DEMOGRAPHY BEYOND THE POPULATION Functional traits as ... · DEMOGRAPHY BEYOND THE POPULATION...

DEMOGRAPHY BEYOND THE POPULATION

Functional traits as predictors of vital rates across thelife cycle of tropical treesMarco D. Visser*,1,2,3, Marjolein Bruijning1,2, S. Joseph Wright3, Helene C. Muller-Landau3,Eelke Jongejans1,2, Liza S. Comita4 and Hans de Kroon1,2

1Department of Experimental Plant Ecology & Animal Ecology, Institute for Water and Wetland Research, RadboudUniversity, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands; 2Department of Physiology, Institute for Waterand Wetland Research, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands;3Smithsonian Tropical Research Institute, Box 0843-03092, Balboa, Anc�on, Panama; and 4School of Forestry andEnvironmental Studies, Yale University, New Haven, CT 06511, USA

Summary

1. The ‘functional traits’ of species have been heralded as promising predictors for species’

demographic rates and life history. Multiple studies have linked plant species’ demographic

rates to commonly measured traits. However, predictive power is usually low – raising ques-

tions about the practical usefulness of traits – and analyses have been limited to size-indepen-

dent univariate approaches restricted to a particular life stage.

2. Here we directly evaluated the predictive power of multiple traits simultaneously across the

entire life cycle of 136 tropical tree species from central Panama. Using a model-averaging

approach, we related wood density, seed mass, leaf mass per area and adult stature (maximum

diameter) to onset of reproduction, seed production, seedling establishment, and growth and

survival at seedling, sapling and adult stages.

3. Three of the four traits analysed here (wood density, seed mass and adult stature) typically

explained 20–60% of interspecific variation at a given vital rate and life stage. There were

strong shifts in the importance of different traits throughout the life cycle of trees, with seed

mass and adult stature being most important early in life, and wood density becoming most

important after establishment. Every trait had opposing effects on different vital rates or at dif-

ferent life stages; for example, seed mass was associated with higher seedling establishment and

lower initial survival, wood density with higher survival and lower growth, and adult stature

with decreased juvenile but increased adult growth and survival.

4. Forest dynamics are driven by the combined effects of all demographic processes across the

full life cycle. Application of a multitrait and full-life cycle approach revealed the full role of

key traits, and illuminated how trait effects on demography change through the life cycle. The

effects of traits on one life stage or vital rate were sometimes offset by opposing effects at

another stage, revealing the danger of drawing broad conclusions about functional trait–demography relationships from analysis of a single life stage or vital rate. Robust ecological

and evolutionary conclusions about the roles of functional traits rely on an understanding of

the relationships of traits to vital rates across all life stages.

Key-words: adult stature, leaf mass per area, model averaging, seed size, tree growth, tree

mortality, wood density

Introduction

Functional biology has raised the possibility that morpho-

logical and physiological traits, henceforth functional

traits, might be strongly related to interspecific variation in

*Correspondence author. E-mail: [email protected]

This article forms part of the British Ecological Society

journals’ Demography Beyond the Population special feature

http://wileyonlinelibrary.com/BES_demography

© 2016 The Authors. Functional Ecology © 2016 British Ecological Society

Functional Ecology 2016, 30, 168–180 doi: 10.1111/1365-2435.12621

http://wileyonlinelibrary.com/BES_demography

vital rates and serve as proxies for life history variation

(McIntyre et al. 1999; Westoby et al. 2002; Westoby &

Wright 2006). However, the predictive power of functional

traits is often very low, raising questions about how ‘func-

tional’ the selected traits really are (Paine et al. 2015). For

example, coefficients of determination (R2) average just

0�08 for predictions of growth and mortality rates of

tropical trees (Poorter et al. 2008; Wright et al. 2010; Iida

et al. 2014a,b). Such low predictive power strongly limits

the potential of traits to serve as proxies for life history

variation or inform global vegetation models (Cox et al.

2013; Friedlingstein et al. 2014).

Previous studies in tropical forest trees have provided an

incomplete picture of the role of traits in tree demography.

Limitations include restriction of analyses to particular life

stages, ignoring size dependence (Poorter et al. 2008; Wright

et al. 2010), and/or consideration of only one trait at a time

(Iida et al. 2014a,b). First, most previous studies consider a

single vital rate and/or life stage, even though traits will gen-

erally have different roles and different predictive power for

different demographic rates. For instance, seed mass is

strongly negatively correlated with seed production (Muller-

Landau et al. 2008), strongly positively correlated with

seedling establishment rates (Moles & Westoby 2006), and

has weaker relationships with growth and survival later in

life (Wright et al. 2010). Secondly, most previous studies

ignore the size dependency of demographic rates, focusing

instead on mean survival and growth over broad size classes

(Poorter et al. 2008; Wright et al. 2010; but see Iida et al.

2014b). This overlooks important interactions between

traits and size, as taller trees experience strikingly different

resource and competitive conditions (Poorter et al. 2005;

H�erault et al. 2011; Falster, FitzJohn & Westoby 2016).

Thirdly, many studies consider only a single trait, thus

inherently limiting the total explanatory power of traits

(Muller-Landau et al. 2008; Iida et al. 2014a,b). Many traits

will influence plant function simultaneously and each trait

can be involved in multiple trade-offs with contradictory

effects on vital rates (Marks & Lechowicz 2006). To improve

understanding of how traits influence plant demography, we

consider size dependence, evaluate trait–demography rela-

tionships across the entire life cycle, and consider multiple

traits in a model-averaging framework.

We evaluate to what degree four key traits can explain

variation in demography among tropical tree species. The

four key traits are adult stature, wood density, seed mass

and specific leaf area, which provide largely independent

information about plant strategies (Table 1; Westoby et al.

2002; Wright et al. 2007). In contrast to previous work,

our analyses are comprehensive including not only growth

and mortality of trees (Poorter et al. 2008; Wright et al.

2010; R€uger et al. 2012; Iida et al. 2014a), but also repro-

ductive schedules; seed production; and seedling establish-

ment, growth and mortality. We use a model-averaging

approach (Burnham & Anderson 2002; Grueber et al.

2011) to simultaneously weigh the effects of all four traits

on each vital rate for up to 136 tree and shrub species from

Barro Colorado Island, Panama. We aim to quantify (i)

which traits explain variation at different life stages; (ii)

the predictive power of all traits combined to explain vari-

ation in all components of the life cycle; and (iii) the rela-

tive effect sizes of each trait at each life stage while

mapping out contradictory effects across life stages.

Materials and methods

STUDY S ITE

Our data are from the moist tropical forest of the 50-ha Forest

Dynamics Plot (FDP) on Barro Colorado Island (BCI; 9°90 N,

79°510 W), Panama. Annual rainfall averages 2650 mm (since

1929), with a dry season between January and April, and tempera-

ture averages 27 °C (see Leigh 1999, for details).

V ITAL RATES

We used five data sets to quantify vital rates for the entire life

cycle.

1. Trees. In the FDP, all free-standing woody stems ≥1 cm diame-

ter breast height (dbh, measured at 1�3 m) were censused in

1981–1982, 1985 and every 5 years thereafter. In each census,

diameters of every stem are measured, and all new individuals

are tagged, mapped, and identified to species. These censuses

Table 1. Details on the functional trait data, including units, mean and standard deviation, range, common ecological associations and

references

Trait Mean (SD) Range Associations Sources

Wood density

(WD: g cm�3)

0�59 (0�14) 0�29, 0�91 Mechanical strength, vulnerability to

hydraulic failure, defence against decay,

growth–survival trade-off

Chave et al. (2009), Anten &

Schieving (2010) and Larjavaara &

Muller-Landau (2010)

Seed mass (SM: g) 0�06 (8�14)* 4�9e-05, 22�87 Seed production, seed dispersal,

seedling tolerance to stress,

seedling competitive ability

Westoby et al. (2002), Moles &

Westoby (2006) and

Muller-Landau (2010)

Leaf mass per area

(LMA: g m�2)

231�84 (314�89) 9�4, 1891�35 Leaf construction cost, photosynthetic

capacity, respiration rates,

leaf life span, leaf herbivory

Westoby et al. (2002),

Wright et al. (2004) and

Osnas et al. (2013)

Adult stature

(Dmax: mm)

398�97 (334�35) 12, 1775�67 Life history variation Kohyama (1993) and

Westoby et al. (2002)

*Geometric mean.

© 2016 The Authors. Functional Ecology © 2016 British Ecological Society, Functional Ecology, 30, 168–180

Traits affect whole life-cycle vital rates 169

provide information on growth and survival for individuals

≥1 cm dbh (hereafter ‘trees’). We analysed data from the 1990

to 2010 censuses, excluding earlier censuses because of small

but important differences in measurement methods (Condit

et al. 1999; R€uger et al. 2009).

2. Seed rain. Seed rain has been recorded in 200 0�5-m2 seed traps

since January 1987 (Wright et al. 2005). Traps are located in a

stratified random manner along trails within the FDP. All

reproductive parts (seeds, flowers, fruits and capsules) are iden-

tified to species and counted weekly (presence is recorded for

flowers). We used seed data from 1993 to 2012, as these years

correspond to records of newly recruiting seedlings (data set 3,

below).

3. Small seedlings. All seedlings and small saplings <1 cm dbh

(with no limits on height) were censused annually in 600 1-m2

seedling plots from 1994 through 2012. These plots are located

2 m from three sides of each of the 200 seed traps (Wright

et al. 2005).

4. Large seedlings. Free-standing woody plants ≥20 cm in height

and<1 cm dbh were censused in 20 000 1-m2 seedling plots each

year from 2001 through 2013, with the exceptions of 2005, 2007

and 2010 (Comita et al. 2007; Comita & Hubbell 2009). These

plots are located in a 5-m grid across the FDP. In each census,

the status (i.e. alive/dead) of previously tagged seedlings is

checked, all individuals are measured for height (except in 2002,

when only new recruits were measured), and new individuals are

tagged and identified to species. In analyses of growth and sur-

vival all census intervals which include missing years were

dropped (i.e. no intervals of>1 year are included for data set 4).

5. Reproductive status. We assessed the reproductive status of

13 358 individual trees to quantify size-dependent probabilities

of reproduction. For each species, a size-stratified sample of

trees was randomly selected and visited during species-specific

reproductive seasons. Reproductive status (fertile or sterile)

was evaluated from the ground using binoculars. For eight

dioecious species, we evaluated sex expression of all individuals

within the FDP. Data were collected between January 1995

and January 1996 for 31 species (Wright et al. 2005), between

2005 and 2007 for 51 wind dispersed species, and between April

2011 and September 2014 for 81 species.

TRA IT DATA

Trait data include seed mass (SM), leaf mass per area (LMA),

adult size (Dmax) and wood density (WD; Table 1; Wright et al.

2010). SM refers to endosperm and embryo dry mass determined

after dissecting diaspores to isolate the endosperm and embryo.

LMA was determined for shade leaves collected from the upper

canopy of the six smallest individuals of each species in the FDP.

We could not use sun-exposed leaves as a basis of comparison

because most FDP species are treelets that complete their entire

life cycle in the shaded understorey (King, Wright & Connell

2006). Dmax is the mean dbh of the six largest individuals in the

FDP (2005 census) and an additional 150 ha of mapped tree

plots located within 30 km and mostly within 10 km of BCI.

Dmax is well correlated with maximum tree height (r = 0�95 on a

log–log scale). Species-specific WD was estimated from tree cores

collected within 15 km of BCI, and was calculated as oven-dried

(60 °C) mass divided by fresh volume (technically wood specific

gravity). Further details can be found in Wright et al. (2010).

The four traits are largely independent of one another, with coef-

ficients of determination (R2 values) of 0�00068, 0�0056, 0�017,0�052, 0�12 and 0�13 for LMA-Dmax, SM-WD, WD-Dmax, SM-

Dmax, LMA-SM and LMA-WD relationships, respectively

(Wright et al. 2010).

We normalized trait values to enable model averaging, and

facilitate comparison of effect sizes among traits with very differ-

ent levels of interspecific variation (Grueber et al. 2011), using all

136 species evaluated here. Species-level trait values were normal-

ized, with SM and Dmax first log-transformed, by subtracting

mean trait values and then dividing by the standard deviation of

the trait values (Table 1).

STUDY SPEC IES

For each life stage and vital rate, we analysed all species with trait

data and sufficient demographic data to ensure reasonable preci-

sion of species-specific vital rate estimates. Table 2 gives exact

selection criteria and the number of species in each analysis.

Table S1 (Supporting information) gives the identities of the spe-

cies in each analysis. Figure S1 shows the distribution of trait val-

ues across all species within each analysis.

