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Guidelines for Biological Monitoring and Research in
Africa’s Rain Forest Protected Areas.
A report to the Center for Applied Biodiversity Science,
Conservation International.
Thomas T. Struhsaker
Department of Biological Anthropology and Anatomy
Duke University, Durham, NC 27708
12 September 2002
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Table of Contents
Executive summary .......4
Introduction.......5
Why monitor PAs?.......5
Objectives of report.......7
General issues in planning a monitoring program.......8
General framework.......8
What species?.......8
Sampling design (spatial and temporal).......8
Evaluation of specific techniques.......11
Stratified sampling.......11
Line-transect censuses.......14
Sweep censuses.......22
Single-observer sweep censuses.......23
Focal animal or social group studies.......24
Point counts.......25
General surveys, rapid assessments, recce walks.......25
Cyber tracking.......27
Dung and track counts.......28
Trap, mark, and recapture.......30
Vegetation sampling.......30
Canopy cover.......34
Plant phenology.......35
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Research priorities.......38
Comparative studies of census techniques.......38
Camera trapping.......38
Population demography.......39
Ecological requirements.......40
Role of key resources on movements and density.......43
Human ecology and impacts on protected areas.......43
Sociological basis of public attitudes toward PA.......47
Acknowledgements.......47
References.......47
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Executive Summary
The majority of Africa’s rain forest protected areas (PAs) lack adequate biological
monitoring programs. As a consequence, management of these PAs is rarely based on
scientific information. This report outlines the reasons for establishing biological
monitoring programs and discusses problems of specific methods used in monitoring. It is
not a comprehensive review of biological monitoring, but, instead, focuses on issues and
techniques that have received little or no attention and those that are controversial.
Although primary attention is given to monitoring medium to large-sized mammals
and trees, much of what is said is relevant to biological monitoring in general. Topics
covered include: sampling design, precision estimates, stratified sampling, census methods
(line transects, multi- and single-observer sweeps, focal studies of individuals or groups,
point counts, general surveys/recce/rapid assessment, cyber tracking, dung and track
counts, mark and recapture), vegetation enumeration, canopy cover, and plant phenology.
In addition, this report offers suggestions for research priorities in Africa’s rain
forest PAs. These include: comparative studies of different census techniques to better
understand each of their biases; camera trapping as a means of estimating abundance;
demography and ecological requirements of key species; role of key resources in
determining movements, densities and habitat impacts of selected species; human ecology
and its impact on the PAs; and the sociological basis of public attitudes toward PAs.
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Introduction:
In a recent study of the problems facing 16 of Africa’s rain forest protected areas
(PAs) it was found that none of them had PA-wide, long-term, biological monitoring
programs (Struhsaker 2001). Only 25% of these PAs had limited biological monitoring
that covered no more than 2-3% of the total PA. As a consequence, objective information
to evaluate the success of PAs as effective conservation areas was either absent or
extremely limited in scope. This also meant that there was little quantitative or scientific
basis for evaluating the relative success of various conservation management strategies.
Instead, evaluation of PA success and management strategies was largely dependent on the
qualitative impressions of PA managers and scientists. Quantitative, scientific data on the
trends in wildlife populations (both fauna and flora) and habitat composition within the
PAs was either lacking or very limited. Likewise, information on the sociological and
other human-based pressures on the PAs was usually unavailable, deficient, or based
largely on interview data.
Clearly there is a need for more biological monitoring programs in PAs throughout
the world and this is particularly obvious for Africa’s rain forest PAs. Until such programs
become a permanent and significant component of PA management practice, we shall be
unable to objectively evaluate the success of these PAs and handicapped in our efforts to
develop effective management practices.
Why Monitor PAs?
Regardless of the overall objectives of a PA (e.g. maintenance of status quo or
improved status of selected species), biological monitoring programs are important for a
number of reasons, including the following (also see Gibbs et al. 1998):
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1) Tropical rain forests, like all other biological communities, are dynamic. None
are static in terms of their proportional species composition and they are becoming
increasingly dynamic due to shifts in global climate. Understanding the nature and extent
of these changes is critical to PA management. Monitoring is the first step in evaluating
these processes.
2) Scientific and objective evaluation of PA success and management practices can
only be achieved with a program that has biological monitoring as its foundation.
3) Regular and frequent biological monitoring is vital to understanding
demographic trends in the flora and fauna of the PA. Monitoring is necessary to
distinguish variation in community composition and population demography that is due to
site differences (habitat), intra- and inter-annual fluctuations, and real trends (e.g. Gibbs et
al. 1998, Larsen et al. 2001).
4) Monitoring is vital to making predictions about population trajectories. It forms
the basis for anticipating conservation problems, such as population declines due to
disease, predator-prey imbalance, invasions by exotic species, impact of human activities
both inside and outside the park, and the effects of episodic events (e.g. landslides, floods,
extensive tree loss due to wind storms).
5) Without frequent and systematic monitoring, there is no objective way to
evaluate and anticipate the threats to the PA from human activities both inside and outside
the PA.
6) Monitoring forms the objective basis for the development of more detailed and
specific scientific studies and for the refinement of PA management plans and practices.
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7) Monitoring may also suggest ways of conserving threatened or endangered
species.
Objectives of Report:
The objectives of this report are two fold. The first is to discuss the problems of
some specific methods that are or should be used in monitoring programs of Africa’s rain
forest PAs. In this section I will also include suggestions for new approaches to
monitoring problems.
Secondly, this report will suggest areas for more detailed study that I consider to be
of high priority for conservation management of Africa’s rain forest PAs.
This report will not review all of the various techniques for biological monitoring
because there is already a wealth of publications on this subject (e.g. Brower and Zar 1977,
Wilson et al. 1996, Krebs 1999, Jachmann 2001), including some that specifically address
issues in the tropics (Rabinowitz 1997) and Africa’s rain forests (White and Edwards
2000). Nor does it attempt to provide a comprehensive review of the monitoring literature.
The reader is referred to the bibliography for a list of some key references, each of which,
in turn, has an extensive bibliography. Instead, this report emphasizes issues and
techniques that have received little or no attention and those over which there is
controversy.
I focus on guidelines for monitoring medium to large-sized mammals and trees
because this is my area of primary experience. However, many of the suggestions given
here have direct relevance to monitoring most other groups of organisms.
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General Issues in Planning a Monitoring Program:
General Framework
Krebs (1999) and Gaines et al. (1999) provide excellent guidelines for designing
monitoring programs. They emphasize the dynamic interrelationship between the
questions being asked, the field methods employed (spatial and temporal sample design),
statistical analysis, interpretation, and management integration (implementation).
What species?
Noss (1990) has suggested that five categories of species be considered when
planning a monitoring program. These are:
1) ecological indicators: ecologically sensitive species that signal the effects of
perturbations.
2) keystone species: species on which many other species depend.
3) umbrella species: species that require large areas.
4) flagship species: popular or charismatic species.
5) vulnerable species: those that are rare and/or prone to extinction.
Apparent examples of these categories in Africa’s forests include for category 1):
red colobus monkeys, gray-cheeked mangabeys, and chimps (Skorupa 1986, 1988,
Struhsaker 1997) and black and white casqued hornbills (Kalina 1988, Struhsaker 1997);
2): Mimusops bagshawei and numerous Ficus species; 3) and 4): elephants, leopard,
chimps; and 5): mountain gorillas, Tana River and Zanzibar red colobus, Abbott’s duiker.
Sampling Design (spatial and temporal)
The spatial and temporal design of a monitoring program will depend on a wide
range of variables, e.g. level of accuracy and precision required, habitat heterogeneity,
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generation time of species under study (e.g. mayflies versus elephants), site accessibility,
budget, etc. Excellent treatments of these issues can be found in Gibbs et al. 1998, Krebs
1999, and Larsen et al. 2001.
