The Open Access Journals Toolkit

Contents: Getting Started 5 • Scope, aims and focus 5 • Choosing a title for your journal 6 • Types of content accepted 7 • Kick-off and ongoing funding 11 • Disciplinary considerations 16 • Journal setup checklist and timeline 18 • Running a journal 20 • Article selection criteria 20 • Publication frequency and journal issues 23 • Attracting authors 25 • Peer review and quality assurance 27 • The costs of running an online open access journal 31 • Running a journal in a local or regional language 34 • Flipping a journal to open access 36 • Indexing 38 • Building and maintaining a profile 38 • Journal and article indexing 41 • Search engine optimisation and technical improvements 43 • Journal and article level metrics 45 • Staffing 49 • Roles and responsibilities 49 • Recruiting journal staff 51 • Building an editorial board 54 • Training and staff development 57 • Policies 59 • Developing author guidelines 59 • Publication ethics and related editorial policies 61 • Compliance with funder policies and mandates 64 • Copyright and licensing 66 • Displaying licensing information 68 • Corrections and retractions 70 • Infrastructure 72 • Software and technical infrastructure 72 • Journal appearance and web design 74 • Article and journal metadata 76 • Structured content 79 • Persistent Identifiers 81 • About the Open Access Journals Toolkit 83 • About 83 • What is an open access journal? 86 • Frequently asked questions 89 • Glossary 92 • Further reading 9

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost