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BiogeochemistryAn International Journal ISSN 0168-2563 BiogeochemistryDOI 10.1007/s10533-020-00643-0
Leaf litter inputs reinforce islands ofnitrogen fertility in a lowland tropical forest
Brooke B. Osborne, Megan K. Nasto,Fiona M. Soper, Gregory P. Asner,Christopher S. Balzotti, CoryC. Cleveland, Philip G. Taylor, et al.
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Leaf litter inputs reinforce islands of nitrogen fertilityin a lowland tropical forest
Brooke B. Osborne . Megan K. Nasto . Fiona M. Soper . Gregory P. Asner .
Christopher S. Balzotti . Cory C. Cleveland . Philip G. Taylor .
Alan R. Townsend . Stephen Porder
Received: 19 December 2018 / Accepted: 1 February 2020
� Springer Nature Switzerland AG 2020
Abstract The role of lowland tropical forest tree
communities in shaping soil nutrient cycling has been
challenging to elucidate in the face of high species
diversity. Previously, we showed that differences in
tree species composition and canopy foliar nitrogen
(N) concentrations correlated with differences in soil
N availability in a mature Costa Rican rainforest.
Here, we investigate potential mechanisms explaining
this correlation. We used imaging spectroscopy to
identify study plots containing 10–20 canopy trees
with either high or low mean canopy N relative to the
landscape mean. Plots were restricted to an uplifted
terrace with relatively uniform parent material and
climate. In order to assess whether canopy and soil N
could be linked by litterfall inputs, we tracked litter
production in the plots and measured rates of litter
decay and the carbon and N content of leaf litter and
leaf litter leachate. We also compared the abundance
of putative N fixing trees and rates of free-living N
fixation as well as soil pH, texture, cation exchange
capacity, and topographic curvature to assess whether
biological N fixation and/or soil properties could
account for differences in soil N that were, in turn,
imprinted on the canopy. We found no evidence of
differences in legume communities, free-living N
fixation, or abiotic properties. However, soils beneath
Responsible Editor: John Harrison.
Electronic supplementary material The online version ofthis article (https://doi.org/10.1007/s10533-020-00643-0) con-tains supplementary material, which is available to authorizedusers.
B. B. Osborne (&) � S. PorderDepartment of Ecology and Evolutionary Biology, Brown
University, Providence, RI 02912, USA
e-mail: brookebosborne@gmail.com
M. K. Nasto
Department of Wildland Resources, Utah Forest Institute,
Utah State University, Logan, UT 84322, USA
F. M. Soper � C. C. ClevelandDepartment of Ecosystem and Conservation Science,
University of Montana, Missoula, MT 59808, USA
G. P. Asner � C. S. BalzottiCenter for Global Discovery and Conservation Science,
Arizona State University, Tempe, AZ 94305, USA
P. G. Taylor
The Institute of Arctic and Alpine Research, University of
Colorado, Boulder, CO 80903, USA
A. R. Townsend
Environmental Science, Colorado College,
Colorado Springs, CO 80303, USA
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high canopy N assemblages received * 60% more Nvia leaf litterfall due to variability in litter N content
between plot types. The correlation of N in canopy
leaves, leaf litter, and soil suggests that, under similar
abiotic conditions, litterfall-mediated feedbacks can
help maintain soil N differences among tropical tree
assemblages in this diverse tropical forest.
Keywords Canopy chemistry � Carnegie airborneobservatory � Imaging spectroscopy � Plant functionaltraits � Soil
Introduction
The effect of plant functional traits on soil nutrient
cycling has been relatively easy to document in low-
diversity systems such as temperate forests, where
distinct communities of trees (e.g., hardwood versus
conifer-dominated forests) and even monodominant
stands are not uncommon (Augusto et al. 2003; Lovett
et al. 2004). However, the effect of tree assemblages
in high-diversity lowland tropical forests on nutrient
cycling is harder to quantify. In contrast to temperate
forests, tropical forests often contain hundreds of
species per hectare (Condit et al. 1996; Losos and
Leigh 2004) and most species are locally rare.
Monodominance does occur in the tropics (Hart
1990 and Torti et al. 2001), but is exceedingly
uncommon in mature Neotropical rainforests. Tropi-
cal trees also have higher levels of inter- and intra-
specific variability in many functional traits than
temperate trees, including foliar nutrient content
(Townsend et al. 2007; Fyllas et al. 2009; Asner
et al. 2014) and rates of litterfall production and decay
(Sundarapandian and Swamy 1999; Scherer-Lorenzen
et al. 2007). This phylogenetic and functional diversity
coupled with overlapping ‘spheres of influence’
between individuals (Zinke 1962; Waring et al.
2015) make it challenging to isolate the role of tree
assemblages in shaping the biogeochemistry of
diverse tropical forests. Nevertheless, the existence
of such a role is plausible, even if it has been
challenging to document (Vitousek 2004; Hobbie
2015).
While tree assemblages can be difficult to delineate
on the ground in the lowland tropics, remote sensing
can discern patterns in canopy characteristics (e.g.,
foliar nutrients) across a range of spatial scales (Asner
et al. 2014). Airborne imaging spectroscopy has been
used to successfully identify links between canopy and
soil properties at the landscape and regional scales in
association with topographic position and/or processes
of landscape evolution (e.g., Porder et al. 2005a;
Chadwick and Asner 2018) as well as the imprint of
different species on local water and soil nutrient
availability in low-diversity tropical forests (Asner
and Vitousek 2005). These results suggest that the
detection of patterns in plant functional traits may help
elucidate relationships between abiotic and biotic
controls of soil nutrient status even in high-diversity
forests.