A few of the key points from these references that I think are particularly relevant
to biological monitoring programs in Africa’s rain forests are:
1) The majority of monitoring programs and other ecological studies use indices of
population size as surrogates for monitoring the actual population size because of the
complexities and difficulties of estimating absolute population size (Gibbs, et al. 1998).
2) The sampling design and effort must be continuously re-evaluated (Larsen et al.
2001), as the extent of temporal and spatial variation become apparent.
3) A distinction must be made between “response design”, which “...incorporates
numerous decisions about how to measure the attribute of interest accurately “ (e.g.
methods of observation and/or measurement, plot size, methods of data analysis) and
“sampling design”, which “...refers to the spatial and temporal pattern of locations where
measurements are to be made” (Larsen et al. 2001).
4) The magnitude of temporal variation within and between years will delineate the
interval between sampling events.
5) Larsen et al. (2001) emphasize four components of variation:
a) within-year variation at a site
b) year to year (interannual) variation has 2 components
i) coherent or synchronous – all sites in the network are affected in a
consistent way.
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ii) independent interannual variation at each site within the network,
i.e. variation that can be attributed to site-specific characteristics.
6) The extent of differences between sites will influence one’s ability to detect
trends (Larsen et al. 2001).
While these are extremely useful points and guidelines, one is usually hampered in
the planning stages of a monitoring program by inadequate information on the extent of
temporal and spatial variation of the parameters being measured or counted. As a result,
monitoring plans must strike a compromise between a) the flexibility needed to deal with
new information on variability as it becomes available and b) some degree of rigid
structure that will permit long-term comparisons within and between sites. The difficulty
here is that all long-term ecological studies in tropical forests show a high degree of
intrasite temporal and spatial variation (e.g. Struhsaker 1997). A minimum of two years
sampling at a given site will be required to gain even a first indication of interannual
variation.
Precision estimates (95% confidence limits expressed as the percentage of the
estimated mean, e.g. Norton-Griffiths 1975, NRC 1981, Krebs 1999, Jachmann 2001) will
help in making decisions about sample design, but even these estimates are likely to be
dynamic. Precision estimates are a function of sampling bias and a populations’ response
to ecological dynamics. For example, estimates of precision for a population in a given set
of study plots or transects may reach an asymptote (i.e. variance levels off) during the first
two to three years of study, but then change with time due to any number of possible
variables, such as changes in weather, shifts in predator-prey dynamics, disease outbreaks,
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introduction of exotic species, etc. Consequently, precision estimates should be made
continuously in order to alter the sampling protocol accordingly.
Gibbs et al. (1998) emphasize that temporal variability inherent in counts is the
most critical variable influencing the power to detect trends in populations: “The
probability that a monitoring program will detect a trend in sample counts when the trend
is occurring despite the noise in the count data, represents its statistical power.” They go
on to caution, “...without multiyear studies, researchers often have little notion of how
variable population indices might be.” Without precise estimates of this variance, it is
difficult to design statistically powerful monitoring programs even though the tools for
power analysis are available. For more details on statistical power analysis and
determination of sample size see Krebs (1999) and most standard statistical textbooks (e.g.
Sokal and Rohlf 1994).
Evaluation of Specific Techniques
Stratified Sampling
The subject of stratified sampling is dealt with in most texts dealing with ecological
methods. In my review, I found that Krebs (1999) presented the greatest detail on
analytical procedures. Stratified sampling involves dividing the area to be sampled into
strata or types and then sampling these different strata according to their proportionally
representation in the entire study area (e.g. Grieg-Smith 1983). Krebs (1999, p. 280)
provides 3 simple guidelines for deciding when one should allocate the sampling effort
among the strata proportionately or optimally. He recommends optimal allocation of
sample size, i.e. sample more in a given strata than its proportional representation would
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indicate, when it is 1) larger, 2) more variable internally, and 3) cheaper to sample.
Optimal allocation of sampling effort is intended to minimize variance.
One of the key problems in stratified sampling is identifying the strata. In most
studies, strata are distinguished on the basis of topography or vegetation. Grieg-Smith
(1983) cautions, however, that “Stratification based on features of the vegetation itself is
undesirable because it involves assumptions about the nature of that vegetation.” In
addition to vegetation and topography, here are some of the other characters that have been
recommended for consideration in defining strata that are relevant to studies of African
rain forests:
1) population density of the species being studied (Krebs 1999, p. 273)
2) human population densities (Barnes et al. 1991 and 1995)
3) distance to nearest road or village (Barnes et al. 1991 and 1995)
4) distance to nearest site of extractive activities, e.g. logging, mining, poaching
camps (White and Edwards 2000)
5) distance to nearest area of past settlement, i.e. relatively large areas of secondary
forest (White and Edwards 2000)
6) distance to any other access route, e.g. navigable river, abandoned logging road,
major footpath
In many cases, if not most, it will be necessary to recognize and sample strata on
the basis of more than one characteristic, i.e. adopt a multi-factorial approach.
Incorporation of an array of environmental variables will complicate the process of
identifying strata, but it will likely lead to both greater accuracy and precision in estimates
of abundance. While vegetation type will likely remain a key factor in distinguishing
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strata, other features must be given greater consideration, such as habitat patch size, shape,
and spatial array. Gaines et al. (1999) stress the importance of including fractal indices
when evaluating habitats. These indices are intended to describe habitat patch shape and
boundary/edge complexity and one of the most common indices used is the patch
perimeter to area relationship.
Distance between similar habitat patches and the vegetation matrix in which these
patches are embedded will also influence faunal populations. In other words, far greater
attention must be given to habitat heterogeneity when attempting to conduct stratified
sampling. Consider, for example, a hypothetical situation in which the study area is
composed of 50% prime forest habitat and 50% unsuitable habitat for a forest specialist
bird that cannot move across the unsuitable habitat. The bird’s population will be
dramatically affected by the extent of forest fragmentation in the study area, i.e. whether
the 50% forested area is in one single block or broken up into numerous isolated forest
patches. Forest edge effects will also be dramatically influenced by the extent of
fragmentation. Conventional random stratified sampling based solely on the proportional
representation of habitat types would not address this issue. Similarly, the location of a
single and relatively small key resource, such as a large fruit tree, waterhole or soil lick,
will greatly influence populations of animals that rely on these resources in spite of gross
topographic and vegetative features. In other words, stratification without consideration of
habitat heterogeneity and the location of small, but key resources may be misleading.
Stratification will initially depend on surveys, pilot studies, topographic and
vegetation maps, as well as data on human demography and land-use patterns in and
around the study area. As more ecological information is collected, it may be necessary to
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refine or even greatly modify the classes of strata (post-stratification, White and Edwards
2000).
Line-transect censuses
Counting animals, animal signs, and plants along line-transects is one of the most
common census methods (e.g. Bibby, et al. 2000, Caughley 1977, Chapman, et al. 1988,
Defler and Pintor 1985, Jachmann 2001, Krebs 1999, NRC 1981, Rabinowitz 1997,
Struhsaker 1997, Sutherland 1996, White and Edwards 2000, Whitesides et al. 1988,
Wilson et al. 1996). It can provide indices of relative abundance and estimates of absolute
density. A critical issue affecting the utility of line-transect censuses is estimating the area
sampled (e.g. see discussion on pp. 57-65 in NRC 1981 and Struhsaker 1997). While the
length of the transect is easy to measure accurately, unless one uses fixed-width transects,
estimating the effective sample-width of the transect is much more difficult.
The use of fixed-width transects usually results in a significantly smaller sample
size of animals counted. Fixed-width transects are most useful when dealing with species
or objects occurring at high densities that do not flee from the observer.
When counting inanimate objects, such as elephant dung or chimpanzee nests, the
actual distance from the transect can be physically measured. In these cases, the width of
the sampled area can be determined with a fairly high degree of accuracy and precision. In
contrast, counting highly mobile animals, such as monkeys, duikers, and birds, presents a
more complicated problem in terms of estimating the effective width of the transect.
