Effects of the training dataset characteristics on the performance of nine species distribution models: application to Diabrotica virgifera virgifera

Modeling potential distribution and above-ground biomass of Scots pine (Pinus sylvestris L.) forests in the Inner Anatolian Region, Türkiye

Scots pine (Pinus sylvestris L.) holds a substantial position as a tree species designated for biomass energy within European forests, covering a significant part of Türkiye's forests. We used the machine learning technique, namely, maximum entropy (MaxEnt), to estimate the suitable areas for Scots pine and to investigate its potential future distribution under various climate change scenarios in Inner Anatolian Region, Türkiye. The distribution data of Scots pine was utilized, and a set of 20 variables was chosen from spectral, topographic, and bioclimatic datasets to train the MaxEnt model. A map depicting the potential distribution of Scots pine in the area was generated, and alterations in its spatial distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios were predicted. The results showed that the most effective factors for the distribution of Scots pine in the region were normalized difference vegetation index (NDVI), Red band of the imagery, and Bio19 variables, and the contribution percentages were 45.6%, 18.5%, and 18.1%, respectively. Current conditions have indicated that 81.11% of the region is not suitable for Scots pine. Highly suitable areas for Scots pine constituted 0.88% of the total area in the east and southeast parts of the region. Considering the SSP2-4.5 and SSP5-8.5 scenarios, it has been determined that there may be a partial increase in highly suitable areas. The above-ground biomass (AGB) data generated based on potential distribution areas were predicted between 0.04 and 168.76 t ha-1, and the areas with dense biomass over 120 t ha-1 were identified in the west, north, and northeast parts of the region. While actual AGB of Scots pine was 6.92 MT, its potential AGB was estimated 125.93 MT in total area. The difference may well be attributed to the wide potential distribution of Scots pine stands in the area apart from the current forest lands. Nevertheless, this research contributes to the holistic management of forests and provides substantial values for formulating well-suited silvicultural interventions, developing sustainable forest management strategies, and furthering research aimed at estimating biomass reserves.

The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models

How to effectively obtain species-related low-dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high-dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Development and application of a multilingual electronic decision-support tool for risk screening non-native terrestrial animals under current and future climate conditions

Electronic decision-support tools are becoming an essential component of government strategies to tackle non-native species invasions. This study describes the development and application of a multilingual electronic decision-support tool for screening terrestrial animals under current and future climate conditions: the Terrestrial Animal Species Invasiveness Screening Kit (TAS-ISK). As an adaptation of the widely employed Aquatic Species Invasiveness Screening Kit (AS-ISK), the TAS-ISK question template inherits from the original Weed Risk Assessment (WRA) and related WRA-type toolkits and complies with the ‘minimum requirements’ for use with the recent European Regulation on invasive alien species of concern. The TAS-ISK consists of 49 basic questions on the species’ biogeographical/historical traits and its biological/ecological interactions, and of 6 additional questions to predict how climate change is likely to influence the risks of introduction, establishment, dispersal and impact of the screened species. Following a description of the main features of this decision-support tool as a turnkey software application and of its graphical user interface with support for 32 languages, sample screenings are provided in different risk assessment areas for one representative species of each of the main taxonomic groups of terrestrial animals supported by the toolkit: mammals, birds, reptiles, amphibians, annelids, insects, molluscs, nematodes, and platyhelminths. The highest-scoring species were the red earthworm Lumbricus rubellus for the Aegean region of Turkey and the New Zealand flatworm Arthurdendyus triangulatus for Croatia. It is anticipated that adoption of this toolkit will mirror that of the worldwide employed AS-ISK, hence allowing to share information and inform decisions for the prevention of entry and/or dispersal of (high-risk) non-native terrestrial animal species – a crucial step to implement early-stage control and eradication measures as part of rapid-response strategies to counteract biological invasions.

