THE EFFECTS OF SPATIALLY HETEROGENEOUS IRRADIANCE AND
SOIL RESOURCES ON NEOTROPICAL SEEDLING GROWTH

By

Ellen K. Holste

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER‟S OF SCIENCE

Forestry

2010

ABSTRACT
THE EFFECTS OF SPATIALLY HETEROGENEOUS IRRADIANCE AND
SOIL RESOURCES ON NEOTROPICAL SEEDLING GROWTH
By
Ellen K. Holste
Plant growth responses to resources may be an important mechanism that
influences species coexistence and community structure. While irradiance is regarded as
the most important resource for seedling growth in wet tropical forests, the spatial
variation of multiple soil resources have yet to be evaluated across a range of species and
at a fine seedling-appropriate scale. The objectives of this research were to compare soil
resource availability among sites and characterize their spatial structure, determine the
appropriate spatial scale to measure soil resources to understand seedling responses, and
identify the resources that best predicted Neotropical seedling growth. We measured
2

irradiance and an array of soil resources over a fine spatial scale (1 m ) in 5 sites of an
old-growth, wet tropical forest at La Selva Biological Station, Costa Rica. Soil resource
availability and heterogeneity appeared to correlate with soil type and their importance in
plant-nutrient cycles, thus to understand seedling responses soil resources, essential plant
nutrients, such as inorganic nitrogen, phosphorus, and base cations, should be measured
at a seedling-level spatial scale, while other resources, such as total nitrogen and carbon
may be measured at coarse spatial scales (>10 m). Light limitation in the forest
understory also did not preclude seedling growth correlations with soil nutrients, which
were equally prevalent and as strong as with irradiance. These results challenge the idea
that soil resources are not important to seedling performance in the light-limited
understory.

This work is dedicated to my family and friends who have always supported me through
all my endeavors, the Guatemalan people who inspired me to study forest ecology, and
Todd Robinson who is a constant source of support and understanding in my life.

iii iii

ACKNOWLEDGEMENTS
A sincere thank you is extended to Dr. Richard Kobe for his guidance, support,
and advice throughout my master‟s work. The skills I have learned from him will
undoubtedly assist me in my future endeavors both professionally and personally. Also, I
would like to offer my sincere appreciation to Drs. David Rothstein and Andy Finley who
dedicated time and energy as members of my graduate committee. Their advice and
insights were invaluable to my thesis research, especially to the methodological and
analytical portions of this research. I am indebted the members of the Kobe lab,
especially Tom Baribault, who have dedicated countless hours of listening, advice, and
assistance in laboratory work throughout my studies.
Additionally, I would like to express a great appreciation for Michigan State‟s
Forestry department and Ecology, Evolutionary Biology and Behavior program. I have
learned a great deal, and I could not have accomplished all that I have without their
support. Thank you to the Organization of Tropical Studies and La Selva Biological
Station staff for their assistance during my field work. I would like to extend my great
appreciation to Ademar Hurtado and Martin Cascante for their invaluable assistance in
species identification, and soil and data collection, also, a big thanks to Corine
Vriesendorp for the experimental design and a solid foundation upon which I could build
my research. Finally, I would like to thank my family and friends for their
encouragement, and Todd Robinson for his constant support and understanding.

iv

iv

TABLE OF CONTENTS
LIST OF TABLES………………………………………………………………… vii
LIST OF FIGURES……………………………………………………..………… viii
CHAPTER I – Introduction……………………………………………………
Literature Cited…………………………………………………………
CHAPTER II - Spatial heterogeneity of soil resources in a wet tropical forest:
influences of substrate and plant-nutrient cycles
Abstract…………………………………...……………………………
Introduction…..…………………………...……………………………
Methods
Site description and sampling design…………..………………
Soil methods……………………………………………………
Statistical analysis and model description……………………..
Adult trees versus soil resources…………………….................
Results
Site differences…………………………………………………
Resource spatial structure, range of autocorrelation, and patch size
Leguminous species not correlative with soil resources………
Discussion
Summary………………………………………………………
Resource availability…………………………………………..
Spatial heterogeneity…………………………………………..
Implications of patch size……………………………………..
Conclusions……………………………………………………
Literature Cited………………………………………………………..

v

1
6

8
10
14
15
17
19
19
20
21
22
22
23
26
28
51

TABLE OF CONTENTS
CHAPTER III – Seedling responses to soil nutrients in the forest understory
Abstract…………………………………...…………………………
Introduction…..…………………………...…………………………
Methods
Site description…………………………………..…………..
Resource measurements…………..…………………………
Data analysis ……………………………..…………………
Results
Summary……………………………………………………
Soil resources and irradiance influenced seedling growth…
Lifespan on compounding of growth………………………
Species growth responses to resources covaried with
shade tolerance……………………………………..
Discussion…………………………………………………………..
Conclusions…………………………………………………
Literature Cited……………………………………………………..

57
59
61
62
65
68
69
71
71
72
76
85

CHAPTER IV – Conclusions………………………………………………..

91

APPENDICES
Appendix A..…………...……………………………………………..
Appendix B. …………...……………………………………………..
Appendix C. …………...……………………………………………..

95
108
121

LIST OF TABLES

Table 2.1. Chemical, physical and biological properties that could affect the
spatial heterogeneity of resources……………………………………

29

Table 2.2 Comparison of soil resources among sites…………………………

30

Table 2.3. Comparison of soil resources among alluvial versus volcanic sites

31

Table 2.4. Summary of resource values per site ……………………………..

32

Table 2.5. Semivariogram values: structural and nugget variances for each
soil resource by site…………………………………………………..

33

Table 2.6. Semivariogram values: effective range and 50% correlation distance
(patch size) for each soil resource by site……………………………

35

Table 2.7. Responsiveness of soil nutrient availability to tree size by species type 37
Table 2.8 Mean resource estimates of patch sizes across all transects and the
biological reasons supporting the ranges……………………………

38

Table 3.1. Resource effect comparisons between irradiance and soil nutrients
across species ………………………………………………………

77

Table 3.2. Resource effect comparisons among soil nutrients across species

78

Table A1.1. Resource parameters of seedling growth rate by species………

96

Table B1.1. Maximum magnitude of resource effects on the final height of
seedling growth by species………………………………………..

109

Table C1.1. Correlations between species‟ magnitude of responses initial
seedling size…………………………..………………………..…

122

LIST OF FIGURES

Figure 2.1. Layout of sampling at each of five study sites at La Selva Biological
Station………………………………………………………………

39

Figure 2.2. Example semivariogram illustrating the sill, partial sill, nugget,
and effective range……………………………….…………………

40

Figure 2.3. Distribution of soil resources by the proportion of cases………

41

Figure 2.4. Semi-variograms of the 50% of the posterior estimate
for each resource……………………………………………………

43

Figure 2.5. Example surface approximation of patch sizes for each resource

45

Figure 2.6. Soil nutrient availability by species type………………………

47

Figure 2.7. Responsiveness of soil nutrient availability to tree size by species type 48
Figure 3.1. Model fit of example species as assessed by r2 values…………

79

Figure 3.2. Proportion of species for which each resource is correlated with
growth …………………………………….………………………..

80

Figure 3.3. Mean absolute values of resource effects on seedling growth across
species………………………………………………………………

81

Figure 3.4. Representative models of height growth with respect to irradiance
and soil nutrients ………………………………………………….

82

Figure 3.5. Species‟ responsiveness to irradiance and soil nutrients across a
continuum of shade tolerance ……………………..………………

83

viii

CHAPTER I
Introduction

Plant growth responses to resources may be an important mechanism that
influences species‟ distributions, coexistence, and community structure. Yet responses to
variation in the location and size of resource patches can differ between plants of
different sizes and species. Gradients of abiotic resources can allow different species to
specialize on different amounts of resources, thus permitting their cohabitation, while
competition for resources within a particular patch may influence whether certain
individuals are destined to be dominant or subordinate within a population (Casper et al.
1999). Tropical forest understories, in particular, are extremely spatially heterogeneous
in both irradiance (Nicotra et al. 1999) and soil resources (Holste et al., in prep), yet
estimates of seedling-appropriate resource availability and heterogeneity have yet to be
examined for multiple soil nutrients and species as well as simultaneously with
irradiance. Because the performance of individual plants depend upon their ability to
compete for and capture resources, understanding the availability and spatial
heterogeneity of abiotic resources is a prerequisite to understanding the effects of
resources on seedling growth.
Resource patches can differ in scale and concentration of supply (Stuefer 1996),
and the sessile nature of plants makes the spatial structure of resources especially
important to the study of plant-resource interactions. The most relevant scale to
understand resource effects on plant performance depends upon the organism of study.
For example, limited rooting extent could makes forest soils extremely heterogeneous
from a tree seedling perspective (Hutchings et al. 2003), but fine scale heterogeneity may
1

be less relevant to a mature tree with a large root system. Thus, seedling responses to
resource patches may be extremely variable depending upon the spatial heterogeneity of
resources.
How plants respond to the availability and heterogeneity of resources within their
environment also may differ depending upon the resource. Irradiance is considered the
most limiting resource in wet tropical forests (Pearcy 2007). Canopy structure,
topography, treefall gaps, and sunflecks all contribute to the understory light
environment. Among seedlings of different species, there is tremendous variation in
growth – irradiance relationships (Augspurger 1984, Lee et al. 1996, Poorter 1999).
Likewise, variability in soil resources originates from multiple biotic and abiotic sources;
parent material, climate, topography, organisms (such as plant-soil feedbacks), and the
time over which organisms, climate and topography have affected the parent material all
affect soil resource availability and heterogeneity. Although typically not considered to
be as important as irradiance in seedling growth, soil nutrients also influence seedling
growth rates, even in the deep shade of the forest understory (Hättenschwiler 2002,
Yavitt and Wright 2008, Palow and Oberbauer 2009). Furthermore, interactions between
irradiance and soil nutrients can modify seedling performance (Kobe 2006, Cai et al.
2008).
In this thesis, I examined the availability and spatial heterogeneity of soil
resources in an old-growth, wet tropical forest at La Selva Biological Station, Costa Rica,
as well as species-specific seedling responses to both irradiance and soil nutrients. Five
sites, spanning a soil fertility gradient, were used to capture a broad array of soil resource
levels. In addition, multiple soil resources (i.e. major nutrients and soil carbon) were

2

measured, including sum of inorganic nitrogen (nitrate and ammonium), total nitrogen,
phosphate, total phosphorus, base cations (potassium, magnesium, and calcium) and total
carbon. By sampling across a fertility gradient, we included two soil types typical of wet
tropical forests: alluvial and volcanic soils. Alluvial soils, commonly formed by recent
deposition of fine silt and nutrients from nearby rivers, are considered moderately fertile
sites (Vitousek and Sanford 1986). At La Selva, alluvial soils are those at the lowest
elevations, so that these soils still flood periodically and their properties are thought to be
still strongly affected by small differences in age and original texture (McDade and
Hartshorn 1994). Volcanic soils are highly weathered of mineral-derived resources and
typically range from moderately to severely infertile (Vitousek and Sanford 1986). At La
Selva, volcanic soils are those found at higher elevations and on the oldest terraces. I
expected these soil types to be very different in resource availability and spatial
heterogeneity due to their dissimilarities in topographic position and relative age.
In Chapter II, I compared soil resource availability among sites, characterized
spatial autocorrelation of resources, and examined whether adult trees imposed a pattern
of fine-scale soil resource availability and heterogeneity in wet tropical forests. I also
determined an appropriate spatial scale to measure soil resources to understand seedling
responses. In Chapter III, I examined the growth responses of 94 species of woody
seedlings to irradiance and soil nutrients, measured over a presumed seedling-appropriate
2

spatial scale (1 m ), to identify the resources that best predicted Neotropical seedling
growth.
The research presented in this thesis contributes to previous studies examining
seedling growth responses along a resource gradient, because it incorporated both

3

irradiance and soil resources, quantified soil fertility in terms of specific soil resources,
methodologically examined whether the scale at which soil resources were measured
corresponded to the scale of the studied organism, and examined a broad range of
Neotropical species. Based upon findings of this thesis, predictions of species-specific
seedling growth responses may be used to understand forest dynamics and promote
ecology-based forest management and restoration.

4

LITERATURE CITED

5

Augspurger, C.K. 1984. Light requirements of Neotropical tree seedlings: a comparative
study of growth and survival. Journal of Ecology 72: 777-795.
Cai, Z.Q., L. Poorter, Q. Han, and F. Bongers. 2008. Effects of light and nutrients on
seedlings of tropical Bauhinia lianas and trees. Tree Physiology 28:1277–1285.
Casper, B.B., J.F. Cahill Jr., and R.B. Jackson. 1999. Plant competition in spatially
heterogeneous environments. In Hutchings, M.J., E.A. John and E.A. Stewart,
editors. The ecological consequences of environmental heterogeneity. Blackwell
Science, Oxford, UK.
Hättenschwiler, S. 2002. Liana seedling growth in response to fertilisation in a
neotropical forest understorey. Basic and Applied Ecology 3:135–143.
Holste, E.K., R.K. Kobe, A.O. Finley, and T.W. Baribault. 201_. Spatial heterogeneity of
soil resources in a wet tropical forest: influences of substrate and plant-nutrient
cycles. In prep.
Hutchings, M.J., E.A. John, and D.K. Wijesinghe. 2003. Toward Understanding the
Consequences of Soil Heterogeneity for Plant Populations and Communities.
Ecology 84: 2322-2334.
Kobe, R.K. 2006. Sapling growth as a function of light and landscape-level variation in
soil water and foliar nitrogen in northern Michigan. Oecologia 147: 119–133.
Lee, D.W., K. Baskaran, M. Mansor, H. Mohamad, and S.K. Yap. 1996. Irradiance and
spectral quality affect Asian tropical rain forest tree seedling development.
Ecology 77:568-580.
McDade, L.A. and G.S. Hartshorn. 1994. La Selva Biological Station. In McDade, L.A.,
K.S. Bawa, H.A. Hespenheide and G.S. Hartshorn, editors. La Selva: Ecology and
Natural History of a Neotropical Rain Forest. The University of Chicago Press,
Chicago, IL.
Nicotra, A.B., R.L. Chazdon, and S.V.B. Iriarte. 1999. Spatial heterogeneity of light and
woody seedling regeneration in tropical wet forests. Ecology 80: 1908-1926.
Palow, D.T. and S.F. Oberbauer. 2009. Soil type affects seedling shade response at low
light for two Inga species from Costa Rica. Plant Soil 319:25–35.
Pearcy, R.W. 2007. Responses of Plants to Heterogeneous Light Environments. Pugnaire,
F.I. and F. Valladares, editors. In Functional Plant Ecology. CRC Press, Boca
Raton, FL.

6

Poorter, L. 1999. Growth response of 15 rainforest tree species to a light gradient: the
relative important of morphological and physiological traits. Functional Ecology
13: 396-410.
Stuefer, J.F. 1996 Potential and limitations of current concepts regarding the response of
clonal plants to environmental heterogeneity. Vegetatio 127:55–70.
Vitousek, P.M. and R.L. Sanford. 1986. Nutrient cycling in moist tropical forest. Annual
Review of Ecology and Systematics 17:137-167.
Yavitt, J.B. and S.J. Wright. 2008. Seedling growth responses to water and nutrient
augmentation in the understorey of a lowland moist forest, Panama. Journal of
Tropical Ecology 24:19–26.

