| 1. |
- Axenrot, Thomas
(författare)
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Including 38 kHz in the standardization protocol for hydroacoustic fish surveys in temperate lakes
- 2019
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Ingår i: Remote sensing in ecology and conservation. - : Wiley. - 2056-3485. ; 5, s. 332-345
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Tidskriftsartikel (refereegranskat)abstract
- Hydroacoustics has become a requisite method to assess fish populations and allows to describe the relationships of fish with other elements of the aquatic ecosystem. This nonintrusive method is currently an integral part of the sampling procedures recommended for fish stock assessment by the Water Framework Directive and has been standardized by the European Committee for Standardization [CEN (2014) CSN EN 15910 - Water quality - Guidance on the estimation of fish abundance with mobile hydroacoustic methods, Category: 7577 Water quality. Biological.]. In Europe, hydroacoustic surveys are performed in freshwater using different frequencies. Consequently, there is a need to evaluate if survey results can be compared. This study aimed to carry out in situ comparisons at the 38 kHz frequency (noted f) with two other commonly used frequencies, 70 and 200 kHz. The 38 kHz frequency has seldom been compared with other frequencies in freshwater although it is widely used worldwide, especially in the Great Lakes of North America and in Sweden. In 2016, hydroacoustic data were acquired in Lakes Annecy and Bourget using methods validated in previous studies that compared the frequencies 70, 120 and 200 kHz. This study showed similar density and biomass estimations as a function of frequency, density(f) and biomass(f), between the frequencies studied for low to moderate fish densities. For higher fish densities, the results were more variable and need to be verified. Fish density(f) and biomass(f) estimations sometimes exhibit differences between frequencies, which is not fully in agreement with theoretical calculations. The aim of this study was to evaluate frequency comparisons in practise. However, if the differences on acoustic metrics, density(f) or biomass(f) between frequencies were occasionally statistically significant, the differences were small enough to be considered negligible for fish population management. These analyses led to better knowledge of the responses from fish in temperate lakes for the studied frequencies. Our findings should be considered when revising the CEN standard.
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| 2. |
- Cromsigt, Joris, et al.
(författare)
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Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data
- 2023
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Ingår i: Remote sensing in ecology and conservation. - 2056-3485. ; 10, s. 283-295
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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
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| 3. |
- Erlandsson, Rasmus, et al.
(författare)
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An innovative use of orthophotos - possibilities to assess plant productivity from colour infrared aerial orthophotos
- 2019
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 5:4, s. 291-301
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Tidskriftsartikel (refereegranskat)abstract
- Studies of ecological processes should focus on a relevant spatial scale, as crude spatial resolution will fail to detect small scale variation which is of potentially critical importance. Remote sensing methods based on multispectral satellite images are used to assess primary productivity and aerial photos to map vegetation structure. Both methods are based on the principle that photosynthetically active vegetation has a characteristic spectral signature. Yet they are applied differently due to technical differences. Satellite images are suitable for calculations of vegetation indices, for example Normalized Difference Vegetation Index (NDVI). Colour infrared aerial photography was developed for visual interpretation and never regarded for calculation of indices since the spectrum recorded and post processing differ from satellite images. With digital cameras and improved techniques for generating colour infrared orthophotos, the implications of these differences are uncertain and should be explored. We tested if plant productivity can be assessed using colour infrared aerial orthophotos (0.5 m resolution) by applying the standard NDVI equation. With 112 vegetation samples as ground truth, we evaluated an index that we denote rel‐NDVIortho in two areas of the Fennoscandian mountain tundra. We compared the results with conventional SPOT5 satellite‐based NDVI (10 m resolution). rel‐NDVIortho was related to plant productivity (Northern area: P = <0.001, R2 = 0.73; Southern area: P = <0.001, R2 = 0.39), performed similar to SPOT5 satellite NDVI (Northern area: P = <0.001, R2 = 0.76; Southern area: P = <0.001, R2 = 0.40) and the two methods were highly correlated (cor = 0.95 and cor = 0.84). Despite different plant composition, the results were consistent between areas. Our results suggest that vegetation indices based on colour infrared aerial orthophotos can be a valuable tool in the remote sensing toolbox, offering a high‐spatial resolution proxy for plant productivity with less signal degradation due to atmospheric interference and clouds, compared to satellite images. Further research should aim to investigate if the method is applicable to other ecosystems.
