Residence time and potential range: crucial considerations in modelling plant invasions

A prime aim of invasion biology is to predict which species will become invasive, but retrospective analyses have so far failed to develop robust generalizations. This is because many biological, environmental, and anthropogenic factors interact to determine the distribution of invasive species. However, in this paper we also argue that many analyses of invasiveness have been flawed by not considering several fundamental issues: (1) the range size of an invasive species depends on how much time it has had to spread (its residence time); (2) the range size and spread rate are mediated by the total extent of suitable (i.e. potentially invasible) habitat; and (3) the range size and spread rate depend on the frequency and intensity of introductions (propagule pressure), the position of founder populations in relation to the potential range, and the spatial distribution of the potential range. We explored these considerations using a large set of invasive alien plant species in South Africa for which accurate distribution data and other relevant information were available. Species introduced earlier and those with larger potential ranges had larger current range sizes, but we found no significant effect of the spatial distribution of potential ranges on current range sizes, and data on propagule pressure were largely unavailable. However, crucially, we showed that: (1) including residence time and potential range always significantly increases the explanatory power of the models; and (2) residence time and potential range can affect which factors emerge as significant determinants of invasiveness. Therefore, analyses not including potential range and residence time can come to misleading conclusions. When these factors were taken into account, we found that nitrogen‐fixing plants and plants invading arid regions have spread faster than other species, but these results were phylogenetically constrained. We also show that, when analysed in the context of residence time and potential range, variation in range size among invasive species is implicitly due to variation in spread rates, and, that by explicitly assuming a particular model of spread, it is possible to estimate changes in the rates of plant invasions through time. We believe that invasion biology can develop generalizations that are useful for management, but only in the context of a suitable null model.     

Filling in the gaps: modelling native species richness and invasions using spatially incomplete data

Detailed knowledge of patterns of native species richness, an important component of biodiversity, and non-native species invasions is often lacking even though this knowledge is essential to conservation efforts. However, we cannot afford to wait for complete information on the distribution and abundance of native and harmful invasive species. Using information from counties well surveyed for plants across the USA, we developed models to fill data gaps in poorly surveyed areas by estimating the density (number of species km⁻²) of native and non-native plant species. Here, we show that native plant species density is non-random, predictable, and is the best predictor of non-native plant species density. We found that eastern agricultural sites and coastal areas are among the most invaded in terms of non-native plant species densities, and that the central USA appears to have the greatest ratio of non-native to native species. These large-scale models could also be applied to smaller spatial scales or other taxa to set priorities for conservation and invasion mitigation, prevention, and control efforts.     

Pine invasions in the southern hemisphere: modelling interactions between organism,environment and disturbance

Current theories of plant invasion have been criticized for their limited heuristic and predictive value. We explore the heuristic and predictive potential of a model which explicitly simulates the mechanisms of plant invasion. The model, a spatially-explicit individual-based simulation, is applied to the invasion of pine trees (Pinus spp.; Pinaceae) in three vegetation types in the southern hemisphere. The model simulates factors which have been invoked as major determinants of invasive success: plant traits, environmental features and disturbance level. Results show that interactions between these determinants of invasive success are at least as important as the main effects. The complexity of invasions has promoted the belief that many factors must be invoked to explain invasions. This study shows that by incorporating interactions and mechanisms into our models we can potentially reduce the number of factors needed to predict plant invasions. The importance of interactions, however, means that predictions about invasions must be context-specific. The search for all-encompassing rules for invasions is therefore futile. The model presented here is of heuristic value since it improves our understanding of invasions, and of management value since it defines the data and models needed for predicting invasions.     

Accounting for imperfect observation and estimating true species distributions in modelling biological invasions

