Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

Functional traits and performance of woody species on

oil-affected soils of the Ecuadorian Amazon

Jaime Villacís1, 3*; https://orcid.org/0000-0001-7752-8506

Fernando Casanoves2; https://orcid.org/0000-0001-8765-9382

Susana Hang3; https://orcid.org/0000-0003-4017-7057

Cristina Armas4; https://orcid.org/0000-0003-0356-8075

1. Departamento Ciencias de la Vida, Universidad de las Fuerzas Armadas (ESPE), Av. General Rumiñahui s/n, P.O.BOX:

171-5-231B, Sangolquí, Ecuador; jevillacis@espe.edu.ec (*Correspondence)

2. CATIE-Centro Agronómico Tropical de Investigación y Enseñanza. 30501, Turrialba, Costa Rica;

casanoves@catie.ac.cr

3. Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, CC 509, 5000 Córdoba, Argentina;

shang@agro.unc.edu.ar

4. Estación Experimental de Zonas Áridas, Consejo Superior de Investigaciones Científicas (CSIC), Almería, Spain;

cris@eeza.csic.es

Received 04-III-2023. Corrected 10-IV-2023. Accepted 10-X-2023.

ABSTRACT

Introduction: Plant functional traits are widely used to predict community productivity. However, they are rarely

used to predict the performance (in terms of growth diameter, growth height, survival, and integral response

index) of woody species planted in degraded soils.

Objective: To evaluate the relationship between the functional traits and the performance of 25 woody species

planted in disturbed soils affected by oil extraction activities in Ecuadorian Amazon.

Methods: Eighteen permanent sampling plots were established and five 6-month-old seedlings of each 25 species

were randomly planted in each plot (125 individuals per plot), at a distance of 4×4 m. Eight quantitative function-

al traits (leaf size, specific leaf area, leaf nitrogen concentration, leaf phosphorus concentration, leaf minimum

unit, leaf dry matter content, stem specific density and leaf tensile strength) were determined for each species.

Results: The woody species with high performance shows greater leaf size, specific leaf area and Stem Specific

Density than those showing low performance. Leaf nitrogen concentration and stem specific density had a direct

relationship with the integral response index. The leaf size, leaf phosphorus concentration, leaf dry matter con-

tent and leaf tensile strength showed a negative relationship with the integral response index.

Conclusions: Our study demonstrated that the performance of woody species o disturbed soils can be predicted

satisfyingly by leaf and stem functional traits, presumably because these traits capture most of environmental

and neighborhood conditions.

Key words: growing species; integral response index; disturbed soils; quantitative traits; multiple regression

model.

https://doi.org/10.15517/rev.biol.trop..v71i1.50333

TERRESTRIAL ECOLOGY

2Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

INTRODUCTION

Tropical forests cover 10 % of the terres-

trial surface and account for 50 % of worldwide

tree diversity (Mayaux et al., 2005). More than

half of tropical forest areas are composed of

tropical rainforests, which are characterized

by a high diversity of tree species (Lewis et al.,

2009). Tropical rainforests have a significant

influence on global patterns of biodiversity,

ecosystem ecology, productivity, and biogeo-

chemical cycles (Malhi, 2010). According to

Guevara-Andino et al. (2019) in the Ecuador-

ian Amazon rainforest there are 2296 tree

species; however, approximately 4.2 million

hectares of this ecosystem have been impacted

by many anthropogenic activities related to oil

extraction (opening of new roads, construc-

tion of platforms, mud and drill cutting cells,

contaminated soil treatment units, and settle-

ment of camps and heliports) (Rivera-Parra

et al., 2020). These activities have resulted in

high deforestation, acceleration of soil ero-

sion, decrease in water infiltration, increase in

superficial runoff (Bertzky et al., 2011), and

reduction of fauna species (Arroyo-Rodríguez

et al., 2007; Pozo-Rivera et al., 2023).

In response, the Ecuadorian government

has implemented reforestation programs on

sites affected by oil extraction processes since

2000 (Villacís, 2016). Reforestation activities

began with the production of seedlings in plant

nurseries, where various native and exotic tree

species are produced (primarily timber, fruit,

and ornamental plants), later, seedlings are

transplanted into the affected soils when reach

30 cm in height. Studies to evaluate the most

suitable species for reforestation sites (previ-

ously affected by oil extraction processes) have

been conducted in plant nurseries (Villacís,

Armas et al., 2016), as well as in open-field

affected sites (Villacís, Casanoves et al., 2016).

Nevertheless, it is unknown whether the

growth and survival rates of the sapling woody

species on disturbed sites over a period of

two years is a good indicator of their perfor-

mance. In this sense, the long-term perfor-

mance of woody species can be determined

based on their functional traits (Poorter &

Bongers, 2006), particularly leaf and stem traits

RESUMEN

Rasgos funcionales y comportamiento de especies leñosas en suelos afectados

por petróleo de la Amazonía ecuatoriana

Introducción: Los rasgos funcionales de las plantas han sido ampliamente utilizados para predecir la productivi-

dad (en términos de crecimiento en diámetro, crecimiento en altura, sobrevivencia e índice de respuesta integral)

de las comunidades vegetales. Sin embargo, rara vez han sido utilizados para predecir el desempeño de las especies

leñosas plantadas en suelos degradados.

Objetivo: Evaluar la relación entre el desempeño y los rasgos funcionales de 25 especies leñosas plantadas en

suelos afectados por actividades de extracción de petróleo en la Amazonía ecuatoriana.

Métodos: Se establecieron 18 parcelas permanentes de muestreo y en cada parcela se sembraron aleatoriamente

cinco plántulas de 6 meses de las 25 especies (125 individuos por parcela), a una distancia de 4×4 m. Se determi-

naron ocho rasgos funcionales (área foliar, área foliar específica, concentración de nitrógeno foliar, concentración

de fósforo foliar, unidad mínima foliar, contenido foliar de materia seca, densidad específica del fuste y fuerza

tensil foliar) de cada especie.

Resultados: Las especies leñosas con alto desempeño presentaron mayor área foliar, área foliar específica y den-

sidad específica del fuste que las especies de bajo desempeño. La concentración de nitrógeno foliar y la densidad

específica del fuste mostraron una relación directa. El área foliar, la concentración de fósforo foliar, el contenido

de materia seca foliar y la fuerza tensil foliar presentaron una relación inversa con el Índice de Respuesta Integral.

