Trait‐based climate vulnerability assessments in data‐rich systems: An application to eastern Bering Sea fish and invertebrate stocks

Trait‐based climate vulnerability assessments based on expert evaluation have emerged as a rapid tool to assess biological vulnerability when detailed correlative or mechanistic studies are not feasible. Trait‐based assessments typically view vulnerability as a combination of sensitivity and exposure to climate change. However, in some locations, a substantial amount of information may exist on system productivity and environmental conditions (both current and projected), with potential disparities in the information available for data‐rich and data‐poor stocks. Incorporating this level of detailed information poses challenges when conducting, and communicating uncertainty from, rapid vulnerability assessments. We applied a trait‐based vulnerability assessment to 36 fish and invertebrate stocks in the eastern Bering Sea (EBS), a data‐rich ecosystem. In recent years, the living marine resources of the EBS and Aleutian Islands have supported fisheries worth more than US $1 billion of annual ex‐vessel value. Our vulnerability assessment uses projections (to 2039) from three downscaled climate models, and graphically characterizes the variation in climate projections between climate models and between seasons. Bootstrapping was used to characterize uncertainty in specific biological traits and environmental variables, and in the scores for sensitivity, exposure, and vulnerability. The sensitivity of EBS stocks to climate change ranged from “low” to “high,” but vulnerability ranged between “low” and “moderate” due to limited exposure to climate change. Comparison with more detailed studies reveals that water temperature is an important variable for projecting climate impacts on stocks such as walleye pollock (Gadus chalcogrammus), and sensitivity analyses revealed that modifying the rule for determining vulnerability increased the vulnerability scores. This study demonstrates the importance of considering several uncertainties (e.g., climate projections, biological, and model structure) when conducting climate vulnerability assessments, and can be extended in future research to consider the vulnerability of user groups dependent on these stocks.     

Insights from ecological niche modeling on the taxonomic distinction and niche differentiation between the black‐spotted and red‐spotted tokay geckoes (Gekko gecko)

The black‐spotted tokay and the red‐spotted tokay are morphologically distinct and have largely allopatric distributions. The black‐spotted tokay is characterized by a small body size and dark skin with sundry spots, while the red‐spotted tokay has a relatively large body size and red spots. Based on morphological, karyotypic, genetic, and distribution differences, recent studies suggested their species status; however, their classifications remain controversial, and additional data such as ecological niches are necessary to establish firm hypotheses regarding their taxonomic status. We reconstructed their ecological niches models using climatic and geographic data. We then performed niche similarity tests (niche identity and background tests) and point‐based analyses to explore whether ecological differentiation has occurred, and whether such differences are sufficient to explain the maintenance of their separate segments of environmental ranges. We found that both niche models of the black‐ and the red‐spotted tokay had a good fit and a robust performance, as indicated by the high area under the curve (AUC) values (“black” = 0.982, SD = ± 0.002, “red” = 0.966 ± 0.02). Significant ecological differentiation across the entire geographic range was found, indicating that the involvement of ecological differentiation is important for species differentiation. Divergence along the environmental axes is highly associated with climatic conditions, with isothermality being important for the “black” form, while temperature seasonality, precipitation of warmest quarter, and annual temperature range together being important for the “red” form. These factors are likely important factors in niche differentiation between the two forms, which result in morphological replacement. Overall, beside morphological and genetic differentiation information, our results contribute to additional insights into taxonomic distinction and niche differentiation between the black‐ and the red‐spotted tokay.     

Are niche‐based species distribution models transferable in space?

Aim To assess the geographical transferability of niche‐based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice‐versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3–0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter‐specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land‐use history, or to species‐specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche‐based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche‐based models for assessing the fate of species in future environments.     

Division within the North American boreal forest: Ecological niche divergence between the Bicknell's Thrush (Catharus bicknelli) and Gray‐cheeked Thrush (C. minimus)

Sister species that diverged in allopatry in similar environments are expected to exhibit niche conservatism. Using ecological niche modeling and a multivariate analysis of climate and habitat data, I test the hypothesis that the Bicknell's Thrush (Catharus bicknelli) and Gray‐cheeked Thrush (C. mimimus), sister species that breed in the North American boreal forest, show niche conservatism. Three tree species that are important components of breeding territories of both thrush species were combined with climatic variables to create niche models consisting of abiotic and biotic components. Abiotic‐only, abiotic+biotic, and biotic‐only models were evaluated using the area under the curve (AUC) criterion. Abiotic+biotic models had higher AUC scores and did not over‐project thrush distributions compared to abiotic‐only or biotic‐only models. From the abiotic+biotic models, I tested for niche conservatism or divergence by accounting for the differences in the availability of niche components by calculating (1) niche overlap from ecological niche models and (2) mean niche differences of environmental values at occurrence points. Niche background similarity tests revealed significant niche divergence in 10 of 12 comparisons, and multivariate tests revealed niche divergence along 2 of 3 niche axes. The Bicknell's Thrush breeds in warmer and wetter regions with a high abundance of balsam fir (Abies balsamea), whereas Gray‐cheeked Thrush often co‐occurs with black spruce (Picea mariana). Niche divergence, rather than conservatism, was the predominant pattern for these species, suggesting that ecological divergence has played a role in the speciation of the Bicknell's Thrush and Gray‐cheeked Thrush. Furthermore, because niche models were improved by the incorporation of biotic variables, this study validates the inclusion of relevant biotic factors in ecological niche modeling to increase model accuracy.     