Previously, we leveraged remote sensing technol-
ogy to identify a strong positive correlation between
canopy foliar nitrogen (N) and soil inorganic N
availability in a hyperdiverse Costa Rican rainforest
(Osborne et al. 2017). Here, we compare a range of
biotic and abiotic factors between 0.25 ha plots with
high and low canopy foliar N to explore potential
mechanisms explaining this relationship, including:
(1) litterfall-mediated feedbacks, (2) variable rates of
biological N fixation, and (3) differing abiotic soil
properties (e.g., texture, cation exchange capacity, or
drainage).
It is possible the observed correlation between
canopy and soil inorganic N availability is a reflection
of local tree assemblages with differing canopy
characteristics and/or variable rates of biological N
fixation. Evidence from South American tropical
forests suggests that species identity, rather than
abiotic site characteristics, drive the majority of
variance in foliar N (Fyllas et al. 2009; Asner et al.
2014). Assemblages of species with relatively high or
low foliar N could perpetuate positive feedbacks,
driving underlying soils from a similar starting point
on divergent nutrient trajectories (Vitousek 2004;
Hobbie 2015). If this is the case, soil nutrient
availability may be modified by differences in litterfall
inputs, which are a dominant source of carbon (C) and
nutrients to tropical forests soils (Tiessen et al. 1994;
Clark et al. 2001; Dent et al. 2006). For example, trees
with high concentrations of foliar N may produce
more decomposable litter, leading to higher soil
nutrient availability and resulting in increased con-
centrations of foliar nutrients. Additionally, it is
possible that biological N fixation rates differ between
the plot types, where nodulating leguminous trees
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support higher rates of symbiotic N fixation and/or
free-living N fixation (FLNF) rates are higher.
In addition to biotic factors, abiotic variability
could influence N availability. We controlled for
several potential abiotic links between canopy and soil
N by confining all study plots to a single, gently
sloping geomorphic surface with relatively uniform
climate and parent material. However, we had not
ruled out the possibility of small-scale variability in
soil properties (e.g., soil texture, cation exchange
capacity, or drainage), which could also influence the
relationship between canopy and soil N. For example,
variation in texture and microtopography could drive
differences in soil moisture and structure and, in turn,
soil and canopy nutrient availability (Silver et al.
2000; Hall et al. 2013). Additionally, variability in
cation exchange capacity (CEC) between plot types
could affect rates of soil nitrate (NO3-) absorption and
leaching losses (Matson et al. 1999).
It was our goal to ascertain which of these factors, if
any, might explain the heterogeneity in canopy and
soil N we observed across the Osa Peninsula. We
predicted that if litterfall-mediated feedbacks are most
important in maintaining differences in soil nutrient
availability, the quantity and N content of litterfall and
leaf litter leachate (an important form of nutrient
transfer from litterfall to the soil matrix in wet tropical
forests; Cleveland et al. 2006) as well as rates of litter
decay, would be higher in high canopy N plots than
low canopy N plots. We also predicted that if N inputs
from biological N fixation are a primary control,
putative N fixing trees would be more abundant in high
canopy N plots and/or rates of FLNF would be greater
in high canopy N plots. Finally, we predicted that if
abiotic factors are most important, we would find
differences in soil pH, texture, CEC, and/or topo-
graphic curvature. These potential controls are not
mutually exclusive; rather we were interested in
documenting the potential mechanisms through which
canopy and soil N might be linked at the plot scale
(0.25 ha) in this setting.
Methods
Site description
Forests on the Osa Peninsula are among the most
diverse on earth and host * 57 plant families and
more than 400 species of trees, with 100–200 species
ha-1 (Janzen 1983; Kappelle et al. 2003). These
forests cycle N more conservatively than many other
lowland tropical forests, with relatively low N losses
via hydrologic and gaseous pathways and rapid
immobilization of bioavailable N (Wanek et al.
2008; Wieder et al. 2013; Taylor et al. 2015b; Soper
et al. 2017, 2018). Previously, we used airborne
imaging spectroscopy from these forests to identify
plots with either high or low canopy foliar N relative to
the landscape mean (Osborne et al. 2017). The canopy
foliar N of low canopy N plots was more representa-
tive of the surrounding landscape, while high canopy
N plots were less common and represented islands of
canopy N fertility. Tree species composition differed
between plot types (high versus low canopy N) and
soil NO3- concentrations, net nitrification, and net N
mineralization rates were higher in the high canopy N
plots (Osborne et al. 2017). Soils in high canopy N
plots also emitted, on average, 3 times more nitrous
oxide (N2O) than nearby low canopy N plots and had a
greater abundance of ammonia-oxidizing archaea
(Soper et al. 2018).
Here, we investigated high and low canopy N
plots in two regions of the Osa. The first region,
hereafter ‘‘Piro’’, is located on the southern end of the
peninsula at the Piro Biological Station (8o 240 N, 83o
190 W) and receives * 3000 mm MAP (Osborneet al. 2017). The second region, hereafter ‘‘San
Pedrillo’’, is located in Corcovado National Park
43 km northwest of Piro (8o 360 N, 84o 160 W) andreceives * 4500 mm MAP (www.worldclim.org;Table 1). Both regions experience a rainy season
between March and November and a short but pro-
nounced dry season (\ 100 mm month-1) betweenDecember and April. Mean annual temperature aver-
ages 26 ± 1 �C in Piro and San Pedrillo (Taylor et al.2015a). At Piro, we logged variability in air temper-
ature and precipitation at 10-min intervals for the
duration of this study using a HOBO Microstation
Data Logger (Onset, Bourne, MA, USA) installed in a
clearing adjacent to our plots. In 2016, Piro’s annual
rainfall exceeded recent averages (4400 mm versus
3000 mm), while seasonal trends followed the pattern
described above. Rainfall at Piro averaged
525 mm month-1 during the 2016 rainy season. We
do not have daily weather data from San Pedrillo,
where we sampled only once (February 2016).
