iEcology: Harnessing Large Online Resources to Generate Ecological Insights
Authors: Ivan Jarić, Ricardo A. Correia, Barry W. Brook, Jessie C. Buettel, Franck Courchamp, Enrico Di Minin, Josh A. Firth, Kevin J. Gaston, Paul Jepson, Gregor Kalinkat, Richard Ladle, Andrea Soriano-Redondo, Allan T. Souza, Uri Roll
Journal: Trends in Ecology & Evolution (2020)

iEcology is a new research approach that seeks to quantify patterns and processes in the natural world using data accumulated in digital sources collected for other purposes.iEcology studies have provided new insights into species occurrences, traits, phenology, functional roles, behavior, and abiotic environmental features.iEcology is expanding, and will be able to provide valuable support for ongoing research efforts, as comparatively low-cost research based on freely available data.We expect that iEcology will experience rapid development over coming years and become one of the major research approaches in ecology, enhanced by emerging technologies such as automated content analysis, apps, internet of things, ecoacoustics, web scraping, and open source hardware. Digital data are accumulating at unprecedented rates. These contain a lot of information about the natural world, some of which can be used to answer key ecological questions. Here, we introduce iEcology (i.e., internet ecology), an emerging research approach that uses diverse online data sources and methods to generate insights about species distribution over space and time, interactions and dynamics of organisms and their environment, and anthropogenic impacts. We review iEcology data sources and methods, and provide examples of potential research applications. We also outline approaches to reduce potential biases and improve reliability and applicability. As technologies and expertise improve, and costs diminish, iEcology will become an increasingly important means to gain novel insights into the natural world. Digital data are accumulating at unprecedented rates. These contain a lot of information about the natural world, some of which can be used to answer key ecological questions. Here, we introduce iEcology (i.e., internet ecology), an emerging research approach that uses diverse online data sources and methods to generate insights about species distribution over space and time, interactions and dynamics of organisms and their environment, and anthropogenic impacts. We review iEcology data sources and methods, and provide examples of potential research applications. We also outline approaches to reduce potential biases and improve reliability and applicability. As technologies and expertise improve, and costs diminish, iEcology will become an increasingly important means to gain novel insights into the natural world. The information age is characterized by rapid accumulation of myriad types of digital data [1.Castells M. The Information Age: Economy. Blackwell, Society and Culture1996Google Scholar]. Central to this revolution is the Internet, which is a source of unprecedented amounts of diverse and readily accessible data, via webpages, social media, and various other data platforms. These data are constantly created and stored in the digital realm and form an omnipresent part of the modern world. They also provide novel opportunities for research that the scientific community is only beginning to explore. Here, we describe an emerging research approach – iEcology (i.e., internet ecology), which we define as the study of ecological patterns and processes using online data generated for other purposes and stored digitally (Figure 1). These data can be used to address fundamental ecological questions and to analyze ecological processes at a range of spatiotemporal scales and across a diverse range of contexts. As such, iEcology has the potential to provide new understandings of ecological dynamics and mechanisms, complementing more traditional methods of obtaining ecological data. While iEcology can be considered to fit within the wider scope of ecological informatics (see Glossary), it is distinct from other uses of Big Data sources in the biological sciences in that data are not specifically and intentionally generated to address ecological and environmental questions [2.Hampton S.E. et al.Big data and the future of ecology.Front. Ecol. Environ. 