Dr. Stephen Malcolm, Department of Biological...

BIOS 6150: Ecology - Dr. S. Malcolm. Week 11: Abundance & metapopulations Slide - 1

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 & density- dependence.

•  Metapopulations.

Ilkka Hanski


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).


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).


4. Nicholson - abiotic vs biotic factors:

•  Although he recognized that density-independent factors like rainfall could influence the level at which density-dependent biotic interactions “governed”, he considered that density-dependent processes play a key role in regulating populations.


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.


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.).


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 & density-dependent processes like competition never became important.


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.


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 density-

dependent and density-independent factors.


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.


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.


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).


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.


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.


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 r-selection 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.


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).


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)


Table 14.1 (15.2 3rd ed.):


Table 14.2 (15.3 3rd ed.):

Slope against ktotal

Slope against log N


Figure 14.4a: Change in Colorado potato beetle k-values with time at 3 sites.


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.


Figure 6.14: Population regulation in Avena fatua in monoculture or in competition with wheat.



Figure 6.15: Density-dependent & density-independent regulation in Avena fatua.



Figure 7.1:

Probability of local extinction against site occupancy: (a) mangrove island insects, (b) leafhoppers, (c) pond molluscs.



Figure 6.17 (15.22, 3rd): Two metapopulations of the silver- studded blue butterfly (a) limestone, (b) heathland (fill: 1983+1990; open: e = 1983, c = 1990).

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