Human occupation explains species invasion better than biotic stability: evaluating Artocarpus heterophyllus Lam. (Moraceae; jackfruit) invasion in the Neotropics

Human occupation explains species invasion better than biotic stability: evaluating Artocarpus... Abstract Aims Biological invasions are recognized to put native species in risk of extinction. In this study, I tested whether the invasion of Artocarpus heterophyllus Lam. (Moraceae; jackfruit) in the Neotropics was explained by its biotic stability, an intrinsic force, or by human occupation, an extrinsic force. Methods I used an ensemble framework combining 12 ecological niche models (ENMs) and 4 atmosphere-ocean general circulation models. ENMs were constructed for the pre-industrial time period in the Indo-Malaya biogeographic region, the native habitat of A. heterophyllus, and were then projected to past (last glacial maximum, 21000 years ago and mid-Holocene, 6000 years ago) and future (end of century, 2080) periods. The ENMs were used to establish the biotic stability of A. heterophyllus in areas where it was predicted to be present concomitantly within these four time periods. This biotic stability was projected onto the Neotropics, and then I used a null model and logistic regression to test what the main driver of A. heterophyllus invasion. Important Findings In general, the presence of A. heterophyllus in the Neotropics was not explained by biotic stability, tested by the null model. However, human occupation explained much of its presence in the invaded habitat, once all standardized coefficients related to this driver was significant positive in the logistic regression. Based on these results, humans sustained the presence of A. heterophyllus in the Neotropics, probably because of the additive influences of propagule pressure and habitat disturbance. Thus, the recommendation is that the cultivation of A. heterophyllus in the Neotropics must be regulated and supervised, primarily near reserve areas. ecological niche models (ENMs), ensemble forecasting, climate change, habitat disturbance, propagule pressure INTRODUCTION Biological invasions are one of the primary concerns in conservation biology, representing a major threat to native species (Dietz and Edwards 2006; Hierro et al. 2005; Tylianakis et al. 2008). At a biogeographic scale, an understanding of the processes that drive species invasions is necessary to interpret spatial patterns of invasion and to infer the primary causes of invasion (Hierro et al. 2005). The following hypotheses are examples that have been proposed to explain the success of species invasions: (i) the absence of natural enemies in invaded habitats; (ii) the availability of empty niches in invaded habitats; (iii) the evolution of increased competitive ability over native species; and (iv) the presence of disturbances caused by human activities (see revisions of these hypotheses in Hierro et al. 2005 and Dietz and Edwards 2006). Tests of these hypotheses have produced contradictory results (González-Moreno et al. 2015; van Kleunen et al. 2010; Pahl et al. 2013), most likely because the causal processes during species invasions were not recognized (Dietz and Edwards 2006). Dietz and Edwards (2006) propose dividing invasions into two phases, the primary phase and secondary phase. The primary phase is where the population growth rate of an alien species is rapid and is associated with the colonization of disturbed habitats. The secondary phase is where the population growth rate of an alien species decreases because of natural habitat resistance as native species impose competitive pressure during the spread of alien species into less disturbed habitats. Ecological niche models (ENMs) are an important tool to evaluate the spatial pattern of species invasions because these models are based on niche conservatism in which species track their ecological niche (Peterson et al. 1999; Wiens and Graham 2005) instead of evolving and adapting to new environmental conditions and shifting their niches (Eldredge et al. 2005; Parmesan and Yohe 2003). For this evaluation, ENMs are calculated for the native habitat of the alien species and then projected onto invaded habitats (e.g. Faleiro et al. 2015). The result of the calculation provides the spatial pattern of potential invasions (e.g. Faleiro et al. 2015), which can be used to test hypotheses on the mechanisms that drive species invasions (e.g. Broennimann et al. 2007; Petitpierre et al. 2012). Furthermore, ENMs can be projected to past climates, e.g. last glacial maximum, (LGM) and mid-Holocene (Hol), to evaluate the climatically suitable areas that were recognized to have high importance in the endemism of that species (Araújo et al. 2008). Accordingly, ENMs can also be projected to future climates, e.g. 2080, under climate changes scenarios to locate the climatically suitable areas in which species will likely persist (de Oliveira et al. 2012, 2015; Terribile et al. 2012). Thus, areas that have high climatic suitability concomitantly in past, present and future time periods have an important role in promoting the spatial persistence of a species (Carnaval and Moritz 2008; Haffer 1969; Prance 1978; Wüster et al. 2005). Consequently, identifying the areas where the species are currently found and where they can persist is a crucial task in species conservation (Terribile et al. 2012). When these areas are identified, predictions are possible for which areas species can invade and also persist in the invaded habitats. The areas that have high climatic suitability for a modeled species in past, present and future time periods were recently called areas of biotic stability (de Oliveira et al. 2015), and these areas were used to establish goals for conservation priorities. Moreover, as defined, biotic stability can also be used as a surrogate of the potential of a species for invasion and persistence. To examine this potential, alien species are modeled in their native habitats for the current time period, with the models then projecting to past and future time periods. This biotic stability can then be projected over invaded habitats, when past, present and future climates are available for those invaded areas. The hypothesis that alien species seek biotic stability in invaded habitats is based on an intrinsic force within those species, suggesting that the cause of invasions is primarily based on the ecological niche of the alien species (e.g. Faleiro et al. 2015). However, the extrinsic force of human occupation also drives the spatial patterns of species invasions (Fuentes et al. 2014; Lenda et al. 2012; Mckinney 2002). More alien individuals are introduced (i.e. propagule pressure; Lockwood et al. 2005) and the intensity of disturbance increases, which favors alien species because they have the highest growth rates in this type of habitats (Dietz and Edwards 2006). Thus, human occupation is an alternative hypothesis to explain the spatial pattern of invasion by a species. I used Artocarpus heterophyllus Lam. (Moraceae), the jackfruit, as an alien tree species to evaluate the primary hypothesis of whether an intrinsic force (i.e. biotic stability) or an extrinsic force (i.e. human occupation) explained the spatial pattern of invasion in the Neotropics. A. heterophyllus is native to Southeast Asia and was introduced into the Neotropics in the 17th century by Portuguese colonization in northeast Brazil (Morton 1965). The introduction of jackfruit was primarily for food, which is important worldwide (Baliga et al. 2011; Thomas 1980), because it has the largest tree-borne fruits in the world and an adult tree can produce between 10 and 200 fruits per year. Thus, A. heterophyllus has tremendous importance in human diets as source of carbohydrates, protein, fat, minerals and vitamins (Baliga et al. 2011). However, in the Neotropics, A. heterophyllus is an alien species that increases the risk of extinction for native species because of competitive pressure (Abreu and Rodrigues 2010; Boni et al. 2009; Fabricante et al. 2012) from dominating invaded ecosystems in both density and biomass (Abreu and Rodrigues 2010). Moreover, it is recognized that the invasion of plant species, mostly, threat native species by competition pressure (see revision in Kumschick et al. 2015). In this study, I tested whether the spatial pattern of invasion by A. heterophyllus in the Neotropics was explained by biotic stability or human occupation by asking the following two questions: (i) Does the spatial distribution of A. heterophyllus in the Neotropics have more or less biotic stability than that expected by chance? and (ii) What is the primary driver of the spatial pattern of A. heterophyllus in the Neotropics, biotic stability or human occupation? To answer these questions, I evaluated ENMs for A. heterophyllus in pre-industrial times and then