2013; 11: 156-162Crossref Scopus (516) Google Scholar, 3.LaDeau S.L. et al.The next decade of big data in ecosystem science.Ecosystems. 2017; 20: 274-283Crossref Scopus (48) Google Scholar, 4.Michener W.K. Jones M.B. Ecoinformatics: supporting ecology as a data-intensive science.Trends Ecol. Evol. 2012; 27: 85-93Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar]. Moreover, iEcology expands on the traditional scope of ecological informatics with new data sources and dedicated methods to analyze them. iEcology is predominantly focused on collecting, collating, and exploring data generated online by human society, either passively or unintentionally (e.g., Internet search activity, social media interactions, and uploaded data and media), a process also referred to as passive crowdsourcing [5.Ghermandi A. Sinclair M. Passive crowdsourcing of social media in environmental research: a systematic map.Glob. Environ. Chang. 2019; 55: 36-47Crossref Scopus (127) Google Scholar]. iEcology uses digital methods to access, handle, and analyze these data, in a manner akin to techniques from other research fields such as sociology, culture and media studies, biomedical sciences, computer sciences, and economics [6.Ekman A. Litton J.E. New times, new needs; e-epidemiology.Eur. J. Epidemiol. 2007; 22: 285-292Crossref PubMed Scopus (98) Google Scholar,7.Bohannon J. Google Books, Wikipedia, and the future of culturomics.Science. 2011; 331e6395Crossref Scopus (20) Google Scholar]. iEcology also shares part of its toolbox with conservation culturomics – an emerging research area in conservation science [8.Ladle R.J. et al.Conservation culturomics.Front. Ecol. Environ. 2016; 14: 269-275Crossref Scopus (134) Google Scholar, 9.Di Minin E. et al.Prospects and challenges for social media data in conservation science.Front. Environ. Sci. 2015; 3: 63Crossref Scopus (165) Google Scholar, 10.Sutherland W.J. et al.A 2018 horizon scan of emerging issues for global conservation and biological diversity.Trends Ecol. Evol. 2018; 33: 47-58Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar] – albeit with a different focus. Specifically, while conservation culturomics is interested in understanding human engagement with nature, iEcology methods focus on the ecological knowledge that can be gained from these human–nature interactions in the digital realm. iEcology data predominantly give rise to insights that are correlative in nature, similar to other large-scale ecological explorations such as much of macroecology [11.Gaston K.J. Blackburn T.M. Pattern and Process in Macroecology. Blackwell Science, 2000Crossref Scopus (176) Google Scholar], and should be viewed as such. Here, we present a broad overview and description of iEcology, including its scope, data types, sources and methods, as well as current major caveats and future prospects for the development of this emerging research approach. Several recent studies have highlighted the potential of iEcology (Figure 2). The most common applications of such methods have been to explore species occurrences and their spatiotemporal trends (Figure 3). For example, a study comparing real-world encounter rates of bird species in the USA with Google Trends data found good agreement between the two sources (Figure 2A) [12.Schuetz J.G. Johnston A. Characterizing the cultural niches of North American birds.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 10868-10873Crossref PubMed Scopus (21) Google Scholar]. This showcases the potential of using voluminous search engine data to explore species distributions in many regions. Others have explored species occurrences and distributions using various sources, such as Flickr, news articles, Twitter, YouTube, Facebook, and Google Trends [13.Barve V. Discovering and developing primary biodiversity data from social networking sites: a novel approach.Ecol. Inform. 2014; 24: 194-199Crossref Scopus (49) Google Scholar, 14.Daume S. Mining Twitter to monitor invasive alien species – an analytical framework and sample information topologies.Ecol. Inform. 