Research ArticleECOLOGY

Spatial models reveal the microclimatic buffering capacity of old-growth forests

+ See all authors and affiliations

Science Advances  22 Apr 2016:
Vol. 2, no. 4, e1501392
DOI: 10.1126/sciadv.1501392
  • Fig. 1 The high biomass, tall canopies, and vertical structure of old-growth forests are associated with lower spring maximum temperatures than in mature plantations.

    Photos of old-growth (A) and mature plantation (B) forest stands at the H. J. Andrews Experimental Forest (HJA) in Oregon, USA. [photo credit: Matthew Betts, Oregon State University].

  • Fig. 2 Relative influence (RI) of variables describing elevation (ELV), microtopography (TOPO), and vegetation structure (VEG) for each temperature metric.

    RI values for 2012 (A) and 2013 (B) were derived from the number of times each variable was selected in the process of model building using boosted regression trees (BRTs). Overall, elevation had the strongest influence on air temperature patterns in the HJA, but microtopography and vegetation also exerted important effects, particularly for maximum temperature of the warmest month, variability measures, and cumulative degree days (CDD) in the winter-spring transition.

  • Fig. 3 Spatially predicted maps of minimum temperature of the coldest month and maximum temperature of the warmest month (in degrees Celsius) based on BRT models.

    Minimum temperatures (A) were primarily influenced by elevation (B), but maximum temperatures (C) were primarily a function of vegetation and microtopography (D). Maps of the elevational gradient (B; in meters) and canopy height (D; in meters) based on LiDAR from 2008 at the HJA. Black dots show the 183 temperature sampling locations. The location of the HJA in the western United States is shown in (A).

  • Fig. 4 Differences in microclimate conditions across a gradient in forest structure.

    (A) Principal components analysis (PCA) showing how vegetation structure metrics differ between mature/old-growth forest sites and plantations. The ellipses represent 68% of the data assuming a normal distribution in each category (plantation and mature/old growth). (B) Three-dimensional LiDAR-generated images of plantation forests [(i) side view; (ii) overhead view] and old-growth forests [(iii) side view; (iv) overhead view] at the Andrews Forest. (C and D) Results from generalized linear mixed models show the modeled relationship between forest structure [PC1, the first component of a PCA on forest structure variables (A)] and the residuals from an elevation-only model of mean monthly maximum during April to June (C) and mean monthly minimum during April to June (D) after accounting for the effects of elevation. Closed circles represent 2012 and open circles represent 2013. Maximum monthly temperatures (C) decreased by 2.5°C (95% confidence interval, 1.7° to 3.2°C) and observed minimum temperatures (D) increased by 0.7°C (0.3° to 1.1°C) across the observed structure gradient from plantation to old-growth forest.

  • Table 1 Generalized linear mixed model results for the relationship between temperature metrics and the first component of a PCA (PC1) representing a gradient in vegetation structure.

    Data from 2012 and 2013 were combined and “site” was included as a random effect in all models. Lower PC1 values indicate forest plantations and higher values indicate old-growth forests. “Change in temperature metrics” reports the average difference in temperature (°C) or degree days (dd) across the range of PC1 values. The effect of old-growth structure (PC1) on microclimate was consistent between years for most variables (“No year effects”). Effects of old-growth forests were stronger in 2012, and the direction of old-growth effects remained consistent for all but SD in weekly temperature. We included elevation (ELV) in all models to statistically account for elevation differences. Elevation had a significant effect on all models (P < 0.0001), except for SD in weekly temperature from January to March (P = 0.062). Coefficients from the interaction models include the 2012 intercept (Embedded Image 2012), the slope of the 2012 PC1 effect (Embedded Image PC1 2012), the 2013 intercept presented as the difference from the 2012 intercept (Embedded Image 2013), and the 2013 PC1 effect presented as the difference from the 2012 PC1 effect (PC1 Embedded Image 2013). P values in boldface indicate a statistically significant effect of PC1 on temperature metrics at P < 0.05. LCL, lower 95% confidence limit; UCL, upper 95% confidence limit.

