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. Information Age, Big Data, and iEcologyThe information age is characterized by rapid accumulation of myriad types of digital data [1]. 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][3][4]. 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]. 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 [6,7]. iEcology also shares part of its toolbox with conservation culturomicsan emerging research area in conservation science [8-10]alb...

Digital Data Sources and Methods approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.

Marine Protected Areas (MPAs) are being established across all marine regions but their validity for the conservation of highly mobile marine vertebrates has been questioned. We tested the hypothesis that French coastal MPAs primarily designed for coastal and benthic biota are also beneficial for the conservation of a pelagic seabird, the Vulnerable yelkouan shearwater (Puffinus yelkouan), an endemic species to the Mediterranean Sea. We used a vast spectrum of electronic devices (GPS, temperature-depth-recorders, satellite transmitters and geolocators) and stable isotopic analyses to study the year-round movements and the trophic status of yelkouan shearwaters from the Hyères archipelago (France). In addition we conducted large-scale ship and aircrafts observation surveys to investigate spatio-temporal density patterns of shearwaters (genus Puffinus) in the western Mediterranean Sea. This extensive investigation permitted the first comprehensive study of the at-sea ecology of yelkouan shearwaters showing strikingly coastal habits, partial migration, unsuspected diving capabilities (max dive depth of 30 m), and a broad diet ranging from zooplankton to small pelagic fish. Importantly, 31% of yelkouan shearwaters GPS positions associated with foraging, 38% of diving positions, and 27% of resting positions were within the three French MPAs during the breeding season. These high scores confirmed by year-round distribution derived from GLS, PTTs, atsea and aerial observations, validated our hypothesis of the major importance of coastal MPAs for the conservation of yelkouan shearwater. Our case-study is therefore a major contribution to research efforts aiming at linking the spatial ecology of highly mobile marine vertebrates with effective conservation of marine biodiversity.

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The development and miniaturization of GPS tracking devices has enabled a better understanding of migration phenology, but it can be challenging to identify where and when migration starts and ends, and researchers rely on multiple methods to infer it. Here, we use GPS tracks of 18 trans‐Saharan migrant White Storks Ciconia ciconia to determine how the choice of method influences the estimation of migratory timing and discuss its implications. We evaluate and provide R code for the implementation of five alternative methods: spatial threshold, absolute displacement, spatio‐temporal displacement, net squared displacement and change point analysis. Spatial threshold, absolute displacement and spatio‐temporal displacement methods produce, in most cases, significantly different estimates of migration timing and duration as compared with net squared displacement and change point analysis.

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Although invasive non-native species have been studied extensively, their monitoring and management are often inadequate (Pergl et al. 2020). Moreover, the great harm invasive non-native species cause tends to be