• Home   /  
  • Archive by category "1"

Research Papers On Human Elephant Conflict Pdf Writer

1. Karanth KK, Gopalaswamy AM, Prasad PK, Dasgupta S. Patterns of human–wildlife conflicts and compensation: Insights from Western Ghats protected areas. Biol Conserv. 2013;166:175–85.

2. Nyhus P, Tilson R. Agroforestry, elephants, and tigers: balancing conservation theory and practice in human-dominated landscapes of Southeast Asia. Agriculture, ecosystems & environment. 2004;104(1):87–97.

3. Zhang L, Ma L, Feng L. New challenges facing traditional nature reserves: Asian elephant (Elephas maximus) conservation in China. Integrative Zoology. 2006;1(4):179–87. doi: 10.1111/j.1749-4877.2006.00031.x[PubMed]

4. Zomer RJ, Trabucco A, Wang M, Lang R, Chen H, Metzger MJ, et al. Environmental stratification to model climate change impacts on biodiversity and rubber production in Xishuangbanna, Yunnan, China. Biol Conserv. 2014;170:264–73.

5. IUCN. The IUCN Red List of Threatened Species 2014 [updated 2014.3; cited 2014 10 November]. Available from: www.iucnredlist.org.

6. China Ministry of Agriculture, State Forestry Administration. National key protected wildlife directory. The state council bulletin of the People's Republic of China. 2003;23.

7. Sun G, Xu Q, Jin K, Wang Z, Lang Y. The historical withdrawal of wild Elephas maximus in China and its relationship with human population pressure. Journal of Northeast Forestry University. 1997;26(4):47–50.

8. Zhang L. Current status and research progress of Asian Elephant in China. The Biological Bulletin. 2006;42(11):1–2.

9. Zhang L. Current conservation status and research progress on Asian elephants in China. Gajah. 2007:35.

10. Sukumar R. The living elephants: evolutionary ecology, behaviour, and conservation: Oxford University Press; 2003.

11. He Q, Wu Z, Zhou W, Dong R. Perception and attitudes of local communities towards wild elephant-related problems and conservation in Xishuangbanna, southwestern China. Chin Geogr Sci. 2011;21(5):629–36. doi: 10.1007/s11769-011-0499-4

12. Xu J, Fox J, Vogler JB, Yongshou ZPF, Lixin Y, Jie Q, et al. Land-use and land-cover change and farmer vulnerability in Xishuangbanna prefecture in southwestern China. Environ Manage. 2005;36(3):404–13. [PubMed]

13. Xu J, Grumbine RE, Beckschäfer P. Landscape transformation through the use of ecological and socioeconomic indicators in Xishuangbanna, Southwest China, Mekong Region. Ecological Indicators. 2014;36(0):749–56. http://dx.doi.org/10.1016/j.ecolind.2012.08.023.

14. XSBN-NNR N. Master plan of Xishuangbanna National Nature reserve Yunnan: XSBN NNR, 2010.

15. Li Z, Ma Y, Li H, Peng M, Liu W. Relation of land use and cover change to topography in Xishuangbanna, Southwest China. Journal of Plant Ecology (Chinese Version). 2008;32(5):1091–103.

16. Chen S, Yi Z-F, Campos-Arceiz A, Chen M-Y, Webb EL. Developing a spatially-explicit, sustainable and risk-based insurance scheme to mitigate human–wildlife conflict. Biol Conserv. 2013;168:31–9.

17. Graham MD, Notter B, Adams WM, Lee PC, Ochieng TN. Patterns of crop-raiding by elephants, Loxodonta africana, in Laikipia, Kenya, and the management of human–elephant conflict. Systematics and Biodiversity. 2010;8(4):435–45.

18. Hoare R. African elephants and humans in conflict: the outlook for co‐existence. Oryx. 2000;34(1):34–8.

19. Sitati N, Walpole M, Smith R, Leader‐Williams N. Predicting spatial aspects of human–elephant conflict. J Appl Ecol. 2003;40(4):667–77.

