Obtain Location Information Research Articles

YOLO-MPAM: Efficient real-time neural networks based on multi-channel feature fusion

Object detection is a crucial foundation in the field of autonomous driving. Therefore, accurate and real-time detection of road objects, including vehicles and pedestrians, is essential for the success of autonomous vehicles. Convolutional neural networks represented by YOLOv8 have made significant progress in autonomous driving. However, there are also various problems, such as false recognition, missed detection, and susceptibility to complex weather. Aiming at the problem of low traffic sign detection effect in automatic driving scenes, an improved YOLOv8 automatic driving target detection algorithm is proposed. Firstly, Mosaic data augmentation and Multi-Path Attention Mechanism (MPAM) are introduced based on the YOLOv8 object detection model. The improved model can obtain location information from the feature map in the early stages of recognition, enabling the model to focus more on the regions of interest. Secondly, because the model pays different attention to global and local features, to make the model pay more attention to fine-grained small targets, so a complex bidirectional fusion structure is proposed in the neural network. Finally, we comprehensively consider the ratio of predicted boxes to ground-truth. The CIoU-tune loss function replaces the original IoU loss function, minimizing the height and width discrepancies between predicted and actual bounding boxes. This improvement accelerates the convergence speed of the model, achieving better localization results. After undergoing ablation analysis, the YOLO-MPAM method is compared with other methods on dataset TT100K. The mAP50 value of the improved algorithm is 86.1%, showing a 9.6% improvement compared to YOLOv8n. Additionally, the model achieves higher accuracy in detecting small objects, effectively enhancing the detection performance of the model at the same inference speed.

Graph-topology-learning-based IoT positioning under incomplete measurement data

Obtaining location information of the multi-sensor internet of things (IoT) is a fundamental requirement. But, two kinds of the incomplete measurement data enhance the difficulty of positioning: 1) The location information is partially missing. 2) The errors of the measurement. Furthermore, the complex environmental impact and the characters of the measurement methods make the error model more changeable at the same time. This paper tries to study a universal method to address these problems, through combining IoT positioning and affinity graph assessment with arbitrary model of the measurement errors or the missing measurement data. First of all, the relationship between the graph signal and the combinatorial graph Laplacian matrix is constructed to link the graph topology learning and the IoT localization problem. Later, the Gaussian mixture model is applied to describe the measurement errors as a general model. Then, the calculations of the graph signal, Laplacian matrix and hyper parameters are obtained via variational Bayesian inference and message passing. The numerical results show the superiority of the proposed algorithm.

Comparison of classical-RTK and network-RTK surveying methods in coastal regions

Since the beginning of human life, human beings have wanted to know their location, and have therefore tried to find out where they are and where they are going throughout their lives. Researches and inventions that have shaped this situation have followed one another. Throughout history, the methods used to obtain location information have improved day by day, and the possibilities of obtaining precise location information faster with more ergonomic devices have emerged. Recently, the Real Time Kinematics have attracted attention in this respect. In this study, "Comparison of Classical-RTK and Network-RTK surveying methods in Coastal Regions" was conducted in Coastal Regions. For this purpose, 12 point locations were determined by creating two separate study areas in two different regions with an approximate surface area of 3.078 km2 and 1.346 km2 in the coastal region. When a base was created between TUSAGA-Active points namely, N4, N5, N6, IN1, IN2, IN3 were located in the inside and N1, N2, N3, DN1, DN2, DN3 points were located in the outside part of the network structure. Static, Classical-RTK and Network-RTK measurement methods were used at 12 points in the study areas in both regions. First of all, static measurements were made at 12 points were taken into account as a reference values for the comparison of the measurements from both Classical-RTK and Network-RTK methods. The differences of the static measurement values and the measurement values obtained by Classical-RTK and Network-RTK measurement methods were taken respectively. These measurement differences were analyzed and compared in various aspects.

