Improving inverse model fitting in trees—Anisotropy,multiplicative effects,and Bayes estimation

Model fitting for individual-based effects in forests has some problems. Because samples measuring the separate influence of each individual are rarely available, the measured value in the sample represents the influence of all surrounding individual trees. Therefore, it is helpful to build inverse models that use the spatial pattern of the variable as well as that of the source trees. For example, since seed dispersal is influenced by wind effects, a model is discussed describing anisotropic effects to ensure an unbiased estimate of the total fruit number. Further, we present a model describing the absorption of radiation by trees. In this case a multiplicative combination of individual effects yields the total effect. Our approach uses logarithmic transformations of the original data to model multiplicative combinations as sum of transformed single effects. For fitting model parameters we propose an approach based on Bayesian statistics, to ensure ecologically interpretable parameters.     

Bayesian Small Area Models for Assessing Wildlife Conservation Risk in Patchy Populations

Abstract:  Species conservation risk assessments require accurate, probabilistic, and biologically meaningful maps of population distribution. In patchy populations, the reasons for discontinuities are not often well understood. We tested a novel approach to habitat modeling in which methods of small area estimation were used within a hierarchical Bayesian framework. Amphibian occurrence was modeled with logistic regression that included third-order drainages as hierarchical effects to account for patchy populations. Models including the random drainage effects adequately represented species occurrences in patchy populations of 4 amphibian species in the Oregon Coast Range (U.S.A.). Amphibian surveys from other locations within the same drainage were used to calibrate local drainage-scale effects. Cross-validation showed that prediction errors for calibrated models were 77% to 86% lower than comparable regionally constructed models, depending on species. When calibration data were unavailable, small area and regional models performed similarly, although poorly. Small area estimation models complement wildlife ecology and habitat studies, and can help managers develop a regional picture of the conservation status for relatively rare species.     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

Not by nutrients alone: a call to broaden the soil fertility initiative

The article discusses agricultural practices in sub-Saharan Africa, especially in terms of maintaining soil fertility. It disagrees with alarmist tendencies in the current debate, exaggerating the state of soil impoverishment in the African region. Nor does it subscribe to the idea that the remedy lies primarily in a massive influx of chemical fertilisers, especially phosphorus.
Instead, the article advocates a more balanced approach, where soil nutrient replenishment, as an improvement in natural capital, is but one part of the investment African agriculture needs. To achieve sustainable rural livelihoods, other forms of capital are also required, such as physical capital, i.e. infrastructure (roads, means of communication); financial capital in the form of credit and savings schemes; and human- and social capital, especially improved access to information and knowledge for farmers as well as improved local organisation and empowerment.
The article reviews nine case studies, taken from different climate zones in the region and representing different soil conditions. All case studies were projects using the participatory learning and action research (PLAR) method, which is discussed in detail.     

Application of Decision Analysis to Forest Road Deactivation in Unstable Terrain

Resource managers require objective methodologies to optimize decisions related to forest road deactivation and other aspects of road management, especially in steep terrain, where road-related slope failures inflict extensive environmental damage. Decision analysis represents a systematic framework that clearly identifies real options and critical decision points. This framework links current decisions with expected future outcomes and provides advantages such as a common currency to systematically explore the liability consequences of limited budget expenditures to road deactivation and other road-related activities. Furthermore, the decision framework prevents the analysis from becoming hopelessly entangled by the vast number of possibilities generated by the alternative occurrences, magnitudes, and consequences of landslide/debris flow events and provides the information required for the first step of an adaptive management process. Here, a structured analysis of potential environmental risks for a road deactivation project in coastal British Columbia, Canada is presented. The application of decision analysis generates a ranking of the expected benefits of proposed deactivation activities on various road sections. The ranking distinguishes between road sections that offer high expected benefit from those that offer moderate to low expected benefit. Seventeen of 171, 100–m road segments accounted for 18% of the cumulative cost and 98% of the cumulative expected net benefits from road deactivation. Furthermore, the cost of deactivating a section of road is related to the expected benefit from such deactivation, thus providing the basis for more effective resource allocation and budgeting decisions.     

Sustainability indicator system and policy processes in Malaysia: a framework for utilisation and learning

Formulation of effective sustainability indicators for national assessment demands a comprehensive understanding of the utilisation, diffusion and dissemination of information in policy processes. To illustrate the dynamic of sustainability assessment within the context of policy processes, this paper uses a case study of national sustainability indicators development in Malaysia. Subsequently, this paper ascribes the limited achievement of national sustainability assessment in Malaysia to four types of constraints: meta-policy issues; technical capacities; communication concerns; and the inherent knowledge gaps within the indicator developer community vis-a-vis their theoretical limitations. It is proposed that such constraints will be encountered in many countries. Drawing from the literature on public policy, this paper outlines a framework for investigating indicator behaviour within policy processes based on well-established concepts such as knowledge utilisation and policy learning. I conclude this paper by elaborating on the corresponding future challenges that must be addressed before effective integration of sustainability indicators within policy systems can occur.     

Enhanced learning from multi-stakeholder partnerships: Lessons from the Enabling Rural Innovation in Africa programme

Despite increasing interest and support for multi‐stakeholder partnerships, empirical applications of participatory evaluation approaches to enhance learning from partnerships are either uncommon or undocumented. This paper draws lessons on the use of participatory self‐reflective approaches that facilitate structured learning on processes and outcomes of partnerships. Such practice is important to building partnerships, because it helps partners understand how they can develop more collaborative and responsive ways of managing partnerships. The paper is based on experience with the Enabling Rural Innovation (ERI) in Africa programme. Results highlight the dynamic process of partnership formation and the key elements that contribute to success. These include: (i) shared vision and complementarity, (ii) consistent support from senior leadership; (iii) evidence of institutional and individual benefits; (iv) investments in human and social capital; (v) joint resources mobilization. However, key challenges require coping with high staff turnover and over‐commitment, conflicting personalities and institutional differences, high transaction costs, and sustaining partnerships with the private business sector. The paper suggests that institutionalizing multi‐stakeholder partnerships requires participatory reflective practices that help structure and enhance learning, and incrementally help in building the capacity of research and development organisations to partner better and ultimately to innovate.     

高校固定资产管理新探

目前，高校资产管理工作在管理意识、管理方法和管理手段等方面都明显滞后，难以适应高校建设与发展的要求。切实加强高校固定资产管理，就要树立全新的固定资产管理理念，建立科学、合理的保障制度，充分发挥财会、审计部门的重要作用，实现资源共建和共享，同时也要建立可行的资产管理绩效考核制度。     

Organizational learning during policy implementation: lessons from U.S. forest planning

ABSTRACT

Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.     

DETERMINATION OF SEDIMENT YIELD BY TRANSFERRING RAINFALL DATA1

ABSTRACT: A methodology for obtaining the optimal design value to allow for sediment storage in a reservoir is presented for the situation where no data on sediment loads in the incoming streams are available. Information concerning the amount of sediment delivered to the reservoir over its life-time is obtained by a sediment yield model which uses data on rainfall amount and duration obtained from a nearby experimental watershed. Bayesian Decision Theory is used to obtain the optimal storage requirements in order to consider the natural variation of rainfall and the sampling error due to the short rainfall record available. The normally difficult calculations involved were made tractable by the use of simplifications and approximations valid in the context of the problem. Results show that sediment storage requirements can be calculated in this manner and that consideration of the uncertainties involved leads to a storage requirement substantially larger than that calculated without such consideration.