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This group provides support for those using geolocator technology to study animal movement and behavior.
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  2. Hi! Your question is a few months old already so I hope you managed fixed the issue already, otherwise it seems to me that it could be a decimal separator issue. In the example you shared, the separator for the temperature is a coma rather than a point and I imagine that the "sep" parameter of the read.table function is set as coma to read csv type files (which seems to be the case of yours). So the function tries to assign 5 values to only 4 headers. This decimal separator issue is common with computers set in certain languages (e.g., french decimal separator is a coma). Hope it hel
  3. Hi Eldar, thank you for a reply. I get overlaping median dates or they seem quite unrealistic (like those in first post where departure date is 11. October and arrival date to next stopover (over 1000 km) is 12. October. I tried adjusting the probability cutoff. The maximum I was able to set was 0.3. If I set 0.5 I get message that the bird didn't move.
  4. Hi Beata, What kind of overlapping dates you get? It is likely that dates will overlap at the ends - around 90-95% percentile. Try adjusting prob.cutoff to e.g. 0.5 in the stationary.migration.summary. I think you should get the same results as from find.times.distribution then. Hope this helps, Eldar
  5. Hi, before calling make.prerun.object you should load saved calibration file for a tag that worked (load(file= path to *Calibration.RData) I think your calibration period is not properly set. Now it is 10 years used for calibration... You should use here only periods with known location of tag.
  6. Hi! I am trying to open my .tem files from MK3 geolocators. The problem is that I don't know how, I downloaded the .tem file with the temperature with comas and not points. So, every time I try to open my file I get an error: > d.deg_prim <- readTem2(paste0(tagdata,".tem"), skip = 1) Error in read.table(file = file, header = header, sep = sep, quote = quote, : more columns than column names So looks my .tem file: 255,19,-0,499986, ok,06/04/19 12:17:54,43561.512431,12,625000 ok,06/04/19 13:13:42,43561.551181,12,750000 ok,06/04/19 14:20:45,43561.597743
  7. Hi all, My concern is related to posts on “plot_slopes_by_location” . I am currently working on GLS data (old BAS tags) on seabirds (albatross) deployed for 2-4 yrs and some of them are circumpolar navigating during the non-breeding period. 1) The first issue is that I do not have properly calibration data, then I tried to use approximate calibration from a very short period, different loggers et site ~ 80km from the deployment site. I see that you suggest to use the calibration extracted from a tag that worked, but I failed to understand how you do that. 2) usi
  8. Hi, I am performing gls analysis of Intigeo devices in FlightR. When I use stationary.migration.summary function I get overlapping dates for stationary period. So I decided to use find.times.distribution to find arrival/departure dates to/from stationary periods calculated by stationary.migration.summary. Now I am wondering what key should I use to unify this approach across all my birds? Any advise? Here is a piece of my results of two stationary periods. Meanlat SDlat Meanlon SDlon Dist2 Arrival.Q.50
  9. Bonjour, I am working with geolocators data collected with Intigeo C65 devices on seabirds breeding in the Arctic (higher red dot on map1) and migrating in southern Atlantic in winter. Parts of the migration occurs during spring and fall equinoxes. I used a rooftop cablibration made in southern Quebec (lower red dot on map1) for about 14 days and I used the findHEZenith function to calculate a new zenith angle on the wintering area. Then I set a list of Zenith changing depending on the period of the year. All my script is running, but for some birds, I have a strange movement up-nor
  10. Hi Melina, please make sure that there are no NA´s in the date: sum(is.na(data$Date)) If this is >0 than this is the fault and you need to remove these rows. Cheers, Simeon
  11. Hi everyone! I am running the preprocessLight of my geolocators data and I have the following error in some of my devices: Error in seq.default(as.numeric(tmin) + dt/2, as.numeric(tmax) - dt/2, : 'from' must be of length 1 My data spans not for more than one year but it includes different years (2019 and 2020). I have tried to subset the .lig original data so that it includes only one year, but it doesn't work! Any idea? Someone with the same problem? Thanks !!! MElina
  12. Hi Eli, Everything works for me at the moment, but after updating R and ggmap to the newest versions. Stamenmaps also fail a lot, and thus are hard to use within a function. I will wait for something more reliable before changing a function myself, but everyone is welcome to write a map_FLightR_stamenmap() Cheers, Eldar
  13. I went around the block with google maps for a couple of hours and could not get past this cryptic error message when trying to download a simple example map: ggmap::register_google(key = APIkey) map <- get_map(location = "texas", zoom = 6, source = "stamen") Source : https://maps.googleapis.com/maps/api/staticmap?center=texas&zoom=6&size=640x640&scale=2&maptype=terrain&key=xxx Error in aperm.default(map, c(2, 1, 3)) : invalid first argument, must be an array In addition: Warning message: In get_googlemap(center = location, zoom = zoom, filename = filename) : H
  14. Hi, I am trying to use TwGeos::preprocessLight() to identify twilights in light data collected by Lotek LAT280 tags. The below is the log of the "Light Intensity" variable. This approach doesn't seem to give great definition between the dark and light sections and makes picking a threshold challenging. Does anyone have any suggestions for a transformation, a threshold, or an alternative approach with the data from this tag? Thanks!
