Combining local- and large-scale models to predict the distributions of invasive plant species

Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L

Climate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.

Assessing genomic and ecological differentiation among subspecies of the Rough-footed Mud Turtle, Kinosternon hirtipes

Combining genetic and ecological measures of differentiation can provide compelling evidence for ecological and genetic divergence among lineages. The Rough-footed Mud Turtle, Kinosternon hirtipes, is distributed from the Trans-Pecos region of Texas to the highlands of Central Mexico and contains six described subspecies, five of which are extant. We use ddRAD sequencing and species distribution models to assess levels of ecological and genetic differentiation among these subspecies. We also predict changes in climatically suitable habitat under different climate change scenarios and assess levels of genetic diversity and inbreeding within each lineage. Our results show that there is strong genetic and ecological differentiation among multiple lineages within K. hirtipes, and that this differentiation appears to be the result of vicariance associated with the Trans-Mexican Volcanic Belt. We propose changes to subspecies designations to more accurately reflect the evolutionary relationships among populations and assess threats to each subspecies.

Preferential habitats prediction in syngnathids using species distribution models

Syngnathids are considered as flagship species for marine conservation. Seahorses and pipefish are highly vulnerable to anthropogenic and environmental disturbances, but most species are currently considered Data Deficient by IUCN, requiring more biological and ecological research. Although syngnathids are well known for their unusual breeding biology, some aspects on the ecology of this family have rarely received attention. The knowledge on the factors governing syngnathids distribution is limited to some species and geographical regions. The present study is the first approach to predict syngnathid habitat preference in Spanish coasts, particularly in a marine National Park. In this study, Species Distribution Models (SDMs) were implemented to investigate the preferential habitat and distribution of the pipefish Syngnathus acus in Cíes Archipelago (Atlantic Islands of Galicia National Park, PNIA). Occurrence data of the species obtained from 2016 to 2018 surveys in PNIA were modeled as a function of bathymetric (depth, slope), substrate (sediment texture) and oceanographic (waves exposure) variables, using GAM, Random Forest and Maxent algorithms. From those SDMs, prediction models were built and the ensemble map of predictions was performed. The variables that most determined the distribution of the species were depth and wave exposure. The results of this study provide information on (1) habitat preference in the most dominant species in PNIA, the pipefish S. acus, towards sustainable management of this species in the National Park, and (2) predictive statistical tools for proper spatial conservation plans of this syngnathid species.

The role of littoral cliffs in the niche delimitation on a microendemic plant facing climate change

Obligate coastline taxa generally occupy very limited areas, especially when there is a close affinity with a specific coast type. Climate change can be a meaningful threat for them, reducing suitable habitat or forcing migration events. Cistus ladanifer subsp. sulcatus is an endemic plant of Portugal, known to occur only in the top of its south-western coast's prominent cliffs. In spite of being included in the annexes II and IV of the European Habitats Directive of Natura 2000 Network, this taxon is still understudied, especially regarding the effects of climate change on its distribution. To overcome such gap, Maxent was used to model the current distribution of C. ladanifer subsp. sulcatus and project its future distribution considering different General Circulation Models, periods (2050 and 2070) and Representation Concentration Pathways (4.5 and 8.5). The results suggested an extensive range contraction in the future, and extinction is a possible scenario. The proximity to littoral cliffs is crucial for this plant's occurrence, but these formations are irregularly distributed along the coast, hindering range expansions, further inhibited by a small dispersal capacity. Cistus ladanifer subsp. sulcatus will probably remain confined to south-western Portugal in the future, where it will continue to face relevant threats like human activity, reinforcing the need for its conservation.

