Dataset Information

A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies.

ABSTRACT: Infrequent, long-distance animal movements outside of typical home range areas provide useful insights into resource acquisition, gene flow, and disease transmission within the fields of conservation and wildlife management, yet understanding of these movements is still limited across taxa. To detect these extra-home range movements (EHRMs) in spatial relocation datasets, most previous studies compare relocation points against fixed spatial and temporal bounds, typified by seasonal home ranges (referred to here as the "Fixed-Period" method). However, utilizing home ranges modelled over fixed time periods to detect EHRMs within those periods likely results in many EHRMs going undocumented, particularly when an animal's space use changes within that period of time. To address this, we propose a novel, "Moving-Window" method of detecting EHRMs through an iterative process, comparing each day's relocation data to the preceding period of space use only. We compared the number and characteristics of EHRM detections by both the Moving-Window and Fixed-Period methods using GPS relocations from 33 white-tailed deer (Odocoileus virginianus) in Alabama, USA. The Moving-Window method detected 1.5 times as many EHRMs as the Fixed-Period method and identified 120 unique movements that were undetected by the Fixed-Period method, including some movements that extended nearly 5 km outside of home range boundaries. Additionally, we utilized our EHRM dataset to highlight and evaluate potential sources of variation in EHRM summary statistics stemming from differences in definition criteria among previous EHRM literature. We found that this spectrum of criteria identified between 15.6% and 100.0% of the EHRMs within our dataset. We conclude that variability in terminology and definition criteria previously used for EHRM detection hinders useful comparisons between studies. The Moving-Window approach to EHRM detection introduced here, along with proposed methodology guidelines for future EHRM studies, should allow researchers to better investigate and understand these behaviors across a variety of taxa.

SUBMITTER: Jacobsen TC

PROVIDER: S-EPMC7703910 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies.

Jacobsen Todd C TC Wiskirchen Kevyn H KH Ditchkoff Stephen S SS

PloS one 20201130 11

Infrequent, long-distance animal movements outside of typical home range areas provide useful insights into resource acquisition, gene flow, and disease transmission within the fields of conservation and wildlife management, yet understanding of these movements is still limited across taxa. To detect these extra-home range movements (EHRMs) in spatial relocation datasets, most previous studies compare relocation points against fixed spatial and temporal bounds, typified by seasonal home ranges ( ...[more]

PMID: 33253220

Similar Datasets

Project description:Purpose To determine the reliability of wrist range of motion (WROM) measurements based on digital photographs taken by patients at home compared with traditional measurements done in the office with a goniometer. Methods Sixty-nine postoperative patients were enrolled in this study at least 3 months postoperatively. Active and passive wrist flexion/extension and radial/ulnar deviation were recorded by one of the two attending surgeons with a 1-degree resolution goniometer at the last postoperative office visit. Patients were provided an illustrated instruction sheet detailing how to take digital photographic images at home in six wrist positions (active and passive flexion/extension, and radial/ulnar deviation). Wrist position was measured from digital images by both the attending surgeons in a randomized, blinded fashion on two separate occasions greater than 2 weeks apart using the same goniometer. Reliability analysis was performed using the intraclass correlation coefficient to assess agreement between clinical and photography-based goniometry, as well as intra- and interobserver agreement. Results Out of 69 enrolled patients, 30 (43%) patients sent digital images. Of the 180 digital photographs, only 9 (5%) were missing or deemed inadequate for WROM measurements. Agreement between clinical and photography-based measurements was "almost perfect" for passive wrist flexion/extension and "substantial" for active wrist flexion/extension and radial/ulnar deviation. Inter- and intraobserver agreement for the attending surgeons was "almost perfect" for all measurements. Discussion This study validates a photography-based goniometry protocol allowing accurate and reliable WROM measurements without direct physician contact. Passive WROM was more accurately measured from photographs than active WROM. This study builds on previous photography-based goniometry literature by validating a protocol in which patients or their families take and submit their own photographs. Clinical Relevance Patient-performed photography-based goniometry represents an alternative to traditional clinical goniometry that could enable longer-term follow-up, overcome travel-related impediments to office visits, improve convenience, and reduce costs for patients.

Project description:BackgroundThere has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.MethodsMEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.Results209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains.ConclusionThis systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field.Systematic review registration[www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].

Dataset Information

A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies.

Publications

A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets