Impact of topographic factors on animal field pathings: Analysis and prediction of deer movement patterns

  1. Valderrama-Zafra, José M.
  2. Rubio-Paramio, Miguel A.
  3. Garcia-Molina, Diego Francisco
  4. Mercado-Colmenero, Jorge Manuel
  5. Oya, Antonia
  6. Carrasco, Rafael
  7. Azorit, Concepción
Journal:
Ecological Informatics

ISSN: 1574-9541

Year of publication: 2024

Volume: 80

Pages: 102487

Type: Article

DOI: 10.1016/J.ECOINF.2024.102487 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Ecological Informatics

Abstract

Understanding and tracking the complexities of animal movement patterns is of paramount importance in wildlife management, conservation efforts, and the sustainable use of natural resources. An infinite number of factors influence the movement path of animals within their respective habitats, including: the structure of the habitat, the availability of resources, the presence of natural predators, social memory, the topographic attributes of the environment, etc.Numerous studies have attempted to delineate the spatial boundaries of animal habitats by elucidating the complexities of their movement dynamics. These investigations have highlighted the profound impact of factors such as environmental topography and the presence of natural impediments and other anthropogenic structures on animal mobility, but very few have analyzed topographic factors at a fine three-dimensional spatial scale.This research focuses on a novel methodology for identifying animal trajectories at a fine scale and evaluating the influence of topographic factors on these trajectories, specifically of deer herds in southern Spain. To understand movement patterns, transects recorded in the field due to continued use by deer are analyzed. Topographical information was obtained in two steps: first with a graphical analysis of orthophotos for the incorporation of the sufficient data set. Secondly, the veracity of this data was verified using Global Positioning System (GPS) tracking technology. The integration of data from multiple sources with Geographic Information Systems (GIS) allowed the analysis to be automated. Next a statistical linear regression model, based on both the ascent and descent lengths and the total length of the path traveled, was designed to infer the trajectories between two designated points within the study area. Using topographical variables obtained in the study environment, such as the slope, the elevation difference (cumulative vertical distance), and the 3D length of the transect paths, the influence of these variables on the movement decisions of animals within their habitat is established in order to facilitate their subsequent prediction.Analytical tests of the trajectories have shown that the movement behavior of cervids is predictable. The results demonstrate the usefulness of the methodology presented which, by providing and collect valuable topographic information on movement and transit areas, can guide sustainable management practices for deer populations and their habitats.

Bibliographic References

  • Abrahms, (2021), In Trends Ecol. Evol., 36, pp. 308, 10.1016/j.tree.2020.10.018
  • Anjitha Krishna, (2019), J. Indian Soc. Remote Sens., 47, pp. 1031, 10.1007/s12524-019-00953-3
  • Azorit, (2010), pp. 215
  • Azorit, (1997), Acta Vertebrata, 24, pp. 181
  • Azorit, (2012), J. Wildl. Dis., 48, pp. 77, 10.7589/0090-3558-48.1.77
  • Azorit, (2020), Hystrix, 31, pp. 1
  • Calenge, (2009), Eco. Inform., 4, pp. 34, 10.1016/j.ecoinf.2008.10.002
  • Cumming, (2012), Divers. Distrib., 18, pp. 1057, 10.1111/j.1472-4642.2012.00908.x
  • Dodge, (2016), Int. J. Geogr. Inf. Sci., 30, pp. 825, 10.1080/13658816.2015.1132424
  • Edelhoff, (2016), Mov. Ecol., 4, pp. 21, 10.1186/s40462-016-0086-5
  • Fernández-Rodríguez, (2023), Eco. Inform., 77
  • Ganskopp, (2000), Appl. Anim. Behav. Sci., 68, pp. 179, 10.1016/S0168-1591(00)00101-5
  • Garrido-Carretero, (2023), J. Wildl. Manag., 87, 10.1002/jwmg.22378
  • Guo, (2019), ISPRS Int. J. Geo Inf., 8, pp. 490, 10.3390/ijgi8110490
  • Horne, (2007), Ecology, 88, pp. 2354, 10.1890/06-0957.1
  • Kauffman, (2021), Vol. 52
  • Kenward, (2018), PLoS One, 13, 10.1371/journal.pone.0206354
  • Klasen, (2022), Eco. Inform., 71
  • Korzeniowska, (2018), Geomorphology, 301, pp. 53, 10.1016/j.geomorph.2017.10.011
  • Lachica, (2000), Br. J. Nutr., 83, pp. 35, 10.1017/S0007114500000064
  • Lachica, (1997), Fr. J. Nutr., 77, pp. 73, 10.1017/S0007114500002890
  • Laffan, (2012), Int. J. Geogr. Inf. Sci., 26, pp. 2003, 10.1080/13658816.2012.721557
  • Laffan, (2014)
  • Laffan, (2016), Int. J. Geogr. Inf. Sci., 30, pp. 1, 10.1080/13658816.2015.1090001
  • Laube, (2011), Trans. GIS, 15, pp. 401, 10.1111/j.1467-9671.2011.01256.x
  • Lele, (2013), J. Anim. Ecol., 82, pp. 1183, 10.1111/1365-2656.12141
  • Lempidakis, (2018), J. Theor. Biol., 457, pp. 101, 10.1016/j.jtbi.2018.08.024
  • Marshall, (2022), Curr. Opin. Neurobiol., 73, 10.1016/j.conb.2022.02.002
  • Masello, (2017), Front. Zool., 14, 10.1186/s12983-017-0219-8
  • (2021)
  • Morita, (2020), Eco. Inform., 56
  • Panzacchi, (2016), J. Anim. Ecol., 85, 10.1111/1365-2656.12386
  • Pinter-Wollman, (2013), Behav. Ecol., 25, pp. 242, 10.1093/beheco/art047
  • Riotte-Lambert, (2020), Trends Ecol. Evol., 35, 10.1016/j.tree.2019.09.009
  • Sahani, (2021), Eco. Inform., 64
  • Schlägel, (2014), Methods Ecol. Evol., 5, pp. 1236, 10.1111/2041-210X.12284
  • Seidel, (2018), Int. J. Geogr. Inf. Sci., 32, pp. 2272, 10.1080/13658816.2018.1498097
  • Shepard, (2013), 182, pp. 298
  • Skidmore, (2011), Int. J. Geogr. Inf. Sci., 25, pp. 337, 10.1080/13658816.2011.554296
  • Spencer, (2012), J. Mammal., 93, 10.1644/12-MAMM-S-061.1
  • Valderrama-Zafra, (2022), Eco. Inform., 101623
  • Wangdi, (2019), Global Ecol. Conserv., 17
  • Wiens, (2005), pp. 390
  • Zaniewski, (2002), Ecol. Model., 157, pp. 261, 10.1016/S0304-3800(02)00199-0
  • Zonneveld, (1995), pp. 199