7

CHAPTER II
Spatial heterogeneity of soil resources in a wet tropical forest:
influences of substrate and plant-nutrient cycles

ABSTRACT
Spatial heterogeneity in soil resources may contribute to tree species distributions,
species coexistence, and population persistence. Yet responses to variation in the
location and size of resource patches can differ between plants of different sizes. For this
reason, estimates of resource availability and heterogeneity are strongly dependent upon
the spatial scale at which they are measured and the organism of study. The objectives of
this research were to compare soil resource availability (i.e. major nutrients and soil
carbon) among sites differing in soil type, characterize spatial autocorrelation of
resources, examine whether adult trees imposed a pattern of fine-scale soil resource
availability and heterogeneity in wet tropical forests, and determine appropriate spatial
scales to measure resources in order to understand individual seedling responses. Five
study sites (three volcanic and two alluvial soils) were sampled in an old growth, wet
tropical forest at La Selva Biological Station, Costa Rica. An array of soil resources were
measured including sum of inorganic nitrogen (inorganic N), total nitrogen (total N),
-

phosphate (PO4 ), total phosphorus (P), total carbon (C), and base cations (potassium,
magnesium, and calcium). Soil type was only weakly correlated with mean resource
availability, but the spatial heterogeneity of soil resources was related to both soil type
and type of mineral nutrient cycle (source and biological mediation). Over half of the
total variance in most resources was spatially structured, indicating a high degree of

8

spatial autocorrelation, and the spatial structure of resources appeared to be related to soil
type. Total C and total N had long effective ranges between 27-174 m, base cations,
-

PO4 and total P between 16 and 97 m, and inorganic N between 14 and 32 m. Patch size
(defined as points within a distance having >50% correlation) were 10-40 m for total C
-

and total N and 3-16 m for base cations, P, PO4 and inorganic N. Adult tree functional
groups did not create patches of similar resource availability and impose of pattern of
spatial heterogeneity in this wet tropical forest. These results suggest that to understand
seedling responses to soil resources in a wet tropical forest, essential plant nutrients, such
as inorganic N, P, and base cations, should be measured at a seedling-level spatial scale,
while other resources, such as total N and C may be measured at coarser scales (>10 m).

9

INTRODUCTION
Spatial heterogeneity in resources can strongly influence local species abundances
(Tilman 1994), distributions (Li et al. 2009), coexistence (Fargione and Tilman 2005),
and overall community structure (Wilson et al. 1995, Finzi et al. 1998). Gradients of
abiotic resources allow species to specialize on different amounts of resources, thus
permitting their cohabitation. Tree species can aggregate on patches that provide suitable
resources for their regeneration, while competition for soil nutrients within a resource
patch may influence whether particular individuals are destined to be dominant or
subordinate within a population (Casper et al. 1999). Because the performance of
individual plants depend upon their ability to compete for and capture resources,
understanding the spatial heterogeneity of abiotic resources is a prerequisite to
understanding their effects on community structure.
Resource patches can differ in scale and concentration of supply (Stuefer 1996),
thus the sessile nature of plants makes the spatial structure of resources especially
important to the study of plant-resource interactions. Responses to variation in the
location and size of nutrient patches would differ between plants of different sizes. For
example, limited rooting extent could make forest soils extremely heterogeneous from a
tree seedling perspective (Hutchings et al. 2003), but fine scale heterogeneity may be less
relevant to a mature tree with a root system large enough to explore both nutrient rich and
poor patches. For this reason, the most relevant scale to characterize resource
heterogeneity depends upon the questions being asked and the studied organism.
Since the size and distribution of resource patches is very important to tree
seedlings, seedling performance may be extremely variable depending upon the spatial

10

heterogeneity of resources. Especially in natural forests, soil nutrients are
heterogeneously distributed (Lechowicz and Bell 1991, Mou et al. 1997, Farley and Fitter
1999). Heterogeneity in soil resources originates from both abiotic and biotic sources.
Parent material, climate, topography, organisms (such as plant-soil feedbacks), and the
time over which organisms, climate and topography have affected the parent material all
create heterogeneity in soil resources (Jenny 1941). Abiotically, the physical properties
of the surrounding environment and chemical properties of soil resources typically create
heterogeneity (see Table 1). Mineral-derived resources such as base cations, phosphorus,
and many micronutrients originate from parent material. Weathering and erosion release
these resources into the soil where they may be occluded (i.e. bound) to other chemicals
or soil colloids, leached through the soil, or taken up by plants. Topographic gradients
and the relative mobility of resources also can influence their spatial heterogeneity across
a landscape.
Heterogeneity also originates from biotic sources, including soil microorganisms,
herbivores, or resource uptake and release by plants (see Table 2.1). Plants create spatial
heterogeneity in soil resources by creating zones of depletion around roots, incorporating
nutrients into foliage and other plant components, and depositing senesced tissues on or
in the soil in another location (Stark 1994). Individuals of different species and
functional types may cause patches of similar resource levels, likely mediated through
senesced tissue chemistry and possibly other plant-soil feedbacks (e.g. McCarthyNeumann and Kobe 2010, Ushio et al. 2010). For example, the presence of N-fixing
microorganisms associated with leguminous species may change the availability of
nitrogen (N) by creating patches of higher N (Vitousek et al. 1987, Rodriguez et al.

11

2009), whereas other species could elevate phosphorus (P) availability or the depletion of
other resources (Chen et al. 2003, Phillips and Fahey 2006). Fungi and bacteria also may
colonize and break down organic materials, making nutrients available. Biologicallyderived resources, such as nitrogen and carbon, often arise from the accumulation and
decomposition of organic residues (e.g. decaying plant material) and/or through
biological N fixation by microorganisms.
Plant growth can increase resource heterogeneity by creating and maintaining
nutrient patches, but the scale of resource distribution has depended upon the ecosystem
(e.g. grasslands vs. shrublands; Schlesinger et al. 1996) and the biotic and abiotic
processes acting upon those resources (Gallardo and Parama 2007). Since inorganic
nitrogen, base cations (potassium, magnesium, and calcium), and phosphate are essential
plant nutrients needed in plant growth and survival, I expected the cycling of these
nutrients to create finer-scale, spatial heterogeneity than total carbon and total nitrogen
(Hinsinger et al 2005). Furthermore, I expected the composition of forest species to
impose a broad-scale pattern of soil resource availability and heterogeneity across the
landscape.
In this study, soils were sampled along a fertility and substrate age gradient in a
wet tropical forest at La Selva Biological Station, Costa Rica. La Selva soils range from
relatively fertile entisols and inceptisols of alluvial origin to large areas of low fertility
ultisols developed on old lava flows (McDade and Hartshorn 1994). By sampling across
a fertility gradient, I included two soil types typical of wet tropical forests: alluvial and
volcanic-origin, residual soils. Topographic position and age are the two dominant
factors thought to determine soil type at La Selva. Alluvial soils are commonly formed

12

by recent deposition of fine silt and nutrients from nearby rivers and are considered
moderately fertile sites (Vitousek and Sanford 1986). At La Selva, alluvial soils are those
at the lowest elevations. These soils still flood periodically and their properties are
thought to be still strongly affected by small differences in age and original texture
(McDade and Hartshorn 1994). Volcanic-origin residual soils are highly weathered of
mineral-derived resources; these soils typically range from moderately to severely
infertile (Vitousek and Sanford 1986). At La Selva, volcanic soils are those found at
higher elevations. These soils are regarded as Pleistocene in age but the actual dates are
lacking (McDade and Hartshorn 1994). Due to the differences in topographic position
and relative age, I expected soil resources to differ in availability according to soil type.
Comparisons of the spatial heterogeneity of particular soil resources have
received little attention in tropical forests, despite the fact that nutrient patches may be
highly variable due to biotic feedbacks through high tree species diversity. The specific
questions address by this study were: 1) Do major soil nutrients and carbon levels differ
across sites that presumably stratify a soil fertility gradient?, 2) How does spatial
structure vary across sites and among different resources?, 3) If adult trees imposed a
pattern of fine-scale soil resource availability and heterogeneity in wet tropical forests?,
and 4) What is an appropriate, or biologically meaningful, spatial scale to measure soil
resources for understanding individual tree seedling responses? I expected soil resources
to differ in availability and spatial heterogeneity according to soil type, their respective
physical, chemical and biological properties (e.g. mobility, occlusion, biological
fixation), as well as their relative importance in plant processes. Capturing the
availability and heterogeneity of soil resources is essential to the study of tropical forest

13

functioning at many scales (Townsend et al. 2008) but is especially important in
understanding seedling performance and future forest dynamics.

METHODS
Site Description and Sampling Design
Five study sites were sampled in a wet tropical forest (sensu Holdridge 1967) at
La Selva Biological Station in the Atlantic lowlands of Costa Rica (10º 26‟N, 84º 00‟ W).
La Selva is located at the convergence of the Sarapiqui and Puerto Viejo rivers; the mean
annual rainfall is 4367 mm, and the mean monthly minimum is 124 mm
(http://www.ots.ac.cr/meteoro/default.php?pestacion=2). To represent landscape-level
occurrence of soils, three study sites on older, weathered volcanic soils and one site each
on older and recent alluvial soils in mainly old growth forests were sampled; sites were
established to span a presumed phosphorus gradient, since this resource is generally
considered the most limiting in tropical ecosystems (Hedin et al. 2009). Soils from all
five sites were fairly acidic (pH 3-5).
2

Each of the five sites was centered on a transect of 200 contiguous 1m quadrats;
along each of the five transects, three soil subsamples were composited from 16, 50 and
2

83 cm along the central axis of each 1m quadrat (n=200 per transect, n=1000 in total).
Each sample was taken with a corer (2 cm diameter) in the upper 20 cm of mineral soil.
In addition, forty-six samples were collected around each transect every ten to twenty
meters and fourteen “irregularly-spaced” samples at a random angle and distance from
the regularly-spaced lattice design (n=60 per transect, n=300 in total); these additional
2

samples were also a composite of three subsamples taken within a 1m quadrat (Fig. 2.1).
14

The combination of forty-six regularly-spaced sampling locations around the transect and
the fourteen “random” sampling points represented a compromise between the predictive
accuracy under a regularly-spaced model versus better estimation of model parameters
through a lattice plus close pairs design (Diggle and Lophaven 2006). Thus, to obtain a
representative soil resource map of each site, a total of 260 soil samples were collected
per site (Fig. 2.1).
Because there can be tremendous variability in points that are very close together,
particularly related to micro-topography (e.g., old root channels, animal disturbance), I
2

used composites of three subsamples within a 1 m sampling unit. By compositing the
samples, I averaged over any sub-meter scale heterogeneity and likely underestimated
2

total variability, but I captured variability among mean conditions in 1 m sampling units,
a typical unit of many plant sampling studies.

Soil Methods
A broad array of soil resources were measured including inorganic nitrogen
(summation of nitrate and ammonium pools), total nitrogen (total N), phosphorus (P),
+

+

+

calcium (Ca ), magnesium (Mg ), potassium (K ), and total carbon (total C) as an
estimate of plant input. All samples were air-dried before being shipped to Michigan
+

+

+

State University for analysis. Phosphorus, K , Ca , and Mg ions were extracted with
Mehlich III solution, which was originally designed as multi-element extractant
(Monterroso et al. 1999, Loide et al. 2005) for acidic, highly weathered soils, such as
+

+

those found in the Neotropics (Mehlich 1984). Calcium, K , Mg , and P concentrations

15

were analyzed with an Optima 2100DV ICP Optical Emission Spectrometer (PerkinElmer, Shelton, CT); phosphorus was also analyzed colorimetrically with a continuous
flow auto-analyzer (ELx808 Absorbance Microplate Reader, BioTek Instruments, Inc,
Winooski, VT).
Mehlich III extractable P was measured with both the ICP and colorimetric
methods using a reagent solution containing acid molybdate, antimony potassium tartrate,
70% sulfuric acid, and absorbic acid (Frank et al. 1998). Phosphorus measured with ICP
analyses sometimes have up to 50% higher P concentrations than measured with the
colorimetric method, suggesting organic forms of P in addition to the orthophosphate
(plant available) P are being measured (Hylander et al. 1995). Measurements of ICP
2

versus colorimetric P were correlated (r =0.49). Consistent with previous studies
(Pittman et al. 2005, Nathan et al. 2002), I interpreted ICP-P concentrations as a measure
-

of “total P” and colorimetric-P as “plant available” phosphorus, or PO4 .
Inorganic nitrogen (N) pools were extracted with potassium chloride and analyzed
colorimetrically using an ammonium salicylate and ammonium cyanurate colorimetric
method to detect ammonium (Sinsabaugh et al. 2000) and vanadium (III), sulfanilamide
and N-(1-naphthyl)-ethylenediamine dihydrochloride (NED) to quantify nitrate (Doane
and Horwath 2003). Total soil N and C were measured via the dry combustion method
using an Elemental Combustion CHNS-O analyzer (ECS 4010, Costech Analytical
Technologies, Valencia, CA).

16

Statistical Analysis and Model Description
Comparisons of soil resource availability among sites were tested with one way
analysis of means with unequal variances and a Bonferroni correction for multiple
comparisons (α=0.05) using the R project computing software (http://www.r-project.org).
Soil type comparisons within soil resources were tested with a mixed model (R software
package lme4), accounting for subsamples within the sites.
Using LiDAR data collected from aerial photographs at La Selva (see Kellner et
al. 2009), maps of geographic variables (elevation, slope, aspect, flow direction, flow
length and curvature) were created using Quantum GIS (QGIS Version 1.0.2-Kore,
http://www.qgis.org) with a GRASS GIS interface (GRASS Version 6.2.3,
http://grass.osgeo.org). Spatial autocorrelation of soil resources was analyzed with and
without these geographic variables as potential covariates in linear univariate models
using spBayes, a statistical package compatible with the R software. spBayes uses a
hierarchical Bayesian approach for computing univariate and multivariate pointreferenced spatial models (Finley et al. 2007). This statistical approach allowed us to
specify prior distributions on the parameters and build hierarchical models that were
estimated using a Gibbs sampler, with Metropolis-Hastings updates (as required), to fit
spatial models. I used empirical variograms to estimate the starting values (i.e.
informative priors) for model parameters. Variance parameter priors followed an inversegamma distribution, while the spatial range priors followed uniform distributions. I
transformed soil resource concentrations to more closely approximate Gaussian
distributions, which is an assumption of the linear model.

17

Four candidate models were used to characterize and predict soil resources: 1) a
null model containing non-informative priors and only soil resources, 2) a model with
non-informative priors, geographic variables and soil resources, 3) a model with
informative priors and only soil resources, and 4) a model containing informative priors,
geographic variables and soil resources. Models were compared according to their DIC
(Deviation information criterion) values. DIC sums the Bayesian deviance as a measure
of model fit and the effective number of parameters as a penalty for model complexity;
thus, lower DIC values indicate preferred models, and in cases of similar DIC values, the
least complex model would be chosen. For each preferred univariate model, three
MCMC (Markov chain Monte Carlo) chains were run for 100,000 iterations each;
summaries were generated from these three chains.
I calculated ninety-five percent credible intervals for each parameter in the model,
2

2

including the semivariogram values of nugget (τ ), partial sill (σ ), lag distance (φ) and
2

effective range (-ln(0.05)/φ) (Fig 2.2). The nugget (τ ) represents the random variation
2

that cannot be explained by the model. The partial sill (σ ), the variation that can be
attributed to autocorrelated spatial effects, provides an index of the spatial dependence of
soil resources. Following Li and Reynolds (1995), the proportion of variance due to
spatial dependence (% structural variance) of each resource was calculated as:
2

2

2

% structural variance (SV) = σ / (σ + τ )
High % SV values indicate a greater degree of spatial autocorrelation (Wang et al. 2007),
whereas high % nugget variance suggests a lack of spatial dependence for the studied
scale:

18

2

2

2

% nugget variance (NV) = τ / (σ + τ ).
The semivariogram effective range is the distance beyond which correlation between
sampling points decreases to < 0.05 and was calculated as -ln(0.05)/φ. To identify an
effective range at which the correlation between points is > 50%, a level that would
indicate ecologically meaningful similarity and provide a measure of resource patch size,
I calculated a 50% correlation distance as: d50% = -ln(0.5)/φ.

Adult trees versus soil resources
Adult woody species > 5 cm in diameter were mapped within a 40 m buffer zone
around each transect. Diameter at breast height (DBH) measurements were taken
between September 2008 and March 2009. Adult species were characterized into
leguminous and non-leguminous species to test whether adult trees impose a pattern of
fine-scale availability and heterogeneity upon soil nutrients. Nearest neighbor analyses
(R software package yaImpute) were used to calculate soil nutrient availability under
+

+

+

adult trees. Essential soil nutrients (e.g. inorganic N, total P, K , Ca , and Mg ) were
used to test this relationship, since these resources may be the most affected by plantnutrient cycling.

