Combining the effects of biological invasion and climate change into systematic conservation planning for the Atlantic Forest

  • Guilherme de Oliveira
  • Bruno de Souza Barreto
  • Daniela da Silva dos Santos
  • Vinícius Queiroz de Matos
  • Maria Cecília Seara Santos
Original Paper


Biological invasions and climate changes are the major causes of changes in biodiversity, which reduce, shift, and extinguish species ranges. While climate changes have been widely used in systematic conservation planning (SCP), biological invasions are rarely considered. Here, we combine the effects of climate changes and Artocarpus heterophyllus Lam. (Moraceae) invasion on the SCP for endemic aromatic fruit tree species from the Atlantic Forest (EFAF). We tested the effect of invasion on SCP measures of species turnover, biotic stability, and irreplaceability. Ecological niche models were used to establish species environmental suitability for the preindustrial period for both invasive species and EFAF and to forecast to the end of the century (2080–2100). We calculated the niche overlap between the invasive species and EFAF and tested the overlap significance using a null model. We tested the biological invasion effect on the results using results with no species invasion correction. The niche overlap between A. heterophyllus and EFAF was significant for 50% of species in the preindustrial period and for 33% in the future. The spatial patterns of species turnover, biotic stability, and irreplaceability had significant effects on biological invasion changing the spatial pattern in both shape and magnitude, which can misplace and overvalue conservation priorities. We showed that the disregard of biological invasion on SCP can cause negative effects on SCP under climate change. We strongly recommend accounting for biological invasion in the evaluation of SCP.


Ensemble forecast Endemism Conservation priorities Species turnover Biotic stability Irreplaceability 


Biological invasions are one of the major causes of biodiversity change and decline, putting native species at risk of extinction (Hierro et al. 2005; Tylianakis et al. 2008; Giakoumi et al. 2016). Invasive species can change the species composition of a natural ecosystem, excluding native species by competitive pressure, predation, and nutrient cycling changes (Levine 2008; Rossiter-Rachor et al. 2009; Vilà et al. 2011; Giakoumi et al. 2016). Moreover, global climate change arising from greenhouse gas emissions is a further threat to the persistence of native species; it can reduce and/or shift their geographical range and displace and/or decrease areas with suitable environmental conditions (Terrible et al. 2012).

Systematic conservation planning (SCP) is a framework that provides efficient allocation of conservation resources using scientific criteria (Margules and Pressey 2000). Establishing priority areas for conservation is mainly based on species composition (e.g., endemism) (Diniz-Filho et al. 2008). Thus, the spatial prioritizations evaluated by SCP aim to assure the persistence of native species, thus avoiding their population decline and consequently their extinction. Usually, the spatial selection of these priority areas is embedded in the analysis of the effects of human occupation (e.g., urbanization, agriculture, and cattle ranching) (de Oliveira et al. 2009), the effects of climate change (e.g., taking into account the reduction and/or shifting of a species range) (Lemes and Loyola 2013), and both effects together (de Oliveira et al. 2015) to assure species persistence in these selected priority areas. However, the effect of biological invasion is rarely taken into account in studies that use SCP to establish spatial conservation priorities (Giakoumi et al. 2016).

Biological invasions and climate changes are of concern to conservation efforts (Bellard et al. 2014) and must be considered in the setting of conservation priorities. Thus, in addition to the primary impact of human occupation in converting natural habitats into cities, industries, agriculture, and cattle ranch lands, human activities are also the primary cause of alien species introduction (Dietz and Edwards 2006) and climate changes (Sih et al. 2011).

Here, we consider the Atlantic Forest with the goal of establishing conservation priorities based on SCP. The biome encompasses 1,300,000 km2 in South America and is the second largest forest in the American continent (Ribeiro et al. 2009). This region presents high levels of habitat heterogeneity with diverse climate zones and vegetational formations ranging from tropical to subtropical zones (Tabarelli et al. 2005). Its large number of different habitats results in a great number of different species with a high level of endemism, making this biome extremely important to biodiversity (Silva et al. 2004). Nonetheless, the biodiversity of this biome is very threatened due to intense human occupation activities, with only 11–16% of its natural area remaining (Ribeiro et al. 2009). This situation makes the region one of the priority hotspots for conservation given its biodiversity uniqueness and threat level (Myers et al. 2000; Mittermeier et al. 2004, 2011).

Considering the negative consequences of biological invasions and climate changes on native biodiversity, as well as the relative neglect of the role of biological invasions on SCP, the aims of this paper are to combine the effect of an invasive aromatic fruit tree species, the jackfruit Artocarpus heterophyllus Lam. (Moraceae), with the effect of climate changes on the SCP analyses for endemic aromatic fruit tree species from the Atlantic Forest (hereafter EFAF) and to evaluate the effect of biological invasion on the results of SCP. In protected areas of the Atlantic Forest, A. heterophyllus is already decreasing the species richness of plants by competitive pressure (Boni et al. 2009) and excluding native species by dominating the ecosystem in both density and biomass (Abreu and Rodrigues 2010; Fabricante et al. 2012).

We constructed ecological niche models (ENMs) to establish species environmental suitability for the preindustrial period for both the invasive species and EFAF and to forecast it into the future (end of the century, 2080–2100) under assumptions of climate change. We then calculated the niche overlap between the invasive species and EFAF and tested the significance of this overlap using a null model. For those EFAF for which this overlap was significant, we corrected their environmental suitability by the environmental suitability of the invasive species. With all EFAF (i.e., corrected by invasion and not corrected), based on SCP and using the preindustrial and future environmental suitability, we established the spatial patterns of conservation priorities of (1) species turnover (Thuiller 2004), (2) biotic stability (Terrible et al. 2012; de Oliveira et al. 2015), and (3) irreplaceability (Pressey et al. 1994; Margules and Pressey 2000; Cabeza and Moilanen 2001). We also repeated the previous analysis without the invasion correction to test the effect of biological invasion on the results.


