Adaptive management for eradication of exotic species

The problems of controlling exotic species have been acknowledged as serious threats to an indigenous ecosystem as well as to society. In response to these threats, various management programs of exotic species have been proposed for supporting eradication in many regions. Although eradication is regarded as the first-best solution, such attempts have been unsuccessful in reality due to mainly two factors: (1) stock-dependent catchability, and (2) uncertainties. This article demonstrates when to aim at eradication through addressing an optimal adaptive management strategy in the framework of a bio-economic model with the aforementioned factors. The study sets out that the sensitivity of catchability in response to a change in the existing stock determines whether or not aiming at eradication is justified. The results also show that process uncertainty associated with stock growth significantly affects the timing of removal actions for eradication, and an increase in the degree of uncertainty could help achieve eradication in a cost–effective manner if we optimally adapt our removal actions to the uncertainty.     

To what extent can ecosystem services motivate protecting biodiversity?

Society increasingly focuses on managing nature for the services it provides people rather than for the existence of particular species. How much biodiversity protection would result from this modified focus? Although biodiversity contributes to ecosystem services, the details of which species are critical, and whether they will go functionally extinct in the future, are fraught with uncertainty. Explicitly considering this uncertainty, we develop an analytical framework to determine how much biodiversity protection would arise solely from optimising net value from an ecosystem service. Using stochastic dynamic programming, we find that protecting a threshold number of species is optimal, and uncertainty surrounding how biodiversity produces services makes it optimal to protect more species than are presumed critical. We define conditions under which the economically optimal protection strategy is to protect all species, no species, and cases in between. We show how the optimal number of species to protect depends upon different relationships between species and services, including considering multiple services. Our analysis provides simple criteria to evaluate when managing for particular ecosystem services could warrant protecting all species, given uncertainty. Evaluating this criterion with empirical estimates from different ecosystems suggests that optimising some services will be more likely to protect most species than others.     

Climate change,uncertainty, and natural resource management

Climate change and its associated uncertainties are of concern to natural resource managers. Although aspects of climate change may be novel (e.g., system change and nonstationarity), natural resource managers have long dealt with uncertainties and have developed corresponding approaches to decision-making. Adaptive resource management is an application of structured decision-making for recurrent decision problems with uncertainty, focusing on management objectives, and the reduction of uncertainty over time. We identified 4 types of uncertainty that characterize problems in natural resource management. We examined ways in which climate change is expected to exacerbate these uncertainties, as well as potential approaches to dealing with them. As a case study, we examined North American waterfowl harvest management and considered problems anticipated to result from climate change and potential solutions. Despite challenges expected to accompany the use of adaptive resource management to address problems associated with climate change, we conclude that adaptive resource management approaches will be the methods of choice for managers trying to deal with the uncertainties of climate change. © 2010 The Wildlife Society.     

The influence of time since introduction on the population growth of introduced species and the consequences for management

Several processes likely act to change the demographic rates of introduced species over time, and this changing demography could influence the optimal management of invasive populations. Optimal management strategies should be derived based on the demography. However, we have a poor understanding of the degree to which the demography of introduced species changes following initial introduction. We used published matrix population models of introduced plant populations to test how population growth rate and elasticity change with time since introduction. We did not find a significant relationship between population growth rate and time since introduction. However, elasticity to stasis increased while elasticity to growth decreased with time since introduction. Broadly, as time since introduction progressed the elasticities of the introduced plant populations became more similar to those that have been reported for native species. These results suggest that the optimal management strategy should be derived incorporating elasticity through time, especially when the time scope of management is long or the available demographic data were obtained in the past.     

Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database

Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100‐year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.     

Assessment of the uncertainty of trace metal and nitrogen concentrations in mosses due to sampling,sample preparation and chemical analysis based on the French contribution to ICP-Vegetation

Moss bio-monitoring is a convenient tool for establishing specific or large-scale exposure to atmospheric trace metals and nitrogen pollution. However, the uncertainty associated with sampling, sample preparation and chemical analysis of bio-monitors has been poorly documented, with the exception of one study dealing with lichens; thus, the uncertainty associated with moss bio-monitoring has never been assessed. Here, we propose following the Eurachem guidelines to determine the uncertainties associated with the concentrations of elements measured in mosses during the sampling survey across France in 2011. In addition, we assess the analytical method used in four surveys from 1996 to 2011. Uncertainties were expressed as linear functions of the element concentrations, with minimum and maximum slopes of 14 and 61%, corresponding to nitrogen and chromium, and a median of 32%. Although the data reveal that some steps of the protocol should be performed carefully, they also indicate that the protocol was executed properly and is reproducible. The chemical analyses contribute to a small proportion of the uncertainty associated with the protocol, except for the analysis of chromium. The sampling period, and intra-site variability largely contribute to this uncertainty. The moss species did not introduce additional uncertainty. This integrative assessment of measurement uncertainty will help improve the protocol for future surveys. Such studies could be useful for developing a standard operating procedure and could be used to improve comparisons between countries and to identify temporal trends across Europe.     

