THE ECOLOGY OF SOME SURFACE-DWELLING

COLLEMBOLA (INSECTA) IN A GRASSLAND HABITAT

by

DONALD RUSSELL REYNOLDS, B. Sc. , A. R. C. S.

A thesis submitted for the degree of Doctor of Philosophy in the

Faculty of Science of the University of London

MARCH 1974. Department of Zoology, & Applied Entomology, Imperial College Field Station, Silwood Park, Sunninghill, Ascot, Berks. 2

Abstract

Some aspects of the biology of surface-dwelling Collembola are reviewed with

emPhasis on adaptations to the surface mode of life.

Possible methods of sampling for these animals are critically reviewed, and a

new sampling method developed giving particular attention to the factors

effecting'its efficiency.

Population estimates have been made for four species of surface-dwelling

Collembola over a period of two years, and for one of the species, Entomobrya

nicoleti, a detailed analysis of the changes in the population size and

structure in relation to various environmental factors was undertaken using

regression methods.

A little-used multivariate procedure (orthogonalized multiple regression) has

been evaluated in this context. Tentative interpretations are made of the

causal mechanisms underlying the statistical relationships revealed in the

analyses.

A laboratory and field investigation of the role of temperature on the development and survival of surface-dwelling Collembola, particularly the lethal effects of sub-zero temperatures, has been carried out.

The dispersion patterns of the Collembola have been analysed by a variety of techniques, and the implications of the patterns for the ecology of the animals discussed.

Finally, a methodological investigation of the applications of some clustering techniques to ecological data has been undertaken with emphasis on the behaviour of the test data under the various procedures. TABLE OF CONTENTS

Page

1) Acknowledgements. 5

2) Introduction. 6

3) Taxonomic Note. 14.

4) Classification of Species Studied. 16

5) Review of Some Aspects of Biology:

a) Habitat and Distribution. 17

b) Food. 25

c) Predation and Parasitism. 4.1

6) Description of the Study Area. 51

7) The Sampling of Surface-Dwelling Collembola:

a) Description of Sampling Procedure. 54.

b) Discussion of Methods of Sampling for Surface-Dwelling 57

Collembola.

8) Stage-Grouping. 84.

9) Collection. 86

10) Culturing. 87

11) Seasonal Abundance of the Four Species of Surface-Dwelling

Collembola. 89

12) Analysis of Stage-Group Data: Life-Histories in the Field. 102

13) Regression Analysis:

a) Methods. 115

b) Selection of Variables. 128

c) Analysis of Relationship between numbers of E. nicoleti,

Suction-Sample Data, and Weather, by Orthogonalised

Multiple Regression. 133 4

d) Analysis of Relationships between Winter populations of 14i.

E. nicoleti and Weather.

e) Analysis of Relationships between E. nicoleti populations

And Weather: Main Analysis. 449

14) Egg Mortality in the Field. 156

15) The Analysis of Dispersion:

a) Review. 163

b) Methods. 167

c) Results and Discussions:

1) Fitting of theoretical frequency distributions. 177

ii) Indices of Aggregation. 199

iii) Taylor's Power Law 2011.

iv) Distribution Pattern as related to various Environmental

Factors. 212

16) Cluster Analysis:

a) Methods. 225

b) Results and Discussion of Single-Linkage Classifications. 231

c) Results and Discussion of Ordinations. 236

17) The Effects of Temperature:

a) Relationship of Egg-Development Rate and Temperature. 259

b) Cold-Hardiness. 266

18) General Discussion. 291

19) Summary. 296

20) References. 299 ACIMOWLEDGEMENTS'

This study has greatly benefit ed from stimulating discussions

I have had with many colleagues in the Departments of Zoology and Botany at Imperial College, and also from their helpful advice and assistance on many occasions; to all who took an interest in the project, my thanks.

In particular I am indebted to:

Professor T. R. E. Southwood for the research

facilities extended to me at Imperial College Field Station,

Silwood Park;

Mr. H. E. Goto for supervising the project, and for

confirming the identifications of the Surface-dwelling

Collembola;

Mr. R. G. Davies for his invaluable assistance with

various statistical aspects of the study;

Drs. B. E. J. Wheeler and G. Jackson for advice on

some mycological problems;

Mr. A. Broodbank for his assistance in carrying out

the sampling programme in the field.

This study was made whilst I held a Natural Environment Research

Council Studentship. Many of the analyses were carried out on the

University of London's C. D. C. Computer using terminal facilities provided under a grant from the Ford Foundation. 6

INTRODUCTION

The ecology of Collembola has been studied for over 60 years, since the early papers of Diem (1903) and Dahl (1912), and a large and scattered literature has accumulated. Salmon (1964) has compiled an extensive bibliography and many papers have been published on the subject since then. (See reviews by Paclt (1956), Christiansen (1964),

Hale (in Burges and Raw 1967), Butcher, Snider and Snider 1971, and

Healey (in Phillipson 1972)).

However, many of the major studies in Continental Europe have been synecological or faunistic in nature, e.g. Gisin (1943, 1951),

Janetschek (1949), Haarlov (1960), Nosek (1967), Cassagnau (1961),

Cassagnau and Rouquet (1962),- Stebajeva (1963) and Rusek (1968). These biocoenological studies have been mainly concerned with distinguishing and classifying associations, or successions of associations and attempt- ing to "label" these by indicator species. This work is often combined with descriptions of various morphological—ecological features of the fauna which are adaptations to its special environment (life—form studies), see Macfadyen (1963 ch.13). Ecology in some countries is almost synonymous with work of this sort (Macdadyen 1963). These studies have given rise to a proliferation of definitions, and have produced descriptions of particular associations but they have, perhaps, been rather unsuccessful in analysing the nature of the dynamic effects of the various environ- mental agencies on the intrinsic attributes of the species which determine population size and quality at any given time and how these change with time.

Peus (1954) (quoted by Clark et. al. 1967) makes the following criticisms of the biocenological approach; that it lacks acuity as an analytical method, that it is not convenient for experimental 7

investigations of numerical regulation and therefore it tends to preclude inductive synthesis.

There has, of course, also been much work on the population ecology of Collembola and some justification may be required for embarking upon yet another study. Firstly, this investigation is only concerned with the surface-dwelling, arthropleonan species of the collembolan fauna.

This group has been rather neglected compared with the soil-dwellers as

Christiansen (1964) has pointed out. This may have been due partially to the difficulties in sampling the surface-dwelling forms as these species usually occur in small numbers in samples which were designed for the soil species. The study of surface-dwelling symphypleonan population dynamics has been greatly aided by the pest status of

Sminthurus viridis and thus more is known about this group (Wallace 1967,

1968 and Walters 1968). While the present study was in progress the situation with regard to the surface-dwelling arthropleonans has been partly rectified by Joosse and her co-workers (1965 onwards).

Secondly, many of the early studies were subject to some of the following drawbacks:-

1) Early workers were unavoidably impeded by the lack of efficient

extraction methods, and until recently there has been little

experimental work designed to elucidate the mechanisms behind

the extraction methods (e.g. the behavioural responses of the

animals under the various stimuli applied during extraction by

dynamic-type methods). However, the recent papers of lief

(1972, 1971), Vannier (1969, 1970, 1971), Macfadyen (1968)

among others, lead one to suppose that there will soon be a

logical basis for the development of more efficient extraction

methods. 8

2)Another drawback was the limited period over which samples

were taken in many of the earlier studies (1 year or even less).

This meant that insufficient evidence was available as to

whether the population curves presented were representative of

regular annual cycles as was often implied by the authors.

Dhillon and Gibson (1962) found that the populations sampled

by them exhibited rather different trends in each of the years

studied. The further analysis of populations to determine

the main disturbing and regulating factors of course requires

several years accurate census data (a minimum of five) for all

stages of the animal, and none is available for a collembolan

population (except that for Sminthurus viridis obtained by

Wallace (1967).

3)Perhaps due to the synecological bias in collembolan ecology,

and also to the fact that a single extraction method auto- ,

matically produces many species, there has been a tendency to

investigate a large range of species rather than concentrate

on a more detailed study of one or a few species combined with

relevant experimental work. This has meant that the work

effort has been spread rather thinly over the species involved.

4)A statistical approach has been lacking in many of the published

studies. It is often difficult to know what level of precision

was being aimed at in the sampling programme, and what con-

fidence can be placed in the population estimates. It is also

difficult to distinguish the significant fluctuations in

population numbers from sampling error. Again, much of the

discussion of the results from these studies consists of rather

vague hypothesising about the effects of various environmental 9

variables on the population numbers with little statistical

evidence. It seems a pity that the powerful tools of multi-

variate data analysis have not been utilised more often during

this preliminary stage of investigations. Multivariate

methods have been little used in soil zoology in spite of the

advocacy of Blackith (1962). Macfadyen (1963 p.191) also

mentions that multivariate analysis may be very useful for

analysing the association of all the combinations of environ-

mental factors with the variation in the numbers of organisms,

under the less well controlled conditions found in the field.

Bonnet, Cassagnau and de Izarra (1970) have demonstrated the

use of multivariate methods in the investigation of the

distribution of Collembola on a rock. Blackith and Reyment

(1971) mention uses in collembolan zoogeography. An inv...?.sti-

gation of the methodology of various multivariate techniques

in the analysis of field data has been attempted in the present

study.

5) Even in the more recent studies not subject to the drawbacks

mentioned above, preliminary hypotheses were not usually tested

by suitably designed experimentation. (A notable exception to

this is the work of Vannier (1970)).

It must be admitted however, that the present author has also

had to omit this essential second stage to an investigation of

this type, namely the testing of the causal nature of the

correlations and interrelationships obtained from the preliminary

analysis, due to lack of time. 1 0

Thus there is a need for more work on the population ecology of the

Collembola as a whole, and the surface-dwelling members of the arthro- pleona in particular. We still have no published survivorship curve for a collembolan population, except the artificial one constructed by ** Healey (1967), and we are still some distance from producing a complete description of a collembolan life-system (Clark et.al. 1967) with the possible exception of Sminthurus viridis. The rather slow progress which has been made in the analysis of natural populations of Collembola is perhaps not surprising in view of the difficulties the group present.

For example, the continuous or protracted recruitment period, the difficulty'of defining and interpreting the age structure, and the difficulty of determining the fecundity per female with time in the field make the construction of conventional life-tables extremely difficult.

Among the other features of natural collembolan populations which cause problems in analysis, is the fact that all the stages are very similar in biology, and live in more or less the same habitat, thus giving no opportunity to census the population during movement from one habitat to another at particular stages in the life history (e.g. Varley & Gradwell, 1963). Lastly, one might mention the fact that the animals are not very amenable to direct observation in the natural habitat.

Considering methods of analysis of collembolan population data, we find that techniques for determining the numbers entering particular stages for populations with some overlap of generations are available

(e.g. Dempster, 1961). But these have the disadvantage of requiring an accurate estimate of one of the stages (e.g. natality) from a separate source, and furthermore are not statistically "robust".

** SEE ALSO TAKEDA 1973. 1 1

Another possibility is the use of Hughes' (1962) method (which measures mortality by comparing the actual population with one expected from a calculated potential rate of increase), on the data from the expanding collembolan population found in spring and early summer.

However, this method is dependent on a stable age-distribution and this is seldom met with in practice, and is especially unlikely to occur quickly in a population of rather uniforth age if the reproductive period is short. It seems quite likely that Hughes' (1962) test for the stable age-distribution is insensitive, and the approximation to a geometric progression of the age-groups is not close enough to

justify the use of the growth equation (P. Furniss personal communication).

If the population was censused exactly at every stage-duration

period (these must be equal), the stable age-distribution requirement

would not be necessary. However, this would be exceptionally difficult

in practice, especially as the sampling occasions have to coincide with

the correct microclimateic conditions, for the successful sampling of

surface-dwelling Collembola.

Other time-specific methods of analysis are unsuitable as the

population is assumed to be growing at a constant rate e.g. Lefkovitch's

(1965) matrix method.