The documentation of biological invasions is often incomplete with records lagging behind the species’ actual spread to a spatio‐temporally heterogeneous extent. Such imperfect observation bears the risk of underestimating the already realised distribution of the invading species, misguiding management efforts and misjudging potential future impacts. In this paper, we develop a hierarchical modelling framework which disentangles the determinants of the invasion and observation processes, models spatio‐temporal heterogeneity in detection patterns, and infers the actual, yet partly undocumented distribution of the species at any particular time. We illustrate the model with a case study application to the invasion of common ragweed Ambrosia artemisiifolia in Austria. The invasion part of the model reconstructs the historical spread of this species across a grid of ～ 6 × 6 km² cells as driven by spatio‐temporal variation in physical site conditions, propagule production, dispersal, and ‘background’ introductions from unknown sources. The observation part models the detection of the species’ occurrences based on heterogeneous sampling efforts, human population density, and estimated local invasion level. We fitted the hierarchical model using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). The actual spread of A. artemisiifolia concentrated on the climatically well‐suited lowlands and was mainly driven by spatio‐temporal propagule pressure from source cells with long‐distance dispersal occurring rather frequently. Annual detection probabilities were estimated to vary between about 1 and up to 28%, depending mainly on sampling intensity. The model suggested that by 2005 about half of the actual distribution of the species was not yet documented. Our hierarchical model offers a flexible means to account for imperfect observation and spatio‐temporal variability in detection efficiency. Inferences can be used to disentangle aspects of the invasion dynamics itself from patterns of data collection, develop improved future surveying schemes, and design more efficient invasion management strategies.     

An integrated,spatio‐temporal modelling framework for analysing biological invasions

Aim

We develop a novel modelling framework for analysing the spatio‐temporal spread of biological invasions. The framework integrates different invasion drivers and disentangles their roles in determining observed invasion patterns by fitting models to historical distribution data. As a case study application, we analyse the spread of common ragweed (Ambrosia artemisiifolia).

Location

Central Europe.

Methods

A lattice system represents actual landscapes with environmental heterogeneity. Modelling covers the spatio‐temporal invasion sequence in this grid and integrates the effects of environmental conditions on local invasion suitability, the role of invaded cells and spatially implicit “background” introductions as propagule sources, within‐cell invasion level bulk‐up and multiple dispersal means. A modular framework design facilitates flexible numerical representation of the modelled invasion processes and customization of the model complexity. We used the framework to build and contrast increasingly complex models, and fitted them using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC).

Results

All modelled invasion drivers codetermined the A. artemisiifolia invasion pattern. Inferences about individual drivers depended on which processes were modelled concurrently, and hence changed both quantitatively and qualitatively between models. Among others, the roles of environmental variables were assessed substantially differently subject to whether models included explicit source‐recipient cell relationships, spatio‐temporal variability in source cell strength and human‐mediated dispersal means. The largest fit improvements were found by integrating filtering effects of the environment and spatio‐temporal availability of propagule sources.

Main conclusions

Our modelling framework provides a straightforward means to build integrated invasion models and address hypotheses about the roles and mutual relationships of different putative invasion drivers. Its statistical nature and generic design make it suitable for studying many observed invasions. For efficient invasion modelling, it is important to represent changes in spatio‐temporal propagule supply by explicitly tracking the species’ colonization sequence and establishment of new populations.

     

A hybrid likelihood algorithm for risk modelling

The risk of radiation-induced cancer is assessed through the follow-up of large cohorts, such as atomic bomb survivors or underground miners who have been occupationally exposed to radon and its decay products. The models relate to the dose, age and time dependence of the excess tumour rates, and they contain parameters that are estimated in terms of maximum likelihood computations. The computations are performed with the software package EPICURE, which contains the two main options of person-by person regression or of Poisson regression with grouped data. The Poisson regression is most frequently employed, but there are certain models that require an excessive number of cells when grouped data are used. One example involves computations that account explicitly for the temporal distribution of continuous exposures, as they occur with underground miners. In past work such models had to be approximated, but it is shown here that they can be treated explicitly in a suitably reformulated person-by person computation of the likelihood. The algorithm uses the familiar partitioning of the log-likelihood into two terms,L ₁ andL ₀. The first term,L ₁, represents the contribution of the events (tumours). It needs to be evaluated in the usual way, but constitutes no computational problem. The second term,L ₀, represents the event-free periods of observation. It is, in its usual form, unmanageable for large cohorts. However, it can be reduced to a simple form, in which the number of computational steps is independent of cohort size. The method requires less computing time and computer memory, but more importantly it leads to more stable numerical results by obviating the need for grouping the data. The algorithm may be most relevant to radiation risk modelling, but it can facilitate the modelling of failure-time data in general.     

Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale

Predicting the probability of successful establishment of plant species by matching climatic variables has considerable potential for incorporation in early warning systems for the management of biological invasions. We select South Africa as a model source area of invasions worldwide because it is an important exporter of plant species to other parts of the world because of the huge international demand for indigenous flora from this biodiversity hotspot. We first mapped the five ecoregions that occur both in South Africa and other parts of the world, but the very coarse definition of the ecoregions led to unreliable results in terms of predicting invasible areas. We then determined the bioclimatic features of South Africa's major terrestrial biomes and projected the potential distribution of analogous areas throughout the world. This approach is much more powerful, but depends strongly on how particular biomes are defined in donor countries. Finally, we developed bioclimatic niche models for 96 plant taxa (species and subspecies) endemic to South Africa and invasive elsewhere, and projected these globally after successfully evaluating model projections specifically for three well‐known invasive species (Carpobrotus edulis, Senecio glastifolius, Vellereophyton dealbatum) in different target areas. Cumulative probabilities of climatic suitability show that high‐risk regions are spatially limited globally but that these closely match hotspots of plant biodiversity. These probabilities are significantly correlated with the number of recorded invasive species from South Africa in natural areas, emphasizing the pivotal role of climate in defining invasion potential. Accounting for potential transfer vectors (trade and tourism) significantly adds to the explanatory power of climate suitability as an index of invasibility. The close match that we found between the climatic component of the ecological habitat suitability and the current pattern of occurrence of South Africa alien species in other parts of the world is encouraging. If species' distribution data in the donor country are available, climatic niche modelling offers a powerful tool for efficient and unbiased first‐step screening. Given that eradication of an established invasive species is extremely difficult and expensive, areas identified as potential new sites should be monitored and quarantine measures should be adopted.     

Biological invasions: the case of planorbid snails

A large number of planorbid snails are now commonly transported by man mainly through the aquatic plant trade. However, only a restricted number of species establish viable populations in a new habitat and a more restricted number spread. Only five planorbid species can be ranked in this last category and can be considered as pests because of their role in the transmission of parasites to humans or domestic animals: Biomphalaria glabrata, B. straminea, B. tenagophila, B. pfeifferi and Indoplanorbis exustus. The neotropical B. glabrata, B. straminea and B. tenagophila have proven their capacity to invade another continent sometimes creating new transmission foci. The African B. pfeifferi and the Indian I. exustus have also expanded their distribution area with long-distance dispersal. Other planorbid species, i.e. Helisoma duryi, Amerianna carinata and Gyraulus spp. have been able to establish viable populations, but not to spread, presumably because they are limited to specific habitats or/and display poor competitive abilities.     

Biological invasions: Lessons for ecology

Anthropogenic introduction of species is homogenizing the earth's biota. Consequences of introductions are sometimes great, and are directly related to global climate change, biodiversity AND release of genetically engineered organisms. Progress in invasion studies hinges on the following research trends: realization that species' ranges are naturally dynamic; recognition that colonist species and target communities cannot be studied independently, but that species-community interactions determine invasion success; increasingly quantitative tests of how species and habitat characteristics relate to invasibility and impact; recognition from paleobiological, experimental and modeling studies that history, chance and determinism together shape community invasibility.     

Earthworm invasions in the tropics

The effects and implications of invasive species in belowground terrestrial ecosystems are not well known in comparison with above-ground terrestrial and marine environments. The study of earthworm invasions in the tropics is limited by a lack of taxonomic knowledge and the potential for loss of species in native habitats due to anthropogenic land use change. Alteration of land use plays a major role in determining the abundance and community structure of earthworms and the establishment of exotic earthworms in areas previously inhabited by worms. Once an exotic species has become established into a new place, site and species characteristics seem to be key factors determining their spread. We reviewed the literature on the distribution and effects of exotic earthworms to understand the interactions of earthworm invasion and land use history in the tropics. Patterns in the abundance, effects and mechanisms of earthworm invasions on ecosystem processes in the tropics are elucidated using Pontoscolex corethrurus as a case study.     

Overlooking the smallest matter: viruses impact biological invasions

Parasites and pathogens have recently received considerable attention for their ability to affect biological invasions, however, researchers have largely overlooked the distinct role of viruses afforded by their unique ability to rapidly mutate and adapt to new hosts. With high mutation and genomic substitution rates, RNA and single‐stranded DNA (ssDNA) viruses may be important constituents of invaded ecosystems, and could potentially behave quite differently from other pathogens. We review evidence suggesting that rapidly evolving viruses impact invasion dynamics in three key ways: (1) Rapidly evolving viruses may prevent exotic species from establishing self‐sustaining populations. (2) Viruses can cause population collapses of exotic species in the introduced range. (3) Viruses can alter the consequences of biological invasions by causing population collapses and extinctions of native species. The ubiquity and frequent host shifting of viruses make their ability to influence invasion events likely. Eludicating the viral ecology of biological invasions will lead to an improved understanding of the causes and consequences of invasions, particularly as regards establishment success and changes to community structure that cannot be explained by direct interspecific interactions among native and exotic species.     

