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

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

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Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.
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10.1126/sciadv.1501392
Spatial models reveal the microclimatic buffering
capacity of old-growth forests
Sarah J. K. Frey,
1
* Adam S. Hadley,
1
Sherri L. Johnson,
2
Mark Schulze,
1
Julia A. Jones,
3
Matthew G. Betts
1
*
Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from
coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate micro-
climate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is
thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative
effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution
of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed inde-
pendent test data well (r= 0.87). As expected, elevation strongly predicted temperatures, but vegetation and
microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light
detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased
by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest
structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and
therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve
old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing
biodiversity persistence in mountainous systems under climate warming.
INTRODUCTION
Macroscale climate patterns are well known to influence range-wide
suitability for biota. However, local-scale climate (hereafter micro-
climate) is often most relevant to animal behavior and demography
(1). Reconciling this mismatch between global climate models and
the scale at which organisms experience their environment should
therefore improve our understanding of biodiversity responses to cli-
mate change (2,3). Furthermore, in heterogeneous mountain landscapes
with complex thermal regimes (4), climate-sensitive species have the
potential to disperse to, and persist in, favorable microclimatic condi-
tions (5). Coarse-scale climate data are not as accurate for predicting
trends in mountains, influencing our ability to assess climate impacts
(6). Identification of factors that generate particular microclimates will
help focus conservation efforts to lessen the impacts of climate change
on biodiversity (7), which are expected to be particularly substantial in
mountainous regions (8). However, to date, the coarse resolution of most
land cover and climate data has precluded such analysis (9).
The decoupling of the surface temperature conditions from those of
the troposphere is commonly attributed to two main factors in moun-
tainous areas: (i) local air-flow dynamics, such as cold air drainage and
pooling, and (ii) variations in slope and aspect (microtopography) (7).
However, vegetation also has the potential to influence microclimatic
patterns via its effects on solar radiation, wind exposure, interception
of precipitation, and retention of understory humidity. Indeed, the in-
fluence of vegetation on microclimate has long been recognized (10,11)
and is the reason why long-term weather stations are situated in open
areas. Unfortunately, such sampling strategies have precluded infer-
ences about the relative influences of microtopography and vegetation
structure on mediating microclimate (2).
If particular vegetation structural characteristics can abate the
effects of regional climate change (12), land management has the
potential to either amplify or buffer these effects on biodiversity
(13). Given the rapid global changes in land use (14), it is critical to
understand the degree to which management influences micro-
climate. An increasing proportion of the worlds forests are secondary,
transformed forests (14,15). Therefore, it is essential to understandthe
implications of secondary forests for climate and biodiversity. Here, we
examine whether the structural characteristics present in old-growth
forests (for example, heterogeneous canopies, high biomass, and com-
plex vertical structure) increase site-scale thermal buffering capacity
over more structurally simple, but mature plantation forest stands.
The substantial biomass associated with western old-growth forests
might be expected to result in slower rates of warming during summer
months (16). Alternatively, the closed and homogeneous canopy con-
ditions of old (>50-year-old) forest plantations could prevent rapid site-
level warming through reduced solar radiation, thereby moderating
climate (17). Although previous work has examined the effects of
substantial differences in canopy cover on microclimate (4,1822), to
our knowledge, ours is the first broad-scale test of whether subtle
changes in forest structure due to differing management practices influ-
ence forest temperature regimes (Fig. 1). Given that old-growth forests
continue to decline globally (23) and that plantations continue to pro-
liferate (24), understanding microclimatic impacts is of great conser-
vation importance.
In 2012 and 2013, we collected understory air temperatures at high
spatial resolution across a complex mountainous landscape at the H. J.
Andrews Experimental Forest (HJA) in Oregon, USA. We obtained
fine-resolution (5 m) data on topography and vegetation structure using
LiDAR (light detection and ranging). We then used machine-learning
techniques [that is, boosted regression trees (BRTs)] to map predicted
1
Forest Biodiversity Research Network, Department of Forest Ecosystems and Society,
Oregon State University, Corvallis, OR 97331, USA.
2
U.S. Forest Service, Pacific Northwest
Research Station, Corvallis, OR 97331, USA.
3
Geography, College of Earth, Ocean, and
Atmospheric Sciences (CEOAS), Oregon State University, Corvallis, OR 97331, USA.
*Corresponding author. E-mail: sarah.frey@oregonstate.edu, sjkfrey@gmail.com (S.J.K.F.);
matt.betts@oregonstate.edu (M.G.B.)
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thermal properties at the landscape scale and to test the hypothesis that
vegetation structure mediates under-canopy microclimates.
RESULTS
All models performed well when tested on independent data (table S1).
The cross-validation correlations were high, showing substantial con-
gruence between training and test data [mean ± SD (range): 2012, r=
0.87 ± 0.09 (0.69 to 0.98); 2013, r= 0.84 ± 0.11 (0.64 to 0.96)]. This
performance was not caused by spatial dependency in the data (table S1).
