Environmental Management DOI 10.1007/s00267-016-0659-5
The Impact of Global Climate Change on the Geographic Distribution and Sustainable Harvest of Hancornia speciosa Gomes (Apocynaceae) in Brazil Joa˜o Carlos Nabout1 • Mara Ru´bia Magalha˜es1 • Marcos Aure´lio de Amorim Gomes1 He´lida Ferreira da Cunha1
•
Received: 15 April 2014 / Accepted: 8 January 2016 Ó Springer Science+Business Media New York 2016
Abstract The global Climate change may affect biodiversity and the functioning of ecosystems by changing the appropriate locations for the development and establishment of the species. The Hancornia speciosa, popularly called Mangaba, is a plant species that has potential commercial value and contributes to rural economic activities in Brazil. The aim of this study was to evaluate the impact of global climate change on the potential geographic distribution, productivity, and value of production of H. speciosa in Brazil. We used MaxEnt to estimate the potential geographic distribution of the species in current and future (2050) climate scenarios. We obtained the productivity and value of production for 74 municipalities in Brazil. Moreover, to explain the variation the productivity and value of production, we constructed 15 models based on four variables: two ecological (ecological niche model and the presence of Unity of conservation) and two socio-economic (gross domestic product and human developed index). The models were selected using Akaike Information Criteria. Our results suggest that municipalities currently harvesting H. speciosa will have lower harvest rates in the future (mainly in northeastern Brazil). The best model to explain the productivity was ecological niche model; thus, municipalities with higher productivity are inserted in regions with higher environmental suitability (indicated by niche model). Thus, in the future, the municipalities harvesting H. speciosa will produce less because there will be less suitable habitat for H. speciosa,
& Joa˜o Carlos Nabout
[email protected] 1
Caˆmpus de Cieˆncias Exatas e Tecnolo´gicas – Henrique Santillo (CCET), Universidade Estadual de Goia´s, BR-153, n8 3.105, Ana´polis, GO CEP 75132-903, Brazil
which in turn will affect the H. speciosa harvest and the local economy. Keywords Ecological niche model MaxEnt Cerrado Caatinga Mangaba
Introduction The study of global climate change has gained great notoriety in both the academic and non-academic communities by means of mass communication, although it is not a recent topic in the scientific community (Parmesan 2009; Nabout et al. 2012). This interest is justified by numerous studies that have evaluated the impact of global climate change on the geographic distribution of species (Diniz-Filho et al. 2010; Nabout et al. 2009) and conservation biology. In addition to investigating the effect of global climate change on the distribution of species, recent studies have discussed the impact of climate change on humanity by examining the effects on the spread of disease (Lafferty 2009) and on agriculture and food security (Lopes et al. 2011; Assad et al. 2010). The impacts of climate change on species, such as changes in the geographic distribution or the biological characteristics (such as intensity of fruiting and flowering, among others), can directly affect human populations. Several native species of plants and animals are used as sustainable harvest contributing to the maintenance of biodiversity and household economic activity (e.g., Lima et al. 2013). Many academic studies have evaluated the impact of climate change on biological characteristics and the geographic distribution of species (Elith and Leathwick 2009), but few studies have investigated the effect of these changes in a species for sustainable use and the magnitude of these impacts on rural communities (e.g., Nabout et al. 2011).
