does niche limitation exist?

Does Niche Limitation Exist?Author(s): J. B. Wilson, H. Gitay and A. D. Q. AgnewReviewed work(s):Source: Functional Ecology, Vol. 1, No. 4 (1987), pp. 391-397Published by: British Ecological SocietyStable URL: http://www.jstor.org/stable/2389796 .

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Functional Ecology 1987, 1, 391-397

391

Does niche limitation exist?

J. B. WILSON*, H. GITAY and A. D. Q. AGNEW Department of Botany and Microbiology, University College of Wales, Aberystwyth SY23 3DA, UK

Abstract

1 Three sets of data are examined for evidence of niche limitation expressed as a deficit of variance in the number of species per sample, as compared to a null model with species occurring indepen- dently of each other. 2 An agricultural field of permanent pasture was sampled with contiguous quadrats. It showed no variance deficit but rather a significant variance excess. Subdividing the field floristically or environmentally to achieve greater homogeneity did not reduce this excess. 3 A fixed dune slope was sampled by a line of contiguous quadrats and analysed as quadrat sizes in the range 20 mm X 20 mm to 1 m X 20 mm. There was no variance deficit at any quadrat size; at quadrat sizes up to 100mm X 20mm there was significant variance excess. 4 A reanalysis of Patrick's (1968) diatom data gave a variance deficit one year (but P = 0 5) and an excess the next. 5 It is questionable whether the concepts of niche limitation, and hence of species packing, are meaningful if they cannot be demonstrated even in favourable circumstances. Key-words: Niche, niche limitation, null model, species diversity, species packing

Introduction

The idea that the number of species in a community is limited by the number of niches is an old one but its recent development is usually traced to MacArthur & Levins (1967).

We accept here Hutchinson's (1957) definition of the niche as a hypervolume in abstract n-dimen- sional space, the variables defining the space being those that permit the species to exist indefinitely. This definition includes both (1) differences

* Present address: Botany Department, University of Otago, PO Box 56, Dunedin, New Zealand.

between species in resource use at one point/ community - alpha niche or 'role' and also (2) geographical differences between points/ communities - beta niche or habitat. We are considering the realised niche (Hutchinson, 1957).

Although the niche is a property of a species, the theory of limiting similarity (Abrams, 1984) suggest that the environment will limit the number of species that can establish alpha niches at one point. The niches will be established by competition and evolution (Roughgarden, 1983). Thus, the environment will determine the number of species that can coexist in a small area, a concept we refer to as 'niche limitation'. It forms the basis for the concept of species packing (May & MacArthur, 1972) because small changes in the environment may alter which species are present without altering the number of species that can occur.

These two related ideas of niche limitation and of species packing have gained wide acceptance (e.g. Williamson, 1973; Whittaker, 1977; Yodzis, 1978; Rappoldt & Hogeweg, 1980; Steinmuller, 1980; Tilman, 1982; Pianka, 1983; Roughgarden, 1983). Their theoretical applicability has been examined for a wide range of conditions (e.g. Roughgarden, 1974; McMurtie, 1976; Abrams, 1984). McNaughton & Wolf (1979) describe this as 'one of the most well-trodden areas of community ecology'. Yet there is very little evidence that niche limitation exists, especially in plant communities.

Another theory widely held is that potential species richness (the number of alpha niches) changes from one point to another, at least over a wide environmental range (e.g. Pianka, 1966, 1983; Huston, 1980; Gentry, 1982; Stocker, Unwin & West, 1985).

Niche limitation as reflected in the number of species present in a quadrat sample was examined here. Any investigation of community structure requires the identification of a null model (Simberloff, 1983). The appropriate null model for this case is obtained by accepting the observed number of occurrences for each species and assuming that they occur at random across the quadrats being considered, independent of the occurrence of the other species. This approach is not clouded by variation in species abundance; only species presence/absence is considered.



