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
. sc iencedi rec t .com
ava i lab le at wwwjournal homepage: www.elsevier .com/ locate /b iocon
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-
.ail.com
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.
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 1909
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
1910 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
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.
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 1911
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).
1912 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
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).
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 1913
1914 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
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).
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 1915
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
1916 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
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
Operational budget �0.187 0.141
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$/km2/year, that was required to reduce
elephant poaching to acceptable levels (60.2% of the popula-
tion) in the central Luangwa Valley between 1989 and 1995
(Jachmann 1998; Jachmann and Billiouw 1997). This, however,
concerned a single key species in one large conservation area
(14,000 km2), surrounded by wilderness and areas with low
t P-values between transformed variables
uman densities Elephants/km2 Operational budget
1.000
0.234 1.000
0.119 �0.409 1.000
sion analyses on transformed variables. SE, standard error,
B SE t P
�8.874 1.152 �7.702 0.00000
0.034 0.010 3.528 0.00211
�0.572 0.268 �2.131 0.04569
�0.214 0.162 �1.325 0.20006
�15.419 1.990 �7.747 0.000003
2.349 0.839 2.801 0.014999
Table 6 – Results of the principal components analysis
Variable Factor 1 Factor 2
Elephants found killed illegally 0.680 �0.412
Elephants encountered/km2 0.928 �0.165
Senior staff visits/camp �0.902 �0.350
Human population densities 0.478 0.779
Operational budget �0.391 0.652
Eigenvalue 2.519 1.351
Variance explained (%) 50.375 27.022
B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 1 9 0 6 – 1 9 1 8 1917
human densities. Mainly due to the larger interface with cul-
tivation, small conservation areas such as in Ghana require
substantially more patrol effort, while the operational budget
for the Luangwa Valley was not corrected for inflation, which
makes the average budgetary allocation for the protected
areas in Ghana much lower in terms of standard dollars.
For the three forest sites, patrol staff densities averaged
0.06/km2, with an average operational budget of roughly
US$ 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.
1918 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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