20. Chartier L, Zimmermann A, Ladle RJ. Habitat loss and human–elephant conflict in Assam, India: does a critical threshold exist?Oryx. 2011;45(4):528–33.

21. Das JP, Lahkar BP, Talukdar BK. Increasing trend of human elephant conflict in Golaghat District, Assam, India: issues and concerns. Gajah. 2012:34.

22. Gubbi S. Patterns and correlates of human–elephant conflict around a south Indian reserve. Biological Conservation. 2012;148(1):88–95.

23. Wilson S, Davies TE, Hazarika N, Zimmermann A. Understanding spatial and temporal patterns of human–elephant conflict in Assam, India. Oryx. 2013:1–10.

24. Goswami VR, Medhi K, Nichols JD, Oli MK. Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models. Conservation Biology. 2015;29(4):1100–10. doi: 10.1111/cobi.12475[PubMed]

25. Lahkar B, Das J, Nath N, Dey S, Brahma N, Sarma P. A study of habitat utilization patterns of Asian elephant Elephas maximus and current status of human elephant conflict in Manas National Park within Chirang-Ripu Elephant Reserve, Assam. Report, Aaranyak, Guwahati, Assam, India. 2007.

26. Liu J, Ouyang Z, Miao H. Environmental attitudes of stakeholders and their perceptions regarding protected area-community conflicts: A case study in China. Journal of Environmental Management. 2010;91(11):2254–62. doi: 10.1016/j.jenvman.2010.06.007[PubMed]

27. Cai J, Jiang Z, Zeng Y, Li C, Bravery BD. Factors affecting crop damage by wild boar and methods of mitigation in a giant panda reserve. Eur J Wildlife Res. 2008;54(4):723–8.

28. Yang W, He S, Shen Y. Spatiotemporal Patterns of Wildlife Damages in Southern Part of Mt. Baima-Xueshan. Journal of Mountain Science. 2009;3:009.

29. Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GA, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403(6772):853–8. [PubMed]

30. Zhang J, Cao M. Tropical forest vegetation of Xishuangbanna, SW China and its secondary changes, with special reference to some problems in local nature conservation. Biol Conserv. 1995;73(3):229–38.

31. MacKinnon. Ecologival guidelines for the development of Xishuangbanna Prefecture, Yunnan Province, China Gland Switzerland: WWF, 1987.

32. Okabe A, Satoh T, Sugihara K. A kernel density estimation method for networks, its computational method and a GIS-based tool. International Journal of Geographical Information Science. 2009;23(1):7–32. doi: 10.1080/13658810802475491

33. Wilson R. Free GIS Data 2014 [cited 2014 24th March]. Available from: http://freegisdata.rtwilson.com/.

34. USGS. Shuttle Radar Topography Mission, Arc Second scene SRTM_ASTGTM2 College Park, Maryland: Global Land Cover Facility, University of Maryland; 2013 [updated February 2000; cited 2013]. Available from: http://glcf.umd.edu/data/srtm/.

35. Tsinghua U. Finer resolution observation and monitoring-global land cover 2013 [cited 2013 20th March 2013]. Available from: http://data.ess.tsinghua.edu.cn/.

36. Senf C, Pflugmacher D, van der Linden S, Hostert P. Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series. Remote Sensing. 2013;5(6):2795–812.

37. Hollander M, Wolfe D. Nonparametric Statistical Methods. John Wiley and Sons, New York; 1973. p. 185–94.

38. Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R: Springer Science & Business Media; 2009.

39. Zeileis A, Kleiber C, Jackman S. Regression Models for Count Data in R. Journal of Statistical Software. 2007;27(8):1–25.

40. Jackman S, Zeileis A. pscl: Political Science Computational Laboratory, Stanford University. 2015.

41. Guerbois C, Chapanda E, Fritz H. Combining multi‐scale socio‐ecological approaches to understand the susceptibility of subsistence farmers to elephant crop raiding on the edge of a protected area. Journal of Applied Ecology. 2012;49(5):1149–58.

