https://cpw.cvlcollections.org/files/original/d0b9933ea8ec50e9d07f1d4971655c28.pdf 2acf8a2851945ce5e1175e7dac8e1021 PDF Text Text | Revised: 30 November 2021 | Accepted: 2 December 2021 DOI: 10.1002/ece3.8466 ACADEMIC PR ACTICE IN ECOLOGY AND EVOLUTION Demography, education, and research trends in the interdisciplinary field of disease ecology Ellen E. Brandell1 | Daniel J. Becker2 1 Department of Biology, Center for Infectious Disease Dynamics, Huck Institute of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA 2 Department of Biology, University of Oklahoma, Norman, Oklahoma, USA 3 Department of Biological Sciences, University of Arkansas, Fayetteville, Arkansas, USA Correspondence Ellen E. Brandell, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-­Madison, Madison, WI 53706, USA. Email: ebrandell08@gmail.com Funding information There is no funding to report. | Laura Sampson1 | Kristian M. Forbes3 Abstract Micro-­ and macroparasites are a leading cause of mortality for humans, animals, and plants, and there is great need to understand their origins, transmission dynamics, and impacts. Disease ecology formed as an interdisciplinary field in the 1970s to fill this need and has recently rapidly grown in size and influence. Because interdisciplinary fields integrate diverse scientific expertise and training experiences, understanding their composition and research priorities is often difficult. Here, for the first time, we quantify the composition and educational experiences of a subset of disease ecology practitioners and identify topical trends in published research. We combined a large survey of self-­declared disease ecologists with a literature synthesis involving machine-­learning topic detection of over 18,500 disease ecology research articles. The number of graduate degrees earned by disease ecology practitioners has grown dramatically since the early 2000s. Similar to other science fields, we show that practitioners in disease ecology have diversified in the last decade in terms of gender identity and institution, with weaker diversification in race and ethnicity. Topic detection analysis revealed how the frequency of publications on certain topics has declined (e.g., HIV, serology), increased (e.g., the dilution effect, infectious disease in bats), remained relatively common (e.g., malaria ecology, influenza, vaccine research and development), or have consistently remained relatively infrequent (e.g., theoretical models, field experiments). Other topics, such as climate change, superspreading, emerging infectious diseases, and network analyses, have recently come to prominence. This study helps identify the major themes of disease ecology and demonstrates how publication frequency corresponds to emergent health and environmental threats. More broadly, our approach provides a framework to examine the composition and publication trends of other major research fields that cross traditional disciplinary boundaries. KEYWORDS host–­pathogen interaction, infectious disease ecology, machine learning, questionnaire, research trends This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Ecology and Evolution. 2021;11:17581–17592. � www.ecolevol.org | 17581 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Received: 10 May 2021 �1 | | BRANDELL et al. I NTRO D U C TI O N disease ecology, including journals and associated organizations (e.g., Wildlife Disease Association, American Society of Tropical Medicine Parasites and the diseases they can cause are an important compo- and Hygiene), and a specialized National Science Foundation and nent of ecosystems and can shape population dynamics, food web National Institutes of Health funding program and conference series structure, and ecosystem health (Hudson et al., 1998, 2006; Lafferty (Scheiner & Rosenthal, 2006), which have helped to direct research et al., 2006). Parasites have both positive and negative impacts on effort and create networks among researchers. the ecosystems in which they occur, yet their negative impacts can Still, many questions remain as to the composition of disease be extremely serious, such that infectious diseases are a leading ecology practitioners, core research foci, and if research trends are source of human, domestic animal, and wildlife mortality, killing an associated with widespread disease outbreaks. Answering these estimated 17 million people each year (Brand, 2013; World Health questions could help improve recruitment and retention and pri- Organization, 1996, 2018), threatening economic security through oritize future research directions. However, understanding these crop and production animal losses (Haseeb et al., 2019; Benavides complex and interrelated factors as they apply to an interdisci- et al., 2017), and causing declines of endangered species (Scheele plinary research field requires diverse and innovative approaches. et al., 2019). Moreover, infectious disease outbreaks are predicted Here, we characterize the field of disease ecology and a subset of its to be exacerbated by contemporary issues such as climate change, practitioners by addressing the following questions: (1) Who com- high human population density, and fragmentation of natural envi- prises the field in terms of education, demographics, and the type ronments (Altizer et al., 2013; Daszak et al., 2001; Plowright et al., of research they conduct? (2) Which scientific articles and journals 2021). It is important to establish a strong foundation and specializa- have been the most influential? (3) And significantly, how has the tion for research on infectious diseases in their ecological and evo- frequency of research topics emerged and changed in the literature lutionary context to promote a high standard of living and enhance over time? For example, do the topics in publications follow global wildlife and ecosystem health; for example, we continue to face health events such as disease outbreaks? challenges such as emerging pathogens (e.g., SARS-­CoV-­2; Andersen To answer these questions, we surveyed self-­declared disease et al., 2020), pathogen evolution (van Boeckel et al., 2019), and the ecologists and conducted a literature synthesis with machine-­ need for innovative interventions (Sokolow et al., 2019). learning topic detection (Bird et al., 2009; Blei, 2012; Loper & Bird, Disease ecology is the study of how micro-­ and macroparasites 2002). Systematic and quantitative approaches to literature synthe- move through and are distributed across host populations, land- ses are increasingly favored over narrative-­based reviews (Haddaway scapes, and ecosystems, considering both abiotic and biotic factors, & Watson, 2016; Hedges & Olkin, 2014; Lajeunesse, 2010). as well as the consequences of their infections. It is a relatively new However, high volumes of published research make theme synthesis and rapidly expanding research focus within ecology and evolution- very difficult and require innovative approaches (Lajeunesse, 2016; ary biology that draws heavily on early foundations in population Nunez-­Mir et al., 2016). Following recent adoptions of data mining biology (Anderson & May, 1979; May & Anderson, 1979) and vector-­ approaches to systematic reviews (Han & Ostfeld, 2019), we apply borne disease (e.g., zooprophylaxis as a precursor to the dilution topic detection using non-­negative matrix factorization to charac- effect literature (Hess & Hayes, 1970; Schmidt & Ostfeld, 2001)). terize the research core and trajectory of disease ecology. More Further, disease ecology integrates many fields that cross multiple broadly, our approach can provide a quantitative synthesis frame- levels of biological organization including but not limited to parasi- work to examine the frequency that topics are published in other tology, microbiology, immunology, and epidemiology (Grenfell et al., fields that cross traditional disciplinary boundaries. 