A global community-sourced assessment of the state of conservation technology

Novel automation, artificial intelligence, and biomimetic engineering advancements for insect studies and management

Entomology has seen remarkable advancements through the integration of robotics, artificial intelligence (AI), and biomimetic engineering. These technological innovations are revolutionizing how scientists study insect behavior, ecology, and management. Robotics and AI offer unprecedented precision and efficiency in monitoring and controlling insect populations. Biomimetics provides new ways to understand and replicate insect abilities in bioengineered systems. This mini-review highlights recent developments in these fields, focusing on key studies describing the transformative potential of these technologies. I explore their applications, benefits, and challenges, aiming at providing an overview of the current state and future directions in insect science and management.

Large-scale and long-term wildlife research and monitoring using camera traps: a continental synthesis

Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources. We predicted that the last decade would have shown: (i) exponentially increasing sampling effort, a continuation of camera usage trends up to 2012; (ii) analytics to have shifted from naive presence/absence and capture rates towards hierarchical modelling that accounts for imperfect detection, thereby improving the quality of outputs and inferences on occupancy, abundance, and density; and (iii) broader research scales in terms of multi-species, multi-site and multi-year studies. However, the results showed that the sampling effort has reached a plateau, with publication rates increasing only modestly. Users reported reaching a saturation point in terms of images that could be processed by humans and time for complex analyses and academic writing. There were strong taxonomic and geographic biases towards medium-large mammals (>500 g) in forests along Australia's southeastern coastlines, reflecting proximity to major cities. Regarding analytical choices, bias-prone indices still accounted for ~50% of outputs and this was consistent across user groups. Multi-species, multi-site and multiple-year studies were rare, largely driven by hesitancy around collaboration and data sharing. There is no widely used repository for wildlife camera images and the Atlas of Living Australia (ALA) is the dominant repository for sharing tabular occurrence records. However, the ALA is presence-only and thus is unsuitable for creating detection histories with absences, inhibiting hierarchical modelling. Workshop discussions identified a pressing need for collaboration to enhance the efficiency, quality and scale of research and management outcomes, leading to the proposal of a Wildlife Observatory of Australia (WildObs). To encourage data standards and sharing, WildObs should (i) promote a metadata collection app; (ii) create a tagged image repository to facilitate artificial intelligence/machine learning (AI/ML) computer vision research in this space; (iii) address the image identification bottleneck via the use of AI/ML-powered image-processing platforms; (iv) create data commons for detection histories that are suitable for hierarchical modelling; and (v) provide capacity building and tools for hierarchical modelling. Our review highlights that while Australia's investments in monitoring biodiversity with cameras position it to be a global leader in this context, realising that potential requires a paradigm shift towards best practices for collecting, curating, sharing and analysing 'Big Data'. Our findings and framework have broad applicability outside Australia to enhance camera usage to meet conservation and management objectives ranging from local to global scales. This review articulates a country/continental observatory approach that is also suitable for international collaborative wildlife research networks.

Navigating scale and interdisciplinary dynamics in conservation social science

To better understand and address global human-environment crises, interdisciplinary collaborations across the natural and social sciences have become increasingly common in conservation. Within such collaborations, the question of scale can cause tensions: how to agree on the unit of measurement and analysis? We contend there is value in scrutinizing the relationship between interdisciplinarity and scale more closely. Drawing on 2 research projects in Indonesia that integrate cultural anthropology and conservation biology, we focused on how these collaborations navigated questions of scale. We sought to illustrate that the relationship between interdisciplinarity and scale choices should be understood as situated in the context of the accelerating drive to scale up conservation science and practice impact. Current conservation discourse around scale deeply affects 3 interconnected factors: ethical and strategic considerations, epistemological parity, and institutional structures. However, interdisciplinary efforts can engage these factors in different ways that have implications for how research unfolds and responds to the push to scale up conservation. To cultivate more robust and resilient interdisciplinary collaborations between the natural and the social sciences, we recommend centering reflexive practices, recognizing the value of rescaling methods and goals, and reforming funding structures.