projected the ENMs to the past (LGM and Hol) and future (end of century, 2080–2100) time periods for the Indo-Malaya biogeographic region (Fig. 1) to establish areas of biotic stability (sensude Oliveira et al. 2015; Terribile et al. 2012) in its native habitat. Then, I projected this biotic stability onto the Neotropical biogeographic region (Fig. 2) as an invaded area to test the intrinsic and extrinsic forces of invasion based on the spatial presence of A. heterophyllus and the spatial pattern of human occupation in the Neotropics. Figure 1: View largeDownload slide indo-Malaya biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); and (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs. Figure 1: View largeDownload slide indo-Malaya biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); and (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs. Figure 2: View largeDownload slide neotropical biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs; and (d) spatial pattern of human occupation. Figure 2: View largeDownload slide neotropical biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs; and (d) spatial pattern of human occupation. MATERIALS AND METHODS Species and environmental data I retrieved 65 records of presence for A. heterophyllus in the Indo-Malaya biogeographic region (Fig. 1a) from the Global Biodiversity Information Facility (GBIF, www.gbif.org) online collection and scientific literature from the ISI Web of Knowledge (apps.webofknowledge.com) by searching for ‘Artocarpus heterophyllus’ with filtering for ‘India’, and ‘Malaya’. I mapped these presences in 3486 grid cells of 0.5° × 0.5° of latitude and longitude that covered the entire Indo-Malaya region (Fig. 1). I obtained the climate layers as 19 bioclimatic variables from the Worldclim database (www.worldclim.org) for past (LGM, 21000 years ago and Hol, 6000 years ago), pre-industrial (simulated for the middle of the 18th century and stabilized across a 200-year time period, representing current climatic conditions), and future (2080–2100, 20-year average for the end of the century, based on emission scenario RCP4.5, see Taylor et al. 2012) climatic conditions, which were derived from four coupled Atmosphere-Ocean General Circulation Models (AOGCM): 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). I choose the emission scenario RCP4.5 because it is an intermediated scenario, assuming that human population will maintain the greenhouse gases emission on the atmosphere as it is presented nowadays (Taylor et al. 2012). The climate layers were compiled from the ecoClimate database (www.ecoclimate.org). To minimize collinearity problems when building the ENMs, I selected a set of four bioclimatic variables, from the 19, for each AOGCM (mean temperature of diurnal range, isothermality, precipitation of the wettest quarter and precipitation of the driest quarter) using a factor analysis based on a correlation matrix (i.e. selecting the variables with the highest loadings in the first four Varimax rotated eigenvectors; Terribile et al. 2012). In addition to these variables, in all AOGCMs, I 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, because it does not have projections to past and future scenarios, to improve the ENM predictions (Collevatti et al. 2012; Lima et al. 2014; de Oliveira et al. 2015). Ecological niche models Ensemble methodologies for modeling the ecological niche of a species (Araújo and New 2007) were implemented following Diniz-Filho et al. (2009), Terribile et al. (2012) and de Oliveira et al. (2015). Twelve different ENMs were used, including six presence-only methods (i.e. BIOCLIM, Euclidian, Gower and Mahalanobis distances, Genetic Algorithm for Rule Set Production—GARP, and Maximum Entropy—MAXENT) and six presence-absence methods (i.e. Generalized Linear Models—GLM, 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 the methods. Parameterization of each ENM is shown separately in online supplementary Appendix S1. For model comparison, in both types of ENM, i.e. presence-only and presence-absence, I used the same pseudo-absence data, but in presence-only ENMs, pseudo-absences were used as background (sensude Oliveira et al. 2014, 2015). I randomly divided species presences and their pseudo-absences (randomly selected on a background region with the same proportion of species records (i.e. with the prevalence of 0.5, resulting 65 pseudo-absences)) into 75% for calibration and 25% for evaluation and repeated this process 50 times. Because I did not correct the presences records for spatial autocorrelation (de Oliveira et al. 2014; Varela et al. 2014), due to small number of presences (sensuPeterson and Samy 2016), I opted to select the pseudo-absences data randomly on background (Barbet-Massin et al. 2012). The 2400 resulting models (i.e. 50 cross-validation × 12 ENMs × 4 AOGCMs) were used to generate consensual occurrence maps based on thresholds established by the ROC curve for which the species frequency of occurrence in each Indo-Malaya grid cell was obtained from each ENM in each AOGCM (i.e. resulting in 48 frequency maps from 12 ENMs × 4 AOGCMs; for methodological details, see Terribile et al. 2012; and de Oliveira et al. 2015). This frequency of occurrence was used as a measure of environmental suitability for A. heterophyllus across the Indo-Malaya region, ranging from 0, with no environmental suitability (i.e. no cell occurrence in any of the 50 models from the 50 randomizations) to 1, with maximum environmental suitability (i.e. cell occurrences in all 50 models). The analyses were performed in the computational platform BioEnsembles (Diniz-Filho et al. 2009; de Oliveira et al. 2014, 2015; Terribile et al. 2012). Biotic stability I followed the protocol proposed by Terribile et al. (2012) and used by de Oliveira et al. (2015) to establish areas of biotic stability across the Indo-Malaya region based on the geographic distributions predicted by ENMs for A. heterophyllus for the four time periods (LGM, Hol, pre-industrial and future). I projected each pre-industrial’s ENM into their respective past and future ENMs for each AOGCM. The continuous values of environmental suitability across cells of each ENM in each AOGCM were converted into binary values (1/0, ‘presence’/‘absence’) to establish occurrence extensions (i.e. species range) using thresholds based on the minimum value of suitability (the least training presences threshold, from Peterson et al. 2011) for A. heterophyllus in the pre-industrial climatic scenario for areas where occurrences of A. heterophyllus in the Indo-Malaya region were recorded. Cells with suitability values above this threshold were assumed to be presences (1), whereas cells with suitability values below this threshold were assumed to be absences (0). A cell was then considered biotically stable when the presence of A. heterophyllus was predicted throughout the four time periods (i.e. LGM, Hol, pre-industrial and future) in each combination of ENM and AOGCM. Therefore, the proportion of presences of A. heterophyllus in all four periods in all 48 ENMs (12 ENMs × 4 AOGCMs) expressed the relative biotic stability of a given grid cell ranging from 0 (no biotic stability for any combination of ENM × AOGCM) to 1 (biotic stability in all 48 combinations of ENM × AOGCM). Hypothesis tests To forecast and to hindcast the biotic stability of A. heterophyllus onto the Neotropics, I overlaid the same four bioclimatic variables and the subsoil pH values used in the Indo-Malaya region from past, pre-industrial and future time periods over 6818 grid cells of 0.5° spatial resolution in the Neotropics (Fig. 2). Each ENM in each AOGCM from the pre-industrial period in the Indo-Malaya region was projected into each ENM in each AOGCM of past, pre-industrial and future time periods in the Neotropics to establish the biotic stability in the invaded habitat. To test the hypothesis whether biotic stability or human occupation explained the A. heterophyllus invasion into the Neotropics, I retrieved 182 presence records for A. heterophyllus for the Neotropical region (Fig. 2a) from GBIF (www.gbif.org) and Species Link (splink.cria.org.br) and mapped those records over 6818 grid cells covering the Neotropical region. Then, I calculated the sum of the biotic stability of the 182 presences of A. heterophyllus. To establish the null model, I randomly selected 182 spatial points in the entire Neotropical region and evaluated the sum of A. heterophyllus biotic stability for those randomized points. This procedure was repeated 500 times to establish a frequency distribution of the sums of biotic stability. Finally, I calculated the probability whether the observed sum of A. heterophyllus biotic stability was higher or lower than that expected by chance with comparisons with the frequency of the 500 random sums of biotic stability