Predicting Invasiveness of Plant Species Based on Biological Information

Abstract: Previous studies suggest that, within particular groups of plant species, biological attributes can be used to predict the potential invasiveness of species that are intentionally introduced for horticultural or agricultural purposes. We examined the broad question of whether commonly available biological information can predict the invasiveness of a wide range of intentionally and accidentally introduced species. We collected information from published floras on 165 pairs of plant species. In each pair, one species originated in Europe and successfully invaded New Brunswick, Canada, and the other was a congeneric species that has not invaded North America. Only three biological characters—lifeform, stem height, and flowering period—and European geographic range were known for all species. We conducted multiple logistic regression analyses using two-thirds (110) of the species pairs and tested the predictive ability of resulting models using the remaining 55 pairs. Although a significant logistic regression model was obtained using the biological attributes, the model could not predict invasiveness of the test species pairs. In contrast, a model using only European range successfully predicted invasiveness in 70% of the test species. The importance of geographic range suggests that prediction of invasiveness on a species-by-species basis is not likely to help stem the flow of accidentally introduced invasive species. Species that are inadvertently picked up and moved to a new location due to their wide distribution are the same species that are likely to succeed in a new environment due to their wide environmental tolerances.     

Forecasting New Zealand Mudsnail invasion range: model comparisons using native and invaded ranges.

Evaluations of the potential distribution of invasive species can increase the efficiency of their management by focusing prevention measures. Generally, ecological models are built using occurrence data from a species' native range to predict the distribution in areas that the species may invade. However, historical and geographical constraints can limit a species' native distribution. Genetic Algorithm for Rule-set Production (GARP), an ecological niche modeling program, was used to predict the potential distribution of the invasive, freshwater New Zealand mudsnail, Potamopyrgus antipodarum, in Australia and North America. We compared the strength of the predictions made by models built with data from the snail's native range in New Zealand to models built with data from the locations invaded by the species. A time-series analysis of the Australian models demonstrated that range-of-invasion data can make better predictions about the potential distribution of invasive species than models built with native range data. Large differences among the model forecasts indicate that uncritical choice of the data set used in training the GARP models can result in misleading predictions. The models predict a large expansion in the range of P. antipodarum in both Australia and North America unless prevention measures are implemented rapidly.     

外来红树植物无瓣海桑的入侵生态特征

无瓣海桑因其具有速生性的特点而被广泛的用于滩涂海岸造林和控制外来杂草互花米草。但是同样作为外来种,无瓣海桑其自身的入侵性及生态风险已经成为近年来研究的一大热点。依据外来种风险评估的侧重点,分别从定居特性、传播特性及其影响等方面总结了国内外无瓣海桑的入侵生态特征的相关研究,表明无瓣海桑具有速生、高生产力、对低温和土壤具有一定的适应性、已在澳门等地出现扩散入侵现象等利于入侵的特点;但是同时它生态位宽度中等,繁殖率较低,并且它的种植会改善红树林生态系统土壤养分,有利于乡土红树植物的生长。现有对无瓣海桑入侵生态特征的定性研究并不能完全确定其入侵性和生态风险。因此今后需采用风险评估模型等手段对无瓣海桑入侵性进行更为深入地评估。     

Demographic models inform selection of biocontrol agents for garlic mustard (Alliaria petiolata).

Nonindigenous invasive plants pose a major threat to natural communities worldwide. Biological control of weeds via selected introduction of their natural enemies can affect control over large spatial areas but also risk nontarget effects. To maximize effectiveness while minimizing risk, weed biocontrol programs should introduce the minimum number of host-specific natural enemies necessary to control an invasive nonindigenous plant. We used elasticity analysis of a matrix model to help inform biocontrol agent selection for garlic mustard (Alliaria petiolata (M. Bieb.) Cavara and Grande). The Eurasian biennial A. petiolata is considered one of the most problematic invaders of temperate forests in North America. Four weevil species in the genus Ceutorhynchus (Coleoptera: Curculionidae) are currently considered potential biocontrol agents. These species attack rosettes (C. scrobicollis), stems (C. roberti, C. alliariae), and seeds (C. constrictus) of A. petiolata. Elasticity analyses using A. petiolata demographic parameters from North America indicated that changes in the rosette-to-flowering-plant transition and changes in fecundity consistently had the greatest impact on population growth rate. These results suggest that attack by the rosette-feeder C. scrobicollis, which reduces overwintering survival, and seed or stem feeders that reduce seed output should be particularly effective. Model outcomes differed greatly as A. petiolata demographic parameters were varied within ranges observed in North America, indicating that successful control of A. petiolata populations may occur under some, but not all, conditions. Using these a priori analyses we predict: (1) rosette mortality and reduction of seed output will be the most important factors determining A. petiolata demography; (2) the root-crown feeder C. scrobicollis will have the most significant impact on A. petiolata demography; (3) releases of single control agents are unlikely to control A. petiolata across its full range of demographic variability; (4) combinations of agents that simultaneously reduce rosette survival and seed production will be required to suppress the most vigorous A. petiolata populations. These predictions can be tested using established long-term monitoring sites coupled with a designed release program. If demographic models can successfully predict biocontrol agent impact on invasive plant populations, a continued dialogue and collaboration between empirical and theoretical approaches may be the key to the development of successful biocontrol tactics for plant invaders in the future.     

