Multivariate Environmental Similarity Surface Research Articles

Climate as a Predictive Factor for Invasion: Unravelling the Range Dynamics of Carpomya vesuviana Costa.

Invasive alien species (IAS) significantly affect global native biodiversity, agriculture, industry, and human health. Carpomya vesuviana Costa, 1854 (Diptera: Tephritidae), a significant global IAS, affects various date species, leading to substantial economic losses and adverse effects on human health and the environment. This study employed biomod2 ensemble models, multivariate environmental similarity surface and most dissimilar variable analyses, and ecological niche dynamics based on environmental and species data to predict the potential distribution of C. vesuviana and explore the environmental variables affecting observed patterns and impacts. Compared to native ranges, ecological niche shifts at invaded sites increased the invasion risk of C. vesuviana globally. The potential geographical distribution was primarily in Asia, Africa, and Australia, with a gradual increase in suitability with time and radiation levels. The potential geographic distribution centre of C. vesuviana is likely to shift poleward between the present and the 2090s. We also show that precipitation is a key factor influencing the likely future distribution of this species. In conclusion, climate change has facilitated the expansion of the geographic range and ecological niche of C. vesuviana, requiring effective transnational management strategies to mitigate its impacts on the natural environment and public health during the Anthropocene. This study aims to assess the potential threat of C. vesuviana to date palms globally through quantitative analytical methods. By modelling and analysing its potential geographic distribution, ecological niche, and environmental similarities, this paper predicts the pest's dispersal potential and possible transfer trends in geographic centres of mass in order to provide prevention and control strategies for the global date palm industry.

Current and future potential distribution of Culex (Melanoconion) (Diptera: Culicidae) of public health interest in the Neotropics.

Anthropogenic activities are altering ecosystem stability and climate worldwide, which is disturbing and shifting arbovirus vector distributions. Although the overall geographic range of some epidemiologically important species is recognized, the spatiotemporal variation for other species in the context of climate change remains poorly understood. Here we predict the current potential distribution of 9 species of Culex (Melanoconion) based on an ecological niche modeling (ENM) approach and assess spatiotemporal variation in future climate change in the Neotropics. The most important environmental predictors were the mean temperature of the warmest season (27 °C), precipitation during the driest month (50 mm), and precipitation during the warmest season (>200 mm). The best current model for each species was transferred to the future general circulation model IPSL-CM6A-LR, using 2 shared socioeconomic pathway scenarios (ssp1-2.6, ssp5-8.5). Under both scenarios of climatic change, an expansion of suitable areas can be observed followed by a strong reduction for the medium-long future under the worst scenario. The multivariate environmental similarity surface analysis indicated future novel climates outside the current range. However, none of the species would occur in those areas. Even if many challenges remain in improving methods for forecasting species responses to global climate change and arbovirus transmission, ENM has strong potential to be applied to the geographic characterization of these systems. Our study can be used for the monitoring of Culex (Melanoconion) species populations and their associated arboviruses, contributing to develop region-specific public health surveillance programs.

Performance of inhomogeneous Poisson point process models under different scenarios of uncertainty in species presence-only data

