2008_Jachmann_Illegal Wildlife Use and Protected Area Management in Ghana

B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 1 9 0 6 – 1 9 1 8

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Illegal wildlife use and protected area management in Ghana

Hugo Jachmann*

SNV-Ghana, Mankata Close 6, Airport Residential, P.O. Box 30284, KIA Accra, Ghana Bergstraat 77, 6174 RP Sweikhuizen, The Netherlands

A R T I C L E I N F O

Article history:

Received 13 March 2008

Received in revised form

16 May 2008

Accepted 17 May 2008

Available online 2 July 2008

Keywords:

Law enforcement

Poaching

Elephants

Resource allocation

Staff performance

Wildlife management

Habitat

0006-3207/$ - see front matter � 2008 Elsevidoi:10.1016/j.biocon.2008.05.009

* Tel.: +233 244143698.E-mail addresses: hjachmann@snvworld.

A B S T R A C T

Starting in 2004, a system to monitor patrol staff performance, illegal wildlife use and

trends in large-mammal populations was established in nine protected areas in Ghana.

The main objectives were to use monitoring feedback as the foundation for informed deci-

sions to aid adaptive and performance management, and to identify the most important

factors contributing to wildlife conservation. The competitive management system

resulted in a doubling of patrol performance. As a result, in the six savannah sites, poach-

ing was reduced to acceptable levels by the end of 2007, but in the three forest sites, poach-

ing remained high. To reverse poaching trends in the forest required a conventional patrol

effort that was 10 times higher than that in the savannah.

The relationship between the amount of illegal activity with the operational budget, senior

staff performance, encounter rates with large mammals, human population densities and

habitat, was investigated for 2005–2007. With three predictor variables, the model

explained 63% of the variation in the encounter rates with illegal activity. Increasing

human population densities gave higher levels of poaching. Increasing frequencies of camp

visits by senior officers and increasing operational budgets gave lower levels of poaching.

In the second model, elephant poaching was used as the response variable and relative ele-

phant density as an additional predictor variable. One predictor variable – that is elephant

density – explained 38% of the total variation in elephant poaching. Elephant density incor-

porated the effects of camp visit frequencies, human densities, and habitat. Commercial

trophy hunting for ivory, as opposed to subsistence hunting, was more sensitive to the den-

sity of the target species and efforts to curtail the activity. Subsistence hunting was propor-

tional to human densities, with mainly members of nearby communities involved, while

elephant poaching was not, mainly involving specialised hunters from towns further away.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

In Ghana, the Wildlife Division of the Forestry Commission

has direct management responsibility for 16 protected areas,

which includes three coastal wetlands, totalling 12,585 km2 or

5.5% of the country. Legislation caters for the protection of all

wildlife, both in and outside of protected areas, but resource

constraints greatly limit the ability to implement conserva-

tion legislation. Prevailing ecological and above all economic

er Ltd. All rights reservedorg, hugojachmann@hotm

conditions (Skonhoft and Solstad, 1998) determine that vol-

untary compliance with conservation legislation does not oc-

cur, and that the protection of wildlife requires effective and

often expensive enforcement mechanisms (Jachmann, 1998;

Rowcliffe et al., 2004). For the majority of protected areas in

Ghana, budgetary allocations were too low to provide ade-

quate protection for their gradually declining wildlife popula-

tions (Jachmann, 2008). Because most of the budget is used for

law-enforcement operations, it is important that law enforce-

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B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 1 9 0 6 – 1 9 1 8 1907

ment is cost-effective. The foundation for all wildlife manage-

ment decisions should include monthly assessments of staff

deployment and performance, patrol effort, trends in the dif-

ferent types of illegal activity, and population trends of key

wildlife species (Jachmann and Billiouw, 1997; Jachmann,

1998, 2001, 2002). In a few protected areas in Ghana, where

donor-funded projects have been operating, GIS-based sys-

tems were used to visualise information collected on patrol

to direct field operations. However, in the majority of pro-

tected areas management was on an ad hoc basis, lacking

structured information to adapt field operations to changing

conditions, and to evaluate whether management was still

on course in achieving its main objectives. Considering the

financial constraints, protected area management was in

need of a cheap, sustainable, and standardised system to col-

lect patrol information. The primary objective was to intro-

duce performance and adaptive management, using

monitoring feedback to make law enforcement more effective

and cost-efficient in the short term (Jachmann, 2008). The

secondary objective was to identify the most important fac-

tors contributing to the conservation of wildlife in a series

of protected areas with varying conditions in terms of habitat,

human pressure, wildlife abundance, resource allocation, and

management.

Fig. 1 – Ghana and its pr

Previous studies showed that fluctuations in the numbers

of elephants and/or rhinoceros poached could be attributed

predominantly to resource allocation for law enforcement,

in terms of patrol effort and funding (Leader-Williams and

Albon, 1988; Leader-Williams et al., 1990; Milner-Gulland

and Leader-Williams, 1992; Dublin and Jachmann, 1992; Jach-

mann and Billiouw, 1997; Jachmann, 1998, 2002). The present

study examined the incidence of all types of illegal activity

combined, as well as elephants found killed illegally, in rela-

tion to key factors that may have been of influence on these,

in a series of nine conservation areas. Here, illegal activity re-

fers to all classes of serious wildlife offences, predominantly

pertaining to subsistence hunting, but including some com-

mercial meat hunting, while elephants found killed illegally

mainly concerned commercial trophy hunting.

In mid 2004, a simple patrol-based monitoring system (Bell,

1985; Bell et al., 1992; Jachmann, 1998) was initiated in Ankasa

and Kakum Conservation Areas, and in Shai Hills and Kalakpa

Resource Reserves (Fig. 1). Early 2005, the same system was

established in Kogyae Strict Nature Reserve and in Digya Na-

tional Park (Fig. 1). Early 2006, the system was in operation

in the Bia Conservation Area (Fig. 1). Patrol data used for the

GIS-based monitoring systems, initiated in Kyabobo and Mole

National Parks (Fig. 1) in 2004, were standardised and

otected area system.

1908 B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 1 9 0 6 – 1 9 1 8

re-analysed. In December 2005, Ankasa, Kakum, Shai Hills,

Kalakpa, Kogyae, and Digya were evaluated in terms of patrol

staff performance, rates of illegal activity and wildlife trends

(Jachmann, 2008). Early 2007, all nine sites were evaluated.

By disseminating the results of the evaluations, a competitive

management system was created, in which each site strived

for the best performance (Jachmann, 2008).

The purpose of this study was twofold. First, to document

the success of the competitive performance management

system in terms of improved patrol effort and reduced poach-

ing. Second, to determine key factors that influenced poach-

ing rates in nine protected areas in Ghana, by examining

the relationships between all classes of illegal activity com-

bined, and elephants found killed illegally, with staff perfor-

mance, relative wildlife densities, elephant densities, habitat

type, human population densities in areas surrounding the

nine study sites, and budgetary allocations. We began by

examining patrol staff performance for all sites combined,

followed by trends in illegal activity for the six savannah sites

combined, and for the three forest sites combined. Then we

continued by examining the univariate relationships between

illegal activity and each of the above key factors, followed by

two multivariate analyses, the first with all classes of illegal

activity combined, and the second with elephants found

killed illegally as the response variables and the key factors

above as the predictor variables.

2. Study areas

A Senior Wildlife Officer and one or more Assistant Wildlife

Officers manage protected areas in Ghana (Senior officers).

Wildlife Rangers make up the hierarchical level below this.

They are in charge of a particular area (range) and a number

of camps from where patrols emanate. Wildlife Rangers

may be stationed in a camp within their range, or they are

posted at the protected area’s headquarters, while they make

regular visits to the camps that come under their supervision.

Senior officers also make regular visits to each camp, but

camp-visit frequencies often depend on motivation and lead-

ership skills. Generally, one striking force of patrol staff oper-

ates from headquarters, and several other teams operate

from camps throughout the protected area. From 2003 to early

2005, using a mobile training unit and external consultants,

patrol staff of all protected areas in Ghana received extensive

law-enforcement training under the Wildlife Division Support

Table 1 – Summary of study areas

Protected area Size (km2) Elevation (m)

Shai hills 48 50–60

Kyabobo 222 300–800

Bia 306 145–230

Kalakpa 320 60–400

Kakum 360 150–250

Kogyae 386 120–230

Ankasa 509 90–150

Digya 3478 90–180

Mole 4577 120–490

a Vegetation types in addition to Guinea savannah for savannah sites an

Project, funded by the Royal Netherlands Embassy in Accra.

As from 2006, this exercise was repeated annually, ensuring

that patrol skills of all Wildlife Division staff remained at a

high and standardised level.

A detailed description of the nine study areas, including

common large-mammal species, was provided in a previous

paper (Jachmann, 2008). A summary of the size, elevation, an-

nual rainfall, and vegetation types that occur in addition to

Guinea savannah for the six savannah sites and moist ever-

green forest for the three forest sites is provided in Table 1.

Briefly, the Bia, Kakum, and Ankasa Conservation Areas con-

sist of moist evergreen forest, with some dry semi-deciduous

forest in the northern part of Bia. The other six protected

areas consist predominantly of Guinea savannah, inter-

spersed with various other vegetation types (Table 1).

Five out of nine study sites contain small resident elephant

populations, whereas Kyabobo shares roughly between 20

and 30 savannah elephants with the adjacent Fazao-Malfak-

assa National Park in Togo. Mole National Park contains the

largest savannah population of at least 401 elephants

(Bouche, 2007), Digya National Park has anywhere between

170 (Jachmann, unpublished data) and 341 savannah ele-

phants (Kumordzi et al., in press). The Ankasa Conservation

Area contains about 38 forest elephants (Protected Areas

Development Programme II, unpublished report, 2007), about

164 forest elephants reside in the Kakum Conservation Area

(CITES/MIKE, unpublished report, 2004), with 115 forest ele-

phants remaining in the Bia Conservation Area (Sam, M.K.,

unpublished report, IUCN/SSC AfESG, 2004).

In Ghana, wildlife related illegal activity falls into two

main classes; commercial hunting of elephants for ivory,

and subsistence hunting. Hunting for subsistence purposes

is either for private use, for selling within the community,

for selling to bush meat traders, or a combination of these.