2016; 31: 70-82Crossref Scopus (56) Google Scholar, 15.Dylewski Ł. et al.Social media and scientific research are complementary –YouTube and shrikes as a case study.Sci. Nat. 2017; 104: 48Crossref Scopus (30) Google Scholar, 16.ElQadi M.M. et al.Mapping species distributions with social media geo-tagged images: case studies of bees and flowering plants in Australia.Ecol. Inform. 2017; 39: 23-31Crossref Scopus (39) Google Scholar, 17.Hong S. et al.Conservation activities for the Eurasian otter (Lutra lutra) in South Korea traced from newspapers during 1962–2010.Biol. Conserv. 2017; 210: 157-162Crossref Scopus (11) Google Scholar, 18.Jeawak S.S. et al.Using Flickr for characterizing the environment: an exploratory analysis.in: 13th International Conference on Spatial Information Theory (COSIT 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017Google Scholar, 19.Jeawak S.S. et al.Mapping wildlife species distribution with social media: Augmenting text classification with species names.in: Winter S. 10th International Conference on Geographic Information Science (GIScience 2018). 2018: 45:1-45:6Google Scholar, 20.Hart A.G. et al.Testing the potential of Twitter mining methods for data acquisition: Evaluating novel opportunities for ecological research in multiple taxa.Methods Ecol. Evol. 2018; 9: 2194-2205Crossref Scopus (16) Google Scholar, 21.Allain S.J. Mining Flickr: a method for expanding the known distribution of invasive species.Herpetol. Bull. 2019; 148: 11-14Crossref Scopus (10) Google Scholar, 22.Fukano Y. Soga M. Spatio-temporal dynamics and drivers of public interest in invasive alien species.Biol. Invas. 2019; 21: 3521-3532Crossref Scopus (6) Google Scholar, 23.Pace D.S. et al.An integrated approach for cetacean knowledge and conservation in the central Mediterranean Sea using research and social media data sources.Aquat. Conserv. 2019; 29: 1302-1323Crossref Scopus (26) Google Scholar, 24.Giovos I. et al.Social media in the service of conservation: a case study of dolphins in the Hellenic seas.Aquat. Mamm. 2016; 42: 12-19Crossref Scopus (13) Google Scholar, 25.Jiménez-Valverde A. et al.Photo-sharing platforms key for characterising niche and distribution in poorly studied taxa.Insect Conserv. Divers. 2019; 12: 389-403Crossref Scopus (12) Google Scholar], as well as population dynamics and phenology [14.Daume S. Mining Twitter to monitor invasive alien species – an analytical framework and sample information topologies.Ecol. Inform. 2016; 31: 70-82Crossref Scopus (56) Google Scholar,20.Hart A.G. et al.Testing the potential of Twitter mining methods for data acquisition: Evaluating novel opportunities for ecological research in multiple taxa.Methods Ecol. Evol. 2018; 9: 2194-2205Crossref Scopus (16) Google Scholar,23.Pace D.S. et al.An integrated approach for cetacean knowledge and conservation in the central Mediterranean Sea using research and social media data sources.Aquat. Conserv. 2019; 29: 1302-1323Crossref Scopus (26) Google Scholar,26.Hentati-Sundberg J. Olsson O. Amateur photographs reveal population history of a colonial seabird.Curr. Biol. 2016; 26: R226-R228Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar, 27.De Frenne P. et al.Using archived television video footage to quantify phenology responses to climate change.Methods Ecol. Evol. 2018; 9: 1874-1882Crossref Scopus (10) Google Scholar, 28.Foglio M. et al.Animal wildlife population estimation using social media images collections.arXiv preprint. 2019; 1908.01875Google Scholar, 29.Francis F.T. et al.Shifting headlines? Size trends of newsworthy fishes.PeerJ. 2019; 7e6395Crossref PubMed Scopus (8) Google Scholar, 30.Jiménez-Alvarado D. et al.Historical photographs of captures of recreational fishers indicate overexploitation of nearshore resources at an oceanic island.J. Fish Biol. 2019; 94: 857-864PubMed Google Scholar, 31.Breckheimer I.K. et al.Crowd-sourced data reveal social–ecological mismatches in phenology driven by climate.Front. Ecol. Environ. 2020; 18: 76-82Crossref Scopus (6) Google Scholar]. A particular illustration comes from assessing seasonal migration patterns of sockeye salmon (Oncorhynchus nerka) and Atlantic salmon (Salmo salar) from Wikipedia pageview frequencies (Figure 2B) [32.Mittermeier J.C. et al.A season for all things: phenological imprints in Wikipedia usage and their relevance to conservation.PLoS Biol. 2019; 17e3000146Crossref PubMed Scopus (27) Google Scholar]. In addition to mapping the distribution and occurrences of known species, images uploaded on social media have also been used to identify new species [33.Gonella P.M. et al.Drosera magnifica (Droseraceae): the largest New World sundew, discovered on Facebook.Phytotaxa. 