The design and implementation of systematic reviews and meta-analyses are often hampered by high financial costs, significant time commitment, and biases due to researchers' familiarity with studies. We proposed and implemented a fast and standardized method for search term selection using Natural Language Processing (NLP) and co-occurrence networks to identify relevant search terms to reduce biases in conducting systematic reviews and meta-analyses.•The method was implemented using Python packaged dubbed benchmarked on the search terms strategy for naïve search proposed by Grames et al.
View Article and Find Full Text PDFThis paper reviews trends in the academic literature on cumulative effects assessment (CEA) of disturbance on forest ecosystems to advance research in the broader context of impact assessments. Disturbance is any distinct spatiotemporal event that disrupts the structure and composition of an ecosystem affecting resource availability. We developed a Python package to automate search term selection, write search strategies, reduce bias and improve the efficient and effective selection of articles from academic databases and grey literature.
View Article and Find Full Text PDFThe decoupling of human-ecosystem relationships in underutilized forested or agricultural regions poses a threat to cultural and biological diversities. Some scholars have proposed transformative strategies involving local-led efforts to reconnect social and ecological systems with the support of bridging organizations (BOs). However, empirically-based understandings about how and under what conditions BOs can address context-specific social conditions to enable transformation work remain limited.
View Article and Find Full Text PDFTransformation of natural land cover (LC) into modified LC has become inevitable due to growing human needs. Nevertheless, landscape transformational patterns during reclamation of mine damaged lands remain vague. Our hypothesis was that post-mining landscapes with different ages since dumping become more diverse in LC transformation over time.
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