Abstract. Withaningsih S, Parikesit, Nasrudin A. 2021. Correlation between landscape structure and distribution of Javan Pangolin (Manis javanica) in an extreme landscape. Biodiversitas 22: 920-932. The Javan Pangolin (Manis javanica) is a unique mammal with hard scales and can roll over when threatened. However, the study of Javan pangolin ecology, particularly using an ecological landscape approach, is limited. Here, a spatial analysis of the presence and distribution of Javan pangolins living in an extreme landscape in Rongga Sub-district, West Bandung District was conducted and was correlated to the landscape structure using a landscape metric approach. A descriptive method was used in conjunction with quantitative statistical analyses using simple linear regressions based on the Pearson correlation coefficient. The variables were features of the extreme landscape structure and the number of Javan pangolin animal signs at the sampling sites. The seven sample sites had variations in land cover classes, and the landscape structure affected the distribution of the Javan pangolins. The pangolin distribution showed a strong, negative correlation with the number of patch types (R2 = 0.628) and a weak, negative correlation with both the landscape heterogeneity (R2 = 0.012) and the percentage of forest cover (R2 = 0.136). Together, the landscape heterogeneity, the number of patch types and the percentage of forest cover negatively affected the distribution of Javan pangolins, showing a strong correlation (R2 = 0.799).

Human activities in modifying land use and land cover increasingly put pressure to many regulatory ecosystem services, one of which is carbon sequestration. If forests, the area with the most vegetation cover are decrease, the amount of carbon sequestered will decrease significantly. Currently, agroforestry systems or Talun (in West Java) in Sumedang was eleven times larger than secondary forest. Carbon stocks in this agricultural area need to be estimated so that their carbon sequestration capacity can be known in order to improve the quality of regulatory ecosystem services. NDVI value of the Landsat 8 OLI was obtained by conducting raster calculation in ArcMap. Field inventory was conducted by measuring stem DBH and height of all vegetation stands in 31 plots measuring 30 x 30 m, a similar plot size to the resolution of the Landsat imagery. Biomass of vegetation stands was calculated using allometric equations and then converted into the carbon content of the biomass. In order to analyze the correlation of NDVI and carbon inventory data, a Pearson product-moment correlation analysis in the form of simple linear regression, non-linear exponential, and polynomial order 2 and order 3 model were carried out. Standard error of estimate (SEE) was performed to identify the best equation to model the aboveground carbon stocks in the area. The results show that the four regression models give a positive correlation between NDVI and carbon stocks. The strongest category was the polynomial order 2 and order 3 regression model with 0.795 coefficient of determination. Yet, the linear simple regression model obtained the highest accuracy with estimated error 0.445 tons/pixel. The estimated carbon stock obtained by linear regression model was 16150.40 tons with an average of 104.95 tons/ha. Visually, according to carbon distribution map, the carbon content of vegetation stands in the mixed garden of Rancakalong was mainly distributed in the north of Rancakalong District which is located in Cibungur Village.

Gunung Leuser National Park (GLNP) priority species is a surrogate species representing four wildlife, Panthera tigris sumatrae, Dicerorhinus sumatrensis, Elephas maximus sumatrensis, and Pongo abelii living within GLNP. GLNP priority species was appointed based on the extinction status, endemicity, population and habitat conditions, threat, and regional representation of wildlife. “GLNP priority species” terminology was considered from flagship species, keystone species, umbrella species, or charismatic species. This literature review aimed to clarify the appropriateness of GLNP priority species to other surrogates by comparing the characters and distributions of GLNP priority species to the definition of surrogate species. This review identified five surrogate species: key-stone species, indicator species, umbrella species, flagship species, and charismatic species. GLNP priority species are appropriate to be GLNP flagship species based on two considerations: (1) the characters of all GLNP priority species which are endemic wildlife of Sumatera and are critically endangered based on the International Union for Conservation of Natures’ status and (2) the condition of the priority species distributions which periodically decreased (elephant and rhino) and are threatened (tiger and orangutan). Besides, tiger and orangutan also can be determined as umbrella species considering their distribution spreads which are extensively covering the GLNP area.

Pengiriman hasil produksi tahu dilakukan berdasarkan jalur yang biasa dilalui oleh driver, tanpa perhitungan yang jelas tentang rute mana yang harus dipilih sehingga driver benar-benar melalui jalur yang optimum . Hal tersebut berpengaruh terhadap biaya pengiriman tahu dan ketepatan waktu barang sampai ke pelanggan . Algoritma Genetika adalah salah satu metode pemecahan masalah yang sangat bagus karena memiliki pilihan pemecahan masalah yang sangat kompleks . Dengan menggunakan data jarak antar titik pengiriman algoritma genetika bisa memberikan solusi yang tepat jalur mana yang paling optimum yang bisa dilalui driver ketika melakukan pengiriman tahu, sehingga biaya distribusi bisa lebih minimum dan waktu yang dibutuhkan untuk melakukan pengriman bisa lebih cepat . Hasil pengujian kelayakan terhadap Aplikasi Rute pengiriman ini mendapatkan nilai presentase sebesar 81,2 % sehingga dapat disimpulkan bahwa aplikasi rute pengiriman layak digunakan.