the phenological interval, and we sought to determine their relative influences by estimating the proportion of variance in the phenological interval explained by each factor. To do so, we ran Gaussian GLMMs (N = 474,421 species-pixel-years) for all species combined that included a random intercept for species, phenological interval as the response variable, and either mid-green-up day or migration midpoint day as the explanatory variable. We also included latitude, longitude, year, dietary guild, and migratory distance as explanatory variables. We then used the “partR2” package in R to partition variance in the phenological interval into portions explained by mid-green-up day and migration midpoint day by obtaining the part R2 values (proportion of variance uniquely explained by each predictor) (54). Next, we fit these models for each species separately to determine part R2s for each species. We compared the sensitivities of species’ phenological intervals to green-up day (i.e., part R2 for mid-green-up day) and migration day (i.e., part R2 for migration midpoint day) in relation to species’ traits by fitting GLMs (N = 150) with species’ part R2 for mid-green-up day or migration midpoint day as the response variable and species’ trait (dietary guild or migratory distance) as the explanatory variable.

Supplementary Material

Appendix 01 (PDF)

We are grateful to eBird citizen scientists for volunteering their time to collect bird data that made this study possible. We also thank Cornell Lab of Ornithology, NASA’S LP DAAC, and the U.S. Geological Survey for providing eBird and green-up phenology data. We thank Matt Strimas-Mackey, Tom Auer, and Daniel Fink for guidance on using eBird data. We thank SC-CASC (South Central Climate Adaptation Science Center) and the Loss and O’Connell labs at OSU for valuable feedback. Computing for this project was performed at the High Performance Computing Center (HPCC) at Oklahoma State University with guidance provided by Jesse Schafer (Manager of Operations) and Evan Linde (Research Cyberinfrastructure Analyst). This work was supported by the SC-CASC, Wolf Creek Charitable Foundation, and NSF (ABI sustaining: DBI-1939187) (HPCC: OAC-1531128).

Author contributions

E.P.R., F.A.L.S., J.D.M., P.J.T., O.J.R., R.J.A., T.J.O., C.A.D., and S.R.L. designed research; E.P.R. and S.R.L. performed research; E.P.R. analyzed data; and E.P.R., F.A.L.S., J.D.M., P.J.T., O.J.R., R.J.A., T.J.O., C.A.D., and S.R.L. wrote the paper.

Competing interests

The authors declare no competing interest.

Data, Materials, and Software Availability

The R code for our models is described within the text. All code and data used in the analyses will be publicly available at Zenodo (72). Raw bird occurrence data are available at eBird: https://science.ebird.org/en/use-ebird-data/download-ebird-data-products (73). Raw green-up (MCD12Q2) and landcover (MCD12Q1) data are available in Google Earth Engine and from NASA/USGS Land Processes Distributed Active Archive Center at https://doi.org/10.5067/MODIS/MCD12Q2.006 (74) and https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (75). Bird phylogeny data are available at https://birdtree.org/ (76). Bird trait data on dietary guilds are available at https://figshare.com/collections/EltonTraits_1_0_Species-level_foraging_attributes_of_the_world_s_birds_and_mammals/3306933 (77).

Supporting Information

This article is a PNAS Direct Submission.