Thus, subsistence hunting includes some commercial meat

hunting. Although commercial hunting of elephants may be

considered a serious class of illegal activity, it occurs at low

intensities, mainly in Mole and Digya National Parks, and in

the Bia and Kakum Conservation Areas. Outside the protected

area system, small numbers of elephants have been killed

each year. This particularly happens during wet-season

movements of elephants from Mole to three mainly unpro-

tected forest reserves south of the park, and in the series of

forest reserves to the east of Bia, where due to extensive log-

ging the remaining habitat has been gradually reduced and

Annual rainfall (mm) Vegetation typesa

900–1000 Dry forest

1200–1300 Various forest types

1500–1700 Semi-deciduous forest

1200–1300 Dry forest

1500–1700 –

1200–1300 Transitional woodland

2000–2200 –

1200–1300 Transitional woodland

950–1050 –

d moist evergreen forest for forest sites.


fragmented. Hunting for subsistence purposes by members of

the communities surrounding the protected areas, targeting a

large range of animal species, from duiker to buffalo, is the

most widespread class of illegal activity throughout the coun-

try. The vast majority of wildlife offences reported by patrol

staff relate to subsistence hunting.

3. Methods

3.1. Data collection

All protected areas use conventional law enforcement in the

form of foot patrols that frequently start from each of the

camps as well as from the area’s headquarters. Standardised

patrol forms were used to keep records of the numbers of

staff on patrol, the exact duration, the area travelled, types,

quantities and locations of illegal activity encountered, and

the numbers of large-mammals encountered by species

and location. Using a grid map, patrol routes were drawn,

and the location of each encounter was recorded. The Wildlife

Ranger in charge of a particular range used the patrol routes

for spatial planning; thereby ensuring that the entire range

was covered at least once a month. Because patrol move-

ments should be unpredictable by nature, rangers were

trained to randomize patrol movements as much as practi-

cally feasible, first to optimise the impact of law enforcement,

and second to enable statistical inference from monitoring

data. Total patrol time was made up of placement, that is time

spent moving between base and the location where the patrol

started or finished, preparations, that is obtaining rations,

firearms and ammunition, and effective patrol time, that is

time spent actively in pursuit of illegal activity. To compare

encounter rates of illegal activity and large mammals in pro-

tected areas with different conditions, a standardised mea-

sure of patrolling effort was required. The most acceptable

measure of effort for comparing areas with each other is

effective patrol man-days, which does not include time spent

on placement and preparations (Jachmann, 1998). The rela-

tionship between the numbers of staff in a patrol group and

the numbers of encounters with large mammals and illegal

activity follows an optimum curve (Jachmann, 1998). First,

an increasing number of staff in a patrol group gives a linear

increase in encounter rates up to a particular optimum patrol

size, then declining, which is partly due to an increased prob-

ability of detection of the patrol group by both poachers and

wildlife (Jachmann, 1998). However, a patrol group size of be-

tween three and four staff on average (range 3–7), which was

the case in all nine sites for the entire study period, falls in the

initial linear part of the curve. This enabled us to multiply

effective patrol time by the number of staff in the patrol group

to give effective patrol man-days. Because we required a mea-

sure of effort that was easy to interpret for management pur-

poses, and closely related to the minimum standard that was

set at 15 effective patrol days/staff/month for all protected

areas in Ghana, the duration of an effective patrol day was

set at 8 h. Thus, for each patrol, independent of its duration,

the number of patrol hours was divided by 8, and multiplied

by patrol size to give effective patrol man-days (effective pa-

trol man-days = ((duration of patrol (hours)/8) · patrol size

(# staff)). All encounters with mammals of a similar size or

larger than a Maxwell’s duiker (Cephalophus maxwelli) were re-

corded. In terms of illegal activity, a standardised number of

classes of serious offences were recorded – that is those,

which directly relate to the illegal killing of wildlife. Classes

of serious offences were; poachers arrested, poachers ob-

served, firearms/cartridges/ivory/skins confiscated, gunshots

heard, poachers’ camps found, animals found killed, wire

snares recovered, and cartridges found.

3.2. Comparing protected areas

Arresting offenders and deterrence may be the main objec-

tives of law enforcement, but with regard to collecting infor-

mation, a patrol may be compared with a sample count.

Along the patrol route, the officers note all encounters with

serious offences and large mammals, in a strip with unknown

and variable width. Because we do not know the width of the

strip searched on patrol, while law enforcement should be

unpredictable by nature, we are not able to estimate absolute

numbers or densities of the indicators measured. Instead,

these particular sample counts yield density indices. Because

we wish to compare the number of encounters during a par-

ticular period in a particular area with that in another area,

we need to correct the encounters for differences in patrol ef-

fort. We used the Catch per unit Effort (C/E) index (Bell, 1985;

Jachmann, 1998), where the catch refers to the total number

of encounters with serious offences or with large mammals

per unit area per unit time, and the effort is the total number

of effective patrol man-days per unit area per unit time. Here,

unit area refers to the size of each protected area (km2), while

unit time refers to either one month or one year. For all pro-

tected areas in Ghana, the acceptable amount of illegal activ-

ity was arbitrarily set at 0.02 encounters with serious

offences/effective patrol man-day/month. We should note

that the unknown and variable width of the strip searched

on patrol depends on visibility, which in turn depends on

the density of the vegetation (habitat type).