Designing nature reserves in the face of uncertainty

Conservation reserves are a fundamental tool for managing biodiversity. The so-called SLOSS debate--should we have a Single Large Or Several Small reserves - is central to conservation theory. Population dynamic models suggest that the design that minimizes the risk of extinction of a species is case-specific, with the optimal number of reserves ranging between one and very many. Uncertainty is pervasive in ecology, but, the previous analyses of the SLOSS debate have not considered how uncertainty in the model of extinction risk might influence the optimal design. Herein, we show that when uncertainty is considered, the SLOSS problem is simplified and driven more by the aspirations of the manager than the population dynamics of the species. In this case, the optimal solution is to have in the order of twenty or fewer reserves for any species. This result shows counter-intuitively that considering uncertainty actually simplifies rather than complicates decisions about designing nature reserves.     

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.     

Multicriteria Bayesian Analysis of Lower Trophic Level Uncertainties and Value of Research in Lake Erie

Human activities have severely disrupted the Lake Erie ecosystem. Recent changes in the structure of the lower trophic level associated with exotic species invasions and reduced nutrient loading have created ecological uncertainties for fisheries management. Decisions that naïvely assume certainty may be different and suboptimal compared to choices that consider uncertainty. Here we illustrate how multiobjective Bayesian decision analysis can recognize the multiple goals of management in evaluations of the effect of ecological uncertainties on management and the value of information from ecological research. Value judgments and subjective probabilities required by the decision analysis were provided by six Lake Erie fishery agency biologists. The Lake Erie Ecological Model was used to project the impacts of each combination of management actions and lower trophic level parameter values. The analysis shows that explicitly considering lower trophic level uncertainties can alter decisions concerning Lake Erie fishery harvests. Of the research projects considered, investigation of goby predation of zebra mussels (Dreissena sp.) and lakewide estimation of secondary production appear to have the greatest expected value for fisheries management. We also find that changes in the weights assigned to management goals affects decisions and value of information more than do changes in probability judgments.     

Model parameter uncertainties in a dual-species biofilm competition model affect ecological output parameters much stronger than morphological ones

Bacterial biofilms are complex microbial depositions on immersed interfaces that form wherever the environmental conditions sustain microbial growth. Despite their name, biofilms can develop in highly irregular structures. Recently several mathematical concepts have been introduced to model these spatially structured microbial populations. Regardless of the type of model, they all have, even for microbially relatively simple systems, many parameters which generally are known at most approximately. We investigate the effect of uncertainties in model parameters on four morphological and four ecological output parameters using a nonlinear diffusion model for a biofilm in which two species compete for a shared nutrient. To this end we conduct an extensive computer simulation experiment for two different levels of data uncertainty, three different hydrodynamic conditions, and two different scenarios of bulk substrate availability. Our results indicate that input model parameter uncertainties have a much larger effect on ecological than on morphological output parameters.     

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.     

Demographic factors affecting population growth in giant gartersnakes

Demographic models provide insight into which vital rates and life stages contribute most to population growth. Integral projection models (IPMs) offer flexibility in matching model structure to a species’ demography. For many rare species, data are lacking for key vital rates, and uncertainty might dissuade researchers from attempting to build a demographic model. We present work that highlights how the implications of uncertainties and unknowns can be explored by building and analyzing alternative models. We constructed IPMs for the threatened giant gartersnake (Thamnophis gigas) based on published studies to determine where management efforts could be targeted to have the greatest effect on population persistence and what unknowns remain for future research. Given uncertainty in the survival of snakes during their first year, and in the form of the size-survival relationship, we modeled a range of scenarios and evaluated where models agree about factors influencing population growth and where discrepancies exist. For most scenarios, the survival of large adult females had the greatest influence on population growth, but the relative importance of juvenile versus adult somatic growth for population growth was dependent on the recruitment probability and the shape of the size-survival function. More data on temporal variation and covariance among vital rates would improve stochastic models for the giant gartersnake. This paper demonstrates the effectiveness of IPMs for studying the demography of reptiles and the value of the model-building process for formalizing what is known and unknown about the demography of rare species. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.     

Challenges and trade-offs in the management of invasive alien trees

Over 430 alien tree species worldwide are known to be invasive, and the list is growing as more tree species are moved around the world and become established in novel environments. Alien trees can simultaneously bring many benefits and cause substantial environmental harm, very often leading to conflicts over how they should be managed. The impacts grow over time as invasions spread, and societal perceptions of the value of alien trees also change as understanding grows and as values shift. This leads to a dynamic environment in which trade-offs are required to maximise benefits and minimise harm. The management of alien tree populations needs to be strategic and adaptive, combining all possible management interventions to promote the sustainable delivery of optimal outcomes. We use examples, mainly from South Africa (where issues relating to invasive alien trees introduced for forestry have received most attention), to argue for holistic and collaborative approaches to alien tree management. Such approaches need to include bold steps, such as phasing out unsustainable plantation forestry that is based on highly invasive species, and in which the costs are externalised. Furthermore, it would be advisable to impose much stricter controls on the introduction of alien trees to new environments, so that problems that would arise from subsequent invasions can be avoided.     