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The harvesting of non-timber forest products is a practice that has gained great attention and support from government entities. This activity can assist in the maintenance and conservation of biodiversity and contribute to rural economic activities (Scherr and Mcneely 2007; Shanley et al. 2002). In Brazil, laws such as the National System of Conservation Units (SNUC, Lei 9.985/00), political actions, and practical activities of local rural communities have contributed to, albeit tentatively, the biological species conservation in biomes. Tropical biomes, such as the Cerrado, Caatinga, and Amazon, have species of native plants that are used as a sustainable resource for rural communities. These communities have commercially used native plants for fruits, oils, soaps, and countless other applications (Almeida et al. 1998). Hancornia speciosa Gomes (Apocynaceae), popularly called Mangaba, is among several native species used by rural population. The H. speciosa is a native fruit species of Brazil that produces latex, whose distribution extends from northeastern Brazil to Bolivia, covering the Cerrado and Caatinga (Tavares 1964; Lorenzi 1992; Rizzini 1997), and represents social, economic, and cultural importance in the areas where it occurs (Almeida et al. 1998; Lima et al. 2013). The plant is an evergreen and semi-deciduous tree of medium size with a height ranging from 2 to 15 (Monachino 1945); it is slow-growing, with a large treetop of 4–5 meters in diameter, and it is often more branched than tall. Therefore, the aim of this study was to evaluate the impact of global climate change on the potential geographic distribution, productivity, and value of production of H. speciosa in Brazil. We specifically sought to (1) investigate the productivity and value of production of H. speciosa in Brazilian municipalities over the past 21 years, (2) determine the current and future (to 2050) potential geographic distribution of H. speciosa based on the ecological niche model, (3) evaluate the influence of ecology (actual environmental suitability and the presence of unity of conservation) and socio-economic variables (gross domestic product and human developed index) in productivity and value of production of H. speciosa; (4) relate the change in geographical distributions of H. speciosa (difference among future and actual environmental suitability) with actual productivity and value of production. For this, we hope determining which municipalities will be affected by change of the environmental suitability of H. speciosa.
Materials and Methods Species The species used in this paper was Hancornia speciosa, a monotypic species belonging to the family Apocynaceae.
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The species has a wide geographic distribution (Almeida et al. 1998) and occurs in areas of the Cerrado, coastal tablelands, and coastal marshlands (Ferreira 1973). The H. speciosa is a semi-deciduous plant with open formations and is common in sandy soils of low fertility (Lorenzi 1992). It flowers from August to November, with a peak in October (Almeida et al. 1998). The climatic conditions for the occurrence of H. speciosa are typically tropical with high annual variation of precipitation (see Table 1). Productivity and Value of Production of H. speciosa The productivity and value of production of H. speciosa by Brazilian municipalities were obtained from the website of the Instituto Brasileiro de Geografia e Estatı´stica (IBGE, www.ibge.gov.br) using the automatic retrieval system (SIDRA) by searching ‘‘Extraction Plant.’’ The search revealed 74 municipalities, and the average extraction and value of production of H. speciosa over the past 21 years were obtained for each municipality. The average extraction was standardized by the size of the municipality; therefore, the unit of extraction was kg/km2. The unit of value of production was Brazilian Real (divided by thousand). Ecological Data: Ecological Niche Model and Unity of Conservation For Ecological Niche Model (ENM) of H. speciosa, the data of the occurrence were obtained from the literature (articles, books, theses, and dissertations) and a database of the Reference Center Environmental Information (CRIA; http://www.cria.org.br/). Altogether, 367 points of occurrence of H. speciosa were obtained, and these points were used in the modeling of the potential geographic distribution (Fig. 1). The data (geographic coordinates) of municipalities that harvested H. speciosa were not included to avoid problems in the circular analysis. We used five climatic variables in the model: (1) Isothermality (Worldclim classification—bio3); (2) Temperature Seasonality (bio4); (3) Temperature Annual Range (bio7); (4) Mean Temperature of Warmest Quarter (bio10); and (5) Precipitation Seasonality (bio15). The same variables were used for the future scenario, derived from the global climate model CCCma (Canadian Centre for Climate Modelling and Analysis), projected to a pessimistic scenario (A2a) (see Karl and Trenberth 2005). The climate scenarios (current and future) were obtained from Worldclim (www.worldclim.org), with values projected to the year 2050 for the future scenario (Hijmans et al. 2005). All climate variables were converted into a grid with 0.0417 degrees of resolution. We selected these variables using the Jackknife criteria (considering all 19 climatic
Environmental Management Table 1 Descriptive statistics of Actual and Future climate variables used in this study to project the distribution of H. speciosa
Actual
Future
bio3
bio4
bio7
bio10
Average
69.93
125.72
15.47
24.79
Maximum
83.02
272.44
22.20
28.65
Minimum
61.83
43.03
8.50
20.25
19.67
55.08
3.67
45.48
2.72
1.96
18.06
5.06
SD
bio15
bio3
bio4
bio7
bio10
bio15
70.49
67.85
132.48
15.65
26.35
73.65
105.83
81.40
231.24
22.80
30.48
107.94
42.98
9.20
21.60
25.80
39.16
2.54
1.89
19.14
The variables were Isothermality (bio3), Temperature Seasonality (bio4), Temperature annual range (bio7), Mean Temperature of Warmest Quarter (bio 10), and Precipitation Seasonality (bio15)
Socio-economic Data For each municipality, we obtained the following socioeconomics data: (1) Gross Domestic Product (GDP) per capita (in real R$) and (2) Municipal Human Development Index (HDI). We expect that HDI and GDP variables will have a negative relationship with productivity and positive with value of production (i.e., a higher production rate in poorer municipality). We obtained the GDP data (year 2009) and Municipal HDI (year 2010) in the IBGE database (www.cidades.ibge.gov.br/). Data Analysis
Fig. 1 Occurrence points used for ecological niche modeling of Hancornia speciosa
variables available in the Worldclim database) (see, for example, Neme´sio et al. 2012). MaxEnt was used to analyze the ENM (Phillips et al. 2006). The input parameters followed the program default choices, except that iterations were set at 1000 and duplicates were removed. We used the AUC (area under the curve) to evaluate the model. The AUC values range from 0.5 to 1 (see Elith et al. 2006). For other ecological predictors, we obtained the municipalities that produce H. speciosa have some kind of Unity of Conservation (UC) according the National System of Conservation Units (SNUC, Lei 9.985/00). Therefore, for each municipality, we had a categorical information (1 = has UC and 0 = not have UC). The UC information was obtained from IBGE and ICMBIO sites (http://www. icmbio.gov.br). Only eighth municipalities were classified with the presence of UC.
We used the Akaike Information Criterion (AIC) to select the best model (with set of variables) that explains the productivity and value of production of H. speciosa in Brazilian municipalities. We used the following variables as predictors: Ecological Niche Model (i.e., suitability to each municipality), Unity of Conservation (dummy variable), Gross Domestic Product Per Capita (GDP), and Municipal Human Development Index (HDI). From the 15 generated models (i.e., linear models with 15 different combinations of predictors), however, we presented only the best models for productivity and value of production (delta AIC \ 2; Burnham and Anderson, 2002). The AIC was performed in Spatial Analysis and Macroecology (SAM V.4) software (Rangel et al. 2010). To reduce the dimensionality and normalize the data, all variables (except Unity of Conservation) were log-transformed (LogX ? 1).
Results The ENM of H. speciosa showed a satisfactory adjustment, with an AUC of 0.97. In addition, for the current scenario, a great portion of the Cerrado and Caatinga biomes shows high habitat suitability (Fig. 2a). However, in the future climate change scenario, there is a large reduction in the geographic distribution of the species. Many regions, especially in the northeast, decreased in climate suitability for this species (Fig. 2b). Moreover, many municipalities
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that harvest H. speciosa will lose environmental suitability in the future scenario. The average productivity of all seventy-four municipalities analyzed was 25.26 kg/km2. The municipalities with the highest productivity of H. speciosa were Baı´a da Traic¸a˜o (339.46 kg/km2) and Marcac¸a˜o, both in Paraı´ba (330.91 kg/km2), and the municipality Ichu (313.31 kg/ km2) in Bahia. In contrast, the municipality with the lowest harvest of H. speciosa was Japoata˜ in Sergipe (0.001 kg/ km2). For value of production, the average was 11.37 thousand of Real (R$), and the Estaˆncia in Sergipe (214.89 thousand of Real), and Salvador in Bahia (0.05 thousand of Real) were, respectively, municipalities with the highest and lowest value of production of H. speciosa in Brazil. The productivity and value of production were not correlated (r = -0.015; P = 0.9), thus, municipalities with more productivity will not have more value of production (Fig. 3). After generating 15 models to explain the variation of productivity and value of production of H. speciosa in Brazilian municipalities, the AIC has listed the best three models to explain productivity, and two to explain the value of production. To productivity, the best model is formed only by a single variable (ENM), in other words, the ENM alone explains 15.7 % of the variation in productivity between the municipalities (Table 2); furthermore, the positive relationship with productivity (Table 3) indicates that municipalities with higher productivity are inserted in regions with higher environmental suitability (indicated by ENM). Regarding the value of production, the best model is formed by the socio-economic variables (GNP and HDI). These two variables explained 18 % of the variation in value of production between municipalities
(Table 2). These two variables were also important in explaining the production value (Table 3), however, have opposed relations with the value of production. Municipalities with the highest value of production are those that have higher GNP and lower HDI. The difference between the environmental suitability (future—actual) of H. speciosa can indicate the local (municipalities) it will present loss or gain of suitability in future scenarios. For producers’ municipalities, it is observed that most of the municipalities present environmental suitability loss in future scenarios of climate change, affecting the productivity and value of production of H. speciosa (Fig. 4).