Wrong,but useful: regional species distribution models may not be improved by range‐wide data under biased sampling

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species‐specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point‐process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor (“prior”) to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias‐free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions.     

No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

Habitat specificity of butterflies along urban environmental gradients in Tama City,Tokyo

Butterfly assemblages were monitored by transect counts in a riverine area along Tamagawa River (RIV), a residential area on the plain (RES1), a residential area on the hill (RES2), the core area of the city (COR), the Tama Experimental Station of Forestry and Forest Products Research Institute (TES) and Tokyo Metropolitan Sakuragaoka Park (MSP) in Tama City, Tokyo, in 2005. The butterfly assemblages in forest‐dominated TES and MSP were more species‐rich than those in the other areas. The assemblage in the grassland‐dominated RIV was characterized by the highest abundance of individuals. Species richness and abundance were lowest in COR. Ordination of the areas by detrended correspondence analysis placed RIV, MSP and TES in increasing order of scores along axis 1, and RES1, RES2 and COR had higher scores along axis 2 than RIV, TES and MSP. In axis 1, grassland species had low scores and forest species high scores; the two groups were generally separated, coinciding with Tanaka's classification. However, the scores for two “forest species”, Papilio xuthus and Ypthima argus, were low and those for two “grassland species”, Potanthus flavus and Anthocharis scolymus, were high, challenging the validity of the classification. Most species recorded in this study were “seminatural type”, with relatively few “natural type” species, according to Sunose's classification. Most “urban type” species occurred in several different habitats and were not specific to highly human‐dominated RES1, RES2 and COR.     

Current food web models cannot explain the overall topological structure of observed food webs

Topological food webs illustrating “who eats whom” in different systems exhibit similar, non‐random, structures suggesting that general rules govern food web structure. Current food web models correctly predict many measures of food web topology from knowledge of species richness and connectance (fraction of possible predator–prey links that actually occur), together with assumptions about the ecological rules governing “who eats whom”. However, current measures are relatively insensitive to small changes in topology. Improvement of, and discrimination among, current models requires development of new measures of food web structure. Here I examine whether current food web models (cascade, niche, and nested hierarchy models, plus a random null model) can predict a new measure of food web structure, structural stability. Structural stability complements other measures of food web topology because it is sensitive to changes in topology that other measures often miss. The cascade and null models respectively over‐ and underpredict structural stability for a set of 17 high‐quality food webs. While the niche and nested hierarchy models provide unbiased predictions on average, their 95% confidence intervals frequently fail to include the observed data. Observed structural stabilities for all models are overdispersed compared to model predictions, and predicted and observed structural stabilities are uncorrelated, indicating that important sources of variation in structural stability are not captured by the models. Crucially, poor model performance arises because observed variation in structural stability is unrelated to variation in species richness and connectance. In contrast, almost all other measures of food web topology vary with species richness and connectance in natural webs. No model that takes species richness and connectance as the only input parameters can reproduce observed variation in structural stability. Further progress in predicting and explaining food web topology will require fundamentally new models based on different input parameters.     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

相似文献   

Speciation and the neutral theory of biodiversity

The neutral theory of biodiversity purports that patterns in the distribution and abundance of species do not depend on adaptive differences between species (i.e. niche differentiation) but solely on random fluctuations in population size (“ecological drift”), along with dispersal and speciation. In this framework, the ultimate driver of biodiversity is speciation. However, the original neutral theory made strongly simplifying assumptions about the mechanisms of speciation, which has led to some clearly unrealistic predictions. In response, several recent studies have combined neutral community models with more elaborate speciation models. These efforts have alleviated some of the problems of the earlier approaches, while confirming the general ability of neutral theory to predict empirical patterns of biodiversity. However, the models also show that the mode of speciation can have a strong impact on relative species abundances. Future work should compare these results to diversity patterns arising from non‐neutral modes of speciation, such as adaptive radiations.     

Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution

Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.     

Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity

Question: What are the effects of the number of presences on models generated with multivariate adaptive regression splines (MARS)? Do these effects vary with data quality and quantity and species ecology? Location: Spain and Ecuador. Methods: We used two data sets: (1) two trees from Spain, representing high‐occurrence number data sets with real absences and unbalanced prevalence; (2) two herbs from Ecuador, representing low‐occurrence number data sets without real absences and balanced prevalence. For model quality, we used two different measures: reliability and stability. For each sample size, different replicates were generated at random and then used to generate a consensus model. Results: Model reliability and stability decrease with sample size. Optimal minimum sample size varies depending on many factors, many of which are unknown. Regional niche variation and ecological heterogeneity are critical. Conclusions: (1) Model predictive power improves greatly with more than 18‐20 presences. (2) Model reliability depends on data quantity and quality as well as species ecological characteristics. (3) Depending on the number of presences in the data set, investigators must carefully distinguish between models that should be treated with skepticism and those whose predictions can be applied with reasonable confidence. (4) For species combining few initial presences and wide environmental range variation, it is advisable to generate several replicate models that partition the initial data and generate a consensus model. (5) Models of species with a narrow environmental range variation can be highly stable and reliable, even when generated with few presences.     

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

Ecological and phenotypic divergence in Iberian shrews (Soricidae)

There are examples of coexisting species with similar morphology and ecology, in apparent contradiction to competition theory. Shrews (Soricidae) are a paradigmatic example of this because members of this group exhibit a conserved body form, relatively low variability in lifestyle, and, in many cases, a sympatric distribution. Here, we combined geometric morphometrics and ecological niche modeling to test whether diversification of soricid species inhabiting the Iberian Peninsula has been driven by niche divergence or, conversely, whether niche conservatism has played a paramount role in this process. We also examined whether pairwise morphological distances increase as the degree of niche overlap between species becomes greater, as would be expected if interspecific competition promotes morphological differentiation. Our results showed that water shrews (Neomys), white‐toothed shrews (Crocidurinae), and red‐toothed shrews (Soricinae) are clearly differentiated in terms of both skull shape and mandible shape. However, we found a lack of phylogenetic signal in most morphological traits, indicating that closely related species are not more similar than expected by random chance. Notably, water shrews show a more “triangular” or sharp skull than white‐toothed and red‐toothed shrews, probably as an adaptation to their semiaquatic lifestyle. In agreement with the phenotypic data, climatic traits (mean annual temperature and annual precipitation) were highly labile and sister taxa showed extensive differentiation in their realized niche space. Finally, we found that phenotypic distances between species tend to increase as the degree of niche overlap increases, suggesting that interspecific competition is an important factor in determining the level of morphological resemblance among relatives. Overall, our results indicate that the existence of limited morphological disparity in a given group does not necessarily imply the existence of a niche conservatism signature.     

Testing the use of best professional judgment to create biological benchmarks for habitat assessment of wetlands and oak savannas in northwestern Indiana

The Tolleston Strandplain at the southern end of Lake Michigan offers a unique “dune and swale” topography supporting oak savannas on the dunes and a mosaic of wetland communities in the swales. Following years of human degradation, sites in this area are now being restored. In this effort, assessments of vegetative quality have been necessary for proper management decisions. However, it is poorly understood what indices best reflect the vegetative quality of these oak savannas and wetlands. A potential method for determining the best indices for these community types is to use metric benchmarks that employ expert best professional judgment (BPJ). In order to confirm the viability and consistency of BPJ for creating benchmarks, Kappa analysis was used to determine the level of agreement among seven experts. They placed each of 63 transects from this unique landscape into one of four quality categories: (1) “good to very good,” (2) “medium,” (3) “poor,” and (4) “very poor.” While experts had good agreement about the quality of severely degraded riverine wetlands, they had fair agreement when assessing the swales, and poor agreement when assessing the oak savannas. Using discriminant analysis, follow-up questions, and a comparison of each expert's quality categories with remnant oak savanna metrics, varying perspectives of quality also were discovered which may have influenced the experts’ assessments of the sites. Therefore, BPJ must be used with caution when creating metric benchmarks.     