42. Songhurst A, Coulson T. Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop-raiding. Ecology & Evolution. 2014;4(5):582–93. [PMC free article][PubMed]

43. Dormann C F., McPherson J M., Araújo M B., Bivand R, Bolliger J, Carl G, et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography. 2007;30(5):609–28. doi: 10.1111/j.2007.0906–7590.05171.x

44. Lacoeuilhe A, Machon N, Julien J-F, Kerbiriou C. Effects of hedgerows on bats and bush crickets at different spatial scales. Acta Oecologica. 2016;71:61–72. http://dx.doi.org/10.1016/j.actao.2016.01.009.

45. Bivand R, Hauke J, Kossowski T. Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods. Geographical Analysis. 2013;45(2):150–79.

46. Bivand R, Piras G. Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software. 2015;63(18):1–36.

47. R Development Core Team R. R: A language and environment for statistical computing Vienna, Austria: R Foundation for Statistical Computing,; 2014. Available from: http://www.R-project.org/.

48. Sakamoto Y, Ishiguro M, Kitagawa G. Akaike information criterion statistics Dordrecht, The Netherlands: D Reidel; 1986.

49. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach: Springer; 2002.

50. Carpenter J, Bithell J. Bootstrap con" dence intervals: when, which, what?A practical guide for medical statisticians. 2000. [PubMed]

51. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006;27(8):861–74.

52. Nagelkerke NJ. A note on a general definition of the coefficient of determination. Biometrika. 1991;78(3):691–2.

53. Quantum GIS Development Team Q. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. 2013. Available from: http://qgis.osgeo.org.

54. Joshi R, Singh R. Feeding behaviour of wild Asian Elephants (Elephas maximus) in the Rajaji National Park. Journal of American Science. 2008;4(2):34–48.

55. Webber CE, Sereivathana T, Maltby MP, Lee PC. Elephant crop-raiding and human–elephant conflict in Cambodia: crop selection and seasonal timings of raids. Oryx. 2011;45(02):243–51. doi: 10.1017/S0030605310000335

56. Chen J, Deng X, Zhang L, Bai Z. Diet composition and foraging ecology of Asian elephants in Shangyong, Xishuangbanna, China. Acta Ecologica Sinica. 2006;26(2):209–316.

57. Yang Z, Chen M, Dong Y, Liu L, Yang S. Analysis on Asian Elephants' Habitat Situation in Menyang Sub- reserve of Xishuanbanna National Nature Reserve. Forest Inventory and Planning. 2006;31(3):49–51.

58. Wu Jinliang JW, Jiansheng Hu, Zongqiang Li, Chunhong Li. Distribution dynamic of Asian Elephant in Xishuangbanna in the past 40 years. Chinese Wildlife. 1999;20(3):8–9.

59. Lin L, Zhang L, Luo A, Wang L, Zhang L. Population dynamics, structure and seasonal distribution pattern of Asian elephant (Elephas maximus) in Shangyong Protected Area, Yunnan, China. Acta Theriologica Sinica. 2011;31(3):226–34.

60. Yuan Z, Zhang L. Population and activity characteristic of wild Asian elephants in the Wild Elephant Valley, Xishuangbanna National Nature Reserve, Yunnan, China. Acta Theriologica Sinica. 2006;26(4):359–67.

61. Zong J, Liu S, Xu H, Wang L, Guo X. Population size and distribution changes of Asian elephant in Menglazi Nature Reserve, Xishuangbanna Nature Reserve. Forest Invenory and Planning. 2014;39(1):89–93.

62. Yi Z-F, Cannon CH, Chen J, Ye C-X, Swetnam RD. Developing indicators of economic value and biodiversity loss for rubber plantations in Xishuangbanna, southwest China: A case study from Menglun township. Ecological Indicators. 2013.

