The recognition of complexity and uncertainty in natural resource management has lead to the development of a wealth of conceptual frameworks aimed at integrated assessment and complex systems monitoring. Relatively less attention has however been given to methodological approaches that might facilitate learning as part of the monitoring process. This paper reviews the monitoring literature relevant to adaptive co-management, with a focus on the synergies between existing monitoring frameworks, collaborative monitoring approaches and social learning. The paper discusses the role of monitoring in environmental management in general, and the challenges posed by scale and complexity when monitoring in adaptive co-management. Existing conceptual frameworks for monitoring relevant to adaptive co-management are reviewed, as are lessons from experiences with collaborative monitoring. The paper concludes by offering a methodological approach to monitoring that actively seeks to engender reflexive learning as a means to deal with uncertainty in natural resource management.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jenvman.2009.05.012 | DOI Listing |
Ann Epidemiol
January 2025
School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China; School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia; Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Carlton, VIC, Australia; Bijie Institute of Shanghai University of Traditional Chinese Medicine, Bijie, China; Doctoral Workstation, Bijie District Center for Disease Control and Prevention, Bijie, China. Electronic address:
Background: From a global perspective, China is one of the countries with higher incidence and mortality rates for cancer.
Objective: Our objective is to create an online cancer risk prediction tool for middle-aged and elderly Chinese adults by leveraging machine learning algorithms and self-reported data.
Method: Drawing from a cohort of 19,798 participants aged 45 and above from the China Health and Retirement Longitudinal Study (2011 - 2018), we employed nine machine learning algorithms (LR: Logistic Regression, Adaboost: Adaptive Boosting, SVM: Support Vector Machine, RF: Random Forest, GNB: Gaussian Naive Bayes, GBM: Gradient Boosting Machine, LGBM: Light Gradient Boosting Machine, XGBoost: eXtreme Gradient Boosting, KNN: K - Nearest Neighbors), which are mainly used for classification and regression tasks, to construct predictive models for various cancers.
Environ Manage
December 2024
TERN Ecosystem Surveillance, School of Biological Sciences, Faculty of Science, Engineering and Technology, University of Adelaide, Adelaide, SA, Australia.
For a long time, ecological monitoring across Australia has utilised a wide variety of different methodologies resulting in data that is difficult to analyse across place or time. In response to these limitations, a new systematic approach to ecological monitoring has been developed in collaboration between the Terrestrial Ecosystem Research Network and the Australian Department of Climate Change, Energy, the Environment and Water - the Ecological Monitoring System Australia (EMSA). A qualitative approach involving focus groups and semi-structured interviews was undertaken to review perceptions of the introduction of the EMSA protocols amongst Natural Resource Management practitioners and other key stakeholders.
View Article and Find Full Text PDFEnviron Manage
December 2024
Okanagan Nation Alliance, Westbank, BC, Canada.
The productivity of Pacific Sockeye salmon (Oncorhynchus nerka) in the Columbia River has been declining over the past century. Yet, the Okanagan River Sockeye salmon population, which spawns in the Okanagan River, a Canadian tributary of the Columbia River, has seen a remarkable turnaround in abundance. Different hypotheses and lines of evidence covering multiple spatial scales have been proposed to explain this recovery; but they have never been comprehensively assessed.
View Article and Find Full Text PDFSci Rep
September 2024
Australian National Centre for Ocean Resources and Security, University of Wollongong, Wollongong, 2522, Australia.
This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes.
View Article and Find Full Text PDFAmbio
December 2024
The Coral Reef Alliance Alliance - Western Caribbean, 548 Market Street, Suite 29802, San Francisco, CA, 94104, USA.
This study explored the transformative journey of community-based natural resource management (CBNRM) in the Bay Islands National Marine Park, Honduras, revealing the interplay of cooperation, funding, and communication in fostering successful conservation initiatives. Using a mixed-method approach, we investigated the historical and legislative process and enabling conditions that led to the transition to CBNRM, based on Gruber's 12 key principles. In regards to the present CBNRM system, we looked at its strengths, its challenges, and whether its functioning is seen as satisfying by local resource-users.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!