Species and environmental data

The criterion used to select the group of species that will potentially be affected by the invasion of A. heterophyllus were species expected to have the same ecosystem role. A. heterophyllus has the largest tree-borne fruit, which is highly aromatic, in the world, and an adult tree can produce between 10 and two hundred fruits per year (Baliga et al. 2011). Thus, the native aromatic-fruit trees will be the most likely species affected by its invasion because disseminators will probably prefer the larger and more abundant fruits provided by the invasive species to the smaller and seasonal fruits provided by the native species (Mello et al. 2015). Thus, we searched for endemic tree species from the Atlantic Forest that are aromatic and fructiferous, and the families Annonaceae and Sapotaceae fulfill these prerequisites. We started with 19 species researched at the Botanical Garden of Rio de Janeiro (JBRJ, that are from the families Annonaceae and Sapotaceae and that are endemic to the Atlantic Forest. However, nine species had insufficient occurrence data (less than five occurrences) for the ENMs (see the next section) and were not included in our analyses, and the remaining ten species are listed in Table 1.
Table 1

Similarity statistic I values of niche overlap between the endemic aromatic fruit tree species from the Atlantic Forest and Artocarpus heterophyllus Lam. (Moraceae), the invasive species, for preindustrial and future time periods



Number of records

Similarity statistic I



Annona acutiflora





Chrysophyllum januariense





Manilkara bela





Manilkara longifolia





Manilkara maxima





Manilkara multifida





Manilkara salzmannii





Manilkara subsericea





Micropholis compta





Pouteria bapeba





The species families and number of records used to build the ecological niche models are also shown. I values greater than expected by chance at a probability level of 0.05 are in bold italics

We retrieved 255 occurrence records of EFAF (Table 1) from Species Link ( and scientific literature from the ISI Web of Knowledge ( and searched for specific species names. All records were examined for synonymies and nomenclature errors (Giovanni et al. 2012). We mapped these presences onto 6818 grid cells with 0.5° spatial resolution overlapping the Neotropical region (Appendix S1).

In the Neotropical grid cells, we overlapped the values of five bioclimatic variables: annual mean temperature, mean diurnal range, isothermality, precipitation in the wettest quarter, and precipitation in the driest quarter. Data were compiled from the ecoClimate database ( (Lima-Ribeiro et al. 2015; Varela et al. 2015). These bioclimatic variables were chosen as the most independent from the 19 bioclimatic variables identified after a factor analysis based on a correlation matrix in which the variables with the highest loadings in the first five Varimax rotated eigenvectors were selected (Terrible et al. 2012). We obtained these variables for the preindustrial period, which was simulated for the middle of the eighteenth century and stabilized across a 200-year period, representing current climatic conditions, and the future, 2080–2100 (20-year average, for the end of the century based on emission scenario RCP4.5 (Taylor et al. 2012). This emission scenario is considered an intermediate one and predicts a peek of greenhouse gases emissions in 2040, and then these emissions will decrease (IPCC 2014). The climatic conditions were derived from four coupled Atmosphere–Ocean General Circulation Models (AOGCM): the Community Climate System Model (CCSM4), Centre National de Recherches Météorologiques (CNMR), Marine-Earth Science and Technology-National Institute for Environmental Studies (MIROC-ESM) and Meteorological Research Institute (MRI-CGCM3). In addition to these variables, in all AOGCMs, we included subsoil pH (30–100 cm; from the Harmonized World Soil Database ver. 1.1, FAO/IIASA/ISRIC/ISS-CAS/JRC 2009) as a constraint variable to improve the ENM predictions because we were dealing with tree species (Collevatti et al. 2012; de Oliveira et al. 2015; de Oliveira 2018).

Ecological niche model (ENM) and niche overlap

We used the ensemble framework (Araújo and New 2007) to establish the environmental suitability of EFAF using the protocol analysis proposed by Diniz-Filho et al. (2009) and applied by several recent studies dealing with ENMs (Collevatti et al. 2012; Terrible et al. 2012; Diniz-Filho et al. 2015; de Oliveira et al. 2015; de Oliveira 2018). Twelve different ENMs were used, including six presence-only methods (i.e., BIOCLIM, Euclidean distance, Gower distance, Mahalanobis distance, Genetic Algorithm for Rule Set Production (GARP), and Maximum Entropy (MAXENT)) and six presence–absence methods (i.e., generalized linear models (GLMs), random forest, generalized additive models (GAM), flexible discriminant analysis (FDA), ecological niche factor analysis (ENFA), and neural network). Franklin (2009) and Peterson et al. (2011) provided general descriptions of these methods. For model comparison, in both types of ENMs (i.e., presence-only and presence–absence), we used the same pseudo-absence data; however, in presence-only ENMs, the pseudo-absences were used as background (de Oliveira et al. 2014, 2015). For each species, we randomly divided their presences and pseudo-absences, which were randomly selected on a background region with the same proportion of species records (i.e., with a prevalence of 0.5), into 75% for calibration and 25% for evaluation and repeated this process 50 times. Because we did not correct the presence records for spatial autocorrelation (de Oliveira et al. 2014; Varela et al. 2014) due to the small number of presence records for most EFAF (see Table 1; Peterson and Samy 2016); we opted to select the pseudo-absence data randomly on a background (Barbet-Massin et al. 2012). The 24,000 resulting models (i.e., ten species × 50 cross-validation × 12 ENMs × four AOGCMs) were used to generate consensus occurrence maps based on thresholds established by the ROC curve for which the frequency of occurrence for each species in each Neotropical grid cell was obtained from each ENM in each AOGCM (i.e., resulting in 48 frequency maps for each species, from 12 ENMs × four AOGCMs) (for methodological details, see Terrible et al. 2012; de Oliveira et al. 2015; de Oliveira 2018). These 48 frequency maps for each species were combined by averaging into a single frequency map that was used as a measure of environmental suitability for each species across the Neotropical region. Values ranged from 0, meaning no environmental suitability (i.e., the cell has no occurrence in any of the 48 models from the 50 randomizations), to 1, equal to maximum environmental suitability (i.e., the cell occurred in all models). The analyses were performed in the computational platform BioEnsembles (Diniz-Filho et al. 2009; Terrible et al. 2012; Collevatti et al. 2013; de Oliveira et al. 2014, 2015; de Oliveira 2018). ENMs were built for the preindustrial period, and then, we forecasted them for the future. The accuracy of each combination of ENM and AOGCM, and the ensemble was measures by True Skill Statistics (TSS—Allouche et al. 2006) and is presented in Appendix S2.

