Causation

Studies dealing with causation try to understand what causes a behavior to be performed. When combined with remotely sensed, freely available data, digital data sources can be used to explore the external mechanisms underlying a behavioral trait. For example, Cabello-Vergel and colleagues [15] combined data on the thermoregulatory behavior of individual storks (Ciconiidae) from georeferenced images and videos found at the Macaulay Library repository (https://www.macaulaylibrary.org) with remotely sensed microclimate data. They investigated the determinants of “urohidrosis” (excreting onto the legs as a form of evaporative cooling) in 19 stork species. They found that high heat loads (high temperature, humidity, and solar radiation, and low wind speed) promoted the use of urohidrosis and thus evaporative heat loss. In the face of global climate change, exploring shifts in mechanisms of control with microclimate data can inform us about mechanisms of adaptation to changing environments and provide profound insights facilitating future conservation efforts.

The study of social learning and the emergence of novel and innovative behaviors in relation to environmental conditions could particularly benefit from digital data sources because people often record surprising or unexpected animal behaviors [7]. For example, data from multiple digital data sources revealed that 10 out of the 16 world’s terrestrial hermit crab species (Coenobitidae) widely use artificial shells, predominantly plastic caps, but also pieces of glass or metal [16]. This novel behavior may be driven by decreased availability of gastropod shells, sexual signaling, lightness of artificial shells, odor cues, and/or camouflage in a polluted environment. Together with controlled preferences experiments and/or records of pollution levels and other environmental conditions, we can address the underlying mechanisms of this behavior, which may ultimately influence the evolutionary trajectory of the species. Other examples include YouTube videos that have been used to describe horses opening doors and gate mechanisms [17] or investigate death-related behavioral responses in Asian elephants (Elephas maximus) such as carrying dead calves [18]. Understanding why and when these rare behaviors occur may not be possible without such online records.

In 2022, Møller and Xia [19] showed that bird species recorded on YouTube videos feeding directly from people’s hands also presented more innovative behaviors, had a higher rate of introduction success, and greater urban tolerance than species not recorded displaying such behavior. This demonstrates the connections between Tinbergen’s questions and highlights that an individual’s (or species) ability to respond behaviorally to external conditions may also rely on its evolutionary history and affects its chances of survival. It further shows that the fields of urban ecology and invasion biology can greatly benefit from integrating these novel digital data sources. For example, with most of the global human population living in cities and the omnipresence of online social platforms, digital data can make global multi-city comparisons of urbanization effects on species behavior feasible. Moreover, human activity can be easily tracked using mobility reports provided by Google (https://www.google.com/covid19/mobility/) and Apple (https://covid19.apple.com/mobility). These can provide a high-resolution understanding of where and when humans are active and how they can play an important role in shaping animal behavior. Such knowledge can help enhance studies of antipredator behavior and wildlife tolerance, as it was used to study the consequences of the COVID-19 pandemic lockdowns [20,21]. Likewise, documenting first arrivals and monitoring the spread of invasive species, their behavior, and interactions with native species can become more efficient by incorporating digital data from online repositories [22].

We acknowledge that digital sources alone cannot offer many insights into internal mechanisms of behavior, such as hunger state or past experience (exceptions may include behaviors that are influenced by temperature, which may be inferred if the data are georeferenced and time stamped). Studying proximate physiological mechanisms often requires extensive field and laboratory experiments. However, addressing what mechanisms drive behavior in terms of changes in the external stimulus (social and physical environment) could greatly benefit from the copious number of available images and videos online. This is particularly true considering current and future global environmental challenges.