Although PNAS asks authors to adhere to United Nations naming conventions for maps (https://www.un.org/geospatial/mapsgeo), our policy is to publish maps as provided by the authors.
==== Refs
1 T. L. Root , Fingerprints of global warming on wild animals and plants. Nature 421 , 57–60 (2003).12511952
2 C. D. Thomas , Extinction risk from climate change. Nature 427 , 145–148 (2004).14712274
3 M. C. Urban, Accelerating extinction risk from climate change. Science 1979 , 571–573 (2015).
4 R. M. B. Harris , Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Chang. 8 , 579–587 (2018).
5 E. A. Beever , Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15 , 299–308 (2017).
6 S. Åkesson, B. Helm, Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8 , 78 (2020).
7 C. Both, S. Bouwhuis, C. M. Lessells, M. E. Visser, Climate change and population declines in a long-distance migratory bird. Nature 441 , 81–83 (2006).16672969
8 D. S. Wilcove, M. Wikelski, Going, going, gone: Is animal migration disappearing. PLoS Biol. 6 , e188 (2008).18666834
9 A. P. Møller, D. Rubolini, E. Lehikoinen, Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl. Acad. Sci. U.S.A. 105 , 16195–16200 (2008).18849475
10 M. E. Visser, P. Gienapp, Evolutionary and demographic consequences of phenological mismatches. Nat. Ecol. Evol. 3 , 879–885 (2019).31011176
11 C. Both , Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats. Proc. R Soc. B Biol. Sci. 277 , 1259–1266 (2010).
12 R. A. Robinson , Travelling through a warming world: Climate change and migratory species. Endanger Species Res. 7 , 87–99 (2009).
13 E. Knudsen , Challenging claims in the study of migratory birds and climate change. Biol. Rev. 86 , 928–946 (2011).21489123
14 B. Abrahms , Emerging perspectives on resource tracking and animal movement ecology. Trends Ecol. Evol. 36 , 308–320 (2021).33229137
15 B. Abrahms , Memory and resource tracking drive blue whale migrations. Proc. Natl. Acad. Sci. U.S.A. 116 , 5582–5587 (2019).30804188
16 S. Åkesson , Timing avian long-distance migration: From internal clock mechanisms to global flights. Philos. Trans. R Soc. B Biol. Sci. 372 , 20160252 (2017).
17 A. P. Tøttrup , Drought in Africa caused delayed arrival of european songbirds. Science 338 , 1307 (2012).23224549
18 C. Youngflesh , Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. 5 , 987–994 (2021).33927370
19 C. Bracis, T. Mueller, Memory, not just perception, plays an important role in terrestrial mammalian migration. Proc. R Soc. B Biol. Sci. 284 , 20170449 (2017).
20 W. F. Fagan , Spatial memory and animal movement. Ecol. Lett. 16 , 1316–1329 (2013).23953128
21 J. A. Merkle , Spatial memory shapes migration and its benefits: Evidence from a large herbivore. Ecol. Lett. 22 , 1797–1805 (2019).31412429
22 F. A. La Sorte, C. H. Graham, Phenological synchronization of seasonal bird migration with vegetation greenness across dietary guilds. J. Animal Ecol. 90 , 343–355 (2021).
23 J. F. Kelly , Novel measures of continental-scale avian migration phenology related to proximate environmental cues. Ecosphere 7 , e01434 (2016).
24 J. A. Merkle , Large herbivores surf waves of green-up during spring. Proc. R Soc. B Biol. Sci. 283 , 20160456 (2016).
25 E. O. Aikens , The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20 , 741–750 (2017).28444870
26 E. M. Wood, J. L. Kellermann, Phenological Synchrony and Bird Migration: Changing Climate and Seasonal Resources in North America (CRC Press, 2015).
27 G. Kudo, T. Y. Ida, Early onset of spring increases the phenological mismatch between plants and pollinators. Ecology 94 , 2311–2320 (2013).24358716
28 M. E. Visser, L. J. M. Holleman, Warmer springs disrupt the synchrony of oak and winter moth phenology. Proc. R Soc. Lond. B Biol. Sci. 268 , 289–294 (2001).