Introduction

Elephants Loxodonta africana africana Blumenbach 1797 and Loxodonta africana cyclotis Matschie 1900, were probably a major obstacle to the evolution of arable farming in precolonial Africa (Parker & Graham 1989a; Barnes 1996). Within elephant range in both savannas and forests, agriculturalists could probably only prosper in large, well-defended villages (Laws, Parker & Johnstone 1975). From the nineteenth and early twentieth centuries, extensive Arab and European penetration of Africa changed the relationship between man and elephant (Hanks 1979; Eltringham 1990). Diverse factors contributing to this change were the advent of a cash value for ivory, firearms, tsetse fly control measures, improved medical and veterinary care, cash crop production and the widespread imposition of colonial government.

Early management of wildlife in African colonies involved widespread elephant control shooting (Swynnerton 1923) but, despite a general decline in range and numbers (Said et al. 1995), elephants have continued to be in conflict with agricultural man in many parts of Africa for most of the twentieth century (Brown 1968; Kinloch 1972; Parker 1983; Parker & Graham 1989b; Eltringham 1990; Barnes 1996). ‘Problem elephants’ are animals that extend their range into human settlement, commonly to feed on a wide variety of cultivated food and cash crops but also sometimes damaging food stores, water installations or fences and barriers, and occasionally injuring or killing people. Efforts to mitigate the conflict at the interface between expanding agriculture and shrinking elephant range (Bell 1984; Hoare 1995) have met with rather limited success.

Human–elephant conflict has recently become a topic of major concern in elephant conservation (Kangwana 1993, 1995; Dublin 1994) because it has immediate negative effects on both people and elephants and is also frequently a precursor to further decline in the African elephant range. Barnes (1996) warns that human–elephant conflict in the forest elephant range is as serious a conservation problem as in the savanna elephant range. There has been an increase in the reported incidence of human–elephant conflict in the last decade (Kangwana 1995). While such conflict is almost certainly becoming more widespread as expanding agriculture lengthens the human–elephant interface, the judgement that conflict is actually becoming more intense remains unsubstantiated. The impression of an increase may have arisen due to widespread publicity and political interest in the problem. Thus, human–elephant conflict must be quantified and hypotheses on causal factors must be tested before any management recommendations can be made to ameliorate its effects.

Accounts of direct interaction between humans and elephants have mostly been descriptive of the problem at one or more sites (Nicholson 1968; Waithaka 1993; Kiiru 1995a; Ngure 1995). Only recently has some quantification of human–elephant conflict been carried out. In the savanna elephant range, problem elephant activity shows a seasonal peak, usually corresponding to the late wet season, because the majority of incidents involve elephants destroying maturing food crops (Hoare 1995; Kangwana 1995; Kiiru 1995a; Tchamba 1995). In some semi-arid areas of Zimbabwe and Kenya, elephant damage to food crops accounts for 75–90% of all incidents by large mammal pest species in each district every year (Hoare & Mackie 1993; Waithaka 1993). In the forest elephant range of Gabon, (Lahm 1996) confirmed that crop raiding by elephants also mostly occurred during the wet season. Crop raiding by elephants is almost exclusively a nocturnal activity (Bell 1984; Thouless 1994; Hillman-Smith et al. 1995; Hoare 1995), suggesting that offenders seek to minimize the associated risk. Where elephants are exceptionally bold, crepuscular raiding activity may be encountered. Irrespective of the circumstances and damage levels inflicted, penetration by an elephant into a settled area demonstrates a temporary expansion of its range that potentially exposes it to disturbance or predation by humans.

Barnes, Asika & Asamoah (1995) offer an hypothesis that increasing crop raiding levels depend upon increasing elephant densities, the latter having been brought about by shrinkage of the elephant range. Even if elephant densities remain static, Barnes, Asika & Asamoah (1995) in Africa and Sukumar (1991) in Asia have proposed that loss of elephant range increases the probability of contact between elephants and human settlement and thus leads to an increase in crop raiding. This suggests an association between the amount of land transformed by agriculture and the level of problem elephant activity. Problem elephant incidents occur in settled areas of Africa with a wide range of human densities (from < 5 km–2 to > 150 km–2; Newmark et al. 1994; Thouless 1994) but for these incidents to occur in the higher range of human density, where permanently resident elephants have been eliminated, a nearby elephant refuge (Bell 1984) must exist. Therefore, it could be predicted that crop raids should increase in proportion to the availability of a secure refuge for elephants (e.g. a protected area). Another hypothesis to consider is that crop-raiding levels depend on rainfall. Higher rainfall, which increases the biomass and yield of dry land crops, could be predicted to lead to an increase in elephant crop raids.