Accessing habitat suitability and connectivity for the westernmost population of Asian black bear (Ursus thibetanus gedrosianus, Blanford, 1877) based on climate changes scenarios in Iran

Climate change, as an emerging phenomenon, has led to changes in the distribution, movement, and even risk of extinction of various wildlife species and this has raised concerns among conservation biologists. Different species have two options in the face of climate change, either to adopt or follow their climatic niche to new places through the connectivity of habitats. The modeling of interpatch landscape communications can serve as an effective decision support tool for wildlife managers. This study was conducted to assess the effects of climate change on the distribution and habitat connectivity of the endangered subspecies of Asian black bear (Ursus thibetanus gedrosianus) in the southern and southeastern Iran. The presence points of the species were collected in Provinces of Kerman, Hormozgan, and Sistan-Baluchestan. Habitat modeling was done by the Generalized Linear Model, and 3 machine learning models including Maximum Entropy, Back Propagation based artificial Neural Network, and Support Vector Machine. In order to achieve the ensemble model, the results of the mentioned models were merged based on the method of "accuracy rate as weight" derived from their validation. To construct pseudo-absence points for the use in the mentioned models, the Ensemble model of presence-only models was used. The modeling was performed using 15 habitat variables related to climatic, vegetation, topographic, and anthropogenic parameters. The three general circulation models of BCC-CSM1, CCSM4, and MRI-CGCM3 were selected under the two scenarios of RCP2.6 and RCP8.5 by 2070. To investigate the effect of climate change on the habitat connections, the protected areas of 3 provinces were considered as focal nodes and the connections between them were established based on electrical circuit theory and Pairwise method. The true skill statistic was employed to convert the continuous suitability layers to binary suitable/unsuitable range maps to assess the effectiveness of the protected areas in the coverage of suitable habitats for the species. Due to the high power of the stochastic forest model in determining the importance of variables, this method was used. The results showed that presence/absence models were successful in the implementation and well distinguished the points of presence and pseudo-absence from each other. Based on the random forests model, the variables of Precipitation of Driest Quarter, Precipitation of Coldest Quarter, and Temperature Annual Range have the greatest impact on the habitat suitability. Comparing the modeling findings to the realities of the species distribution range indicated that the suitable habitats are located in areas with high humidity and rainfall, which are mostly in the northern areas of Bandar Abbas, south of Kerman, and west and south of Sistan-Baluchestan. The area of suitable habitats, in the MRI-CGCM3 (189731 Km2) and CCSM4 (179007 Km2) models under the RCP2.6 scenario, is larger than the current distribution (174001 Km2). However, in terms of the performance of protected areas, the optimal coverage of the species by the boundary of the protected areas, under each of the RCP2.6 and RCP8.5 scenarios, is less than the present time. According to the electric circuit theory, connecting the populations in the protected areas of Sistan-Baluchestan province to those in the northern Hormozgan and the southern Kerman would be based on the crossing through the heights of Sistan-Baluchestan and Hormozgan provinces and the plains between these heights would be the movement pinch points under the current and future scenarios. Populations in the protected areas of Kerman have higher quality patch connections than that of the other two provinces. The areas such as Sang-e_Mes, Kouh_Shir, Zaryab, and Bahr_Aseman in Kerman Province and Kouhbaz and Geno in Hormozgan Province can provide suitable habitats for the species in the distribution models. The findings revealed that the conservation of the heights along with the caves inside them could be a protective priority to counteract the effects of climate change on the species.

Potential Distribution of the Biocontrol Agent Toxorhynchites rutilus By 2070

Climate change projections indicate that mosquito distributions will expand to include new areas of North America, increasing human exposure to mosquito-borne disease. Controlling these vectors is imperative, as mosquito-borne disease incidence will rise in response to expansion of mosquito range and increased seasonality. One means of mosquito control used in the USA is the biocontrol agent, Toxorhynchites rutilus. Climate change will open new habitats for its use by vector control organizations, but the extent of this change in habitat is currently unknown. We used a maximum entropy approach to create species distribution models for Tx. rutilus under 4 climate change scenarios by 2070. Mean temperature of warmest quarter (22.6°C to 29.1°C), annual precipitation (1,025.15 mm to 1,529.40 mm), and precipitation seasonality (≤17.86) are the most important bioclimatic variables for suitable habitat. The center of current possible habitat distribution of Tx. rutilus is in central Tennessee. Depending upon the scenario, we expect centroids to shift north-northeast by 97.68 km to 280.16 km by 2070. The extreme change in area of greater than 50% suitable habitat probability is 141.14% with 99.44% area retained. Our models indicate limited change in current habitat as well as creation of new habitat. These results are promising for North American mosquito control programs for the continued and potential combat of vector mosquitoes using Tx. rutilus.

