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</o:shapelayout></xml><![endif]--></head><body lang=DE-CH link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span lang=EN-US>Hi all,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>I am performing a home range analysis with GPS data of some leopards and lions. The input data has a highly variable point density and result in quite large areas (roughly a magnitude of 500 to 1’000 km2 for the 95% isopleth). In agreement with the tutorial, I begin with selecting the value for the temporal parameter s and then select suitable k values. As an orientation for that I use the rule of thumb ( k = sqrt(numberOfPoints)) and the plots. When a k-value has been chosen, the tutorial recommends to use the auto.a() function (lxy.nn.add(toni.lxy, s=0.003, a=auto.a(nnn=15, ptp=0.98))).<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>However, the recommended a-value is massively too high and results in a oversmoothed home range that lacks any details. The higher the s-value, the more severe is this issue. While the result of the suggested a-value still shows a few weak spatial details for s=0, almost circular home ranges result for all isopleths with s>0. I checked whether this issue occurs only for one dataset, but it is the same for all 5 datasets I have checked. I attached two images that present the result when using the recommended a-value (auto_) and one that presents a manually selected a-value (manually_). For example, for s=0.005, I would rather take an a-value between 150’000 and 190’000 than the recommended value of 1’150’000. The auto.a() function should thereby include at least k points for 90% of all hulls.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Therefore the question: Has anyone experienced the same issue or is it even a known technical problem with the package? My datasets contain 5’000 to 30’000 fixes, have some gaps and includes sometimes different sampling intervals. May the auto.a() function have severe problems due to that? The choice of an a-value is rather subjective and not really intuitive, especially when s>0. But when the auto.a() function can’t be used to get an approximate reference, what other measures are available to be able to justify your choice of the a-value?<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>PS: I use T-LoCoH version 1.34.00 with RStudio 0.98.1103.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Best regards,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>André Zehnder<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>