While the distribution and frequency of problem elephant activity is easily recorded, its intensity has to be judged quantitatively, often alongside the effects of other agricultural pest species (Msiska & Deodatus 1991; Lahm 1996; Wunder 1997). Measurement of conflict incident levels have used ‘raid frequency indices’ at conflict sites. At Kasungu and Liwonde, Malawi, an ordinal 4-value scale was used, based on incident levels per growing season (Deodatus & Lipiya 1991; Simons & Chirambo 1991) while at Shimba Hills, Kenya, the statistic ‘mean incidents per household per fortnight’ was employed (Kiiru 1995b). In India, Sukumar (1990) used ‘raiding days per village per month’. Judgements based on economic assessments of the damage are problematic in Africa because data are usually provided from multiple sources in eco-climatic zones of inherently low agricultural potential (Thouless 1994). Economic assessments also exclude many of the social ‘opportunity costs’ associated with living with elephants (DHV 1992; Thouless 1994).

This study recorded problem elephant incidents in spatial subdivisions of one ecosystem over 3 years. The analysis is concerned with judging the intensity of elephant raids and exploring associations with possible explanatory variables: local elephant density, proximity of a protected wildlife area, local human density, amount of human settlement, and rainfall. A simple raid-frequency index was proposed that can be used to compare different sites suffering from problem elephant activity. Sizes and types of elephant groups responsible for problem incidents were also analysed.

Study area

The northern Sebungwe is a region of some 15 000 km2 situated in north-west Zimbabwe, forming part of the Zambezi river basin immediately to the south of Lake Kariba. The elevation varies from 475 m above sea level at the Kariba lakeshore to over 1200 m on the Zambezi escarpment inland; the mean elevation is 700–800 m with generally undulating topography. The climate is semi-arid, characterized by a single wet season from November to March and a long dry season from April to October. Mean annual rainfall shows great variation both within the region and between years, but the long-term mean is 680–750 mm per year (Hutton 1991). The natural land cover is deciduous woodland savanna dominated by ‘mopane’Colophospermum mopane Kirk, and ‘miombo’Brachystegia spp. Taub. and Julbernadia globiflora Troupin vegetation, interspersed with abundant riparian fringes on the extensive northward drainage and occasional small, dense thickets (Timberlake, Nobanda & Mapaure 1993).

Land tenure consists of protected areas (PAs) for wildlife and communal lands with varying degrees of human settlement (Fig. 1). PAs are national parks and safari areas in which the settlement of people is prohibited; they form three large but separate blocks and are under the control of the wildlife authorities of central government. Intervening communal lands (CLs) are areas where people and some wildlife are both resident and have to coexist. The communal land areas fall into the three administrative districts of Binga, Gokwe and Kariba, which since 1990 have each had authority to manage their own wildlife under a national programme called CAMPFIRE (Communal Areas Management Programme for Indigenous Resources) (Taylor 1993a; Child 1995). Districts are further subdivided administratively into wards, which vary from approximately 150 to 700 km2 in area. Some wildlife management functions of the districts are being devolved to ward level.

The regional elephant population is dispersed throughout both PAs and CLs in the region but is isolated by extensive human settlement from other regions of Zimbabwe that contain elephants (DNP & WLM 1996). Continuous immigration of people caused severe loss of elephant range in the region, which resulted in rising elephant densities in the contracting range up to 1980 (Cumming 1981). The region was fully cleared of tsetse fly in the mid 1980s, allowing accelerated immigration of people from other parts of Zimbabwe who have continued to transform the land cover for subsistence agriculture (Timberlake, Nobanda & Mapaure 1993). This region thus encapsulates many of the factors acting simultaneously upon rural African people and elephants. In the land-use mosaic, human expansion has been continuous for 45 years and the full spectrum of wildlife conservation endeavour, both traditional (Cumming 1981) and contemporary (Taylor 1993b), is represented.

Methods

Data collection

As part of the technical support to CAMPFIRE, training workshops were organized in the three study districts to teach enumerators (reporters) in wards (Fig. 1) to collect information on ‘problem animals’ causing damage to human life or property. To ensure full coverage, larger wards had several reporters. In practice, these problem animal reporters (PARs) placed reporting emphasis on incidents involving the potentially dangerous wildlife species: elephant, buffalo Syncerus caffer Sparrmann, hippopotamus Hippopotamus amphibius L., lion Panthera leo L., spotted hyaena Crocuta crocuta Erxleben, and crocodile Crocodilus niloticus Laurenti. A PAR visited the site of each individual problem animal incident in his area as soon as possible after the occurrence and described the details on a data sheet.

This reporting system provided standardized data that could be summarized to establish the frequency, distribution and severity of problem animal activity in each ward. Data on all elephant incidents for the years 1993, 1994 and 1995 were summarized by ward. A total of 1823 problem elephant incidents occurring in 21 wards, was used in the analysis (Table 1).

Mola B1– 3842401·1 6·40531150·49
Negande26311735721·0 4·80654140·81
Mola A3– 9825620·913·56295390·53
Nenyunka4423114290·713·319627350·13
Tyunga513294160·5 4·628627110·22
Sinansengwe6403019300·4 4·067397180·42
Nabusenga717179150·3 9·514606230·08
Nagangala8443414310·3 7·050448460·13
Madzivazvido9343951410·220·516518500·16
Simchembu106410926660·122·173359510·36
Nsenga11366013370·110·630225350·46
Sikalenge1218258170·1 8·016499300·11
Kabuba1323234170·110·548608210·13
Muchesu14282121240·119·09114400·41
Chireya15621270·0220·901021680·01
Lubu1614548017·50157– 
Manjolo177705023·40133– 
Sai 21858455252036·025332– 
Sai 3199387019·616554– 
Masuka20281195567027·933235– 
Nemangwe213534021·86440– 
Totals 549857419   4499277  
Means 264118290·3915·121442330·30

Detailed maps of the Sebungwe region exist (Department of the Surveyor General 1985) and a 1:25 000 aerial photography series was taken in mid-1993 (Cumming & Lynam 1997). A regional elephant census is undertaken annually by aerial survey (DNP & WLM 1996). Data on land tenure and administrative boundaries, human settlement patterns, and elephant distribution and abundance, obtained from these sources, have been stored in electronic format, using a geographical information system (GIS) computer program (Atlas GIS: Strategic Mapping Inc., 3135 Kifer Rd, Santa Clara, CA 95051, USA). Area measurements, multilayered maps and some analyses have been produced for the region with the aid of this program (Cumming & Lynam 1997).

Elephant density (elephants km–2) was calculated from annual aerial census data in the years 1993, 1994 and 1995 (Mackie 1994; Mackie 1995; DNP & WLM 1996). A 3-year arithmetic mean of elephant density for each census stratum was used in the analysis, to smooth out unavoidable census variation due to differences in habitat conditions between years. As census strata do not match ward boundaries, the ward boundaries (Fig. 1) were overlaid onto the mean elephant density in each stratum. The GIS calculated the mean elephant density for each ward (Table 1). Elephants in the Sebungwe region exhibit very little seasonal movement (Hoare 1997), so dry season density is also considered applicable to the wet season when problem elephant activity peaks. The six wards without figures for elephant density (Table 1) were not covered by the annual elephant census or the aerial photography series, because they are known to have no permanently resident elephants.

Human density (persons km–2) for wards (Table 1) was available directly from the 1992 national census (Government of Zimbabwe 1992). If the ward abutted a PA, the length of this boundary was measured by the the GIS computer program and termed ‘PA frontage’ for the ward (Table 1).

