Correction to: Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states.
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ABSTRACT:
SUBMITTER: Hance DJ
PROVIDER: S-EPMC8063432 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundStudies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.MethodsWe developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event.ResultsParameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively.ConclusionsWe demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.
| S-EPMC8025504 | biostudies-literature
Project description:Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers.
| S-EPMC10213493 | biostudies-literature
Project description:Human-carnivore conflict continues to present a major conservation challenge around the world. Translocation of large carnivores is widely implemented but remains strongly debated, in part because of a lack of cost transparency. We report detailed translocation costs for three large carnivore species in Namibia and across different translocation scenarios. We consider the effect of various parameters and factors on costs and translocation success. Total translocation cost for 30 individuals in 22 events was $80,681 (US Dollars). Median translocation cost per individual was $2,393, and $2,669 per event. Median cost per cheetah was $2,760 (n = 23), and $2,108 per leopard (n = 6). One hyaena was translocated at a cost of $1,672. Tracking technology was the single biggest cost element (56%), followed by captive holding and feeding. Soft releases, prolonged captivity and orphaned individuals also increased case-specific costs. A substantial proportion (65.4%) of the total translocation cost was successfully recovered from public interest groups. Less than half the translocations were confirmed successes (44.4%, 3 unknown) with a strong species bias. Four leopards (66.7%) were successfully translocated but only eight of the 20 cheetahs (40.0%) with known outcome met these strict criteria. None of the five habituated cheetahs was translocated successfully, nor was the hyaena. We introduce the concept of Individual Conservation Cost (ICC) and define it as the cost of one successfully translocated individual adjusted by costs of unsuccessful events of the same species. The median ICC for cheetah was $6,898 and $3,140 for leopard. Translocations are costly, but we demonstrate that they are not inherently more expensive than other strategies currently employed in non-lethal carnivore conflict management. We conclude that translocation should be one available option for conserving large carnivores, but needs to be critically evaluated on a case-by-case basis.
| S-EPMC4134276 | biostudies-literature
Project description:Globally, human activities have led to the impoverishment of species assemblages and the disruption of ecosystem function. Determining whether this poses a threat to future ecosystem stability necessitates a thorough understanding of mechanisms underpinning community assembly and niche selection. Here, we tested for niche segregation within an African small carnivore community in Kibale National Park, Uganda. We used occupancy modeling based on systematic camera trap surveys and fine-scale habitat measures, to identify opposing preferences between closely related species (cats, genets, and mongooses). We modeled diel activity patterns using kernel density functions and calculated the overlap of activity periods between related species. We also used co-occupancy modeling and activity overlap analyses to test whether African golden cats Caracal aurata influenced the smaller carnivores along the spatial and/or temporal axes. There was some evidence that related species segregated habitat and activity patterns. Specialization was particularly strong among forest species. The cats and genets partitioned habitat, while the mongooses partitioned both habitat and activity period. We found little evidence for interference competition between African golden cats and other small carnivores, although weak interference competition was suggested by lower detection probabilities of some species at stations where African golden cats were present. This suggests that community assembly and coexistence in this ecosystem are primarily driven by more complex processes. The studied carnivore community contains several forest specialists, which are typically more prone to localized extinction. Preserving the observed community assemblage will therefore require the maintenance of a large variety of habitats, with a particular focus on those required by the more specialized carnivores.
| S-EPMC6787825 | biostudies-literature
Project description:More and more devices are equipped with global positioning system (GPS). However, those handheld devices with consumer-grade GPS receivers usually have low accuracy in positioning. A position correction algorithm is therefore useful in this case. In this paper, we proposed an evolutionary computation based technique to generate a correction function by two GPS receivers and a known reference location. Locating one GPS receiver on the known location and combining its longitude and latitude information and exact poisoning information, the proposed technique is capable of evolving a correction function by such. The proposed technique can be implemented and executed on handheld devices without hardware reconfiguration. Experiments are conducted to demonstrate performance of the proposed technique. Positioning error could be significantly reduced from the order of 10 m to the order of 1 m.
| S-EPMC3918392 | biostudies-other
Project description:Throughout Africa, lions are thought to have experienced dramatic population decline and range contraction. The greatest declines are likely occurring in human-dominated landscapes where reliably estimating lion populations is particularly challenging. By adapting a method that has thus far only been applied to animals that are habituated to vehicles, we estimate lion density in two community areas in Kenya's South Rift, located more than 100 km from the nearest protected area (PA). More specifically, we conducted an 89-day survey using unstructured spatial sampling coupled with playbacks, a commonly used field technique, and estimated lion density using spatial capture-recapture (SCR) models. Our estimated density of 5.9 lions over the age of 1 year per 100 km2 compares favorably with many PAs and suggests that this is a key lion population that could be crucial for connectivity across the wider landscape. We discuss the possible mechanisms supporting this density and demonstrate how rigorous field methods combined with robust analyses can produce reliable population estimates within human-dominated landscapes.