1995; Hudson et al., 2002; Wilson et al., 2019). Disease ecologists investigate a range of practical and fundamental questions relevant to humans, other animals, and plants, such as the natural origins of disease outbreaks; heterogeneities in pathogen susceptibility, transmission, and impact; and the effectiveness of intervention strategies 2 | M ATE R I A L S A N D M E TH O DS 2.1 | Survey (Condeso & Meentemeyer, 2007; Hudson et al., 1998; Joseph et al., 2013; Olival et al., 2017; Vanderwaal & Ezenwa, 2016). We developed a survey questionnaire to quantify the demographics Disease ecology, in part, adapted and developed population and research core of disease ecology (Pennsylvania State University biology theory to address societal needs (Johnson et al., 2015; Institutional Review Board Study 00010582; Appendix S1). The sur- Koprivnikar & Johnson, 2016; Scheiner & Rosenthal, 2006). Key vey was disseminated on disease ecology email listservs such as con- among these is the urgency to understand and address novel dis- ference attendees (e.g., the past five years of Ecology and Evolution ease threats, which are rooted in natural systems but are often of Infectious Disease conferences, Ecological Society of America), exacerbated by societal inequalities (Carlson & Mendenhall, scientific organizations and networks (e.g., VectorBiTE, American 2019). For example, the impacts of habitat degradation on patho- Society of Parasitologists, Ecological Society of America disease ecol- gen spillover are an expanding area of research that can be used ogy section), and institutional research centers (e.g., Pennsylvania to guide risk assessments and environmental policy (Plowright State University Center for Infectious Disease Dynamics, University et al., 2021). At the same time, infrastructure has developed around of Georgia Center for the Ecology of Infectious Diseases). We also 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17582 �17583 distributed the survey to prominent non-­USA research groups (in, using Boolean filters, including a focus on studying a pathogen or e.g., South America, Europe, Australia) to diversify our survey partic- parasite, host infections (to distinguish from solely environmen- ipants. However, we acknowledge that survey reach was heavily bi- tal persistence of microorganisms), and individual-­level or higher-­ ased toward established research centers and active researchers in order dynamics (e.g., not cellular processes, with the exception of the disciplinary community, primarily in North America and Europe, those analyzed as a population-­level process). The full list of search and surely missed certain individuals and groups, particularly those terms is provided in the Appendix S1, alongside a set of exclusion- who conduct relevant research on infectious disease but may not ary terms to remove similar but non-­disease ecology articles. Web necessarily identify as disease ecologists (e.g., medical entomolo- of Science categories were used to narrow our search and also re- gists and historians). duce false-­p ositive inclusions. To reduce bias, both search terms The survey was open from November 2018 until January 2019, and included journals were based on survey results. We included closing once the response rate dropped below two new responses journals that were listed by at least four survey participants as sig- per day for one consecutive week. All survey participants were self-­ nificant to the field (n = 42), as well as Nature and Science. Finally, declared disease ecology practitioners, who were informed about articles with fewer than four citations were removed as a form of the potential use of results in a consent statement. The survey asked quality control. participants questions on their demographics, institution, educa- To evaluate false positives, two authors (DJB and KMF) inde- tion, types and topics of current research, and influential scientific pendently evaluated the same 100 randomly selected articles and articles and journals. It included a combination of multiple choice classified them as “disease ecology” or “outside the field.” Papers and short answer response questions. A full copy of the survey that fell outside the field predominantly described pathogenesis, and description of the data cleaning procedure is available in the bacterial communities, or genetics/genomics (Figure S5). Within-­ Appendix S1. host studies were accepted if they focused on population-­level processes (Cressler et al., 2014) or parasite manipulation. 75% of the articles in the final corpus were classified as disease ecology, and 2.2 | Literature search consensus was strong among evaluators (94% agreement, Cohen's κ = 0.84). Within false-­positive papers, there was no association be- Our objective was to compile an extensive corpus robustly repre- tween topical and temporal trends (χ2 = 72.84, p = .29, Appendix S1: sentative of publications in disease ecology rather than to include False-­Positive Literature Assessment). every article per se. Literature search terms are often generated To evaluate false negatives, we cross-­validated our corpus using by the authors, which may impose bias. To generate a list of search our survey data. Specifically, we assessed whether articles that terms with reduced author bias, we compiled a set of papers that were identified by at least two survey participants as influential cited the foundational paper in disease ecology and was considered were present in our corpus. We calculated the proportion of papers to be highly influential to the survey participants (Anderson & May, that were included in our corpus out of the list of such articles, with 1979; Table 1). Using this set of papers, we performed topic detec- the requirement that at least 70% of papers had to be included. Of tion algorithms (nltk library, Python 2.7; Bird et al., 2009) to gener- the influential articles identified by survey participants (written ≥2 ate the list of 13 base keywords (e.g., the word parasite could have times) restricted to journals used in building the corpus, approxi- multiple prefixes and suffixes) that were used to search the wider mately 71% (50/70) were present in the corpus. The “most influen- literature (see Appendix S1: Literature Search Methods). To this end, tial” articles had a higher probability of being included: the corpus our literature search terms emerged from disease ecology literature included 85% of articles written four or more times, 75% of articles itself and then were refined through the process described below written three times, and 63% of articles written twice. We adjusted and in Figure 1. the search and exclusion terms twice using the workflow described The final literature search was conducted in Web of Science for the years 1975 to 2018. Each article had to meet specific criteria in Figure 1 (unfilled arrow) to obtain a corpus with high classification and cross-­validation success. TA B L E 1 Five highest ranking scientific journals (left) and articles (right) based on survey responses. Survey participants were asked to pick journals other than Science or Nature Influential journals n Influential articles n Proceedings of the Royal Society—­Biology 98 Hudson et al. (1998). Prevention of Population Cycles by Parasite Removal. Science 18 Ecology Letters 90 Anderson and May (1979). Population biology of infectious diseases: Part I. Nature 13 Proceedings of the National Academy of Sciences of the United States of America 85 Anderson and May (1978). Regulation and Stability of Host-­Parasite Population Interactions: I. Regulatory Processes. Journal of Animal Ecology 12 Ecology 72 Lloyd-­Smith et al. (2005). Superspreading and the effect of individual variation on disease emergence. Nature 10 Journal of Animal Ecology 60 Keesing et al. (2006). Effects of species diversity on disease risk. Ecology Letters 9 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | BRANDELL et al. �| BRANDELL et al. F I G U R E 1 Workflow of systematic literature search and corpus development. Box coloration denotes different stages of corpus development: literature compilation (blue), corpus assessment and validation (orange), and corpus completion (red). The unfilled arrow denotes repeating of the workflow to optimize corpus accuracy 2.3 | Literature analysis S1). To ensure topic trends were not confounded by an increase in the total number of published articles through time, we con- We conducted topic detection on the validated corpus using non-­ structed a baseline topic using neutral words that should be in all negative matrix factorization. Topic clusters represent a set of co-­ disease ecology articles: analysis, study, and paper. We evaluated occurring words that can be used to define an area of research. The temporal trends in publications for each theme using generalized number of topic clusters (i) and words per topic (j) were the only additive models (GAMs) fit using the mgcv