Less favourable climates constrain demographic strategies in plants

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

Considering weed management as a social dilemma bridges individual and collective interests

Weeds pose severe threats to agricultural and natural landscapes worldwide. One major reason for the failure to effectively manage weeds at landscape scales is that current Best Management Practice guidelines, and research on how to improve such guidelines, focus too narrowly on property-level management decisions. Insufficiently considered are the aggregate effects of individual actions to determine landscape-scale outcomes, or whether there are collective practices that would improve weed management outcomes. Here, we frame landscape-scale weed management as a social dilemma, where trade-offs occur between individual and collective interests. We apply a transdisciplinary system approach—integrating the perspectives of ecologists, evolutionary biologists and agronomists into a social science theory of social dilemmas—to four landscape-scale weed management challenges: (i) achieving plant biosecurity, (ii) preventing weed seed contamination, (iii) maintaining herbicide susceptibility and (iv) sustainably using biological control. We describe how these four challenges exhibit characteristics of ‘public good problems’, wherein effective weed management requires the active contributions of multiple actors, while benefits are not restricted to these contributors. Adequate solutions to address these public good challenges often involve a subset of the eight design principles developed by Elinor Ostrom for ‘common pool social dilemmas’, together with design principles that reflect the public good nature of the problems. This paper is a call to action for scholars and practitioners to broaden our conceptualization and approaches to weed management problems. Such progress begins by evaluating the public good characteristics of specific weed management challenges and applying context-specific design principles to realize successful and sustainable weed management

A detailed quantification of differential ratings of perceived exertion during team-sport training

Objectives: To investigate the application of differential ratings of perceived exertion (dRPE) to team-sport training. Design: Single cohort, observational study. Methods:Twenty-nine professional rugby union players were monitored over a six-week intensified training period. Training sessions were classified as: high-intensity intervals, repeated high-intensity efforts, speed, skill-based conditioning, skills, whole-body resistance, or upper-body resistance. After each session, players recorded a session rating of perceived exertion (sRPE; CR100®), along with differential session ratings for breathlessness (sRPE-B), leg muscle exertion (sRPE-L), upper-body muscle exertion (sRPE-U), and cognitive/technical demands (sRPE-T). Each score was multiplied by the session duration to calculate session training loads. Data were analysed using mixed linear modelling and multiple linear regression, with magnitude-based inferences subsequently applied. Results: Between-session differences in dRPE scores ranged from very likely trivial to most likely extremely large and within-session differences amongst dRPE scores ranged from unclear to most likely very large. Differential RPE training loads combined to explain 66–91% of the variance in sRPE training loads, and the strongest associations with sRPE training load were with sRPE-L for high-intensity intervals (r = 0.67; 90% confidence limits ±0.22), sRPE-B for repeated high-intensity efforts (0.89; ±0.08) and skill-based conditioning (0.67; ±0.19), sRPE-T for Speed (0.63; ±0.17) and Skills (0.51; ±0.28), and sRPE-U for resistance training (whole-body: 0.61; ±0.21, upper-body: 0.92; ±0.07). Conclusions: Differential RPE can provide a detailed quantification of internal load during training activities commonplace in team sports. Knowledge of the relationships between dRPE and sRPE can isolate the specific perceptual demands of different training modes

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 invader's 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

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

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

Appendix A. Mean field approximation of the simulation.

Mean field approximation of the simulation

Best models predicting high-impact species using a statistical learning approach.

<p>Model weighting assumption was tested by comparing true positives and false negatives equally (<i>w</i> = 0.5) (comparable to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068678#pone-0068678-t002" target="_blank">Table 2</a>) and weighting true positives more heavily than false negatives) (<i>w</i> = 0.9). <i>Weuc</i> is expressed as a proportion of the maximum possible value given the value of <i>w</i>, thus in both cases a perfect classifier would have a <i>Weuc</i> of 0, and a classifier that is guessing randomly will have a <i>Weuc</i> of 1.</p

Spread rate of each species (n = 155) including high impact species in each sector.

<p>High impact species in each sector are highlighted in separate panels (black dots). Data points are randomly jittered across the y-axis to make visualisation clearer. The very large outlier is explained in the bottom panel.</p