3D Position Estimation of Objects for Inventory Management Automation Using Drones

With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

Opportunistic navigation applied to integration of alternative positioning and electronic mapping based on spline technology

The problem of vulnerability and local incapacity of global satellite navigation systems is formulated. The expediency of alternative positioning methods to create a precedent for high-precision location determination in the satellitedenied environment is revealed. The hypothesis about the impossibility of a separate alternative to meet the requirements of full autonomy of modern navigation when it is necessary to comprehensively solve geolocation scenarios based on the combinatorics of various technologies is put forward. An assumption about the probable advantage of combining data from several information sources to compensate for the disadvantages of each option when predicting an increase in the overall reliability and accuracy of navigation parameters is made. An opportunistic navigation paradigm with the objective function of obtaining location information from mixing additional radio frequency signals with simultaneous visualization of a spline positioning map to determine the location inside a graphic fragment is proposed. The confidence that the approach from the standpoint of spline functions expands the horizon of possibilities of the concept of opportunistic navigation from the purely technical synergetics of the landscape of «signals of opportunity» to the creation of a perspective positioning system using effective electronic mapping is expressed. It is determined that the positioning accuracy of navigation systems is practically necessary to build an optimal trajectory for the safe routing of the vessel. Attention is focused on understanding the optimality of the trajectory as a multi-way planning strategy within the framework of the «receding horizon» paradigm with the obligatory condition that a waypoint with a user-defined probability is achievable by a moving object. It is noted that the modeling of optimal trajectories is implemented by a method similar to the technology of interpolation of navigation contours based on the composition of a B-spline with the provision of an attribute parameter to maintain the same value at each point as a result of the analysis of the spatial anisotropy of the source data. The synthesis of threedimensional spreading of magnetic map anomalies has been tested to create a mapping standard based on a hybrid approximation algorithm as an implementation of a method for computer rendering a geometric primitive of a function of two variables. The postulate of increasing the potential of high-precision localization of the position of any mobile object in space is interpreted, provided that the ontology of opportunistic navigation is applied in practice as the realization of situational awareness of interdependent technical systems in order to maximize the reliability of observation.

Bayesian detection and tracking of odontocetes in 3-D from their echolocation clicks.

Localization and tracking of marine animals can reveal key insights into their behaviors underwater that would otherwise remain unexplored. A promising nonintrusive approach to obtaining location information of marine animals is to process their bioacoustic signals, which are passively recorded using multiple hydrophones. In this paper, a data processing chain that automatically detects and tracks multiple odontocetes (toothed whales) in three dimensions (3-D) from their echolocation clicks recorded with volumetric hydrophone arrays is proposed. First, the time-difference-of-arrival (TDOA) measurements are extracted with a generalized cross-correlation that whitens the received acoustic signals based on the instrument noise statistics. Subsequently, odontocetes are tracked in the TDOA domain using a graph-based multi-target tracking (MTT) method to reject false TDOA measurements and close gaps of missed detections. The resulting TDOA estimates are then used by another graph-based MTT stage that estimates odontocete tracks in 3-D. The tracking capability of the proposed data processing chain is demonstrated on real acoustic data provided by two volumetric hydrophone arrays that recorded echolocation clicks from Cuvier's beaked whales (Ziphius cavirostris). Simulation results show that the presented MTT method using 3-D can outperform an existing approach that relies on manual annotation.

Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP

To address the problems of poor recognition effect, low detection accuracy, many model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study proposes a lightweight tennis ball detection algorithm, YOLOv5s-Z, based on the YOLOv5s algorithm and Robomater EP. The main work is as follows: firstly, the lightweight network G-Backbone and G-Neck network layers are constructed to reduce the number of parameters and computation of the network structure. Secondly, convolutional coordinate attention is incorporated into the G-Backbone to embed location information into channel attention, which enables the network to obtain location information of a larger area through multiple convolutions and enhances the expression ability of mobile network learning features. In addition, the Concat module in the original feature fusion is modified into a weighted bi-directional feature pyramid W-BiFPN with settable learning weights to improve the feature fusion capability and achieve efficient weighted feature fusion and bi-directional cross-scale connectivity. Finally, the Loss function EIOU Loss is introduced to split the influence factor of the aspect ratio and calculate the length and width of the target frame and anchor frame, respectively, combined with Focal-EIOU Loss to solve the problem of imbalance between complex and easy samples. Meta-ACON’s activation function is introduced to achieve an adaptive selection of whether to activate neurons and improve the detection accuracy. The experimental results show that compared with the YOLOv5s algorithm, the YOLOv5s-Z algorithm reduces the number of parameters and computation by 42% and 44%, respectively, reduces the model size by 39%, and improves the mean accuracy by 2%, verifying the effectiveness of the improved algorithm and the lightweight of the model, adapting to Robomaster EP, and meeting the deployment requirements of embedded devices for the detection and identification of tennis balls.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