package in R (Wood, parameters imposed on the literature analysis. To select appropriate 2006). The proportion of words in each topic relative to all words values for i and j, we ran topic detection for a range of values and was modeled as a binomial response using thin-­p late splines with combinations of i and j and assessed outputs. If i was too small or shrinkage for publication year. Lastly, to assess covariation among too large, we were unable to detect temporal variation in that topic. topics, we estimated Spearman's rank correlation coefficients (ρ) If j was too small or too large, the topics were not clearly defined. at the zero-­year lag. For example, a topic with only five words may not be interpretable; similarly, a topic with 30 words may be too broad to assign meaning. We used i = 15 and j = 15, so our corpus was analyzed for 15 topics with 15 words each. A “topic” therefore describes the frequency of co-­occurring words or phrases in the literature corpus; 3 | R E S U LT S 3.1 | Survey topics could span any number of interests, methods, taxa, or set of words/phrases that emerged from the literature or are of interest to A total of 413 self-­declared disease ecologists participated in the researchers. survey. The average respondent was 36.1 years old (range: 21–­76, We used K-­m eans clustering from the nltk Python library to median: 34, n = 348; categorized as: ≤25, 26–­30, 31–­35, 36–­4 0, 41–­ construct topics, where each topic comprised 15 commonly co-­ 50, 51–­60, >60). 76.7% of participants (n = 408) considered at least occurring words. We assigned a name to each topic to describe half of their research to fall within disease ecology. Participants that its theme. For example, we named a topic containing immunodefi- considered ≥75% of their research to be disease ecology were con- ciency, HIV, patient, therapy, drug, AIDS, background, treatment, centrated from ages 26–­4 0 and most self-­identified as women (60%, and risk, as an HIV topic. We gave each topic name a “confidence” n = 344). More broadly, 56.1% of participants identified as women (n measurement of 1–­3 , from high to low confidence in identifying = 231), 42.5% as men (n = 175), 0.7% as non-­binary (n = 3), and 0.7% the topic (Appendix S1: Topic detection). In addition to topics that preferred not to say (n = 3). We report on participants that chose to emerged from the literature, we also generated and assessed our disclose a gender identity for results regarding gender. own topic lists based on key research areas: climate change, di- Most participants identifying as women were younger (age ≤35) lution effect, superspreaders, network analysis, EIDs, infectious than most participants identifying as men (age 26–­50). The youngest diseases in bats and rodents, chytrid fungus, theoretical modeling, age category (≤25 years) was 68.9% women (n = 45), and the oldest and field experiments (Figure 4; full topic lists are in the Appendix age category (>60 years) was 85.0% men (n = 20). Current positions 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17584 �17585 held by survey participants were as follows: undergraduate student statistics, environmental science, public health, or agricultural pro- (1.2%, n = 5), master's student (2.9%, n = 12), PhD student (24.5%, n grams. Among surveyed disease ecologists who graduated from = 100), postdoctoral researcher (21.1%, n = 86), faculty (39.5%, n = 1990–­2018, biology programs have consistently comprised about 161), researcher (9.1%, n = 37), and other (1.7%, n = 7). Participants half of all earned PhDs, with ecology closely tracking but slightly de- identifying as women comprised most of each academic position ex- creasing since 2005 (Figures 3d and S2). Over the same time, PhDs in cept Master's student and faculty (Table S1). In general, most PhD mathematics/statistics and wildlife/fisheries programs have slightly students and postdoctoral researchers were young and identified as declined and remained at approximately 10% (Figure S2). women. Most masters’ students were young and identified as men, Most participants identifying as non-­white were less than and most faculty were middle-­aged and identified as men (Tables S1–­ 36 years old, indicating a recent, though minor (Figure 3b), diver- S3). Non-­binary participants were distributed across age (<50) and sification of disease ecology practitioners. This was especially pro- position categories (n = 3), as were participants who preferred not to nounced when analyzed by education: the average proportion of provide a gender identity (n = 3). participants identifying as non-­white who have earned a master's We identified clear trends in education and demographics of degree has nearly doubled from 1980–­1999 to 2000–­2018 (9.6% to survey participants. 92.7% of participants had completed their un- 19.1%) and has risen to 23.9% in the last decade (Figure 2d). The pro- dergraduate degree by 2018 (n = 382), 50.0% had completed their portion of participants identifying as non-­white who have earned a master's degree by 2018 (n = 206), and 73.3% had completed their PhD slightly increased from 1980–­1999 to 2000–­2018 but has since PhD by 2018 (n = 302). 8.3% of participants had also earned an ad- remained stable (15%–­19%; Figure 2d). The proportion of master's ditional degree, most commonly as a Doctor of Veterinary Medicine and PhD degrees earned in low-­ and middle-­income countries has (n = 18). Nearly half of participants had a PhD but not a master's also recently increased (Figure 2b). degree (45.4%; n = 187). The total number of graduate degrees The proportion of participants identifying as women who have earned per year among disease ecology researchers has dramatically earned master's degrees approximately doubled from a mean of increased since the year 2000 (Figure 2a). Broadly, 50% of PhDs 27.5% during 1980–­1999 to 53.2% during 2000–­2018. From 2000 earned were in biology graduate programs (e.g., biology, biological to 2018, this proportion was approximately stable at around 55%. sciences, microbiology), 25% were in ecology graduate programs Similarly, the proportion of participants identifying as women who (e.g., ecology, ecology and evolution, plant science), 11% were in have earned PhDs substantially increased from a mean of 34.3% wildlife or fisheries graduate programs (e.g., wildlife, fisheries, zool- during 1980–­1999 to 52.4% from 2000 to 2018; this proportion has ogy, entomology, animal science), and <10% were in mathematics/ continued to increase to 62.6% since 2010 (Figure 2c). F I G U R E 2 Plots showing demographic trends in survey participants that indicated they earned a master's (top row) or PhD (bottom row) degree by 2018. (a) Displays the number of degrees earned by year, and the stacked bar plots show the proportion of degree earners by (b) high-­or low/middle-­income countries, (c) self-­declared gender identity and (d) self-­declared race/ethnicity. Sample sizes are displayed above their respective bars and years included 1980–­2018; some years may be excluded (from the x-­axis) if no degree earners indicated the information of interest 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | BRANDELL et al. �| BRANDELL et al. F I G U R E 3 Summary of disease ecology demographics and research topics/types from survey participants. Pie charts display (a) current position or institution type, (b) self-­declared race or ethnicity, and (c) country of residence; research topics and types are categorized by (d) field of PhD thesis, (e) current study taxa, and (f) type of primary research The most popular areas of research within disease ecology were in Ecology & Evolution (n = 9), Ecology Letters (n = 8), and Proceedings ranked as: epidemiology, mathematical modeling, population ecol- of the National Academy of Sciences (n = 6). Interestingly, however, ogy, wildlife ecology/management, parasitology, community ecol- Proceedings of the Royal Society -­ Biology was ranked as the most in- ogy, and infectious disease evolution/life history (up to 5 responses fluential journal by survey participants, followed by Ecology Letters, were included per person; n participants = 410, n responses = 1739). Proceedings of the National Academy of Sciences, Ecology, and Journal The least common areas included behavioral ecology, bioinformat- of Animal Ecology (Table 1). See Appendix S1 for full lists of both ar- ics, field and laboratory techniques, movement ecology, virology, ticles and journals. landscape ecology, and zoology. In brief, most participants fell into a few distinguishable categories based on location, study taxa, and research type 3.2 | Literature search and analyses (Figure 3; see Appendix S1 for more). 