F ITT ING TRAIT -BASED MODELS FOR VITAL RATES

We evaluated relationships between size-dependent vital rates and

traits, including trait–size interactions, using generalized linear

mixed models (GLMMs), with species and individual included as

random effects. The most complex full model had the following

form:

Table 2. Species selection criteria and sample sizes for each analysis

Analysis Selection criteria Number of species Years

Reproduction (mm dbh) Reproductive status assessed for >20 trees. Species with too wide

confidence intervals were excluded after visual inspection of fit

60 (8891) 1995–2014

Seed production Species had at least 30 seeds captured in traps, and must have been

included in the reproductive analysis to estimate reproductive basal area

38 (NA) 1993–2010

Seedling establishment 30 or more seedling recruits observed between 1995 and 2011, with

>30 seeds observed for the fruiting years corresponding to

1995–2011 seedling recruitment (taking account of species-specific

germination delays)

68 (NA) 1994–2011

Seedling survival (mm height) >100 individuals in data set 80 (93 082) 2001–2013Seedling growth (mm height) >100 individuals in data set 80 (75 990) 2001–2013Tree survival (mm dbh) >100 individuals in data set 117 (267 469) 1990–2010Tree growth (mm2 basal area) >100 individuals in data set 117 (214 373) 1990–2010

Each vital rate under the column ‘Analysis’ is defined in the text, with corresponding units for size given in parenthesis when the analysis

was carried out on individual data. Under ‘Number of species’, the total number of species in each analysis is given with the total number

of individuals between parentheses. This value is ‘NA’ when the corresponding analysis concerns species-level data. The column ‘Years’

gives the time span of data used in each analysis. A total of 136 unique species were included across all analyses.


170 M. D. Visser et al.

y� bo þ b1sþX4

i¼1

biþ1Ti þ biþ5Tis� �þ esp þ eind þ eresidual

eqn1

where y is growth, (logit) survival or (logit) reproductive fraction; and s is

size in mm height, dbh or mm2 basal area for analyses of seedlings, repro-

ductive size or tree survival, and growth, respectively. Trait effects including

their interactions with size are given by the expression in parentheses, where

Ti represents trait i (corresponding to SM, WD, LMA or Dmax). The ran-

dom effects of species and individuals are denoted by esp and eind, respec-tively, and eresidual is the residual error. For each size-dependent vital rate,

we fit 82 possible models including eqn. 1 and all subsets involving different

combinations of the trait and trait by size effects (Table S2).

Two vital rates, seed production and seedling establishment, were mea-

sured and analysed at species level, and we related these to traits directly

using generalized linear models (GLMs) without size effects. Here too, we

evaluated a suite of models including all subsets of SM, LMA, Dmax and

WD (16 models per vital rate; Tables S3 and S4). Details of model fitting

for each vital rate follow.

Reproduction

The size-dependent probability of reproduction was evaluated

with a logistic GLMM (eqn. 1 with binomial error) using data set

5.

Seed production

Species-specific seed production (fseeds, seeds per year per m2 of

reproductive basal area) was quantified as the mean flux of

seeds arriving (seeds per year per m2 of trap area) divided by

mean reproductive basal area density (m2 of reproductive basal

area per m2 of plot area). We used seed trap and tree census

data from 1993 through 2012. Reproductive basal area was cal-

culated from the tree census data in combination with the fitted

logistic models for size-dependent probability of reproduction.

The logistic models predicted each individual’s reproductive

probability as a function of its size. We then weighted each

individual’s basal area by its reproductive probability to calcu-

late total reproductive basal area. Total reproductive basal area

was interpolated between FDP censuses to calculate annual val-

ues of fseeds, which were then averaged over years to obtain a

single mean value for each species. These simple estimates of

seed production were qualitatively similar to more sophisticated

estimates obtained using inverse modelling (Text S1, Fig. S2).

We chose to use the simple estimates because they were avail-

able for more species. Estimates of fseeds were then related to

traits using linear regression.

Seedling establishment

Species-specific mean seed to seedling establishment probabilities

were calculated as the mean flux of newly recruiting seedlings

per year per m2 in seedling plots in years 1995 to 2012 (data set

3) divided by the mean flux of seeds arriving per year per m2 in

seed traps for the corresponding fruiting years after accounting

for germination delays (Wright et al. 2005; data set 2). Seedling

establishment rates were related to traits using GLMs (i.e. logit

transform).

Growth

Growth was modelled as height growth for seedlings (mm per

year) and basal area growth for trees (mm2 per year) using LMMs

(eqn 1). We used basal area growth because general additive mod-

els (GAMs) showed that basal area growth was generally linearly

related to size (mm2 basal area). Growth rates were calculated as

the difference in sizes divided by the time in years between cen-

suses (data sets 1, 3 and 4). For data set 1, we excluded individuals

marked as ‘resprout’, ‘buttressed’, ‘leaning’ and ‘broken above

1�3 m’ in each census, as well as those with growth rates more

than four 4 standard deviations from the mean. These are likely

measurement errors (R€uger et al. 2011).

Survival

The size-dependent (mm height and mm dbh for seedlings and

trees, respectively) probability of survival was evaluated with a

logistic GLMM (eqn 1 with binomial error) using data sets 1, 3

and 4.

We used model averaging to calculate average parameters

(Burnham & Anderson 2002). All models were assigned a weight

based on their AIC score and fitted parameters were averaged

over the full set of models using these weights to obtain a final

average model. The final average model provides a basis to com-

pare effect sizes. Model averaging is superior to selecting the best

model because models with similar fits are not ignored (Burnham

& Anderson 2002; Whittingham et al. 2006; Bolker et al. 2009).

For this reason, model averaging provides a more robust basis for

inference and prediction, reducing bias in estimation of effect sizes,

especially in cases where multiple variables influence the response

variable (Grueber et al. 2011). This contrasts with stepwise multi-

ple regression, which is seen as poorly suited to disentangle contri-

butions of multiple traits to vital rates (Wittingham et al. 2006).

We averaged parameters over all models having AIC weights >0using the ‘zero method’ in which parameters are assigned the value

zero where absent from models. This is a conservative approach

to model averaging (i.e. leading to lower effect sizes) and is recom-

mended when comparing effect sizes among variables (Burnham &

Anderson 2002; Grueber et al. 2011). Confidence intervals for

each weighted parameter were estimated following Buckland,

Burnham & Augustin (1997).

We evaluated model fits to the full data sets, including varia-

tion among individuals, using marginal and conditional R2 val-

ues developed for mixed-effects models (Nakagawa & Schielzeth

2013). These R2 values provide information on how well the

trait-based hierarchical models (the GLMMs) explain individual-

level variation in vital rates over all species in the community.

To evaluate linearity, we plotted model residuals against size for

each model (Fig. S3). Residuals deviated from linearity for seed-

lings taller than 2�5 m and for trees with dbh >50 cm in the

growth and survival analyses. To ensure linearity, we therefore

excluded trees with dbh >50 cm and seedlings taller than 2�5 m

(corresponding to 0�41% and 0�47% of the data, respectively).

Nonlinearity was not detected in our reproduction analysis. All

analyses were performed in R 3.1.1 (R-core 2014), making use

of the LME4 package for mixed-effects models (Bates et al.

2015). An example R-code is provided for model averaging

(Appendix S2).

THE POWER OF TRA ITS TO EXPLA IN INTERSPEC IF IC

VAR IAT ION

We performed a second set of analyses to estimate the contribu-

tions of traits to explaining interspecific variation in demographic

rates at particular sizes, and thereby to enable more direct com-

parisons of our results with earlier studies based on species-level

estimates of vital rates (Poorter et al. 2008; Wright et al. 2010;

Iida et al. 2012). We first calculated trait-based predictions for

each species and each vital rate based on the fitted average mod-

els. We then compared these predictions with observed mean



vital rates and calculated associated R2 values. For seed produc-

tion and seedling establishment, these comparisons used observed

species-specific mean rates. For size-dependent vital rates

(growth, survival, reproduction), we estimated species-specific

moving averages using generalized additive models (GAMs;

example code in Appendix S2), which make no prior assumptions

on the functional shape of the relationship (Figs S4–S8). We then

estimated size-specific R2 values in three steps: (i) species-specific

mean rates were given from GAM predictions for each vital rate

at size points ranging from 0 to 1�5 m height for seedlings and

from 1 to 25 cm dbh for trees (at these size ranges, analyses

always included 15 or more species); (ii) demographic rates for

these sizes were predicted using only fixed effects from each aver-

aged trait models; and (iii) we calculated R2 values for correla-

tions between trait-based and GAM estimated mean rates at each

size. To evaluate the predictive power of individual single traits,

we repeated the calculation of R2 values for single-trait average

models for every trait, where single-trait models were based on

averaging over models including only size, the single focal trait

and/or the trait–size interaction as predictors. Single-trait models

represent the best-case scenario, in terms of R2, when using a sin-

gle trait.

SEPARATE ANALYSES AT EVERY SIZE

Out of an abundance of caution, we performed a final analysis to

guard against the possibility that underlying assumptions of lin-

ear relationships with size or the random effects structure in the

mixed-effects model may unduly influence results. The (G)LMMs

allow only linear relationships between vital rates and size, and

might misstate the influence of predictors if there are underlying

nonlinearities with size. Additionally, to improve computational

feasibility, we included only random species intercepts, but

ignored random slopes with size, which potentially may impact

effect sizes (Schielzeth & Forstmeier 2009). To address these con-

cerns, we evaluated relationships between traits and species-speci-

fic vital rates separately for each size. We fit GLMs (sets of 16

models, as shown in Tables S3 and S4) to species mean rates (es-

timated from the GAMs) for seedlings between 0 and 1�5 m tall

and trees between 1 and 25 cm dbh. We then compared effect

sizes and R2 values with the mixed-effect models (eqn 1). This

analysis allows for varying intercepts, slopes and functional

shapes between size and vital rates for each species. However, the

analysis is also far less parsimonious and weights all species

equally regardless of sample size and hence is likely to overesti-

mate the strength of trait–vital rate relationships. When results

are qualitatively similar this indicates no major problems with

our assumptions. In this case, our initial analyses (eqn 1) are less

biased for inference and yield the greatest predictive accuracy

(Gelman 2006).

Results

Our analyses included 136 different species, with 38 to

117 species for each vital rate (Table 2). The full range

of trait values observed among BCI trees was well repre-

sented for each vital rate (Fig. S1). The species included

a wide range of growth forms (shrubs to understorey

and canopy trees), seed dispersal mechanisms (ballistic,

wind, mammals and/or birds), and relative abundances

[from 13�5% (Faramea occidentalis) to 0�29% (Hampea

appendiculata) of all stems in the FDP]. WD, SM, LMA

and Dmax varied by 0�5, 5�7, 2�3 and 2�2 orders of mag-

nitude, respectively (Table 1).

SEED PRODUCT ION

The average model for seed production was based on nine

models with nonzero weights (weights above 0�001;Table S3), included all four traits and explained 65% of

interspecific variation (Table 3). Seed production was neg-

atively related to SM (slope �1�13; Table 3, Fig. 1a) with

all other traits having approximately 7–50 times smaller

effect sizes (Table 3, Fig. 1a).

SEEDL ING ESTABL ISHMENT

The average model for seedling establishment was based

on four models with nonzero weights (weights above

0�001; Table S4), included all four traits and explained

66% of interspecific variation (Table 3). Seedling establish-

ment increased with seed mass (slope 1�32; Table 3,

Fig. 1b) and decreased with Dmax (slope �1�06; Table 3,

Fig. 1b). LMA and WD hardly influenced seedling estab-

lishment rates (Table 3, Fig. 1b).

SEEDL ING GROWTH RATES

The average model for seedling growth rates was based on

nine models with nonzero weights (weights above 0�001;Table S5). It included all four size–trait interactions and

explained 10% of the individual-level variation, with the

fixed effects of size, traits and their interactions explaining

only 1% of the individual growth variation (Table 3). Seed-

ling growth rates decreased with SM and WD, with smaller

effects for larger seedlings (Table 3, Fig. 2a,b). LMA and

Dmax hardly influenced seedling growth rates (Fig. 2c,d).

SEEDL ING SURV IVAL RATES

The full model, including all size–trait interactions, con-

tained 100% of the weight for seedling survival (Table S6).

This model explained 25% of the individual-level variation

in seedling survival, with the fixed effects of size, traits and

their interactions explaining 16% of the individual varia-

tion (Table 3). Seedling survival increased with WD and

decreased with Dmax, and these effects diminished with

seedling size (Fig. 2f,h). The direction of relationships

between seedling survival and SM and LMA changed with

seedling size (Fig. 2e,g). For small seedlings, survival was

greater for species with smaller seeds and larger LMA. For

larger seedlings, survival was greater for species with larger

seeds and smaller LMA.