These results indicate that BRTs, which are now used extensively in
species distribution models (SDMs) (25), seem to offer a powerful new
approach to examining spatial distributions in abiotic conditions. As in
SDM applications, the advantage of such machine-learning methods
lies in their capacity to incorporate many independent variables and
their flexibility to include nonlinearities and variable interactions. Al-
though parametric alternatives are available (for example, generalized
linear models), our results indicate that BRTs also represent a promising
option for distinguishing the relative importance (RI) of complex cli-
mate drivers and for generating detailed spatial climate predictions
(fig. S1).
Elevation was the dominant predictor for most of the temperature
metrics (Fig. 2), including cumulative degree days (CDDs); monthly mini-
mum, maximum, and mean temperatures during spring-summer (April
to June); and minimum temperatureofthecoldestmonth(Fig.3A).
High-elevation sites were generally cooler and had fewer CDDs. Micro-
topographic features showed high RI for predicting CDDs during winter-
spring (January to March; RI 2012: 45.9%; RI 2013: 53.9%) and max-
imum temperature of the warmest month (RI 2012: 37.9%; RI 2013:
32.2%; Fig. 3C). Steeper, exposed, south-facing sites were generally
warmer and more variable, and accumulated more CDDs (fig. S2).
Among all temperature metrics, maximum temperature of the
warmest month (RI 2012: 35.5%; RI 2013: 39.8%) and variability in
weekly spring-summer temperature (RI 2012: 36.7%; RI 2013: 10.2%)
were most strongly influenced by vegetation structure (that is, canopy
height, biomass, understory cover, and vertical structure; Fig. 2). Vege-
tation structure also had a strong effect on mean monthly maximum
temperature from April to June (RI 2012: 20.4%; RI 2013: 18.9%; Fig. 2).
Furthermore, vegetation structure was an important predictor for tem-
perature variability (RI 2012: 28.9%; RI 2013: 31.6%) and CDDs during
winter (RI 2012: 31.2%; RI 2013: 34.3%). Sites with old-growth forest
traits (for example, taller canopies, higher biomass, and more complex
vertical structure) had reduced temperatures and greater temperature
stability (fig. S2). Old-forest characteristics, such as taller canopies, more
canopy cover >10 m, and biomass >500 Mg/ha, reduced maximum
temperature of the warmest month and mean monthly maximum tem-
perature from April to June (fig. S3, A to C). Old-forest traits also had an
important influence on climate variability; for example, increased co-
efficient of variation in canopy height and greater midcanopy cover
(2 to 10 m) both reduced variability in mean weekly temperature from
January to March (fig. S3, D and E). Areas with the lowest biomass
variability (for example, even-aged stands such as plantations) showed
more microclimate variability in this winter-spring transition (fig. S3F).
However, topographic position (valleys versus topographically ex-
posed sites) appeared to accentuate the effect of forest structure; exposed
sites with low variability in biomass (for example, plantation stands)
accumulated the most degree days (fig. S4A). A vegetation-elevation in-
teraction revealed that maximum monthly temperature during spring-
summer was lowest at high elevations with high amounts of canopy cover
surrounding a site (fig. S4C). In both years and for most temperature
metrics, variables at the local scale (25-m radius) had a greater relative
influence than variables averaged across a 250-m radius [overall aver-
age RI at the local scale (25 m) across all metrics: 2012: 60.7 ± 10%;
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)foreststandsattheH.J.AndrewsExperimental Forest (HJA) in Oregon,
USA. [photo credit: Matthew Betts, Oregon State University].
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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)wereprimarilya
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. 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 micro-
topography 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.
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2013: 60.4 ± 10.1%]. However, vegetation metrics tended to have more
influence at the broader (250-m) spatial scale (fig. S5). Despite inter-
annual differences in the RI of variables, we found remarkable between-
year consistency in both predicted and observed under-canopy thermal
conditions across sites (7 of 10 variables with r> 0.9; table S2 and figs. S6
and S7). This interannual consistency in site-level conditions, which
occurred despite substantial differences in annual climatic conditions,
lends support to the notion that thermally buffered sites may provide
temporally consistent refugia for biodiversity (7).
Despite being well suited to prediction given our complexity of input
variables, BRTs do not provide information on effect sizes (that is,
regression coefficients) (25). Therefore, we used principal components
analysis (PCA) to integrate vegetation structure variables into a reduced
number of components (table S3). The first two principal components
of the PCA explained 74.7% of the variability [principal component 1
(PC1) = 44.7%; principal component 2 (PC2) = 30%]. The first com-
ponent (PC1) strongly reflected a gradient in forest structure from closed-
canopy plantations to mature/old-growth forests (Figs. 1 and 4A); this
gradient represents the predominant forest types on federal land in the
region (26). Sites with low PC1 values had less biomass (mean and SD),
lower canopies (mean and SD), and less cover (2 to 10 and >10 m) (table
S3). The individual LiDAR metrics effectively distinguished between
plantation sites and mature/old-growth forest sites (table S4); a dis-
criminant function analysis (27) showed that prediction accuracy was
85.3% for plantation sites and 90.4% for mature/old-growth forest
sites. Furthermore, our LiDAR metrics were congruent with previously
Fig. 4. Differences in microclimate conditions across a gradient in forest structure. (A) Principal components analysis (PCA) showing how veg-
etation 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 struc-
ture gradient from plantation to old-growth forest.
