arizona state university etal bgc 20… · university of montana, missoula, mt 59808, usa g. p....

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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: [email protected]

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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Biogeochemistry

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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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Biogeochemistry


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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Biogeochemistry


http://www.worldclim.org

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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Biogeochemistry


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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Biogeochemistry


http://www.ars-grin.gov

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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Biogeochemistry


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

123

Biogeochemistry


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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Biogeochemistry


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)

123

Biogeochemistry


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)

123

Biogeochemistry


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.

123

Biogeochemistry


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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