Influence of external factors on the success of transmissing messages during the monitoring of wild reindeer movements using the “Argos” satellite system (Rangifer tarandus)

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Currently, satellite telemetry is increasingly in use in environmental research. As a result, researchers obtain a large amount of data on the use of space by animals. However, despite the perfection of modern satellite navigation and data transmission systems, reports on the positions of animals are extremely uneven. We consider here the main technical and natural factors that may influence the success of spacecraft in the “Argos” satellite system receiving messages emitted by radio beacons installed on animals. Among the natural factors when an animal is under the forest canopy, the greatest influence has been established to be exerted by the closure of tree crowns, which can be offset by the abundance of snow in the crowns after heavy snowfalls. Dense clouds have a weaker effect. Of the technical factors associated with the characteristics of flights of satellites of the “Argos” system, the success of receiving messages is influenced, first of all, by the maximum angle of elevation of the satellite above the horizon and the intensity of flights of satellites with a maximum angle of elevation above the horizon of more than 10° per unit time. This is due to the high unevenness of message receipt. At night and in the afternoon, due to a reduction in the number of satellite flights and a decrease in the altitude of their trajectories, the success of reception may decrease to 3% of the number of transmitted messages.

Толық мәтін

Рұқсат жабық

Авторлар туралы

V. Mamontov

N. P. Laverov Federal Center for Integrated Arctic Research, Ural Branch, Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: mamont1965@list.ru
Ресей, Arkhangelsk, 163020

A. Salman

“ES-PAS”

Email: a.salman@es-pas.com
Ресей, Moscow, 125171

Әдебиет тізімі

  1. Amstrup S. C., Mcdonald T. L., Durner G. M., 2004. Using satellite radiotelemetry data to delineate and manage wildlife populations // Wildlife Society Bulletin. V. 32. № 3. P. 661–679. https://doi.org/10.2193/0091-7648(2004)032[0661:USRDTD]2.0.CO;2
  2. Cagnacci F., Boitani L., Powell R. A., Boyce M. S., 2010. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges // Philosophical Transactions of the Royal Society B. Biol. Sci. V. 365. № 1550. P. 2157–2162. https://doi.org/10.1098/rstb.2010.0107
  3. Coxen Ch.L., Frey J. K., Carleton S. A., Collins D. P., 2017. Species distribution models for a migratory bird based on citizen science and satellite tracking data // Glo-bal Ecology and Conservation. V. 11. P. 298–311. https://doi.org/10.1016/j.gecco.2017.08.001
  4. Csermak-Jr A.C., de Araujo G. R., Pizzutto C. S., de Deco-Souza T., Jorge-Neto P.N., 2022. GPS collars as a tool to uncover environmental crimes in Brazil: The jaguar as a sentinel // Animal Conservation. V. 26. № 2. P. 137–275. https://doi.org/10.1111/acv.12826
  5. De Groeve J., Cagnacci F., Ranc N., Bonnot N. C., Gehr B., Heurich M., Hewison A. J.M., Kroeschel M., Linnell J. D., Morellet N., Mysterud A., Sandfort R., Van De Weghe N., 2020. Individual movement-sequence analysis method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes // International Journal of Geographical InformationScience. V. 34. P. 1530–1551. https://doi.org/10.1080/13658816.2019.1594822
  6. DeCesare N.J., Squires J. R., Kolbe J. A., 2005. Effect of forest canopy on GPS-based movement data // Wildlife Society Bulletin. V. 33. № 3. P. 935–941. https://doi.org/10.2193/0091-7648(2005)33[935:EOFCOG]2.0.CO;2
  7. Fernandez-Rodriguez P., Carrasco R., Moro J., Garrido-Carretero M.S., Azorit C., 2023. Working with GNSS collar data. The importance of pre-analysis when setting the sampling interval // Ecological Informatics. V. 77. Ar. 102219. https://doi.org/10.1016/J.ECOINF.2023.102219
  8. Forin-Wiart M.-A., Hubert P., Sirguey P., Poulle M.-L., 2015. Performance and accuracy of lightweight and low-cost GPS data loggers according to antenna positions, fix intervals, habitats and animal movements // PLoS One. V. 10. № 6. Ar. 0129271. https://doi.org/10.1371/journal.pone.0129271
  9. Frair J. L., Fieberg J., Hebblewhite M., Cagnacci F., DeCesare N.J., Pedrotti L., 2010. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data // Philosophical Transactions of the Royal Society B: Biological Sciences. V. 365. № 1550. P. 2187–2200. https://doi.org/10.1098/rstb.2010.0084
  10. Garcia-Jimenez R., Margalida A., Perez-Garcia J.M., 2020. Influence of individual biological traits on GPS fix-loss errors in wild bird tracking // Scientific Reports. V. 10. Ar. 19621.
  11. https://doi.org/10.1038/s41598-020-76455-x
  12. Hebblewhite M., Haydon D. T., 2010. Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology // Philosophical Transactions of the Royal Society B: Biological Sciences. V. 365. № 1550. P. 2303–2312. https://doi.org/10.1098/rstb.2010.0087
  13. Leonard J. P., Tewes M. E., Lombardi J. V., Wester D. W., Campbell T. A., 2020. Effects of sun angle, lunar illumination, and diurnal temperature on temporal movement rates of sympatric ocelots and bobcats in South Texas // PLoS ONE. V. 15. № 4. Ar. e0231732. https://doi.org/10.1371/journal.pone.0231732
  14. Lombardi J. V., Perotto-Baldivieso H.L., Hewitt D. G., Scognamillo D. G., Campbell T. A., Tewes M. E., 2022. Assessment of appropriate species-specific time intervals to integrate GPS telemetry data in ecological niche models // Ecological Informatics. V. 70. Ar. 101701. https://doi.org/10.1016/j.ecoinf.2022.101701
  15. Lombardi J. V., Perotto-Baldivieso H.L., Sergeyev M., Veals A. M., Schofield L., Young J. H., Tewes M. E., 2021. Landscape structure of woody cover patches for endangered ocelots in southern Texas // Remote Sensing. V. 13. № 19. Ar. 4001. https://doi.org/10.3390/rs13194001
  16. Oeser J., 2022. Leveraging big satellite image and animal tracking data for characterizing large mammal habitats. Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium. Berlin: Humboldt-Universität zu Berlin – Geographisches Institut. 179 p.
  17. Sager-Fradkin K.A., Jenkins K. J., Hoffman R. A., Happe P. J., Beecham J. J., Wright R. G., 2007. Fix Success and Accuracy of Global Positioning System Collars in Old-Growth Temperate Coniferous Forests // Journal of Wildlife Management. V. 71. № 4. P. 1298–1308. https://doi.org/10.2193/2006-367
  18. Street G. M., Potts J. R., Börger L., Beasley J. C., Demarais S., Fryxell J. M., McLoughlin P.D., Monteith K. L., Prokopenko C. M., Ribeiro M. C., Rodgers A. R., Strickland B. K., Van Beest F. M., Bernasconi D. A., Beumer L. T., Dharmarajan G., Dwinnell S. P., Keiter D. A., Keuroghlian A., Newediuk L. J., Oshima J. E.F., Rhodes Jr. O., Schlichting P. E., Schmidt N. M., Wal E. V., 2021. Solving the sample size problem for resource selection functions // Methods in Ecology and Evoluton. V. 12. № 12. P. 2421–2431. https://doi.org/10.1111/2041-210X.13701
  19. Walton Z., Samelius G., Odden M., Willebrand T., 2018. Long-distance dispersal in red foxes Vulpes vulpes revealed by GPS tracking // European Journal of Wildlife Research. V. 64. Ar. 64 https://doi.org/10.1007/s10344-018-1223-9
  20. Webb S. L., Dzialak M. R., Mudd J. P., Winstead J. B., 2013. Developing spatially-explicit weighting factors to account for bias associated with missed GPS fixes in resource selection studies // Wildlife Biology. V. 19. № 3. P. 257–273. https://doi.org/10.2981/12-038