Methodological uncertainties may occur in the prediction from the assembled species environmental suitability due to differences in the ENMs and AOGCMs used to model the EFAF (Diniz-Filho et al. 2009; Buisson et al. 2010). Thus, to locate the sources of these uncertainties, we partitioned and mapped the variation among the 12 ENMs and four AOGCMs using a hierarchical ANOVA in each grid cell of the Neotropical biogeographic region. The environmental suitability was used as the response variable, and the 12 different ENMs and four AOGCMs were nested within the ten different EFAF, which were nested within the two time periods, as explanatory variables (Diniz-Filho et al. 2009; Collevatti et al. 2012; de Oliveira et al. 2015; de Oliveira 2018). For detailed results, see Appendix S3.

The environmental suitability of A. heterophyllus was obtained from a study by de Oliveira (2018), which used the same analytical protocol applied here. However, the ENM was built in the native habitat of A. heterophyllus, the Indo-Malaya biogeographic region (Thomas 1980), and projected onto the Neotropical biogeographic region, the invaded habitat. To quantify the niche overlap between the EFAF and invasive species, we calculated the similarity statistic I, which is a measure of overlap, using ENMTOOLS (Warren et al. 2008) and using the environmental suitability of the invasive species A. heterophyllus and the environmental suitability of each EFAF. The statistic I ranges from 0 (no niche overlap) to 1 (total niche overlap). The statistic I was measured in preindustrial and future time periods.

The environmental suitability of A. heterophyllus presented a scattered spatial pattern (de Oliveira 2018), whereas the spatial patterns of environmental suitability of endemic aromatic-fruit trees were restricted to the northeastern region of the Atlantic Forest (see Appendix S4). Thus, the statistic I could be reached by chance, and we cannot infer the real effect of this invasion. We tested the significance of this niche overlap by building a null model. We randomized the values of the environmental suitability of the invasive species in each grid cell across the 6818 cells, with values ranging from 0 to 1, until the observed sum of environmental suitability of A. heterophyllus across the Neotropical region was reached. We then calculated the statistic I between this randomized environmental suitability and that of each EFAF. This process was repeated 1000 times to establish a frequency distribution of I values by randomization. Then, we calculated the probability of the observed I value being reached by chance and assumed significance at a 0.05 probability level. We performed these analyses for preindustrial and future time periods.

Species turnover, biotic stability, and irreplaceability

For those EFAFs that had significant niche overlap with the invasive species (Table 1) we applied a protocol to correct their environmental suitability by the environmental suitability of the invasive species (Fig. 1). First, we used the value of the statistic I as a coefficient of correction and multiplied each cell value of the environmental suitability of A. heterophyllus by this value (Fig. 1b). We then calculated the complement of this value by subtracting 1 from each cell value of the corrected environmental suitability of A. heterophyllus to represent habitats that were not invaded by the species. Thus, 1 represents 100% of the cell, and the corrected value of A. heterophyllus environmental suitability in each cell represents the invaded proportion, subtracting each other, i.e., the complement, represents the proportion that was not invaded in each cell. Finally, we multiplied the values of the complement by the values of EFAF environmental suitability (Fig. 1c). The final EFAF environmental suitability values (Fig. 1d) represent their environmental suitability without the effect of A. heterophyllus invasion (to see all EFAF that were corrected by A. heterophyllus invasion, see Appendix S4).
Fig. 1

Illustration of the protocol used to correct the spatial pattern of environmental suitability of endemic aromatic fruit tree species from the Atlantic Forest (EFAF) by the spatial pattern of the invasive fruit tree species Artocarpus heterophyllus (Moraceae). We used Annona acutiflora (Sapotaceae) in the preindustrial time period as an example, showing a the histogram with the frequency of values of the niche overlap index by the similarity statistic I expected by chance, b the coefficient of correction from the significant value of the statistic I multiplying A. heterophyllus environmental suitability, c the multiplication of the values of the complement of the corrected A. heterophyllus environmental suitability by the values of EFAF environmental suitability, and d the final EFAF environmental suitability used in systematic conservation planning analyses

However, for the SCP analyses, we needed the EFAF ranges; thus, to generate the range maps, their environmental suitability values were converted to an ensemble binary distribution using a threshold of 0.5 (Mata et al. 2017). We used this threshold as a fine one assuming that if 50% of all 2400 ENMS (12 ENMs × four AOGCMs × 50 randomizations) predicted species presence then the species will be considered present. As follows, cells with environmental suitability values above this threshold were assumed as species presences, and cells below this threshold were assumed as species absences.

Species turnover is a measure that summarizes species persisting, disappearing, and colonizing areas (Peterson et al. 2002). Consequently, this measure is assumed to be a good surrogate of ecosystem perturbation due to climate changes (Peterson et al. 2002; Thuiller 2004). Thus, we calculated species turnover based on the number of species gaining (G) or losing (L) presences in the future within each grid cell; this was given by (G + L)/(S + G), where S is the species richness in the preindustrial period (Thuiller 2004; Diniz-Filho et al. 2009). Thus, the spatial pattern of species turnover measures the dissimilarity of species composition in each Neotropical grid cell from preindustrial to future time periods (Thuiller 2004).

In this study, biotic stability was taken as a measure of which grid cells in the Neotropical region would be suitable for species from the preindustrial to the future, thus becoming a refuge for EFAF. Thus, we modified the protocol proposed by Terrible et al. (2012) to establish areas of long-term environmental stability, once we did not corrected for A. heterophyllus invasion and projected the environmental stability of EFAF into past climate conditions. A cell was considered environmentally stable if a given species was predicted to be present throughout preindustrial and future environmental conditions (de Oliveira et al. 2015). Therefore, the proportion of the ten EFAF that are present in the two periods expresses the relative biotic stability of a given grid cell.