87.1% were currently employed/studying at a university (Figure 3a), primarily in the United We compiled a list of 42 journals that at least four survey partici- States (74.7%) or United Kingdom (15.1%, Figure 3c). Most partic- pants said were the most important in disease ecology, plus Science ipants studied wildlife hosts, microparasites, and/or vectors and and Nature. We searched these 44 journals for relevant articles in macroparasites (Figure 3e); wildlife–­m icroparasite, ectoparasite/ the field using the algorithm described above, and our final corpus vector–­w ildlife, and human–­vector were the most common co-­ comprised 18,695 articles. Our validation processes demonstrated occurring pairs of study taxa selected by individual participants. that at least 75% of these articles were properly classified, and we Finally, participants were approximately evenly split among type did not detect any systematic bias in falsely positive articles. Articles of primary research: experimental (31.6%), observational (33.6%), span from 1975 to 2018, with most published after 2000, indi- or computational (34.8%) approaches to infectious disease re- cating a rapid and considerable expansion of the field since early search (Figure 3f). foundational work in population biology and vector-­borne disease Survey participants were asked to write in scientific journals and (Anderson & May, 1979; Hess & Hayes, 1970; May & Anderson, articles that they believed were the most influential in disease ecol- 1979). However, some journals were not available in Web of Science ogy (Table 1). Influential articles (written in at least twice, n = 76) until the 1980s-­1990s, so article availability may slightly bias our were most often published in Science (n = 17), Nature (n = 10), Trends corpus in the early years. 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17586 �17587 F I G U R E 4 Timelines of events relevant to disease ecology and research trends. (a) A timeline of select human (filled circles) and wildlife (open circles) infectious disease events from 1979–­2018. The approximate number of humans infected is represented by circle size (scaled by log/4). Analogous estimates are rare for wildlife diseases; thus, these circles are an equivalent arbitrary size. Stars denote notable events for the development of the field of disease ecology. (b) Frequency of publication on selected topics (green = emerged from the literature corpus, purple = selected by authors) compared with the neutral topic (gray) from 1976–­2018; 1976–­1989 are binned in the first data point. Thick lines and ribbons show the fitted values and 95% confidence intervals from the GAMs. Plots are ordered by thematic categorization: host–­pathogen study systems, concepts, and research methods/approaches Topic clusters were classified into two categories: (1) those that Overall, we had high confidence assigning names to topic clusters emerged and were identifiable from the literature and (2) those that emerging from the literature, indicating defined areas of research in we deliberately searched for using key term searches. Of emer- the corpus (see Appendix S1). gent topics, malaria and mosquito-­borne pathogens appeared most Many of the topics that emerged from the disease ecology liter- frequently in the topic clusters (3/15), followed by experimental ature, such as malaria, influenza, and vaccination research and de- infection trials (2/15). Other clear topics included HIV, influenza, velopment, have remained constant in publication frequency over vaccine research, and host–­pathogen coevolution. Some topics were time (Figure 4b). Others, such as HIV and serology, have declined more ambiguous but still identifiable, such as wildlife pathogens, in publication frequency over time, and host–­pathogen coevolution tick-­borne pathogens with rodent hosts, and serological analyses. has instead steadily increased. These emergent topics comprised 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | BRANDELL et al. �| BRANDELL et al. a notable portion of the disease ecology literature and were more have remained prominent foci of disease ecology, whereas an in- prominent in the literature than author-­selected topics. A neutral crease in the frequency of publication of a priori selected topics topic, constructed for comparison, had a constant publication fre- such as emerging infectious diseases, climate change, and effects of quency through time (Figure 4b, gray line in panels), thus validating biodiversity loss emphasizes how this expanding field has mirrored the observed temporal changes in these topics. global events and priorities. Using key term searches, we next explored the frequency of Self-­declared disease ecology practitioners are becoming more publication of select topics: climate change, emerging infectious diverse in terms of country of education, gender identity, and in- diseases (EIDs), the dilution effect, superspreaders, network anal- stitution (Figures 2 and 3). The gender trends identified here are ysis, pathogens in rodents, pathogens in bats, chytrid fungus in am- echoed in engineering, computer science, and mathematics/statis- phibians, theoretical modeling, and field experiments (Figure 4b). As tics where the proportion of women earning graduate degrees has with emergent topics, our topic detection was sensitive to detecting increased over the past two decades (20%–­43% of master's and changes in frequency over time, identifying peaks and troughs. For doctorates earned in 2014), yet remains low in physics (18.7% of instance, published research on pathogens in bats had a small peak doctorates earned in 2014) (National Science Foundation, 2020). around the time of the first SARS epidemic (2002–­2004), and bat Women authorship has increased in ecology and evolution literature disease research has steadily increased since 1979; however, over- every year from 2009 to 2015 (Fox et al., 2018), and the proportion all, literature on bat pathogens has been a small proportion of all of women journal editors also increased over that time but was still disease ecology literature. Published research on rodent pathogens low relative to men (Fox et al., 2019). In terms of race/ethnicity, the was greatest in the 1990s and has generally declined, although re- rate of people identifying as Hispanic earning bachelor's degrees in cent years have also seen an increase in rodent-­related disease ecol- science and engineering has increased slowly since the 1990s, but ogy publications. The frequency of publications on many topics has remained approximately constant for people identifying as black, steadily grown and will likely continue to grow based on this trend, African American, or Asian (National Science Foundation, 2020). such as EIDs, climate change, the dilution effect, network analyses, Similarly, persons who identify as women and those who do not and superspreaders. Chytrid fungus literature, on the other hand, identify as white remain underrepresented in a prominent ecological appears to have declined or plateaued in recent years. Lastly, publi- organization, the Ecological Society of America; representation has cation frequency of both theoretical modeling of infectious disease improved for women in this group over the past 30 years, but not for and field experiments has remained constant but rare over time. most racial/ethnic minorities (Beck et al., 2014). Therefore, our find- Frequencies of published topics displayed strong degrees of ings are largely reflected in other scientific and mathematical fields cross-­correlation, with both positive and negative covariation in an- such that gender representation is improving at a more rapid pace nual trends (Spearman's rank correlation coefficient ρ = −0.89–­0.88; than racial/ethnic representation. Figure S8). Particularly strong positive correlations were observed Diversity in the workplace and educational institutions is fun- for superspreaders and network analyses (ρ = 0.88), superspreaders damentally important and increases performance, cooperation, and the dilution effect (ρ = 0.85), EIDs and bats (ρ = 0.83), EIDs and problem-­solving, and