Conclusión: Se demostró que el desempeño de las especies leñosas plantadas en suelos alterados puede predecirse

satisfactoriamente por rasgos funcionales de hoja y de tallo, debido posiblemente a que los rasgos influyen en el

crecimiento y supervivencia de las especies, y reflejan la mayoría de las condiciones ambientales.

Palabras clave: crecimiento de especies; índice de respuesta integral; suelos perturbados; rasgos cuantitativos;

modelo de regresión múltiple.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

(Martínez-Encino et al., 2013). The functional

traits are fundamental to the understanding of

plant adaptations and can be useful for selecting

suitable species to restore degraded ecosystems

(Wang et al., 2021). Additionally, function-

al traits encompass the morpho-phenologi-

cal characteristics of plants that have indirect

effects on their physical structure, affecting

key biological processes such as growth, repro-

duction, and survival (Yadav et al., 2022). For

example, Freschet et al. (2010) and Pérez-

Harguindeguy et al. (2016) indicated that trees

with high foliar concentrations of nitrogen and

phosphorus, as well as large specific leaf areas,

demonstrated rapid growth rates. Moreover,

Sterck et al. (2006) found that trees with low

stem specific density exhibited faster growth

rates, whereas trees with high stem specific

density had a higher survival rate.

Because the long-term growth assessment

of woody species could be expensive, functional

traits could be good indicators of forest species

performance. Maire et al. (2013) found that

functional traits were related to the growth of

individual plants. In other studies, the high

specific leaf area and low stem specific density

of the trees have been found to be associated

with their fast growth; furthermore, the low

specific leaf area and high stem specific density

of the trees were associated with high survival

(Poorter et al., 2008; Rüger et al., 2012). Leaf

functional traits have been used to explain spe-

cies-specific growth rates and have contributed

to a better understanding of tree growth strate-

gies and the structure and dynamics of forest

communities (Adler et al., 2014). Although the

relationships between functional traits and spe-

cies growth in tropical forest ecosystems have

been often studied (Werden el at., 2018), no

studies have been conducted in sites disturbed

by oil extraction processes. The information

about the performance of woody species in

restored Amazonian tropical ecosystems and

their relationships with functional traits will

constitute a valuable contribution to scientific

knowledge. In this study, the performance of

twenty-five woody species planted on soils

affected by oil extraction activities years was

evaluated. Using data for a 24 month growing

period for twenty-five woody species, we cor-

related species performance (growth diameter,

growth height, survival and integral response

index) with seven functional traits related to

leaf (Leaf Size, Leaf Minimum Unit, Specif-

ic Leaf Area, Leaf Dry Matter Content, Leaf

Tensile Strength, Leaf Nitrogen Concentration

and Leaf Phosphorus Concentration), and one

stem functional traits (Stem Specific Density).

The following two hypotheses were tested: (i)

some plant functional traits differed between

woody species according to their performance

on soils affected for oil extraction activities,

and (ii) the performance of all woody species

can be predicted simultaneity by leaf and stem

functional traits.

MATERIALS AND METHODS

Study area: The study was performed in

the Sucumbíos (0°5’ S & 76°53’ W) province

of Ecuador. The region has an average altitude

of 328 m a.s.l., a mean annual rainfall of 3 000

mm, a mean annual temperature of 25 °C, a rel-

ative humidity of 85 % and a heliophany of 12 h

day-1. The area is classified as tropical rainforest

(Peel et al., 2007). The soils of the Ecuadorian

Amazon are acidic, have low levels of nutrients

and high aluminum contents (Villacís, Armas

et al., 2016).

Selection of species and disturbed sites:

The 25 woody species selected were mostly

produced in the Ecuadorian Amazon (Table 1).

These species have been used in remediation

programs of disturbed soils, mainly for their

timber, forage and fruit uses (Villacís, Armas, et

al., 2016; Villacís, Casanoves et al., 2016).

In sites affected by the oil extraction pro-

cesses, the superficial layer of soil and all veg-

etation have been removed, and they contain

residues of hydrocarbons and heavy metals.

These sites present an average pH of 4.6, 0.5 %

of organic matter, 5.3 mg kg-1 of Al, 5 341.73 mg

kg-1 of total petroleum hydrocarbons, 5.3 mg

kg-1 of Cd, 33.2 mg kg-1 of Ni and 23.76 mg kg-1

of Pb. More details on the physical and chemical

4Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

characteristics of these sites can be found in

Villacís, Casanoves et al. (2016). Eighteen per-

manent sampling plots were established and

five 6-month-old seedlings of each 25 species

were randomly planted in each plot (125 indi-

viduals per plot), at a distance of 4×4 m. The

saplings that died due to post-transplant shock

in the first month were replanted with new

individuals of the same species. Every 4 months

weed controls were carried out with a motor

trimmer, eliminating all the vegetation within a

1 m radius from the stem of the seedlings.

Measured variables: In each plot sapling

survival was measured by dividing the number

of two-year-old living plants by the initial num-

ber of plants transplanted. The diameter and

height at the time of transplantation and two

years after transplantation were determined.

The diameter of saplings was measured at 10

cm from the base of the plant, using a digital

caliper and plant height was measured from the

base to the apical meristem of the tallest stem,

using a laser hypsometer. Subsequently, we

estimated the integrated response index (IRI)

which considers survival rates and growth vari-

ables and serves to determine the performance

of saplings in an integrated way. The IRI was

estimated according to the following equation:

IRI= survival percentage × RGR height ×

RGR diameter. Where: RGR height (height

rate growth in cm / month) = [ln (final

height) -ln (initial height)] / 24 months; RGR

Table 1

Taxonomic classification and uses of restoration woody species.