The spatial pattern of irreplaceability for EFAF in the Neotropical region was calculated as the frequency at which each cell was selected across 100 runs of the complementarity algorithm (Meir et al. 2004) with 2 × 107 iterations in each run (Bini et al. 2006; Diniz-Filho et al. 2008; de Oliveira et al. 2009, 2015). Here, we used the simulated annealing algorithm as a complementarity algorithm, available in the software SITES v. 1.0 (Andelman et al. 1999; Andelman and Willig 2002), to select minimum networks of cells needed to achieve conservation goals. The conservation goals were 25% of each EFAF range, with maximum proportions of biotic stability previously calculated in each cell. Simulated annealing used the biotic stability of cells as a conservation cost, aiming to maximize the proportions of stable cells in each run.

Testing the biological invasion effect

For comparison, we analyzed species turnover, biotic stability, and irreplaceability without the effect of A. heterophyllus invasion using the EFAF ranges without the correction illustrated in Fig. 1. We used a linear regression analysis (ordinary least squares (OLS)) between the spatial patterns of species turnover, biotic stability, and irreplaceability without correction as the response variables and the spatial patterns of species turnover, biotic stability, and irreplaceability with invasion correction as the predictor variables.

These types of data usually display strong spatial autocorrelation, which can inflate the Type I error of the significance tests due to non-independence of the grid cells (Legendre 1993; Diniz-Filho et al. 2003). To overcome this spatial autocorrelation, we inserted eigenvector spatial filters in the OLS as response variables (see Griffith 2003). These filters were obtained using the spatial eigenvector mapping methodologies (Diniz-Filho and Bini 2005; Griffith and Peres-Neto 2006) and are eigenvectors resulting from a pairwise distance matrix. Here, we extracted the eigenvectors truncating the distances at 78.291 km, where spatial autocorrelation was not significant. We selected all filters that together minimized the spatial autocorrelation in the residuals model. We used the uncorrected spatial patterns of species turnover, biotic stability, and irreplaceability as the response variables and the corrected spatial patterns of species turnover, biotic stability, and irreplaceability as the predictor variables. We used a Moran’s I as low as 0.05 for the first distance class (Griffith and Peres-Neto 2006; Bini et al. 2009; de Oliveira and Diniz-Filho 2011). The correlograms are shown in Appendix S5.

We evaluated whether the OLS intercept and slope of the corrected spatial patterns of species turnover, biotic stability, and irreplaceability differed from values of one for the slope and zero for the intercept using Student’s t test. If this difference does not exist, it would indicate no influence of species invasion on EFAF spatial patterns of species turnover, biotic stability, and irreplaceability in terms of the magnitude for the intercept and in terms of shape for the slope (Jetz and Rahbek 2001; Hawkins and Diniz-Filho 2002; de Oliveira and Diniz-Filho 2011).


The niche overlap between A. heterophyllus and EFAF was high for all ten species, reaching values greater than 0.87 (Table 1). However, five species (50%) in the preindustrial period and three species (33%) in the future reached values of I greater than expected by chance compared with the null model (Table 1).

The spatial pattern of species turnover without the effect of species invasion was clumped, showing most of the high values in the center-south region of the Atlantic Forest (Fig. 2a). On the other hand, this spatial pattern, including the effect of the invasive species, had the highest values of species turnover scattered across the entire region of the biome (Fig. 2d). The effect of the invasive species on the EFAF species turnover was significant both in magnitude and in shape (Table 2).
Fig. 2

The spatial patterns of the endemic aromatic fruit tree species from the Atlantic Forest regarding species turnover without a) and with d) the effect of Artocarpus heterophyllus (Moraceae) invasion, biotic stability without b) and with e) the effect of species invasion, and irreplaceability without c) and with f) the effect of species invasion

Table 2

Results of the regressions between the species turnover, biotic stability, and irreplaceability without and with the effect of Artocarpus heterophyllus (Moraceae) invasion



t (β = 0)

t (β = 1)

t (α = 0)

Species turnover



− 21.881


Biotic stability



− 5.834





− 9.109


Shown are the proportions of variance explained by species turnover, biotic stability, and irreplaceability with the invasion effect (R2) of the ordinary least squares regression model and Student’s t tests for β = 0, for β = 1, and for α = 0. All t tests were significant at the 0.05 level

The spatial pattern of biotic stability without the effect of species invasion showed high values in the coastal region of the Atlantic Forest from north to south, with a gradual decay toward the interior (Fig. 2b). The spatial pattern of biotic stability with the effect of species invasion remained the same, with high values in the coastal region of the Atlantic Forest (Fig. 2e). However, the decaying of values toward the interior was more abrupt than that observed without the effect of species invasion (Fig. 2e). The effect of the invasive species on the EFAF biotic stability was significant both in magnitude and in shape (Table 2).

The spatial pattern of irreplaceability without the effect of species invasion showed high numbers of cells with high values concentrated in the eastern region and the coastal southern region of the Atlantic Forest (Fig. 2c). The spatial pattern of irreplaceability with the effect of species invasion diminished the number of cells with high irreplaceability and concentrated these irreplaceable areas in the coastal region of the biome (Fig. 2f). The effect of the invasive species on EFAF irreplaceability was significant both in magnitude and in shape (Table 2).


Our results showed that the effect of A. heterophyllus invasion on EFAF is emphasized when we see the amount of species that have significant niche overlap in both preindustrial and future time periods. This effect is still apparent even when we only consider the value of the statistics I for all EFAF without the comparison with the null model. Thus, it is recognized that A. heterophyllus dominates Atlantic Forest ecosystems (Bergallo et al. 2016; Freitas et al. 2017) where it is present; consequently, it will reduce the richness of native species (Abreu and Rodrigues 2010; Fabricante et al. 2012), including EFAF (Freitas et al. 2017). It is widely known that frugivorous species play an important role in ecosystems worldwide, mainly in tropical ecosystems (Bregman et al. 2016), providing ecosystem services in ecological succession by seed dispersal (Donoso et al. 2016). If the invasion of A. heterophyllus persists, Atlantic Forest habitats will become homogeneous both in plant diversity, reducing the number of EFAF, and in frugivore diversity. This situation would likely occur due to frugivorous species that consume the jackfruit as a food resource having a selective advantage (e.g., Mello et al. 2015) and excluding other frugivores that are dispersers of EFAF. Thus, A. heterophyllus invasion must be considered in conservation practices such as SCP to avoid the collapse of biodiversity in Atlantic Forest habitats by the homogenization of ecosystem function (Bregman et al. 2015).