2015; 220: 257-267Crossref Scopus (44) Google Scholar,34.Rahayu S. Rodda M. Hoya amicabilis sp. nov. (Apocynaceae, Asclepiadoideae), from Java discovered on Facebook.Nord. J. Bot. 2019; 37e02563Crossref Scopus (3) Google Scholar]. Trait dynamics, evolutionary trends, and biogeographic patterns can also be explored using iEcology methods. For instance, Google Images were used to identify the presence and distribution of hybrid zones of hooded (Corvus cornix) and carrion (Corvus corone) crows in Europe (Figure 2C) [35.Leighton G.R. et al.Just Google it: assessing the use of Google Images to describe geographical variation in visible traits of organisms.Methods Ecol. Evol. 2016; 7: 1060-1070Crossref Scopus (38) Google Scholar]. Furthermore, spatiotemporal dynamics of biophysical environments, such as solar radiation and various other climatic parameters were characterized using Flickr tags [18.Jeawak S.S. et al.Using Flickr for characterizing the environment: an exploratory analysis.in: 13th International Conference on Spatial Information Theory (COSIT 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017Google Scholar].Figure 3Overview of the Studied Taxa, Data Sources Used, and Knowledge Categories Addressed by the iEcology Studies Cited in this Article.Show full captionColors represent different taxa, width of lines represents relative number of publications connecting different categories.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Colors represent different taxa, width of lines represents relative number of publications connecting different categories. iEcology sources, tools, and methods can also be used to explore biotic and abiotic interactions within and across species and their environments. For example, feeding patterns of yellow anaconda (Eunectes notaeus) and green anaconda (Eunectes murinus) were studied using online videos [36.Miranda E.B. et al.The ecology of human-anaconda conflict: a study using internet videos.Trop. Conserv. Sci. 2016; 9: 43-77Crossref Scopus (25) Google Scholar], while online images that simultaneously depicted African birds and herbivorous mammals were used to construct a web of associations between these two groups (Figure 2D) [37.Mikula P. et al.Large-scale assessment of commensalistic–mutualistic associations between African birds and herbivorous mammals using internet photos.PeerJ. 2018; 6e4520Crossref PubMed Scopus (16) Google Scholar]. iEcology also provides new opportunities to study animal behavior [15.Dylewski Ł. et al.Social media and scientific research are complementary –YouTube and shrikes as a case study.Sci. Nat. 2017; 104: 48Crossref Scopus (30) Google Scholar]. For instance, YouTube videos have been used to compare the behavior of red (Sciurus vulgaris) and grey squirrels (Sciurus carolinensis) in different habitats (Figure 2E) [38.Jagiello Z.A. et al.What can we learn about the behaviour of red and grey squirrels from YouTube?.Ecol. Inform. 2019; 51: 52-60Crossref Scopus (9) Google Scholar]. The sheer volume and coverage of such sources could also prove fertile ground for identifying and tracking the spread of new behaviors [39.Fisher J. Hinde R.A. The opening of milk bottles by birds.Br. Birds. 1949; 42: 347-357Google Scholar, 40.Gil M.A. et al.Social iformation links individual behavior to population and community dynamics.Trends Ecol. Evol. 2018; 33: 535-548Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar, 41.Firth J.A. Considering complexity: animal social networks and behavioural contagions.Trends Ecol. Evol. 2020; 35: 100-104Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar]. Disease ecology, including knowledge of the occurrence, distribution, prevalence, and severity of diseases, has also recently benefited from iEcology methods [42.Elmer F. et al.Black spot syndrome in reef fishes: using archival imagery and field surveys to characterize spatial and temporal distribution in the Caribbean.Coral Reefs. 