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ARTICLE
Elevated growth and biomass along temperate
forest edges
Luca L. Morreale 1,2✉, Jonathan R. Thompson2, Xiaojing Tang 1, Andrew B. Reinmann3,4,5 & Lucy R. Hutyra1
Fragmentation transforms the environment along forest edges. The prevailing narrative,
driven by research in tropical systems, suggests that edge environments increase tree
mortality and structural degradation resulting in net decreases in ecosystem productivity. We
show that, in contrast to tropical systems, temperate forest edges exhibit increased forest
growth and biomass with no change in total mortality relative to the forest interior. We
analyze >48,000 forest inventory plots across the north-eastern US using a quasi-
experimental matching design. At forest edges adjacent to anthropogenic land covers, we
report increases of 36.3% and 24.1% in forest growth and biomass, respectively. Inclusion of
edge impacts increases estimates of forest productivity by up to 23% in agriculture-
dominated areas, 15% in the metropolitan coast, and +2% in the least-fragmented regions.
We also quantify forest fragmentation globally, at 30-m resolution, showing that temperate
forests contain 52% more edge forest area than tropical forests. Our analyses upend the
conventional wisdom of forest edges as less productive than intact forest and call for a
reassessment of the conservation value of forest fragments.
https://doi.org/10.1038/s41467-021-27373-7 OPEN
1Department of Earth & Environment, Boston University, Boston, MA, USA. 2Harvard Forest, Harvard University, Petersham, MA, USA. 3Environmental
Science Initiative, CUNY Advanced Science Research Center, New York, NY, USA. 4Graduate Program in Earth and Environmental Sciences and Biology,
CUNY Graduate Center, New York, NY, USA. 5Department of Geography and Environmental Sciences, Hunter College, New York, NY, USA.
✉email: lmorreal@bu.edu
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Deforestation is a pervasive consequence of land-use
change1and is impactful not just due to what is lost,
but also due to its effects on the forest fragments that
remain. Forest fragmentation is globally ubiquitous, with over
70% of forests located less than 1 km from a non-forest edge2.
Fundamental constraints on forest growth3,4and carbon cycling
are altered near edges relative to interior forests5,6, with increases
in light availability, temperature, wind, and reactive nitrogen
deposition, as well as altered water availability7,8. While frag-
mentation occurs across biomes, reported effects of these per-
turbations on higher-order dynamics in fragmented forests (i.e.,
structure, composition, function, and mortality) have largely
focused on tropical ecosystems, where sharp increases in mor-
tality and long-term forest degradation are reported at the forest
edge9–13. Expanded analyses suggest significant reductions in
tropical ecosystem net carbon sequestration and, more broadly,
the terrestrial carbon sink10,11,14. However, environmental con-
trols on temperate forests differ from the tropics, and temperate
forest fragmentation studies are both fewer and more limited in
scale, c.f. 15,16. Temperate forest edges have similar microclimatic
differences, but contrasting biomass and productivity responses,
emphasizing a need for a better understanding of edge ecosystems
in non-tropical biomes6,15,17,18.
Here, we offer a large-scale estimation of fragmentation
impacts on temperate forest growth and structure along forest
edges, with broader implications for global evaluation of frag-
mented forests. Hereafter, we use the term edge to refer to forest
area bounded, in part, by a non-forest land cover and, conversely,
interior as a designation of forest area bounded fully by forest. We
report differences in tree basal area (BA; a metric of forest
structure, strongly correlated with biomass), BA increment (BAI;
a measure of forest growth), tree mortality, and average stem
density and diameter, between the forest edge (edge plots; <15 m
from a non-forest land cover) and forest interior (interior plots;
nonadjacent to non-forest land cover). We show that the tem-
perate forest edges within our study area exhibit dramatically
increased growth, tree stem density, and total BA, with negligible
changes in mortality. We then scale these results to estimate
regional increases in forest growth attributable to the distinct
forest edge environment. Finally, we place our results in context
of global patterns of forest fragmentation.
Results and discussion
Distinct characteristics of forest edges. To examine forest edges
in the northeastern US, we used inventory data from the US
Department of Agriculture Forest Inventory and Analysis (FIA)
program. The FIA program has established permanent, fixed-area
(675 m2), forest plots in a hexagonal grid across the United
States19. This national forest inventory includes measurements of
tree size, growth, and land use; re-measuring every 5–7 years in
our study area. Using >48,000 FIA plots distributed throughout
20 northeastern US states (Supplementary Fig. 1), we compared
structural and growth dynamics along temperate forest edges to
those of interior forests. Individual tree measurements are col-
lected within four fixed-radius subplots (168.7 m2area) with a
fixed orientation; subplot characteristics are recorded even if the
subplot contains partially forested or non-forest area. We leverage
partially forested subplots to identify forest edges within the FIA
database.
