Nitrogen inputs promote the spread of an invasive marsh grass.

Excess nutrient loading and large-scale invasion by nonnatives are two of the most pervasive and damaging threats to the biotic and economic integrity of our estuaries. Individually, these are potent forces, but it is important to consider their interactive impacts as well. In this study we investigated the potential limitation of a nonnative intertidal grass, Spartina alterniflora, by nitrogen (N) in estuaries of the western United States. Nitrogen fertilization experiments were conducted in three mud-flat habitats invaded by S. alterniflora in Willapa Bay, Washington, USA, that differed in sediment N. We carried out parallel experiments in San Francisco Bay, California, USA, in three habitats invaded by hybrid Spartina (S. alterniflora x S. foliosa), in previously unvegetated mud flat, and in native S. foliosa or Salicornia virginica marshes. We found similar aboveground biomass and growth rates between habitats and estuaries, but end-of-season belowground biomass was nearly five times greater in San Francisco Bay than in Willapa Bay. In Willapa Bay, aboveground biomass was significantly correlated with sediment N content. Addition of N significantly increased aboveground biomass, stem density, and the rate of spread into uninvaded habitat (as new stems per day) in virtually all habitats in both estuaries. Belowground biomass increased in Willapa Bay only, suggesting that belowground biomass is not N limited in San Francisco Bay due to species differences, N availability, or a latitudinal difference in the response of Spartina to N additions. The relative impact of added N was greater in Willapa Bay, the estuary with lower N inputs from the watershed, than in San Francisco Bay, a highly eutrophic estuary. Nitrogen fertilization also altered the competitive interaction between hybrid Spartina and Salicornia virginica in San Francisco Bay by increasing the density and biomass of the invader and decreasing the density of the native. There was no significant effect of N on the native, Spartina foliosa. Our results indicate that excess N loading to these ecosystems enhances the vulnerability of intertidal habitats to rapid invasion by nonnative Spartina sp.     

Native ecotypic variation and the role of host identity in the spread of an invasive herbivore, Cactoblastis cactorum

Environmental niche models (ENMs) have gained enormous popularity as tools to investigate potential changes in species distributions resulting from climate change and species introductions. Despite recognition that species interactions can influence the dynamics of invasion spread, most implementations of ENMs focus on abiotic factors as the sole predictors of potential range limits. Implicit in this approach is the assumption that biotic interactions are relatively unimportant, either because of scaling issues, or because fundamental and realized niches are equivalent in a species' native range. When species are introduced into exotic landscapes, changes in biotic interactions relative to the native range can lead to occupation of different regions of niche space and apparent shifts in physiological tolerances. We use an escaped biological control organism, Cactoblastis cactorum (Berg.), to assess the role of the environmental envelope as compared with patterns of host-herbivore associations based on collections made in the native range. Because all nonnative populations are derived from a single C. cactorum ecotype, we hypothesize that biotic interactions associated with this ecotype are driving the species' invasion dynamics. Environmental niche models constructed from known native populations perform poorly in predicting nonnative distributions of this species, except where there is an overlap in niche space. In contrast, genetic isolation in the native range is concordant with the observed pattern of host use, and strong host association has been noted in nonnative landscapes. Our results support the hypothesis that the apparent shift in niche space from the native to the exotic ranges results from a shift in biotic interactions, and demonstrate the importance of considering biotic interactions in assessing the risk of future spread for species whose native range is highly constrained by biotic interactions.     

Local population dynamics of an invasive tree species with a complex life-history cycle: A stochastic matrix model

Biological invasions are widely accepted as having a major impact on ecosystem functioning worldwide, giving urgency to a better understanding of the factors that control their spread. Modelling tools have been developed for this purpose but are often discrete-space, discrete-time spatial-mechanistic models that adopt a computer simulation approach and resist mathematical analysis. We constructed a simple demographic matrix model to explore the local population dynamics of an invasive species with a complex life history and whose invasive success depends on resource availability, which occurs stochastically. As a case study we focused on the American black cherry (Prunus serotina Ehrh.), a gap-dependent tree able both to constitute a long-living seedling bank under unfavourable light conditions and to resprout vigorously once cut-down, which is invading European temperate forests. The model used was a stage-classified matrix population model (i.e., Lefkovitch matrix), integrating environmental stochasticity. Stochastic matrix projection analysis was combined with elasticity analysis and stochastic simulations to search for the species’ ‘Achille heel’. As expected, the population growth rate (i.e., Lyapunov exponent), which measures the risk of P. serotina invasion at the stand scale, increased with light frequency. There was a critical value above which the population of P. serotina explodes and below which it locally goes extinct. The resprouting capacity usually speed up the invasion but appeared to play a minor role. The mean duration of stand invasion was measured and important life stage transitions that mostly contribute to the local stochastic growth rate were identified. Some relevant management implications are discussed and the interest of such models for the understanding of demographic characteristics of invasive species is stressed.     

Soil biota drive expression of genetic variation and development of population-specific feedbacks in an invasive plant

Invasive plant species alter soils in ways that may affect the success of subsequent generations, creating plant-soil feedbacks. Ailanthus altissima is an invasive tree introduced two centuries ago to North America. We hypothesized that geographically distinct populations of A. altissima have established feedbacks specific to their local environment, due to soil communities cultivated by A. altissima. We collected seeds and soils from three populations in the eastern United States, and in the greenhouse reciprocally planted all families in all collected soils as well as in a control mixed soil, and in soils that had been irradiated for sterilization. There were positive plant-soil feedbacks for two populations in the live field-collected soils, but strong negative feedbacks for the third population. There were no population-level performance differences or feedbacks in the sterilized population locale soils, supporting a soil biotic basis for feedbacks and for the expression of genetic differentiation in A. altissima. If populations of Ailanthus altissima vary in the extent to which they benefit from and promote these plant-soil biota feedbacks, the interaction between invader and invaded community may be more important in determining the course of invasion than are the characteristics of either alone.     

Importance of individual and environmental variation for invasive species spread: a spatial integral projection model

Plant survival, growth, and flowering are size dependent in many plant populations but also vary among individuals of the same size. This individual variation, along with variation in dispersal caused by differences in, e.g., seed release height, seed characteristics, and wind speed, is a key determinant of the spread rate of species through homogeneous landscapes. Here we develop spatial integral projection models (SIPMs) that include both demography and dispersal with continuous state variables. The advantage of this novel approach over discrete-stage spread models is that the effect of variation in plant size and size-dependent vital rates can be studied at much higher resolution. Comparing Neubert-Caswell matrix models to SIPMs allowed us to assess the importance of including individual variation in the models. As a test case we parameterized a SIPM with previously published data on the invasive monocarpic thistle Carduus nutans in New Zealand. Spread rate (c*) estimates were 34% lower than for standard spatial matrix models and stabilized with as few as seven evenly distributed size classes. The SIPM allowed us to calculate spread rate elasticities over the range of plant sizes, showing the size range of seedlings that contributed most to c* through their survival, growth and reproduction. The annual transitions of these seedlings were also the most important ones for local population growth (lambda). However, seedlings that reproduced within a year contributed relatively more to c* than to lambda. In contrast, plants that grow over several years to reach a large size and produce many more seeds, contributed relatively more to lambda than to c*. We show that matrix models pick up some of these details, while other details disappear within wide size classes. Our results show that SIPMs integrate various sources of variation much better than discrete-stage matrix models. Simpler, heuristic models, however, remain very valuable in studies where the main goal is to investigate the general impact of a life history stage on population dynamics. We conclude with a discussion of future extensions of SIPMs, including incorporation of continuous time and environmental drivers.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Analytical expression for predicting the reduced settling velocity of small particles in turbulence

The influence of turbulence on the settling velocity of small particles remains an inconclusive research subject. Both enhanced and retarded particle settling compared to quiescent settling have been reported in previous literature, and several theories have been proposed. Among the mechanisms that account for reduced settling velocities in turbulence, the loitering effect is an important one that generally exists in various conditions. This study focuses on an analytical prediction of reduced particle settling velocities due to the loitering effect. By considering the velocity autocorrelation function as a step function analogous to the free path theory, and using a modified integral time scale to capture the essence of the loitering effect, an analytical expression is derived for predicting the reduction in particle settling velocities in turbulent flows. Calculation results of the expression are then examined by comparing with the results of a random walk model, direct numerical simulations that conditionally captured the reduction of particle settling velocities due to the loitering effect, and several representative experiments. Major possible influencing factors on the prediction of the analytical expression and applicable conditions of the expression are then further discussed. The proposed analytical expression is shown to be suitable for predicting the reduced settling velocities of small particles with relatively weak inertia in turbulent flows and could provide a reasonable explanation for reported cases in which small to moderate reductions in particle settling velocities were observed.

