Research ArticlePOPULATION ECOLOGY

Land-cover changes predict steep declines for the Sumatran orangutan (Pongo abelii)

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Science Advances  04 Mar 2016:
Vol. 2, no. 3, e1500789
DOI: 10.1126/sciadv.1500789
  • Fig. 1 Map of the study area showing the Sumatran orangutan distribution in northern Sumatra.

    The locations of transects (centroids), the borders of the Leuser Ecosystem and Batang Toru (lower right on figure), and the intermediate areas between the Leuser Ecosystem and the Batang Toru area are shown.

  • Fig. 2 Predicted density of the Sumatran orangutan.
  • Fig. 3 Estimated sizes of Sumatran orangutan populations (defined as all adjacent and occupied patches below a distance of 1 km) based on recent surveys and under nine different land-use scenarios for the years 2020 and 2030, respectively.

    Populations of the Leuser Ecosystem population are represented by circles, and populations outside the Leuser Ecosystem and the Sidiangkat area are represented by circles bounded by quadrats. The size and color of each circle are proportional to the area inhabited by each population, and the size of the largest population is provided in numbers. At the bottom, the estimated population sizes are given for the entire present (T) and projected Sumatran orangutan populations (in black, A) and for the northern population only, with the areas outside the Leuser Ecosystem and Sidiangkat area omitted (in gray, N). Populations smaller than 250 individuals are not included in the estimates except for the current total number of 14,613. Note that the y-axis values are log-transformed for better visibility of small subpopulations.

  • Table 1 Loadings of the predictor variables on the four factors.

    Loadings greater than 0.5 are in bold.

    Factor 1Factor 2Factor 3Factor 4
    Elevation0.880−0.420−0.1090.083
    Carbon0.507−0.156−0.0030.484
    ft_1 (forest cover)0.0410.045−0.0070.767
    ht_3 (peat swamp)−0.2150.2430.5450.098
    ht_4 (lowland forest)−0.4350.417−0.7480.269
    ht_5 (lower montane forest)0.693−0.3710.2310.002
    Rain−0.0770.9660.1000.013
    Rain.var0.261−0.8590.0270.021
    Temperature range0.8180.201−0.0070.076
    Temperature mean−0.8900.3520.135−0.085
    % Variance explained3324109
  • Table 2 Results for full and null models.

    Significant models are in bold.

    ModelTermEstimateSEz valuePr(>|z|)
    Full model
    (AIC = 1721)
    Intercept−0.1970.061*
    Factor 1−0.2130.068−3.1520.002
    Factor 20.2340.0683.465<0.001
    Factor 30.0590.0630.9310.352
    Factor 4−0.0520.078−0.6690.504
    z.human.pop−0.1570.072−2.1950.028
    z.roads0.0090.0690.1320.895
    ac.term0.7730.06412.088<0.001
    Null model
    (AIC = 1734)
    Intercept−0.1480.064*
    ac.term0.8070.06412.611<0.001

    *Not shown because of lack of a meaningful interpretation.

    • Table 3 Abundance estimates for current orangutan distribution and future land-use scenarios based on a 1-km barrier (see figs. S3 to S12 and details in the Supplementary Materials).

      Not all land-cover scenarios cover the areas outside the Leuser Ecosystem and Sidiangkat area; so for some models, estimates without those areas are provided for comparative purposes. Note that scenario 5 is for Aceh only. Populations smaller than 250 individuals are not included in the estimates, except for the current estimated total population of 14,613. NA, not applicable.

      Scenario no.Scenario nameYearTotal abundanceAbundance of northern populations only
      0Current status201014,613/13,83513,231
      1Prediction Indonesia deforestation 1202010,63710,283
      2Prediction Indonesia deforestation 2202013,08512,546
      3Aceh and North Sumatra land use plan 1203012,52912,019
      4Aceh and North Sumatra land use plan 2203098249824
      5Aceh land-use plan2030NA7874
      6Predicted forest cover without roads low rate2030NA12,722
      7Predicted forest cover with roads low rate2030NA12,355
      8Predicted forest cover without roads high rate2030NA10,879
      9Predicted forest cover with roads high rate2030NA9085

    Supplementary Materials

    • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/3/e1500789/DC1

      Distance analysis

      Fig. S1. Histogram of detection distances: truncation distance, 32.5 m; six intervals.

      Fig. S2. Model diagnostics for the best and full model.

      Fig. S3. Distribution of orangutan populations separated by at least 1 km: Situation as of 2012.

      Fig. S4. Distribution of orangutan populations separated by at least 1 km: Scenario 1 (year 2020).

      Fig. S5. Distribution of orangutan populations separated by at least 1 km: Scenario 2 (year 2020).

      Fig. S6. Distribution of orangutan populations separated by at least 1 km: Scenario 3 (year 2030).

      Fig. S7. Distribution of orangutan populations separated by at least 1 km: Scenario 4 (year 2030).

      Fig. S8. Distribution of orangutan populations separated by at least 1 km: Scenario 5 (year 2030; note that this scenario is based on land-use change prediction for Aceh only).