Using a quasi-experimental statistical matching framework
followed by a generalized linear model (GLM) regression analysis,
we compared BA, BAI, and tree mortality on FIA subplots that
are adjacent to a non-forest land cover, to matched subplots
within the forest interior. Matching approximates an experi-
mental design where control plots (interior) were selected based
on similarity to the treatment plots (edge) in relation to
confounding predictors (light, water, temperature, nitrogen
deposition, and forest type; Supplementary Fig. 5)20. We report
the results from GLM regression models as percent differences
with significance derived from Wald tests on regression
coefficients and we include Nagelkerke Pseudo-R2from the most
parsimonious models as a goodness-of-fit metric21. Detailed
descriptions of plot filtering, statistical matching, GLM selection
and analysis are provided in the Methods section.
Edges come in many forms. Natural edges exist as both
transitions in growing conditions (e.g., forest–grassland ecotones,
and wetlands) and sharp boundaries (e.g., lakes, rivers, and
geologic features) with variable effects on forest growth. In
contrast, anthropogenic edges often exist as abrupt transitions in
areas that were once fully forested (e.g., agricultural fields, roads,
and developments). Average BAI along anthropogenically formed
edges is 36.3% greater (p< 0.001; R2=0.149) than interior forest,
while BAI along all edges (encompassing anthropogenic, natural,
and unspecified edges) is 24.1% greater (p< 0.001; R2=0.153)
than interior forest (Fig. 1). BA exhibits smaller differences, but
the same trend: anthropogenic edges have 21.0% greater
(p < 0.001; R2=0.059) BA and along all edges BA is 13.9%
greater (p< 0.001; R2=0.069) than the forest interior. Notably,
our analyses exclude trees smaller than 12.7 cm in diameter.
Given that densities of small diameter woody vegetation are
typically higher along forest edges6, it follows that the differences
in BA and BAI between edge and interior forests observed here
represent a conservative estimate.
There are just three pathways to increased BA in edge forests:
more trees, larger trees, or some combination thereof. We find no
significant difference in the average tree diameter between the
forest edge and interior, even when comparing with only
anthropogenic edges. In contrast, by averaging individual tree
measurements within each subplot, we find a mean increase of 58
trees per hectare (p< 0.001) across all edges as compared with the
forest interior (Fig. 2). Along anthropogenic edges, the difference
increases to 82.6 additional trees per hectare (p< 0.001), which is
consistent with the observed patterns of BA in all versus
anthropogenic edges.
Along tropical edges, the primary driver of decreased
productivity is heightened tree mortality, frequently attributed
to increased impacts of wind, lianas, and more frequent
droughts22. In contrast, we find no significant differences in
biogenic mortality between edge and interior forests (Supple-
mentary Fig. 3b). Within our study area, the largest cause of
mortality in forests is anthropogenic removals23. While we do
find a statistically significant (p< 0.001) increase in anthropo-
genic removals in both edge groups compared to the interior
(Supplementary Fig. 3c), there is no difference in overall total
mortality (Supplementary Fig. 3a). Given the prevalence of forest
management in this region, we performed a robustness test of our
main result to quantify any potential impacts of harvesting. We
withheld all plots that had a record of tree removal (n=3642)
within the FIA inventory and found no changes in the overall
pattern between edge and interior in either BA or BAI.
Tree species composition mediates forest response to anthro-
pogenic environmental perturbations24. Individual species
responses to altered energy and biogeochemical inputs at the
edge can vary due to climatic tolerance and successional
characteristics25. Therefore, we quantified differences in structure
and growth responses to edges by species composition groups23
(Fig. 1). In most compositional groups, BAI increases significantly
at all forest edges, but with varying magnitudes: Northern Pines—
Hemlock forests exhibit the smallest increase in BAI, 16.9%
(p< 0.001); Oak—Pine forests have the largest, 32.5% (p< 0.001).
The effect size increases across almost all compositional groups
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when comparing BAI specifically along anthropogenic edges with
forest interiors. Of the eight forest type groups, only the Southern
Conifers group has no statistically significant difference in BAI.
