Camouflage mismatch and evolutionary rescue in snowshoe hares

Introduction

  • Climate change imposes rapid, novel stressors; camouflage mismatch due to shorter snow duration is a critical example for seasonal color-molting species.
  • Snowshoe hares are a model because background matching is essential for camouflage and predation risk is high.
  • Research questions: how does camouflage mismatch affect fitness and can populations adapt plastically or evolutionarily?
  • Key finding: mismatch costs reduce survival; there is substantial among-individual variation in molt timing that could enable evolutionary rescue.

Study design and area

  • Location: two sites in Western Montana, Seeley Lake and Gardiner, ~330 km apart.
  • Subjects: 186 radiocollared snowshoe hares monitored weekly for survival; coat color molt and snow cover observed weekly.
  • Monitoring period: roughly 2009–2012; predators accounted for ~67% of deaths, with some right-censoring.
  • Measurements:
    • Coat colour molt (percent white) estimated weekly (0–100%).
    • Snow cover within 10 m around each hare (0–100%).
    • Color contrast defined as the absolute difference between hare whiteness and mean snow cover for the site.
  • Key threshold: camouflage mismatch defined as ext{contrast}_{i,j} \geq 0.60 (60%).

Measurements and modeling

  • Color-contrast modeling: a nonlinear model to handle missing color observations; links whiteness to Julian day via individual random effects.
    • Form (illustrative):
    • W{i,j} = 100 ig(1 + \, \exp(-a{0,i} - a{1,i} \cdot JDj)\big)^{-1} + e
    • Where W{i,j} is whiteness, JDj is standardized Julian day, and a{0,i}, a{1,i} are individual random effects.
  • Color contrast used in survival analysis:
    • ext{contrast}{i,j} = |W{i,j} - Sj|, where Sj is mean snow cover at the week/site.
  • Survival modeling (Bayesian, known-fate):
    • Basic form: \text{logit}(\pi{i,j}) = b0 + b1 \cdot x{i,j} + c_i
    • \pi{i,j} = weekly survival probability, x{i,j} = covariate (color contrast), c_i = individual random effect.
  • Selection coefficient: temporally standardized weekly color contrast to estimate directional selection on mismatch.
    • Standardization: subtract weekly mean and divide by weekly SD; slope gives the standardized selection coefficient.
    • Result: standardized selection coefficient ≈ -0.04 (95% credible interval: [-0.061, -0.017]).
  • Survival costs and population projections:
    • Weekly survival costs under mismatch used to project annual survival under future snow-duration scenarios.
    • Annual survival projection (two-weekend extremes):
    • \text{AnnualSurv} = \mathcal{S}{0\%\text{contrast}}^{c} \cdot \mathcal{S}{60\%\text{contrast}}^{(52-c)}
    • where c = weeks with 0% contrast (matched weeks), and 52-c = weeks with 60% contrast.
  • Future snow-duration scenarios:
    • Time periods: mid-century (2030–2059) and late-century (2070–2099).
    • Emission scenarios: IPCC AR5 RCP4.5 (medium-low) and RCP8.5 (high).
  • Population dynamics:
    • Baseline hare population: asymptotic geometric growth rate k = 1.15 from a separate, intensive study.
    • Population-growth projections modify spring/fall survival according to mismatch costs; reproductive rates left unchanged.
    • Sensitivity analysis indicates post-weaning survival drives population dynamics in these models.

Key results

  • Individual variation: strong among-individual variation in molt phenology, leading to notable variation in color contrast across individuals; some hares show >50% whiteness differences for ~7 weeks/year.
  • Mismatch frequency: camouflage mismatch (contrast ≥ 60%) is relatively infrequent per individual ( < 1 week/year on average).
  • Survival costs and selection:
    • Weekly survival cost of mismatch is negative and substantial:
    • \mathcal{S}{\text{match}} \approx 0.96\, ,\quad \mathcal{S}{60\%} \approx 0.92\, ,\quad \mathcal{S}_{100\%} \approx 0.89 per week (approximate values from the model).
    • Per-week effect size on survival for color contrast: b_{\text{Contrast}} = -0.95 (95% CRI [-1.82, -0.035]).
    • Under temporally standardized weekly contrast, standardized selection coefficient: \approx -0.04 (95% CRI [-0.061, -0.017]).
    • No detectable difference in mean survival between the two study sites: b_{\text{Site}} = 0.004\, (95\%\ CRI: [-0.54, 0.52]).
  • Annual survival projections (cost of 60% contrast):
    • Mid-century and late-century survival under high emissions (RCP8.5):
    • \text{AnnualSurv}{\text{mid,8.5}} \approx 0.082\,,\; \text{AnnualSurv}{\text{late,8.5}} \approx 0.070
    • Baseline annual survival was 0.093.
    • Under medium-low emissions (RCP4.5): \text{AnnualSurv}{\text{mid,4.5}} \approx 0.085\,,\; \text{AnnualSurv}{\text{late,4.5}} \approx 0.080
  • Population growth rate (k) projections:
    • Baseline k = 1.15.
    • Under RCP8.5: mid-century k ≈ 1.02, late-century k ≈ 0.88 (strong declines expected).
    • Under RCP4.5: mid-century k ≈ 1.05, late-century k ≈ 1.00 (near replacement by late century).
    • Overall, under high-emissions, demographic costs from mismatch could drive populations toward decline unless evolutionary rescue occurs.

Implications and conclusions

  • Evolutionary rescue is potentially critical for maintaining camouflage-molting species under rapid snow-duration decline.
  • Necessary conditions for rescue include: sufficient additive genetic variation in molt phenology, large population sizes, ongoing gene flow, and mitigation of anthropogenic stressors including climate change.
  • Phenotypic plasticity could aid rapid responses, but its capacity to fully counteract increasing mismatch remains uncertain.
  • The study provides direct field evidence of anthropogenic climate-change-induced selection on a highly variable trait and demonstrates how selection can translate into meaningful demographic consequences.

Key concepts to recall

  • Camouflage mismatch: mismatch between an animal’s coat color and background due to changes in snow duration.
  • Molt phenology: timing of seasonal coat-color changes (fall and spring molts).
  • Evolutionary rescue: rapid evolutionary response that prevents population extinction under environmental change.
  • Selection coefficient: standardized measure of how a trait affects fitness; here, about -0.04 for color contrast.
  • Population growth rate: the long-term growth proxy k; values around 1 indicate replacement-level dynamics, >1 growth, <1 decline.
  • Climate scenarios: RCP4.5 and RCP8.5 used to project future snow duration and mismatch frequency.

Equations snapshot (all in LaTeX)

  • Color contrast definition: ext{contrast}{i,j} = |W{i,j} - S_j|
  • Mismatch threshold: ext{contrast}_{i,j} \geq 0.60
  • Survival model:\text{logit}(\pi{i,j}) = b0 + b1 \cdot x{i,j} + c_i
  • Standardized contrast (selection):z{i,j} = \frac{\text{contrast}{i,j} - \mu{\text{week}}}{\sigma{\text{week}}}
  • Annual survival under contrast levels:\text{AnnualSurv} = \mathcal{S}{0\%\text{contrast}}^{c} \cdot \mathcal{S}{60\%\text{contrast}}^{52-c}
  • Baseline population growth:k = 1.15
  • Projected k under scenarios (example values):
    • Middle of century (RCP8.5): k \approx 1.02
    • Late century (RCP8.5): k \approx 0.88
    • Middle of century (RCP4.5): k \approx 1.05
    • Late century (RCP4.5): k \approx 1.00