Data · dataset · 2021
Data from: Resource selection and landscape change reveal mechanisms suppressing population recovery for the world's most endangered antelope
Listed in Borealis and Agri-environmental Research Data Dataverse — shown once because both records carry DOI 10.5683/sp2/tasg7f
Description
Abstract
Understanding how bottom-up and top-down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations.
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Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators.
We quantified: (1) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 years; (2) how tree cover has influenced resource selection by hirola; and (3) interactions between tree cover and predation. Between 1985 and 2012, tree cover increased by 251% across the historical range of hirola. Tree encroachment was associated with a 98% decline of hirola and elephant Loxodonta africana populations, a 74% decline in cattle Bos indicus, an increase in browsing livestock by 327%, and a reduction in rainfall.
Although hirola avoided tree cover, we found no evidence that predation on hirola increased with increasing tree cover. Synthesis and applications. Hirola may qualify as a refugee species, in which contemporary populations are restricted to suboptimal habitat and exhibit low survival, reproduction, or both.
The extinction of hirola would be the first of a mammalian genus on the African continent in modern history. We conclude that contemporary low numbers of hirola are due at least partly to habitat loss via tree encroachment, triggered by some combination of elephant extirpation, overgrazing, drought, and perhaps fire suppression. We recommend a combination of rangeland restoration efforts (including conservation of elephants, manual clearing of trees, and grass seeding), increased enforcement of an existing protected area (Arawale National Reserve), and reintroductions to enhance recovery for this endangered species.
These efforts will rely on enhanced support from the international conservation community and the cooperation of pastoralist communities with which the hirola coexist.
Usage notes
Livestock, Cattle, elephants abundance in hirola's range_77_2011 This is aerial survey data from the Department of Resource Survey and Remote Sensing Kenya from 1977 to 2011 to give historical estimates wildlife and livestock abundances. We estimated the trends of livestock, hirola, elephants abundances from this data. Masalani_daily rainfall The rainfall data file contains daily rainfall observations for Masalani station in the centre of hirola's geographic range for the period from 1970-2009.
Hirolacollardatamay2nd 2015 The hirola collar data contains the telemetry data of collared hirola individuals within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset. Imagery analysis P166R61zip contains details of the imagery analysis (1985 and 2012) for the hirola's geographic range.
P166R61_zip.zip Hirolakillsites_2015 Hirola kill sites data contains observed hirola kill sites collected by network of scouts within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset.
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP2%2FTASG7F ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp2/tasg7f ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP2%2… ↗
metadata API · from borealisdata ca
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Disease 75% · Evolutionary biology 73% · Satellite remote sensing 65%
Provenance · 2 source records, 23 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP2/TASG7F | 12 d ago | JSON v1 |
| Agri-environmental Research Data Dataverse | doi:10.5683/SP2/TASG7F | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[disease].local:disease:disease | enrichment · borealisdata ca | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:group:3104 | enrichment · borealisdata ca | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].dataverse_subject:other | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:other | source · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:economics-finance | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:mathematics-statistics | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[modality].local:modality:remote-sensing | enrichment · borealisdata ca | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |