Search Results (11)

Understanding how environmental enrichment influences behavioural time allocation is particularly important for threatened primate species maintained under human care. Accordingly, we investigated whether environmental enrichment (EE) influences behavioural time allocation in yellow-breasted capuchin monkeys (Sapajus xanthosternos), aiming to inform evidence-based husbandry practices in zoological settings. Employing the standard ethological approach of behavioural coding, we observed 20 capuchins housed in three groups comprising adult and juvenile males and females. We recorded behavioural categories including: aggressive, exploratory, affiliative/play, general activity, alert, inactivity, and abnormal behaviour. To evaluate individual engagement with EE, we applied the ABA paradigm, wherein phases A1 and A2 (controls) represented standard zoo conditions, while phase B corresponded to the implementation of an EE programme. Each phase spanned 10 days, and behavioural data were collected via focal animal sampling (2 × 10 min focal sessions per animal per day), resulting in a total of 1200 focal sessions. Behavioural time allocation was analysed using a multivariate generalized linear mixed modelling approach that accounted for the interdependence among behavioural categories. Based on previous studies, we predicted that environmental enrichment may promote higher levels of play and exploration and lower aggression and inactivity. However, despite by-eye suggestions of increases in play and decreases in activity during enrichment, when behavioural categories were analysed simultaneously within the multivariate framework, overall behaviour time budgets and behavioural diversity were found not to change significantly across experimental phases. There were also no sex or age effects on behaviour. This indicates that for S. xanthosternos, the enrichment protocol used here did not provide sufficient novelty or complexity to alter established activity patterns. Integrated analytical approaches are needed to further evaluate the effectiveness of enrichment strategies to ensure they are tailored to specific cognitive and social needs of complex species; future studies could explore how social dynamics, enclosure design, and environmental complexity interact to shape behavioural responses to enrichment. Full article

Computer vision and machine learning have accelerated the automation of animal re-identification pipelines used in conservation programs worldwide. For species with distinctive markings, such as the spot patterns of giraffes, these automated methods are crucial for research and population monitoring purposes. However, many tools are designed for experts, and their implementation requires substantial technical expertise. Research teams often use specialist software and workflows that are not accessible to the general public. In a zoo setting, visitors lack a simple way to identify an individual animal, and unique features are easily missed by untrained visitors. This study presents a three-part solution: a web interface for zoo visitors to upload photos, a deep learning model for giraffe torso detection, and a fast re-identification method for matching observations to a gallery of known individuals using server-side processing. We compare several re-identification methods (RootSIFT, MiewID, and MegaDescriptor) using a consistent evaluation protocol and report both identification performance and system latency for this closed-set zoo setting. Taken together, this study presents a visitor-facing web system that integrates existing re-identification models into a modular, real-time pipeline for zoo deployment, lowering the barrier to visitor participation and making state-of-the-art re-identification methods more accessible to the general public. Full article

We quantified how exhibit design and routine management shape behavior and space use in captive harbor seals (Phoca vitulina). Using a species-specific ethogram, scan sampling and focal follows on adult females housed in a modern zoo exhibit, we estimated time budgets, mapped space use across depth-defined zones, and modeled behavior sequences as first-order transition networks. Locomotion dominated activity (swimming/active travel), with resting and enrichment-related behaviors next most frequent; social and play behaviors occurred at low but non-negligible rates. Seals showed clear depth preferences, concentrating active swimming in deeper zones and using liminal/shallow areas for rest. Transition graphs revealed stable, low-entropy loops (e.g., swim → turn/pace → swim) consistent with repetitive locomotor routines, while enrichment and feeding windows temporarily diversified sequences and increased exploration. Overall, integrating time budgets with Markov-style transition analysis and spatial heatmaps provides a compact welfare-oriented dashboard: it identifies where exhibit depth and refuge availability support positive behavioral diversity, flags repetitive cycles as targets for enrichment, and offers actionable metrics to evaluate husbandry changes over time. Full article

Recent advancements in space exploration have significantly increased the volume of astronomical data, heightening the demand for efficient analytical methods. Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. We introduce the first large-scale Green AI benchmark for galaxy morphology classification, evaluating over 30 machine learning architectures (classical, ensemble, deep, and hybrid) on CPU and GPU platforms using a balanced subset of the Galaxy Zoo dataset. Beyond traditional metrics (precision, recall, and F1-score), we quantify inference latency, energy consumption, and carbon-equivalent emissions to derive an integrated EcoScore that captures the trade-off between predictive performance and environmental impact. Our results reveal that a GPU-optimized multilayer perceptron achieves state-of-the-art accuracy of 98% while emitting 20× less CO₂ than ensemble forests, which—despite comparable accuracy—incur substantially higher energy costs. We demonstrate that hardware–algorithm co-design, model sparsification, and careful hyperparameter tuning can reduce carbon footprints by over 90% with negligible loss in classification quality. These findings provide actionable guidelines for deploying energy-efficient, high-fidelity models in both ground-based data centers and onboard space observatories, paving the way for truly sustainable, large-scale astronomical data analysis. Full article

Zoo, aquarium, and sanctuary exhibit designers, both specialist “zoo architects”, and general practice architects, as well as landscape architects generally do not closely follow the evolving scientific literature on zoo biology, visitor experience, and managed animal welfare. Reportedly, this is because most zoo and aquarium clients do not require these efforts. Detailed requirements are provided by clients as project programs or briefs, which vary widely in quality and currency. Many clients and designers copy or adapt popular enclosure models without regard to their scientific foundations. Research papers frequently focus on discrete subject areas, such as animal behavior and welfare, visitor experience, or education, using their own methods and vocabulary. Relatively few studies integrate findings in ways useful to designers in preparing widely integrated systems. Regulatory standards set minimum rather than ideal standards. Knowledge of in situ animal behavior is lacking for many managed species. How can zoo and aquarium managers and designers be encouraged to increase research within the design process? This review article suggests that the long-term benefits of greater and better science integration outweigh initially higher design costs, resulting in improved facility and management design, benefiting all zoo, aquarium, and sanctuary stakeholders, and providing factual evidence underpinning community support. Full article

