No abstract

Abstract. Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorithms of species distribution modeling that widely used to predict the potential distribution pattern of diverse organisms. Finding the best model in terms of predicting the potential distribution of many species remains a challenge. The objective of this study is to compare six different algorithms for predicting the potential current distribution pattern of Guettarda speciosa (zebra wood). The occurrence records of G. speciosa are derived from herbarium database, Bogor Botanic Gardens’s plant inventory database and direct field surveys through NKRI expedition.  Seven climatic variables and elevation data are extracted from global data. R open-source software is used to run those algorithms and QGIS is used to prepare the spatial data.  The result shows that MAXENT outperforms other predictive models with the highest AUC score 0.89, followed by SVM (0.87), RF (0.86), and GLM (0.82), DOMAIN (0.73), and BIOCLIM (0.62). Based on the AUC score, the four predictive models (MAXENT, SVM, RF, GLM) are categorized into good predictive models, indicating those are quite better to predict the potential current distribution pattern of G. speciosa. Whereas, DOMAIN is fair predictive model and BIOCLIM is poor predictive model. The predictive map derived from four models (MAXENT, SVM, RF, and GLM) shows almost similar appearance in predicting of potential current distribution of G. speciosa. The predictive map of current distribution would be useful to provide information regarding the potential habitat of G. speciosa across the landscape of Indonesia.

Pengelolaan biodiversitas nasional hanya akan efektif jika penggalian potensi sejalan dengan upaya konservasinya secara berkesinambungan. Status lahan Kebun Raya (KR) yang tetap dan tidak dapat dialihfungsikan merupakan jaminan kelestarian tumbuhan di dalamnya. Penelitian ini bertujuan untuk mengungkap potensi tutupan vegetasi Kebun Raya Indonesia (KRI) sebagai bentuk sinergi antara konservasi tumbuhan termasuk pemanfaatannya dengan program lintas tema Pemerintah dalam upaya penurunan emisi karbon. Peranan koleksi KRI telah diukur Purnomo et al. (2013), dimana sebesar 24% tumbuhan terancam kepunahan telah dikoleksi di 25 KRI. Perhitungan kandungan karbon pada tutupan vegetasi Kebun Raya dapat diukur dengan metode pendugaan cepat dengan menghitung luas tutupan dikalikan kandungan karbon jenis tutupan. Nilai C stock pada tiap tipe tutupan vegetasi ditentukan berdasarkan tetapan Masripatin et al. (2010). Hasil perhitungan nilai C stock pada semua tutupan vegetasi KRI adalah 336.058,62 tonC. Kebun Raya yang memiliki lahan luas dengan tutupan vegetasi rapat seperti KR Jambi dan KR Balikpapan berkontribusi tertinggi dengan nilai C stock masing-masing 47.293,45 tonC dan 41.033,96 tonC. Koleksi KR tua yang diwakili 4 KR LIPI memiliki C stock rata-rata 138,32 tonC/ha, sedangkan pada KR baru yang diwakili KR Batam, KR Kendari, KR Banua, dan KR Sriwijaya memiliki C stock rata-rata 45,71 tonC/ha. Kandungan karbon pada kebun raya yang telah mencapai tutupan vegetasi ideal adalah 105,81 tonC/ha.

Abstract. Yudaputra A. 2020. Modelling potential current distribution and future dispersal of an invasive species Calliandra calothyrsus in Bali Island, Indonesia. Biodiversitas 21: 674-682.  Calliandra calothyrsus Meisn. is relatively well-adapted in abandoned areas, degraded lands, and poor nutrient soils. It tends to reproduce rapidly and be invasive in certain landscapes as it often dominates the vegetation. This study aimed to understand the potential current distribution and the population dispersal of C. calothyrsus across Bali Island using Random Forest (RF) and Maximum Entropy (MaxEnt) models. Thirteen environmental variables, including several climatic variables, topography, soil characteristics were used as predictors. The occurrence records of C. calothyrsus were obtained from direct field survey in which square plots 10 x 10 m were used to collect the population structure data. The Rangeshifter software was used to understand the population dynamic and dispersal pattern. The results showed that the two models (RF and MaxEnt) have the AUC>0.9 which means those models are excellent in predicting the potential current distribution of C. calothyrsus. Furthermore, the RF model has the TSS and Kappa value of >0.90 which means it has almost perfect agreement between the prediction and the real observation. On the other hand, the TSS and Kappa value of MaxEnt were >0.70 indicating it has a substantial agreement. The population structure in the field showed that the number of juvenile individuals dominated all plots compared to seedlings and mature individuals. The simulation analysis showed that the population tends to have bigger population in the next 50 years by dispersing throughout neighbor cells or areas in which the origin occurrence points were recorded.

