[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119467-en":3,"doc-seo-119467-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119467,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","WELCOME TO THE MACHINE - A PAN-CONTINENTAL OVERVIEW OF MACHINE LEARNING APPLICATIONS IN ECOLOGY AND CONSERVATION","Machine learning offers an effective alternative for understanding ecological patterns and processes across different spatiotemporal scales. This study provides a global overview of machine-learning usage in ecology and conservation by indexing relevant publications through Scopus keywords and summarizing trends with descriptive statistics and regression models. Results show concentration of studies in economically affluent countries and northern regions, with early emergence in 2003 and exponential growth thereafter. Most works address landscape and vertebrate ecology, using methods from maximum entropy to random forests and deep learning, while key predictive variables show limited effects.","Biodiversity Informatics, 19, 2025, pp. 33-45  \nWELCOME TO THE MACHINE:  \nA PAN-CONTINENTAL OVERVIEW OF MACHINE LEARNING APPLICATIONS IN ECOLOGY AND CONSERVATION  \nJulianoA. Bogoni 1*, Derick Victor de Souza Campos2*, Claumir C. Muniz2, Manoel dos Santos-Filho 1, Jéssica Eloá Poletto3  \n1 Universidade do Estado de Mato Grosso, Centro de Pesquisa de Limnologia, Biodiversidade e Etnobiologia do Pantanal, Programa de Pós-Graduação em Ciências Ambientais, Laboratório de  \nMastozoologia, Cáceres, MT, Brazil.  \n2 Universidade do Estado de Mato Grosso, Centro de Pesquisa de Limnologia, Biodiversidade e Etnobiologia do Pantanal, Programa de Pós-Graduação em Ciências Ambientais, Laboratório de Investigação Ambiental do Pantanal Norte – LIPAN, Cáceres, MT, Brazil.  \n3 Faculdade de Ciências Médicas da Universidade Estadual de Campinas, Piracicaba, SP, Brasil.  \nAbstract. Machine-learning emerged as an excellent alternative to understanding ecological patterns and processes at different spatiotemporal scales. The study aimed to offer a global overview of the status quo on the use of machine-learning in ecology and conservation globally. Using keywords in the Scopus engine, we indexed all publications in ecology and conservation using machine-learning. We employed descriptive statistics and regressions models to provide an overview and predict geopolitical patterns. The majority of manuscripts were condensed in economically affluent countries, such as the United States (USA) and China (CHN) which together amount to 91 (36.8%) studies. There is a spatial aggregation in the authors’ affiliations, once 182 (73.7%) studies derived from both Nearctic and Palearctic teams, whereas Tropical teams published 65 (26.3%) manuscripts and the most-cited papers also are concentrated in northern regions. In ecology and conservation, machine-learning first appear in the literature in 2003. Since then, the number of publications has increased exponentially, from 09 manuscripts in 2010, to 120 manuscripts 10 years later. Most studies (N = 173; 70.1%) focused on landscape and vertebrate ecology. The primary aims of the publications were widely variable but strongly adherent to providing the best-information on both landscape-scale classifications and species distribution modelling. The manuscripts encompass different methods, from maximum entropy to boosted regression trees and random forest, sometimes using a range of deep-learning architectures. Finally, the predictive variables (i.e., mammal diversity and per capita GDP) do not exert significant influences on the number of studies published. Finally, we recommend a well-structured and collaborative agenda aiming to integrate less-resourced countries into scientific advancements, fostering more equitable and effective responses to global environmental challenges.  \nKeywords: data analysis, global-scale, informatics, numerical ecology, tropical forest.  \n*corresponding authors: [bogoni.ja@gmail.com](bogoni.ja@gmail.com | derick@unemat.br)[ | ](bogoni.ja@gmail.com | derick@unemat.br)[derick@unemat.br](bogoni.ja@gmail.com | derick@unemat.br)  \nJulianoA. Bogoni et al.– Machine-Learning Applications in Ecology and Conservation  \nIntroduction  \nEcology is a relatively young science that fundamentally seeks to understand the causes and consequences (i.e., processes) of diversity patterns and species distributions across global environments (Brown, 1995; Haeckel, 1866) . Universal features of ecological processes exhibit mathematical properties that are inherently non-linear and complex, historically addressed only through mathematical approximations (Bogoni et al., 2019; Conway, 1977; May, 1976) . Since the 1920s, explicit models as in Volterra (1926), Lotka (1925), Elton (1924) have been employed in ecology to predict and describe synchronization mechanisms in animal behaviour as Araujo et al. (2013), predator-prey relationships and dynamics in Sherratt et al.(1997), Kar et al. (2010), host-parasitoid interactio","cbCais0Pt5UT2M6m","https://ap.wps.com/l/cbCais0Pt5UT2M6m","pdf",2240801,1,13,"English","en",105,"# Introduction\n## Ecological patterns, diversity, and non-linear processes\n## Biodiversity loss and conservation urgency in the tropics\n## Machine learning as an alternative predictive approach\n## Methods and evidence base (global overview)","[{\"question\":\"What is the purpose of this study on machine learning in ecology and conservation?\",\"answer\":\"It aims to provide a global overview of the current status of machine-learning applications in ecology and conservation worldwide.\"},{\"question\":\"How were publications collected and analyzed in the study?\",\"answer\":\"Publications were indexed using Scopus keywords, then descriptive statistics and regression models were used to summarize patterns and predict geopolitical trends.\"},{\"question\":\"What kinds of topics and methods dominate the literature?\",\"answer\":\"Most studies focus on landscape and vertebrate ecology, commonly using approaches such as maximum entropy, boosted regression trees, random forests, and sometimes deep-learning architectures.\"}]","WELCOME TO THE MACHINE - 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