Kajian Konservasi Gua Gajah Di Gianyar Bali

Suhartono, Yudi; Sutopo, M.Hum Anam; Haldoko, Liliek Agung; Purnama, Yudhi Atmaja Hendra; Rachmad, Basuki

doi:10.33374/jurnalkonservasicagarbudaya.v12i2.187

Cited by 1 publication

(3 citation statements)

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“…With so few Sumatran elephants remaining in the wild, there is a demand for accurate maps to inform conservation activities (Suhartono et al, 2007 ). We integrated the best available data on Sumatran elephant occurrence to identify the areas that provide suitable habitat for the species.…”

Section: Conservation Implications and Next Stepsmentioning

confidence: 99%

“…Efforts to conserve natural landscapes for wildlife have not accounted for elephant's habitat requirements, with only 12.3% of lands on Sumatra having a protected status (52,766.04 km 2 ; Nurbaya et al, 2020 ). Further, most core elephant ranges are located outside of the protected areas network (Moßbrucker et al, 2016 ; Suhartono et al, 2007 ). Given the large area requirements of elephants, protected areas alone will likely not suffice to conserve and support substantial wild populations over multiple generations (Moßbrucker, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Despite calls for scientifically rigorous distribution and connectivity maps (Suhartono et al, 2007 ), no habitat suitability or cost of movement analyses have been conducted. Our aim was to use currently available data of elephant occurrence to (a) predict and identify potential habitat suitability for elephants across the island of Sumatra and (b) model landscape connectivity among the extant elephant populations.…”

Section: Introductionmentioning

confidence: 99%

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Beyond protected areas: The importance of mixed‐use landscapes for the conservation of Sumatran elephants (Elephas maximus sumatranus)

Imron,

Glass,

Tafrichan

et al. 2023

Ecology and Evolution

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Elephants were once widely distributed across the Indonesian island of Sumatra but now exist in small, isolated populations. Using the best data available on elephant occurrence, we aimed to (a) predict potential habitat suitability for elephants (Elephas maximus sumatranus) across the island of Sumatra and (b) model landscape connectivity among the extant elephant populations. We used direct sightings and indirect observations of elephant signs, as well as six remotely sensed proxies of surface ruggedness, vegetation productivity and structure, and human land use and disturbance, to model habitat suitability in a Google Earth Engine (GEE) environment. We validated the habitat suitability prediction using 10‐fold spatial block cross validation and by calculating the area under the precision‐recall curve (AUC‐PR), sensitivity, and specificity for each model iteration. We also used a geolocation dataset collected from global positioning system (GPS) collars fitted on elephants as an independent validation dataset. Models showed good predictive performance with a mean AUC‐PR of 0.73, sensitivity of 0.76, and specificity of 0.68. Greater than 83% of the independent GPS collar geolocations were located in predicted suitable habitat. We found human modification, surface ruggedness, and normalized difference vegetation index to be the most important variables for predicting suitable elephant habitat. Thirty‐two percent, or 135,646 km2, of Sumatra's land area was predicted to be suitable habitat, with 43 patches of suitable habitat located across Sumatra. Areas with high connectivity were concentrated in the Riau and North Sumatra provinces. Though our analysis highlights the need to improve the quality of data collected on Sumatran elephants, more suitable habitat remains on Sumatra than is used by known populations. Targeted habitat conservation, especially of the suitable habitat in and around the Lamno, Balai Raja, Tesso Tenggara, Tesso Utara, Bukit Tigapuluh, Seblat, Padang Sugihan, and Bukit Barisan Selatan ranges, may improve the long‐term viability of this critically endangered species.

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Section: Conservation Implications and Next Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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