RESULTS
Site differences
Mean availability of soil resources differed among sites and were generally
-

associated with soil type (Table 2.2). Not surprisingly, phosphate (PO4 ) and total

19

phosphorus (total P) concentrations were greatest on alluvial soils since sites were chosen
to stratify a presumed phosphorus gradient (Table 2.3, 2.4; Fig. 2.3A, B). Potassium
values also had greater concentrations on alluvial soils (Table 2.3, 2.4; Fig. 2.3C), while
+

+

Ca and Mg availability were not associated with soil type (Table 2.3, 2.4; Fig. 2.3D, E).
On the other hand, inorganic N concentrations were greatest on volcanic soils (Table 2.3,
2.4; Fig. 2.3F), but neither total N nor total C were associated with soil type (Table 2.3,
2.4; Fig. 2.3G-H). Interestingly, even though alluvial sites are considered to be “nutrient
rich”, these sites generally had resource concentrations as low as the assumed “infertile”
or volcanic sites for all resources (Fig. 2.3). Variability of soil resources also differed
among sites. The coefficient of variation (% CV) in all soil resources ranged from 18190%, with a mean value of ~70% (Table 2.4), suggesting moderate to high variability
for all soil nutrients (per Wilding (1985)).

Resource spatial structure, range of autocorrelation, and patch size
Of the four candidate models that were tested to explain spatial patterns and site
differences in soil resources, the informative models with or without geographic variables
provided the best fit. No geographic variables were significant, probably due to the small
land areas that the transects encompassed (only 240m x 40m). Thus I used only measured
resource levels to characterize spatial structure in resources.
Over half of the total variance in most of the soil resources was spatially
structured (% structural variance; Table 2.5), indicating a high degree of spatial
autocorrelation. The spatial structure (% SV) of resources was site-specific (Fig. 2.4).
Generally the recent alluvial site (transect 5) had the least structural variance ~58% SV

20

(with the exception of total N where transect 2 had a high % NV), while two of the
volcanic sites (transects 1 and 2) had the highest spatial structure ~72% SV. The spatial
-

heterogeneity of PO4 , total P, base cations (calcium, potassium, and magnesium), and
total C all had a mean % SV between 60-75% (Table 2.5). For total N, four of the five
sites had over 70% SV for total N; the fifth site (transect 2) had 11% SV (Table 2.5),
suggesting its variance could be attributed more to random noise in the data or
unmeasured variables in the site. On the other hand, inorganic N had the lowest % SV
(less than half of the total variance) and highest % NV (Table 2.5), indicating its
heterogeneity was due to unmeasured factors, occurred at finer than the measured 1 m
spatial scale, or arose from random noise in the data.
The range of autocorrelation was analogous within each resource across all sites
and fell into three categories: total C and total N had long effective ranges between 27+

+

+

-

174 m, the base cations (Ca , K , and Mg ), PO4 and total P between 16 and 97 m, and
inorganic N ranged between 14 and 32 m (Table 2.6). Similarly, the 50% correlation
distances (approximating patch sizes), of total C and total N were generally long, varying
between 6 (transect 2, total N) to 40 m across sites, between 7 and 22 m for base cations,
-

PO4 and total P, and the resource with the highest % NV, inorganic N, had the smallest
patch size varying between 3-7 m (Table 2.6, Fig. 2.5).

Leguminous species not correlative with soil resources
The availability of soil nutrients did not differ between locations occupied by
leguminous versus non-leguminous trees (Fig. 2.6). A few soil nutrients were correlated

21

with tree size when divided by functional group (leguminous vs. non-leguminous; Table
2.7, Fig. 2.7), but these relationships were very weak.

DISCUSSION
Summary
Substrate type and type of mineral nutrient cycle (source and biological
mediation) influenced soil resource availability and heterogeneity in this wet tropical
forest. Soil resource availability overlapped across sites yet weakly corresponded to soil
type, while the spatial structure of resource heterogeneity was similar across sites but
differed among resources. Contrary to expectation, adult tree functional groups
(leguminous vs. non-leguminous) did not create patches of similar resource availability or
heterogeneity. In general, resource patch sizes were larger than the mean root length of
most tropical seedlings, so that seedlings would find a range of favorable to unfavorable
habitats. The tremendous fine scale variability of these soil resources calls into question
the use of site-level mean nutrient levels to approximate seedling nutrient availability.
Thus, in order to capture the variability in resources that seedling are actually
experiencing, it is especially important to sample resources according to the type of
nutrient cycle and intrinsic physical and chemical properties to make sure that the
perceived resource environment, or patch size, is estimated appropriately.

Resource availability
In this study, the availability of soil resources was associated with soil type in
about half of the resources. Inorganic N levels were highest on the older, volcanic sites,

22

-

+

while PO4 , total P, and K were highest on alluvial sites. The high N and low P
concentrations found in the volcanic sites are consistent with previous studies at La Selva
Biological Station that found high rates of N mineralization and highly efficient P cycling
(Robertson 1984, Vitousek 1984, Vitousek and Denslow 1986). This study suggests that
the alluvial soils could be N-limited because readily weatherable minerals, such as
mineral-derived P and base cations, are in relatively greater supply than on older,
volcanic soils (Hedin et al. 2009, Townsend et al. 2008). Although not specifically tested
in this study, these results are consistent with a global nutrient limitation hypothesis of Plimitation on older sites and N-limitation on younger sites (Walker and Syers 1976,
Vitousek 1984, Hedin 2003, Vitousek 2004). Under this hypothesis, P availability
decreases with time as weathering dissolves phosphates from newly exposed rock, while
N may be more available in older sites due to the biologically renewable capacity of soil
N. In the alluvial sites, the constant flooding and disturbance by nearby rivers may have
replenished weatherable minerals by depositing mineral-derived resources such as soil P
and base cations and washed away mobile N ions, whereas volcanic soils could have
experienced P and base cation losses from weathering and occlusion, without
replenishment, and the long-term accumulation of N.

Spatial heterogeneity
Spatial heterogeneity of the soil resources appeared to be related to not only soil
type but also to the type of mineral nutrient cycles. Because spatial heterogeneity can be
the result of the physical, chemical, and biological processes in a system, similar degrees
of structural variances of these resources could indicate that similar environmental factors

23

are acting on these resources. Consistent with this view, the spatial dependence of
resources appeared to be correlated with the relative age of the soils, with the recent
alluvial site having the least structural variance whereas the “older” volcanic sites had the
most spatial structure in soil resources. This suggests that the time over which climate,
topography and organisms had to affect soil resources is important to spatial patterns in
these sites.
Recent biotic processes also may influence the spatial heterogeneity of soil
resources. The range of autocorrelation for total C and total N were similar, while base
-

cations, PO4 , total P, and inorganic N demonstrated their own autocorrelative patterns
(Table 2.8). Individual plants and species can influence nutrient distributions (Zheng et
al. 2008, Diekmann et al 2007). Townsend et al. (2008) proposed that in highly diverse
tropical forests, such as these La Selva sites, individual species may create tree-specific
„biogeochemical footprints‟ that extend from the canopy to the soil, and significant
associations between local-scale variation in soil chemistry and tree species composition
have been found in the forests of Panama, Colombia and Ecuador (John et al. 2007).
However, somewhat surprisingly, soil nutrients were not associated with the presence of
leguminous species in this study. Thus, whole tree inputs may not be influencing the fine
scale heterogeneity of essential soil nutrients. Yet, the lack of correlation also may be a
result of the fast and tight cycling of soil nutrients in wet tropical forests. Due to the
dynamic nature of these nutrients, there is still the possibility that a relationship could be
found with more dynamic measures of resource estimation (e.g., N mineralization) rather
than simple standing pools.

24

The similar spatial structure and ranges of all mineral-derived soil nutrients
suggest that they are influenced by similar factors. The short ranges of the base cations,
-

PO4 , total P, and inorganic N also support this hypothesis. Furthermore, the short range
but highly variable structure of biologically-derived inorganic N suggests finer scale
controls, such as localized microbial transformations and immobilization (Wang et al
2002, Jackson and Caldwell 1993). On the other hand, total C and total N are not derived
from minerals and are directly taken up by plants. The long effective ranges of total C
and total N suggest similar inputs of organic matter across fairly broad areas, perhaps due
to leaf litter dispersal (Ferrari 1999). Mean crown diameters of La Selva canopy trees are
~24 m (Clark et al. 2005), which correspond to the long ranges of total C and total N.
Thus, the type of mineral nutrient cycle may explain the different autocorrelative patterns
in soil resources.
These results generally correspond to a similar study conducted in an old growth,
wet tropical forest in Panama (Yavitt et al. 2009). Established on highly weathered soil,
the Panamanian study measured soil resources from 87 base points, sampled at 0, 2, 8 and
20 m along a transect, to quantify spatial patterns of soil resources from 2 to ~500m.
Yavitt et al.‟s study had a spatial dependence scale of 10-100 m for most soil properties,
-

and the effective ranges of the base cations and PO4 were around 50-60 m, which was on
the high end of the La Selva ranges. On the other hand, inorganic N‟s spatial
heterogeneity differed between the studies. Although the Panamanian study had similar
% NV for inorganic N, the resource also showed long effective ranges (74.2 m and 111.3
m for nitrate and ammonium, respectively) compared to La Selva‟s short inorganic N
range. Yet in support of a short range for inorganic N, several studies in other
25

ecosystems, with similar fine scale sampling designs to this study, found comparable
effective ranges consistent with these results (Gallardo et al. 2005, Gross et al. 1995).

Implications of patch size on seedlings
Patch size of a resource may be equally important to its availability for seedlings,
which have limited rooting extent. The long range of total C and total N indicated that
similar concentrations of these resources occurred in large, smoothly continuous patches
-

(Fig. 2.5). The effective ranges of the base cations, PO4 and total P indicated finer scale
heterogeneity, as illustrated by steeper semivariogram functions as they approached the
sill; these nutrient patches were smaller and more sharply discontinuous (Fig. 2.5). The
short effective range of inorganic N indicated the smallest, most frequent and
discontinuous patches of all the resources (Fig. 2.5).
Since the spatial distribution of forest soil nutrients is highly heterogeneous and
soil nutrients are frequently a growth-limiting factor, plasticity in seedling rooting length
is important to understand the implications of resource heterogeneity on plant
performance. In this study, I examined horizontal, spatial heterogeneity of soil resources
across the La Selva sites, since the sampling depth was uniform. Patch size varied
depending upon the soil resource of interest; 50% correlation distances for total C and
-

total N was on average between 10-40 m, 4-16 m for base cations, PO4 , and total P, and
3-7 m for inorganic N. A study examining the roots of 37 species from a Bolivian
st

lowland wet tropical forest found 1 year (mean seedling height = 14 cm, range mean
seedling height of species = 5-29 cm) seedling root length varied between 0.07-1.86 m

26

with a mean length of ~0.5 m (not distinguished as either vertical or horizontal,
Markesteijn and Poorter 2009). By comparing the patch size of soil resources with mean
rooting length of tropical seedlings, it appears that resource patch sizes may be larger
st

than the mean root length of most 1 year tropical seedlings. Therefore, seedlings may be
experiencing “hot” and “cold” patches of nutrients that exist independent of soil type, as
proposed by Townsend et al. (2008), which is especially important since establishment
within the first year of growth is important to seedling growth and survival. In the La
Selva soils, many of the “fertile” alluvial sites had similarly low levels of soil resources
as the “infertile” volcanic sites, creating islands of infertility. Therefore, in contrast to a
mature tree that can explore both the nutrient rich and poor habitats, tropical seedlings
may find a range of habitats from favorable to unfavorable (Hutchings et al. 2003,
Gallardo et al. 2006).
By not measuring soil resources at levels commensurate with seedlings, studies
may not be capturing the variation in resources that seedling growth and survivorship are
dependent upon. Studies that measured soil resources at fine scales found significant
effects of resources on seedlings (Baraloto and Goldberg 2004), while those studies that
measured coarse, stand-level resources did not find effects (Valdecantos 2005, VargasRodriguez et al. 2005). Thus, it is especially important to sample resources at the
seedling-appropriate scale to make sure that the perceived resource environment, or patch
size, is estimated appropriately.

27

Conclusions
The scale at which heterogeneity emerges is especially important when compared
to plant size. If samples are taken at a larger spatial scale than what seedlings may have
access to, studies may not capture those equally low nutrient levels that seedlings are
actually experiencing. Despite differences among resources‟ spatial autocorrelation and
effective ranges, fine scale sampling is important with respect to most soil nutrients.
Therefore, to understand seedling responses to soil resources and by extension, the
potential for species to partition gradients of soil resource availability in wet tropical
forests, essential plant nutrients, such as inorganic N, P, and base cations, should be
measured at a seedling-level spatial scale, while other soil resources, such as total N and
total C could be measured at coarser spatial scales (>10 m).

28

TABLE 2.1. Chemical, physical and biological properties that could affect the spatial heterogeneity of resources.
Source: M = Mineral-derived, originated from parent material; B = Biological-derived. Plant Uptake: Y = Yes; N = No.
Resource

Source

Processes affecting availability

PO4

M

Weathering, erosion, occlusion

Y

Mainly immobile

Total P

M

Weathering, erosion, occlusion

Y/N

Mainly immobile

M

Weathering, erosion, leaching

Y

Somewhat mobile

M

Weathering, erosion, occlusion

Y

Mg

M

Weathering, erosion, occlusion

Y

Inorganic N

B

Y

Total N

B

Total C

B

Biological fixation, decomposition of
organic residues by bacteria/fungi
Accumulation/decomposition of
organic residues, biological fixation
Accumulation of organic residues

Mainly immobile,
held fairly tight to soil colloids
Mainly immobile,
held fairly tight to soil colloids
Immobile as ammonium form, but
more mobile as nitrate form
See inorganic N; organic N is
bound in organic material
Bound in organic material

-

+

K

+

Ca

+

Plant Uptake

29

Y/N
N

Mobility in tropical soils

TABLE 2.2. Comparison of soil resources among sites (one way analysis of means with unequal variances and a Bonferroni
correction for multiple comparisons, p<0.05).

Resource
-

PO4
Total P
+
K
+

Ca

+

Mg
Inorganic N
Total N
Total C

F statistic

df

P

85.87
49.92
58.31

4, 621.35
4, 622.14
4, 619.57

< 0.001
<0.001
<0.001

55.95

4, 621.21

<0.001

61.36
62.98
121.89
134.89

4, 620.66
4, 577.72
4, 564.72
4, 573.59

<0.001
<0.001
<0.001
<0.001

30

TABLE 2.3. Comparison of soil resources among alluvial versus volcanic sites.