TREE GROWTH RATES

The full model, including all size–trait interactions, also

contained 100% of the weight for tree growth rates

(Table S7). This model explained 66% of the individual-

level variation in tree growth, with the fixed effects of size,

traits and their interactions explaining 26% of the individ-

ual variation (Table 3). Tree growth rates decreased with



Table

3.Coeffi

cients

(andstandard

errors)from

thefullaveraged

model

foreach

oftheevaluatedvitalrates(columns)

Seedproduction

Seedlingestablishment

Seedlinggrowth

Treegrowth

Seedlingsurvival

Treesurvival

Reproduction

Intercept

�2�95

(0�07

81)

�3�21

(0�08

16)

42(2�07

)108(8�7)

1�31

(0�05

82)

2�19

(0�06

63)

�3�71

(0�09

39)

SM

�1�13

(0�06

18)

1�32

(0�08

66)

�11�1

(2�31

)�2

2�5

(9�5)

�0�22

(0�06

5)

0�38

7(0�07

22)

�0�22

6(0�07

24)

WD

0�01

52(0�03

12)

0�01

37(0�03

49)

�13(2�2)

�70�4

(9�79

)0�45

1(0�06

07)

0�53

6(0�07

48)

0�12

3(0�06

93)

LMA

�0�16

6(0�08

44)

�0�00

413(0�03

5)

�0�06

05(2�32

)�2

�19(9�41

)0�20

4(0�06

65)

0�24

9(0�07

24)

�0�28

1(0�08

75)

Dmax

�0�02

47(0�04

48)

�1�06

(0�08

26)

0�43

4(1�96

)36�6

(9�97

)�0

�348(0�05

78)

�0�29

7(0�07

58)

�0�38

6(0�11

)

Size

�0�01

24(0�00

0477)

0�00

934(7�63

e-05)

0�00

116(2�34

e-05)

�0�00

282(0�00

0116)

0�01

74(0�00

0224)

Size:SM

0�00

479(0�00

0332)

�0�00

031(3�14

e-05)

0�00

0458(2�1e

-05)

�0�00

0976(9�73

e-05)

0�00

0675(5�99

e-05)

Size:WD

0�00

496(0�00

0596)

0�00

0858(4�83

e-05)

8�65

e-06(1�52

e-05)

�0�00

121(0�00

0107)

�0�00

0109(7�53

e-05)

Size:LMA

0�00

112(0�00

0772)

�0�00

0946(5�03

e-05)

�0�00

0529(3�04

e-05)

�0�00

0642(0�00

0104)

�0�00

0143(9�62

e-05)

Size:Dmax

�1�98

e-05(2�29

e-05)

0�00

255(9�66

e-05)

0�00

0272(2�25

e-05)

0�00

581(0�00

0161)

�0�00

877(0�00

0197)

R2 size

0�00

15

0�16

76

0�03

21

1e-04

0�17

75

R2 fixed

0�00

84

0�25

91

0�15

81

0�06

54

0�24

69

R2 species

0�07

76

0�40

15

0�25

07

0�18

70�50

63

R2 individual

0�03

43

0�51

72

0�15

81

0�06

54

0�34

51

R2 full

0�65

0�66

0�10

35

0�65

95

0�25

07

0�18

70�60

45

Bold

face

indicatesconfidence

intervalsnotincludingzero

(a<0�05

).Themeasure

ofsize

washeight(m

m)forseedlings,dbh(m

m)forsurvivalandreproduction,andbasalareaforgrowth

(mm

2).

Complete

listsofevaluatedmodelswithAIC

values

andAIC

weights

are

given

inTablesS4–S

9.Withtheexceptionofseed

productionandseedlingestablishment,theR2values

inthetable

are

specificto

mixed-effects

models(N

akagawa&

Schielzeth2013),andreflectthefitofthemodelincludingfixed

effects

ofsize

only

(‘R

2 size’),fixed

effects

ofsize

andtraits(‘R

2 fixed’),fixed

andspeciesran-

dom

effects

(‘R

2 species’),fixed

andindividualrandom

effects

(‘R

2 individual’),andthefullmixed

model

(‘R

2 full’).Traitvariableswerenorm

alizedpriorto

modelfits.



WD and increased with Dmax (Table 3, Fig. 2j,l). The effect of

WD decreased with tree size (Fig. 2j), and the effect of Dmax

increased with tree size (Table 3, Fig. 2l). SM and LMA had

much smaller effects on tree growth rates (Fig. 2I,k).

TREE SURV IVAL RATES

The full model, including all size–trait interactions, con-

tained 99�7% of the weight for tree survival rates

(Table S8). The average model explained 19% of the indi-

vidual-level variation in seedling survival, with the fixed

effects of size, traits and their interactions explaining 7%

of the individual variation (Table 3). Tree survival was

strongly influenced by Dmax, and was generally larger for

species with larger Dmax (Fig. 2p). Tree survival was also

greater for species with larger SM, WD and LMA over

nearly the full range of tree sizes, with the exception of the

very largest individuals (Fig. 2m–o).

REPRODUCT ION

Nine models with nonzero weights contributed to the aver-

age model for reproduction (Table S9), which included all

size–trait interactions and explained 60% of the individ-

ual-level variation, with the fixed effects of size, traits and

their interactions explaining 25% of the variation (Table 3

and Table S9). Dmax had the largest effect on reproductive

status, with larger-statured species becoming reproductive

at larger sizes than smaller-statured species (Fig. 2t). The

threshold size at which 50% of individuals are reproduc-

tive is well predicted by the following simple equation:

R50 = ½Dmax (R2 = 0�81; Fig. S9).

THE POWER OF TRA ITS TO EXPLA IN INTERSPEC IF IC

VAR IAT ION

Figure 3 summarizes the proportion of interspecific varia-

tion in vital rates explained by Dmax, LMA, SM and WD

throughout tree life cycles on BCI. These proportions are

consistently higher than the proportions explained by traits

and size in the GLMMs (Table 3) because the latter includes

additional variation among individuals. The R2 values for

each trait separately are presented in Tables S10–12.

SEPARATE ANALYSES AT EVERY SIZE

Traits had qualitatively similar influences on vital rates

(Fig. S10) and explained similar proportions of interspecific

−2 −1 0 1 20·005

0·010

0·020

0·050

0·100

0·200

0·500

1·000Seed production

See

ds p

er u

nit b

asal

are

a (N

/mm

−2/y

r)

SMWDLMADmax

−2 −1 0 1 20·001

0·002

0·005

0·010

0·020

0·050

0·100

0·200

0·500 Seedling establishment

See

dlin

g re

crui

ts p

er s

eed

Standardized trait value

(a)

(b)

Fig. 1. Fitted effects of deviations in seed

mass (SM), wood density (WD), leaf mass

per area (LMA) and maximum stature

(Dmax) from their mean values on seed pro-

duction (a), and the rate of seedling estab-

lishment (b) when other traits are held at

their mean values. Predictions are plotted

against standardized trait values (standard

deviations from the mean). Observed trait

value means and standard deviations are

given in Table 1. Panels a and b show that

traits can have opposing effects not only

between life stages (SM in a and b) but also

between different traits within a single life

stage (Dmax and SM in Panel b).



variation (Fig. S11) in analyses that fit separate models for

every size and in our main analyses.

Discussion

We systematically quantified trait–demography relation-

ships across the entire life cycle of multiple co-occurring

species of tropical trees for the first time while incorporat-

ing individual-level, size-dependent variation in growth,

survival and reproduction (Table 3, Fig. 2). Full models,

including random effects for individuals and species,

explained 10–25% of the overall variation for seedling

growth, seedling survival and tree survival (Table 3). Fac-

tors missing from our models clearly affect these three vital

rates. Likely candidates include abiotic and biotic environ-

mental variation associated with soils, local competitive

effects and plant pests. The full models performed much

better for tree growth rates and reproductive status,

explaining 66% and 60% of overall variation, respectively.

The fixed effects of size, wood density (WD), seed mass

(SM), leaf mass per area (LMA), adult size (Dmax) and

interactions between size and traits explained 1–26% of

the variation observed over all individuals of all species for

the five size-dependent vital rates (Table 3). Our trait-

based average models explained between 4% and 65% of

interspecific variation in mean size-specific demographic

rates, depending on the size and demographic rate, with

more variation explained for small than for large size

classes (Fig. 3). In comparison with previous studies of

species-level trait–demography relationships among tropi-

cal trees (Poorter et al. 2008; Wright et al. 2010; Iida et al.

2012, 2014a), our analyses provide clear improvements in

predictive power and new insights into how effects vary

with size.

(a)

20

30

40

50(b) (c) (d)

(e)

0 300 600 900 1200 15000·700·750·800·850·900·95

(f)

0 300 600 900 1200 1500

(g)

0 300 600 900 1200 1500

(h)

0 300 600 900 1200 1500

(i)

0500

1000150020002500 (j) (k) (l)

(m)

0·85

0·90

0·95

1·00(n) (o) (p)

(q)

0 100 200 300 400 5000·00·20·40·60·81·0

(r)

0 100 200 300 400 500

(s)

0 100 200 300 400 500

(t)

0 100 200 300 400 500

Rep

rodu

ctio

nTr

ee s

urvi

val

Tree

gro

wth

Sdl

. sur

viva

lS

dl. g

row

thfra

ctio

n re

prod

uctiv

ean

nual

rate

mm

^2/y

ear

annu

al ra

tem

m/y

ear

Seedmass Wood density Leaf mass per area Max. d.b.h.

Size (mm)

Fig. 2. Fitted effects of each trait (columns) on size-dependent vital rates (rows) in the averaged models. The black lines present the vital

rate–size relationships with all traits set to their mean values. The red plus signs and blue minus lines present the same relationships with

one trait set to its mean plus or minus one standard deviation, respectively, and the three remaining traits set to their mean values. The

trait whose value varies among the blue, black and red lines is named at the top of each column. Observed trait value means and standard

deviations are given in Table 1. Grey text at the right outermost column gives the corresponding measure of size (in mm) for each row of

panels. The figure shows that the effect sizes corresponding to different traits differ greatly throughout the life cycle of trees among species,

as shown by the + and � lines from the top to the bottom of the graph.



WOOD DENS ITY

Higher WD is associated with higher resistance to hydrau-

lic failure and to decay and with higher structural strength

for a given diameter, but at the cost of slower diameter

growth rates (reviewed by Chave et al. 2009). Previous

studies concur that wood density (WD) is the single trait

best able to predict growth and survival among tropical

tree species, with coefficients of determination (R2) averag-

ing 0�093 (�0�077 SD) and 0�076 (�0�079 SD) for relation-

ships with growth and survival, respectively (using the

maximum R2 values reported in Poorter et al. 2008;

Wright et al. 2010; Iida et al. 2014a,b). Our size-specific

coefficients of determination, which were always greater

for wood density than for other traits for analyses of

growth and survival (Table 3), are consistent with this con-

clusion. Wood density had progressively less predictive

power for the survival and growth of larger trees, which is

consistent with previous comparisons of broad sizes classes

(Poorter et al. 2008; Wright et al. 2010). The benefits and

costs associated with variation in WD affect basal area

growth and survival directly, but have negligible effects on

reproduction, seed production and seedling establishment

(Fig. 3).

In interpreting the relationship of WD to growth and sur-

vival at constant diameter or constant height in this and

other studies, it is important to keep in mind that these rela-

tionships are dependent upon the size (and growth) cur-

rency used as the basis for comparison. The same ‘size’ in

diameter or height is associated with larger biomass in

higher WD species, and the same biomass growth translates

to less diameter, basal area, and height growth in higher

WD species (Larjavaara & Muller-Landau 2010). Relation-

ships of growth with WD may weaken or even disappear

when growth is expressed on the basis of biomass instead of

diameter (R€uger et al. 2012). Thus, the strong relationships

of growth with WD might in part be seen as an artefact of

our choice of currency, much as mass-normalized leaf traits

show stronger interrelationships than area-normalized leaf

traits (Osnas et al. 2013). Similarly, the use of height and

diameter as measures of size might introduce bias towards

positive relationships between survival and WD, because

the same ‘size’ in diameter or height is associated with lar-

ger biomass in higher WD species, and survival increases

with biomass on BCI (Muller-Landau et al. 2006). Future

analyses should evaluate how much variation in growth

and survival is explained by WD when these currency

effects are eliminated.

(a)

(b)

(c)

Fig. 3. The proportion of interspecific variation in various demographic rates explained by the four functional traits throughout the life

cycle, as measured by R2 values. The rows show results for (top to bottom) size-dependent growth, size-dependent survival, and vital rates

associated with reproduction. The R2 value at the upper edge of the stacked colours represents the proportion of the total variation among

species explained by the fixed effect terms in the full averaged model (i.e. including traits Dmax, LMA, SM and WD), where the observed

values for each species are based on species-specific GAMs. The relative importance of different traits is indicated by the relative height of

each colour band as a proportion of the total, with height scaled to the R2 values for averaged models including only one trait (i.e. includ-

ing only Dmax, LMA, SM or WD; Tables S10–12). The number of species included in the analyses varies with size; for reference, the num-

bers of species included at various sizes are shown in solid circles above each graph. Variation in vital rates explained among species

shows that seed mass is initially influential but diminishes in importance with wood density becoming more important.



SEED MASS

Previous studies and our analyses (Fig. 3) concur that seed

mass (SM) explains minimal variation in growth and sur-

vival among tropical trees (R2 averages 0�002 � 0�0022 SD

and 0�101 � 0�041 SD, respectively; Fig. 3; Poorter et al.