2019; 38: 1303-1315Crossref Scopus (7) Google Scholar]. iEcology methods have also been used to investigate ecosystem and habitat dynamics in response to increasing anthropogenic impacts. For example, videos of the Tour of Flanders cycling race from over 35 years have been used to track phenological changes to vegetation in response to climate change (Figure 2F) [27.De Frenne P. et al.Using archived television video footage to quantify phenology responses to climate change.Methods Ecol. Evol. 2018; 9: 1874-1882Crossref Scopus (10) Google Scholar]. Images of corals and tweets referring to corals have both been used to evaluate the state and trends of coral reefs in different areas, suffering from various human impacts [43.Haas A.F. et al.Can we measure beauty? Computational evaluation of coral reef aesthetics.PeerJ. 2015; 3e1390Crossref PubMed Scopus (33) Google Scholar,44.Becken S. et al.A hybrid is born: integrating collective sensing, citizen science and professional monitoring of the environment.Ecol. Inform. 2019; 52: 35-45Crossref Scopus (9) Google Scholar]. Aspects of invasion dynamics [14.Daume S. Mining Twitter to monitor invasive alien species – an analytical framework and sample information topologies.Ecol. Inform. 2016; 31: 70-82Crossref Scopus (56) Google Scholar,45.Proulx R. et al.Googling trends in conservation biology.Conserv. Biol. 2014; 28: 44-51Crossref PubMed Scopus (80) Google Scholar] and overexploitation of fish [29.Francis F.T. et al.Shifting headlines? Size trends of newsworthy fishes.PeerJ. 2019; 7e6395Crossref PubMed Scopus (8) Google Scholar,30.Jiménez-Alvarado D. et al.Historical photographs of captures of recreational fishers indicate overexploitation of nearshore resources at an oceanic island.J. Fish Biol. 2019; 94: 857-864PubMed Google Scholar] have also been studied using image analysis, tweets, and news articles. In the same way, behavioral changes in animals in response to anthropogenic impacts [46.Snijders L. et al.Animal social networks can help wildlife conservation.Trends Ecol. Evol. 2017; 32: 567-577Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar, 47.Brakes P. et al.Animal cultures matter for conservation.Science. 2019; 363: 1032-1034Crossref PubMed Scopus (62) Google Scholar, 48.Sullivan M. et al.Social media as a data resource for monkseal conservation.PLoS One. 2019; 14e0222627Crossref PubMed Scopus (15) Google Scholar] can be tracked by such methods. While inherently varied in scope, other fields within ecology and environmental science could conceivably benefit from iEcology tools and methods, such as functional ecology, macroecology, landscape ecology, and urban ecology. At their core, iEcology data sources fall into two categories: (i) new data uploaded by users for different purposes; and (ii) data on online activity, including data access and search engine usage. Types of data within the first category can comprise text, images, videos, and sounds (Figure 1). The second category is aggregated data and the exploration of frequencies (e.g., the number of times a term was searched or a webpage visited, but could also include interactions on social media such as shares and likes). Both categories have different types of associated metadata that are particularly important for iEcology, such as locality, timestamp, user identity, and links across data. iEcology data sources differ in their scope, availability, ease of access, associated metadata, and therefore utility for different types of research. Potential data sources range from various social media platforms (e.g., Twitter and Flickr) [49.Chamberlain J. Using social media for biomonitoring: how Facebook, Twitter, Flickr and other social networking platforms can provide large-scale biodiversity data.Adv. Ecol. Res. 2018; 59: 133-168Crossref Scopus (9) Google Scholar], search engines (e.g., Google, Baidu, and Bing), online encyclopedias (e.g., Wikipedia and Encyclopedia Britannica online), and other online repositories (blogs, discussion forums, popular articles, books, etc.). Many of these sources can also be accessed through search engines. The scope of sources differs based on spatiotemporal coverage, linguistic or cultural breadth, data resolution, and the degree of multimedia composition (e.g., text, images, and