Genetics and Distribution Modeling: The Demographic History of the Cactophilic Drosophila buzzatii Species Cluster in Open Areas of South America

Biodiversity is the result of historical and recurrent events acting on populations and species. The Drosophila buzzatii species cluster is distributed along a diagonal of open areas in South America. Combining genetic analyses with species distribution models we evaluated the influence of climatic changes in the demography history of this cluster. We performed a Bayesian Skyline analysis and reconstructed the ancestral areas based on mitochondrial cytochrome oxidase I (mtCOI) gene. We modeled the current distribution and projected it to past (mid-Holocene and Last Glacial Maximum) and future. Our results demonstrate that climate change plays a critical role in historical demography and in defining the current and future geographic ranges of these species. The inter-Andean dry valleys and the Chiquitano Seasonally Dry Tropical Forests (SDTF) in Bolivia are considered the ancestral area for the D. buzzatii cluster. From this area, the migration route was through a west-east corridor from central Andes, throughout Bolivia, Paraguay and Argentina toward eastern and northeastern Brazil, along the edges of rain forest. The responses of the species to the climatic changes differ from the dominant assumptions of expansion during dry/cold weather and contraction during wet/warm weather that characterized the glacial cycles. We suggest that the influence of ecology and the potential responsiveness of each taxon to the environmental dynamics should be considered as well. Predictions for the future suggest a decline of suitable areas for the cluster, threatening biodiversity of these habitats. This work showed the importance of an integrative analysis of genetics and geography information to improve the inferences about demographic history hypotheses for the cluster.

Potential distribution of Notopterygium incisum Ting ex H. T. Chang and its predicted responses to climate change based on a comprehensive habitat suitability model

Notopterygium incisum Ting ex H. T. Chang is a rare and endangered traditional Chinese medicinal plant. In this research, we built a comprehensive habitat suitability (CHS) model to analyze the potential suitable habitat distribution of this species in the present and future in China. First, using nine different algorithms, we built an ensemble model to explore the possible impacts of climate change on the habitat distribution of this species. Then, based on this model, we built a CHS model to further identify the distribution characteristics of N. incisum-suitable habitats in three time periods (current, 2050s, and 2070s) while considering the effects of soil and vegetation conditions. The results indicated that the current suitable habitat for N. incisum covers approximately 83.76 × 103 km2, and these locations were concentrated in the Tibet Autonomous Region, Gansu Province, Qinghai Province, and Sichuan Province. In the future, the areas of suitable habitat for N. incisum would significantly decrease and would be 69.53 × 103 km2 and 60.21 × 103 km2 in the 2050s and 2070s, respectively. However, the area of marginally suitable habitat would remain relatively stable. This study provides a more reliable and comprehensive method for modelling the current and future distributions of N. incisum, and it provides valuable insights for highlighting priority areas for medicinal plant conservation and resource utilization.

Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-e-Sayad protected area, Iran

The maximum entropy (Maxent) model was used to predict the distribution of Persian leopards and wild sheep in the Tang-e-Sayad protected area in Iran. For this purpose, eight variables, as well as 30 occurrence points of leopard and 98 points of wild sheep, were used. Two techniques, density-based occurrence points thinning and performance-based predictor variables selection were used to improve the results of the model. The model results were analyzed based on four threshold limit-based statistics (sensitivity, specificity, kappa and true skill statistics) and area under the curve (AUC), followed by determining the relative importance of variables based on the jackknife procedure. The results of threshold limit-based statistics revealed that the success of the model for distribution prediction of leopard and wild sheep were good and relatively good, respectively. According to the jackknife procedure, for wild sheep and for leopard, slope and distance to road, respectively, were the most important predictor variables. The results also indicated that the efficiency of the model did not improve by reducing the density of occurrence points for the wild sheep (AUC=0.784–0.773). However, the selection of predictor variables slightly improved the performance of the model (AUC=0.794–0.819). The results of the study also showed overlapping habitat for two species due to both human and ecological reasons for which we proposed some conservation actions such as excluding domestic grazing, controlling illegal poaching and restoration of old migratory corridors.