The presence of transformed land cover on aerial photographs (indicating fields or villages) was used to quantifiy human settlement at a resolution of a quarter of a square kilometre(Cumming & Lynam 1997). The total area transformed by human activity in each ward in km2 was termed ‘settlement coverage’ and expressed as a percentage of the total area of each ward (Table 1). The six wards without figures for settlement coverage (Table 1) are those not covered by the aerial photography series. These are the same wards that are not covered by the elephant census.

Monthly rainfall figures were obtained from eight rainfall stations in the region (Fig. 1) and annual rainfall was calculated for each of the three study years. Wards were assigned the annual rainfall figure of the nearest measuring station.

Sizes and types of elephant groups responsible for problem elephant incidents were recorded by the PARs. Size–frequency distribution and sex–frequency composition of problem elephant groups were calculated and compared with those of male and female groups in the CL elephant population, obtained from other parts of simultaneous elephant study in the region (Hoare 1997).

An assumption was made that reporting effort was uniform in each ward since the training programme for the PARs was similar in all districts. Three years of records (n = 1823) may have helped to minimize some inevitable local variation in reporting effort.

Data analysis

Associations between the number of problem elephant incidents and five independent variables (elephant density, protected area frontage, settlement coverage, annual rainfall and human density) were tested. A total of 61 data points were available from 21 wards over 3 years. With these data there are valid theoretical concerns that (i) using all 61 data points sampled from only 21 wards constitutes pseudoreplication of observations (Hurlbert 1984), and (ii) nonparametric methods using ranks do not readily extend to the analysis of several explanatory variables (Zar 1996). In practice, however, huge variation of problem incidents between years in the same wards reinforces the contention that elephant–human interactions are primarily spatial and thus poorly described by numeric means (Hoare & du Toit 1999). Also, the main variable of interest, elephant density, can only be derived from aerial counts, which are not of sufficient frequency or accuracy to meet the assumptions of a parametric analysis like regression, especially at the spatial resolution of a ward.

Therefore, the relative contribution of each variable is presented using two statistical methods: (i) Spearman rank correlation (rs) using all data points over 3 years gives a significance level but cannot account for the variation between years so (ii) a summary of percentage variation (r2) across wards between years reveals the magnitude of effect. The latter method uses a log-transformation of the data to try to approximate assumptions of a parametric model.

In relationships where the correlation was significant but weak (elephant density and protected area frontage), the independent variable was subdivided into high, medium and low categories. Differences in the number of problem elephant incidents per category were compared.

In the 15 wards for which settlement coverage was available, a raid frequency index was calculated, using the area of human settlement. The mean number of elephant incidents for the three study years was divided by the settlement area in each ward to give a ‘ward problem elephant index’, i.e. an arithmetic mean of problem elephant incidents per km2 of settled area per year (Table 1). Association between the problem elephant index and settlement coverage in each ward was examined.

Results

Elephants in the Sebungwe consumed or damaged maize Zea mays L., millet Pennisetum glaucum L., sorghum Sorghum bicolor Pers., cotton Gossypium hirsutum L., beans Vigna unguiculata Macf. groundnuts Arachis hypogaea L., melons Citrullus lanatus Mansf. and sunflowers Helianthus annuus L., raided grain stores and occasionally injured or killed people.

Problem elephant activity occurred in all wards examined, whether elephants were resident or not, the range of incident totals being similar in wards with or without resident elephants (Fig. 2a–c). Six wards without resident elephants suffered attack from animals in nearby refuges, but not all these refuges were protected areas. In two cases (ward numbers 16 and 17) the elephant refuges may have been other CL wards.