     

An analytical model of an urban heat island circulation in calm conditions

Environmental Fluid Mechanics - We obtain an analytical model solution to an idealization of an urban heat island (UHI) circulation—the steady shallow convective flow of a viscous stably...     

A state-dependent model for the optimal management of an invasive metapopulation.

Management of invasive species involves choosing between different management strategy options, but often the best strategy for a particular scenario is not obvious. We illustrate the use of optimization methods to determine the most efficient management strategy using one of the most devastating invasive forest pests in North America, the gypsy moth (Lymantria dispar), as a case study. The optimization approach involves the application of stochastic dynamic programming (SDP) to a metapopulation framework with different infestation patch sizes, with the goal of minimizing infestation spread. We use a novel "moving window" approach as a way to address a spatially explicit problem without being explicitly spatial. We examine results for two cases in order to develop general rules of thumb for management. We explore a model with limited parameter information and then assess how strategies change with specific parameterization for the gypsy moth. The model results in a complex but stable, state-dependent management strategy for a multiyear management program that is robust even under situations of uncertainty. The general rule of thumb for the basic model consists of three strategies: eradicating medium-density infestations, reducing large-density infestations, and reducing the colonization rate from the main infestation, depending on the state of the system. With specific gypsy moth parameterization, reducing colonization decreases in importance relative to the other two strategies. The application of this model to gypsy moth management emphasizes the importance of managing based on the state of the system, and if applied to a specific geographic area, has the potential to substantially improve the efficiency and cost-effectiveness of current gypsy moth eradication programs, helping to slow the spread of this pest. Additionally, the approach used for this particular invasive species can be extended to the optimization of management programs for the spread of other invasive and problem species exhibiting metapopulation dynamics.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Flow and temperature dynamics in an urban canyon under a comprehensive set of wind directions,wind speeds,and thermal stability conditions

Environmental Fluid Mechanics - Atmospheric flow and temperature dynamics in the urban roughness sublayer exhibit numerous complexities that cannot all be investigated using models or scaled-down...     

Modeling the longitudinal profiles of streamwise velocity in an open channel with a model patch of vegetation

This paper proposes a model for predicting the longitudinal profiles of streamwise velocities in an open channel with a model patch of vegetation. The governing equation was derived from the momentum equation and flow continuity equation. The model can estimate the longitudinal profiles of velocities both inside and outside a vegetation patch. Laboratory experiments indicate that the longitudinal profiles of velocities inside a patch and in the adjacent bare channel have the same adjustment distance in the longitudinal direction, but the profiles have different trends because the vegetation drag drives the flow from the patch to the adjacent bare channel. The model considers different dimensionless parameters in two flow adjustment regions upstream of and inside the patch. Sixteen sets of experimental data from different sources are used to verify the model. The model is capable of modeling the longitudinal profiles of velocities inside and outside patches of cylinders or cylinder-like plants. Compared to a previous model, the current model improves the modeling accuracy of longitudinal profiles of velocities.

     

The invasive gastropod Crepidula fornicata: reproduction and recruitment in the intertidal at its northernmost range in Wales, UK, and implications for its secondary spread

The establishment and spread of a non-native species in an introduced range depends to a large extent on the performance of the species under the prevailing environmental conditions. The spawning, larval and spatfall periods of the invasive gastropod Crepidula fornicata were monitored in the intertidal zone at its northernmost range in Wales, UK, between February 2010 and January 2011. The duration of the reproductive season was similar to that recorded from more southerly European populations. Spawning and larval release occurred throughout most of the year even at low seawater temperatures of <7 °C, but benthic recruitment was observed over a much shorter period at seawater temperatures >16 °C. Recruitment was low and likely controlled by post-settlement mortality. These observations suggest that C. fornicata’s northwards spread in Welsh waters will not be limited by seawater temperature negatively affecting reproduction, but by processes acting after larval release. These data show the importance of incorporating settlement and post-settlement processes into studies on recruitment success when aiming to predict the potential spread of a potentially harmful invader such as C. fornicata.     