It would of course be possible to simulate the growth of the

population by computer, but this was not thought to be worth while as

the estimates of fecundity and growth obtained from laboratory cultures

were not thought to be close enough to those pertaining in the field,

due to differences in diet (Healey, 1970). As the mimicking of

natural conditions in the laboratory is very difficult due to our

ignorance of the precise diet in the wild, the estimation of development

rates would best be carried out in animals confined in field-cages. 12

Observations on the confined animals would in themselves be difficult due to their small size and concealing habits. There would also be little indication of whether the animals were confined in a favourable location.

In the face of the above difficulties the present author has not attempted to construct life-budgets, but has approached the problem by analysing the effects of various (mainly physical) environmental factors on the size of the population, by the use of regression analysis.

Some little-used multivariate techniques have been evaluated in this context. A thorough analysis of the dispersion pattern of the animals using a variety of statistical techniques has also been undertaken.

The general biology of surface-dwelling Collembola has been reviewed, with emphasis on adaptations to the epigeic niches, and comparisons made with the soil-dwelling members of the group.

Major advances in the ecology of Collembola will probably only be made when studies are related to general ecological theory. Thus it is gratifying to see that recently the functional roles of small arthropods in litter and soil habitats have been the subject of considerable

• speculation and study from an energetics view-point. Interest has centred on the processes of energy flow, production and nutrient cycling especially in connection with the decomposer cycle, Healey (1967),

Phillipson (ed.) (1971) and Edwards,Reichle and Crossley (1970),

Witkamp (1971). In grassland ecosystems in particular it is known that a large percentage of the net primary production goes into the decomposer cycle, (Macfadyen in Phillipson 1971).

Interest has also been shown in the related problems of the trophic inter-relationships of Collembola and other organisms in the soil community e.g. Torne (1967, 1968), and the reasons behind the co- existence of a large number of detritivore species apparently living in 13

the same habitat and utilising similar resources (Anderson and Healey

1972). It is to be hoped that interest in these topics will act as a spur to the future study of the population dynamics of Collembola.

The present study is one of several on the ecology of grassland habitats presently in progress at Silwooa Park and it is hoped that a complete picture of this ecosystem will eventually emerge. 14

TAXONOMIC NOTE

The four species mainly dealt with in the present study are:-

Entomobrya nicoleti (Lubbock, 1867); Lepidocyrtus lignorum Febricius, J.,

1775, sensu Gisin, 1964; Tomocerus longicornis (Muller, 1776); and

Isotoma viridis Bourlet, 1839. The classification of the four species is shown on page 16 .

Gisin (1943) and Christiansen (1964) have classified the life—forms of Collembola. The species dealt with here are probably best placed in the "Epigeon" ("Atmosbios"), although Gisin (1943) placed Isotoma viridis in his "Hemiedaphon" category. However, Hale (1966) regards

I. viridis as a member of the "Atmosbios". It should be noted however that these epidaphic species quite often perform vertical migrations down into the deeper layers of the profile, and that some of the species particularly Lepidocyrtus lipnorum, have a large part of their juvenile population resident in the intermediate layers of the profile, rather than the top layers.

Surface—dwelling, as I have used the term here, means that a large proportion of the population of the species was amenable to the vacuum sampling method as used in this study.

The taxonomy of the genus Entomobrya followed in the present study was that of South* (1959, 1961). He allows specific status for Entomobrya nicoleti. The taxonomy of the group has, in the past, been somewhat confused, due to the use of colour pattern for identification without recording the limits of the range of the variation in pattern in each species described. Thus South (1959) notes that Entomobrya nicoleti, for example was described as "Entomobrya multifasciata var. nicoleti" and as "Entomobrya muscorum var. nicoleti", by various early authors.

More recently it has been recorded as a variety of Entomobrya nivalis

(Linne) by a number of workers, e.g. Gisin (1944). 15

Lepidocyrtus lignorum has had an even more confusing taxonomic history.

It was resurrected as a good species by Gisin (1964). Before this paper,

Lepidocyrtus lignorum material was probably frequently referred to Lepido- cyrtus curvicollis or Lepidocyrtus lanuginosusl (see Hale, 1966). Further- more, the characters used to separate the latter two species were unreliable (Hale, 1966). Thus there is little datm in the literature which can, without doubt, be applied to LeLilocyLAus lignorum, although it seems likely that this species was often the one in question.

With regard to Tomocerus longicornis, it is interesting to note that the first instar of this species was described in a different genus

(as Architomocerura litsteriana Bagnall, 1940) until this mistake was corrected by Goto, (1956). 1 6►

The classification of the four

collembolan species studied

Class and Order COLLEMBOLA

Suborder ARTHROPLEONA

Superfamily ENTOMOBRYOIDEA

Family ISOTOMIDAE

Isotoma viridis Bourlet, 1839

Family ENTOMOBRYIDAE

Subfamily ENTOMOBRYINAE

Entomobrya nicoleti (Lubbock, 1867)

Subfamily LEPIDOCYRTINAE

Lepidocyrtus lignorum (Fabricus, J. , 1775) sensu Gisin, 1964.

Family TOMOCERIDAE

Tomocerus po the nu. s ion icor ilia (Muller, 1776) 17

REVIEW OF SOME ASPECTS OF THE BIOLOGY OF

SURFACE-DWELLING COLLEMBOLA

In the following section some aspects of collembolan biology not dealt with in the body of the thesis are reviewed, some original observations added and the whole discussed with regard to the effects on the population ecology of the group. Particular emphasis is placed on observations concerning the biology of the four genera studied here. Many of these data are incomplete or inprecise but as the knowledge of the population ecology of these animals is poor it seemed useful to bring together all the relevant items of information, from the scattered sources in the literature, into a convenient form for future reference. The literature relating to aspects of the biology which have been the subject of particular study here are reviewed in the relevant sections of the thesis.

HABITAT AND DISTRIBUTION

As the taxonomic status of Entomobrya nicoleti has, until recently, been unclear, there are few reliable references on its distribution and habitat in the literature. Most of the information on the species is contained in the work of South (1959, 1961). The species appears to be widely distributed in Europe, having been taken in Sweden, Finland, England, Scotland, Wales, Ireland, Germany,

Hungary, Albania and Malta according to South (loc. cit.). The species is said to be characteristic of rough grassland, especially

Dactylis glomerata, according to South, and this agrees with my own . 18

observations. South occasionally recorded the species from

Pteridium and the lower branches of Ulex europeus, and dead

herbaceous plants, but in these cases he maintained that the

Collembola had "strayed" up from the grass below?

It is well known, that the genus Entomobrya prefers drier

places than most Collembola, (for example Joosse 1970),

and that at least some species of the genus are more resistant to

low humidity than most Collembola (see Davies, 1928; Mais, 1970;

Joosse, 1970). It appears however that Entomobrya nicoleti

requires a higher humidity than most members of the genus; this being an adaptation to its grassland habitat where the relative humidity would be higher than in many other Entomobrya habitats. Thus

South (1959) found that E. nicoleti showed less mortality at a .

100% 11,H., but was more susceptible below 90% R. H. , compared

to six other species of Entomobrya.

Among the few other records for the species are the following:—

The species has been recorded from permanent pasture, in most

of the months of the year, and within the top three inches of the

profile, by Thompson (1924). She records only two specimen s (in a

five month period), in arable land, indicating that this does not form

a favourable habitat for the species compared to pasture land. Hale

(1963, 1966a, 1966b) gives habitat notes on Entomobrya: nicoleti

from a moorland area. He states that the Entomobrya nicoleti were

mainly pale with a few dark markings, (this form was commonest at

Silwood also), and the species was most common on uncropped Festuca

on alluvial grassland, and in Eriophorum vaginatum, small numbers

were found on the cropped vegetation of mineral soils, (like limestone

grassland), in Calluna litter, in Sphagnum and Polytrichum and in 19

Eriophorum augustifolium. The reasons for the difference in numbers in these habitats appeared to be partly due to the amount of cover given by the vegetation (the n-iore tussocky E. vaginatum forming a better habitat than E. augustifolium, or the uncropped

Festuca being better than the cropped vegetation). It may also be due to the water content of the soil (the mixed moor, for example, appeared to be partly too dry; and partly too wet, i. e. in the

Sphagnum areas.)

Lepidocyrtus lignorum, as it has only been recently

resurrected as a species, is not mentioned widely in the literature.

Szepticki (1967), working in Poland, records Lepidocyrtus lignorum as frequent and numerous in his Corylo-Peucedanetum association, very frequent and not numerous or singly in the Cteniditalia, and

Pino-quercetum with fir associations. He also states that it is

common under pieces of wood and on plants, in litter and on tree

trunks and in forest ground flora, and says most of the data

concerning Lepidocyrtus lanuginosus are to be applied here.

Hale (1966 a and b), working in Westmorland, frequently

recorded this species on peat soils of all types, also on Sphagnum

and Polytrichum, and it was not uncommon on alluvial grassland,

but was rare from lime3tone grassland. Goddard (1972) records

the species from arctic Norway, and Valpas (1969) has recorded it

from Finland. In Silwood Park, it appears to be quite common in

leaf litter and decaying vegetable matter generally. It appears to

occur in slightly damper places and where the vegetable matter is

in a more advanced state of decay, as compared to the typical 20

habitats occupied by Entomobrya nicoleti. It often occurs with

Lepidocyrtus lanuginosus but this smaller species appears to be typically more hypogeal, occurring deeper in the profile than the adults of L. lignorum at least. The species is probably widely distributed throughout Europe.

Because of its large and conspicuous appearance, Tomocerus longicornis is recorded frequently in the literature. Cassagnau.

(1961) gives its geographical distribution as Europe and North

America, while Gisin (1960) states that within Europe it is found from mid-Finland and Great Britain to Italy. Cassagnau (1961) states that it is found commonly everywhere beneath wood, and also in humid litter. Gisin (1943) places this species in the

"Atmosbios" and in the "Synusie des Waldes", and Gisin (1960) states that it occurs in woods and high-shrub vegetation, particularly of lowlands. Among other habitats from which it has been taken are the following: Calluna moor (Delany, 1956, 1960), Beech forest

(Van der Drift, 1951), associated with decomposing mushrooms

(Marlier, 1942), in the litter of a fir wood (Poole, 1959, 1961).

Knight (1961) quotes literature to show that the Tomoceridae only exist in areas with a definite litter stratum, or a litter-like environment, i. e. thick grass prairie. For example, Bellinger

(1954) showed that in a series of stands, there was a tendency for

Tomocerus to become more abundant and constant in the communities with a thicker litter layer. An examination of more recent literature appears to bear out this contention, for example Tamura

(1967) found Tomocerus absent in only one of his five habitats and 21

here the surface litter was very thin. It is known that a thick litter layer is an efficient moderator of environmental extremes

(note experiments where the natural litter has been replaced by an

artificial cover, e. g. Gill, 1969). Knight and Read (1969) state

that substrate moisture was an important factor controlling the

distribution of the Tomoceridae, the optimum substrate producing

a constant high humidity but without being water-logged. The

genus Tomocerus is not very resistant to low humidity (Joosse and

Groen, 1970), and according to Mais (1970) the genus has a high

transpiration rate and a rather low rate of moisture absorption.

As the animal, in the adult stage at least, is of large body size,

it cannot easily escape the effects of desiccation by downward

migration. Thus a thick litter layer would be required to protect

the animal against desiccation during periods of drought.

But as Knight and Read (1969) point out, the genus does not

favour poorly drained soils, and this seems particularly the case

for Tomocerus longicornis, which appears to be more common in

drier places than some other members of the genus, e. g. Tomocerus

minor. For example, at Silwood lake where conditions were rather

wet, T. minor was by far the most common of the two, in contrast,

on the sandy, well-drained soil of the study plot, T. longicornis was

common and T. minor virtually absent. In the present study, there

was no significant association between T. longicornis numbers and

soil moisture, as was found for T. minor by Joosse (1970). Knight

and Read (1969) attribute the lower numbers of the Tomoceridae found

in badly drained soils to an indirect effect of the excess soil moisture 22 on the fungal food of the animals, but they do not produce any convincing evidence on this point. Gisin (1948) states that

T. longicornis is usually found in "xerothermic" forests, as opposed to humid ones.

Isotoma viridis has a holarctic distribution (Cassagnau,

1961; Stach, 1947), being found from the arctic islands to Mexico and 'Mesopotamia'. In the north of its range, i. e. in the tundra, it is often an important constituent of the fauna, e. g. Chernov

(1968), Challet and Bohnsack (1968), Goddard (1972). Goddard

(1972) states that I. viridis composed 90% of the total fauna in a birch wood habitat in arctic Norway. Towards the south of its range it becomes gradually rarer, due, according to Stach (1947), to its requirement for cold, damp conditions.

