Environment and dispersal paths override life strategies and residence time in determining regional patterns of invasion by alien plants

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparing the establishment of an invasive and an endemic palm species in the Atlantic rainforest

Background: Biological invasions are one of the major causes of biodiversity loss, yet remain rather understudied in tropical environments. The Australian palm tree Archontophoenix cunninghamiana was introduced into Brazil for ornamental purposes, but has become an invasive species in urban and suburban forest patches. The substitution of A. cunninghamiana by the native palm Euterpe edulis has been proposed as a management action. Aims: We aimed to evaluate the regeneration potential of these two palm species in an Atlantic forest remnant in south-eastern Brazil where both species occur. Methods: We compared seedling establishment and seed longevity of both species through seed sowing, and also measured the contribution of A. cunninghamiana to the local seed rain and seed bank. Results: Nearly half of the non-anemochoric diaspores collected from the seed rain belonged to A. cunninghamiana, which represented a high propagule pressure in the community. The distribution of the alien palm seeds in the seed rain correlated with the distribution of nearby young and adult individuals inside the forest. Neither A. cunninghamiana nor E. edulis appeared to have a persistent seed bank in a burial experiment; seedling survival experiments suggested a much better performance for A. cunninghamiana, which had a survival rate of ca. 30% compared with a rate of only 3.5% for E. edulis. Conclusions: The results suggest a higher regeneration capacity for the alien palm over the native species when co-occurring in a forest fragment. Management actions are thus proposed to reduce a potential biological invasion process.

Island Invasion by a Threatened Tree Species: Evidence for Natural Enemy Release of Mahogany (Swietenia macrophylla) on Dominica, Lesser Antilles

Despite its appeal to explain plant invasions, the enemy release hypothesis (ERH) remains largely unexplored for tropical forest trees. Even scarcer are ERH studies conducted on the same host species at both the community and biogeographical scale, irrespective of the system or plant life form. In Cabrits National Park, Dominica, we observed patterns consistent with enemy release of two introduced, congeneric mahogany species, Swietenia macrophylla and S. mahagoni, planted almost 50 years ago. Swietenia populations at Cabrits have reproduced, with S. macrophylla juveniles established in and out of plantation areas at densities much higher than observed in its native range. Swietenia macrophylla juveniles also experienced significantly lower leaf-level herbivory (~3.0%) than nine co-occurring species native to Dominica (8.4–21.8%), and far lower than conspecific herbivory observed in its native range (11%–43%, on average). These complimentary findings at multiple scales support ERH, and confirm that Swietenia has naturalized at Cabrits. However, Swietenia abundance was positively correlated with native plant diversity at the seedling stage, and only marginally negatively correlated with native plant abundance for stems ≥1-cm dbh. Taken together, these descriptive patterns point to relaxed enemy pressure from specialized enemies, specifically the defoliator Steniscadia poliophaea and the shoot-borer Hypsipyla grandella, as a leading explanation for the enhanced recruitment of Swietenia trees documented at Cabrits.

Risk assessment for invasive exotic plants using multi-criteria risk models

Invasive exotic plants have altered natural ecosystems across much of North America. In the Midwest, the presence of invasive plants is increasing rapidly, causing changes in ecosystem patterns and processes. Early detection has become a key component in invasive plant management and in the detection of ecosystem change. Risk assessment through predictive modeling has been a useful resource for monitoring and assisting with treatment decisions for invasive plants. Predictive models were developed to assist with early detection of ten target invasive plants in the Great Lakes Network of the National Park Service and for garlic mustard throughout the Upper Peninsula of Michigan. These multi-criteria risk models utilize geographic information system (GIS) data to predict the areas at highest risk for three phases of invasion: introduction, establishment, and spread. An accuracy assessment of the models for the ten target plants in the Great Lakes Network showed an average overall accuracy of 86.3%. The model developed for garlic mustard in the Upper Peninsula resulted in an accuracy of 99.0%. Used as one of many resources, the risk maps created from the model outputs will assist with the detection of ecosystem change, the monitoring of plant invasions, and the management of invasive plants through prioritized control efforts.

