Global Network of SDM Research

The global network of collaboration was composed by 155 countries, of which the 10 most productive, in descending order of productivity, were United States, United Kingdom, China, Spain, Germany, Australia, France, Brazil, Mexico, and Canada (Figure 1(b)). The United States led the global scientific production extensively with 1,863 papers (Figure 1(b)). Further, the 15 countries that represented the nodes with the highest centrality degree (with collaborations with more than 30 countries) were, in descending order, United States, Germany, United Kingdom, France, Spain, Australia, Canada, Italy, Netherlands, Switzerland, Brazil, China, Belgium, Denmark, and South Africa, indicating their roles as a collaboration hubs (Figure 1(c)). Worldwide, the most important collaboration networks occurred between Portugal and Spain (n = 84 published papers in collaboration), United Kingdom and Spain (n = 69), Germany and United Kingdom (60), as well as between United States and United Kingdom (n = 144), Australia (n = 135), Mexico (n = 112), China (n = 95), Canada (n = 94), Spain (n = 89), Brazil (n = 86), France (n = 86), and Germany (n = 85; Figure 1(c)). From this network of collaboration, the most productive Latin American countries were Brazil, Mexico, Argentina, Colombia, Chile, and Ecuador, with 356, 350, 145, 103, 57, and 39 papers, respectively (Figure 1(b, c)).

Latin American Networks and Their Articulation With the World

The Latin American collaboration network was composed of 26 countries that mainly interact with 60 countries worldwide, from which the most productive collaborations (represented by links known as edges) are with United States, Spain, United Kingdom, Germany, and Australia (Figure 2(a, b)). The strongest network of collaboration was evidenced between United States with Mexico (n = 112 collaborations), Brazil (n = 86), Colombia (n = 44), Chile (n = 21), and Ecuador (n = 21); followed by the interactions of Brazil with United Kingdom (n = 23) and Germany (n = 21); as well as between Spain and Mexico (n = 28), Brazil (n = 15), Argentina (n = 11), Ecuador (n = 11), Colombia (n = 8), and Chile (n = 8; Figure 2(b)). Further, the 10 countries that represented the nodes with the highest centrality degree (with collaborations with more than 30 countries) between Latin America and the rest of the world are, in descending order, United States, Brazil, Australia, United Kingdom, Mexico, Colombia, Spain, Germany, France, and Canada (Figure 2(a, b)).

Figure 2.

(a) Productivity of SDM literature in which Latin American institutions participate (blue circles) with other countries worldwide (pale blue circles). Circle size represents the number of published documents per country. (b) Collaboration network between the top 26 Latin American countries (orange circles) with other 60 countries (red circles) to produce documents on SDM in the scientific literature. Circle size represents the number of papers published by country. The thickness of the connections (links or edges) indicates the strength of collaboration that exists between countries.

The top 10 of the most prolific Latin American countries are Brazil (n = 356), Mexico (n = 350), Argentina (n = 145), Colombia (n = 103), Chile (n = 57), Ecuador (n = 39), Costa Rica (n = 20), Peru (n = 20), Uruguay (n = 19), and Bolivia (n = 18; Figure 2(a, b)). However, when considering the number of published documents per country population density (population/km²), a new ranking pattern appears. In descendant order, the top 10 prolific countries are Brazil, Argentina, Mexico, Colombia, Chile, Bolivia, Uruguay, Peru, Ecuador, and Venezuela. In contrast, when considering the number of published documents per country population, the ranking pattern changes: Uruguay, Costa Rica, Argentina, Chile, Mexico, Guyana, Panama, Ecuador, Colombia, and Suriname.