This species is frequently encountered in a large range of surface habitats, and it is recorded from a high proportion of soil faunal investigations. Gisin (1943) placed it in his "mesophile or hydrophile hemiedaphon" category, but it is mainly the juveniles which tend to occur at lower levels (Milne, 1962; Weis-Fogh, 1948), the adults being largely surface-dwelling. Several authors, e. g.

Gisin (1943, 1960), Joosse (1969), have indicated that the species prefers rather moist places, and according to Gisin (1943) is characteristic of lush pastures, edges of 'pools, etc. , and does not shun marshy soils. Certainly the species is not very resistant to low humidity (Davies, 1928; Joosse and Groen, 1970), but this does not presuppose a'wet substrate is necessary: e. g. Gisin

(1943) also states that it is present in dry, chalky soils if these are manured; also, Chernov (1968) found it dominant in dry mossy 2 3 tundra, and again, Joosse (1970) did not find a significant partial regression of I. viridis numbers to soil moisture in her study. As with Tomocerus longicornis, what does seem to be necessary is that the "architecture" of the soil profile should provide sufficient humid cover, (for an animal which presumably cannot penetrate easily into the soil to escape desiccation because of its size). Thus in the habitats studied by Haarlov (1960),

I. viridis was only found in numbers in the level pasture, due to the unsuitable profile-structure in the other habitats.

Among the habitats that I. viridis has been recorded from are the following: pasture (Gisin, 1943), tundra meadows (Chernov, 1968), humid moss (Cassagnau, 1961), moorland (both mineral and peat soils) (Hale, 1966),

e. in fairly open habitats. Where it is common in woodland, e.g. birch forest of northern Scandinavia (Agrell, 1941; Goddard, 1972), or oak-hickory wood (Cole, 1947), these were generally fairly open with good grass cover. Gisin (1943, 1960) states that the species avoids dense forests. It also, apparently, avoids high altitudes, and is not found above

1200 metres in the Tatras, 1700 metres in the Alps (Stach, 1947), or above

1650 metres in the Central Pyrenees (Cassagnau, 1961). The species is not very tolerant of high cation concentrations, as Strenke (1963) noted that it appeared only in the final phase of decay in marine algal wrack, where the NaC1 content fell to a low level.

Several authors mention the appearance of I. viridis on snow

(Gisin, 1960; Stach, 1947), and some races of the species at least seem to be very cold tolerant. (Stach, 1947, reports large numbers on a frozen river where the ambient temperature was - 22°C).

The species has many colour varieties whose significance is not clear. Stach (1947) mentions that the younger individuals tend 24 to be light in colour, but the colour varieties also show some correlation with "biotopes". He found that adults collected in winter were always dark coloured, but suggested that this was an adaptation to winter conditions rather than proper to adult animals. In the present study area mauve, light-green, dark grey-green, light-grey, bluish-grey and dark-grey coloured animals were taken. However, the hatchlings produced under culture conditions were always a light, reddish-mauve colour, and this darkened to produce adults of a dark, slate-grey colouration. 25

FOOD

A good deal of information, admittedly of uneven quality, has

accumulated on the food of collembola. (For references and

discussion see e.g. Dunger (1963), Poole (1959), Bodvarsson (1970),

Christiansen (1964), Hale (in Burgess and Haw, 1967), Butcher,

Snider and Snider (1971), Wallwork (1970), and Adams and Salmon

(1972).

However, much work is required both on the nutrition of

natural population of Collembola throughout the year and on the

precise effects of food quantity and quality on the population

dynamics of the species. For most species the answers to the

following elementary questions are still required: -

What precisely does the natural diet consist of? Is all of the

material ingested important nutritionally? Christiansen (1964)

suggests that the animals may only digest microflora on the

residues ingested. How much selection of food material is

there? Does the availability of food largely control the diet,

with little specificity or selectivity on the part of the animal?

What are the reasons for, and the consequences of, the apparently

similar foods for different species in the same site? Is there an

optimum food for each species?

The apparently similar diet between different species in the

same habitat poses some interesting questions on niches occupied and the intensity of competition between the species (Anderson and

Healey 1972). Laboratory work is also required on the food preferences and on the effects of different materials on fecundity 2 6

and growth.

The studies of the population dynamics of a collembolan species should, ideally, be undertaken simultaneously with population studies of that part of the microflora utilised as food by the Collembola. An investigation of the stage of humification of plant debris at different seasons of the year, and the nutrient status of these stages, may also have to be studied. Thirdly, the indirect effect of various physical factors on the collembolan population through the agency of the microflora should also be studied. Multidisciplinary studies of this type are not numerous, and to the best of the author's knowledge none have been undertaken with Collembola specifically in mind. This is, perhaps, not surprising considering the complexity of collembolan trophic interactions with the environment (see e. g. Torne, 1968).

There is, however, much interest recently in the functioning of decomposer systems from an energetics point of view (Healey, 1971).

If these studies take the form of precise investigations of the quantitative and qualitative role of the Collembola in such systems, rather than mere balance sheets of the gross energy fluxes, results will no doubt be produced on the nutrition of Collembola which would be of great interest to population ecologists.

Such experiments and observations on aspects of the feeding habits and nutrition of Collembola that have been made, have given indications of the type of effects on the population abundance which can result from these processes. For example, species may have an infinity for a certain microfloral species. Thus Kuhnelt (1963) 27

found Lepidocyrtus curvicollis was more numerous in the immediate neighbourhood of leaves attacked by the fungus Clitocybe infundibuliformis. The collembolan may have been more common on the yellow, attacked leaves than on the brown, unattacked leaves because of the fungus itself, or the increased pH, or tannin content of the leaves due to the fungal action. In the same experiment,

Entomobrya nivalis was not found except on the yellow, attacked leaves. Again, a collembolan species may show an infinity for a certain component of the fungus, for example the apparent preferences of a Tomocerus species for fungal spores rather than hyphae (Knight and Angel, 1967).

Some interactions between the microflora and the collembolan fauna may be very complex. Torne (1968), for example found that a soil-dwelling collembolan could be cultured on sterile cellulose as certain micro-organisms were present in the gut which could digest this material. However if certain substances, including humic matter, were present these micro-organisms were injured so that the Collembola could no longer nourish themselves sufficiently on sterile food.

An interesting example of intra-specific effects of feeding behaviour on population numbers of a surface-dwelling collembolan is given by Wallace (1967). Here, density-induced mortality resulting from the tendency of the newly hatched nymphs of

Sminthurus viridis to eat the bodies of dead nymphs and adults of the same species, played a dominant role at high densities. It was believed that the early death of the young nymphs, due to the 28

ingestion of large quantities of uric acid from the corpses could lead to a dramatic collapse in population numbers.

Another example of intra-specific effects of feeding behaviour was described by Waldorf (1971); Sinella curviseta was cannibalistic but showed a greater tendency to eat smooth eggs rather than eggs in which the chorion had ruptured. Thus there was an increasing probability as time passed of the cannibals only taking the infertile eggs, and this was viewed as a form of resource conservation in the species. Finally, the "head wagging" contact behaviour noted by Christianse,tt Lyman and Johnson (1972) may be mentioned. The 'bouts' between individuals may represent the effects of competition for food or space in high density population.

Similar behaviour has been observed by the present author in

Entomobrya nicoleti on occasion.

Considering the literature concerned particularly with the nutrition of the four genera dealt with in the present study, we find that information on Entomobrya, Lepidocyrtus and Isotoma viridis is rather meagre. Rather more data is available for Tomocerus.

Because its large size makes hand-collection easy and facilitates the examination of the gut contents, it has frequently been used in studies of food of Collembola.

The methods used in previous research on the present topic for the above genera include:

(a) Analysis of gut contents and observations of feeding in

nature.

(b) Feeding preference experiments. 29

(c) Growth and fecundity experiments.

In spite of its drawbacks, careful analysis of the gut contents of natural populations (by some technique such as that of McMillan

and Healey (1971) is probably one of the most useful methods for

determining which of the range of foods available in the habitat has

actually been consumed. The various components of the diet may,

however, not be identified with similar ease using techniques such

as this; for example, non-solid food materials would not be

recorded, and materials like bacteria would not be visible in a normal

gut-content preparations in lacto-phenol, or lactic acid. Gut content

analysis does not give information on either the feeding rate, or on

the assimilation efficiency of the ingested materials.

Most of the information on the natural food of Entomobrya is

contained in South (1959, 1961). South examined the gut contents of

several of the British species of the genus throughout the year, for

Entomobrya nicoleti in particular, South's work represents the

only previous observations on the food of this species. For the

genus as a whole, the most common gut contents were stated to be

fungal spores, mycelia, pollen grains, and Pleurococcus sp. Some

species of the genus were also recorded eating live plant material,

and in this connection it is worth noting that Entomobrya .has

occasionally been recorded as a pest (e. g. on hops and currant

leaves by Theobald (1908, 1910), on cotton by Owen and Owen (1958)).

As South's observations on Entomobrya nicoleti were made

at Silwood Park and in grassland, they are particularly relevant to

the present study. He found that between July and November, fungal

spores and mycelium were the main constituents of the gut contents, 30

with some debris towards the end of this period. In December the contents consisted of fungal mycelia and debris with only a few spores. From January through till April the contents were reasonably similar but with rather more fungal spores than in

December. One sample in April contained material which appeared to be grass leaf parenchyma. During May and June, some samples contained large amounts of grass pollen, and correspondingly small quantities of fungal matter and debris. Cladosporium species was frequently the most common fungal constituent. In general,

South's observations are in agreement with such observations as I have been able to make. On several occasions (16/5, 9/6, 8/7), the present author has noticed that large numbers of Entomobrya nicoleti in the samples have a greenish tinge to them, due to the large amounts of pollen in the gut. These periods coincide with the flowering of Holcus species in the habitat. The question arises as to whether pollen is eaten at these times merely because of its widespread occurrence in the habitat, or whether there is a definite preference fOr this material. The subject of Collembola as pollen feeders is reviewed by Kevan and Kevan (1970), and in this paper and in Kevan (1972) a species of Entomobrya is reported feeding on pollen directly from the anthers of the flower, during a short sensitive period in its life history.

South concluded that the principal food of the genus Entomobrya was the spores and mycelia of a Fungi imperfecti. He also concluded that the actual food consumed appeared to be directly

related to the food available in the habitat at a particular time, and 31

that there was little specificity of diet with regard to the fungal food, and little difference in diet between the different species of the genus. These opinions will be commented upon below. Karg

(1964) was of the opinion that the large population increase of

Entomobrya species in some of his experimental plots was due to the strong growth of soil fungi. Farahat (1966), working with

Entomobrya lanuginosa, found that this species could be reared successfully on the fungi Trichoderma viridi and Rhisopus nigrians, or a ground-up extract of these fungi. Wheeler (1972) found

E. nivalis populations were highest when the amount of Alternaria sp. fungus on alfalfa terminals was greatest.

Entomobrya has also been observed to eat animal material.

South (1959) observed an adult of E. corticalis eating an adult of the same species in culture, although it was possible that the victim was already dead. I have observed cannibalism in E. nicoleti on one occasion, when an adult was seen eating another adult of the same species which had been accidentally stunned but was definitely alive.

There is an old record by Handschin (1926) of E. multifasciata with nematode larvae filling the mid gut, with the suggestion that these were taken in as food.

Entomobrya does not eat its own exuviae after moulting, as do some other surface-dwelling Collembola.

Now turning to the diet of Tomocerus; with this genus, due to its size and conspicuousness, there are quite numerous observations.

