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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Online Access: | http://dx.doi.org/10.1080/23311843.2017.1420364 |
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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 |
work_keys_str_mv |
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