ORCID Profile
0000-0001-6893-2187
Current Organisation
Randbee Consultants
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Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.SCITOTENV.2018.10.416
Abstract: Green and Blue Infrastructure (GBI) is a network designed and planned to deliver a wide range of ecosystem services and to protect bio ersity. Existing GBI designs lacked a systematic method to allocate restoration zones. This study proposes a novel approach for systematically selecting cost-effective areas for restoration on the basis of bio ersity, ecosystem services, and ecosystem condition to give an optimal spatial design of GBI. The approach was tested at a regional scale, in a transboundary setting encompassing the Intercontinental Biosphere Reserve of the Mediterranean in Andalusia (Spain) - Morocco (IBRM), across three aquatic ecosystems: freshwater, coastal and marine. We applied Marxan with Zones to stakeholder-defined scenarios of GBI in the IBRM. Specifically, we aimed to identify management zones within the GBl that addressed different conservation, restoration and exploitation objectives. Although almost all conservation targets were achieved, our results highlighted that the proportion of conservation features (i.e., bio ersity, ecosystem services) that would be compromised in the GBl, and the proportion of provisioning services that would be lost due to conservation (i.e., incidental representation) are potentially large, indicating that the probability of conflicts between conservation and exploitation goals in the area is high. The implementation of restoration zones improved connectivity across the GBI, and also achieved European and global policy targets. Our approach may help guide future applications of GBI to implement the flexible conservation management that aquatic environments require, considering many areas at different spatial scales, across multiple ecosystems, and in transboundary contexts.
Publisher: Springer Science and Business Media LLC
Date: 11-12-2020
DOI: 10.1038/S41467-020-20142-Y
Abstract: Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention s ling were used by just 23% of intervention studies in bio ersity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
No related grants have been discovered for Beatriz Martín.