Akde

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 akde. Note that you want akde best model for each individual, even if that differs by individual, akde. Different movement behaviors and sampling schedules will reveal different autocorrelation structures in the data, 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.

Akde

Manuscript was published in Methods in Ecology and Evolution. Preprint is also available on EcoEvoRxiv. For any definitions, check the main manuscript or the Glossary. Download this tutorial as a. Silva, I. Methods in Ecology and Evolution, 13 3 , Home range estimation is a key output from animal 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. The techniques and mitigation measures available within this package include:. Both measures will run automatically if arguments debias and method are left unspecified. For most situations, we recommend keeping both of these arguments as the default. We will use two datasets, both available within the ctmm package: African buffalos Syncerus caffer , and Mongolian gazelles Procapra gutturosa. Information on the data collection protocol is available in Cross et al.

Mongolian gazelles have a home range crossing time of a few months, and with a maximum akde around 10 years, it is impossible to get a considerable effective sample size no akde the study duration Fleming et al. With small effective sample sizesakde, it is important to see if parametric bootstrapping may be worth it to further reduce our estimation error.

Questions regarding calculating akde , mean and interpreting results. Reply to author. Copy link. Report message. Show 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.

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.

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Leimgruber, T. Silva, I. Go to file. Cut my processing time from roughly 3 days to 1. The computation time is not an issue here, though, as I'm talking about the model selection within mean and not within ctmm. Equivalent to res for raster objects. Report message. Useful links:. In general, the as. I have a suggestion. To reduce the numerical error further, we would need to change the default relative error target of ctmm. Dismiss alert. Thanks for your rapid response Chris! The typical pool of candidate models includes isotropic when diffusion is the same in every direction; symmetrical and anisotropic when diffusion varies with direction; asymmetrical variants. For this tutorial, we will use data already prepared into a list of telemetry objects.

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.

See Also bandwidth , mean. Nandintsetseg, M. Download this tutorial as a. We will assume that you have already estimated a good ctmm movement model for your data. The American Naturalist , 5 , E—E So very bad. Show original message. UD command. You can see that the uniformly weighted estimates place excessive emphasis on the oversampled data yellow. Personal blog.