video) per source. Data also differ in availability: while many sources are freely available, some platforms may restrict availability by limiting data collection (i.e., limits on volume, time frame, or number of queries) or use (e.g., privileged access or paywall restrictions). Sources also differ in their ease of access, from simple online tools embedded at the source (e.g., Google Trends webpage), through open application programming interfaces (APIs) accessible via various dedicated computer scripts (e.g., Wikipedia and Flickr), to APIs with restricted access (e.g., Facebook). However, data availability and ease of access to different sources can also change over time. The analysis of iEcology data faces similar challenges and uses the same solutions as many other approaches for analysis of Big Data [2.Hampton S.E. et al.Big data and the future of ecology.Front. Ecol. Environ. 2013; 11: 156-162Crossref Scopus (516) Google Scholar,50.Bollier D. Firestone C.M. The Promise and Peril of Big Data. Aspen Institute, 2010Google Scholar]. Many of the methods used in iEcology rely on high levels of automation, frequently adopting machine-learning techniques [51.Christin S. et al.Applications for deep learning in ecology.Methods Ecol. Evol. 2019; 10: 1632-1644Crossref Scopus (135) Google Scholar]. There are different tools that can aid each stage of the research: data access, downloading, handling, extraction, storage, pattern identification and recognition, data analysis, and visualization. These tools are in a constant state of evolution, as illustrated by developments in deep neural network analysis and other emerging technologies (Box 1).Box 1Emerging Technologies Relevant for iEcologyFuture development within iEcology will be enhanced by rapidly developing technologies:Apps and GamesApps on mobile devices are ubiquitous, and often within a person's reach 24-7. Using these to support augmented reality could also provide an interface to generate more detailed data with real-time diagnostics [64.Jepson P. Ladle R.J. Nature apps: waiting for the revolution.Ambio. 2015; 44: 827-832Crossref PubMed Scopus (36) Google Scholar, 65.Buettel J.C. Brook B.W. Egress! How technophilia can reinforce biophilia to improve ecological restoration.Restor. Ecol. 2016; 24: 843-847Crossref Scopus (11) Google Scholar, 66.Dorward L.J. et al.Pokémon Go: benefits, costs, and lessons for the conservation movement.Conserv. Lett. 2017; 10: 160-165Crossref Scopus (71) Google Scholar]. In addition, apps that 'gamify' nature can motivate the public to interact with their environment, and thus provide more data on species and the environment. Overall, apps and games have the potential to transform how humans interact with nature (both positive and negative) and cause a fundamental shift in the quantity and quality of iEcology data.Automated Content AnalysisThe application of algorithms for analyzing visual, textual, and audio content from digital sources. These methods have allowed, for example, automatic identification, counting, and description of species and individuals from images and videos [67.Di Minin E. et al.Machine learning for tracking illegal wildlife trade on social media.Nat. Ecol. Evol. 2018; 2: 406-407Crossref PubMed Scopus (62) Google Scholar,68.Norouzzadeh M.S. et al.Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: E5716-E5725Crossref PubMed Scopus (392) Google Scholar], and the extraction from text of information on species and their interactions [69.Kaur K.M. et al.Using text-mined trait data to test for cooperate-and-radiate co-evolution between ants and plants.PLoS Comput. Biol. 2019; 15e1007323Crossref PubMed Scopus (8) Google Scholar]. Further developments will allow combining visual, textual, and audio analysis of large volumes of iEcology data [70.Di Minin E. et al.A framework for investigating illegal wildlife trade on social media with machine learning.Conserv. Biol. 2019; 33: 210-213Crossref PubMed Scopus (53) Google Scholar]. All these methods should be used carefully and consider ethical concerns [71.Wearn O.R. et al.Responsible AI for conservation.Nat. Mach. Intell. 