392 J. B. Wilson et al.

There may be two opposing forces causing departure from the null model. Firstly, changes in the environment will change the competitive bal- ance and one species may displace another in a particular alpha niche, leaving the total number of species constant; 'niche limitation'. We would not expect exactly zero variance in species number per quadrat because of within-quadrat environmental variation, because of differences between species in the width of niche they occupy and because any ecological and evolutionary non-equilibrium would impair the efficiency of species packing. (Additionally, Yodzis (1978) suggested from theory that species evenness would also have an effect but we do not yet know how to adjust for this.) However, we would expect, if there is any niche limitation, variance lower than that of the null model; a variance deficit. Secondly, in some environments, normally called more 'favourable', there may be more alpha niches available and therefore higher species richness (Pianka, 1983). Pielou (1975) called this the 'Waterhole Effect', by example of the clustering of animals round one. This would lead to a variance greater than that of the null model due to differences between favourable and unfavourable sites. Since both these forces operate, it will be hard to see one for the other and especially hard to see niche limitation for the overlaid Waterhole Effect.

The solution should be to look over a narrow environmental range, so the sites are uniform enough for the number of alpha niches to be constant according to the niche limitation/species packing theory. If there is niche limitation we should then be able to see species replacing each other within a limited set of alpha niches, as chance or subtle changes in the environment determine which species occupies each niche at any point, such niche limitation resulting in a variance deficit. Disturbance is sometimes seen as allowing release from niche limitation (Armesto & Pickett, 1985), so we should seek niche limitation in a little-disturbed community. We sought niche limitation in situations that seemed to approach this ideal.

The number of species is used here rather than any quantitative measure because:

(1) the estimation of any quantitative measure is more open to subjectivity;

(2) most theory on niche limitation has addressed limitations on the number of niches limiting the number of species (e.g. MacArthur, 1968; MacArthur & Levins, 1967; Maynard Smith, 1974; Ricklefs, 1977; Whittaker, 1977) and it was this theory that we wished to test;

(3) in spite of Yodzis' suggestion that there would be an effect of species evenness on species number, no theory has been developed as to what effect this would be; although null models for species abundance have been suggested (Caswell, 1976), it is not clear which is appropriate (Engen, 1978). In contrast, the appropriate null model for species number is clear and a test against it available.

Methods

Fron Goch field

An agricultural field is often cited as an example of a uniform plant community. We sampled a field of permanent pasture on the University College of Wales (UCW) College Farm at Fron Goch, Wales (National Grid reference SN607827). The field has received no fertilizer within record and is grazed very little. Therefore, there is no regular disturbance. We recorded the presence and absence of all plant species (bryophytes and vascular plants) in contiguous 5 m X 5 m quadrats. To further increase vegetational uniformity, we excluded from the analysis any quadrat within lOm of the edge of the field. This left 164 quadrats, containing a total of 71 species. We calculated the variance of the number of species per quadrat and compared it with the variance to be expected from the null model by the method of Barton & David (1959).

Results

The expected variance from a null model, by Barton & David's formula, is 6 138. The actual variance over the field was 8 977. This represents a variance excess over the null model of +46/2% Barton & David warn that their formula is useful only when the variance in species frequencies is 'not too large'. They show that their formula is adequate for an example in which frequencies vary from 7% to 73%/. the frequency of the 71 species in our data varies from 1 % to 98%/. Therefore a Monte Carlo simulation of our null model was performed, allocating the 71 species to the 164 quadrats at random but with the same number of occurrences for each species as found in our sampling. The variance of the resulting quadrat totals was calculated. We repeated the process 2500 times. This gave a mean variance in quadrat totals of 6 139, confirming that the Barton & David formula was still appropriate. Barton & David did not give a test of significance for departure from the null model. However, the Monte Carlo simulations above per-



393 Does niche limitation exist?

mit a randomization test from which an unbiased estimate of significance can be made (Edgington, 1980). None of the 2500 simulations produced a variance as high as the 8 977 that we found in our real community; the highest was 8 013. This indicates that the variance excess was highly significant; allowing for a 2-tailed test (the probability of such a deviation under the null model can be given as P < 0 001). An alternative test of significance is obtained by calculating a calculate

2.