Addressing the astronomical challenges posed by the interplay of data volume, AI sophistication, and energy consumption is crucial for the future of astronomy. As astronomical surveys continue to produce vast amounts of data, the computational and energy demands for galaxy classification have escalated, necessitating more efficient and sustainable approaches. This study presents a novel application of tensor factorization within the Faster R-CNN framework, resulting in the development of our model, T-Faster R-CNN, designed to enhance both the energy efficiency and computational performance of deep learning models used in galaxy classification. By integrating tensor factorization, our T-Faster R-CNN significantly reduces the model’s complexity, memory footprint, and CO₂ emissions, while maintaining, and in some cases even improving, the accuracy of morphological classification. The effectiveness of this optimized model is validated using data from the Galaxy Zoo DECaLS, where it demonstrates substantial improvements in computational efficiency without compromising classification precision. Furthermore, this research incorporates green code principles, emphasizing reductions in energy consumption and environmental impact in computational astronomy. The T-Faster R-CNN model offers a resource-efficient, sustainable methodology for analyzing large-scale astronomical data, addressing the critical need for greener computational practices in the era of big data. Full article

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper. Full article

Environmental enrichment is a management tool used to promote positive animal welfare by stimulating species-specific behaviors and providing animals with opportunities to exert choice and control over the environment. Our study aimed to evaluate the combined effect of three enrichment types and environmental/individual factors (i.e., individual age and rank position) on the behavior of six adult Lemur catta hosted at Pistoia Zoo (Italy). We collected data from June to September 2013 using a within-subject experimental design consisting of five conditions: Baseline, Food-based enrichment, Physical enrichment, Auditory enrichment and No enrichment provided. We conducted six 30-minute observation sessions per sampling day (total = 107 h). We recorded the animals’ behavior via 2-minute focal animal sampling per individual per observation period and analyzed data with Generalized Linear Models. The study group only performed normal species-specific behaviors. Enrichments decreased stress-related behavioral patterns, whreas environmental and individual factors influenced the other recorded behaviors. Our study confirmed the usefulness of employing an integrated methodological approach to enrichment assessment for enhancing captive lemur care. Full article

This review commentary focuses on traditional management practices and facility design with suggested improvements in non-public primate management areas, often called “back-of-house”, (henceforth BOH) in zoos, sanctuaries, and research facilities. Progress has been made toward improving animal quality of life in larger, more naturalistic, and enriched indoor and outdoor display areas. However, the quality of life in BOH areas has improved little in comparison. Basic management, regulatory, structural, and spatial BOH environments are lagging, especially in the developing world, and animals may be confined in less enriching spaces for substantial periods of the 24 h day. We reviewed traditional management policy and practice, as well as newer training, enrichment, and welfare policies and actions, and suggested alternatives for structural environments and spatial environments. The suggestions included using more animal-friendly construction materials and animal–computer interaction, providing greater control of the ambient environment and choice of access to multiple areas by the animals themselves, and designing for optimal animal wellbeing at all times, including when caregivers are no longer present. Case studies focused on primates were included. We concluded by suggesting a new, integrated design model based not upon rote standards and old models but building on empirical foundations while embracing empathy and innovation. Full article

Due to the rapid changes caused by globalization and internationalization, this study focused on achieving Sustainable Development Goals (SDGs) 4 and 15 via specific digital learning materials—animal conservation apps—particularly designed for enlarging primary school pupils’ knowledge of biodiversity and conservation of natural habitats, and promoting sustainable development and lifelong learning abilities. Through a collaboration with Taipei Zoo, this study recruited 37 undergraduates who took the Learning Design and Practice course to develop the digital learning-assisted materials, namely animal conservation apps and assessment tools that suited the digital learning materials. In the initiative stage of the course, the undergraduates were required to work as a team and to learn in a group by observing and experiencing the model apps provided in the class. The provided apps were developed in compliance with the ADDIE model. In the middle stage of the course, each team was asked to develop their team app and assessment tools following the ADDIE model. In the final stage, each team’s design results were evaluated based on the digital learning material scale and core competency test evaluation: art domain. The results show that the undergraduates were able to integrate the expertise they gained in the course to developing high-quality digital learning materials. According to the educational professionals’ evaluation, the assessment tools designed by the undergraduates scored high marks. Full article

There is a growing interest and need for zoos to develop and implement welfare assessment tools that are practical to use and provide meaningful results that can inform management decisions. This paper presents a process that was developed to support this type of evidence-based management in zoo animal welfare. The process is configured to facilitate institutional risk assessment, using an adapted version of the Five Domains Model for animal welfare assessment. It is designed to systematically analyse information gathered from zoo personnel in order to highlight areas of welfare risk, as well as areas that are performing well and areas requiring further investigation. A trial was conducted on three zoos over three years. Results of the trial suggest the process developed is practical and effective in identifying areas of welfare risk in a wide range of species in a zoo setting. It represents a further step towards achieving high-level animal welfare in zoos by integrating animal welfare as an institutional priority. The more zoos that employ such strategies, the greater the ability of the sector to advance the welfare of the animals in their care. Full article