Innovation in practice: The influences of technological and social advances on ecology and conservation

We live in an era of innovation. Innovation is underwritten by technological and social development, and we see the results of unprecedented technological advancement all around us. For example, in the last 50 years, humanity has gone from recording and storing information by writing in notebooks and ledgers to storing millions of searchable, digital documents available on devices strapped to our wrists. Massive cultural, social and economic innovations happen alongside such technological changes. Technological innovation has fundamentally altered everyday life, changing how we search for and find information, diagnose illnesses, move through space and communicate with one another. And many of the fastest growing companies are developing or using new technologies such as artificial intelligence. In some ways, however, the impacts of these technological revolutions on basic and applied ecology and conservation have flown under the radar. Many aspects of management appear unchanged over the past half century: planting and cutting trees, erecting fences or decommissioning dams, controlled burns and fire suppression, herbicide and pesticide applications or releasing agents of biological control, and a plethora of earthworks (digging, disking, ploughing, filling, etc.) continue to be mainstays of management strategies. Similarly, the roles of those tasked with implementation, such as professional biologists, government scientists and land managers, appear unchanged. This reliance on the same players and strategies masks a quiet revolution in management practices and applications of new technologies. For example, CRISPR-Cas9 is used in powerful new tools to manage problematic invaders (Lester et al., 2020), control pathogens or their vectors (Dong et al., 2018) and aid in endangered species recovery (Yin et al., 2024). High-resolution, remotely sensed spatial data are now used routinely to map, analyse and predict environmental change (Cavender-Bares et al., 2022; Rose et al., 2015). Communities of non-specialists use apps on smartphones to contribute observations and data that are reshaping our understanding of ecological patterns and processes worldwide (Di Cecco et al., 2021). Concomitant with these technological changes, more individuals, stakeholders and communities, many with different epistemologies and conceptions of nature, are included in discussions, framing policies, and making and implementing decisions about the environment we share (Drew & Henne, 2006; Golden et al., 2014). Finally, the adoption and use of technology is not value-free and are paralleled by evolving considerations of what we can and should do with large amounts of data, to ensure privacy, and to manage nature. This Special Feature is about innovation in practice. By innovation we mean any new method, idea or product that alters the way something is done for the better—that is, with more effectiveness, efficiency or improved processes. By practice, we mean applying such innovations to provide the basis for designing or implementing a management intervention that aims to improve natural or designed habitats and support the species that occur in them. This Special Feature is a collaboration between Ecological Solutions and Evidence and Methods in Ecology and Evolution because the manuscripts were expected to present novel innovations with broad applications and direct relevance for practice. We put out an open call to the ecological, management and conservation communities, through our own networks, and on social-media platforms to solicit proposals for manuscripts that developed, examined or promoted innovations with direct utility for addressing applied issues. The articles in this Special Feature not only highlight the development and application of new ideas and technologies to help with challenging applied problems but also reflect on the promise and potential pitfalls of new innovations. Most of the submissions we received were about innovations that were augmenting and overcoming human limitations in observing (with sight and sound), recording, remembering and analysing natural phenomena and nuanced ecological patterns. We expect there to be broad interest amongst practitioners to learn about emerging technological advances and to understand how they can be applied to specific applied problems in particular contexts. In the last few decades, the use of uncrewed aerial vehicles (a.k.a. drones) and remote sensing that allow simultaneous visualization of large areas at high resolution along with spectra within and beyond human perception (Croft et al., 2020; Lu et al., 2020) has revolutionized ecology and conservation to the same degree that van Leeuwenhoek's single-lens microscope revolutionized our understanding of the microbial world 350 years ago. The value of these visual innovations is that they allow rapid and reliable assessments of attributes that require extensive sampling and can be difficult to observe. In the Special Feature, Flynn and colleagues (Flynn et al., 2024) show how drones capturing three-dimensional colour images can quickly and reliably detect the prevalence of ash-dieback disease in Surrey, United Kingdom. Nyberg and colleagues (Nyberg et al., 2024) show that drone images of difficult-to-access places like cliffs can greatly increase the accuracy and precision of estimates of rare plant abundances. Further, Nyberg et al. (2024) modified drones to collect physical plant samples. Lappin et al. (2024) used thermal cameras to provide valuable and reliable observations about ground bird abundance and spatial arrangement, with an application to Northern Bobwhites in Mississippi, USA. Oikawa et al. (2025) developed an easy-to-use app that uses new LiDAR sensors on iPhones that lets community scientists measure tree sizes and construct 3D images of tree and forest structure. Innovations in collecting visual data have led to enhanced power to make robust predictions by combining spectral data with other sources of information. Hayden et al. (2024) used high-resolution measurements of spectral variance to predict plant taxonomic diversity in recently burned grasslands in Colorado, USA. Their approach is robust for low-diversity sites, but less reliable for high-diversity ones, highlighting the need for further methodological refinements. Bowler et al. (2025) combined longer term sequences of images of Arctic ice formation with both caribou radio telemetry and stakeholder knowledge to predict caribou migration under changes in the timing of ice formation. Although all these visual innovations and applications empower ecologists and conservation biologists, Rossi and Wiesmann (2024) remind us that putting innovations into practice can be challenging. For example, employees in the oldest national park in the Swiss Alps had to overcome hurdles including selecting a drone model, operating