Towards a multisensor station for automated biodiversity monitoring

Using motion-detection cameras to monitor foraging behaviour of individual butterflies

The activity of many animals follows recurrent patterns and foraging is one of the most important processes in their daily activity. Determining movement in the search for resources and understanding temporal and spatial patterns in foraging has therefore long been central in behavioural ecology. However, identifying and monitoring animal movements is often challenging. In this study we assess the use of camera traps to track a very specific and small-scale interactions focused on the foraging behaviour of Heliconiini butterflies. Data on floral visitation was recorded using marked individuals of three pollen-feeding species of Heliconius (H. erato, H. melpomene and H. sara), and two closely related, non-pollen feeding species (Dryas iulia and Dryadula phaetusa) in a large outdoor insectary. We demonstrate that camera traps efficiently capture individual flower visitation over multiple times and locations and use our experiments to describe some features of their spatial and temporal foraging patterns. Heliconiini butterflies showed higher activity in the morning with strong temporal niche overlap. Differences in foraging activity between males and females was observed with females foraging earlier than males, mirroring published field studies. Some flowers were more explored than others, which may be explained by butterflies foraging simultaneously affecting each other's flower choices. Feeding was grouped in short periods of intense visits to the same flower, which we refer to as feeding bouts. Heliconius also consistently visits the same flower, while non-Heliconius visited a greater number of flowers per day and their feeding bouts were shorter compared with Heliconius. This is consistent with Heliconius having more stable long-term spatial memory and foraging preferences than outgroup genera. More broadly, our study demonstrates that camera traps can provide a powerful tool to gather information about foraging behaviour in small insects such as butterflies. © 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Exploring the uptake of advanced digital technologies in environmental assessment practice - Experiences from Austria and Germany

Environmental assessment (EA) evaluates the environmental impacts of proposed projects, plans or policies to inform decision making. While several studies have highlighted the potential and opportunities of digitalisation for EA, few have explored practitioners' perceptions using a mixed methods approach in order to discover concerns and risks identified by EA of novel technological approaches. In addition, this initial exploratory study examines the perception of benefits and contributions to quality and effectiveness of advanced digital approaches, such as the introduction of artificial intelligence, in EA practice. The research process was based on focus group discussions and exploratory interviews with EA consultants, environmental authorities, researchers, environmental associations and NGOs. Relevant technologies were identified from the existing scientific literature and their applicability, benefits and use were discussed in context of real-world experience made by the practitioner. It became evident that the majority of practitioners in the field of EA in Austria and Germany are not familiar with advanced digital approaches and tools. While other planning disciplines are exploiting the potential of advanced digital tools, EA practitioners still share concerns about data quality, security, legal uncertainties, but also skills and know-how. The study identifies a gap and a need for training and confidence building. It aims to contribute to the promotion of inter- & transdisciplinary exchange involving the wider EA community.

Emerging technologies in citizen science and potential for insect monitoring

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Towards a toolkit for global insect biodiversity monitoring

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.

Environmental DNA methods for biosecurity and invasion biology in terrestrial ecosystems: Progress, pitfalls, and prospects

Analysis of environmental DNA (eDNA) enables indirect detection of species without the need to directly observe and sample them. For biosecurity and invasion biology, eDNA-based methods are useful to address biological invasions at all phases, from detecting arrivals to confirming eradication of past invasions. We conducted a systematic review of the literature and found that in biosecurity and invasion biology, eDNA has primarily been used to detect new incursions and monitor spread in marine and freshwater ecosystems, with much slower uptake in terrestrial ecosystems, reflecting a broader trend common to the usage of eDNA tools. In terrestrial ecosystems, eDNA research has mostly focussed on the use of eDNA metabarcoding to characterise biodiversity, rather than targeting biosecurity threats or non-native populations. We discuss how eDNA-based methods are being applied to terrestrial ecosystems for biosecurity and managing non-native populations at each phase of the invasion continuum: transport, introduction, establishment, and spread; across different management options: containment, control, and eradication; and for detecting the impact of non-native organisms. Finally, we address some of the current technical issues and caveats of eDNA-based methods, particularly for terrestrial ecosystems, and how these might be solved. As eDNA-based methods improve, they will play an increasingly important role in the early detection and adaptive management of biological invasions, and the implementation of effective biosecurity controls.