Prevalence dependence in model goodness measures with special emphasis on true skill statistics

It has long been a concern that performance measures of species distribution models react to attributes of the modeled entity arising from the input data structure rather than to model performance. Thus, the study of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) identifying the true skill statistics (TSS) as being independent of prevalence had a great impact. However, empirical experience questioned the validity of the statement. We searched for technical reasons behind these observations. We explored possible sources of prevalence dependence in TSS including sampling constraints and species characteristics, which influence the calculation of TSS. We also examined whether the widespread solution of using the maximum of TSS for comparison among species introduces a prevalence effect. We found that the design of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) was flawed, but TSS is indeed independent of prevalence if model predictions are binary and under the strict set of assumptions methodological studies usually apply. However, if we take realistic sources of prevalence dependence, effects appear even in binary calculations. Furthermore, in the widespread approach of using maximum TSS for continuous predictions, the use of the maximum alone induces prevalence dependence for small, but realistic samples. Thus, prevalence differences need to be taken into account when model comparisons are carried out based on discrimination capacity. The sources we identified can serve as a checklist to safely control comparisons, so that true discrimination capacity is compared as opposed to artefacts arising from data structure, species characteristics, or the calculation of the comparison measure (here TSS).

Projected changes in climatic suitability for Kinosternon turtles by 2050 and 2070

Chelonians are expected to be negatively impacted by climate change due to limited vagility and temperature‐dependent sex determination. However, few studies have examined how freshwater turtle distributions may shift under different climate change scenarios. We used a maximum entropy approach to model the distribution of five widespread North American Kinosternon species (K. baurii, K. flavescens, K. hirtipes, K. sonoriense, and K. subrubrum) under four climate change scenarios. We found that areas with suitable climatic conditions for K. baurii and K. hirtipes are expected to decline substantially during the 21st century. In contrast, the area with suitable climate for K. sonoriense will remain essentially unchanged, while areas suitable for K. flavescens and K. subrubrum are expected to substantially increase. The centroid for the distribution of four of the five species shifted northward, while the centroid for K. sonoriense shifted slightly southward. Overall, centroids shifted at a median rate of 37.5 km per decade across all scenarios. Given the limited dispersal ability of turtles, it appears unlikely that range shifts will occur rapidly enough to keep pace with climate change during the 21st century. The ability of chelonians to modify behavioral and physiological responses in response to unfavorable conditions may allow turtles to persist for a time in areas that have become increasingly unsuitable, but this plasticity will likely only delay local extinctions.

Worldwide Alien Invasion: A Methodological Approach to Forecast the Potential Spread of a Highly Invasive Pollinator

The ecological impacts of alien species invasion are a major threat to global biodiversity. The increasing number of invasion events by alien species and the high cost and difficulty of eradicating invasive species once established require the development of new methods and tools for predicting the most susceptible areas to invasion. Invasive pollinators pose serious threats to biodiversity and human activity due to their close relationship with many plants (including crop species) and high potential competitiveness for resources with native pollinators. Although at an early stage of expansion, the bumblebee species Bombus terrestris is becoming a representative case of pollinator invasion at a global scale, particularly given its high velocity of invasive spread and the increasing number of reports of its impacts on native bees and crops in many countries. We present here a methodological framework of habitat suitability modeling that integrates new approaches for detecting habitats that are susceptible to Bombus terrestris invasion at a global scale. Our approach did not include reported invaded locations in the modeling procedure; instead, those locations were used exclusively to evaluate the accuracy of the models in predicting suitability over regions already invaded. Moreover, a new and more intuitive approach was developed to select the models and evaluate different algorithms based on their performance and predictive convergence. Finally, we present a comprehensive global map of susceptibility to Bombus terrestris invasion that highlights priority areas for monitoring.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population

Background

Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts.

Methods

Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent.

Results

Results from our land change modeling indicate that about 70,000 km² of forest land would be lost by 2050 and 78,000 km² by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution.

Conclusion

As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1033-9) contains supplementary material, which is available to authorized users.

