Search

Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway.

The phenotypic information of soybean has been one of the most important indicators for the variety selection of soybean. Most research has been focused on soybean pods for phenotypic traits at present. However, the phenotypic information of soybean plants can also be a very important indicator for soybean seed breeding. Furthermore, the current manual measurement cannot fully meet the large-scale production in recent years, due to the time and labor-consuming. More recently, computer technologies have been started to automatically acquire the soybean phenotypic parameters. For instance, some shallow machine learning and image description were used for the image feature extraction and target detection, such as the Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradient (HOG), and Support Vector Machine (SVM). Although the automatic acquisition of phenotypic data was realized to a certain extent, the high generalization ability can be a high demand suitable for practical application scenarios. In this study, an improved model was proposed to rapidly and accurately acquire the soybean plant phenotype using Re-YOLOv5 and area search algorithm. The circular smooth labels were also introduced in the Re-YOLOv5 to process the angle information for the detection of the rotating objects. The angle regression was then converted into a simple classification. As such, the rotating objects were accurately detected to reduce the redundant information in the detection area during object detection. In addition, the Coordinate attention mechanism was added to the Neck part of YOLOv5 (CSL). The cross-channel information was then obtained to capture the position and orientation information. As such, the improved network was used to more accurately locate and recognize the soybean branching than before. The weights greatly contributed to the YOLOv5 on the processing of feature-related parts. More importantly, the original 3×3 convolution kernels were placed in the backbone with a RepVGG block structure. The feature information was then fused to extract using different convolution modules. The information extraction of the overall structure was enhanced for the parallel fusion of the multiple convolution layers while reducing the number of model parameters. Taking the detected branch as the search area, an area search algorithm was also proposed to input into the algorithm, in order to extract the relevant information of the stem nodes in the area, and then connect the nodes in sequence. Thus, the soybean skeleton was reconstructed to obtain phenotypic information about soybean. The experimental results showed that the improved Re-YOLOv5 performed better to detect the rotating objects in various phenotypic indicators, compared with the YOLOv5. Specifically, the mAP of the improved Re-YOLOv5 increased by 1.70 percentage points, the number of parameters decreased by 0.17 M, and the detection accuracy of stem nodes was improved by 9.90 percentage points. An excellent ability was also achieved to detect the small targets suitable for the acquisition of the soybean plant phenotype information. Among them, the average absolute errors of plant height, and the number of stem nodes and branches were 2.06 cm, 1.37, and 0.03, respectively, fully meeting the accuracy requirements of actual collection. At the same time, the detection area search algorithm can also be expected to accurately locate the stem nodes on each branch for the single-, double-, and complex-branched soybean plants, and then reconstruct an accurate soybean skeleton. Anyway, the improved model can also be used to accurately and efficiently acquire the phenotypic information of soybean branch angle, and soybean plant type. The finding can provide a strong reference for the subsequent acquisition of phenotypic information during soybean production. [ABSTRACT FROM AUTHOR]

For organisms tracking a chemical cue to its source, the motion of their surrounding fluid provides crucial information for success. Swimming and flying animals engaged in olfaction-driven search often start by turning into the direction of an oncoming wind or water current. However, it is unclear how organisms adjust their strategies when directional cues are absent or unreliable, as is often the case in nature. Here, we use the genetic toolkit of Drosophila melanogaster to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences for free-flying animals in either laminar wind or still air. We first confirm that in laminar wind flies turn upwind. Furthermore, we show that they achieve this using a rapid (∼100 ms) turn, implying that flies estimate the ambient wind direction prior to "surging" upwind. In still air, flies adopt a remarkably stereotyped "sink and circle" search state characterized by ∼60° turns at 3-4 Hz, biased in a consistent direction. Together, our results show that Drosophila melanogaster assesses the presence and direction of ambient wind prior to deploying a distinct search strategy. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight. We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm that is able to consider sensor characteristics, the uncertainty of target detection, and complexity of needed maneuvers in order to make a multiagent search autonomous. This article provides a methodology for an autonomous 2-D search using multiple unmanned (aerial or possibly other) vehicles. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via a heat equation-driven area coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents that are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides a near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve the proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of an HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

The recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. Some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. This report presents new likelihood-based spatially explicit capture-recapture (SECR) methods for such data. The methods are shown to be at least as robust in simulations as an equivalent Bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. It is recommended on the basis of simulation that plots for SECR should be at least as large as the home range of the target species. The R package "seer" may be used to fit the models. The likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. It is also shown how the method may be adapted for detections along a linear transect. [ABSTRACT FROM AUTHOR]

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture-recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Field Ornithology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)