Resource
-

PO4
Total P
+
K
+

Ca

+

Mg
Inorganic N
Total N
Total C

F statistic

df

P

141.16
7.57
11.53

1, 3
1, 3
1, 3

0.001
0.071
0.043

0.22

1, 3

0.67

0.00
11.02
0.67
0.80

1, 3
1, 3
1, 3
1, 3

0.96
0.045
0.47
0.44

31

TABLE 2.4. Summary of resource values per site (Transect 1, 2, 3 = volcanic sites, Transect 4 = alluvial site, Transect 5 = recent
alluvial site). Letters denote differences (p<0.05, one-way analysis of means with unequal variances and a Bonferroni correction)
among soil resources, SD = standard deviation, and % CV = coefficient of variation.
+
+
+
Transect
PO4
Total P
Ca
K
Mg
Inorganic N Total N
Total C
(µg/g)
(µg/g)
(µg/g)
(µg/g)
(µg/g)
(µg/g)
(%)
(%)
Transect 1
mean
3.06 a
4.45 a
207.26 d
103.64 c
76.92 c
341.87 b
0.39 b
4.91 b
SD
4.05
4.86
192.26
61.5
58.09
181.21
0.15
1.92
Median
1.76
3.01
139.6
87.63
61.25
315.60
0.38
4.61
% CV
132.52
109.29
92.77
59.34
75.52
53.01
38.46
39.10
Transect 2
mean
3.06 ab
4.64 a
130.52 b
78.50 a
61.09 b
440.57 c
3.62 e
4.77 b
SD
3.25
3.85
160.73
43.20
55.80
262.18
6.88
2.77
Median
2.08
4.15
93.98
71.21
49.90
410.09
0.38
4.38
% CV
106.51
83.06
123.15
55.04
91.35
59.51
190.06
58.07
Transect 3
mean
3.51 b
4.61 a
93.91 a
87.97 b
48.28 ab
341.69 b
0.49 d
6.13 d
SD
3.07
3.89
138.34
34.39
22.27
220.97
0.09
1.26
Median
2.97
3.79
77.46
83.91
45.09
257.09
0.47
6.00
% CV
87.55
84.31
147.30
39.10
46.12
64.67
18.37
20.55
Transect 4
mean
8.75 c
9.67 c
192.62 d
119.63 d
77.67 c
203.74 a
0.43 c
5.29 c
SD
9.91
5.90
140.42
54.15
42.90
119.07
0.10
1.33
Median
6.17
9.55
153.19
111.4
70.04
173.40
0.45
5.14
% CV
113.25
61.04
72.90
45.26
55.23
58.44
23.26
25.14
Transect 5
mean
7.74 c
6.33 b
139.94 c
133.06 d
44.96 a
245.25 a
0.32 a
3.87 a
SD
5.19
4.11
94.61
61.81
23.34
158.83
0.09
1.03
Median
6.90
5.81
119.78
118.74
39.73
202.76
0.34
3.70
% CV
67.09
6.90
67.61
46.45
51.91
64.76
28.13
26.61
32

TABLE 2.5. Structural and nugget variances for each soil resource by site (Transect 1, 2, 3 = volcanic sites, Transect 4 = alluvial site,
Transect 5 = recent alluvial site). Letters denote differences (no overlap in 95% credible intervals) across a resource.
Nutrient

Transect 1

Transect 2

Transect 3

Transect 4

Transect 5

PO4
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

71.80 c
(72.21, 73.20)
28.20
(26.80, 27.79)

70.49 b
(70.68, 71.84)
29.51
(28.16, 29.32)

46.24 a
(24.19, 44.34)
53.76
(55.66, 75.81)

84.15 d
(80.60, 88.48)
15.85
(11.52, 19.40)

35.29 a
(27.21, 46.50)
64.71
(53.50, 72.79)

Total P
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

51.56 a
(45.50, 59.07)
48.44
(40.93, 54.50)

76.20 b
(76.14, 77.94)
23.80
(22.06, 23.86)

55.55 a
(48.96, 65.62)
44.45
(34.38, 51.04)

84.82 c
(83.57, 89.15)
15.18
(10.85, 16.43)

48.25 a
(40.64, 56.08)
51.75
(43.92, 59.36)

86.93 d
(84.99, 91.28)
13.07
(8.72, 15.01)

81.38 c
(80.73, 84.49)
18.62
(15.51, 19.27)

43.72 a
(38.27, 51.66)
56.28
(48.34, 61.73)

68.88 b
(68.36, 72.28)
31.12
(27.72, 31.64)

68.88 b
(38.27, 51.66)
56.28
(48.34, 61.73)

73.38 b
(67.86, 82.79)
26.62
(17.21, 32.14)

90.77 c
(89.48, 93.64)
9.23
(6.36, 10.52)

64.97 ab
(56.09, 76.54)
35.03
(23.46, 43.91)

63.00 ab
(58.83, 74.78)
37.00
(25.22, 41.17)

50.55 a
(47.04, 63.40)
49.45
(36.60, 52.96)

-

+

Ca
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)
+

K
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

33

TABLE 2.5 (cont). Structural and nugget variances for each soil resource by site (Transect 1, 2, 3 = volcanic sites, Transect 4 =
alluvial site, Transect 5 = recent alluvial site). Letters denote differences (no overlap in 95% credible intervals) across a resource.
Nutrient

Transect 1

Transect 2

Transect 3

Transect 4

Transect 5

Mg
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

74.59 bc
(69.09, 83.00)
25.41
(17.00, 30.91)

79.75 c
(77.86, 84.56)
20.25
(15.44, 22.14)

67.63 ab
(59.88, 78.71)
32.37
(21.29, 40.12)

69.29 b
(67.02, 75.79)
30.71
(24.21, 32.98)

51.18 a
(44.56, 65.72)
48.82
(34.28, 55.44)

Inorganic N
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

57.74 b
(55.94, 60.80)
42.26
(39.20, 44.06)

54.26 b
(49.63, 60.41)
45.74
(39.59, 50.37)

22.85 a
(14.42, 33.72)
77.15
(66.28, 85.58)

55.50 b
(48.89, 65.55)
44.42
(34.45, 51.11)

28.51 a
(20.34, 36.50)
71.49
(63.50, 79.66)

Total N
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

77.27 b
(71.97, 86.10)
22.73
(13.90, 28.03)

11.04 a
(6.64, 19.23)
88.96
(80.77, 93.36)

79.52 b
(74.52, 83.18)
20.48
(16.82, 25.48)

75.11 b
(70.27, 79.35)
24.89
(20.65, 29.73)

77.54 b
(70.66, 82.43)
22.46
(17.57, 29.34)

Total C
% Structural variance
(95% credible interval)
% Nugget variance
(95% credible interval)

76.78 ab
(72.44, 85.95)
23.22
(14.05, 27.56)

63.95 a
(56.06, 72.78)
36.05
(27.22, 43.94)

81.01 b
(75.89, 84.39)
18.99
(15.61, 24.11)

78.13 b
(73.37, 82.09)
21.87
(17.91, 26.63)

7625 ab
(68.23, 81.71)
23.75
(18.29, 31.77)

+

34

TABLE 2.6. Effective range and 50% correlation distance (patch size) for each soil resource by site (Transect 1, 2, 3 = volcanic sites,
Transect 4 = alluvial site, Transect 5 = recent alluvial site).
Nutrient

Transect 1

Transect 2

Transect 3

Transect 4

Transect 5

PO4
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

15.91
(9.33, 30.09)
3.68
(2.16, 6.95)

20.67
(11.98, 41.55)
4.78
(2.77, 9.60)

32.64
(11.98, 198.68)
7.54
(2.77, 45.90)

97.40
(49.75, 189.87)
22.50
(11.50, 43.87)

26.22
(10.79, 123.46)
6.06
(2.49, 28.52)

Total P
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

23.95
(12.43, 59.88)
5.53
(2.87, 13.84)

18.82
(11.50, 36.28)
4.35
(2.66, 8.38)

33.98
(17.08, 92.88)
7.85
(3.95, 21.46)

49.51
(27.45, 122.95)
11.44
(6.34, 28.41)

20.63
(10.88, 56.29)
4.77
(2.51, 13.00)

58.03
(30.83, 147.73)
13.41
(7.12, 34.13)

35.76
(20.83, 79.58)
8.26
(4.81, 18.39)

19.17
(10.62, 61.22)
4.43
(2.45, 14.14)

18.37
(8.99, 56.18)
4.24
(2.08, 12.98)

19.17
(10.62, 61.22)
4.43
(2.45, 14.14)

53.57
(23.08, 158.73)
12.38
(5.34, 36.67)

52.08
(27.68, 128.21)
12.03
(6.39, 29.62)

69.28
(19.32, 185.19)
16.01
(4.46, 42.79)

37.36
(11.64, 184.05)
8.63
(2.69, 42.52)

19.34
(9.49, 154.64)
4.47
(2.19, 35.73)

-

+

Ca
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)
+

K
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

35

TABLE 2.6 (cont). Effective range and 50% correlation distance (patch size) for each soil resource by site (Transect 1, 2, 3 = volcanic
sites, Transect 4 = alluvial site, Transect 5 = recent alluvial site).
Nutrient

Transect 1

Transect 2

Transect 3

Transect 4

Transect 5

Mg
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

60.73
(27.52, 161.29)
14.03
(6.36, 37.27)

37.22
(21.51, 85.23)
8.60
(4.97, 19.69)

62.24
(22.69, 174.42)
14.38
(5.24, 40.30)

24.86
(10.41, 90.36)
5.74
(2.41, 20.88)

37.31
(12.68, 153.06)
8.62
(2.93, 35.36)

Inorganic N
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

14.28
(10.22, 29.53)
3.27
(2.36, 6.82)

25.84
(12.60, 64.24)
5.97
(2.91, 14.84)

32.19
(12.39, 116.73)
7.44
(2.86, 26.97)

32.33
(13.84, 104.90)
7.47
(3.20, 24.24)

13.85
(10.14, 38.86)
3.20
(2.34, 8.98)

Total N
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

53.38
(22.16, 165.75)
12.28
(5.10, 38.12)

26.88
(10.29, 171.43)
6.18
(2.36, 39.43)

167.60
(89.02, 198.68)
38.55
(20.47, 45.70)

174.42
(103.81, 198.68)
40.12
(23.88, 45.70)

149.25
(62.50, 197.37)
34.32
(14.38, 45.39)

Total C
Range (m)
(95% credible interval)
50% correlation distance (m)
(95% credible interval)

45.18
(19.57, 153.85)
10.39
(4.50, 35.38)

136.36
(24.94, 197.37)
31.36
(5.74, 45.39)

164.04
(79.58, 198.68)
37.50
(18.30, 45.70)

167.60
(85.71, 198.68)
38.55
(19.71, 45.70)

144.23
(49.10, 197.37)
33.17
(11.29, 45.39)

+

36

TABLE 2.7. Responsiveness of soil nutrient availability to tree size by species type.

Resource
Inorganic N
Total P
+
K
+

Ca

+

Mg

Size of leguminous species
F
P
r
(df = 1, 580)
0.21 0.65 -0.019
1.55 0.21 -0.052
0.27 0.61 -0.021

Size of non-leguminous species
F
P
r
(df = 1, 3520)
3.92 0.048 -0.033
0.85 0.36 0.016
3.36 0.067 0.031

5.63

0.018 -0.098

5.90

0.015 0.041

9.82

0.002 -0.129

4.12

0.042 0.034

37

TABLE 2.8. Mean resource estimates of patch sizes across all transects and the biological reasons supporting the ranges.

Resource
-

Range of point estimates across transects (m)
(“relative patch size”)

Biological reasoning for patch size

3.7 –22.5
4.4 - 11.4

Mineral weathering, plant uptake
Mineral weathering, plant uptake
(includes both POand organic P forms)

4.4 - 16.0

Mineral weathering, plant uptake

4.2 - 13.4

Mineral weathering, plant uptake

Mg
Inorganic N

5.7 - 14.0
3.2 - 7.5

Total N

6.2 - 40.1

Total C

10.4 - 38.6

Mineral weathering, plant uptake
Biological-derived origin, microbial transformations,
N immobilization, plant uptake
Biological-derived origin, litterfall inputs,
driven by organic N forms
Biological-derived origin, litterfall inputs

PO4
Total P
+

K

+

Ca

+

38

FIGURE 2.1. Layout of sampling at each of five study sites (200 m transect) at La Selva
Biological Station. (Filled-in circles = Regular lattice sampling points, Open circle =
Random sampling points, Short arrows = 10 m, and Longer arrows = 20 m).

39

2

2

2

FIGURE 2.2. Example semivariogram illustrating the sill (σ + τ ), partial sill (σ ),
2

1.5

nugget (τ ) and effective range.

2

1.0

Sill

0.5

Semivariance

Partial Sill (σ )

2

Nugget (τ )
0.0

Effective range
0

20

40

Distance (m)

40

60

FIGURE 2.3. Distribution of soil resources by the proportion of cases. (A) Phosphate (B)
Total phosphorus, (C) Potassium, (D) Calcium, (E) Magnesium, (F) Sum of inorganic
nitrogen, (G) Total nitrogen, and (H) Total carbon (Transect 1/volcanic=solid, Transect
2/volcanic=dashed, Transect 3/volcanic=dotted, Transect 4/alluvial=dot-dash, Transect
5/recent alluvial=long-dash).
20
15
10
0
0

10
20
30
40
Soil phosphate (ppm)

0

30
20
10

30
20
10

40

(D) Soil calcium
Proportion of cases (%)

40

(C) Soil potassium

10
20
30
40
Total soil phosphorus (ppm)

0

50 100 150 200 250 300
Soil potassium (ppm)

41

0

0

Proportion of cases (%)

(B) Total soil phosphorus

5

Proportion of cases (%)

15
10
5
0

Proportion of cases (%)

20

(A) Soil phosphate

0

200
400
600
Soil calcium (ppm)

800

FIGURE 2.3 (cont). Distribution of soil resources by the proportion of cases. (A)
Phosphate (B) Total phosphorus, (C) Potassium, (D) Calcium, (E) Magnesium, (F) Sum
of inorganic nitrogen, (G) Total nitrogen, and (H)Total carbon (Transect
1/volcanic=solid, Transect 2/volcanic=dashed, Transect 3/volcanic=dotted, Transect
4/alluvial=dot-dash, Transect 5/recent alluvial=long-dash).
(F) Sum of inorganic nitrogen

40
30
20
0

10

Proportion of cases (%)

40
30
20
10
0

Proportion of cases (%)

50

(E) Soil magnesium

0

50

100

150

200

0

250

Soil magnesium (ppm)

1000

10

20

30

40

(H) Total soil carbon
Proportion of cases (%)

40
30
20

800

0

0

10

400 600

Sum of inorganic nitrogen (ppm)

(G) Total soil nitrogen
Proportion of cases (%)

200

0.0

0

0.2
0.4
0.6
0.8
Total soil nitrogen (%)

42

2
4
6
8
Total soil carbon (%)

10

FIGURE 2.4. Semi-variograms of the 50% of the posterior estimate for each resource.
(A) Phosphate (B) Total phosphorus, (C) Potassium, (D) Calcium, (E) Magnesium, (F)
Sum of inorganic nitrogen, (G) Total nitrogen, and (H) Total carbon (Transect
1/volcanic=solid, Transect 2/volcanic=dashed, Transect 3/volcanic=dotted, Transect
4/alluvial=dot-dash, Transect 5/recent alluvial=long-dash).

2.5
2.0
1.5
1.0

60

0.8

1.0

(D) Soil calcium

0.6

Calcium semivariance

0.8
0.6
0.4
0.2

20
40
Distance (m)

0.4

(C) Soil potassium

1.0

0.5
0

60

0.0

0.0

Potassium semivariance

20
40
Distance (m)

0.2

0

(B) Total soil phosphorus

0.0

2.0
1.5
1.0
0.5
0.0

Phosphate semivariance

2.5

Total phosphorus semivariance

(A) Soil phosphate

0

20
40
Distance (m)

0

60

43

20
40
Distance (m)

60

20
40
Distance (m)

60

4
3
2
1
0

20
40
Distance (m)

60

1.0
0.8
0.6
0.4
0.0 0.2

2.5
2.0
1.5
1.0
0.5
0

20
40
Distance (m)

(H) Total soil carbon
Total soil carbon semivariance

(G) Total soil nitrogen

0.0

Total soil nitrogen semivariance

0

(F) Sum of inorganic nitrogen

0

1.0
0.8
0.6
0.4
0.2
0.0

Soil magnesium semivariance

(E) Soil magnesium

Sum of inorganic nitrogen semivariance

FIGURE 2.4 (cont). Semi-variograms of the 50% of the posterior estimate for each
resource. (A) Phosphate (B) Total phosphorus, (C) Potassium, (D) Calcium, (E)
Magnesium, (F) Sum of inorganic nitrogen, (G) Total nitrogen, and (H) Total carbon
(Transect 1/volcanic=solid, Transect 2/volcanic=dashed, Transect 3/volcanic=dotted,
Transect 4/alluvial=dot-dash, Transect 5/recent alluvial=long-dash).

0

60

44

20
40
Distance (m)

60

FIGURE 2.5. Example surface approximation of patch sizes for each resource across site
5 (alluvial soil). (A) Phosphate, (B) Total Phosphorus, (C) Calcium, (D) Potassium, (E)
Magnesium, (F) Sum of Inorganic Nitrogen, (G) Total Nitrogen, and (H) Total Carbon.
(B) Total soil phosphorus

(A) Soil phosphate
1153900

1153900

15

10

10
1153800

1153800

Northing

20

5

0

0
828140 828180 828220 828260

828140 828180 828220 828260
Easting

Easting
(D) Soil magnesium

(C) Soil calcium
1153900

1153900

100
80
60

1153800

400
1153800

Northing

700

40
20

100
828140 828180 828220 828260

828140 828180 828220 828260
Easting

Easting

45

FIGURE 2.5 (cont). Example surface approximation of patch sizes for each resource
across site 5 (alluvial soil). (A) Phosphate, (B) Total Phosphorus, (C) Calcium, (D)
Potassium, (E) Magnesium, (F) Sum of Inorganic Nitrogen, (G) Total Nitrogen, and
(H) Total Carbon.
(E) Soil potassium

(F) Sum of inorganic nitrogen

1

Northing
1153900

2

500
400
300

1153800

1153900

3

1153800

Northing

600

200
100
828140 828180 828220 828260
Easting

828140 828180 828220 828260
Easting
(G) Total soil nitrogen

(H) Total soil carbon
0.12
0.10

1153900

Northing1153900
1153800

0.08
0.06
0.04

828140 828180 828220 828260

1.0
1153800

Northing

1.6

828140 828180 828220 828260

Easting

Easting

46

0.4

6
4
0

2

Total P (ppm)

8

400
300
200

Inorganic N (ppm)

FIGURE 2.6. Soil nutrient availability by species type. (A) Inorganic N by species type;
(B) Total P by species type, (C) Potassium by species type, (D) Calcium by species type,
(E) Magnesium by species type.
(A) Inorganic N by species type
(B) Total P by species type

Leguminous

Non-leguminous

Leguminous

50

100

150 200 250

(D) Calcium by species type

Calcium (ppm)

100 120 140
80
60

Potassium (ppm)

(C) Potassium by species type

Leguminous

Non-leguminous

Leguminous

40

60

80

100

(E) Magnesium by species type

Magnesium (ppm)

Non-leguminous

Leguminous

Non-leguminous

47

v

Non-leguminous

FIGURE 2.7. Responsiveness of soil nutrient availability to tree size by species type.
(Transect 1 = square, Transect 2 = circle, Transect 3 = triangle, Transect 4 = X,
Transect 5 = diamond.)