The increase in BAI ranges from 25.5% (p< 0.001) in Northern
Pines—Hemlock, to 67.7% in Spruce—Fir. The Oak—Hickory
group exhibits 41.1% (p< 0.001) higher tree growth at anthro-
pogenic edges than the forest interior, an effect >28% larger than
when all edges are pooled. Interior-to-edge enhancements of BA
are smaller than BAI, but five compositional groups have
significantly greater BA along edges: Oak—Hickory (16.5%;
p< 0.001), Northern Hardwood (16.1%; p< 0.001), Northern
Pines—Hemlock (15.1%; p< 0.001), Oak—Pine (18.5%;
p< 0.001), and Bottomland Forests (12.5%; p< 0.001). When
comparing anthropogenic edges with the interior, the effect is
again stronger, and five compositional groups exhibit significant
increases in edge BA. Of these groups, Aspen—Birch have the
largest increase in BA (31.7%; p< 0.001); Northern Hardwoods
have the smallest (19.5%; p< 0.001).
Estimating the regional impact of enhanced growth. To scale
the edge impacts on growth across our study area, we coupled the
results from the GLM regression analysis with a land-cover map26
and a forest-type map27. We aggregate our results to ecoregions,
geographic areas that are ecologically and climatically similar, to
account for mismatches in spatial resolution between our gridded
inputs28,29. For these analyses, we focused on the effects of
anthropogenic edges. The increases in growth and biomass we
observe at temperate forest edges are greatest adjacent to
anthropogenic edges and are evidence of a largely unrecognized
impact of the ongoing process of forest fragmentation. Large
variability was observed in fragmentation patterns across our
Fig. 1 Forest edges have elevated growth and basal area. BAI (a) and BA (b) show the average marginal effects of edge-class and forest-type from GLM
outputs. Results are presented in Interior, All edges, and Anthropogenic edge groups and ordered by forest type abundance (Supplementary Fig. 5). Interior
and All Edge groups have n=6607 independent subplots, anthropogenic edges have n=4327 independent subplots. Data are presented as the mean
marginal effects with inner error bars showing 95% confidence intervals on the marginal effects; outer error bars on interior group are for comparison with
anthropogenic edges.
*
*
*
**
a
b
Fig. 2 Temperate forest edges have higher mean stem density than the
forest interior but exhibit no difference in mean tree diameter.
aDistributions of mean subplot stem density (# of trees per hectare).
bDistributions of mean subplot tree diameter (diameter in centimeters).
Dashed lines show mean values of all subplots within each edge class.
Asterisks denote significance (*p< .00001; **p=0.0078) as calculated
with two-sided pairwise ttests using a Bonferroni adjustment. Interior and
all edge groups have n=6607 independent subplots, anthropogenic edges
have n=4327 independent subplots.
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study region. The proportion of forest area within 30 m of an
edge varies across ecoregions from <5 to 68% of all forest area,
with an area-weighted average of 18.5% (Fig. 3a). We quantified
the expected difference between interior and edge forest based on
ecoregion-specific forest composition (Fig. 3b) and abiotic pre-
dictors, then combined the proportion of forest within 30 m of an
edge with ecoregion BAI differences to quantify the effect of edges
on overall forest productivity. We estimated the total increase in
annual BAI within each ecoregion associated with increased
growth at anthropogenic forest edges (Supplementary Fig. 5).
Estimates determined that elevated BAI found at anthropogenic
forest edges represents a >6% increase in total forest growth
across the entire region (Fig. 3c). The BAI response varied across
our study domain; increases in forest growth range from 23%
increase in agricultural-dominated areas (region shown in Sup-
plementary Fig. 6b), a 2% increase in the least-fragmented
northern regions (region shown in Supplementary Fig. 6c), and a
15% increase within the metropolitan east coast (region shown in
Supplementary Fig. 6d).
Our findings contrast with the conventional narrative based on
tropical forest studies, that forest edges decrease net forest
productivity and, consequently, lower forest aboveground carbon
storage. Temperate and tropical forests have distinct ecologies
and climate; it follows that similar perturbations can have
markedly different effects. The absence of any increase in tree
mortality, as repeatedly observed in tropical forest edges, suggests
that temperate forest edges are less wind-threatened and less
sensitive to the elevated temperatures and water stress that occur
along all forest edges. Rather, increases in radiation may release
the most-limiting biogeochemical constraints on temperate
forests (temperature and light)3,6,18. The growth response is
almost certainly related to greater light availability, which affects
tree canopy architecture and can increase forest leaf area index
and, in turn, stimulate productivity18,30.