Haphazard and opportunistic species occurrence (PO) data are widely used in species distribution models (SDMs) instead of high-quality species data gathered using appropriate and structured sampling methods, which is expensive and often spatially limited. Despite their widespread use in ecology, PO data are prone to errors and uncertainties, such as imperfect detectability, positional imprecision, and spatial niche truncation, which make their use analytically challenging for effective and adaptive biodiversity management and conservation. Using simulated data, this study investigates the effects of these uncertainties on the performance of spatial point process based presence-only and integrated SDMs. We investigated three SDMs in this study, one that ignores imperfect detectability: the presence-only model (PO model), and two that account for it: the thinned presence-only model (THINPO model) and the integrated model (PBPC model). The ability of these SDMs to produce accurate maximum likelihood estimates of intensity model coefficients and reliable predictions of species distributions under different data quality scenarios was investigated. The results show that SDMs that account for imperfect detectability (THINPO or PBPC models) are not applicable in situations of high detectability. In this situation, the PO model produces the most accurate maximum likelihood estimates of the models’ coefficients ({hat{beta }}_k), and consequently the most accurate predictions of species distributions ({hat{lambda }}(s)). The effects of positional uncertainty and spatial niche truncation on this SDM output are minimal. However, in situations of low detectability, it is preferable to use the PBPC model. Positional uncertainty and spatial niche truncation have negligible effects on the output of this SDM, except when positionally uncertain PO data are analyzed along with truncated PC data. These minimal effects of spatial niche truncation on SDM outputs demonstrate the transferability of SDMs. However, the effects of all these uncertainties may depend on the characteristics of the species. Prior to modeling species distributions, a multivariate environmental similarity surface analysis should be performed to test the similarity between data from the restricted region to be used for model calibration and data from the entire range. If this analysis reveals dissimilarities, larger spatial and ecological scales should be considered to address the issue of spatial niche truncation. Further efforts could address the effects of species characteristics on SDMs performance and assess the effects of species-specific uncertainties.

Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment

The main goal of this study was to develop a reliable rockfall susceptibility map for Valchiavenna (275 km2), located in the Italian Central Alps, through the introduction of outcrop-scale geomechanical properties (Joint Volumetric Count—Jv, rock mass Weathering Index—Wi and Equivalent Permeability—Keq) as spatially distributed predictors. Specific objectives were: (i) to increase the representativeness over the study area of an existing geomechanical dataset by adding new surveys, (ii) to effectively regionalize the geomechanical properties and (iii) to evaluate the performance and the physical plausibility of a rockfall susceptibility model combining geomechanical, topographical, geomorphological, and geological predictors.We optimized new survey locations by means of Spatial Simulated Annealing (SSA) and Multivariate Environmental Similarity Surface (MESS). For the regionalization of predictors we tested several interpolation techniques and evaluated them through performance indices and leave-one-out-validation. We performed the susceptibility analysis using rockfall data from the official Italian inventory, later updated with several field-mapped rockfalls, and different combinations of predictors. We applied Generalized Additive Models, which we evaluated through spatial k-fold cross-validation in terms of model performance (AUROC) and physical plausibility. Also, we investigated the importance of the predictors in the model through penalization and the calculation of the mean decrease of deviance explained (mDD%) upon recursive removal of each predictor.Through SSA we added 25 survey locations that reduced the study area with negative MESS from 26.2 % to 15.9 %. We calculated he geomechanical predictor maps applying ordinary kriging to Jv (NRMSE = 13.7 %) and Wi (NRMSE = 14.5 %) and using Thin Plate Splines for Keq (NRMSE = 18.5 %).The model containing the geomechanical predictors resulted in acceptable rockfall discrimination capabilities (mean AUROC > 0.7), with high-susceptibility areas located in plausible geomorphological contexts, characterized by currently active deformations (verified by means of inSAR data), which were not revealed by the topographic predictors alone. Regarding importance, Jv showed an mDD% of 7.5 % comparable to those of secondary topographic predictors (e.g., profile curvature, northness), while Wi and Keq were penalized out of the model. Models built with the non-updated inventory resulted in physically implausible susceptibility maps and predictor behavior (unreasonable smoothing functions), highlighting a model bias.