100 200 300 400
0

Inorganic N (ppm)

100 200 300 400
0

Inorganic N (ppm)

(A) Leguminous size vs. Inorganic N (B) Non-leguminous size vs. Inorganic N

0 20 40 60 80 100 120
Diameter at breast height (cm)

0 20 40 60 80 100 120
Diameter at breast height (cm)
(D) Non-leguminous size vs. Total P

6
2

4

Total P (ppm)

6
4

0

2
0

Total P (ppm)

8

8

(C) Leguminous size vs. Total P

0 20 40 60 80 100 120
Diameter at breast height (cm)

(E) Leguminous size vs. Potassium

(F) Non-leguminous size vs. Potassium

100
0

50

Potassium (ppm)

100
50
0

Potassium (ppm)

150

150

0 20 40 60 80 100 120
Diameter at breast height (cm)

0 20 40 60 80 100 120
Diameter at breast height (cm)

0 20 40 60 80 100 120
Diameter at breast height (cm)
48

250
100 150 200

Calcium (ppm)

100 150 200

0

0

50

50

Calcium (ppm)

250

FIGURE 2.7 (cont). Responsiveness of soil nutrient availability to tree size by species
type. (Transect 1 = square, Transect 2 = circle, Transect 3 = triangle, Transect 4 = X,
Transect 5 = diamond.)
(H) Non-leguminous size vs. Calcium
(G) Leguminous size vs. Calcium

0 20 40 60 80 100 120
Diameter at breast height (cm)

0 20 40 60 80 100 120
Diameter at breast height (cm)

20 40 60 80 100

Magnesium (ppm)

20 40 60 80 100

(J) Non-leguminous size vs. Magnesium

0

0

Magnesium (ppm)

(I) Leguminous size vs. Magnesium

0 20 40 60 80 100 120
Diameter at breast height (cm)

0 20 40 60 80 100 120
Diameter at breast height (cm)

49

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56

CHAPTER III
Seedling responses to soil nutrients in the forest understory

ABSTRACT
Plant growth responses to resources may be an important mechanism that
influences species‟ distributions, coexistence, and community structure. Irradiance is
considered the most important resource for seedling growth in the understory of wet
tropical forests, but multiple soil nutrients and species have yet to be examined
simultaneously with irradiance. To identify the resources that best predicted Neotropical
tree seedling growth, irradiance and soil nutrients were measured over a seedling2

appropriate spatial scale (1 m ) in five sites, spanning a soil fertility gradient, in oldgrowth wet tropical forests at La Selva Biological Station, Costa Rica. An array of soil
-

+

nutrients were measured including nitrate (NO3 ), ammonium (NH4 ), total nitrogen
-

(total N), phosphate (PO4 ), and sum of base cations (SBC; including potassium,
magnesium, and calcium). Light limitation in the forest understory did not preclude
seedling growth correlations with soil nutrients; growth was correlated with both
irradiance and soil nutrients in about 45% of species, but in an additional 48% of species,
growth was associated with only soil nutrients. Irradiance was a significant predictor of
-

+

growth in 52% of the species, total N in 47%, SBC in 39%, NO3 in 32%, NH4 in 34%,
-

and PO4 in 29%. Contrary to expectations, the realized magnitudes of growth effects,
assessed as the maximum growth response for each species, were similar for irradiance
and most soil nutrients. Among species whose growth correlated with soil nutrients, the
57

-

rank importance of nutrient effects was first SBC, followed by PO4 and N (total N, NO3
+

and NH4 ). Species‟ growth responsiveness (i.e. magnitudes of effect) to irradiance,
-

+

-

SBC, NO3 , NH4 , and PO4 was negatively correlated with species‟ shade tolerance
(survival under 1% full sun). In this broad survey of species and resources, the nearly
ubiquitous effects of soil nutrients on seedling growth challenges the idea that soil
nutrients are less important than irradiance in the light-limited understory of wet tropical
forests.

58

-

INTRODUCTION
Tropical forest understories are spatially heterogeneous in irradiance (Nicotra et
al. 1999) and soil resources (Holste et al., in prep), and species-specific seedling growth
and survivorship responses to resource levels could influence community structure. In
both temperate and tropical forests, there is an inter-specific trade-off between
survivorship under low light (i.e. shade-tolerant species) versus growth under high light
(i.e. light-demanding species) that could promote species coexistence across light
gradients (Kobe et al.1995, Kobe 1999). Similarly, inter-specific trade-offs between
growth and survivorship also exist along edaphic gradients, potentially contributing to
species‟ distributions (Schreeg et al. 2005, Russo et al. 2008). These resource-based
trade-offs could be a mechanism for resource partitioning (Grubb 1977, Denslow 1980),
which could sustain tree species diversity in tropical forests. Therefore, understanding
species-specific seedling growth responses to resources may be crucial for predicting
forest dynamics and promoting ecology-based forest management and restoration.
In tropical forests, irradiance is considered to be “the single-most limiting
resource” (Pearcy 2007), with sometimes less than 1% of above canopy radiation
transmitted to the forest floor (Chazdon and Fetcher 1984). Wet tropical forest seedlings
exhibit inter-specific variation in growth to differing light levels (Augspurger 1984,
Poorter 1999). Even in low light environments, such as beneath closed canopy forests,
tropical seedling growth varies with light availability (Montgomery and Chazdon 2002).
Soil nutrients also can influence tree growth (Burslem et al. 1995, Dent and
Burslem 2009). In tropical forests, species differ in the direction and strength of growth
responses to (Baraloto et al. 2006) and distributions with (John et al. 2007) soil factors.

59

Phosphorus (P) is widely thought to be more limiting than nitrogen (N) in mature lowland
tropical forests, because most tropical soils are N rich relative to other nutrients (Hedin et
al. 2009). Seedling growth responses to P additions support limitation by P (Lawrence
2001, Ceccon et al. 2003). However, N also could alleviate P limitations by providing
the substrate to construct extracellular phosphatases to acquire P (Treseder and Vitousek
2001). Based on reviews of tropical soils and plant-soil relations, Sollins (1998)
proposed (in decreasing order of importance) that P, base cations (potassium, calcium,
and magnesium), and N could influence species‟ performance in lowland wet tropical
forests.
Soil nutrients may interact with irradiance to influence tree seedling performance
(e.g. Kobe 2006). A common assumption is that light-demanding species invest more in
traits that maximize growth (Herms and Mattson 1992), while shade-tolerant species
invest in functions that improve survivorship, such as defense against natural enemies
(Coley and Barone 1996) and carbohydrate storage (Myers and Kitajima 2007). The
growth of both shade-tolerant and light-demanding seedlings respond to soil nutrients
under high light availability (reviewed by Lawrence 2003), likely because faster growth
increases demand for nutrients (Peace and Grubb 1982). Despite presumed lower
nutrient demand under low irradiance, soil nutrients influence seedling growth even in
the deep shade of the forest understory (Yavitt and Wright 2008, Palow and Oberbauer
2009). Seedling dependency on external sources generally develops earlier for light than
mineral nutrients, suggesting greater importance of light than soil nutrients during early
seedling establishment (Kitajima 2002). Yet seedlings in the shaded understory could
benefit from increased soil nutrient availability via improved light capture (Fahey et al.

60

1998). Thus, I expect light-demanding species to be more dependent upon soil resources
during seedling growth than more shade-tolerant species. I also expect growth-irradiance
correlations to be more prevalent and to have a greater effect than soil nutrients on
seedling growth in the shaded forest understory.
In this study, I examined the growth responses of 94 species of woody seedlings
(represented by 12,800 individuals) to irradiance and soil nutrients to better understand
species-specific seedling performance and implications for forest dynamics. I
hypothesized that:
H1: Irradiance limitations to seedling growth are more prevalent among species and are
of a greater magnitude than soil resource limitations to seedling growth.
H2: Among the species whose seedling growth is sensitive to soil nutrients, the rank
importance of soil nutrients is (in decreasing order): P, base cations, and N.
H3: The effect of irradiance and soil nutrients on species-specific seedling growth is
negatively correlated with a species shade tolerance.

METHODS
Site description
Irradiance and soil nutrients were measured over a seedling-appropriate spatial
2

scale (1 m ) within five sites in mainly old-growth wet tropical forest at La Selva
Biological Station, Costa Rica. A portion of one site contained secondary forest, which
was associated with higher ecosystem productivity (T.W. Baribault, personal
communication) but not higher seedling growth. La Selva is located in the Atlantic
lowlands, at the convergence of the Sarapiqui and Puerto Viejo rivers (10º 26‟N, 84º 00‟

61

W) and receives a mean annual rainfall of 3859 mm with a mean monthly minimum of
100 mm. La Selva soils span a fertility gradient from relatively fertile entisols and
inceptisols of alluvial origin to low fertility ultisols developed on old lava flows (McDade
and Hartshorn 1994); sites were established on three volcanic and two alluvial soils.
2

Each site was centered on a transect of 200 contiguous 1m quadrats. In each transect,
seedlings were identified and monitored, with height measurements taken every six
weeks from February 2000 to September 2009.

Resource measurements
Light availability was measured in Fall 2000, July 2004, 2006, and 2007, and
February and July 2008 as percent canopy openness. A hemispherical photograph was
2

taken at the center of each 1 m quadrat with a digital camera (Nikon Coolpix 950) and
fish-eye lens (Nikon FC-E8). All photos were taken at 0.25 m height (+/- 0.05m) during
uniformly overcast or twilight conditions. To increase contrast between sky and
vegetation in the images, the 2000 and 2004 photos were under-exposed and for
subsequent photos the automatic exposure level was locked based on spot metering of
open sky (Ishida 2004). For 2000 through 2006 images, thresholds for images were set
manually to classify pixels as sky or vegetation and percent canopy openness was
calculated using Gap Light Analyzer (Gap Light Analyzer 2.0, Frazer et al. 1999). Later
images were taken in black and white, classified with an edge detection algorithm
(Sidelook 1.1, Nobis 2005), and resulting binary images were analyzed with Gap Light
Analyzer to estimate percent canopy openness. I used percent canopy openness rather
than irradiance metrics that include direct beam radiation, because percent canopy
62

openness is a better predictor of seedling / sapling performance (Kobe and Hogarth 2007,
Kobe and Vriesendorp, in review).
Because the 2000-2006 images were analyzed differently, measurements from
these years were disproportionately higher in canopy openness compared to later years.
Thus, canopy openness for individual quadrats was normalized across years (i.e.
proportionately adjusted to result in the same mean percent canopy openness for each
year). Because there were strong correlations of canopy openness through time (transect
1,

between measurements = 0.40; transect 2,

= 0.40; transect 5,

= 0.64; transect 3,

= 0.54; transect 4,

= 0.36), I approximated the light environment experienced by each

seedling by calculating mean canopy openness over each individual‟s lifespan. I
assumed that a single measurement of percent canopy openness applied to half of the
interval between the preceding and subsequent measurements.
Along each of the five, 200 m transects, three soil subsamples from 16, 50 and 83
2

cm along the central axis of each 1 m seedling quadrat (n=200 per transect, n=1000 in
total) were taken with a corer (2 cm diameter) in the upper 20 cm of mineral soil in June
2008. Soil subsamples were composited, homogenized, air-dried for one week and
shipped to Michigan State University for analysis. An array of soil resources were
-

+

measured including nitrate (NO3 ), ammonium (NH4 ), total nitrogen (total N), potential
-

+

+

net N mineralization rate (Nmin), phosphate (PO4 ), potassium (K ), calcium (Ca ), and
+

magnesium (Mg ).
-

+

Plant available inorganic N (NO3 and NH4 ) pools were extracted with
potassium chloride and analyzed colorimetrically with an absorbance microplate reader
63

(ELx808 Absorbance Microplate Reader, BioTek Instruments, Inc, Winooski, VT). I
used an ammonium salicylate and ammonium cyanurate colorimetric method to detect
+

NH4 (Sinsabaugh et al. 2000) and vanadium (III), sulfanilamide and N-(1-naphthyl)-

ethylenediamine dihydrochloride (NED) to colorimetrically quantify NO3 (Doane and
Horwath 2003). I calculated Nmin as the difference between final and initial pools of
-

inorganic N after a 28 day incubation period. However, Nmin was correlated with NO3
(P < 0.001, r = -0.36) and led to similar species‟ growth responses to resources as NO3

-

+

and NH4 . Some Nmin rates also were negative. To avoid redundant independent
-

+

variables and multicollinearity, I used initial NO3 and NH4 pools in the analyses. Total
soil N, which included both organic and inorganic N forms, was also measured via the
dry combustion method using an Elemental Combustion CHNS-O analyzer (ECS 4010,
Costech Analytical Technologies, Valencia, CA).
+

+

+

Phosphate, K , Ca , and Mg ions were extracted with Mehlich III solution, a
multi-element extractant (Monterroso et al. 1999) for acidic, highly weathered soils, such
+

+

as those found at La Selva (Mehlich 1984). Potassium, Ca , and Mg were analyzed
with an Optima 2100DV ICP Optical Emission Spectrometer (Perkin-Elmer, Shelton,
-

CT). Since previous studies found colorimetrically analyzed PO4 correlated more
-

closely to “plant available” P (Pittman et al. 2005), PO4 was analyzed colorimetrically
with a reagent solution containing acid molybdate, antimony potassium tartrate, 70%
sulfuric acid, and absorbic acid (Frank et al. 1998). Total P was also analyzed (ICP

64

-

analyzed P; Hylander et al. 1995), but since total P and PO4 were correlated, only PO4
+

-

+

was used to avoid multicollinearity in estimating seedling growth. Extractable K , Ca ,
+

and Mg also were highly correlated; these elements were combined into a composite
metric of sum of base cations expressed in charge equivalents (SBC).
Because soil texture can potentially alter nutrient availability (Neill et al 1997)
and influence drainage, water-holding capacity, and species composition (Sollins 1998), I
estimated soil texture as the percent sand, silt and clay in the soil using the hydrometer
texture classification method. However, since I did not find differences within or across
sites, soil texture was not likely to influence seedling growth, and thus was not
considered as a factor.

Data analyses
Across all five sites, the entire dataset encompassed 522 woody species (including
lianas and canopy, subcanopy, and understory trees) and 14,000 individuals. Seedlings
were included if they were alive for at least two censuses so that height growth over an
interval could be calculated (i.e., ∆Height = Heightfinal – Heightinitial). Twelve species
contained 50% of the individuals, and 5 of those species (Colubrina spinosa,
Pentaclethra macroloba, Coussarea hondensis, Parathesis trichogyne, and Welfia regia)
had over 500 seedlings and made up 33% of the individuals. C. spinosa had the most
individuals (n=1432) and was more abundant than P. macroloba, the most common adult
species in La Selva (Hartshorn 1983).

