ANALYSIS O F PROCESSES IN CONTROL O F INSECTS
27
determine whether this predation causes a higher or lower percent
mortality when the prey is (or has recently been) sparser or more
abundant (cf. Fig. 4). There is no need to dwell on this, for examples
of density-relationships or of density-dependent (etc.) factors are often
presented in such terms.
2. By Key Fmtor Analy&
One of the first phases of the study of a field population is often an
attempt to discover which factors are mainly responsible for the variations in abundance. Whether or not their action is dependent on the
density, we need to know about them in order to carry the analysis to
the stage of understanding regulation, to say nothing of the needs of
economic entomology and the importance of being able to predict
increases in abundance.
Morris (1959, 1963a, b) argues the practical advantages of this approach, and shows how it has been developed in the study of Canadian
forest insects. Life-table data for the spruce budworm (to quote from the
former paper) “suggested that the factors affecting this species in any
one place are of two types - those that cause a relatively constant
mortality from year to year and contribute little to population variation,
and those that cause a variable, though perhaps smaller, mortality and
appear to be largely responsible for the observed changes in population
(Morris, 1957). A factor of the latter type will here be called a ‘key
factor’, meaning simply that changes in population density from generation to generation are closely related to the degree of mortality caused
by this factor, which therefore has predictive value. . . . If a key factor
is suspected in a population, can its existence be demonstrated effectively by a very limited ‘single-factor’ approach in which only population
density and the mortality caused by this factor are measured in each
generation? ”
He gives two examples of this single factor analysis (Morris, 1959).
We need refer to only one of them, based on the observations over
twelve generations of the black-headed budworm referred to earlier
(Figs. 2 and 1 2 ~ ) .
Parasitism in the larval stage was the suspected key
factor. Population density was estimated at the appropriate stage of
larval development, and parasitism was measured by dissecting or
rearing the larvae from this sample.
The test was made by comparing the numbers surviving after this
factor had acted (8,) with the total numbers of larvae in the next
generation (N,+,). The correlation had to be significantly better than
in a straight-forward comparison of N , and N,+l, if the parasitism were
really acting as a key factor. In fact, it was found to be so ; r = 0.93 for
log N,+l and log S,,, r =0.67 for log N,,, and log N,. Squaring these
27
determine whether this predation causes a higher or lower percent
mortality when the prey is (or has recently been) sparser or more
abundant (cf. Fig. 4). There is no need to dwell on this, for examples
of density-relationships or of density-dependent (etc.) factors are often
presented in such terms.
2. By Key Fmtor Analy&
One of the first phases of the study of a field population is often an
attempt to discover which factors are mainly responsible for the variations in abundance. Whether or not their action is dependent on the
density, we need to know about them in order to carry the analysis to
the stage of understanding regulation, to say nothing of the needs of
economic entomology and the importance of being able to predict
increases in abundance.
Morris (1959, 1963a, b) argues the practical advantages of this approach, and shows how it has been developed in the study of Canadian
forest insects. Life-table data for the spruce budworm (to quote from the
former paper) “suggested that the factors affecting this species in any
one place are of two types - those that cause a relatively constant
mortality from year to year and contribute little to population variation,
and those that cause a variable, though perhaps smaller, mortality and
appear to be largely responsible for the observed changes in population
(Morris, 1957). A factor of the latter type will here be called a ‘key
factor’, meaning simply that changes in population density from generation to generation are closely related to the degree of mortality caused
by this factor, which therefore has predictive value. . . . If a key factor
is suspected in a population, can its existence be demonstrated effectively by a very limited ‘single-factor’ approach in which only population
density and the mortality caused by this factor are measured in each
generation? ”
He gives two examples of this single factor analysis (Morris, 1959).
We need refer to only one of them, based on the observations over
twelve generations of the black-headed budworm referred to earlier
(Figs. 2 and 1 2 ~ ) .
Parasitism in the larval stage was the suspected key
factor. Population density was estimated at the appropriate stage of
larval development, and parasitism was measured by dissecting or
rearing the larvae from this sample.
The test was made by comparing the numbers surviving after this
factor had acted (8,) with the total numbers of larvae in the next
generation (N,+,). The correlation had to be significantly better than
in a straight-forward comparison of N , and N,+l, if the parasitism were
really acting as a key factor. In fact, it was found to be so ; r = 0.93 for
log N,+l and log S,,, r =0.67 for log N,,, and log N,. Squaring these