高山植物天山花楸的适宜分布及其环境驱动因子

气候变化是影响物种分布的决定性因素之一, 研究高山植物天山花楸的适宜分布及其对未来气候变化的可能响应, 为了解西北地区高山植物类群的生态适宜性及其对未来气候情景的响应提供参考案例。利用天山花楸51个自然分布点和10个环境因子, 整合GIS空间分析和MAXENT模型, 分析基准气候(1970-2000)及未来气候下(2050时段, 基于RPC4.5情景)其在西北地区的适宜分布范围与空间分布特征。利用多元环境相似度面和最不相似变量分析研究区未来气候相比基准气候的波动情况, 利用环境变量贡献值、置换重要性值及刀切法明晰影响天山花楸分布的关键环境因子。利用GIS工具和R软件ggplot2程序包分析基准和未来气候下天山花楸适宜分布区内关键因子变化的数值范围。研究结果表明：(1)基准气候下, 天山花楸的适宜面积占研究区总面积的13%, 主要集中在阿尔泰山西段、准噶尔西部山地、天山西段及祁连山中段的高海拔山地等区域；(2)加入归一化植被指数显著提高了天山花楸模型模拟的准确性, 最干月降水量(0-18mm)、最湿月降水量(6-127mm)和平均气温日较差(8.2-16.3℃)主要限制了天山花楸的适宜分布；(3)相对基准气候, 2050时段下受降水因子影响, 天山花楸的适宜分布在阿尔泰山、准噶尔西部山地、天山及祁连山区域略有扩增, 适宜分布区的质心将向北迁移。本研究的结果表明天山花楸在西北地区的适宜生境面积较小而且破碎, 在未来气候情景下仅在局部适宜山地呈破碎化扩张, 而且将预测的该植物的适宜分布区与其适宜的海拔数值范围叠合分析后, 仅占研究区面积的4%, 但对该植物尚未建立自然保护区, 本研究结果建议将阿尔泰山西段、准噶尔西部山地、天山西段及祁连山中段作为天山花楸中风险保护区域, 将河西走廊东部、青海南山作为高风险保护区域。;Climate change is one of the key factors affecting species distribution. In order to understand the ecological suitability of alpine plant in taxa Northwest China and their responses to future climate scenarios, the suitable distribution of Sorbus tianschanica and its possible response to future climate change were studied. In this study, 51 natural distribution sites and 10 environmental factors of Sorbus tianschanica were used to integrate GIS spatial analysis and MAXENT model to analyze the suitable distribution area and spatial characteristics of Sorbus tianschanica in Northwest China under baseline climate (1970-2000) and future climate (2050 period, based on RCP 4.5 scenario). Based on the multivariate environmental similarity surface and the least similar variables, the future climate fluctuations in the study area compared with the baseline climate were analyzed. The key environmental factors affecting the distribution of Sorbus tianschanica were identified by integrating the contribution value of environmental variables, the replacement importance value and the knife cutting method. GIS tools and R software ggplot2 package were integrated to analyze the numerical range of the changes of key factors in the suitable distribution area of Sorbus tianschanica in the future climate. The results show that: (1) under the baseline climate, the suitable area of Sorbus tianschanica accounts for 13% of the total area of the study area, which is concentrated in the western part of Altay, the western part of Junggar, the middle and western part of Tianshan, Bogda Mountain, Qilian Mountain, South Mountain of Qinghai, and parts of Pamir Plateau and north part of Karakoram Mountain; (2) The accuracy of the model simulation is significantly improved by adding the Normalized Vegetation Index. The distribution of Sorbus tianschanica is mainly limited by the driest monthly precipitation, the wettest monthly precipitation, and the daily range of average temperature. Regarding the suitable distribution range, the driest monthly precipitation is 0-18 mm, the wettest monthly precipitation is 6-127 mm, and the daily range of average temperature is 8.2-16.3 ℃; (3) Compared with the baseline climate, influenced by precipitation factors in 2050, the suitable distribution of Sorbus tianschanica in Altai Mountain, western Junggar Mountain, Tianshan Mountain, and Qilian Mountain will expand slightly, and the center of mass of suitable distribution area will move northward. The results of the study show that the suitable habitat area of Sorbus mandshurica in Northwest China is small and fragmented. In the future climate scenario, only the local suitable mountains will be fragmented and expanded. The predicted suitable distribution area of Sorbus tianschanica and its suitable altitude range only account for 4% of the study area. However, there is no nature reserve for this plant. It is suggested that the western part of Altay, the western part of Junggar, the western part of Tianshan Mountain and the middle part of Qilian Mountain should be taken as the risk protection areas of Sorbus tianschanica, and the eastern part of Hexi Corridor and the southern mountain of Qinghai should be taken as the high risk protection areas. The suitable and most suitable distribution area of the plant predicted in this study is helpful to guide the protection of the Sorbus tianschanica.

