Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources

The understanding of animal distribution in habitats located farther from water sources has not been dealt with adequately in the literature, yet this knowledge enables better prediction of species occurrence across an entire landscape. We tested whether elephant occurrence peaks away from water in...

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Main Authors: Henry Ndaimani, Amon Murwira, Mhosisi Masocha, Fadzai M. Zengeya
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:Cogent Environmental Science
Subjects:
Online Access:http://dx.doi.org/10.1080/23311843.2017.1420364
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spelling doaj-c205569d4f8549fdb8134b152444affc2021-03-02T14:23:43ZengTaylor & Francis GroupCogent Environmental Science2331-18432017-01-013110.1080/23311843.2017.14203641420364Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sourcesHenry Ndaimani0Amon Murwira1Mhosisi Masocha2Fadzai M. Zengeya3University of ZimbabweUniversity of ZimbabweUniversity of ZimbabweUniversity of ZimbabweThe understanding of animal distribution in habitats located farther from water sources has not been dealt with adequately in the literature, yet this knowledge enables better prediction of species occurrence across an entire landscape. We tested whether elephant occurrence peaks away from water in addition to the known peak that is associated with water sources. We used the Maximum Entropy Modelling (MaxEnt) algorithm to predict the potential distribution of elephants in the Gonarezhou National Park, Zimbabwe. Elephant tracking data from Global Positioning System (GPS) collars were used as the response variable while NDVI (a proxy for forage quantity) and water sources data were the environmental variables. Results showed multiple peaks of elephant occurrence with increasing distance from water sources. Additionally, results illustrated that the peaks occur in high NDVI areas. Our findings emphasise the utility of GIS and remote sensing in enhancing our understanding of animal occurrence driven by water sources.http://dx.doi.org/10.1080/23311843.2017.1420364area under curvemaxentnormalised difference vegetation indexpiospherereceiver operating characteristic curve
collection DOAJ
language English
format Article
sources DOAJ
author Henry Ndaimani
Amon Murwira
Mhosisi Masocha
Fadzai M. Zengeya
spellingShingle Henry Ndaimani
Amon Murwira
Mhosisi Masocha
Fadzai M. Zengeya
Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
Cogent Environmental Science
area under curve
maxent
normalised difference vegetation index
piosphere
receiver operating characteristic curve
author_facet Henry Ndaimani
Amon Murwira
Mhosisi Masocha
Fadzai M. Zengeya
author_sort Henry Ndaimani
title Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
title_short Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
title_full Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
title_fullStr Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
title_full_unstemmed Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources
title_sort elephant (loxodonta africana) gps collar data show multiple peaks of occurrence farther from water sources
publisher Taylor & Francis Group
series Cogent Environmental Science
issn 2331-1843
publishDate 2017-01-01
description The understanding of animal distribution in habitats located farther from water sources has not been dealt with adequately in the literature, yet this knowledge enables better prediction of species occurrence across an entire landscape. We tested whether elephant occurrence peaks away from water in addition to the known peak that is associated with water sources. We used the Maximum Entropy Modelling (MaxEnt) algorithm to predict the potential distribution of elephants in the Gonarezhou National Park, Zimbabwe. Elephant tracking data from Global Positioning System (GPS) collars were used as the response variable while NDVI (a proxy for forage quantity) and water sources data were the environmental variables. Results showed multiple peaks of elephant occurrence with increasing distance from water sources. Additionally, results illustrated that the peaks occur in high NDVI areas. Our findings emphasise the utility of GIS and remote sensing in enhancing our understanding of animal occurrence driven by water sources.
topic area under curve
maxent
normalised difference vegetation index
piosphere
receiver operating characteristic curve
url http://dx.doi.org/10.1080/23311843.2017.1420364
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