| S-EPMC8888262 | biostudies-literature
Project description:BACKGROUND: Health studies and mHealth applications are increasingly resorting to tracking technologies such as Global Positioning Systems (GPS) to study the relation between mobility, exposures, and health. GPS tracking generates large sets of geographic data that need to be transformed to be useful for health research. This paper proposes a method to test the performance of activity place detection algorithms, and compares the performance of a novel kernel-based algorithm with a more traditional time-distance cluster detection method. METHODS: A set of 750 artificial GPS tracks containing three stops each were generated, with various levels of noise.. A total of 9,000 tracks were processed to measure the algorithms' capacity to detect stop locations and estimate stop durations, with varying GPS noise and algorithm parameters. RESULTS: The proposed kernel-based algorithm outperformed the traditional algorithm on most criteria associated to activity place detection, and offered a stronger resilience to GPS noise, managing to detect up to 92.3% of actual stops, and estimating stop duration within 5% error margins at all tested noise levels. CONCLUSIONS: Capacity to detect activity locations is an important feature in a context of increasing use of GPS devices in health and place research. While further testing with real-life tracks is recommended, testing algorithms' performance with artificial track sets for which characteristics are controlled is useful. The proposed novel algorithm outperformed the traditional algorithm under these conditions.
| S-EPMC3637118 | biostudies-literature
Project description:BackgroundARGOS satellite telemetry is one of the most widely used methods to track the movements of free-ranging marine and terrestrial animals and is fundamental to studies of foraging ecology, migratory behavior and habitat-use. ARGOS location estimates do not include complete error estimations, and for many marine organisms, the most commonly acquired locations (Location Class 0, A, B, or Z) are provided with no declared error estimate.Methodology/principal findingsWe compared the accuracy of ARGOS Locations to those obtained using Fastloc GPS from the same electronic tags on five species of pinnipeds: 9 California sea lions (Zalophus californianus), 4 Galapagos sea lions (Zalophus wollebaeki), 6 Cape fur seals (Arctocephalus pusillus pusillus), 3 Australian fur seals (A. p. doriferus) and 5 northern elephant seals (Mirounga angustirostris). These species encompass a range of marine habitats (highly pelagic vs coastal), diving behaviors (mean dive durations 2-21 min) and range of latitudes (equator to temperate). A total of 7,318 ARGOS positions and 27,046 GPS positions were collected. Of these, 1,105 ARGOS positions were obtained within five minutes of a GPS position and were used for comparison. The 68(th) percentile ARGOS location errors as measured in this study were LC-3 0.49 km, LC-2 1.01 km, LC-1 1.20 km, LC-0 4.18 km, LC-A 6.19 km, LC-B 10.28 km.Conclusions/significanceThe ARGOS errors measured here are greater than those provided by ARGOS, but within the range of other studies. The error was non-normally distributed with each LC highly right-skewed. Locations of species that make short duration dives and spend extended periods on the surface (sea lions and fur seals) had less error than species like elephant seals that spend more time underwater and have shorter surface intervals. Supplemental data (S1) are provided allowing the creation of density distributions that can be used in a variety of filtering algorithms to improve the quality of ARGOS tracking data.
| S-EPMC2806907 | biostudies-literature
Project description:Neurons in posterior parietal cortex contribute to the execution of goal-directed navigation and other decision-making tasks. Although molecular studies have catalogued over fifty cortical cell types, it remains unknown what distinct functions they serve during goal-directed navigation. Here, we identified a molecularly defined subset of somatostatin (Sst) inhibitory neurons that, in mouse posterior parietal cortex, carry a novel cell type-specific error correction signal for navigation. We obtained repeatable experimental access to these cells using an adeno-associated virus (AAV) in which gene expression is driven by an enhancer that functions specifically in a subset of Sst cells. We found that during goal-directed navigation in a virtual environment, this subset of Sst neurons activates in a synchronous pattern that is distinct from the activity of surrounding neurons, including other Sst neurons. Using in vivo two-photon photostimulation and ex vivo paired patch clamp recordings, we show that nearby cells of this Sst subtype excite each other through gap junctions, revealing a self-excitation circuit motif that contributes to the synchronous activity of this cell type. Remarkably, these cells selectively activate as mice execute course corrections for deviations in their virtual heading during navigation toward a reward location, both for self- and experimentally-induced deviations. We propose that this subtype of Sst neurons provides a self-reinforcing and cell type-specific error-correction signal in posterior parietal cortex that may aid the execution and learning of accurate goal-directed navigation trajectories.
2023-05-11 | GSE232200 | GEO
Project description:Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error.
| S-EPMC6339035 | biostudies-literature