Climate Warming May Threaten Reproductive Diapause of a Highly Eusocial Bee

Climate changes are predicted to affect the diapause of many insect species around the world adversely. In this context, bees are of interest due to their pollination services. In southern Brazil, the highly eusocial bee species Plebeia droryana (Friese) (Hymenoptera: Apidae: Meliponini) exhibits reproductive diapause in response to the region's rigorous winters. That diapause is characterized by a temporary interruption in brood cell construction by nurse bees and egg-laying by the queen, regardless of other internal tasks underway in the nests. In this study, we evaluated whether P. droryana enter diapause under experimental conditions. P. droryana colonies were kept in a germination chamber, and the temperature was progressively reduced from 20°C over a period of a few weeks until diapause was detected. Additionally, we also estimated the environmental conditions in the actual geographic range occupied by P. droryana and modeled it for predicted changes in climate up to the year 2080. Our findings indicate that P. droryana enter diapause between 10 and 8°C. We also found that the current minimum winter temperature (10.1°C, median) in the distributional range of P. droryana will probably rise (13.4°C, median). Thus, if our experimental data are somewhat accurate, ∼36% of the southern Brazilian P. droryana population may be active during the expected milder winter months in 2080. In this scenario, there may be a larger demand for pollen and nectar for that bee species. Greater conservation efforts will be required to preserve P. droryana populations and keep them viable in the coming decades.

New baseline environmental assessment of mosquito ecology in northern Haiti during increased urbanization

The catastrophic 2010 earthquake in Port-au-Prince, Haiti, led to the large-scale displacement of over 2.3 million people, resulting in rapid and unplanned urbanization in northern Haiti. This study evaluated the impact of this unplanned urbanization on mosquito ecology and vector-borne diseases by assessing land use and change patterns. Land-use classification and change detection were carried out on remotely sensed images of the area for 2010 and 2013. Change detection identified areas that went from agricultural, forest, or bare-land pre-earthquake to newly developed and urbanized areas post-earthquake. Areas to be sampled for mosquito larvae were subsequently identified. Mosquito collections comprised five genera and ten species, with the most abundant species being Culex quinquefasciatus 35% (304/876), Aedes albopictus 27% (238/876), and Aedes aegypti 20% (174/876). All three species were more prevalent in urbanized and newly urbanized areas. Anopheles albimanus, the predominate malaria vector, accounted for less than 1% (8/876) of the collection. A set of spectral indices derived from the recently launched Landsat 8 satellite was used as covariates in a species distribution model. The indices were used to produce probability surfaces maps depicting the likelihood of presence of the three most abundant species within 30 m pixels. Our findings suggest that the rapid urbanization following the 2010 earthquake has increased the amount of area with suitable habitats for urban mosquitoes, likely influencing mosquito ecology and posing a major risk of introducing and establishing emerging vector-borne diseases.

The distributional ecology of the maned sloth: environmental influences on its distribution and gaps in knowledge

The maned sloth Bradypus torquatus (Pilosa, Bradypodidae) is endemic to a small area in the Atlantic Forest of coastal Brazil. It has been listed as a threatened species because of its restricted geographic range, habitat loss and fragmentation, and declining populations. The major objectives of this study were to estimate its potential geographic distribution, the climatic conditions across its distributional range, and to identify suitable areas and potential species strongholds. We developed a model of habitat suitability for the maned sloth using two methods, Maxent and Mahalanobis Distance, based on 42 occurrence points. We evaluated environmental variable importance and the predictive ability of the generated distribution models. Our results suggest that the species distribution could be strongly influenced by environmental factors, mainly temperature seasonality. The modeled distribution of the maned sloth included known areas of occurrence in the Atlantic Forest (Sergipe, Bahia, Espírito Santo, and Rio de Janeiro), but did not match the observed distributional gaps in northern Rio de Janeiro, northern Espírito Santo or southern Bahia. Rather, the model showed that these areas are climatically suitable for the maned sloth, and thus suggests that factors other than climate might be responsible for the absence of species. Suitable areas for maned sloth were located mainly in the mountainous region of central Rio de Janeiro throughout Espírito Santo and to the coastal region of southern Bahia. We indicate 17 stronghold areas and recommended survey areas for the maned sloth. In addition, we highlight specific areas for conservation, including the current network protected areas. Our results can be applied for novel surveys and discovery of unknown populations, and help the selection of priority areas for management and conservation planning, especially of rare and relatively cryptic species directed associated with forested habitats.

