Agr. Nat. Resour. 55 (2021) 377–386

AGRICULTURE AND NATURAL RESOURCES

Journal homepage: http://anres.kasetsart.org

Research article Causal factors and models of human-Tapanuli orangutan conflict in Batang Toru landscape, ,

Wanda Kuswandaa,*, Ramadhan Hamdani Harahapb,†, Hadi Sukadi Alikodrac,†, Robert Sibaranid,† a Environmental and Forestry Research and Development Institute of Aek Nauli, Simalungun 21139, Indonesia b Faculty of Social and Political Science, Universitas Sumatera Utara, 20222, Indonesia c Faculty of Forestry, Bogor Agricultural Institute-IPB, Bogor 16680, Indonesia d Faculty of Cultural Science, Universitas Sumatera Utara, Medan 20222, Indonesia

Article Info Abstract

Article history: The number of human-Tapanuli orangutan conflicts continues to increase and is increasing Received 28 June 2020 the threat of extinction for this animal. The factors and models causing human-Tapanuli Revised 3 March 2021 Accepted 16 April 2021 orangutan conflicts were analyzed for use as a reference in developing mitigation Available online 18 June 2021 strategies in the Batang Toru Landscape, North Sumatra, Indonesia. Data collection used the strip transect method, questionnaires, interviews and observations, followed Keywords: by logistic regression analysis. The results indicated that human-orangutan conflicts Batang Toru, Community, increased with the development of community activities such as firewood collection Logistic regression, (89.97%), gardening (84.52%), logging (80.38%) and frequent orangutan foraging in Tapanuli orangutan, farmer gardens (incidence/transect area). The cultivated plants were generally a suitable Wildlife conflict orangutan food source, such as Durio zibethinus Murray and Parkia speciosa Hassk. Factors causing the significant (p < 0.05) increase in conflict opportunities were the number of food species at tree level, logging activities and community crop damage. Recommended conflict mitigation strategies to improve the ecological conditions were patrolling in conservation forests and habitat rehabilitation with a focus on State forests. Alternative economic ventures could be developed, such as providing compensation and increasing community access rights in social forestry schemes.

Introduction (Karanth et al., 2018; Matseketsa et al., 2019). The most intense conflicts occur in developing countries where the majority of Human-wildlife conflicts are increasing due to deforestation, the population lives in rural areas as farmers (Seoraj–Pillai agricultural expansion, climate change and human activities and Pillay, 2017). Such conflict has resulted in wildlife, including orangutans, suffering from stress, poisoning, capture, † Equal contribution. injury and death caused by humans, resulting in their greater * Corresponding author. E-mail address: [email protected] (W. Kuswanda)

online 2452-316X print 2468-1458/Copyright © 2021. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/), production and hosting by Kasetsart University of Research and Development Institute on behalf of Kasetsart University.