Our results on the spatial pattern of species turnover were very different without and with the effect of A. heterophyllus invasion. Without the biological invasion effect, most of the impact of climate change on the dissimilarity of EFAF richness between preindustrial and future time periods will be concentrated in regions where EFAF have low probability of being present in both preindustrial and future time periods. Most of the EFAF have high values of environmental suitability in the northeastern region of the Atlantic Forest, and the highest values of species turnover without the effect of invasion occur in the center-south region. This spatial pattern of species turnover reflects species that can reach the region of high values of species turnover (i.e., center-south region of the Atlantic Forest). Medium values of irreplaceability (i.e., near to the threshold 0.5) will result in high dissimilarity of EFAF richness from the present to the future. Thus, climate changes without the effect of A. heterophyllus invasion will not have much effect on the core environmentally suitable regions for EFAF in the Atlantic Forest.

However, when biological invasion is considered in the spatial pattern of species turnover, the impact of the combined effects of biological invasion and climate changes will reach regions where EFAF have their core environmentally suitable areas, affecting the probabilities of presence. This situation will likely trigger impacts on the structure of native communities (e.g., Eisenhauer et al. 2012), changing the species interaction within communities due to changes in species functional roles and resulting in a decrease in EFAF species richness (Brice et al. 2017).

In this study, we followed the recommendations of Terrible et al. (2012) and Collevatti et al. (2013) to establish the coastal regions of the Atlantic Forest as refugia for EFAF under climate changes because these regions have high values of biotic stability (de Oliveira et al. 2015). However, the difference between the spatial patterns without and with the effect of A. heterophyllus invasion showed that these highly biotically stable areas will be reduced with invasion and be concentrated in a narrow coastal zone of the Atlantic Forest. Therefore, if these refugia, which are the foundations for the establishment of conservation priorities for EFAF, do not consider the biological invasion of A. heterophyllus, they will not fulfill the role of maintaining the persistence of EFAF under climate changes (sensu de Oliveira et al. 2015). Thus, to effectively conserve EFAF, considering the combined effects of climate change and biological invasion, the remaining few areas that will play the role of refugia must be considered in SCP.

Considering the spatial conservation priorities measured by irreplaceability, the results without the effect of A. heterophyllus invasion showed many highly irreplaceable areas compared with the results with the effect of species invasion. This result, at first sight, seems contradictory since we expect that invasion by A. heterophyllus will raise the conservation priorities across the Atlantic Forest (e.g., Boni et al. 2009) due to its negative role in reducing native biodiversity (Abreu and Rodrigues 2010; Fabricante et al. 2012; Bergallo et al. 2016; Freitas et al. 2017).

Nonetheless, note that for those EFAF that had their environmental suitability corrected by the environmental suitability of A. heterophyllus, their ranges decreased in size (i.e., number of cells) compared with their range without the correction for species invasion. Thus, as we opted to establish as conservation goals 25% of EFAF ranges, larger ranges will result in more priority areas for conservation than small ranges due to the number of cells needed to reach this 25%. Thus, we must interpret the number of conservation priority areas as possibilities for conservation because this spatial pattern of irreplaceability represents the flexibility of the areas to implement reserves (Pressey et al. 1994; Margules and Pressey 2000; Cabeza and Moilanen 2001; Andelman and Willig 2002). We must bear in mind that if only the effect of climate changes is taken into account, the possibility of creating conservation units for EFAF is greater than when one considers the combined effects of climate changes and biological invasion. However, we must recognize the negative effect of A. heterophyllus on EFAF and assume that possible areas for EFAF conservation are narrow and concentrated in the coastal region of the Atlantic Forest.

In this way, the congruence between the spatial pattern of biotic stability and irreplaceability, including the effect of A. heterophyllus invasion, on a narrow coastal region of the Atlantic Forest raises another concern about EFAF conservation. This coastal zone is recognized as the most human-occupied region of the biome (Ribeiro et al. 2009) and is subject to the conversion of natural habitats by activities of urbanization, agriculture, and cattle ranching, which may cause a conservation conflict scenario (Balmford et al. 2001; Luck 2007; Dobrovolski et al. 2013). This conservation conflict due to human occupation is another challenge to the SCP for EFAF, in addition to the combined effects of biological invasion and climate change. Moreover, human occupation can reinforce the threat to EFAF conservation because utilization of the jackfruit as a food resource will sustain the persistence of A. heterophyllus in the Neotropical region, raising the propagule pressure and thus promoting its establishment by habitat disturbance (de Oliveira 2018).

All analyses used to apply SCP to EFAF in the Atlantic Forest regarding species turnover, biotic stability, and irreplaceability were affected in shape and magnitude by the invasion of A. heterophyllus. These results indicate that considering the effect of biological invasion is valuable, and if this effect is not considered in SCP, regions of high value for conservation can be misplaced due to changes in shape and overvalued due to changes in magnitude. Therefore, we strongly recommend the evaluation and the inclusion of the effect of biological invasions into the design of spatial priorities based on SCP when there are invasive species causing risk to native biodiversity.



This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [442103/2014-0] and developed in the context of the National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq [465610/2014-5] and FAPEG. DSS and MCSS are grateful for the scholarship provided by FAPESB [6166/2014 and 5878/2015]. We are grateful to Dr. Thiago F. Rangel for providing the use of BioEnsembles, to the World Climate Research Programmer’s Working Group on Coupled Modeling for providing CMIP5, to the climate-modeling group from NCAR for producing and making available CCSM, to Dr. Alessandra N. Caiafa for the first insight on the risk of jackfruit invasion, and to two anonymous reviewers that helped with their suggestions to improve and clarify previous versions of our manuscript.