“Reconstructions of the past distribution of Testudo graeca mitochondrial lineages in the Middle East and Transcaucasia support multiple refugia since the Last Glacial Maximum”: A response to Turkozan et al. (2021)

Species distribution models (SDMs) are frequently used to characterise current, past or future realised environmental niches. Two recent studies applied different approaches to infer range dynamics in eastern subspecies of the spur-thighed tortoise Testudo graeca Linnaeus, 1758. We discuss differences in the conclusions of the two papers and use multivariate environmental similarity surface (MESS) analyses to show that the results of the study by Turkozan et al. (2021), recently published in the Herpetological Journal, are compromised by extrapolation and therefore have to be interpreted with caution.

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for conservation purposes and supporting the delineation of marine protected areas, but model predictions are rarely associated with extrapolation uncertainty maps.In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i.e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e.g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species.Considering the unexpectedly high levels of extrapolation uncertainty measured in SDM predictions, we strongly recommend that studies report information related to the level of extrapolation. Waiting for improved datasets, adapting modelling methods and providing such uncertainy information in distribution modelling studies are a necessity to accurately interpret model outputs and their reliability.

High-resolution habitat suitability model for Phlebotomus pedifer, the vector of cutaneous leishmaniasis in southwestern Ethiopia

BackgroundPhlebotomus pedifer is the vector for Leishmania aethiopica causing cutaneous leishmaniasis (CL) in southwestern Ethiopia. Previous research on the transmission dynamics of CL resulted in recommendations for vector control. In order to target these interventions towards affected areas, a comprehensive understanding of the spatial distribution of P. pedifer at high spatial resolution is required. Therefore, this study determined the environmental predictors that facilitate the distribution of P. pedifer and created a map indicating the areas where conditions are suitable for survival of the vector in southwestern Ethiopia with high spatial resolution.MethodsPhlebotomus pedifer presence points were collected during two entomological surveys. Climate, vegetation and topographic variables were assembled. Climate variables were interpolated with variables derived from high-resolution digital elevation models to generate topoclimatic layers representing the climate conditions in the highlands. A Maximum Entropy model was run with the presence points, predicting variables and background points, which were selected based on a bias file.ResultsPhlebotomus pedifer was the only captured Phlebotomus species in the study area and was collected at altitudes ranging between 1685 and 2892 m. Model projections indicated areas with suitable conditions in a ‘belt’ surrounding the high mountain peaks. Model performance was high, with train and test AUC values being 0.93 and 0.90, respectively. A multivariate environmental similarity surface (MESS) analysis showed that the model projection was only slightly extrapolated for some of the variables. The mean annual temperature was the environmental variable, which contributed most to the model predictions (60.0%) followed by the seasonality in rainfall (13.2%). Variables representing steep slopes showed very low importance to model predictions.ConclusionsOur findings indicate that the suitable habitats for P. pedifer correspond well with the altitudes at which CL was reported previously, but the predictions are more widely distributed, in contrast with the description of CL to occur in particular foci. Moreover, we confirm that vector distribution is driven by climate factors, suggesting inclusion of topoclimate in sand fly distribution models. Overall, our model provides a map with a high spatial resolution that can be used to target sand fly control measures in southwestern Ethiopia.

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).