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The Osa Peninsula is a highly active tectonic region
with rapid rates of uplift and incision. The forest
overlies a complex lithology resulting from the
accretion of basaltic volcanic arc material during the
subduction of the Cocos Ridge and the intercolation of
basaltic and andesitic volcanic debris flows associated
with arc-volcanism with shallow water marine sedi-
ments (Buchs et al. 2009). The lithology in both Piro
and San Pedrillo can be broadly defined in these terms,
but finer scale differences have not been identified at
our sites because detailed geologic maps of the region
are based primarily on outcrops exposed at the coast.
Thus, while all plots are located on similar parent
material according to the best available geologic
maps, it was not possible to rule out small-scale
variation in soil parent material. Similarly, soil maps
of the Osa Peninsula, and our sites in particular, are
dominated by Ultisols (Perez et al. 1978; Vasquez
1989), but there is undoubtedly unmapped variation
that is biogeochemically relevant. (e.g., Weintraub
et al. 2015). The topography in Piro and San Pedrillo is
characterized by elevated terraces being rapidly
incised by streams, similar to other terraces on the
Pacific Coast of North and Central America (Jenny
et al. 1969; White et al. 2009). In Piro, terraces are
broader and dissected by fewer streams than in San
Pedrillo. We purposefully isolated our plots to terraces
rather than slopes, which can have substantially
different biogeochemical properties on the Osa
(Weintraub et al. 2015; Osborne et al. 2017) and
elsewhere (Silver et al. 1999; Porder et al. 2005b;
Hilton et al. 2013; Chadwick and Asner 2018).
Experimental design
We identified study plots using high fidelity imaging
spectroscopy (HiFIS) in conjunction with LiDAR-
based digital elevation models (DEM) of the Osa
Peninsula created by the Carnegie Airborne Observa-
tory’s Airborne Taxonomic Mapping System (CAO
Table 1 Soil characteristics (0–10 cm) reported as means (± 1 standard error) of four sampling dates for Piro plots (n = 5 for eachcanopy type) and of one sampling date (February 2016) for San Pedrillo plots (n = 4 for each canopy type)
Region Piro San Pedrillo
MAP (mm) * 3000 * 4500
Plot type High canopy N Low canopy N High canopy
N
Low canopy N
Pixel count per plot 360 (23) 400 (36) 500 (9.5) 500 (11)
Relative canopy N 1 1.2 (0.14) 2 0.43 (0.13) 1 1.1 (0.20) 2 0.6 (0.07)
Canopy foliar N (%) 2.9 (0.09) 1.9 (0.08) 3.0 (0.14) 1.8 (0.05)
Standard curvature - 0.68 (1.0) - 0.18 (0.16) - 0.03 (0.27) 0.11 (0.10)
Soil pH 5.8 (5.4–6.1) 5.7 (5.6–6.0) – –
CEC (cmol kg soil-1) 16 (1.5) 15 (0.92) – –
Soil C (g kg-1) 52 (4.4) 59 (4.9) 65 (7.8) 62 (3.5)
Soil N (g kg-1) 4.8 (0.31) 5.0 (0.30) 6.0 (0.50) 5.4 (0.29)
Soil C:N 13 (0.32) 14 (0.48) 13 (0.53) 13 (0.30)
Soil d15N (o/oo vs. AIR) 5.1 (0.21) 4.9 (0.25) 4.9 (0.48) 4.8 (0.27)
NH4?–N (mg kg-1) 1.3 (0.33) 1.2 (0.42) 3.1 (1.6) 1.4 (0.79)
NO3-–N (mg kg-1) 2.7 (0.62) 0.17 (0.02) 5.9 (2.0) 0.74 (0.33)
Net N min (mg kg-1 day-1) 2.8 (0.77) 1.5 (0.77) 4.9 (1.2) 1.6 (0.83)
Net nit (mg kg-1 day-1) 2.2 (0.37) 0.49 (0.21) 5.0 (1.4) 1.5 (0.69)
MAP is mean annual precipitation. Pixel counts represent the number of pixels in the plots after processing the dataset to remove
poorly illuminated and/or non-canopy structures. Relative canopy N reports the standard deviation of canopy N relative to the
landscape mean. Standard curvature was calculated using the curvature function in ArcGIS 10.6. CEC is cation exchange capacity.
Significant differences between plot types within regions are in bold font (P\ 0.05). Canopy foliar N data for Piro were previouslyreported by Soper et al. (2018). MAP data were previously reported by Taylor et al. (2015a) for Piro and by WorldClim for San
Pedrillo
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AToMS) (Asner et al. 2012). A detailed description of
the collection and analysis of these data can be found
in Osborne et al. (2017) and Balzotti et al. (2016). In
short, after atmospheric correction, brightness nor-
malization, and canopy shade removal, the HiFIS data
were converted to canopy foliar N using partial least
squares regression. Training and test data for canopy N
were obtained from 22 tropical forests across a
3500 m elevation gradient in Peru (Asner et al.
2014, 2015). Independent validation in the Osa
Peninsula across three flight lines for canopy N
(RMSE values of 0.07, 0.11, 0.20; Balzotti et al.
2016) had similar results to the Peru data (RMSE =
0.31; Asner et al. 2015).
In both study regions, we established circular
0.25 ha plots located on relatively flat, uneroded
terraces with either high or low mean canopy N
relative to the mean of their surrounding landscapes.
