Akde

Manuscript was published in Methods in Ecology and Evolution, akde. Preprint is also available on EcoEvoRxiv.

In this vignette we walk through autocorrelated kernel density estimation. We will assume that you have already estimated a good ctmm movement model for your data. Note that you want the best model for each individual, even if that differs by individual. Different movement behaviors and sampling schedules will reveal different autocorrelation structures in the data. The exact algorithm is the easiest to implement, but it can be prohibitively slow on larger datasets 10kk. On the other hand, the fast algorithm can scale to extremely large datasets, but requires an appropriate discrete-time grid dt argument, which should be a divisor of the most frequent sampling intervals that can approximate the smallest sampling intervals. The default will try to intelligently choose among these methods, and the above plot depicts the selected dt in red.

Akde

This repository is a companion piece to the manuscript "Autocorrelation-informed home range estimation: a review and practical guide" , published in Methods in Ecology and Evolution. Click here to download the full-text. Preprint is also available on EcoEvoRxiv. Home range estimation is a key output from tracking datasets, but the inherent properties of animal movement can lead traditional methods to under- or overestimated their size. Autocorrelated Kernel Density Estimation AKDE methods were designed to be statistically efficient while explicitly dealing with the complexities and biases of modern movement data, such as autocorrelation , small sample sizes , and missing or irregularly sampled data. Silva, I. Methods in Ecology and Evolution, 13 3 , If you are not familiar with R , make sure you follow these steps:. We provide a guide to home range estimation using the following workflow:. Click here for the tutorial as a GitHub page or here as a.

Report message. I'll make a note to automatically remove these bad Akde returns automatically from functions like mean.

Questions regarding calculating akde , mean and interpreting results. Reply to author. Copy link. Report message. Show original message. Either email addresses are anonymous for this group or you need the view member email addresses permission to view the original message.

In this vignette we walk through autocorrelated kernel density estimation. We will assume that you have already estimated a good ctmm movement model for your data. Note that you want the best model for each individual, even if that differs by individual. Different movement behaviors and sampling schedules will reveal different autocorrelation structures in the data. The exact algorithm is the easiest to implement, but it can be prohibitively slow on larger datasets 10kk. On the other hand, the fast algorithm can scale to extremely large datasets, but requires an appropriate discrete-time grid dt argument, which should be a divisor of the most frequent sampling intervals that can approximate the smallest sampling intervals. The default will try to intelligently choose among these methods, and the above plot depicts the selected dt in red. In this case, the user needs to thin their data if optimal weights are required.

Akde

Movement Ecology volume 7 , Article number: 16 Cite this article. Metrics details. Kernel density estimation KDE is a major tool in the movement ecologist toolbox that is used to delineate where geo-tracked animals spend their time.

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R akde R Documentation Calculate an autocorrelated kernel density estimate Description These functions calculate individual and population-level autocorrelated kernel density home-range estimates from telemetry data and a corresponding continuous-time movement models. On this page Introduction Data Preparation Step 1. However, default arguments should be able to render any quantiles of reasonable accuracy. Last commit date. The XX. Mueller, K. Autocorrelation-informed home range estimation: a review and practical guide. Go to file. I'm working with a small set of data, 43 individuals, for one month, roughtly 3 locations a day. See Also bandwidth , mean. We can see that the variogram flattens i. You should contact the package authors for that.

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Equivalent to res for raster objects. Question 4: Finally, If I subset this small dataset further looking to see how it impacts the DOF and try to only mean a subset of the akde object, the code fails with the following error. Worth redownloading? I'm running the ctmm dev package version 1. We provide a guide to home range estimation using the following workflow:. The alternative is that the confidence intervals are so wide that the UD raster is absurdly large and you get an out-of-memory error. Functions If needed, Movebank allows you to keep your data private. ID timestamp UTM. Cheers, Ingo.