The global extent of forest fragmentation. Comparison of our
results and those of previous tropical studies is complicated by
differences in land-use history, specifically the time since edge
creation. Forests in our study region and, more broadly, the
temperate forest biome have undergone centuries of deforesta-
tion, forest transitions, and fragmentation. Some forest edges
included in our study have existed for decades. However, research
on newly created edges in this region has shown large growth
increases in remaining trees, without associated increases in
mortality, immediately following edge creation31. Given that
abrupt formation of edges can expose the previously intact forest
to secondary disturbances, individual tree characteristics,
including height, drought tolerance, and rooting depth, may
determine whether the cascading perturbations induce mortality.
Shorter, more wind-firm trees, prevalent in temperate forests,
may not experience altered biogeochemical conditions only as
negative perturbations and, instead, are more likely to be
advantaged by increased resource availability. In contrast, the
taller trees found in temperate forests of the Pacific Northwestern
US, in which fragmentation patterns are characterized by defor-
estation and clear-cut timber harvests, might exhibit a similar
initial mortality response to tropical forests29. However, forestry
research from the same US Pacific Northwest region also finds
large increases in BAI in surviving conifers adjacent to silvi-
cultural treatments32, analogous to the edge enhancements in BAI
that we report. Furthermore, a recent study on European tem-
perate forests similarly found that temperate forest edges exhibit a
95% increase in aboveground carbon stock within 5 m of an
edge33. Together, these results suggest that the pattern of elevated
growth along forest edges holds true across large portions of the
temperate forest biome.
The implications of these findings on global estimates of tree
growth and carbon storage are proportional to the amount of
fragmentation within temperate and tropical forest biomes. We
quantified forest fragmentation throughout both types of forests
using a 30-meter resolution, global, forest-cover map29,34 (Fig. 4).
a
b
c
Fig. 3 Edges increase productivity in temperate forests. a The percent of
forest area within 30 m of an anthropogenic edge within each ecoregion.
bSpatial distribution of aggregated forest types used in study. cThe
percent increase in ecoregion total BAI attributable to elevated growth at
anthropogenic edges.
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Temperate forests have >50% more forest area within 30 m of a
forest edge than tropical forests (217 million ha compared to 143
million ha, respectively) (Fig. 4b). Europe has the highest percent
of edge temperate forests (21.5%), while North America has the
highest percent of edge tropical forests (29.1%) (Fig. 4a).
Fragmented forests are often perceived as degraded remnants.
However, the prevalence of temperate forest edges and their
distinctive ecosystem functions, demonstrated here, argue for a
reassessment of forest edges and fragments. These are the forests
that people interact with most, they are distinct from interior
forests in ways that need to be better understood, and, in some
functions, are of disproportional value. The large increases in
growth near forest edges that we observe here have major
implications for understanding how these ecosystems will
respond to ongoing fragmentation and climate change.
Emphatically, this research does not argue for proactive forest
fragmentation as a prescription to increase carbon sequestration.
The increased carbon storage along the edges of fragmented
remnants does not come close to offsetting the loss of terrestrial
carbon stocks and future sequestration capacity associated with
forest loss15. Furthermore, there is evidence that the temperate
edge responses are hindered by extreme heat, suggesting that
rising global temperatures may exacerbate heat stress at
temperate forest edges15 and cause them to respond more
similarly to tropical forest edges. Instead, this is a call to
acknowledge the complexity of interactions between global
change drivers across diverse ecosystems. Centuries of fragmen-
tation have created a permanent shift in the microenvironment of
a large and growing proportion of the global temperate forest
area. With rising populations, expanding urban and agricultural
areas, and ongoing deforestation, the critical need to understand
fragmented forests as distinct ecosystems only grows. Any
attempt to predict future forests must account for ongoing
changes in the prevalence of forest edges and the potential
contributions of fragments to terrestrial carbon storage.
Methods
Overview. We used data from the national forest inventory conducted by the US
Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA)
program to quantify tree biomass and growth along forest edges and within the
forest interior. We estimated the causal impact of the forest edge environment on
patterns of tree biomass and growth, while accounting for potentially confounding
variables. We then used the regression models to estimate the aggregate difference
in growth attributable to forest edges throughout the northeastern U.S. Finally, to
better understand the implications of our findings, we quantified the degree of
forest fragmentation throughout temperate and tropical forest biomes world-wide,
using a 30 m forest cover map.
Study area. Our analyses of edge impacts on forest biomass and growth were
conducted throughout twenty-states (1.7 million km2) in the northeastern and
upper mid-west of the United States (Supplementary Fig. 1). This region contains
765,000 km2of forest and encompasses gradients of dominant land-uses, climatic
conditions, and forest composition while remaining within deciduous, coniferous,
and mixed temperate forest ecosystems.