In Piro, where terraces are wider and there is more
mature forest, we identified ten plots within 1 km2
(n = 5 for each canopy type). In San Pedrillo, where
terraces are narrower and secondary forests are more
prevalent, we were only able to identify eight plots
(n = 4 for each canopy type) within a * 2 km2 area.Mean canopy height is similar in both regions
(* 33 ± 10 m) and among plot types, with sometrees reaching over 60 m (Taylor et al. 2015a; Balzotti
et al. 2017). The high canopy N plots in Piro and San
Pedrillo had similar canopy foliar N, which aver-
aged 1.1 ± 0.30 SD above the local landscape means.
Canopy N in the low N plots was also similar between
regions and averaged 0.5 ± 0.2 SD below the local
landscape means (Table 1). Thus, canopy N in the high
canopy N plots was * 50% higher than in the lowcanopy N plots (Table 1). The difference between high
and low canopy N plots was similar to the spread of
canopy N observed across the entire Osa Peninsula
(Balzotti et al. 2016).
Tree species composition
We compared upper canopy tree species composition
in the high and low canopy N plots of Piro and San
Pedrillo by identifying and recording the diameter at
breast height (DBH) of all trees with a minimum DBH
of 40 cm (the DBH at which trees are likely to be in the
upper canopy of these forests; Taylor et al. 2015a).
Taken together, the Piro and San Pedrillo plots
included 63 upper canopy species, with a mean of 15
individuals per plot. In Piro, we extended our
comparison of species composition beyond those in
the upper canopy to also include trees with 10–40 cm
DBH. This group of trees (C 10 cm DBH) included a
total of 97 species and an average of 55 individuals per
plot. In addition to statistical comparisons between the
tree communities in high and low canopy N plots (see
Statistical analyses section below), we used a database
of nodulating leguminous trees to ascertain whether
there were more putative N fixing tress in high versus
low canopy N plots (Sprent 2009; www.ars-grin.gov).
Litterfall production, nutrient content, and decay
rates
We collected litterfall in Piro only. In each Piro plot,
we captured litter using four 50 9 50 cm traps made
of 1.2 mm netting elevated 1 m off the ground. One
trap was located in the center of each plot, while the
other three were spaced evenly at a 10 m radius from
the plot center. We collected, separated, dried, and
weighed leaf and reproductive litter from each trap
every two weeks between September 2015 and
February 2017. We weighed leaf and reproductive
litter (i.e., fruits and flowers) separately because
production of the two litter types can vary over
different time scales and has been shown to respond in
distinct ways to nutrient inputs (Kaspari et al. 2008).
Here, we present leaf litter data from three sampling
dates that spanned the wet and dry seasons (February,
April and July 2016). We ground samples collected
from each trap on those dates and analysed them
individually for C and N on a NC2100 Elemental
Analyzer (CE Elantech, Lakewood, NJ, USA). To
analyze the stoichiometry of leaf litter leachate, we
created four composite samples of leaf litter from the
high and low canopy N plots. Each composite
represented roughly three months of homogenized
leaf litterfall from the high or low canopy N plots
collected over the course of 2016. For each sample of
homogenized litter, we took five subsamples (25 g
each) and soaked each in 0.5 L of deionized water for
24 h. We filtered the resulting solutions to 0.45 lmand analyzed dissolved organic carbon (DOC) and
nitrogen (TDN) concentrations using a TOC-V TN
analyzer (Shimadzu, Kyoto, Japan).
To quantify litter decomposition, we filled 300
12 9 12 cm mesh bags (1 mm netting) with 8 g of
homogenized leaf litter trapped in either high or low
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canopy N plots between September 2015 and February
2016. In late April 2016 (the beginning of the rainy
season), we distributed 12 strands of bags into each
plot, which included a total of 30 bags of ‘‘native’’ leaf
litter (i.e., homogenized high canopy N plot litter was
decomposed in the high N plots and visa versa). We
installed the strands 3 m from the plot centers and
extended them radially. We collected two strands (5
bags) from each plot after 4, 8, 12, 16, 24, and
36 weeks of deployment. After collection, we dried
the bags at 60 �C for four days and extracted andweighed the remaining leaf litter. We estimated
annual mass loss rates on a plot by plot basis by
solving for the negative exponential decay constant
k in the model y = e-kt, where y is the fraction of mass
remaining at a specific time, and t is time since the start
of the experiment (Olson 1963).
Free-living N fixation by acetylene reduction
We used acetylene reduction assays (ARA; Hardy
et al. 1968) to compare FLNF rates in the soil and litter
of Piro’s high and low canopy N plots in February (dry
season), August (wet season), and November 2016
(late wet season). We collected ten 0–2 cm samples of
mineral soil and ten * 2 g samples of surface litterfrom random locations within a 10 m radius of each
plot’s center. When individual leaves were larger
than * 2 g, we cut them to size with scissors. Weimmediately sealed the soil and litter samples in
individual, clear, 50 mL acrylic tubes with fitted
rubber stoppers and incubated them on the forest floor
for 18 h with a 10% acetylene atmosphere (made from
calcium carbide). Following incubation, we mixed and
then sampled 15 mL of headspace gas from each
tube and injected it into a pre-evacuated 10 mL glass
Vacutainers (Becton–Dickinson, Inc., Franklin Lakes,
NJ, USA). Ethylene (C2H4) concentrations were
measured in all samples along with non-acetylene
and acetylene-only blanks by gas chromatography on
a Shimadzu GC-2014 equipped with a flame ioniza-
tion detector (Shimadzu Inc., Kyoto, Japan). We based
our comparison of FLNF between plot types on C2H4production rates. Results are expressed on a mass basis
in units of nmol C2H4 g-1 h-1.