student retention (Drury et al., 2011; Milem, the dilution effect (ρ = 0.83), EIDs and network analysis (ρ = 0.82), 2003; Roberge & van Dick, 2010). The highest demographic and and superspreaders and bat pathogens (ρ = 0.80). Especially institutional diversity we identified was in younger age groups strong negative correlations were observed for EIDs and serology (<36 years old), graduate programs, and postdoctoral positions (ρ = −0.89), serology and the dilution effect (ρ = −0.87), serology and (Tables S2 and S3). This may be due to increasing levels of educa- bat disease (ρ = −0.81), influenza and HIV (ρ = −0.77), climate change tion globally (Group of Eight, 2013; UNESCO Institute for Statistics, and HIV (ρ = −0.76), network analysis and serology (ρ = −0.70), in- 2020), or targeted programs to increase diversity in science and fluenza and rodent disease (ρ = −0.68), climate change and serology mathematics, particularly focused on recruiting women (Burke (ρ = −0.68), malaria and network analysis (ρ = −0.65), and malaria et al., 2007; Huntoon & Lane, 2007). Another non-­mutually exclu- and rodent disease (ρ = −0.65). sive driver of these trends could be the failed retention of minorities and women in later career stages (Blickenstaff, 2005; Diekman et al., 4 | DISCUSSION 2010; Shaw & Stanton, 2012). Yet significantly, although we identified some relative increases in diversity within disease ecology, the field as a whole remains quite homogenous in terms of gender, race Interdisciplinary research fields can rapidly grow to address impor- and ethnicity, and geography, and marginalized groups face con- tant societal needs, and retrospective analysis of their evolution can siderable inequities and discrimination in science fields—­for exam- help improve their future trajectory and growth. By combining a ple, experience harassment and exclusion, lower likelihood to have survey with a powerful quantitative literature synthesis, we dem- grants funded, hold fewer faculty positions, and have limited access onstrate the increasing gender and institutional diversity of disease to academic experiences and resources (Allen et al., 2000; Jones & ecology practitioners alongside the breadth of research activities. Solomon, 2019; Rissler et al., 2020). Concerted efforts to improve Certain topical themes that emerged from our literature corpus, equity must continue and explicitly address recruitment and reten- such as influenza, malaria, and vaccine research and development, tion, especially for fostering racial and ethnic diversity. 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17588 �17589 We acknowledge that surveys can be a biased source of informa- the late 1990s. This likely reflects ongoing and continued efforts to tion because researchers rely on voluntary participation. For exam- reduce public health burdens of this disease and to understand com- ple, studies of academic survey participation have shown that people plex interactions between mosquito vectors, human hosts, and the who identify as women are more likely to respond to surveys, while environment (Suh et al., 2020), especially in the context of emerg- academic rank had little influence on response rate (e.g., tenured ver- ing human pathogens (e.g., Plasmodium knowlesi, Lee et al., 2011). sus tenure track) (Saleh & Bista, 2017; Smith, 2008). Additionally, as Mosquito-­borne pathogens and influenza have been defining topics our survey was shared via email listservs, it is likely that many people over the entire time series, which is reflected in human–­vector re- did not see or receive our request for participation. Because the field search being the third most commonly studied disease system by of disease ecology is relatively new and multidisciplinary, it is more participants; we expect this trend to persist for the foreseeable fu- challenging to identify smaller research groups both at universities ture. We observed exceptions for theoretical and field experimental (i.e., individual laboratory groups) or decentralized working groups approaches to disease ecology. While publications with these ap- (e.g., Bat One Health Research Network; BOHRN). Our survey dis- proaches have remained constant over time, publication frequency semination and participation likely reflect broader geographic biases was rare relative to other themes in disease ecology. This could signal in ecology research and publishing (Nuñez et al., 2021), which could that these approaches are relatively uncommon, but we suspect that subsequently affect the influential literature identified (Table 1); publications using theoretical modeling or field experiments may not however, we were unable to assess these limitations. While there use the same set of co-­occurring words, thus making them harder to are shortcomings of surveys, they remain a widely used method of identify as distinct approaches using topic detection methods. data collection, and the survey developed here provides the first We also identified broader concept-­based trends in disease ecol- description of the composition of disease ecologists and important ogy literature. In particular, the frequency of published research on literature that we hope is built upon in future. the dilution effect has undergone several spikes following key find- The second part of our study comprised an extensive literature ings (Civitello et al., 2015; Keesing et al., 2006) and a steady increase synthesis. Literature reviews can be compromised by author bias in publication rate. Similarly, predicting how climate changes may when search terms are subjectively selected (reviewed in Okoli, alter pathogen spread continues to be a growing research interest 2015). There is a trade-­off between the scope and errors when (Altizer et al., 2013; Ryan et al., 2019), as does research on super- constructing a literature corpus. For example, narrower ecologi- spreaders (Lloyd-­Smith et al., 2005). More broadly, these temporal cal literature reviews usually consist of a <2000 article corpus and patterns in publications suggest that work related to biodiversity often much less (Han & Ostfeld, 2019; Lowry et al., 2013; Poff & and infectious disease, climate change, pathogen spillover, hetero- Zimmerman, 2010; Wortley et al., 2013), and papers may be individ- geneity in pathogen transmission, and new tools to analyze epidemi- ually assessed for inclusion (e.g., Uehlinger et al., 2016 sample size = ological data will all continue to be active areas of inquiry. 9 papers). Our analysis, on the other hand, captured a diverse range Published research on emerging infectious diseases and bats of literature topics within a broader field, resulting in a corpus of has increased through time, consistent with bats being established over 18,500 papers. We quantified the false positives (type I error) reservoir hosts for pathogens such as Nipah virus, SARS, Marburg and true positives in our corpus, which is rarely accounted for or re- virus, and Hendra virus, as well as with infection-­related population ported in ecological literature reviews (Haddaway & Watson, 2016). declines in bats through white-­nose syndrome (Calisher et al., 2006; False positives are inevitable in such a large body of literature, but Frick et al., 2010; Figure 4a). However, although the frequency the false-­positive papers identified were unbiased with respect to of disease ecology publications on bat pathogens has increased year or topic. Our true-­positive rate was high—­85% of articles writ- markedly in recent years, they still remain relatively understudied ten in by participants four or more times were present in our corpus. compared to our neutral term and rodent pathogens and comprise Large-­scale quantitative reviews are imperfect and, even with the only a small proportion of emerging infectious disease research in development and implementation of our robust corpus formulation general. When additionally considering that wildlife–­microparasite and validation (Figure 1), relevant papers were likely excluded in our systems were the most commonly studied systems among survey corpus and topic analyses. Nonetheless, we are confident that we participants, it appears that bat–­pathogen research is relatively un- were able to identify true, broad patterns across the disease ecology derrepresented. It is worth noting that our search does not include literature at a large scale. the SARS-­CoV-­2 