A Freshwater Classification Approach for Biodiversity Conservation Planning

Abstract:  Freshwater biodiversity is highly endangered and faces increasing threats worldwide. To be complete, regional plans that identify critical areas for conservation must capture representative components of freshwater biodiversity as well as rare and endangered species. We present a spatially hierarchical approach to classify freshwater systems to create a coarse filter to capture representative freshwater biodiversity in regional conservation plans. The classification framework has four levels that we described using abiotic factors within a zoogeographic context and mapped in a geographic information system. Methods to classify and map units are flexible and can be automated where high-quality spatial data exist, or can be manually developed where such data are not available. Products include a spatially comprehensive inventory of mapped and classified units that can be used remotely to characterize regional patterns of aquatic ecosystems. We provide examples of classification procedures in data-rich and data-poor regions from the Columbia River Basin in the Pacific Northwest of North America and the upper Paraguay River in central South America. The approach, which has been applied in North, Central, and South America, provides a relatively rapid and pragmatic way to account for representative freshwater biodiversity at scales appropriate to regional assessments.     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Predicting Risk of Habitat Conversion in Native Temperate Grasslands

Abstract: Native grasslands that support diverse populations of birds are being converted to cropland at an increasing rate in the Prairie Pothole Region of North America. Although limited funding is currently available to mitigate losses, accurate predictions of probability of conversion would increase the efficiency of conservation measures. We studied conversion of native grassland to cropland in the Missouri Coteau region of North and South Dakota (U.S.A.) during 1989–2003. We estimated the probability of conversion of native grassland to cropland with satellite imagery and logistic regression models that predicted risk of conversion and by comparing the overlap between areas of high biological value and areas most vulnerable to conversion. Annualized probability of conversion was 0.004, and 36,540 ha of native grassland were converted to cropland during the period of our study. Our predictive models fit the data and correctly predicted 70% of observed conversions of grassland. Probability of conversion varied spatially and was correlated with landscape features like amount of surrounding grassland, slope, and soil productivity. Tracts of high biological value were not always at high risk of conversion. We concluded the most biologically valuable areas that are most vulnerable to conversion should be prioritized for conservation. This approach can be applied broadly to other systems and offers great utility for implementing conservation in areas with spatially variable biological value and probability of conversion.     

Using habitat suitability index and particle dispersion models for early detection of marine invaders.

Eradication and control of invasive species are often possible only if populations are detected when they are small and localized. To be efficient, detection surveys should be targeted at locations where there is the greatest risk of incursions. We examine the utility of habitat suitability index (HSI) and particle dispersion models for targeting sampling for marine pests. Habitat suitability index models are a simple way to identify suitable habitat when species distribution data are lacking. We compared the performance of HSI models with statistical models derived from independent data from New Zealand on the distribution of two nonindigenous bivalves: Theora lubrica and Musculista senhousia. Logistic regression models developed using the HSI scores as predictors of the presence/absence of Theora and Musculista explained 26.7% and 6.2% of the deviance in the data, respectively. Odds ratios for the HSI scores were greater than unity, indicating that they were genuine predictors of the presence/ absence of each species. The fit and predictive accuracy of each logistic model were improved when simulated patterns of dispersion from the nearest port were added as a predictor variable. Nevertheless, the combined model explained, at best, 46.5% of the deviance in the distribution of Theora and correctly predicted 56% of true presences and 50% of all cases. Omission errors were between 6% and 16%. Although statistical distribution models built directly from environmental predictors always outperformed the equivalent HSI models, the gain in model fit and accuracy was modest. High residual deviance in both types of model suggests that the distributions realized by Theora and Musculista in the field data were influenced by factors not explicitly modeled as explanatory variables and by error in the environmental data used to project suitable habitat for the species. Our results highlight the difficulty of accurately predicting the distribution of invasive marine species that exhibit low habitat occupancy and patchy distributions in time and space. Although the HSI and statistical models had utility as predictors of the likely distribution of nonindigenous marine species, the level of spatial accuracy achieved with them may be well below expectations for sensitive surveillance programs.     

