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.

Avian blood analysis is a fundamental method for investigating a wide range of topics concerning individual birds and populations of birds. Determining precise blood cell counts helps researchers gain insights into the health condition of birds. For example, the ratio of heterophils to lymphocytes (H/L ratio) is a well-established index for comparing relative stress load. However, such measurements are currently often obtained manually by human experts. In this article, we present a novel approach to automatically quantify avian red and white blood cells in whole slide images. Our approach is based on two deep neural network models. The first model determines image regions that are suitable for counting blood cells, and the second model is an instance segmentation model that detects the cells in the determined image regions. The region selection model achieves up to 97.3% in terms of F1 score (i.e., the harmonic mean of precision and recall), and the instance segmentation model achieves up to 90.7% in terms of mean average precision. Our approach helps ornithologists acquire hematological data from avian blood smears more precisely and efficiently.

Disruption-tolerant networks (DTNs) have a wide range of applications, such as emergencies where traditional communication infrastructure has been destroyed, remote rural deployments where communication infrastructure has never existed, and environmental monitoring where animals are equipped with sensors and transmit data whenever they come into contact with a base station. Using the de-facto DTN protocol standard, i.e., Bundle Protocol version 7 (BPv7), nodes transmit data via so-called Convergence Layer Protocols (CLPs) that act as general abstractions for the underlying communication technologies. BPv7 specifies MTCP and TCPCL as the two current CLPs for DTNs. However, both of them have different but equally undesirable shortcomings in terms of functionality, complexity, performance, and reliability. In this paper, we present QUICL, a novel CLP for DTNs. QUICL is based on the QUIC transport protocol and fully leverages QUIC's advantages over TCP-based transport protocols in a DTN environment. In particular, QUICL provides improved congestion control, allows multiplexing, ensures reliable transmission, effectively manages unreliable links, and uses encryption by default. Our prototypical implementation, already merged upstream, is based on the free and open-source DTN7-go protocol suite and the QUIC-go implementation. Our experimental evaluation shows that even with 30% packet loss, QUICL can still deliver data with minimal CPU overhead in scenarios where most other DTN/CLP combinations fail to transmit any data successfully.