Potential Distribution Modeling of Useful Brazilian …...Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance 403 Fig. 1 Process of potential distribution

Journal of Agricultural Science and Technology B 6 (2016) 400-410 doi: 10.17265/2161-6264/2016.06.005

Potential Distribution Modeling of Useful Brazilian Trees

with Economic Importance

Vitor Augusto Cordeiro Milagres1 and Evandro Luiz Mendonça Machado2

1. Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, 13418-900, São Paulo,

Brazil

2. Forest Engineering Department, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, MG, 39100-000, Brazil

Abstract: Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, tourism riches, as well as ecological benefits, such as nutrient regulation and carbon sequestration. Thus, this work aimed to generate potential distribution modeling for the Brazilian forest species, to provide information that will serve as a strategy for conservation, restoration and commercial plantation of them, that is, encouraging the use of legal native species in the forest sector. Eleven tree species and 19 bioclimatic variables were selected. The software Maxent 3.3.3 was applied in the generation of the distribution models and the area under the curve of receiver operating characteristic (AUC) was used to analyze the model. The Jackknife test contributed to identify which bioclimatic variables are most important or influential in the model. The models showed AUC values ranged from 0.857 to 0.983. The species with higher AUC values were Araucaria angustifolia, Mimosa scabrella and Euterpe edulis, respectively. The maximum temperature of warmest month showed the highest influence for the most species, followed by the mean diurnal range and annual precipitation. It was observed that for some species, there were restricted areas of environmental suitability, such as Araucaria angustifolia, Ilex paraguariensis and Mimosa scabrella. The models used could trace the potential distribution areas using the environmental variables, and these models contribute significantly to sustainable forest management. Key words: Brazilian flora, Maxent, bioclimatic variables, distribution models, potential occurrence.

1. Introduction

The Brazilian territory is covered by the most

varied forest ecosystems, such as Amazon Rainforest,

Atlantic Forest, Cerrado, Caatinga and others, which

ranks it among the countries with the greatest

diversity on the planet. In addition, Brazil has

plantations of native and exotic species distributed

throughout practically the entire national territory [1].

Trees planted for industrial purposes are the source

of hundreds of products and byproducts, and they

produce diverse cultural, recreational, tourist and other

services related to research and regulation of water

flow and nutrients, as well as generate climatic

benefits with carbon sequestration [2].

Native species with ecological and economic

Corresponding author: Vitor Augusto Cordeiro Milagres,

M.Sc. student, research fields: soil and plant nutrition.

importance are being studied. One of the technologies

used is the potential distribution models [3]. Some

authors give it different names, such as ecological

niche models [4] and species distribution models

(SDMs) [5].

These models aim to complement or infer

information on the geographic distribution of the

species. This approach is a numerical tool that

combines observations of species occurrence or

abundance with environmental parameters, predicting

distribution through landscapes, sometimes requiring

extrapolation in space [6].

One of the desired characteristics of the SDMs is

the transferability, that is, the possibility to indicate

potential areas of occurrence of the species beyond the

known sites [7].

Potential distribution models can be useful in

identifying suitable sites for the species due to climate

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Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance

401

change and areas susceptible to invasive species [8],

estimating the broad limits of the distribution of an

endangered species and identifying new suitable

habitat for their reintroduction [9].

The productive capacity of a site can be defined as

the potential to produce wood (or other product), in a

given area, for a particular species or clone [10].

According to Clutter et al. [11], the models for

evaluating the local productive capacity are classified

as direct and indirect.

This study therefore aimed to generate potential

distribution models for Brazilian forest species,

encouraging the use of legal native species for

management, and contribute to the evaluation of

productive capacity in an indirect way. In addition,

this study will provide tools for forest producers to

find better combinations of trees for their farms,

increasing their productivity and variability of species.

2. Materials and Methods

2.1 Obtaining Occurrence Data

Eleven tree species of Brazilian flora were defined

(Table 1) with economic potential, whether wood or

non-wood product. The selected species are contained

in the species occurrence database, called TreeAtlan 2.1.

The TreeAtlan 2.1 program gathers information

from herbarium around the world that contains

information on tree species for neotropical regions. By

2010, there were over 1,216 checklists with 6,188

species [12]. Therefore, species were chosen referring

only to the Brazilian territory.