Weighing empirical and hypothetical evidence for assessing potential invasive species range limits: a review of the case of Burmese pythons in the USA

Range expansion potential is an important consideration for prioritizing management actions against an invasive species. Understanding the potential for range expansion by invasive reptiles such as the Burmese python can be challenging, because the lack of knowledge on fundamental physiological and behavioral constraints initially forces reliance on modeling to predict hypothetical invasive range potential. Hypothetical predictions for Burmese python range limits in the USA have been highly divergent, from only extreme South Florida and the extreme southern Gulf edge of Texas to a broad swath over the southern third of the continental USA. Empirical observations on python thermal tolerances and behavioral abilities to cope with more temperate temperatures became evident during a cold spell in December 2009-January 2010. We review and highlight important considerations for improving invasive range estimation methodology, deciding between competing range predictions, and the importance of having, and applying, empirical data to aid in decision making.

Macroclimate determines the global range limit of Aedes aegypti

Aedes aegypti is the main vector of dengue and a number of other diseases worldwide. Because of the domestic nature of this mosquito, the relative importance of macroclimate in shaping its distribution has been a controversial issue. We have captured here the worldwide macroclimatic conditions occupied by A. aegypti in the last century. We assessed the ability of this information to predict the species' observed distribution using supra-continental spatially-uncorrelated data. We further projected the distribution of the colonized climates in the near future (2010-2039) under two climate-change scenarios. Our results indicate that the macroclimate is largely responsible for setting the maximum range limit of A. aegypti worldwide and that in the near future, relatively wide areas beyond this limit will receive macroclimates previously occupied by the species. By comparing our projections, with those from a previous model based strictly on species-climate relationships (i.e., excluding human influence), we also found support for the hypothesis that much of the species' range in temperate and subtropical regions is being sustained by artificial environments. Altogether, these findings suggest that, if the domestic environments commonly exploited by this species are available in the newly suitable areas, its distribution may expand considerably in the near future.

Global decline in suitable habitat for Angiostrongylus ( = Parastrongylus) cantonensis: the role of climate change

Climate change is implicated in the alteration of the ranges of species worldwide. Such shifts in species distributions may introduce parasites/pathogens, hosts, and vectors associated with disease to new areas. The parasite Angiostrongylus ( = Parastrongylus) cantonensis is an invasive species that causes eosinophilic meningitis in humans and neurological abnormalities in domestic/wild animals. Although native to southeastern Asia, A. cantonensis has now been reported from more than 30 countries worldwide. Given the health risks, it is important to describe areas with potentially favorable climate for the establishment of A. cantonensis, as well as areas where this pathogen might become established in the future. We used the program Maxent to develop an ecological niche model for A. cantonensis based on 86 localities obtained from published literature. We then modeled areas of potential A. cantonensis distribution as well as areas projected to have suitable climatic conditions under four Representative Concentration Pathways (RCP) scenarios by the 2050s and the 2070s. The best model contained three bioclimatic variables: mean diurnal temperature range, minimum temperature of coldest month and precipitation of warmest quarter. Potentially suitable habitat for A. cantonensis was located worldwide in tropical and subtropical regions. Under all climate change RCP scenarios, the center of the projected distribution shifted away from the equator at a rate of 68–152 km per decade. However, the extent of areas with highly suitable habitat (>50%) declined by 10.66–15.66% by the 2050s and 13.11–16.11% by the 2070s. These results conflict with previous studies, which have generally found that the prevalence of tropical pathogens will increase during the 21st century. Moreover, it is likely that A. cantonensis will continue to expand its current range in the near future due to introductions and host expansion, whereas climate change will reduce the total geographic area of most suitable climatic conditions during the coming decades.

An iterative and targeted sampling design informed by habitat suitability models for detecting focal plant species over extensive areas