Two Sides of the Same Desert: Floristic Connectivity and Isolation Along the Hyperarid Coast and Precordillera in Peru and Chile

In this study we aim at refining our understanding of the floristic connectivity of the loma- and precordillera floras of southern Peru and northern Chile and the parameters determining vegetation cover in this region. We used multivariate analyses to test for floristic- and environmental similarity across 53 precordillera and loma locations in Peru and Chile. We propose the use of predictive modeling in estimating the extent of desert vegetation as a complementary method to remote sensing. We created habitat suitability models for the vegetation on the coast and in the precordillera based on a combination of latent bioclimatic variables and additional environmental predictors using Maxent. We found Peruvian and Chilean lomas to be strongly floristically differentiated, as are the Chilean precordillera and lomas. Conversely, there is clear connectivity between both the Peruvian loma- and precordillera floras on the one hand and the Peruvian and Chilean precordillera floras on the other. Divergent environmental conditions were retrieved as separating the precordillera and lomas, while environmental conditions are not differentiated between Peruvian and Chilean lomas. Peruvian and Chilean precordilleras show a gradual change in environmental conditions. Habitat suitability models of vegetation cover retrieve a gap for the loma vegetation along the coast between Peru and Chile, while a continuous belt of suitable habitats is retrieved along the Andean precordillera. Unsuitable habitat for loma vegetation north and south of the Chilean and Peruvian border likely represents an ecogeographic barrier responsible for the floristic divergence of Chilean and Peruvian lomas. Conversely, environmental parameters change continuously along the precordilleras, explaining the moderate differentiation of the corresponding floras. Our results underscore the idea of the desert core acting as an ecogeographic barrier separating the coast from the precordillera in Chile, while it has a more limited isolating function in Peru. We also find extensive potentially suitable habitats for both loma- and precordillera vegetation so far undetected by methods of remote sensing.

RAD-Seq and Ecological Niche Reveal Genetic Diversity, Phylogeny, and Geographic Distribution of Kadsura interior and Its Closely Related Species

Most plants of Kadsura have economic value and medicinal application. Among them, K. interior and its closely related species have been demonstrated to have definite efficacy. However, the taxonomy and phylogenetic relationship of Kadsura in terms of morphology and commonly used gene regions remain controversial, which adversely affects its rational application. In this study, a total of 107 individuals of K. interior, K. heteroclita, K. longipedunculata, K. oblongifolia, and K. coccinea were studied from the perspectives of genetic diversity, phylogeny, and ecology via single nucleotide polymorphisms (SNPs) developed through restriction site-associated DNA sequencing (RAD-seq). Based on these SNPs, the genetic diversity, phylogenetic reconstruction, and population genetic structure were analyzed. Subsequently, divergence time estimation and differentiation scenario simulation were performed. Meanwhile, according to the species distribution records and bioclimatic variables, the Last Glacial Maximum and current potential distributions of five species were constructed, and the main ecological factors affecting the distribution of different species were extracted. The F ST calculated showed that there was a moderate degree of differentiation among K. heteroclita, K. longipedunculata, and K. oblongifolia, and there was a high degree of genetic differentiation between K. interior and the above species. The phylogenetic tree indicated that each of the species was monophyletic. The results of population genetic structure and divergence scenario simulation and D-statistics showed that there were admixture and gene flow among K. heteroclita, K. longipedunculata, and K. oblongifolia. The results of ecological niche modeling indicated that the distribution areas and the bioclimatic variables affecting the distribution of K. interior and its related species were different. This study explored the differences in the genetic divergence and geographical distribution patterns of K. interior and its related species, clarifying the uniqueness of K. interior compared to its relatives and providing a reference for their rational application in the future.

Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change

The rapid expansion of soybean-growing areas across Europe raises questions about the suitability of agroclimatic conditions for soybean production. Here, using data-driven relationships between climate and soybean yield derived from machine-learning, we made yield projections under current and future climate with moderate (Representative Concentration Pathway (RCP) 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The selected model showed high R² (>0.9) and low root-mean-squared error (0.35 t ha^-1) between observed and predicted yields based on cross-validation. Our results suggest that a self-sufficiency level of 50% (100%) would be achievable in Europe under historical and future climate if 4-5% (9-11%) of the current European cropland were dedicated to soybean production. The findings could help farmers, extension services, policymakers and agribusiness to reorganize the production area distribution. The environmental benefits and side effects, and the impacts of soybean expansion on land-use change, would need further research.