Species Family Use Origin Nitrogen

fixer

Acnistus arborescens (L.) Schltdl. Solanaceae Timber/Medicinal Native No

Apeiba membranacea Spruce ex. Benth. Malvaceae Ornamental/Medicinal Native No

Averrhoa carambola L. Oxalidaceae- Timber/Fruit Exotic No

Cedrela odorata L. Meliaceae Timber Native No

Cedrelinga cateniformis (Ducke) Ducke. Fabaceae Timber Native Yes

Gmelina arborea Roxb. ex Sm. Lamiaceae Timber Native No

Guarea purusana C. DC. Meliaceae Timber Native No

Inga densiflora Benth. Fabaceae Timber/Fruit Native Ye s

Leucaena leucocephala (Lam.) de Wit Fabaceae Forage Exotic Yes

Morinda citrifolia L. Rubiaceae Fruit Exotic No

Myrcia aff. fallax Myrtaceae Timber Native No

Myroxylon balsamum (L.) Harms Fabaceae Timber/Medicinal Native Ye s

Nephelium lappaceum L. Sapindaceae Fruit Native No

Ochroma pyramidale (Cav. ex Lam.) Urb. Malvaceae Timber Native No

Ormosia macrocalyx Ducke Fabaceae Timber Native Yes

Piptadenia pteroclada Benth. Fabaceae Timber Native Ye s

Platymiscium pinnatum (Jack.) Dougand Fabaceae Timber Native Yes

Pourouma cecropiifolia Mart. Urticaceae Fruit/Medicinal Native No

Schizolobium parahyba (Vell.) S.F. Blake Fabaceae Timber Native Yes

Stryphnodendron porcatum D.A.Neill & Occhioni f. Fabaceae Timber Native Yes

Syzygium jambos (L.) Alston Myrtaceae Fruit Native No

Syzygium malaccense (L.) Merr. & L.M.Perry Myrtaceae Fruit Exotic No

Tapirira guianensis Aubl. Anacardiaceae Timber Native No

Vitex cymosa Bertero ex Spreng. Lamiaceae Timber Native No

Zygia longifolia (Humb. & Bond. ex Willd.) Britton & Rose Fabaceae Timber Native Yes

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

diameter (diameter rate growth in mm / month)

= [ln (final diameter) -ln (initial diameter)] / 24

months (Elliott et al., 2003).

For the evaluation of functional traits, five

adult individuals from the natural forest located

along a longitudinal gradient in Sucumbíos

Province were selected (17 947 km²; Fig. 1).

Leaf and stem plant functional traits that

are commonly used to characterize growth

strategy of plants were evaluated. In each speci-

men, seven leaf functional traits [(leaf size (LS),

leaf minimum unit (LMU), specific leaf size

(SLA), leaf dry matter content (LDMC), leaf

nitrogen concentration (LNC), leaf phospho-

rus concentration (LPC), leaf tensile strength

(LTS], and one stem functional traits [(stem

specific density (SSD)] were measured (Chave,

2006; Cornelissen et al., 2003).

The LS, LMU and SLA are indicators of

the adaptation to the environment and to the

strategies and the use of plant resources (Ven-

dramini et al., 2002). The LDMC is related to

the average density of leaf tissues (Cornelissen

et al., 2003) and reflects an exchange between

plant performance and rapid biomass produc-

tion. To estimate the LS, SLA, and LDMC, five

leaves of each individual were collected. Each

leave using the Midebmp Image Analysis ver-

sion 4.2 program was scanned and the LS in

mm2 was measured (Ordiales-Plaza, 2000). The

SLA in mm2 mg-1 by dividing the leaf size by the

leaf dry weight was estimated (Hunt, 1990). The

LDMC in mg g-1 by dividing the leaf dry weight

by leaf fresh weight was calculated.

The LNC and the NPC foliar are vital ele-

ments of the photosynthesis functioning of the

leaves (Bongers & Popma, 1990). A sample of

fresh leaves without petioles or rachis of each

sampled specimen to measure the LNC (Micro

Kjheldal total combustion method) and the

LPC (colorimeter method) was estimated (Cor-

nelissen et al., 2003). The LTS is an indicator of

the inverted carbon ratio in the structural pro-

tection of the photosynthetic tissues (Cornelis-

sen et al., 2003). A rectangular section 1 cm

wide by 4 cm long in the direction of the vein of

Fig. 1. Geographical location of 18 plots, and 125 sampled trees of 25 woody species in the Ecuadorian Amazon.

6Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

a fully sound expanded leaf was extracted; the

section was subjected to traction in a Tearing

Apparatus and the LTS in N mm-1 (maximum

force resisted by leaf section at the instant of

its rupture) was obtained (Hendry & Grime,

1993). Finally, the SSD is directly related to the

carbon accumulation and flows and inverse-

ly related to the stem growth rate, mortality

rate and plant reproduction time (Swenson &

Enquist, 2007). A cylindrical wood samples

from each individual with a 6.2 mm auger drill

was collected; the volume and dry weight of

cylinder was measured, and the SSD in g cm-3

by dividing dry weight by cylinder volume was

calculated. These data are available in the TRY

database (Kattge et al., 2020).

Data analysis: In order to group woody

species according to their performance a cluster

analysis (Ward method and Euclidean distance)

for integral response index (IRI) was performed

to obtain two groups (high and low perfor-

mance). Differences in IRI and quantitative

traits between species of high and low perfor-

mance were analyzed, by using general linear

models; subsequently a less significant differ-

ence (LSD) post hoc test was performed (α =

0.05). Correlation analyses between functional

traits and growth height, growth diameter and

survival of species using Pearson correlations

analysis were performed. To fit the perfor-

mance of woody species in function of plant

functional traits, a multiple regression models

were built. Using leaf size, specific leaf area,

leaf nitrogen concentration, leaf phosphorus

concentration, leaf minimum unit, leaf dry

matter content, stem specific density and leaf

tensile strength, as predictor variables, we pre-

dicted the IRI. The multiple regression model

was based on the assumption that residuals

are normally distributed and their variance are

homogeneous (Wright & Cannon, 2001). Nor-

mality and homogeneity of variances assump-

tions were evaluated with graphical analysis of

residuals. We used partial residuals graphs to

evaluate when was necessary or not the inclu-

sion of polynomial terms for each trait. The

selection and evaluation of the best regression

model was based on the coefficient of deter-

mination (adjusted R2) and mean squared

prediction error (MSPE), Akaike information

criterion (AIC), Bayesian information criterion

(BIC) and p-value. We use Variance Inflation

Factor (VIF) values to evaluate multicollinear-

ity. In order to compare the effect of measured

traits on the IRI we use the relative variation

explained for each trait. All analyzes were per-

formed using InfoStat statistical software (Di

Rienzo et al., 2020).