The most robust collaboration edges between Latin American countries were evidenced between Brazil with Argentina (n = 18), Mexico (n = 17), and Colombia (n = 11) and between Mexico with Colombia (n = 17), Ecuador (n = 10), and Argentina (n = 8); these collaborations represent just between 2.2% and 10.7% of the country’s publications. From Central American institutions, Costa Rica, Panama, and Nicaragua were the most prolific with 60, 27, and 11 published documents, respectively, followed by Honduras, Guatemala, and El Salvador (with 7, 6, and 2 publications, respectively). From the Caribbean islands, Cuba and Puerto Rico were the most prolific with 16 and 5 publications, respectively, followed by Jamaica, Antigua and Barbuda, Dominican Republic, and Haiti (with 3, 3, 2, and 1 publications, respectively; Figure 2(a)).

From an institutional perspective, the global collaboration network was composed of 4,329 institutions, from which 51 published more than 34 papers each (mean 61.6), shaping a major component of the network (Figure 3(a)). Top 10 institutions included two from Latin America and, in descending order of production, were National Autonomous University of Mexico (UNAM), Chinese Academy of Sciences, University of Kansas, U.S. Geological Survey (USGS), Consejo Superior de Investigaciones Científicas, Federal University of Goiás (Brazil), University of Melbourne, University of Porto, U.S. Forest Service, and Colorado State University. Based on the amount of productivity, it was possible to identify that the UNAM had the highest centrality degree among institutions worldwide, being the most prolific institution, with 199 published papers, collaborating with other 225 institutions (38.2% from Latin American countries) in the network. In contrast, the second most prolific institution, the Chinese Academy of Sciences, collaborated with 17 institutions but published 70% of the documents in collaboration with a single institution (University of Chinese Academy of Sciences). There was no collaboration between the UNAM with the Chinese Academy of Sciences (Figure 3(a)).

Figure 3.

(a) Collaboration network between the top 51 worldwide institutions (red circles) in the scientific literature on SDM, from which six institutions are in Latin American countries (orange circles). Circle size represents the number of papers published by the institution. The thickness of the connections (links) indicates the strength of collaboration that exists between institutions. (b) Collaboration network between the top 45 Latin American institutions (orange circles) with 13 institutions from other regions (red circles) to produce scientific literature on SDM. Circle size represents the number of papers published by each institution. The thickness of the connections (links) indicates the strength of collaboration that exists between institutions.

University of Kansas and USGS (USA) are in the third and fourth level of productivity, with 161 and 125 published documents, respectively. Within the major component of the global network, University of Kansas has collaborated with 28 institutions of which five are from Latin America (UNAM, Institute of Ecology A.C.—INECOL, University of São Paulo (USP), Federal University of Goiás, and University of Brasilia), while USGS collaborated with just 18 institutions showing no interaction with Latin American institutions (Figure 3(a)).

The Latin American network was composed of 556 Latin American institutions that collaborate with 519 other institutions outside Latin American countries (online Appendix C). A total of 45 Latin American institutions and 13 institutions from outside, published more than 10 papers each (mean 22.8) shaping the major component of the network (Figure 3(b)). From those, the top institution is UNAM, mainly collaborating with University of Kansas, INECOL, and University of Texas at Austin (17%, 10.5%, and 6.5% of UNAM publications, respectively). The second most productive Latin American institution was Federal University of Goiás collaborating with 24 institutions, mainly with State University of Goias, Consejo Superior de Investigaciones Científicas, Federal University of Paraná, UNAM, and National Institute of Amazonian Research (between 5 and 7 shared papers). The other four most prolific Latin American institutions were INECOL (55 papers published with 17 institutions), National Scientific and Technical Research Council in Argentina—CONICET (52 papers published with 19 institutions), USP (51 papers published with 28 institutions), and University of Brasilia (36 papers published with 20 institutions; online Appendix C).

Thematic Characterization of Latin American Research on SDM

From the characterization of keywords reported among the 1,000 SDM papers published with collaboration by Latin American institutions, the main patterns were as follows:

Modeling Methods Used to Develop SDM in Latin American Research

From the detailed reading (title, abstract, and keywords) of the 1,000 papers in which Latin American institutions collaborated, we identified 673 papers (excluding 14 review papers that did not conduct any SDM modeling and were excluded from the bibliometric mapping) that used 36 methods (in three categories) to model species distributions (see online Appendix F). The first modeling method to model distributions in which Latin American institutions participated, was GARP (Sánchez-Cordero & Martínez-Meyer, 2000; Soberón, Golubov, & Sarukhán, 2001).