In December 2005, patrol staff performance and the law-

enforcement programs in six protected areas were evaluated

(Jachmann, 2008). Early 2006, the results were disseminated

to all protected areas, as well as to other relevant stakehold-

ers. The second evaluation involved nine study areas, and

took place between March and June 2007. The results were

widely circulated, and presented to all field staff in each of

the sites. The objective was to improve patrol staff perfor-

mance by creating a spirit of competition between protected

areas.

Most of the information presented in this paper was de-

rived from patrol reports that were assumed reliable accounts

of the activities of the patrol staff, both in terms of technical

precision and in terms of being a true account of events. The

subject of reliability of self-reporting in a competitive man-

agement system, and checks on the system at the various

hierarchical levels, was discussed in detail in a previous paper

(Jachmann, 2008).

The patrol data for the Kakum Conservation Area for 2006

was not included in the analysis. Mainly due to changes in

mid- and senior level staff in late 2005, the patrol data were

considered unreliable (Jachmann, 2008). In 2007, we did not


manage to visit the Bia Conservation Area to check on data

collection, while after submission some of the data were

missing. Consequently, the 2007 data for Bia was excluded

from the multivariate analysis.

3.3. Analyses

We began by examining the trend in patrol staff performance

for all sites combined, from 2005 to 2007. We continued with

trends of the incidence of poaching for the six savannah sites

combined, and for the three forest sites combined. Combining

protected areas has the advantage that C/E indices do not re-

quire correction for size, but the disadvantage of pronounced

seasonal oscillations in encounter rates, mainly due to sea-

sonal differences in visibility and accessibility. Consequently,

for the multivariate analysis described below, we used totals/

km2/year for most of the variables.

To examine the influence of resource allocation, senior

staff performance, human population densities, relative den-

sities of large mammals, and habitat type on the incidence of

illegal activity, a stepwise multiple linear regression analysis

was performed with the program STATISTICA (Statsoft Inc.,

Tulsa, OK). The number of encounters with serious offences/

km2/effective patrol man-day was used as the response vari-

able. As a measure of resource allocation, we used the opera-

tional budget/km2 (US$). As a measure of the performance of

senior officers, we used the average number of visits by senior

officers/camp. Only camp visit frequencies verified through

camp visitor books, senior officer diaries or through other

trustworthy sources were used for the analysis. As a measure

of human pressure, we used the human population density

for the districts were the protected area is located (Ghana

0

2

4

6

8

10

12

14

16

18

Jan 2

005

MarMay Ju

lSep Nov

Jan 2

006

MarMay

Month (

Perf

orm

ance

(effe

ctiv

e pa

trol

day

s/st

aff/m

onth

)

Fig. 2 – Patrol staff performance in average effective patrol days/

and moving average (n = 6; Microsoft Office, Program Excel) (sol

Government, district demographical data). The number of

encounters with large mammals/km2 was used as a measure

of relative wildlife density. As an indication of habitat type

and therefore vegetation density, we made a simple division

between forest sites, and sites with predominantly Guinea

savannah (forest = 2 and savannah = 1). First, the univariate

relationships between each of the individual predictor vari-

ables and the response variable were explored. Using the non-

linear components module in program STATISTICA, the

transformation that provided the best fit for the modelling

procedure was used. Four different transformations were ap-

plied, i.e. logarithmic, exponential, square root, and square.

Next, Pearson product-moment correlation coefficients were

calculated amongst transformed variables, and correlations

between predictor variables examined. This was followed by

a stepwise forward linear multiple regression analysis, with

the objective of explaining the most variation with the least

number of variables.

In Ghana, elephant numbers have been declining rapidly

over the past few decades. To examine the factors that are

of influence on an important class of illegal wildlife use –

that is elephant poaching – we repeated the forward step-

wise multiple linear regression, but replaced the response

variable (serious offences) with elephants found killed ille-

gally, and added elephants encountered/km2 (relative ele-

phant density) as a predictor variable. To detect further

structure in the relationships between the variables, thereby

complementing the results of the regression analysis, we

performed a principal components analysis (program

STATISTICA).

Multiple linear regression assumes linear relationships be-

tween the variables, a more or less constant variance of the

Jul

Sep Nov

Jan 2

007

MarMay Ju

lSep Nov

2005 - 2007)

staff/month for nine protected areas combined (broken line),

id line), from January 2005 to December 2007.


response variable, and a normal distribution of residuals. Vio-

lations of these assumptions were checked with normal prob-

ability plots of residuals (program STATISTICA).

We used repeated measures of the same variables from

three consecutive years as independent data points to enlarge

the sample size. This design may include potentially con-

founding variables when repeated measures of the same var-

iable are more or less the same over time. To test for

associations between temporal changes in illegal activity (re-

sponse variable) and those for each of the predictor variables,

we performed a series of one-way repeated measures Analy-

ses of Covariance (ANCOVA), (program STATISTICA). Predictor

variables without a significant temporal association with ille-

gal activity were omitted from the regression analyses.