RESULTS

Performance of woody species: Based

on the integral response index, cluster analy-

sis exhibited two well-differentiated groups

of plants (Fig. 2). The first group formed

by Cedrelinga cateniformis, Myrcia aff. fallax,

Piptadenia pteroclada, Platymiscium pinnatum,

Tapirira guianensis, Vitex cymosa and Zygia

longifolia species, showed high IRI mean value

(high performance woody species). The second

group formed for Acnistus arborescens, Apeiba

membranaceae, Averrhoa carambola, Cedrela

odorata, Gmelina arborea, Guarea purusana,

Inga densiflora, Leucaena leucocephala, Myroxy-

lon balsamum, Morinda citrifolia, Nephelium

lappaceum, Ochroma pyramidale Ormosia mac-

rocalyx Pourouma cecropiifolia, Schizolobium

parahyba, Stryphnodendron porcatum, Syzygium

jambos and Syzygium malaccensis species, dis-

played a low IRI mean value (low performance

woody species). The growth diameter, growth

height and integral response index was higher

in high performance woody species than in

low performance woody species. Nevertheless,

the survival was similar for the high and low

performance sapling woody species (Table 2).

Mean values for leaf size, specific leaf

area and Stem Specific Density were higher in

saplings of high performance woody species.

The rest of plant functional traits did not differ

between saplings woody species of high and

low performance (Table 2).

Correlations between variables: Correla-

tion analysis showed a negative relationship

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

Fig. 2. Dendrogram of classification of 25 sapling woody species, obtained by hierarchical cluster analysis (Ward method and

Euclidean distance) using integral response index (IRI).

Table 2

Performance variables and functional traits for high and low performance woody species on soils affected for oil extraction

activities.

Variables Species performance F-Fisher P-value

High Low

Performance

Survival (%) 85.38 ± 4.52 a 85.18 ± 2.55 a 0.00 0.9687

Growth diameter (mm) 45.79 ± 3.08 a 19.50 ± 1.92 b 52.29 0.0001

Growth height (cm) 1.45 ± 0.12 a 1.01 ± 0.08 b 9.71 0.0049

Integral response index (IRI) 0.37 ± 0.08 a 0.22 ± 0.03 b 6,19 0.0205

Functional traits

Leaf size (mm2)62 702.99 ± 14 146.56 a 26 696.30 ± 8 821.93 b 4.66 0.0415

Specific Leaf Area (mm2 mg-1)59 564.60 ± 13 809.31 a 17 950.28 ± 8 611.62 b 6.54 0.0176

Leaf Nitrogen Concentration (%) 2.64 ± 0.41 a 2.79 ± 0.25 a 0.10 0.7593

Leaf Phosphorus Concentration (%) 0.20 ± 0.03 a 0.19 ± 0.02 a 0.08 0.7788

Leaf Minimum Unit (mm2)29 396.69 ± 12 782.49 a 13 104.35 ± 7 971.29 a 1.17 0.2907

Leaf Dry Matter Content (mg g-1)0.38 ± 0.04 a 0.37 ± 0.02 a 0.17 0.6835

Stem Specific Density (g cm-3)0.56 ± 0.04 a 0.45 ± 0.03 b 4.52 0.0444

Leaf Tensile Strength (N mm-1)4.63 ± 0.49 a 3.88 ± 0.30 a 1.72 0.2027

Values are means ± error standard. Different letters in each row indicate significant differences (LSD, post hoc test, P < 0.05).

8Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

between Survival and Leaf Tensile Strength.

The rest of correlation were non-significant

(Table 3).

The best multiple regression model shows

the higher adjusted R2 and the lowest values of

AIC and BIC. For LS, SLA, LDMC and LNC a

second order polynomial was used (Table 4).

The estimated regression model predicted

90 % of the total IRI variability, and it is repre-

sented by the following equation:

IRI = 0.066 - 1.19 × LPC + 4.59 E-6 × LMU +

0.98 * SSD - 0.05 * LTS 5.53 E-6 × LS + 2.68 E-11

× LS2 + 1.31E-5 × SLA - 6.22 E-11 × SLA2 - 3.12

× LDMC + 5.21 × LDMC2 + 0.23 × LNC - 0.032

× LNC2

The IRI showed a positive relationship

with SLA, LMU, LNC and SSD and a negative

relationship with LS, LPC, LDMC and LTS.

Table 3

Pearson correlation coefficients (r) between functional traits and average absolute growth in height (height growth), diameter

(diameter growth) and survival after 24 months (survival, %) for 25 woody species planted on degraded soils in Amazonian

Basin.

Functional traits

Growth diameter

(mm year-1)

Growth height

(cm year-1)

Survival

(%)

r P-value r P-value R P-value

Leaf traits

Leaf Size (mm2) -0.05 0.8208 -0.22 0.2864 0.07 0.7289

Specific Leaf Area (mm2 mg-1) -0.08 0.6907 -0.15 0.4790 -0.16 0.4378

Leaf Nitrogen Concentration (%) 0.30 0.1479 0.36 0.0798 -0.01 0.9610

Leaf Phosphorus Concentration FPC (%) -0.01 0.9667 -0.09 0.6668 -0.09 0.6622

Leaf Minimum Unit (mm2) -0.10 0.6223 -0.28 0.1704 0.02 0.9109

Leaf Dry Matter Content (mg g-1) 0.18 0.3768 0.11 0.6081 0.18 0.4019

Leaf Tensile Strength (N mm-1) -0.31 0.1305 -0.32 0.1131 -0.45 0.0236

Stem trait

Stem Specific Density (g cm-3) -0.05 0.7973 0.07 0.7441 0.11 0.5915

Table 4

Regression coefficients and summary statistics for the best model to predict IRI from functional traits.

Coefficient Estimation Standard error t-Student P-value CpMallows

Constant 0.0660 0.3077 0.2146 0.8337

LPC % -1.1901 0.5454 -2.1822 0.0497 15.7619

LMU (mm2) 4.6E-06 1.6E-06 2.8510 0.0146 19.1281

SSD (g cm-3) 0.9830 0.1751 5.6122 0.0001 42.4972

LTS (N mm-1) -0.0479 0.0197 -2.4283 0.0318 16.8967

LS (mm2) -5.5E-06 2.1E-06 -2.6226 0.0223 17.8781

LS2 (mm2) 2.7E-11 1.4E-11 1.9828 0.0708 14.9317

SLA (mm2 mg-1) 1.3E-05 2.6E-06 4.8415 0.0004 34.4398

SLA2 (mm2 mg-1) -6.2E-11 1.8E-11 -3.3817 0.0055 22.4361

LDMC (mg g-1) -3.1276 1.2474 -2.5073 0.0275 17.2863

LDMC2 (mg g-1) 5.2198 1.6910 3.0868 0.0094 20.5285

LNC % 0.2376 0.1011 2.3494 0.0368 16.5198

LNC2 % -0.0323 0.0155 -2.0883 0.0588 15.3608

SLA = Specific Leaf Area; LDMC = Leaf Dry Matter Content; LTS = Leaf Tensile Strength; LS = Leaf Size; LMU = Leaf

Minimum Unit; SSD = Stem Specific Density; LNC = Leaf Nitrogen Concentration; LPC = Leaf Phosphorus Concentration.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

DISCUSSION

According to our knowledge, this is the

first report that approach the functional traits

and the performance of woody species planted

on soils affected for oil extraction activities. The

woody species of high performance showed

greater leaf size, specific leaf area and Stem

Specific Density than low performance woody

species. The performance of woody species

using IRI was explained using leaf and stem

functional traits as regressor variables.

