With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise. Species Distribution Models in EcologyLarge-scale ecological models of how species distributions and abundances vary over space and time are a critical tool in macroecology, biogeography, and conservation biology. They underpin our understanding of how biodiversity is shaped, how it is responding to anthropogenic activities, and how it might change in the future [1][2][3]. There is now a substantial literature on statistical tools for building species distribution models (SDMs) (see Glossary) and best practice in how to fit them [4][5][6][7]. SDMs also form a building block upon which more complex models, incorporating occupancy and/or abundance in space and time, can be built [8,9].

High‐throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, technological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small‐scale, proof‐of‐concept studies. The arrival of low‐cost, open‐source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities. We show that terrestrial and marine PAM applications are advancing rapidly, facilitated by emerging sensor hardware, the application of machine learning innovations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open‐source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods. In order to improve possibilities for PAM globally, we emphasise the need for collaborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities.

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.