Extension de l'aire de distribution du mangabey agile (Cercocebus agilis) et du mandrill (Mandrillus sphinx) dans l'est du Gabon, documentée grâce aux pièges photographiques.

peer reviewe

Miscellanea Herpetologica Gabonica V & VI

We report the first observations of the orange morph and new locality records for Atherissquamigera (Viperidae) in Gabon, and new Gabonese locality records, ecological data orunpublished museum material for Pelusios castaneus and P. chapini (Pelomedusidae),Kinixys erosa (Testudinidae), Trionyx triunguis (Trionychidae), Crocodylus niloticus,Mecistops cataphractus and Osteolaemus tetraspis (Crocodylidae), Agama agama and A.lebretoni (Agamidae), Chamaeleo dilepis, C. oweni and Rhampholeon spectrum(Chamaeleonidae), Hemidactylus echinus and H. mabouia (Gekkonidae), Gerrhosaurusnigrolineatus (Gerrhosauridae), Trachylepis maculilabris and T. p. polytropis (Scincidae),Varanus ornatus (Varanidae), Crotaphopeltis hotamboeia, Dipsadoboa underwoodi,Hapsidophrys smaragdinus, Philothamnus carinatus and P. heterodermus, Rhamnophisaethiopissa, Thrasops flavigularis (Colubridae), Pseudohaje goldii (Elapidae), Aparallactusmodestus, Atractaspis boulengeri, Buhoma depressiceps, Hormonotus modestus,Psammophis cf. phillipsii (Lamprophiidae), Python sebae (Pythonidae), Indotyphlopsbraminus (Typhlopidae), Bitis nasicornis and Causus lichtensteinii (Viperidae). We add onespecies each to Estuaire, Haut-Ogooué and Ogooué-Ivindo provinces’ reptile lists. Twosnake species are added to Ivindo National Park, bringing the total number of reptile speciesrecorded from the park to 64, i.e., half of the species currently recorded from Gabon. Wedocument predation cases of Pycnonotus barbatus (Aves: Pycnonotidae) on Hemidactylusmabouia, Philothamnus heterodermus on Arthroleptis variabilis (Amphibia: Arthroleptidae),Hormonotus modestus on Hemidactylus mabouia, Psammophis cf. phillipsii onGerrhosaurus nigrolineatus, Causus lichtensteinii on Sclerophrys sp. (Amphibia:Bufonidae) and feeding of Varanus ornatus on spaghetti

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. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to 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. All reference materials required to build the system are provided in open-source repositories. Results show the system can operate for a minimum of 3 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 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network. We anticipate significant developments in this field 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 including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

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.We modified an off‐the‐shelf camera trap (Bushnell™) and customised existing open‐source hardware to 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. All reference materials required to build the system are provided in open‐source repositories.Results show the system can operate for a minimum of 3 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 min, though some outliers showed delays of 5‐days or more when the system was incorrectly positioned and unable to connect to the Iridium network.We anticipate significant developments in this field 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 including real‐time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

Strengthening and stretching for rheumatoid arthritis of the hand (SARAH):Design of a randomised controlled trial of a hand and upper limb exercise intervention-ISRCTN89936343

Background: Rheumatoid Arthritis (RA) commonly affects the hands and wrists with inflammation, deformity, pain, weakness and restricted mobility leading to reduced function. The effectiveness of exercise for RA hands is uncertain, although evidence from small scale studies is promising. The Strengthening And Stretching for Rheumatoid Arthritis of the Hand (SARAH) trial is a pragmatic, multi-centre randomised controlled trial evaluating the clinical and cost effectiveness of adding an optimised exercise programme for hands and upper limbs to best practice usual care for patients with RA.Methods/design: 480 participants with problematic RA hands will be recruited through 17 NHS trusts. Treatments will be provided by physiotherapists and occupational therapists. Participants will be individually randomised to receive either best practice usual care (joint protection advice, general exercise advice, functional splinting and assistive devices) or best practice usual care supplemented with an individualised exercise programme of strengthening and stretching exercises. The study assessors will be blinded to treatment allocation and will follow participants up at four and 12 months. The primary outcome measure is the Hand function subscale of the Michigan Hand Outcome Questionnaire, and secondary outcomes include hand and wrist impairment measures, quality of life, and resource use. Economic and qualitative studies will also be carried out in parallel.Discussion: This paper describes the design and development of a trial protocol of a complex intervention study based in therapy out-patient departments. The findings will provide evidence to support or refute the use of an optimised exercise programme for RA of the hand in addition to best practice usual care.Trial registration: Current Controlled Trials ISRCTN89936343Keywords: Randomised controlled trial, Rheumatoid arthritis, Exercise, Hand, Rehabilitatio

Pangolins in global camera trap data: Implications for ecological monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (<0.05) for all species. Occupancy was associated with distance from rivers for M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Pangolins in Global Camera Trap Data: Implications for Ecological Monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

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

The British Army, information management and the First World War revolution in military affairs

Information Management (IM) – the systematic ordering, processing and channelling of information within organisations – forms a critical component of modern military command and control systems. As a subject of scholarly enquiry, however, the history of military IM has been relatively poorly served. Employing new and under-utilised archival sources, this article takes the British Expeditionary Force (BEF) of the First World War as its case study and assesses the extent to which its IM system contributed to the emergence of the modern battlefield in 1918. It argues that the demands of fighting a modern war resulted in a general, but not universal, improvement in the BEF’s IM techniques, which in turn laid the groundwork, albeit in embryonic form, for the IM systems of modern armies. KEY WORDS: British Army, Information Management, First World War, Revolution in Military Affairs, Adaptatio

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