Ontogeny

Digital data in the forms of images, audio, videos, and live-streaming videos can also be used to study and quantify different behavioral shifts in individuals over their lifetimes. For example, using online-sourced photographs, Naude and colleagues [23] showed that adult martial eagles (Polemaetus bellicosus) preyed more on birds than juveniles and subadults, which preferred less agile reptiles and mammals. They attributed this pattern to an improvement in hunting skills with age. Another study found evidence for “ontogenetic deepening”—the phenomenon that older and larger fish are found in deeper water, whereas younger and smaller fish stay in shallower water—in dusky groupers (Epinephelus marginatus) using YouTube videos of recreational fishers [24]. Exploring videos over several years, they further showed that fishing depth did not change over time and thus suggested that this ontogenetic deepening may not be solely driven by changes in harvesting pressure. Combining acoustic recordings from various sources (field recordings, a museum sound library, and citizen science records), Riós-Chelén and colleagues [25] found that birds can adapt their songs to environmental acoustic conditions. The fact that songbirds (known as oscines), who learn their songs, showed stronger associations between environmental noise and song modifications than other closely related bird species with innate songs (suboscines) indicates the involvement of ontogenetic processes in this adjustment.

Other studies can use similar approaches to further explore ontogenetic changes in different species’ hunting skills, aggressiveness, mating rituals, and parental care, with or without complementing intensive fieldwork (see S1 Table). Exploring such changes in behavior in response to anthropogenic environmental changes worldwide can be of great importance for conservation science, urban ecology, and agroecology. For example, live-streaming videos of bird nests—which have become very common for many species and sites (e.g., https://camstreamer.com/blog/streaming-birds-with-an-eagle-eye and https://www.viewbirds.com/)—can provide rich information to study the development of nestling vocal signals, the learning of songs, or the establishment of siblings relationships, as well as differences in such behaviors as a function of the distance to urban areas, human disturbance level, or levels of noise or light pollution [26]. Nonetheless, similar to exploring causation mechanisms, answering questions related to ontogeny cannot solely rely on digital data sources since ontogenetic processes often involve studying individuals over time. Furthermore, developing a deep understanding of external factors affecting the development of behavior may also require well-designed controlled experiments, which can be more challenging to accomplish with currently available digital databases.

Evolution

With images and videos from around the world spanning several decades available online, it is now possible to use digital data to explore intra- and interspecific traits and behaviors, as well as study their evolution in light of anthropogenic environmental changes. For example, using crowd-sourced images and videos, Mikula and colleagues [10] showed that predator–prey interactions between diurnal birds and bats, which were previously thought to be rare, have been commonly reported around the world (Fig 1). This indicates that diurnal bird predation might act as one of the drivers of the evolution of bat nocturnality. Similarly, using social media videos and phylogenetic modeling, Bastos and colleagues [27] showed that tool-using behavior in parrots is far more common than previously thought and that these new sources of data can be used to better understand the origin, evolution, and drivers of rare behaviors. In another example, Pearse and colleagues [28] were able to explore evolutionary patterns in bird song at a broad scale (in terms of pitch and complexity) using a large citizen science digital repository, combined with scientific data on bird biology, life history, and geographical distribution, and advanced machine learning techniques. Surprisingly, they showed that suboscine and oscine birds have similar song complexity. They further noted that using Artificial intelligence (AI) tools to help analyze citizen science data can further facilitate research on bird song evolution. However, such tools may also have limitations and need to be routinely validated and assessed.

The fact that digital repositories can potentially hold decades-old data allows retrospective explorations of data collected long before the research has commenced. For example, the COVID-19 pandemic highlighted the importance and usefulness of citizen science data sets, as past records could be compared with records under the novel environmental settings created by the pandemic [21]. Similar data sets may be obtained from various social media platforms that are far more popular than citizen science platforms, both in volume and in geographic coverage. For example, there are 3 million iNaturalist users (https://www.inaturalist.org/stats) compared with 300 million X (formerly Twitter) users (https://www.statista.com/statistics/303681/twitter-users-worldwide/). While most of the content on X would probably be irrelevant for ecology and conservation, the potential to reach and engage new audiences, and access diverse data could be valuable. Using these novel data sources can further facilitate large spatial scale explorations of evolutionary changes in animal behavior. It may also help researchers to better plan and choose field sites before embarking on intensive fieldwork.