The level of agreement among experts applying best professional judgment to assess the condition of benthic infaunal communities

Benthic infaunal communities are frequently used to assess aquatic environmental condition, but interpretation of benthic data is often subjective and based on best professional judgment. Here, we examine the repeatability of such assessments by providing species-abundance data from 35 sites to 9 independent benthic experts who ranked the sites from best to worst condition. Their site rankings were highly correlated, with an average correlation coefficient of 0.91. The experts also evaluated the sites in terms of four condition categories: (1) unaffected, (2) marginal deviation from reference, (3) affected, or (4) severely affected. At least two-thirds of the experts agreed on site categorization for 94% of the samples and they disagreed by more than one category for less than 1% of the assessment pairs. The experts identified seven parameters used in making their assessments, with four of those parameters (dominance by tolerant taxa, presence of sensitive taxa, species richness, and total abundance) used by all of the experts. Most of the disagreements in site categorization were due to philosophical rather than technical differences, such as whether the presence of invasive species indicates a degraded community. Indices are increasingly being used as an alternative to best professional judgment for assessing benthic condition, but there have been inconsistencies in how sites are selected for validating such indices; the level of agreement found among experts in this study suggests that consensus expert opinion can be a viable benchmark for such evaluations.     

Differences in habitat quality explain nestedness in a land snail meta-community

We set up two alternative hypotheses on how environmental variables could foster nestedness; one of “nested habitats” and another of “nested habitat quality”. The former hypothesis refers to situations where the nestedness of species depends on a nestedness of discrete habitats. The latter considers situations where all species in an assemblage increase in abundance along the same environmental gradient, but differ in specialisation or tolerance. We tested whether litter‐dwelling land snails (terrestrial gastropods) in boreal riparian forest exhibited a nested community structure, whether such a pattern was related to differences in environmental variables among sites, and which of the two hypotheses that best could account for the found pattern. We sampled litter from 100 m² plots in 29 mature riparian forest sites along small streams in the boreal zone of Sweden. The number of snail species varied between 3 and 14 per site. Ranking the species‐by‐site matrix by PCA scores of the first ordination axis revealed a similarly significant nested pattern as when the matrix was sorted by number of species, showing that the species composition in this meta‐community can be properly described as nested. Several environmental variables, most notably pH index, were correlated with the first PCA axis. All but two species had positive eigenvectors in the PCA ordination and the abundance increased considerably along the gradient for most of the species implying that the hypothesis of “nested habitats” was rejected in favour of the “nested habitat quality” hypothesis. Analyses of nestedness have seldom been performed on equal sized plots, and our study shows the importance of understanding that variation in environmental variables among sites can result in nested communities. The conservation implications are different depending on which of our two hypotheses is supported; a conservation focus on species “hotspots” is more appropriate if the communities are nested because of “nested habitat quality”.     

Mobile Communication in the Public Mind: Insights from Free Associations Related to Mobile Phone Base Stations

The number of people using mobile phones has dramatically increased. At the same time, many people are unsettled about the potential health effects from the electromagnetic fields generated by mobile phone base stations. Research indicates that the risks associated with base stations are perceived differently by experts, laypeople, and base station opponents. Using a free association method, we analyzed these differences in more detail. In our first study, we found no difference between experts and laypeople but a marked distinction in the associations of opponents as opposed to the first two groups. The prevalence of free associations in a large random sample from the general population was explored via correspondence analysis in the second study. People who assign high risks to mobile communication had different, more negative associations in mind (e.g., “senselessness,” “hazard”) compared to people with low risk-perception (e.g., “mast,” “acceptance”). Our research is in line with the assumption that the affect heuristic guides risk and benefit assessments, and highlights the role of affect in risk perception and communication.     

Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models

Aim Niche‐based distribution models are often used to predict the spread of invasive species. These models assume niche conservation during invasion, but invasive species can have different requirements from populations in their native range for many reasons, including niche evolution. I used distribution modelling to investigate niche conservatism for the Asian tiger mosquito (Aedes albopictus Skuse) during its invasion of three continents. I also used this approach to predict areas at risk of invasion from propagules originating from invasive populations. Location Models were created for Southeast Asia, North and South America, and Europe. Methods I used maximum entropy (Maxent ) to create distribution models using occurrence data and 18 environmental datasets. One native model was created for Southeast Asia; this model was projected onto North America, South America and Europe. Three models were created independently for the non‐native ranges and projected onto the native range. Niche overlap between native and non‐native predictions was evaluated by comparing probability surfaces between models using real data and random models generated using a permutation approach. Results The native model failed to predict an entire region of occurrences in South America, approximately 20% of occurrences in North America and nearly all Italian occurrences of A. albopictus. Non‐native models poorly predict the native range, but predict additional areas at risk for invasion globally. Niche overlap metrics indicate that non‐native distributions are more similar to the native niche than a random prediction, but they are not equivalent. Multivariate analyses support modelled differences in niche characteristics among continents, and reveal important variables explaining these differences. Main conclusions The niche of A. albopictus has shifted on invaded continents relative to its native range (Southeast Asia). Statistical comparisons reveal that the niche for introduced distributions is not equivalent to the native niche. Furthermore, reciprocal models highlight the importance of controlling bi‐directional dispersal between native and non‐native distributions.