Measuring the distance to the first animal seen with a tape measure is usually not practical
and may actually interfere with the census itself by creating more disturbance and by
slowing down the pace of the census. Some of these problems might be reduced by using
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a range-finder, but one is still faced with the problem of poor visibility in a rain forest and
obtaining a clear line of sight. In fact, the majority of line-transect census studies in
Africa’s forests have not measured these distances, but, instead, relied upon estimated
distances. Although not rigorously studied, there is ample evidence of highly significant
differences between observers in their abilities to accurately estimate distances in forests
(e.g. Mitani et al. 2000). Three or four observers comparing their distance estimations can
prove this to themselves in less than 30 minutes. If two observers differ by 10m in their
estimations of an actual distance of 50m, e.g. 40m vs 50m, they will differ by 20% in their
estimations of area sampled.
There is also the question of whether one should measure the actual distance to the
animal seen or the horizontal distance to a point directly below the animal seen. This
applies to arboreal creatures and points out the fundamental problem of estimating
population densities in a volume rather than in a two-dimensional space. Virtually all
programs, analyses, and publications assume a two-dimensional space, which, of course, is
an unrealistic abstraction when estimating the densities of arboreal species in tall forest
habitats. Estimating the volume of a rain forest and the volume of available habitat to the
species in question will likely remain elusive for some time, but it is an important factor
that must be considered in the interpretation of population density estimates.
The DISTANCE program (Buckland et al. 1993) is commonly used to analyze line
transect data. It is considered particularly important in the generation of detection
functions to estimate the effective sample width of the transect, which in turn is used to
estimate population densities. While this important analytical tool is of obvious use in
counting inanimate objects, its use in generating detection functions and population density
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estimates of highly mobile animals is questionable because of the frequent, if not usual,
violation of its assumptions. The following assumptions of the DISTANCE program are
usually violated in line-transect censuses of mobile animals:
1) all animals on the transect line are detected with certainty
2) animals do not move in response to the observer before being detected by the
observer
3) distances are measured accurately
4) transect lines are placed randomly with respect to the distribution of the animals
being censused: this assumption will most likely be violated when animals preferentially
use the transect (e.g. duikers in heavily logged forest) or when the transect follows a
topological feature, e.g. ridge top, stream bed, etc.
5) individuals are detected independently of one another: this will most often be
violated when dealing with species that move in polyspecific associations or mixed flocks
or when individuals and groups aggregate at common resources, e.g. large fruiting trees or
tree groves, soil licks, water holes, etc.
6) minimum sample size of 60-80 detections (Buckland et al. 1993) or 100 (Bibby
et al. 2000).
In addition, DISTANCE and most other programs used to estimate detection
functions rely on the perpendicular distance (P) of the animal or group of animals from the
transect rather than the actual sighting distance, i.e. the distance between the observer and
the animal(s) (A-O). The A-O distance is usually significantly greater than the P distance
and, as a consequence, use of the P distance underestimates the area sampled and over-
estimates the population densities. This is true for both primates and duikers (Struhsaker
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1997). The discrepancy between A-O and P distance lies in the fact that the observer
usually sees the animals at some distance ahead. In many cases, the animal is either on the
census trail or directly above it, resulting in a zero P distance and an A-O distance much
greater than zero.
There have been few studies that actually compare the results of these two methods
(A-O vs P distance) for estimating population densities from line-transect data with the
more accurate estimates that are based on detailed study of the home ranges of specific
animals. The primate studies that have compared these methods to the most accurate
estimates that are based on detailed focal-animal studies, show that use of the animal to
observer (A-O) distance yields more accurate density estimates than does the perpendicular
(P) distance between the animal and the transect (NRC 1981, Chapman, et al. 1988, Defler
and Pintor 1985, Struhsaker 1997). Estimates based on P distance consistently
overestimate densities by magnitudes.
The one study of primates that found comparable estimates between focal group
studies and P distance did so only after adding a fairly substantial correction factor to the
estimates of P distance, thereby increasing the P distance to one that would be closer to the
A-O distance (Whitesides, et al. 1988). The correction factor added to the P distance was
half the estimated distance of the spread of the monkey social groups. The estimated
distance of group spread was based on detailed studies of specific social groups at other
times, i.e. not during the transect censuses. Each species of monkey had a different spread.
The assumptions were that monkey groups were arranged in circles and the first animal
detected was at the edge of this circle. Half of the dispersion distance was assumed to be
the radius of a group’s spread. This assumed radius was then added to the estimated P
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distance because it was thought that the distance to the center of the group should be
plotted rather than the distance to the first individual seen. Use of these correction factors
resulted in an increase in the estimated transect width of between 28% and 267%
depending on the species (tab. 2 in Whitesides et al. 1988). So, what was analyzed were
assumed rather than observed distances, i.e. the estimated perpendicular distance to a
hypothetical group center. Because these distance estimates are meant to generate
detection functions, it is not at all clear why one would use the distance to an unseen and
hypothetical center of a social group rather than the observed distance to the first animal
seen.
There are other problems with this method. One of the most important is that there
is no empirical evidence that monkey social groups are arranged in circles. To the
contrary, a great many species of primates, if not the majority, move in single file or in a
broad and narrow front as they forage. Although the use of these correction factors
resulted in density estimates similar to those derived from detailed studies of focal social
groups, the methods are unnecessarily complex, require information on group spread, and
involve far too many assumptions that are not well founded.
There is a clear need for more studies in a wider range of forest habitats that
compare density estimates based on detailed studies of specific individuals or groups with
those based on line-transect censuses. These comparative studies will improve our
understanding of the nature and extent of the biases inherent in density estimates based on
line-transect data. The few comparative studies we have of primates suggest that what we
are sampling during line-transect censuses is an area approximating that of a semicircle in
front of the observer as he/she walks along the transect. Estimating this area is best done
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with A-O distances, not P distances. Computer programs, such as TransAn, can be used to
analyze these A-O data resulting in density estimates that closely approximate those
derived from focal-group studies (e.g. tab. 5.2 in Struhsaker 1997). Finally, it must be
emphasized that when censusing animals rather than inanimate objects, use of the P
distance to estimate the area sampled is usually unrealistic because it is an abstraction and
does not usually reflect the actual detection distance except in cases where P and A-O
distances are the same. In contrast, A-O distance is a more realistic index of detection
distance because this is what is actually seen and then estimated or measured.
There are some situations where there is no significant difference between the
estimated A-O and P distances of monkey groups, such as in logged forests with tall and
dense thickets (Struhsaker 1997). In these situations, visibility was much more limited
than in old-growth forests.
The density of vegetation not only affects visibility, but also appears to affect the
response of some species to the approach of the observer. The duikers of Kibale, for
example, appear to flee much more readily and at greater distances in old-growth forest
with its open understory than they do in the dense thickets of heavily logged, secondary
forest. In the latter habitat, they tend to freeze until the observer is close to them.
Furthermore, duikers appear to use the cut census trail much frequently in heavily logged
forest than in old-growth forest (McCoy 1995, Struhsaker 1997). As a result, detection
distances are greater in old-growth than in heavily logged forest. These differences have a
profound effect on estimates of both area sampled and duiker densities. Density estimates
of duikers in areas of dense thickets are likely to be biased on the high side.
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Terrain is another variable that will affect visibility and the estimates of A-O and P
distances. For example, census transects in mountainous areas will often allow great
visibility across valleys to distant slopes and ridge tops. These distances, however, do not
reflect the area sampled because much of the area between the observer or transect and the
animal is not suitable habitat, i.e. it is open space.