65

Among the 522 potential species, only 95 species (represented by 12,800
individuals) had sample sizes of > 20 individuals; one of the 95 species was excluded
from analyses because all individuals were found in only two quadrats. In general,
seedlings of a given species were broadly distributed across transects and quadrats (also
see Kobe and Vriesendorp 2009). Species with the most clumped spatial distribution
occupied at least 4 seedling quadrats in 2 transects. Thus, I tested for spatial dependence
of seedlings with semivariograms of residual growth variance. For most species, the
lowest model semivariance occurred at a lag distance other than zero, meaning that
seedlings located in the same plot or in close proximity were not more similar in residual
growth variance than seedlings located farther away. The lack of spatial structure in
residual growth likely was due to the generally good fit of the models in which spatial
variation in growth was accounted for in the resource measurements and models. Thus,
seedlings were treated as independent units.
Measurements of Heightfinal were truncated at September 2009, the last available
seedling census. Because a decrease in seedling height can signal the start of the death
process (and often was), I determined Heightfinal as the last census at which ∆Height for
the most recent interval was zero or positive. Seedling growth can be characterized as a
compound interest formula:
Heightfinal = Heightinitial (1 + R)

lifespan

(equation1)

In this equation, R is the growth rate and is specified as a linear function of
-

+

-

resources (irradiance, NO3 , NH4 , total N, PO4 , and/or SBC). Seedling growth rate
may slow over time due to exhaustion of seed reserves, the accumulation of respiring

66

non-photosynthetic tissue (e.g. MacFarlane and Kobe 2005), or cumulative effects of
natural enemies. I tested whether growth rate declined over time by estimating an
exponent () on lifespan:
(equation 2)
When  = 1, compounding of growth rate (R) is constant over seedlings of different
lifespans;  < 1 indicates a diminishing while  > 1 indicates increasing compounding of
R for longer-lived seedlings.
I expected growth rate (R) to increase with increasing irradiance and soil
resources. Seedling growth often is asymptotically related to irradiance, especially across
broad ranges of irradiance (e.g. Pacala et al. 1994, Kobe 2006). However the range of
canopy openness encountered in this study was limited (0 to 6.72%), so I used linear
models to characterize the relationship between growth and irradiance.
Models were fit using maximum likelihood based non-linear regression in Rproject computing software (http://www.r-project.org). The best models were chosen
based upon 1) lowest Akaike's Information Criterion (AIC) with a minimum AIC
difference of at least two, 2) parameter estimates that were significantly different from
2

zero (or unity for ), and 3) model fit as assessed by r values (calculated as the square of
the Pearson correlation coefficient between observed and predicted final heights). For
each species, 10% of the seedlings were randomly selected as a holdout set (i.e. not used
for model calibration) for model validation. For each species, the best fit model was used
2

to predict final height for hold-out set seedlings, and r values were calculated as above.
To express resource effects on a comparable scale, potential influence of a
resource was calculated as the product of the maximum observed resource level for a
67

species and the corresponding resource parameter estimate for that species. The absolute
value of each maximum effect was compared across resources. To estimate the rank
importance of soil nutrients and compare their effects on seedling growth with irradiance,
resources effects were considered significantly different (P < 0.05) when their maximum
effects had < 29% overlap in 95% support (Austin and Hux 2002).
I characterized species‟ shade tolerance as the probability of seedling survival at
1% full sun and zero conspecific seedling density, calculated from mortality models that
were calibrated with survival data from the same sites (Kobe and Vriesendorp, in
review). Since shade-tolerant species generally have larger seed size (total seed mass),
resulting in a proportionally larger initial seedling size than light-demanding species (e.g.
Osunkoya et al. 1994), I obtained seed sizes for 31 of the species (KEW Seed
Information Database, http://data.kew.org/sid/sidsearch.html) and calculated the mean
initial seedling height for each species. To assess negative density dependence (Janzen
1970, Connell 1971), I tested for effects of both con- and heterospecific seedling density
on growth for each species; neither con- nor heterospecific seedling densities were
correlated with seedling growth rate for any species (mean r = -0.01, r = 0.02,
respectively).

RESULTS
Summary
In this broad survey of species and resources, I found support for both soil
nutrients and irradiance correlations with seedling growth in a wet tropical forest
understory (Appendix A). Across all species, a mean of 58% of the variation in growth

68

2

(median r =0.70) was explained by simple size- and resource-based growth models (e.g.
Fig. 3.1). On average, resources explained ~25% of the variation in growth (mean of
2

differences in r values between models containing initial height and resources versus
only initial height) and initial height ~33% of growth variation. The holdout dataset
2

provided strong validation of the growth models (mean r = 0.69 across species). Of the
94 species with >20 individuals (and sufficient variation in growth and resources), three
species had several single resource models that contained significant parameters with
2

similar AIC support and r values. Growth of two species was influenced by outliers,
which were excluded for subsequent analyses.

Soil resources and irradiance influenced seedling growth
Contrary to expectation, irradiance was not the most prevalent or strongest
correlate of seedling growth. With regards to prevalence, growth of 48% of the species
was correlated with only soil nutrients, and an additional 45% were related to both soil
nutrients and irradiance (see Appendix A). In 7% of species, growth was correlated with
only irradiance. Among soil nutrients, total N was a significant predictor of growth in
-

+

almost half (47%) of the species, SBC in 39% of species, NO3 in 32%, and NH4 in
34%. Phosphate, typically assumed to be the most limiting nutrient in tropical systems,
was least prevalent as a correlate of growth (29%). The range of resources for each
species did not influence whether that factor was empirically supported (for irradiance, z
-

+

-

= 1.47, P = 0.14; for NO3 , z = 1.11, P = 0.27; for NH4 , z = 0.80, P = 0.42; for PO4 , z =

69

0.25, P = 0.81; for SBC, z = -0.40, P = 0.69), with the exception of total N (z = -2.13, P =
0.03). Positive growth-resource correlations occurred in 45% of species for irradiance,
-

+

38% for total N, 30% for SBC, 23 and 22% for NO3 and NH4 , respectively, and 18%
-

for PO4 ; ~6-12% of species‟ resource-growth relationships were negative (Fig. 3.2).
-

Contrary to expectation, most soil nutrients (with the exception of NO3 , t = -2.06,
df = 77.77, P = 0.04) had similar magnitudes of effect on seedling growth as irradiance
(Fig. 3.3). Across the range of measured resources among species, irradiance‟s mean
potential effect on growth was 0.10 mm/month. Mean potential magnitudes of effect for
-

soil nutrients were 0.11 mm/ month for total N, 0.09 mm/month for PO4 , 0.08
+

-

mm/month for SBC, 0.08 mm/month for NH4 , and 0.06 mm/month for NO3 (see
Appendix B). Among species for which both irradiance and a soil nutrient(s) were
correlated with growth, irradiance had a greater effect on growth in about half of the
species, whereas the effects of a soil nutrient were greater than or indistinguishable from
irradiance in the other half of species (Table 3.1, e.g. Fig. 3.4A-C). Among species for
which growth was correlated with soil nutrients, the rank importance of nutrients was
-

first SBC, followed by PO4 and N (e.g. Fig. 3.4D-F); SBC affected 42-59% of species
-

more than any other nutrient, PO4 had a greater effect on 0-39% of species, and total N,
-

+

NO3 and NH4 affected 7-43% of species more than other nutrients (Table 3.2). All N
-

forms affected species similarly and had analogous effects as PO4 on species (Table 3.2).

70

Lifespan effects on compounding of growth
Of the 94 species, 24 species (27%) experienced diminishing compounding of
70
growth ( < 1) and 7 species (8%) experienced accelerating growth ( > 1) with
increasing lifespan. Species estimates of  were weakly correlated with initial seedling
size (F1,92 = 4.36, P = 0.04, r = 0.21) but not shade tolerance (F1,89 = 0.47, P = 0.49, r =
0.07).

Species growth responses to resources covaried with shade tolerance
Across species, seed size was strongly correlated with mean initial seedling height
(F1,31 = 50.17, P < 0.001, r = 0.79). Since I had seed size for only one-third of species, I
used species mean initial height as a surrogate for seed size. As expected, species of
smaller initial seedling height tended be less shade-tolerant (F1,89 = 21.66, P < 0.001, r =
-0.44).
Across species, magnitude of growth responses to resources were negatively
correlated with shade tolerance but not with initial seedling size, with the exception of
-

larger seedlings affected less by NO3 (Appendix C). Shade tolerance was negatively
-

+

-

correlated with species‟ responses to irradiance, SBC, NO3 , NH4 , and PO4 (Fig. 3.5AE). Species‟ growth responsiveness to total N was similar for light-demanding and
shade-tolerant species (Fig. 3.5F).

71

DISCUSSION
In wet tropical forest understories, both irradiance and growth rates of seedlings
are generally very low (Clark and Clark 1992). Despite low light levels in this study, the
results suggest that soil nutrients may be equally important as irradiance for understory
seedling growth, which was frequently and strongly correlated with soil nutrients.
Species‟ growth responsiveness to many soil nutrients was negatively correlated with
shade tolerance, suggesting that faster growth by light-demanding species created a
higher demand for soil nutrients. This study highlights the potential importance of both
irradiance and soil nutrients to tropical seedling growth in low light and challenges the
idea that soil nutrients are not important in the light-limited understory.
Contrary to H1, irradiance and soil nutrient correlations with seedling growth had
similar prevalence and magnitudes of influence. Growth of 93% of species correlated
with at least one soil nutrient and many soil nutrients had similar potential magnitudes of
effect as irradiance. Seedlings growing in very low light levels might benefit from
increased nutrient availability through increasing light capture (Fahey et al. 1998).
Nitrogen is a key component of chlorophyll for light capture and Rubisco for initial CO2
fixation (Evans 1989); thus higher N acquisition by seedlings could lead to increased
carbon gain even under light-limited conditions (Walters and Reich 1997). Base cations
+

also were correlated with seedling growth; Mg is the central atom in the chlorophyll
molecule (Shabala and Hariadi 2005), and even under low light conditions (<1% PAR),
+

seedling growth increased in response to Mg additions (Burslem 1993). Therefore,
irradiance and soil nutrients may be simultaneously limiting to seedling growth in the

72

+

forest undestory and increased Mg and N may assist growth through improved light
capture.
It was surprising that growth correlations with light were not stronger and more
prevalent as numerous studies have shown the importance of increased light availability
to seedling growth (Augsburger 1984, Poorter 1999). The unexpectedly weak growthirradiance correlations could have arisen from the limited range of existing light
conditions but which nevertheless were representative of variation across 1 km of forest
understory over 9 years. In contrast to other studies (e.g. Kobe 1999), I did not
intentionally stratify variation in irradiance but wanted to represent typical conditions
experienced by seedlings. I offer the caveat that infrequent light measurements early in
the study could have partly obscured growth-light relationships and thus underestimated
irradiance effects. However, strong correlations of irradiance through time suggest that
low intensity of light measurements early in study was not a strong influence.
Contrary to H2, base cations (SBC) had greater prevalence and greater
magnitudes of effect than P, which is consistent with accumulating empirical evidence
about the importance of calcium and magnesium (see Lawrence 2003). For example,
+

Antidesma cuspidatum seedling growth responded to Mg additions but not to any other
macronutrient added singly, including P and N (Burslem et al. 1996). Base cation levels
also were found to more strongly correlate with tree species distributions than either P or
N (John et al. 2007). Base cations are more mobile than P and could be depleted more
rapidly, especially in tropical ecosystems where high precipitation levels increase nutrient
leaching (Chadwick et al. 1999). Base cations could influence seedling growth through:
+

+

the structural role of Mg in chlorophyll, Ca as a constituent of woody tissue (Baribault
73

+

et al. 2010) and correlate of photosynthetic rates (Reich et al. 1995), and role of K and
+

Ca in signal transduction and nutrient uptake (Stevens et al. 1993). The availability of
base cations also could limit mycorrhizal associated tropical tree seedling growth
(Denslow et al. 1987, Burslem et al. 1994, Burslem et al. 1995) through an unknown
mechanism. In temperate forest, base cation amendments increased mycorrhizal
colonization and photosynthesis (St. Clair and Lynch 2005).
Even though N is generally more abundant than P in tropical forests (Hedin
2009), growth of about 50% of species were correlated with total N and another third
-

+

-

with inorganic N (NO3 and NH4 ), while growth correlations with PO4 were much less
prevalent. Similarly, N fertilization increased tropical plant growth (Lewis and Tanner
2000). One possible explanation is that N may be used to alleviate P limitation through
extracellular phosphatase production (Treseder and Vitousek 2001). Phosphatases
hydrolyze the ester–phosphate bonds in soil organic P and release the phosphate into soil
solution for uptake by nearby roots or microbes (Pant and Warman 2000). Phosphatases
require a significant investment of N (N concentrations between 8% and 32%).
Supporting this interpretation, N fertilization increased phosphatase activity in temperate
soils under Norway spruce (Clarholm and Rosengren-Brinck 1995), while increased P
availability reduced extracellular acid phosphatase activity (McLachlan 1980).
-

-

+

Instantaneous pools of available P (PO4 ) and inorganic N (NO3 and NH4 )
might have been too dynamic to provide an integrated estimate of long-term availability;
-

-

+

thus, seedling growth responses to PO4 , NO3 , and NH4 may have been
underestimated. Under limitation, P would be quickly assimilated (Vandecar et al. 2009),
74

-

and this study‟s one time measurement of PO4 may not have accounted for temporal
-

heterogeneity Seedling growth correlated more with total N than inorganic N (NO3 and
+

NH4 ) perhaps because total N better approximates long-term N availability or provides
-

an integrated measure of fertility. Since total P was highly correlated to PO4 , it was not
included in predicting seedling growth, but similar to total N, total P may have provided a
better estimate of P fertility.
Consistent with H3, the effects of most resources (except total N) were negatively
correlated with shade tolerance. Of the 52 species for which seedling growth correlated
with light availability, irradiance had greater effects on shade intolerant species. The
growth of six shade tolerant species (Borojoa panamensis, Byrsonima arthropoda,
Coussarea talamancana, Doliocarpus multiflorus, Rauvolfia purpurascens, and
Tetragastris panamensis) was negatively correlated to irradiance. Shade-tolerant species
may grow poorly under high irradiance due to inhibition of photosynthetic capacity
(Krause et al. 2007). However, it is unlikely that irradiance levels encountered in the
present study would have been high enough for photo inhibition. Nevertheless, this
study‟s results are consistent with a broader species trade-off between low-resource
survival and high resource-growth (Kobe et al.1995, Kobe 1999, Russo et al. 2008).
Of the 92 species for which growth correlated with a soil nutrient(s), effects of
-

SBC, inorganic N, and PO4 affected light-demanding species more than shade-tolerant
species. This result supports that species‟ demand for nutrients increases with average
species growth, which is inversely correlated with low-light survival (shade tolerance).
The inverse relationship between species shade tolerance and magnitude of growth
75

responses to nutrients has not always been supported (Denslow et al. 1987, Lawrence et
al. 2001). However, most nutrient studies have included only P and inorganic N; this
study‟s examination of a broader array of nutrients and nutrient forms suggests
relationships between shade tolerance /growth and growth responsiveness to resources.

Conclusions
As seedlings represent the next generation of forest individuals, understanding
and predicting seedling growth is important to understanding forest dynamics and
regeneration. Although irradiance has been regarded as the primary determinant of
seedling growth in wet tropical forests, growth correlations with soil nutrients were
equally prevalent and as strong as growth correlations with irradiance. Seedling growth
in these 94 species was generally affected more by SBC than any other soil nutrient,
including P which is widely assumed to be most limiting. Furthermore, species‟
responses to resources may arise from covariance in life-history traits between shade
tolerance and the demand for soil nutrients to achieve faster growth. Both irradiance and
soil nutrients likely affect seedling growth under the low light conditions of the forest
understory, and, therefore, must be simultaneously examined across a broad array of
species with different life-history traits in order to begin to understand potential
mechanisms of species coexistence and diversity in wet tropical forests.

76

TABLE 3.1. Resource effect comparisons between irradiance and soil nutrients across
species; percentages denote the portion of species that contained both irradiance and a
particular soil nutrient. Resources effects are considered significantly different when
P < 0.05 and there is < 29% overlap in 95% support.

Comparison
Greater irradiance effect
Greater soil nutrient effect
Effects indistinguishable

SBC
35
35
30

PO4
39
28
33

77

-

Resource effects (%)
Total N
NO3
45
54
5
8
50
38

+

NH4
55
5
40

TABLE 3.2. Resource effect comparisons among soil nutrients across species;
percentages denote the portion of species that contained two particular soil nutrients
(e.g. SBC and PO4 , or SBC and total N). Resources effects are considered significantly
different when P < 0.05 and there is < 29% overlap in 95% support.