(n-1) x observed variance 2 _ X (n-i) expected variance

This gives a test statistic of 238 35 for our data, again P < 0 001.

Subdivision for greater uniformity

Even within a field of permanent pasture there will be vegetational differences, probably correlated with the environment. Goodall's (1954, 1970) comment that no area of vegetation has ever been shown to be uniform still holds true (Whittaker & Levin, 1977; Greig-Smith, 1979). There is environmental heterogeneity at all scales (e.g. Frankland, Ovington & Macrae, 1963) and hence vegetational pattern at all scales (e.g. Weir & Wilson, in prepar- ation). Subdividing the area for greater unformity subjectively would risk a division subconsciously based partly on species number. Almost all methods of numerical classification are sensitive to species richness and would introduce bias. There- fore we chose to divide the area on the presence or absence in each 5 m x 5 m quadrat of one particular species. There were four species with frequencies near to 50% over the field: Agrostis capillaris L., Galium palustre L., Molinia caerulea (L.) Moench and Ran unculus acris L.; we used each of them in turn.

With a different set of quadrats the species frequencies will be different and the expected variance will therefore change. Having established the validity of the Barton & David method for the dataset, we used their formula for the expected variance. None of the subdivisions of the area gave an appreciable decrease in variance excess (Table 1), certainly none approached the null model let alone showed a variance deficit.

It is possible that no one species acts as an effective indicator of the environment. It seems likely that the major environmental factor determining vegetational differences across the field is topography, affecting the water status of different patches. Therefore, the elevations of the four corners of each quadrat were surveyed with surveyor's level and the mean of these was taken as mean quadrat elevation. These elevations were expressed in three ways:

1. Simple mean elevation of each quadrat; 2. Topography, measured as the difference

between the mean elevation of a quadrat and its eight neighbours (adjacent and diagonal);

3. Drainage. In order to estimate the efficiency of drainage from each quadrat the lowest corner was selected as the corner through which most drainage would occur. Then the lowest of the three surveyed points adjacent to that corner and away from the quadrat was found. Drainage was expressed by substracting the elevation of that point from the mean elevation of the quadrat.

For each of these three measures we divided the quadrats into two groups, those below or at the median value and those above. Since we have divided the area on the factor likely to be of the greatest importance, directly and via effects on other factors, we assume these groups have even greater environmental homogeneity. We calcu-

Table 1. Variance in number of species per quadrat in floristic subdivisions of the Fron Goch Field, compared to that expected from the null model, with results from the full dataset for comparison.

Criterion species Variance for subdivision Absent or Expected Observed excess/ of the area present variance variance deficit

Agrostis capillaris - 5652 8 073 +42 8% + 5-809 9 573 +64-8%

Galium palustre - 6129 8 315 +35-7% + 5-794 9-554 +64 9%

Molinia caerulea - 5966 8-762 +46 8% + 5-890 8 601 +46-0%

Ranunculus acris - 5-988 8 909 +48-9% + 5-968 8-192 +37-3%

mean of the eight values above +48-4% all 164 quadrats 6-138 8-977 +46-2%




lated the variance excess for each group (Table 2). In some cases the variance excess was lower than for the whole field but for others it was higher and the mean excess was very little different. Cer- tainly, the variance did not approach that for the null model in any case, let alone show the variance deficit that would indicate niche limitation.