for each ENM in each AOGCM. Furthermore, I evaluated the spatial pattern of human occupation in the Neotropics by using anthropogenic biomes (Ellis et al. 2010, available at: databasin.org). Anthromes, or anthropogenic biomes, are maps that classify the human influence in ecosystems and consequently, are a measure of worldwide human occupation (Ellis and Ramankutty 2008; Ellis et al. 2010). I used the proportion of presences of the following classes of anthropogenic occupation in each cell across the Neotropics: (i) dense settlements, (ii) villages, (iii) croplands and (iv) rangeland. These levels of human occupation are from Ellis et al. (2010; Fig. 2e). Then, I applied a logistic regression using the 182 A. heterophyllus presences and 182 A. heterophyllus pseudo-absences (randomized across the Neotropical region) as response variables and their respective values of biotic stability and human occupation as explanatory variables. The standardized partial regression coefficient of each explanatory variable was used to infer the primary spatial driver of A. heterophyllus invasion. The logistic regression was performed in SAM software (Rangel et al. 2006, 2010). Methodological uncertainties may occur due to differences in the ENMs and AOGCMs used to model species ENMs (Buisson et al. 2010; Diniz-Filho et al. 2009), and these uncertainties might bias the interpretation of the spatial pattern of the invasion of A. heterophyllus into the Neotropics. Thus, I presented the maps with the means and the variances between the 12 ENMs and 4 AOGCMs (i.e. 48 models). Moreover, aiming to spatially locate the source of these uncertainties, I also partitioned and mapped the variation among the 12 ENMs and 4 AOGCMs using a hierarchical analysis of variance in each grid cell of the Indo-Malaya and Neotropical biogeographic regions. The environmental suitability was used as the response variable and the 12 different ENMs and 4 AOGCMs were nested in the four time periods as explanatory variables (Collevatti et al. 2012; Diniz-Filho et al. 2009; de Oliveira et al. 2015; see detailed results in online supplementary Appendix S2). RESULTS The means of the spatial pattern for the biotic stability of A. heterophyllus from all combinations of the 12 ENMs and 4 AOGCMs in the Indo-Malaya biogeographic region showed high values in the Western Ghats region, the north of India, and the south of Thailand, Cambodia, Vietnam and China (Fig. 1b). The lowest values of A. heterophyllus biotic stability in the Indo-Malaya region were placed in Indonesia and the Philippines (Fig. 1b). The spatial pattern of the variances of biotic stability across all combinations of the 12 ENMs and 4 AOGCMs revealed an inverted pattern compared with that of biotic stability, with low values in the Western Ghats region, the north of India, and the south of Thailand, Cambodia, Vietnam and China (Fig. 1c). The true skill statistics of each combination of ENM and AOGCM are provided in online supplementary Appendix S3. The projection of the means of A. heterophyllus biotic stability across all combinations of the 12 ENMs and 4 AOGCMs onto the invaded habitat of the Neotropical biogeographic region showed high values on the Atlantic Coast in the northeast of Brazil, the center-south of Brazil, the north of Paraguay, and the center of Bolivia and Colombia (Fig. 2b). The lowest values of A. heterophyllus biotic stability in the Neotropics were placed in Chile and the south of Argentina (Fig. 2b). The spatial pattern of variances of biotic stability across all combinations of the 12 ENMs and 4 AOGCM showed low values in the Brazilian northeast, the center of Bolivia and Colombia, and the north of Peru (Fig. 2c). Of the 48 models (12 ENMs × 4 AOGCMs), 29% (14 models) showed that the sum of biotic stability for the 182 Neotropical presences of A. heterophyllus was higher than that expected by chance (at 0.05 probability level; Table 1), and these models were those primarily based on distance (e.g. Euclidian and Mahalanobis distance) and BIOCLIM. Twenty-five percent (12 models) showed biotic stability sums lower than that expected by chance and were primarily those models based on machine learning (e.g. MAXENT, GARP and Random Forests; Table 1). For the other models, 46% (22 models), the sums of biotic stability were neither higher nor lower than that expected by chance (Table 1). Table 1: probability values for the sums of biotic stability of the 182 Artocarpus heterophyllus presences in the Neotropical biogeographical region from the 48 combinations of the 12 ecological niche models and 4 atmosphere-ocean circulation models   Higher than expected by chance  Lower than expected by chance    CCSM  CNMR  MIROC  MRI  CCSM  CNMR  MIROC  MRI  Bioclim  1.000  0.008  0.024  0.022  0.000  0.992  0.976  0.978  ENFA  0.918  0.804  0.564  0.982  0.082  0.196  0.436  0.018  EuclDist  0.002  0.000  0.004  0.016  0.998  1.000  0.996  0.984  FDA  1.000  0.986  0.812  0.468  0.000  0.014  0.188  0.532  GAM  0.998  0.966  0.010  0.000  0.002  0.034  0.990  1.000  GARP  0.102  0.056  1.000  0.992  0.898  0.944  0.000  0.008  GLM  0.560  0.980  0.734  0.510  0.440  0.020  0.266  0.490  GoweDist  0.606  0.002  0.114  0.052  0.394  0.998  0.886  0.948  MahaDist  0.012  0.066  0.004  0.026  0.988  0.934  0.996  0.974  MAXENT  0.974  0.998  0.694  0.916  0.026  0.002  0.306  0.084  NeurNet  0.086  0.220  0.882  0.022  0.914  0.780  0.118  0.978  RandFor  0.998  0.486  1.000  0.088  0.002  0.514  0.000  0.912    Higher than expected by chance  Lower than expected by chance    CCSM  CNMR  MIROC  MRI  CCSM  CNMR  MIROC  MRI  Bioclim  1.000  0.008  0.024  0.022  0.000  0.992  0.976  0.978  ENFA  0.918  0.804  0.564  0.982  0.082  0.196  0.436  0.018  EuclDist  0.002  0.000  0.004  0.016  0.998  1.000  0.996  0.984  FDA  1.000  0.986  0.812  0.468  0.000  0.014  0.188  0.532  GAM  0.998  0.966  0.010  0.000  0.002  0.034  0.990  1.000  GARP  0.102  0.056  1.000  0.992  0.898  0.944  0.000  0.008  GLM  0.560  0.980  0.734  0.510  0.440  0.020  0.266  0.490  GoweDist  0.606  0.002  0.114  0.052  0.394  0.998  0.886  0.948  MahaDist  0.012  0.066  0.004  0.026  0.988  0.934  0.996  0.974  MAXENT  0.974  0.998  0.694  0.916  0.026  0.002  0.306  0.084  NeurNet  0.086  0.220  0.882  0.022  0.914  0.780  0.118  0.978  RandFor  0.998  0.486  1.000  0.088  0.002  0.514  0.000  0.912  Comparisons with the null model determined whether values were higher or lower than that expected by chance (see the main text). Probabilities <0.05 are italicized. View Large Human occupation was positively correlated with the presence of A. heterophyllus in the Neotropics, with all standardized partial coefficients with positive and significant values (P > 0.05; Table 2). By contrast, in general, biotic stability showed no relationship with the presence of A. heterophyllus; 40 standard coefficients (83%) were not significant and only 8 standard coefficients (17%) were significant, with five showing a negative relationship and three showing a positive relationship (Table 2). Table 2: standardized partial coefficients of the logistic regression that used the 182 Artocarpus heterophyllus presences and 182 pseudo-absences in the Neotropical biogeographic region as response variables and the human occupation (HO) and the biotic stability (BS) in the Neotropics as the explanatory variables   CCSM  CNMR  MIROC  MRI    HO  BS  HO  BS  HO  BS  HO  BS  Bioclim  1.911  −0.364  1.711  0.263  1.763  0.126  1.788  0.027  ENFA  1.894  −0.938  2.304  −1.196  2.194  −1.301  1.794  −0.006  EuclDist  1.916  1.341  1.728  0.564  1.851  1.108  1.861  −0.212  FDA  1.805  −0.355  2.307  −1.808  1.884  −0.716  2.065  −0.579  GAM  1.869  −0.577  1.773  −0.2  1.791  0.01  1.933  −0.283  GARP  1.852  −0.34  1.948  −0.395  1.797  −0.062  1.799  −0.023  GLM  1.65  −0.547  1.823  −0.438  1.79  0.091  1.795  −0.033  GoweDist  1.778  0.156  1.737  0.412  1.636  0.66  1.872  −0.256  MahaDist  1.778  0.528  1.773  0.394  1.919  1.277  1.762  0.198  MAXENT  2.61  −2.517  1.792  −0.769  2.396  −1.492  2.099  −0.908  NeurNet  1.784  −0.205  1.81  0.423  1.812  0.087  1.849  −0.234  RandFor  1.95  −0.571  1.801  −0.038  2.041  −0.789  1.941  −0.284    CCSM  CNMR  MIROC  MRI    HO  BS  HO  BS  HO  BS  HO  BS  Bioclim  1.911  −0.364  1.711  0.263  1.763  0.126  1.788  0.027  ENFA  1.894  −0.938  2.304  −1.196  2.194  −1.301  1.794  −0.006  EuclDist  1.916  1.341  1.728  0.564  1.851  1.108  1.861  −0.212  FDA  1.805  −0.355  2.307  −1.808  1.884  −0.716  2.065  −0.579  GAM  1.869  −0.577  1.773  −0.2  1.791  0.01  1.933  −0.283  GARP  1.852  −0.34  1.948  −0.395  1.797  −0.062  1.799  −0.023  GLM  1.65  −0.547  1.823  −0.438  1.79  0.091  1.795  −0.033  GoweDist  1.778  0.156  1.737  0.412  1.636  0.66  1.872  −0.256  MahaDist  1.778  0.528  1.773  0.394  1.919  1.277  1.762  0.198  MAXENT  2.61  −2.517  