External environmental conditions impact nocturnal activity levels in proboscis monkeys (Nasalis larvatus) living in Sabah, Malaysia

Recently, several diurnal nonhuman anthropoids have been identified displaying varying degrees of nocturnal activity, which can be influenced by activity “masking effects”—external events or conditions that suppress or trigger activity, temporarily altering normal activity patterns. Environmental masking characteristics include nocturnal temperature, rainfall, cloud cover, and moon brightness. Similarly, other ecological characteristics, including proximity to humans and predators and daytime activity, may also trigger or suppress nocturnal activity. Understanding the effects of external conditions on activity patterns is pertinent to effective species conservation. We investigated the presence of nocturnal activity and the influence of masking effects on the level of nocturnal activity displayed by wild proboscis monkeys (Nasalis larvatus) in Sabah, Malaysian Borneo. Dual-axis accelerometers were attached by collar to six male proboscis monkeys from different one-male, multi-female groups to record activity continuously (165–401 days each). We measured the monkeys' nocturnal and diurnal activity levels and investigated the effects of seven potential masking effects. Nocturnal activity was much lower than diurnal activity. Still, proboscis monkeys did display varying levels of nocturnal activity. Generalized linear mixed models identified higher nocturnal activity in the study individuals during nights with cooler temperatures, higher rainfall, and after higher diurnal activity. These three masking effects affected nocturnal activity levels during the observation period that informed our model, although they did not predict nocturnal activity outside of this period. While the generalizability of these results remains uncertain, this study highlights the utility of accelerometers in identifying activity patterns and masking effects that create variability in these patterns

Drivers of Dyadic Cofeeding Tolerance in Pan: A Composite Measure Approach

This study aimed to construct a composite model of Dyadic Cofeeding Tolerance (DCT) in zoo-housed bonobos and chimpanzees using a validated experimental cofeeding paradigm and to investigate whether components resulting from this model differ between the two species or vary with factors such as sex, age, kinship and social bond strength. Using dimension reduction analysis on five behavioral variables from the experimental paradigm (proximity, aggression, food transfers, negative food behavior, participation), we found a two-factor model: "Tolerant Cofeeding" and "Agonistic Cofeeding". To investigate the role of social bond quality on DCT components alongside species effects, we constructed and validated a novel relationship quality model for bonobos and chimpanzees combined, resulting in two factors: Relationship Value and Incompatibility. Interestingly, bonobos and chimpanzees did not differ in DCT scores, and sex and kinship effects were identical in both species but biased by avoidance of the resource zone by male-male dyads in bonobos. Social bonds impacted DCT similarly in both species, as dyads with high Relationship Value showed more Tolerant Cofeeding, while dyads with higher Relationship Incompatibility showed more Agonistic Cofeeding. We showed that composite DCT models can be constructed that take into account both negative and positive cofeeding behavior. The resulting DCT scores were predicted by sex, kinship and social bonds in a similar fashion in both Pan species, likely reflecting their adaptability to changing socio-ecological environments. This novel operational measure to quantify cofeeding tolerance can now be applied to a wider range of species in captivity and the wild to see how variation in local socio-ecological circumstances influences fitness interdependence and cofeeding tolerance at the dyadic and group levels. This can ultimately lead to a better understanding of how local environments have shaped the evolution of tolerance in humans and other species

Chimpanzees organize their social relationships like humans

Human relationships are structured in a set of layers, ordered from higher (intimate relationships) to lower (acquaintances) emotional and cognitive intensity. This structure arises from the limits of our cognitive capacity and the different amounts of resources required by different relationships. However, it is unknown whether nonhuman primate species organize their affiliative relationships following the same pattern. We here show that the time chimpanzees devote to grooming other individuals is well described by the same model used for human relationships, supporting the existence of similar social signatures for both humans and chimpanzees. Furthermore, the relationship structure depends on group size as predicted by the model, the proportion of high-intensity connections being larger for smaller groups

A novel methodological approach for group classification during fission of a semi-free-ranging group of Japanese macaques (Macaca fuscata)