Western Corn Rootworm (Diabrotica virgifera virgifera LeConte) in Europe: Current Status and Sustainable Pest Management

Simple Summary

Diabrotica virgifera virgifera, also known as western corn rootworm (WCR), is a maize-specific pest that has been a serious threat in Europe since the mid-1990s. Between 1995 and 2010, European countries were involved in international projects to plan pest control strategies. However, since 2011, collaborative efforts have declined and the overview of knowledge on WCR is in great need of updating. Therefore, a review of scientific papers published between 2008 and 2020, in addition to direct interviews with experts responsible for WCR management in several European countries, was conducted to (1) summarize the research conducted over the last 12 years and (2) describe the current WCR distribution and population in the EU, and the management strategies implemented. A considerable amount of new knowledge has been gained over the last 12 years, which has contributed to the development of pest management strategies applicable in EU agricultural systems. There is no EU country reporting economic damage on a large scale. In many countries, solutions based on crop rotation are regularly implemented, avoiding insecticide use. Therefore, WCR has not become as serious a pest as was expected when it was discovered in much of Europe.

Abstract

Western corn rootworm (WCR), or Diabrotica virgifera virgifera LeConte, became a very serious quarantine maize pest in Europe in the mid-1990s. Between 1995 and 2010, European countries were involved in international projects to share information and plan common research for integrated pest management (IPM) implementation. Since 2011, however, common efforts have declined, and an overview of WCR population spread, density, and research is in serious need of update. Therefore, we retained that it was necessary to (1) summarize the research activities carried out in the last 12 years in various countries and the research topics addressed, and analyze how these activities have contributed to IPM for WCR and (2) present the current distribution of WCR in the EU and analyze the current population levels in different European countries, focusing on different management strategies. A review of scientific papers published from 2008 to 2020, in addition to direct interviews with experts in charge of WCR management in a range of European countries, was conducted. Over the past 12 years, scientists in Europe have continued their research activities to investigate various aspects of WCR management by implementing several approaches to WCR control. A considerable amount of new knowledge has been produced, contributing to the development of pest management strategies applicable in EU farming systems. Among the 10 EU countries analyzed, there is no country reporting economic damage on a large scale. Thanks to intensive research leading to specific agricultural practices and the EU Common Agricultural Policy, there are crop-rotation-based solutions that can adequately control this pest avoiding insecticide use.

Models of Diabrotica Populations: Demography, Population Genetics, Geographic Spread, and Management

Simple Summary

Two beetles that are serious pests of maize, Diabrotica virgifera virgifera and Diabrotica barberi, have caused problems for farmers in the USA and Europe for many years. Because both species have developed resistance to several management tactics, including insecticides and crop rotation, mathematical modeling has been used to evaluate their life cycles for weaknesses and new tactics for value. This review highlights lessons learned from the past 35 years. Some models have focused on the probability of the beetles spreading across regions. Other models have been developed to estimate the risk of the evolution of resistance. These models are thoroughly reviewed with respect to the biological attributes incorporated in these models and the impact of those attributes on the evolution of resistance.

Abstract

Both Diabrotica virgifera virgifera LeConte and D. barberi Smith and Lawrence are among the most damaging insects impacting corn in North America. D. virgifera virgifera has also invaded Europe and has become an important pest in that region. Computer models have become an important tool for understanding the impact and spread of these important pests. Over the past 30 years, over 40 models have been published related to these pests. The focus of these models range from occupancy models (particularly for Europe), impact of climate change, range expansion, economics of pest management, phenology, to the evolution of resistance to toxins and crop rotation. All of these models share characteristics. We elaborate on the methods in which modelers have incorporated the biology of these pests, including density-dependence, movement, fecundity and overwintering mortality. We discuss the utility of both spatially-explicit, complex models and spatially-implicit, generational models and where each might be appropriate. We review resistance models that either explain past evolution to crop rotation, insecticides or insecticidal traits or attempt to predict the consequences of resistance management strategies.

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Background

Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions.

Results

According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse.