1.792  −0.769  2.396  −1.492  2.099  −0.908  NeurNet  1.784  −0.205  1.81  0.423  1.812  0.087  1.849  −0.234  RandFor  1.95  −0.571  1.801  −0.038  2.041  −0.789  1.941  −0.284  The results are shown for all 48 combinations of the 12 ecological niche models and 4 atmosphere-ocean general circulation models. Coefficients with a significance level <0.05 are italicized. View Large DISCUSSION The comparison between the spatial pattern of A. heterophyllus biotic stability in Indo-Malaya and Neotropical biogeographic region showed that this stability probably is not the main driver of invasion. The regions of highest values for the biotic stability of A. heterophyllus in the Indo-Malaya region were expected because these regions are recognized as the centers of the original distribution of A. heterophyllus (Baliga et al. 2011; Thomas 1980). Moreover, because the spatial patterns of biotic stability of all ENMs converged on these regions, strong support was provided for the predictive power of the models (see de Oliveira et al. 2015). Additionally, the variance levels from all combinations of the 12 ENMs and 4 AOGCMs were the lowest in these regions, which indicated the ENMs used in this study were reliable (sensuDiniz-Filho et al. 2009; de Oliveira et al. 2015, and also see online supplementary Appendix S2). In the invaded habitat of the Neotropical biogeographic region, the reliability of the models generating the spatial pattern of biotic stability of A. heterophyllus remained at the same high level as observed for the Indo-Malaya region because the biotic stability also had an inverted spatial pattern from its variance and high values of biotic stability were associated with low values of variance (compare Fig. 2b with Fig. 2c). Do the spatial presences of A. heterophyllus in the Neotropics have more or less biotic stability than that expected by chance? In general, the presences of A. heterophyllus in the Neotropical region were expected only by chance, compared with the 500 randomized sums for biotic stability for most of the combinations of ENMs and AOGCMs, which suggested that biotic stability was not a significant driver of the A. heterophyllus invasion of habitats. An alternative explanation might be a niche shift (Eldredge et al. 2005; Parmesan and Yohe 2003) in which A. heterophyllus occupies different ecological niches in the switch from the native Indo-Malaya region into the invaded habitats of the Neotropics (Broennimann et al. 2007). However, this differentiation of the niche from native to invaded habitats might be explained beyond this intrinsic force based on ENMs, and the forces beyond the niche, which are primarily expressed by climate, as the extrinsic force of the human occupation, may limit the spatial distribution of a species (Early and Sax 2014, and see explanation in the next section). Another important finding was that ENMs based on distance and BIOCLIM showed that the biotic stability of A. heterophyllus was higher than that expected by chance. Compared with other ENMs, although they have more clarity with less complexity, these ENMs likely overestimated the results (Rangel and Loyola 2012). This overestimation might explain the higher biotic stability of A. heterophyllus than that expected by chance. In this way, the environmental suitability resulting from these ENMs had a widespread spatial pattern and the presences of A. heterophyllus in the Neotropics likely reached high values. VanDerWal et al. (2009) and Tôrres et al. (2012) also found the same widespread spatial pattern of ENMs based on distance in tests on the relationship between environmental suitability, from ENMs, and local species abundances. By contrast, ENMs based on machine learning resulted in A. heterophyllus having biotic stability lower than that expected by chance. These ENMs have high precision, although they usually have problems related to ENMs over fitness, and the predictions may be highly contingent on the geographic positions of species presences (Rangel and Loyola 2012). This underestimation of environmental suitability, which resulted from ENMs over fitness, might narrow the spatial pattern of environmental suitability and the presences of A. heterophyllus in the Neotropics would not reach high values. Thus, the ensemble approach used in this study overcame the pros and cons of choosing one ENM over another ENM. The tradeoffs between the transparency and overestimation of the distance-based ENMs and the precision and underestimation of machine learning ENMs were combined using all possible types of ENMs. This ensemble framework brings transparency to the results from the ENMs by showing the sources of their variations (Buisson et al. 2010; Diniz-Filho et al. 2009). Is biotic stability or human occupation the primary driver of the spatial pattern of the presences of A. heterophyllus in the Neotropics? In this study, I found that human occupation was the primary driver of the spatial pattern of the invasion of A. heterophyllus in the Neotropics because it had a significant positive relationship in all combinations of ENMs and AOGCMs. Generally, when species do not track their ecological niche from native to invaded habitats, a niche shift is an alternative explanation (e.g. Broennimann et al. 2007; Cornuault et al. 2015; Kumar et al. 2015; Petersen 2013). This niche shift implies that species tolerate conditions and levels of resources in the invaded habitat that were not tolerated in their native habitats, highlighting the importance of the intrinsic force of invasion. Nevertheless, this acquired tolerance in the invaded habitats might be sustained by other forces than only the intrinsic, for example, the force of human occupation. When the primary cause for alien species introduction and invasion is anthropogenic activities (i.e. agriculture, recreation, ornamentation and creation of pets; Lockwood et al. 2005), human occupation can have a role as an extrinsic force of invasion. Human occupation likely acted in two ways that explain the spatial pattern of A. heterophyllus invasion in the Neotropics. First, the jackfruit is a popular fruit that is used as a source of food worldwide (Baliga et al. 2011); thus, because of the demand and the positive perspective held by humans, with the consumption of this fruit, the propagule pressure increases (Lockwood et al. 2005) and contributes to the persistence of A. heterophyllus in invaded habitats. Second, A. heterophyllus can establish in the primary phase of invasion, which is associated with habitat disturbance that is linked to the intensity of human occupation (Dietz and Edwards 2006). Thus, A. heterophyllus remains dependent on human occupation for establishment, despite almost 400 years from its first invasion (Abreu and Rodrigues 2010; Boni et al. 2009; Fabricante et al. 2012), and cannot invade less disturbed habitats because of competitive pressure from native species (i.e. the secondary phase of invasion, Dietz and Edwards 2006). The first explanation does not exclude the second, and they can act additively with increases in propagule pressure increasing the persistence of A. heterophyllus in disturbed habitats that are closer to human occupation. In conclusion, the invasion of A. heterophyllus puts native species at risk of extinction, as stated by Boni et al. (2009), Abreu and Rodrigues (2010) and Fabricante et al. (2012), and therefore is a problem for conservation. Based on the evaluation in this study, this species remains dependent of human occupation for both establishment and persistence. Therefore, the cultivation of jackfruit must be regulated and supervised, primarily near reserve areas, once records of presences of this alien species it has been found in conservation units (e.g. Boni et al. 2009). SUPPLEMENTARY MATERIAL Supplementary data are available at Journal of Plant Ecology online. ACKNOWLEDGEMENTS This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant number 442103/2014-0]. I am grateful to Thiago Fernando Rangel to provide the use of BioEnsembles. To the World Climate Research Programmer’s Working Group on Coupled Modeling for providing CMIP5 and the climate-modeling group from NCAR for producing and making available CCSM. To Dr. Alessandra Nasser Caiafa for the first insight on the risk of jackfruit invasion. And to two anonymous reviewers that helped to improve and clarify the latest version of this work with their comments and suggestions. Conflict of interest statement. None declared. 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Human occupation explains species invasion better than biotic stability: evaluating Artocarpus heterophyllus Lam. (Moraceae; jackfruit) invasion in the Neotropics

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© The Author(s) 2017. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China. All rights reserved. For permissions, please email: journals.permissions@oup.com