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reported structural differences between old-growth forests and secondary
Douglas-fir (Pseudotsuga menzisii) forests in the Pacific Northwest (28).
After we statistically controlled for the effects of elevation, PC1 was
associated with most temperature variables (8 of 10 variables; Table 1).
Effects were largest for maximum and minimum temperatures, as well
as for CDDs in the spring and summer months. Temperature differences
were substantial across the gradient in forest structure; for instance, in
2012, maximum spring monthly temperatures decreased by 2.5°C (95%
confidence interval, 1.7° to 3.2°C; Fig. 4C) across the observed gradient
in forest structure (from structurally simple plantations to complex
old-growth forests). Minimum temperatures during this same period
were 0.7°C (95% confidence interval, 0.3° to 1.1°C; Fig. 4D) warmer
across the same gradient. Overall, these influences of old-growth
forests on thermal conditions were consistent between years (although
we found statistical evidence for year × PC1 interactions, parameter
estimates of the interactions tended to be small and only resulted in
a sign change for variability in temperature from January to March;
Table 1).
DISCUSSION
Elevation was a powerful predictor for air temperatures across years,
variables, and scales, confirming the importance of macrotopography
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 sitewas included as a random effect in all models.
Lower PC1 values indicate forest plantations and higher values indicate old-growth forests. Change in temperature metricsreports 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 differ-
ences. 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 (
^
b02012), the slope of the 2012 PC1 effect (
^
b1PC1 2012), the 2013 intercept presented as the
difference from the 2012 intercept (D
^
b02013), and the 2013 PC1 effect presented as the difference from the 2012 PC1 effect (PC1D
^
b12013). Pvalues in
boldface indicate a statistically significant effect of PC1 on temperature metrics at P< 0.05. LCL, lower 95% confidence limit; UCL, upper 95% confi-
dence limit.
Variable
Intercept PC1 Change in temperature metrics ~ PC1
^
b0SE
^
b1SE PUnits Change LCL UCL
No year effects
CDD > 0°C January to March 178.66 3.66 0.47 3.92 0.9051 dd 1.96 37.01 14.25
CDD > 0°C April to June 820.27 4.17 10.25 4.47 0.0229 dd 42.89 82.83 2.95
Mn mo MEAN TApril to June 8.98 0.05 0.11 0.05 0.0235 °C 0.47 0.92 0.03
Mn mo MAX TApril to June 13.68 0.08 0.59 0.09 <0.0001 °C 2.47 3.24 1.70
Mn mo MIN TApril to June 5.29 0.04 0.16 0.05 0.0006 °C 0.68 0.26 1.10
Significant year effects
Intercept 2012 PC1 2012 2012 Change in temperature metrics ~ PC1
^
b0SE
^
b1SE PUnits Change LCL UCL
CDD > 10°C April to June 115.06 1.73 7.84 1.85 <0.0001 dd 32.82 49.36 16.28
SD wkly TJanuary to March 1.62 0.03 0.07 0.03 0.0341 °C 0.27 0.55 0.00
SD wkly TApril to June 3.78 0.01 0.01 0.01 0.4512 °C 0.03 0.04 0.13
MAX Twarmest mo 25.22 0.14 0.68 0.15 <0.0001 °C 2.82 4.15 1.49
MIN Tcoldest mo 1.14 0.06 0.33 0.06 <0.0001 °C 1.39 0.88 1.91
Intercept 2013 PC1 2013 2013 Change in temperature metrics ~ PC1
D
^
b0SE D
^
b1SE PUnits Change LCL UCL
CDD > 10°C April to June 73.36 0.71 2.72 0.71 0.0002 dd 21.43 37.97 4.90
SD wkly TJanuary to March 0.98 0.04 0.13 0.04 0.0014 °C 0.26 0.02 0.53
SD wkly TApril to June 1.08 0.01 0.04 0.01 0.0071 °C 0.11 0.21 0.02
MAX Twarmest mo 1.17 0.06 0.19 0.06 0.0047 °C 2.05 3.38 0.72
MIN Tcoldest mo 0.61 0.08 0.26 0.08 0.0013 °C 0.32 0.16 0.84
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in temperature patterns (7,20). However, although elevation predicted
most temperature metrics well, it was less effective in predicting tem-
perature variability and degree-day accumulation from January to
Marchboth of which are microclimatic factors that are likely to
influence species behavior and demography (29). Microtopographic
variables, including slope, aspect, and relative topographic position,
also influenced temperature patterns, but this effect varied markedly
by time of year. Depressions and other topographically sheltered
areas are thought to contribute to the decoupling of surface tempera-
tures from regional patterns, thereby potentially generating microre-
fugia in complex terrain (7). These topographic features exerted a
large influence on understory microclimate during winter, when
persistent cold air pools form in depressions and valleys.
Vegetation characteristics associated with older forest stands ap-
peared to confer a strong, thermally insulating effect. Older forests with
tall canopies, high biomass, and vertical complexity (Fig. 1A) provided
cooler microclimates compared with simplified stands (Fig. 1B). This
resulted in differences as large as 2.5°C between plantation sites and
old-growth sites, a temperature range equivalent to predicted global
temperature increases over the next 50 years (30). This effect was po-
tentially attributable to large differences in biomass between forest
types (16), rather than canopy cover, as we observed less variation in
canopy cover between old-growth sites and plantation sites (table S5).