https://doi.org/10.34044/j.anres.2021.55.3.07 378 W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386 vulnerability to extinction (Atmoko et al., 2014; Anand and regency cover approximately 520 km2 spread over two habitat Radhakrishna, 2017). blocks, namely the East and West (South) Blocks, which are The Tapanuli orangutan (Pongo tapanuliensis) is one of located in conservation forests (nature reserves) and their species that often comes into conflict with humans due to land buffer zones, both in production forests and other use areas clearing for monoculture plantations and mining, competition (Kuswanda, 2014; Rahman et al., 2019) in Fig. 1. The altitude for food and hunting (Kuswanda, 2014; Wich et al., 2016). of the study sites is in the range 400–1,875 m above sea level The Tapanuli orangutan was recognized as the third species (asl) with undulating topography and slopes of 8–60%. At least of orangutans based on its mitochondrial DNA differing 20 villages (17,347 people) co-exist in this orangutan habitat, from other orangutans in Sumatra (Pongo abelii Lesson) and and most of these villages have experienced human-orangutan Kalimantan (Pongo pygmaeus Linnaeus) (Nater et al., 2017). conflict (Statistics of South Tapanuli Regency, 2019). The remaining population of Tapanuli orangutans is limited to the Batang Toru Landscape, Tapanuli, North Sumatra (Kuswanda, 2014; Haryanto et al., 2019). The orangutan distribution is estimated to cover approximately 1,460 km2 or 59% of the Batang Toru Landscape and is administratively included in the South, Central and North Tapanuli regencies (Rahman et al., 2019). Human-Tapanuli orangutan conflicts generally occur in the South Tapanuli regency because the orangutan habitat around conservation forests and buffer zones has been converted into community cultivation land. Tapanuli orangutan nest and find food in gardens and fields (Atmoko et al., 2014; Kuswanda, 2014), which sometimes results in damage to community crops (Hockings and Humle, 2010; Davis et al., 2013; Soulsbury and White, 2015; Noga et al., 2018). In some buffer villages Fig. 1 Tapanuli orangutan habitats in South Tapanuli regency, North in South Tapanuli regency, orangutans entering plantations and Sumatra, Indonesia fields will be driven off because farmers consider them as plant pests (Kuswanda, 2014; Nater et al., 2017). Data collection Conflict mitigation is essential as Tapanuli orangutans have been categorized as a critically endangered species in the Factors predicted to cause orangutan conflicts were used IUCN Red List of Threatened Species (Nowak et al., 2017). as the independent variable (X) and the potential conflict (Y) The objectives of the current study were to analyze the factors were identified through direct observation on transect and and to develop models of human-Tapanuli orangutan conflicts vegetation plots. The data collected focused on the habitat in the Batang Toru Landscape, South Tapanuli regency, North component, referring to some research results such as Wich Sumatra. Studies regarding the Tapanuli orangutan, especially et al. (2012), Alikodra (2019), Haryanto et al. (2019), Deák et on conflict mitigation with humans, are still limited (Haryanto al. (2020), human activity factors (Dickman, 2010; Margulies et al., 2019). The results of the current study could be used and Karanth, 2018; Mukeka et al., 2019; Siljander et al., 2020), to formulate recommendations for developing orangutan other specific factors such as damage to community crops conservation strategies in Indonesia. and the presence of animals/orangutans (Hill et al., 2012; Soulsbury and White, 2015; Anand and Radhakrishna, 2017; Materials and Methods Rusch and Gavrilets, 2017; Torres et al., 2018). Field observations were made along line transects (500 Research sites m long) based on a stratified sampling method using habitat blocks and forest status. In total, 49 transect observations This study was undertaken in the Batang Toru Landscape, were installed, with 17 on the East Block and 32 on the West South Tapanuli regency, Indonesia, from July to December (South) Block. Habitat potential was recorded using vegetation 2019. Tapanuli orangutan habitats in the South Tapanuli analysis based on the strip transect method. In one transect, six W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386 379 plots were established 100 m apart, with a total of 294 plots. Ghosal, 2020). The data were analyzed using the Statistical The observed crops were recorded on differing plot sizes and Package for Social Science (SPSS) 23.0 software for Windows the focused at the level of poles and