Building bridges between natural and social science disciplines: a standardized methodology to combine data on ecosystem quality trends

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’.

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

“…In 2005, the PCSDS utilised the Satellite Pour I’Observation de la Terre (SPOT) satellite sensor’s images to delineate the extent of mangroves in Palawan and generated approximately 58,400 ha. Based on the mangrove data extraction made by Richter et al [ 91 ] from the Global Mangrove Watch (GMW), in accordance with the same mangrove areal estimates that were originally created by Bunting et al [ 92 ], the GMW figures from 2007 to 2010 had a very slight difference with the 2008 estimate (53,877 ha) for this study. Unsurprisingly, among all the references cited in this study, NAMRIA recorded the highest estimates at 63,532 ha in 2010 [ 15 ], which was higher than the GMW data in the same year (53,731 ha) and even higher than our most recent estimate for 2020.…”

Multi-Spatiotemporal Analysis of Changes in Mangrove Forests in Palawan, Philippines: Predicting Future Trends Using Support Vector Machine Algorithm and Markov Chain Model

“…[91] recently reported that communities interviewed generally perceived mangrove condition in Palawan had improved over the last 10 years. [91] reported that the perception of the local communities in Taytay, in reference with the mangrove forest ecosystem quality in their area, suggested no change in condition compared with the findings from this study that showed a decrease in extent over the past 10 years, although it is apparent that the extent has increased significantly over the interviewee's lifetime. Similarly, [91] reported that the communities in Aborlan and Puerto Princesa City perceived an improvement in mangroves over the last 10 years This study indicates while there was a gain in mangrove extent between 2008-2013, since 2013 there has been slight decline in mangrove cover or cover has remained stable in these areas (Figure 6, Table 3).…”

“…In 2005, the PCSDS utilized the Satellite Pour I'Observation de la Terre (SPOT) satellite sensor's images to delineate the extent of mangroves in Palawan and generated approximately 58,400 ha. Based on the mangrove data extraction made by[91] from the Global1,…”

“…The '*' symbol denotes the estimates from this study. The GMW estimates were sourced from[91] and are based on the measurements by[92].…”

Connecting physical and social science datasets: challenges and pathways forward

“…• Developing Shared Understandings of Assumptions, Biases, and Uncertainties: Build in time across all research phases for communicating the underlying assumptions, complexities, and uncertainties associated with data collection, analyses, and interpretation (Halvorsen et al 2016, Morss et al 2021, Bukvic et al 2022, Richter et al 2022, Mahmoudi et al 2022. This includes feedback and learning during data integration processes, and developing recognition for how uncertainty and complexity may be communicated upon integration.…”