Differences in functional traits between

woody species according to their perfor-

mance: The High performance woody spe-

cies (Cedrelinga cateniformis, Myrcia aff. fallax,

Piptadenia pteroclada, Platymiscium pinnatum,

Tapirira guianensis, Vitex cymosa and Zygia

longifolia) showed a high Leaf Size, that enables

them to maximize light capture (Poorter at

al., 2006). Moreover, the nutrient cycling pro-

cesses of these species are more efficient due

to their high photosynthetic capacity per unit

of leaf area (Denslow, 1996; Le Roux et al.,

2001; Mooney et al., 1981; Reich et al., 1998 ).

This character trait is favorable at the begin-

ning of restoration processes because the dis-

turbed sites are totally exposed to the sun and

do not have any type of vegetation. Similar

results were reported by Poorter and Evans

(1998), who found that fast-growing tree spe-

cies presented high leaf Area. Additionally,

high performance woody species tend to have

a high Leaf Dry Matter Content. This allows

them capture and use available resources in

an efficient way (Tecco et al., 2013), due to the

greater stiffness of its leaves (Niinemets, 2001)

because of the lignification of the thick and

rigid cell walls (Niinemets & Kull, 1998). Simi-

lar results were reported by Martínez-Garza

et al., (2013), who found that tree plants with

higher Leaf Dry Matter Content had higher

survival. The high Specific Stem Density of

high performance woody species reflects an

elevated growth rate (Santiago et al., 2004),

which is essential to compete with aggressive

colonizing vegetation. This allows the saplings

increase their performance under the adverse

conditions of disturbed soils (Villacís, Casa-

noves et al., 2016). However, other studies have

reported that trees with higher Specific Stem

Density have presented higher survival, but

lower diameter growth (Fortunel et al., 2016;

Poorter et al., 2008). Finally the low Leaf Tensile

Strength of high performance woody species

favors the rapid growth and decomposition of

its biomass (Obando-Vargas, 2003).

On the other hand, the low performance

woody species showed a low Leaf Size and

Specific Leaf Area, that indicate that the spe-

cies of this group must invest more amounts

of energy into the structural protection of their

leaves (Wright et al., 2004). In addition, spe-

cies with low Leaf Size and Specific Leaf Size

have low assimilation and respiration rates, and

high survival (Lusk, 2002). Aditionally, the low

Leaf Dry Matter Content reflects a slow process

of nutrient conservation in low performance

woody species (Messier et al., 2010). Pérez-

Harguindeguy et al. (2016), reported that the

species with low growth rates presented low

levels of Leaf Dry Matter Content. The low per-

formance woody species showed less Specific

Stem Density than other species. Similar results

were found in several studies concerning rain-

forest saplings (Muller-Landau, 2004) and trees

(King et al., 2005). Finally, due to their high

Leaf Tensile Strength, low performance woody

species have a greater proportion of vascular

tissues, fibers or sclerenchyma, and high tissue

density (Tecco et al., 2010; Wright & Cannon,

2001), which was reflected in lower growth

rates and lower performance compared to high

performance woody species.

Relationship between functional traits

and performance of woody species: Consider-

ing that stem specific density is directly related

with the content, accumulation, and fluctua-

tions of carbon (Wiemann & Williamson, 2002)

and inversely related with stem growth rate,

mortality rate, and breeding time (Swenson &

Enquist, 2007), woody species with lower stem

specific density will have greater possibilities

of growing under the adverse conditions of

10 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

degraded sites, due to their capacity to produce

biomass quickly (Díaz et al., 2004) and survive

the adverse conditions of the disturbed soils.

In our study, the leaf and stem functional

traits were good predictors of the performance

of the woody species evaluated with IRI. Simi-

lar studies carried out with plants grown under

controlled conditions and in the open field,

have also reported mathematical models that

have explained the performance of tree spe-

cies based on their functional traits (Grime &

Hunt, 1975; Rüger et al., 2012). However, Paine

et al. (2015) point out that functional traits are

not good predictors of tree plant growth at a

global scale. These differences could possibly

be explained because the woody species per-

formance is not only affected by its functional

traits, but also by the adaptation of the traits to

its environment. In this sense, the conditions

of the disturbed soils may have influenced the

performance of the trees in addition to their

functional traits, for which more in-depth stud-

ies are necessary to validate this hypothesis.

The estimated regression model predicted 90

% of the total IRI variability, indicating that

there are other traits or factors that should be

considered to explain greater variability. Within

the best multiple regression model estimated

to predict the performance of the trees of the

woody species planted on disturbed soils, eight

quantitative functional traits (seven of the leaf

and one of the stems) explained the perfor-

mance of the woody species.

The functional traits SLA, LMU, LNC and

SSD, showed a directly proportional relation-

ship with the IRI of the woody species. Among

these traits, SSD explained the highest relative

percentage of the variance. Although the lit-

erature does not report studies on relationships

between the IRI and functional traits (growth

in diameter + growth in height + survival),

studies carried out by Iida et al. (2014) and

Gibert et al. (2016) reported that the stem spe-

cific density represents the best trait to predict

the woody species growth and survival. On the

other hand, LS, LPC, LDMC and LTS showed

an inverse relationship with the IRI of the

woody species. LDMC explained the highest

relative percentage of the total variability. Nega-

tive relationships between the LDMC and the

relative growth rates of the species have been

reported in studies by Pérez-Harguindeguy et

al. (2016). The negative rate of change between

IRI and LPC differs from the results found by

Chaturvedi et al., (2011) and Cernusak et al.,

(2010), who reported positive relationships

between the growth rate and the LPC.