trees because orangutans (IBM Corp. SPSS Statistics; Somers, NY, USA). are arboreal: plants with a diameter at breast height over bark The ecological factors analyzed referred to Wich et al. (dbh) of 10–20 cm (poles) used a 10 m × 10 m plot and trees (2012), Kuswanda (2014), Haryanto et al. (2019) and Deák with a dbh above 20 cm were recorded on a 20 m × 20 m plot et al. (2020) and were defined as: dominant land cover (X1), (Franzreb, 1981; Sugardjito et al., 1987; Alatar et al., 2012; number of vegetation species of trees (X2) and poles (X3), Hardus et al., 2012; Lillo et al., 2019). The data observed vegetation density of trees (X4) and poles (X5), number of included land cover type based on the classification in the forage species of trees (X6) and poles (X7), and food density Indonesian National Standard (2014), species and numbers for of trees (X8) and poles (X9). Human activity factors that were each species of vegetation and forage trees. thought to cause human-wildlife conflict were: land clearing Descriptive observations were made to determine the (X10), tree cutting (X11), animal hunting (X12) and extraction presence of orangutans (direct, nesting and former food of non-timber forest products (X13) based on Dickman (2010), collection), crop damage caused by orangutans and potential Ranjan (2016), Margulies and Karanth (2018) and Siljander conflicts, with the assessment classification being present (1) et al. (2020). Specific factors that could lead to conflict and abstaining (0) according to Ministry of Forestry (2008). were: damage to community crops (X14) and the presence Potential conflicts were events that could lead to human- of orangutans (X15), based on Soulsbury and White (2015), Tapanuli orangutan conflicts. The observation criteria were Anand and Radhakrishna (2017) and Rusch and Gavrilets (2017). based on finding an orangutan nest, past activity or at least one human activity event (Nailufa et al., 2015; Mukeka et al., 2019; Results Siljander et al., 2020). Data collection was also carried out on the local community Vegetation density and orangutan food plants using mixed methods through the distribution of questionnaires, interviews and descriptive observations (Rakshya, 2016; Noga The analysis results of the vegetation and plant food for et al., 2018). About 15% (172 people) of the population in nine orangutans based on observations in 294 plots are presented in conflict villages were purposively selected as respondents who Table 1. had met or interacted with Tapanuli orangutangs that had often visited their land. Each respondent represented one family, both Community activities in orangutan habitat men and women (Kuswanda and Barus, 2018). Communities in the Batang Toru Landscape have used Data analysis forest products and are regularly active in the habitat of the Tapanuli orangutan (Table 2). The densities of vegetation and food trees were analyzed The majority of people living in Tapanuli orangutan habitat using equations by Alatar et al. (2012) and Alikodra (2019). used their gardens, fields or forest almost every day, with only The frequency tables describing the levels of community 2.99% of them visiting occasionally every month. A few people activity in orangutan habitat were analyzed using equations rarely went to the fields because they had other livelihoods, from Kuswanda (2014). The conflict causes were analyzed such as being traders or government employees. The activities using logistic regression following Jain et al. (2020) and Wei mostly carried out in the fields were taking firewood (89.97%), and Ghosal (2020). A logistic regression model was developed gardening (84.52%), cutting trees (80.38%) and farming to identify the main factors causing conflict. The independent (79.81%). Every day, farmers conducted various activities variable (X) was based on ecological variables, human socio- within the orangutan habitat, causing conflicts with orangutans. economic activities and specific variables. The dependent variable (Y) was whether or not there was a chance of conflict Causal factors and models of human-Tapanuli orangutan conflicts occurring at each study location (Nailufa et al., 2015; Mukeka et al., 2019; Siljander et al., 2020). The use of logistic regression Based on the descriptive statistics, there were differences in is suitable for Y variables that can be dichotomous and where the average values ​​of each variable causing conflict, from the the X data are not spread normally (Jain et al., 2020; Wei and observed results on 49 transects, as presented in Table 3. 380 W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386

