Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance
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Journal of Agricultural Science and Technology B 6 (2016) 400-410 doi: 10.17265/2161-6264/2016.06.005 D DAVID PUBLISHING 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 importance are being studied. One of the technologies used is the potential distribution models [3]. Some The Brazilian territory is covered by the most authors give it different names, such as ecological varied forest ecosystems, such as Amazon Rainforest, niche models [4] and species distribution models Atlantic Forest, Cerrado, Caatinga and others, which (SDMs) [5]. ranks it among the countries with the greatest These models aim to complement or infer diversity on the planet. In addition, Brazil has information on the geographic distribution of the plantations of native and exotic species distributed species. This approach is a numerical tool that throughout practically the entire national territory [1]. combines observations of species occurrence or Trees planted for industrial purposes are the source abundance with environmental parameters, predicting of hundreds of products and byproducts, and they distribution through landscapes, sometimes requiring produce diverse cultural, recreational, tourist and other extrapolation in space [6]. services related to research and regulation of water One of the desired characteristics of the SDMs is flow and nutrients, as well as generate climatic the transferability, that is, the possibility to indicate benefits with carbon sequestration [2]. potential areas of occurrence of the species beyond the Native species with ecological and economic known sites [7]. Potential distribution models can be useful in Corresponding author: Vitor Augusto Cordeiro Milagres, M.Sc. student, research fields: soil and plant nutrition. identifying suitable sites for the species due to climate Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance 401 change and areas susceptible to invasive species [8], information on tree species for neotropical regions. By estimating the broad limits of the distribution of an 2010, there were over 1,216 checklists with 6,188 endangered species and identifying new suitable species [12]. Therefore, species were chosen referring habitat for their reintroduction [9]. only to the Brazilian territory. The productive capacity of a site can be defined as 2.2 Environmental Variables the potential to produce wood (or other product), in a given area, for a particular species or clone [10]. In this study, 19 bioclimatic environmental According to Clutter et al. [11], the models for variables (Table 2) were used with 1 km resolution at evaluating the local productive capacity are classified the equator extracted from WorldClim database. With as direct and indirect. the software DIVA-GIS version 7.5, the bioclimatic This study therefore aimed to generate potential variables were extracted to Brazil extension [13]. distribution models for Brazilian forest species, encouraging the use of legal native species for 2.3 Development of Models management, and contribute to the evaluation of The distribution models were generated for each productive capacity in an indirect way. In addition, species using the maximum entropy species this study will provide tools for forest producers to distribution modeling (Maxent). It is a find better combinations of trees for their farms, general-purpose machine learning method with a increasing their productivity and variability of species. simple and precise mathematical formulation, and it 2. Materials and Methods has a number of aspects that make it well-suited for species distribution modeling [14] (Fig. 1). 2.1 Obtaining Occurrence Data When comparing Maxent with other SDMs, such as Eleven tree species of Brazilian flora were defined GARP, BIOCLIM, for example, Maxent demonstrated (Table 1) with economic potential, whether wood or better predictability [6, 15-17]. Also, Maxent can non-wood product. The selected species are contained effectively and accurately model species with multiple in the species occurrence database, called TreeAtlan 2.1. forms of rarity, that is, with few points of occurrence, The TreeAtlan 2.1 program gathers information to the point of leading to new population discovery from herbarium around the world that contains [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. 402 Potential Distribution Modeling of Useful Brazilian Trees with Economic Importance 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 in relation to predicted absences predictions [20]. The probability distribution by finding the probability area under the ROC curve (AUC) was used to distribution of the maximum entropy subject to a set evaluate the distribution models. It ranges from 0 to 1, of constraints that represent the incomplete and predicted values above 0.5 are acceptable [21]. information about the target distribution [14]. 2.4 Mapping Distribution Maxent also evaluates through the Jackknife test. In this analysis, each variable is deleted and a new model DIVA GIS 7.5 was used to create the maps [13]. is created with only the other variables. Furthermore, The points of presence were superimposed on the other models are created using each variable expected areas of occurrence to evaluate the separately [19]. Thus, it is known which bioclimatic performance of the models [22]. For each cell’s variables are the most important or influential in the probability, the Maxent indicates a percentage ranging model. A low gain variable, close to 0, presents from 0 to 100, indicating environmental suitability (not probability of occurrence) [23]. prediction as poor