2019; 1: 72-73Crossref Scopus (37) Google Scholar].Bioacoustics and EcoacousticsThe recording and analysis of sounds produced by biological entities and entire environments. Increase in sonic and video recording and publicized soundscapes could provide an untapped source of data for iEcology [72.Aide T.M. et al.Real-time bioacoustics monitoring and automated species identification.PeerJ. 2013; 1e103Crossref PubMed Scopus (218) Google Scholar, 73.Harris S.A. et al.Ecoacoustic indices as proxies for biodiversity on temperate reefs.Methods Ecol. Evol. 2016; 7: 713-724Crossref Scopus (106) Google Scholar, 74.Linke S. et al.Freshwater ecoacoustics as a tool for continuous ecosystem monitoring.Front. Ecol. Environ. 2018; 16: 231-238Crossref Scopus (57) Google Scholar, 75.Rajan S.C. et al.Rapid assessment of biodiversity using acoustic indices.Biodivers. Conserv. 2019; 28: 2371-2383Crossref Scopus (16) Google Scholar].BlockchainCryptographically linked and growing data lists. Further development of dedicated iEcology blockchains or plug-ins will allow the creation of immutable complex data of various formats that will be permanently recorded into a decentralized platform at the moment of their creation. This would increase security, traceability, decrease errors associated with multiple data entries, and allow imprinting of the technical details of data generator [76.Firdaus A. et al.The rise of "blockchain": bibliometric analysis of blockchain study.Scientometrics. 2019; 120: 1289-1331Crossref Scopus (67) Google Scholar].Internet of ThingsA network of computers, machines, and other objects that share information and interact. This will greatly increase the amount of data pertaining to humans and their actions [77.Atzori L. et al.The internet of things: A survey.Comput. Netw. 2010; 54: 2787-2805Crossref Scopus (9681) Google Scholar].Open Source HardwarePhysical objects with design specifications that allow them to be widely studied, modified, created, and distributed. As more knowledge and expertise on construction of various sensors are produced and shared, larger volumes of high-quality and more specialized data could be produced [78.Berger-Tal O. Lahoz-Monfort J.J. Conservation technology: the next generation.Conserv. Lett. 2018; 11e12458Crossref Scopus (35) Google Scholar,79.Hill A.P. et al.Leveraging conservation action with open-source hardware.Conserv. Lett. 2019; 12e12661Crossref Scopus (5) Google Scholar].Web ScrapingThe fetching and extraction of relevant information from web content, mostly done automatically. Further developments in these technologies will enable better and quicker access to larger volumes of iEcology data, and potentially continuous monitoring of patterns and trends [80.Galaz V. et al.Can web crawlers revolutionize ecological monitoring?.Front. Ecol. Environ. 2010; 8: 99-104Crossref Scopus (38) Google Scholar]. Future development within iEcology will be enhanced by rapidly developing technologies: Apps and Games Apps on mobile devices are ubiquitous, and often within a person's reach 24-7. Using these to support augmented reality could also provide an interface to generate more detailed data with real-time diagnostics [64.Jepson P. Ladle R.J. Nature apps: waiting for the revolution.Ambio. 2015; 44: 827-832Crossref PubMed Scopus (36) Google Scholar, 65.Buettel J.C. Brook B.W. Egress! How technophilia can reinforce biophilia to improve ecological restoration.Restor. Ecol. 2016; 24: 843-847Crossref Scopus (11) Google Scholar, 66.Dorward L.J. et al.Pokémon Go: benefits, costs, and lessons for the conservation movement.Conserv. Lett. 2017; 10: 160-165Crossref Scopus (71) Google Scholar]. In addition, apps that 'gamify' nature can motivate the public to interact with their environment, and thus provide more data on species and the environment. Overall, apps and games have the potential to transform how humans interact with nature (both positive and negative) and cause a fundamental shift in the quantity and quality of iEcology data. Automated Content Analysis The application of algorithms for analyzing visual, textual, and audio content from digital sources. These methods have allowed, for example, automatic identification, counting, and description of species and individuals from images and videos [67.Di Minin E. et al.Machine learning for tracking illegal wildlife trade on social media.Nat. Ecol. Evol. 2018; 2