The authorship analysis conducted in the present study has identified a total of 13,122 authors who have made significant contributions to the field of ENM through their research documents. Among these authors, only 91 have authored single-authored documents, while substantial 13, 031 have collaborated on multi-authored documents. “Peterson AT,” a distinguished professor at the University of Kansas Lawrence, United States, has emerged as the leading contributor with a remarkable 37 publications to his name. Professor Peterson works on a broad spectrum of topics including ecology, biogeography, and evolutionary biology. Following closely to him is “Guisan, A,” a professor at the University of Lausanne, Switzerland, specializing in niche modelling, habitat suitability, and distribution modeling, with 31 publications. “Roedder D,” working as an associate professor and curator of Herpetology at the Leibniz Institute for Biodiversity Change Analysis (LIB)–Research Museum Alexander Koenig, Germany, has authored 26 publications. Furthermore, “Thuiller, W,” a Research Director at Grenoble Alps University, France, known for his work in biogeography, ecology, and biostatistics, has 25 publications to his name, and “Svenning, JC,” a Professor at Aarhus University, Denmark, working in the section for Eco-informatics and Biodiversity (ECOINF) and the Centre for Biodiversity Dynamics in a Changing World (BIOCHANGE), has contributed 24 publications (Table 1).

Lotka’s productivity law, introduced by Alfred J. Lotka in 1926, characterizes the distribution of publication frequency among authors in a given field (Lotka, 1926). According to this law, the distribution is typically uneven, with a small number of highly productive authors responsible for the majority of articles (Ridwan et al., 2023). The relationship can be described as inversely proportional, meaning that as the number of works contributed increases, the number of authors contributing to that level of output decreases (López-Montero et al., 2022). Applying Lotka’s Law to our findings, it becomes evident that 81.8% of authors have contributed only one publication, while 10.6% have authored two publications. Furthermore, 3.7% have authored three publications, 1.5% contributed with four publications and 2.4% have contributed more than four publications (Table 2). This distribution aligns with Lotka’s law where a small number of authors are highly productive, while the majority has lower publication output.

On the basis of number of documents published, out of the total 564 sources on the topic of ENM, the most relevant source is “Journal of Biogeography,” in which maximum number of documents (290) was published, it is followed by “Diversity and Distributions” (214 documents), “Molecular Ecology” (115 documents), “PLOS ONE” (102 documents) and “Global Ecology and Biogeoghraphy” (93 documents) (Figure 2). The Journal of Biogeography is a peer-reviewed, renowned scientific journal that focuses on research at the intersection of biology and geography, with a strong emphasis on scientific significance and broad general interest. The journal seeks to publish papers that investigate patterns and mechanisms that influence biodiversity across various temporal and spatial scales, ranging from the distant past to the future and from local to global perspectives. The Journal of Biogeography was founded in 1974 and is closely affiliated with two sister journals, Global Ecology and Biogeography and Diversity and Distributions. These three journals, while interconnected, maintain distinct yet complementary areas of focus within the expansive domain of biogeography. ENM often deals with both historical and predictive aspects of species distributions, making the “Journal of Biogeography” “Global Ecology and Biogeography” and “Diversity and Distributions” ideal outlets for research on this subject. The journal “Molecular Ecology” specializes in the molecular and genetic aspects of ecology, evolution, behavior and conservation, making it particularly relevant for ENM studies that incorporate genetic data. The journal “PLOS ONE” is known for its interdisciplinary approach and open-access publishing model. While it covers a wide range of scientific fields, including biology, it is also relevant to ENM research due to its inclusivity and willingness to publish research from various domains.

Bradford’s law of scattering describes how literature pertaining to a particular subject is distributed across various journals (Bradford, 1934). According to Bradford’s Law, a set of core journals, known as “Bradford’s core” emerges as central to that subject. These core journals typically contain a significant number of articles and receive substantial citations, signifying their importance (Borgohain et al., 2021). Applying Bradford’s Law to our study, we identified that among a total of 564 sources, only eleven journals were classified as core sources, as shown in Figure 3. This finding suggests that these eleven journals likely encompass a substantial proportion of articles pertaining to the topic of ENM. Conversely, the remaining sources are likely to have lower relevance or frequency of articles on this specific subject, emphasizing the concentration of valuable literature within the core journals.

When analyzing country-wise document production, it is evident that the United States stands as the most prolific contributor with a substantial output of 2,215 documents. Following the United States, the United Kingdom closely trails with 1,290 documents, followed by Australia with 1,180, Brazil with 1,136, China with 1,054, Spain with 1,052, Germany with 1,014, and France with 804 (Figure 4). This information may be helpful for gaining insights about the global distribution of research activity within the field of ENM.

Figure 5 presents a comprehensive overview of the top twenty prominent affiliations of corresponding authors. Notably, the “National Autonomous University of Mexico” emerges as the leading institution, demonstrating its significant contribution with a total of 158 documents. It is closely followed by the “University of Kansas” with 157 documents, the “University of Porto” with 138, the “University of Helsinki” with 123, and the “University of Lausanne” with 118 documents. This ranking facilitates precise comparisons among institutions with respect to the global reach of their publication patterns, their frequency of publishing in peer-reviewed sources, their presence in esteemed journals within the specific discipline, and the distribution of their publications across scientific research and high-level synthetic literature (Ridwan et al., 2023).