Soil analyses
We compared the soil texture and CEC of high versus
low canopy N plots in Piro in June 2018. We sampled
soils by removing standing litter and collecting and
homogenizing ten 0–10 cm soil cores from within a
10 m radius of the plot centers. Soil samples consisted
of mineral soil only, as O horizons are typically
minimal across the study sites. Soils were shipped to a
commercial lab (Ward Labs, Kearney, NE, USA) for
analysis. Soil texture was determined by hydrometer,
while CEC was calculated based on the extraction of
exchangeable Ca2?, Mg3?, K?, and Na? in a neutral
ammonium acetate solution, following a standard
protocol (Haby et al. 1990).
We sampled soils from the Piro plots in February,
May, August, and November 2016 to measure pH and
soil inorganic N availability. As with the soil sampling
described above, we removed standing litter and
collected three 0–10 cm samples of mineral soil from
the inner 10 m of each plot (cores were not homog-
enized). We measured soil pH (1:2 soil/deionized
water solutions, InLab 413 glass electrode, Mettler
Toledo, Schwezenbach, Switzerland) and bulk soil C,
N and d15N (Europe 20–20 continuous-flow isotoperatio mass spectrometer interfaced with a Europe
ANCA-SL elemental analysis Sercon Ltd., Cheshire,
UK). Additionally, within three hours of collection,
we shook 8 ± 0.1 g of field moist soil in 30 mL of
2 M KCl for 1 min every hour for 4 h (Weintraub et al.
2015), then filtered and froze extracts until analysis for
NO3- and ammonium (NH4
?) as described below. To
quantify net N mineralization and nitrification, we
incubated a second set of fresh soil samples in the dark
at field temperature for five days before extracting
them in 2 M KCl following the same protocol.
In addition to measuring instantaneous concentra-
tions of soil NO3- using KCl extractions (which were
near or below detection limits in some low canopy N
plot soils), we also quantified cumulative soil NO3-
availability over two-week intervals using anion-
exchange membranes (Pure Flow Inc., Peterborough,
NH, USA). To charge the membranes we shook
2 9 10 cm strips in 1 M NaCl for 24 h. Then we
inserted 10 strips into the top 10 cm of Piro plot soils
at * 45-degree angles in February, May, August, andNovember 2016. Each time, we retrieved the strips
2 weeks later, rinsed them with deionized water, and
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stored them at 4 �C prior to extracting them with 2 MKCl.
We analyzed all KCl extracts on a Westco
Smartchem 200 discrete element analyzer (Brookfield,
CT, USA). Net nitrification was calculated as the
difference between KCl-extractable NO3- at the end
of the 5-day incubation and at the time of soil
collection, net N mineralization was calculated as
the difference in KCl-extractable NO3- and NH4
?.
Soil extract data are presented as mg kg-1 on a soil dry
mass basis, while data from the membrane extracts are
reported in units of lg N cm resin-2 day-1.
Topographic analysis
We used the LiDAR-based DEMs created by the
Carnegie Airborne Observatory to compare the local
topography in the high and low canopy N plots from
both regions. DEM pixels were 1.25 m2. We used the
curvature function in ArcGIS 10.6 (ESRI, Redlands,
CA) to determine the mean standard curvature values
for all pixels within a 10 m radius of each plot’s center
(the area from which all soil and litter samples were
collected).
Statistical analyses
To test for differences in canopy tree communities
between high and low canopy N plots, we used a
Monte Carlo permutation test in CANOCO 4.5 (Ter
Braak and Šmilauer 2002; Šmilauer and Lepš 2014).
We analyzed species data from the Piro and San
Pedrillo plots together using region as a covariate. We
compared leaf and reproductive litter production as
well as soil inorganic N availability and net nitrifica-
tion and N mineralization rates over time in the Piro
plots using repeated measures multivariate analyses of
variance (MANOVAs). We used MANOVA rather
than a univariate approach because some datasets did
not meet the assumption of sphericity (the variances of
all variables were not equal). To analyze rates of
FLNF in the soil and litter of high versus low canopy N
plots we used mixed-effects models, with plot type and
sampling date as fixed effects and plot number as a
random variable. We compared the C and N content of
leaf litter and the DOC and TDN content of leaf litter
leachate between plot types using two-way analysis of
variance. We used t-tests to compare soil metrics
between high and low N plots that were measured only
once, such as soil nutrient concentrations and net N
processing rates in San Pedrillo, soil %C, %N, d15N,CEC, and pH in both regions, as well as putative N
fixer counts and rates of litter decay (k). Statistical
analyses were conducted using SAS JMP Pro software
version 13.2.0 (SAS Institute Inc., Cary, North
Carolina). Unless otherwise specified, data are
reported as means ± standard error.
Results
Canopy tree species and putative N fixer
abundance
Upper canopy tree species assemblages differed
between high and low canopy N plots (P = 0.006;
n = 9). No species were common, but the most
abundant genera in the high canopy N plots were
Brosimum, Tetragastris, and Virola and in the low
canopy N plots were Castilla,Qualea, and Symphonia.
This difference did not reflect varying abundances in
putative N fixers, which were rare in all plots. In Piro,
only 3 of the 61 upper canopy trees in high canopy N
plots were species known to nodulate, compared to
just 1 of the 71 individuals in low canopy N plots.
Even when smaller trees (C 10 cm DBH) were
considered in Piro, just 12 of the 265 individuals in
high canopy N plots and 7 of the 287 in low canopy N
plots were putative N fixers. In San Pedrillo, 6 of the
69 upper canopy trees in the high canopy N plots were
potential fixers, compared with 4 out of the 71 in low
canopy N plots.
Litterfall C and N content and rates of production
and decay
Annual rates of leaf and reproductive litter production
were similar among plot types. Leaf litterfall averaged
1080 ± 83 g m-2 year-1 in high canopy N plots and
911 ± 89 g m-2 year-1 in low canopy N plots
(Fig. 1c; shown across time in Online Resource 1).