A hybrid representation approach for modelling complex dynamic bioprocesses

This paper considers the use of hybrid models to represent the dynamic behaviour of biotechnological processes. Each hybrid model consists of a set of non linear differential equations and a neural model. The set of differential equations attempts to describe as much as possible the phenomenology of the process whereas neural networks model predict some key parameters that are an essential part of the phenomenological model. The neural model is obtained indirectly, that is, using the prediction errors of one or more state variables to adjust its weights instead of successive presentations of input-output data of the neural network. This approach allows to use actual measurements to derive a suitable neural model that not only represents the variation of some key parameters but it is also able to partly include dynamic behaviour unaccounted for by the phenomenological model. The approach is described in detail using three test cases: (1) the fermentation of glucose to gluconic acid by the micro-organism Pseudomonas ovalis, (2) the growth of filamentous fungi in a solid state fermenter, and (3) the propagation of filamentous fungi growing on a 2-D solid substrate. Results for the three applications clearly demon- strate that using a hybrid model is a viable alternative for modelling complex biotechnological bioprocesses.     

Gene regulatory networks modelling using a dynamic evolutionary hybrid

Background  

Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.     

Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio‐temporal dynamics of alien invasions using distribution data

A novel, yet generic, Bayesian approach to parameter inference in a stochastic, spatio‐temporal model of dispersal and colonisation is developed and applied to the invasion of a region by an alien plant species. The method requires species distribution data from multiple time points, and accounts for temporal uncertainty in colonisation times inherent in such data. Covariates, such as climate parameters, altitude and land use, which capture variation in the suitability of sites for plant colonisation, are easily incorporated into the model. The method assumes no local extinction of occupied sites and thus is primarily applicable to modelling distribution data at relatively coarse spatial resolutions of plant species whose range is expanding over time. The implementation of the model and inference algorithm are illustrated through application to British floristic atlas data for the widespread alien Heracleum mantegazzianum (giant hogweed) assessed at a 10 × 10 km resolution in 1970 and 2000. We infer key characteristics of this species, predict its future spread, and use the resulting fitted model to inform a simulation‐based assessment of the methodology. Simulated distribution data are used to validate the inference algorithm. Our results suggest that the accuracy of inference is not sensitive to the number of distribution time points, requiring only that there are at least two points in time when distributions are mapped. We demonstrate the utility of the modelling approach by making future forecasts and historic hindcasts of the distribution of giant hogweed in Great Britain. Giant hogweed is one of the worst alien plants in Britain and has rapidly increased its range since 1970, yet we highlight that a further 20% of land area remains susceptible to colonisation by this species. We use the robustness of this case study to discuss the potential for modelling distribution data for other species and at different spatial scales.     

Correction to: Managing biological invasions: the cost of inaction

Biological Invasions -     

Dammed rivers: impoundments facilitate fish invasions

相似文献   

Biological invasions and the neutral theory

Aim  The invasion of natural communities by alien species represents a serious threat, but creates opportunities to learn about community functions. Neutral theory proposes that the niche concept may not be needed to explain the assemblage and diversity of natural communities, challenging the classical view of community ecology and generating a lasting debate. Biological invasions, when considered as natural experiments, can be used to contrast some of the predictions of neutral and classic niche theories.
Location  Global.
Methods  We use data from biological invasions as natural experiments to contrast some of the fundamental predictions of neutral theory.
Results  Some emerging patterns did not differ from neutral model expectations (e.g. the relationship between native and exotic species richness, invasibility of resource-rich habitats, and the relationship between propagule release and invasion success). Nevertheless, other patterns (e.g. experimental evidence of the relationship between diversity and susceptibility to invasion, the invasion of communities with a low resource availability, invasiveness related to species traits) contrasted with the predictions that can be inferred from neutral theory.
Main conclusions  Neutral theory correctly highlights the need to include randomness in models of community structure. Biological invasion patterns show that neutral forces are important in structuring natural communities, but the patterns differ from those inferred from a complete neutral model. For biodiversity-conservation purposes, the implications of accepting or not accepting neutral theory as a process-based theory are very important.