Akde

In akde vignette we walk through autocorrelated kernel density estimation.

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.

Akde

These functions calculate individual and population-level autocorrelated kernel density home-range estimates from telemetry data and a corresponding continuous-time movement models. Locations are assumed to be inside the SP polygons if SP. Optimally weight the data to account for sampling bias See bandwidth for akde details. For weighted AKDE, please note additional When feeding in lists of telemetry and ctmm objects, all UDs will be calculated on the same grid. These UDs can be averaged with the mean. UD command. If a UD or raster object is supplied in the grid argument, then the estimate will be calculated on the same grid. Alternatively, a list of grid arguments can be supplied, with any of the following components:. A vector setting the x and y cell widths in meters. Equivalent to res for raster objects. The x - y extent of the grid cells, formatted as from the output of extent.

For more details, check our manuscript here. UDraster,UD-methodrevisitation. Fully aware this akde not be enough data, akde, but starting small before working with the larger dataset.

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 ,

File Exchange. Fast adaptive kernel density estimation in high dimensions in one m-file. OUTPUT: pdf - the value of the estimated density at 'grid' X1,X2 - default grid used only for 2 dimensional data see example on how to construct grid on higher dimensions. Reference: Kernel density estimation via diffusion Z. Botev, J.

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.

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Fleming, C. Locations are assumed to be inside the SP polygons if SP. Fully aware this may not be enough data, but starting small before working with the larger dataset. To check the full model selection table, we can run the following command:. A vector setting the x and y cell widths in meters. Buffalo tracking data 5. Dec 6, References C. Or is that inadequate, would it need to be higher? Report 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.

These functions calculate individual and population-level autocorrelated kernel density home-range estimates from telemetry data and a corresponding continuous-time movement models. With small effective sample sizes , it is important to see if parametric bootstrapping may be worth it to further reduce our estimation error. In v0. Mongolian gazelles have a home range crossing time of a few months, and with a maximum longevity around 10 years, it is impossible to get a considerable effective sample size no matter the study duration Fleming et al. Olson, P. Noonan, K. UD A list of individual UD objects corresponding to data. Removes the tendency of Gaussian reference function GRF methods to overestimate the area of home ranges. Report message. Click here for the tutorial as a GitHub page or here as a. If a UD or raster object is supplied in the grid argument, then the estimate will be calculated on the same grid. We can also see that our effective sample size is only 4. Copy link.