Another source of error in line-transect censusing concerns differences between
observers in their abilities to see the animals. It is often assumed that animal censuses are
something that anyone can do with equal precision regardless of experience. The fallacy
of this assumption is only too apparent. However, I do not know of a single study in the
primate literature that objectively evaluates the problem of inter-observer reliability in line-
transect censuses (but, see Mitani et al. 2000 for a partial exception).
A pragmatic and cautionary note: once line transects are established (i.e. cut,
measured and marked) it is important that they are monitored frequently (at least once per
month) to reduce the chances they will be used by poachers.
The extent to which line transect data are representative of the area they traverse
can be evaluated by sampling additional transects that are randomly placed perpendicular
to the baseline or main transect (e.g. see Sutherland 1996). These perpendicular transects
enlarge the width of the area sampled.
The ability of line-transect census data to provide reliable indices of abundance or
absolute population density estimates is further complicated when the unit being scored in
these censuses is a social group. This is because social groups change in size and age-sex
composition over time. In line-transect censuses one is rarely able to make a complete and
accurate count of a group. Instead, one records the number of groups seen. Numbers of
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individuals are then extrapolated by multiplying the number of groups seen by a mean
group size that is derived from detailed studies of specific groups. A common assumption
when comparing different populations or subpopulations is that group size is the same.
Studies of primates have shown that this is not the case because some subpopulations have
fusion-fission societies and others have undergone declines in group size corresponding to
changes in habitat (e.g. Skorupa 1988, Decker 1994, Struhsaker 1997, 2001, Siex and
Struhsaker 1999). The only way these problems can be overcome is by collecting more
accurate data on group size and composition for the population being censused.
When counting social groups or aggregations of primates rather than individuals, it
is recommended that only visual detections be scored rather than detections based only on
adult-male loud calls. This is because in many species solitary males give loud calls and
also because males from the same group can be temporarily separated from one another by
great distances while calling, thereby giving the false impression of more than one group.
Summary Recommendations Regarding Line-transect Censuses: Given the
various problems outlined above, data on mobile animals from line-transect censuses are
most useful as indices of relative abundance, i.e. number of groups or animals detected per
km walked (e.g. Mitani et al. 2000). Their utility in estimating absolute population
densities (number per unit area) is often highly questionable and problematic at best.
When used for estimating densities of primate social groups, the evidence indicates that the
most accurate estimates will be obtained using A-O distances rather than P distances. In
situations where one has more accurate estimates of density, such as from detailed studies
of known individuals or groups, the accuracy of the estimates based on line-transect data
can be evaluated objectively. There is great need for more studies comparing these
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different methods. Until the nature and extent of the biases inherent in line-transect data
are better understood, I recommend that line-transect data be used primarily as indices of
abundance. These indices are usually sufficient for detecting likely trends within sites over
time and between similar habitats.
Data on sighting (detection) distances will facilitate interpretation and comparisons
of these indices between observers, habitats, and sites. Detection distances are particularly
important when comparing the abundance of animals between habitats that afford radically
different visibility, such as logged vs unlogged forest (e.g. Struhsaker 1975, Skorupa 1987)
and when comparing data collected by different observers in the same habitat (e.g. Mitani
et al. 2000).
Detailed studies of focal groups or individuals yield the most accurate estimates of
absolute population density. More comparisons of abundance indices derived from line-
transects with population density estimates derived from focal-animal or group studies in
the same block of forest will enhance our interpretation of line-transect data elsewhere.
This is particularly true when the unit being counted is a social group.
Sweep Censuses
In this method, several observers simultaneously census transects that are parallel
to one another and spaced at intervals that attempt to provide complete coverage of an area.
For most rain forest primates this usually means the transects are about 100m apart,
assuming that all animals within 50m of the transects are detected (NRC 1981, Whitesides
et al. 1988, White and Edwards 2000). This method is particularly appropriate for
estimating densities in relatively small areas and is likely to be most accurate when dealing
with species whose home range size is comparable to the area covered by the sweep.
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More comparative studies of this method need to be made, but the variance should
be lower than the conventional line transect censuses described above because a larger area
is covered during each census. The sweep census also has the advantage of reducing, if not
eliminating the need for estimating detection distances because it assumes that the entire
area has been sampled, i.e. a total count of all individuals or groups has been made in the
sample area. This method is also advantageous in that each census can be completed in a
much shorter time period than line-transect censuses, thereby reducing the time-of-day
effect. For example, most line-transect censuses of primates cover about 4 kms. and
require 5 hours, e.g. 7am until 1 pm. In contrast, a sweep census involving 4 observers
each walking one km would complete the census in 1.25 hours, i.e. 7-8:30am. This is
important because primates, like most other creatures, have activity cycles that usually
vary with time of day.
The main disadvantage of this method is that it requires several qualified observers
(i.e. the problem of interobserver reliability) and that it usually covers a smaller section of
the forest. Furthermore, the sweep census does not overcome the problem of changes in
group size when social groups are the unit of measure.
Single-Observer Sweep Census
In situations where there are not enough qualified observers to do a conventional
multi-observer sweep census, I propose a new and untested method that I call the single-
observer sweep census.
In this method the observer follows a grid of transects that attempts to cover an
entire area (total count), as opposed to the conventional line-transect that only samples an
area. For example, in areas with a trail grid, the observer walks back and forth over the
24
grid in an attempt to cover the entire area. This method could also be applied to sites with
steep sided valleys where the observer would follow contours around the valley,
attempting to count all individuals or groups within the valley.
The potential advantages of this method are that it would provide more accurate
and precise data than that derived from conventional straight-line, square or rectangular
transects. Accordingly, less time and fewer repetitions would be required.
The disadvantages are that it would cover a smaller segment of the forest and might
be prone to a greater frequency of duplicate counting of subjects and, therefore, an inflated
estimate of abundance than line-transect censuses. This method should be compared with
conventional line-transect, multi-observer sweep censuses, and detailed studies of specific
individuals or groups.
Focal animal or social group studies
Detailed studies of recognizable individuals usually provide the most accurate data
on home range size, population densities, food habits, and most other aspects of ecology.
The quality of this information will generally depend on the degree of habituation,
accessibility, visibility, and home range size of the study subjects. Diurnal, well-
habituated animals that have small home ranges and who are readily observable will yield
the best data.
The disadvantages of these detailed studies are that they require large investments
of time and labor. Furthermore, they generally apply only to relatively small areas of the
forest.
There is a great need for studies that compare estimates of abundance based on
detailed studies of individuals or social groups with those derived from other sampling
25
methods that require less time and that cover a greater area (e.g. see Chapman et al. 1988,
Defler and Pintor 1985, NRC 1981, Struhsaker 1975, 1997).
Point Counts
In this method the observer records all detections (visual and/or auditory) at
predetermined locations (points) over a predetermined sample time period. Point counts
are most often used in censusing birds, although they have also been employed in primate
studies of species that have a dawn chorus, e.g. gibbons and siamangs (e.g. Marsh and
Wilson 1981) and howlers (e.g. Struhsaker 1974, Scott et al. 1976).
The main disadvantages of this method are the problems of estimating the area
sampled, i.e. the distance of the callers from the observer, and determining the number of
different animals heard, i.e. distinguishing between individuals of the same species.
Except for conspicuous species with small home ranges, point counts based on sightings
will yield few data and, therefore, require many repetitions at a given point (Bibby et al.
2000).
As with line-transect censuses, point counts are most useful in providing indices of
relative abundance rather than accurate estimates of absolute population density.
General Surveys, Rapid Assessments, Reconnaissance (Recce) Walks
Survey walks are commonly employed during the initial stages of more detailed
studies and when the objective is to gain a general and qualitative impression of the flora
and fauna of an area. These walks can provide information on the relative abundance of
animals, such as the number seen or dung piles counted per km or hour walked (e.g. Oates
et al. 1996/1997, White and Edwards 2000), but, by definition, they are expected to be
biased and not necessarily representative of the larger area being surveyed. This is because
26
the observer tends to follow the path of least resistance and, when actively looking for
animals, will follow tracks, other spoor, and sounds.