The multiple regression model estimated

by the combination of leaf and stem func-

tional traits, explained better the woody species

performance. This finding provides empirical

evidence that the woody species performance

is the integrated result of several leaf and stem

functional traits (Iida et al. 2014). Therefore, in

our research the woody species performance

was explained better by several foliar and stem

functional traits, than for a single functional

trait (Li et al., 2017).

Implications for forest restauration: Our

results are specific to predict the performance

of young woody species planted in soils affected

by oil extraction processes in Amazon Basin.

However, our findings should not be correlated

with the performance of adult woody species

growing in other environments. Additionally,

the performance of saplings not only depends

on the local environment, but also of the func-

tional traits of the species and their interac-

tion (Uriarte et al., 2016). The environmental

variation was not considered in our models and

therefore contributes to the non-explained vari-

ability. On the other hand, the search of addi-

tional functional traits that provide predictions

on the performance of woody species must also

be specific for each phonological stage. In this

sense, others functional traits such as physi-

ological or allometric, which are well correlated

with common performance must be identified.

We must need to consider the possibility that

the relationship between any functional trait

and performance of woody species depends

on the values of other unidentified functional

traits (Marks & Lechowicz, 2006).

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

Ethical statement: the authors declare that

they all agree with this publication and made

significant contributions; that there is no con-

flict of interest of any kind; and that we fol-

lowed all pertinent ethical and legal procedures

and requirements. All financial sources are fully

and clearly stated in the acknowledgments sec-

tion. A signed document has been filed in the

journal archives.

REFERENCES

Adler, P. B., Salguero-Gómez, R., Compagnoni, A., Hsu, J.

S., Ray-Mukherjee, J., Mbeau-Ache, C., & Franco, M.

(2014). Functional traits explain variation in plant

life history strategies. Proceedings of the National

Academy of Sciences of the United States of America,

111(2), 740–745.

Arroyo-Rodríguez, V., Mandujano, S., Benítez-Malvido, J.,

& Cuende-Fanton, C. (2007). The influence of large

tree density on howler monkey (Alouatta palliata

mexicana) presence in very small rain forest frag-

ments. Biotropica, 39, 760–766.

Bertzky, M., Ravilious, C., Araujo, A. L, Kapos, V., Carrión,

D., Chíu, M., & Dickson, B. (2011). Carbono, bio-

diversidad y servicios ecosistémicos: Explorando los

beneficios múltiples. Ecuador. UNEP-WCMC.

Bongers, F., & Popma, J. (1990). Leaf Characteristics of the

Tropical Rain Forest Flora of Los Tuxtlas, Mexico.

Botanical Gazette, 151(3), 354–365.

Cernusak, L. A., Winter, K., & Turner, B. L. (2010). Leaf

nitrogen to phosphorus ratios of tropical trees: Expe-

rimental assessment of physiological and environ-

mental controls. New Phytologist, 185, 770–779.

Chaturvedi, R. K., Raghubanshi, A. S, & Singh, J. S. (2011).

Leaf attributes and tree growth in a tropical dry forest.

Journal of Vegetation Science, 22, 917–931.

Chave, J. (2006). Measuring wood density for tropical

forest trees a field manual. PAN-AMAZONIA. https://

rainfor.org/wp-content/uploads/sites/129/2022/06/

wood_density_english1.pdf

Cornelissen, J. H. C., Lavorel, S., Garnier, E., Díaz, S., Buch-

mann, N., Gurvich, D. E., Reich, P. B., ter Steege, H.,

Morgan, H. D., van der Heijden, M. G. A., Pausas, J.

G., & Poorter, H. (2003). A handbook of protocols for

standardised and easy measurement of plant functio-

nal traits worldwide. Australian Journal of Botany,

51(4), 335–380.

Denslow, J. S. (1996). Functional Group Diversity and

Responses to Disturbance. In G. H. Orians, R. Dirzo,

& J. H. Cushman (Eds.), Biodiversity and Ecosystem

Processes in Tropical Forests. Ecological Studies (p.

122). Springer.

Di Rienzo, J. A., Casanoves, F., Balzarini, M. G., Gonzalez,

L., Tablada, M., & Robledo, C. W. (2020). InfoStat

(Versión 2020, Software). Centro de Transferencia

Infostat, Argentina. https://www.infostat.com.ar/

Díaz, S., Hodgson, J. G., Thompson, K., Cabido, M., Cor-

nelissen, J. H., Jalili, A., Montserrat-Marti, G., Grime,

J. P., Zarrinkamar, F., Asri, Y., Band, S. R., Basconcelo,

S., Castro-Diez, P., Funes, G., Hamzehee, B., Khosh-

nevi, M., Pérez-Harguindeguy, N., Perez-Rontome,

M. C., Shirvany F. A., … Zak, M. R. (2004). The plant

traits that drive ecosystems: evidence from three con-

tinents. Journal of Vegetation Science, 15(3), 295–304.

Elliott, S., Navakitbumrung, P., Kuarak, C., Zangkum, S.,

Anusarnsunthorn, V., & Blakesley, D. (2003). Selec-

ting framework tree species for restoring seasonally

dry tropical forests in northern Thailand based on

field performance. Forest Ecology and Management,

184, 177–191.

Fortunel, C., Valencia, R., Wright, S. J., Garwood, N.

C., & Kraft, N. J. B. (2016). Functional trait diffe-

rences influence neighbourhood interactions in a

hyperdiverse Amazonian forest. Ecology Letters, 19,

1062–1070.

Freschet, G. T., Cornelissen, J. H. C., van Logtestijn, R. S.

P., & Aerts, R. (2010). Substantial nutrient resorption

from leaves, stems and roots in a subarctic flora: What

is the link with other resource economics traits? New

Phytologist, 186, 879–889.

Gibert, A., Gray, E. F., Westoby, M., Wright, I. J., & Falster,

D. S. (2016). On the link between functional traits

and growth rate: meta-analysis shows effects change

with plant size, as predicted. Journal of Ecology, 104,

1488–1503.

Grime, J. P., & Hunt, R. (1975). Relative Growth-Rate: Its

Range and Adaptive Significance in a Local Flora. The

Journal of Ecology, 63, 393.

Guevara-Andino, J. E., Pitman, N. C., Ulloa, C., Romole-

roux, K., Fernández-Fernández, D., Ceron, C., Pala-

cios, W., Neill, D. A., Oleas, N., Altamirano, P.,

Rivas-Torres, G., & ter Steege, H. (2019). Trees of

Amazonian Ecuador: a taxonomically verified species

list with data on abundance and distribution. Ecology,

100(12), e02894.