Resource effects (%)
-

-

+

Comparison
Greater SBC effect
Lesser SBC effect
Greater PO4 effect

SBC
―
―
0

PO4
47
0
―

Total N
42
25
18

NO3
47
18
21

Lesser PO4 effect
Greater Total N effect
Lesser Total N effect
Greater NO3 effect

47
25
42
18

―
27
18
43

27
―
―
27

43
33
27
―

11
8
33
33

Lesser NO3 effect

47

21

33

―

7

Greater NH4 effect

12

11

33

7

―

Lesser NH4 effect

59

39

8

33

―

-

-

+

+

78

NH4
59
12
39

2

200
150
0

0
50 100 150 200
Observed final height (mm)

0

150
100

2

r = 0.58

40

60 80 100

(F) Psychotria sp. 3

20

400
200
0

0
50
100
150
Observed final height (mm)

0

r = 0.65

Predicted final height (mm)

2

600

800

0
200 400 600 800
Observed final height (mm)
(E) Virola koschnyi

2

r = 0.82

50

200

400

600

r = 0.85

(D) Aegiphila sp.

0

2

Predicted final height (mm)

(C) Heteropterys laurifolia
Predicted final height (mm)

2

r = 0.49

100

1000

0
1000
2000
3000
Observed final height (mm)

Predicted final height (mm)

(B) Colubrina spinosa

50

r = 0.89

Predicted final height (mm)

2

2000

3000

(A) Hampea appendiculata

0

Predicted final height (mm)

FIGURE 3.1. Model fit of representative species as assessed by r values; 95%
confidence intervals and prediction intervals are represented by dashed and solid lines,
respectively.

0
200
400
600
Observed final height (mm)

0 20 40 60 80 100
Observed final height (mm)

79

FIGURE 3.2. Proportion of species for which each resource is correlated with growth.

Proportion of cases (%)

70

Negative

60

Non-significant

50

Positive

40
30
20
10
0

Resource

80

FIGURE 3.3. Mean absolute values of resource effects on seedling growth across species.
Error bars denote standard error; letters represent significantly different pairwise
comparisons at P=0.05.

Maximum absolute resource effects
(mm/month)

0.14
0.12

A
A

AB

0.10

AB

AB
B

0.08

0.06
0.04
0.02
0.00
Irradiance

Total N

PO4

SBC

Resources

81

NO3

NH4

FIGURE 3.4. Representative models of height growth (mm/month) with respect to
irradiance and soil nutrients.

82

FIGURE 3.5. Species‟ responsiveness to irradiance and soil nutrients across a continuum
of shade tolerance (for (A) irradiance, F1,48 = 12.30, P = 0.001, r = 0.45; for (B) SBC,
-

F1,35 = 18.79, P = 0.0001, r = 0.59; for (C) NO3 , F1,28 = 5.13, P = 0.03, r = 0.39; for
+

-

(D) NH4 , F1,31 = 4.84, P = 0.04, r = 0.37; for (E) PO4 , F1,27 = 3.42, P = 0.08, r = 0.34;

0

0

10

10

20

20

30

30

40

40

(A) Irradiance effects vs. shade tolerance (B) SBC effects vs. shade tolerance

0.0
0.1
0.2
0.3
0.4
Species irradiance effects (mm/mo)

0.0
0.10
0.20
0.30
Species SBC effects (mm/mo)

-

+

(C) NO3 effects vs. shade tolerance

0

0

10

10

20

20

30

30

40

40

(D) NO4 effects vs. shade tolerance

0.0 0.1 0.2 0.3 0.4 0.5
+
Species NO4 effects (mm/mo)

0.0
0.05 0.10 0.15 0.20
Species NO3 effects (mm/mo)
40
10

10

20

20

30

30

40

-

(E) PO4 effects vs. shade tolerance (F) Total N effects vs. shade tolerance

0

0

Species mortality at 1% full sun (%) Species mortality at 1% full sun (%) Species mortality at 1% full sun (%)

for (F) total N, F1,44 = 0.17, P = 0.07, r = 0.68).

0.0 0.1 0.2 0.3 0.4 0.5
Species PO4 effects (mm/mo)

0.0 0.1 0.2 0.3 0.4 0.5
Species Total N effects (mm/mo)
83

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90

CHAPTER IV
Conclusions

Understanding soil resource availability and heterogeneity within a system is
critical to determining the factors that influence plant performance. In this research, soil
type was weakly correlated with resource availability. Inorganic nitrogen (inorganic N)
-

levels were lower on the younger sites (alluvial) and phosphate (PO4 ), total phosphorus
+

(total P), and potassium (K ) were lower on older sites (volcanic), which is consistent
with ideas of global patterns of nutrient limitation. Over half of the total variance in most
soil resources was spatially structured, indicating a high degree of spatial autocorrelation,
and the spatial structure of resources appeared to be based upon soil type. Adult tree
functional groups did not create patches of similar resource availability and impose of
pattern of spatial heterogeneity in this wet tropical forest. Patch size (defined as points
within a distance having >50% correlation) were 10-40 m for total C and total N and
-

3-16 m for base cations, P, PO4 and inorganic N.
In general, resource patch sizes were larger than the mean root length of most
tropical seedlings, so that seedlings would find a range of favorable to unfavorable
habitats. The tremendous fine scale variability of these soil resources calls into question
the use of site-level mean nutrient levels to approximate seedling nutrient availability.
Thus, in order to capture the variability in resources that seedling are actually
experiencing, it is especially important to sample resources according to the type of

91

nutrient cycle and intrinsic physical and chemical properties to make sure that the
perceived resource environment, or patch size, is estimated appropriately.
As seedlings represent the next generation of forest individuals, understanding
and predicting seedling growth is important to future forest dynamics and regeneration.
Through modeling 94 species, we were able to observe the overall resource trends
affecting seedling growth in the light-limited understory. Based on the prevalence and
the strength of soil nutrient correlations with growth, soil nutrients appeared to be equally
important as irradiance to seedling growth in the forest understory. Across species,
growth was correlated with both irradiance and soil nutrients in about 45% of species, but
an additional 48% of species associated growth with only soil nutrients. Irradiance was a
significant predictor of growth in 52% of the species, total N in 47%, SBC in 39%, NO3
+

-

-

in 32%, NH4 in 34%, and PO4 , commonly considered the main limiting nutrient in
many tropical systems, was significant in only in 29%. Contrary to expectation, the
realized magnitudes of growth effects, assessed as the maximum growth response for
each species, were similar for irradiance and most soil nutrients, and among species
whose growth was correlated with soil resources, the rank importance of nutrient effects
-

-

+

was first SBC, followed by PO4 and N (total N, NO3 and NH4 ). Additionally, species‟
growth responsiveness (i.e. magnitudes of effect) to irradiance, SBC, inorganic N, and
-

PO4 was negatively correlated with species‟ shade tolerance, assessed as survival under
1% full sun.
In this broad survey of species and soil resources, the nearly ubiquitous effects of
soil nutrients on seedling growth challenges the idea that soil resources are less important

92

than irradiance in the light-limited understory of wet tropical forests. This research has
shown that irradiance and soil resources may both affect seedling growth and, therefore,
must be simultaneously examined across a broad array of species and at a spatial scale
commensurate with the soil environment experienced by seedlings in order to understand
their effects on forest regeneration. Based upon these findings, seedling growth
responses to resources could be used in understanding possible mechanisms of species
coexistence and diversity in a wet tropical forest and to predict species-specific
performance in ecology-based forest management and restoration.

93

APPENDICES

94

APPENDIX A

95

TABLE A1.1. Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence intervals are
denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Abuta panamensis
0.63
1.49
(0.60, 2.25)
Aegiphila sp.

0.82

2.19
(0.47, 3.99)

Anaxagorea
crassipetala

0.89

2.85
(2.29, 3.36)

Apeiba
membranacea

0.95

3.99
(3.43, 4.50)

Ardisia
fimbrillifera

0.04

Ardisia
nigropunctata

0.53

Ardisia sp.

0.74

1.12
(0.39, 1.81)

Arisolochia sp.

0.55

6.44
(6.12, 6.78)

Asterogyne
martiana

0.35

0.12
(0.07, 0.16)

0.53
0.94
(0.004, 0.94) (0.03, 1.56)
0.70
(0.35, 1.04)

-12.30
(-17.70, -6.14)

-0.38
-2.88
(-0.69, -0.004) (-4.63, -1.23)
5.73
(3.80, 7.37)

-2.94
(-4.62, -1.00)

0.49
(0.27, 0.65)
67.0
(55.3, 77.6)

11.3
(3.43, 16.6)
6.43
(5.90, 6.90)

96

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Bauhinia
0.93
0.04
2.10
1.62
-8.47
guianensis
(0.01, 0.07)
(0.64, 3.33) (0.69, 2.58) (-11.7, -5.34)
Bignoniaceae morph 0.82
Model 1

1.67
(1.37, 1.91)

Bignoniaceae morph 0.80
Model 2
Borojoa
panamensis

0.13

2.34
(1.91, 2.70)
-34.7
(-44.3, -25.6)

Brosimum lactescens 0.98

1.15
(0.80, 1.52)

Byrosonima
arthropoda

3.77
(0.89, 6.41)

-0.12
(-0.22, -0.015)

0.25

Callichlamys latifolia 0.28

Calyptrogyne
ghiesbreghtiana

0.48

Capparis pittieri

0.47

0.59
0.48, 0.69)

7.28
(6.58, 8.03)

9.85
(7.44, 12.4)

-0.42
-1.58
-1.33
(-0.83, -0.06) (-1.91, -1.27) (-1.82, -0.90)
6.30
(5.62, 6.97)

2.07
(1.89, 2.26)

1.09
(0.85, 1.32)
9.89
(7.77, 11.6)

0.61
(0.27, 0.88)

0.37
(0.15, 0.54)

97

0.77
(0.12, 1.28)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Casearia corymbosa 0.83
2.72
(1.51, 3.82)
Cassipourea
elliptica

0.34

2.03
(1.45, 2.48)

Castilla elastica

0.92

2.92
(2.34, 3.49)

3.63
(0.64, 6.40)

Colubrina spinosa

0.49

0.86
(0.13, 1.56)

3.08
(2.12, 4.02)

Cordia alliodora

0.32

Cordia bicolor

0.09

-2.33
(-3.67, -0.83)
1.53
(1.30, 1.77)

6.25
(0.28, 12.2)
24.1
(10.5, 37.2)

Coussarea hondensis 0.06

44.8
(3.34, 85.2)

-34.0
(-60.4, -11.2)
0.17
(0.08, 0.24)

Coussarea
talamancana

0.92

-2.83
-0.05
(-4.65, -0.96) (-0.08, -0.01)

Cryosophila
warscewiczii

0.98

1.82
(1.17, 2.35)

4.34
(1.82, 6.52)

2.33
(1.86, 2.78)
2.34
(1.44, 3.07)

98

2.57
(1.41, 3.70)

-7.02
(-8.86, -5.05)

-1.64
-1.22
5.62
(-2.63, -0.63) (-1.71, -0.72) (2.11, 5.29)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Dendropanax
0.45
6.03
arboreus
(3.80, 8.07)
Dicranostyles ampla 0.85

1.38
(0.96, 1.80)

0.36
(0.04, 0.66)

Doliocarpus
multiflorus

0.34

-1.27
(-2.35, -0.21)

2.79
(1.78, 3.72

Eugenia sp.

0.74

13.7
(8.78, 18.8)

Euterpe precatoria

0.89

3.67
(2.27, 4.85)

-14.3
(-22.9, -7.11)

-5.36
(-8.60, -2.10)

-1.24
(-1.90, -0.53)
-1.71
(-2.64, -0.78)

Faramea parvibractea 0.13

0.60
(0.23, 0.98)

Geonoma cuneata

0.13

2.49
(2.02, 3.01)

Geonoma cuneata
(without outliers)

0.00005

1.44
(0.60, 2.28)

Goethalsia meiantha 0.81

2.62
(1.62, 3.60)

2.56
(1.53, 3.70)

10.7
(1.51, 18.8)
1.66
(1.15, 2.14)
2.02
(1.48, 2.46)

1.01
(0.23, 1.79)

-3.68
(-6.24, -1.47)

1.64
(1.22, 2.06)

-4.11
(-5.53, -2.69)
46.2
(16.2, 71.8)

99

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Guarea guidonia
0.02
1.73
(0.45, 1.93)
Guatteria
amplifolia

0.80

2.29
(1.69, 2.84)

3.18
(1.42, 4.70)

Gurania sp.

0.46

Hampea
appendiculata

0.89

2.36
(2.17, 2.55)

-0.97
-0.57
0.42
(-1.36, -0.61) (-0.68, -0.46) (0.24, 0.59)

Hernandia
didymantha

0.76

1.28
(1.18, 1.38)

1.09
(0.96, 1.19)

Herrania purpurea

0.23

Heteropterys
laurifolia

0.85

Inga alba

0.72

Inga pezizifera

0.68

3.53
(1.49, 5.18)
7.23
(6.10, 8.38)

11.2
(7.81, 13.4)
2.55
(2.13, 3.00)

-4.03
6.42
(-6.12, -2.03) (5.03, 7.76)

-6.29
(-8.58, -3.97)
0.78
(0.63, 0.90)

0.66
(0.46, 0.83)

0.60
(0.39, 0.79)

100

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Inga thibaudiana
0.84
0.47
2.64
(0.56, 0.76)
(0.48, 4.48)
Iriartea deltoidea

0.33

Jubelina wilburii

0.54

Lacmellea
panamensis

0.78

Laetia procera

0.84

Machaerium
seemannii

1.37
(0.71, 1.82)

0.87

0.76
(0.02, 1.26)
1.51
(1.26, 1.74)

0.13

Mendoncia sp.

0.10

0.46
(0.02, 0.89)
-26.5
18.0
(-32.4, -20.5) (13.3, 21.8)

85.8
(66.3, 102.0)
0.91
(0.51, 1.25)

Maripa nicaraguensis 0.72

Mendoncia tonduzii

1.74
(1.19, 2.27)

-1.99
2.52
(-3.22, -0.73) (1.90, 3.13)
3.04
(1.95, 4.06)

2.05
(1.39, 2.63)

0.15
(0.008, 0.27)

1.51
(0.96, 2.05)

2.77
(0.88, 4.54)

1.13
(0.09, 2.07)

101

-4.06
(-7.30, -0.86)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Nectandra sp.
0.33
1.24
(0.46, 1.83)
Neea elegans

0.80

Neea sp.

0.15

Odontadenia
macrantha

0.91

Paragonia pyramidata 0.76
Model 1

14.7
(11.6, 17.3)
9.90
(4.98, 14.1)
15.4
(3.74, 17.6)

-27.1
29.9
(-48.8, -5.45) (7.47, 33.9)

2.02
(1.28, 2.63)

Paragonia pyramidata 0.77
Model 2

1.51
(0.96, 1.96)

Paragonia pyramidata 0.77
Model 3

7.57
(4.87, 9.82)

Parathesis trichogyne 0.25

Paullinia sp. A

0.93

0.04
(0.02, 0.07)

-5.01
5.22
(-7.06, -3.08) (4.00, 6.34)

2.09
(2.06, 2.11)

-7.64
(-10.2, -5.05)
-0.39
(-0.56, -0.25)

102

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Paullinia fibrigera 0.0008
1.20
(0.82, 1.55)
Pentaclethra
macroloba

0.67

0.90
(0.69, 1.10)

0.09
(0.04, 0.13)

-0.43
0.42
(-0.65, -0.25) (0.26, 0.58)

0.37
(0.22, 0.51)

-1.58
(-2.26, -0.93)

Perebea hispidula

0.79

3.92
(0.49, 5.19)

8.64
(2.71, 13.7)

-8.77
(-14.9, -2.20)

-2.80
13.4
(-5.15, -0.22) (4.98, 22.1)

Pholidostachys
pulchra

0.01

1.36
(6.45, 19.8)

1.06
(0.03, 1.56)

4.61
(3.15, 6.07)

Pinzona coriacea

0.68

14.5
(12.4, 16.3)

Plukenetia stipellata 0.78

10.4
(8.79, 13.5)

-24.5
(-39.7, -16.5)

Pourouma bicolor

0.90

2.73
(2.59, 2.89)

0.82
(0.28, 1.40)

Pourouma minor

0.44

1.61
(0.93, 2.19)

Prestoea decurrens

0.01

7.17
(6.08, 8.48)

48.2
(37.8, 58.7)
-1.74
0.88
(-2.11, -1.39) (0.61, 1.14)

3.01
(1.94, 4.18)

-1.68
1.46
(-3.32, -0.30) (0.58, 2.48)
12.5
(11.1, 13.9)

103

-10.56
-3.06
12.9
(-12.30, -9.03) (-4.07, -2.00) (3.32, 19.9)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Prestoea decurrens 0.0003
2.13
(without outliers)
(1.62, 2.58)
Protium confusum
Model 1

0.16

Protium confusum
Model 2

0.19

Protium confusum
Model 3

1.30
(0.05, 2.09)

0.17

1.82
(0.21, 3.43)
0.79
(0.13, 1.45)

Protium panamense 0.96

1.29
(1.25, 1.32)

0.12
(0.11, 0.12)

Psychotria
panamensis

0.38

3.39
(1.93, 4.62)

0.35
(0.14, 0.50)

Psychotria sp.1

0.16

10.7
(2.90, 16.9)

Psychotria sp.2

0.96

Psychotria sp.3

0.58

1.58
(0.74, 2.34)

9.31
(8.97, 9.63)
6.60
(4.73, 8.22)

104

3.04
(1.44, 4.47)

-4.60
(-8.63, -0.89)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Pterocarpus rohrii 0.32
1.56
(0.90, 2.07)
Randia genipoides

0.87

3.18
(2.35, 3.90)

Rauvolfia
purpurascens

0.78

-2.75
0.66
(-5.14, -3.54) (0.43, 0.90)

Richeria obovata

0.12

Rinorea deflexiflora 0.08

1.09
(0.20, 1.87)

-1.30
(-1.66, -0.90)
2.83
(2.07, 3.59)

-2.66
(-3.76, -1.64)

1.22
(0.77, 1.66)

0.76
(0.55, 0.94)

1.84
(0.74, 2.81)
1.44
(1.29, 1.58)

-2.79
(-3.91, -1.68)

Securidaca
diversifolia

0.41

1.95
(1.27, 2.54)

Serjania sp.