Identifying edges in forest inventory data. The FIA collects measurements of
tree size, growth, and land-use within a nested plot design across the country19.
Each FIA plot is composed of four individual subplots; within each subplot, the
diameter at breast height (dbh) of every tree >12.7 cm is measured during each
measurement period. The re-measurement frequency for FIA plots in our study
area is between 5 and 7 years, but this can differ between Forest Service regions. In
addition to tree measurements, the database details land-use condition data that
includes the proportion of the area that is forested and, on some plots, the land-
cover class of the non-forest area (FIA User’s Manual, Condition Table). FIA plots
are considered forested if some portion of the plot includes a contiguous forest
patch (including potentially outside of the plot area) of greater than 4047 m2that
has more than 10% canopy cover. With a memorandum of understanding between
the USFS and Harvard University, we had access to the true, unfuzzed plot
coordinates, which are not publicly available. Evaluating >48,000 plots in the USFS
Northern Region sampled from 2010 to 2020 and selecting the most recent mea-
surement cycle for each plot, we identified subplots that contained both a forest
and a non-forest condition and categorized these as edges (Supplementary
Table 1). Only subplots that included a forest condition in both the most recent
and previous measurement were included. Subplots where the mapped condition
changed from forest to non-forest were excluded. Changes in the amount of
mapped forest condition were included and are incorporated into the calculation of
response variables using the most recent condition area. We identified FIA plots
where all four subplots were fully forested as interior plots to be used for com-
parison. Subplots located within the same plot as an edge subplot (i.e., edge-
proximate subplots) were excluded from this study due to limitations in our ability
to quantify their distance from an edge. The spatial configuration of subplots is
such that a fully forested subplot may be up to ~65 m away from an identified
forest edge within another subplot. Studies suggest that the distance of edge
influence in temperate forest does not extend more than 30 m into the forest
interior15,33. Since the FIA does not contain information about the geometry of
non-forest conditions beyond the subplot boundary, we deemed that the large
uncertainty in the relationship between these subplots to a non-forest edge pre-
cluded their inclusion in the study. The FIA plot configuration prevented quan-
tification of the distance of edge influence in our analysis; the exclusion of subplots
adjacent to edge-subplots may limit direct comparisons with other fragmentation
studies.
We used the FIA condition data to characterize the non-forest land use in edge
subplots. Information on adjacent non-forest land cover is not collected on all FIA
plots (4327 of 6607 edge subplots). We aggregated FIA land-cover classification to
a binary anthropogenic or unknown edge type designation and present results from
all edge subplots and the anthropogenic edge subset (FIA User’s Manual Condition
Table, Section 2.4.50).
For each subplot (168 m2in area), we calculated two primary response variables
of interest: total live tree BA and BAI. Notably, trees smaller than <12.7 cm
diameter are only recorded within a small portion of the plot, called the microplot.
Our study design prevented the inclusion of the microplot and therefore excludes
trees beneath this diameter threshold. Trees that grew into the measurement size
class between the previous and most recent measurement are included. The
exclusion of small trees and saplings may result in a conservative estimate of the
difference between edge and interior BA and BAI, as other studies have found a
higher density of small-stemmed woody vegetation along forest edges35.BAis
calculated from a single plot measurement, as the summed BA of all live adult trees
(>12.7 cm dbh) in m2. BAI was calculated on a per-tree basis as the difference
in radial growth of live adult trees between the most recent and previous
measurements, and then divided by the number of years between measurements
(m2yr−1). In addition, we aggregated individual tree diameter measurements to
Africa
Asia
Austr alia
Europe
N. America
Oceania
S. America
Temp. Trop. Temp. Trop. Temp. Trop. Temp. Trop. Temp.Trop. Temp.Trop. Temp.Trop.
0
200
400
600
Biome
)
seratce
h n
oillim( aer
A
Interior
Edge
Total
0 500 1000 1500
Temp.
Trop.
Area ( million h ectares )
ab
Fig. 4 Temperate forests are nearly 1.5 times more fragmented than tropical forests. a The percent of temperate and tropical forest area within 30 m of
an anthropogenic edge within each global ecoregion. bThe area (in millions of hectares) of edge and interior forest, grouped by biome and continent.
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calculate mean stem density (stems ha−1) and mean tree diameter for each subplot
(Fig. 2).