pandemic, which we expect to lead to a large spike Topic detection of publications revealed how published re- in disease ecology research on pathogens with their evolutionary or- search priorities changed over time. For example, the frequency igins in bats and the role of intermediate hosts in pathogen spillover of published HIV research peaked in the 1990s but in recent years (Andersen et al., 2020). has declined to be lower than the frequency of most other topics in In general, published research on epidemics tended to lag rather the field. While a direct association is difficult to demonstrate, the than precede events such that we observed a spike in the frequency decrease in HIV-­related publications has roughly coincided with ad- of publications on high-­profile pathogens followed by a decline or vances in HIV treatments (hiv.gov, 2019). Likewise, although we also plateau (e.g., chytrid fungus). Emergent topics were remarkably sta- observed significant temporal fluctuations in publications related ble through time, with the exception of HIV and host–­pathogen co- to malaria, their frequency has remained remarkably constant since evolution, which have, respectively, decreased and increased. The 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | BRANDELL et al. �| BRANDELL et al. frequency of published research focusing on concepts (e.g., the Becker: Conceptualization (equal); Formal analysis (supporting); dilution effect, superspreaders, coevolution) or approaches (e.g., Methodology (equal); Visualization (equal); Writing –­ review & edit- network analyses) rather than specific hosts or pathogens tended ing (equal). Laura Sampson: Conceptualization (equal); Data curation to rise more gradually and remain a notable proportion of the lit- (supporting); Formal analysis (equal); Methodology (equal); Writing –­ erature. Interestingly, self-­declared disease ecologists performed review & editing (supporting). Kristian Forbes: Conceptualization experimental, observational, and computational research equally (equal); Methodology (equal); Visualization (supporting); Writing –­ (Figure 3f); however, computational research such as methodolog- review & editing (equal). ical development (e.g., network analyses), epidemiology, and mathematical modeling was popular in the literature and among survey DATA AVA I L A B I L I T Y S TAT E M E N T participants. We suspect that disease ecologists have broad skillsets Additional data and code files are available on the Dryad Digital that intersect multiple types of research, such as performing exper- Repository: https://doi.org/10.5061/dryad.c2fqz​619f. iments to calibrate mathematical models, which may be a defining ORCID feature of practitioners. Although our analysis of cross-­correlation between the topic fre- Ellen E. Brandell https://orcid.org/0000-0002-2698-7013 quency time series is associative, we observed several interesting Daniel J. Becker https://orcid.org/0000-0003-4315-8628 relationships. The frequency of publications on bat disease, chytrid Kristian M. Forbes https://orcid.org/0000-0002-2112-2707 fungus, climate change, the dilution effect, superspreaders, and emerging infectious diseases all increased over time, suggesting a REFERENCES general increase in research on emergent disease risks to wildlife Allen, W. R., Epps, E. G., Guillory, E. A., Suh, S. A., & Bonous-­Hammarth, M. (2000). Knocking at Freedom’s Door: Race, Equity, and Affirmative Action in U.S. Higher Education. Journal of Negro Education, 69(1), 112–­127. https://www.jstor.org/stabl​e/2696268 Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S., & Harvell, C. D. (2013). Climate change and infectious diseases: From evidence to a predictive framework. Science, 341(6145), 514–­519. https://doi. org/10.1126/scien​ce.1239401 Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C., & Garry, R. F. (2020). The proximal origin of SARS-­CoV-­2. Nature Medicine, 26(4), 450–­452. https://doi.org/10.1038/s4159​1-­020-­0 820-­9 Anderson, R. M., & May, R. M. (1978). Regulation and stability of host-­ parasite population interactions: I. Regulatory processes. Journal of Animal Ecology, 47, 219–­247. Anderson, R. M., & May, R. M. (1979). Population biology of infectious diseases: Part I. Nature, 280(5721), 361–­367. https://doi.org/10.1017/ CBO97​81107​415324.004 Beck, C., Boersma, K., Tysor, C. S., & Middendorf, G. (2014). Diversity at 100: women and underrepresented minorities in the ESA. Frontiers in Ecology and the Environment, 12(8), 434–­436. https:// doi.org/10.1890/14.WB.011 Benavides, J. A., Rojas Paniagua, E., Hampson, K., Valderrama, W., & Streicker, D. G. (2017). Quantifying the burden of vampire bat rabies in Peruvian livestock. PLoS Neglected Tropical Diseases, 11(12), 1–­17. https://doi.org/10.1371/journ​al.pntd.0006105 Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: Analyzing text with the natural language toolkit. O’Reilly Media Inc. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–­8 4. https://doi.org/10.1145/21338​06.2133826 Blickenstaff, J. C. (2005). Women and science careers: leaky pipeline or gender filter? Gender and Education, 17(4), 369–­386. https://doi. org/10.1080/09540​25050​0145072 Brand, C. J. (2013). Wildlife mortality investigation and disease research: Contributions of the USGS National Wildlife Health Center to endangered species management and recovery. EcoHealth, 10(4), 446–­454. https://doi.org/10.1007/s1039​3-­013-­0 897-­4 Burke, R. J., Mattis, M. C., editors. (2007). Women and minorities in science, technology, engineering, and mathematics: Upping the numbers. Edward Elgar Publishing. Calisher, C., Childs, J., Field, H., Holmes, K., & Schountz, T. (2006). Bats: important reservoir hosts of emerging viruses. Clinical Microbiology Reviews, 19(3), 531–­545. https://doi.org/10.1128/CMR.00017​- ­06 and humans in relation to anthropogenic change and heterogeneities in pathogen transmission (Daszak et al., 2001; Jones et al., 2008; Lloyd-­Smith et al., 2005). From another perspective, the frequency of publications on serology has steadily declined over time, in contrast to topics such as bat disease and emerging infectious diseases. This may indicate advances in rapid and affordable sequencing efforts to quantify pathogen diversity (Lipkin, 2013). These associations provide testable hypotheses for a future analysis that examines the combination of concepts and methods in published literature. Using a survey and quantitative literature synthesis, this study demonstrates that disease ecology is a rapidly growing field, albeit one that will require continued efforts to enhance recruitment and retention to improve diversity. Our analysis identified trends and publication patterns of research topics addressed by disease ecologists. More broadly, our quantitative synthesis framework could help examine the composition and trends of other major research topics that cross traditional disciplinary boundaries. AC K N OW L E D G M E N T S Our survey was considered exempt under the Pennsylvania State University Institutional Review Board (STUDY00010582). We thank all participants who completed the online survey as well as helpful feedback from participants at the 2019 Ecology and Evolution of Infectious Diseases conference. We are grateful to Vanessa Ezenwa and Peter Hudson for constructive comments on an earlier version of this report. C O N FL I C T O F I N T E R E S T The authors declare no conflicts of interest. AU T H O R C O N T R I B U T I O N S Ellen E. Brandell: Conceptualization (equal); Data curation (lead); Formal analysis (lead); Methodology (equal); Project administration (lead); Writing –­ original draft (lead). Daniel 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17590 �Carlson, C. J., & Mendenhall, E. (2019). Preparing for emerging infections means expecting new syndemics. The Lancet, 394(10195), 297. https://doi.org/10.1016/S0140​-­6736(19)31237​-­1 Civitello, D. J., Cohen, J., Fatima, H., Halstead, N. T., Liriano, J., McMahon, T. A., Ortega, C. N., Sauer, E. L., Sehgal, T., Young, S., & Rohr, J. R. (2015). Biodiversity inhibits parasites: broad evidence for the dilution effect. Proceedings of the National Academy of Sciences, 112(28), 8667–­8671. https://doi.org/10.1073/pnas.15062​79112 Condeso, T. E., & Meentemeyer, R. K. (2007). Effects of landscape heterogeneity on the emerging forest disease sudden oak death. Journal of Ecology, 95(2), 