Akde

Manuscript was published in Akde in Ecology and Evolution. 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

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. Making progress on my analysis of looking at caribou herd akde's but have a few questions about how to interpret some of the results. Bit of a rambling list, but hopefully others find the answers helpful! I have tried to stay up to date on the various manuscripts, but if there is one I'm missing that would answer these technical questions, please point it out to me! I'm working with a small set of data, 43 individuals, for one month, roughtly 3 locations a day.

A vector setting the x and akde cell widths in meters, akde. With small effective sample sizesakde, it is important to see if parametric bootstrapping may be worth it to further reduce our estimation error.

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.

To conserve the mobility of species across changing land and seascapes, we must first understand how much space is necessary to maintain stable, interconnected populations. Home range estimation allows managers and policymakers to easily visualize the habitats most commonly used by species of conservation concern. Figure 1: GPS location data top panel can be used to determine both where an animal might have traveled during observation occurrence distribution and predict where it might go in the future range distribution. Home range estimation presents several quantitative challenges and is prone to statistical biases that can lead to underestimation Fig. This can have negative impacts on conservation outcomes as it may result in conservation managers underestimating how much land should be protected or overestimating the number of animals that a region can sustain.

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. Because KDE bandwidth optimizers are sensitive to temporal autocorrelation, statistically-robust alternatives have been advocated, first, data-thinning procedures, and more recently, autocorrelated kernel density estimation AKDE. These yield asymptotically consistent, but very smoothed distributions, which may feature biologically unrealistic aspects such as spilling beyond impassable borders. I introduce a semi-parametric variant of AKDE designed to extrapolate more realistic home range shapes by incorporating movement mechanisms into the bandwidth optimizer and into the base kernels. I implement a first approximative version based on the step selection framework. This method allows accommodating land cover selection, permeability of linear features, and attraction for select landscape features when delineating home ranges. In a plains zebra Equus quagga , the reluctance to cross a railway, the avoidance of dense woodland, and the preference for grassland when foraging created significant differences between the estimated home range contours by the new and by previous methods.

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FIT function took almost 3 days to run, so hoping to avoid, although I'm going to have to tackle parallel processing fairly quickly, as we are looking at running these akde's often. This movement process features a home range, correlated positions, and correlated velocities. Cut my processing time from roughly 3 days to 1. VMM An optional vertical ctmm object for 3D home-range calculation. We can see that the variogram flattens i. Note In the case of coarse grids, the value of PDF in a grid cell corresponds to the average probability density over the entire rectangular cell. Disease, predation and demography: Assessing the impacts of bovine tuberculosis on African buffalo by monitoring at individual and population levels. As each of these UDs need to be on the same grid, and I specified the grid, why am I getting this printout? Leimgruber, J. The American Naturalist , 5 , E—E If a UD or raster object is supplied in the grid argument, then the estimate will be calculated on the same grid. R tutorial:. If these low DOFs are true, and not a model selection issue, could it just be I don't have enough data.

Manuscript was published in Methods in Ecology and Evolution.

Locations are assumed to be inside the SP polygons if SP. Method When to run? I can't wrap my head about why, if it works with the entire group, it would fail on a subset. Thanks for your rapid response Chris! Making progress on my analysis of looking at caribou herd akde's but have a few questions about how to interpret some of the results. The techniques and mitigation measures available within this package include:. This gives a rough guideline as to what spatial details are and are not important in the density estimate. Report repository. Working these suggestions In now! We can see that the variogram flattens i. Thanks Ingo! We can see that the expected order of bias was reduced to 2. Bit of a rambling list, but hopefully others find the answers helpful!