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Fig 1.

Conceptual diagram illustrating possible topographic effects on diurnal and seasonal fluctuations.

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Fig 2.

Pepperwood Preserve with 50 study sites.

Each site was equipped with a microclimate logger (Onset HOBO Model U23, Onset Corp., Bourne, MA) that was installed at 1.2m height above the ground (see Fig A.3 in S1 Appendix for a picture of the logger). Image credits © Stamen Design, © OpenStreetMap.

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Table 1.

Derived climate variables and their minimum, mean, and maximum across the 50 logger sites.

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Table 2.

Topographic and canopy variables measured at each of the 50 sites.

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Fig 3.

Seasonal minimum, maximum average temperatures with corresponding variability (lines are loess fits).

(A) Mean hourly temperatures of 50 sites by season. (B) Maximum hourly temperatures of 50 sites by season. (C) Minimum hourly temperatures of 50 sites by season. (D) Standard deviation of seasonal mean, minimum and maximum across sites.

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Table 3.

Partial coefficients from the multiple linear regression of climate variables by topographic drivers (p-value significance codes in bold: ‘***’ < 0.001, ‘**’ < 0.01, ‘*’ < 0.05, ‘.’ < 0.1, ‘‘ < 1).

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Fig 4.

Biplots of climate and physiographic PCA analysis (A) PCA of climate space (B) PCA of physiographic space. Dots on the panels represent sites, and arrows represent variables, with their length indicating their contribution to the principal component.

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Fig 5.

Topographic variables contributions deduced by RDA across all the seasons.

Elevation was found to be a prominent driver of variability in all seasons, with smaller contributions from other topographic features.

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Fig 6.

Linear fits of seasonal minima and maxima across the elevational gradient.

(A) Lower elevation sites are found to be colder than the high elevation suggesting inversion (temperature increasing with elevation); autumn minimum temperatures exhibiting higher rate of increase by elevation than in the spring; autumn (R2 = 0.65), spring (R2 = 0.28), summer (R2 = 0.41), and winter (R2 = 0.58) all significant at p-value < 0.001; (B) Summer maximum temperatures are found to be declining with elevation, but is less pronounced in winter; maximum temperatures association with elevation in autumn is found to be less pronounced compared to spring; autumn (R2 = 0.31), spring (R2 = 0.31), summer (R2 = 0.45) significant at p-value <0.001, and winter (R2 = 0.02) not significant at p-value = 0.31.

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Fig 7.

Linear fits of seasonal diurnal fluctuations as a function of elevation.

Higher elevation sites exhibit lower diurnal variation in all seasons; autumn (R2 = 0.69, p-value < 0.001), spring (R2 = 0.57, p-value < 0.001), summer (R2 = 0.57, p-value < 0.001), and winter (R2 = 0.31, p-value < 0.001) (A) and lower seasonal variation (difference between mean summer and winter fluctuation); R2 = 0.25, p-value < 0.001(B).

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Fig 8.

Linear fit of mean daily temperatures of HOBOs against the Pepperwood Preserve weather station (A) and slope of the relationship with elevation (B). Relationship highlighting that most of the sites are cooler in comparison to the weather station, but also show some of the high elevation sites are fundamentally aligned with the weather station climatically. Note that in panel A, dots refer to the day of the year, solid black line is 1:1, and the colored lines are individual linear fits in relation to the weather station.

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