Gilmore (1970) records two species of Tomocerus taking nematodes 32

(Panagrellus sp.) from charcoal/ plaster discs in culture, and Singh

(1964) observed Tomocerus longicornis feeding upon an aphid, but the usual diet appears to consist of fungi and decaying plant material. Savely (1939) states that Tomocerus flavescens fed on rotting wood and fungi, and Schaller (1950) states that the same species flourished on decaying leaf litter, and would feed on the excretement of larger arthropods. Schaller (1950) also states that this collembolan fed more on elm and hornbeam leaves than on oak and beech leaves, he also reports that the animals were unable to utilize fresh green leaf material, and is supported in this by Singh

(1969) who mentions that Tomocerus would not feed on decaying plant material if it had been sterilised. Gilmore and Raffensperger (1970) compared the diets of several species of Tomocerus from several habitats, and found that differences in diet were related to habitat differences rather than reflecting species differences. Humus and fungal hyphae appeared to be more important constituents in populations from a site with a heavy litter layer, while fungal spores were more important in sites with a sparse litter layer. Barnardi and.Parisi (1968) found the gut-contents of T. minor to consist of fungal spores, fungal hyphae, plant cuticle, parenchyma, pollen and blue algae. There was

considerable seasonal change in the proportions of those materials present in the gut.. Christiansen (1964) gave Tomocerus as an example of a Collembolan genus which fed mainly on decaying and dead plants, but this conclusion seems more applicable to some of the smaller, less epigeal members of the genus, than to Tomocerus longicornis. 33

Considering this species in particular, Poole (1959) found that in a fir plantation T. longicornis fed very largely on the hyphae

(9/10 of the identifiable material in the gut) and the spores (0. 5/10) of Fungi Imperfecti of the family Dematiaceae. These observations

are given some support by the work of Singh (1969) who reported that

Tomocerus longicornis appeared to show some preference for fungi

of this family in laboratory food-preference experiments. Poole (1959) believed that Collembola sought out fungal colonies in the soil and only

turned to decaying litter as a second choice. Anderson and Healey

(1972) in their careful study of the gut contents of Collembola from a

chestnut woodland, found that about 46% of the particles in the gut

contents of T. longicornis were of fungal origin. Anderson and Healey

also noted that 42% of the specimens of Tomocerus longicornis had

empty guts, and they attributed this to the operation of the moulting

cycle. Singh (1964) has described the periodicity in feeding in

T. longicornis associated with moulting. It was long been known,

(Handschin, (1926); Macnamara, (1924)) that Tomocerus eat their

own exuviae after moulting.

The work of Poole (1959) represents the main observations on the natural food of Isotoma viridis. Poole found that the gut contents

varied seasonally; on 13 December the specimens of Isotoma viridis 7 examined had been feeding predominantly on the litter, /10 of the 3 identifiable material in the gut being lignin or cellulose, the other /10 being fungal hyphae. By the 4 May of the next year, the Collembola

examined were feeding on litter and fungi in approximately equal 9 quantities, and by 7 July of the same year /10 of the identifiable material 34

1 in the gut was of fungal origin, with only /10 or less of lignin or cellulose. Poole speculated that either the Collembola prefered fungal hyphae when this was available, or that litter was only palatable in certain states of decay which only occurred at certain seasons of the year. Isotoma viridis will also eat a large number of other organic materials; for example pollen (Kevan and Kevan,

(1970)), fresh plant tissue (Sharma and Kevan, (1963) quote Davies,

(1925) who associated I. viridis with damage to mangolds), Pleurococcus sp. (Joossee and Veltkamp, (1970)). The species can also be carnivorous, as Macnamara reports laboratory observations on Isotoma viridis killing and devouring Isotoma notabilis, and Gilmore (1970) records

I. viridis taking nematodes.

As Lepidocyrtus lignorum has only recently been resurrected as a good species (Gisin, (1964)), little dietary information exists in the literature. McMillan and Healey (1971) have quantitatively analysed the gut contents of some individuals of this species from oak/grass compost.

They found that 72% of the identitiable material in the gut consisted of as eptate spores, and 26. 7% consisted of plant material particles of size less than 10 Am.

Observations on Lepidocyrtus lanuginosus and L. curvicollis may be of some relevance here. Poole (1959) found the gut contents of

Lepidocyrtus lanuginosus from Douglas fir plantations to consist mainly of hyphae (8/10) and spores (2/10). Bodvarrson (1970) found that 51. 5% of Lepidocyrtus lanuginosus specimens had no gut contents, presumably 35

due to the moulting cycle. Considering the specimens with gut

contents, he found that 95. 4% of the guts had amorphous material in

them, 74. 1% of the guts had mineral particles, 49.4% had a fairly large

amount of hyphae, and 39. 1% had fungal spores. Lepidocyrtus lanuginosus

represented one of the more epigeal species in his study, and he notes that

this species had a higher proportion of specimens with an empty gut,

which he attributes to a lower feeding rate because of better quality

food in surface-dwelling Collembolan species. L. lanuginosus also had a greater frequency of fungal materials and pollen in the guts compared

with more subterranean Collembola.

Some species of Lepidocyrtus are capable of eating living plant tissue, as the records of Folsom (1933) with regard to L. violenta eating

sugar cane demonstrate. Backlund (1945) records two species of

Lepidocyrtus living on dry wrack seaweed.

Considering food preference experiments as a method of elucidating the dietary requirements of Collembola:- As many species of

Collembola will eat and apparently thrive on materials which are obviously not part of their natural diet, the early work on food preferences is of rather limited use. Even in the more recent studies e. g. Knight and Angel (1967) and Singh (1969), where the range of food materials presented was more like that which would be available in the natural habitat, the results were still rather ambivalent. Knight and Angel's experiments (1967) showed that Tomocerus would preferentially ingest 36

fungal spores rather than fungal hyphae humus or litter, although the survival rate of cultures provided with fungi alone was very low.

(It was possible that this low survival was due to other reasons).

The fact that the guts of wild specimens contained less spores than the laboratory individuals was explained by the unavailability of spores in the natural habitat. Healey (1970) believes that the sensory equipment of many forms is so poor that food selection experiments are meaningless, and food preferences can only be demonstrated by growth rates and fecundity. While his conclusion that Collembola are indiscriminate feeders, is probably more applicable to subterranean forms rather than epigeal Collembola, it is probably true that food selection experiments are of somewhat limited value. Although growth and fecundity rates on different food materials have been determined for some species of Collembola (e. g. Vail, (1965)), none have been carried out on the surface-dwelling Arthropleona: Other tools which are available for investigating these problems include radionuclide labelling of possible fungal foods as has been done for mite populations by Coleman and McGinnis (1970), or the use of precipitin tests,

(although the small size of Collembola would make the use of the latter technique somewhat difficult).

Discussion of food and feeding

Many authors have stated or implied that Collembola are indiscriminate feeders, that the food consumed is related simply to what occurs in their habitat, and that there is little food specificity or 37

interspecific differentiation in dietary requirements. These statements appear to need some qualification. Reference to the

Order as a whole does not seem very rigorous, as some ,species with suctorial mouth parts are carnivorous, or largely so e. g. Friesia mirablis. Even considering the more typical fungivore and detritivore

Collembola, these statements do not seem very useful. On the whole, it is true that monophagy in Collembola is extremely rare or absent,

(as it is for soil animals generally), but in some of the surface-dwelling

Collembola at least, selectivity for type and quality of food can be quite marked, and specific differences are noticeable.

Surface-dwelling Collembola probably have a large range of normal foods, than do soil dwellers (as suggested by Christiansen, (1964)).

No doubt this is because the range of food materials available to the surface dwelling fauna is a greater e. g. pollen, algae. These materials however do not seem to be as important in the diet as was believed by some early authors e. g. Gisin, (1948), and the fungal constituent of the diet remains the most important.

This greater diversity and probable higher quality of the foods available, has led, apparently, to a greater selectivity for food type and quality in surface-dwelling Collembola than in subterranean forms.

The greater the specialisation to a surface niche, the greater the selectivity; thus in the laboratory, Isotoma viridis (the least specialised to a surface niche) would readily eat materials reluctantly eaten by 38

Tomocerus longicornis , which in turn would eat materials rejected by

Entomobrya nicoleti . For example, T. longicornis would consume old, feaces-contaminated mycelia of Cladosporium herbarum, which would not be eaten by Entomobrya nicoleti, although this fungus was readily acceptable to the latter species when fresh. (Incidentally, these observations rule out the possibility (in Entomobrya at least) that digestion may be a co-operative venture, the food material becoming more and more utilizable after passing through several digestive tracts, Christiansen (1964). It was also noticed, that Isotoma viridis would eat a greater range of fungal species, than say Entomobrya nicoleti, and would more readily consume materials like Bemax and yeast than would the latter species.

There are, of course, many factors to be born in mind when considering the comparative selectivity of the various genera, e.g. genera like Tomocerus which have a comparatively high growth rate

(Joosse and Veltkamp, (1970)) may be obliged to be less selective than slower growing genera. In spite of qualifications like the above the following scheme probably still holds:

Increasing specialisation to surface life.

Onychiurus Entomobrya

(Subterranean) (surface dwelling)

Greater range of food materials available.

More high quality food available.

More selectivity for food type.

More selectivity for the quality of a particular food material.

Smaller quantity of food required. 39

Several authors have commented on the lack of dietary differentiation between different species or even genera of Collembola e. g. Poole, (1959); Gilmore and Raffensperger, (1970); and Anderson and Healey, (1972). However, I incline to the view that even with the simple methodology employed in these studies, which would not be capable of detecting many of the nutritional differences which may have been at present, there were still indications of inter-specific differences in diet. Furthermore where a number of species of a genus of surface-dwelling Collembola are present within a general area, they are typically in separate micro-habitats (South (1959),

Knight (1963), and personal observations, see page / ) and thus competition for food among the species is reduced. Thirdly, as discussed by Anderson and Healey, there may be a low ratio of biomass to resources in the soil/litter ecosystem, and food may not be limiting for much of the time. •

Due to the euryphagic habits of most Collembola, it seems unlikely that direct starvation would be an important mortality factor in the most habitats (Wallwork 1970). On the other hand, it does not seem likely that the food supply would be optimum at all times. Thus the effects of intra-specific competition for high quality food (say high nitrogen sources), at least during some parts of the seasonal cycle, seem likely to influence population level in a density dependent fashion through effects on fecundity, or perhaps reduced survival under stressful conditions. In soil Collembola at least, there are indications that the 40

numbers of several species may be regulated mainly by the influence of the micro-flora on the food material determining the rate of

reproduction of the animals (TOrne, 1967). We may also note in passing that the availability of food played the greatest part in regulating the rate of population growth, and in determining the maximum

population density in lab./ cultures of soil Collembola (Usher, /__ Longst aff and Southall, 1970). 41

PREDATION.

Predation on Collembola has been reviewed by Christiansen (1964) and Simon (1964), and some predators of the four surface-dwelling genera dealt with in the present study are listed on ph.9 . Christiansen (1964) states that in general the following groups are important collembolan

predators: Mites (six families), Pseudoscorpions, Staphylinid and

Garabid beetlesi and centipedes. He also states that spiders, predaceous

Hemiptera, ants and vertebrates feed on Collembola "in unusual habitats and under special conditions". One gains the impression, however, that as far as surface-dwelling Collembola are concerned, spiders may rival or supersede mites as predatory agents. KUhnelt (1961) states that ground

beetles (eg Notiophilus), Staphylinids (eg Stenus) and various spiders

(eg Clubiona sp.) are enemies of surface Collembola, while soil-dwellers are attacked by mites (eg Gamasids, Bdellids and Trombidiids) and beetle larvae.

Spiders gave the impression of being important in the present study

because of (a) the numerical dominance of the group in the surface-layers, and (b) the known efficiency of the group as predators. (Christiansen

(1971) found spiders to be much more efficient predators of surface

Collembola than Opilionids, Chilopods or Trechine beetles). During the

present study, surface-dwelling Collembola were seen from time to time/

in spiders' webs in the field, and an unidentified spider was seen carrying a T. longicornis juvenile on one occasion. Clark and Grant (1963) found an increase in the numbers of Collembola on plots where substantial numbers of spiders had been removed, and concluded that predation by

spiders was one of the main factors limiting arthropleonan collembolan

populations in litter. Again, spiders were recorded as predators of

Sminthurus viridis by Maclagan (1932), and of Tomocerus by Moulder, Reichle

and Auerbach (1970). 42

Predatory mites in general seemed more common lower in the litter,

but some Erythraeidae occurred abundantly in the suction samples,

particularly in May which would coincide with the build-up of juvenile

Collembola.) Also one assumes that adults of the larger collembolan species, ie Tomocerus and Isotoma are of too great a size to be preyed on (see also Agrell 1941),and further, Hurlbutt's (1965) work seems to indicate that Veigtia at least appears to have difficulty capturing the

more active genera (eg Lepidocyrtus). This does not mean that predation of the juveniles and/or moulting individuals by mites cannot be important.