We accounted for variable subplot area by normalizing both BA and BAI to a
per-hectare of forested area basis, resulting in units of m2ha−1and m2ha−1yr−1,
respectively. To account for potential small-area bias, we performed a sensitivity
analysis on the relationship between BA and subplot forested area (Supplementary
Fig. 2). We subsequently excluded 1284 subplots under 30 m2in area as the area to
BA relationship asymptotes relationship above this threshold. Finally, we
accounted for errors in field dbh measurements, sometimes resulting in negative
BAI values, by excluding the <2.5% and >97.5% quantiles of both BA and BAI
distributions.
Given their spatial configuration, FIA subplots are not fully independent
measurements, potentially introducing issues with pseudo-replication and spatial
autocorrelation within our dataset. To test for spatial autocorrelation we examined
the semivariance of model residuals36, and found that there was high correlation
only at distances of less than 1 km. The spatial stratification of the FIA plot design
minimizes issues of plot–plot proximity within our study. However, to account for
autocorrelation between subplots, we filtered our pre-matched dataset to only
including one subplot from each FIA plot. For plots containing multiple edge
subplots, we selected the subplot with the largest forested area. For interior plots,
we selected the central subplot and excluded all others.
Isolating the effect of edges on growth
Abiotic controls. To account for environmental controls on forest growth we
included the most critical abiotic predictors of terrestrial vegetation productivity
(light, water, temperature, and nitrogen deposition) as covariates in the regression
models (Supplementary Fig. 4, Supplementary Table 2). Light, water, and tem-
perature data were drawn from spatial raster maps (0.5° resolution) as unit-less
indices of relative limitation on vegetation productivity, ranging from 0 to 13.
Nitrogen data were drawn from the 2018 NADP gridded inorganic wet nitrogen
deposition product (4 km spatial resolution; kg of N ha−1)37. To interpolate across
small gaps in the raster data (usually along water bodies), we used the Nibble tool
from ArcGis Pro (ESRI Team). We then used FIA plot locations to extract values
from each raster layer for all FIA subplots.
Forest composition. Tree species may vary in their responses to biogeochemical
changes that occur on forest edges. Overall forest community response emerges
from complex interactions between species. We used aggregations of tree species,
termed forest composition groups (or forest types)38, to assess if species compo-
sition influenced the response to altered edge condition. Forest type classifications
for each subplot are provided by the FIA (FIA User’s Manual, Condition Table)
and are defined in Appendix D therein. We aggregated the FIA forest types into
eight broader species groups, following Thompson et al.23, and defined in Sup-
plementary Table 1.
Matching, GLM regressions, and model selection. All statistical analyses and
most of the data processing were conducted in R, version 3.439. Using a causal
inference framework, we created a quasi-experimental statistical design that
included pre-matching followed by a GLM regression analysis40. Matching emu-
lates an experimental design using observational data by identifying control groups
of untreated (forest interior) plots that were as similar as possible to treated (forest
edge) plots in terms of observable confounders. By capturing key differences in
abiotic variables we control for the fundamental drivers of forest productivity,
allowing for a direct estimation of the average treatment effect of edges. Similarity
was defined by nearest-neighbor covariate matching determined by Malahanobis
distance, implemented in the MatchIt library in R41, the simplest and best method
when the dataset is robust enough to find a match for every treated plot20. This
method excludes forest interior plots that are not matched with an edge plot. Given
differences in sample size between the full edge dataset and the subset designated as
anthropogenic edges, we performed matching separately on the two datasets. To
assess the efficacy of matching on reducing the differences in covariate distribu-
tions, we used summary statistics calculated with the MatchIt library and report the
pre- and post-matched covariate balance in Supplementary Table 4 and Supple-
mentary Table 5 (sensu Schleicher et al.42). Matching was highly successful, largely
eliminating differences in all covariate distributions in both datasets.
Our primary response variables of interest, BA and BAI, were right-skewed,
non-normally distributed and violated the assumptions of normality necessary for
ordinary least squares regression43. We, therefore, used a GLM to better fit the
structure of our data. GLMs are an extension of linear regression that allow more
freedom in the choice of probability distribution function through the use of a link
function to model relationships between predictors and response variables44. The
gamma probability distribution is frequently chosen to model BA, given its
assumptions of positive, continuous values and flexible model form23,45.We
performed a series of GLM regressions on our post-matched datasets, using a
gamma probability distribution with an inverse link function to model the
relationship of BA and BA with a suite of predictor variables, using the glm
function as implemented in the R Core stats package39. Due to differences in
sample size between the all-edge dataset and the anthropogenic-edge subset, we
modeled these two datasets separately for each of BA and BAI, resulting in four
separate regression analyses. We used a model selection framework to identify the
most parsimonious model within each of the model sets based on the Akaike
Information Criterion (AIC) and residual deviance statistic46,47. We report the
model-selection and model-fit results for each of our separate analyses, including
model forms, AIC, Nagelkerke Pseudo-R2, and residual deviance in Supplementary
Table 2. Across all four regression analyses, the best-performing model was one
that included an interaction between the edge-status and forest type categorical
variables, as well as the variables of temperature-limitation, light-limitation, water-
limitation, and nitrogen deposition.