2.2 Environmental Variables

In this study, 19 bioclimatic environmental

variables (Table 2) were used with 1 km resolution at

the equator extracted from WorldClim database. With

the software DIVA-GIS version 7.5, the bioclimatic

variables were extracted to Brazil extension [13].

2.3 Development of Models

The distribution models were generated for each

species using the maximum entropy species

distribution modeling (Maxent). It is a

general-purpose machine learning method with a

simple and precise mathematical formulation, and it

has a number of aspects that make it well-suited for

species distribution modeling [14] (Fig. 1).

When comparing Maxent with other SDMs, such as

GARP, BIOCLIM, for example, Maxent demonstrated

better predictability [6, 15-17]. Also, Maxent can

effectively and accurately model species with multiple

forms of rarity, that is, with few points of occurrence,

to the point of leading to new population discovery

[18].

Table 1 List of species chosen for the potential species distribution model.

Family Scientific name Common name Uses

Anacardiaceae Anacardium occidentale Cashew tree Culinary, medicinal, CC

Aquifoliaceae Ilex paraguariensis Yerba mate RDA, beverage, chemical industry

Araucariaceae Araucaria angustifolia Araucaria CC, culinary, medicinal, afforestation

Arecaceae Euterpe edulis Juçara palm Culinary, CC, handicraft

Arecaceae Euterpe oleracea Acai palm Culinary, CC, handicraft

Calophyllum Calophyllum brasiliense Guanandi CC, medicinal

Euphorbiaceae Hevea brasiliensis Rubber tree Natural rubber, culinary

Fabaceae Hymenaea courbaril Jatobá CC, RDA, culinary

Fabaceae Mimosa scabrella Bracatinga RDA, CC, charcoal, beekeeping, animal feed

Fabaceae Schizolobium amazonicum Parica CC, RDA, medicinal

Meliaceae Swietenia macrophylla Brazilian mahogany CC, landscaping

RDA: recovery of degraded areas; CC: civil construction.


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Table 2 Nineteen environmental variables used in the potential distribution of species and their codes.

Variables Descriptions

BIO1 Annual mean temperature

BIO2 Mean diurnal range (mean of monthly (max. – min.) temperature) BIO3 Isothermality (BIO2/BIO7) × 100) BIO4 Temperature seasonality

BIO5 Max. temperature of the warmest month

BIO6 Min. temperature of the coldest month

BIO7 Temperature annual range (BIO5 – BIO6)

BIO8 Mean temperature of the wettest quarter*

BIO9 Mean temperature of the driest quarter

BIO10 Mean temperature of the warmest quarter

BIO11 Mean temperature of the coldest quarter

BIO12 Annual precipitation

BIO13 Precipitation of the wettest month

BIO14 Precipitation of the driest month

BIO15 Precipitation seasonality

BIO16 Precipitation of the wettest quarter

BIO17 Precipitation of the driest quarter

BIO18 Precipitation of the warmest quarter

BIO19 Precipitation of the coldest quarter

* Quarter: period of 13 weeks (three months) consecutive.

The idea of Maxent is to estimate a target

probability distribution by finding the probability

distribution of the maximum entropy subject to a set

of constraints that represent the incomplete

information about the target distribution [14].

Maxent also evaluates through the Jackknife test. In

this analysis, each variable is deleted and a new model

is created with only the other variables. Furthermore,

other models are created using each variable

separately [19]. Thus, it is known which bioclimatic

variables are the most important or influential in the

model. A low gain variable, close to 0, presents

prediction as poor as a random model. However, the

variables with gains close to 1 present information

highly correlated to the occurrences and they perform

good predictions [20].

To analyze the performance of the model, that is, its

accuracy, a technique known as receiver operating

characteristic (ROC) curve was used. The curve is

made plotting on one axis—the proportion of true

presences of total presences predicted and specificity,

and on the other axis—the proportion of true absences

in relation to predicted absences predictions [20]. The

area under the ROC curve (AUC) was used to

evaluate the distribution models. It ranges from 0 to 1,

and predicted values above 0.5 are acceptable [21].

2.4 Mapping Distribution

DIVA GIS 7.5 was used to create the maps [13].

The points of presence were superimposed on the

expected areas of occurrence to evaluate the

performance of the models [22]. For each cell’s

probability, the Maxent indicates a percentage ranging

from 0 to 100, indicating environmental suitability

(not probability of occurrence) [23].