Temporal limits to simulating the future spread pattern of invasive species: Buddleja davidii in Europe and New Zealand

The spread of invasive species is a major ecological and economic problem. Dynamic spread modelling is a potentially valuable tool to assist regional and central government authorities to monitor and control invasive species. To date a lack of suitable data has meant that most broad scale dispersal models have not been validated with independent datasets, and so their predictive ability and reliability has remained unscrutinised. A dynamic, stochastic dispersal model of the widely invasive plant Buddleja davidii was calibrated on European spread data and then used to project the temporal progression of B. davidii's distribution in New Zealand, starting from several different historical distributions. To assess the model's performance, we constructed an occupancy map based on the average number of simulation realisations that have a population present. The application of Receiver Operating Characteristic (ROC) curves to occupancy maps is introduced, but with specificity substituted by the proportion of available area used in a realisation. A derivative measure, the partial area under these curves when assessed through time (pAUC), is introduced and used to assess overall performance of the spread model. The model was able to attain a high level of model sensitivity, encompassing all of the known locations within the occupancy envelope. However, attempting to simulate the spread of this invasive species beyond a decade had very low model specificity. This is due to several factors, including the exponential process of spread (the further a population spreads the more sites exist from which it can spread stochastically), and the Markovian chain property of the stochastic system whereby differences between realisations compound through time. These features are seen in many reports of spread models, without being explicitly acknowledged. Our measure of pAUC through time allows a model's temporal performance and its specificity to be simultaneously assessed. While the rapid deterioration in model performance limits the utility of this type of modelling for forecasting long-term broad-scale strategic management of biological invasions, it does not necessarily limit its attractiveness for informing smaller scale and shorter term invasion management activities such as surveillance, containment and local eradication.     

The importance of space, time, and stochasticity to the demography and management of Alliaria petiolata

As population modeling is increasingly called upon to guide policy and management, it is important that we understand not only the central tendencies of our study systems, but the consequences of their variation in space and time as well. The invasive plant Alliaria petiolata (garlic mustard) is actively managed in the United States and is the focus of a developing biological control program. Two weevils (Coleoptera: Curculionidae: Ceutorhynchus) that reduce fecundity (C. alliariae) and rosette survival plus fecundity (C. scrobicollis) are under consideration for release pending host specificity testing. We used a demographic modeling approach to (1) quantify variability in A. petiolata growth and vital rates and (2) assess the potential for single- or multiple-agent biocontrol to suppress growth of 12 A. petiolata populations in Illinois and Michigan studied over three plant generations. We used perturbation analyses and simulation models with stochastic environments to estimate stochastic growth rates (lambda(S)) and predict the probability of successful management using either a single biocontrol agent or two agent species together. Not all populations exhibited invasive dynamics. Estimates of lambda(S) ranged from 0.78 to 2.21 across sites, while annual, deterministic growth (lambda) varied up to sevenfold within individual sites. Given our knowledge of the biocontrol agents, this analysis suggests that C. scrobicollis alone may control A. petiolata at up to 63% of our study sites where lambda >1, with the combination of both agents predicted to succeed at 88% of sites. Across sites and years, the elasticity rankings were dependent on lambda. Reductions of rosette survival, fecundity, or germination of new seeds are predicted to cause the greatest reduction of lambda in growing populations. In declining populations, transitions affecting seed bank survival have the greatest effect on lambda. This contrasts with past analyses that varied parameters individually in an otherwise constant matrix, which may yield unrealistic predictions by decoupling natural parameter covariances. Overall, comparisons of stochastic and deterministic growth rates illustrate how analyses of individual populations or years could misguide management or fail to characterize complex traits such as invasiveness that emerge as attributes of populations rather than species.     

SPAnDX: a process-based population dynamics model to explore management and climate change impacts on an invasive alien plant,Acacia nilotica

This paper describes a process-based metapopulation dynamics and phenology model of prickly acacia, Acacia nilotica, an invasive alien species in Australia. The model, SPAnDX, describes the interactions between riparian and upland sub-populations of A. nilotica within livestock paddocks, including the effects of extrinsic factors such as temperature, soil moisture availability and atmospheric concentrations of carbon dioxide. The model includes the effects of management events such as changing the livestock species or stocking rate, applying fire, and herbicide application. The predicted population behaviour of A. nilotica was sensitive to climate. Using 35 years daily weather datasets for five representative sites spanning the range of conditions that A. nilotica is found in Australia, the model predicted biomass levels that closely accord with expected values at each site. SPAnDX can be used as a decision-support tool in integrated weed management, and to explore the sensitivity of cultural management practices to climate change throughout the range of A. nilotica. The cohort-based DYMEX modelling package used to build and run SPAnDX provided several advantages over more traditional population modelling approaches (e.g. an appropriate specific formalism (discrete time, cohort-based, process-oriented), user-friendly graphical environment, extensible library of reusable components, and useful and flexible input/output support framework).