Calculating the target location based on sliding fiducials

The use of trilateration and triangulation methods to obtain location information is cumbersome and error-prone. Aiming to solve these problems, this paper designs a method of calculating target location, establishes an error model and improves the accuracy of the location calculation. It uses a laser to provide a linear relationship, sliding fiducials provide basic data, calculates the location of the target point. Experimental results show that within the measurement range of 10 meters, the average error of solving the east and west coordinates 30 times is 4.7 cm.

Visualization of Dust Generation in Outdoor Workplaces Using A Wearable Particle Monitor and Global Navigation Satellite System.

We manufactured a wearable particle monitor (WPM), which is a simple and low-cost dust monitor. We aimed to evaluate the usefulness of the device by using it and location information of a Global Navigation Satellite System (GNSS) to measure dust generation in outdoor workplaces. We used nine WPMs and a particle counter KC-52 to measure in parallel the dust concentration diffusing standard particles in a dust exposure apparatus to evaluate the measurability of the WPM, and visualized dust generation in outdoor workplaces to evaluate its usability. We obtained location information using a GNSS in parallel with measuring with the WPM. The measured values of the WPM followed the measured values of the KC-52, with a strong correlation of the values between the KC-52 and each WPM. The discrepancy among devices tended to increase, however, because the measured values of the WPMs increased. For outdoor measurements, we could create a heat map of the relative values of dust generation by combining two data of the WPM and the GNSS. The methods of using the WPM could overview the conditions needed to produce dust emissions in dust-generating workplaces.

Location-Based Hybrid Precoding Schemes and QoS-Aware Power Allocation for Radar-Aided UAV–UGV Cooperative Systems

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) complement each other, promising a variety of benefits for unmanned cooperative systems. When infrastructures for communication are unavailable, such as disaster relief and patrol mission in sparsely populated areas, UGVs can be controlled to go to the esignated areas with the aid of a UAV. Specifically, the UAV hovers over the UGVs, sending commands to the UGVs and obtaining location information from the UGVs. In this paper, we consider integrating millimeter wave communication and UAV–UGV cooperative system. For high mobile unmanned cooperation systems, we develop two joint radar-aided hybrid precoding schemes, which are suitable for a fully connected (FC) hybrid precoding structure and partially connected (PC) hybrid precoding, respectively. Specifically, an effective hybrid precoding scheme is first proposed by using location information to jointly design hybrid precoding and power allocation. Moreover, we also derive the radar parameter estimation problem Cramér-Rao lower bound (CRLB). In order to ensure the minimum quality-of-service (QoS) of UGVs, we propose a QoS-aware power allocation scheme based on the difference-of-two-convex-function (D.C.) programming. Finally, a heuristic hybrid precoding design algorithm, where the analog precoding matrix is usually implemented by low-resolution phase shifters (PSs), is proposed based on antenna dynamic grouping and radar parameter acquisition, with the aim of maximizing the sum-rate of the UAV–UGV cooperative systems. Extensive computer simulation results indicated the validity of the proposed radar-aided location-based hybrid precoding schemes in the UAV–UGV heterogeneous cooperative system.

Joint Beam Training and Positioning for Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications

Intelligent reflecting surface (IRS) offers a cost-effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training designs for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to estimate angle of arrival (AoA) and angle of departure (AoD) of the line of sight (LoS) path between BS/AP (or IRSs) and MT. Then, with the estimated AoDs, we propose an iterative positioning algorithm that achieves centimeter-level positioning accuracy. The obtained location information is not only a fringe benefit but also enables us to cross verify and enhance the estimation of AoA and AoD, and it also facilitates the estimation of blockage indicator. Numerical results show the superiority of our proposed beam training scheme and verify the performance gain brought by location information.

