‘Learning by doing’: adaptive planning as a strategy to address uncertainty in planning

Adaptive management, an established method in natural resource and ecosystem management, has not been widely applied to landscape planning due to the lack of an operational method that addresses the role of uncertainty and standardized monitoring protocols and methods. A review of adaptive management literature and practices reveals several key concepts and principles for adaptive planning: (1) management actions are best understood and practiced as experiments; (2) several plans/experiments can be implemented simultaneously; (3) monitoring of management actions are key; and (4) adaptive management can be understood as ‘learning by doing’. The paper identifies various uncertainties in landscape planning as the major obstacles for the adoption of an adaptive approach. To address the uncertainty in landscape planning, an adaptive planning method is proposed where monitoring plays an integral role to reduce uncertainty. The proposed method is then applied to a conceptual test in water resource planning addressing abiotic-biotic-cultural resources. To operationalize adaptive planning, it is argued that professionals, stakeholders and researchers need to function in a genuinely transdisciplinary mode where all contribute to, and benefit from, decision making and the continuous generation of new knowledge.     

MM5/WRF气象场模拟差异对CMAQ空气质量预报效果的影响

评估了为公共多尺度空气质量模式(CMAQ)提供气象输入场的第五代NCAR/Penn State中尺度 (MM5) 模式与天气研究和预报(WRF)模式模拟的多种气象要素的准确性;比较了2个模式提供的气象场对华北地区SO₂和NO₂源同化反演效果及其质量浓度预报的差异;分析了相对湿度和边界层高的变化对ρ(SO₂),ρ(NO₂)预报的影响及其物理机制.结果表明:WRF模式模拟的各气象要素准确性优于MM5模式,其中MM5模式对相对湿度和边界层高度的模拟值与实测值的偏差较明显,而WRF模式的模拟值与实测值较接近;相对湿度和边界层高度参数是影响CMAQ空气质量预报的关键气象要素,这2个参数的变化对ρ(SO₂)和ρ(NO₂)的预报有显著影响,因此,对2个参数的改进可显著减小预报误差;ρ(SO₂)模拟误差减小的主要原因是垂直输送和质量调整过程对ρ(SO₂)的贡献减小;而ρ(NO₂)模拟误差减小的主要原因是化学反应过程对ρ(NO₂)的贡献明显减小.      

Unpacking pathways to diversified livelihoods from projects in Pacific Island coastal fisheries

Livelihood diversification has become an integral focus of policies and investments aiming to reduce poverty, vulnerability, and pressure on fishery resources in coastal communities around the globe. In this regard, coastal fisheries in the Pacific Islands have long been a sector where livelihood diversification has featured prominently. Yet, despite the widespread promotion and international investment in this strategy, the ability of externally funded livelihood diversification projects to facilitate improved resource management and rural development outcomes often remains inconsistent. We argue these inconsistencies can be attributed to a conceptual ambiguity stemming from a lack of attention and awareness to the complexity of livelihood diversification. There is still much to learn about the process of livelihood diversification, both in its theoretical conceptualizations and its practical applications. Herein, we utilize a common diversity framework to clarify some of this ambiguity by distinguishing three diversification pathways. These pathways are illustrated using an ideal–typical Pacific Island coastal household and supported by examples provided in the literature that detail livelihood diversification projects in the Pacific. Through this perspective, we seek a more nuanced understanding of what is meant within the policy and practice goal of livelihood diversification. Thereby enabling more targeted and deliberate planning for development investments that facilitates outcomes in support of sustainable livelihoods.     