4. Results

4.1. Patrol staff performance and poaching trends

For all study areas combined, patrol staff performance in-

creased from about 8 effective days/staff/month on average

in 2005 to about 16 by the end of 2007 (Fig. 2). As a result of

the improved patrol staff performance, for the six savannah

sites combined, the incidence of illegal activity dropped from

an average of approximately 0.16 encounters with serious of-

fences/effective patrol man-day/month in early 2005 to a low

of about 0.02 by the end of 2007 (Fig. 3). However, for the three

forest sites combined, the incidence of illegal activity re-

mained more or less the same throughout the study period,

fluctuating between 0.20 and 1.00 encounters with serious of-

fences/effective patrol man-day/month (Fig. 4). Thus, encoun-

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Jan 2

005

MarMay Ju

lSep Nov

Jan 2

006

MarMay

Month

Serio

us o

ffenc

es e

ncou

nter

ed/e

ffect

ive

patr

ol m

an-d

ay

(sav

anna

h si

tes)

Fig. 3 – Encounter rates with illegal activity (serious offences/ef

combined, from January 2005 to December 2007. Y = �0.043 ln(x

ter rates with serious offences – that is poaching – were

between 10 and 50 times higher in the forest as compared

to the savannah, while a doubling of patrol effort did not ap-

pear to have a significant effect on the incidence of illegal

activity. On the contrary, in the forest, encounter rates with

serious offences exponentially increased with an increasing

patrol effort (Y = 93.290e0.001x; P 6 0.001). This exponential in-

crease was mainly due to a sharp increase in wire snares

recovered with an increasing patrol effort (Fig. 5). When we

omitted wire snares, a polynomial relationship between the

incidence of illegal activity and increasing patrol effort

emerged, with encounter rates peaking between about 1200

and 1400 effective patrol man-days/month (Fig. 6). The mean

size of the three forest sites is about 392 km2 (Table 1), imply-

ing that it requires at least 3–4 effective patrol man-days/km2

to reduce the incidence of poaching with firearms, dogs or

other active means. In the forest, where wire snares are a

common means to trap animals, any increase in patrol effort

will merely result in an increase in wire snares detected. In

the savannah sites, however, poaching was reduced to

acceptable levels, by increasing the patrol effort from about

0.25 effective man-days/km2 in early 2005 to about 0.40 by late

2007. To reverse poaching trends in the forest, which does not

include the incidence of snaring, a conventional patrol effort

of at least 10 times that of the savannah is required, but much

higher if poaching needs to be reduced to acceptable levels. In

summary, the relationship between conventional patrol effort

and poaching follows a detection/deterrence curve, peaking

at much lower efforts in the savannah than in the forest,

but for the incidence of snaring alone, peaking at patrol ef-

forts that may not be sustainable.

Jul

Sep Nov

Jan 2

007

MarMay Ju

lSep Nov

(2005 - 2007)

fective patrol man-day/month) for six savannah sites

) + 0.170 (P 6 0.001).

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Jan 2

005

MarMay Ju

lSep Nov

Jan 2

006

MarMay Ju

lSep Nov

Jan 2

007

MarMay Ju

lSep Nov

Month (2005 - 2007)

Serio

us o

ffenc

es e

ncou

nter

ed/e

ffect

ive

patr

ol m

an-d

ay

(fore

st s

ites)

Fig. 4 – Encounter rates with illegal activity (serious offences/effective patrol man-day/month) for three forest sites combined,

from January 2005 to December 2007.

0

100

200

300

400

500

600

0 200 400 600 800 1000 1200 1400 1600

Effective patrol man-days/month (forest sites)

Snar

es re

cove

red/

mon

th

Fig. 5 – Relationship between the numbers of wire snares recovered/month and the numbers of effective patrol man-days/

month for three forest sites combined (2005–2007). Y = 43.035e0.0015x (P 6 0.001).


4.2. Univariate relationships

A summary of the response variable and the five predictor

variables for the nine protected areas, from 2005 to 2007, is

provided in Table 2. The relationship between the average

numbers of camp visits by senior officers with serious of-

fences encountered/km2/effective patrol man-day was highly

significant, with poaching sharply declining with increasing

0

20

40

60

80

100

120

140

160

180

0 200 400 600 800 1000 1200 1400 1600

Effective patrol man-days/month (forest sites)

Serio

us o

ffenc

es e

ncou

nter

ed/e

ffect

ive

patr

ol m

an-d

ay (s

nare

s no

t inc

lude

d)

Fig. 6 – Relationship between serious offences (not including wire snares) encountered/effective patrol man-day/month and

effective patrol man-days/month for three forest sites combined (2005 – 2007). Y = �1E�07x3 + 0.0003x2 � 0.0841x + 63.3020

(P 6 0.001).

Table 2 – Summary of variables that may be of influence on the incidence of illegal activity in nine protected areas inGhana, from 2005 to 2007

Area Year Serious offences/km2/epmd

Predictor Variables

Visits/camp Human densities Large mammals Operational budget Habitat

Shai hills 2005 0.0000349 18.00 74.49 76.25 43.71 1

Shai hills 2006 0.0005945 24.00 76.05 84.00 58.08 1

Shai hills 2007 0.0000024 24.00 77.65 69.54 134.06 1

Kyabobo 2005 0.0000533 57.00 39.23 1.03 140.07 1

Kyabobo 2006 0.0000388 71.00 40.41 1.07 9.28 1

Kyabobo 2007 0.0000455 40.00 41.62 0.78 173.90 1

Bia 2006 0.0016012 3.25 81.39 2.69 5.68 2

Kalakpa 2005 0.0008552 1.44 54.95 4.72 10.90 1

Kalakpa 2006 0.0002635 4.67 56.60 16.48 13.28 1

Kalakpa 2007 0.0001652 17.14 58.30 38.90 24.15 1

Kakum 2005 0.0009106 3.33 99.15 5.37 11.38 2

Kakum 2007 0.0008849 1.43 103.56 8.95 35.74 2

Kogyae 2005 0.0006089 9.96 65.53 1.60 8.75 1

Kogyae 2006 0.0001434 16.67 66.39 10.35 9.96 1

Kogyae 2007 0.0000522 14.57 67.25 7.89 25.11 1

Ankasa 2005 0.0004168 22.20 84.28 1.08 7.75 2

Ankasa 2006 0.0010960 – 86.89 2.88 3.87 2

Ankasa 2007 0.0002917 32.00 89.58 1.96 6.47 2

Digya 2005 0.0000248 44.04 10.46 2.79 3.32 1

Digya 2006 0.0000202 43.85 10.65 1.87 2.55 1

Digya 2007 0.0000137 24.00 10.84 1.63 4.94 1

Mole 2005 0.0000209 32.04 10.69 8.66 14.67 1

Mole 2006 0.0000130 8.33 11.02 11.95 1.19 1

Mole 2007 0.0000066 24.67 11.36 10.08 83.73 1

Response variable: serious offences encountered/km2/effective patrol man-day. Predictor variables: average number of camp visits by senior

officers; human population densities in surrounding areas (people/km2); number of large-mammals encountered/km2; operational budget in

US$/km2; Habitat (forest = 2, savannah = 1).



camp visit frequencies (Fig. 7). With increasing human densi-

ties in the districts where the nine protected areas are lo-

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

0.0018

0 10 20 30 40

Average number of sen

Serio

us o

ffenc

es/k

m2 /e

ffect

ive

patr

ol m

an-d

ay

Fig. 7 – Relationship between the average numbers of camp visi

effective patrol man-day for nine protected areas, from 2005 to

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

0.0018

0 20 40

Human populatio

Serio

us o

ffenc

es/k

m2 /e

ffect

ive

patr

ol m

an-d

ay

Fig. 8 – Relationship between human population densities in the

km2), and serious offences encountered/km2/effective patrol ma

cated, levels of poaching increased significantly (Fig. 8).

Although poaching declined exponentially with an increasing

50 60 70 80

ior staff visits/camp

ts by senior officers, and serious offences encountered/km2/

2007. Y = �0.0003 ln(x) + 0.0011 (R2 = 0.554; P = 0.001).

60 80 100 120

n density (people/km2)

districts where the nine protected areas are located (people/

n-day. Y = 1E�05e0.0437x (R2 = 0.638; P = 0.000).


operational budget and with increasing wildlife densities,

these relationships were not statistically significant.

4.3. Correlations between transformed variables

All continuous variables required a logarithmic (ln) transfor-

mation. An increasing number of camp visits by senior offi-

cers gave a significant increase in staff performance (Fig. 9).

The variation in numbers of annual camp visits between

protected areas was not related to the availability of finan-

cial resources, while adequate transport was available in

each of the sites. The most important factors appeared to

be leadership skills and motivation of senior officers. How-

ever, camp-visit frequencies explained only about 23% of

the variability in patrol performance (Fig. 9). A summary

of Pearson product-moment correlations and P-values is

provided in Table 3.

Increasing densities of elephants gave a significant in-

crease in elephants found killed illegally (Table 4), suggesting

that poaching occurs in high elephant density areas.

0

5

10

15

20

25

0 10 20 30 40

Senior staff vis

Perf

orm

ance

(ave

rage

effe

ctiv

e pa

trol

day

s/st

aff)

Fig. 9 – Relationship between the average numbers of camp vis

effective patrol days/staff) for nine protected areas, from 2005 to

Table 3 – Pearson product-moment correlations and significan

Variable Serious offences Visits/camp Hu

Serious offences 1.000

Visits/camp �0.625 1.000

P = 0.001

Human densities 0.676 �0.483

P = 0.000 P = 0.020

Large mammals �0.142 �0.237

Operational budget �0.038 �0.015

Although none of the other predictor variables was directly

correlated with elephant poaching, significant correlations

existed between elephants encountered/km2 and camp visits,

and between human population densities and camp visits

(Table 4).

4.4. The models

The results of the one-way repeated measures ANCOVAs

showed that temporal changes between serious offences/

km2/effective patrol man-day (response variable) and large

mammals/km2 (predictor variable) were not significant

(P = 0.471). This predictor variable was omitted from the

regression analyses.