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

The Potential of Diffusion-Based Near-Infrared Image Colorization

Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion.

Lack of local genetic representation in one of the regions with the highest bird species richness, the Peruvian Amazonia

Peru ranks among the three countries with the highest bird species diversity globally and a majority of those species are found in the Peruvian Amazon. However, birds in this area are currently facing serious anthropogenic threats. Genetic and genomic methods are becoming important tools for avian biodiversity monitoring and conservation planning. Comprehensive molecular libraries that are publicly available are key to the effective deployment of these tools. We analyze the information gaps for four molecular markers in the most important genetic sequence databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, for bird species of the Peruvian Amazonia. We found that 64% of Peruvian Amazonian bird species have gene sequences for COI, 59.5% have CYTB sequences, 16.4% have 12S sequences, and only 0.6% have 18S sequences. However, these numbers decrease drastically to 4.3% for COI sequences when we only consider specimens sampled in Peru. Our data also showed that 43.8% of Peruvian Amazonian endemic species (n = 32) are missing sequences of any screened marker uploaded to GenBank or BOLD. Our results will encourage and guide efforts of the scientific community to complete reference libraries for Peruvian avian species that will be useful for future DNA-based monitoring projects that include birds.

Nature 4.0: A networked sensor system for integrated biodiversity monitoring

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

Bioacoustic IoT Sensors as Next-Generation Tools for Monitoring: Counting Flying Insects through Buzz

The global loss of biodiversity is an urgent concern requiring the implementation of effective monitoring. Flying insects, such as pollinators, are vital for ecosystems, and establishing their population dynamics has become essential in conservation biology. Traditional monitoring methods are labour-intensive and show time constraints. In this work, we explore the use of bioacoustic sensors for monitoring flying insects. Data collected at four Italian farms using traditional monitoring methods, such as hand netting and pan traps, and bioacoustic sensors were compared. The results showed a positive correlation between the average number of buzzes per hour and insect abundance measured by traditional methods, primarily by pan traps. Intraday and long-term analysis performed on buzzes revealed temperature-related patterns of insect activity. Passive acoustic monitoring proved to be effective in estimating flying insect abundance, while further development of the algorithm is required to correctly identify insect taxa. Overall, innovative technologies, such as bioacoustic sensors, do not replace the expertise and data quality provided by professionals, but they offer unprecedented opportunities to ease insect monitoring to support conservation biodiversity efforts.

Metabolite Profiling of Conifer Needles: Tracing Pollution and Climate Effects

In the face of escalating environmental challenges, understanding the intricate relationship between plant metabolites, pollution stress, and climatic conditions is of paramount importance. This study aimed to conduct a comprehensive analysis of metabolic variations generated through 1H and 13C NMR measurements in evergreen needles collected from different regions with varying pollution levels. Multivariate analyses were employed to identify specific metabolites responsive to pollution stress and climatic factors. Air pollution indicators were assessed through ANOVA and Pearson correlation analyses. Our results revealed significant metabolic changes attributed to geographical origin, establishing these conifer species as potential indicators for both air pollution and climatic conditions. High levels of air pollution correlated with increased glucose and decreased levels of formic acid and choline. Principal component analysis (PCA) unveiled a clear species separation, largely influenced by succinic acid and threonine. Discriminant analysis (DA) confirmed these findings, highlighting the positive correlation of glucose with pollution grade. Beyond pollution assessment, these metabolic variations could have ecological implications, impacting interactions and ecological functions. Our study underscores the dynamic interplay between conifer metabolism, environmental stressors, and ecological systems. These findings not only advance environmental monitoring practices but also pave the way for holistic research encompassing ecological and physiological dimensions, shedding light on the multifaceted roles of metabolites in conifer responses to environmental challenges.