2008; Wright et al. 2010). This is unsurprising because seed

reserves and direct effects of seed size are exhausted well

before the large minimum sizes (≥1 cm dbh) used to deli-

mit trees. Those relationships with SM that remain at these

large sizes reflect indirect effects, whose unknown causa-

tion must involve unrecognized correlations among traits

and life histories.

SM has much stronger effects at the earliest stages of

regeneration. SM was strongly negatively related to seed

production and strongly positively related to seedling

establishment on BCI (Fig. 1). This is consistent with the

well documented trade-off between seed quantity and per-

seed investment (Henery & Westoby 2001; Muller-Landau

2010). SM also influenced seedling performance. Small-

seeded species consistently grew faster than large-seeded

species, although this effect diminished with seedling size

(Fig. 2a). Small-seeded species also had lower survival

rates than large-seeded species, but only at larger seedling

sizes. Among the smallest seedlings, small-seeded species

actually had higher survival rates than large-seeded species

(Fig. 2e). Environmental variation associated with germi-

nation sites likely confounds all of these relationships

(Lichstein et al. 2010; Muller-Landau 2010). Small-seeded

species tend to establish in the least stressful locations, and

those locations improve subsequent performance. Large-

seeded species are able to establish in more competitive

environments, which limit subsequent performance. On a

population level, this causes smaller-seeded species to have

lower establishment rates, because appropriate resource-

rich sites are infrequent, and larger growth and survival

rates than larger-seeded species that establish widely in less

favourable environments. Clearly, observed vital rates are

influenced not only by species traits, but by the habitats in

which individuals are found (i.e. environmental filtering;

Lasky et al. 2013). We discuss this issue in more detail

below.

LEAF MASS PER AREA

Previous studies and our results concur that LMA explains

minimal variation in growth and survival among tropical

trees (R2 averages 0�08 � 0�025 SD and 0�11 � 0�071 SD,

respectively; Fig. 3, this study; Poorter et al. 2008; Wright

et al. 2010; Iida et al. 2014b). LMA is thought to be a

minor factor affecting carbon gain in larger trees where

crown architecture determines light interception (Sterck &

Bongers 2001). Surprisingly, LMA was also vanishingly

unimportant for seedling establishment, growth and sur-

vival (Figs 1b, 2c,g and 3). LMA had non-negligible corre-

lations only with reproductive status, with species with

lower LMA tending to reproduce at smaller sizes (Figs 2

and 3). This relationship between LMA and reproductive

status provides another example, as with seed mass, of an

indirect effect, whose unknown causation must involve

unrecognized correlations among traits and life histories.

There are at least two possible reasons for the negligible

relationships of LMA with seedling growth and survival

(Figs 2c,g and 3). First, our LMA values are for saplings

(≥1 cm dbh), and seedling LMA values may differ. Previ-

ous studies show that leaf traits at juvenile and adult

stages are generally strongly correlated (Iida et al. 2014a);

however, as LMA measurements for recently established

seedlings remain rare, we cannot discount ontogenetic

changes in LMA between seedlings and saplings (Spasoje-

vic et al. 2014). LMA values determined for seedlings

might yet yield stronger relationships between LMA and

seedling performance. A second possible cause of weak

relationships between LMA and growth and survival

applies to both seedlings and trees. Costs and benefits

associated with LMA variation might balance, yielding

similar growth and survival rates on a population level.

Low LMA species tend to have low construction costs and

large leaf turnover rates, whereas high LMA species tend

to have larger construction costs and lower leaf turnover

rates (Wright et al. 2004). This may result in similar net

carbon gain over time, minimizing potential relationships

between LMA and demographic rates.

ADULT STATURE

Previous studies and our results concur that larger-statured

species have larger growth and survival rates among tropi-

cal trees (Fig. 2l,p) R2 values average 0�11 � 0�039 SD

and 0�2 � 0�066 SD, respectively (using the maximum of

reported R2 values; Fig. 3; Poorter et al. 2008; Wright

et al. 2010; Iida et al. 2014a,b). This led to the conclusion

that smaller-statured species have lower survival and

growth rates as they may have less access to light (Poorter

et al. 2008; Wright et al. 2010; Iida et al. 2014a). We see

opposing effects on seedlings, however, with taller species

at an inherent disadvantage in early life (King, Wright &

Connell 2006). Seedling establishment and survival

decreased with increasing adult stature (Figs 1b and 2h).

These opposing relationships between maximum size and

performance between life stages are a condition for coexis-

tence in the ‘forest architecture hypothesis’. Large-statured

species will out-compete smaller species, if not handi-

capped during establishment (Kohyama 1993). The handi-

cap observed among large-statured species during

establishment might be related to a trade-off between allo-

cation to traits and architectures that enable survival in

the forest understorey vs. rapid vertical growth towards

the canopy (for a more detailed discussion see Kohyama

et al. 2003; Poorter et al. 2005).

Dmax was the single most influential trait explaining inter-

specific variation in reproductive size thresholds (cf.

Fig. 2q–s vs. 2t). Species that grow larger only begin to

reproduce at larger sizes. The threshold size for reproduction



also increased for taller species in a Malaysian forest

(Thomas 1996; Davies & Ashton 1999). For BCI trees, the

simple equation R50 = ½Dmax explains 81% of interspecific

variation in reproductive size thresholds (Fig. S9). That is,

at the time individuals attain half of their species’ maximum

observed diameter, they have a 50% probability of being

reproductive. This is consistent with expectations from game

theory models (reviewed by Falster & Westoby 2003), which

predict that to maximize reproductive output individuals

should first invest heavily in growth, and then only after

reaching an optimal size start to invest in reproduction.

Extending this, we would expect large-statured species to

become reproductive only when they attain a position in the

forest canopy (Thomas 1996; Zuidema & Boot 2002), while

smaller understorey species will likely reproduce when they

obtain optimal crown depth (Kohyama et al. 2003) or foli-

age cover. However, whether this optimal size on average

corresponds to the ½Dmax size threshold reported here

remains to be tested.

TRA ITS AS QUANT ITAT IVE PRED ICTORS OF TREE

DEMOGRAPHY

Westoby (1998) suggested that specific leaf area (the

inverse of LMA), seed mass and adult stature are three

readily measurable traits that represent important dimen-

sions of variation in plant ecology. Our results suggest

that, at least for tropical forests, a more promising combi-

nation would be adult stature, seed mass and wood den-

sity. In our study system, adult stature, seed mass and

wood density (but not LMA) each explained substantial

interspecific variation in particular vital rates or particular

life stages (Fig. 3). Nevertheless, a large proportion of

interspecific and individual variation remained unexplained

(Table 3 and Fig. 3). Why is the explained variation not

higher, and what are the implications for the functional

trait research agenda?

Variance partitioning suggests that a considerable fraction

of the unexplained interindividual variation is due to species

effects not captured by the functional traits included in this

study. Variance partitioning quantifies the unexplained vari-

ation and, thus, the potential for additional factors to

explain variation at each grouping level. Mixed-effects mod-

els allow variance partitioning (Bolker et al. 2009), and we

calculated conditional R2 values to quantify variation

explained at the individual and species levels (Nakagawa &

Schielzeth 2013). The addition of species random effects

increased conditional R2 values on average by a factor of 3�2(compare R2

fixed with R2species in Table 3). This demonstrates

that substantial unexplained interspecific variation remains,

variation that might potentially be explained by additional

traits. However, species–environment associations might

also contribute to unexplained interspecific variation (Mes-

sier, McGill & Lechowicz 2010; Lasky et al. 2013).

Variation in plant performance among individuals

depends strongly on local environment as well as on spe-

cies traits and their interaction (Uriarte et al. 2016). Local

environmental variation includes both abiotic factors such

as soil nutrients (Condit et al. 2013) and water availability

(Comita & Engelbrecht 2009), and biotic factors such as

local competitive neighbourhoods (Uriarte et al. 2004).

Such environmental variation is not explicitly included in

our models, and thus contributes to variation among indi-

viduals captured here by individual-level random effects.

Environmental variation can also confound interspecific

comparisons of vital rates (Lichstein et al. 2010; McMa-

hon, Metcalf & Woodall 2011; Baraloto et al. 2012). For

example, in closed canopy tropical forests, small-seeded

species only establish successfully in relatively high light

microsites and higher light levels then contribute to higher

initial growth and survival rates (Fig. 2a,e). Inclusion of

more information on key environmental covariates for

each individual would make it possible to control for any

systematic differences in environments among species, and

thereby better estimate the true effects of traits on perfor-

mance (Paine et al. 2011; Lasky et al. 2014). Other sources

of variation will remain stochastic and unpredictable.

These include negative height growth due to stem breakage

for seedlings (e.g. from falling branches), which happens

regularly on BCI (Paciorek et al. 2000).

Our full-life cycle approach shows that individual traits

can have opposing effects on different vital rates. This

raises the possibility that effects at one life stage or vital

rate may be offset by opposite effects at another life stage

or vital rate. Robust ecological and evolutionary conclu-

sions, based on findings at single life stages or vital rates,

will therefore depend on how effect sizes translate to net

effects over the full life cycle. Trait-based models that map

full-life cycle demographic patterns across trait axes

(Figs 1 and 2) may help resolve full-life cycle effects. For

instance, when an empirical study finds an effect of

increased seed production – which comes at the cost of

reduced seed size – trait-based models can be used to cal-

culate whether a net positive effect on seedling recruitment

can be expected. A trait-based framework may even be

used in a population modelling context (sensu Visser et al.

2011; Merow et al. 2014), to calculate expected net effects

when information on the whole life cycle is lacking. In this

context, a full-life cycle trait-based approach may add

value to ecological research by enabling robust assessments

of relationships between traits and population fitness.

The holy grail of the functional traits research agenda is

the identification of easily measured traits that are good

predictors of life history and demographic performance,

and the parameterization of associated models for inferring

life history and demography from these traits. With increas-

ing amounts of vital rate variation across species explained,

researchers start daring to ask the exciting question of

whether trait-based vital rate models can be used to inter-

polate vital rates for species for which they have trait infor-

mation but lack demographic data. Such interpolations

would increase the number of species that can be included

in Earth system models and community-wide studies. How

certain do we have to be about these interpolations for the



resulting multispecies analyses to be trustworthy? Our

analyses still show a large proportion of unexplained inter-

specific variation, implying that trait-based models should

be used with caution. Nevertheless, we have shown that a

single individual measurement (size) and three species-level

traits (Dmax, SM and WD) explained on average 41% of

interspecific variation in vital rates (mean R2 ranged

between 0�11 and 0�66; Fig. 3), despite unquantified envi-

ronmental effects on vital rates. This is quite remarkable

and represents a substantial improvement over earlier stud-

ies. Functional biology may yet improve understanding of

tropical forest dynamics.

Acknowledgements

We thank Timothy Paine and an anonymous reviewer for helpful com-

ments. This study was supported by the Netherlands Organization for Sci-

entific Research (NWO-ALW 801-01-009; MDV), the Smithsonian Tropical

Research Institute (MDV, MB) and the HSBC Climate Partnership

(HCM). The data sets were collected with funding from the National

Science Foundation (DEB 0425651 & 0948585 to SPH and 1242622 &

1464389 to LSC), the Smithsonian Tropical Research Institute, the Centre

for Tropical Forest Science, the John D. and Catherine T. MacArthur

Foundation, the Mellon Foundation and the Small World Institute Fund.

Data accessibility

Previously archived BCI data set is available from Dryad (datadryad.org)

or the Smithsonian DSpace repository (repository.si.edu). Data sets 1, 2, 3

and 5 are achieved at DSpace with DOIs 10.5479/data.bci.20130603,

10.5479/si.data.201511251137, 10.5479/si.data.201511251134 and 10.5479/

si.data.201511251100, respectively, while data set 4 is archived at Dryad

http://dx.doi.org/10.5061/dryad.fm654 (Visser et al. 2016).

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Received 2 June 2015; accepted 4 December 2015

Handling Editor: Cory Merow

Supporting Information

Additional Supporting information may be found in the online

version of this article:

Appendix S1. Including Tables S1–S12, Figures S1–S11 and

Text S1.

Appendix S2. R-code example for model averaging.



https://www.R-project.org/

https://www.R-project.org/

SI: Functional traits as predictors of vital rates

across the life-cycle of tropical trees.