Abstract. Yudaputra A, Fijridiyanto I, Cropper WPJr. 2020. The potential impact of climate change on the distribution pattern of Eusideroxylon zwageri (Bornean Ironwood) in Kalimantan, Indonesia. Biodiversitas 21: 326-333. Eusideroxylon zwageri Teijsm & Binn. is a vulnerable tree species with considerable economic value. The high demand for its wood makes this species vulnerable. Population vulnerabilities of E. zwageri include habitat loss, land-use change, and forest encroachment. An additional potential risk factor is climate change, with a possible increase in temperature of about 2.5 to 10 degrees Fahrenheit over the next century. Climate is considered to be a principal factor that determines the distribution of many species. This study addresses the potential current and future distribution of E. zwageri under climate change. Seven predictor climate variables are selected from 19 climatic variables using VIF (Variance Inflation Factor) to eliminate multicollinearity among variables. The spatial data is prepared using Geographic Information System (GIS). Six species distribution models (RF, SVM, MARS, GAM, GLM) and the ensemble model are applied to understand the potential current geographical distribution of E. zwageri. For risk assessment, the potential future distribution is predicted using the ensemble model only. All models are run using R open-source software. In model evaluation, all models have AUC value >0.80, indicating those models are good predictive models. All predictive models have the TSS >0.60 which means those models having a useful agreement between prediction and real observation. Precipitation seasonality, isothermality, and precipitation of the coldest quarter are the most important model variables that influence the current and future distribution of E. zwageri. Four models (RF, SVM, GAM, GLM) produce similar predictive maps of potential current distribution. MARS produces a slightly different predictive map. The future projection of ensemble model shows that the distribution area is more likely shifted and decreased from the current to 2050 and 2070.

Abstract. Yudaputra A, Rahardjo P. 2020. Short Communication: Plant species richness and diversity in Karangsambung-Karangbolong National Geopark, Indonesia. Biodiversitas 21: 1735-1742. The information on plant species richness and diversity in Karangsambung-Karangbolong National Geopark, Central Java is very limited. This study aimed: (1) to investigate plant species richness and abundance as well as floristic composition in Karangsambung-Karangbolong National Geopark; (2) to reveal the potential uses of plant species recorded in the area. Square sampling plots were applied for nine sampling locations. Square plot of 10 x 10 m was applied to record tree, while nested plots of 5 x 5 m and 2 x 2 m were applied to record sapling and understorey plants including shrubs and herbs, respectively. The highest plant species richness was found in the location with higher elevation and mountainous topography. The species abundance reaches its maximum values at low to moderate elevation. Shannon Diversity Index (H) showed that tree and sapling have moderate diversity, whereas understorey plant has high diversity. Melastoma malabathricum, Clidemia hirta, Zingiber zerumbet, and Ageratum conyzoides are the most abundant plants in this Geopark. Most of plants recorded have potential benefit as medicinal uses.

Climate change becomes a major threat to the global biodiversity. It alters the ecological niche of species, even small change in temperature could have a significant impact to the distribution pattern of biodiversity. Cecropia peltata is an invasive species with wide range geographic distribution. The aim of this study is to understand the impact of climate change to the current and future distribution of invasive plant C. peltata. The Support Vector Machine (SVM) and Boosted Regression Tress (BRT) algorithm of machine learning were used to predict the current and future distribution. The occurrence records of C. peltata was obtained from Global Biodiversity Information Facility (GBIF). There were 2691 occurrence records in GBIF database. The global climatic variables with the resolution 2.5 km were used as predictors of model. VIF was used to select the multicollinearity among those variables using threshold of 0.7. The CIMP5 of Global Circulation Model (GCM) was used to understand the impact of climate change to the distribution of the plant. The future projection on year 2070 with the worst climate scenario RCP 8.5 was used on these predictive models. The SVM and BRT models were actually relevant to be used as predictive models with AUC >0.90 and categorised as excellent predictive models. The future distribution pattern was likely to be shifted compared to the current distribution prediction. The output of this study as predictive current and future distribution maps would be useful to provide an information about the potential area where the species might be invading based on the training data (observation data). Furthermore, the prediction of future distribution would be necessary to understand how the climate change literally affects the range of distribution of the invasive plant species.

No abstract