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This book offers an overview of some recent advances in the Computational Bioacoustics methods and technology.In the focus of discussion is the pursuit of scalability, which would facilitate real-world applications of different scope and purpose, such as wildlife monitoring, biodiversity assessment, pest population control, and monitoring.

Traditional methods of biodiversity monitoring are often logistically challenging, time‐consuming, require experienced experts on species identification, and sometimes include destruction of the targeted specimens. Here, we investigated a non‐invasive approach of combining the use of drones and environmental DNA (eDNA) to monitor insect biodiversity on vegetation. We aimed to assess the efficiency of this novel method in capturing insect diversity and comparing insect composition across different vegetation types (grassland, shrub and forest) in Switzerland. A commercial, off‐the‐shelf drone was equipped with a specialised probe that autonomously swabbed vegetation and collected eDNA. Then, samples were processed using rapid third‐generation Oxford Nanopore sequencing. The obtained data were analysed for insect diversity, comparing taxonomic richness, evenness and community composition across the three habitat types using statistical techniques. Sequencing of the samples yielded 76 hexapod taxa, revealing an insect community with notable differences in taxonomic richness but not in evenness across grassland, shrub and forest habitats. Our study demonstrates the potential of drone‐based sampling integrated with eDNA and nanopore sequencing for biodiversity monitoring, offering a non‐destructive method for detecting insect occurrence on plant surfaces. Integrating robotics and eDNA technology provides a promising solution for fast, large‐scale, non‐invasive biodiversity monitoring, potentially improving conservation efforts and ecosystem management. We developed a novel method combining drone‐based sampling and environmental DNA (eDNA) with Oxford Nanopore sequencing to monitor insect biodiversity on vegetation. Using a DJI Matrice 3 drone, we collected eDNA samples from grassland, shrub and forest habitats in Switzerland, detecting 64 insect taxa and revealing habitat‐specific differences in diversity. This approach offers a non‐invasive, scalable solution for biodiversity monitoring, enhancing conservation and ecosystem management efforts.

In this study, we trained an object‐detection model to classify 17 benthic invertebrate taxa in archived footage of a study site on the northern west coast of Sweden (a wall section of the Koster Fjord) within the Swedish marine protected area Kosterhavet National Park. The model displayed a mean average precision score of 0.738 and was applied to footage from 1997 to 2023, generating a dataset of 72,369 occurrence records. The dataset was used to quantify depth distributions and abundance trends of both individual taxa and functional groups over time. Depth distributions for 15 of 17 taxa occurred at depths ≥ 45 m. Distributions of 11 taxa aligned with empirical observations, and for the remaining six taxa, we propose expanded depth distributions in the area. Abundances over time significantly increased for eight taxa and decreased for five taxa, while the overall community structure throughout the study period shifted toward smaller, more heat‐tolerant suspension feeders. We found that temperature preference and size were significant drivers of the observed abundance trends in individual taxa. Community structure was altered by the loss of large, heat‐sensitive taxa to greater depths due to increased temperatures. We also observed a strong trend of increasing abundances in the remaining community, including six trawling‐sensitive taxa, highlighting the effectiveness of the park's protective measures. To protect key cold‐water species, we suggest that current fishery regulations of the national park should be expanded to deeper (colder) waters and that new marine protected areas should also be established in deep waters. Our study demonstrates the application potential of video surveillance combined with deep‐learning technology, and we recommend the implementation of standardized video monitoring in marine ecosystem management. We applied an object‐detection model to archived videos from 1997‐2023 of a submarine rock wall in a Swedish marine protected area. We modeled depth distributions and abundance trends of 17 invertebrate taxa. Most taxa resided at deeper wall sections and abundance trends were generally positive, but heat sensitivity was associated with population decline. This highlights a partially successful management strategy of the protected area that should be adapted to preserve key cold‐water species.

This research level text provides an up-to-date, authoritative review of the methods of measuring biological diversity, as well as the application of these methods.

Conserving biodiversity in the face of ever-increasing human pressure is hampered by our lack of basic information on species occurrence, distribution, abundance, habitat requirements, and threats. Obtaining this information requires efficient and sensitive methods capable of detecting and quantifying true occurrence and diversity, including rare, cryptic and elusive species. Environmental DNA (eDNA) is an emerging technique that can increase our ability to detect and quantify biodiversity, by overcoming some of the challenges of labor-intensive traditional surveys. The application of eDNA in ecology and conservation has grown enormously in recent years, but without a concurrent growth in appreciation of its strengths and limitations. In many situations, eDNA may either not work, or it may work but not provide the information needed. Problems with (1) imperfect detection, (2) abundance quantification, (3) taxonomic assignment, (4) eDNA spatial and temporal dynamics, (5) data analysis and interpretation, and (6) assessing ecological status have all been significant. The technique has often been used without a careful evaluation of the technical challenges and complexities involved, and a determination made that eDNA is the appropriate method for the species or environment of interest. It is therefore important to evaluate the scope and relevance of eDNA-based studies, and to identify critical considerations that need to be taken into account before using the approach. We review and synthesize eDNA studies published to date to highlight the opportunities and limitations of utilizing eDNA in ecology and conservation. We identify potential ways of reducing limitations in eDNA analysis, and demonstrate how eDNA and traditional surveys can complement each other.

Large‐scale biodiversity databases have great potential for quantifying long‐term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias – how spatial bias has changed over time – has been largely overlooked. We examined the spatial bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well‐known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long‐term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land‐use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long‐term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the spatial bias through time.

Global species extinction rates are orders of magnitude above the background rate documented in the fossil record. However, recent data syntheses have found mixed evidence for patterns of net species loss at local spatial scales. For example, two recent data meta-analyses have found that species richness is decreasing in some locations and is increasing in others. When these trends are combined, these papers argued there has been no net change in species richness, and suggested this pattern is globally representative of biodiversity change at local scales. Here we reanalyze results of these data syntheses and outline why this conclusion is unfounded. First, we show the datasets collated for these syntheses are spatially biased and not representative of the spatial distribution of species richness or the distribution of many primary drivers of biodiversity change. This casts doubt that their results are representative of global patterns. Second, we argue that detecting the trend in local species richness is very difficult with short time series and can lead to biased estimates of change. Reanalyses of the data detected a signal of study duration on biodiversity change, indicating net biodiversity loss is most apparent in studies of longer duration. Third, estimates of species richness change can be biased if species gains during post-disturbance recovery are included without also including species losses that occurred during the disturbance. Net species gains or losses should be assessed with respect to common baselines or reference communities. Ultimately, we need a globally coordinated effort to monitor biodiversity so that we can estimate and attribute human impacts as causes of biodiversity change. A combination of technologies will be needed to produce regularly updated global datasets of local biodiversity change to guide future policy. At this time the conclusion that there is no net change in local species richness is not the consensus state of knowledge.

Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in government-funded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale.