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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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