Developing scientific information that is used in policy and practice has been a longstanding challenge in many sectors and disciplines, including climate change adaptation for natural resource management. One approach to address this problem encourages scientists and decision-makers to co-produce usable information collaboratively. Researchers have proposed general principles for climate science co-production, yet few studies have applied and evaluated these principles in practice. In this study, climate change researchers and natural resource managers co-produced climate-related knowledge that was directly relevant for on-going habitat management planning. We documented our methods and assessed how and to what extent the process led to the near-term use of co-produced information, while also identifying salient information needs for future research. The co-production process resulted in: 1) an updated natural resource management plan that substantially differed from the former plan in how it addressed climate change, 2) increased understanding of climate change, its impacts, and management responses among agency staff, and 3) a prioritized list of climate-related information needs that would be useful for management decision-making. We found that having a boundary spanner-an intermediary with relevant science and management expertise that enables exchange between knowledge producers and users-guide the co-production process was critical to achieving outcomes. Central to the boundary spanner's role were a range of characteristics and skills, such as knowledge of relevant science, familiarity with management issues, comfort translating science into practice, and an ability to facilitate climate-informed planning. By describing specific co-production methods and evaluating their effectiveness, we offer recommendations for others looking to co-produce climate change information to use in natural resource management planning and implementation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510579PMC
http://dx.doi.org/10.1007/s00267-022-01718-4DOI Listing

Publication Analysis

Top Keywords

natural resource
20
climate change
20
resource management
16
management
9
management planning
8
co-production process
8
relevant science
8
climate
7
natural
5
resource
5

Similar Publications

Complete Valorization of Cashew Nutshell Waste Enriched with Sulfur Copolymer for Efficient Mercury Removal.

Chem Asian J

March 2025

Materials Chemistry Laboratory, Department of Chemistry, School of Natural Sciences, Shiv Nadar Institution of Eminence, Delhi NCR, India.

Integrating sustainable raw materials with efficient synthesis is key to advancing eco-friendly solutions. Renewable feedstocks like cashew nutshells (CNS) and elemental sulfur, an industrial byproduct, are underutilized resources. This study presents a simple method to valorize CNS and sulfur, creating a copolymer composite designed for efficient mercury removal from contaminated water.

View Article and Find Full Text PDF

Precise Identification of Inhibitors for Coagulation Reactions from Complex Extracts through Monitoring of Biological Aggregates Combined with a Targeted Fishing Technique.

Anal Chem

March 2025

Department of Pharmaceutical Analysis, School of Pharmacy, Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China.

Biological aggregates play a crucial role in the pathogenesis of thrombotic diseases, especially thrombin-induced biological aggregates. Therefore, the efficient discovery of thrombin inhibitors is of great significance for the prevention and treatment of thrombotic diseases. In this study, the aggregation precursor protein fluorescent probe was successfully prepared for monitoring the production of biological aggregates induced by thrombin.

View Article and Find Full Text PDF

In January 2024, the Australian state of Victoria committed to ending native forest logging six years ahead of schedule, a decision that has been advocated for by scientists and conservationists for decades. However, the direct benefits for threatened species from this policy change has not been quantified. This study assesses the spatial overlap between areas approved for logging and the habitats of nationally listed threatened species, to estimate the potential impacts of continued logging and the likely benefits of its cessation.

View Article and Find Full Text PDF

Sustainable visions: unsupervised machine learning insights on global development goals.

PLoS One

March 2025

Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques.

View Article and Find Full Text PDF

Starvation tolerance and effects on fitness of predatory mite Amblyseius orientalis.

Exp Appl Acarol

March 2025

State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.

Amblyseius orientalis Ehara (Mesostigmata: Phytoseiidae) has garnered significant attention as an effective predatory mite for controlling spider mites in fruit production in China. However, despite its considerable potential for pest management, A. orientalis may face food shortages during transportation and field application.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!