Migrating mule deer compensate en route for phenological mismatches

Abstract

Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. Using GPS movement data collected from 72 adult female deer over a 10-year duration, we study a population of mule deer (Odocoileus hemionus) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. We show that individual deer arrive at their summer range within an average 6-day window by adjusting movement speed and stopover use. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Our findings suggest that ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.

Fig. 1. Probability density of mean days from peak Instantaneous Rate of Green-up (IRG) at the start and end of spring migration.

a Each spring, mule deer leave their desert winter range to make a 240-km, one-way, migration in western Wyoming. b The start date of spring migration was standardized to the mean date of peak IRG on winter ranges for each year of the study (2011–2012, 2014, 2016–2020). The vertical dashed line represents zero days from peak IRG. c The end date of spring migration was standardized to the mean date of peak IRG on summer ranges for each year. Mule deer started spring migration asynchronously (70 days ahead to 52 days behind peak IRG) but, on average, arrived on summer range within a narrow window of time (6 days).

Fig. 2. Green wave surfing en route for early, mid and late migrants.

Green wave surfing en route was measured as mean days from peak Instantaneous Rate of Green-up (IRG) ± 95% confidence intervals as a function of distance from winter range (solid lines represent loess regressions). Despite being strongly mismatched ahead or behind the green wave when they began their spring migration, early migrants (n = 47 animal-years; purple), mid-migrants (n = 58 animal-years; green), and late migrants (n = 47 animal-years; orange) ended spring migration largely synchronized and closer to peak IRG. Early and late migrants seemingly compensated for being mismatched with the green wave during migration. The horizontal dashed line represents perfect surfing (0 days from peak IRG).

Fig. 3. Comparisons in the location of mule deer on the green wave at the start and end of spring migration.

a Despite starting spring migration 30 ± 5 days (x̄ ± 95% CI) ahead peak Instantaneous Rate of Green-up (IRG; dashed horizontal line), early migrants (n = 47 animal-years; purple) ended their spring migration 4 ± 3 days behind peak IRG. b Mid-migrants (n = 58 animal-years; green) were 2 ± 5 days ahead peak IRG at the start of spring migration but 7 ± 3 days behind peak IRG at the end of spring migration. c Although late migrants (n = 47 animal-years; orange) started spring migration 20 ± 5 days behind peak IRG, they ended spring migration 11 ± 4 days behind peak IRG. The boxplots represent the median value of days from peak IRG (horizontal bar). Whiskers extend to the minima (25^th percentile – 1.5 * interquartile range) and maxima (75th percentile + 1.5 * interquartile range).

Fig. 4. Movement rate and stopover use by migrating mule deer relative to the propagation of the green wave across western Wyoming.

a Plotting mean date of peak Instantaneous Rate of Green-up (IRG) along the 240-km migration corridor indicates that the wave of green-up did not propagate consecutively across the landscape for the first 32 km of migration (dotted segments indicate a negative slope of green-up) but propagated as a wave for the remainder of the migration corridor (solid segments indicate a positive slope of green-up). b The spring of 2017 illustrates the movements of full compensators (blue dots) relative to the weekly propagation of the green wave. Mule deer that were behind the green wave tended to move more quickly, while those ahead of the wave moved more slowly. c Mule deer migrated 134–293 km over 50 ± 4 (x̄ ± 95% CI) days from a desert sagebrush shrubland to a montane ecosystem. d The start date of spring migration (i.e., whether deer were early or late) positively influenced the rate of movement by mule deer during spring migration (predicted coefficients ± 95% CI; GAMM, R² = 0.56, p < 2.20 × 10⁻¹⁶). Colored points correspond with the same individuals in b and indicate the average rate of movement over the entire migration for those individuals. e The start date of spring migration negatively influenced the number of days mule deer allocated to high-use stopovers (predicted coefficients ± 95% CI; GAMM, R² = 0.53, p < 2.20 ×10⁻¹⁶). The start date of spring migration for each animal-year was standardized relative to the median start date of spring migration for each year of the study.

Fig. 5. The probability of mule deer to behaviorally compensate based on their degree of mismatch with the green wave at the start of spring migration.

Mule deer were classified as (a) full compensators, (b) partial compensators, (c) perfect surfers, and (d) non-compensators. Based on an ordinal logistic regression model, mule deer were 1.21 times more likely to become full compensators for every 1-day increase in mismatch with peak Instantaneous Rate of Green-up at the start of spring migration (β = 0.19, 95% CI = 0.15–0.24, p = 6.98 ×10⁻¹⁵). Beyond 40 days mismatched, nearly 100% of mule deer behaviorally compensated to realign their movements with the green wave. Grey bands represent the 95% confidence intervals of the predicted probabilities.