Reproductive litter averaged 235 ± 41 g m-2 year-1
in high canopy N plots and 264 ± 53 g m-2 year-1 in
low canopy N plots. Although the quantity of litter
produced did not vary between plot types, leaf litter
chemistry did (we did not measure reproductive litter
chemistry). The C:N ratio of leaf litter from low
canopy N plots (measured in February, April, and July
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2016) was, on average, 1.3 times greater than the C:N
ratio of leaf litter from high canopy N plots (P =
0.0001; Table 2; shown across time in Online
Resource 2). Mean leaf litter %N was higher in high
canopy N plots (P = 0.0003; Fig. 1b), but %C did not
differ between plot types (Table 2; Online Resource
2). Similarly, the mean C:N ratio of leachate extracted
from low canopy N plot leaf litter (measured in four
composite samples spanning all of 2016) was 2.3
times greater than leachate from high canopy N plot
leaf litter (P = 0.0012; Table 2). On average across the
four time points, leachate TDN was 1.8 times greater
in high canopy N plot leachate (P = 0.04; Fig. 1d),
and mean leachate DOC was 1.3 times greater in low
canopy N plot leachate (P = 0.08; Table 2; Online
Resource 3). Despite differences in leaf litter and
leachate quality, the mean decay rates (k) of high N
leaf litter in high canopy N plots and low N leaf litter in
low canopy N plots were similar (k = 1.3 ± 0.08,
R2 = 0.8 and k = 1.3 ± 0.11, R2 = 0.85, respectively;
Table 2; Fig. 1e).
Soil conditions and free-living N fixation rates
Soil pH did not differ significantly beneath high and
low N canopies (with true means of 5.8 and 5.7,
respectively), nor did CEC or texture (Table 1). All
plots contained either clay or clay loam soils with
mean 46% clay. Microtopography (analyzed as mean
standard curvature values based on the curvature
function in ArcGIS 10.6) was also similar between
plot types (Table 1). The mean standard curvature
values for high canopy N plots (- 0.68 ± 1.03) and
low canopy N plots (- 0.18 ± 0.16) were low and
with standard errors overlapping zero, indicating that
all of the plots were essentially flat.
Mean rates of FLNF (expressed as rates of C2H4production) were similar in soils among plot types.
They averaged 0.10 ± 0.04 nmol C2H4 g-1 h-1 in
high canopy N plot soils and 0.09 ± 0.01 nmol C2H4g-1 h-1 in low canopy N plot soils. Rates of FLNF in
standing litter were more than 5 times greater in low
canopy N plots (9.1 ± 2.6 nmol C2H4 g-1 h-1) than
high canopy N plots (1.7 ± 0.31 nmol C2H4 g-1 h-1;
P = 0.011).
Soil inorganic N availability and cycling rates
In Piro, KCl-extractable NO3- was higher in high
canopy N plot soils (2.7 ± 0.62 mg kg-1) than in low
canopy N plot soils (P = 0.014), in which NO3- was
near or below detection limits throughout the year
(0.17 ± 0.02 mg kg-1; Table 1; Fig. 2). Concentra-
tions of KCl-extractable NH4? did not differ between
the Piro high and low canopy N plot soils in this study
(1.3 ± 0.33 mg kg-1and 1.2 ± 0.42 mg kg-1,
respectively; Table 1; Fig. 2). However, net nitrifica-
tion (P = 0.001) and N mineralization (P = 0.008)
were higher in high canopy N plot soils (Table 1;
Fig. 2). Membrane-extractable NO3- was higher in
high canopy N plot soils (1.0 ± 0.36 versus
Fig. 1 Histograms illustrate mean canopy foliar %N (a), leaflitter %N (b), rates of leaf litter production (c), total dissolved N(TDN) in leaf litter leachate (d), rates of leaf litter decay (e), andsoil NO3
- concentrations (f) in the high and low canopy N plotsfrom the Piro region only (n = 5 for each canopy type). All plots
were located within 1 km2 under similar abiotic conditions.
*P\ 0.05
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0.06 ± 0.03 lg N cm resin-2 day-1; P = 0.003) andwas consistently near detection limits in low canopy N
plot soils (Fig. 2).
The differences in soil N availability between high
and low canopy N plots in San Pedrillo (sampled in
February 2016) were similar to those observed in Piro.
Concentrations of KCl-extractable NO3- were 8 times
greater in high canopy N plot soils
(5.9 ± 2.0 mg kg-1) than low canopy N plot soils
(0.74 ± 0.33 mg kg-1; P = 0.04; Table 1; Fig. 2).
Extractable NH4? did not differ between high and low
canopy N plots (Table 1; Fig. 2), but net nitrification
and net N mineralization were significantly higher in
high canopy N plots soils in San Pedrillo (P = 0.03 for
both). In February 2016, the only time when Piro and
San Pedrillo plots were sampled simultaneously, soil
NO3- and NH4
? concentrations were similar between
the two regions. However, high canopy N plots in San
Pedrillo had higher rates of net nitrification and net N
mineralization than high canopy N plots in Piro by 3.8
and 3.7 times, respectively (Fig. 2).
Soil bulk C:N ratios were slightly lower in high
versus low canopy N plot soils in Piro (13 ± 0.32
versus 14 ± 0.48 in the high and low canopy N plots,
respectively; P = 0.07), but were similar in San
Pedrillo (Table 1). Soil C and N, along with d15Nwere also similar among plot types in both regions
(Table 1).