    VariableInterceptPC1Change in temperature metrics ~ PC1
    Embedded ImageSEEmbedded ImageSEPUnitsChangeLCLUCL
    No year effects
    CDD > 0°C January to March178.663.66−0.473.920.9051dd−1.96−37.0114.25
    CDD > 0°C April to June820.274.17−10.254.470.0229dd−42.89−82.83−2.95
    Mn mo MEAN T April to June8.980.05−0.110.050.0235°C−0.47−0.92−0.03
    Mn mo MAX T April to June13.680.08−0.590.09<0.0001°C−2.47−3.24−1.70
    Mn mo MIN T April to June5.290.040.160.050.0006°C0.680.261.10
    Significant year effects
    Intercept 2012PC1 20122012 Change in temperature metrics ~ PC1
    Embedded ImageSEEmbedded ImageSEPUnitsChangeLCLUCL
    CDD > 10°C April to June115.061.73−7.841.85<0.0001dd−32.82−49.36−16.28
    SD wkly T January to March1.620.03−0.070.030.0341°C−0.27−0.550.00
    SD wkly T April to June3.780.010.010.010.4512°C0.03−0.040.13
    MAX T warmest mo25.220.14−0.680.15<0.0001°C−2.82−4.15−1.49
    MIN T coldest mo−1.140.060.330.06<0.0001°C1.390.881.91
    Intercept 2013PC1 20132013 Change in temperature metrics ~ PC1
    Embedded ImageSEEmbedded ImageSEPUnitsChangeLCLUCL
    CDD > 10°C April to June73.360.712.720.710.0002dd−21.43−37.97−4.90
    SD wkly T January to March0.980.040.130.040.0014°C0.260.02−0.53
    SD wkly T April to June1.080.01−0.040.010.0071°C−0.11−0.21−0.02
    MAX T warmest mo1.170.060.190.060.0047°C−2.05−3.38−0.72
    MIN T coldest mo−0.610.08−0.260.080.0013°C0.320.160.84

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/4/e1501392/DC1

    Supplementary Materials and Methods

    fig. S1. Fine-resolution (5 m) spatial predictions of temperature metrics at the HJA based on BRT models.

    fig. S2. Partial dependence plots showing the relationship between selected microtopographic variables and microclimate.

    fig. S3. Partial dependence plots showing the relationship between selected vegetation structure variables and microclimate.

    fig. S4. Key interactions identified from BRT models testing the effects of elevation, microtopography, and vegetation structure on microclimate.

    fig. S5. RI of variables measured at 25- and 250-m scales for each temperature metric in both years.

    fig. S6. Comparison of observed microclimate data by year.

    fig. S7. Comparison of predicted microclimate metrics by year.

    fig. S8. Photo of the HOBO temperature sensor in the field.

    table S1. BRT model settings (learning rate, number of trees), performance diagnostics (deviance, deviance SE, CV corr, CV SE), and tests for spatial autocorrelation in the BRT model residuals (Moran’s I and P).

    table S2. Pearson’s correlation coefficients (r) and associated P values for both observed and predicted values between years.

    table S3. Results from a PCA of all vegetation structure predictor variables.

    table S4. Summary statistics and t tests showing differences in LiDAR metrics between mature plantations and mature/old-growth forests.

    table S5. Results from Welch two-sample t tests comparing measures of biomass and canopy cover for plantation sites and mature/old-growth forest sites.

    table S6. Temperature metrics used in our study and associated summary statistics.

    table S7. Predictor variables used to predict patterns in microclimate metrics.

    References (5965)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • fig. S1. Fine-resolution (5 m) spatial predictions of temperature metrics at the HJA based on BRT models.
    • fig. S2. Partial dependence plots showing the relationship between selected microtopographic variables and microclimate.
    • fig. S3. Partial dependence plots showing the relationship between selected vegetation structure variables and microclimate.
    • fig. S4. Key interactions identified from BRT models testing the effects of elevation, microtopography, and vegetation structure on microclimate.
    • fig. S5. RI of variables measured at 25- and 250-m scales for each temperature metric in both years.
    • fig. S6. Comparison of observed microclimate data by year.
    • fig. S7. Comparison of predicted microclimate metrics by year.
    • fig. S8. Photo of the HOBO temperature sensor in the field.
    • table S1. BRT model settings (learning rate, number of trees), performance diagnostics (deviance, deviance SE, CV corr, CV SE), and tests for spatial autocorrelation in the BRT model residuals (Moran’s I and P).
    • table S2. Pearson’s correlation coefficients (r) and associated P values for both observed and predicted values between years.
    • table S3. Results from a PCA of all vegetation structure predictor variables.
    • table S4. Summary statistics and t tests showing differences in LiDAR metrics between mature plantations and mature/old-growth forests.
    • table S5. Results from Welch two-sample t tests comparing measures of biomass and canopy cover for plantation sites and mature/old-growth forest sites.
    • table S6. Temperature metrics used in our study and associated summary statistics.
    • table S7. Predictor variables used to predict patterns in microclimate metrics.
    • References (59–65)

    Download PDF

    Files in this Data Supplement:

Related Content