COMMUNITY ATTITUDES TOWARD HIROLA CONSERVATION AND RANGE RESTORATION IN EASTERN KENYA INTRODUCTION

Transcription

1 COMMUNITY ATTITUDES TOWARD HIROLA CONSERVATION AND RANGE RESTORATION IN EASTERN KENYA INTRODUCTION Pastoralists all over the world are knowledgeable about their environments and regulated resource use through a traditional system. However, a combination of growing human populations, growing livestock populations, and postindependence government policies undermines traditional pastoralism and may have a devastating impact for critically endangered species. The hirola is the world s most endangered antelope, having suffered a >95% population decline across its native range comprising a narrow belt of arid savanna along the Kenya-Somalia Figure 1: Ijara and Fafi districts, Kenya showing study villages and border (Fig.1). Throughout adjacent protected areas the region, hirola and other wildlife exist on communal land outside of formally protected areas. We carried out this study in Ijara and Fafi districts in northeastern Kenya (Fig.1) to understand attitudes and perceptions of Somali communities toward hirola conservation. We therefore asked the following pertinent questions. What are the predictor variables accounting for community perception and attitudes? Do community attitude differ between districts and what are the reasons behind these perceptions? For example, are local residents aware of conservation strategies directed toward hirola conservation? Do they support community based initiatives? We assessed the drivers of these attitudes relative to district, age, gender, and opinion towards conservation. We appointed and trained local scouts to administer the questionnaires to homesteads. Interviewers briefly conducted self-introductions, stating the purpose of the research before engaging their respondents. The surveys focused on four critical areas that have been identified as knowledge gaps in our interactive session with the stakeholders and from the literature review. The survey consisted of: (1) an introductory text explaining why the research was conducted 2) knowledge and attitudes on hirola and other wildlife including interface with livestock, 3) causes and consequences of range degradation and species decline in the area and 4) impact of traditional pastoralism on hirola and other wildlife conservation 5) ways of integrating pastoralism and hirola conservation. Questions specific to

2 hirola inquired as to historical hirola distribution in the region, attitudes toward hirola, threats to livestock of humans from hirola, threats to hirola from people, and the future of hirola and other wildlife in the areas. Through the collection of this important data we hope to guide future hirola conservation programs and provide guidelines for integrating traditional pastoralism alongside conservation in the long-term. Statistical analysis We have done initial analysis of the survey data using SPSS 16.0 and data were presented as mean (±SE parametric tests), and percentage (non-parametric tests) with p 0.05 considered statistically significant. Response frequencies of categorical variables were compared using Chisquare tests. Continuous variables were compared using two-sample t-tests. We used ANOVA to test the difference in the number of animals owned. I have used Classification and Regression Tree (CART) to test predictor variables accounting for community perception. CART allows the exploration of data, making it easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. We run the model using rpart. The results are a series of rules that partition ranges of the response variable based on the covariates. For this specific model we set the splitting function to ANOVA. In trees created by rpart( ), move to the LEFT branch when the stated condition is true. Comment [A1]: I will probably do this with R to ensure consistency RESULTS: General characteristics of respondents A total of 220 individuals from Fafi and Ijara Districts were interviewed and respondents comprised of four age groups: 10 percent were young Somali herders (aged >21), 34% were youth (aged 22 35) and 33% were middle aged persons (aged between 36 to 50 years) and 23% were village elders aged above 50 years. All of the 220 respondents identified themselves as Muslims and speak the same language (Somali). Seventy seven percent of all the respondents were male while twenty three percent were females. Fifty seven percent of those interviewed had never attended school, and were primarily livestock herders. Mean livestock owned per household in Fafi district is 38.47±4.645 compared to Ijara district with a mean of 29.46± There was no significant difference between the number animals owned (t (2) (219) =-1.237, P=0. 218) between the two districts. However, there was a significant difference between the number of animals owned across age categories (F 3, 214 =4.087, P=0. 008). Tukey test showed the difference is between years old and years old as well as between years old and >51 years old. Seventy six percent of the respondents said they are pure pastoralist while twenty four percent said they practiced agro-pastoralism. There was a significant relationship between number of livestock owned by an individual and the type of livelihood activities practiced (t (2) (165) =-1.996, P=0. 048). In addition there was a significant difference in the number of cattle born per month (Mean±SE; 2±0.224) and the household livestock off-takes, that is either sold or given away as gift (1±0.074) from the herds (t (218) = 8.47, P<0.001). There was also a significant difference between the number of small stock born per month (3±0.326) and a small stock of-takes (1±0.056), that is either sold or given as a gift (t (218) = 7.95, P<0.001). Eighty percent of the respondents said they get income from the sale of their livestock while less than 5% said they get income from tourism or wildlife related enterprises. Classification and Regression Analysis