Ants may also be important predators (see eg Wilson 1950, Panic 1963, and Vannier 1971a). Ants such as Myrmica laevinodis were able to efficiently attack the; surface Collembola using the weapon of sprayed formic acid, which caused paralysis, particularly in juvenile Collembola

(Panic 1963, Vannier 1971a). Some species, eg Lepidocyrtus domesticus appeared to be resistant to the secretions of the ant however (Vannier

1971a). Soil-dwelling , Podmomorph Collembola eg Onychiurus armatus and Hybogastrura viatica were avoided by ants due to their autohaemoragic defence system, which appeared to have a definite toxic effect on the ants, Panic (1963). [This repellant action of the blood of Onychiurus spp. apparently protects them to a certain extent against predation from some mites, Hurlbutt (1965), but not apparently against other predators such as the Staphylinids mentioned by Delany (1960).] Ants may have been important as predators in the present study population as Myrmica

Scabrinodis was common on the experimental plot, and was seen carrying

Tomocerus juveniles.

Although certain Carabids eg Notioohilus spp., Loricera pilicornis,

Leistus suinibarbis, are noted predators of surface-dwelling Collembola

(Anderson 197 , Schaller 1949, Davies 1953, 1959, South 1959). Other species seem inefficient, at least in laboratory conditions, and evidence 43

from gut contents should be interpreted with caution. In the present study

Dromius melanocephalus was common on the plot, and clubbed setae like those of Entomobrya were observed in the gut contents, but the animal

showed no predatory inclination when confined with surface-living

Collembola in the laboratory. It seems possible that with many Caralid

species in which Collembola remains are found (Davies 1953),the Collembola are only caught if they are debilitated, trapped, or in moult, or the dead bodies may be eaten by the beetles.

Some other predators were very efficient under laboratory conditions

eg the Pseudoscorpion Neobisium muscorum which was observed to snatch even the most active collembolan species before the latter could jump. The

effects on the collembolan population of these predators in the present study seems limited due to their low density in the field.

Egg mortality due to predation appeared to be appreciable in the

present study in Tomocerus longicornis and Entomobrya nicoleti (see later section) and Wallwork (1967) suggests that some Cryptostigmatid mites may

eat the eggs of Collembola. Maddison (1969) has noticed that meso-

stigmatids eg Pergamasus crassipes eat the eggs of Collembola.

Istoma viridis lays its eggs in batches like those of many soil- dwelling Collembolal and in contrast to Entomobrya and Tomocerus which lay

eggs singly, or in twos and threes, concealed in crevices. It appeared that losses from I. viridis batches in egg-predation experiments were less than losses of Entomobrya or Tomocerus eggs, and it is possible that

Collembola which lay in batches also have eggs which are unpalatable to some predators, (although other explanations of the differences in oviposition behaviour between Entomobryids and Tomocerids on one handl and Istomids, many Onychiurids and Hypogastrurids on the other are possible). Hale (1965) found significant differences in egg-batch size even within the Onychiuridae, 44

between the more hemiedaphic species and the more euedaphic species, and so the phenomenon seems quite general. He suggested reduced predation in the lower soil levels as an explanation. Williamson (1971) in a categorisation of the interactions between organisms states that consumers which eat lots of whole organisms (ie most general predators) take a range of prey species specified by a) size, b) occurrence in time and space, c) taste (to a certain extent), but not species. Predators of Collembola appear to be mostly of this type, eg spiders are known to select prey mainly by size rather than species (see eg Moulder, Riechle and Auerbach

1970), and predatory mites and carabids also seem to be unselective for species. The activity of the prey should be added to the list as far as

Collembola are concerned.

The very high diversity of predatory mites present within a small area of soil and apparently feeding on the same prey is in itself interesting from the competition point of view.

An example of the taste of the prey affecting selection by the predator is the autohaemoragy defense mechanism of Onychiurids and Hypogastrurids / but this phenomenon, to the best of the author's knowledge, is not known to be present in surface Collembola. Avoidance of predation by the latter appears to be due to: a) size of the prey species, eg in Orchesella and

Tomocerus, (predation may have acted to produce the large body size in these genera), b) agility, eg in Lepidocyrtus and Entomobrya (it is interesting that these genera which are very active running forms have not evolved the large body size found in the less agile Tomocerus), c) some species may possibly avoid some effects of predation by being able to continue breeding at low temperatures in the winter when some predators are not present or not active.

It is interesting that Collembola as a group are not subject to another predation strategy, that of the insect parasitoid, No explanation for the absence of host species among Collembola is known to me. 45

In conclusion, it might be said that while it is plain that a large range of predators will prey on surface-dwelling Collembola, there is little precise work on the population effects of this predation. In spite of this many authors (eg Knight & Read 1967) have hinted that certain population declines in Collembola have been due to predation especially by mites, or that mite predators were limited by their collembolan prey

(eg Karg 1961). It seems fairly clear that populations of the symphpleonan

Sminthurus viridis are partly determined by predatory Bdellid mites

(Wallace 1967, Norris 1938). It now appears unlikely that predation is the main regulatory factor of primary consumers (contrary to Hairston,

Smith & Slobodkin 1960), and there seems no theoretical reason why Collembola

(if these can be regarded as mainly primary consumers) should be necessarily regulated by predation rather than sub-optimality in the diet, say, or climatic factors (Wallwork 1970). As the predators of Collembola seem generally rather unspecific one imagines that a species of collembolan will only regulate a predator if no other prey is available. Decreases in a number of collembolan or other soil faunal species could, however, take place at the same time due to an inclement weather factor, like drought say, and this could conceivably limit predator numbers.

ANIMAL PARASITES

The absence of parasitoid insects preying on Collembola has been

noted above.

Considering true parasites: Paclt (1958) and Christiansen (1964)

mention that gregarines, nematodes and larval Gordiidae have been recorded

from Collembola. Tomocerus spp. have been observed to harbour nematodes

by several authors (perhaps because the size of the genus facilitates

ease of observation) eg Phillips (1946) quotes Summer (1885) who reported

several young nematodes in T. "plumbea". Populations of Ttlangicornis

at Silwood Park have been observed with high infestations of nematodes 6

in the haemocoel (H.E. Goto, personal communication), but the population studied by the present author did not appear to be infected. Miles (1970) found nematodes (possibly larval Cheilobus sp.) attached to the antennae, legs, head and furca of another surface-living collembolan, Orchesella villosa. The nematodes were attached by the head, and the body was coiled-up "like a watch-spring". The precise effects of the nematode infestations on the Collembola are not known, but Christiansen (1964) quotes Delamare - Deboulteville to the effect that some of the cases of supposed parasitism may be in fact cases of phoresis.

Gregarines are also found in Tomocerus (see Paclt (1956)). The later reference of Chiba, Uchida and Fujita (1966) might also be mentioned. They frequently observed many gregarine trophozoids in the space between the peritrophic membrane and the columnar epithelium of the mid-intestine of T. minutus. Once again nothing is known about the effects of these parasites on natural populations of Collembola.

Finally, the presence of microsporidian infections has been noted by

South( 1959) in Entomobrya cultures.

ENTOMOPHAGOUS FUNGI

Leatherdale (1958) has produced a host list of Entomophagous fungi, but for the Collembola it is taken entirely from Goto's (1956) paper which thus appears to contain all the references for Collembola to date.

The only record of an entomophagous fungus attacking a surface-dwelling collembolan species is Goto's record of Cenhalosoorium muscarium Petch effecting Tomocerus longicornis and T. minor eggs.

In the present work some apparent instances of fungi attacking

Collembola were investigated. Fungus is, of course, often found growing from dead individuals in cultures, but in the vast majority of cases 47 these were saprophytes growing on the decomposing corpses. Two species of fungi found associated with Tomocerus longicornis in cultures were however suspected of being genuinely Entomophagous and investigated further.

In one case, several specimens from one outside insectary culture was seen to have a quantity of Entomophthora - like fungus sprouting from the joints of the legs, antennae, and the body sclerites. This fungus was cultured on potato-dextrose agar, and was observed to discharge spores explosively thus producing a white powdery area on the lid of the petri dish. However an isolate of this fungus was identified as Conidiobolus coronatus (Cost)

Batko, by Mrs Tulloch of the Commonwealth Mycological Institute. According to Macleod (1963) this genus of the Entomophthoraceae is not parasitic on

insects. He mentions that Conidiobolus has been isolated from plant debris, and as weak parasite or sapraphite on pilei of Basidiomycetes. The reproductive structures of the pathogen itself may become associated with or attacked by secondary fungi Madelin (1963), so that there is a remote

possibility that the isolate identified was not the one which may have caused the death of the Tomocerus individual.

The second fungal species suspected of being Entomophagous was noted

in constant temperature room culture, again of Tomocerus longicornis on

five occasions. It was observed growing out from the integumental joints

either very shortly after death, or in two specimens, while the animal was

still alive but apparently rather unhealthy. Slide preparations of some of the infected specimens were made in lactophenol and cotton blue. The other

specimens were dipped in methanol to kill surface contaminants, and

inoculated on potato dextrose agar and V-8 agar. The fungus in question

grew well upon these media, but unfortunately in the sterile form. Sub-

cultures were treated with ultra-violet light, grown on water agar, and also on chitin agar in an attempt to induce sporulation. This was

unsuccessful. Thus two of the original slide preparations (which contained a small amount of mycelia which had sporangia and spores present) were 48

submitted to the Commonwealth Mycological Institute. This material was examined by Mrs Tulloch and she reported that a positive identification was not possible although one of the slides "could be a genus in the

Zoopagales". The Zoopagales (= zoopagaceae) is a group of the

Entomophthorales, some members of which attack small terrestrial animals eg nematodes and amoebae.

Thus it must be admitted, that the status of the two species as entomophagous parasites of Collembola is, unfortunately, still open to doubt. 49

Some predators of surface—dwelling Collembola (Entomobrya, Tomocerus,

Leoidocyrtus, Isotoma viridis) reported in the literature.

Isotoma Taxon Entomobrya Tomocerus Lepidocyrtus Authority viridis

Pergamasus Bhattacharyya crassi.es 1962 Lab

Pergamasus Maddison crassites 1969 Lab Mites - Unidentified South 1959 Gamasid Lab

Gamasids Karg 1962

Veigaia sp Veigaia Hurlbutt 1 ocha 1965 Lab

Unidentified South 1959 Lab Spiders Salticidae Moulder et al Gnathosidae 1970 Lab Agelemidae ,

Oligolophus South 1959 meadii Opiliones Lab Mitopus Mitopus, Phillipson morio morio 1960 Lab

Neobisium N. muscorum Neobisium Reynolds Pseudo— muscorum muscorum this study,la scorpions N. muscorum N.muscorum Delany 1960 Lab Chilopoda Lithobius Lithobius Cole forticatus forticatus 1946

Hemiptera Nabis South flavomaginatus 1959 TNabidaer---- Diptera Tachypeza Tachyreza South 1959 nubila nubila Laurence 195 7757) field 50

Some predators of surface—dwelling Collembola (contd.)