Discussion In this study, we used ecological (ENM and UC) and socioeconomic (HDI and GNP) variables to predict the productivity and value of production of H. speciosa in Brazilian municipalities. The socio-economic variables were import to explain the value of production, which showed the importance of rural economy to municipality. However, the productivity was explained by ecological variables (only ENM), which showed the influence of climate in sustainable harvest. The ENM has been widely used in the scientific literature, mainly to evaluate the impact of global climate change on species distribution (Peterson et al. 2011; Beck 2013). Moreover, several studies have investigated the relationships among suitability (obtained by ENMs), genetic variables (e.g., Fst, He and others; Diniz-Filho et al. 2009a, b, c, 2015; Soares et al. 2015), variations in
Fig. 2 Potential geographic distribution of Hancornia speciosa, considering current (a) and future (b) climate scenarios
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(a)
2.4 2.3 2.2 2.1 2 1.9 1.8 1.7
2.3
(b)
2.2 2.1 2 1.9 1.8 1.7 1.6
1.6
1.5
1.5
1.4
1.4
1.3
1.3
1.2
1.2
1.1
1.1 1 0.9 0.8 0.7 0.6
1 0.9 0.8 0.7 0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
Fig. 3 Brazilian municipalities that harvest Hancornia speciosa. Each circle indicates the central position of the municipality. The gradient of the monochromatic color shows the productivity (a) and
value of production (b) log-transfomed (LogX ? 1) of Hancornia speciosa by municipality
Table 2 Best models (DAIC \ 2) for productivity and value of production of Harconia speciosa in brazilian municipalities
production of H. speciosa, indicating the increased use of these resources. In this study, the ecological variables, indicated by suitability in Ecological Niche Models, were most important to explain the productivity of H. speciosa. Our results indicated that suitability (obtained by the ecological niche model) can be a surrogate for the harvest of H. speciosa. However, this relation is triangular (i.e., constraint envelope), thus, regions of low suitability will always have a small extraction of H. speciosa (similar results were obtained for Caryocar brasiliense; see Nabout et al. 2011). Nonetheless, several municipalities located in the regions of high suitability may have low harvests of H. speciosa. This result may be an indication that these municipalities harvested H. speciosa below its production potential. However, the low harvest of H. speciosa in the regions of high suitability may be due to two aspects: (1) There are few (if any) plants of this species in the region. The species may be absent at the site due to historical reasons or biotic interactions (Peterson et al. 2011); moreover, the anthropogenic impact may have eliminated individuals of those species that occur in regions of high suitability, as the Cerrado has undergone an intense process of land conversion for grazing and agriculture (Sawyer 2008). (2) There are numerous individuals, but few have been explored. The exploitation of natural resources is encouraged by state and local governments. Thus, regions with little government support may have a low harvest of H. speciosa because it is more profitable to invest in crops such as soy and sugar cane than in plants for sustainable use.