Broad-scale species distribution models applied to data-poor areas

Species distribution models (SDMs) have been increasingly used over the past decades to characterise the spatial distribution and the ecological niche of various taxa. Validating predicted species distribution is important, especially when producing broad-scale models (i.e. at continental or oceanic scale) based on limited and spatially aggregated presence-only records. In the present study, several model calibration methods are compared and guidelines are provided to perform relevant SDMs using a Southern Ocean marine species, the starfish Odontaster validus Koehler, 1906, as a case study. The effect of the spatial aggregation of presence-only records on modelling performance is evaluated and the relevance of a target-background sampling procedure to correct for this effect is assessed. The accuracy of model validation is estimated using k-fold random and spatial cross-validation procedures. Finally, we evaluate the relevance of the Multivariate Environmental Similarity Surface (MESS) index to identify areas in which SDMs accurately interpolate and conversely, areas in which models extrapolate outside the environmental range of occurrence records.Results show that the random cross-validation procedure (i.e. a widely applied method, for which training and test records are randomly selected in space) tends to over-estimate model performance when applied to spatially aggregated datasets. Spatial cross-validation procedures can compensate for this over-estimation effect but different spatial cross-validation procedures must be tested for their ability to reduce over-fitting while providing relevant validation scores. Model predictions show that SDM generalisation is limited when working with aggregated datasets at broad spatial scale. The MESS index calculated in our case study show that over half of the predicted area is highly uncertain due to extrapolation. Our work provides methodological guidelines to generate accurate model assessments at broad spatial scale when using limited and aggregated presence-only datasets. We highlight the importance of taking into account the presence of spatial aggregation in species records and using non-random cross-validation procedures. Evaluating the best calibration procedures and correcting for spatial biases should be considered ahead the modelling exercise to improve modelling relevance.

Evaluating Potential Distribution of High‐Risk Aquatic Invasive Species in the Water Garden and Aquarium Trade at a Global Scale Based on Current Established Populations

Aquatic non-native invasive species are commonly traded in the worldwide water garden and aquarium markets, and some of these species pose major threats to the economy, the environment, and human health. Understanding the potential suitable habitat for these species at a global scale and at regional scales can inform risk assessments and predict future potential establishment. Typically, global habitat suitability models are fit for freshwater species with only climate variables, which provides little information about suitable terrestrial conditions for aquatic species. Remotely sensed data including topography and land cover data have the potential to improve our understanding of suitable habitat for aquatic species. In this study, we fit species distribution models using five different model algorithms for three non-native aquatic invasive species with bioclimatic, topographic, and remotely sensed covariates to evaluate potential suitable habitat beyond simple climate matches. The species examined included a frog (Xenopus laevis), toad (Bombina orientalis), and snail (Pomacea spp.). Using a unique modeling approach for each species including background point selection based on known established populations resulted in robust ensemble habitat suitability models. All models for all species had test area under the receiver operating characteristic curve values greater than 0.70 and percent correctly classified values greater than 0.65. Importantly, we employed multivariate environmental similarity surface maps to evaluate potential extrapolation beyond observed conditions when applying models globally. These global models provide necessary forecasts of where these aquatic invasive species have the potential for establishment outside their native range, a key component in risk analyses.

Integrating indigenous local knowledge and species distribution modeling to detect wildlife in Somaliland

AbstractWith wildlife populations in decline, understanding their distributions across the landscape are needed for management and conservation efforts, particularly in remote or hazardous regions. We used indigenous local knowledge to inform species distribution models (SDMs) to predict the distribution of 38 wildlife species historically documented in Somaliland, one of the most isolated, data‐poor regions in Africa. We conducted 195 interviews with agro‐pastoral men and women in 2016 and 2017 throughout Somaliland based on the environmental range captured by performing a Multivariate Environmental Similarity Surface (MESS) analysis. Respondents were asked if each species (1) occurred in the vicinity, and if they did, (2) whether it was common, rare, extirpated, or unknown. We tested two commonly used SDMs, Maxent and boosted regression trees (BRTs), to map the potential distribution of wildlife using interview data. We present detailed case studies of two species of high conservation priority: cheetah (Acinonyc jubatus) and African wild ass (Equus africanus somaliensis). More than half of the respondents reported the presence of 25 of the wildlife species in the survey. Additionally, we developed Maxent models for all surveyed wildlife known to develop a species richness map. The least reported species known to have inhabited Somaliland in recent decades were lion (Panthera leo), African wild ass, and Somali wild dog (Lycaon pictus somalicus). Surprisingly, 177 of respondents reported the presence of cheetah, which was presumed by many to be extirpated from Somaliland. Given the high number of reports, coupled with the BRT model results and evaluations, it may be that cheetahs persist throughout Somaliland. Unfortunately, our results also suggest that African wild ass may have been extirpated from Somaliland. Our models carry the uncertainties of the raw data, but provide habitat suitability maps to guide future targeted surveys. Our methods and results demonstrate that integrating indigenous local knowledge with SDMs may be an essential tool for the conservation of wildlife in data‐poor regions.