In the field of ENM, from 1992–2023, it was revealed that on an average, each document has received 31.41 citations. The most globally cited documents on ENM include Pearson et al. (2007), Liu et al. (2005), and Karger et al. (2017) (Table 3). The top most cited article by Pearson et al. (2007) revolves around the development and testing of a novel jackknife validation approach to predict the distribution of 13 secretive leaf-tailed gecko species (Uroplatus spp.) in Madagascar, even when the available occurrence records for these species are limited (ranging from 4 to 23 occurrences). They utilized environmental data and two modelling approaches (Maxent and GARP) and found that the Maxent model performed well with sample sizes as low as five occurrences, while GARP was less effective, especially with very limited data. The study emphasizes that models with small sample sizes indicate regions with similar environmental conditions to known occurrences rather than precise species ranges. While this approach offers valuable insights for guiding field surveys and discovering unknown populations, it also recognizes the need for cautious interpretation and highlights the potential for overestimating predictive power with larger sample sizes.

Liu et al. (2005) addresses the critical issue of selecting appropriate thresholds when converting species distribution modeling results from probabilistic predictions to binary presences and absences. This article compares twelve different threshold determination approaches using two European species and artificial neural networks. The evaluation of modeling results employs four indices: sensitivity, specificity, overall prediction success, and Cohen’s kappa statistic. The findings highlight that several effective approaches include the prevalence approach, average predicted probability/suitability approach, and three sensitivity-specificity combined methods, such as sensitivity-specificity sum maximization, sensitivity-specificity equality, and the approach based on the shortest distance to the top-left corner (0,1) in ROC plot. In contrast, the commonly used kappa maximization approach is less effective, and the fixed threshold approach is the least favorable. Furthermore, the study recommends using datasets with a prevalence of 50% for model building whenever possible. It often leads to optimal thresholds satisfying multiple criteria simultaneously, simplifying the threshold selection process.

Karger et al. (2017) introduces the CHELSA dataset (Climatologies at high resolution for the earth’s land surface areas), which provides high-resolution temperature and precipitation estimates for the Earth’s land surface. These data are derived by downscaling the ERA-Interim climatic reanalysis to a 30-arc-second resolution. The temperature estimation is based on statistical downscaling of atmospheric temperatures, while the precipitation estimation incorporates factors like wind patterns, valley orientation, and boundary layer height, followed by bias correction. The dataset covers monthly temperature and precipitation climatology for the period from 1979 to 2013. Comparisons with other established gridded datasets and station data from the Global Historical Climate Network demonstrate the accuracy of the CHELSA algorithm. On comparison of the performance of new climatologies, the study shows that using CHELSA data can improve the precision of species distribution modelling or ecological niche modelling (ENM), particularly in predicting species ranges, and highlights its superior performance in predicting precipitation patterns compared to other products.

During the present study it was revealed that among the authors, “Thuiller W” stands out with the highest impact, having received 5,646 citations and possessing an h-index of 22, a g-index of 25, and an m-index of 1.1. Following closely are “Pearson RJ” with 5,059 citations, an h-index of 6, a g-index of 6, and an m-index of 0.31, “Guisan A” with 5,052 citations, an h-index of 19, a g-index of 3, and an m-index of 0.864, and “Peter AT” with 5,018 citations, an h-index of 24, a g-index of 37, and an m-index of 1.091 (Table 1). The “h-index,” first introduced by Jorge Hirsch in 2005, serves as a valuable metric for assessing a scientist’s contribution and impact. It signifies that a researcher has authored H papers, each of which has been cited at least H times (Hirsch, 2005). The h-index can be applied not only to evaluate an individual researcher’s influence but also to assess the impact of a collection of publications with a substantial quantity (Bornmann and Daniel, 2007). However, it may have limitations and inaccuracies when applied to a small set of articles. The “g-index,” proposed as an enhancement of the h-index, identifies the largest rank (when papers are arranged in descending order of citation count) at which the first g papers have at least g2 citations combined (Egghe, 2006). It places more emphasis on highly cited articles while retaining the positive aspects of the h-index, offering a better overall representation of achievements than the h-index (Egghe, 2006). The “m-index” represents another variation of the h-index, emphasizing the h-index in relation to the time of the researcher’s initial publication (Zaki et al., 2023).

The USA ranks at the top with 19,899 citations, followed by the Australia (13,777 citations), United Kingdom + (9,010 citations), Spain (7,550), and Switzerland (7,383 citations) (Supplementary Figure S1). Among the sources, the journal Ecography stands out to be the most cited source with 8,485 citations, it is followed by Ecological Modelling with 7,084 citations, and Journal of Biogeoghraphy with 7,025 citations (Supplementary Figure S2). The highest citation count of the journal Ecography highlights its influential role in advancing ecological and biodiversity research. Ecography is a peer-reviewed journal owned by the Nordic Society of Oikos, specializes in publishing groundbreaking articles that contribute significantly to the advancement of knowledge concerning ecological and biodiversity patterns in both spatial and temporal dimensions. While the journal welcomes papers centered on conservation and restoration, it places emphasis on their integration with ecological theory and the communication of broader, generalized insights rather than relying solely on individual case studies. The second most cited journal “Ecological Modelling,” focuses on the publication of innovative mathematical models and systems analyses that effectively describe ecological processes. It also highlights novel applications of these models in the context of environmental management, showcasing its significance in bridging the gap between ecological research and practical environmental solutions.