734 J. O. Afriyie et al.


dedicated to conservation. The 79 km2 Special-use Zone (20%) is an area where some farming activities by local inha- bitants are allowed, but not hunting or logging. The 86 km2 Restoration Zone (22%) are those lands that have been de- graded or significantly altered by farming, logging and char- coalmaking; they are leased to immigrants for settlement and farming. The management priorities in this Zone exclude all forms of destructive activities and the Zone is dedicated to the recoveryof vegetationandwildanimalpopulations.The 1km2 Development Zone (1%) has been set aside for staff accom- modation, administration facilities, a research station and a centre for conservation education.


Methods


Patrol operations management Kogyae uses conventional lawenforcement in the form of foot patrols that operate from the headquarters and from camps established in each of seven communities at the periphery of the Reserve. A gridmap is used for planning of patrol routes, to ensure that the entire Reserve is patrolled eachmonth (de- scribed indetail by Jachmann, 2008a).Afoot patrol comprises at least five rangers, led by the most senior of the group. Standardized forms are used to record data: the number of staff on patrol, duration, total distance travelled, and types, number and locations of illegal activity encountered. Illegal activities recorded include poachers arrested, poachers observed, firearms confiscated, gunshots heard, poachers’ camps found, animals found killed, snares recovered and cartridges found.


Evaluation of patrol staff performance


In evaluating the performance of patrol staff, we used the monthly distancewalked by all patrols and the effective patrol time, which is ameasure of time spent in the field by a patrol team without including deployment time (sensu Bell, 1985,as applied by Jachmann, 2008a; Nyirenda & Chomba, 2012). To facilitate comparison of law enforcement performance across protected areas, two standardized measures of monthly patrolling effort were used: (1) effective patrol man-days calculated as the monthly effective patrol time divided by 8 hours (assigned time unit as standard for 1 patrol day), multiplied by the number of staff in the patrol group, and (2) effective patrol days calculated as the total effective patrol man-days for the month divided by the number of active staff on duty for themonth. We used catch per unit effort (Bell, 1985; Jachmann,


2008a) to measure the level of encounter rates with indi- cators of illegal activities per given period. Catch refers to the total number of monthly encounters with indicators of


illegal activity, and the effort is the total number of effective patrol man-days per month. Akilometric index of abundance, which is the ratio of the


number of illegal activities encountered to distance walked by patrols per month, was used as a second measure of en- counter rate. The kilometric index of abundance was multi- plied by 100, to give the number of encounters per 100 km.


Data collection and analyses


We collected data on law enforcement operations during January 2006–August 2017, and we carried out field visits and informal interviews with the manager and patrol staff to gain insights into patrol operations. Locations of the il- legal activities encountered were not available and therefore spatial aspects of law enforcement could not be evaluated. Total distance walked by patrols was only available for 2006–2014, when the GPS units were functioning. All data parameters recorded were examined with the Kolmogorov– Smirnov test and found to be normally distributed. To examine any annual,monthly or seasonal (wet vs dry)


trends in patrol staff performance, general linearmodelswere applied for each parameter separately as the dependent vari- able,with year,month and season as the independent predic- tors. In the case of significant differences, we used post hoc Tukey HSD tests to examine any further differences. Catch per unit effort and the kilometric index of abun-


dance were highly correlated (Pearson’s r = 0.94,P,0.001) and therefore only the catch per unit effort was used for fur- ther analyses. To examine differences in encounter rates with various types of illegal activities and their temporal trends,we used general linear models, with catch per unit effort as the dependent variable and year,month, illegal activity type, year × illegal activity typeandmonth × illegal activity type interac-


tions as the independentpredictors.Post hocTukeyHSDtests were used to examine any further differencesamong the levels of predictors if the general linear model was significant. To examine the effect of patrol staff performance and the number of inhabitants in the district surrounding Kogyae (which in- creased annually during the years ofmonitoring) on encoun- ter ratewith illegal activities,we used simple linear regression. We used STATISTICA 13 (TIBCO Software, Palo Alto, USA) to performall statistical analyses.


Results


Patrol staff performance The mean monthly distancewalked by patrols in the Kogyae Strict Nature Reserve during 2006–2014 was 1,221 ± SE 47 km/month, with a minimum of 623 ± SE 0 km/month in 2012 to a maximum of 1,874 ± SE 64 km/month in 2010. Mean monthly effective patrol days were 17.5 ± SE 0.3 and


Oryx, 2021, 55(5), 732–738 © The Author(s), 2021. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605320000228


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