Although previous studies have shown strong influences of vegeta-
tion on microclimate, most of these demonstrated differences between
significantly different vegetation types or stages, such as mature forests
versus grasslands (18,31)orclearcuts(19,32). At the global scale, forests
have been shown to have a broad-scale cooling effect (33); however, to
ourknowledge,thisisthefirstevidencethatsubtlerstructuraldiffer-
ences within mature forest types (that is, mature plantations versus
old-growth forests) mediate under-canopy temperature regimes.
Our findings indicate that management practices that result in
single-species, even-aged plantations are likely to reduce the thermal
buffering capacity of forest sites, potentially limiting the availability
of favorable microclimates for some species. Unlike most predictors,
which were primarily useful when summarized at fine spatial scales
(25-m radius), vegetation at broader scales exerted the strongest in-
fluences on temperature. This is consistent with results from studies
that examine the thermal edge effects of high-contrast cover types (34);
smaller forest patches tend to be more susceptible to changes in tem-
perature (20), and such edge effects also limit microclimatic buffering
of tropical forests (22). In jurisdictions where biodiversity maintenance
is the goal, conservation and restoration of structures associated with
old-growth forests are more likely to sustain favorable microclimates
(35) and to reduce climate change impacts on temperature-sensitive
species. Recent work shows that the understory microclimate differ-
ences documented here could be highly relevant to biodiversity conser-
vation in temperate forests; cooler forest types have attenuated the
widespread loss of cool-adapted understory plant species (13)andhave
promoted tree recruitment (36). Amphibians, lizards, insects, and even
large mammals are shown to take advantage of microclimate con-
ditions when regional climate moves beyond the range of thermal pref-
erences (5,37,38). However, our findings apply to species inhabiting
forest understory. Although a high proportion of forest biodiversity is
found in this stratum (3941), species associated with upper canopies
may not benefit from the microclimate buffering capacity of old-
growth forests. Furthermore, because older seral stages provide the high-
est levels of buffering, management options may be limited for species
inhabiting early successional forests (42), unless they are able to take
advantage of the microclimatic buffering of older forests or cooler mi-
croclimates that are near old-forest edges (34). Currently, early seral spe-
cies are of high conservation concern in the Pacific Northwest, largely as
a result of habitat loss (42); given that early seral forests may not have
equivalent thermal refuges, we predict synergistic negative effects on
these species when combined with climate.
We conclude that the substantial influence of vegetation structure
on microclimate presents the opportunity to manage for conditions
that favor the persistence of biodiversity (43). By conserving or creating
forest conditions that buffer organisms from the impacts of regional
warming and/or slow the rate at which organisms must adapt to a
changing climate, it may be possible to ameliorate some of the severe
negative effects of regional warming. Given the time frame for forests
to acquire old-growth structural conditions, understanding thresholds
in forest structure where important ecosystem services are lost is of
pressing concern (44). With ~3.5 million ha of old-growth forests re-
maining in the coastal region of the Pacific Northwest (much of which
is now protected under the federal Northwest Forest Plan) (45), there
is substantial potentialin this region at leastto reduce the effects of
warming on native populations of forest species.
Understanding the fine-resolution effects of vegetation structure
on mediating microclimate could also improve predictions about bio-
diversity responses to climate change. Without considering the detailed
information on forest structural conditions reported in this paper, pub-
lished model estimates of changes in speciesclimatic niches (and ex-
tinction risk) (46,47) could be substantially underestimated or
overestimated, depending on the amount of old forests in a landscape.
We demonstrate how combining new remote sensing technologies
(48) with machine-learning techniques provides an effective option
to develop high-resolution, spatially explicit models of under-canopy
temperature characteristics. As under-canopy temperature data be-
come more readily available, microclimate variability can thus be
mapped across broad spatial scales. Under-canopy temperature
modeling, coupled with models predicting future vegetation dynamics
(49), offers the potential to enhance our understanding of microclimate
and species persistence in the face of climate change.
MATERIALS AND METHODS
Experimental design
We collected fine-scale temperature data using calibrated HOBO pen-
dant data loggers (Supplementary Materials) at 183 locations across
the 6400-ha HJA in the Cascade Mountains of central Oregon, USA
(44°12N, 122°15W). Data loggers were placed 1.5 m above the forest
floor; this ground- and shrub-level forest stratum is characterized by
particularly high biodiversity in Douglas-fir forests of the Pacific North-
west (3941). The HJA spans an elevational gradient from 410 to 1630 m
above sea level. It is a conifer forest mosaic that is largely composed of a
mix of old-growth forests, mature forests, and 40- to 60-year-old Douglas-
fir plantations. Sampling locations were stratified across elevation, forest
type, and distance to roads, ensuring that the full environmental gradi-
ent was sampled (Fig. 3) with a minimum distance between sampling
points of 300 m (Supplementary Materials). We collected air tempera-
ture data at the 183 sample locations from January 2012 to July 2013. In
total, we used 7,417,320 temperature loggings to calculate summary me-
trics (table S6).