Unraveling climate influences on the distribution of the parapatric newts Lissotriton vulgaris meridionalis and L. italicus

BackgroundClimate is often considered as a key ecological factor limiting the capability of expansion of most species and the extent of suitable habitats. In this contribution, we implement Species Distribution Models (SDMs) to study two parapatric amphibians, Lissotriton vulgaris meridionalis and L. italicus, investigating if and how climate has influenced their present and past (Last Glacial Maximum and Holocene) distributions. A database of 901 GPS presence records was generated for the two newts. SDMs were built through Boosted Regression Trees and Maxent, using the Worldclim bioclimatic variables as predictors.ResultsPrecipitation-linked variables and the temperature annual range strongly influence the current occurrence patterns of the two Lissotriton species analyzed. The two newts show opposite responses to the most contributing variables, such as BIO7 (temperature annual range), BIO12 (annual precipitation), BIO17 (precipitation of the driest quarter) and BIO19 (precipitation of the coldest quarter). The hypothesis of climate influencing the distributions of these species is also supported by the fact that the co-occurrences within the sympatric area fall in localities characterized by intermediate values of these predictors. Projections to the Last Glacial Maximum and Holocene scenarios provided a coherent representation of climate influences on the past distributions of the target species. Computation of pairwise variables interactions and the discriminant analysis allowed a deeper interpretation of SDMs’ outputs. Further, we propose a multivariate environmental dissimilarity index (MEDI), derived through a transformation of the multivariate environmental similarity surface (MESS), to deal with extrapolation-linked uncertainties in model projections to past climate. Finally, the niche equivalency and niche similarity tests confirmed the link between SDMs outputs and actual differences in the ecological niches of the two species.ConclusionsThe different responses of the two species to climatic factors have significantly contributed to shape their current distribution, through contractions, expansions and shifts over time, allowing to maintain two wide allopatric areas with an area of sympatry in Central Italy. Moreover, our SDMs hindcasting shows many concordances with previous phylogeographic studies carried out on the same species, thus corroborating the scenarios of potential distribution during the Last Glacial Maximum and the Holocene emerging from the models obtained.

A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization

Fine-resolution soil maps constitute important data for many different environmental studies. Digital soil mapping techniques represent a cost-effective method to obtain detailed information about soil types and soil properties over large areas. The main objective of the study was to extend predictions from 1:25,000 legacy soil surveys (including WRB soil groups, soil depth and soil texture classes) to the larger area of Cyprus. A multiple-trees classification technique, namely Random Forest (RF), was applied. Specific objectives were: (i) to analyze the role and importance of a large data set of environmental predictors, (ii) to investigate the effect of the number of training points, forest size (ntree), the numbers of predictors sampled per node (mtry) and tree size (nodesize) in RF; (iii) to compare RF-derived maps with maps derived with a multinomial logistic regression model, in terms of validation error (test set and independent profiles) and map uncertainty, using the confusion index and a newly developed reliability index. The optimized RF model was run using half of the input points available (over a million) and with ntree equal to 350. The mtry parameter was set to 5 (close to half the number of the environmental variables used) for both soil series and soil properties. The nodesize calibration showed no relevant performance increase and was kept at its default value (1). In terms of environmental variables, the model used 10 predictors, covering all the soil formation factors considered in the scorpan formula, to derive the three maps. Soil properties, derived from geochemistry data, showed a high importance in deriving soil groups, depths and texture. Random Forest constructed a better predictive model than multinomial logistic regression, showing comparable predictive uncertainty but much lower validation error. The RF-derived maps show very low out of bag (OOB) errors (around 10% for both soil groups and soil properties) but relatively high validation error from independent profiles (45% for soil depth, 51% for soil texture). The resulting reliability index was low in the main mountainous area of Cyprus, where predictions were extrapolations as indicated by the multivariate environmental similarity surface, but medium to high in the main agricultural areas of the country.