外来入侵植物在中国的分布及入侵能力研究进展

以近十几年来备受关注的外来植物入侵为背景,综述关于入侵植物在我国分布和入侵能力及其相关因素的研究进展,并探讨了未来研究需要加强的几个方面。不同起源地的入侵植物在我国分布区域不一样,在控制了起源地的作用后,入侵植物主要分布在经济较发达、人为活动较多的省份或地区（大尺度）,以及干扰较严重的生境（小尺度）。入侵能力强的多年生植物常有强大的无性繁殖能力、高光合速率和资源利用效率、强化感作用、以及适应异质生境的能力;起源于美洲的入侵能力强的1年生植物常能产生大量的易传播的种子、可自交亲和、强化感作用等;起源于欧亚大陆的入侵能力强的1年生植物常与作物非常相似,在农田中危害严重。未来需要研究干扰促进入侵的机制、化感作用与野外实际情况相结合研究、化感物质通过改变土壤微生态环境间接促进入侵的机制研究、1年生和多年生外来植物的入侵能力与其物种特性之间的关系、以及加强对弱入侵性外来植物的预警研究。这些研究不仅能使我们深入理解外来植物发生入侵和危害的规律,也可为人侵植物的防控和管理提供科学依据。     

Plant invasions, generalist herbivores, and novel defense weapons

One commonly accepted mechanism for biological invasions is that species, after introduction to a new region, leave behind their natural enemies and therefore increase in distribution and abundance. However, which enemies are escaped remains unclear. Escape from specialist invertebrate herbivores has been examined in detail, but despite the profound effects of generalist herbivores in natural communities their potential to control invasive species is poorly understood. We carried out parallel laboratory feeding bioassays with generalist invertebrate herbivores from the native (Europe) and from the introduced (North America) range using native and nonnative tetraploid populations of the invasive spotted knapweed, Centaurea stoebe. We found that the growth of North American generalist herbivores was far lower when feeding on C. stoebe than the growth of European generalists. In contrast, North American and European generalists grew equally well on European and North American tetraploid C. stoebe plants, lending no support for an evolutionary change in resistance of North American tetraploid C. stoebe populations against generalist herbivores. These results suggest that biogeographical differences in the response of generalist herbivores to novel plant species have the potential to affect plant invasions.     

Using parasites to inform ecological history: comparisons among three congeneric marine snails

Species introduced to novel regions often leave behind many parasite species. Signatures of parasite release could thus be used to resolve cryptogenic (uncertain) origins such as that of Littorina littorea, a European marine snail whose history in North America has been debated for over 100 years. Through extensive field and literature surveys, we examined species richness of parasitic trematodes infecting this snail and two co-occurring congeners, L. saxatilis and L. obtusata, both considered native throughout the North Atlantic. Of the three snails, only L. littorea possessed significantly fewer trematode species in North America, and all North American trematodes infecting the three Littorina spp. were a nested subset of Europe. Surprisingly, several of L. littorea's missing trematodes in North America infected the other Littorina congeners. Most likely, long separation of these trematodes from their former host resulted in divergence of the parasites' recognition of L. littorea. Overall, these patterns of parasitism suggest a recent invasion from Europe to North America for L. littorea and an older, natural expansion from Europe to North America for L. saxatilis and L. obtusata.     

Phenotypic plasticity of native vs. invasive purple loosestrife: a two-state multivariate approach

The differences in phenotypic plasticity between invasive (North American) and native (German) provenances of the invasive plant Lythrum salicaria (purple loosestrife) were examined using a multivariate reaction norm approach testing two important attributes of reaction norms described by multivariate vectors of phenotypic change: the magnitude and direction of mean trait differences between environments. Data were collected for six life history traits from native and invasive plants using a split-plot design with experimentally manipulated water and nutrient levels. We found significant differences between native and invasive plants in multivariate phenotypic plasticity for comparisons between low and high water treatments within low nutrient levels, between low and high nutrient levels within high water treatments, and for comparisons that included both a water and nutrient level change. The significant genotype x environment (G x E) effects support the argument that invasiveness of purple loosestrife is closely associated with the interaction of high levels of soil nutrient and flooding water regime. Our results indicate that native and invasive plants take different strategies for growth and reproduction; native plants flowered earlier and allocated more to flower production, while invasive plants exhibited an extended period of vegetative growth before flowering to increase height and allocation to clonal reproduction, which may contribute to increased fitness and invasiveness in subsequent years.     

