BIOS 6150: Ecology
Dr. Stephen Malcolm, Department of Biological Sciences
•  Week 11: Abundance &
Metapopulations.
•  Lecture summary:
•  Based on:
•  Chapters 6 and 7 Begon, Mortimer
& Thompson, (1996).
•  Chapters 15 and 23 in Begon,
Harper & Townsend (1996).
•  Chapters 6, 14 & 15 in Begon,
Townsend & Harper (2006).
•  Population regulation:
•  A.J. Nicholson.
•  H.G. Andrewartha and L.C. Birch.
•  Key-factor analysis & densitydependence.
•  Metapopulations.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
Ilkka Hanski
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2. Explaining distribution and abundance contrasting views:
•  (1) A.J. Nicholson (1954):
•  Australian.
•  Considered that density-dependent, biotic
interactions most influenced population size.
•  (2) H.G. Andrewartha and L.C. Birch (1954):
•  Also Australians.
•  Considered that density-dependent processes:
•  Are “... in general, of minor or secondary importance,
and ... play no part in determining the abundance of
some species” (from Clark et al., 1967).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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3. Nicholson:
•  “Governing reaction induced by density
change holds populations in a state of
balance in their environments”,
•  “...the mechanism of density governance is
almost always intraspecific competition,
either amongst animals for a critically
important requisite, or amongst natural
enemies for which the animals concerned
are requisites” (Nicholson, 1954).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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4. Nicholson - abiotic vs biotic factors:
•  Although he recognized that densityindependent factors like rainfall could
influence the level at which densitydependent biotic interactions
“governed”, he considered that densitydependent processes play a key role in
regulating populations.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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5. Andrewartha & Birch:
•  Numbers of animals limited by:
•  (1) Shortage of resources,
•  (2) Unavailability of these resources in
comparison to dispersal abilities, and
•  (3) shortage of time when r is positive
•  Fluctuations caused by weather, predators etc.
•  So they rejected divisions of:
•  density-dependent vs density-independent,
or,
•  biotic vs physical factors.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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6. Andrewartha & Birch - their thrips example:
•  For nearly 14 years they counted thrips on roses in
South Australia and measured local temperatures
and rainfall (Fig. 15.4 3rd ed.).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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7. Andrewartha & Birch analysis:
•  By multiple regression analysis they accounted for
78% of the variance in the yearly peak of thrips
numbers in relation to 4 climatic factors:
•  1. Temperature suitability for development < 31 August.
•  2. Temperature suitability for development in September
& October.
•  3. Temperature suitability for development in August of
the previous season.
•  4. Rainfall in September & October.
•  Using these data they could predict quite accurately how many
thrips would occur in the following year.
•  Concluded that everything was a race against time & densitydependent processes like competition never became important.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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8. Problems with interpretation:
•  The interpretation of Andrewartha and Birch could not
invoke density-dependence because the
regression technique could not detect it:
•  Hides what is going on!
•  Using techniques that can detect density-dependence,
the data are clearly density-dependent:
•  (Figs. 15.4 & 6.3).
•  Weather caused density-dependent mortality because
refuges from winter weather were limited.
•  Therefore it was not weather but refuges that were
density-related.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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9. Regulation vs determination of abundance:
•  Regulation of abundance can only
occur via density-dependent
processes, but abundance can still be
determined by the combined effects of
all processes that impact a population:
•  Probably includes both densitydependent and density-independent
factors.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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10. Key-factor analysis:
•  Mostly promoted by George Varley:
•  Used to assess the relative importance of k-values
in determining population size
•  By regression of individual k-values against total
mortality (Ktotal).
•  Determines whether separate mortalities vary randomly
or vary with the overall mortality (see Tables 14.1, 14.2).
•  k6 agrees most with ktotal (Fig. 14.4) and so has the
highest regression coefficient in Table 14.2. But
these variables are not independent and so cannot be
compared statistically, although this is a measure of
relative importance.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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11. Density-dependent regulation:
•  Plotting k-values against log population size
shows degree of density-dependent
population regulation (see Fig. 15.9).