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Environmental predictor variables
To model temperature metrics, we selected 19 predictor variables
(table S7) that we hypothesized to be important for influencing air
temperatures in forested mountain landscapes and categorized these
into three main groups: (i) elevation (ELV), (ii) microtopography
(TOPO), and (iii) vegetation structure (VEG). We derived all vege-
tation variables from LiDAR data collected at the HJA in August
2008 during the leaf-on period (50). LiDAR is a relatively new tech-
nology that allows for fine-scale mapping of forest structure across
broad spatial extents (51,52). The VEG category variables described
vegetation structure using metrics relating to (i) canopy height, (ii) cover
at multiple strata, and (iii) vertical distribution of canopy elements.
Spatial scales
We assessed the importance of our predictor variables at two spatial
extents around each sample point because the scale at which drivers
of microclimate act is largely unknown (29): (i) at a radius of 25 m,
which represents local-scale predictors, and (ii) at a radius of 250 m,
which represents stand-scale conditions. The relative roles of the biotic
and abiotic aspects of the environment could influence microclimate
differently at these two spatial scales (3).
Response variables
Our 10 response variables included CDDs, mean monthly minimum
and maximum temperatures, and variability in temperature (table S6).
CDDs are linked closely to timing of spring plant bud burst, leaf out,
and flowering, as well as insect emergence, and therefore have the
potential to influence higher trophic levels (53). Variability in weekly
temperature in both time periods (as measured by SD) may also de-
termine the quality of sites (54). We chose the two time periods of
January to March (winter-spring transition) and April to June (spring-
summer transition) because they are relevant to the phenology of
many organisms on our landscape. In temperate regions, pheno-
logical events during spring also have direct implications for repro-
duction and growth in both plants and animals (55). We also
included the minimum and maximum temperatures of the coldest
and warmest months, respectively, as they represent extremes at
the annual time scale.
Statistical analysis: BRTs
We used a machine-learning approach (BRTs) to explore the relation-
ship between our suite of predictor variables (19 × 2 spatial scales = 38
total predictor variables; table S7) and air temperature at our 183 sam-
ple locations. BRTs have recently been used extensively in species dis-
tribution modeling because of their capacity for uncoveringnonlinear
relationships between predictors and response variables, as well as
their flexibility in testing interactions among predictors (25). BRTs
can also handle large numbers of predictor variables and the colinear-
ity between them (25), which is advantageous in studies such as ours,
where there are many categorized predictor variables (table S7) but
little prior information about which are most important or at which
spatial scales. This modeling method afforded the flexibility to explore
multiple potential correlates of microclimate without arbitrarily re-
stricting our predictor set. We used the R program version 3.0.1 (56)
in combination with the dismopackage version 0.8-17 (57)forall
analyses (Supplementary Materials). We used predictive deviance,
measured as the mean deviance from the held-out data in all folds,
as our primary measure of model performance. We also tested the
correlation between predicted and observed temperature metrics
via a built-in 10-fold cross-validation that uses 10% independent test
data. These tests thus represent an entirely independent test of model
performance. If overfitting occurred, these tests should show low cor-
relations between predicted and observed values. We also tested for
spatial autocorrelation in our temperature data set (Supplementary
Materials).
We assessed the contribution of each group of predictor variables
(ELV, TOPO, and VEG) to the explained variance in temperature
metrics by summing the RI values of the variables in each category (Sup-
plementary Materials). To determine the direction and nature of the
relationships between the temperature metrics and the most influential
individual predictor variables (>2% RI), we examined the partial de-
pendence plots for the visualization of the fitted functions.
Statistical analysis: PCA and generalized linear
mixed models
We performed a PCA on all of our LiDAR-derived vegetation varia-
bles at the 25-m scale (13 variables) to test whether we could reliably
differentiate between plantations and older forests (table S3). This also
aided in determining whether our vegetation structure variables cap-
tured the gradient in forest structure present across the landscape. A
weakness of BRTs is that they do not produce effect sizes (that is, re-
gression slopes) that can be easily related to differences in response
variables (degrees Celsius or degree days). Therefore, we used gener-
alized linear mixed models [R package nlme(58)] to examine the
relationship between values of principal components (reflecting a
gradient in forest structure) and temperature metrics. We combined
data from both years and included siteas a random effect to account
for a lack of independence within location between years. Elevation
was included as a covariate in all models. We tested for a year effect
on the influence of PC1 on microclimate via an interaction model. In
this fashion, we were able to quantify differences in temperature across
the old-growth forest structure gradient and to test for consistency
between years.
SUPPLEMENTARY MATERIALS
Supplementa ry material for this art icle is available at http: //advances.sc iencemag.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. RIof variables measured at 25- and 250-mscales 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 (MoransIand P).
table S2. Pearsons correlation coefficients (r) and associated Pvalues 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 ttests showing differences in LiDAR metrics between mature
plantations and mature/old-growth forests.
RESEARCH ARTICLE
Frey et al. Sci. Adv. 2016; 2 : e1501392 22 April 2016 7of9
on April 22, 2016http://advances.sciencemag.org/Downloaded from
table S5. Results from Welch two-sample ttests 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.
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Acknowledgments: We extend our special thanks to T. Valentine for her help with GIS (geo-
graphic information system), to C. Murphy and E. Miles for assistance with the temperature data
processing, and to J. Sexton for providing logistical support for field work. We also thank C. Still,
whose comments greatly improved this manuscript. This work would not have been possible
without our field assistants (E. Jackson, A. Bartelt, S. Ashe, S. Yegorova, A. Mott, and K. Stanley).
Funding: This research was made possible with support from multiple grants and a wards: an
NSFIntegrative Graduate Educa tion and Research Traineeship fellowship (NSF-033 3257), a De-
partment of the Interior North west Climate Science Center graduate fellowship,and an Andrews
Forest Long-Term Ecological Research graduate research assistantship (NSF DEB-0823380) all
awarded to S.J.K.F. Research and support were provided by the HJA research program, funded by
the NSFsLong- TermEcologica l Research Program (NSF DEB-0823380), the U.S. Forest Service Pacific
Northwest Research Station, and Oregon State University. The project described in this publication
was also supported by a grant awarded to M.G.B. from the Department of the Interior through Co-
operative Agreement No. G11AC20255 from the U.S. Geological Survey and an NSF grant awarded
to M.G.B. and J.J. (NSF ARC-0941748). The contents of this paper are solely the responsibility of the
authors and do not represent the views of the Northwest Climate Science Center, the U.S. Geological
Survey, or the U.S. Forest Service. Au thor contribut ions: S.J.K.F. and M.G.B. conceived thestudy and
planned the analysis. S.J.K.F. analyzed the data. S.J.K.F., M.G.B., and A.S.H. co-wrote the manuscript. S.J.K.F.
and A.S.H. collected the data. All authors discussed the results and edited and commented on the
manuscript. Competing interests:The a uthorsde claretha t they have no competing interests. Data
and materials availability: All data needed to evaluate the conclusions in the paper are present in
the paper and/orthe Supplementary Materials. Air temperature and LiDAR data areavailable online
at the Andrews Forest Webpage (air temperature: http://andrewsforest.oregonstate.edu/data/
abstract.cfm?dbcode=MS045; LiDAR: http://andrewsforest.oregonstate.edu/data/abstract.cfm?
dbcode=GI010). Additional data related to this paper may be requested from the authors.
Submitted 6 October 2015
Accepted 25 March 2016
Published 22 April 2016
10.1126/sciadv.1501392
Citation: S. J. K. Frey, A. S. Hadley, S. L. Johnson, M. Schulze, J. A. Jones, M. G. Betts, Spatial
models reveal the microclimatic buffering capacity of old-growth forests. Sci. Adv. 2, e1501392
(2016).
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2016, 2:.Sci Adv
Schulze, Julia A. Jones and Matthew G. Betts (April 22, 2016)
Sarah J. K. Frey, Adam S. Hadley, Sherri L. Johnson, Mark
old-growth forests
Spatial models reveal the microclimatic buffering capacity of
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... However, most studies of near-ground temperatures in disturbed forests have focused on logged and managed forests (e.g. Frey et al. 2016, Greiser et al. 2018, Hardwick et al. 2015, Jucker et al. 2018b, Kermavnar et al. 2020; few have quantified the relationships between microclimate, canopy, and topography within burnt forests. ...
... As in other studies (Frey et al., 2016;Wolf et al., 2021), we found that the impact of forest structure on microclimate varied seasonally. Models of early-and late-season temperature had higher explanatory power than models of growing season mean temperatures. ...
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Forest canopies can buffer seedlings from extreme climate conditions. Yet, how disturbed forest canopies influence microclimate is not well understood, despite the important implications of microclimate for seedling establishment and post-disturbance successional trajectories. Better understanding of the relationship between a forest canopy and sub-canopy temperature and moisture conditions requires easily acquired and continuous forest canopy data, which is increasingly available due to new technology. Here, we measured canopy height using a remotely piloted aircraft (RPA) and monitored microclimate with low-cost temperature and soil moisture sensors in a sub-boreal forest impacted by fires of variable severity. We used regression models to investigate how differences in canopy height influenced microclimate variables. Mean growing season temperatures at -8 cm (soil), 0 cm (surface), and 15 cm (near-surface) relative to the ground surface were higher under shorter more disturbed canopies. Soil temperature was most sensitive to canopy height differences: linear models for the observed data range predicted a 2.0 °C increase in mean growing season soil temperature with every 10 m decrease in canopy height. We observed a weak negative relationship between canopy height and mean growing season soil moisture. We found that canopy height summarized at moderate resolution (15 m) better explained differences in temperature in our disturbed landscape. This work informs future methods to produce gridded microclimate datasets and outlines the impact of disturbed forest structure on microclimate variables. Our results show that the characteristics of the forest canopy remaining after a burn impact microclimates, which has important implications for post-fire ecosystems.
