Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

This paper presents a microfluidic device capable of performing genetic analysis on dung samples to identify White Rhinoceros (Ceratotherium simum). The development of a microfluidic device, which can be used in the field, offers a portable and cost‐effective solution for DNA analysis and species identification to aid conservation efforts. Optimization of the DNA extraction processes produced equivalent yields compared to conventional kit‐based methods within just 5 minutes. The use of a color‐changing loop‐mediated isothermal amplification reaction for simultaneous detection of the cytochrome B sequence of C. simum enabled positive results to be obtained within as little as 30 minutes. Field testing was performed at Knowsley Safari to demonstrate real‐world applicability of the microfluidic device for testing of biological samples.

Primates are some of the most cognitively advanced species held in zoos, and their interactions with visitors are complex. The COVID-19 pandemic provided a unique opportunity to understand the impact of zoo visitors on animals, in comparison to “empty zoos”. This study sought to understand the impact of facility closures and subsequent reopenings on behavioural and physiological parameters of welfare in four primate species housed in the UK: bonobos (Pan paniscus) (n = 8), chimpanzees (Pan troglodytes) (n = 11), and western lowland gorillas (Gorilla gorilla gorilla) (n = 6) held at Twycross Zoo (TZ); and olive baboons (Papio anubis) (n = 192) held at Knowsley Safari (KS). Behavioural data were collected from April–September 2020 (KS) and November 2020–January 2021 (TZ). Faecal samples were collected during morning checks from October–November (TZ) and July–November 2020 (KS). Faecal glucocorticoid metabolites (FGMs) were measured using ELISA kits. Statistical analysis for behavioural observations was undertaken using general linear models. Enclosure usage was assessed using t-tests and Mann–Whitney U-tests as appropriate. Bonobos and gorillas spent less time alone when facilities were open to the public (p = 0.004, p = 0.02 respectively). Gorillas spent less time resting when the facility was open to the public (p = 0.04), and chimpanzees engaged in more feeding (p = 0.02) and engagement with enrichment (p = 0.03) when the zoo was open to the public than when it was closed. Olive baboons performed less sexual and dominance behaviour and approached visitor cars more frequently when the safari park was opened to the public than they did the ranger’s vehicle during closure periods. There were no significant changes in physiological parameters for any of the study species. The results suggest variable impacts of the zoo closures on zoo-housed primates. We recommend future work that seeks to understand the impact of individual-level differences on “visitor effects” and that differences between animal experiences in zoos and safari parks are further explored in a range of species.

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata), a ground-nesting bird experiencing significant population declines. A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. The system processes camera trap data in real time, significantly enhancing monitoring efficiency. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections and a sensitivity of 92.35%, specificity of 100%, and F1-score of 96.03% for curlew chick detections. These results demonstrate the capability of AI-driven monitoring systems to deliver accurate, timely data for biodiversity assessments, facilitating early conservation interventions and advancing the use of technology in ecological research.

Visitors are a prominent feature of the zoo environment and lives of zoo animals. The COVID-19 pandemic led to repeated and extended closure periods for zoos worldwide. This unique period in zoological history enabled the opportunity to investigate the consistency of behavioural responses of zoo animals to closures and subsequent reopenings. Bennett’s wallabies (Notamacropus rufogriseus), meerkats (Suricata suricatta), macaws (red and green: Ara chloropterus; blue and yellow: Ara ararauna; military: Ara militaris) and rabbits (Oryctolagus cuniculus domesticus) held at four zoological collections in the United Kingdom were studied during COVID-19 closures and subsequent reopening periods. Facilities were closed for three time periods during 2020 and 2021: March–June/July 2020; November–December 2020; January–April/May 2021. Behavioural data were captured during closures (maximum n = 3) and reopening periods (maximum n = 3) during five-min scans using instantaneous scan sampling with a one-minute inter-scan interval. General linear models (GLMs) and general linear mixed models (GLMMs) were used to investigate the relationship between observed behaviours and open/closed periods. Changes were observed in behaviour between open and closure periods in all species, and in some instances changes were also observed over time, with animals responding differently to different closure and reopening periods. However, no overt positive or negative impacts of the closures or reopening periods were identified for these species. The study species may have different relationships with zoo visitors, but no clear differences were seen across the species studied. The unique opportunity to study animals over a long period of time during repeated closure periods enabled a greater understanding of the impact of zoo visitors on animals. As with other work in this sphere, these data support the adaptability of zoo animals to zoo visitors. This work contributes to the growing field of research undertaken during the COVID-19 periods and enhances our understanding of the impact that these zoological closures had on a wider body of species in a number of facilities.

It is important to examine the animal welfare implications of all aspects of zoo operations, including out-of-hours public events. Research to date has indicated variable responses across species and event types. The current research aimed to understand and quantify the impact of a Christmas lights event. Four species: Rothschild giraffe (Giraffa camelopardalis rothschildi; n = 2) in one exhibit and capybara (Hydrochoerus hydrochaeris; n = 4), lowland tapir (Tapirus terrestris; n = 3) and vicuña (Lama vicugna; n = 5) in a mixed species exhibit were observed. Data were collected from 16:00–20:00 between 28 October 2021 and 11 January 2022. The event ran from mid-November to the end of December 2021. Five-minute behavioural observations were undertaken once per hour using instantaneous scan sampling with a one-minute inter-scan interval. A further six days of 12 h observations were conducted to enable a more detailed investigation post-event. Data collected were compared on non-event and event days using Mann–Whitney U tests (event vs. non-event) and Kruskal–Wallis tests (pre-event, event, post-event periods). Kruskal–Wallis tests and one-way ANOVAs were undertaken to compare behaviours during three time periods (12:00–16:00, 16:00–20:00, 20:00–00:00) over 12 h. Mixed behavioural responses were seen across the study species. Capybara spent more time in their house from 16:00–20:00 on event nights compared to non-event nights (p < 0.001) and tapir only engaged in vigilant behaviour from 16:00–20:00 when the event was held, (p = 0.044). There were no differences in frequency of behaviour between pre-event, event, and post-event observation periods, with the exception of capybara, who spent more time OOS in the pre-event period than during (p < 0.001) or after the event (p < 0.001). The results of the project, undertaken as part of an evidence-based management programme, highlighted that the event did not have any overtly negative impacts on the individuals studied. Except for the giraffe, all individuals had free access to inside and outside environments, and it is believed this choice enabled animals to be active in managing their response to the event. It is recommended that future work observe animals over 24 h to understand whether events lead to behavioural changes the day after events or if animals reverted to normal activity once the event ended.