Many aspects of the evolution of animal behavior are challenging to document directly because numerous phenotypic traits co-evolve over large spatial and phylogenetic scales, making comparative studies useful. For example, body coloration may be an important factor in answering fundamental questions in behavioral ecology that provides insights into local behavioral adaptations [29,30]. Online image repositories have already been used to document geographical and phylogenetic variation in color patterns in birds and mammals, including color polymorphism [31], mutations [32], and variation in the morphology of color strips and patches [33]. In addition to readily available data, people can be encouraged to upload their images, videos, and sound recordings for specific studies through citizen science platforms [34] or social media platforms [35]. Spatial data on the phenotypic distributions are often collected via field observations and inspection of voucher specimens.

We envision that online images, videos, and acoustic recordings may provide a rich resource of information on large-scale variation in many phenotypic traits closely linked to animal behavior, such as nest morphology in fish and birds, or the size and shape of ornaments and armaments (e.g., antlers in deer or bony spurs in birds). Yet, we must acknowledge the limitations of using digital data to answer questions of an evolutionary nature that require some genomic knowledge. Still, the sheer volume of digital data and the ability to compare data of many species and populations inhabiting different areas and environments can provide valuable information for the processes and mechanisms involved in evolutionary adaptation and speciation.

Function

Answering function-related questions—how a behavior increases one’s fitness through survival and reproduction—can also gain much from using digital data. With the ubiquity of the Internet, we can explore external drivers of current utility and sexual selection regarding behavioral contributions to overall fitness. These may include intra- and interspecific interactions, migratory patterns, predation risk, and mating rituals. For example, using live-streaming underwater cameras, Coleman and Burge [36] showed a higher association between sand tiger sharks (Carcharias taurus) and round scads (Decapterus punctatus) in the presence of scad mesopredators, which enhances foraging opportunities for sand tiger sharks and reduces predation risk for the scads. Such behaviorally mediated indirect interactions may have far-reaching implications for trophic interactions, including predator and prey strategies. Studies like this highlight the potential of these novel data and technologies in ecological research.

Digital data can be further used to study the timing of biological processes (i.e., phenology) in animals and how these are being affected by external cues such as climate change, land use changes, or human disturbance. For example, using Wikipedia page views, Mittermeier and colleagues [37] tracked seasonal migration patterns in sockeye salmon (Oncorhynchus nerka) and Atlantic salmon (Salmo salar). Atsumi and Koizumi [38] used X (formerly Twitter) and Google Images to explore spatial variations in breeding timing in Japanese dace fish (Tribolodon hakonensis) and how they may have been affected by climate change. Combined with data on breeding success or the costs of not adjusting breeding timing, these studies could greatly advance function-related research. Given the ongoing global environmental change, such explorations can be invaluable to understanding how these changes impact various species in terms of range shifts and/or expansions. Again, digital data has limits, and complementing it with traditional methods may be required to accurately assess the fitness value of a behavior.

The challenges and limitations of using digital data to study animal behavior

Addressing questions related to any of Tinbergen’s 4 levels of analysis is challenging. While digital data and approaches can greatly advance the fields of behavioral ecology and conservation behavior, these data sources and tools currently cannot replace empirical work and field studies. We acknowledge the limitations of digital data, particularly in answering questions related to internal mechanisms such as endocrine or neural control of behavior. Available digital data may not provide reliable information on an individual’s physiological state, its developmental history, or its reproductive state. Nonetheless, digital data sources can provide new opportunities to explore many aspects of Tinbergen’s 4 questions in a noninvasive way and without manipulation of free-living animals, thus solving underlying ethical and welfare issues associated with the use of animals in research [39]. It is important to note, however, that digital data research also raises ethical questions and should follow rules to avoid disruption to the focal animal(s), the animals’ population, or the wider ecosystem. Viewing digital data as complementary to more traditional sources of data may be very useful. Moreover, in some areas traditional data sources are lacking, and so adequately reliable digital data may be the best source of behavioral data available. Nevertheless, we must consider the biases, technical challenges, and ethical concerns associated with digital data.