it in heterogeneous landscapes and conflicts with visitors to realize the benefits of drone imagery for documenting ecological processes and restoration outcomes (Rossi & Wiesmann, 2024). Landscapes are filled with sounds that provide biologically meaningful data to understand both species diversity and the impacts of human activity (Pijanowski et al., 2011). Soundscapes now can be monitored continuously, and acoustic features can be identified and quantified with automated methods. Soh et al. (2024) use deep neural-network machine-learning algorithms to analyse passive recordings and estimate roost sizes of invasive Mynas in Singapore. There also has been an explosion of methods for quantifying and analysing acoustic data, and a concomitant proliferation of new indices representing facets of spectra, frequencies and amplitudes of acoustic data. This plethora of indices can lead to confusion about which index to use under what circumstances, and to errors in interpretation. Bradfer-Lawrence et al. (2024) provide a useful guide for selecting, using, interpreting acoustic indices and provide an interactive Shiny app to aid index selection (https://ecohack.shinyapps.io/Acoustic_Index_Users_Guide/). Collecting data is a core process of research, and new tools let us capture large volumes of meaningful data. Mühlbauer et al. (2023) developed a low-cost, high-resolution sensor network for monitoring environmental data in real time. Managers using this method can monitor site conditions to detect abrupt changes and evaluate management actions. For several decades, a large diversity of contributors has been accumulating and uploading a massive amount of scientifically useful information (Kobori et al., 2016) about some organisms (birds, plants, insects, etc.) or their interactions (e.g. plant–pollinator interaction). Even though researchers regularly use these valuable observations, they infrequently include ‘secondary’ observations on, for example, time, location, background conditions and weather that could accompany the primary data target. Pernat et al. (2024) outline the opportunities and hurdles associated with collecting and using community-contributed secondary data and illustrate that standardizing the collection and organization of these data could greatly help with analyses. It is not enough to simply amass data. The methods by which the data are collected need to be robust and transparently documented, and there also need to be procedures in place to standardize the addition of new data and observations to existing datasets. Further, data pipelines need to assure that datasets are openly available for use by the broader community for future research and to inform policymaking and management decisions. Marstein et al. (2024) developed blockchain processes to allow for decentralized and secure data storage, and these processes can be combined with subsequent data collection in transparent ways. Martin et al. (2024) developed an Extinctions Solution Index (https://conservationxlabs.com/esi) that provides a framework to evaluate, compare and rank the most effective and efficient solutions to stem biodiversity loss. In a similar vein, Salgado-Rojas et al. (2023) developed ‘prioriactions’, an R package with functions that are designed to find optimal conservation planning outcomes based on multiple factors and limitations such as spatial constraints, resources and so on (https://cran.r-universe.dev/prioriactions/doc/manual.html). Finally, ecology and conservation science have greatly expanded the individuals, stakeholders and groups who should be included in the planning and execution of research, and the development and implementation of policies and management strategies (Gould et al., 2018; Johri et al., 2021; Ramirez et al., 2018). Two of the papers in the Special Feature highlight the necessity to value the contributions from communities outside of traditional researchers. Contributions by diverse communities become more powerful by adding secondary data to enhance biodiversity data and research (Pernat et al., 2024). Rosa et al. (2024) introduce the Black Earth Restoration Collective (https://aliciafoxx.github.io/berc/) to ensure inclusion of the deep knowledge of Black, Indigenous and People of Colour into conservation and restoration practice. Successful innovation enhances the efficacy and efficiency of research and practice, yet these innovations often lack systematic communication pipelines ensuring their adoption. New technology and approaches are put into practice in a variety of contexts, providing valuable lessons that can influence implementation elsewhere or aid in further technological refinements. Those practical innovations that work for applied management frequently evaluate methodology and effectiveness in overcoming hurdles, but then usually rely on informal networks to share their experiences (Kittredge et al., 2013). This Special Feature serves as a call to more formally communicate the development, use and evaluation of technological innovations. Across this Special Feature, the authors show how the application of new visual and acoustic methods can refine our understanding of biodiversity patterns and change; improvements in handling large datasets enable us to store and disseminate them to ensure that the evidence needed to improve practice is robust and available; and inclusion of diverse voices can improve all aspects of data collection and implementation. Although we might be inclined to think of innovation as purely technological, the papers in Special Feature make clear that the diversity of actions—from development to evaluation and implementation—are of great value to those working to improve management. Our journals should be seen as the obvious homes for papers that document the development, application and accessibility of new methods to improve practice. Methods in Ecology and Evolution publishes Practical Tools, Applications, Research Articles, Perspectives, Reviews and Forum articles that identify a notable gap in existing methodologies and present generalizable solutions to them (Ellison, 2023). Ecological Solutions and Evidence also publishes a diversity of article types (e.g. Practice Insights, Practical Tools, Data Articles and Forum Articles) that provide opportunities to communicate the development and implementation or innovations, with the explicit aim to make available the key information needed by practitioners (Cadotte et al., 2020). To this end, the British Ecological Society also maintains Applied Ecology Resources (https://www.britishecologicalsociety.org/applied-ecology-resources/), a repository for ‘grey literature’ materials like case studies, best management practices and policy briefs. Both authors contributed equally to this article. Both authors are Editors of British Ecological Society journals. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/2688-8319.70061.

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