Shaun R. Coutts

What are the key drivers of spread in invasive plants: Dispersal, demography or landscape: And how can we use this knowledge to aid management?

Invasive plants disrupt ecosystems from local to landscape scales. Reduction or reversal of spread is an important goal of many invasive plant management strategies, but few general guidelines exist on how to achieve this aim. We identified the main drivers of spread, and thus potential targets for management, using a spatially explicit simulation model tested on different life history categories in different spread and landscape scenarios. We used boosted regression trees to determine the parameters that most affected spread. Additionally, we analysed how spread reacted to changes in those parameters over a broad realistic range. From our results we deduce four simple management guidelines: (1) Manage dispersal if possible, as mean dispersal distance was an important driver of spread for all life history categories; (2) short bursts of rapid spread or more usual year on year spread can have different drivers, therefore managers need to decide what type of spread they want to slow; (3) efforts to manage spread will have variable outcomes due to interactions between, and non-linear responses to, key drivers of spread; and (4) the most useful demographic rates to target depend on dispersal ability, life history and how spread is measured. Fecundity was found to be important for driving spread only when reduced to low levels and particularly when the species was short lived. For longer lived species management should target survival, or age of maturity, especially when dispersal ability is limited.

Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants

Invasive plants cause substantial economic and environmental damage throughout the world. However, eradication of most invasive species is impossible and, in some cases, undesirable. An alternative is to slow the spread of an invasive species, which can delay impacts or reduce their extent. We identify three main areas where models are used extensively in the study of plant spread and its management: (i) identifying the key drivers of spread to better target management, (ii) determining the role spatial structure of landscapes plays in plant invasions, and (iii) integrating management structures and limitations to guide the implementation of control measures. We show how these three components have been approached in the ecological literature as well as their potential for improving management practices. Particularly, we argue that scientists can help managers of invasive species by providing information about plant invasion on which managers can base their decisions (i and ii) and by modeling the decision process through optimization and agent‐based models (iii). Finally, we show how these approaches can be articulated for integrative studies.

Meta-models as a straightforward approach to the sensitivity analysis of complex models

Complex simulation models are important tools in applied ecological and conservation research. However sensitivity analysis of this important class of models can be difficult to conduct. High level interactions and non-linear responses are common in complex simulations, and this necessitates a global sensitivity analysis, where each parameter is tested at a range of values, and in combination with changes in many other parameters. We reviewed the literature, searching for population viability analyses that used simulation models. We found only 9 out of the 122 simulation population viability analysis used global sensitivity analysis. This result is typical of other simulation models in applied ecology, where global sensitivity analysis is rare. We then demonstrate how to conduct a meta-modeling sensitivity analysis, where a simpler statistically fit function (the meta-model, also known as the surrogate model or emulator) is used to approximate the behavior of the complicated simulation. This simpler meta-model is interrogated to inform on the behavior of simulation model. We fit two example meta-models, a generalized linear model and a boosted regression tree, to exemplify the approach. Our hope is that by going through these techniques thoroughly they will become more widely adopted.

The behavior of multiple independent managers and ecological traits interact to determine prevalence of weeds

Management of damaging invasive plants is often undertaken by multiple decision makers, each managing only a small part of the invaders population. As weeds can move between properties and re-infest eradicated sites from unmanaged sources, the dynamics of multiple decision makers plays a significant role in weed prevalence and invasion risk at the landscape scale. We used a spatially explicit agent-based simulation to determine how individual agent behavior, in concert with weed population ecology, determined weed prevalence. We compared two invasive grass species that differ in ecology, control methods, and costs: Nassella trichotoma (serrated tussock) and Eragrostis curvula (African love grass). The way decision makers reacted to the benefit of management had a large effect on the extent of a weed. If benefits of weed control outweighed the costs, and either net benefit was very large or all agents were very sensitive to net benefits, then agents tended to act synchronously, reducing the pool of infested agents available to spread the weed. As N. trichotoma was more damaging than E. curvula and had more effective control methods, agents chose to manage it more often, which resulted in lower prevalence of N. trichotoma. A relatively low number of agents who were intrinsically less motivated to control weeds led to increased prevalence of both species. This was particularly apparent when long-distance dispersal meant each infested agent increased the invasion risk for a large portion of the landscape. In this case, a small proportion of land mangers reluctant to control, regardless of costs and benefits, could lead to the whole landscape being infested, even when local control stopped new infestations. Social pressure was important, but only if it was independent of weed prevalence, suggesting that early access to information, and incentives to act on that information, may be crucial in stopping a weed from infesting large areas. The response of our model to both behavioral and ecological parameters was highly nonlinear. This implies that the outcomes of weed management programs that deal with multiple land mangers could be highly variable in both space and through time.

Integrating ecological knowledge, public perception and urgency of action into invasive species management.

Recently Prévot-Julliard and colleagues presented a concept paper on biological conservation strategies using exotic species as a case study. They emphasized the difficulty of integrating conservation into a broad picture that accounts for public perception as well as scientific knowledge. We support this general call for better integration of society in conservation research, but we believe that the original framework might misguide conservation practices if wrongly interpreted. Our objective is to complement their paper and correct a few misleading points, by showing that (1) for regions of high endemicity “reservation” may be the best conservation practice, and does not prevent public participation, (2) aiming for broad societal agreement is valuable, but in some cases risky, and always complex, and (3) calling a harmful invasive species harmful shouldn’t be an issue. The Australian context provides us with many cases of the labeling of exotic species as harmful or not, using inputs from scientists, industry, and the public. Integration of social and scientific points of view can only improve conservation on the ground if it allows managers to use the ecological, economic and social impacts of exotic species to prioritize conservation actions in an operative way.

Decision science for effective management of populations subject to stochasticity and imperfect knowledge

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.

Are high-impact species predictable? An analysis of naturalised grasses in northern Australia

Predicting which species are likely to cause serious impacts in the future is crucial for targeting management efforts, but the characteristics of such species remain largely unconfirmed. We use data and expert opinion on tropical and subtropical grasses naturalised in Australia since European settlement to identify naturalised and high-impact species and subsequently to test whether high-impact species are predictable. High-impact species for the three main affected sectors (environment, pastoral and agriculture) were determined by assessing evidence against pre-defined criteria. Twenty-one of the 155 naturalised species (14%) were classified as high-impact, including four that affected more than one sector. High-impact species were more likely to have faster spread rates (regions invaded per decade) and to be semi-aquatic. Spread rate was best explained by whether species had been actively spread (as pasture), and time since naturalisation, but may not be explanatory as it was tightly correlated with range size and incidence rate. Giving more weight to minimising the chance of overlooking high-impact species, a priority for biosecurity, meant a wider range of predictors was required to identify high-impact species, and the predictive power of the models was reduced. By-sector analysis of predictors of high impact species was limited by their relative rarity, but showed sector differences, including to the universal predictors (spread rate and habitat) and life history. Furthermore, species causing high impact to agriculture have changed in the past 10 years with changes in farming practice, highlighting the importance of context in determining impact. A rationale for invasion ecology is to improve the prediction and response to future threats. Although our study identifies some universal predictors, it suggests improved prediction will require a far greater emphasis on impact rather than invasiveness, and will need to account for the individual circumstances of affected sectors and the relative rarity of high-impact species.

Less favourable climates constrain demographic strategies in plants

Abstract Correlative species distribution models are based on the observed relationship between species’ occurrence and macroclimate or other environmental variables. In climates predicted less favourable populations are expected to decline, and in favourable climates they are expected to persist. However, little comparative empirical support exists for a relationship between predicted climate suitability and population performance. We found that the performance of 93 populations of 34 plant species worldwide – as measured by in situ population growth rate, its temporal variation and extinction risk – was not correlated with climate suitability. However, correlations of demographic processes underpinning population performance with climate suitability indicated both resistance and vulnerability pathways of population responses to climate: in less suitable climates, plants experienced greater retrogression (resistance pathway) and greater variability in some demographic rates (vulnerability pathway). While a range of demographic strategies occur within species’ climatic niches, demographic strategies are more constrained in climates predicted to be less suitable.

Reproductive ecology of Pinus nigra in an invasive population: individual- and population-level variation in seed production and timing of seed release

Abstract• ContextThe details of fecundity, such as its distribution and timing, can have important consequences for forest dynamics.• AimsWe detail two aspects of the reproductive ecology of an exotic population of Pinus nigra in New Zealand. We compare our findings with those reported for P. nigra in southern France and Britain.• MethodsWe describe variation in fecundity, both within the population and through time, and relate seed release to climatic conditions.• ResultsOn average, trees entered reproduction earlier than reported in European studies. Although the mean number of cones per tree varied through time, the distribution of cone production among trees was consistently best described using a negative binomial or mixed gamma-exponential distribution. Both distributions are right skewed and trees maintained fecundity hierarchies over time, suggesting that some trees in the population have much higher lifetime reproduction than others. We found that trees released significantly more seeds when conditions were dry and windy, potentially increasing the proportion of seeds that disperse long distances.• ConclusionsRight-skewed fecundity distributions have the potential to slow spread rates, while preferentially releasing seeds in dry windy conditions is likely to increase spread rates. The net effect of these processes is an open question.

Extrapolating demography with climate, proximity and phylogeny: approach with caution

Plant population responses are key to understanding the effects of threats such as climate change and invasions. However, we lack demographic data for most species, and the data we have are often geographically aggregated. We determined to what extent existing data can be extrapolated to predict population performance across larger sets of species and spatial areas. We used 550 matrix models, across 210 species, sourced from the COMPADRE Plant Matrix Database, to model how climate, geographic proximity and phylogeny predicted population performance. Models including only geographic proximity and phylogeny explained 5-40% of the variation in four key metrics of population performance. However, there was poor extrapolation between species and extrapolation was limited to geographic scales smaller than those at which landscape scale threats typically occur. Thus, demographic information should only be extrapolated with caution. Capturing demography at scales relevant to landscape level threats will require more geographically extensive sampling.