Isotoma Taxon Entomobrya Tomocerus Lenidocyrtus viridi. . s Authority

Stenus South 1959 , similist Reynolds (this study) S.impressus Lab

Aleochara Sp Aleochara Delany 1960 Sp Lab

Staphilin- Olophrum Olophrum n tt idae 212201 piceum

Othius Othius If ft angustus angustus

O. punctulatus tt tt

Quedius . Quedius ft tt boops boops

Tachyporus ft ft obtusus •

Leistus South 1959 spinibarbist Lab

Leistus South 1959 Sp.la. Field

Notiophilus . South 1959 biguttatus Lab

Carabidae *Notionhilus Anderson 1972 biguttatus Field

Nebria Penny 1966 brevicollis Field (from gut contents) Loricera Davies pilicornis La. 1959 Several spp.Several spp. Christiansen of Trechini of Trechini 1971 Lab

NYrmica 1',laevinodis Panic 1963 laevinodis & others Field and & other ant Lab Myrmica Reynolds r.uginodis (this study) Ants ;;;; ;.47aljj1241g Field Vannier 1971 Lasius L.emarginatus emarginatus Field & Lab

WIIMAIMMINOiMIIMIIIMMY11•••■••••••■•■■•■• Wilson 1950 Dacetines Dacetines Field & Lab Davies 1 95.9 notes larvae of N. biguttatus & N.ru ipes preying on Orcheselia villosa 51

DESCRIPTION OF THE STUDY AREA

This study was carried out at Imperial College Field Station,

Silwood Park, near Ascot, Berkshire. Some general accounts of the vegetation and geology of the Park can be found in Greenslade (1961) and

Richards and Waloff (1954). The study area (see sketch map, Fig. 1) is the southern part of the area described by Bera (1966) and McNeill (1969).

The studies of McNeill (1971, 1972) were also carried out on this site.

The area was 1850 sq. metres of rough grassland situated on an east-facing bank of Gunnes Hill. To the west is a plantation of old and mainly dead broom bushes Sarothamnus scoparius (area 4 of Waloff, 1968). The cultivated fields of Hill Bottom lie on the east. More grassland exists to the north with the difference that Agrostis tenuis and Holcus lanatus are more dominant here than on the study area, and this_area is over hung by a large oak Quercus robor. To the south is more grassland of a similar nature to that of the study area but with more stands of

Cirsium arvense.

The grasses of the study area itself are: Holcus mollis Linn., H. lanatus Linn. and Agrostis tenuis Sibth. distributed approximately as shown on the sketch map. Cirsium arvense stands are present along the southern edge and along the east of the area. The grass is unmown but small oaks are removed from the area at intervals. Most disturbance on the plot is due to trampling during sampling and to the activity of rabbits. A feature of the Holcus areas are clumps of dead or dying grass which are not removed and gradually decompose and grade into the litter.

They form canopies over the numerous vole runs. The soil profile consists of a mat of vegetation composed of stems and roots cf the grasses mixed with decomposing grass litter which lies on top of a poor, acid, 52

sandy soil. jH of the litter is 4. 9 (range 4. 3 - 5. 8), PH of the soil in

the top inch is 4. 3 (range 4. 6 - 4.1). The underlying strata are eocene

sands of the Bagshot series. At the top of the slope of the study area

there are many water-worn flint pebbles from Plateau Gravel deposits

which lie just under the vegetation mat. Podsolization is very slight.

As the weather at Silwood Park tends to approach the continental type

the summers are long and rather warm, this combined with the nature

of the soil means that the litter is liable to dry out in times of drought.

The severity of this effect depends partly on the nature of the vegetation.

In the Agrostis areas where the vegetation mat is relatively thin,

drying out is more severe than in the Holcus areas which have a greater

accumulation of litter and swathes of dying grass which tends to slow

down the drying out process. • ( FIG. ! MAP O EXPERIMENTAL AREA BROOM 7'7

o FIELD c.0 tvAT

H. MOLL'S AGROSTIS to METRES H. LANATUS DACTYLIS GLOMERATA 54

THE SAMPLING OF SURFACE-DWELLING COLLEMBOLA

DESCRIPTION OF SAMPLING PROCEDURE

An outline of the sampling procedure actually employed in this

study is given here. Discussion of the merits and demerits of this and alternative sampling methods, and related matters such as efficiency,

accuracy and cost of sampling is deferred to a following section.

The study area was divided into 100m 2squares by posts along its perimeter, and a simple random stratified sampling design adopted.

Within a square the position of each sample unit was located by throwing

a ring of the same diameter as the unit.

The sampling methods consisted of:-

(a) Vacuum sampling with a "D-vac" insect sampling machine

(back-pack model) made by the D-vac Co., Riverside,

California (Dietrick 1961).

(b) Taking soil cores directly below the vegetation sampled by

the D-vac, and extracting these in Tullgren funnels.

The sampling occasions were timed so that micro-climatic

conditions were comparable between each occasion, this was to prevent

errors arising due to the differing availability of the collembola to the

vacuum sampling machine due to changes in their vertical distribution.

(see next section). In practice this meant that sampling was carried out

at the same time of day (early afternoon), and in conditions of 100%

humidity at the level of the vegetation. The lower vegetation and litter

were damp on these occasions but not extremely wet. Windy, very cold or very hot days were avoided. Because of these requirements the

timing of the sampling occasions was slightly irregular, but samples 55

were taken atroughly fortnightly intervals during the period October 1969 -

November 1970 and at monthly intervals thereafter until March 1972.

The number of sub-samples taken on each occasion was generally 10, though for some special purposes 15 or 20 were taken. The area covered by each sub-sample was either 1 square foot (0. 0929 sq. m.) or 0.0873 square foot (81. 073 sq. cm.). The smaller sub-samples were of four inches diameter.

The procedure was best carried out by two persons.

Firstly the collecting-head of the "D-vac" was quickly lowe red over the area to be sampled, and agitated vigorously up and down on the vegetation for a few seconds. The collecting-head was then lifted and a second person quickly parted the vegetation in the sampling area and pulled away the upper layers of dead grass, before the collecting-head was replaced for one minute again with constant agitation.

The parting of the vegetation and removal of the upper layers of dead material was mainly necessary in the Holcus stands, where large mounts of decaing grass from the previous seasons growth formed a canopy over the litter proper. If not removed this formed something of a barrier to the extraction of animals on the litter surface. This part of the procedure was not necessary on the shorter vegetation e. g. the Agrostis patches.

The vacuum sampler was then removed and the catch transfered to a polythene bag. Then a 4" diameter x 2. 5" deep core (10. 2cm. x

6. 4cm.) was taken by the second person in the centre of the vacuumed area. For a few of the early sampling occasions a wireworm corer was used, but this was soon replaced by a specially made, and more

efficient corer resembling that of Macfadyen (1961), 1. e. it consisted 56 of an outer metal sleeve with a sharpened end, enclosing 2 inner rings which held the soil core intact.

The soil cores were transferred to the lab in these inner rings and enclosed in plastic bags. Tullgren funnels, of a similar design to that of Edwards, Thompson and Lofty (1967) were used for extraction of the Collembola. The cores were placed intact and inverted on grids of 8 meshes per inch which formed the bottom of circular trays 11 cm. in diameter. These trays were then placed in the tops of the funnels.

The funnels were fitted with 40 watt light bulbs and these were turned on after two days. The total extraction time was 7 days. 70% alcohol was used as a collecting medium. This apparently does not have a repellent effect on the descending Collembola (Edwards and

Fletcher 1971).

The "D-vac" catches were sorted from the debris by sieving out the larger pieces of debris over a tray, and collecting the animals with a mechanical pooter as they ran out from the smaller particles of debris left on the tray. Then this smaller sized debris was tipped into 80% alcohol and examined under a stereo-microscope. It was necessary to do this if some of the very youhg instars were not to be missed. The Collembola were stored until required for examination by placing them in 80% alcohol and allowing them to sink by adding a few drops of ether. The most accurate way of counting the collembola was found to be to filter off the alcohol in a funnel leaving the animals on black filter-paper which had previously been marked into a grid by scoring with a mounting needle. The animals were counted on the filter paper square by square. A proportion of them were picked off for examination of the instar, sex, gut contents etc. 57 DISCUSSION OF METHODS OF SAMPLING

FOR SURFACE-DWELLING COLLEMBOLA

Methods suitable for the separation of arthropods from litter and soil habitats are reviewed by MacFadyen (1962), Southwood

(1966), and in Murphy (1962) and Phillipson (1971) among others.

These authors also give an introduction to the design of sampling programmes for soil and litter arthropods, and discuss the statistical aspects.

The objective of the present study was to obtain absolute population estimates, and information on spatial distribution for all stages (other than the egg) of four species of surface-dwelling

Collembola.

Methods which have actually been utilised or might be potentially useful for sampling surface-dwelling Collembola appear to include the following: -

1. Suction apparatus.

2. Taking of cores of vegetation and litter, inverting and

dislodging collembola by tapping the corer.

3. Coring and extraction by a Tullgren funnel method.

4. Coring and extraction by flotation and differential wetting.

5. Hand collection by removing debris from quadrats, sieving

this onto a tray and removing the animals by pooter.

6. Pitfall trapping.

7. Cryptozoa boards.

8. Sweeping with net.

9. Soil embedding and sectioning. 58 1. Suct ion Sampling

This method has been little used for the sampling of

Collembola except as part of the general fauna of a habitat.

(Turnbull 1966, Southwood and Van Emden 1967, Johnson, Southwood and Entwistle 1957, Bulan and Barratt 1971, Dietrick, Schlinger and

Van Den Bosch 1959). Vacuum sampling for collembola is not mentioned in the reviews of MacFadyen (1962), Edwards and Fletcher

(1971) and Healey (1971).

South (1959) however used an electrically-powered suction apparatus for the sampling of Entomobrya multifasciata on Cedrus needle litter. He found that the vacuum method was considerably better than Tullgren funnel extraction or flotation with a xylene/water interface for the extraction of Entomobrya, perhaps due to escape during sampling by the latter two methods. The numbers of

Lepidocyrtus sp. were, on the other hand, higher in the funnel and flotation samples. South estimated the percentage efficiency of his

vacuum sampling as 99 - 100% for Entomobrya multifasciata as the population was confined to the fo layer, and approximately 66% for the Lepidocyrtus sp. due to its frequenting the deeper litter layers.

Johnson, Southwood and Entwistle (1957) claimed a mean

percentage extraction of 99. 3 for arthropleonan collembola from rough

grassland. Turnbull (1966) using a Dietrick machine like the one used

in the present study, removed 96. 2% of the surface collembola from

grazed pasture land. He recommended the method as the best for

obtaining quantatative samples for a range of arthropods from grass

land habitats. 59

Vacuum sampling was quite satisfactory for the species studied here, provided some simple precautions were taken (see below). It is possible that in some habitats, the large amounts of dead leaves or other debris would make sorting of the catches more difficult and costly. This problem can be overcome partially by a) sampling while the ground is damp to prevent dust being sucked up, b) screening with a coarse-mesh bag in front of the collecting bag whilst sampling, c) use of Tullgren funnel or flotation methods as aids to further sorting of the catch (Dietrick et. al. (1959) and

Turnbull (1966). This last procedure of course adds further errors and was not resorted to here.

The advantages of vacuum sampling in grass-lands are:

1. Suction sampling is (at least in theory) an absolute

method, the sample being related to a definite unit

area (cf. sweeping). _ This is of course very important

in the construction of numerical and energy budgets.

2. As the sample is related to a definite area it is

relatively easy to check the sampling efficiency of the

method.

3. For many low density arthropod species it is probably

the only convenient method whereby enough animals can

be gathered to make the study quantitative.

4. The method is claimed to collect all of the life-stages for

more species than any other method suitable for the

habitat.

5. The method is less effected than some, '-)y the position of

the animals on the vegetation, thus comparable samples 60

can often be taken under various stages of plant growth,

times of day and weather conditions. Notwithstanding

the above statement the percentage efficiency must be

determined for all species sampled under various

environmental conditions and sampling procedures.

6. In studies of associated species all the species concerned

are gathered at the same time from within identical

sampling areas.

The special advantages of the Dietrick machine as used here are the following:

i. The apparatus is powered by a single cylinder, air-

cooled, 2-/engine of the type used in lawn mowers.

The lightness of this motor unit (27 lbs.) and the back-

pack mounting make it possible for the operator to carry

out the sampling procedure alone, and have both hands free

to manipulate the collecting head.

ii. The carbure ter is of the non-float type which feeds

petrol in any position of the motor.

iii. No electrical leads are necessary as the machine is

petrol driven.

iv. Many different collecting attachments can be fitted,

i. e. can take different sized sample units.

v. The collecting bag is as near the collecting head as possible

(not at the machine head of the suction hose as in some other

designs). This overcomes the problem of animals being

trapped in a film of water on the inside of the hose.