Help from under ground: soil biota facilitate knotweed invasion

Soil biota can be important drivers of plant community structure. Depending on the balance between antagonistic and mutualistic interactions, they can limit or promote the success of plant species. This is particularly important in the context of exotic plant invasions where soil biota can either increase the biotic resistance of habitats, or they can shift the balance between exotic and native plants towards the exotics and thereby greatly contribute to their dominance. Here, we explored the role of soil biota in the invasion success of exotic knotweed (Fallopia × bohemica), one of the world's most noxious invasive plants. We created artificial native plant communities that were experimentally invaded by knotweed, using a range of substrates where we manipulated different fractions of soil biota. We found that invasive knotweed benefited more from the overall presence of soil biota than any of the six native species. In particular the presence of the full natural soil biota strongly shifted the competitive balance in favor of knotweed. Soil biota promoted both regeneration and growth of the invader, which suggests that soil organisms may be important both in the early establishment of knotweed and possibly its later dominance of native communities. Addition of activated carbon to the soil made the advantage of knotweed disappear, which suggests that the mechanisms underlying the positive soil biota effects are chemically mediated. Our study demonstrates that soil organisms play a key role in the invasion success of exotic knotweed.

Slowing down a pine invasion despite uncertainty in demography and dispersal

1. Management decisions regarding invasive plants often have to be made quickly and in the face of fragmentary knowledge of their population dynamics. However, recommendations are commonly made on the basis of only a restricted set of parameters. Without addressing uncertainty and variability in model parameters we risk ineffective management, resulting in wasted resources and an escalating problem if early chances to control spread are missed. 2. Using available data for Pinus nigra in ungrazed and grazed grassland and shrubland in New Zealand, we parameterized a stage-structured spread model to calculate invasion wave speed, population growth rate and their sensitivities and elasticities to population parameters. Uncertainty distributions of parameters were used with the model to generate confidence intervals (CI) about the model predictions. 3. Ungrazed grassland environments were most vulnerable to invasion and the highest elasticities and sensitivities of invasion speed were to long-distance dispersal parameters. However, there was overlap between the elasticity and sensitivity CI on juvenile survival, seedling establishment and long-distance dispersal parameters, indicating overlap in their effects on invasion speed. 4. While elasticity of invasion speed to long-distance dispersal was highest in shrubland environments, there was overlap with the CI of elasticity to juvenile survival. In shrubland invasion speed was most sensitive to the probability of establishment, especially when establishment was low. In the grazed environment elasticity and sensitivity of invasion speed to the severity of grazing were consistently highest. Management recommendations based on elasticities and sensitivities depend on the vulnerability of the habitat. 5. Synthesis and applications. Despite considerable uncertainty in demography and dispersal, robust management recommendations emerged from the model. Proportional or absolute reductions in long-distance dispersal, juvenile survival and seedling establishment parameters have the potential to reduce wave speed substantially. Plantations of wind-dispersed invasive conifers should not be sited on exposed sites vulnerable to long-distance dispersal events, and trees in these sites should be removed. Invasion speed can also be reduced by removing seedlings, establishing competitive shrubs and grazing. Incorporating uncertainty into the modelling process increases our confidence in the wide applicability of the management strategies recommended here.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

Population genetics of the invasive plant species Impatiens glandulifera in Southern Finland

Biological invasions affect biodiversity worldwide, and, consequently, the invaded ecosystems may suffer from significant losses in economic and cultural values. Impatiens glandulifera Royle (Balsaminaceae) is an invasive annual herb, native to the western Himalayas and introduced into Europe in the 19th century as a garden ornamental plant. The massive invasion of I. glandulifera is due to its high reproductive output, rapid growth and its ability to outcompete native species. In Finland, the first observations regarding the presence of I. glandulifera date from the year 1947, and today it is considered a serious problem in riparian habitats. The aim of this master’s thesis research is to reveal the population genetic structure of I. glandulifera in Finland and to find out whether there have been one or multiple invasions in Finland. The study focuses on investigating the origin of I. glandulifera in Southern Finland, by comparing plant samples from the Helsinki region with those from its native region and other regions of invasion. Samples from four populations in Helsinki and from the United Kingdom, Canada, India and Pakistan were collected and genotyped using 11 microsatellite markers. The genetic analyses were evaluated using the programs Arlequin and Structure. The results of the genetic analyses suggested that I. glandulifera has been introduced to Finland more than once. Multiple introductions are supported by the higher level of genetic diversity detected within and among Finnish populations than would be expected for a single introduction. Results of the Bayesian Structure analysis divided the four Finnish populations into four clusters. This geographical structure was further supported by pairwise Fst values among populations. The causes and potential consequences of such multiple introductions of I. glandulifera in Finland and further perspectives are discussed.