The genetic effective population size,
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of
- NSF-PAR ID:
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Monographs
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The genetic effective population size,
N e, can be estimated from the average gametic disequilibrium ( ) between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed‐neis a suite of matlabcomputer code functions to estimate from with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed‐neincludes functions to either generate or input simulated genotype data to facilitate comparative studies of estimators under various population genetic scenarios. speed‐newas validated with data simulated under both time‐forward and time‐backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on ,how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact . Estimators differed greatly in precision in the scenarios examined, and a widely employed estimator exhibited the largest variances among replicate data sets. speed‐neimplements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed‐neprovides an open‐source extensible tool for estimation of from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism ( SNP) data types to develop expectations and to compare estimators.
Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space‐use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner.
We developed ‘
movedesign’, a user‐friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).
Leveraging the ‘
ctmm’ Rpackage, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous‐time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data.
This application addresses key challenges faced by researchers when designing tracking studies, including the trade‐off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘
movedesign’ has broad applications for researchers and decision‐makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade‐off associated with these choices.
This study is the first community engagement phase of a project to develop a residential formaldehyde detection system. The objectives were to conduct a feasibility assessment for device use, and identify factors associated with concerns about environmental exposure and community interest in this device.
Design and Sample
A cross‐sectional, internet‐based survey employing community‐based participatory research principles was utilized. 147 individuals participated from a focused Waycross, Georgia (58.5%) and broader national sample (41.5%).
Variables included acceptable cost and number of testing samples, interest in conducting tests, levels of concern over pollutants, health status, housing, and demographics.
The majority of participants desired a system with fewer than 10 samples at ≤$15.00 per sample. Statistically significant higher levels of concern over air quality, formaldehyde exposure, and interest in testing formaldehyde were observed for those with overall worse health status and living in the Waycross, Georgia geographic region. Significant differences in formaldehyde testing interest were observed by health status (
OR= 0.31, 95% CI= 0.12–0.81 for home testing) and geographic location ( OR= 3.16, 95% CI= 1.22–8.14 for home and OR= 4.06, 95% CI= 1.48–11.12 for ambient testing) in multivariate models. Conclusions
Geographic location and poorer general health status were associated with concerns over and interest in formaldehyde testing.
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