It is well recognized that there is no guarantee that a single modeling method will be optimal to model species distributions for all data sets and geographical realms (Mateo, Felicísimo, & Muñoz, 2011). However, It was only after 2006 that researchers began to compare SDM with more than two modeling methods (generalized additive models, GARP, BIOCLIM, generalized linear models—GLM, MaxEnt, DOMAIN, FloraMap, Weights of evidence, Mahalanobis Distance and Random Forests, among others; online Appendix G). Until 2018, only 50 articles (7.4%) compared between 3 and 10 modeling methods, from which only 6 compared more than 7 methods (Escobar, Qiao, Cabello, & Peterson, 2018; Ochoa-Ochoa, Flores-Villela, & Bezaury-Creel, 2016; Queiroz et al., 2013; Reiss, Cunze, Konig, Neumann, & Kroncke, 2011; Tessarolo, Rangel, Araújo, & Hortal, 2014; Tognelli, Roig-Juñent, Marvaldi, Flores, & Lobo, 2009; online Appendix F, G).

We found that 73.5% of the analyses used MaxEnt, followed by GARP (18.7%), GLM (6.4%), BIOCLIM (4.6%), Random Forests (5%), and generalized additive models (3.1%; Figure 5(a)). The most common combination of performed analyses are MaxEnt models with GARP (n = 47), BIOCLIM (n = 22), GLM (n = 17), and Random Forests (n = 16; Figure 5(a), online Appendix F, G). Our results highlight MaxEnt as the most commonly used software to model the distribution of species from Latin American institutions. This finding is further supported by MaxEnt download data showing that between December 12, 2016, and June 8, 2018, 178 days after the new open source version of MaxEnt became available (Phillips et al., 2017), 27,472 downloads were made from 156 countries, of which 19.7% were made in the United States, 9.2% in Mexico, 7.5% in Brazil, 6% in China, and 5.5% in Colombia (Figure 5(b); data from the American Museum of Natural History). Latin American countries made 8,861 downloads, 32.2% of total downloads, during this period. Also, after controlling for the population size of countries, 5 Latin American countries emerged among the top 10 countries with the highest number of downloads per million people during this period: Costa Rica, French Guiana, Colombia, Ecuador, and Chile.

Interaction Opportunities Visualized From Institutional Collaboration Patterns

We conclude that the United States is the country with the highest scientific production in SDM, as it is for other scientific areas as shown in Table 1 (SCImago, 2019). However, the United States is important for collaborations between countries as well, but our study presents a different per country publication pattern than the one reported by Barbosa et al. (2012) for SDM of invasive species (Table 1).

Table 1.

Top Five Most Prolific Countries in Different Subject Areas and Categories of Knowledge.

Countries in Latin America collaborate in 18% of the global production of SDM academic publications including the development of new modeling tools (Figure 1(a)). Brazil and Mexico are among the top 10 countries worldwide and become a reference at the international level, especially among other Latin American countries. This correlates with the results of the SCImago Journal & Country Rank, a portal that provides scientific indicators to assess scientific domains, where at a global level Brazil is in the top 15 and Mexico in the top 29 for academic production in all subject areas category (Table 1; SCImago, 2019).

Within the Latin American region, the most productive institutions on SDM is the UNAM, which is also number one worldwide, followed by Federal University of Goiás, USP, and University of Brasilia, those institutions are ranked by SCImago (2019) in Latin America as the 3rd, 45th, 1st, and 25th positions, respectively.

The geographic scope of the SDM research in Latin America coincides with the four more scientifically productive countries Mexico, Brazil, Argentina, and Colombia (Figure 2(a, b); online Appendix D). Although the collaboration network in Latin America is complex, and involves 12 countries, research in the region might be reducing the Wallacean shortfall only on those countries. It would be desirable that future collaboration reduces the Wallacean shortfall not only in the most productive countries but expands to other less studied geographic areas.