Adaptive social impact management for conservation and environmental management

Concerns about the social consequences of conservation have spurred increased attention the monitoring and evaluation of the social impacts of conservation projects. This has resulted in a growing body of research that demonstrates how conservation can produce both positive and negative social, economic, cultural, health, and governance consequences for local communities. Yet, the results of social monitoring efforts are seldom applied to adaptively manage conservation projects. Greater attention is needed to incorporating the results of social impact assessments in long‐term conservation management to minimize negative social consequences and maximize social benefits. We bring together insights from social impact assessment, adaptive management, social learning, knowledge coproduction, cross‐scale governance, and environmental planning to propose a definition and framework for adaptive social impact management (ASIM). We define ASIM as the cyclical process of monitoring and adaptively managing social impacts over the life‐span of an initiative through the 4 stages of profiling, learning, planning, and implementing. We outline 14 steps associated with the 4 stages of the ASIM cycle and provide guidance and potential methods for social‐indicator development, predictive assessments of social impacts, monitoring and evaluation, communication of results, and identification and prioritization of management responses. Successful ASIM will be aided by engaging with best practices – including local engagement and collaboration in the process, transparent communication of results to stakeholders, collective deliberation on and choice of interventions, documentation of shared learning at the site level, and the scaling up of insights to inform higher‐level conservation policies‐to increase accountability, trust, and perceived legitimacy among stakeholders. The ASIM process is broadly applicable to conservation, environmental management, and development initiatives at various scales and in different contexts.     

Wildlife impacts and vulnerable livelihoods in a transfrontier conservation landscape

Interactions between humans and wildlife resulting in negative impacts are among the most pressing conservation challenges globally. In regions of smallholder livestock and crop production, interactions with wildlife can compromise human well-being and motivate negative sentiment and retaliation toward wildlife, undermining conservation goals. Although impacts may be unavoidable when human and wildlife land use overlap, scant large-scale human data exist quantifying the direct costs of wildlife to livelihoods. In a landscape of global importance for wildlife conservation in southern Africa, we quantified costs for people living with wildlife through a fundamental measure of human well-being, food security, and we tested whether existing livelihood strategies buffer certain households against crop depredation by wildlife, predominantly elephants. To do this, we estimated Bayesian multilevel statistical models based on multicounty household data (n = 711) and interpreted model results in the context of spatial data from participatory land-use mapping. Reported crop depredation by wildlife was widespread. Over half of the sample households were affected and household food security was reduced significantly (odds ratio 0.37 [0.22, 0.63]). The most food insecure households relied on gathered food sources and welfare programs. In the event of crop depredation by wildlife, these 2 livelihood sources buffered or reduced harmful effects of depredation. The presence of buffering strategies suggests a targeted compensation strategy could benefit the region's most vulnerable people. Such strategies should be combined with dynamic and spatially explicit land-use planning that may reduce the frequency of negative human–wildlife impacts. Quantifying and mitigating the human costs from wildlife are necessary steps in working toward human–wildlife coexistence.     

Examining the impacts of disaster resettlement from a livelihood perspective: a case study of Qinling Mountains,China

Disaster resettlement, as a mitigation and preparedness measure, entails significant economic, physical, and social impacts, which continue to challenge understanding of recovery from major events, especially regarding the extent of the context and environmental efforts to rebuild livelihoods. Based on a case study of Qinling Mountains, China, this research investigates the effects of disaster resettlement from a livelihoods perspective. Methodologically, it proposes a framework that combines the pressure–state–response framework and the sustainable livelihoods approach, and it employs a structural equation model to examine how specific factors affect disaster resettlement. The results indicate that conflicts may occur during and after resettlement owing to the difference or disparity between the concerns of resettled peasants and those of the government. Consequently, the risks related to livelihoods need to be taken seriously. Effective risk communication is critical to bridge the gap between different stakeholders. The paper concludes with some practical and policy recommendations.     