Table 1 Species number and densities of vegetation and food plants in orangutan habitat Vegetation Food plant Growth Plot area Block Site Number Density Number Density level (ha) of species (individuals/ha) of species (individuals/ha) Dolok Sipirok Tree 67 261.57 40 168.06 2.16 Nature Reserve Pole 53 492.59 32 316.67 East Tree 77 186.46 42 112.50 Buffer zone 1.92 Pole 69 529.17 36 262.50 Dolok Sibuali Tree 76 194.44 48 128.70 2.16 West Nature Reserve Pole 62 416.67 40 250.00 (South) Tree 102 156.34 55 112.32 Buffer zone 5.52 Pole 91 405.07 47 245.65 Data source: Kuswanda et al. (2021)

Table 2 Types of community activities in orangutan habitat East block West (south) block Activity by respondent Total Percentage Total Percentage (3 villages, n = 61) (%) (6 villages, n = 111) (%) Frequency of entering habitat (forests and gardens) a. Never/once in a while (once a month) 2 3.28 3 2.70 b. Rarely (2–times a month) 14 22.95 21 18.92 c. Often (more than five times a month) 45 73.77 86 77.48 Activities carried out in orangutan habitat Yes No Yes No Yes No Yes No a. Cutting down trees 53 8 86.89 13.11 82 29 73.87 26.13 b. Gardening 52 9 85.25 14.75 93 18 83.78 16.22 c. Planting paddy/rice 49 12 80.33 19.67 88 23 79.28 20.72 d. Handling cattle 7 54 11.48 88.52 14 97 12.61 87.39 e. Taking firewood 57 4 93.44 6.56 96 15 86.49 13.51 f. Taking sap (sugar) water 33 28 54.10 45.90 72 39 64.86 35.14 g. Hunting for wildlife 14 47 22.95 77.05 82 29 73.87 26.13 h. Taking non-timber forest products 38 23 62.30 37.70 54 57 48.65 51.35 i. Crossing into other villages 21 40 34.43 65.57 34 77 30.63 69.37

Table 3 Results of descriptive statistics of causal factors of human-orangutan conflicts Variable Mean (n =4 9) SD Y Potential conflicts 0.69 0.47 X1 Dominant land cover 4.18 2.82 X2 Number of vegetation species of tree-level 18.69 6.47 X3 Number of vegetation species of pole-level 13.88 5.67 X4 Tree vegetation density (individuals/ha) 176.02 46.51 X5 Pole vegetation density (individuals/ha) 419.73 108.90 X6 Number of food species of tree-level 11.80 4.60 X7 Number of food species of pole-level 7.78 3.31 X8 Tree food density (individuals/ha) 116.83 39.77 X9 Pole food density (individuals/ha) 232.65 90.07 X10 Land clearing (incidence/transect area) 0.53 0.50 X11 Tree logging (incidence/transect area) 0.71 0.46 X12 Wildlife hunting (incidence/transect area) 0.22 0.42 X13 Taking non-timber forest products (incidence/transect area) 0.71 0.46 X14 Damage to community plants (incidence/transect area) 0.31 0.47 X15 Presence of orangutans (incidence/transect area) 0.86 0.35 W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386 381

The indicators of multicollinearity were analyzed based on (XI, X2, X4 and X15) with a 50% correlation value was the tolerance value (TOL) and from the variance inflation factor excluded from the model (Appendix 1). (VIF). Three independent variables (X1, X2, X4) had TOL Table 5 shows the estimated values of the parameters values less than 0.10 and VIF values more than 10, indicating and the interpretation of the independent variables for the that other variables could explain these variables (Table 4). The preparation of the logistic regression model. The SPSS output correlation matrix showed that some independent variables in the last step (7th) shows that the model can predict with an (X1-X10, X2-X6, X4-X8, X14-X15) had correlation values accuracy of 93.9% so that the model of fit (acceptable), as above 50% (moderate-high correlation value). One variable presented in Appendix 2.

Table 4 Result of multicollinearity testing on 15 independent variables Unstandardized Collinearity statistics Model t p-value coefficient (β) Tolerance Variance inflation factor 1 (Constant) -1.11 -3.65 0.00 X1 0.03 0.75 0.46 0.09 11.10 X2 -0.01 -0.48 0.64 0.08 11.87 X3 -0.01 -0.83 0.41 0.27 3.66 X4 0.00 0.67 0.51 0.08 12.75 X5 0.00 -0.08 0.93 0.50 2.01 X6 0.03 1.84 0.08 0.17 6.00 X7 0.01 0.49 0.63 0.12 8.27 X8 0.00 -1.06 0.30 0.10 9.61 X9 0.00 0.45 0.66 0.30 3.35 X10 0.00 -0.01 1.00 0.14 7.25 X11 0.36 2.97 0.01 0.32 3.12 X12 0.26 3.03 0.01 0.77 1.31 X13 0.36 3.61 0.00 0.46 2.16 X14 0.00 0.01 0.99 0.33 3.02 X15 1.05 7.64 0.00 0.41 2.45