Conclusion

Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

Small genome separates native and invasive populations in an ecologically important cosmopolitan grass

The literature suggests that small genomes promote invasion in plants, but little is known about the interaction of genome size with other traits or about the role of genome size during different phases of the invasion process. By intercontinental comparison of native and invasive populations of the common reed Phragmites australis, we revealed a distinct relationship between genome size and invasiveness at the intraspecific level. Monoploid genome size was the only significant variable that clearly separated the North American native plants from those of European origin. The mean Cx value (the amount of DNA in one chromosome set) for source European native populations was 0.490 ± 0.007 (mean ± SD), for North American invasive 0.506 ± 0.020, and for North American native 0.543 ± 0.021. Relative to native populations, the European populations that successfully invaded North America had a smaller genome that was associated with plant traits favoring invasiveness (long rhizomes, early emerging abundant shoots, resistance to aphid attack, and low C:N ratio). The knowledge that invasive populations within species can be identified based on genome size can be applied to screen potentially invasive populations of Phragmites in other parts of the world where they could grow in mixed stands with native plants, as well as to other plant species with intraspecific variation in invasion potential. Moreover, as small genomes are better equipped to respond to extreme environmental conditions such as drought, the mechanism reported here may represent an emerging driver for future invasions and range expansions.

Multi-Scenario Species Distribution Modeling

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls - collinearity among environmental predictors - is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called "idiosyncrasy in species responses" to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

Potential global distribution of Diabrotica species and the risks for agricultural production

BACKGROUND

Despite efforts in the last few decades to prevent biological invasions, agricultural pests continue to spread as a result of globalization and international trade. This study was conducted to identify suitable areas for the occurrence of four Diabrotica species and to assess the potential impact of these species in a scenario of invasion followed by spread throughout the estimated suitable regions.

RESULTS

Our findings reveal that a large proportion of the suitable areas for Diabrotica species overlap with cultivated areas. Niche analyses also demonstrated that these species occupy a small proportion of the suitable habitats available to them, indicating that, if new areas are invaded, there is a risk of spread throughout adjacent regions.

CONCLUSION

Most of the suitable areas for Diabrotica species overlap with highly productive agricultural areas, suggesting that a potential spread of these species may cause economic loss. Our study provides a valuable contribution to the development of tools aiming to predict the potential spread of these species throughout the world. © 2018 Society of Chemical Industry.

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species' geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist.

Environmental factors associated with the distribution of visceral leishmaniasis in endemic areas of Bangladesh: modeling the ecological niche

Background

Visceral leishmaniasis (VL) is a parasitic infection (also called kala-azar in South Asia) caused by Leishmania donovani that is a considerable threat to public health in the Indian subcontinent, including densely populated Bangladesh. The disease seriously affects the poorest subset of the population in the subcontinent. Despite the fact that the incidence of VL results in significant morbidity and mortality, its environmental determinants are relatively poorly understood, especially in Bangladesh. In this study, we have extracted a number of environmental variables obtained from a range of sources, along with human VL cases collected through several field visits, to model the distribution of disease which may then be used as a surrogate for determining the distribution of Phlebotomus argentipes vector, in hyperendemic and endemic areas of Mymensingh and Gazipur districts in Bangladesh. The analysis was carried out within an ecological niche model (ENM) framework using a maxent to explore the ecological requirements of the disease.

Results

The results suggest that VL in the study area can be predicted by precipitation during the warmest quarter of the year, land surface temperature (LST), and normalized difference water index (NDWI). As P. argentipes is the single proven vector of L. donovani in the study area, its distribution could reasonably be determined by the same environmental variables. The analysis further showed that the majority of VL cases were located in mauzas where the estimated probability of the disease occurrence was high. This may reflect the potential distribution of the disease and consequently P. argentipes in the study area.

Conclusions

The results of this study are expected to have important implications, particularly in vector control strategies and management of risk associated with this disease. Public health officials can use the results to prioritize their visits in specific areas. Further, the findings can be used as a baseline to model how the distribution of the disease caused by P. argentipes might change in the event of climatic and environmental changes that resulted from increased anthropogenic activities in Bangladesh and elsewhere.

Geographic structuring and transgenerational maternal effects shape germination in native, but not introduced, populations of a widespread plant invader

PREMISE OF THE STUDY

Germination is critical in determining species distributions and invasion dynamics. However, is it unclear how often invasive populations evolve germination characteristics different from native populations, because few studies have isolated genetic variation by using seed from garden-grown plants. Additionally, while herbivore-induced transgenerational effects are common, it is unknown whether maternal herbivory differentially shapes germination in native and introduced offspring.

METHODS

We explored germination in native and introduced populations of the North American invader Verbascum thapsus using seed from garden-grown maternal plants, half of which were protected from herbivores. To elucidate (1) germination niche breadth and (2) whether germination conditions affected expression of genetic structuring among populations, we germinated seed under four ecologically relevant temperature regimes.