Controlling range expansion in habitat networks by adaptively targeting source populations

Controlling the spread of invasive species, pests, and pathogens is often logistically limited to interventions that target specific locations at specific periods. However, in complex, highly connected systems, such as marine environments connected by ocean currents, populations spread dynamically in both space and time via transient connectivity links. This results in nondeterministic future distributions of species in which local populations emerge dynamically and concurrently over a large area. The challenge, therefore, is to choose intervention locations that will maximize the effectiveness of the control efforts. We propose a novel method to manage dynamic species invasions and outbreaks that identifies the intervention locations most likely to curtail population expansion by selectively targeting local populations most likely to expand their future range. Critically, at any point during the development of the invasion or outbreak, the method identifies the local intervention that maximizes the long‐term benefit across the ecosystem by restricting species’ potential to spread. In so doing, the method adaptively selects the intervention targets under dynamically changing circumstances. To illustrate the effectiveness of the method we applied it to controlling the spread of crown‐of‐thorns starfish (Acanthaster sp.) outbreaks across Australia's Great Barrier Reef. Application of our method resulted in an 18‐fold relative improvement in management outcomes compared with a random targeting of reefs in putative starfish control scenarios. Although we focused on applying the method to reducing the spread of an unwanted species, it can also be used to facilitate the spread of desirable species through connectivity networks. For example, the method could be used to select those fragments of habitat most likely to rebuild a population if they were sufficiently well protected.     

Improving credibility and transparency of conservation impact evaluations through the partial identification approach

The fundamental challenge of evaluating the impact of conservation interventions is that researchers must estimate the difference between the outcome after an intervention occurred and what the outcome would have been without it (counterfactual). Because the counterfactual is unobservable, researchers must make an untestable assumption that some units (e.g., organisms or sites) that were not exposed to the intervention can be used as a surrogate for the counterfactual (control). The conventional approach is to make a point estimate (i.e., single number along with a confidence interval) of impact, using, for example, regression. Point estimates provide powerful conclusions, but in nonexperimental contexts they depend on strong assumptions about the counterfactual that often lack transparency and credibility. An alternative approach, called partial identification (PI), is to first estimate what the counterfactual bounds would be if the weakest possible assumptions were made. Then, one narrows the bounds by using stronger but credible assumptions based on an understanding of why units were selected for the intervention and how they might respond to it. We applied this approach and compared it with conventional approaches by estimating the impact of a conservation program that removed invasive trees in part of the Cape Floristic Region. Even when we used our largest PI impact estimate, the program's control costs were 1.4 times higher than previously estimated. PI holds promise for applications in conservation science because it encourages researchers to better understand and account for treatment selection biases; can offer insights into the plausibility of conventional point‐estimate approaches; could reduce the problem of advocacy in science; might be easier for stakeholders to agree on a bounded estimate than a point estimate where impacts are contentious; and requires only basic arithmetic skills.     

Accounting for adaptive capacity and uncertainty in assessments of species’ climate‐change vulnerability

Climate‐change vulnerability assessments (CCVAs) are valuable tools for assessing species’ vulnerability to climatic changes, yet failure to include measures of adaptive capacity and to account for sources of uncertainty may limit their effectiveness. We took a more comprehensive approach that incorporates exposure, sensitivity, and capacity to adapt to climate change. We applied our approach to anadromous steelhead trout (Oncorhynchus mykiss) and nonanadromous bull trout (Salvelinus confluentus), threatened salmonids within the Columbia River Basin (U.S.A.). We quantified exposure on the basis of scenarios of future stream temperature and flow, and we represented sensitivity and capacity to adapt to climate change with metrics of habitat quality, demographic condition, and genetic diversity. Both species were found to be highly vulnerable to climate change at low elevations and in their southernmost habitats. However, vulnerability rankings varied widely depending on the factors (climate, habitat, demographic, and genetic) included in the CCVA and often differed for the 2 species at locations where they were sympatric. Our findings illustrate that CCVA results are highly sensitive to data inputs and that spatial differences can complicate multispecies conservation. Based on our results, we suggest that CCVAs be considered within a broader conceptual and computational framework and be used to refine hypotheses, guide research, and compare plausible scenarios of species’ vulnerability to climate change.