Exploring the landscape of automated species identification apps: Development, promise, and user appraisal

Two decades ago, Gaston and O'Neill (2004) deliberated on why automated species identification had not become widely employed. We no longer have to wonder: This AI-based technology is here, embedded in numerous web and mobile apps used by large audiences interested in nature. Now that automated species identification tools are available, popular, and efficient, it is time to look at how the apps are developed, what they promise, and how users appraise them. Delving into the automated species identification apps landscape, we found that free and paid apps differ fundamentally in presentation, experience, and the use of biodiversity and personal data. However, these two business models are deeply intertwined. Going forward, although big tech companies will eventually take over the landscape, citizen science programs will likely continue to have their own identification tools because of their specific purpose and their ability to create a strong sense of belonging among naturalist communities.

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Closing the gap between existing large-area imaging research and marine conservation needs

Emerging technology has immense potential to increase the scale and efficiency of marine conservation. One such technology is large-area imaging (LAI), which relies on structure-from-motion photogrammetry to create composite products, including 3-dimensional (3-D) environmental models, that are larger in spatial extent than the individual images used to create them. Use of LAI has become widespread in certain fields of marine science, primarily to measure the 3D structure of benthic ecosystems and track change over time. However, the use of LAI in the field of marine conservation appears limited. We conducted a review of the coral reef literature on the use of LAI to identify research themes and regional trends in applications of this technology. We also surveyed 135 coral reef scientists and conservation practitioners to determine community familiarity with LAI, evaluate barriers practitioners face in using LAI, and identify applications of LAI believed to be most exciting or relevant to coral conservation. Adoption of LAI was limited primarily to researchers at institutions based in advanced economies and was applied infrequently to conservation, although conservation practitioners and survey respondents from emerging economies indicated they expect to use LAI in the future. Our results revealed disconnect between current LAI research topics and conservation priorities identified by practitioners, highlighting the need for more diverse, conservation-relevant research using LAI. We provide recommendations for how early adopters of LAI (typically Global North scientists from well-resourced institutions) can facilitate access to this conservation technology. These recommendations include developing training resources, creating partnerships for data storage and analysis, publishing standard operating procedures for LAI workflows, standardizing methods, developing tools for efficient data extraction from LAI products, and conducting conservation-relevant research using LAI.

Conservation tools: the next generation of engineering-biology collaborations

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that use technology to aid in the conservation of wildlife. In this review, we present five case studies and infer a framework for designing conservation tools (CT) based on human-wildlife interaction. Successful CT range in complexity from cat collars to machine learning and game theory methodologies and do not require technological expertise to contribute to conservation tool creation. Our goal is to introduce researchers to the field of conservation technology and provide references for guiding the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Identifying conservation technology needs, barriers, and opportunities

Amid accelerating threats to species and ecosystems, technology advancements to monitor, protect, and conserve biodiversity have taken on increased importance. While most innovations stem from adaptation of off-the-shelf devices, these tools can fail to meet the specialized needs of conservation and research or lack the support to scale beyond a single site. Despite calls from the conservation community for its importance, a shift to bottom-up innovation driven by conservation professionals remains limited. We surveyed practitioners, academic researchers, and technologists to understand the factors contributing to or inhibiting engagement in the collaborative process of technology development and adoption for field use and identify emerging technology needs. High cost was the main barrier to technology use across occupations, while development of new technologies faced barriers of cost and partner communication. Automated processing of data streams was the largest emerging need, and respondents focused mainly on applications for individual-level monitoring and automated image processing. Cross-discipline collaborations and expanded funding networks that encourage cyclical development and continued technical support are needed to address current limitations and meet the growing need for conservation technologies.

Coproduce Conservation Technology With Conservation Decision Makers and Practitioners to Increase Its Impact

Addressing the biodiversity crisis will mean developing and adopting new resources and methods that effectively improve public conservation efforts. Technologies have a long track record of increasing the efficiency of carrying out time-consuming tasks or even making new feats possible, and if applied thoughtfully, can serve as a key means of strengthening conservation outcomes. Yet technology development sometimes proceeds without clear mechanisms for application and scaling, or key adopters like government agencies are not able to use the technologies. To overcome these discrepancies, we recommend the use of a coproduction model of conservation technology development that starts from detailed knowledge of conservation laws, regulations, policies, and their implementation; identifies choke points in those processes amenable to technological solutions; and then develops those solutions while integrating existing users and needs. To illustrate the model, we describe three tools recently developed to help improve the efficiency and effectiveness of implementing the U.S. Endangered Species Act. We also highlight several outstanding questions and challenges that the broad conservation technology and policy communities may help address.