Marco D. Visser∗1,2, Marjolein Bruijning†1, Stuart J. Wright‡2,Helene C. Muller-Landau§2, Eelke Jongejans¶1, Liza S. Comita‖3,

and Hans de Kroon∗∗1

1Radboud University, Departments of Experimental Plant Ecology& Animal Ecology and Physiology, The Netherlands

2Smithsonian Tropical Research Institute, Republic of Panama3Yale University, School of Forestry and Environmental Studies

December 1, 2015

Contents

S1 TEXT: Comparison of results using inverse modeling 2

S2 Tables 4

S3 Figures 21

S1 TEXT: Comparison of results using

inverse modeling

When multiple adult trees contribute seeds to a single location it becomes com-plicated to assign individual seeds to their respective sources. A now commonlyused method pioneered by Ribbens et al (1994) to overcome this problem isinverse modeling (IM) of seed shadows (Clark et al, 1999; Muller-Landau etal, 2008). A seed shadow describes the distribution of seeds with distance from

∗[email protected]†[email protected]‡[email protected]§[email protected]¶[email protected]‖[email protected]∗∗[email protected]

1

S1 TEXT: COMPARISON OF RESULTS USING INVERSE MODELING

their parent, and consists of two elements; (1) a component describing the fecun-dity of a tree, usually as a function of size and (2) a dispersal kernel describingthe distribution of seeds as a function of distance from the source. The seedshadow is the product of these two elements (Clark et al., 1999).

Seed shadows from individual trees will overlap within a stand and the seedrain of these multiple sources can be viewed as a smoothed version of the individ-ual shadows (Clark et al., 1999). This smoothed shadow can be expressed as thesummed contributions of each individual seed shadow (hereafter the summedseed shadow SSS). The number of seeds in a certain quadrat is predicted by sum-ming the densities the quadrat receives from each of the surrounding sources, asprojected by each individual seed shadow. IM has been shown to be a promisingestimate of dispersal distances, as it is not inherently biased to long or shortdistance dispersal (Jones and Muller-Landau, 2008; Muller-Landau et al, 2008).With IM the predicted seed density in a sample quadrat at location j is givenby the summed seed shadow of all N trees at location j (SSSj);

SSSj = α ·T∑

i=1

F (pibi|β) ·Q(rij |µ, σ) (1)

where SSSj is the predicted seed number in trap j, which is the sum of seedcontributions from T trees multiplied by trap size α (0.5 m2). For each treei, the seed shadow is a function of the fecundity F , and a two-dimensionaldispersal kernel Q. Q depends on the distance rij between tree i and trapj, while F is a function of basal area bi (calculated as [dbh/2]2π) and seedproduction probabilities pi (obtained from the reproductive census as describedin the main text). Only females were included for dioecious species.

The parameters β, µ and σ were fitted per species. We tested four differentdispersal kernels: the Exponential, 2dt (Clark et al., 1999), 2dt1k (which is the2dt distribution with the degrees of freedom parameter set to 3) (Muller-Landauet al., 2008) and the Cauchy density distribution.

Off plot integrationTraps within 20 meter from the edge of the plot were excluded since they couldbe highly influenced by trees outside the plot (Muller-Landau et al., 2008). Datafrom the remaining 188 traps were used. We then estimated the contribution tothe seed rain from unknown sources outside the mapped plot. Here it is assumedthat the per unit area seed production off plot is uniform to that within theplot (see Muller-Landau et al., 2008, for details). The total expected seed count

ˆSSSj in trap j then becomes the sum of the expected contributions from treesinside the plot SSSj and the expected contributions from trees outside the plot

(immigrant seeds) Ij to seed trap j.

ˆSSSj = SSSj + Ij (2)

2

S2 TABLES

Parameter estimationModels were fit using maximum likelihood, assuming seed counts in traps weredistributed according to a negative binomial distribution, with an additinalclumping parameter k (Muller-Landau et al., 2008), as previous studies haveshown that seed arrival tends to be clumped (Muller-Landau et al., 2008).

The results from the above described inverse modeling approach are comparedto the alternative estimates (see maintext) in figure S2.

S2 Tables

3

S2

TABLES

Table S1. Overview of species used in each analysis, ordered alphabetically. True indicates a species was included.

Species Reproduction Seed production Seedlingestablishment

Seedlingsurvival

Seedlinggrowth

Tree survival Treegrowth

Adelia triloba FALSE FALSE FALSE FALSE FALSE TRUE TRUEAlchornea costaricensi TRUE TRUE TRUE TRUE TRUE TRUE TRUEAlibertia edulis FALSE FALSE TRUE TRUE TRUE TRUE TRUEAllophylus psilospermus FALSE FALSE FALSE FALSE FALSE TRUE TRUEAlseis blackiana TRUE TRUE TRUE TRUE TRUE TRUE TRUEAnacardium excelsum FALSE TRUE FALSE TRUE TRUE FALSE FALSEAndira inermis FALSE FALSE FALSE FALSE FALSE TRUE TRUEAnnona acuminata FALSE FALSE FALSE TRUE TRUE TRUE TRUEAnnona spraguei FALSE FALSE FALSE TRUE TRUE TRUE TRUEApeiba membranacea TRUE FALSE TRUE TRUE TRUE TRUE TRUEApeiba tibourbou TRUE FALSE FALSE FALSE FALSE FALSE FALSEAspidosperma spruceanum TRUE FALSE TRUE TRUE TRUE TRUE TRUEAstronium graveolens TRUE TRUE FALSE FALSE FALSE TRUE TRUEBeilschmiedia pendula TRUE TRUE TRUE TRUE TRUE TRUE TRUEBrosimum alicastrum TRUE TRUE TRUE TRUE TRUE TRUE TRUECalophyllum longifolium FALSE FALSE TRUE TRUE TRUE TRUE TRUECapparis frondosa FALSE FALSE TRUE TRUE TRUE TRUE TRUECasearia aculeata TRUE FALSE FALSE FALSE FALSE TRUE TRUECasearia arborea FALSE FALSE FALSE FALSE FALSE TRUE TRUECassipourea elliptica TRUE FALSE TRUE TRUE TRUE TRUE TRUECecropia insignis FALSE FALSE FALSE FALSE FALSE FALSE FALSEChrysochlamy eclipes FALSE FALSE FALSE FALSE FALSE TRUE TRUEChrysophyllum argenteum TRUE TRUE TRUE TRUE TRUE TRUE TRUEChrysophyllum cainito TRUE TRUE TRUE TRUE TRUE TRUE TRUECoccoloba coronata FALSE FALSE FALSE FALSE FALSE TRUE TRUECoccoloba manzinellens FALSE FALSE FALSE FALSE FALSE TRUE TRUECordia alliodora TRUE TRUE TRUE TRUE TRUE TRUE TRUECordia bicolor TRUE TRUE TRUE TRUE TRUE TRUE TRUECoussarea curvigemmia TRUE TRUE TRUE TRUE TRUE TRUE TRUECupania rufescens FALSE FALSE FALSE FALSE FALSE TRUE TRUECupania seemannii FALSE FALSE FALSE FALSE FALSE TRUE TRUE

Continued on next page4

S2

TABLES



Seedlingsurvival

Seedlinggrowth


Dendropanax arboreus FALSE FALSE TRUE FALSE FALSE FALSE FALSEDesmopsis panamensis TRUE TRUE TRUE TRUE TRUE TRUE TRUEDipteryx oleifera TRUE TRUE FALSE TRUE TRUE FALSE FALSEDrypetes standleyi FALSE TRUE TRUE TRUE TRUE TRUE TRUEErythroxylum panamense FALSE FALSE FALSE FALSE FALSE TRUE TRUEEugenia coloradoensi FALSE FALSE TRUE TRUE TRUE TRUE TRUEEugenia galalonensis FALSE FALSE FALSE TRUE TRUE TRUE TRUEEugenia nesiotica FALSE FALSE TRUE TRUE TRUE TRUE TRUEEugenia oerstediana TRUE TRUE TRUE TRUE TRUE TRUE TRUEFaramea occidentalis TRUE TRUE TRUE TRUE TRUE TRUE TRUEFicus insipida FALSE FALSE TRUE FALSE FALSE FALSE FALSEGarcinia intermedia TRUE TRUE TRUE TRUE TRUE TRUE TRUEGarcinia madruno FALSE FALSE FALSE FALSE FALSE TRUE TRUEGenipa americana FALSE FALSE TRUE FALSE FALSE FALSE FALSEGuapira standleyana FALSE FALSE TRUE TRUE TRUE TRUE TRUEGuarea guidonia TRUE TRUE TRUE TRUE TRUE TRUE TRUEGuatteria dumetorum TRUE FALSE TRUE TRUE TRUE TRUE TRUEGustavia superba TRUE TRUE TRUE TRUE TRUE TRUE TRUEHamelia axillaris FALSE FALSE FALSE TRUE TRUE TRUE TRUEHampea appendiculat FALSE FALSE TRUE TRUE TRUE TRUE TRUEHasseltia floribunda TRUE FALSE TRUE FALSE FALSE TRUE TRUEHeisteria acuminata FALSE FALSE TRUE FALSE FALSE TRUE TRUEHeisteria concinna TRUE TRUE TRUE TRUE TRUE TRUE TRUEHerrania purpurea FALSE FALSE FALSE FALSE FALSE TRUE TRUEHirtella triandra TRUE TRUE TRUE TRUE TRUE TRUE TRUEHybanthus prunifolius FALSE FALSE TRUE TRUE TRUE TRUE TRUEHieronyma alchorneoide TRUE TRUE FALSE FALSE FALSE TRUE TRUEInga goldmanii FALSE FALSE FALSE FALSE FALSE TRUE TRUEInga marginata TRUE TRUE TRUE TRUE TRUE TRUE TRUEInga nobilis FALSE FALSE FALSE FALSE FALSE TRUE TRUEInga acuminata FALSE FALSE TRUE TRUE TRUE TRUE TRUE


S2

TABLES



Seedlingsurvival

Seedlinggrowth


Inga sapindoides FALSE FALSE FALSE FALSE FALSE TRUE TRUEInga thibaudiana FALSE FALSE FALSE TRUE TRUE TRUE TRUEInga umbellifera FALSE FALSE FALSE TRUE TRUE TRUE TRUEJacaranda copaia TRUE FALSE TRUE FALSE FALSE TRUE TRUELacistema aggregatum FALSE FALSE FALSE TRUE TRUE TRUE TRUELacmellea panamensis FALSE FALSE TRUE TRUE TRUE TRUE TRUELaetia thamnia TRUE FALSE FALSE FALSE FALSE TRUE TRUELicania hypoleuca FALSE FALSE FALSE FALSE FALSE TRUE TRUELicania platypus FALSE FALSE FALSE FALSE FALSE TRUE TRUELonchocarpus heptaphyllus TRUE FALSE TRUE TRUE TRUE TRUE TRUELuehea seemannii TRUE TRUE TRUE TRUE TRUE TRUE TRUEMosannona garwoodii TRUE FALSE FALSE FALSE FALSE TRUE TRUEMaquira guianensis FALSE FALSE FALSE FALSE FALSE TRUE TRUEMouriri myrtilloides TRUE TRUE TRUE TRUE TRUE TRUE TRUENectandra cissiflora FALSE FALSE FALSE FALSE FALSE TRUE TRUEOcotea cernua FALSE FALSE FALSE TRUE TRUE TRUE TRUEOcotea puberula FALSE FALSE TRUE TRUE TRUE TRUE TRUETrophis caucana FALSE FALSE FALSE TRUE TRUE TRUE TRUEOrmosia coccinea FALSE FALSE FALSE FALSE FALSE TRUE TRUEOrmosia macrocalyx FALSE FALSE FALSE FALSE FALSE TRUE TRUEOuratea lucens FALSE FALSE TRUE TRUE TRUE TRUE TRUEPalicourea guianensis FALSE FALSE TRUE TRUE TRUE TRUE TRUEPentagonia macrophylla FALSE FALSE FALSE FALSE FALSE TRUE TRUEPerebea xanthochyma FALSE FALSE FALSE FALSE FALSE TRUE TRUECinnamomum triplinerve FALSE FALSE TRUE TRUE TRUE FALSE FALSEPicramnia latifolia FALSE FALSE TRUE TRUE TRUE TRUE TRUEPlatymiscium pinnatum FALSE FALSE FALSE TRUE TRUE TRUE TRUEPlatypodium elegans TRUE FALSE FALSE FALSE FALSE TRUE TRUEPoulsenia armata TRUE FALSE TRUE FALSE FALSE TRUE TRUEPourouma bicolor FALSE FALSE FALSE FALSE FALSE TRUE TRUEPouteria reticulata TRUE TRUE TRUE TRUE TRUE TRUE TRUE


S2

TABLES



Seedlingsurvival

Seedlinggrowth


Prioria copaifera TRUE TRUE FALSE TRUE TRUE TRUE TRUEProtium panamense TRUE FALSE FALSE TRUE TRUE TRUE TRUEProtium tenuifolium TRUE TRUE TRUE TRUE TRUE TRUE TRUEPseudobombax septenatum TRUE FALSE FALSE FALSE FALSE FALSE FALSEPsychotria acuminata FALSE FALSE TRUE TRUE TRUE FALSE FALSEPsychotria deflexa FALSE FALSE FALSE TRUE TRUE FALSE FALSEPsychotria horizontalis FALSE FALSE TRUE TRUE TRUE TRUE TRUEPsychotria marginata FALSE FALSE TRUE TRUE TRUE TRUE TRUEPsychotria racemosa FALSE FALSE TRUE TRUE TRUE FALSE FALSEPterocarpus rohrii TRUE FALSE FALSE FALSE FALSE TRUE TRUEQuararibea asterolepis TRUE TRUE TRUE TRUE TRUE TRUE TRUEQuassia amara FALSE FALSE FALSE FALSE FALSE TRUE TRUERandia armata TRUE TRUE TRUE TRUE TRUE TRUE TRUESimarouba amara TRUE TRUE TRUE TRUE TRUE TRUE TRUESloanea terniflora TRUE FALSE FALSE FALSE FALSE TRUE TRUESorocea affinis FALSE FALSE TRUE TRUE TRUE TRUE TRUESpondias mombin FALSE FALSE FALSE FALSE FALSE TRUE TRUESpondias radlkoferi FALSE FALSE FALSE TRUE TRUE TRUE TRUESterculia apetala FALSE FALSE FALSE TRUE TRUE FALSE FALSEStylogyne turbacensis FALSE FALSE FALSE TRUE TRUE TRUE TRUESwartzia simplex FALSE FALSE FALSE TRUE TRUE FALSE FALSESymphonia globulifera FALSE FALSE FALSE FALSE FALSE TRUE TRUETabebuia guayacan FALSE FALSE TRUE FALSE FALSE FALSE FALSETabebuia rosea TRUE TRUE TRUE TRUE TRUE TRUE TRUETabernaemont arborea TRUE FALSE TRUE TRUE TRUE TRUE TRUETachigali versicolor FALSE FALSE FALSE TRUE TRUE TRUE TRUETalisia nervosa TRUE FALSE FALSE FALSE FALSE TRUE TRUETalisia princeps FALSE FALSE FALSE TRUE TRUE TRUE TRUETerminalia amazonia TRUE TRUE FALSE FALSE FALSE FALSE FALSETerminalia oblonga TRUE FALSE FALSE FALSE FALSE FALSE FALSETetragastris panamensis TRUE TRUE TRUE TRUE TRUE TRUE TRUE


S2

TABLES



Seedlingsurvival

Seedlinggrowth


Thevetia ahouai FALSE FALSE FALSE FALSE FALSE TRUE TRUETrattinnicki aspera TRUE TRUE FALSE FALSE FALSE FALSE FALSETrichilia pallida TRUE TRUE TRUE FALSE FALSE TRUE TRUETrichilia tuberculata TRUE TRUE TRUE TRUE TRUE TRUE TRUETriplaris cumingiana TRUE TRUE TRUE FALSE FALSE TRUE TRUETurpinia occidentalis FALSE FALSE FALSE FALSE FALSE TRUE TRUEUnonopsis pittieri TRUE FALSE TRUE TRUE TRUE TRUE TRUEVirola sebifera TRUE FALSE TRUE TRUE TRUE TRUE TRUEVirola surinamensis FALSE FALSE FALSE FALSE FALSE TRUE TRUEVochysia ferruginea TRUE FALSE TRUE FALSE FALSE FALSE FALSEXylopia macrantha TRUE FALSE FALSE TRUE TRUE TRUE TRUEZanthoxylum panamense FALSE FALSE TRUE TRUE TRUE TRUE TRUE

8

S2

TABLES

Table S3. All evaluated model forms, model weights, and ∆AIC values for the seed production analysis (fseeds).

formulas weight ∆AIC

fec ∼ sm + dmax + lma 0.212 0.000fec ∼ sm + dmax + wd 0.203 0.091fec ∼ sm + lma 0.178 0.354fec ∼ sm + dmax 0.120 1.144fec ∼ sm + wd 0.087 1.778fec ∼ sm + dmax + wd + lma 0.084 1.859fec ∼ sm + lma + wd 0.072 2.166fec ∼ sm 0.043 3.211fec ∼ lma 0.001 11.975fec ∼ lma + wd 0.000 12.628fec ∼ wd 0.000 13.034fec ∼ dmax + wd 0.000 13.535fec ∼ dmax + lma 0.000 13.779fec ∼ dmax + lma + wd 0.000 14.241fec ∼ 1 0.000 19.228fec ∼ dmax 0.000 20.645

10

S2

TABLES

Table S4. All evaluated model forms, model weights, and ∆AIC values for the seedling establishment analysis.


qlogis(p) ∼ sm + dmax 0.484 0.000qlogis(p) ∼ sm + dmax + wd 0.246 1.356qlogis(p) ∼ sm + dmax + lma 0.178 1.999qlogis(p) ∼ sm + dmax + wd + lma 0.093 3.303qlogis(p) ∼ dmax + wd 0.000 31.452qlogis(p) ∼ dmax + lma + wd 0.000 33.443qlogis(p) ∼ dmax 0.000 34.448qlogis(p) ∼ dmax + lma 0.000 35.980qlogis(p) ∼ sm + wd 0.000 39.262qlogis(p) ∼ sm + lma 0.000 39.317qlogis(p) ∼ sm + lma + wd 0.000 39.588qlogis(p) ∼ sm 0.000 40.278log(p) ∼ 1 0.000 47.684qlogis(p) ∼ wd 0.000 48.810qlogis(p) ∼ lma + wd 0.000 49.492qlogis(p) ∼ lma 0.000 51.928

11

S2

TABLES

Table S5. Evaluated model forms, model weights, and ∆AIC values for the seedling growth analysis, for the top 30 models(out of 82). A full list of model forms can be found in table S2.


growth ∼ height ∗ wd + height ∗ sm + lma + dmax + (1|species) + (1|tag) 0.599 0.000growth ∼ height ∗ wd + height ∗ sm + lma + (1|species) + (1|tag) 0.166 2.567growth ∼ height ∗ wd + height ∗ sm + dmax + (1|species) + (1|tag) 0.143 2.870growth ∼ height ∗ wd + height ∗ sm + (1|species) + (1|tag) 0.041 5.368growth ∼ height ∗ wd + height ∗ sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.021 6.727growth ∼ height ∗ wd + height ∗ sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.020 6.752growth ∼ height ∗ wd + height ∗ sm + height ∗ lma + (1|species) + (1|tag) 0.006 9.293growth ∼ height ∗ wd + height ∗ sm + lma + height ∗ dmax + (1|species) + (1|tag) 0.004 10.255growth ∼ height ∗ wd + height ∗ sm + height ∗ dmax + (1|species) + (1|tag) 0.001 13.125growth ∼ height + wd + height ∗ sm + lma + dmax + (1|species) + (1|tag) 0.000 16.569growth ∼ height + wd + height ∗ sm + lma + (1|species) + (1|tag) 0.000 19.128growth ∼ height + wd + height ∗ sm + dmax + (1|species) + (1|tag) 0.000 19.429growth ∼ height + wd + height ∗ sm + (1|species) + (1|tag) 0.000 21.918growth ∼ height + wd + height ∗ sm + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 22.950growth ∼ height ∗ sm + lma + dmax + (1|species) + (1|tag) 0.000 24.422growth ∼ height + wd + height ∗ sm + height ∗ dmax + (1|species) + (1|tag) 0.000 25.810growth ∼ height ∗ sm + lma + (1|species) + (1|tag) 0.000 27.096growth ∼ height ∗ sm + dmax + (1|species) + (1|tag) 0.000 27.599growth ∼ height ∗ sm + (1|species) + (1|tag) 0.000 30.311growth ∼ height ∗ sm + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 30.802growth ∼ height + wd + height ∗ sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 31.209growth ∼ height + wd + height ∗ sm + height ∗ lma + (1|species) + (1|tag) 0.000 33.768growth ∼ height ∗ sm + height ∗ dmax + (1|species) + (1|tag) 0.000 33.972growth ∼ height + wd + height ∗ sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 34.538growth ∼ height ∗ sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 39.062growth ∼ height ∗ sm + height ∗ lma + (1|species) + (1|tag) 0.000 41.735growth ∼ height ∗ sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 42.380growth ∼ height ∗ wd + sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 61.905growth ∼ height ∗ wd + sm + height ∗ lma + (1|species) + (1|tag) 0.000 64.478growth ∼ height ∗ wd + sm + lma + dmax + (1|species) + (1|tag) 0.000 65.205

12

S2

TABLES

Table S6. Evaluated model forms, model weights, and ∆AIC values for the seedling survival analysis, for the top 30 models(out of 82). A full list of model forms can be found in table S2.


survive ∼ height ∗ wd + height ∗ sm + height ∗ dmax + height ∗ lma + (1|species) + (1|tag) 1.000 0.000survive ∼ height ∗ wd + height ∗ sm + height ∗ dmax + (1|species) + (1|tag) 0.000 81.243survive ∼ height ∗ wd + height ∗ sm + height ∗ dmax + lma + (1|species) + (1|tag) 0.000 83.232survive ∼ height + wd + height ∗ sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 114.721survive ∼ height + wd + height ∗ sm + height ∗ lma + (1|species) + (1|tag) 0.000 120.806survive ∼ height + wd + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 205.165survive ∼ height ∗ wd + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 206.284survive ∼ height + wd + sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 207.109survive ∼ height ∗ wd + sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 208.230survive ∼ height + sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 231.883survive ∼ height ∗ wd + (1|species) + height ∗ dmax + (1|tag) 0.000 279.614survive ∼ height ∗ wd + sm + height ∗ dmax + (1|species) + (1|tag) 0.000 281.542survive ∼ height ∗ wd + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 281.589survive ∼ height ∗ wd + sm + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 283.529survive ∼ height + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 314.015survive ∼ height + wd + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 517.184survive ∼ height + wd + sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 519.107survive ∼ height + wd + height ∗ lma + (1|species) + (1|tag) 0.000 525.973survive ∼ height + wd + sm + height ∗ lma + (1|species) + (1|tag) 0.000 526.077survive ∼ height ∗ wd + lma + (1|species) + (1|tag) 0.000 526.516survive ∼ height ∗ lma + dmax + (1|species) + (1|tag) 0.000 542.056survive ∼ height + sm + height ∗ lma + dmax + (1|species) + (1|tag) 0.000 543.147survive ∼ height + lma + (1|species) + (1|tag) 0.000 549.065survive ∼ height ∗ lma + (1|species) + (1|tag) 0.000 549.670survive ∼ height + sm + height ∗ lma + (1|species) + (1|tag) 0.000 551.647survive ∼ 1 + (1|species) + (1|tag) 0.000 2721.140survive ∼ height + wd + height ∗ sm + height ∗ lma + height ∗ dmax + (1|species) + (1|tag) 0.000 4640.336survive ∼ height + wd + height ∗ sm + lma + height ∗ dmax + (1|species) + (1|tag) 0.000 4752.207survive ∼ height + wd + height ∗ sm + height ∗ dmax + (1|species) + (1|tag) 0.000 4763.285survive ∼ height + wd + height ∗ sm + lma + dmax + (1|species) + (1|tag) 0.000 4820.751

13

S2

TABLES

Table S7. Evaluated model forms, model weights, and ∆AIC values for the tree growth analysis, for the top 30 models (outof 82). A full list of model forms can be found in table S2. The variable BA refers to basal area (mm2).

formulas weight ∆AICgrowth ∼ (BA) ∗ wd + (BA) ∗ sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 1.000 0.000growth ∼ (BA) ∗ wd + (BA) ∗ sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 722.011growth ∼ (BA) ∗ wd + (BA) ∗ sm + BA ∗ dmax + (1|species) + (1|tag) 0.000 727.452growth ∼ (BA) ∗ wd + (BA) ∗ sm + (BA) ∗ lma + dmax + (1|species) + (1|tag) 0.000 1434.112growth ∼ (BA) ∗ wd + (BA) ∗ sm + (BA) ∗ lma + (1|species) + (1|tag) 0.000 1455.667growth ∼ (BA) ∗ wd + sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 1732.065growth ∼ (BA) ∗ wd + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 1745.412growth ∼ (BA) + wd + (BA) ∗ sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 1809.328growth ∼ (BA) ∗ sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 1857.663growth ∼ (BA) ∗ wd + sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 2075.792growth ∼ (BA) ∗ wd + sm + BA ∗ dmax + (1|species) + (1|tag) 0.000 2080.841growth ∼ (BA) ∗ wd + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 2088.759growth ∼ (BA) ∗ wd + BA ∗ dmax + (1|species) + (1|tag) 0.000 2094.665growth ∼ (BA) + wd + (BA) ∗ sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 2680.176growth ∼ (BA) + wd + (BA) ∗ sm + BA ∗ dmax + (1|species) + (1|tag) 0.000 2685.318growth ∼ (BA) ∗ sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 2730.689growth ∼ (BA) ∗ sm + BA ∗ dmax + (1|species) + (1|tag) 0.000 2736.455growth ∼ (BA) ∗ wd + (BA) ∗ sm + lma + dmax + (1|species) + (1|tag) 0.000 2785.345growth ∼ (BA) ∗ wd + (BA) ∗ sm + dmax + (1|species) + (1|tag) 0.000 2791.423growth ∼ (BA) ∗ wd + (BA) ∗ sm + lma + (1|species) + (1|tag) 0.000 2807.186growth ∼ (BA) ∗ wd + (BA) ∗ sm + (1|species) + (1|tag) 0.000 2812.767growth ∼ (BA) + wd + sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 3643.162growth ∼ (BA) + wd + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 3656.590growth ∼ (BA) + sm + (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 3689.670growth ∼ (BA) ∗ lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 3707.314growth ∼ (BA) + wd + sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 4086.731growth ∼ (BA) + wd + sm + BA ∗ dmax + (1|species) + (1|tag) 0.000 4091.493growth ∼ (BA) + wd + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 4099.658growth ∼ (BA) + wd + BA ∗ dmax + (1|species) + (1|tag) 0.000 4105.179growth ∼ (BA) + sm + lma + BA ∗ dmax + (1|species) + (1|tag) 0.000 4135.374

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S2 TABLES

Table S10. R2 values for average models including only single traits for seedproduction and seedling establishment.

dmax lma sm wd

Fecundity 0.01 0.04 0.63 0.00Establishment 0.20 0.05 0.33 0.10

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S2

TABLES

Table S11. R2 values at different sizes for average models including only single traits for seedlings. Dmax, LMA, SM and WDrepresent adult stature, leaf mass per area, seed mass and wood density.