Modeling and Predicting Species Occurrence Using Broad-Scale Environmental Variables: an Example with Butterflies of the Great Basin

Abstract: If occurrence of individual species can be modeled as a function of easily quantified environmental variables (e.g., derived from a geographic information system [GIS]) and the predictions of these models are demonstrably successful, then the scientific foundation for management planning will be strengthened. We used Bayesian logistic regression to develop predictive models for resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (segments of canyons) in the Toquima Range ( Nevada, U.S.A.) were used to build the models. Squares of the environmental variables were also used to accommodate possibly nonmonotonic responses. We obtained statistically significant models for 36 of 56 (64%) resident species of butterflies. The models explained 8–72% of the deviance in occurrence of those species. Each of the independent variables was significant in at least one model, and squared versions of five variables contributed to models. Elevation was included in more than half of the models. Models included one to four variables; only one variable was significant in about half the models. We conducted preliminary tests of two of our models by using an existing set of data on the occurrence of butterflies in the neighboring Toiyabe Range. We compared conventional logistic classification with posterior probability distributions derived from Bayesian modeling. For the latter, we restricted our predictions to locations with a high ( 70%) probability of predicted presence or absence. We will perform further tests after conducting inventories at new locations in the Toquima Range and nearby Shoshone Mountains, for which we have computed environmental variables by using remotely acquired topographic data, digital-terrain and microclimatic models, and GIS computation.     

Making more out of sparse data: hierarchical modeling of species communities

Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence-absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.     

Predicting invasion risk using measures of introduction effort and environmental niche models.

The Chinese mitten crab (Eriocheir sinensis) is native to east Asia, is established throughout Europe, and is introduced but geographically restricted in North America. We developed and compared two separate environmental niche models using genetic algorithm for rule set prediction (GARP) and mitten crab occurrences in Asia and Europe to predict the species' potential distribution in North America. Since mitten crabs must reproduce in water with >15% per hundred salinity, we limited the potential North American range to freshwater habitats within the highest documented dispersal distance (1260 km) and a more restricted dispersal limit (354 km) from the sea. Applying the higher dispersal distance, both models predicted the lower Great Lakes, most of the eastern seaboard, the Gulf of Mexico and southern extent of the Mississippi River watershed, and the Pacific northwest as suitable environment for mitten crabs, but environmental match for southern states (below 35 degrees N) was much lower for the European model. Use of the lower range with both models reduced the expected range, especially in the Great Lakes, Mississippi drainage, and inland areas of the Pacific Northwest. To estimate the risk of introduction of mitten crabs, the amount of reported ballast water discharge into major United States ports from regions in Asia and Europe with established mitten crab populations was used as an index of introduction effort. Relative risk of invasion was estimated based on a combination of environmental match and volume of unexchanged ballast water received (July 1999-December 2003) for major ports. The ports of Norfolk and Baltimore were most vulnerable to invasion and establishment, making Chesapeake Bay the most likely location to be invaded by mitten crabs in the United States. The next highest risk was predicted for Portland, Oregon. Interestingly, the port of Los Angeles/Long Beach, which has a large shipping volume, had a low risk of invasion. Ports such as Jacksonville, Florida, had a medium risk owing to small shipping volume but high environmental match. This study illustrates that the combination of environmental niche- and vector-based models can provide managers with more precise estimates of invasion risk than can either of these approaches alone.     

Invasive Microstegium populations consistently outperform native range populations across diverse environments