We then used the best performing model from each analysis to compare the
differences in BA and BAI between forest edge and interior across each forest type.
We estimated the treatment effect of edge-state within each forest type using the
ggeffects package48 to calculate marginal effects with the continuous predictors
(temperature, light, water, and nitrogen deposition) held at their within-forest type
regional means. The results of this analysis are displayed in Fig. 1and
Supplementary Table 3; primary error bars on the interior point show the 95%
confidence interval of the marginal effect from the full edge model, while secondary
error bars show the CI from the anthropogenic edge model. Due to the smaller
sample size in the anthropogenic model, estimates of the mean marginal effect of
the interior plots vary slightly (though non-significantly) from those from the full
dataset. The main text description reports outputs from both models, calculated
from separate interior mean estimates. For visual clarity, we only display one set of
interior means in Fig. 1.
Mortality and timber harvest. In tropical forests, large reductions in productivity
along edges are associated with increased tree mortality.9To assess differences in
tree mortality across our study region, we applied a simplified GLM analysis,
including edge-state as our only predictor variable. The FIA differentiates between
mortality attributed to timber harvest and that attributed to other, non-harvest
causes. The results of this analysis are presented as marginal effects of each edge
category in Supplementary Fig. 3. There are no significant differences in biogenic
mortality between edge groups and no difference in overall mortality (combined
biogenic and anthropogenic); there is a small, but statistically significant
(p< 0.001), increase in harvested BA within both all-edge and anthropogenic edges
as compared with the forest interior. We note that the exclusion of small-diameter
trees from our study could alter these results if there was differential mortality
between edge and interior in smaller tree size classes.
Temperate forests are heavily impacted by forest management49. We tested the
robustness of the effect of edges on growth and biomass by withholding all subplots
with a record of anthropogenic removals on the full FIA plot (i.e., management;
n=3642). We found no difference in the overall effect of edges nor meaningful
differences within forest type groups.
Scaling edge effects on forest growth across the Northeast. Ecoregions are a
widely used geographic partitioning of ecosystems into coherent spatial units as
defined by abiotic, biotic, and anthropogenic characteristics28. EPA Level IV
ecoregions are delineated by differences in environmental characteristics analogous
to those that we used to model forest growth and thus are a comparable spatial unit
to quantify the aggregated effects of fragmentation.
Quantifying fragmentation. To quantify anthropogenic forest edge area, we identify
forest cover within 30 m of a road, development, or agricultural field (sensu Smith
et al.6) using a 30 m resolution land-cover product from 2016 of the National Land
Cover Database (NLCD)50. Edge forest was defined as all forest pixels adjacent
(queen’s rule) to a non-forest cultivated or developed pixel (Supplementary
Fig. 6a). Figure 3a shows the percentage of total forest area classified as edge within
each ecoregion. We report that 18% of the total forest area in our study domain is
adjacent to an anthropogenic edge. Differences from the reported 22% in Smith
et al. are likely attributable to the use of a different NLCD product. Note that the
definition of forest edge here may differ from that of the FIA analysis, given the
constraints on quantification of the distance of edge influence and the spatial
resolution of the land cover products.
Ecoregion edge impacts. To scale the effects as illustrated in Fig. 3, we quantified
ecoregion forest composition by (1) using a 250 m resolution USFS forest type
map27, we aggregated raw forest type values to the aggregated forest type groups
included in our regression models (Figs. 3b), (2) we calculated the total area of each
forest type group within each ecoregion, then used the average temperature, light,
water, and nitrogen deposition in each ecoregion as inputs to our GLM regression
models to calculate the BAI of edge and interior forest for each forest type. With
the proportional area of each forest type, we calculated an area-weighted mean and
then differenced the estimated edge and interior BAI to produce an expected
difference of forest growth (BAI m2ha−1) between edge and interior within each
ecoregion (Supplementary Fig. 5). Finally, we combined the proportion of edge
forest with the expected growth difference to quantify the estimated difference in
percent increases in ecoregion BAI within each ecoregion attributable to increases
of forest growth at the edge (Fig. 3c).