•  b =2.65 for k6 (Table 14.2, Fig. 15.9a) is >1:
•  Shows overcompensating density-dependent
regulation.
•  Inverse density-dependence (Fig. 15.9b).
•  Undercompensating density-dependence (Fig. 15.9c).
•  Based on observed k-values, predictions can be
made into the future.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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12. Population regulation of wild oats:
•  Density-dependent and density-independent
regulation of wild oat plants in monoculture or in
competition with wheat (Figs. 6.14 & 6.15).
•  Underlying cause of regulation is intraspecific through
reduced seed production at high density (6.14c). But:
•  Interspecific seed predation is also density dependent, and
•  Interspecific competition with wheat (Fig. 6.14a) also shows
density-dependent reduction of reproductive rates (Fig. 6.15)
which are depressed further by the application of herbicide as a
density-independent process.
•  Adult survivorship is density-independent (6.14b).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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13. Metapopulations:
•  Most populations are fragmented and
patchy.
•  Dispersal among patches with variable
dynamics is important.
•  Most populations are subject to repeated
episodes of local immigrations and
extinctions.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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14. Population size:
•  Fragmented populations may remain small because:
•  1. There are few habitable patches.
•  2. Habitable sites are small.
•  3. Habitable sites are far apart:
•  Relative to dispersing ability of the species.
•  4. Habitable sites support few individuals:
•  Low carrying capacity.
•  5. Sites are habitable for only short periods of time.
•  6. Slow population growth after colonization.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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15. Development of metapopulation theory:
•  1. Local populations are linked genetically to form a
metapopulation.
•  Population genetics to describe gene flow among populations linked
by dispersal.
•  2. Equilibrium Theory of Island Biogeography of MacArthur &
Wilson (1967):
•  Focused on extinction and colonization of species on islands as
influenced by their life histories along the continuum between rselection and K-selection.
•  3. Levin’s model of “metapopulation” dynamics also published
in 1967 described population dynamics at 2 levels:
•  (i) Within patches.
•  (ii) Among patches.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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16. Metapopulation persistence:
•  Metapopulation persists stably through
balance between:
•  Random extinctions and recolonizations.
•  Even though none of the local populations are
stable in their own right (Fig. 7.1 bmt 1996).
•  In addition, the greater the variation in patch size
the more likely a metapopulation will persist:
•  An important argument in conservation.
•  With mosaic of source (↑donor) and sink (↓receiver)
patches (Fig. 6.17).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 6.3: NA adults produce NL larvae after k1 random
mortality. (a) k2 = weakly density dependent,
(b) k2 = strongly density-dependent mortality.
Begon, Mortimer & Thompson (1996)
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Table 14.1 (15.2 3rd ed.):
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Table 14.2 (15.3 3rd ed.):
Slope against
ktotal
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
Slope against
log N
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Figure 14.4a: Change in Colorado potato
beetle k-values with time at 3 sites.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 15.9, 3rd ed. (14.4b 4th ed.):
Colorado potato beetle mortalities: (a) density-dependent
emigration, (b) inversely density-dependent pupal parasitism,
(c) density dependent larval starvation.
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 6.14: Population regulation in Avena fatua
in monoculture or in competition with wheat.
Begon, Mortimer & Thompson (1996)
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 6.15: Density-dependent & densityindependent regulation in Avena fatua.
Begon, Mortimer & Thompson (1996)
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 7.1:
Probability of local
extinction against site
occupancy:
(a) mangrove island
insects,
(b) leafhoppers,
(c) pond molluscs.
Begon, Mortimer & Thompson (1996)
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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Figure 6.17 (15.22, 3rd): Two metapopulations of the silverstudded blue butterfly (a) limestone, (b) heathland
(fill: 1983+1990; open: e = 1983, c = 1990).
BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations
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