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

Environmental risk analysts need to draw from a clear typology of uncertainties when qualifying risk estimates and/or significance statements about risk. However, categorizations of uncertainty within existing typologies are largely overlapping, contradictory, and subjective, and many typologies are not designed with environmental risk assessments (ERAs) in mind. In an attempt to rectify these issues, this research provides a new categorization of uncertainties based, for the first time, on the appraisal of a large subset of ERAs, namely 171 peer-reviewed environmental weight-of-evidence assessments. Using this dataset, a defensible typology consisting of seven types of uncertainty (data, language, system, extrapolation, variability, model, and decision) and 20 related sub-types is developed. Relationships between uncertainties and the techniques used to manage them are also identified and statistically evaluated. A highly preferred uncertainty management option is to take no action when faced with uncertainty, although where techniques are applied they are commensurate with the uncertainty in question. Key observations are applied in the form of guidance for dealing with uncertainty, demonstrated through ERAs of genetically modified higher plants in the European Union. The presented typology and accompanying guidance will have positive implications for the identification, prioritization, and management of uncertainty during risk characterization.     

A systematic review of adaptive wildlife management for the control of invasive,non-native mammals,and other human–wildlife conflicts

1. We are entering an era where species declines are occurring at their fastest ever rate, and the increased spread of non-native species is among the top causes. High uncertainty in biological processes makes the accurate prediction of the outcomes of management interventions very challenging. Adaptive management (AM) offers solutions to reduce uncertainty and improve predictability so that the outcomes of interventions can continuously improve.
2. We quantitatively assess the extent to which AM is used for managing vertebrates, with a focus on invasive non-native species (INNS). Using the Web of Science, we evaluated 3992 articles returned by the search terms ‘adaptive management’ or ‘adaptive harvest management’ against seven recommended elements of AM (engagement with stakeholders, defining objectives, forecasting and estimating uncertainty, implementing management, monitoring populations, adjusting management in response to monitoring, and improving forecasting and reducing uncertainty in response to monitoring populations).
3. The use of AM for vertebrates was reported in 56 (1%) of the evaluated studies; including four for managing INNS. Of these, ten studies excluding INNS and no studies of INNS management implemented all seven recommended elements of AM. Those elements infrequently implemented were as follows: the use of analysis or models to forecast and represent uncertainty (44%) and the feedback of monitoring data to improve forecasting and reduce uncertainty (25%).
4. Complete active AM has rarely been implemented and reported for managing INNS, despite the significant advantages it offers. Among studies purporting to have implemented AM, most did not use analyses or models to forecast and represent uncertainty, while most defined objectives, implemented management, and monitored populations.
5. Improvements to ongoing control programmes and much broader adoption of the AM approach are required to increase the efficiency and success of INNS management campaigns and reduce their negative impacts on native species.

     

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*) 相似文献   

Bio-economic resource management under threats of environmental catastrophes

We combine ecological and economic dynamics to study the management of a natural resource that supports both ecosystem and human needs. Shrinking the resource base introduces a threat of occurrence of catastrophic ecological events, such as sudden ecosystem collapse. The occurrence conditions involve uncertainty of various types, and the distinction among these types is important for optimal resource management. When uncertainty is due to our ignorance of some aspects of the underlying ecology, the isolated equilibrium states characterizing optimal exploitation for many renewable resource problems become equilibrium intervals. Genuinely stochastic events shift the optimal equilibrium states, but maintain the structure of isolated equilibria.     

Uncertainty of Measurement: A Review of the Rules for Calculating Uncertainty Components through Functional Relationships

The Evaluation of Measurement Data - Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides general rules for evaluating and expressing uncertainty in measurement. When a measurand, y, is calculated from other measurements through a functional relationship, uncertainties in the input variables will propagate through the calculation to an uncertainty in the output y. The manner in which such uncertainties are propagated through a functional relationship provides much of the mathematical challenge to fully understanding the GUM.The aim of this review is to provide a general overview of the GUM and to show how the calculation of uncertainty in the measurand may be achieved through a functional relationship. That is, starting with the general equation for combining uncertainty components as outlined in the GUM, we show how this general equation can be applied to various functional relationships in order to derive a combined standard uncertainty for the output value of the particular function (the measurand). The GUM equation may be applied to any mathematical form or functional relationship (the starting point for laboratory calculations) and describes the propagation of uncertainty from the input variable(s) to the output value of the function (the end point or outcome of the laboratory calculation). A rule-based approach is suggested with a number of the more common rules tabulated for the routine calculation of measurement uncertainty.     

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.