364–­375. https://doi. org/10.1111/j.1365-­2745.2006.01206.x Cressler, C. E., Nelson, W. A., Day, T., & Mccauley, E. (2014). Disentangling the interaction among host resources, the immune system and pathogens. Ecology Letters, 17(3), 284–­293. https://doi. org/10.1111/ele.12229 Daszak, P., Cunningham, A. A., & Hyatt, A. D. (2001). Anthropogenic environmental change and the emergence of infectious diseases in wildlife. Acta Tropica, 78, 103–­116. https://doi. org/10.1046/j.1365-­2664.1998.00307.x Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E. K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science, 21(8), 1051–­1057. https://doi. org/10.1177/09567​97610​377342 Drury, B. J., Siy, J. O., & Cheryan, S. (2011). When do female role models benefit women? The importance of differentiating recruitment from retention in STEM. Psychological Inquiry, 22(4), 265–­269. https://doi.org/10.1080/10478​4 0X.2011.620935 Fox, C. W., Duffy, M. A., Fairbairn, D. J., & Meyer, J. A. (2019). Gender diversity of editorial boards and gender differences in the peer review process at six journals of ecology and evolution. Ecology and Evolution, 9(24), 13636–­13649. https://doi.org/10.1002/ ece3.5794 Fox, C. W., Ritchey, J. P., & Paine, C. E. T. (2018). Patterns of authorship in ecology and evolution: First, last, and corresponding authorship vary with gender and geography. Ecology and Evolution, 8(23), 11492–­11507. https://doi.org/10.1002/ece3.4584 Frick, W. F., Pollock, J. F., Hicks, A. C., Langwig, K. E., Reynolds, D. S., Turner, G. G., & Kunz, T. H. (2010). An emerging disease causes regional population collapse of a common North American bat species. Science, 329(5992), 679–­682. Grenfell, B. T., Dobson, A. P., & Moffatt, H. K. (1995). Ecology of infectious diseases in natural population (Volume 7). Cambridge University Press. Group of Eight Australia (2013). The Changing PhD. http://hdl.voced.edu. au/10707/​251914 Haddaway, N. R., & Watson, M. J. (2016). On the benefits of systematic reviews for wildlife parasitology. International Journal for Parasitology: Parasites and Wildlife, 5(2), 184–­191. Han, B. A., & Ostfeld, R. S. (2019). Topic modeling of major research themes in disease ecology of mammals. Journal of Mammalogy, 100(3), 1008–­1018. https://doi.org/10.1093/jmamm​al/gyy174 Haseeb, A., Jonathan, Y., William, A. D. G., Alicia, D., Tito, J. K., Blandina, T. M., Felix, L., Emmanuel, S. S., & Sarah, C. (2019). Economic burden of livestock disease and drought in Northern Tanzania. Journal of Development and Agricultural Economics, 11(6), 140–­151. https:// doi.org/10.5897/jdae2​018.1028 Hedges, L. V., & Olkin, I. (2014). Statistical methods for meta-­analysis. Academic press. Hess, A. D., & Hayes, R. O. (1970). Relative Potentials of domestic animals for Zooprophylaxis against Mosquito Vectors of Encephalitis. The American Journal of Tropical Medicine and Hygiene, 19(2), 327–­ 334. https://doi.org/10.4269/ajtmh.1970.19.327 hiv.gov. (2019). A timeline of HIV/AIDS. https://www.hiv.gov/sites/​defau​ lt/files/​aidsg​ov-­timel​ine.pdf | 17591 Hudson, P. J., Dobson, A. P., & Lafferty, K. D. (2006). Is a healthy ecosystem one that is rich in parasites? Trends in Ecology & Evolution, 21(7), 381–­385. https://doi.org/10.1016/j.tree.2006.04.007 Hudson, P. J., Dobson, A. P., & Newborn, D. (1998). Prevention of Population Cycles by Parasite Removal. Science, 282(5397), 2256–­ 2258. https://doi.org/10.1126/scien​ce.282.5397.2256 Hudson, P. J., Rizzoli, A., Grenfell, B. T., Heesterbeek, H., & Dobson, A. P. (2002). Ecology of wildlife diseases. Oxford University Press. Huntoon, J. E., & Lane, M. J. (2007). Diversity in the geosciences and successful strategies for increasing diversity. Journal of Geoscience Education, 55(6), 447–­457. https://doi. org/10.5408/1089-­9995-­55.6.447 Johnson, P. T., de Roode, J. C., & Fenton, A. (2015). Why infectious disease research needs community ecology. Science, 349(6252), 1259504. https://doi.org/10.1126/scien​ce.1259504 Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L., & Daszak, P. (2008). Global trends in emerging infectious diseases. Nature, 451(7181), 990–­993. Jones, M. S., & Solomon, J. (2019). Challenges and supports for women conservation leaders. Conservation Science and Practice, 1(6), e36. https://doi.org/10.1111/csp2.36 Joseph, M. B., Mihaljevic, J. R., Arellano, A. L., Kueneman, J. G., Preston, D. L., Cross, P. C., & Johnson, P. T. (2013). Taming wildlife disease: bridging the gap between science and management. Journal of Applied Ecology, 50(3), 702–­712. https://doi. org/10.1111/1365-­2664.12084 Keesing, F., Holt, R. D., & Ostfeld, R. S. (2006). Effects of species diversity on disease risk. Ecology Letters, 9(4), 485–­498. https://doi. org/10.1111/j.1461-­0248.2006.00885.x Koprivnikar, J., & Johnson, P. T. J. (2016). The Rise of Disease Ecology and Its Implications for Parasitology—­A Review. Journal of Parasitology, 102(4), 397–­4 09. https://doi.org/10.1645/15-­942 Lafferty, K. D., Dobson, A. P., & Kuris, A. M. (2006). Parasites dominate food web links. Proceedings of the National Academy of Sciences, 103(30), 11211–­11216. https://doi.org/10.1073/pnas.06047​55103 Lajeunesse, M. J. (2010). Achieving synthesis with meta-­analysis by combining and comparing all available studies. Ecology, 91(9), 2561–­ 2564. https://doi.org/10.1890/09-­1530.1 Lajeunesse, M. J. (2016). Facilitating systematic reviews, data extraction and meta-­analysis with the metagear package for R. Methods in Ecology and Evolution, 7(3), 323–­330. https://doi. org/10.1111/2041-­210X.12472 Lee, K.-­S., Divis, P. C. S., Zakaria, S. K., Matusop, A., Julin, R. A., Conway, D. J., Cox-­Singh, J., & Singh, B. (2011). Plasmodium knowlesi: reservoir hosts and tracking the emergence in humans and macaques. PLoS Path, 7(4), e1002015. https://doi.org/10.1371/journ​al.ppat.1002015 Lipkin, W. I. (2013). The changing face of pathogen discovery and surveillance. Nature Reviews Microbiology, 11, 133–­141. https://doi. org/10.1038/nrmic​ro2949 Lloyd-­Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005). Superspreading and the effect of individual variation on disease emergence. Nature, 438(7066), 355–­359. Loper, E., & Bird, S. (2002). NLTK: the natural language toolkit. ArXiv Preprint Cs/0205028. Lowry, E., Rollinson, E. J., Laybourn, A. J., Scott, T. E., Aiello-­L ammens, M. E., Gray, S. M., Mickley, J., & Gurevitch, J. (2013). Biological invasions: a field synopsis, systematic review, and database of the literature. Ecology and Evolution, 3(1), 182–­196. https://doi. org/10.1002/ece3.431 May, R. M., & Anderson, R. M. (1979). Population biology of infectious diseases: Part II. Nature, 280(5722), 455–­461. https://doi. org/10.1038/280455a0 Milem, J. F. (2003). The educational benefits of diversity: Evidence from multiple sectors. In M. J. Chang, D. Witt, J. Jones, & K. Hakuta (Eds.), Compelling interest: Examining the evidence on racial dynamics in higher education (pp. 126–­169). 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BRANDELL et al. �| National Science Foundation (2020). Women, Minorities, and Persons with Disabilities in Science and Engineering. https://nsf.gov/stati​stics/​ 2017/nsf17​310/diges​t/fod-­women/​engin​eering.cfm Nuñez, M. A., Chiuffo, M. C., Pauchard, A., & Zenni, R. D. (2021). Making ecology really global. Trends in Ecology & Evolution, 36(9), 766–­769. https://doi.org/10.1016/j.tree.2021.06.004 Nunez-­Mir, G. C., Iannone, B. V., Pijanowski, B. C., Kong, N., & Fei, S. (2016). Automated content analysis: addressing the big literature challenge in ecology and evolution. Methods in Ecology and Evolution, 7(11), 1262–­1272. https://doi.org/10.1111/2041-­210X.12602 Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37, 879–­910. https://doi.org/10.17705/​1CAIS.03743 Olival, K. J., Hosseini, P. R., Zambrana-­Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–­650. Plowright, R. K., Reaser, J. K., Locke, H., Woodley, S. J., Patz, J. A., Becker, D. J., Oppler, G., Hudson, P. J., & Tabor, G. M. (2021). April 1). Land use-­induced spillover: a call to action to safeguard environmental, animal, and human health. The Lancet Planetary Health, 5(4), e237–­e245. https://doi.org/10.1016/S2542​-­5196(21)00031​- ­0 Poff, N. L., & Zimmerman, J. K. (2010). Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biology, 55(1), 194–­205. https://doi.org/10.1111/j.1365-­2427.2009.02272.x Rissler, L. J., Hale, K. L., Joffe, N. R., & Caruso, N. M. (2020). Gender differences in grant submissions across science and engineering fields at the NSF. BioScience, 70(9), 814–­820. https://doi.org/10.1093/ biosc​i/biaa072 Roberge, M.-­É., & van Dick, R. (2010). Recognizing the benefits of diversity: When and how does diversity increase group performance? Human Resource Management Review, 20(4), 295–­3 08. https://doi. org/10.1016/j.hrmr.2009.09.002 Ryan, S. J., Carlson, C. J., Mordecai, E. A., & Johnson, L. R. (2019). Global expansion and redistribution of Aedes-­borne virus transmission risk with climate change. PLoS Neglected Tropical Diseases, 13(3), e0007213. https://doi.org/10.1371/journ​al.pntd.0007213 Saleh, A., & Bista, K. (2017). Examining factors impacting online survey response rates in educational research: perceptions of graduate students. Journal of MultiDisciplinary Evaluation, 13(29), 63–­73. http://www.jmde.com Scheele, B. C., Pasmans, F., Skerratt, L. F., Berger, L., Martel, A., Beukema, W., & de la Riva, I. (2019). Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science, 363(6434), 1459–­1463. Scheiner, S. M., & Rosenthal, J. P. (2006). Ecology of infectious disease: Forging an alliance. EcoHealth, 3(3), 204–­208. https://doi. org/10.1007/s1039​3-­0 06-­0 035-­7 Schmidt, K. A., & Ostfeld, R. S. (2001). Biodiversity and the dilution effect in disease ecology. Ecology, 82(3), 609–­619. Shaw, A. K., & Stanton, D. E. (2012). Leaks in the pipeline: Separating demographic inertia from ongoing gender differences in academia. Proceedings of the Royal Society B: Biological Sciences, 279(1743), 3736–­3741. https://doi.org/10.1098/rspb.2012.0822 Smith, G. (2008). Does gender influence online survey participation?: A record-­linkage analysis of university faculty online survey response behavior. ERIC Document Reproduction Service No. ED 501717. BRANDELL et al. Sokolow, S. H., Nova, N., Pepin, K. M., Peel, A. J., Pulliam, J. R. C., Manlove, K., Cross, P. C., Becker, D. J., Plowright, R. K., McCallum, H., & De Leo, G. A. (2019). Ecological interventions to prevent and manage zoonotic pathogen spillover. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1782), 20180342. https://doi. org/10.1098/rstb.2018.0342 Suh, E., Grossman, M. K., Waite, J. L., Dennington, N. L., Sherrard-­Smith, E., Churcher, T. S., & Thomas, M. B. (2020). The influence of feeding behaviour and temperature on the capacity of mosquitoes to transmit malaria. Nature Ecology & Evolution, 4(7), 940–­951. https://doi. org/10.1038/s4155​9-­020-­1182-­x Uehlinger, F. D., Johnston, A. C., Bollinger, T. K., & Waldner, C. L. (2016). Systematic review of management strategies to control chronic wasting disease in wild deer populations in North America. BMC Veterinary Research, 12(1), 1–­16. https://doi.org/10.1186/s1291​ 7-­016-­0 804-­7 UNESCO Institute for Statistics (UIS) Higher Education (2020). UNESCO Institute for Statistics (UIS). http://data.uis.unesco.org/http://uis. unesco.org/en/topic/​highe​r-­educa​tion Van Boeckel, T. P., Pires, J., Silvester, R., Zhao, C., Song, J., Criscuolo, N. G., Gilbert, M., Bonhoeffer, S., & Laxminarayan, R. (2019). Global trends in antimicrobial resistance in animals in low-­ And middle-­ income countries. Science, 365(6459), https://doi.org/10.1126/ scien​ce.aaw1944 Vanderwaal, K., & Ezenwa, V. (2016). Heterogeneity in pathogen transmission: mechanisms and methodology. Functional Ecology, 30(10), 1606–­1622. https://doi.org/10.1111/1365-­2435.12645 Wilson, K., Fenton, A., & Tompkins, D. (2019). Wildlife disease ecology: Linking theory to data and application. Cambridge University Press. Wood, S. N. (2006). Generalized Additive Models: An Introduction with R (Edition 1). Chapman & Hill/CRC. World Health Organization (1996). Press release: Infectious diseases kill over 17 million people a year: WHO warns of global crisis. https:// www.who.int/whr/1996/media_centr​e/press_relea​se/en/ World Health Organization (2018). The top 10 causes of death. https:// www.who.int/news-­r oom/fact-­s heet​s /detai​l /the-­t op-­10-­c ause​ s-­of-­death Wortley, L., Hero, J. M., & Howes, M. (2013). Evaluating ecological restoration success: a review of the literature. Restoration Ecology, 21(5), 537–­543. https://doi.org/10.1111/rec.12028 S U P P O R T I N G I N FO R M AT I O N Additional supporting information may be found in the online version of the article at the publisher’s website. How to cite this article: Brandell, E. E., Becker, D. J., Sampson, L., & Forbes, K. M. (2021). Demography, education, and research trends in the interdisciplinary field of disease ecology. Ecology and Evolution, 11, 17581–­17592. https://doi. org/10.1002/ece3.8466 20457758, 2021, 24, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.8466 by Colorado Parks & Wildlife, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 17592 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Demography, education, and research trends in the interdisciplinary field of disease ecology Description An account of the resource Micro-and macroparasites are a leading cause of mortality for humans, animals, and plants, and there is great need to understand their origins, transmission dynamics, and impacts. Disease ecology formed as an interdisciplinary field in the 1970s to fill this need and has recently rapidly grown in size and influence. Because interdisciplinary fields integrate diverse scientific expertise and training experiences, understanding their composition and research priorities is often difficult. Here, for the first time, we quantify the composition and educational experiences of a subset of disease ecology practitioners and identify topical trends in published research. We combined a large survey of self-declared disease ecologists with a literature synthesis involving machine-learning topic detection of over 18,500 disease ecology research articles. The number of graduate degrees earned by disease ecology practitioners has grown dramatically since the early 2000s. Similar to other science fields, we show that practitioners in disease ecology have diversified in the last decade in terms of gender identity and institution, with weaker diversification in race and ethnicity. Topic detection analysis revealed how the frequency of publications on certain topics has declined (e.g., HIV, serology), increased (e.g., the dilution effect, infectious disease in bats), remained relatively common (e.g., malaria ecology, influenza, vaccine research and development), or have consistently remained relatively infrequent (e.g., theoretical models, field experiments). Other topics, such as climate change, superspreading, emerging infectious diseases, and network analyses, have recently come to prominence. This study helps identify the major themes of disease ecology and demonstrates how publication frequency corresponds to emergent health and environmental threats. More broadly, our approach provides a framework to examine the composition and publication trends of other major research fields that cross traditional disciplinary boundaries. Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. Brandell, E.E., D. J. Becker, L. Sampson, and K. M. Forbes. 2021. Demography, education, and research trends in the interdisciplinary field of disease ecology. Ecology and evolution 11:17581-17592, https://doi.org/10.1002/ece3.8466 Creator An entity primarily responsible for making the resource Brandell, Ellen E. Becker, Daniel J. Sampson, Laura Forbes, Kristian M. Subject The topic of the resource Host-pathogen interaction Infectious disease Machine learning Extent The size or duration of the resource. 12 pages Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/">IN COPYRIGHT - NON-COMMERCIAL USE PERMITTED</a> Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Ecology and Evolution Date Accepted Date of acceptance of the resource. Examples of resources to which a Date Accepted may be relevant are a thesis (accepted by a university department) or an article (accepted by a journal). 2021/12/02 Date Issued Date of formal issuance (e.g., publication) of the resource. 2021/12/14 Date Modified Date on which the resource was changed. 2021/11/30 Date Submitted Date of submission of the resource. Examples of resources to which a Date Submitted may be relevant are a thesis (submitted to a university department) or an article (submitted to a journal). 2021/05/10 Type The nature or genre of the resource Article