Appendix 1 The correlation matrix between independent variables Correlation coefficient Model X15 X9 X12 X14 X3 X11 X8 X5 X13 X6 X10 X7 X4 X1 X2 X15 1.00 0.00 0.08 -0.50 0.05 -0.24 -0.10 0.18 0.14 0.00 0.16 0.06 0.02 0.28 -0.14 X9 0.00 1.00 0.11 0.19 0.10 -0.07 -0.21 -0.41 0.09 -0.05 -0.34 -0.51 -0.11 0.12 0.42 X12 0.08 0.11 1.00 -0.07 0.25 -0.05 0.16 0.00 -0.07 0.08 0.26 -0.29 -0.16 -0.17 -0.05 X14 -0.50 0.19 -0.07 1.00 -0.11 -0.02 -0.25 -0.01 -0.11 0.25 -0.39 -0.15 0.13 -0.01 0.05 X3 0.05 0.10 0.25 -0.11 1.00 0.02 0.35 -0.07 0.02 0.06 0.07 -0.55 -0.35 -0.17 -0.06 X11 -0.24 -0.07 -0.05 -0.02 0.02 1.00 0.28 -0.27 -0.31 -0.05 -0.04 0.04 -0.16 -0.40 -0.02 X8 -0.10 -0.21 0.16 -0.25 0.35 0.28 1.00 0.11 -0.26 -0.39 0.15 -0.33 -0.86 -0.45 0.36 X5 0.18 -0.41 0.00 -0.01 -0.07 -0.27 0.11 1.00 -0.06 0.19 0.18 0.00 -0.07 -0.04 -0.25 X13 0.14 0.09 -0.07 -0.11 0.02 -0.31 -0.26 -0.06 1.00 -0.06 -0.11 0.13 0.23 0.06 -0.11 X6 0.00 -0.05 0.08 0.25 0.06 -0.05 -0.39 0.19 -0.06 1.00 0.28 -0.09 0.32 -0.14 -0.67 X10 0.16 -0.34 0.26 -0.39 0.07 -0.04 0.15 0.18 -0.11 0.28 1.00 -0.01 -0.02 -0.58 -0.39 X7 0.06 -0.51 -0.29 -0.15 -0.55 0.04 -0.33 0.00 0.13 -0.09 -0.01 1.00 0.54 0.31 -0.40 X4 0.02 -0.11 -0.16 0.13 -0.35 -0.16 -0.86 -0.07 0.23 0.32 -0.02 0.54 1.00 0.42 -0.53 X1 0.28 0.12 -0.17 -0.01 -0.17 -0.40 -0.45 -0.04 0.06 -0.14 -0.58 0.31 0.42 1.00 0.14 X2 -0.14 0.42 -0.05 0.05 -0.06 -0.02 0.36 -0.25 -0.11 -0.67 -0.39 -0.40 -0.53 0.14 1.00

Table 5 Variable in equation of logistic regression model 95% C.I. for EXP(β) Variable β SE df p-value Exp (β) Lower Upper Step 7a X6 0.75 0.27 1 0.01 2.12 1.26 3.59 X11 4.43 1.79 1 0.01 83.76 2.52 2,779.95 X12 3.82 1.96 1 0.05 45.43 0.98 2,109.93 X14 5.46 2.12 1 0.01 234.01 3.65 14,999.01 Constant -12.25 4.40 1 0.01 0.00 C.I. = confidence interval; β = parameter estimate (value of regression coefficient) 382 W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386