The apparatus may thus be used to sample damp foliage. 61

vi. The collecting head limits the area to be sampled.

(No separate cylinder has to be carried for this purpose.)

vii. As mentioned above, some sieving-out of coarse debris

is possible while the sample is being taken, if the coarse-

meshed sieve bag is used.

Variations in the efficiency of the vacuum sampling could be

due to the following: .

1. Behaviour differences

a. Between the different species

b. Between the different age groups or sexes

c. Due to weather and microclimate variation

d. Due to diurnal rhythms.

e. Due to seasonal differences .

2. Mechanical reasons.

In the case of Collembola most of the behavioural

differences would be manifested in a change in the vertical or

horizontal distribution of the animals. Firstly, considering the

behavioural differences between the species: the proportion of, for

example, the Lepidocyrtus lignorum population occuring in the deeper

litter layer is greater than that of Entomobrya nicoleti. Thus the

proportions of the populations taken by vacuum sampling and coring

will be different for the two species, and the methods cannot be

expected to be equally efficient.

As metamorphosis is wanting in the Collembola, drastic

changes in behaviour due to age or stage are absent. However, there is a slight tendency for a larger proportion of the very young 62

instars to occur in the lower layers, This

phenomena has also been remarked on for Isotoma viridis by Milne

(1962). This might cause a slight underestimate of the first and

second instars if the coring and Tullgren extraction is slightly less

efficient than the vacuum method.

Efficiency changes due to behaviour in response to weather

and microclimatic conditions are important. As is well known the

animals are noticeably sensitive to alterations in humidity and

substrate moisture. Short term vertical migrations can occur

daily as the humidity drops and the litter dries locally (also reported

by Davies (1928), Voltz (1934), Gisin (1948) and Kuhnelt (1961). Jens en

and Corbin (1966) found evidence of daily rhythms in Isotoma viridis

which were thought to be due to the animals avoiding hot or dry

conditions.

Thus in the present study the same population sampled in early

morning when there was dew, and again at noon when the top layer of

herbage had become dry, produced quite different population estimates.

Another example was that sampling on 9th June 1970, after 18 days of

coke^ drought, andAthe vegetation and top litter were very dry, gave an

apparent density for Entomobrya nicoleti only half that of the next day

after rain had dampened the substrate and humidity was therefore high,

This difference was not due to hatching, as an

analysis of the age-structure showed, but simply to variation in

sampling efficiency due to microclimate.

There may also be longer term migrations in response to

seasonal variation and weather e. g. downward migraton due to low temperatures (Voltz (1934), Blake (1931) and Strenzke (1949). Also 63

Kuhnelt (1961) mentions Tomocerus sp. migrating into the soil during

spells of dry weather, and even Entomobrya spp, which are more

resistant to low humidity, migrated under plant roots and into the

litter.

Heavy rain or thawing snow may also affect behaviour. Some

species, e. g. Entomobrya seem to avoid free surface water (also

noted by South (1959). Okely (1965) observed in Tomocerus longicornis

that collection was more difficult under very wet conditions as the

animals seemed less available. Joosse (1965) has shown activity

differences occur in several surface-dwelling Collembola after heavy

rain.

Thus it was imperative to standardise microclimatic conditions

under which sampling took place. The sampling was only carried out

on occasions where the humidity at the level of the top of the vegetation '

was 100% R. H. or very near to this, but without the vegetation being

extremely wet. Other extremes of weather, e. g. very hot, very cold

or windy days, and days just after heavy rain, were avoided if possible

as sampling occasions.

Daily cycles in response to microclimate have been mentioned

above, but there might also be behavioural cycles due to light/dark

periods, i. e. true diurnal rhythms. Joosse (1965) showed that some

surface-dwelling Collembola have distinct activity peaks in either day

or night time. Jensen and Corbin (1966) found highest numbers of

Isotoma viridis under cryptozoa boards in the morning and late

afternoon and lowest numbers in early afternoon. To eliminate any

such effects sampling was always undertaken at the same time of day, viz, in the early afternoon. It would perhaps have been better to 64

sample in early morning or late evening when the humidity was more likely to be ideal, but this was inconvenient.

Considering variations in sampling efficiency which are due to mechanical reasons: wet herbage may cause the Collembola to become trapped in water films on the collecting head of the "D-vac", but this can be checked. The design of the "D-vac" is particularly good in this respect as the collecting bag is at the front end of the hose, and the animals cannot become trapped on the hose wall as in some other designs. The main difficulty remaining is the tendency for the animals to become trapped in surface-water films either on the vegetation while they are being sucked up, or on the debris in the collecting bag. This latter would interfere with the sorting out of the catch which depends partly on the Collembola moving out from the debris where they can be pooted up. This difficulty was another reason for not sampling if the

vegetation was very wet.

Another mechanical cause of sampling efficiency was due to the

height and nature of the vegetation. For example, in some of the Holcus

stands the grass was fairly long and tended to get flattened over when the

year's growth was finished, thus forming swathes of dead grass which

over-lay the litter layer and probably tended to form a barrier to the

extraction of the Collembola. This situation contrasted. with some

areas of Agrostis which were short stemmed, and did not have an

accumulation of dead material shielding the litter. Thus the stage of

growth and species of grass influenced the ease of extraction of the

Collembola. In this context the Dietrick machine was found to be superior

to two other models which were of the narrow hose type. One was an 65

electrically driven vacuum machine as described in South (1959) and

the other a petrol driven trolley-mounted machine mentioned in

Southwood (1966). On 2nd October 1969 an experiment was performed

to test the efficiency of the "D-vac" machine and the electric, narrow

hosed type. Samples were taken randomly with both machines and

the results compared by an analysis of variance. A two-factor with

replication design was adopted. The factors (methods and species)

were assumed to exert fixed or systematic effects. The "D-vac"

method was significantly better than the electrically powered machine

(see table p.66). The reasons for the better performance of the

Dietrick model were perhaps that firstly, the area to be sampled is

delimited by the collecting-head itself and the vegetation was raised

upright and drawn into the mouth by the suction tube as this was lowered

onto the vegetation, c. f, the electric, narrow-hosed design which entailed

a separate ring to delimit the sampling unit and this tended to press the'

grass over on the denser stands. The separate ring also tended to

remain "perched" up above the surface of the litter in the very dense

stands and thus there was a distinct possibility of animals being able to

escape out below the sampling ring. The greater suction force exerted

by. the "D-vac" may also have contributed to its greater efficiency,

however, the "Dtvac" also produced better results when compared

with the petrol driven narrow-hosed vacuum machine which had a very

powerful suction.

Some of the above considerations also affected the choice of

sample unit size (area). Originally, it was intended to use a 4-inch

diameter unit (which would have matched the diameter of the soil cores).

However, this small sample unit was found to be inefficient compared to the square foot size (see table p.T), particularly for Entomobrya. Comparison of the Dietrick vacuum sampler and the Electric vacuum sampler by an Analysis of Variance (two Factors with equal numbers of Replicates) for E. nicoleti, T. longicornis and L. lignorum.

Occasion 2.10.69 Data transformed = Logic, (n + 1) Rows = Sampling methods = 2 NB. Methods or species were assumed to Columns = Species = 3 exert systematic effects, while replicates Layers = Replicates = 9 assumed to exert random effects.

ROW MEANS: Dietrick Sampler 1.7379; Electric Sampler .9628;

COLUMN MEANS: E. nicoleti 1.6593; T. longicornis .7228; L. lignorum 1.6689.

Source of Sum of Degrees of Mean Variance variance sauares freedom square ratio

Rows .81097029E+01 1 .81097029E+01 53.32 Columns .10631688E+02 2 .53158438E+01 34.95 * * * RXC .38434762E+00 2 .19217381E+00 1.26 Residual .73002981E+01 48 .15208954E+00

Total .26426036E+02 53

Between Rows LSD at P=0.05 = .2134 Between Columns LSD at P=0.05 = .2614. LSD at P=0.01 = .2847 LSD at P=0.01 = .3487

Ci\ 67

On the first occasion (26th January 1970) the Dietrick square foot sample-unit size was appreciably better than the Dietrick with

4-inch diameter sample-unit size when three species were considered.

However, on 2nd March 1970 when the experiment was repeated the methods were not significantly different from each other in the analysis of variance considering all four species. However a U-test showed that the methods were significantly different for Entomobrya nicoleti but not for Lepidocyrtus and Isotoma. Also note that the methods x species interaction was quite significant on both occasions.

The explanation of the difference in sampling efficiency between the species is due, perhaps, to the escape of the active Entomobrya nicoleti from the smaller unit. Lepidocyrtus and Isotoma are perhaps not quite so active and also tend to live deeper in the profile and thus the greater suction of the smaller nozzle might be more efficient for these species.

Generally speaking, the smaller unit was less satisfactory due to the fact that its diameter was less than the average length of the

grass stems in the higher stands, thus the grass was pushed over and this hindered extraction from the lower layers. The square foot size

was, on the other hand, of large enough diameter to allow the vegetation to be raised during collection. Another reason for the rejection of the four-inch diameter unit was that it produced a more powerful suction

as mentioned above, which combined with the small area being sampled

probably led to the area actually sampled being slightly larger than the

intended 4-inch diameter circle, due to the suction force being exerted

outside this area. With a larger sample unit size this effect becomes Comparison of the efficiencies of the Dietrick Vacuum sample with a) square-foot sample unit b) 4. inch diameter sample unit, by an analysis of variance (2 factors with equal numbers of replicates).

EXPERIMENT I. Occasion 26.1.70 Rows = 2 (Difference in sample unit size; Assumed systematic in effects) Column = 3 (Species; Assumed systematic in effects) Layers = 7 (Replicates; Assumed random in effects) Data transformed Log.10 (n + 1)

ROW MEANS: Square-foot sample unit .7236; 4 inch-diameter sample unit .3540.

COLUMN MEANS: E. nicoleti .5299; T. longicornis .2053; L. lignorum .8812.

Source of Sum of Degrees of Mean Variance variance squares freedom square ratio

Rows .14338007E+01 1 .14338007E+01 25.00 Columns .31994921E+01 2 .15997461E+01 27.90 RXC .66998819E+00 2 .33499409E+00 5.84 Residual .20643050E+01 36 .57341804E-01

Total .73675859E+01 41

Between Rows LSD at P=0.05 = .1499 Between Columns LSD at P=0.05 = .1836 LSD at P=0.01 = .2010 LSD at P=0.01 = .2461

00 EXPERIMENT II. Occasion = 2.3.70. As above but with: Columns = Species = 4 e Layers = Replicates = 6

ROW MEANS: Square-foot sample unit .5711; 4 inch:diameter sample unit .4944. COLUMN MEANS: E. nicoleti .8413; T. longicornis .1446; L. lignorum .7477; I. viridis .3974.

Source of Sum of Degrees of Mean Variance variance squares freedom souare ratio P

Rows .70692532E-01 1 .70692532E-01 .86 =11 Columns .37249253E+01 3 .12416418E+01 15.13 *** RXC .13118446E+01 3 .43728154E+00 5.33 Residual .32831395E+01 40 .82078489E-01

Total .83906019E+01 47

Between Rows LSD at P=0.05 = .1672 Between Columns LSD at P=0.05 = .2364. LSD at P=0.01 = .2237 LSD at P=0.01 = .3163

Mann-Whitney tiltest for difference in medians of sample

Species Test criterion Significance Species Test criterion Significance

E. nicoleti 0 Significant difference lignorum 13 Not significant T. longicornis 15 Not significant 15 Not significant I. viridis k0 (but very small numbers 7 . less important. Thirdly, the small unit was rejected as the patchy distribution of the species led to the sample variance being very high with the small unit and to attain a reasonable precision given this high variance would have meant a prohibitively high sampling cost.

The larger unit of course reduced the variance and thus the cost of sampling, even allowing for the extra time needed for counting the larger number of individuals in each unit. Another consideration is, of course, the differing densities of the species involved. There must be a compromise here. The small samples although perhaps producing adequate number per samples, of, say,

Entomobrya, would result in large numbers of empty units for Tomocerus.