Plant species introduced into novel ranges may become invasive due to evolutionary change, phenotypic plasticity, or other biotic or abiotic mechanisms. Evolution of introduced populations could be the result of founder effects, drift, hybridization, or adaptation to local conditions, which could enhance the invasiveness of introduced species. However, understanding whether the success of invading populations is due to genetic differences between native and introduced populations may be obscured by origin x environment interactions. That is, studies conducted under a limited set of environmental conditions may show inconsistent results if native or introduced populations are differentially adapted to specific conditions. We tested for genetic differences between native and introduced populations, and for origin x environment interactions, between native (China) and introduced (U.S.) populations of the invasive annual grass Microstegium vimineum (stiltgrass) across 22 common gardens spanning a wide range of habitats and environmental conditions. On average, introduced populations produced 46% greater biomass and had 7.4% greater survival, and outperformed native range populations in every common garden. However, we found no evidence that introduced Microstegium exhibited greater phenotypic plasticity than native populations. Biomass of Microstegium was positively correlated with light and resident community richness and biomass across the common gardens. However, these relationships were equivalent for native and introduced populations, suggesting that the greater mean performance of introduced populations is not due to unequal responses to specific environmental parameters. Our data on performance of invasive and native populations suggest that post-introduction evolutionary changes may have enhanced the invasive potential of this species. Further, the ability of Microstegium to survive and grow across the wide variety of environmental conditions demonstrates that few habitats are immune to invasion.     

Testing the Generality of Bird-Habitat Models

Bird-habitat models are frequently used as predictive modeling tools—for example, to predict how a species will respond to habitat modifications. We investigated the generality of the predictions from this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km². This assemblage of species is relatively static with respect to both occupancy and spatial positioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom's richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predictive models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models' predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The results from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable.     

Behavioral responses to a novel predator and competitor of invasive mosquitofish and their non-invasive relatives (Gambusia sp.)

Attributes of the recipient community may affect the invasion success of arriving non-indigenous organisms. In particular, biotic interactions may enhance the resistance of communities to invasion. Invading organisms typically encounter a novel suite of competitors and predators, and thus their invasiveness may be affected by how they cope with these interactions. Behavioral plasticity may help invaders to respond appropriately to novelty. We examined the behavioral responses of highly invasive mosquitofish to representative novel competitors and predators they might encounter as they spread through North America. We compared the behavior of invasive Gambusia holbrooki and G. affinis to that of two close relatives of lower invasive potential (G. geiseri and G. hispaniolae) in order to elucidate whether responses to novelty related to invasiveness. In short-term assays, female Gambusia were paired with a novel competitor, Pimephales promelas, and a novel predator, Micropterus dolomieu. Behavioral responses were measured in terms of foraging success and efficiency, activity, refuge use, predator inspections, and interspecific aggression. Contrary to a priori predictions, invasive and non-invasive responses to novel interactions did not differ consistently. In response to novel competition, both invasive species increased foraging efficiency, but so did G. geiseri. In response to novel predation, only G. holbrooki decreased consumption and activity and increased refuge use. No antipredator response was observed in G. affinis. We found consistent differences, however, between invasives and non-invasives in foraging behavior. Both in the presence and absence of the competitor and the predator, invasives foraged more efficiently and consumed more prey than non-invasives.Communicated by P. Bednekoff     

Stochastic gravity models for modeling lake invasions

Freshwater aquatic systems in North America are being invaded by many different species, ranging from fish, mollusks, cladocerans to various bacteria and viruses. These invasions have serious ecological and economic impacts. Human activities such as recreational boating are an important pathway for dispersal. Gravity models are used to quantify the dispersal effect of human activity. Gravity models currently used in ecology are deterministic. This paper proposes the use of stochastic gravity models in ecology, which provides new capabilities both in model building and in potential model applications. These models allow us to use standard statistical inference tools such as maximum likelihood estimation and model selection based on information criteria. To facilitate prediction, we use only those covariates that are easily available from common data sources and can be forecasted in future. This is important for forecasting the spread of invasive species in geographical and temporal domain. The proposed model is portable, that is it can be used for estimating relative boater traffic and hence relative propagule pressure for the lakes not covered by current boater surveys. This makes our results broadly applicable to various invasion prediction and management models.     

Plague bacterium as a transformer species in prairie dogs and the grasslands of western North America

Invasive transformer species change the character, condition, form, or nature of ecosystems and deserve considerable attention from conservation scientists. We applied the transformer species concept to the plague bacterium Yersinia pestis in western North America, where the pathogen was introduced around 1900. Y. pestis transforms grassland ecosystems by severely depleting the abundance of prairie dogs (Cynomys spp.) and thereby causing declines in native species abundance and diversity, including threatened and endangered species; altering food web connections; altering the import and export of nutrients; causing a loss of ecosystem resilience to encroaching invasive plants; and modifying prairie dog burrows. Y. pestis poses an important challenge to conservation biologists because it causes trophic‐level perturbations that affect the stability of ecosystems. Unfortunately, understanding of the effects of Y. pestis on ecosystems is rudimentary, highlighting an acute need for continued research.