Appendix 2 The analysis results of the classification table Classification Tablea Predicted Observed Y Percentage Correct 0 1 0 12 3 80.0 Y Step 1 1 1 33 97.1 Overall Percentage 91.8 0 12 3 80.0 Y Step 2 1 0 34 100.0 Overall Percentage 93.9 0 12 3 80.0 Y Step 3 1 1 33 97.1 Overall Percentage 91.8 0 12 3 80.0 Y Step 4 1 1 33 97.1 Overall Percentage 91.8 0 12 3 80.0 Y Step 5 1 0 34 100.0 Overall Percentage 93.9 0 12 3 80.0 Y Step 6 1 0 34 100.0 Overall Percentage 93.9 0 12 3 80.0 Y Step 7 1 0 34 100.0 Overall Percentage 93.9 a. The cut value is 0.500

In the last step (7th) of the backward stepwise regression, Discussion there were three significant (p < 0.05) independent variables: the number of food species at the tree level (X6), tree cutting Land cover, vegetation and community activities activities (X11) and damage to community crops (X14), with one independent no significant variable that was the activity The habitat of the Tapanuli orangutan, primary and of hunting animals (X12). No significant variable (close to secondary forests were dominant in natural reserves, while α = 0.05) was excluded in the modeling because of its slight other land cover types were located in the buffer zones. In the effect on the change in the dependent variable or possibility for nature reserves, agroforestry lands were also found because human-orangutan conflicts (Y). With or without entering these the community had cleared the forest before its designation variables, the impacts on the dependent variable tended to be as a nature reserve (Kuswanda et al., 2021). The Tapanuli the same. The logistic regression model for the probabilities of people have managed the land for hundreds of years in the the formed conflicts are presented in Equation 1 or 2: Batang Toru Landscape as a livelihood (Kuswanda, 2014). Table 1 shows that the amount of vegetation and forage plants p Ln = = -12.25+0.75 X6+4.43 X11+5.46 X14 (1) in the buffer zone was higher than for the nature reserve. The p-1 buffer zone contained species of community cultivation plants, or such as mixed forest types and agroforestry, which are also orangutan food plants. -12.25+0.75 X6+4.43 X11+5.46 X14 The number of plant species and orangutan food plants y = p = e (2) 1+e-12.25+0.75 X6+4.43 X11+5.46 X14 identified in the West (South) Block was higher than in the East Block, though they were present at lower densities. The This model showed that increases in the community crop Eastern Block was still dominated by primary forests, while damage (X14), illegal logging activities (X11) or forage the West (South) Block was dominated by cultivated land. species at the tree level (X6) in orangutan habitat would lead to Some tree species were also limited West (South) Block, such high conflict. as Lithocarpus pseudomoluccus (Blume) Rehder, Podocarpus W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386 383 imbricatus Bl. Var. and Cinnamomum burmannii Nees & Th. community anger and had resulted in individuals driving out Nees. The dominant altitude of the West Block (South) was and even killing the orangutans (Cheyne et al., 2013; Davis et 500–900 m asl, while in the East Block it was 700–1,110 m asl al., 2013; Kanamori et al., 2017). The current study confirmed (Kuswanda et al., 2021). that orangutan conflicts continued to increase in buffer areas, The differences in the vegetation and forage plant densities both in production forest areas and in other use areas, on between the West (South) and East Blocks had affected the locations that had become community-cultivated land. distribution of the Tapanuli orangutans. The orangutan density Other activities by communities were collecting sap water, in the West (South) Block was predicted to be approximately cooking palm sugar (63.05% of respondents), looking for 0.53 individuals/km2 with 0.44 individuals/km2 in the East Block non-timber forest products (58.15%) and hunting for wildlife (Kuswanda et al., 2020). However, the high density of orangutans, (52.48%). Taking rattan, bark, latex, traditional medicines and especially on the community lands, had increased the number hunting for wildlife were conducted in the nature reserve area. of conflicts. Five orangutans were identified that frequently ate People who hunted for wildlife sometimes stayed 2–3 days in community crops in the West (South) Block buffer area. the forest and took any species that they could catch, such as In the West (South) Block, extensive logging to convert birds, anteaters, deer and forest goats. Community members forests into agricultural land was continuing (Wich et al., also set wire snares or made pitfall traps for pigs, deer and 2016), so that orangutan habitat had become secondary even Sumatran tigers. Rogan et al. (2018) stated that as long forest dominated by pioneer plants (fast-growing) such as as law enforcement remained weak, hunters would continue to Macaranga hosei King and Macaranga lowii King ex Hook.f., carry out their activities without concern for the importance of and community cultivation crops like those found in the wildlife conservation. Enforcement of the rule of law was very Gunung Leuser National Park (Kuswanda and Sunandar, important to drive the suppression of illegal hunting of animals, 2019). The conversion of primary forest to agricultural land has especially in conservation forests, such as nature reserves been reported to be positively correlated with human-wildlife (Lindsey et al., 2013). The variety of community activities conflicts (Mukeka et al., 2019). and the high frequency of people entering orangutan habitat Collecting firewood was the most frequent activity carried resulted in a very high incidence of orangutan encounters with out by local people. Local people always brought wood when humans, which in turn increased the probability of conflict and returning from the garden or fields. The firewood was obtained a serious threat on the life of orangutans (Hockings and Humle, by removing branches and even cutting down trees on their 2010; Scanes, 2018). land. The firewood was used for producing palm sugar, cooking and for selling. Other daily activities by local people in the Causal factors of human-orangutan conflicts fields were maintaining, harvesting and protecting plants from wildlife disturbance. When there was no work in the garden, The multicollinearity test and correlation matrix (Appendix 1) they cultivated palawija (alternative plants) and vegetables in showed that X1 could be explained by X10, X2 by X6, X4 the fields. Intensive human activities in the wildlife habitat, by X8, X7 by X9 and X14 by X15. These variables showed especially in unprotected areas (buffer zones), could disturb the possibility of a high correlation, such as X10 strongly animals and increase conflicts, such as has been reported in influenced X1, while X2 could affect X6. We observed that Africa (Anand and Radhakrishna, 2017). land clearing activity (X10) in developing gardens had an Human-orangutan conflicts often occur during the fruiting impact on decreasing forest cover/canopy (X1) that orangutan season because the plants cultivated by the community such movement was increasingly limited. Likewise, an increase as durian (Durio zibethinus Murray), petai (Parkia speciosa in the presence of orangutans (X15) on community land can Hassk) and jengkol (Archidendron jeringa [Jack] I.C. Nielsen) increase crop damage and failure to cultivated crops (X14). The are also food for orangutans (Kuswanda et al., 2021). Orangutan acceptable fit of a logistic regression model can be seen from 2 activities on farmers’ lands cause crop failure and economic the value of the likelihood ratio χ counting using the value of Cox losses up to IDR 10 million. According to the respondents, & Snell’s R2 (R Square) or Nagelkerke R2 (Ahmed, 2017). In orangutan activities in a durian or petai tree can damage the current study, the Nagelkerke R2 value shows that the Y axis 80–90% of fruits. The current study identified that orangutans can be explained by the X axis by 74.3% and the remaining also consume raw (unripe) fruits. On some community lands, 26.7% by other factors. The Nagelkerke R2 value also shows hundreds of raw durians were knocked to the ground when that the model fits a correlation value of 74.3% (Appendix 3). orangutans were active in a tree. This condition triggered 384 W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386