Discussion
Research into the role of tree species in tropical forest
nutrient cycling has largely focused on the scales of
landscapes or individual trees, which each pose
challenges. At the landscape scale, the influence of
trees may be confounded by abiotic heterogeneity and,
due to high levels of biodiversity, it is difficult to scale
up the effects of individual trees or species (e.g., Van
Haren et al. 2010; Keller et al. 2013; Waring et al.
2015). However, remote sensing data reveal that some
functional traits (e.g., canopy N) are spatially clustered
in lowland tropical rainforests of southwestern Costa
Rica, allowing the delineation of ‘functional assem-
blages’. Unlike previous work in the Amazon, which
has identified differences in canopy chemistry associ-
ated with geologic, geomorphic, and climatic variation
(Asner et al. 2016), we identified clusters of trees with
high or low canopy N that were correlated with soil N
availability under similar abiotic conditions. Our
results are consistent with the idea that positive
plant-soil feedbacks reinforce the N heterogeneity
we observed between these ‘functional assem-
blages’ (Vitousek 2004; Hobbie 2015).
Litterfall N inputs are greater beneath ‘functional
assemblages’ with high canopy foliar N
Studies across a broad range of scales and ecosystems
support the theory that plant traits can both reflect and
reinforce soil fertility through litter-mediated feed-
backs (e.g., decomposition and litter N release;
Vitousek 2004; Hobbie 2015; Fig. 1). Our observation
that tree species composition and canopy and soil N
were correlated in a biologically diverse but abioti-
cally similar tropical forest (Osborne et al. 2017) led
us to hypothesize that such feedbacks may be at work
in our study plots. Based on this hypothesis, we
Table 2 Leaf litter and leaf litter leachate characteristics reported as means (± 1 standard error)
Plot type Leaf litter
%C
Leaf litter
%N
Leaf litter
C:N
Leachate DOC (mg
g-1)
Leachate TDN (mg
g-1)
Leachate
C:N
Decay rates
(k)
High canopy
N
44 (0.57) 1.5 (0.04) 32 (1.6) 8.6 (2.3) 0.30 (0.05) 27 (3.7) 1.3 (0.08)
Low canopy
N
44 (0.39) 1.1 (0.04) 42 (1.4) 12 (3.3) 0.19 (0.04) 59 (3.7) 1.3 (0.11)
All litter was collected in Piro
Leaf litter %C and %N values were calculated from samples collected in February, April, and July 2016. Leachate values come from
composite samples collected quarterly over the course of one year (February, May, August, and November 2016), each representing
roughly 3 months of litterfall. DOC is dissolved organic carbon. TDN is total dissolved nitrogen. Significant differences between plot
types are in bold font (P\ 0.05)
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thought it plausible that a suite of traits (litter
production, leaf litter N content, leachate N content,
and rates of decomposition) would be higher in high
canopy N plots. Only some of these predictions were
supported. Rates of litter production and decay were
similar between plot types, but leaf litter N content
differed significantly (Fig. 1). The disconnect between
decomposition rates and litter N is surprising because
nutrient content has been identified as an important
predictor of decomposition in the tropics (e.g., San-
tiago 2007; Szefer et al. 2017). However, it is possible
that other unmeasured indices of litter chemistry,
namely those related to the quality of C content, were
more dominant controls of decay in our study plots
(e.g., lignin content, micronutrients, polyphenols;
Kaspari et al. 2008; Wieder et al. 2009; Coq et al.
2010; Hättenschwiler et al. 2011). Nevertheless, based
on rates of leaf litter production and litter N content
(Fig. 1), total N inputs via leaf litterfall are * 60%higher in high canopy N plots than low canopy N plots
(* 16 g m-2 year-1 and 10 g m-2 year-1, respec-tively). Thus, despite their similar rates of litter
production and decay, differences in the quality of
leaf litterfall inputs may help reinforce N availability
differences between plot types. In contrast to litterfall
inputs, it is unlikely that biological N fixation is an
important factor in the observed N heterogeneity.
Rates of FLNF were * 5.5 times greater in lowcanopy N plot litter and, although we did not measure
symbiotic N fixation directly, we found that putative N
fixing trees were uncommon in both plot types.
High canopy N plots represent hotspots of N
fertility
Although many lowland tropical forests are relatively
N-rich (Vitousek 1984; Martinelli et al. 1999; Cleve-
land et al. 2011) others, including those in wet regions
like our study area in southwestern Costa Rica, cycle
N more conservatively (Nardoto et al. 2008; Posada
and Schuur 2011; Hilton et al. 2013; Fisher et al.
2013). In these forests, soils have low concentrations
of KCl-extractable NO3- (Wanek et al. 2008; Wieder
et al. 2013) and small losses of bioavailable N via
denitrification (Taylor et al. 2015b; Soper et al.
2017, 2018). A lack of NO3- indicates that uptake
by plants and immobilization by microbes exceed
gross nitrification (Vitousek et al. 1982; Davidson
et al. 2000). Our low canopy N plots exhibit these
characteristics. However, unlike our low canopy N
plots and prior findings in our study area in general,
high canopy N plot soils contain high levels of soil
NO3-, along with relatively high rates of net nitrifi-
cation (Fig. 1) and N2O fluxes (see also Osborne et al.
2017; Soper et al. 2018). Although N acquisition
strategies vary among plants and microbes (Houlton
et al. 2007; Schimann et al. 2008), our results suggest
that NO3- may be available in excess of biological
demand in high canopy N plots, while it remains
Fig. 2 Mean soil NO3- and NH4
? concentrations as well as net
rates of nitrification and N mineralization (± 1 standard error)
measured in high and low canopy N plots in 2016. Plots in the
Piro region were sampled quarterly (n = 5 for each canopy
type), while San Pedrillo plots were sampled only in February
(n = 4 for each canopy type)
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below detection in low canopy N plots throughout the
year.