The self-initiated split of a social group, known as fission, is a challenge faced by many group-living animals. The study of group fission and the social restructuring process in real time provides insights into the mechanism of this biologically important process. Previous studies on fission in Japanese macaques (Macaca fuscata) assigned individuals to newly reorganized groups mainly using behavioral observations and group attendance records based on periods before or after fission itself. Here, we present a novel framework for group classification during the process of fission that uses quantifiable behavioral variables and statistical analyses. The framework was tested on a group fission process at Affenberg Landskron (Austria), a park that housed around 160 semi-free-ranging Japanese macaques. The behavioral data were collected for 26 days during fission. We analyzed three behavioral developments recurrent in fissions in Japanese macaques, that is, independence of behavior, participation in group movements, and separation of nomadic ranges. These analyses were combined to assign individuals to different groups. Our study resulted in one main group (N = 33), one subgroup (N = 36) and 56 individuals whose group membership was still undefined. The demographic characteristics of these newly formed groups were comparable with those of fissioned groups in wild populations. Furthermore, we found that these newly forming groups showed early social dynamics of fission five months before group level movements, that is: grouping based on spatial proximity and spatial withdrawal of the subgroup to the periphery. These results underline the validity of our novel framework to study social dynamics in Japanese macaques during the process of fission. It represents an important addition to existing methods, and we recommend testing its scope in other primate societies

Glucocorticoids in relation to behavior, morphology, and physiology as proxy indicators for the assessment of animal welfare. A systematic mapping review

Translating theoretical concepts of animal welfare into quantitative assessment protocols is an ongoing challenge. Glucocorticoids (GCs) are frequently used as physiological measure in welfare assessment. The interpretation of levels of GCs and especially their relation to welfare, however, is not as straightforward, questioning the informative power of GCs. The aim of this systematic mapping review was therefore to provide an overview of the relevant literature to identify global patterns in studies using GCs as proxy for the assessment of welfare of vertebrate species. Following a systematic protocol and a-priory inclusion criteria, 509 studies with 517 experiments were selected for data extraction. The outcome of the experiments was categorized based on whether the intervention significantly affected levels of GCs, and whether these effects were accompanied by changes in behavior, morphology and physiology. Additional information, such as animal species, type of intervention, experimental set up and sample type used for GC determination was extracted, as well. Given the broad scope and large variation in included experiments, meta-analyses were not performed, but outcomes are presented to encourage further, in-depth analyses of the data set. The interventions did not consistently lead to changes in GCs with respect to the original authors hypothesis. Changes in GCs were not consistently paralleled by changes in additional assessment parameter on behavior, morphology and physiology. The minority of experiment quantified GCs in less invasive sample matrices compared to blood. Interventions showed a large variability, and species such as fish were underrepresented, especially in the assessment of behavior. The inconclusive effects on GCs and additional assessment parameter urges for further validation of techniques and welfare proxies. Several conceptual and technical challenges need to be met to create standardized and robust welfare assessment protocols and to determine the role of GCs herein

Towards an animal-centred ethics for Animal–Computer Interaction

The emerging discipline of Animal–Computer Interaction (ACI) aims to take what in Interaction Design is known as a user-centred approach to the design of technology intended for animals, placing them at the centre of the design process as stakeholders, users, and contributors. However, current regulatory frameworks for the involvement of animals in research are not animal-centred, regarding them as research instruments, unable to consent to procedures that may harm them, rather than consenting research participants and design contributors. Such frameworks aim to minimise the impacts of research procedures on the welfare of individual animals, but this minimisation is subordinated to specific scientific and societal interests, and to the integrity of the procedures required to serve those interests. From this standpoint, the universally advocated principles of replacement, reduction and refinement aim to address the ethical conflicts arising from the assumed inability of individual animals to consent to potentially harmful procedures, but such principles in fact reflect a lack of individual centrality. This paper makes the case for moving beyond existing regulations and guidelines towards an animal-centred framework that can better support the development of ACI as a discipline. Firstly, recognising animal welfare as a fundamental requirement for users and research participants alike, the paper articulates the implications of a welfare-centred ethics framework. Secondly, recognising consent as an essential requirement of participation, the paper also defines criteria for obtaining animals׳ mediated and contingent consent to engaging with research procedures. Further, the paper argues for the methodological necessity, as well as the ethical desirability, of such an animal-centred framework, examining the boundaries of its applicability as well as the benefits of its application. Finally, the paper puts forward a series of practical principles for conducting ACI research, which imply but also essentially exceed the welfare and ethics requirements of current regulatory frameworks

Robust ecological analysis of camera trap data labelled by a machine learning model

1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. 2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

African penguins follow the gaze direction of conspecifics

This work was supported by a British Academy/Leverhulme Small Research Grant to C.N. and L.F. (SG160975) and by a fellowship from the Deutsche Forschungsgemeinschaft (NA 1233/1-1) to C.N. and L.F. was also supported by the University of Torino through a System S.p.A. research grant for bioacoustics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

Human head orientation and eye visibility as indicators of attention for goats (Capra hircus)

This work was supported by grants from the Deutsche Forschungsgemeinschaft (NA 1233/1-1) to C. Nawroth, and a grant from the Farm Sanctuary ‘The Someone Project’ to A.G. McElligott and C. Nawroth