Appendix 3 The analysis results of the model summary from The Villagers who found signs of the presence of orangutans Nagelkerke R2 value would clean their gardens and build huts to make it easier to Step -2 Log Cox & Snell Nagelkerke monitor orangutans and drive them for the village gardens. likelihood R Square R Square 1 17.951a 0.579 0.818 Farmers always had a fire daily producing smoke near their 2 22.439b 0.539 0.761 huts to repel animals, especially orangutans. Orangutans found 3 22.446b 0.539 0.761 it difficult to take food and would move to other locations when b 4 22.599 0.537 0.759 they were evicted by the owners often visiting the land. Moving 5 23.141b 0.532 0.751 to other zones would have negative impacts for the orangutans 6 23.947c 0.524 0.740 7 24.408c 0.520 0.734 since there they would have to compete with individuals or a. Estimation terminated at iteration number 20 because maximum other primate species, have low food availability or possibly iterations has been reached. Final solution cannot be found. have increased encounters with humans. b. Estimation terminated at iteration number 8 because parameter estimates changed by less than 0.001. Implications of mitigation for human-orangutan conflicts c. Estimation terminated at iteration number 7 because parameter estimates changed by less than 0.001. Mitigation programs to save orangutans involved in Logging causes a decrease in the habitat carrying capacity, conflicts need to be developed immediately (Siljander et al., diversity of vegetation and food plants, as well as fragmenting 2020). When conflicts occur, orangutans tend to be excluded habitat, so that orangutans will move to safer habitats with even if they survive their needs for nutritional food, a home adequate foods and available nest trees (Casteren et al., 2012: range and social behavior (Makindi et al., 2014). Human- Wich et al., 2014; Kanamori et al., 2017). Crop damage has Tapanuli orangutan conflicts often occurred in buffer areas, also been reported as a trigger for human-wildlife conflicts in especially on the community cultivated land around the nature various countries (Rogan et al., 2018; Mukeka et al., 2019). reserves. Conflict mitigation strategies can be developed to The model simulation was conducted by entering real increase the carrying capacity and to reduce the activity of conditions based on observations, as presented in Fig. 2. utilizing forest and land resources in orangutan habitat. Conflicts will occur if X6 is less than six species and there is Based on the current study, various strategies and programs logging activity (X11) or damage to the community cultivated can be recommended to mitigate human-orangutan conflicts plants (X14). Conflicts will continue to increase in locations in the Batang Toru Landscape and protecting habitat. 1) In where fodder species and human activities are high. The the short period, it is suggested to increase security and the possibility of conflicts occurring in buffer zones was correlated capacity of conservation forest managers, such as forest with the number of community plants consumed by orangutans. rangers and add patrol locations that can be focus on villages In buffer zones, orangutans also prefer locations with abundant where communities have interactions and high levels of conflict food sources (Sugardjito et al., 1987; Casteren et al., 2012). with orangutans (Pandong et al., 2019). 2) In the medium term, The habitat preferred by orangutans was mixed-garden and habitat could be enriched through forest and land rehabilitation agroforestry. In agroforestry, people do not cut down the trees with various species of food plants and nest trees. 3) In the from the Moraceae, Lauraceae or Fagaceae families that can be longer term, consideration should be given to developing used as a food source by orangutans. human-orangutan coexistence areas in production forests and