3. Results

3.1 Prediction Maps of Species

The resulting maps are referents of areas of wide

occurrence for the species studied (Figs. 2 and 3).

It was observed that for some species, there were

restricted areas of environmental suitability, such as

Araucaria angustifolia (Fig. 2b), Ilex paraguariensis

(Fig. 3b) and Mimosa scabrella (Fig. 3c).


403

Fig. 1 Process of potential distribution modeling of the species [22].

Fig. 2 Potential distribution model of Anacardium occidentale (a), Araucaria angustifolia (b), Calophyllum brasiliense (c), Euterpe edulis (d), Euterpe oleracea (e) and Hevea brasiliensis (f), containing the environmental suitability in Brazil with their occurrence points.

: 0.0000-0.1806; : 0.1806-0.3613; : 0.3613-0.5419; : 0.5419-0.7225; : 0.7225-1.0000. Modelling was performed using Maxent with 19 environmental variables.

(a) (b) (c)

(d) (e) (f)


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Fig. 3 Potential distribution model of Hymenaea courbaril (a), Ilex paraguariensis (b), Mimosa scabrella (c), Schizolobium amazonicum (d) and Swietenia macrophylla (e), containing the environmental suitability in Brazil with their occurrence points.

: 0.0000-0.1806; : 0.1806-0.3613; : 0.3613-0.5419; : 0.5419-0.7225; : 0.7225-1.0000. Modelling was performed using Maxent with 19 environmental variables.

A relevant factor for the restriction of areas is the

reduced number of records (below 100). However,

species, such as Euterpe oleracea (Fig. 2e),

Schizolobium amazonicum (Fig. 3d) and Swietenia

macrophylla (Fig. 3e), despite the low occurrence,

presented a notable area of occurrence widely

distributed.

It was highlighted that the species Anacardium

occidentale, Calophyllum brasiliense and Hymenaea

courbaril showed greater abundance. These species

are found from the north to the south of the country,

covering several biomes with high environmental

resilience.

3.2 Evaluation of the Models

The AUC values found by the Maxent program

ranged from 0.857 to 0.983 (nine species with values

in 0.9-1.0 and two species with values in 0.8-0.9)

(Table 3).

The species with higher AUC were Araucaria

angustifolia, Euterpe edulis and Mimosa scabrella,

and with lower AUC values were Hymenaea courbaril,

Schizolobium amazonicum and Anacardium

occidentale.

3.3 Standardization of Models

In this study, these distinct patterns were observed:

(1) Pattern I: species with few occurrence records

and wide distribution and adaptation, such as

Schizolobium amazonicum and Swietenia

macrophylla;

(2) Pattern II: species with many occurrence records

and wide distribution and adaptation, such as

Hymenaea courbaril, Anacardium occidentale,

Calophyllum brasiliense and Hevea brasiliensis;

(3) Pattern III: species with many occurrence records

and restricted areas of environmental suitability,

such as Araucaria angustifolia, Ilex paraguariensis,

(a) (b) (c)

(d) (e)


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Table 3 AUC values to the potential distribution models of the 11 species.

No. Species AUC

1 Anacardium occidentale 0.900

2 Araucaria angustifolia 0.983

3 Calophyllum brasiliensis 0.912

4 Euterpe edulis 0.975

5 Euterpe oleracea 0.928

6 Hevea brasiliensis 0.960

7 Hymenaea courbaril 0.894

8 Ilex paraguariensis 0.970

9 Mimosa scabrella 0.982

10 Schizolobium amazonicum 0.857

11 Swietenia macrophylla 0.947

Table 4 Order of the most important environmental variables for each species, according to results of the Jackknife statistics.

No. Species Environmental variables

1st 2nd 3rd

1 Ilex paraguariensis

BIO5

BIO18 BIO3

2 Mimosa scabrella BIO18 BIO3

3 Euterpe edulis BIO2 BIO3

4 Araucaria angustifolia BIO12 BIO2

5 Calophyllum brasiliense BIO11 BIO2

6 Swietenia macrophylla BIO3 BIO4

7 Anacardium occidentale BIO2

BIO12 BIO4

8 Euterpe oleracea BIO7 BIO12

9 Hevea brasiliensis BIO12

BIO2 BIO4

10 Hymenaea courbaril BIO4 BIO17

11 Schizolobium amazonicum BIO4 BIO11 BIO16

Mimosa scabrella and Euterpe edulis;

(4) Pattern IV: species with few records and

restricted areas of environmental suitability, such as

Euterpe oleracea.