Prioritizing areas for management of non-native invasive plants is critical, as invasive plants can negatively impact plant community structure. Extensive and multi-jurisdictional inventories are essential to prioritize actions aimed at mitigating the impact of invasions and changes in disturbance regimes. However, previous work devoted little effort to devising sampling methods sufficient to assess the scope of multi-jurisdictional invasion over extensive areas. Here we describe a large-scale sampling design that used species occurrence data, habitat suitability models, and iterative and targeted sampling efforts to sample five species and satisfy two key management objectives: 1) detecting non-native invasive plants across previously unsampled gradients, and 2) characterizing the distribution of non-native invasive plants at landscape to regional scales. Habitat suitability models of five species were based on occurrence records and predictor variables derived from topography, precipitation, and remotely sensed data. We stratified and established field sampling locations according to predicted habitat suitability and phenological, substrate, and logistical constraints. Across previously unvisited areas, we detected at least one of our focal species on 77% of plots. In turn, we used detections from 2011 to improve habitat suitability models and sampling efforts in 2012, as well as additional spatial constraints to increase detections. These modifications resulted in a 96% detection rate at plots. The range of habitat suitability values that identified highly and less suitable habitats and their environmental conditions corresponded to field detections with mixed levels of agreement. Our study demonstrated that an iterative and targeted sampling framework can address sampling bias, reduce time costs, and increase detections. Other studies can extend the sampling framework to develop methods in other ecosystems to provide detection data. The sampling methods implemented here provide a meaningful tool when understanding the potential distribution and habitat of species over multi-jurisdictional and extensive areas is needed for achieving management objectives.

Assessing current and projected suitable habitats for tree-of-heaven along the Appalachian Trail

The invasion of ecosystems by non-native species is a major driver of biodiversity loss worldwide. A critical component of effective land management to control invasion is the identification and active protection of areas at high risk of future invasion. The Appalachian Trail Decision Support System (A.T.-DSS) was developed to inform regional natural resource management by integrating remote sensing data, ground-based measurements and predictive modelling products. By incorporating NASA's remote sensing data and modelling capacities from the Terrestrial Observation and Prediction System (TOPS), this study examined the current habitat suitability and projected suitable habitat for the invasive species tree-of-heaven (Ailanthus altissima) as a prototype application of the A.T.-DSS. Species observations from forest surveys, geospatial data, climatic projections and maximum entropy modelling were used to identify regions potentially susceptible to tree-of-heaven invasion. The modelling result predicted a 48% increase in suitable area over the study area, with significant expansion along the northern extremes of the Appalachian Trail.

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Migrate or evolve: options for plant pathogens under climate change

Findings on climate change influence on plant pathogens are often inconsistent and context dependent. Knowledge of pathogens affecting agricultural crops and natural plant communities remains fragmented along disciplinary lines. By broadening the perspective beyond agriculture, this review integrates cross-disciplinary knowledge to show that at scales relevant to climate change, accelerated evolution and changing geographic distribution will be the main implications for pathogens. New races may evolve rapidly under elevated temperature and CO2 , as evolutionary forces act on massive pathogen populations boosted by a combination of increased fecundity and infection cycles under favourable microclimate within enlarged canopy. Changing geographic distribution will bring together diverse lineages/genotypes that do not share common ecological niche, potentially increasing pathogen diversity. However, the uncertainty of model predictions and a lack of synthesis of fragmented knowledge remain as major deficiencies in knowledge. The review contends that the failure to consider scale and human intervention through new technology are major sources of uncertainty. Recognizing that improved biophysical models alone will not reduce uncertainty, it proposes a generic framework to increase focus and outlines ways to integrate biophysical elements and technology change with human intervention scenarios to minimize uncertainty. To synthesize knowledge of pathogen biology and life history, the review borrows the concept of 'fitness' from population biology as a comprehensive measure of pathogen strengths and vulnerabilities, and explores the implications of pathogen mode of nutrition to fitness and its interactions with plants suffering chronic abiotic stress under climate change. Current and future disease management options can then be judged for their ability to impair pathogenic and saprophytic fitness. The review pinpoints improving confidence in model prediction by minimizing uncertainty, developing management strategies to reduce overall pathogen fitness, and finding new sources of data to trawl for climate signatures on pathogens as important challenges for future research.