It is interesting how the number of collaborations with other institutions has a contrasting pattern between the top 1 and 2 global ranking institutions. While UNAM collaborated with 225 other institutions, the Chinese Academy of Sciences collaborates with only 17 other institutions. This suggest that in contrast to China, collaboration is a crucial issue for ranking universities in Latin America. The Chinese Academy of Sciences is the second institution that globally has the most publications on SDM. Based on its research performance, innovation outputs and societal impact, the Chinese Academy of Sciences ranks first among global institutions as measured by their web visibility (SCImago, 2019). However, in Latin America, this institution only collaborates with Guatemala and Chile (online Appendix C).

The implementation of collaboration strategies between Latin American institutions and those outside Latin America should continue to be enhanced through fellowships, capacity building, sabbatical years between researchers, or shared advisors of postgraduate students. Latin American collaboration networks will tend to grow as more researchers return to their country of origin, maintaining collaboration networks created from postgraduate studies and fellowships, and beginning new collaborations with other Latin American countries.

Closing the Gap From Software User to Coauthor: A MaxEnt Study Case

MaxEnt is the most commonly used software to model the distribution of species from Latin American institutions (Figure 5(a)). The first nine documents published, by Latin American institutions, using MaxEnt software were from 2007 to 2008 (online Appendix F, G), between 1 and 2 years after the release of the first version of the software (Phillips, Anderson, & Schapire, 2006; Figure 1(a)). MaxEnt users see this tool as one of those that requires the fewest actions needed to implement the analysis being at the top among modeling tools in terms of usefulness, learning curve, system capabilities, and overall user satisfaction (Ahmed et al., 2015). As mentioned earlier, after the new open source version of MaxEnt was made available (Phillips et al., 2017), it was followed by an enormous number of downloads (Figure 5(b)); 32.3% of which took place in Latin America.

Also, half of the top 10 countries downloading the software (when controlling for population size) were in Latin America. So, if Latin America is indeed powerful within the global community of MaxEnt users, why do Latin American authors and institutions represent only 18% of the global scientific literature on SDM? Looking deeper into download trends, we can examine the 13,155 or 48% of global downloads that included optional institution information. Of this number, 3,385 or 38.2% of total downloads from Latin America (8,861) included institutional information. Of those, the vast majority (2,944 or 87%) of MaxEnt downloads were from academic institutions (universities or university centers/institutes). Downloads from government institutions including research institutes, agencies, national park system administrations, and ministries comprised 384 or 11%, nongovernmental organizations (NGOs) including conservation organizations, private not-for-profit research institutes, and foundations accounted for 102 or 3%. Only 5 downloads or 0.1% were from for-profit companies including consultants.

As mentioned earlier, the most prolific Latin American institutions in global scientific literature were universities (online Appendix C). The software download information also reflects this trend, but the number of MaxEnt downloads from government institutions is also notable, which is not as evident in the observed scientific literature trends. We might assume that if we examined beyond the scientific literature to include white papers and government reports, we might find additional written contributions from these institutions and probably several examples of translations of SDM to decision-making related to biodiversity conservation at NGOs and government agencies (see some examples later). We can also probably assume that there is a great deal of SDM development and research ongoing at universities that is not being published in the global scientific literature, or that is being used for training only. This research may be available instead as student theses or reports in university databases. There may also be further challenges to publishing results such as costs, language, and other factors.

Implementing Best Practices and Integrating SDM Into Monitoring Biodiversity Change

Strengthening of collaborative networks between academic institutions and institutions outside of academia in Latin America would further enhance the translation of SDM research to biodiversity conservation decision-making (as discussed later) and would likely increase the visibility of ongoing and future research using SDM in the global scientific literature. Open source SDM training opportunities and online networks in the Spanish language (such as the Colombian initiative BioModelos—see later—and more informally, the Facebook group “Modelado de Nichos Ecológicos y Distribuciones Geográficas”) are a robust and well-appropriated strategy to facilitate online training and the formation of communities of practice across researchers and practitioners from different sectors and different Latin American countries (Cuervo-Robayo et al., 2017).

We identified that some Latin American countries have great potential to become world leaders in the area of ​SDM, to the extent that the collaboration between institutions increases and diversifies. As SDM research areas increase in Latin America, good practices must also acquire by researchers, input data should be open access, codes should be shared with complete documentation, and prediction layers should be made available (Breiman, 2001; Guisan & Thuiller, 2005; Humphries & Huettmann, 2018). Latin American countries are moving into open science culture, the number of open access mandates adopted has grown from 1 in 2005 to 54 in 2018 (ROARMAP  http://roarmap.eprints.org/, last visit on April 18, 2019).

Megadiverse countries in Latin America, have important reservoirs of mining resources such as gold, nickel, chromium, and copper, among others (USGS, 2018). Those resources attract higher pressure from big companies, that together with oil and timber extraction, push governments to give priority to extractive activities over preservation of biodiversity (Loyola, 2014; Villarroya et al., 2014). Under such scenarios, SDM could provide critical information to make a difference in the decision-making process by improving the understanding and monitoring of biodiversity, as well as focusing spatial conservation strategies and their effective implementation by responsible authorities (Scheldeman & van Zonneveld, 2010); for example, by combining SDMs with other key information as a part of a systematic conservation planning framework (e.g., as in Marxan: Ball, Possingham, & Watts, 2009; or Zonation: Moilanen et al., 2005). Enhancing the training of new generations of biologists and ecologists in computational techniques is needed so that machine learning methods can play an essential role in understanding whole ecosystems by holistic modeling (Humphries & Huettmann, 2018).

New technology can easily be misused when they are not correctly applied, as explicitly seen for SDM (Guillera-Arroita et al., 2015; Humphries & Huettmann, 2018) and model quality should be evaluated (Araújo et al., 2019). SDM are particularly prone to problems arising from a mismatch between data type and intended purpose. Hence for conservation practice is critical to know if the output from an SDM will be appropriate for the intended application (Guillera-Arroita et al., 2015). The majority of the SDM produced in Latin America comes from presence-background data, meaning that their outputs represent in most cases relative likelihoods of observation, with subsequent limitations in their applications. As such Latin America needs to move toward the collection of more informative survey data of the occupancy-detection type so that better inference of species distribution can be made (Guillera-Arroita et al., 2015).

SDM in Latin America have been used in multiple realms of application but with a considerable bias toward vertebrates and insects, and in three main ecosystems (rainforests, tropical dry forests, and mountain ecosystems). Researchers in Latin America have explored multiple applications for SDM; we encourage that this diverse level of application continues in an interdisciplinary manner. Is also important to notice that one of the most threatened ecosystems worldwide (tropical dry forest: Hoekstra, Boucher, Ricketts, & Roberts, 2005) is also one of the best represented in SDM research (online Appendix E). Also, the most critical global change drivers, land use change, biological invasions, and climate change (Sala et al., 2000) are among the most studied thematic from SDM perspective (Figure 4(b)). Those results show indeed the awareness by the research community of the importance of SDM for conservation in the regions.

Figure 4.

Carrot Search FoamTree visualization of pertinent words classified by categories: (a) taxonomic group and (b) realm of application.

Figure 5.

(a) Network of modeling methods used to develop SDM in documents including collaborators from Latin American institutions. (b) Global downloads of MaxEnt software from December 12, 2016, to June 8, 2018 (n = 27,472; data from the American Museum of Natural History). Circle size represents the number of downloads per country, in 178 days.

ENFA = ecological niche factor analysis; GARP = Genetic Algorithm for Rule-Set Prediction; GLM = generalized linear models; GAM = generalized additive models.

For research results to have a higher incidence in the decision-making process, the importance of SDM needs to be also understood by decision-makers. There is no clear sustainable future in some megadiverse neotropical countries in which biodiversity is not a priority for central governments that are committed with leading economy countries (such as China and US), while local poverty increases in some regions, in which illegal drug business and big companies exhibit local power on land decisions (Brocket, 1990; Huettmann, 2015). As noticed by Humphries and Huettmann (2018), SDM study is rarely linked to effective conservation management, as they are not referred to in most policy or legal decisions, and SDM studies widely lack a reflective component that advances ethical and societal questions. How can we move to translate scientific results into effective conservation management? We propose that in Latin America, SDM must be placed in a broader context between monitoring data and specific management and conservation contexts. New biodiversity monitoring frameworks may provide robust tools to articulate user needs with scientific outputs, Biodiversity Observation Networks could be developed in Latin America and facilitate explicit knowledge to transit into tacit knowledge, addressing societal and economic needs, and thus increase research incidence on conservation (Navarro et al., 2017).

Building Communities of Practice Around SDM: The Case of BioModelos in Colombia

SDM ideally must rely on enough occurrence data as well as knowledge regarding species’ environmentally limiting factors and dispersal limitations (Anderson, 2012; Araújo & Peterson, 2012). Although occurrence data are increasingly available in open access platforms such as Global Biodiversity Information Facility (Meyer et al., 2015), albeit, with questionable quality (Anderson et al., 2016), knowledge on species ecology and evolution is also insufficient but crucial for the development of biologically meaningful models (Diniz-Filho, Loyola, Raia, Mooers, & Bini, 2013). To remedy this shortfall, the Colombian initiative BioModelos brings together modelers with experts who can inform model development and assess the reliability of models (Velásquez-Tibatá et al., 2019). We present this initiative here as an example of collaborative knowledge building for improving SDM.

In BioModelos, experts are arranged into groups according to thematic areas of interest, and each group has a defined set of species of interest. Experts contribute through an online platform (biomodelos.humboldt.org.co) to any of the following activities for each species in their group: (a) occurrence data cleaning; (b) delineation of accessible area; (c) identification of suitable land covers; (d) identification of areas of model over/underprediction; (e) selection of suitability thresholds to create binary models; and (f) qualitative evaluation of model accuracy. A core team develops and edits models according to expert inputs. Once this collaborative modeling process is completed, models become available for visualization and download in standard GIS formats along with metadata documenting the modeling process on the BioModelos webpage. Importantly, the metadata acknowledges the contribution of everyone involved in model development.

Thus far, 475 experts have joined at least one of the 20 expert groups in BioModelos, where they contribute to the development of models for 980 species (Velásquez-Tibatá et al., 2019). These models have been particularly crucial for the Colombian government in the evaluation of species extinction risk (Renjifo, Amaya-Villareal, Burbano-Girón, & Velásquez-Tibata, 2016) and the development of national conservation action plans (e.g., cycads and primates:  http://www.minambiente.gov.co/index.php/bosques-biodiversidad-y-servicios-ecosistematicos/fauna-y-flora/programas-de-conservacion#documentos). Along those assessments and plans, qualitative assessments of the biological realism of spatial predictions by qualified experts are essential to minimize costly commission errors (Guisan et al., 2013; Lobo, Jiménez-Valverde, & Hortal, 2010) that cannot be measured unbiasedly with presence-only data (Lobo, Jiménez-Valverde, & Real, 2008). However, model users also report their use for basic research (33%) and educational activities (32%) and varied activities such as environmental impact assessments, bioprospecting, and applied research (Velásquez-Tibatá et al., 2019).

The implementation of BioModelos in Colombia shows that it is possible to go a long way toward addressing data incompleteness and quality issues by involving experts in model development. By using transparent workflows and making data freely available, this approach contributes to diminishing the duplication of efforts in data cleaning and modeling, thereby accelerating the use of modeling outcomes in basic and applied research. Building SDM in a collaborative way is the first step to create communities of practice that discuss and overcome the different caveats of SDM. A next step is to integrate stakeholders from governmental institutions, NGOs, and productive sectors to develop derived products from SDM that can be directly incorporated into decision-making.