Association of incident levels with elephant density (Fig. 2a) and with protected area frontage (Fig. 2b) was significant but weak (Tables 2 & 3). There was a significant difference in incidents between elephant density categories (Hoare 1997) (high > 0·7 km–2; medium 0·3–0·6 km–2; low < 0·3 km–2) with a chi-squared goodness-of-fit test (χ2 = 175·8; d_f. = 2; P < 0·005) but these followed no predicted trend: the highest density was the least different from expected values; intermediate density was the most different. There was a significant difference in incidents between frontage categories (0 km; 1–20 km; 21–40 km; 41–60 km; 61–80 km [χ2 = 288·1; d.f. = 2; P < 0·005]) but no predicted trend: the longest frontage contributed the least difference from expected values.

Elephant density0·385610·0020·047210·053
Protected area frontage0·352610·0050·298210·190
Human density– 0·04761 > 0·5 – 0·18821
Annual rainfall0·228610·0760·08521 > 0·5
Settlement coverage– 0·311 43*0·0420·038 15* > 0·5
Elephant density2120·813·04·612·8
Protected area frontage2112·83·40·55·6
Human density211·60·15·12·3
Annual rainfall210·071·87·13·0
Settlement coverage 15*0·010·20·50·2

Incident levels showed very little statistical association with the human density range in wards (Fig. 2c; Tables 2 & 3). Total annual rainfall varied considerably between years in different wards (range 158–740 mm) but the range of incident levels remained similar (Fig. 2d). In the region overall, the most incidents occurred in the year with intermediate rainfall (1994) but the fewest occurred in the 1995 drought year when many crops failed to mature (Table 1). Association of incident levels with rainfall was statistically weak (Tables 2 & 3).

In the 15 wards with resident elephants, the problem elephant index ranged from 0·01 to 0·81 incidents km–2 of settled area per year with an overall mean of 0·3 incidents km–2 per year over a total of 2624 km2 of settlement (Table 1; Fig. 3). The relationship between incidents and settlement coverage had not previously revealed any strong association (Tables 2 & 3).

The distribution of elephant raiding group sizes recorded on the reporting forms illustrates that most incidents are due to small groups (Fig. 4). Lone males accounted for 19% of total incidents. The range of raiding group size was 1–47 with 89% of raiding groups consisting of 10 animals or less. This agrees closely with reports where raiding elephant groups could be sexed with some confidence by PARs (n = 337): 79% of raids were perpetrated by male groups or lone males. A further 9% were mixed herds where both males and females were present. In only 12% of cases were cow groups recorded as being solely responsible and 50% of these were recorded from one district (Gokwe). The median size of raiding groups reported (M = 4; range 1–47; n = 1688) was the same as the median size of male groups sighted in the CL male population (M = 4; range 1–49; n = 381) but half the median size of female groups sighted in the CL population (M = 8; range 2–49; n = 396) (Fig. 5). The ratio of male to female groups in the CL elephant population at large, found in other parts of the present study (Hoare 1997) was 40:60%.

Because of problems quantifying availability of the different crop types to raiding elephants, analysing the selection of these crop types was not attempted. The ages of crops damaged by elephants, however, were assessed. A sample of incidents on all types of crops over the 3 years (n = 1122) showed that mature crops were selected in 62% of cases, intermediate growth stages selected in 35% of cases and early growth stages selected in only 3% of cases.

Discussion

This study examined the intensity of problem elephant activity in a sample of rural settlements practising subsistence agriculture within the same semi-arid ecosystem. Incident levels varied widely without corresponding local changes in the elephant population. PAs acting as elephant refuges in the Sebungwe are known to have almost twice the elephant density of refuges within the CL wards (DNP & WLM 1996) but neither the type of refuge nor the elephant density within it appeared to determine levels of problem elephant activity in adjacent human settlement. Substantial local variations in rainfall also suggest that better crops are not necessarily attracting more elephants to cultivated fields. Elephants appear to feed on whatever crops are available, preferring the mature growth stages.

Across the many differing situations where problem elephant activity occurred, only one condition was consistent: the preponderance of male elephant involvement in the problem incidents. Although this sex difference has been reported (Nicholson 1968; Bell 1984;

One thought on “Research Papers On Human Elephant Conflict Pdf Writer

Leave a comment

L'indirizzo email non verrà pubblicato. I campi obbligatori sono contrassegnati *