The advantage of these surveys is that they cover relatively large areas with
minimal investment of time and effort. The obvious disadvantage comes with interpreting
the data and understanding the nature and extent of the biases inherent in this method.
Walsh and White (1999) have attempted to address this problem when estimating
elephant population densities from dung counts by combining survey (recce) walks with
conventional line-transects. While this study represents an important initiative, especially
when counting inanimate objects like dung piles, there are potential problems with this
method. For example, the sample width (distance from transect to dung pile) was only
measured in the samples along the conventional line-transects and not along the recce path.
The apparent assumption was that visibility was equivalent in both methods even though
the authors state that recces follow paths of least resistance. Consequently, they may have
sampled different microhabitats that afford different visibility. If true, then one possible
result is that the actual sample width of the recce differed from that of the line-transect.
This problem could be readily addressed by measuring the distance of dung piles from the
recce path. Were this done, however, it would increase the time and effort invested in
recce walks, thereby reducing the cost effectiveness of this method.
In addition, the survey/recce walks do not address the problem of the tremendous
temporal variation associated with most census data of rain forest animals. More research
is needed that compare census methods. This is especially so when censusing animals
rather than nests or dung. Understanding the biases of survey walks would be improved if
they were repeated on a regular basis in an area where conventional line-transects are
27
sampled. This would allow comparisons of the two methods over time. Ideally, these two
methods, as well as all other census methods should be compared with density estimates
based on studies of focal-individuals or social group, which provide the most accurate data.
Cyber tracking
Cyber tracking is a technique that is being used for monitoring wildlife and human
activities within conservation areas in some African countries, such as The Republic of
Congo, Uganda, Namibia, and S. Africa. The technique involves use of GPS and Palm
Pilots that allow the observer to record data on location, identity, and numbers of animals,
time of sighting, date, etc. Visual images of different species or activities can be
programmed into the Palm Pilot, which allows even those observers who are illiterate to
collect data.
The advantages of this method are obvious. The disadvantages are, however, rarely
discussed. There are two obvious problems with the way this technique is being employed
in many areas.
1) This method has been advocated for use by rangers or guards who are on law
enforcement patrols. Consequently, their sample of the park is not random nor is it random
according to habitat strata. While cyber tracking will provide useful information about
when and where the guards have been, the data they collect may be biased because the
park is not sampled according to principles of random stratified sampling.
2) The quality of the data entered will depend on the way this technique is used and
on the experience, capability, and professional integrity of the recorders. When this
technique is employed in the course of antipoaching activities, for example, the guards will
most likely give priority to finding poachers rather than wildlife. So, except for large and
28
conspicuous species, like elephants, they will likely miss observations of many species.
Establishing professional integrity among the event recorders is often a difficult task and
one that does not necessarily depend on the level of education of the observers. The
objective in training personnel for use of this method should be to emphasize that honest
data is more important than data that indicate large numbers of animals. An indication of
interobserver reliability can be objectively studied by sending different teams of observers
to the same areas at different times.
The main point to be made here is that the value of the data collected by cyber
tracking will depend largely on how it is employed and the professional capabilities and
integrity of the observers.
Dung and track counts
Dung and track counts are often used to estimate the abundance of medium and
large-sized mammals. In Africa this technique has been particularly well developed for
elephants (see White and Edwards 2000 for a succinct summary of methods). This
method has also been used to a much lesser extent and less successfully for duikers (e.g.
Koster and Hart 1988, Payne 1992, McCoy 1995, Struhsaker 1997).
The use of dung counts to estimate abundance is strongly influenced by seasonal
variables, such as rainfall and plant phenology, both of which influence rates of defecation
and decay. These variables must be studied and taken into account, thereby limiting the
utility of this method in censusing mammals.
Established census trails that have been cut and maintained can also introduce
biases, particularly in areas with dense understory vegetation, such as in heavily logged
forests. In these situations, many species, such as duikers, bushpig and elephants
29
preferentially use these trails. Counts of dung on these census trails can, therefore, lead to
over estimates of animals (e.g. Payne 1992, McCoy 1995 and Struhsaker 1997). McCoy
(1995), for example, found no correlation between sightings of duikers and dung counts
along established census trails. In old-growth forest with open understory, significantly
more dung was found off the trail than on it.
Another potential problem with this method is that for some species, such as
duikers, dung piles are distributed in clumps, rather like toilet areas. In other words, dung
piles are neither uniformly nor randomly dispersed.
McCoy’s (1995) study of duikers in Kibale, Uganda compared 3 methods for
estimating abundance: visual and auditory detections during systematic censuses; dung
counts along the same census trails; and track counts on one-meter square plots that were
cleared of vegetation and tilled down to 10cms and situated at 250m intervals along these
census trails. Her results indicate that there was greater consistency in measures of duiker
abundance between direct detections and track counts than between either of these methods
and dung counts.
In summary, while the use of dung counts may be reliable for estimating population
densities of elephants, the use of dung and track counts for other medium and large sized
mammals is useful only in giving indices of relative abundance. Estimating more accurate
population densities of duikers is best done through direct detection, radio tracking, and
capture-recapture studies. Camera trapping has the potential to provide yet another method
for estimating densities of medium sized forest mammals, like duikers (see section on
recommended research).
30
Trap, mark, and recapture
There are numerous publications outlining the fundamentals of this method. Here I
only deal with the problem of estimating the effective size of the area that is being trapped.
This issue has been addressed in numerous publications, e.g. Fleming (1971), Cheeseman
(1975), Smith et al. (1975), O’Farrell, et al. (1978), and Rabinowitz (1997), but I think the
most comprehensive treatment is given by Krebs (1999). Detailed studies of home range
size of marked individuals will provide the best estimates of population density, but this
sampling method usually involves large investments of time and effort. Krebs (1999)
describes three methods: boundary strip; nested grid; and trapping web. Estimating the
boundary strip around the trapping grid is the least complex and perhaps the most efficient
method for long-term and wide spread monitoring programs. It relies on adding a
boundary that either incorporates data on home range size or on the average radius between
recapture sites of marked individuals. Krebs (1999) emphasizes that the main weakness of
using the average radius of recaptures is its reliance on the spacing between recapture
points (i.e. distance between traps) and the number of recaptures. These methods are
appropriate for both conventional recapture studies and for camera trapping (see research
recommendations).
Vegetation sampling
Monitoring the vegetation of parks is critical to conservation management for
obvious reasons. One of the most important reasons is because changes in vegetation
usually precede and indicate likely changes in the fauna.
Coarse-grained information from remote sensing (e.g. aerial photography, satellite
imagery) is essential and particularly useful in providing a broad overview of the dynamics
31
of gross habitat types and of patterns of land-use both within and outside the park. This
information is also vital to developing vegetation maps and in estimating the amount and
distribution of various habitat types, which are crucial to the planning of more-detailed,
stratified sampling on the ground. Although, remote sensing should be a fundamental
component of any conservation monitoring program, I will not deal with it in this report
because it is not my area of expertise.
Problems of habitat stratification have been addressed earlier. Here I will focus on
issues related to plot size and spatial distribution as they relate to monitoring vegetation
dynamics. A central question in terms of study design is whether one should establish
single large plots that are divided into a number of subplots (e.g. 10-50 ha. see Hubbell
1998 and Makana et al. 1998) or many small plots that are distributed randomly amongst
the various habitats of the park, i.e. random stratified sampling. Krebs (1999) summarizes
the literature regarding plot size and shape, concluding that long, narrow plots reduce
confidence limits because they deal better with problems of habitat heterogeneity than do
circular or square plots. However, some caution against the use of rectangular plots
because of increased edge effects (e.g. Greenwood 1996). Krebs (1999) also shows that
greater precision (i.e. lower standard errors) is achieved with many small plots than with
fewer large plots.