Ynys Las sand dune slope

It was difficult to survey the Fron Goch field on a very fine scale but possible on the dune slope in Ynys Las Nature Reserve, Wales (National Grid reference SN607937). The area sampled was a uniform dune slope facing south on a fixed 'grey' dune (Tansley, 1939). The sand was very low in organic matter. Rabbit grazing has produced a short turf. Shoot/rhizome presence of all plant species was recorded in 20 mm X 20 mm quadrats, rhizomes being detected to a depth of 80 mm. There were 236 such quadrats, laid contiguously in a line. By adding the results from adjacent quadrats it was possible to obtain results for a range of quadrat sizes, from 20mm X 20mm to 20 mm X 1 m. Calculations were not made for sizes larger than the 20mm x 1 m since the number of quadrats available of such sizes was too small for any conclusions to be drawn. In pattern investiga- tions that have employed similar sampling it has been found that the result can depend on the starting position for the summations. Adapting the suggestion of Hill (1973), we repeated calculations of observed variance and randomization tests for each of the possible starting positions. For example, with 40mm X 20mm quadrats there will be two possible starting positions, with 60mm X 20mm there will be three, etc. Since all starting positions are a priori equally valid we obtained means over all starting positions for observed variance, expected variance, and probability of the observed variance under the null model (Table 3,

Fig. 1). Again, the Barton & David result for the variance under the null model was very close to that from the Monte Carlo simulation (Table 3); the latter was used.

At all quadrat sizes there was a variance excess. This was significant for the quadrat sizes between 40mm X 20mm and 100mm X 20mm, reaching a peak of +61% with a quadrat 100mm X 20mm. Above the 200mm x 20mm size the variance

+60% - (a)

,,+40%a/

> +20%

a 2 0/-

C . > r 20%/ I l l I

002-i

e)_0 005 _________

E-m 0-1

021 co 0-2_

0 5 - _1

0 20 40 60 80 100 Quadrat length (cm)

Fig. 1. (a) Excess/deficit of variance in the number of species per quadrat on the Ynys Las dune slope, in comparison with the null model. (b) Significance level (i.e. probability of the observed result under the null model). 0 indicates that there was variance excess for some starting points and deficits for others.

Table 2. Variance in number of species per quadrat in environmental subdivisions of the Fron Goch Field, compared to that expected from the null model, with results from the full dataset for comparison.

Criterion for Variance subdivision Low or Expected Observed excess/ of the area high variance variance deficit

Simple elevation low 6 124 9 769 +59 5% high 5 484 7264 +32 5%

Topography low 6 066 9400 +55-0% high 5-520 7 694 +39-4%

Drainage low 6-225 9 499 +52 6% high 5-816 7435 +278%

mean of the six values above +44 8% all 164 quadrats 6 138 8 977 +46 2%



395 Table 3. Variance in number of species per quadrat on the Ynys Las Dune Slope, at selected quadrat sizes, compared to Does niche that expected from the null model (calculated by two methods). limitation exist? Expected variance

Variance Quadrat Monte Barton & Observed excess/ size Carlo David variance deficit

20mm X 20mm 3 323 3 315 3 729 +12 2% 40mm X 20mm 3 544 3 542 4 544 +28 2% 80mm X 20mm 3 434 3 449 5.495 +60 0%

160 mm X 20mm 3 274 3 270 4 872 +4808% 320mm X 20mm 3 051 3 045 3 973 +30 2% 640 mm X 20mm 2 771 2 770 3 468 +25 1%

excess fluctuated between +250% and +400%/. At these larger quadrat sizes the reduced number of quadrats makes the test insensitive and for no quadrat size was the variance significantly different from that expected from the null model. At the 880mm, 960mm and 980mm sizes we found variance deficits significant at P = 0 05 but in each case in only 4% of the starting positions, slightly less than the number of Type I errors expected on a random basis.

We conclude that there is no evidence for niche limitation in these Ynys Las data.

Ruth Patrick's diatom boxes

The most uniform set of habitats imaginable would be glass microscope slides in artificial ponds, fed by uniform water. Such an experiment was con- ducted by Patrick (1968). In that experiment, the slides were suspended in eight concrete boxes, fed by stream water that had been thoroughly mixed to ensure uniformity between the boxes. Patrick presents results for 2 yr.