Are Mosses Used in Atmospheric Trace Metal Deposition Surveys Impacted by Their Substrate Soils? A National Study in Albania

This research used moss biomonitoring to assess the atmospheric deposition of selected trace metals across the whole territory of Albania, a country of diverse lithology, and topography. Here, we assess three elements (Cr, Ni, and Co) that were identified in high concentrations compared to values reported by European moss surveys of 2010 and 2015. The possibility of element uptake by moss from substrate soils was assessed by analyzing moss and topsoil samples from the same areas. For this purpose, moss (Hypnum cupressiforme (Hedw.)) and topsoil samples were collected throughout Albania. Higher concentrations of elements in moss were found in areas of very high element content in soil characterized by no/or thin humus layer and sparse vegetation that stimulates soil dust generation. To compensate for the natural variation of the elements and to show their anthropogenic variation, geochemical normalization was conducted as the ratio of Co, Cr, and Ni concentration data to be concentration. Associations between elements in moss and soil samples, investigated by Spearman-Rho correlation analysis, indicated strong and significant correlations (r > 0.81, p = 0.000) between elements' data in moss or soil, and weak or no correlations (r < 0.4, p > 0.05) between the same data of moss and soil. Factor analysis revealed two main factors that selectively affect the elements in moss and top soil samples. Findings from this research suggested negligible interactions between moss and substrate soils, with the exception of soils with high concentrations of elements.

Future of DNA-based insect monitoring

Insects are crucial for ecosystem health but climate change and pesticide use are driving massive insect decline. To mitigate this loss, we need new and effective monitoring techniques. Over the past decade there has been a shift to DNA-based techniques. We describe key emerging techniques for sample collection. We suggest that the selection of tools should be broadened, and that DNA-based insect monitoring data need to be integrated more rapidly into policymaking. We argue that there are four key areas for advancement, including the generation of more complete DNA barcode databases to interpret molecular data, standardisation of molecular methods, scaling up of monitoring efforts, and integrating molecular tools with other technologies that allow continuous, passive monitoring based on images and/or laser imaging, detection, and ranging (LIDAR).

A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation

The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species' continuous and rapid decline, information and intelligent technology have provided innovative knowledge about how functional biological diversity interacts with environmental changes. Especially in complex natural scenes, identifying bird species with a real-time and accurate pattern is vital to protect the ecological environment and maintain biodiversity changes. Aiming at the fine-grained problem in bird image recognition, this paper proposes a fine-grained detection neural network based on optimizing the YOLOV5 structure via a graph pyramid attention convolution operation. Firstly, the Cross Stage Partial (CSP) structure is introduced to a brand-new backbone classification network (GPA-Net) for significantly reducing the whole model's parameters. Then, the graph pyramid structure is applied to learn the bird image features of different scales, which enhances the fine-grained learning ability and embeds high-order features to reduce parameters. Thirdly, YOLOV5 with the soft non-maximum suppression (NMS) strategy is adopted to design the detector composition, improving the detection capability for small targets. Detailed experiments demonstrated that the proposed model achieves better or equivalent accuracy results, over-performing current advanced models in bird species identification, and is more stable and suitable for practical applications in biodiversity conservation.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer the 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75), but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is openly provided.

Comparison of destructive and non-destructive DNA extraction methods for the metabarcoding of arthropod bulk samples

DNA metabarcoding is routinely used for biodiversity assessment, especially targeting highly diverse groups for which limited taxonomic expertise is available. Various protocols are currently in use, although standardization is key to its application in large-scale monitoring. DNA metabarcoding of arthropod bulk samples can be either conducted destructively from sample tissue, or non-destructively from sample fixative or lysis buffer. Non-destructive methods are highly desirable for the preservation of sample integrity but have yet to be experimentally evaluated in detail. Here, we compare diversity estimates from 14 size sorted Malaise trap samples processed consecutively with three non-destructive approaches (one using fixative ethanol and two using lysis buffers) and one destructive approach (using homogenized tissue). Extraction from commercial lysis buffer yielded comparable species richness and high overlap in species composition to the ground tissue extracts. A significantly divergent community was detected from preservative ethanol-based DNA extraction. No consistent trend in species richness was found with increasing incubation time in lysis buffer. These results indicate that non-destructive DNA extraction from incubation in lysis buffer could provide a comparable alternative to destructive approaches with the added advantage of preserving the specimens for post-metabarcoding taxonomic work but at a higher cost per sample.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.