We performed a series of forward stepwise multiple linear

regression analyses on transformed variables, with serious

offences/km2/effective patrol man-day as the response vari-

able. By replacing variables with an insignificant contribution

to the equation with new ones, we arrived at a highly signif-

icant model with only three predictor variables (F = 10.68,

50 60 70 80

its/camp

its by senior officers, and patrol staff performance (average

2007. Y = 0.123x + 7.937 (R2 = 0.232; P 6 0.020).

t P-values between transformed variables

man densities Large mammals Operational budget

1.000

0.197 1.000

0.161 0.340 1.000


P < 0.0002) that accounted for about 63% of the total variation.

The regression Eq. (1) is:

ln y ¼ � 8:874þ 0:034 ln Humandensities

� 0:572 ln Visits=camp� 0:214 ln Operationalbudget

ð1Þ

where y = prediction of the number of serious offences

encountered/km2/effective patrol man-day.

The density of the human populations in the areas sur-

rounding the nine study sites was the most important factor

influencing levels of illegal activity, with the second most

important factor being the frequency of camp visits by senior

officers (Table 5). The third factor, the operational budget, had

the least influence on illegal activity (Table 5). Human popula-

tion densities had a positive impact – that is with increasing

densities, poaching also increased. Both camp visit frequen-

cies and the operational budget had a negative impact – that

is with increasing camp visit frequencies and financial re-

sources, poaching declined (Table 5). In the areas surrounding

the three forest sites, human densities were twice as high

(mean = 90.81/km2) as compared to those surrounding the

six savannah sites (mean = 43.53/km2; P = 0.00031). Thus, the

influence of habitat on the incidence of illegal wildlife use

was incorporated in the predictor variable human densities.

The forward stepwise multiple linear regression analysis,

with elephants found killed illegally as the response variable,

resulted in a significant model with only one predictor vari-

able (F = 7.846; P 6 0.015) that accounted for about 38% of

the total variation. The regression Eq. (2) is:

ln y ¼ �15:419þ 2:349 lnðelephantsencountered=km2Þ ð2Þ

where y = prediction of numbers of elephants found killed

illegally/km2/effective patrol man-day.

Increasing elephant densities gave higher levels of poach-

ing (Table 5). Because this predictor variable was correlated

Table 4 – Pearson product-moment correlations and significan

Variable Elephants killed Visits/camp H

Elephants killed 1.000

Visits/camp �0.417 1.000

Human densities �0.028 �0.637

P = 0.011

Elephants/km2 0.614 �0.798

P = 0.015 P = 0.000

Operational budget �0.257 0.107

Table 5 – Results of the forward stepwise multiple linear regresB, slope

Variable Beta SE

Intercept

Human population densities 0.566 0.160

Senior staff visits/camp �0.337 0.158


Intercept

Elephants encountered/km2 0.614 0.219

with camp visits, which in turn was correlated with human

densities, it incorporated the effects of patrol effort, leader-

ship skills and motivational levels of senior officers, as well

as human densities and habitat type. This was confirmed by

the results of the principal components analysis that yielded

two factors that together accounted for 77.4% of the total var-

iance (Table 6). In the first factor, elephants found killed ille-

gally, elephants encountered/km2, and camp visits had the

highest factor loadings (Table 6). In the second factor, human

population densities and the operational budget had the

highest factor loadings (Table 6). Although elephant poaching

was more or less influenced by the same factors as other clas-

ses of illegal wildlife use, it mainly occurred in high elephant

density areas, while as opposed to subsistence hunting, it was

not proportional to human densities.

5. Discussion

Performance management through annual evaluations of

law-enforcement programs, followed by wide dissemination

of the results, proved to be a cheap and sustainable method

of improving patrol performance, applicable in most pro-

tected areas on the continent (Jachmann, 2008). In the six

savannah sites, it required a patrol staff density of 0.02

staff/km2 on average, a patrol effort of about 0.40 effective pa-

trol man-days/km2/month, and an average operational bud-

get of US$ 51/km2/year to reduce illegal wildlife use to

acceptable levels. This compares with 0.02 staff/km2, between

0.10 and 0.14 effective patrol man-days/km2/month, and be-

tween 22 and 52 US$/km2/year, that was required to reduce

elephant poaching to acceptable levels (60.2% of the popula-

tion) in the central Luangwa Valley between 1989 and 1995

(Jachmann 1998; Jachmann and Billiouw 1997). This, however,

concerned a single key species in one large conservation area

(14,000 km2), surrounded by wilderness and areas with low

t P-values between transformed variables

uman densities Elephants/km2 Operational budget

1.000

0.234 1.000

0.119 �0.409 1.000

sion analyses on transformed variables. SE, standard error,

B SE t P

�8.874 1.152 �7.702 0.00000

0.034 0.010 3.528 0.00211

�0.572 0.268 �2.131 0.04569

�0.214 0.162 �1.325 0.20006

�15.419 1.990 �7.747 0.000003

2.349 0.839 2.801 0.014999

Table 6 – Results of the principal components analysis

Variable Factor 1 Factor 2

Elephants found killed illegally 0.680 �0.412

Elephants encountered/km2 0.928 �0.165

Senior staff visits/camp �0.902 �0.350

Human population densities 0.478 0.779


Eigenvalue 2.519 1.351

Variance explained (%) 50.375 27.022


human densities. Mainly due to the larger interface with cul-

tivation, small conservation areas such as in Ghana require

substantially more patrol effort, while the operational budget

for the Luangwa Valley was not corrected for inflation, which

makes the average budgetary allocation for the protected

areas in Ghana much lower in terms of standard dollars.