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Abstract

Abstract Aims Biological invasions are recognized to put native species in risk of extinction. In this study, I tested whether the invasion of Artocarpus heterophyllus Lam. (Moraceae; jackfruit) in the Neotropics was explained by its biotic stability, an intrinsic force, or by human occupation, an extrinsic force. Methods I used an ensemble framework combining 12 ecological niche models (ENMs) and 4 atmosphere-ocean general circulation models. ENMs were constructed for the pre-industrial time period in the Indo-Malaya biogeographic region, the native habitat of A. heterophyllus, and were then projected to past (last glacial maximum, 21000 years ago and mid-Holocene, 6000 years ago) and future (end of century, 2080) periods. The ENMs were used to establish the biotic stability of A. heterophyllus in areas where it was predicted to be present concomitantly within these four time periods. This biotic stability was projected onto the Neotropics, and then I used a null model and logistic regression to test what the main driver of A. heterophyllus invasion. Important Findings In general, the presence of A. heterophyllus in the Neotropics was not explained by biotic stability, tested by the null model. However, human occupation explained much of its presence in the invaded habitat, once all standardized coefficients related to this driver was significant positive in the logistic regression. Based on these results, humans sustained the presence of A. heterophyllus in the Neotropics, probably because of the additive influences of propagule pressure and habitat disturbance. Thus, the recommendation is that the cultivation of A. heterophyllus in the Neotropics must be regulated and supervised, primarily near reserve areas. ecological niche models (ENMs), ensemble forecasting, climate change, habitat disturbance, propagule pressure INTRODUCTION Biological invasions are one of the primary concerns in conservation biology, representing a major threat to native species (Dietz and Edwards 2006; Hierro et al. 2005; Tylianakis et al. 2008). At a biogeographic scale, an understanding of the processes that drive species invasions is necessary to interpret spatial patterns of invasion and to infer the primary causes of invasion (Hierro et al. 2005). The following hypotheses are examples that have been proposed to explain the success of species invasions: (i) the absence of natural enemies in invaded habitats; (ii) the availability of empty niches in invaded habitats; (iii) the evolution of increased competitive ability over native species; and (iv) the presence of disturbances caused by human activities (see revisions of these hypotheses in Hierro et al. 2005 and Dietz and Edwards 2006). Tests of these hypotheses have produced contradictory results (González-Moreno et al. 2015; van Kleunen et al. 2010; Pahl et al. 2013), most likely because the causal processes during species invasions were not recognized (Dietz and Edwards 2006). Dietz and Edwards (2006) propose dividing invasions into two phases, the primary phase and secondary phase. The primary phase is where the population growth rate of an alien species is rapid and is associated with the colonization of disturbed habitats. The secondary phase is where the population growth rate of an alien species decreases because of natural habitat resistance as native species impose competitive pressure during the spread of alien species into less disturbed habitats. Ecological niche models (ENMs) are an important tool to evaluate the spatial pattern of species invasions because these models are based on niche conservatism in which species track their ecological niche (Peterson et al. 1999; Wiens and Graham 2005) instead of evolving and adapting to new environmental conditions and shifting their niches (Eldredge et al. 2005; Parmesan and Yohe 2003). For this evaluation, ENMs are calculated for the native habitat of the alien species and then projected onto invaded habitats (e.g. Faleiro et al. 2015). The result of the calculation provides the spatial pattern of potential invasions (e.g. Faleiro et al. 2015), which can be used to test hypotheses on the mechanisms that drive species invasions (e.g. Broennimann et al. 2007; Petitpierre et al. 2012). Furthermore, ENMs can be projected to past climates, e.g. last glacial maximum, (LGM) and mid-Holocene (Hol), to evaluate the climatically suitable areas that were recognized to have high importance in the endemism of that species (Araújo et al. 2008). Accordingly, ENMs can also be projected to future climates, e.g. 2080, under climate changes scenarios to locate the climatically suitable areas in which species will likely persist (de Oliveira et al. 2012, 2015; Terribile et al. 2012). Thus, areas that have high climatic suitability concomitantly in past, present and future time periods have an important role in promoting the spatial persistence of a species (Carnaval and Moritz 2008; Haffer 1969; Prance 1978; Wüster et al. 2005). Consequently, identifying the areas where the species are currently found and where they can persist is a crucial task in species conservation (Terribile et al. 2012). When these areas are identified, predictions are possible for which areas species can invade and also persist in the invaded habitats. The areas that have high climatic suitability for a modeled species in past, present and future time periods were recently called areas of biotic stability (de Oliveira et al. 2015), and these areas were used to establish goals for conservation priorities. Moreover, as defined, biotic stability can also be used as a surrogate of the potential of a species for invasion and persistence. To examine this potential, alien species are modeled in their native habitats for the current time period, with the models then projecting to past and future time periods. This biotic stability can then be projected over invaded habitats, when past, present and future climates are available for those invaded areas. The hypothesis that alien species seek biotic stability in invaded habitats is based on an intrinsic force within those species, suggesting that the cause of invasions is primarily based on the ecological niche of the alien species (e.g. Faleiro et al. 2015). However, the extrinsic force of human occupation also drives the spatial patterns of species invasions (Fuentes et al. 2014; Lenda et al. 2012; Mckinney 2002). More alien individuals are introduced (i.e. propagule pressure; Lockwood et al. 2005) and the intensity of disturbance increases, which favors alien species because they have the highest growth rates in this type of habitats (Dietz and Edwards 2006). Thus, human occupation is an alternative hypothesis to explain the spatial pattern of invasion by a species. I used Artocarpus heterophyllus Lam. (Moraceae), the jackfruit, as an alien tree species to evaluate the primary hypothesis of whether an intrinsic force (i.e. biotic stability) or an extrinsic force (i.e. human occupation) explained the spatial pattern of invasion in the Neotropics. A. heterophyllus is native to Southeast Asia and was introduced into the Neotropics in the 17th century by Portuguese colonization in northeast Brazil (Morton 1965). The introduction of jackfruit was primarily for food, which is important worldwide (Baliga et al. 2011; Thomas 1980), because it has the largest tree-borne fruits in the world and an adult tree can produce between 10 and 200 fruits per year. Thus, A. heterophyllus has tremendous importance in human diets as source of carbohydrates, protein, fat, minerals and vitamins (Baliga et al. 2011). However, in the Neotropics, A. heterophyllus is an alien species that increases the risk of extinction for native species because of competitive pressure (Abreu and Rodrigues 2010; Boni et al. 2009; Fabricante et al. 2012) from dominating invaded ecosystems in both density and biomass (Abreu and Rodrigues 2010). Moreover, it is recognized that the invasion of plant species, mostly, threat native species by competition pressure (see revision in Kumschick et al. 2015). In this study, I tested whether the spatial pattern of invasion by A. heterophyllus in the Neotropics was explained by biotic stability or human occupation by asking the following two questions: (i) Does the spatial distribution of A. heterophyllus in the Neotropics have more or less biotic stability than that expected by chance? and (ii) What is the primary driver of the spatial pattern of A. heterophyllus in the Neotropics, biotic stability or human occupation? To answer these questions, I evaluated ENMs for A. heterophyllus in pre-industrial times and then projected the ENMs to the past (LGM and Hol) and future (end of century, 2080–2100) time periods for the Indo-Malaya biogeographic region (Fig. 1) to