3 Key question: What are the predictor variables accounting for community opinions towards hirola conservation? Building the tree model-- OPINION~ factor (District) + factor (Age)+ Factor (Gender) + factor (Formal education) + factor(education level) + factor(livestock wealth) +factor (Depredation loss) + factor (Awareness) + factor(income) + factor(disease) +factor (competition). print(summary(opinion)) Call: rpart(formula = OPINION ~ factor(district) + factor(age) + factor(education) + factor(livestock_w) + factor(p_proportions) + factor(income_l) + factor(income_c) + factor(income_p) + factor(income_t) + factor(identify) + factor(competation) + factor(diseases) + factor(awareness), data = Attitude_data, control = rpart.control(cp = 0.025)) n=205 (16 observations deleted due to missingness) Comment [A2]: As I mentioned this morning, I just got this idea on Thursday, after the committee meeting, while I have realized it is easy to compute; I will need time to study it, and learn interpretation of the results and also cleaning the figures they call it pruning the tree in this CART language. Therefore was not able to give a good result and a discussion something I will certainly do in the coming weeks as I have to submit this paper next spring. Thanks for understanding and teaching this difficult language! CP nsplit rel error xerror xstd Variable importance factor(livestock_w) factor(p_proportions) factor(district) factor(awareness) factor(age) factor(income_c) factor(identify) factor(education) factor(diseases) 1 Node number 1: 205 observations, complexity param= mean= , MSE= left son=2 (102 obs) right son=3 (103 obs) factor(livestock_w) splits as RRRRRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLL-R-RLLLLRRLLLLLRLLLLLLL, improve= , (0 missing)

4 factor(p_proportions) splits as LLLLLLLRRRRLLL, improve= , (0 missing) factor(income_c) splits as LR, improve= , (0 missing) factor(district) splits as RL, improve= , (0 missing) factor(awareness) splits as LR, improve= , (0 missing) Surrogate splits: factor(district) splits as RL, agree=0.741, adj=0.480, (0 split) factor(p_proportions) splits as LLLRLRRRRRRRRR, agree=0.707, adj=0.412, (0 split) factor(awareness) splits as LR, agree=0.698, adj=0.392, (0 split) factor(income_c) splits as LR, agree=0.649, adj=0.294, (0 split) factor(age) splits as RRLL, agree=0.580, adj=0.157, (0 split) Node number 2: 102 observations mean=1, MSE=0 Node number 3: 103 observations, complexity param= mean= , MSE= left son=6 (88 obs) right son=7 (15 obs) factor(p_proportions) splits as L--L-LLRRRRLLL, improve= , (0 missing) factor(education) splits as LR, improve= , (8 missing) factor(income_l) splits as LR, improve= , (0 missing) factor(livestock_w) splits as LLLRL R----R-R----RR-----R , improve= , (0 missing) factor(district) splits as RL, improve= , (0 missing) Node number 6: 88 observations, complexity param= mean= , MSE= left son=12 (77 obs) right son=13 (11 obs) factor(livestock_w) splits as LLLLL R----R-R----RR-----R , improve= , (0 missing) factor(age) splits as LLLR, improve= , (3 missing) factor(diseases) splits as LR, improve= , (10 missing) factor(education) splits as LR, improve= , (8 missing) factor(income_l) splits as LR, improve= , (0 missing) Node number 7: 15 observations mean= , MSE= Node number 12: 77 observations, complexity param= mean= , MSE= left son=24 (64 obs) right son=25 (13 obs) factor(age) splits as LLLR, improve= , (2 missing)