For the three forest sites, patrol staff densities averaged

0.06/km2, with an average operational budget of roughly

US$ 21/km2/year. This included some project support to both

Ankasa and Bia Conservation Areas. In the forest, visibility is

low and strips searched on patrol are narrow. To reduce

poaching to acceptable levels in the forest requires substan-

tially more conventional patrol effort, supported by opera-

tional funds, than in the savannah. Moreover, human

population densities in the areas surrounding the three forest

sites are on average twice as high as in those surrounding the

savannah sites (Table 2). High human densities combined

with poverty result in high levels of subsistence hunting.

Low visibility, an inhospitable environment, and often prob-

lems of access require at least 3–4 effective man-days/km2/

month to reverse poaching trends, which does not include

the incidence of snaring. The latter may prove to be extremely

difficult and highly expensive to bring under control with con-

ventional foot patrols alone. Next to a variety of community

approaches, law-enforcement strategies in the forest require

the use of trackers (Nellemann et al., 2007), and investigations

in the main villages and towns outside the protected areas

(Jachmann, 1998).

For the multiple regression analysis, the predictor variable

‘large mammals/km2’ had to be omitted, because temporal

changes with illegal activity were not significant. This was

mainly due to minor changes in patrol coverage, which was

a direct result of the sharp increase in patrol effort. In 2007,

in the majority of protected areas, patrols spent proportion-

ally more time in low wildlife density areas as compared to

previous years, which resulted in declining large-mammal

encounter rates. With an uneven and often shifting distribu-

tion of wildlife, encounter rates with large mammals (direct

observations) are more susceptible to minor changes in patrol

coverage than encounter rates with serious offences, which

include many indicators (indirect observations) that remain

visible for extended periods. Thus, changes in patrol coverage

have a greater impact on large-mammal encounter rates than

on encounter rates with illegal activity.

As an important class of illegal wildlife use, commercial

trophy hunting for ivory was influenced by more or less the

same factors as hunting for subsistence purposes. In the

Luangwa Valley, between 1989 and 1995, most of the variation

in the numbers of elephants found killed illegally could be ex-

plained by resource allocation in terms of financial input and

patrol effort alone (Jachmann, 1998; Jachmann and Billiouw,

1997). This, however, concerned a single population of ele-

phants in a single large conservation area covered by wood-

land savannah. In Ghana, in the six protected areas that

contain elephants, as opposed to subsistence hunting, com-

mercial trophy hunting was more sensitive to the density of

the target species and efforts to curtail the activity. Moreover,

subsistence hunting was proportional to human densities in

the areas surrounding the parks, whereas commercial trophy

hunting for ivory was not. Members of communities located

near protected areas mainly carried out subsistence hunting.

Specialised hunters, frequently originating from towns fur-

ther away, were involved in commercial ivory hunting.

In the six savannah sites, a doubling of patrol effort resulted

in a sharp decline in illegal activity (Figs. 2 and 3). However,

neither of our two regression models, the first one pertaining

to hunting for subsistence purposes, and the second one per-

taining to commercial trophy hunting, included a predictor

variable for patrol effort. With our current analytical design,

this was not feasible, first, because patrol effort was used to

correct encounters in the field (C/E index) for widely varying

patrol intensities, and second, because patrol effort, through

patrol performance, was indirectly correlated with camp vis-

its. Camp visit frequencies, however, only explained 23% of

the variability in patrol performance. This implies that much

of the unexplained variation in poaching rates, both for subsis-

tence hunting and trophy hunting, can be attributed to patrol

effort. Most unfortunately, our current data set is too small

and some of the information too heterogeneous. With a larger

data set, differentiating between forest and savannah and cor-

recting for financial investment under project management,

most of the variation in incidences of illegal wildlife use on

one hand, and elephants found killed illegally on the other

hand, may be explained by human density and resource allo-

cation, and elephant density and resource allocation respec-

tively. Here, resource allocation should include the

operational budget, the capital expenditure, and patrol effort,

whereby patrol effort is the product of patrol staff numbers

and performance. Although competent and dedicated senior

officers with adequate leadership skills are required for sound

wildlife management, this is partly incorporated in patrol staff

performance and therefore patrol effort.

Acknowledgments

I would like to thank Mr. M. Adu-Nsiah (Executive Director of

the Wildlife Division), Mr. A. Akwoviah (Director Operations),

and Mr. C. Nateg (Manager Special Services) for their continu-

ous support. I am indebted to the senior management staff in

each of the protected areas for their hospitality, patience and

cooperation. SNV-Netherlands Development Organisation

supported the work, under a bilateral agreement with the

Wildlife Division of the Forestry Commission. My gratitude

goes to Peter de Haan, the Country Director of SNV-Ghana,

for his continued logistical and moral support. I am grateful

to Christian Nellemann, and several unknown reviewers for

providing useful comments on an earlier draft.


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Documents

2008_Jachmann_Illegal Wildlife Use and Protected Area Management in Ghana