Decisions about plot size and their spatial distribution will depend on the questions
asked and on the extent of habitat heterogeneity. In terms of monitoring habitat dynamics
in parks and other conservation areas, there is no question that the plots must be distributed
according to habitat strata, including not only trees and other plants, but other critical
variables as well (see earlier discussion on habitat stratification). From a pragmatic
32
perspective, this will mean that the vegetation plots will have to be relatively small.
Comiskey, et al. (2000) have outlined a sampling protocol based on the Whittaker plot.
The basic plot is 0.1 ha.(20m x 50m) within which there are a number of smaller plots.
Plants of different sizes are enumerated according to subplot size, i.e. all trees >10cm dbh
are measured and enumerated in the entire 0.1 ha. plot, whereas in the smallest subplots
(2m x 0.5m) all vegetation is identified and counted, including herbs and grasses.
While this method may be appropriate for many forest types, it may not be the best
method for evaluating vegetation in parks with a high degree of habitat heterogeneity and
fragmentation, e.g. forests with varying degrees of disturbance due to past human activities
(e.g. Bia, Kakum, Tai, Korup, Odzala, Kibale), those in mountainous areas with steep
slopes and unstable substrates (e.g. Udzungwa), those with pronounced vegetation catenas
(e.g. Kibale, Udzungwa), those with distinct riverine and swamp vegetation (e.g. most
African forest parks). In these situations, smaller plots are recommended so that the
sampling effort may be distributed over a larger and more representative area.
One conventional sampling method that is particularly useful for relating numbers
of animals to quantitative assessments of vegetation is to enumerate all trees of a certain
size (e.g. >10cms dbh) that are located within 2.5 m of the center of the same transects that
are used to census animals (e.g. Struhsaker 1975). The transect is divided into 50m
sections, giving vegetation plots that are 5m x 50m. Only trees whose bole center lies
within this area are measured and enumerated. The advantage of this method is that it
utilizes existing transects, spans a wider range of forest and habitat types, and can be
related directly to estimates of abundance of animals being censused. Smaller plots can be
established along these transects at regular intervals to permit sampling of smaller sized
33
plants. These smaller plots are particularly important in terms of understanding patterns of
forest regeneration and describing stand curves (size-class frequency distributions) of trees
critical to the fauna. Without details on stand-curves, one cannot understand numerous
topics of concern to conservation biology, e.g. size (age)-specific mortality and growth
rates, potential regeneration, and changes in species composition of the community.
These long, narrow plots permit the calculation of indices of dispersion, clumping,
etc. of each species. They also have the potential to give better information on patterns of
regeneration of forest trees than the Whittaker plot. This will be particularly so for a great
many tree species where regeneration is poor or entirely absent under parent trees, e.g.
seed and seedling shadows. A specific example of this concerns regeneration patterns of
Parinari excelsa in the Kibale Forest, Uganda. The seeds of this species are dispersed by
fruit bats who carry the fruit to feeding perches at some distance from the parent tree.
After eating the fruit, the bat drops the seed. Consequently, Parinari occurs in cohort
clumps away from parent trees. A few large or even medium-sized vegetation plots will be
less likely to reflect this pattern than will many, smaller and more widely distributed plots.
A possible disadvantage of using faunal census transects to enumerate vegetation,
as described above, concerns its utility in predicting and understanding the abundance of
different animal groups in areas with extremely heterogeneous habitats. The vegetation
sample plot is only 5m wide and, unless the habitat is very homogeneous, this will not
necessarily describe all of the habitat being used by the animals counted along the same
route. This will be especially true for large, mobile species, e.g. primates. Consequently, it
may be necessary to establish additional vegetation plots along transects running
perpendicular to the main line transect (see earlier section on line transects).
34
An alternative to the line transect tree enumeration method described above is the
point-center-quarter (PCQ) method (e.g. Krebs 1999). This method is usually faster
because it involves less sampling effort than line-transects or other rectangular plots. The
sample points could, for example, be placed at 50m intervals along the faunal transect and
again at 50m intervals out to a distance of 100m perpendicular to these points on the faunal
transect. In this way the sample would yield a broader coverage of the habitat without the
same investment of time and effort required by total counts of trees along the transect and
along lines perpendicular to this transect.
All of the methods considered above should be compared within the same area to
better understand their respective biases and their utility for addressing the questions being
asked. The ultimate goal in sample design is to maximize efficiency while minimizing loss
of information and understanding of trends. In this regard, Krebs (1999, pp. 105-114)
offers useful suggestions for determining appropriate quadrat size and shape.
Canopy Cover
In at least one study (Skorupa 1986, 1988, Struhsaker 1997), estimates of canopy
cover proved to be an important variable accounting for variance in African rain forest
primate densities. However, estimates of canopy cover are often highly subjective and
probably have low precision and accuracy. Often the observer simply selects points at
predetermined distances along a transect, looks straight up at the canopy and estimates the
percentage of canopy cover at predetermined heights, e.g. 9m and 15m (Skorupa 1988).
Brower and Zar (1977, p. 32) describe a method for estimating screening efficiency, i.e.
the relative amount of shading or concealment of the ground by the vegetation. They
recommend using a transparent plastic square (0.5m2) that is marked off in a 10x10 grid.
35
One holds the grid directly overhead and counts the number of squares that do or don’t
contain visible sky. White and Edwards (2000) recommend use of a clinometer in a
modification of a method advocated by Grieg-Smith (1983). Densiometers and
densitometers (available from Forestry Suppliers) provide a faster and probably more
precise method for estimating canopy cover, but like the preceding methods, only allows
readings to be taken at the level of the observer, i.e. about 1.7 to 1.8 meters. All of these
methods are time-consuming and relatively imprecise and inaccurate.
For most purposes of monitoring rain forest habitats, the use of alternative
measurements will be sufficient. For example, Brower and Zar (1977) acknowledge the
difficulty of obtaining direct measurements of foliage coverage in trees and suggest that
basal area be used as an index because it is generally proportional to canopy cover. In
support of this, Skorupa’s (1986, 1988) study in Kibale demonstrated a strong correlation
between his estimates of canopy cover at 9m and 15m heights and the density of large trees
and with the mean basal area of trees in the study plots. Because tree densities and basal
areas will be routine measurements in any monitoring program, it is suggested that these
two measurements be used as indices of canopy cover.
Plant Phenology
An understanding of long-term phenological patterns of plants is critical to any
ecological monitoring program. Most of the work on this subject in tropical forests has
focused on phenological patterns in trees (see reviews in van Schaik et al. 1993 and
Struhsaker 1997). Although the importance of phenological studies is widely accepted,
there remains considerable debate about how they should be done. I will not address the
36
issue of sample size, frequency or location because these fall under the general topic of
study design.
The sampling procedure selected will depend on the questions and problems of
concern. In general, there are two basic methods for estimating the production of specific
phytophases: 1) counting the items that have fallen beneath the tree (directly to ground or
into traps and 2) estimating or counting the items while they are on the tree.
There is also an obvious dichotomy in the literature between phenological studies
that simply report the presence or absence of a specific phytophase (e.g. Watts and Mitani
2002, McConkey et al. 2002) and those that present information on the abundance of the
phytophase, i.e. either scores of relative abundance (0-4, e.g. Struhsaker 1975) or attempts
at counting total numbers or biomass produced (e.g. Levy 1988, Chapman et al. 1992,
Leighton 1993, Tutin and White 1998). I strongly recommend against the use of the
presence or absence method for obvious reasons. It does not distinguish between a tree that
produces one fruit from a tree that produces 2,000 and, as a result, the method is extremely
inaccurate and misleading.
Methods that rely on collecting fallen items (usually fruit) from the ground or from
traps placed on the ground score only that which is not eaten on the tree and, consequently,
will likely underestimate productivity. This is particularly true in cases where the
phytophase of concern is readily eaten by large populations of consumers. This method
will also be of limited use in the study of highly perishable phytophases, such as buds,
young leaves, and flowers.
Indirect measures of abundance, such as the use of dbh or estimates of crown
volume, are also inadequate because they fail to deal with the tremendous temporal
37
variation in productivity of any given tree, e.g. see appendices in Struhsaker 1997 and
discussion in Chapman et al. 1992.
Although more time consuming, estimating the abundance of specific phytophases
on the plant is, in my experience, the best approach to evaluating its phenology. High
levels of interobserver reliability can be achieved with adequate training (although see
Chapman et al. 1992 for a different perspective). One of the easiest methods for providing
estimates of abundance is to scan the entire plant (e.g. crown of tree) and give it a score of
relative abundance (zero to 4) for each phytophase (e.g. floral bud, flower, unripe fruit,
ripe fruit, leaf bud, small young leaves, large young leaves, mature leaves, etc.).
In a more quantitative approach, Levy (1988) and Leighton (1993) estimated fruit
crop size on an exponential scale. This is particularly useful when attempting estimates of
biomass productivity and when counting fruit. Exponential scales have not been applied to
studies of other phytophases.
The temporal spacing of phenological samples should be designed according to the
duration of and interval between the cycles of each phytophase. For the majority of
tropical forest trees, this will mean sampling at least once each month and marking
individual plants.
Special attention should be given to variation in phenological patterns within and
between species and sites in the same forest (e.g. see Struhsaker 1997). Combining the
phenological data of different species should be avoided because lumping many species
together ignores important interspecific differences in phenological variation and their
relative value as food to consumers. For example, when phenological data from different
species are combined one could obtain identical community patterns when in one year an
38
unpalatable species bears fruit and in the subsequent year a different and highly palatable
species fruits.
Research Priorities relevant to Ecological Monitoring and Conservation Management
in Africa’s Rain Forests:
Comparative studies of different census techniques at same site
Although detailed studies of focal individuals or social groups provide the most
accurate and precise estimates of population density, these studies are time and labor
intensive. Other techniques that require less time and effort all have their biases, but the
nature and extent of these biases are poorly understood. Consequently, there is a need for
more studies that compare the results of the fine-grained, focal method with other, coarser-
grained census techniques, such as line-transects using different estimators of strip width,
fixed-width transects, sweep censuses, survey walks, dung and track counts, camera
trapping, etc. There is also a need for more studies that examine the effects of walking
speed, terrain, and visibility on the results from line-transect censuses.
Camera trapping has been extremely important in understanding the distribution
of cryptic species. With few exceptions, however, has camera trapping been used to
estimate population densities. The exceptional studies have dealt only with species that
have very distinct markings, e.g. tigers (Karanth 1995). I recommend that greater effort be
given to experimental studies that attempt to extend the use of camera traps to estimate
population densities of species that are much more difficult to recognize as individuals,
e.g. duikers. Although individually distinctive marks would contribute to these estimates,
greater reliance would be placed on integrating information on time, date and location of
photographic records and likely rates of movement. For example, if 5 photographic
39
records of duikers were obtained at the same time and date in a 50 ha study plot, we would
know that there was a minimum of 5 duikers using the 50 ha plot. In reality, one is
unlikely to obtain many simultaneous images. The probability of capturing photographic
images will depend on the number and spacing of cameras, as well as on the densities and
rates of movements of the study species. In the case of duikers, the results from camera
trapping would be compared with more conventional techniques, such as line-transect
visual censuses, dung and track counts, sweep censuses, and mark and release studies. If
camera trapping can be refined to estimate population densities of cryptic species, it has
the obvious advantages of being relatively non-invasive, requiring relatively little labor,
and applicable over large areas.
Population demography of selected animal and plant species
The first step in understanding populations is to obtain some index of abundance.
This is, however, insufficient when trying to understand population trends and their
processes. Life-table data (e.g. age-sex composition, natality, age-specific mortality and
recruitment) are mandatory components of long-term monitoring and population modeling.
This kind of information is rarely available for the plants and animals of Africa’s rain
forests, but some of the most detailed data come from studies of primates, e.g. Struhsaker
and Pope 1991, Siex and Struhsaker 1999.
Similar studies have been done on forest tree species in an attempt to understand
patterns of forest dynamics and regeneration (e.g. Langdale-Brown, et al. 1964, Kasenene
1980, 1987, Struhsaker et al. 1989, Lwanga 1994, Struhsaker 1997). In general, however,
this has been a neglected area in the study of tree population dynamics in Africa’s rain
forests. We require far more studies that provide information on size-class frequency
40
distributions for individual tree species and how these are affected by edaphic, hydrologic,
and other ecological parameters, such as the effects of competition, allelopathy, disease,
composition of the animal community (browsers and seed predators and dispersers), and
various kinds of perturbation (logging, natural gaps, landslides, etc.).
One of the more important questions that should be addressed by demographic
studies of most forest trees (not only Africa’s), is why the sapling and pole-sized
individuals of so many species occur at such low densities? The few data available
indicate that for most tree species in Africa’s rain forests there are very high densities of
seedlings, modest to low densities of adults and extremely low densities or a virtual
absence of saplings and pole-sized individuals. Similar patterns are also found among
many tree species in temperate forests. It has been hypothesized that this pattern is the
result of either higher mortality and/or faster growth rates in these size classes (e.g.
Harcombe 1987). Long-term studies of marked individuals would help distinguish
between an incipient population decline and a normal pattern of growth. Without these
kinds of long-term and detailed population studies, our ability to practice scientific
conservation management will be severely limited.
Ecological requirements of selected species
Ecological monitoring relies heavily on counting and estimating the abundance of
plants and animals. It should not, however, be limited to this. Understanding the
ecological requirements and interspecific relations, such as food habits (predator-prey
balance/imbalance, browsing impacts, seed dispersal, and competition) is critical to
predicting trends and potential problems at the community level.
41
Beyond gross distribution and habitat type, we know very little about the ecological
requirements of the vast majority of Africa’s rain forest species. Long-term, detailed data
are needed on food habits, home range size, and population density in relation to habitat
type. There is an outstanding need for greater integration of floral and faunal studies. For
example, two recent tomes on biodiversity in the tropics concentrate almost exclusively on
the flora and primarily on trees with scarcely a mention of the role of animals in shaping
forest composition and dynamics (Dallmeier and Comiskey 1998a, b).
A major advance in our understanding of ecological requirements would be the
development of a powerful predictive model relating vegetation to animal populations.
Research projects should be designed to determine which vegetative characteristics provide
the greatest predictive value in terms of densities and demographic structure of selected
animal species. For example, studies could build upon the pioneering work of Skorupa
(1988) who compared various characteristics of the tree community (density, species
diversity, richness, basal area, etc.) to the primate community. Future studies should
expand this approach to include not only primates and total densities, but also data on other
species and on age-sex composition, natality and age-specific survivorship in these
populations. Furthermore, information on forest patch size and shape (edge effects) should
be incorporated and integrated with the data on tree species diversity, density, and basal
area. This type of research could be readily integrated with the long-term monitoring of
plant and animal populations that is conducted on permanent transects and plots and with
remote sensing. Information of this sort is of obvious value for understanding ecological
requirements and interrelations of key species. In addition, a predictive model like this
would greatly enhance the interpretation of landsat imagery and gross vegetation maps.
42
Without this kind of predictive model, extrapolation of PA-wide population estimates for
specific animal species based on a gross vegetation map are likely to be of limited value.
Inseparable from studies of the basic ecology of selected species are those that
address more specific questions about interspecific relationships, such as predator-prey
relations, impact of herbivores and granivores on forest composition and structure,
pollination and seed dispersal, competition, etc. Only with these kinds of studies will one
be able to understand the processes driving forest dynamics. This is crucial to conservation
management.
