DOI: 10.5061/dryad.p5hqbzkst
title: Data for: Five decades of biogeography: A view from the Journal of Biogeography

description: Since the first issue of Journal of Biogeography (JBI) was published in
 1974, the discipline and its eponymous journal have grown in scope and
 consequence. On this 50th anniversary of the journal, we reflect on
 changes in biogeography and publishing, describe trends of the past five
 decades, and present lists of the 50 most-cited articles from JBI’s back
 catalogue. We describe current initiatives intended to chart a course for
 continued success in the coming 50 years, during what may well be a period
 of global biogeographic crises.
description: <em>Literature
 search </em> We carried out a
 search of peer-reviewed literature associated with biogeography research
 and indexed in Web of Science’s Core Collection using the functions
 <em>query_wos</em> and <em>pull_wos</em> from the
 ‘wosr’ package (Baker, 2018) for R statistical software (R Core Team,
 2020). For this, we used a search string composed that included variations
 of the word biogeography as well as articles published in journals
 associated with biogeography research, as follows:  
 "TS=biogeograph* OR SO=(Journal of
 Biogeography OR Diversity and Distributions OR Global Ecology and
 Biogeography OR Ecography OR Global Ecology and Biogeography
 Letters) The search resulted in a list of
 66,812 references which were considered for further analysis of the main
 topics addressed in this sample of the literature using topic
 modelling. <em>Topic
 modelling</em> We used latent
 Dirichlet allocation (LDA) on the combined text of titles and abstracts to
 assess the dominant themes being discussed in the sampled literature (Blei
 et al. 2015). LDA is a statistical model that estimates the number of
 topics in a set of documents based on the premise that words used to
 discuss a particular topic across documents will tend to occur together
 more frequently when compared to the rest of the words. Prior to the topic modelling implementation, we
 stemmed the words featuring in the titles and abstracts, removed
 stopwords, punctuation and numbers, converted all words to lowercase, and
 removed keywords featuring in less than 2% or more than 98% of the
 references. The remaining list of keywords was converted into a document
 per term matrix used to run the LDA analysis. The total number of topics
 that are discovered in the data needs to be set manually in LDA, so we
 varied the number of topics between 2 and 25 to identify the ideal number
 of topics. We used the function <em>FindTopicsNumber</em> from
 package ‘ldatuning’ (Nikita, 2020) to identify the ideal number of topics
 based on four metrics, proposed by Arun et al. (2010), Deveaud et al.
 (2014), Juan et al. (2009), and Griffiths & Steyvers (2004). We
 identified a good balance between the metrics when the model was trained
 on 22 topics based on an exploration of the resulting topics; the optimal
 number of topics represents the fewest topics that maximise the
 information covered as close to the original text as possible. It should
 be noted that while such metrics can be a useful guiding principle, the
 best judgement is often achieved by human interpretations of resultant
 topics (Chang et al., 2009). Once
 the final model was set, we assigned each reference to the topic with the
 highest probability of being associated with it. This classification was
 then used to analyse the set of publications associated with 15 topics
 that were identified as strongly-related to biogeography research and
 cover the majority of the sampled literature (&gt; 80% of references)
 published since 1974. Specifically, we calculated the percentage of
 references associated with these topics during each decade (1974–1979,
 1980–1989, 1990–1999, 2000–2009, 2010–2019, 2020–present) in the set of
 references associated with (i) Journal of Biogeography, (ii) a set of
 biogeography-related journals (<em>Journal of
 Biogeography</em>, <em>Ecography</em>, <em>Global
 Ecology and Biogeography</em>, and <em>Diversity and
 Distributions</em>), and in (iii) all journals.
description: The data and scripts included in this submission can be opened
 using the free and open R software.