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| 4. |
- Hentati-Sundberg, Jonas, et al.
(författare)
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Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
- 2023
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:4, s. 568-581
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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.
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| 5. |
- Hofmeester, Tim
(författare)
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Random encounter model is a reliable method for estimating population density of multiple species using camera traps
- 2022
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Ingår i: Remote sensing in ecology and conservation. - : Wiley. - 2056-3485. ; 8, s. 670-682
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Tidskriftsartikel (refereegranskat)abstract
- Population density estimates are important for wildlife conservation and management. Several camera trapping-based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for species-specific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REM-reference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation-based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals.
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| 6. |
- Laporte-Devylder, Lucie, et al.
(författare)
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A camera trap-based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore
- 2023
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:2, s. 210-221
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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.
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| 7. |
- Monsimet, Jérémy, et al.
(författare)
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UAV data and deep learning : efficient tools to map ant mounds and their ecological impact
- 2024
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Ingår i: Remote Sensing in Ecology and Conservation. - : John Wiley & Sons. - 2056-3485.
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Tidskriftsartikel (refereegranskat)abstract
- High-resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient-limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi-sensor data and deep learning for efficient ecological tracking and monitoring of mound-building ants and their spatial impact.
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| 8. |
- Pfeffer, Sabine, et al.
(författare)
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Pictures or pellets? Comparing camera trapping and dung counts as methods for estimating population densities of ungulates
- 2018
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Ingår i: Remote sensing in ecology and conservation. - : Wiley. - 2056-3485. ; 4, s. 173-183
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Tidskriftsartikel (refereegranskat)abstract
- Across the northern hemisphere, land use changes and, possibly, warmer win- ters are leading to more abundant and diverse ungulate communities causing increased socioeconomic and ecological consequences. Reliable population esti- mates are crucial for sustainable management, but it is currently unclear which monitoring method is most suitable to track changes in multi-species assem- blages. We compared dung counts and camera trapping as two non-invasive census methods to estimate population densities of moose Alces alces and roe deer Capreolus capreolus in Northern Sweden. For camera trapping, we tested the random encounter model (REM) which can estimate densities without the need to recognize individual animals. We evaluated different simplification options of the REM in terms of estimates of detection distance and angle (raw data vs. modelled) and of daily movement rate (camera trap based vs. telemetry based). In comparison to density estimates from camera traps, we found that, dung counts appeared to underestimate population density for roe deer, but not for moose. Estimates of detection distance and angle from modelled versus raw camera data resulted in nearly identical outcomes. The telemetry-derived daily movement rate for moose and roe deer resulted in much higher density estimates than the camera trap-derived estimates. We suggest that camera trap- ping may be a robust complement to dung counts when monitoring ungulate communities, particularly when similarities between dung pellets from sympatric deer species make unambiguous assignment difficult. Moreover, we show that a simplified use of the REM method holds great potential for large-scale citizen science-based programmes (e.g. involving hunters) that can track the rapidly changing European wildlife landscape. We suggest to include camera trapping in management programmes, where the analysis can be verified via web-based applications.
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| 9. |
- Scotson, Lorraine, et al.
(författare)
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Best practices and software for the management and sharing of camera trap data for small and large scales studies
- 2017
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 3:3, s. 158-172
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Forskningsöversikt (refereegranskat)abstract
- Camera traps typically generate large amounts of bycatch data of non-target species that are secondary to the study's objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi-collaborator projects that use standardized methods to answer broad-scale research questions are rare and limited in geographical scope. Many small, fixed-term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range-wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research.
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