ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2022
Building bridges between natural and social science disciplines: a standardized methodology to combine data on ecosystem quality trends.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 34 citations in OpenAlex.
- Stakeholder diversity matters: employing the wisdom of crowds for data-poor fisheries assessments.Scientific reports · 2025Article
- Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model.UCL open. Environment · 2023Article
- Building bridges between natural and social science disciplines: a standardized methodology to combine data on ecosystem quality trends.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Introduction to the Special Issue-Nurturing resilient marine ecosystems.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 11 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite a growing interest in interdisciplinary research, systematic ways of how to integrate data from different disciplines are still scarce. We argue that successful resource management relies on two key data sources: natural science data, which represents ecosystem structure and processes, and social science data, which describes people's perceptions and understanding. Both are vital, mutually complementing information sources that can underpin the development of feasible and effective policies and management interventions. To harvest the added value of combined knowledge, a uniform scaling system is needed. In this paper, we propose a standardized methodology to connect and explore different types of quantitative data from the natural and social sciences reflecting temporal trends in ecosystem quality. We demonstrate this methodology with different types of data such as fisheries stocks and mangrove cover on the one hand and community's perceptions on the other. The example data are collected from three United Nations Educational Scientific and Cultural Organization (UNESCO) Biosphere reserves and one marine park in Southeast Asia. To easily identify patterns of convergence or divergence among the datasets, we propose heat maps using colour codes and icons for language- and education-independent understandability. Finally, we discuss the limitations as well as potential implications for resource management and the accompanying communication strategies. This article is part of the theme issue 'Nurturing resilient marine ecosystems'.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.