The 1965 results are for eight of the boxes. The number of species per box, the variance and the expected variance under the null model were determined (Table 4). Significance was determined by the randomization test. The observed variance in species richness is about two thirds of that expected from the null model. However, because of the small number of boxes, this departure is far from significance (P = 0.500).

Table 4. Variance in number of species per box in Ruth Patrick's Diatom Boxes, compared to that expected from the null model (calculated by two methods).

Expected variance Variance Monte Barton & Observed excess/

Year Carlo David variance deficit

1965 19-812 19 750 12-688 -35-9% 1966 20 717 20-188 51 688 +156-0%

The records for four boxes in 1966 cannot be used to increase the sensitivity of the test because they show a non-significant (P = 0 094) variance excess (Table 4).

Discussion

Two processes may be operative in determining the variance of species richness: niche limitation and the Waterhole Effect.

Niche limitation would tend to reduce the variance between quadrats in their species richness below that of the null model, i.e. to give a variance deficit. Because the number of niches is limited, the number of species in a quadrat would be limited. This theory is effective only if there are sufficient species in an area to fill all the niches (Walker & Valentine, 1984). Insufficiency of species might be because of insufficient time for their evolution or insufficient time for migration after glaciation (Ricklefs, 1977). However, suffi- ciency of species is a usually unstated assumption of all niche packing theory. Naveh & Whittaker (1979) suggested that species availability would be high in areas with long histories of human agricul- ture, which is true of the permanent pastures of mid Wales. In Patrick's experiment it is difficult to imagine empty diatom niches, since 182 species were found. Rather, the question is how there can be more than a few niches in such a simple environment. Whether on the dune slope 28 species are sufficient to fill all niches is difficult to say.

The opposing process is the Waterhole Effect (Pielou, 1975); the tendency of species to congre- gate in certain sites. Pielou saw these as favourable sites but it is also possible that the greater numbers in some sites are caused by greater within-site environmental differences. The name Waterhole Effect still seems appropriate.

If we are to demonstrate niche limitation, it will have to be where the Waterhole Effect is small,




examining quadrats that are physically close in a uniform environment. Wilson & Sykes (in prepar- ation) looked for niche limitation amongst quadrats 10m X lom in size, randomly positioned through areas 200m X 200m and failed to find it. Here, we examined adjacent quadrats, which is as close as quadrats can be. We sampled an agricultural field, which would generally be given as an example of a uniform plant community. We examined a dune slope, a very uniform environment above and below ground. We sampled down to the scale of 20 mm X 20 mm, which is as fine a level as any other recorded. Still we could see no evidence of niche limitation.

It is still possible that niche limitation is oper- ating but cannot be seen above the larger Waterhole Effect. It is difficult to draw firm conclusions from the inability to observe an effect. However, we question the value of the concept of niche limitation if it cannot be demonstrated even in the most favourable circumstances. There must be a suspicion that, as Whittaker (1977) suggested, the number of niches is in real communities no limitation to the number of species in a plant community, i.e. in Whittaker's terminology communities are never 'saturated'. If there is a fixed number of niches, some of them must be filled by more than one species. This would contradict the Principle of Gauss and suggest rather Connell's (1978) Equal Chance Hypothesis. Therefore, our result throws further doubt on the Clements/MacArthur/Roughgarden concept of integrated communities (Leps, Osbornova- Kosinova & Rejmanek, 1982) and points towards Gleason's (1926) Individualistic model.

Acknowledgments

We thank Julia Williams and Shirley Agnew for field assistance; David Causton and Bryan Manly for statistical advice; Peter Bannister, Jill Rapson and Alan Mark for helpful comments.

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Received 4 March 1987; revised 6 July 1987; accepted 6 August 1987



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