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Әрекет
1. JATS XML
2. Fig. 1. Average number of satellites rising above the horizon and average number of messages received per hour.

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3. Fig. 2. Average number of satellites with a maximum elevation angle above the horizon greater than 10°, and the average number of messages received per hour.

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4. Fig. 3. Effect of tree crown density on the success of message reception by satellites with the minimum (A1) and maximum (MC) number of received messages, all other conditions being taken as standard. Classification of factors and their designations in the model formula above (in brackets): Along the x-axis – the altitude of the highest point of the satellite trajectory (in degrees above the horizon) (Elevation); along the y-axis – (Messages) – the number of messages (Messages) received during one satellite pass. Factors: tree crown density = 0 (Canopy = 0) – open spaces (crown density no more than 10%); tree crown density = 1 (Canopy = 1) – sparse forests (crown density from 10 to 30%); Canopy density = 2 – dense forests (crown density over 30%); Snow depth in crowns = 0 (Kuhta = 0) – no snow on tree branches; Snow depth in crowns = 1 (Kuhta = 1) – a thin layer of snow on tree branches; Snow depth in crowns = 2 (Kuhta = 2) – dense caps of snow on tree branches; Clouds = 0 – clear (cloudiness less than 20%); Clouds = 1 – cloudy (cloudiness 20–80%); Clouds = 2 – overcast (cloudiness over 80%); Satellite = A1, Satellite = MS – satellite models (see description in the section “Materials and Methods”).

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5. Fig. 4. Effect of cloudiness on the success of receiving messages by satellites with the minimum (A1) and maximum (MC) number of received messages under other conditions taken as standard. The classification of factors corresponds to the caption to Fig. 3.

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6. Fig. 5. The influence of the kitchen on the success of receiving messages by satellites with the minimum (A1) and maximum (MC) number of received messages under other conditions taken as standard. The classification of factors corresponds to the caption to Fig. 3.

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7. Fig. 6. Differences in the success of message reception by different satellite models under other conditions taken as standard. The classification of factors corresponds to the caption to Fig. 3.

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8. Fig. 7. The influence of natural factors on the number of messages received per hour, against the background of changes in the intensity of flights of satellites rising above 10° above the horizon during the same period. Classification of factors and their designations in the model formula above (in brackets). Along the x-axis – the number of satellites with a maximum elevation angle above the horizon of more than 10°, during an hour (Satellite > 10); along the y-axis – the number of messages received during an hour (Messages). Factors: canopy density = 0 (Canopy = 0) – open spaces (canopy density no more than 10%); canopy density = 1 (Canopy = 1) – sparse forests (canopy density from 10 to 30%); canopy density = 2 (Canopy = 2) – dense forests (canopy density more than 30%); Snow cover in tree crowns = 0 (Kuhta = 0) – no snow on tree branches; Snow cover in tree crowns = 1 (Kuhta = 1) – a thin layer of snow on tree branches; Snow cover in tree crowns = 2 (Kuhta = 2) – dense caps of snow on tree branches; Cloudiness = 0 (Clouds = 0) – clear (cloudiness less than 20%); Cloudiness = 1 (Clouds = 1) – cloudy (cloudiness 20–80%); Cloudiness = 2 (Clouds = 2) – overcast (cloudiness over 80%).

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