Quantifying global forest fragmentation. We quantified the extent of forest
fragmentation throughout temperate and tropical forests worldwide at the scale of
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ecoregions using the Hansen Global Forest Change (v1.7)51 dataset on Google
Earth Engine (GEE)52. Tropical and temperate biomes were delineated in a global
ecoregion map53, analogous to the more detailed ecoregions described earlier. The
tree canopy cover layer from the Hansen dataset provided estimates of percent tree
canopy cover for the year 2000 at 30 m resolution globally produced by time series
analysis of Landsat images51. To calculate the percentage of edge forest in each
ecoregion: (1) a 10% threshold (following the FIA definition of minimum forest
cover19) was applied to the tree canopy cover layer to separate forest and non-
forest pixels, (2) each forest pixel adjacent (queen’s rule) to a non-forest pixel was
classified as edge forest on GEE, and (3) ArcGIS Zonal Statistics Tool was used to
calculate the percentage of edge forest in each ecoregion. Definitions of forest cover
via % canopy cover vary between studies, therefore we performed a robustness
check on our results to the threshold definition of forest cover by re-analyzing with
a 30% canopy threshold. While there were differences in the calculated raw area of
forest edges, the ratio of area fragmented between temperate and tropical forests
did not change meaningfully (Supplementary Fig. 7). We then compared the
Hansen-derived forest fragmentation to the 2016 NLCD-derived forest fragmen-
tation used in our previous analysis to assess comparability of the two products.
Supplementary Fig. 8 shows the agreement between the percent edge forest values
calculated based on the two forest maps for the 247 ecoregions in the Northeast US.
The agreement is strong especially in large and more forested ecoregions. The
Hansen-derived percent edge forest explained 84.5% of the variance in NLCD-
derived percent edge forest with RMSE of 6.1 (%) at ecoregion level. The spatial
aggregation to ecoregion level largely reduced the uncertainty in the mapping of
forest pixels in both products.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The processed, post-matching FIA data generated in this study and used to generate
Figs. 1and 2have been deposited in the Harvard Forest Data Archive under accession
code HF41954. The spatially aggregated estimates of BAI presented in Fig. 3c and
summaries of global forest edge area displayed in Fig. 4a are available in the HF Data
Archive under accession code HF419. The un-fuzzed FIA location data are protected and
are not available due to data privacy laws. Unprocessed FIA inventory data is available at
https://apps.fs.usda.gov/fia/datamart/. The National Land Cover Database land cover
layer is at https://www.mrlc.gov/data. The forest cover map we use for the global analysis
is available on Google Earth Engine.
Code availability
Statistical analyses and FIA data-processing were conducted in the R programming
environment, version 3.4. Generalized linear model regressions were performed using the
Rstats package, version 3.4.3. Marginal effect estimates were calculated using R package
ggeffects, version 1.1.1. Other GIS analyses were performed in the ESRI software ArcGIS
Pro, version 2.4. The global analysis of forest fragmentation was performed in Google
Earth Engine. The code used to analyze and process FIA data are not available publicly
due to data privacy laws.
Received: 13 November 2020; Accepted: 8 November 2021;
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Acknowledgements
We thank the many colleagues who gave us friendly feedback throughout this research,
in particular to C. Canham, S.C. Wofsy and D. Foster for their thoughtful suggestions, J.
Holt and A. Kalinin for their statistical guidance. FIA plot location data was made
available via Memorandum of Understanding 09MU11242305123 between the U.S.
Forest Service and Harvard University. Funding: This work was supported, in part, by the
United States Department of Agriculture National Institute of Food and Agriculture
Award 2017-67003-26487, the Harvard Forest LTER Program (NSF DEB 18-32210), the
Rafiki B. Hariri Institute at Boston University and by a National Science Foundation
Research Traineeship (NRT) grant to Boston University (DGE 1735087).
Author contributions
L.M., J.T., L.H., and A.R. conceived the project and designed the study. L.M. processed
the FIA data and performed the subsequent analyses. X.T. and L.M. performed the global
edge analysis. All authors contributed to the writing and intellectual development, and
gave feedback throughout the project.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-021-27373-7.
Correspondence and requests for materials should be addressed to Luca L. Morreale.
Peer Review Information Nature Communications thanks Rico Fischer and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work. Peer
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