Obviously, one cannot include studies of all species in designing a long-term
monitoring and research program. Species will have to be selected based on conservation
priorities (e.g. rare and/or keystone species) and preliminary studies and impressions about
which species are having the greatest impact on the community as a whole, as well as on
the species of primary conservation interest. Any research and monitoring program should
be flexible enough to allow for new insights gained from ongoing research and to allow for
the opportunistic sampling and study of unexpected ecological events (e.g. storms,
droughts, fires, etc.).
An obvious gap in our knowledge of Africa’s rain forest PAs is long term data on
temperature and rainfall. There is an obvious need for more meteorological stations
throughout Africa’s rain forests (several widely spaced in each PA) and an ongoing
program that analyzes data from these stations in relation to temporal patterns of biological
events, e.g. phenology, births, diet, population trends, etc. (see Struhsaker 1997 for an
example of what can be done in Africa). Without quality meteorological data, our
understanding and predictive powers of biological events will be severely limited.
43
Role of key resources on spatial distribution, movements and density of
selected animal species
Much greater attention should be given to the study of how key resources (e.g.
waterholes, rivers, soil licks, low density tree species of high nutritional value) influence
population densities, movements and habitat use by the fauna. The way in which these key
resources are used by the fauna will, in turn, be expected to shape the habitat around these
resources. A conspicuous example of this is the use of the large forest clearings by
elephants, buffalo, gorilla, and several other species in central Africa. Areas of past
disturbance, whether due to logging or cultivation, are often used heavily by elephants who
play an important role in maintaining these areas in a state of dense, secondary growth (e.g.
Struhsaker 1997). While some of these key resources might be detected by aerial surveys
or landsat imagery, many will not (e.g. soil licks under closed canopy forest, large trees or
groves of trees bearing favored fruit). In most cases, these resources will have to be
mapped on the ground and their use and relative importance to the fauna determined by
focal studies. This kind of research is vital to our understanding of community ecology, to
refinement of ecological stratification for the purposes of estimating total populations (see
section on stratification), and to explaining some of the spatial and temporal variation in
animal population densities apparent in many African forests.
Human Ecology and Impacts on Protected Areas
Human activities in and around PAs are likely to generate the majority of problems
facing the effective conservation of these areas. Consequently, it is important to include
the study of human ecology and how it impacts on the PAs as part of a long-term
monitoring program. This subject can be divided into the following categories:
44
1) human demography around the PA: The objective of this kind of
study would be to quantify the human population dynamics due to intrinsic growth and
immigration. Data would include details on age, sex and tribal composition. The size of
the perimeter around the PA to be studied would be determined by information regarding
the likely area of significant influence on the PA. This is a problem of scaling and will
often be difficult to determine because it will certainly change over time. However, for
practical purposes, I would recommend beginning with studies that deal with a 10-km wide
perimeter.
2) Land-use activities (economy) around the PA: Emphasis in this topic
must include a quantitative analysis of economic activities that are likely to affect the PA
both directly and indirectly. A spatial and temporal evaluation (mapping) of these
activities is critical. The frequency of sampling will depend on the socio-economic
dynamics, e.g. frontier situations may need to be sampled more frequently than older and
more conservative communities
3) Direct human pressures on the PA: This topic addresses the issue of
what and how much humans are removing from the PA, either legally or illegally. Illegal
activities will be the most difficult to study and will rely primarily on samples within the
PA and information from law enforcement officers and undercover agents. In contrast,
legal activities can be studied throughout a combination of field studies in the PA and
inventories in the households.
4) Distance-related human impacts on PAs and the effectiveness of law
enforcement: This research topic would examine the status of wildlife and habitat as a
function of distance from roads, navigable waterways, major footpaths, human settlements,
45
PA headquarters and ranger posts. For example, in Gabon, Barnes, et al. (1991 and 1995)
found an inverse relationship between elephant densities and human population densities
and a direct relationship between elephant densities and distance from the nearest roads.
These relationships, however, are likely to vary between areas depending on a number of
variables, such as the species of concern and the culture of the people living around the
PA. For example, in Kibale, Uganda elephant numbers were greater near human
settlements than in more remote areas where most of the elephant poaching occurred. In
both the Kibale and Udzungwa Mts. National Parks (Tanzania) monkeys are often seen
along roads and major footpaths because most of the people living near these parks do not
hunt or eat monkeys. This is not the case in most forest parks of central and west Africa
where it is difficult to see a primate anywhere, much less along roads or paths. One would
also expect to see more wildlife near areas that receive the greatest amount of protection,
i.e. near park headquarters and ranger posts. If not, then it would indicate inadequate law
enforcement and the need for improvements.
5) Indirect pressures by humans on PAs: Human activities outside the
park can also influence its flora and fauna. For example, water and air pollution
(pesticides, fertilizers, and disease) from distant sources may have negative impacts on
parks, especially aquatic systems (plants, arthropods, amphibians, fish, and piscivores),
which then have a wave effect that eventually impacts the entire ecosystem. Air pollution
directly or in the form of acid rain can have a direct negative impact on terrestrial
vegetation and possibly frogs too. Exotic plants intentionally or unintentionally planted
outside the PA can invade the PA leading to ecological disasters, e.g Chromalaena odorata
in many west African forest parks (e.g. Marahoue and Kakum).
46
Modification of stream and river flow into parks, either through dams or irrigation
projects, can impact the parks whether they are placed upstream or downstream of the
park. Deforestation along watercourses upstream of the parks can also be expected to have
serious impacts. These alterations will influence the hydrology, sedimentation rates,
nutrient flow, and movements of aquatic organisms within the parks.
Deforestation outside of parks can lead to the immigration of fauna into the parks
resulting in population compression of species, which in turn can have negative effects on
the park populations and habitat. Compression of elephant populations is a prime example
of how this can result in negative impacts on the tree community (e.g. Struhsaker 1997).
Likewise, population compression of the Zanzibar red colobus due to habitat loss has
apparently resulted in overbrowsing and increased mortality of key food species (Siex and
Struhsaker 1999 and personal observations).
Studies of land-use patterns outside the PA are also critical to understanding the
basis for actual or potential human-wildlife conflicts near PA boundaries and for
understanding how some species (e.g. birds, bats, and migratory species) that readily move
in and out of the PA are affected by various land uses outside the PA. The impacts of
land-use patterns on parks are likely to be particularly strong for species dependent on
resources in addition to those found in the parks, i.e. in situations where the park is not a
self-contained unit and species must move in and out of the park. These effects can be
profound even for a seemingly benign activity like rural residential development (e.g.
Hansen, et al. 2002).
47
All of these problems of human ecology and impacts on parks will require a
combination of study techniques, including remote sensing, ground truthing, and
interviews.
Study of Sociological Basis of Public Attitudes toward PA: In an earlier study it
was found that one of the strongest correlates of PA success was public attitude toward the
PA (Struhsaker 2001). Public attitude is a rather vague concept, but clearly the managers
and scientists working in PAs agreed that if the general public were supportive of the PA
then the PA was more effectively as a conservation area. The basis for a positive public
attitude, however, was not at all clear. Financial benefits were not sufficient to explain the
variance in public attitude. We need are more detailed studies from a representative
sample of PAs to better understand the relationship between public attitude and PA
effectiveness. How best can one generate a positive public attitude? While this is not a
conventional subject for ecological research, it is extremely important for conservation.
Unless the parks being monitored and studied are effectively conserved, the best research
available will amount to little more than an academic exercise.
Acknowledgements
This project was funded by the Center for Applied Conservation Biology of
Conservation International. I thank Drs. Gustavo Fonseca and Mohamed Bakarr for their
support of this work and Ms. Kirstin Siex for technical assistance.
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