establish areas of biotic stability (sensude Oliveira et al. 2015; Terribile et al. 2012) in its native habitat. Then, I projected this biotic stability onto the Neotropical biogeographic region (Fig. 2) as an invaded area to test the intrinsic and extrinsic forces of invasion based on the spatial presence of A. heterophyllus and the spatial pattern of human occupation in the Neotropics. Figure 1: View largeDownload slide indo-Malaya biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); and (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs. Figure 1: View largeDownload slide indo-Malaya biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); and (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs. Figure 2: View largeDownload slide neotropical biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs; and (d) spatial pattern of human occupation. Figure 2: View largeDownload slide neotropical biogeographic region showing (a) presence records of Artocarpus heterophyllus; (b) biotic stability that resulted from the proportion of each grid cell present in all time periods (past, pre-industrial and future) in each combination of ecological niche model (ENM) and atmosphere-ocean general circulation model (AOGCM); (c) the variance across all biotic stabilities for each combination of the 12 ENMs and 4 AOGCMs; and (d) spatial pattern of human occupation. MATERIALS AND METHODS Species and environmental data I retrieved 65 records of presence for A. heterophyllus in the Indo-Malaya biogeographic region (Fig. 1a) from the Global Biodiversity Information Facility (GBIF, www.gbif.org) online collection and scientific literature from the ISI Web of Knowledge (apps.webofknowledge.com) by searching for ‘Artocarpus heterophyllus’ with filtering for ‘India’, and ‘Malaya’. I mapped these presences in 3486 grid cells of 0.5° × 0.5° of latitude and longitude that covered the entire Indo-Malaya region (Fig. 1). I obtained the climate layers as 19 bioclimatic variables from the Worldclim database (www.worldclim.org) for past (LGM, 21000 years ago and Hol, 6000 years ago), pre-industrial (simulated for the middle of the 18th century and stabilized across a 200-year time period, representing current climatic conditions), and future (2080–2100, 20-year average for the end of the century, based on emission scenario RCP4.5, see Taylor et al. 2012) climatic conditions, which were derived from four coupled Atmosphere-Ocean General Circulation Models (AOGCM): 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). I choose the emission scenario RCP4.5 because it is an intermediated scenario, assuming that human population will maintain the greenhouse gases emission on the atmosphere as it is presented nowadays (Taylor et al. 2012). The climate layers were compiled from the ecoClimate database (www.ecoclimate.org). To minimize collinearity problems when building the ENMs, I selected a set of four bioclimatic variables, from the 19, for each AOGCM (mean temperature of diurnal range, isothermality, precipitation of the wettest quarter and precipitation of the driest quarter) using a factor analysis based on a correlation matrix (i.e. selecting the variables with the highest loadings in the first four Varimax rotated eigenvectors; Terribile et al. 2012). In addition to these variables, in all AOGCMs, I 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, because it does not have projections to past and future scenarios, to improve the ENM predictions (Collevatti et al. 2012; Lima et al. 2014; de Oliveira et al. 2015). Ecological niche models Ensemble methodologies for modeling the ecological niche of a species (Araújo and New 2007) were implemented following Diniz-Filho et al. (2009), Terribile et al. (2012) and de Oliveira et al. (2015). Twelve different ENMs were used, including six presence-only methods (i.e. BIOCLIM, Euclidian, Gower and Mahalanobis distances, Genetic Algorithm for Rule Set Production—GARP, and Maximum Entropy—MAXENT) and six presence-absence methods (i.e. Generalized Linear Models—GLM, 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 the methods. Parameterization of each ENM is shown separately in online supplementary Appendix S1. For model comparison, in both types of ENM, i.e. presence-only and presence-absence, I used the same pseudo-absence data, but in presence-only ENMs, pseudo-absences were used as background (sensude Oliveira et al. 2014, 2015). I randomly divided species presences and their pseudo-absences (randomly selected on a background region with the same proportion of species records (i.e. with the prevalence of 0.5, resulting 65 pseudo-absences)) into 75% for calibration and 25% for evaluation and repeated this process 50 times. Because I did not correct the presences records for spatial autocorrelation (de Oliveira et al. 2014; Varela et al. 2014), due to small number of presences (sensuPeterson and Samy 2016), I opted to select the pseudo-absences data randomly on background (Barbet-Massin et al. 2012). The 2400 resulting models (i.e. 50 cross-validation × 12 ENMs × 4 AOGCMs) were used to generate consensual occurrence maps based on thresholds established by the ROC curve for which the species frequency of occurrence in each Indo-Malaya grid cell was obtained from each ENM in each AOGCM (i.e. resulting in 48 frequency maps from 12 ENMs × 4 AOGCMs; for methodological details, see Terribile et al. 2012; and de Oliveira et al. 2015). This frequency of occurrence was used as a measure of environmental suitability for A. heterophyllus across the Indo-Malaya region, ranging from 0, with no environmental suitability (i.e. no cell occurrence in any of the 50 models from the 50 randomizations) to 1, with maximum environmental suitability (i.e. cell occurrences in all 50 models). The analyses were performed in the computational platform BioEnsembles (Diniz-Filho et al. 2009; de Oliveira et al. 2014, 2015; Terribile et al. 2012). Biotic stability I followed the protocol proposed by Terribile et al. (2012) and used by de Oliveira et al. (2015) to establish areas of biotic stability across the Indo-Malaya region based on the geographic distributions predicted by ENMs for A. heterophyllus for the four time periods (LGM, Hol, pre-industrial and future). I projected each pre-industrial’s ENM into their respective past and future ENMs for each AOGCM. The continuous values of environmental suitability across cells of each ENM in each AOGCM were converted into binary values (1/0, ‘presence’/‘absence’) to establish occurrence extensions (i.e. species range) using thresholds based on the minimum value of suitability (the least training presences threshold, from Peterson et al. 2011) for A. heterophyllus in the pre-industrial climatic scenario for areas where occurrences of A. heterophyllus in the Indo-Malaya region were recorded. Cells with suitability values above this threshold were assumed to be presences (1), whereas cells with suitability values below this threshold were assumed to be absences (0). A cell was then considered biotically stable when the presence of A. heterophyllus was predicted throughout the four time periods (i.e. LGM, Hol, pre-industrial and future) in each combination of ENM and AOGCM. Therefore, the proportion of presences of A. heterophyllus in all four periods in all 48 ENMs (12 ENMs × 4 AOGCMs) expressed the relative biotic stability of a given grid cell ranging from 0 (no biotic stability for any combination of ENM × AOGCM) to 1 (biotic stability in all 48 combinations of ENM × AOGCM). Hypothesis tests To forecast and to hindcast the biotic stability of A. heterophyllus onto the Neotropics, I overlaid the same four bioclimatic variables and the subsoil pH values used in the Indo-Malaya region from past, pre-industrial and future time periods over 6818 grid cells of 0.5° spatial resolution in the Neotropics (Fig. 2). Each ENM in each AOGCM from the pre-industrial period in the Indo-Malaya region was projected into each ENM in each AOGCM of past, pre-industrial and future time periods in the Neotropics to establish the biotic stability in the invaded habitat. To test the hypothesis whether biotic stability or human occupation explained the A. heterophyllus invasion into the Neotropics, I retrieved 182 presence records for A. heterophyllus for the Neotropical region (Fig. 2a) from GBIF (www.gbif.org) and Species Link (splink.cria.org.br) and mapped those records over 6818 grid cells covering the Neotropical region. Then, I calculated the sum of the biotic stability of the 182 presences of A. heterophyllus. To establish the null model, I randomly selected 182 spatial points in the entire Neotropical region and evaluated the sum of A. heterophyllus biotic stability for those randomized points. This procedure was repeated 500 times to establish a frequency distribution of the sums of biotic stability. Finally, I calculated