Supplementary material

10530_2018_1727_MOESM1_ESM.docx (888 kb)
Supplementary material 1 (DOCX 887 kb)


  1. Abreu RCR, Rodrigues PJFP (2010) Exotic tree Artocarpus heterophyllus (Moraceae) invades the Brazilian Atlantic Rainforest. Rodriguésia 61:677–688CrossRefGoogle Scholar
  2. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232CrossRefGoogle Scholar
  3. Andelman S, Willig MR (2002) Alternative configurations of conservation reserves for Paraguayan bats: considerations of spatial scale. Conserv Biol 16:1352–1363CrossRefGoogle Scholar
  4. Andelman S, Ball I, Davis F, Toms D (1999) SITES v. 1.0, an analytical toolbox for designing ecoregional conservation portfolios. Technical report, The Nature Conservancy,
  5. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47CrossRefPubMedGoogle Scholar
  6. Baliga MS, Shivashankara AR, Haniadka R, Dsouza J, Bhat HP (2011) Phytochemistry, nutritional and pharmacological properties of Artocarpus heterophyllus Lam (jackfruit): a review. Food Res Int 44:1800–1811CrossRefGoogle Scholar
  7. Balmford A, Moore JL, Brooks T, Burgess N, Hansen LA, Williams P, Rahbek C (2001) Conservation conflicts across Africa. Science 291:2616–2619CrossRefPubMedGoogle Scholar
  8. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: How, where, and how many? Methods Ecol Evol 3:327–338CrossRefGoogle Scholar
  9. Bellard C, Leclerc C, Leroy B, Bakkenes M, Veloz S, Thuiller W, Courchamp W (2014) Vulnerability of biodiversity hotspots to global change. Global Ecol Biogeogr 23:1376–1386CrossRefGoogle Scholar
  10. Bergallo HG, Bergallo AC, Rocha HB, Rocha CFD (2016) Invasion by Artocarpus heterophyllus (Moraceae) in an island in the Atlantic Forest Biome, Brazil: distribution at the landscape, level, density and need for control. J Coast Conserv 20:191–198CrossRefGoogle Scholar
  11. Bini LM, Diniz-Filho JAF, Rangel TLFVB, Bastos RP, Pinto MP (2006) Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot. Divers Distrib 12:475–482CrossRefGoogle Scholar
  12. Bini LM, Diniz-Filho JAF, Rangel TFLVB, Akre TSB, Albaladejo RG, Albuquerque FS, Aparicio A, Araújo MB, Baselga A, Beck J, Belloq I, Böhning-Gaese K, Borges PAV, Castro-Parga I, Chey VK, Chown SL, De Marco P, Dobkin DS, Ferrer-Gastán D, Field R, Filloy J, Fleishman E, Gómez JF, Hortal J, Iverson JB, Kerr JT, Kissling D, Kitching IJ, León-Cortés JL, Lobo JM, Montoya D, Morales-Castilla I, Moreno JC, Oberdoff T, Olalla-Tárraga MÁ, Pausas JG, Qian H, Rahbek C, Rodríguez MÁ, Rueda M, Ruggiero A, Sackmann P, Sanders NJ, Terribile LC, Vetaas OR, Hawkins BA (2009) Coefficients shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. Ecography 32:193–204CrossRefGoogle Scholar
  13. Boni R, Novelli FZ, Silva AG (2009) Um alerta para os riscos de bioinvasão de jaqueiras, Artocarpus heterophyllus Lam., na Reserva Biológica Paulo Fraga Rodrigues, antiga Reserva Biológica Duas Bocas, no Espírito Santo, Sudeste do Brasil. Natureza on Line 7:51–55Google Scholar
  14. Bregman TP, Lees AC, Seddon N, MacGregor HEA, Darski B, Aleixo A, Bonsall MB, Tobias JA (2015) Species interactions regulate the collapse of biodiversity and ecosystem function in tropical forest fragments. Ecology 96:2692–2704CrossRefPubMedGoogle Scholar
  15. Bregman TP, Lees AC, MacGregor HEA, Darski B, de Moura NG, Aleixo A, Barlow J, Tobias JA (2016) Using avian functional traits to assess the impact of land-cover change on ecosystem processes linked to resilience in tropical forests. Proc R Soc B 283:20161289CrossRefPubMedPubMedCentralGoogle Scholar
  16. Brice MH, Pellerin S, Poulin M (2017) Does urbanization lead to taxonomic and functional homogenization in riparian forests? Divers Distrib 23:828–840CrossRefGoogle Scholar
  17. Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G (2010) Uncertainty in ensemble forecasting of species distribution. Global Change Biol 16:1145–1157CrossRefGoogle Scholar
  18. Cabeza M, Moilanen A (2001) Design of reserve networks and the persistency of biodiversity. Trends Ecol Evol 16:242–248CrossRefPubMedGoogle Scholar
  19. Collevatti RG, Terribile LC, Lima-Ribeiro MS, Nabout JC, de Oliveira G, Rangel TF, Rabelo SG, Diniz-Filho JAF (2012) A coupled phylogeographical and species distribution modelling approach recovers the demographical history of a neotropical seasonally dry forest tree species. Mol Ecol 21:5843–5863CrossRefGoogle Scholar
  20. Collevatti RG, Lima-Ribeiro MS, Diniz-Filho JAF, de Oliveira G, Dobrovolski R, Terribile LC (2013) Stability of Brazilian seasonally dry forests under climate change: inferences for long-term conservation. Am J Plant Sci 4:792–805CrossRefGoogle Scholar
  21. De Oliveira G (2018) Human occupation explains species invasion better than biotic stability: evaluating Artocarpus heterophyllus Lam. (Moraceae; jackfruit) invasion in the Neotropics. J Plant Ecol. Google Scholar
  22. De Oliveira G, Diniz-Filho JAF (2011) Evaluating environmental and geometrical constraints on endemic vertebrates of the semiarid Caatinga (Brazil). Basic Appl Ecol 12:664–673CrossRefGoogle Scholar
  23. De Oliveira G, Diniz-Filho JAF, Bini LM, Rangel TFLVB (2009) Conservation biogeography of mammals in the Cerrado biome under the unified theory of macroecology. Acta Oecol 35:630–638CrossRefGoogle Scholar