Dmax LMA SM WD

Size (mm hght) Growth Survival Growth Survival Growth Survival Growth Survival200 0.283 0.283 0.002 0.022 0.103 0.053 0.151 0.219260 0.261 0.261 0.002 0.032 0.114 0.036 0.14 0.263320 0.234 0.234 0.003 0.041 0.104 0.021 0.142 0.305380 0.126 0.126 0 0.018 0.086 0.002 0.173 0.357440 0.1 0.1 0.001 0.017 0.081 0.01 0.174 0.385500 0.08 0.08 0.002 0.016 0.074 0.021 0.168 0.405560 0.063 0.063 0.003 0.014 0.066 0.036 0.161 0.418620 0.064 0.064 0.005 0.015 0.058 0.042 0.152 0.42680 0.054 0.054 0.008 0.014 0.037 0.06 0.141 0.42740 0.041 0.041 0.01 0.012 0.031 0.074 0.124 0.431800 0.04 0.04 0.009 0.01 0.026 0.082 0.116 0.429860 0.032 0.032 0.008 0.008 0.019 0.092 0.101 0.423920 0.027 0.027 0.01 0.008 0.012 0.1 0.086 0.42980 0.028 0.028 0.011 0.02 0.008 0.094 0.068 0.4081040 0.025 0.025 0.015 0.019 0.004 0.105 0.06 0.41100 0.024 0.024 0.007 0.015 0.008 0.11 0.067 0.4071160 0.022 0.022 0.011 0.016 0.006 0.109 0.042 0.41220 0.019 0.019 0.004 0.015 0.005 0.104 0.027 0.3931280 0.021 0.021 0.008 0.019 0.004 0.097 0.039 0.3921340 0.03 0.03 0.003 0.011 0.003 0.087 0.009 0.4511400 0.028 0.028 0.001 0.019 0.002 0.081 0.001 0.5081460 0.032 0.032 0.011 0.021 0.011 0.068 0.004 0.5021490 0.036 0.036 0.01 0.022 0.013 0.061 0.01 0.495

18

S2

TABLES

Table S12. R2 values at different sizes from average models for growth, survival and reproduction including only single traitsfor trees (>1cm dbh). Dmax, LMA, SM and WD represent adult stature, leaf mass per area, seed mass and wood density.

Dmax LMA SM WD

Size (mm dbh) Growth Survival Repr. Growth Survival Repr. Growth Survival Repr. Growth Survival Repr.10 0.1108 0.0172 0.4429 0.0258 3e-04 0.4525 0.0564 0.1666 0.0269 0.3446 0.2765 0.384240 0.1187 0.0062 0.4429 0.0091 4e-04 0.4525 0.0863 0.0938 0.0269 0.3596 0.2609 0.384270 0.0701 0.0344 0.5423 0.0206 0.0068 0.3898 0.0799 0.0544 0.0035 0.4181 0.2547 0.1874100 0.0012 6e-04 0.5808 0.0257 0.0191 0.433 0.0532 0.0411 0.003 0.4112 0.2253 0.426130 0.025 0.0024 0.3845 0.0196 0.0214 0.1971 0.0208 0.0143 1e-04 0.371 0.193 0.1509160 0.0139 0.0164 0.3976 0.0046 0.018 0.0618 0.0184 0.0048 0.0095 0.2693 0.1712 0.0534190 0.0035 0.1314 0.2418 0.011 0.008 0.0423 0.0343 7e-04 7e-04 0.2859 0.1176 0.0175220 0.0096 0.1105 0.195 8e-04 0.0028 0.0611 0.0812 0.0048 0.0082 0.3293 0.0922 0.0028250 0.0418 0.2496 0.2374 0.0216 0.0025 0.0693 0.0716 1e-04 0.009 0.2707 0.0922 0.0046

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Figure S3. Plots of residuals of the full averaged model against size for treegrowth (A,B), tree survival (C,D), seedling growth (E,F), seedling survival (G,H,and reproduction (I). Black dots indicate residual values from each model-averaged mixed-effect model (predicted - observed), while the red line is themoving average. The first column (A,C,E,G,I) shows residuals for models fit tothe full datasets, while the second column (B,D,F,H) shows residuals for modelsfit to datasets truncated above at 500 mm diameter (B,D) or 2500 mm height(F, H) to avoid non-linearities with size.

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Figure S4. Observed and fitted proportion of individuals that are reproductive as a function of tree diameter (mm) for eachspecies. Black dots show observations, red dashed lines show species-specific moving averages (from a Generalized AdditiveModel using a loess smoother), blue dashed lines show the fitted trait based average model, and grey lines show a fitted modelbased only on size (not on traits, thus identical for all species).

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Jacaranda copaia

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Laetia thamnia

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Lonchocarpus heptaphyllus

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Luehea seemannii

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Figure S4. Continued.

25

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Mosannona garwoodii

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Mouriri myrtilloides

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Platypodium elegans

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Poulsenia armata

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Pouteria reticulata

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Prioria copaifera

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Protium panamense

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Protium tenuifolium

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Pseudobombax septenatum

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Pterocarpus rohrii

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Quararibea asterolepis

Dbh (mm)R

epro

duct

ion

prob

abili

ty

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Randia armata

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Simarouba amara

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Sloanea terniflora

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Tabebuia rosea

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Tabernaemont arborea

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility


26

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Talisia nervosa

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Terminalia amazonia

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Terminalia oblonga

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Tetragastris panamensis

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Trattinnicki aspera

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Trichilia pallida

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Trichilia tuberculata

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Triplaris cumingiana

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Unonopsis pittieri

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Virola sebifera

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Vochysia ferruginea

Dbh (mm)R

epro

duct

ion

prob

abili

ty

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

0 100 200 300 400 500

0.0

0.4

0.8

Xylopia macrantha

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility

Dbh (mm)

Rep

rodu

ctio

n pr

obab

ility


27

S3

FIG

URES

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Alibertia edulis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Alseis blackiana

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Anacardium excelsum

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Annona acuminata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Annona spraguei

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Apeiba membranacea

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Brosimum alicastrum

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Calophyllum longifolium

Height (mm)Ye

arly

gro

wth

(m

m h

eigh

t)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Capparis frondosa

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Cordia alliodora

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Figure S5. Observed and predicted annual seedling height growth (mm/yr) as a function of initial height (mm). Black dotsshow observations, red dashed lines show species-specific moving averages (from a Generalized Additive Model using a loesssmoother), blue dashed lines show the fitted trait based average model, and grey lines show a fitted model based only on size(not on traits, thus identical for all species).

28

S3

FIG

URES

500 1000 1500 2000 2500

−20

00

100

Cordia bicolor

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Dipteryx oleifera

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Drypetes standleyi

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Eugenia coloradoensi

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Eugenia galalonensis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Eugenia nesiotica

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Eugenia oerstediana

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Garcinia intermedia

Height (mm)Ye

arly

gro

wth

(m

m h

eigh

t)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Guapira standleyana

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Guarea guidonia

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Guatteria dumetorum

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Gustavia superba

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Hamelia axillaris

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)


29

S3

FIG

URES

500 1000 1500 2000 2500

−20

00

100

Hampea appendiculat

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Heisteria concinna

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Hirtella triandra

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Hybanthus prunifolius

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Inga marginata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Inga acuminata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Inga thibaudiana

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Inga umbellifera

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Lacistema aggregatum

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Lacmellea panamensis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)Ye

arly

gro

wth

(m

m h

eigh

t)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Luehea seemannii

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Ocotea cernua

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Ocotea puberula

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Trophis caucana

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)


30

S3

FIG

URES

500 1000 1500 2000 2500

−20

00

100

Ouratea lucens

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Palicourea guianensis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Cinnamomum triplinerve

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Picramnia latifolia

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Platymiscium pinnatum

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Pouteria reticulata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Prioria copaifera

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Protium panamense

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Protium tenuifolium

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Psychotria acuminata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Psychotria deflexa

Height (mm)Ye

arly

gro

wth

(m

m h

eigh

t)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Psychotria horizontalis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Psychotria marginata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Psychotria racemosa

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Randia armata

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)


31

S3

FIG

URES

500 1000 1500 2000 2500

−20

00

100

Simarouba amara

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Sorocea affinis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Spondias radlkoferi

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Sterculia apetala

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Stylogyne turbacensis

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Swartzia simplex_var.

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Tabebuia rosea

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Tachigali versicolor

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Talisia croatii

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)Ye

arly

gro

wth

(m

m h

eigh

t)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100


Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Unonopsis pittieri

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Virola sebifera

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Xylopia macrantha

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

500 1000 1500 2000 2500

−20

00

100

Zanthoxylum panamense

Height (mm)

Year

ly g

row

th (

mm

hei

ght)

Height (mm)

Year

ly g

row

th (

mm

hei

ght)


32

S3

FIG

URES

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Alibertia edulis

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Alseis blackiana

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Anacardium excelsum

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Annona acuminata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Annona spraguei

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Apeiba membranacea

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Brosimum alicastrum

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)Ye

arly

sur

viva

l pro

babi

lity

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Capparis frondosa

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Cordia alliodora

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Figure S6. Observed and predicted annual seedling survival as a function of initial height (mm). Black dots show observations,red dashed lines show species-specific moving averages (from a Generalized Additive Model using a loess smoother), bluedashed lines show the fitted trait based average model, and grey lines show a fitted model based only on size (not on traits,thus identical for all species).

33

S3

FIG

URES

500 1000 1500 2000 2500

0.0

0.4

0.8

Cordia bicolor

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Dipteryx oleifera

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Drypetes standleyi

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Eugenia nesiotica

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Eugenia oerstediana

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Garcinia intermedia

Height (mm)Ye

arly

sur

viva

l pro

babi

lity

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Guapira standleyana

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Guarea guidonia

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Guatteria dumetorum

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Gustavia superba

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Hamelia axillaris

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y


34

S3

FIG

URES

500 1000 1500 2000 2500

0.0

0.4

0.8

Hampea appendiculat

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Heisteria concinna

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Hirtella triandra

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Inga marginata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Inga acuminata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Inga thibaudiana

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Inga umbellifera

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)Ye

arly

sur

viva

l pro

babi

lity

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Luehea seemannii

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Ocotea cernua

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Ocotea puberula

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Trophis caucana

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y


35

S3

FIG

URES

500 1000 1500 2000 2500

0.0

0.4

0.8

Ouratea lucens

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Cinnamomum triplinerve

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Picramnia latifolia

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Pouteria reticulata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Prioria copaifera

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Protium panamense

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Protium tenuifolium

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Psychotria acuminata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Psychotria deflexa

Height (mm)Ye

arly

sur

viva

l pro

babi

lity

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Psychotria racemosa

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Randia armata

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y


36

S3

FIG

URES

500 1000 1500 2000 2500

0.0

0.4

0.8

Simarouba amara

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Sorocea affinis

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Spondias radlkoferi

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Sterculia apetala

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Swartzia simplex_var.

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Tabebuia rosea

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Talisia croatii

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)Ye

arly

sur

viva

l pro

babi

lity

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Unonopsis pittieri

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Virola sebifera

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8

Xylopia macrantha

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

500 1000 1500 2000 2500

0.0

0.4

0.8


Height (mm)

Year

ly s

urvi

val p

roba

bilit

y

Height (mm)

Year

ly s

urvi

val p

roba

bilit

y


37

S3

FIG

URES

0 10000 20000 30000

−10

000

500

Adelia triloba

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 20000 40000 60000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Alibertia edulis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Allophylus

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Alseis blackiana

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Andira inermis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Annona acuminata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 20000 30000

−10

000

500

Annona spraguei

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0e+00 4e+04 8e+04

−10

000

500

Apeiba membranacea

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 20000 40000 60000

−10

000

500


Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Brosimum alicastrum

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Capparis frondosa

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Casearia aculeata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Figure S7. Observed and predicted annual tree growth (mm2 basal area/yr) as a function of initial size (mm2 basal area).Black dots show observations, red dashed lines show species-specific moving averages (from a Generalized Additive Model usinga loess smoother), blue dashed lines show the fitted trait based average model, and grey lines show a fitted model based onlyon size (not on traits, thus identical for all species).

