| 1. |
- Cromsigt, Joris, et al.
(författare)
-
Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data
- 2023
-
Ingår i: Remote sensing in ecology and conservation. - 2056-3485. ; 10, s. 283-295
-
Tidskriftsartikel (refereegranskat)abstract
- Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, "Camera Trap Metadata Standard" (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.We present a new data exchange format for camera trap data, the Camera Trap Data Package (Camtrap DP; ), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP is being developed under the umbrella of the Biodiversity Information Standards (TDWG), and through outreach and collaboration, it is now supported by GBIF. Importantly, Camtrap DP is the consensus of a long, in depth consultation process among the main existing camera trap data management platforms, as well as some of the major global players in the field of camera trapping. As an open, evolving standard for the FAIR exchange and archive of camera trap data, Camtrap DP represents an important step towards a global data sharing workflow with rapid results and thus more timely science based wildlife management recommendations.image
|
|
| 2. |
- Hentati-Sundberg, Jonas, et al.
(författare)
-
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
- 2023
-
Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:4, s. 568-581
-
Tidskriftsartikel (refereegranskat)abstract
- Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high-resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame-by-frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high-resolution up-to-date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up-to-date support for conservation and ecosystem management.
|
|
| 3. |
- Laporte-Devylder, Lucie, et al.
(författare)
-
A camera trap-based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore
- 2023
-
Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:2, s. 210-221
-
Tidskriftsartikel (refereegranskat)abstract
- For many species, the ability to rapidly adapt to changes in seasonality is essential for long-term survival. In the Arctic, seasonal moulting is a key life-history event that provides year-round camouflage and thermal protection. However, increased climatic variability of seasonal events can lead to phenological mismatch. In this study, we investigated whether winter-white (white morph) and winter-brown (blue morph) Arctic foxes (Vulpes lagopus) could adjust their winter-to-summer moult to match local environmental conditions. We used camera trap images spanning an eight-year period to quantify the timing and rate of fur change in a polymorphic subpopulation in south-central Norway. Seasonal snow cover duration and temperature governed the phenology of the spring moult. We observed a later onset and longer moulting duration with decreasing temperature and longer snow season. Additionally, white foxes moulted earlier than blue in years with shorter periods of snow cover and warmer temperatures. These results suggest that phenotypic plasticity allows Arctic foxes to modulate the timing and rate of their spring moult as snow conditions and temperatures fluctuate. With the Arctic warming at an unprecedented rate, understanding the capacity of polar species to physiologically adapt to a changing environment is urgently needed in order to develop adaptive conservation efforts. Moreover, we provide the first evidence for variations in the moulting phenology of blue and white Arctic foxes. Our study underlines the different intraspecific selective pressures that can exist in populations where several morphs co-occur, and illustrates the importance of integrating morph-based differences in future management strategies of such polymorphic species.
|
|