First, data sets obtained from online platforms—particularly ones provided by the general public—have an inherited bias linked to Internet coverage and use such that different regions of the world are not equally represented in digital records. Similarly, different sectors of society based on, for example, ethnicity, language, socioeconomic status, and education level, are currently not equally represented in the digital realm, complicating research on human–nature interactions using digital data.

Second, only a fraction of the global biodiversity is digitally recorded and has an online presence [1,40]. This limits the number of species that can be explored using digital data sets and leads to an uneven sampling effort across different taxa and clades. Such biases, for example, towards charismatic or larger-bodied species, are widespread and well known from more traditional approaches of scientific research [41], but may be exacerbated using data from social media. Furthermore, this limitation of unequal human interest goes beyond which species are predominantly documented, but also to which behaviors are recorded. Such human preferences and biases, and how they may differ across cultures, may compromise analyses and conclusions if not properly accounted for [42]. Furthermore, search algorithms of search engines like Google or platform-internal ones may also introduce biases affecting the results returned.

The lack of rigorous collection protocols across various digital platforms, especially in light of the complexity and variety of animal behavior, makes applying digital data sources in behavioral ecology research even more challenging. For example, in exploring bird plumage color aberrations using various digital sources (Google Images and several local platforms devoted to bird watching and photography), Zbyryt and colleagues [32] highlighted how digital sources and public participation can advance our understanding of less-studied natural phenomena. They showed that color aberrations are more prevalent in urban, larger, and sedentary birds. However, the nature of the input data prevented them from concluding whether these patterns were biologically driven or resulted from inherent biases in their data set where people more easily spot and report large sedentary birds in human settlements. Thus, it is essential to address these and other biases and limitations to understand when and where it is appropriate to use various digital data sources. As a start, combining data from novel digital sources—such as various social media platforms and Google Images—with more rigorous scientific data sets, dedicated fieldwork, or literature surveys, can help validate digital sources and ensure meaningful results. Another approach is creating well-designed question-first citizen science data sets in which researchers recruit and train citizen scientists to collect dedicated data to answer specific questions [43].

When exploring user-generated content—for example, videos uploaded on social media platforms—we must also consider legal and ethical aspects such as data protection and privacy [44]. In order to minimize the risk of misusing sensitive data (e.g., IP address, localization details, or user name), we advocate for establishing and following protocols for data protection [44]. It is also important to note that many social media recordings may be associated with unintentional or even intentional disturbances and harmful actions towards the animal being recorded [5,45], raising ethical concerns as well as questions of interpretation and relevance. Even if individuals are not directly harmed, the context under which data were recorded (e.g., Were domestic animals like dogs present? Did the humans feed the animals before filming?) is not always known, and this may have substantial impacts on the recorded behavior [17]. Such human disturbances, combined with partial recording and suboptimal recording quality, necessitate extensive filtering processes and the implementing of clear protocols for the inclusion of records. Furthermore, it may limit the use of digital data sources in certain explorations [7]. While we encourage sharing nature observations online, we strongly discourage any actions that could harm the animals and the environment in the process. By contrast, recording people’s negative interactions with nature can potentially be helpful for both legal and conservation interventions, as well as for related research.

Finally, while these readily available data sets are relatively easy to obtain, using them requires programming skills, computational power, and storage capacity, among other things [46]. Accessing various platforms may further require data-sharing agreements, proprietary companies opening their data sets for researchers, and consistency in how data is managed [47]. Once obtained, data filtering and cleaning processes and analysis would further require advanced technological tools, such as machine learning methods and machine vision models. Such filtering process should also consider for example AI-generated content and ensure only reliable data are used. Post-analysis challenges may include repeatability and reproducibility [4,48] as data may not be archived on different platforms, and downloading and sharing all records may face legal issues (copyrights), as well as storage space limitations. While some of these aspects are beyond our control, keeping clear records of protocols, versions, and codes, as well as publishing metadata and when possible raw data, could increase transparency and help address some limitations [4].