... Ecological studies have observed air temperature extremes to be tempered under denser forests and canopies because of reduced incoming and outgoing radiation, higher evapotranspiration and lower air mixing (Chen et al., 1999;Hardwick et al., 2015;von Arx et al., 2012;Zellweger et al., 2019). More specifically, air temperature buffering was shown to be increased by basal area and canopy closure (Greiser et al., 2018), leaf area index (Hardwick et al., 2015;von Arx et al., 2013), biomass and structural complexity (Frey et al., 2016) and below-canopy vegetation density (Kovács et al., 2017). Similarly aligned with our results, canopy height was previously found important (Frey et al., 2016;Jucker et al., 2018), and could be compared to the thickness of an insulating cover. ...
... More specifically, air temperature buffering was shown to be increased by basal area and canopy closure (Greiser et al., 2018), leaf area index (Hardwick et al., 2015;von Arx et al., 2013), biomass and structural complexity (Frey et al., 2016) and below-canopy vegetation density (Kovács et al., 2017). Similarly aligned with our results, canopy height was previously found important (Frey et al., 2016;Jucker et al., 2018), and could be compared to the thickness of an insulating cover. We revealed that these conclusions appear transposable to human thermal comfort. ...
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... A similar explanation can be given for the higher functional diversity of SLA that was found at sites that were dominated by beech and with higher leaf litter cover. However, the higher richness and diversity of SLA can also be explained by the microclimatic buffering that is happening in dense and shaded sites of old-growth forests [83], in which less competitive forest specialist species are favored [15,70,84]. These results are consistent with the relationships that were found between the functional richness and diversity of SLA and the two ordination axes, showing that the higher richness and diversity of SLA appeared in communities that were located at shaded sites with higher leaf litter cover and that host a low number of species such as Athyrium filix-femina, Helleborus viridis subsp. ...
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... iii) N-fixing organisms such as lichens, mosses, liverworts are more abundant in OGF (Lesica et al. 1991). iv) They create stable macroclimates that function as refuges for many species, of which some being endangered and mitigating indirectly in this way the climate change (Frey et al. 2016). ...
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Old-growth forests are considered a benchmark for naturalness and models to which one compares managed forests. The comparison led to debates around biodiversity and its conservation, structure and dynamics, polarizing conservation-ists and forest practitioners. Plant pathogens are frequently disregarded as components of forests biodiversity, the common perception referring to this category of organisms as important biotic stressors. However, pathogens are building several functional groups such as necrotrophic and biotrophic pathogens, endophytic pathogens, sapro-parasitic species, in highly natural forests including old-growth forests. They are establishing interaction networks with several other functional groups of organisms such as hyperparasites, consumers, disease facilitators, indirect opportunistic species (such as tree hollow dwellers), saprotrophs or mutualists. Being connected to old trees (rare or missing components of managed forests) or to endangered forest plants, pathogens become indicators of naturalness and biodiversity. The gradient going from saprotrophs, to sapro-pathogens and generalist/spe-cialist pathogens characterizing forest ecosystems is linked at great extent to wood and bark, being connected to nutrient cycling as ecosystem level process. As long as disease is maintained within the baseline mortality of the trees, pathogens play the important role of control factors and contribute directly and indirectly to forest biodiversity. The current disease ecology progresses, the biodiversity integrative studies, and the new holistic approaches shaping modern ecology bring in the focus rare, endemic pathogens. These play several roles: as important control factors for plant populations, as components of the plants' phenotypic niche and of the global biodiversity, as potential providers of services (sources of medicines) and as components of the intricate ecological webs. However, the comparisons between old-growth and managed forests biodiversity should be focused on species evenness (high evenness in old-growth forests versus low evenness in managed forests) and not particularly on species richness. Still, there are pathogens responsible for major biotic disturbances in forests worldwide, the invasive, alien or emerging pathogens threatening both old-growth forests and managed forests.
... Population declines in species associated with regenerating forest are particularly cryptic because habitat amount for these species tends to be increasing. One hypothesis is that populations of some species that prefer early seral stages, despite having potentially more habitat, are declining due to climate change over the past three-and-a-half decades (~1 °C increase over 30 years 37 ) Given that such stand types are probably warmer due to more open and/or shallow canopies 38 , any increases in ambient temperatures are likely to be more severe in plantations and naturally regenerating forests than in mature forests, which would exert physiological stresses and potentially have population consequences on birds. This effect could be magnified by the fact that several early seral species are more associated with young coniferous forest, which is typically found further to the north in boreal forests 20 . ...
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In many regions of the world, forest management has reduced old forest and simplified forest structure and composition. We hypothesized that such forest degradation has resulted in long-term habitat loss for forest-associated bird species of eastern Canada (130,017 km2) which, in turn, has caused bird-population declines. Despite little change in overall forest cover, we found substantial reductions in old forest as a result of frequent clear-cutting and a broad-scale transformation to intensified forestry. Back-cast species distribution models revealed that breeding habitat loss occurred for 66% of the 54 most common species from 1985 to 2020 and was strongly associated with reduction in old age classes. Using a long-term, independent dataset, we found that habitat amount predicted population size for 94% of species, and habitat loss was associated with population declines for old-forest species. Forest degradation may therefore be a primary cause of biodiversity decline in managed forest landscapes.