  24. De Oliveira G, Rangel TF, Lima-Ribeiro MS, Terribile LC, Diniz-Filho JAF (2014) Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography 37:637–647CrossRefGoogle Scholar
  25. De Oliveira G, Lima-Ribeiro MS, Terribile LC, Dobrovolski R, Telles MPC, Diniz-Filho JAF (2015) Conservation biogeography of the Cerrado’s wild edible plants under climate change: linking biotic stability with agricultural expansion. Am J Bot 102:1–8CrossRefGoogle Scholar
  26. Dietz H, Edwards PJ (2006) Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology 87:1359–1367CrossRefPubMedGoogle Scholar
  27. Diniz-Filho JAF, Bini LM (2005) Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecol Biogeogr 14:177–185CrossRefGoogle Scholar
  28. Diniz-Filho JAF, Bini LM, Hawkins BA (2003) Spatial autocorrelation and red herrings in geographical ecology. Global Ecol Biogeogr 12:53–64CrossRefGoogle Scholar
  29. Diniz-Filho JAF, Bini LM, Pinto MP, Terribile LC, de Oliveira G, Vieira CM, Blamires D, Barreto BS, Carvalho P, Rangel TFLVB, Tôrres NM, Bastos R (2008) Conservation planning: a macroecological approach using the endemic terrestrial vertebrates of the Brazilian Cerrado. Oryx 42:567–577CrossRefGoogle Scholar
  30. Diniz-Filho JAF, Bini LM, Rangel TF, Loyola RD, Hof C, Nogues-Bravo D, Araújo MB (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32:897–906CrossRefGoogle Scholar
  31. Diniz-Filho JAF, Rodrigues H, Telles MP, de Oliveira G, Terribile LC, Soares TN, Nabout JC (2015) Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account. Mol Ecol Resour 15:1059–1066CrossRefPubMedGoogle Scholar
  32. Dobrovolski R, Loyola RD, Guilhaumon F, Gouveia SF, Diniz-Filho JAF (2013) Global agricultural expansion and carnivore conservation biogeography. Biol Conserv 165:162–170CrossRefGoogle Scholar
  33. Donoso I, Garcia D, Roduiguez-Perez J, Martinez D (2016) Incorporating seed fate into plant-frugivore networks increases interaction diversity across plant regeneration stages. Oikos 125:1762–1771CrossRefGoogle Scholar
  34. Eisenhauer N, Fisichelli NA, Frelich LE, Reich PB (2012) Interactive effects of global warming and ‘global worming’ on the initial establishment of native and exotic herbaceous plant species. Oikos 121:1121–1133CrossRefGoogle Scholar
  35. Fabricante JR, Araújo KCT, Andrade LA, Ferreira JVA (2012) Invasão biológica de Artocarpus heterophyllus Lam. (Moraceae) em um fragmento de Mata Atlântica no Nordeste do Brasil: impactos sobre a fitodiversidade e os solos dos sítios invadidos. Acta Bot Bras 26:339–407CrossRefGoogle Scholar
  36. Franklin J (2009) Mapping species distribution: spatial inference and prediction. Cambridge University Press, CambridgeGoogle Scholar
  37. Freitas WK, Magalhães LMS, Resende AS, Brasil FC, Vivès LR, Pinheiro MAS, Filho PL, Luz RV (2017) Invasion impact of Artocarpus heterophyllus Lam. (Moraceae) at the edge of an Atlantic Forest fragment in the municipality of Rio de Janeiro, Brazil. Biosci J 33:422–433CrossRefGoogle Scholar
  38. Giakoumi S, Guilhaumon F, Kark S, Terlizzi A, Claudet J, Felline S, Cerrano C, Coll M, Danovaro R, Fraschetti S, Koutsoubas D, Ledoux J, Mazor T, Merigot B, Micheli F, Katsanevakis S (2016) Space invaders; biological invasions in marine conservation planning. Divers Distrib 22:1220–1231CrossRefGoogle Scholar
  39. Giovanni RL, Bernacci C, Siqueira MF, Rocha FS (2012) The real task of selecting records for ecological niche models. Nat Conservacao 10:139–144CrossRefGoogle Scholar
  40. Griffith DA (2003) Spatial autocorrelation and spatial filtering—gaining understanding through theory and scientific visualization. Springer, BerlinCrossRefGoogle Scholar
  41. Griffith DA, Peres-Neto PR (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analysis. Ecology 87:2603–2613CrossRefPubMedGoogle Scholar
  42. Hawkins BA, Diniz-Filho JAF (2002) The mid-domain effect cannot explain the diversity gradient of Neartic birds. Global Ecol Biogeogr 11:419–426CrossRefGoogle Scholar
  43. Hierro JL, Maron JL, Callaway RM (2005) A biogeographical approach to plant invasions: the importance of studying exotics in their introduced and native range. J Ecol 93:5–15CrossRefGoogle Scholar
  44. IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Summaries, frequently asked questions, and cross-chapter boxes. A contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. World Meteorological Organization, Geneva, Switzerland, p 190Google Scholar
  45. Jetz W, Rahbek C (2001) Geometric constraints explain much of the species richness pattern in African birds. Proc Natl Acad Sci USA 98:5661–5666CrossRefPubMedPubMedCentralGoogle Scholar
  46. Legendre P (1993) Spatial autocorrelation—trouble or new paradigm. Ecology 74:1659–1673CrossRefGoogle Scholar
  47. Lemes P, Loyola RD (2013) Accommodating species climate-forced dispersal and uncertainties in spatial conservation planning. PLoS ONE 8:e54323CrossRefPubMedPubMedCentralGoogle Scholar
  48. Levine JM (2008) Biological invasions. Curr Biol 18:57–60CrossRefGoogle Scholar
  49. Lima-Ribeiro MS, Varela S, González-Hernandez J, de Oliveira G, Diniz-Filho JAF, Terribile LC (2015) ecoClimate: a database of climate data from multiple models for past, present, and future for macroecologists and biogeographers. Biodivers Inform 10:1–21CrossRefGoogle Scholar
  50. Luck GW (2007) A review of the relationships between human population density and biodiversity. Biol Rev 82:607–645CrossRefPubMedGoogle Scholar
