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Datasets, sample descriptions and codes from Building bridges between natural and social science disciplines: a standardized methodology to combine data on ecosystem quality trends

Authors :
Richter, I.
Roberts, B. R.
Sailley, S. F.
Sullivan, E.
Cheung, V. V.
Eales, J.
Fortnam, M.
Jontila, J. B.
Maharja, C.
Nguyen, T. Ha.
Pahl, S.
Praptiwi, R. A.
Sugardjito, J.
Sumeldan, J. D. C.
Syazwan, W. M.
Then, A. Y.
Austen, M. C.
Publication Year :
2022
Publisher :
The Royal Society, 2022.

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 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’.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....153fd546ff8a5c9ed03280bc4ba0061b
Full Text :
https://doi.org/10.6084/m9.figshare.19646500.v1