... In undisturbed conditions, forest bryoflora is rich and diversified and hosts most rare and endangered bryophyte species, whose survival is strictly linked to the preservation of these conditions (Gustafsson and Hallingback 1988;Cooper-Ellis 1998). That is because forests, by constantly supplying environmental moisture through evapotranspiration and buffering the understory from temperature extremes and water loss thanks to the shading effects of tree canopies, provide stable climatic conditions (Chen et al. 1999;Suggitt et al. 2011;von Arx et al. 2013;Frey et al. 2016), which ensure suitable habitats for poikilohydric organisms such as bryophytes. Canopy Forests also supply a variety of micro-habitats acting as microrefugia for specialized bryophytes, e.g. ...
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Cryptogamic diversity is a reliable indicator of the state of forest ecosystems. In this study we analysed the variations in both bryophyte species richness overall and number of hemerophobic bryophyte species in Central European managed forests over a 20-year time span, based on data collected in 132 plots scattered across Poland. We tested differences in species richness among five temporal replicates, as well as among site types grouped based on elevation a.s.l., dominant tree species and stand age. The analyses revealed no significant trend in species richness across years. Meanwhile, species richness significantly increased along with elevation a.s.l., especially in broadleaved forests. No significant difference in species number between spruce and pine dominated stands emerged for mature stands, while there was a strong difference for young stands, with spruce forest hosting a much higher number of species. Species richness exhibited a slight, but not significant, increase over time in broadleaved forests, no significant variations in pine dominated stands and significant fluctuations in spruce dominated stands, yet without a significant trend. Out of the tested drivers, dominant tree species exhibited the strongest impact on species community composition. Number of hemerophobic and strongly hemerophobic species did not undergo significant variations across years either. The lack of bryophyte diversity trends highlighted in this study suggests Central European managed forests are in an equilibrium sate, maintained by the opposing effects of climate changes, on one side and of more sustainable forest management and pollutant deposition decline, from the other.
... This would mean setting higher targets for strict protection than 10% of land that has been suggested for the EU (European Commission, 2020). Conservation of mature forests is important not only for the protection of species those areas are designated for, but they also play a crucial role in the mitigation of climate change (Chen et al., 1995(Chen et al., , 1993Frey et al., 2016) and of its impacts on species distribution (Lehikoinen et al., 2018). ...
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Species distribution modelling is an important tool to inform conservation, particularly if combined with site prioritization approaches. Results can then be used in both suggestion of new protected areas as well as evaluation of the existing ones. We applied MaxEnt analysis to a species of mature forests, the Eurasian Pygmy Owl (Glaucidium passerinum), which is recognized as a biodiversity indicator species. Furthermore, we used habitat suitability and uncertainty maps in site prioritization for conservation and evaluation of the existing nature conservation area (NCA) network. We found species to be strongly positively associated with time since forestry activities at a local scale (25ha) and abundance of mature forests from local through home range (450ha) to landscape (1960ha) scales. The sum of habitat suitability as a proxy for apparent population allowed us to estimate that the existing NCAs hold only 23% of the population. We found 68% of priority sites (PS) for species conservation to be outside NCAs. Strict forestry restrictions form the most suitable conservation regimes, with more than 51% of PS in NCA network having insufficient regimes. Inclusion of PS in NCAs would increase the network to 26% of the national territory with 41% of the species apparent population. Currently, the most suitable conservation regimes cover less than 2% of the country area.
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Chapter
This chapter focuses on historical microclimates and how they can help us to predict the future. It summarizes the drivers and effects of past, present and future climate, land‐use and forest management on temporal dynamics in understory microclimate, and methods to infer historical microclimates. The chapter outlines the implications for forest biodiversity. The focus is on temperatures near the forest floor (0–1 m), because these temperatures are of high biological importance for understory plants and animals. The focus on temperature also provides a direct link to climate warming, although we clearly acknowledge that other microclimatic parameters, such as soil moisture and light availability, are also important. The forest microclimate is not only affected by plot and stand characteristics, but also by the landscape in which it is embedded.
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Currently, commercial forestry applications of airborne scanning lidar are limited to geo-technical applications such as creation of digital terrain models for layout of roads or logging systems. We investigated the feasibility of predicting characteristics of forest stands with lidar data in a university-industry partnership. Lidar lends itself well to such applications because it allows direct measurement of important structural characteristics of height and canopy closure. We found that lidar data can be used to predict the stand characteristics of height, basal area, and volume quite well. The potential for commercial applications appears bright. Lidar data can be used to estimate stand characteristics over large areas or entire forests. After the process is streamlined, it should be possible to provide maps of height, basal area, and volume in such areas within a few weeks of the lidar collection flight.
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CONTENTS: INTRODUCTION; THE RADIATION BALANCE; SOIL HEAT FLUX AND SOIL TEMPERATURE; SENSIBLE HEAT FLUX, SURFACE AND AIR TEMPERATURE; WIND AND TURBULENT TRANSPORT; ATMOSPHERIC HUMIDITY; MODIFICATION OF THE SOIL TEMPERATURE REGIME; EVAPOTRANSPIRATION; PHOTOSYNTHESIS; CARBON DIOXIDE BALANCE; WINDBREAKS AND THE SHELTER EFFECT; FROST AND FROST CONTROL; IMPROVING WATER USE EFFICIENCY - SOME NEW METHODS; APPENDIX - NOTATION AND CUSTOMARY UNITS.