The results of the Jackknife tests are in relation to

the importance of environmental variables for each

species. BIO5 showed a higher gain for a larger

number of species, followed by BIO2 and BIO12

(Table 4).

In this study, 10 test variables did not present

significant influence in the models, among them, such

as annual mean temperature (BIO1) and the

precipitation of the coldest quarter (BIO19).

4. Discussion

Through the chosen environmental variables, it was

possible to represent the fundamental niche of the 11

species studied.

This result can be evaluated using two components:

the ROC curve and the Jackknife test. The first

provides a standard quantitative measure of model

performance. The higher the AUC value, the better the

model [24]. On the other hand, in the Jackknife

analysis, each variable is deleted and a new model is

created with only the other variables. In this way, it is

possible to visualize which bioclimatic variable had

the greatest influence on the model [19].

The AUC indexes found were satisfactory.

According to Metz [25], an evaluation test may adopt

AUC values as: excellent (0.9-1.0), good (0.8-0.9),

fair (0.7-0.8), poor (0.6-0.7) and fail (0.5-0.6). In this

study, the AUC values ranged from 0.983 to 0.857,


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with the Araucaria angustifolia, Mimosa scabrella

and Euterpe edulis species having the highest values,

respectively.

Considering the data used in this work, the results

suggest that the Maxent program presented high

predictive power. Comparing different models

generated (GARP, BIOCLIM, Maxent and SVM), it

was concluded that the model generated by Maxent

was the most efficient, with the highest AUC value

among all the algorithms used [26].

Maxent is one of the few algorithms that make a

prediction consistent with low sample numbers (n <

30), and can be used with data below 10 points of

occurrence [27]. Other models, such as BIOCLIM

stabilizes after 50 points [28]. For some species, a

small number of occurrence points may be sufficient

to characterize the environmental niche, whereas in

other cases, the same number of occurrence may be

inadequate to represent the range of conditions under

which the species exists [23]. One can therefore

understand why some species have restricted areas

and other large areas. The importance of each record

will depend on whether this location represents an

exclusive environment, not represented by the other

sampling points [23].

The variable with greater training gain is the one of

greater importance and the one that most influenced in

the modeling of the potential distribution of the

species [22]. In the generation of the models, the

variable that influenced most species was the

maximum temperature of the warmest month (BIO5),

as observed for Ilex paraguariensis and Mimosa

scabrella. According to Oliveira and Rotta [29] in the

climatic mapping for Ilex paraguariensis, using the

classification of Köppen, it is evidenced that the

predominant distribution of Yerba mate is covered by

climatic types Cfb, that is, temperate climate with

mild summer.

The probability of relative occurrence from a

combination of environmental variables depends on

the density of species records in the fundamental niche

of them [30]. This was observed for the species

Anacardium occidentale, Calophyllum brasiliense and

Hymenaea courbaril. These were the ones with the

highest occurrence and consequently the greatest

ecological niche.

The model indicates that the environmental

conditions are similar to the places where the known

species occurs [23]. In the same way, Bracatinga

(Mimosa scabrella) is native to the colder climates of

Brazil, that is, rainy temperate, constantly humid with

average temperatures of the month warmer and cooler

inferior to 22 °C and 18 °C, respectively [31]. From

their distribution models, it is observed that the

species are sensitive to high temperatures, and with

this, it is verified that the areas with greater

environmental suitability are regions with lower

temperatures, as it happens in the south of the country.

In addition, the species obtained similar results in

other variables of greater importance, such as

precipitation of warmest quarter (BIO18) and

isothermality (BIO3). Such information confirms the

sensitivity of both to temperature.

Species, such as Hymenaea courbaril and

Calophyllum brasiliense are considered as plastic

species, since they occur in different regions of the

country. Calophyllum brasiliense grows well in

alluvial, clayey, sandy-loamy or sandy soils with acids

(pH 4.5-6.0), and presents excellent adaptation to both

wet and dry environments [32]. Due to its high

adaptability, the species can be an alternative

plantation for farmers.