r2
AIC
ENM
0.157
129.073
ENM, HDI
0.164
130.885
ENM, GNP
0.162
131.006
Model Productivity
Value HDI, GPD
0.188
82.543
ENM, HDI, GPD
0.201
83.857
The predictor variables were ENM (Ecological Niche Model), UC (Unity of conservation), GPD (Gross Domestic Product per Capita), and HDI (Municipal Human Development Index)
functional traits (Thuiller et al. 2010), population variables (density; Van Der Wal et al. 2009; Toˆrres et al. 2012), and the production/extraction of fruits (Nabout et al. 2011, 2012). The potential geographic distribution of H. speciosa suggests a wide distribution, occupying much of the Cerrado and Caatinga biomes. Despite its wide distribution, this natural resource has been heavily used by municipalities in the northeast (especially on the coast). Therefore, H. speciosa can potentially be exploited in other regions of Brazil. The prevalence of the use of this plant in the northeast can be justified by encouraging and organizing local communities in the sustainable harvest of H. speciosa fruit (see Schmitz et al. 2009). Furthermore, most of the investigated municipalities tended to increase in their
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2.8 2.6
Productivity and value of production (Log X+1)
Fig. 4 Relationship between delta suitability (future–actual suitability) and the harvest of Hancornia speciosa for the 74 investigated municipalities. Negative values in X axis indicate loss of suitability in future scenarios
Value of production Productivity
Decrease of suitability
Increase of suitability
2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.6
0.0
-0.5
0.2
Delta Suitabilty
Indeed, H. speciosa has been used to supplement the financial resources for human populations that use these resources (see Lima et al. 2013). In addition, the positive relationship of average harvest with the ecological niche model suggests that it is possible to make inferences about the impact on the fruit harvest considering predicted climate changes. H. speciosa species will lose suitable habitat in futures scenarios. The loss of suitable habitat for species has been recorded in several studies with plants in Brazil (Diniz-Filho et al. 2009a, b, c; Nabout et al. 2011; Simon et al. 2013). Moreover, it is expected that in future climate scenarios, the municipalities that actually extracted H. speciosa will lose or decrease their amount of extraction. This scenario is conservative because other variables, such as land use, change of pollinators, and pests, were not considered (Hannah et al. 2002). This is especially important considering that the Cerrado has gone through an intense process of occupation and conversion of land use, mainly for grazing and agriculture (e.g., soybeans and sugar cane) (Myers et al. 2000; Sawyer 2008) . The loss of suitable habitat for H. speciosa due to the consequences of global climate change might affect municipalities that currently use this species as a sustainable use product, thus hurting the local economy. In fact, some future projections are designing scenarios in which the economy will undergo major changes (Grossmann et al. 2009, Nabout et al. 2011). Thus, it is of great importance to predict the impact of global climate change in a local and regional context, as in the context of this study. However, strategies to minimize their effects must be conducted on a
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global scale through partnerships and scientific international political treaties (Kyoto Protocol) (Reid et al. 2010; Grossmann et al. 2009). Finally, this study highlights the importance of investigating the relationship between sustainable use and the impact of climate change because, as in the case of H. speciosa, municipalities that use this fruit may be strongly affected by global climate change. Thus, some considerations and final guidelines should be highlighted: (1) Conservation of H. speciosa—strategies such as creating Unity of Conservation and Unity of sustainable harvesting are important for biodiversity conservation and management of the species. Moreover, considering future changes in the geographical distribution of the species, it is important to delineate future protected areas, or areas where future appropriate presentation for the occurrence of this species. Table 3 Importance and angular coefficient (stand. coeff.) for each predictor variables of productivity and value of production of Harconia speciosa in brazilian municipalities Productivity Importance
Value of production Stand coeff
Importance
Stand coeff
ENM
0.98
0.397
0.352
0.128
UC
0.265
-0.063
0.241
-0.012
GPD
0.264
-0.057
0.945
0.443
HDI
0.277
-0.073
0.985
-0.529
The importance considers the AIC across all models in the set this variable occurs. It ranges from 0 to 1, in which the closer to 1 indicates that the variable occurred in the best models
Environmental Management
(2) Proactive actions—Actions in Federal, State, or Municipal level should be taken to reduce the risk of loss of H. speciosa. Such as selection of resistant plants, germplasm banks as well as creating conservation areas in appropriate regions considering future climate conditions as highlighted in the previous item. (3) New producers municipalities—Encourage increased productivity in new municipalities that are currently in suitable climatic conditions and does not have loss in future climate scenarios and also stimulate municipalities that will be in the future in better climatic conditions. Thus, future studies are needed to investigate the adequate management of populations of this species, knowing the extraction limits allowed for the maintenance of the individuals of this species. These information together are critical for decision making for the conservation and management of H. speciosa to Brazil. Acknowledgments We dedicate this work to Professor. Dr. Roberto Prado de Morais (in memoriam) for encouragement and dedication to the study of Environmental Sciences. JCN and HFC were partially supported by CAPES and Fundac¸a˜o de Amparo a Pesquisa do Estado de Goia´s (Auxpe 2036/2013). The work by JCN has been supported by CNPq grant (306719/2013-4). HFC was supported by University Research and Scientific Production Support Program (PROBIP/UEG).
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