  15. Hi, I'm analysing some geolocator data from a common sandpiper using the geolight package. When I run my analysis I get a weird migration pattern, the individual appears to stop in two locations (one of which is in the sea) before settling in West Africa. I'm not sure whether these are true movements from west to east or if something else is going on. Does anyone have any suggestions? These movements are not around the equinoxes either. The output of siteMap() and schedule() are attached. Thanks, Thomas ADplot.tiff AD_migration_schedule.csv
  16. Hi Ana, these cut-off probs just measure how many particles movet between time t and t+1. 0.1 will say that there was movement between two periods if at least 10% of particles moved >25 km. 0.5 would mean 50% chance. Generally you better make it higher than lower. There is so far no established approach on how to select a proper value here. Hope this helps, Eldar
  17. Hi all, I estimated stopover locations by using 0.1, 0.2, and 0.3 cutt-off prob. Results are quite consistent specially during the first part of migration. Can the 0.1, 0.2, and 0.3 values be translated into something more "meaningful"? Like km? I'm not quite sure how to explain these three values. Many thanks, Ana
  18. Finally, an almost complete user`s guide for geolocator analyses. Check out the paper: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.13036 As well as the online manual: https://geolocationmanual.vogelwarte.ch/ Lisovski_et_al-2019-JAnimEcol.pdf
  19. So you can install it directly from there with the install.packages(). 0.4.9 version introduces FLightR2Movebank( ) function that saves result formovebank upload. The latest version is on GitHub, as always.
  20. Sorry - I am not sure what is happening.. are you working on a mac? If not try gr.Device = "default"
  21. Thanks for your help, Eldar! Distance does seem to be key. Increasing max distance in particle.filter has improved the return track. Now working on adjusting mean and sd. Forgive a beginner question, but is there an easy way to estimate appropriate values? I have the result of a particle.filter run that produced an OK-looking track, but not quite sure what I should use. Should I include all movements in my estimates? These are birds that remain in a relatively small area for most of the year, but travel long distances quickly during migration...
  22. Hi Simeon, Thank you so much for responding. I checked my NA's and didn't get any. summary(raw) Date Light Min. :2013-02-22 00:00:02 Min. : 0.0002 1st Qu.:2013-04-28 17:58:47 1st Qu.: 0.0002 Median :2013-07-03 11:57:32 Median : 6.4847 Mean :2013-07-03 11:57:32 Mean : 5.1559 3rd Qu.:2013-09-07 05:56:17 3rd Qu.: 8.8506 Max. :2013-11-11 23:55:02 Max. :11.2176 I set the threshold to -.6, .6, 1, 2. I went through the steps to select the beginning, ending date and the darkest spot in th
  23. Hi Jenny, 1. You could try increasing the distance they are allowed to fly. The maximum distance can be fixed in particle.filter and the mean and sd in make.prerun.object. 2. Try running first without any spatial constrains. It sometimes happen that particle filter just cannot find a proper solution if bir migrated to far every day for a long period. Hope this helps, Eldar
  24. Hi, all I'm using FLightR 0.4.9 to track migration of shorebirds. Tags were deployed in Alaska and recovered at the same site a year later. FLightR gives me nice-looking tracks for the breeding season in Alaska, fall migration to Chile, and the non-breeding season in Chile. But the track for spring migration is consistently problematic -- instead of returning north to Alaska, it heads south toward Antarctica. Has anyone experienced something similar? Any ideas for what I can do? Calibration periods are from the study site (lon = -150.725, lat = 61.272): one from the beginning
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