Darwin's naturalization hypothesis: scale matters in coastal plant communities

Darwin proposed two seemingly contradictory hypotheses for a better understanding of biological invasions. Strong relatedness of invaders to native communities as an indication of niche overlap could promote naturalization because of appropriate niche adaptation, but could also hamper naturalization because of negative interactions with native species ('Darwin's naturalization hypothesis'). Although these hypotheses provide clear and opposing predictions for expected patterns of species relatedness in invaded communities, so far no study has been able to clearly disentangle the underlying mechanisms. We hypothesize that conflicting past results are mainly due to the neglected role of spatial resolution of the community sampling. In this study, we corroborate both of Darwin's expectations by using phylogenetic relatedness as a measure of niche overlap and by testing the effects of sampling resolution in highly invaded coastal plant communities. At spatial resolutions fine enough to detect signatures of biotic interactions, we find that most invaders are less related to their nearest relative in invaded plant communities than expected by chance (phylogenetic overdispersion). Yet at coarser spatial resolutions, native assemblages become more invasible for closely-related species as a consequence of habitat filtering (phylogenetic clustering). Recognition of the importance of the spatial resolution at which communities are studied allows apparently contrasting theoretical and empirical results to be reconciled. Our study opens new perspectives on how to better detect, differentiate and understand the impact of negative biotic interactions and habitat filtering on the ability of invaders to establish in native communities.

Habitat productivity constrains the distribution of social spiders across continents - case study of the genus Stegodyphus

INTRODUCTION

Sociality has evolved independently multiple times across the spider phylogeny, and despite wide taxonomic and geographical breadth the social species are characterized by a common geographical constrain to tropical and subtropical areas. Here we investigate the environmental factors that drive macro-ecological patterns in social and solitary species in a genus that shows a Mediterranean-Afro-Oriental distribution (Stegodyphus). Both selected drivers (productivity and seasonality) may affect the abundance of potential prey insects, but seasonality may further directly affect survival due to mortality caused by extreme climatic events. Based on a comprehensive dataset including information about the distribution of three independently derived social species and 13 solitary congeners we tested the hypotheses that the distribution of social Stegodyphus species relative to solitary congeners is: (1) restricted to habitats of high vegetation productivity and (2) constrained to areas with a stable climate (low precipitation seasonality).

RESULTS

Using spatial logistic regression modelling and information-theoretic model selection, we show that social species occur at higher vegetation productivity than solitary, while precipitation seasonality received limited support as a predictor of social spider occurrence. An analysis of insect biomass data across the Stegodyphus distribution range confirmed that vegetation productivity is positively correlated to potential insect prey biomass.

CONCLUSIONS

Habitat productivity constrains the distribution of social spiders across continents compared to their solitary congeners, with group-living in spiders being restricted to areas with relatively high vegetation productivity and insect prey biomass. As known for other taxa, permanent sociality likely evolves in response to high predation pressure and imposes within-group competition for resources. Our results suggest that group living is contingent upon productive environmental conditions where elevated prey abundance meet the increased demand for food of social groups.

Using habitat suitability models to target invasive plant species surveys

Managers need new tools for detecting the movement and spread of nonnative, invasive species. Habitat suitability models are a popular tool for mapping the potential distribution of current invaders, but the ability of these models to prioritize monitoring efforts has not been tested in the field. We tested the utility of an iterative sampling design (i.e., models based on field observations used to guide subsequent field data collection to improve the model), hypothesizing that model performance would increase when new data were gathered from targeted sampling using criteria based on the initial model results. We also tested the ability of habitat suitability models to predict the spread of invasive species, hypothesizing that models would accurately predict occurrences in the field, and that the use of targeted sampling would detect more species with less sampling effort than a nontargeted approach. We tested these hypotheses on two species at the state scale (Centaurea stoebe and Pastinaca sativa) in Wisconsin (USA), and one genus at the regional scale (Tamarix) in the western United States. These initial data were merged with environmental data at 30-m2 resolution for Wisconsin and 1-km2 resolution for the western United States to produce our first iteration models. We stratified these initial models to target field sampling and compared our models and success at detecting our species of interest to other surveys being conducted during the same field season (i.e., nontargeted sampling). Although more data did not always improve our models based on correct classification rate (CCR), sensitivity, specificity, kappa, or area under the curve (AUC), our models generated from targeted sampling data always performed better than models generated from nontargeted data. For Wisconsin species, the model described actual locations in the field fairly well (kappa = 0.51, 0.19, P < 0.01), and targeted sampling did detect more species than nontargeted sampling with less sampling effort (chi2 = 47.42, P < 0.01). From these findings, we conclude that habitat suitability models can be highly useful tools for guiding invasive species monitoring, and we support the use of an iterative sampling design for guiding such efforts.

Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population.