KEY RESULTS

Native populations had a wide germination niche breadth, germinating as well as or better than introduced populations. At cooler temperatures, native populations exhibited a genetically based environmental cline indicative of local adaptation, with populations from warmer locales germinating better than populations from cooler locales. However, this cline was obscured when maternal plants were attacked by herbivores, revealing that local stressors can override the expression of geographic structuring. Introduced populations did not exhibit clinal variation, suggesting its disruption during the introduction process.

CONCLUSIONS

Native and introduced populations have evolved genetic differences in germination. The result of this difference manifests in a wider germination niche breadth in natives, suggesting that the invasive behavior of V. thapsus in North America is attributable to other factors.

Reconstructing the Mexican Tropical Dry Forests via an Autoecological Niche Approach: Reconsidering the Ecosystem Boundaries

We used Ecological Niche Modeling (ENM) of individual species of two taxonomic groups (plants and birds) in order to reconstruct the climatic distribution of Tropical Dry Forests (TDFs) in Mexico and to analyze their boundaries with other terrestrial ecosystems. The reconstruction for TDFs' distribution was analyzed considering the prediction and omission errors based upon the combination of species, obtained from the overlap of individual models (only plants, only birds, and all species combined). Two verifications were used: a primary vegetation map and 100 independent TDFs localities. We performed a Principal Component (PCA) and Discriminant Analysis (DA) to evaluate the variation in the environmental variables and ecological overlap among ecosystems. The modeling strategies showed differences in the ecological patterns and prediction areas, where the "all species combined" model (with a threshold of ≥10 species) was the best strategy to use in the TDFs reconstruction. We observed a concordance of 78% with the primary vegetation map and a prediction of 98% of independent locality records. Although PCA and DA tests explained 75.78% and 97.9% of variance observed, respectively, we observed an important overlap among the TDFs with other adjacent ecosystems, confirming the existence of transition zones among them. We successfully modeled the distribution of Mexican TDFs using a number of bioclimatic variables and co-distributed species. This autoecological niche approach suggests the necessity of rethinking the delimitations of ecosystems based on the recognition of transition zones among them in order to understand the real nature of communities and association patterns of species.

Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata)

The bean leaf beetle, Cerotoma trifurcata, has become a major pest of soybean throughout its North American range. With a changing climate, there is the potential for this pest to further expand its distribution and become an increasingly severe pest in certain regions. To examine this possibility, we developed bioclimatic envelope models for both the bean leaf beetle, and its most important agronomic host plant, soybean (Glycine max). These two models were combined to examine the potential future pest status of the beetle using climate change projections from multiple general circulation models (GCMs) and climate change scenarios. Despite the broad tolerances of soybean, incorporation of host plant availability substantially decreased the suitable and favourable areas for the bean leaf beetle as compared to an evaluation based solely on the climate envelope of the beetle, demonstrating the importance of incorporating biotic interactions in these predictions. The use of multiple GCM-scenario combinations also revealed differences in predictions depending on the choice of GCM, with scenario choice having less of an impact. While the Norwegian model predicted little northward expansion of the beetle from its current northern range limit of southern Ontario and overall decreases in suitable and favourable areas over time, the Canadian and Russian models predict that much of Ontario and Quebec will become suitable for the beetle in the future, as well as Manitoba under the Russian model. The Russian model also predicts expansion of the suitable and favourable areas for the beetle over time. Two predictions that do not depend on our choice of GCM include a decrease in suitability of the Mississippi Delta region and continued favourability of the southeastern United States.

Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach

Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological niche modeling (ENM) to confirm the validity of these seven taxa and an additional taxon S. johnii belonging to the same genus. MaxEnt modeling tool was used with 19 bioclimatic, 12 vegetation and 6 hydrological environmental layers. We reduced total environmental variables to 14 layers after testing for collinearity and an independent test for model prediction was done using ENMTools. A total of 196 non-overlapping data points from primary and secondary sources were used as inputs for ENM. Results showed eight distinct ecological boundaries, corroborating the eight taxa mentioned above thereby confirming validity of these eight taxa. The study, for the first time provided ecological variables that determined the ecological requirements and distribution of members of the Hanuman langur species complex in the Indian peninsula.