      Fig. S9. Distribution of orangutan populations separated by at least 1 km: Scenario 6 (year 2030).

      Fig. S10. Distribution of orangutan populations separated by at least 1 km: Scenario 7 (year 2030).

      Fig. S11. Distribution of orangutan populations separated by at least 1 km: Scenario 8 (year 2030).

      Fig. S12. Distribution of orangutan populations separated by at least 1 km: Scenario 9 (year 2030).

      Fig. S13. The estimated sizes of orangutan populations (defined as all adjacent and occupied patches below a distance of 5 km) based on the recent survey and under nine different land-use scenarios for the years 2020 and 2030, respectively.

      Fig. S14. Distribution of orangutan populations separated by at least 5 km: Situation as of 2012.

      Fig. S15. Distribution of orangutan populations separated by at least 5 km: Scenario 1 (year 2020).

      Fig. S16. Distribution of orangutan populations separated by at least 5 km: Scenario 2 (year 2020).

      Fig. S17. Distribution of orangutan populations separated by at least 5 km: Scenario 3 (year 2030).

      Fig. S18. Distribution of orangutan populations separated by at least 5 km: Scenario 4 (year 2030).

      Fig. S19. Distribution of orangutan populations separated by at least 5 km: Scenario 5 (year 2030; note that this scenario is based on land-use change prediction for Aceh only).

      Fig. S20. Distribution of orangutan populations separated by at least 5 km: Scenario 6 (year 2030).

      Fig. S21. Distribution of orangutan populations separated by at least 5 km: Scenario 7 (year 2030).

      Fig. S22. Distribution of orangutan populations separated by at least 5 km: Scenario 8 (year 2030).

      Fig. S23. Distribution of orangutan populations separated by at least 5 km: Scenario 9 (year 2030).

      Table S1. Detection model selected, parameters estimated and their variance, probability density function evaluated at distance zero [f(0)], detection probability (p), and ESW.

      Table S2. Results of χ2 goodness of fit test on detection model.

      Table S3. Decay times, 95% confidence levels (lower confidence level, upper confidence level) and nest sample sizes from five sites.

      Table S4. Summed AIC weights for the six predictors.

      Table S5. Selected predictors for the Sumatran orangutan density distribution model.

      Table S6. Spearman correlations between all predictors.

      Table S7. Abundance estimates for current orangutan distribution and future land-use scenarios based on a 5-km barrier.

      References (5356)

    • Supplementary Materials

      This PDF file includes:

      • Fig. S1. FESEM characterizations of SiO2@TiO2/HDA core-shell spheres and SiO2@aTiO2 yolk-shell spheres.
      • Fig. S2. DLS analysis of SiO2 templates and SiO2@TiO2/HDA spheres.
      • Fig. S3. Small-angle XRD analysis of the particles with mesostructured TiO2 shells.
      • Fig. S4. FTIR study on the interactions between TiO2 and HDA.
      • Fig. S5. Wide-angle XRD analysis of the particles with aTiO2 shells.
      • Fig. S6. N2 sorption analysis of SiO2@aTiO2 and SiO2@TiO2/HDA samples.
      • Fig. S7. FTIR study of surfactant removal.
      • Fig. S8. TEM characterizations of SiO2@TiO2/HDA spheres and aTiO2 hollow spheres.
      • Fig. S9. N2 sorption analysis of aTiO2 hollow spheres.
      • Fig. S10. FESEM characterizations of mesoporous aTiO2 hollow spheres.
      • Fig. S11. TEM characterizations of SiO2@cTiO2 spheres.
      • Fig. S12. FTIR and N2 sorption analysis of SiO2@cTiO2 spheres.
      • Fig. S13. Wide-angle XRD analysis of the particles with cTiO2 shells.
      • Fig. S14. N2 sorption analysis of cTiO2 hollow spheres.
      • Fig. S15. Elemental analysis of cTiO2 hollow spheres.
      • Fig. S16. TEM and XRD characterizations of SiO2@TiO2/HDA spheres treated with 0.1 M HCl and 0.1 M NaOH solutions.
      • Fig. S17. TEM characterizations of the formation process of Au@TiO2 yolk-shell spheres.
      • Fig. S18. FESEM and TEM characterizations of the formation process of Fe2O3@TiO2 yolk-shell particles.
      • Fig. S19. FESEM and TEM characterizations of the formation process of TiO2-polymer double-shell hollow spheres.
      • Fig. S20. FESEM characterizations of different TiO2 core-shell composites.
      • Fig. S21. FESEM characterizations of CN and MOF templates.
      • Fig. S22. Wide-angle XRD analysis of different functional cores.
      • Fig. S23. TEM characterizations of CNT@TiO2 nanofibers.
      • Fig. S24. TEM characterizations and small-angle XRD analysis of different TiO2 core-shell composites.
      • Fig. S25. Cyclic voltammetry characterization of cTiO2 hollow spheres.
      • Fig. S26. Electrochemical characterizations of cTiO2 hollow spheres as an anode material in LIBs.
      • Fig. S27. FESEM characterization of cTiO2 hollow spheres before and after cycling test.

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