29 A. J. Allstadt , Spring plant phenology and false springs in the conterminous US during the 21st century. Environ. Res. Lett. 10 , 104008 (2015).
30 S. Piao , Plant phenology and global climate change: Current progresses and challenges. Glob. Chang. Biol. 25 , 1922–1940 (2019).30884039
31 J. B. Armstrong, G. Takimoto, D. E. Schindler, M. M. Hayes, M. J. Kauffman, Resource waves: Phenological diversity enhances foraging opportunities for mobile consumers. Ecology 97 , 1099–1112 (2016).27349088
32 N. Pettorelli , The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology. Clim. Res. 46 , 15–27 (2011).
33 K. Thorup , Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3 , e1601360 (2017).28070557
34 S. P. Saunders , Multiscale seasonal factors drive the size of winter monarch colonies. Proc. Natl. Acad. Sci. U.S.A. 116 , 8609–8614 (2019).30886097
35 P. P. Marra, E. B. Cohen, S. R. Loss, J. E. Rutter, C. M. Tonra, A call for full annual cycle research in animal ecology. Biol. Lett. 11 , 20150552 (2015).26246337
36 B. L. Sullivan , The eBird enterprise: An integrated approach to development and application of citizen science. Biol. Conserv. 169 , 31–40 (2014).
37 B. G. Freeman, M. Strimas-Mackey, E. T. Miller, Interspecific competition limits bird species’ ranges in tropical mountains. Science 1979 , 416–420 (2022).
38 M. Friedl, J. Gray, D. Sulla-Menashe, MCD12Q2 MODIS/Terra+ aqua land cover dynamics yearly L3 global 500m SIN grid V006 (NASA EOSDIS Land Processes DAAC, 2019). 10.5067/MODIS/MCD12Q2.006. Accessed 31 July 2022.
39 S. J. Mayor , Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7 , 1–10 (2017).28127051
40 M. Shariatinajafabadi , Migratory herbivorous waterfowl track satellite-derived green wave index. PLoS One 9 , e108331 (2014).25248162
41 C. Youngflesh , Demographic consequences of phenological asynchrony for North American songbirds. Proc. Natl. Acad. Sci. U.S.A. 120 , e2221961120 (2023).37399376
42 P. P. Marra, C. M. Francis, R. S. Mulvihill, F. R. Moore, The influence of climate on the timing and rate of spring bird migration. Oecologia 142 , 307–315 (2005).15480801
43 B. Haest, O. Hüppop, F. Bairlein, Weather at the winter and stopover areas determines spring migration onset, progress, and advancements in Afro-Palearctic migrant birds. Proc. Natl. Acad. Sci. U.S.A. 117 , 17056–17062 (2020).32601181
44 B. Haest, O. Hüppop, F. Bairlein, Challenging a 15-year-old claim: The North Atlantic Oscillation index as a predictor of spring migration phenology of birds. Glob. Chang. Biol. 24 , 1523–1537 (2018).29251800
45 E. F. Cole, P. R. Long, P. Zelazowski, M. Szulkin, B. C. Sheldon, Predicting bird phenology from space: Satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment. Ecol. Evol. 5 , 5057–5074 (2015).26640682
46 M. E. Visser, L. te Marvelde, M. E. Lof, Adaptive phenological mismatches of birds and their food in a warming world. J. Ornithol. 153 , 75–84 (2012).
47 M. E. Visser, L. J. M. Holleman, P. Gienapp, Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147 , 164–172 (2006).16328547
48 T. Emmenegger, S. Hahn, S. Bauer, Individual migration timing of common nightingales is tuned with vegetation and prey phenology at breeding sites. BMC Ecol. 14 , 1–8 (2014).24438134
49 L. Z. Garamszegi, Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology: Concepts and Practice (Springer, 2014).
50 E. Gwinner, Circadian and circannual programmes in avian migration. J. Exp. Biol. 199 , 39–48 (1996).9317295
51 T. Usui, S. H. M. Butchart, A. B. Phillimore, Temporal shifts and temperature sensitivity of avian spring migratory phenology: A phylogenetic meta-analysis. J. Animal Ecol. 86 , 250–261 (2017).
52 S. S. Renner, C. M. Zohner, Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49 , 165–182 (2018).
53 H. M. Kharouba, E. M. Wolkovich, Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Chang. 10 , 406–415 (2020).
54 M. A. Stoffel, S. Nakagawa, H. Schielzeth, partR2: Partitioning R2 in generalized linear mixed models. PeerJ. 9 , e11414 (2021).34113487
55 IPCC, “Climate Change 2022: Impacts, Adaptation, and Vulnerability” in Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, H.-O. Pörtner et al., Eds. (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022), pp. 3056, 10.1017/9781009325844.
56 D. S. Wilcove, No Way Home: The Decline of the World’s Great Animal Migrations (Island Press, 2010).
57 K. V. Rosenberg , Decline of the North American avifauna. Science 1979 , 120–124 (2019).
58 Ebird, eBird: An online database of bird distribution and abundance (eBird, Cornell Lab of Ornithology, 2017).
59 M. Strimas-Mackey , Best Practices for Using eBird Data (Version 1.0., Cornell Laboratory of Ornithology, Ithaca, NY, 2020).
60 R. S. Ridgely , Digital distribution maps of the birds of the Western Hemisphere (version 1.0, NatureServe, Arlington, VA, 2003).
61 H. Wilman , EltonTraits 1.0: Species‐level foraging attributes of the world’s birds and mammals: Ecological archives E095‐178. Ecology 95 , 2027 (2014).
62 W. Jetz, G. H. Thomas, J. B. Joy, K. Hartmann, A. O. Mooers, The global diversity of birds in space and time. Nature 491 , 444–448 (2012).23123857
63 M. Strimas-Mackey, S. Ligocki, T. Auer, D. Fink, ebirdst: Access and Analyze eBird Status and Trends Data Products, R package version 3.2022.0. https://ebird.github.io/ebirdst/. Accessed 31 July 2022.
64 F. A. La Sorte, W. M. Hochachka, A. Farnsworth, A. A. Dhondt, D. Sheldon, The implications of mid-latitude climate extremes for North American migratory bird populations. Ecosphere 7 , e01261 (2016).
65 A. H. Hurlbert, Z. Liang, Spatiotemporal variation in avian migration phenology: Citizen science reveals effects of climate change. PLoS One 7 , e31662 (2012).22384050
66 N. Gorelick , Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 , 18–27 (2017).
67 D. Sulla-Menashe, M. A. Friedl, User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product (USGS, Reston, VA, 2018), vol. 1 , p. 18.
68 D. Bates, M. Mächler, B. Bolker, S. Walker, Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67 , 1–48 (2015).
69 R Core Team, R: A Language and Environment for Statistical (R Foundation for Statistical Computing, 2022).
70 P. G. P. Ericson , Higher-level phylogeny and morphological evolution of tyrant flycatchers, cotingas, manakins, and their allies (Aves: Tyrannida). Mol. Phylogenet. Evol. 40 , 471–483 (2006).16678446
71 J. D. Hadfield, MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33 , 1–22 (2010).20808728
72 E. P. Robertson , Data and Code from “Decoupling of bird migration from the changing phenology of spring green-up”. Zenodo. 10.5281/zenodo.10625004. Deposited 14 February 2024.
73 eBird, An online database of bird distribution and abundance [web application]. eBird. http://www.ebird.org. Accessed 31 July 2022.
74 M. Friedl, J. Gray, D. Sulla-Menashe, MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. 10.5067/MODIS/MCD12Q2.006. Accessed 31 July 2022.
75 D. Sulla-Menashe, M. A. Friedl, User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS. https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf. Accessed 31 July 2022.
76 W. Jetz, G. H. Thomas, J. B. Joy, K. Hartmann, A. O. Mooers, The global diversity of birds, space and time. Nature 491 , 444–448 (2012).23123857
77 H. Wilman , EltonTraits 1.0: Species‐level foraging attributes of the world’s birds and mammals: Ecological archives E095‐178. Ecol. 95 , 2027 (2014).