5 factor(education) splits as LR, improve= , (7 missing) factor(livestock_w) splits as RLRLR , improve= , (0 missing) factor(district) splits as RL, improve= , (0 missing) factor(income_l) splits as LR, improve= , (0 missing) Node number 13: 11 observations mean= , MSE= Node number 24: 64 observations, complexity param= mean= , MSE= left son=48 (41 obs) right son=49 (23 obs) factor(livestock_w) splits as RLLLL , improve= , (0 missing) factor(competation) splits as RL, improve= , (0 missing) factor(income_l) splits as LR, improve= , (0 missing) factor(education) splits as LR, improve= , (4 missing) factor(p_proportions) splits as R--L-RL----RLL, improve= , (0 missing) Surrogate splits: factor(district) splits as LR, agree=0.734, adj=0.261, (0 split) factor(p_proportions) splits as R--L-LL----LLL, agree=0.719, adj=0.217, (0 split) factor(diseases) splits as LR, agree=0.688, adj=0.130, (0 split) factor(awareness) splits as RL, agree=0.672, adj=0.087, (0 split) factor(identify) splits as RL, agree=0.656, adj=0.043, (0 split) Node number 25: 13 observations mean= , MSE= Node number 48: 41 observations mean= , MSE= Node number 49: 23 observations, complexity param= mean= , MSE= left son=98 (14 obs) right son=99 (9 obs) factor(district) splits as RL, improve= , (0 missing) factor(income_l) splits as LR, improve= , (0 missing) factor(competation) splits as RL, improve= , (0 missing) factor(awareness) splits as LR, improve= , (0 missing) factor(age) splits as LLR-, improve= , (2 missing) Surrogate splits: factor(income_c) splits as LR, agree=0.783, adj=0.444, (0 split) factor(identify) splits as LR, agree=0.783, adj=0.444, (0 split) factor(education) splits as LR, agree=0.739, adj=0.333, (0 split) factor(awareness) splits as LR, agree=0.739, adj=0.333, (0 split)

6 factor(p_proportions) splits as L----R , agree=0.652, adj=0.111, (0 split) Node number 98: 14 observations mean= , MSE= Node number 99: 9 observations mean= , MSE= n=205 (16 observations deleted due to missingness) node), split, n, deviance, yval * denotes terminal node 1) root ) factor(livestock_w)=5,6,7,8,9,10,11,14,15,16,20,24,25,26,27,28,30,33,35,36,38,40,42,43,4 4,50,54,55,57,65,70,80,90,95,100,112,125,130,136,141,154,165,191,200,210,211,293, * 3) factor(livestock_w)=0,1,2,3,4,60,85,87,120,122, ) factor(p_proportions)=0,0.3,0.5,0.6,2,3, ) factor(livestock_w)=0,1,2,3, ) factor(age)=1,2, ) factor(livestock_w)=1,2,3, * 49) factor(livestock_w)= ) factor(district)= * 99) factor(district)= * 25) factor(age)= * 13) factor(livestock_w)=60,85,87,120,122, * 7) factor(p_proportions)=0.7,0.8,1, * Regression tree: rpart(formula = OPINION ~ factor(district) + factor(age) + factor(education) + factor(livestock_w) + factor(p_proportions) + factor(income_l) + factor(income_c) + factor(income_p) + factor(income_t) + factor(identify) + factor(competation) + factor(diseases) + factor(awareness), data = Attitude_data, control = rpart.control(cp = 0.025)) Variables actually used in tree construction: [1] factor(age) factor(district) factor(livestock_w) factor(p_proportions) Root node error: /205 = n=205 (16 observations deleted due to missingness) CP nsplit rel error xerror xstd

7 Fig.2 Anova tree for community opinions; the label at each node indicare the mean value of each factor at the node

8 text(opinion, all = T, use.n=t, fancy = T) Fig 5: plot(predict(opinion),resid(opinion))