Hendry, G. A., & Grime, J. P. (1993). Methods in compa-

rative plant ecology: a laboratory manual. Springer

Science & Business Media.

Hunt, R. (1990). Basic Growth Analysis. Plant Growth

Analysis for Beginners. Springer Science & Business

Media.

Iida, Y., Kohyama, T. S., Swenson, N. G., Su, S. H., Chen,

C. T., Chiang, J. M., & Sun, I. F. (2014). Linking

12 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

functional traits and demographic rates in a subtro-

pical tree community: The importance of size depen-

dency. Journal of Ecology, 102, 641–650.

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C.,

Leadley, P., Tautenhahn, S., Werner, G. D. A., Aaka-

la, T., Abedi, M., Acosta, A. T. R., Adamidis, G. C.,

Adamson, K., Aiba, M., Albert, C. H., Alcántara, J.

M., Alcázar, C., Aleixo, I., Ali, H., ... Wirth, C. (2020).

TRY plant trait database–enhanced coverage and

open access. Global Change Biology, 26(1), 119–188.

King, D. A., Davies, S. J., Nur Supardi, M. N., & Tan, S.

(2005). Tree growth is related to light interception

and wood density in two mixed dipterocarp forests of

Malaysia. Functional Ecology, 19, 445–453.

Le Roux, X., Walcroft, A. S., Daudet, F. A., Sinoquet, H.,

Chaves, M. M., Rodrigues, A., & Osorio, L. (2001).

Photosynthetic light acclimation in peach leaves:

Importance of changes in mass:area ratio, nitrogen

concentration, and leaf nitrogen partitioning. Tree

Physiology, 21, 377–386.

Lewis, S. L., Lloyd, J., Sitch, S., Mitchard, E. T. A., & Lauran-

ce, W. F. (2009). Changing ecology of tropical forests:

Evidence and drivers. Annual Review of Ecology, Evo-

lution, and Systematics, 40, 529–549.

Li, Y., Kröber, W., Bruelheide, H., Härdtle, H., & Von Ohe-

imb, G. (2017). Crown and leaf traits as predictors of

subtropical tree sapling growth rates. Journal of Plant

Ecology, 10(1), 136–145.

Lusk, C. H. (2002). Leaf area accumulation helps juvenile

evergreen trees tolerate shade in a temperate rainfo-

rest. Oecologia, 132, 188–196.

Maire, V., Gross, N., Hill, D., Martin, R., Wirth, C., Wright,

I. J., & Soussana, J. F. (2013). Disentangling Coordi-

nation among Functional Traits Using an Individual-

Centred Model: Impact on Plant Performance at

Intra- and Inter-Specific Levels. PLOS ONE, 8, 1–16.

Malhi, Y. (2010). The carbon balance of tropical forest

regions, 1990-2005. Current Opinion in Environmen-

tal Sustainability, 2, 237–244.

Marks, C. O., & Lechowicz, M. J. (2006). A holistic tree

seedling model for the investigation of functional trait

diversity. Ecological Modelling, 193(3), 141–181.

Martínez-Encino, C., Villanueva-López, G., & Casanova-

Lugo, F. (2013). Densidad y composición de árboles

dispersos en potreros en la Sierra de Tabasco, México.

Agrociencia, 47, 483–496.

Martínez-Garza, C., Bongers, F., & Poorter, L. (2013). Are

functional traits good predictors of species perfor-

mance in restoration plantings in tropical abandoned

pastures? Forest Ecology and Management, 303, 35–45.

Mayaux, P., Holmgren, P., Achard, F., Eva, H., Stibig, H. J, &

Branthomme, A. (2005). Tropical forest cover change

in the 1990s and options for future monitoring. Philo-

sophical Transactions of the Royal Society B: Biological

Sciences, 360, 373–384.

Messier, J., McGill, B. J., & Lechowicz, M. J. (2010). How do

traits vary across ecological scales? A case for trait-

based ecology. Ecology Letters, 13, 838–848.

Mooney, H. A., Field, C., Gulmon, S. L., & Bazzaz, F. A.

(1981). Photosynthetic capacity in relation to leaf

position in desert versus old-field annuals. Oecologia,

50, 109–112.

Muller-Landau, H. C. (2004). Interspecific and inter-site

variation in wood specific gravity of tropical trees.

Biotropica, 36, 20–32.

Niinemets, Ü. (2001). Global-scale climatic controls of leaf

dry mass per area, density, and thickness in trees and

shrubs. Ecology, 82, 453–469.

Niinemets, Ü., & Kull, O. (1998). Stoichiometry of foliar

carbon constituents varies along light gradients in

temperate woody canopies: implications for folia-

ge morphological plasticity. Tree Physiology, 18(7),

467–479.

Obando-Vargas, F. V. (2003). Árboles del trópico húmedo.

Importancia socioeconómica. Editorial Tecnológica de

Costa Rica.

Ordiales-Plaza, R. (2000). MideBMP (Versión 4.2, Soft-

ware). Estación experimental de zonas áridas, España.

Paine, C. T., Amissah, L., Auge, H., Baraloto, C., Baruffol,

M., Bourland, N., Bruelheide, H., Dainou, K., de

Gouvenain, R. C., Doucet, J. L., Doust, S., Fine, P. V.

A., Fortunel, C., Haase, J., Holl, K. D., Jactel, H., Li,

X., Kitajima, K., Koricheva, J., … Hector, A. (2015).

Globally, functional traits are weak predictors of juve-

nile tree growth, and we do not know why. Journal of

Ecology, 103(4), 978–989.

Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007).

Updated world map of the Köppen-Geiger climate

classification. Hydrology and Earth System Sciences,

11, 1633–1644.

Pérez-Harguindeguy, N., Diaz, S., Garnier, E., Lavorel, S.,

Poorter, H., Jaureguiberry, P., Bret-Harte, M. S., Cor-

nwell, W. K., Craine, J. M., Gurvich, D. E., Urcelay,

C., Veneklaas, E. J., Reich, P. B., Poorter, L., Wright,

I. J., Ray, P., Enrico, L., Pausas, J. G., de Vos, A. C., …

Cornelissen, J. H. C. (2016). Corrigendum to: New

handbook for standardised measurement of plant

functional traits worldwide. Australian Journal of

Botany, 64(8), 715–716.