38

S3

FIG

URES

0 20000 60000

−10

000

500

Casearia arborea

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Chrysochlamy eclipes

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Coccoloba coronata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Coccoloba manzinellens

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0e+00 4e+04 8e+04

−10

000

500

Cordia alliodora

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 20000 40000 60000

−10

000

500

Cordia bicolor

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Cupania rufescens

Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Cupania seemannii

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Drypetes standleyi

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Erythroxylum panamense

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


39

S3

FIG

URES

200 400 600 800 1200

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Eugenia nesiotica

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Eugenia oerstediana

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Garcinia intermedia

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Garcinia madruno

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 20000 40000 60000

−10

000

500

Guapira standleyana

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 20000 30000

−10

000

500

Guarea guidonia

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Guatteria dumetorum

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500

Gustavia superba

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

50 60 70 80 90 100

−10

000

500

Hamelia axillaris

Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500

Hampea appendiculat

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Heisteria acuminata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500

Heisteria concinna

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Herrania purpurea

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


40

S3

FIG

URES

0 5000 10000 15000 20000

−10

000

500

Hirtella triandra

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

5000 15000 25000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Inga goldmanii

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Inga marginata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Inga nobilis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Inga acuminata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Inga sapindoides

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500

Inga thibaudiana

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Inga umbellifera

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 50000 100000 150000

−10

000

500

Jacaranda copaia

Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Laetia thamnia

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Licania hypoleuca

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Licania platypus

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


41

S3

FIG

URES

0 5000 10000 15000 20000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500

Luehea seemannii

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Mosannona garwoodii

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Maquira guianensis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

50 60 70 80 90 100

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Nectandra cissiflora

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Ocotea cernua

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Ocotea puberula

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Trophis caucana

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Ormosia coccinea

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Ormosia macrocalyx

Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Ouratea lucens

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Pentagonia macrophylla

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Perebea xanthochyma

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Picramnia latifolia

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


42

S3

FIG

URES

0 10000 30000 50000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Platypodium elegans

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Poulsenia armata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500

Pourouma bicolor

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Pouteria reticulata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Prioria copaifera

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Protium panamense

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Protium tenuifolium

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

50 60 70 80 90 100

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

50 60 70 80 90 100

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Pterocarpus rohrii

Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

1500 2500 3500

−10

000

500

Quassia amara

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Randia armata

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Simarouba amara

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Sloanea terniflora

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


43

S3

FIG

URES

0 1000 2000 3000

−10

000

500

Sorocea affinis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Spondias mombin

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500

Spondias radlkoferi

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 4000 6000 8000

−10

000

500

Symphonia globulifera

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Tabebuia rosea

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Talisia nervosa

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

200 400 600 800 1200

−10

000

500

Talisia croatii

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500


Basal area (mm2)Ye

arly

gro

wth

(m

m2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 1000 2000 3000

−10

000

500

Thevetia ahouai

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 10000 15000 20000

−10

000

500

Trichilia pallida

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 5000 15000 25000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 20000 30000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 20000 30000

−10

000

500

Turpinia occidentalis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


44

S3

FIG

URES

0 5000 15000 25000

−10

000

500

Unonopsis pittieri

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500

Virola sebifera

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500

Virola surinamensis

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 2000 6000 10000

−10

000

500

Xylopia macrantha

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )

0 10000 30000 50000

−10

000

500


Basal area (mm2)

Year

ly g

row

th (

mm

2 )

Basal area (mm2)

Year

ly g

row

th (

mm

2 )


45

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Adelia triloba

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Alibertia edulis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Allophylus

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Alseis blackiana

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Andira inermis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Annona acuminata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Annona spraguei

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Apeiba membranacea

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Brosimum alicastrum

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Capparis frondosa

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Casearia aculeata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Figure S8. Observed and predicted annual tree survival as a function of initial dbh (mm). Black dots show observations, reddashed lines show species-specific moving averages (from a Generalized Additive Model using a loess smoother), blue dashedlines show the fitted trait based average model, and grey lines show a fitted model based only on size (not on traits, thusidentical for all species).

46

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Casearia arborea

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Chrysochlamy eclipes

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Coccoloba coronata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Coccoloba manzinellens

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Cordia alliodora

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Cordia bicolor

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Cupania rufescens

Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Cupania seemannii

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Drypetes standleyi

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Erythroxylum panamense

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


47

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Eugenia nesiotica

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Eugenia oerstediana

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Garcinia intermedia

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Garcinia madruno

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Guapira standleyana

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Guarea guidonia

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Guatteria dumetorum

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Gustavia superba

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Hamelia axillaris

Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Hampea appendiculat

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Heisteria acuminata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Heisteria concinna

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Herrania purpurea

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


48

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Hirtella triandra

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga goldmanii

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga marginata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga nobilis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga acuminata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga sapindoides

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga thibaudiana

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Inga umbellifera

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Jacaranda copaia

Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Laetia thamnia

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Licania hypoleuca

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Licania platypus

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


49

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Luehea seemannii

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Mosannona garwoodii

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Maquira guianensis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Nectandra cissiflora

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Ocotea cernua

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Ocotea puberula

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Trophis caucana

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Ormosia coccinea

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Ormosia macrocalyx

Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Ouratea lucens

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Pentagonia macrophylla

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Perebea xanthochyma

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Picramnia latifolia

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


50

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Platypodium elegans

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Poulsenia armata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Pourouma bicolor

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Pouteria reticulata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Prioria copaifera

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Protium panamense

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Protium tenuifolium

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Pterocarpus rohrii

Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Quassia amara

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Randia armata

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Simarouba amara

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Sloanea terniflora

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


51

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Sorocea affinis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Spondias mombin

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Spondias radlkoferi

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Symphonia globulifera

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Tabebuia rosea

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Talisia nervosa

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Talisia croatii

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)Ye

arly

sur

viva

l pro

babi

lity

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Thevetia ahouai

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Trichilia pallida

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Turpinia occidentalis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


52

S3

FIG

URES

0 100 200 300 400 500

0.0

0.4

0.8

Unonopsis pittieri

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Virola sebifera

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Virola surinamensis

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8

Xylopia macrantha

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

0 100 200 300 400 500

0.0

0.4

0.8


Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y

Dbh (mm)

Year

ly s

urvi

val p

roba

bilit

y


53

S3 FIGURES

●

●

●

●

●

●●

●●●

●

●●

●

●

● ●

●

●●

●

●●

●

●●

● ● ●

●●

● ●●

●●●

●

●●

●

●

●

●

●●

●

●

●

●

●● ●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●●

●●

●

●●

●

●

●

●

●●

●

●

●

●

●●

50 100

200

500

1000

2000

5

10

20

50

100

200

500

1000

Maximum diameter (mm)

Rep

rodu

ctiv

e th

resh

old

size

(m

m d

iam

eter

)

R2=0.807

Figure S9. Relationship between species’ maximum observed tree diameter(Dmax) and the onset of reproduction (50 percent quantile of reproduction;R50). The dashed line indicates the relationship R50 = 0.5Dmax).

54

S3

FIG

URES

200 400 600 800 1200

2040

60SM

size

sdl g

rowt

h −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−++++++++++++++++

++++++++++++++++++++++++++++

200 400 600 800 1000 1400

2040

60

WD

size

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−++++++++++++++++++++++++++

++++++++++++++++++

200 400 600 800 1000 1400

2040

60

LMA

size

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

++++++++++++++++++++++++++++++++++++++++++++

200 400 600 800 1000 1400

2040

60

Dmax

size

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

++++++++++++++++++++++++++++++++++++++++++++

200 400 600 800 1200

0.75

0.85

0.95

SM

size

sdl s

urviv

al

−−−−−−

−−−−−−−−−−−−

−−−−−−−−−−−−−−−−−−−−−−−−−−

+++++

+++++++

++++++++++++++++++

++++++++++++++

200 400 600 800 1000 1400

0.75

0.85

0.95

WD

size

−−−−−

−−−−−−−−

−−−−−−−−−−−−−−−−−−−−−

−−−−−−−−−−+++++

++++++++

+++++++++++++++++++++++++++++++

200 400 600 800 1000 1400

0.75

0.85

0.95

LMA

size

−−−−−

−−−−−−−

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

+++++

+++++++++

++++++++++++++++++++++++++++++

200 400 600 800 1000 1400

0.75

0.85

0.95

Dmax

size

−−−−−−−−−

−−−−−−−−−−−−−−−−−−−−−−−

−−−−−−−−−−−−

++++

++++

++++++++++

++++++++++++++++++++++++++

50 100 150 200 250

200

600

1000 SM

size

tree

grow

th

− − − −− −

− −−

+ + ++

+ ++ +

+

50 100 150 200 250

200

600

1000 WD

size

− − −−

− −− − −

+ + + ++ +

+ +

+

50 100 150 200 250

200

600

1000 LMA

size

− − −−

− −− −

−

+ + ++

+ ++ +

+

50 100 150 200 250

200

600

1000 Dmax

size

− − −−

− −− −

−

+ + + + ++

++

+

50 100 150 200 250

0.88

0.92

0.96

SM

size

tree

surv

ival

−−

− − − − − −−+ + + + + + + ++

50 100 150 200 250

0.88

0.92

0.96

WD

size

− −− − − − − −

−+ + + + + + + + +

50 100 150 200 250

0.88

0.92

0.96

LMA

size

− − − − − − − −−

+ + + + + + + ++

50 100 150 200 250

0.88

0.92

0.96

Dmax

size

− − − − −− − −

−+ + + + + + + + +

50 100 150 200

0.0

0.4

0.8

SM

size

Repr

. fra

ctio

n

− − − − − − −−

+ + + + + + ++

50 100 150 200

0.0

0.4

0.8

WD

size

−− −

− − − −−

+ ++ + + + +

+

50 100 150 200

0.0

0.4

0.8

LMA

size

−− − − − − −

−

+ + + ++ + + +

50 100 150 200

0.0

0.4

0.8

Dmax

size

−− − −

− − −−

+ + + + + + + +

Figure S10. Separate Analyses at Every Size. Fitted effects of each trait (columns) on size-dependent vital rates (rows), asin Figure 2, but based on separate models for each size (the robustness analysis). The black lines present the vital rate-sizerelationships with all traits set to their mean values (with the corresponding predictions from the hierarchical model in grey).The blue and red lines present the same relationships with one trait set to its mean plus or minus one standard deviation,respectively, and the two remaining traits set to their mean values (with exception of Dmax, see below). The trait whose valuevaries among the blue, black and red lines is named at the top of each column. Actual mean values and standard deviations foreach trait are given in table 1. An important difference from figure 3 is that for this analysis here, small statured species dropout at larger sizes and thus a different subset of species are used at each size (mean Dmax increases); in contrast, the result infigure 3 based on the hierarchical model simply shows the main effects as fitted across sizes. The size ranges of the axis differfrom Fig. 2 also because species drop out with increasing size, and we limited the analysis include at least 15 species.

55

S3

FIG

URES

Figure S11. Separate Analyses at Every Size. Among-species variance explained by traits (as measured by R2 values) throughout thelife-cycle of tree species from Barro Colorado Island, as in Figure 3, but based on separate models for each size (the robustness analysis).The rows show results for (top to bottom) size-dependent growth, size-dependent survival, and vital rates associated with reproduction.The R2 value at the upper edge of the stacked colors represents the proportion of the total variation among species explained by traits,as reflect in the averaged model when species-specific values for that vital rate are regressed on all four traits (i.e. including traits Dmax,LMA, SM and WD) and model averaging is applied, where the observed values for each species are based on species-specific GAMs. Therelative importance of different traits is indicated by the relative height of each color band as a proportion of the total, with height scaledto the R2 values for averaged models including only one trait (i.e. including only Dmax, LMA, SM or WD). The number of speciesincluded in the analyses depends on size; for reference, species numbers are shown in solid circles above each graph at fixed intervals,with their diameters proportional to log species number.

56

REFERENCES

References

James. S Clark, Miles R Silman, Ruth. Kern, Eric. Macklin, and Janneke. Hil-leRisLambers. Seed dispersal near and far: patterns across temperate andtropical forests. Ecology, 80(5):1475–1494, 1999.

H C Muller-Landau, S J Wright, O Calderon, R Condit, and S P Hubbell.Interspecific variation in primary seed dispersal in a tropical forest. Journalof Ecology, 96:653–667, 2008.

57

DEMOGRAPHY BEYOND THE POPULATION Functional traits as ... · DEMOGRAPHY BEYOND THE POPULATION...

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