The Guanandi has light to moderately dense wood,

good durability and resistance, which allows its use in

civil and naval construction, barrels for wine storage

and general carpentry [33]. Hymenaea courbaril

occurs from the north to the southeast, both in high

and medium fertility soils [34]. Its wood is used in

hydraulic works, bodywork, poles, barrels, various

constructions, furniture, laminates, among other forms

[35]. Both species have a high environmental

amplitude, therefore, being a common characteristic of


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the species more found in the Brazilian territory.

Paricá (Schizolobium amazonicum) was more

influenced by temperature seasonality (BIO4). This

variable represents the coefficient of variation of the

mean temperature, expressed as a percentage of the

average annual temperature. It was observed a

feasibility of a large part of the Brazilian North to

Paricá. This region has a spatial and seasonal

homogeneity of temperature, which does not happen

in relation to precipitation [36].

Although it shelters the largest commercial growing

area in the country, the 3D figure suggests that the

species still has potential for exploration. The wood

has potential use in the manufacture of phosphorus

sticks, shoe heels, toys, concrete forms, laminates,

plywood, cellulose and paper, due to its easy

processing [37].

The mean diurnal range (BIO2) was the most

important variable for the species Anacardium

occidentale and Euterpe edulis. The main producing

regions of cashew in the world present climate Aw,

characterized in tropical, hot, humid, with rains from

November to April and dry, or too little rainfall

intensity, in other months, according to Köppen

classification [38]. The same was observed in the

potential distribution of the species. Anacardium

occidentale shows greater suitability in Northeast and

Southeastern Brazil. In the present study, it was

observed that the species is susceptible to prolonged

periods under temperatures below 22 °C, since young

plants are harmed by cold [38].

The Euterpe edulis species presented a greater

adaptation in the Atlantic Forest region, from the

Northeast to the South of the country. Juçara palm

develops well in tropical and subtropical regions, with

high precipitation, i.e., regions with high precipitation

and no pronounced drought [39]. Due to its

environmental requirements, Brazil is one of the few

countries that present adequate climatic conditions for

the cultivation and commercial exploitation of this

plant [39].

Agroforestry systems have grown widely in Brazil.

Consortia of tree species with palm trees show

themselves to be increasingly promising. Examples of

this are rubber tree plantations (Hevea brasiliensis)

with the Acai (Euterpe oleracea) or the Juçara palm

(Euterpe edulis).

Analyzing Figs. 2d and 2f, it is possible to observe

the viability of both cultivations for the states of

Espírito Santo (Southeast) and Acre (North), where

their effectiveness is already proven. The consortium

between rubber tree and palm tree was investigated by

Bovi et al. [40] and it was concluded that production

could be feasible with adequate spacing.

The Parica (Schizolobium amazonicum) consortium

with the Brazilian mahogany (Swietenia macrophylla)

has also been promising. Similar regions of

environmental suitability are observed for the species,

especially in the north of the country. Santos et al. [41]

analyzed the Paricá, mahogany and other crops

consortium and the economic viability, and presented

a positive profitability for this system. In addition, it

was observed that the consortium exceeded the

monoculture results with adequate management.

In addition to what has been discussed so far, the

evaluation of productive capacity may be done by

means of direct methods (relation between dominant

height and age) or indirect methods, based on

indicative vegetation and edaphoclimatic factors [42].

This work is expected to contribute to the indirect

methods, since it used climate data for model

generation.

5. Conclusions

It was possible to trace potential occurrence areas,

using environmental variables appropriate to the

species. Species distribution models provide important

contribution to the design and implementation of

strategies for sustainable forest management.

The relevance of the use of predictive modeling of

distribution for the forest agribusiness is also

highlighted. Species, such as Anacardium occidentale,


408

Calophyllum brasiliense and Hymenaea courbaril

were the ones with the highest occurrence. Others,

such as Araucaria angustifolia, Ilex paraguariensis

and Mimosa scabrella were specific species of

Southern Brazil, and their planting in other regions is

not recommended. In addition, it is observed that the

species with higher AUC values were Araucaria

angustifolia, Mimosa scabrella and Euterpe edulis,

respectively.

This work can be a tool to evaluate the productive

capacity of the place, using climatic data as an indirect

method. It is necessary for the forest management the

incorporation of the knowledge between the

interactions of the climate with the forest.

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Potential Distribution Modeling of Useful Brazilian …...Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance 403 Fig. 1 Process of potential distribution

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