Poorter, L., & Bongers, F. (2006). Leaf traits are good pre-

dictors of plant performance across 53 rain forest

species. Ecology, 87, 1733–1743.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 71: e50333, enero-diciembre 2023 (Publicado Oct. 30, 2023)

Poorter, L., Bongers, L., & Bongers, F. (2006). Architecture

of 54 moist-forest tree species: Traits, trade-offs, and

functional groups. Ecology, 87, 1289–1301.

Poorter, H., & Evans, J. R. (1998). Photosynthetic nitrogen-

use efficiency of species that differ inherently in spe-

cific leaf area. Oecologia, 116, 26–37.

Poorter, L., Wright, S. J., Paz, H., Ackerly, D. D., Condit,

R., Ibarra-Manríquez, G., Harms, K. E., Licona, J. C.,

Martínez-Ramos, M., Mazer, S. J., Muller-Landau,

H. C., Peña-Claros, M., Webb, C. O., & Wright, I. J.

(2008). Are functional traits good predictors of demo-

graphic rates? Evidence from five neotropical forests.

Ecology, 89(7), 1908–1920.

Pozo-Rivera, W. E., Quiloango-Chimarro, C., Paredes, X.,

Landívar, M., Chiriboga, C., Hidalgo, D., García, K.,

& Villacís, J. (2023). Response of dung beetle diversity

to remediation of soil ecosystems in the Ecuadorian

Amazon. PeerJ, 11, e14975.

Reich, P. B., Walters, M. B., Tjoelker, M. G., Vanderklein,

D. W., & Buschena, C. (1998). Photosynthesis and

respiration rates depend on, leaf and root morphology

in nine boreal tree species differing in relative growth

rate. Functional Ecology, 12, 395–405.

Rivera-Parra, J. L., Vizcarra, C., Mora, K., Mayorga, H.,

& Dueñas, J. C. (2020). Spatial distribution of oil

spills in the north eastern Ecuadorian Amazon: A

comprehensive review of possible threats. Biological

Conservation, 252, 108820.

Rüger, N., Wirth, C., Wright, S. J., & Condit, R. (2012).

Functional traits explain light and size response of

growth rates in tropical tree species. Ecology, 93(12),

2626–2636.

Santiago, L. S., Goldstein, G., Meinzer, F. C., Fisher, J. B.,

Machado, K., Woodruff, D., & Jones, T. (2004). Leaf

photosynthetic traits scale with hydraulic conducti-

vity and wood density in Panamanian forest canopy

trees. Oecologia, 140, 543–550.

Sterck, F. J., Poorter, L., & Schieving, F. (2006). Leaf traits

determine the growth-survival trade-off across rain

forest tree species. The American Naturalist, 167(5),

758–765.

Swenson, N. G., & Enquist, B. J. (2007). Ecological and

evolutionary determinants of a key plant functional

trait: wood density and its community-wide variation

across latitude and elevation. American Journal of

Botany, 94(3), 451–459.

Tecco, P. A., Díaz, S., Cabido, M., & Urcelay, C. (2010).

Functional traits of alien plants across contrasting

climatic and land-use regimes: do aliens join the

locals or try harder than them? Journal of Ecology,

98(1), 17–27.

Tecco, P. A., Urcelay, C., Díaz, S., Cabido, M., & Pérez-

Harguindeguy, N. (2013). Contrasting functional trait

syndromes underlay woody alien success in the same

ecosystem. Austral Ecology, 38, 443–451.

Uriarte, M., Lasky, J. R., Boukili, V. K., & Chazdon, R. L.

(2016). A trait-mediated, neighbourhood approach

to quantify climate impacts on successional dynamics

of tropical rainforests. Functional Ecology, 30(2),

157–167.

Vendramini, F., Díaz, S., Gurvich, D. E., Wilson, P. J.,

Thompson, K., & Hodgson, J. G. (2002). Leaf traits

as indicators of resource-use strategy in floras with

succulent species. New Phytologist, 154(1), 147–157.

Villacís, J. (2016). Evaluación de las técnicas de remediación

vegetal utilizadas en plataformas petroleras mediante

estudios del desempeño de especies y análisis de diver-

sidad funcional (Doctoral dissertation). Universidad

Nacional de Córdoba, Argentina.

Villacís, J., Armas, C., Hang, S., & Casanoves F. (2016).

Selection of Adequate Species for Degraded Areas by

Oil-Exploitation Industry as a Key Factor for Reco-

very Forest in the Ecuadorian Amazon. Land Degra-

dation and Development, 27, 1771–1780.

Villacís, J., Casanoves, F., Hang, S., Keesstra, S., & Armas,

C. (2016). Selection of forest species for the rehabi-

litation of disturbed soils in oil fields in the Ecuado-

rian Amazon. Science of the Total Environment, 566,

761–770.

Wiemann, M. C., & Williamson, G. B. (2002). Geographic

variation in wood specific gravity: Effects of latitude,

temperature, and precipitation. Wood and Fiber Scien-

ce, 34, 96–107.

Wang, C., Jian, S., Ren, H., Yan, J., & Liu, N. (2021). A Web-

Based Software Platform for Restoration-Oriented

Species Selection Based on Plant Functional Traits.

Frontiers in Ecology and Evolution, 9, 570454.

Werden, L. K., Alvarado, J. P., Zarges, S., Calderón, E.,

Schilling, E., Gutierrez, M., & Powers, J. (2018). Using

soil amendments and plant functional traits to select

native tropical dry forest species for the restoration

of degraded Vertisols. Journal of Applied Ecology, 55,

1019–1028.

Wright, I. J., & Cannon, K. (2001). Relationships between

leaf lifespan and structural defences in a low-nutrient,

sclerophyll flora. Functional Ecology, 15, 351–359.

Wright, I. J., Groom, P. K., Lamont, B. B., Poot, P., Prior, L.

D., Reich, P. B., Schulze, E. D., Veneklaas, E. J., & Wes-

toby, M. (2004). Leaf trait relationships in Australian

plant species. Functional Plant Biology, 31, 551–558.

Yadav, M. R., Choudhary, M., Singh, J., Lal, M. K., Jha, P.

K., Udawat, P., Gupta, N. K., Rajput, V. D., Garg, N.

K., Maheshwari, C., Hasan, M., Gupta, S., Jatwa, T.

K., Kumar, R., Yadav, A. K., & Prasad, P. V. (2022).

Impacts, tolerance, adaptation, and mitigation of heat

stress on wheat under changing climates. Internatio-

nal Journal of Molecular Sciences, 23(5), 2838.