Innovation of Business Model to Expand New Revenue Stream and Exploring Willingness to Pay in Café, Restaurant, and Business Sector to Waste Logistic Service in Urban Area: Study Case of NYAMPIH

The purpose of this study is to find out solutions and implementation of business model systems to get new customers when NYAMPIH starts to scale-up and market expansion to urban areas such as coffee shops, restaurants, and also other sector businesses in the city of Bandung. NYAMPIH is a smart application and educational platform that connects waste officers with their consumers. In this case, the transported waste has been categorized properly according to the facilities provided. After that, consumers can collect points given by officers when picking up their garbage and can be exchanged for prizes. Consumers only need to subscribe, so all organic and inorganic waste will not end up in the TPA/TPS, because we will process it back into a new value. NYAMPIH faces an obstacle, how to change one's habits to care about their own waste. This study will use a qualitative approach by conducting observations, observations, and also interviews with a total of 30 respondents who have the position of manager and also the location cleaning division according to the market segment appointed by the researcher. The primary data used is the history of NYAMPIH operations with financial data, and for secondary data from literature reviews, journals, and books. Previously, the NYAMPIH market segment was housewives who lived in housing, but there was still a lack of comprehensive implementation when viewed from NYAMPIH's income so far. Therefore, this research is devoted to analyzing new business models with sales scenarios for new market segments. The location observation is used to obtain location information and also the problems faced by the respondents directly. Interviews were conducted to obtain validation regarding the business model which will later become the new guidelines for NYAMPIH. The results of this study indicate that NYAMPIH must expand its market segment broadly and large according to the results of observations and interviews that when market expansion will be NYAMPIH will be more prepared and can also be implemented comprehensively through the results of business models with new revenue streams There is also to implement the location of the sector business that subscribes to NYAMPIH which will later become a zerowaste city area. After evaluation and scenario analysis, all divisional activities, including marketing, operations, finance, will be further improved for each activity that will be carried out in terms of developing NYAMPIH in the future.

Design of intelligent real-time safety supervision system for pension institutions based on LBS

Abstract With the gradual ageing of China's population and the single-child family system becoming the backbone of society, the traditional pension way of relying on children at home not only does not guarantee the quality of life of the elderly, but also brings great pressure to the work and life of young people. The way of relying on pension institutions to provide for the aged is becoming gradually accepted by society. In elderly care institutions, safety supervision systems provide an important link to protect the safety of the elderly, thereby ensuring accurate analysis and supervision of the elderly persons’ activity routes and activity rules, and thus allowing an early warning to be given in a dangerous situation. The system realises the real-time positioning and monitoring of the elderly activity track, and can give early warning and timely rescue to the elderly who enter into any dangerous area. The system includes a server monitoring program and mobile APP; ASP.NET Technology to develop the monitoring program; SQL Server 2012 for data storage; and model view controller (MVC) architecture for the APP to improve the efficiency of program development. The supervision program comprises the functions of personnel information management, fence management, positioning service, activity track record, danger warning and rapid rescue. The APP deploys the location-based service (LBS) technology of Android, uses Baidu map API to obtain location information and draw activity track, and stores the information in the mobile phone SQLite database. The APP is installed on mobile phones, bracelets and other devices carried by the elderly and staff, which can record the current position of the person in real-time and simultaneously transmit it to the computer. When the elderly enter into a dangerous area, the system can quickly select the nearest staff and inform them for rescue, so as to ensure the safety of the elderly. After simulation test, the system has now reached the preset function.

Artificial Intelligence based Intelligent Tourism System-Go My Way

At the moment, we find that tourists usually spend more time planning their trip because they need to spend every minute. In this context, this application aims to identify the main computer needs to support the improvement of the tourist promotion point, using the mobile application proposal. Currently, for regular tourists and travelers they spend a lot of time planning and deciding on their trip to achieve maximum satisfaction. In this case, the app aims to identify the main computer needs to support the development of the tourist promotion point. This paper suggests a model for use in an intelligent visitor information system. It uses the concept of knowledge-base. The model will be based on a study of human behavior as a tourism guide. It builds a relationship between an information-based system and a guide, to provide a service to any visitor who meets their needs and the purpose of obtaining location information. There are different modules, different methods of acquisition methods and a shorter way to acquire the ingenuity of the artificial intelligence in this thesis. The proposed system should be designed in such a way that it works on most devices namely palmtop and mobiles. So it can be helpful when visiting new places. This application will find the route using user terms. The short-term method of finding an algorithm should work well and in the right way in most cases. The system must find a method that fulfills the user's terms, indicating the name of the item, images related to a brief description of the location. It should also be able to find the distance, time and travel costs to your destination and over time the user can also make bookings using the app interface only.