the probability whether the observed sum of A. heterophyllus biotic stability was higher or lower than that expected by chance with comparisons with the frequency of the 500 random sums of biotic stability for each ENM in each AOGCM. Furthermore, I evaluated the spatial pattern of human occupation in the Neotropics by using anthropogenic biomes (Ellis et al. 2010, available at: databasin.org). Anthromes, or anthropogenic biomes, are maps that classify the human influence in ecosystems and consequently, are a measure of worldwide human occupation (Ellis and Ramankutty 2008; Ellis et al. 2010). I used the proportion of presences of the following classes of anthropogenic occupation in each cell across the Neotropics: (i) dense settlements, (ii) villages, (iii) croplands and (iv) rangeland. These levels of human occupation are from Ellis et al. (2010; Fig. 2e). Then, I applied a logistic regression using the 182 A. heterophyllus presences and 182 A. heterophyllus pseudo-absences (randomized across the Neotropical region) as response variables and their respective values of biotic stability and human occupation as explanatory variables. The standardized partial regression coefficient of each explanatory variable was used to infer the primary spatial driver of A. heterophyllus invasion. The logistic regression was performed in SAM software (Rangel et al. 2006, 2010). Methodological uncertainties may occur due to differences in the ENMs and AOGCMs used to model species ENMs (Buisson et al. 2010; Diniz-Filho et al. 2009), and these uncertainties might bias the interpretation of the spatial pattern of the invasion of A. heterophyllus into the Neotropics. Thus, I presented the maps with the means and the variances between the 12 ENMs and 4 AOGCMs (i.e. 48 models). Moreover, aiming to spatially locate the source of these uncertainties, I also partitioned and mapped the variation among the 12 ENMs and 4 AOGCMs using a hierarchical analysis of variance in each grid cell of the Indo-Malaya and Neotropical biogeographic regions. The environmental suitability was used as the response variable and the 12 different ENMs and 4 AOGCMs were nested in the four time periods as explanatory variables (Collevatti et al. 2012; Diniz-Filho et al. 2009; de Oliveira et al. 2015; see detailed results in online supplementary Appendix S2). RESULTS The means of the spatial pattern for the biotic stability of A. heterophyllus from all combinations of the 12 ENMs and 4 AOGCMs in the Indo-Malaya biogeographic region showed high values in the Western Ghats region, the north of India, and the south of Thailand, Cambodia, Vietnam and China (Fig. 1b). The lowest values of A. heterophyllus biotic stability in the Indo-Malaya region were placed in Indonesia and the Philippines (Fig. 1b). The spatial pattern of the variances of biotic stability across all combinations of the 12 ENMs and 4 AOGCMs revealed an inverted pattern compared with that of biotic stability, with low values in the Western Ghats region, the north of India, and the south of Thailand, Cambodia, Vietnam and China (Fig. 1c). The true skill statistics of each combination of ENM and AOGCM are provided in online supplementary Appendix S3. The projection of the means of A. heterophyllus biotic stability across all combinations of the 12 ENMs and 4 AOGCMs onto the invaded habitat of the Neotropical biogeographic region showed high values on the Atlantic Coast in the northeast of Brazil, the center-south of Brazil, the north of Paraguay, and the center of Bolivia and Colombia (Fig. 2b). The lowest values of A. heterophyllus biotic stability in the Neotropics were placed in Chile and the south of Argentina (Fig. 2b). The spatial pattern of variances of biotic stability across all combinations of the 12 ENMs and 4 AOGCM showed low values in the Brazilian northeast, the center of Bolivia and Colombia, and the north of Peru (Fig. 2c). Of the 48 models (12 ENMs × 4 AOGCMs), 29% (14 models) showed that the sum of biotic stability for the 182 Neotropical presences of A. heterophyllus was higher than that expected by chance (at 0.05 probability level; Table 1), and these models were those primarily based on distance (e.g. Euclidian and Mahalanobis distance) and BIOCLIM. Twenty-five percent (12 models) showed biotic stability sums lower than that expected by chance and were primarily those models based on machine learning (e.g. MAXENT, GARP and Random Forests; Table 1). For the other models, 46% (22 models), the sums of biotic stability were neither higher nor lower than that expected by chance (Table 1). Table 1: probability values for the sums of biotic stability of the 182 Artocarpus heterophyllus presences in the Neotropical biogeographical region from the 48 combinations of the 12 ecological niche models and 4 atmosphere-ocean circulation models   Higher than expected by chance  Lower than expected by chance    CCSM  CNMR  MIROC  MRI  CCSM  CNMR  MIROC  MRI  Bioclim  1.000  0.008  0.024  0.022  0.000  0.992  0.976  0.978  ENFA  0.918  0.804  0.564  0.982  0.082  0.196  0.436  0.018  EuclDist  0.002  0.000  0.004  0.016  0.998  1.000  0.996  0.984  FDA  1.000  0.986  0.812  0.468  0.000  0.014  0.188  0.532  GAM  0.998  0.966  0.010  0.000  0.002  0.034  0.990  1.000  GARP  0.102  0.056  1.000  0.992  0.898  0.944  0.000  0.008  GLM  0.560  0.980  0.734  0.510  0.440  0.020  0.266  0.490  GoweDist  0.606  0.002  0.114  0.052  0.394  0.998  0.886  0.948  MahaDist  0.012  0.066  0.004  0.026  0.988  0.934  0.996  0.974  MAXENT  0.974  0.998  0.694  0.916  0.026  0.002  0.306  0.084  NeurNet  0.086  0.220  0.882  0.022  0.914  0.780  0.118  0.978  RandFor  0.998  0.486  1.000  0.088  0.002  0.514  0.000  0.912    Higher than expected by chance  Lower than expected by chance    CCSM  CNMR  MIROC  MRI  CCSM  CNMR  MIROC  MRI  Bioclim  1.000  0.008  0.024  0.022  0.000  0.992  0.976  0.978  ENFA  0.918  0.804  0.564  0.982  0.082  0.196  0.436  0.018  EuclDist  0.002  0.000  0.004  0.016  0.998  1.000  0.996  0.984  FDA  1.000  0.986  0.812  0.468  0.000  0.014  0.188  0.532  GAM  0.998  0.966  0.010  0.000  0.002  0.034  0.990  1.000  GARP  0.102  0.056  1.000  0.992  0.898  0.944  0.000  0.008  GLM  0.560  0.980  0.734  0.510  0.440  0.020  0.266  0.490  GoweDist  0.606  0.002  0.114  0.052  0.394  0.998  0.886  0.948  MahaDist  0.012  0.066  0.004  0.026  0.988  0.934  0.996  0.974  MAXENT  0.974  0.998  0.694  0.916  0.026  0.002  0.306  0.084  NeurNet  0.086  0.220  0.882  0.022  0.914  0.780  0.118  0.978  RandFor  0.998  0.486  1.000  0.088  0.002  0.514  0.000  0.912  Comparisons with the null model determined whether values were higher or lower than that expected by chance (see the main text). Probabilities <0.05 are italicized. View Large Human occupation was positively correlated with the presence of A. heterophyllus in the Neotropics, with all standardized partial coefficients with positive and significant values (P > 0.05; Table 2). By contrast, in general, biotic stability showed no relationship with the presence of A. heterophyllus; 40 standard coefficients (83%) were not significant and only 8 standard coefficients (17%) were significant, with five showing a negative relationship and three showing a positive relationship (Table 2). Table 2: standardized partial coefficients of the logistic regression that used the 182 Artocarpus heterophyllus presences and 182 pseudo-absences in the Neotropical biogeographic region as response variables and the human occupation (HO) and the biotic stability (BS) in the Neotropics as the explanatory variables   CCSM  CNMR  MIROC  MRI    HO  BS  HO  BS  HO  BS  HO  BS  Bioclim  1.911  −0.364  1.711  0.263  1.763  0.126  1.788  0.027  ENFA  1.894  −0.938  2.304  −1.196  2.194  −1.301  1.794  −0.006  EuclDist  1.916  1.341  1.728  0.564  1.851  1.108  1.861  −0.212  FDA  1.805  −0.355  2.307  −1.808  1.884  −0.716  2.065  −0.579  GAM  1.869  −0.577  1.773  −0.2  1.791  0.01  1.933  −0.283  GARP  1.852  −0.34  1.948  −0.395  1.797  −0.062  1.799  −0.023  GLM  1.65  −0.547  1.823  −0.438  1.79  0.091  1.795  −0.033  GoweDist  1.778  0.156  1.737  0.412  1.636  0.66  1.872  −0.256  MahaDist  1.778  0.528  1.773  0.394  1.919  1.277  1.762  0.198  MAXENT  2.61  −2.517  1.792  −0.769  2.396  −1.492  2.099  −0.908  NeurNet  1.784  −0.205  1.81  0.423  1.812  0.087  1.849  −0.234  RandFor  1.95  −0.571  1.801  −0.038  2.041  −0.789  1.941  −0.284    CCSM  CNMR  MIROC  MRI    HO  BS  HO  BS  HO  BS  HO  BS  Bioclim  1.911  −0.364  1.711  0.263  1.763  0.126  1.788  0.027  ENFA  1.894  −0.938  2.304  −1.196  2.194  −1.301  1.794  −0.006  EuclDist  1.916  1.341  1.728  0.564  1.851  1.108  1.861  −0.212  FDA  1.805  −0.355  2.307  −1.808  1.884  −0.716  2.065  −0.579  GAM  1.869  −0.577  1.773  −0.2  1.791  0.01  1.933  −0.283  GARP  1.852  −0.34  1.948  −0.395  1.797  −0.062  1.799  −0.023  GLM  1.65  −0.547  1.823  −0.438  1.79  0.091  1.795  −0.033  GoweDist  1.778  0.156  1.737  0.412  1.636  0.66  1.872  −0.256  MahaDist  1.778  0.528  1.773  0.394  1.919  1.277  1.762  0.198  MAXENT  2.61  −2.517  1.792  −0.769  2.396  −1.492  2.099  −0.908  NeurNet  1.784  −0.205  1.81  0.423  1.812  0.087  1.849  −0.234  RandFor  1.95  −0.571  1.801  −0.038  2.041  −0.789  1.941  −0.284  The results are shown for all 48 combinations of the 12 ecological niche models and 4 atmosphere-ocean general circulation models. Coefficients with a significance level <0.05 are italicized. View Large DISCUSSION The comparison between the spatial pattern of A. heterophyllus biotic stability in Indo-Malaya and Neotropical biogeographic region showed that this stability probably is not the main driver of invasion. The regions of highest values for the biotic stability of A. heterophyllus in the Indo-Malaya region were expected because these regions are recognized as the centers of the original distribution of A. heterophyllus (Baliga et al. 2011; Thomas 1980). Moreover, because the spatial patterns of biotic stability of all ENMs converged on these regions, strong support was provided for the predictive power of the models (see de Oliveira et al. 2015). Additionally, the variance levels from all combinations of the 12 ENMs and 4 AOGCMs were the lowest in these regions, which indicated the ENMs used in this study were reliable (sensuDiniz-Filho et al. 2009; de Oliveira et al. 2015, and also see online supplementary Appendix S2). In the invaded habitat of the Neotropical biogeographic region, the reliability of the models generating the spatial pattern of biotic stability of A. heterophyllus remained at the same high level as observed for the Indo-Malaya region because the biotic stability also had an inverted spatial pattern from its variance and high values of biotic stability were associated with low values of variance (compare Fig. 2b with Fig. 2c). Do the spatial presences of A. heterophyllus in the Neotropics have more or less biotic stability than that expected by chance? In general, the presences of A. heterophyllus in the Neotropical region were expected only by chance, compared with the 500 randomized sums for biotic stability for most of the combinations of ENMs and AOGCMs, which suggested that biotic stability was not a significant driver of the A. heterophyllus invasion of habitats. An alternative explanation might be a niche shift (Eldredge et al. 2005; Parmesan and Yohe 2003) in which A. heterophyllus occupies different ecological niches in the switch from the native Indo-Malaya region into the invaded habitats of the Neotropics (Broennimann et al. 2007). However, this differentiation of the niche from native to invaded habitats might be explained beyond this intrinsic force based on ENMs, and the forces beyond the niche, which are primarily expressed by climate, as the extrinsic force of the human occupation, may limit the spatial distribution of a species (Early and Sax 2014, and see explanation in the next section). Another important finding was that ENMs based on distance and BIOCLIM showed that the biotic stability of A. heterophyllus was higher than that expected by chance. Compared with other ENMs, although they have more clarity with less complexity, these ENMs likely overestimated the results (Rangel and Loyola 2012). This overestimation might explain the higher biotic stability of A. heterophyllus than that expected by chance. In this way, the environmental suitability resulting from these ENMs had a widespread spatial pattern and the presences of A. heterophyllus in the Neotropics likely reached high values. VanDerWal et al. (2009) and Tôrres et al. (2012) also found the same widespread spatial pattern of ENMs based on distance in tests on the relationship between environmental suitability, from ENMs, and local species abundances. By contrast, ENMs based on machine learning resulted in A. heterophyllus having biotic stability lower than that expected by chance. These ENMs have high precision, although they usually have problems related to ENMs over fitness, and the predictions may be highly contingent on the geographic positions of species presences (Rangel and Loyola 2012). This underestimation of environmental suitability, which resulted from ENMs over fitness, might narrow the spatial pattern of environmental suitability and the presences of A. heterophyllus in the Neotropics would not reach high values. Thus, the ensemble approach used in this study overcame the pros and cons of choosing one ENM over another ENM. The tradeoffs between the transparency and overestimation of the distance-based ENMs and the precision and underestimation of machine learning ENMs were combined using all possible types of ENMs. This ensemble framework brings transparency to the results from the ENMs by showing the sources of their variations (Buisson et al. 2010; Diniz-Filho et al. 2009). Is biotic stability or human occupation the primary driver of the spatial pattern of the presences of A. heterophyllus in the Neotropics? In this study, I found that human occupation was the primary driver of the spatial pattern of the invasion of A. heterophyllus in the Neotropics because it had a significant positive relationship in all combinations of ENMs and AOGCMs. Generally, when species do not track their ecological niche from native to invaded habitats, a niche shift is an alternative explanation (e.g. Broennimann et al. 2007; Cornuault et al. 2015; Kumar et al. 2015; Petersen 2013). This niche shift implies that species tolerate conditions and levels of resources in the invaded habitat that were not tolerated in their native habitats, highlighting the importance of the intrinsic force of invasion. Nevertheless, this acquired tolerance in the invaded habitats might be sustained by other forces than only the intrinsic, for example, the force of human occupation. When the primary cause for alien species introduction and invasion is anthropogenic activities (i.e. agriculture, recreation, ornamentation and creation of pets; Lockwood et al. 2005), human occupation can have a role as an extrinsic force of invasion. Human occupation likely acted in two ways that explain the spatial pattern of A. heterophyllus invasion in the Neotropics. First, the jackfruit is a popular fruit that is used as a source of food worldwide (Baliga et al. 2011); thus, because of the demand and the positive perspective held by humans, with the consumption of this fruit, the propagule pressure increases (Lockwood et al. 2005) and contributes to the persistence of A. heterophyllus in invaded habitats. Second, A. heterophyllus can establish in the primary phase of invasion, which is associated with habitat disturbance that is linked to the intensity of human occupation (Dietz and Edwards 2006). Thus, A. heterophyllus remains dependent on human occupation for establishment, despite almost 400 years from its first invasion (Abreu and Rodrigues 2010; Boni et al. 2009; Fabricante et al. 2012), and cannot invade less disturbed habitats because of competitive pressure from native species (i.e. the secondary phase of invasion, Dietz and Edwards 2006). The first explanation does not exclude the second, and they can act additively with increases in propagule pressure increasing the persistence of A. heterophyllus in disturbed habitats that are closer to human occupation. In conclusion, the invasion of A. heterophyllus puts native species at risk of extinction, as stated by Boni et al. (2009), Abreu and Rodrigues (2010) and Fabricante et al. (2012), and therefore is a problem for conservation. Based on the evaluation in this study, this species remains dependent of human occupation for both establishment and persistence. Therefore, the cultivation of jackfruit must be regulated and supervised, primarily near reserve areas, once records of presences of this alien species it has been found in conservation units (e.g. Boni et al. 2009). SUPPLEMENTARY MATERIAL Supplementary data are available at Journal of Plant Ecology online. ACKNOWLEDGEMENTS This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant number 442103/2014-0]. I am grateful to Thiago Fernando Rangel to provide the use of BioEnsembles. To the World Climate Research Programmer’s Working Group on Coupled Modeling for providing CMIP5 and the climate-modeling group from NCAR for producing and making available CCSM. To Dr. Alessandra Nasser Caiafa for the first insight on the risk of jackfruit invasion. And to two anonymous reviewers that helped to improve and clarify the latest version of this work with their comments and suggestions. Conflict of interest statement. None declared. 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Journal of Plant EcologyOxford University Press

Published: Jun 1, 2018

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