0.86

-3.50
(-6.83, -0.33)

Simarouba amara

0.94

1.45
(1.04, 1.87)

Socratea exorrhiza

0.85

1.57
(1.08, 1.99)

3.67
(1.89, 5.45)
-3.33
3.01
(-4.42, -2.27) (2.40, 3.59)
-0.18
(-0.31, -0.05)

105

-4.55
(-8.00, -1.17)
1.87
(1.21, 2.49)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Synechanthus
0.99
8.38
warscewiczianus
(8.28, 8.47)
Tapiria guianensis

0.05

0.87
(0.42, 1.20)

Tetragastris
panamensis

0.88

Trophis racemosa

0.93

Unonopsis pittieri

0.66

Virola koschnyi

0.65

Virola sebifera

0.94

2.21
(1.60, 2.74)

-0.68
-0.78
0.45
(-1.23, -0.18) (-1.08, -0.45) (0.19, 0.72)

Vitex cooperi

0.99

0.58
(0.30, 0.87)

5.67
(4.76, 6.54)

-2.78
(-3.79, -1.76)

-4.81
4.23
(-6.81, -2.83) (3.18, 5.25)

2.71
(0.50, 4.81)
3.31
(2.06, 4.48)

6.89
(0.59, 13.0)

5.26
(1.08, 9.12)
-3.34
1.86
(-4.67, -1.89) (1.32, 2.36)

106

5.56
(3.00, 7.88)

TABLE A1.1 (cont.). Resource parameters (in mm/month per unit resource) of seedling growth rate by species. 95% confidence
intervals are denoted by parentheses for each resource parameter.
2
+
Species
r
Irradiance total N
PO4
SBC
NO3
NH4
(10e-2)
(10e-3)
(10e-3)
(10e-4)
(10e-5)
Vochysia ferruginea 0.79
3.70
0.06
0.55
-0.76
2.57
(2.75, 4.59) (0.03, 0.09) (0.07, 1.01)
(-1.40, -0.12) (1.16, 4.00)
Welfia regia

0.08

1.07

0.05

1.58

107

APPENDIX B

108

TABLE B1.1. Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Abuta panamensis
0.21
(0.08, 0.32)
Aegiphila sp.

0.43
(0.09, 0.78)

Anaxagorea
crassipetala

1.28
(1.03, 1.52)

Apeiba membranacea

1.82
(1.57, 2.06)

Ardisia fimbrillifera

-2.11
(-3.02, -1.05)

0.22
(0.07, 0.37)

0.27
(0.13, 0.40)

0.25
(0.16, 0.34)

0.29
(0.02, 0.52)

-0.09
-0.18
(-0.17, -0.01) (-0.29, -0.08)
1.31
(0.87, 1.68)

-0.18
(-0.28, -0.06)

2.14
(1.21, 2.86)

Ardisia nigropunctata

1.03
(0.85, 1.20

Ardisia sp.

0.22
(0.07, 0.33)

Aristolochia sp.

4.33
(4.11, 4.56)

Asterogyne martiana

0.72
(0.22, 1.06)
0.65
(0.59, 0.69)

109

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Bauhinia guianensis
0.10
0.98
0.69
-0.85
(0.04, 0.18)
(0.30, 1.55) (0.29, 1.09) (-1.17, -0.53)
Bignoniaceae morph
Model 1

0.29
(0.24, 0.33)

Bignoniaceae morph
Model 2

0.22
(0.18, 0.26)

Borojoa panamensis

-3.06
(-3.90, -2.25)

Brosimum lactescens

0.44
(0.30, 0.58)

Byrsonima arthropoda

-0.06
(-0.11, -0.007)

Callichlamys latifolia

0.16
(0.13, 0.19)

Calyptrogyne ghiesbreghtiana

Capparis pittieri

1.07
(0.25, 1.81)
0.72
(0.65, 0.79)

2.38
(1.79, 2.99)

-0.07
-1.19
-0.32
(-0.13, -0.009) (-1.44, -0.95) (-0.44, -0.22)
0.80
(0.71, 0.88)

0.57
(0.52, 0.63)

0.12
(0.10, 0.15)
1.09
(0.86, 1.28)

0.14
(0.06, 0.20)

0.17
(0.07, 0.24)

110

0.25
(0.04, 0.42)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Casearia corymbosa
0.22
(0.12, 0.31)
Cassipourea elliptica

0.72
(0.51, 0.88)

Castilla elastica

1.39
(1.11, 1.66)

0.58
(0.10, 1.03)

Colubrina spinosa

0.21
(0.03, 0.38)

0.44
(0.31, 0.58)

Cordia alliodora

Cordia bicolor

-0.48
(-0.76, -0.17)
1.01
(0.86, 1.17)

1.28
(0.06, 2.50)
4.48
(1.96, 6.93)

Coussarea hondensis

1.66
(0.12, 3.15)

-3.64
(-6.47, -1.20)
0.60
(0.27, 0.82)

Coussarea talamancana

-0.43
-0.21
(-0.71, -0.15) (-0.37, -0.05)

Cryosophila warscewiczii

0.44
(0.28, 0.57)

0.49
(0.21, 0.74)

1.27
(1.02, 1.51)
0.46
(0.28, 0.60)

111

0.58
(0.32, 0.83)

-0.44
(-0.55, -0.31)

-0.67
-0.53
0.50
(-1.08, -0.26) (-0.74, -0.31) (0.19, 0.82)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Dendropanax arboreus
1.87
(1.18, 2.50)
Dicranostyles ampla

0.28
(0.19, 0.36)

0.15
(0.02, 0.27)

Doliocarpus multiflorus

-0.31
(-0.58, -0.05)

1.57
(1.01, 2.10)

Eugenia sp.

2.70
(1.74, 3.71)

Euterpe precatoria

1.10
(0.68, 1.45)

-1.42
(-2.27, -0.71)

-0.48
(-0.77, -0.19)

-0.68
(-1.04, -0.29)
-0.82
(-1.26, -0.37)

Faramea parvibractea

0.22
(0.09, 0.37)

Geonoma cuneata

0.53
(0.43, 0.64)

Geonoma cuneata
(without outlier)

0.31
(0.13, 0.48)

Goethalsia meiantha

0.92
(0.57, 1.26)

1.15
(0.69, 1.67)

0.72
(0.10, 1.26)
0.66
(0.46, 0.85)
2.22
(1.62, 2.69)

0.52
(0.12, 0.92)

-0.31
(-0.53, -0.12)

1.79
(1.33, 2.26)

-0.35
(-0.47, -0.23)
3.32
(1.16, 5.16)

112

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Guarea guidonia
3.51
(0.91, 3.93)
Guatteria amplifolia

1.05
(0.77, 1.30)

Gurania sp.

0.32
(0.14, 0.47)
0.39
(0.17, 0.58)

Hampea appendiculata

1.08
(0.99, 1.17)

-0.25
-0.37
0.14
(-0.34, -0.15) (-0.45, -0.30) (0.08, 0.19)

Hernandia didymantha

0.32
(0.30, 0.35)

0.26
(0.23, 0.28)

Herrania purpurea

Heteropterys laurifolia

0.43
(0.30, 0.52)
1.21
(1.01, 1.43)

-0.38
1.61
(-0.58, -0.19) (1.26, 1.95)

Inga alba

Inga pezizifera

0.64
(0.54, 0.74)

-0.42
(-0.57, -0.26)
0.20
(0.16, 0.24)

0.29
(0.20, 0.36)

0.14
(0.09, 0.19)

113

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Inga thibaudiana
0.09
0.22
(0.05, 0.12)
(0.04, 0.38)
Iriatea deltoidea

0.50
(0.26, 0.67)

Jubelina wilburii

Lacmellea panamensis

1.67
(0.03, 2.76)

0.40
(0.27, 0.52)

0.31
(0.26, 0.36)

Laetia procera

0.03
(0.002, 0.07)
-3.18
4.03
(-3.89, -2.46) (2.98, 4.88)

Machaerium seemannii

0.28
(0.16, 0.39)

Maripa nicaraguensis

Mendoncia tonduzii

Mendoncia sp.

4.70
(3.63, 5.59)

-0.28
0.40
(-0.45, -0.10) (0.30, 0.50)
0.61
(0.39, 0.82)

0.73
(0.49, 0.94)

0.57
(0.03, 1.01)

0.53
(0.33, 0.72)

6.11
(1.93, 10.00)

1.35
(0.11, 2.48)

114

-0.34
(-0.62, -0.07)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Nectandra sp.
0.09
(0.03, 0.13)
Neea elegans

1.07
(0.85, 1.26)

Neea psychotriodes

1.39
(1.17, 1.61)

Neea sp.

0.89
(0.45, 1.27)

Odontadenia macrantha

2.43
(0.59, 2.78)

Paragonia pyramidata
Model 1

-2.52
1.23
(-4.54, -0.51) (0.31, 1.39)

0.48
(0.31, 0.63)

Paragonia pyramidata
Model 2

0.67
(0.43, 0.87)

Paragonia pyramidata
Model 3
Parathesis trichogyne

0.51
(0.33, 0.66)
0.19
(0.09, 0.29)

-0.67
1.26
(-0.94, -0.41) (0.96, 1.53)

115

-0.56
(-0.74, -0.37)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Paullinia sp. A
0.95
-0.15
(0.94, 0.96)
(-0.21, -0.10)
Paullinia fibrigera

0.23
(0.16, 0.30)

Pentaclethra macroloba

0.53
(0.41, 0.65)

0.38
(0.16, 0.55)

-0.33
0.43
(-0.50, -0.19) (0.26, 0.59)

0.16
(0.10, 0.22)

Perebea hispidula

1.16
(0.14, 1.54)

0.99
(0.31, 1.57)

-1.23
(-2.09, -0.31)

-0.83
1.20
(-1.53, -0.07) (0.45, 1.98)

Pholidostachys pulchra

0.26
(0.12, 0.37)

3.43
(0.09, 5.05)

0.83
(0.57, 1.09)

Pinzona coriacea

-0.19
(-0.27, -0.11)

1.28
(1.10, 1.44)

Plukenetia stipellata

4.20
(3.55, 5.45)

-5.17
(-8.37, -3.48)

Pourouma bicolor

1.30
(1.23, 1.37)

0.21
(0.07, 0.35)

Pourouma minor

0.41
(0.23, 0.55)

4.81
(3.77, 5.86)
-0.96
0.37
(-1.16, -0.76) (0.26, 0.48)
-0.44
0.40
(-0.87, -0.08) (0.16, 0.67)

116

0.22
(0.14, 0.30)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Prestoea decurrens
3.40
3.45
-5.96
-1.09
0.85
(2.88, 3.97)
(3.06, 3.83) (-6.94, -5.10) (-1.45, -0.71) (0.22, 1.31)
Prestoea decurrens
(without outlier)
Protium confusum
Model 1

4.69
(3.56, 5.69)
0.19
(0.008, 0.31)

Protium confusum
Model 2

0.34
(0.04, 0.64)

Protium confusum
Model 3

0.36
(0.06, 0.65)

Protium panamense

0.59
(0.57, 0.60)

0.51
(0.48, 0.55)

Psychotria panamensis

0.81
(0.46, 1.11)

1.46
(0.58, 2.07)

Psychotria sp.1

1.71
(0.46, 2.69)

Psychotria sp.2

1.73
(0.81, 2.56)

0.93
(0.90, 0.96)

117

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Psychotria sp.3
1.22
1.72
-0.39
(0.87, 1.52) (0.81, 2.52)
(-0.73, -0.08)
Pterocarpus rohrii

0.43
(0.25, 0.58)

Randia genipoides

0.49
(0.36, 0.60)

1.11
(0.20, 1.89)

Rauvolfia purpurascens

-0.69
1.99
(-1.30, -0.09) (1.28, 2.69)

Richeria obovata

Rinorea deflexiflora

-0.29
(-0.37, -0.20)
1.39
(1.02, 1.77)

-0.77
(-1.08, -0.48)

0.55
(0.35, 0.75)

0.33
(0.24, 0.41)

0.28
(0.12, 0.44)
0.66
(0.59, 0.72)

-0.33
(-0.46, -0.20)

Securidaca diversifolia

0.38
(0.25, 0.49)

Serjania sp.

-0.39
(-0.76, -0.04)

Simarouba amara

0.69
(0.50, 0.89)

0.89
(0.46, 1.32)
-0.65
1.70
(-0.86, -0.44) (1.36, 2.03)

118

-0.38
(-0.67, -0.10)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Socratea exorrhiza
0.21
-0.06
0.15
(0.15, 0.27)
(-0.10, -0.02)
(0.10, 0.20)
Synechanthus warscewiczianus

1.01
(1.00, 1.02)

Tapiria guianensis

0.17
(0.08, 0.23)

Tetragastris panamensis

-0.39
(-0.53, -0.25)

-0.64
1.02
(-0.91, -0.38) (0.77, 1.26)

Trophis racemosa

0.24
(0.05, 0.43)
0.55
(0.34, 0.74)

Unonopsis pittieri

0.38
(0.03, 0.72)

0.60
(0.12, 1.05)

Virola koschnyi

-0.78
1.20
(-1.09, -0.44) (0.85, 1.52)

Virola sebifera

0.80
(0.58, 0.99)

-0.17
-0.30
0.13
(-0.31, -0.04) (-0.41, -0.17) (0.06, 0.21)

Vitex cooperi

0.32
(0.17, 0.480

2.31
(1.94, 2.66)

119

0.43
(0.23, 0.60)

TABLE B1.1 (cont.). Maximum magnitude of resource effects (in mm/month) on the final height of seedling growth by species.
95% confidence intervals are denoted by parentheses for each resource‟s magnitude of effect.
+
Species
Irradiance total N
PO4
SBC
NO3
NH4
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
(10e-1)
Vochysia ferruginea
0.55
0.23
0.10
-0.32
0.30
(0.41, 0.69) (0.12, 0.34) (0.01, 0.18)
(-0.59, -0.05) (0.14, 0.47)
Welfia regia

0.35
(0.30, 0.40)

0.19
(0.10, 0.26)

0.14
(0.08, 0.21)

120

APPENDIX C

121

TABLE C1.1. Correlations between species‟ magnitude of responses and initial seedling
size.
Resource
Irradiance
SBC
PO4
Total N
+
NH4

r
-0.20
-0.27
-0.14
-0.26
-0.18

F
1.98
2.88
0.53
3.37
1.05

df
1, 49
1, 37
1, 27
1, 45
1, 32

P
0.17
0.10
0.47
0.07
0.31

NO3

-0.45

7.53

1, 30

0.01

-

122