Can canopy tree assemblages create N hotspots?
If, as our data suggest, litterfall-mediated feedbacks do
reinforce high and low soil N patches (Vitousek 2004;
Hobbie 2015), one logical follow-up question is which
came first: did elevated soil N availability increase
canopy N or did the functional traits of local canopy
tree species increase soil N availability? Because we
cannot directly compare soil N availability in our plots
prior to the inception of the proposed feedbacks, we
looked for evidence that abiotic drivers of soil N
availability differed between plot types. Specifically,
we compared soil pH, texture, CEC, and local
topographic curvature because of the importance of
soil structure and moisture as controls of nutrient
cycling and availability (Silver et al. 1999; Hall et al.
2013). We found no evidence that soil abiotic
conditions varied between plot types. Soil pH, texture,
and CEC were similar, and LiDAR-generated DEMs
did not reveal any systematic differences in microto-
pography that might affect water saturation (as would
be the case if the high canopy N plots were all convex
and the low canopy N plots were all concave, for
example; Table 1).
The absence of measured abiotic differences in our
high and low canopy N plots at the regional (i.e.,
rainfall, geomorphic surface age) or plot-scale (i.e.,
soil pH, texture, CEC, curvature), in conjunction with
the differences in tree species composition and
litterfall chemistry, lead us to hypothesize that tree
species assemblages may drive the formation of N
hotspots in this relatively low-N tropical forest.
‘Functional assemblages’ of tree species with inher-
ently high canopy N could enrich local soil inorganic
N pools and initiate positive plant-litter feedbacks,
driving the observed spatial structuring of canopy N
(Laughlin et al. 2015). N-fixing tree species, for
example, have been shown to form ‘‘islands of
fertility’’ in tropical forests (Corti et al. 2002).
Although putative N fixers were not abundant in the
plots, foliar N, which in tropical forests is linked more
closely to species traits rather than site characteristics
(e.g., Asner et al. 2014; Balzotti et al. 2016), varies
widely among individual trees in mature tropical
forests (Hättenschwiler et al. 2008; Xia et al. 2015).
Species with high N lifestyles may have therefore
contributed to the formation of these patches of high
and low N availability.
Conclusions
Our findings suggest that upper canopy tree assem-
blages may perpetuate areas of high and low N
availability via differences in leaf litterfall N inputs,
even in abiotically similar settings. Our findings also
indicate that tree species may promote the formation
of N hotspots in tropical ecosystems with relatively
low N availability. Given the higher rates of N cycling
and losses in these plots, understanding their distribu-
tion may be important for understanding landscape-
scale fluxes of N out of tropical forest ecosystems. A
direct experimental comparison of the effects of high
and low canopy N plot leaf litter on soil biogeochem-
istry is needed to address the question of whether or
not the observed differences in litterfall inputs are
capable of driving (as opposed to only reinforcing) soil
nutrient availability. If they are, tree species assem-
blages may play a larger role in the local-scale
biogeochemistry of tropical forests than previously
understood and future changes in species composition
may have as yet unrecognized consequences for
nutrient cycling. With the increasing availability of
high resolution but extensive coverage remote sens-
ing, quantifying the distribution of canopy N may be a
way to understand spatial patterns in N cycling and
losses across heterogeneous tropical forests.
Acknowledgements A collaborative National ScienceFoundation Grant (DEB-0918387) awarded to S.P., C.C., and
A.T. supported this work. The collection and processing of
Carnegie Airborne Observatory (CAO) data was funded
privately by the Carnegie Institution for Science. The CAO
has been made possible by grants and donations to G.P. Asner
from the Avatar Alliance Foundation, Margaret A. Cargill
Foundation, David and Lucile Packard Foundation, Gordon and
Betty Moore Foundation, Grantham Foundation for the
Protection of the Environment, W.M. Keck Foundation, John
D. and Catherine T. MacArthur Foundation, Andrew E. Mellon
Foundation, Mary Anne Nyburg Baker and G. Leonard Baker
Jr., and William R. Hearst III. From the CAO, we thank R.
Martin, C. Anderson, D. Knapp, and N. Vaughn for assistance
with data collection and processing. The authors also thank Osa
Conservation and M. Porras of the Organization for Tropical
Studies as well as the Ministeria de Ambiente y Energı́a for
assistance with research permits and forest access. M. Lopez, B.
Cannon, K. Cushman, R. Ho, B. Munyer, and A. Swanson
assisted with field and laboratory work, and L. Carlson and M.
Rejmanek provided guidance on data analyses.
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Author contributions BBO, MKN, CCC, and SP conceived ofthe study. BBO, MKN, FMS, CSB, CCC, PGT, and SP designed
the project and performed the research. The Carnegie Airborne
Observatory team, lead by GPA, collected and analyzed all
remote sensing data. BBO analyzed all other data. All authors
interpreted results and contributed to the MS. Writing was led by
BBO and SP.
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Leaf litter inputs reinforce islands of nitrogen fertility in a lowland tropical forestAbstractIntroductionMethodsSite descriptionExperimental designTree species compositionLitterfall production, nutrient content, and decay ratesFree-living N fixation by acetylene reductionSoil analysesTopographic analysisStatistical analyses
ResultsCanopy tree species and putative N fixer abundanceLitterfall C and N content and rates of production and decaySoil conditions and free-living N fixation ratesSoil inorganic N availability and cycling rates
DiscussionLitterfall N inputs are greater beneath ‘functional assemblages’ with high canopy foliar NHigh canopy N plots represent hotspots of N fertilityCan canopy tree assemblages create N hotspots?
ConclusionsAcknowledgementsAuthor contributionsReferences
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