  51. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253CrossRefPubMedGoogle Scholar
  52. Mata RA, Tidon R, de Oliveira G, Vilela B, Diniz-Filho JAF, Rangel TF, Terribile LC (2017) Stacked species distribution and macroecological models provide incongruent predictions of species richness for Drosophilidae in the Brazilian savanna. Insect Conserv Divers 10:415–424CrossRefGoogle Scholar
  53. Meir E, Andelman S, Possingham HP (2004) Does conservation planning matter in a dynamic and uncertain world? Ecol Lett 7:615–622CrossRefGoogle Scholar
  54. Mello JHF, Moulton TP, Raíces DSL, Bergallo HG (2015) About rats and jackfruit trees: modeling the carrying capacity of a Brazilian Atlantic Forest spiny-rat Trinomys dimidiatus (Günther, 1877)—Rodentia, Echimyidae—population with varying jackfruit tree (Artocarpus heterophyllus L.) abundances. Braz J Biol 75:208–215CrossRefPubMedGoogle Scholar
  55. Mittermeier RA, Robles-Gil P, Hoffman M, Pilgrim J, Brooks T, Mittermeier CG, Lamoreaux J, Fonseca GAB (2004) Hotspots revisited: earth’s biologically richest and most endangered terrestrial ecoregions. CEMEX, Mexico CityGoogle Scholar
  56. Mittermeier RA, Turner WR, Larsen FW, Brooks TM, Gascon C (2011) Global biodiversity conservation: The critical role of hotspots. In: Zachos FE, Habel JC (eds) Biodiversity hotspots: distribution and protection of conservation priority areas. Springer, Berlin, pp 3–22CrossRefGoogle Scholar
  57. Myers N, Mittermeier RA, Mittermeier CG, Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–855CrossRefPubMedGoogle Scholar
  58. Peterson AT, Samy AM (2016) Geographic potential of disease caused by Ebola and Marburg viruses in Africa. Acta Trop 162:114–124CrossRefPubMedGoogle Scholar
  59. Peterson AT, Ortega-Huerta MA, Bartley J, Sánchez-Cordero V, Soberón J, Buddemeier RH, Stockwell DRB (2002) Future projections for Mexican faunas under global climate change scenarios. Nature 416:626–629CrossRefPubMedGoogle Scholar
  60. Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological niches and geographical distributions. Princeton University Press, PrincetonGoogle Scholar
  61. Pressey RL, Johnson IR, Wilson PD (1994) Shades of irreplaceability—towards a measure of contribution of sites to a reservation goal. Biodivers Conserv 3:242–262CrossRefGoogle Scholar
  62. Ribeiro MC, Metzer JP, Martensen AC, Ponzoni FJ, Hirota MM (2009) The Brazilian Atlantic Forest: how much is left, and how is the remaining forest distributed? Implications for conservation. Biol Conserv 142:1141–1153CrossRefGoogle Scholar
  63. Rossiter-Rachor NA, Setterfield SA, Douglas MM, Hutley LB, Cook GD, Schimdt S (2009) Invasive Andropogon gayanus (gamba grass) is an ecosystem transformer of nitrogen relations in Australian savanna. Ecol Appl 19:1546–1560CrossRefPubMedGoogle Scholar
  64. Sih A, Ferrari MCO, Harris DJ (2011) Evolution and behavioural responses to human- induced rapid environmental change. Evol Appl 4:367–387CrossRefPubMedPubMedCentralGoogle Scholar
  65. Silva JMC, Sousa MC, Castelletti CHM (2004) Areas of endemism for passerine birds in the Atlantic Forest. Global Ecol Biogeogr 13:85–92CrossRefGoogle Scholar
  66. Tabarelli M, Pinto LP, Silva JMC, Hirota MM, Bedê LC (2005) Desafios e oportunidades para a conservação da biodiversidade na Mata Atlântica brasileira. Megadiversidade 1:132–138Google Scholar
  67. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  68. Terrible LC, Lima-Ribeiro MS, Araújo M, Bizao N, Collevatti RG, Dobrovolski R, Franco A, Guilhaumon F, Lima JS, Murakami DM, Nabout JC, de Oliveira G, Oliveira LK, Rabello SG, Rangel TF, Simon LM, Soares TN, Telles MPC, Diniz-Filho JAF (2012) Areas of climate stability of species ranges in the Brazilian Cerrado: disentangling uncertainties through time. Nat Conservacao 10:152–159CrossRefGoogle Scholar
  69. Thomas CA (1980) Jackfruit, Artocarpus heterophyllus (Moraceae), as source of food and income. Econ Bot 34:154–159CrossRefGoogle Scholar
  70. Thuiller W (2004) Patterns and uncertainties of species’ range shifts under climate change. Global Change Biol 10:2020–2027CrossRefGoogle Scholar
  71. Tylianakis JM, Didham RK, Bascompte J, Wardle DA (2008) Global change and species interaction in terrestrial ecosystems. Ecol Lett 11:1351–1363CrossRefPubMedGoogle Scholar
  72. Varela S, Anderson RP, García-Valdés R, Fernádez-Gonzáles F (2014) Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37:1084–1091Google Scholar
  73. Varela S, Terribile LC, de Oliveira G, González-Hernandez J, Lima-Ribeiro MS (2015) ecoClimate versus Worldclim: variables climáticas SIG para trabajar en biogeografia. Ecosistemas 24:88–92CrossRefGoogle Scholar
  74. Vilà M, Espinar JL, Hejda M, Hulme PE, Jarosik V, Maron JL, Pergl J, Schaffner U, Sun Y, Pysek P (2011) Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol Lett 14:702–708CrossRefPubMedGoogle Scholar
  75. Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868–2883CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratório de Biogeografia da Conservação, Instituto de Biologia, Centro de Ciências Agrárias, Ambientais e BiológicasUniversidade Federal do Recôncavo da BahiaCruz das AlmasBrazil
  2. 2.Laboratório de Modelagem Ecológica, Coordenação de Ciência da Terra e EcologiaMuseu Paraense Emílio GoeldiBelémBrazil

Personalised recommendations