1.100 buffer zones, especially along rivers and on steep slope (> 45%) 1.000 and in high conservation value forest, with the development of 0.900 0.800 linking corridors for use by orangutans currently cut off due to 0.700 0.600 human activities and land use (Tscharntke et al., 2012). 0.500 Some programs are needed to minimize community 0.400 X11=0 X14=0

Potential of conflict 0.300 X11=1 X14=0 activities in orangutan habitats. 1) In the short term, there 0.200 X11=0 X14=1 X11=1 X14=1 0.100 could be compensation for the losses in the community as the 0.000 X6=4 X6=5 X6=6 X6=7 X6=11 X6=13 X6=15 X6=17 X6=19 X6=21 X6=23 ‘victims’ conflicts do not involve cash (Karanth et al., 2018; Variable X Matseketsa et al., 2019) and the creation of opportunities for Fig. 2 Model simulation of causes of human-orangutan conflict where alternative economic businesses, such as ecotourism, animal X6 = number of forage species of tree, X11= amount of tree cutting and husbandry and agriculture to reduce forest deforestation and X14=damage to community crops animal migration (Weiler et al., 2020). 2) For the medium W. Kuswanda et al. / Agr. Nat. Resour. 55 (2021) 377–386 385 period, access rights could be provided for social forestry and arthropod species. Biol. 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