Integrating local pastoral knowledge, participatory mapping, and species distribution modeling for risk assessment of invasive rubber vine (Cryptostegia grandiflora) in Ethiopia’s Afar region

The threats posed by invasive plants span ecosystems and economies worldwide. Local knowledge of biological invasions has proven beneficial for invasive species research, but to date no work has integrated this knowledge with species distribution modeling for invasion risk assessments. In this study, we integrated pastoral knowledge with Maxent modeling to assess the suitable habitat and potential impacts of invasive Cryptostegia grandiflora Robx. Ex R.Br. (rubber vine) in Ethiopia's Afar region. We conducted focus groups with seven villages across the Amibara and Awash-Fentale districts. Pastoral knowledge revealed the growing threat of rubber vine, which to date has received limited attention in Ethiopia, and whose presence in Afar was previously unknown to our team. Rubber vine occurrence points were collected in the field with pastoralists and processed in Maxent with MODIS-derived vegetation indices, topographic data, and anthropogenic variables. We tested model fit using a jackknife procedure and validated the final model with an independent occurrence data set collected through participatory mapping activities with pastoralists. A Multivariate Environmental Similarity Surface analysis revealed areas with novel environmental conditions for future targeted surveys. Model performance was evaluated using area under the receiver-operating characteristic curve (AUC) and showed good fit across the jackknife models (average AUC = 0.80) and the final model (test AUC = 0.96). Our results reveal the growing threat rubber vine poses to Afar, with suitable habitat extending downstream of its current known location in the middle Awash River basin. Local pastoral knowledge provided important context for its rapid expansion due to acute changes in seasonality and habitat alteration, in addition to threats posed to numerous endemic tree species that provide critical provisioning ecosystem services. This work demonstrates the utility of integrating local ecological knowledge with species distribution modeling for early detection and targeted surveying of recently established invasive species.

Contrasting spatio‐temporal climatic niche dynamics during the eastern and western invasions of spotted knapweed in North America

AbstractAimThe spotted knapweed (Centaurea stoebe), a plant native to south‐east and central Europe, is highly invasive in North America. We investigated the spatio‐temporal climatic niche dynamics of the spotted knapweed in North America along two putative eastern and western invasion routes. We then considered the patterns observed in the light of historical, ecological and evolutionary factors.LocationEurope and North America.MethodsThe niche characteristics of the east and west invasive populations of spotted knapweed in North America were determined from documented occurrences over 120 consecutive years (1890–2010). For this investigation, the 2.5 and 97.5 percentiles of values along temperature and precipitation gradients, as given by the two first axes of a principal components analysis (PCA), were calculated. We additionally measured the climatic dissimilarity between invaded sites and the native niche using a multivariate environmental similarity surface (MESS) analysis.ResultsAlong both invasion routes, the species established in regions with climatic conditions that were similar to those in the native niche. An initial spread in ruderal habitats always preceded spread in (semi‐)natural habitats. In the east, the niche gradually increased over time until it reached limits similar to the native niche. Conversely, in the west the niche abruptly expanded after an extended time lag into climates not occupied in the native range; only the native cold niche limit was conserved.Main conclusionsOur study reveals that different niche dynamics have taken place during the eastern and western invasions. This pattern indicates different combinations of historical, ecological and evolutionary factors in the two ranges. We hypothesize that the lack of a well‐developed transportation network in the west at the time of the introduction of spotted knapweed confined the species to a geographically and climatically isolated region. The invasion of dry rangelands may have been favoured during the agricultural transition in the 1930s by release from natural enemies, local adaptation and less competitive vegetation, but further experimental and molecular studies are needed to explain these contrasting niche patterns fully. Our study illustrates the need and benefit of applying large‐scale, temporally explicit approaches to understanding biological invasions.

Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas

Correlational models of species’ ecological niches are commonly used to transfer model rules onto other sets of conditions to evaluate species’ distributional potential. As with any model fitting exercise, however, interpretation of model predictions outside the range of the independent variables on which models were calibrated is perilous, herein denoted as strict extrapolation to distinguish from extrapolation onto novel combinations of variables. We use novel visualization techniques to characterize model response surfaces for several niche modeling algorithms for a virtual species (wherein the truth is known) and for two transfer-based studies published by one of our group. All modeling algorithms for each species showed strict extrapolation, such that biologically unrealistic response surfaces were reconstructed. We discuss the implications of these results for calibration and interpretation of niche models and analysis of ecological niche evolution. We present Mobility-Oriented Parity (MOP), a modification and extension of the Multivariate Environmental Similarity Surface (MESS) metric currently in use, as a means of both quantifying environmental similarity between calibration and transfer regions and highlighting regions in geographic space where strict extrapolation occurs.

Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions

During the last decades the disease vector Aedes albopictus ( Ae. albopictus) has rapidly spread around the globe. The spread of this species raises serious public health concerns. Here, we model the present distribution and the future climatic suitability of Europe for this vector in the face of climate change. In order to achieve the most realistic current prediction and future projection, we compare the performance of four different modelling approaches, differentiated by the selection of climate variables (based on expert knowledge vs. statistical criteria) and by the geographical range of presence records (native range vs. global range). First, models of the native and global range were built with MaxEnt and were either based on (1) statistically selected climatic input variables or (2) input variables selected with expert knowledge from the literature. Native models show high model performance (AUC: 0.91–0.94) for the native range, but do not predict the European distribution well (AUC: 0.70–0.72). Models based on the global distribution of the species, however, were able to identify all regions where Ae. albopictus is currently established, including Europe (AUC: 0.89–0.91). In a second step, the modelled bioclimatic envelope of the global range was projected to future climatic conditions in Europe using two emission scenarios implemented in the regional climate model COSMO-CLM for three time periods 2011–2040, 2041–2070, and 2071–2100. For both global-driven models, the results indicate that climatically suitable areas for the establishment of Ae. albopictus will increase in western and central Europe already in 2011–2040 and with a temporal delay in eastern Europe. On the other hand, a decline in climatically suitable areas in southern Europe is pronounced in the Expert knowledge based model. Our projections appear unaffected by non-analogue climate, as this is not detected by Multivariate Environmental Similarity Surface analysis. The generated risk maps can aid in identifying suitable habitats for Ae. albopictus and hence support monitoring and control activities to avoid disease vector establishment.

American bullfrog invasion in Argentina: where should we take urgent measures?

Argentina is the country with the most geographically extended biological invasion of the American bullfrog (Lithobates catesbeianus) in South America after Brazil. Here, we used a maximum entropy ecological niche modeling algorithm (using records of the native range of American bullfrog) to project the model onto the whole of Argentina. We determined the most suitable habitats for this invasive alien species and where we consider urgent measures should be taken. Our projections showed good agreement with known feral populations of American bullfrog in Argentina. By implementing the “Multivariate Environmental Similarity Surface” analysis, we be able to determine that factors such as low precipitations or highest altitudes could be limiting the species’ ability to invade the west and south of the country. We suggest that strategies should focus on detecting established feral populations of the American bullfrog and preventing further introductions or range expansion of feral populations in the northeast portion of the country. Lastly, we report a new feral population of bullfrogs in Argentina.