Research on a Novel Locating Method for Track Inspection Based on Onboard Sensors in Maglev Train.

Guideway inspection is of great significance to the operation safety and riding quality of a commercial high-speed maglev transportation system. When analyzing guideway inspection data, it is important to obtain the location information for each piece of raw data and convert it from the time domain to the spatial domain for the analysis afterward. Previous studies have used the method of adding additional hardware such as GPS (global positioning system) receivers, LRF (location reference flag) readers, or onboard CAN (controller area network) bus adaptors to obtain location information. This paper presents a novel method for indirectly obtaining the location information via the use of data from the levitation and guidance control sensors perpendicular to the longitudinal direction to extract the characteristic information from the track. The method can be used for a long stator linear motor-driven maglev system and similar contactless rail transit systems. The results showed that the method could accurately identify the required location information in each stator tooth during the entire operation simultaneously with the operating information such as train velocity, direction, and track ID, without additional hardware installation and vehicle network connection. Thus, it could improve the pertinence of the results of guideway inspection equipment, and at the same time, facilitate the miniaturization and independence of guideway inspection equipment.

Automatic Positioning of Mobile Users via GSM Signal Measurements

Today the need for mobile communication systems and the high increase in the number of users have also made the development of new generation mobile applications indispensable. Obtaining location information has been one of the most interesting and significant areas of improvement. The purpose of the services used to determine the location is generally to obtain the information of the users such as approximate location, speed and time. The GPS system is the most preferred and globally accurate positioning system among global positioning systems. However, in addition to requiring a high installation cost of this system, it is one of the biggest constraints that galactic and meteorological factors, high buildings and other physical obstacles, and especially closed areas can lead to serious signal weaknesses and losses which may cause the system to be out of service. Considering these issues, it is seen that there is an urgent need for positioning systems that will be alternative and complementary to global positioning systems. The cellular network is widely used by almost everyone and its coverage area is increasing day by day. The employed data sets were created by recording the received signal strength (RSS), location information of the GSM base station and the user measured in indoor and outdoor areas through a mobile application we have developed in the Android Studio environment for mobile phones. The network has been trained by machine learning algorithms; extreme learning machine (ELM), generalized regression neural network (GRNN) and k nearest neighborhood (kNN). In the tests conducted with indoor, outdoor and combined data sets, it has been observed that the proposed positioning system works well with distance error rates below a meter (m) at the minimum, and between 76-216 m on average.

Environmental correlates of distribution across spatial scales in the intertidal gastropods Littoraria and Echinolittorina of the Indian coastline

ABSTRACT Marine habitats are a mosaic of environmental conditions, which can limit species distributions despite the high dispersal potential offered by ocean currents. Periwinkle snails from the genera Littoraria and Echinolittorina disperse using planktotrophic larvae and have wide ranges in the Indo-Pacific, but patchy local distributions along the Indian coastline. To understand the environmental drivers of distribution in these intertidal snails, we used species distribution models executed in Maxent at both coastline and ocean basin scales. We obtained location information from primary and secondary sources, and used environmental predictors related to species survival, growth and reproduction spanning terrestrial and marine realms. Explanatory environmental variables broadly corresponded between scales, with maximum sea surface salinity and tidal range being common factors across species differing in their dispersal potential and habitat specificity. The Indian coastline exhibits a steep salinity and tidal range gradient, which may influence adult or larval survival through desiccation stress. In contrast to global-scale studies, temperature and productivity rarely emerged as important at the Indian coastline scale, indicating that variation along other environmental axes can drive distribution patterns at smaller spatial scales and within tropical coastlines. The results from our study provide specific hypotheses related to species-environment relationships that can be tested using field experiments to understand the drivers of species range along the Indian coastline.