Lastly, as can be seen from table p.71, the 4-inch diameter unit was less satisfactory as it was much more affected by the wetness of the vegetation than was the square foot size unit.

Now considering the other potential sampling methods:

For certain habitats and with Collembola of marked epigeal habit, e. g. the Sminthuridae, the taking of cores of vegetation and dislodging of the

Collembola by tapping the corer has been used, e. g. Wallace (1967).

For animals that have a tendency to migrate into the litter this method would have to be augmented by Tullgren extraction. It seems a useful

combination as it would ensure that the active surface-dwelling Collembola

would not be crushed during transport of cores to the laboratory and would

not escape out of the Tullgrens during extraction. Here however the

removal of the Collembola on the surface was accomplished already by

the suction sampling. 71

Differences in the efficiency of Dietrick square-foot samples and 4" diameter samples with the wetness of the vegetation (for E.nicoleti).

Square foot 4" dia samples: Square foot Date Wetness of samples: Numbers of numbers Vegetation Numbers of E. nicoleti E. nicoleti " dia numbers

9.6.70 Very dry 35.80 17.19 2.08 2.3.70 Slightly damp 163.33 34.38 4.75 28.10.70 Wet 323.57 52.71 6.14 20.8.70 Wet 481.25 68.75 6.99

26.1.70 Very wet 85.40 9.79 8.77

5.75 Average 72

Coring and Tullgren extraction

This method was used here as a backup to the vacuum method, to sample the residual part of the population in the litter layer. The

size of the corer was determined by the apparatus available. On the first few sampling occasions a wire-worm corer was used. However this design was obviously unsuitable as it used a plunger to force the

core out from the tube. As most of the Collembola will be in the

upper layers of the core, compression losses due to the plunger action

would be great (Haarlov 1960). Thus another corer was constructed.

It was similar in design to that of MacFadyen (1961) but with two inner

rings made of "Tufnol" instead of "Bakerlite", and provided with a

T-shaped handle jointed to the top of the metal outer tube. The rings

were particularly important for avoiding compression and preserving

the core structure intact during transportation to the laboratory in the

plastic bags. Efficiency of the two types of corer were compared by /

taking pairs of cores, the members of each pair were located adjacent

to one another and taken with the two types of corer. The cores were

extracted by Tullgren funnel and the numbers for each Collembolan

group obtained with each coring implement compared by the analysis of

variance (see Table p.73-4).The new corer is significantly better than

the wire-worm corer, both for Collembola as a whole, and the four

species studied here in particular. It was important to rotate the

corer into the litter with the minimum of downward pressure rather

than force it in vertically; thus the vertical compression on the litter

is kept to a minimum. It follows from this that the cutting edge of the

corer must be kept razor sharp so that it slices cleanly through the Comparison of Coring Methods (Wireworm Corer V Macfadyen Corer) by an analysis of variance (two factors with equal numbers of replicates)

Occasion: 24. June 1970 NB. Methods or species assumed to Rows = Methods = 2 exert systematic effects. Columns = Species or Groups = 8 Replicates exert random effects. Layer = Replicates = 10 Data Transformed = Logic) (n + 1)

Row Means: Wireworm Corer .6466; Macfadyen Corer 1.0547.

Column Means: E. nicoleti 1.2262; T. longicornis .0903; L. lignorum 1.4164; Isotomids 1.6068; Folsomia sp. .8190; Onychiurus 'sp. 1.0322; Hypogastrurids .3768; Sminthurids .2378.

Source of Sum of Degrees of Mean Variance Significance Variance Squares Freedom Square Ratio Level Rows .66605283E+01 1 .66605283E+01 38.25 * * * Columns .44901292E+02 7 .64144703E+01 36.84. * * * RXC .21270430E+01 7 .30386328E+00 "1.74 Residual .25076023E+02 144 .17413905E+00

Total .78764.886E+02 • 159

Between Rows Between Columns LSD at P=0.05 = 434 LSD at P=0.05 = .2608 LSD at P=0.01 = .1722 LSD at P=0.01 = .3445 N

As above but Considering 4 Species Only (i.e.4 columns. )

Row Means:- Wireworm Corer .8985; Macfadyen Corer 1.2713.

Column Means: E. micoleti 1.2262; T. longicornis .0903; L. lignonum 1.4164; Isotomils1.6068.

Source of Sum of Degrees of Mean Variance P Ratio Variance Squares Freedom Square •MM

Rows .27800479E+01 1 .27800479E+01 24.57 Columns .27830168E+02 3 .92767226E+01 82.00 RXC .11627291E+01 3 .38757637E+00 3.43 Residual .81454906E+01 72 .1/313181E+00

Total .39918435E+02 79

Between Rows Between Columns

LSD at P=0.05 = .1499 LSD at P=0.05 = .2120

LSD at P=0.01 = .1990 LSD at P=0.01 = .2814. 75

stems of the vegetation and the litter. Hughes (1954) showed that an

only slightly blunt corer could considerably reduce the number of

animals obtained.

The funnels used have been described above. This design was

chiefly used because of availability and it could probably be improved

upon. However this would have required considerable experimentation

and was not thought justifiable for what was only an accessory sampling

method. The percentages of the four species which were collected by

the funnel method as opposed to the vacuum method were calculated.

It was seen that at some times of the year very large numbers of small would individuals in the coresAmean that the vacuum method alone would miss

a considerable portion of the younger instars, particularly in Lepidocyrtus.

There is the possibility that the vacuum method could be dispensed

with and coring alone used. Comparing vacuum estimations of the

population with those from cores not taken under the vacuum samples,

i. e. coring alone, it was found that for Entomobrya particularly the

vacuum method could not be dispensed with, see page 76 .

The efficiency of the funnel method in sampling the residual

population was not known. An attempt to check the funnel extraction

by flotation of the cores after extraction probably did not produce very

reliable results due to the unsuitability of the flotation method for this

soil type (see b elow). Very few additional animals were obtained from

these funnel extracted cores however.

Incidentally, a line of approach little studied is the use of

chemical expellants in funnels for Collembola, Murphy (1962).

Tamura (1967) obtained larger numbers of Tomocerus sp. using paradichlorobenzene as an expellant in the funnel than with heat from 76

Comparison of numbers s• . ft. of 4 species of surface-dwellin

Collembola obtained by a) "D-vac" suction and b) Coring and

Tull gren extraction. (Cores not taken below suction samples)

SAMPLING DATE -E. nicoleti T. longicornis L. lignorum I. viridis

2/April/70 93.25 2.25 40.00 6.25 8.59 6.68 553.86 8.59

15/Sept./70 498.38 3.50 254.25 26.88 103.13 3.44 304.81 37.82

24/Nov. /70 170.73 4.89 30.89 19.84 10.31 10.31 48.13 5.73

9/4larch/71 111.50 7.30 32.45 11.20 26.36 6.88 311.69 9.17

15/June /71 181.87 8.26 220.13 62.13 123.76 16.04 236.06 51.56

Upper Figures = Suction sample averages 77

electric light bulbs (although for Collembola in general the chemical method was less efficient).

Flotation

There was a possihility that the soil cores would be better extracted by flotation methods, and an experiment was undertaken to test this. The method used was similar to that of Salt and Rollick

(1944) with the addition of Raw's (1955) differential wetting technique using benzene. However the numbers of surface-dwelling Collembola obtained were very low compared to those from the Tuligren extraction.

Wood (1965) has compared Salt and Hollick/Raw flotation with a funnel method, (the funnel design was fairly similar, though a smaller dimension, to that used in the present study). He found that for

Entombyra nivalis the funnel method was significantly more efficient but for another Entombyra sp. , albocincta, the flotation method was clearly superior. There was also a significant method x sites interaction for this species, with lower numbers of gleyed, podsolic brown earths with a surface accumulation of organic matter. It was noticed in the present study that Entombrya albocincta was fairly common in the cores but rarely taken in the suction samples. Thus it may live deeper in the profile in grass-lands than other Entombrya sp., and this may possibly account for its better extraction by flotation in Wood's study. Alternatively, the difference in extraction efficiency could be due to some behavioural difference between the Entombrya spp.

In the present study only the Onychiuridae were recovered in larger numbers by the flotation method as compared to the funnel method, and this agrees with the results of Edwards and Fletcher (1971) for this group of Collembola. 78

The major problem with the flotation method was that the large amount of vegetable material present in the samples Meant that the method relied heavily on the interface technique which did not completely overcome the problem of separating these small animals from the plant matter. Hale's (1964) flotation technique may have been more appropriate for the conditions encountered here as it was designed for soils with high organic content. Hale added Lepidocyrtus lanuginosus to manufactured peat and obtained 100% extraction by his method, although this procedure probably would not mimic extraction from the natural substrate. Ancither surface-dwelling Collembolan Isotoma viridis was extracted very much better by Hale's method than with a high-gradient canister method. This could have been due to escape from canisters before and during the extraction process, or to the animal's size preventing it from passing through the grid in the canister extractor.

Hale's technique would not be very suitable for the large amounts of soil which would need to be processed in a study with large sample

unit size and many replicates, unless a specially-made apparatus was constructed. Edwards and Fletcher (1971) have recently made a thorough study of the extraction methods for terrestrial arthropods.

Their results indicate funnel methods are superior flotation for the

Isotomidae in pasture as well as in woodlands and fallow habitats, and

in all soil types. For Entomobryids funnel extractors were generally best (except on clayloam pasture where Salt and Hollick flotation was

as good). On sandloam pasture which probably approximated to the

conditions of the present study, Tullgrens was significantly better than

flotation.

Perhaps more important than efficiency considerations, the flotation method was unattractive due to its high cost (especially if sub-samples were to be extracted separately as here), for what was only an accessory method. Flotation would have had to be very much more efficient than Tullgrens to justify this high cost. Thirdly, greater damage to the specimens results with the flotation method, and this hinders further examination of the animals for age-grouping, gut-content examination, etc.

Pitfall trapping

This method has many disadvantages when used for estimating populations (Southwood 1966). The method is not an absolute one and the area sampled is not precisely known. Thus, it is rather difficult to assess the efficiency of the method. Another serious disadvantage is that population size is only one of the components that influence the numbers trapped, and it is difficult to separate this from the complex behavioural and mechanical factors which influence the catch. Its low cost is the main

advantage. It should probably not be used for population estimates unless

no other suitable method is available. Pedigo (1970 a & b) has used pitfall

trapping to study season activity and abundance in surface-dwelling

collembola, but it was not clear how the effects of these two factors were

separated from each other. Pitfall trapping can be used with caution for

obtaining information on diurnal cycles of activity or short-term activity

variations due to weather. Joosse (1965) studied activity in some

surface-dwelling collembolan species using pitfalls, and Joosse (1965) and

Joosse and Kapteijn (1968) have analysed the effect on the catch of factors

such as design of trap, weather, and mechanical disturbance in the field.

Mellanby (1962) points out that sticky traps laid on the ground catch

many Collembola. However, this method is subject to similar

disadvantages to pitfall trapping for population studies. 80

Cryptozoa boards

Cryptozoa boards suffer from many of the disadvantages of pitfall traps e. g. difficulty in relating sample to a definite arealand in testing the efficiency of the method. Cole (1946) has studied animals

under boards and found that the method did not satisfactorily sample

the surface-dwelling collembolan species (Entomobrya sp. Tomocerus

sp. and Isotoma viridis). These were characteristic of the "cryptozoa

niche" but tended to migrate downward under unfavourable conditions

e. g. drought and low temperatures.

In the present study, five 1 sq. ft. wooden boards were used.

The area under the boards had the vegetation cut down to litter level.

Only the Collembola on the underside of the boards were counted, not

those on the sub-strate under the board. The numbers obtained varied

rather erratically even over short periods of time. It was noticed that / during dry spells very few or no animals were obtained under some

boards, migration presumably having taken place (c. f. Cole 1946).

The boards also seem to have a qualatative effect on the fauna. Some

Collembola e. g. Hypogastrura sp. being much more commonly found

under the boards than in comparable areas of the surface litter sampled

by vacuum machine. The use of the cyptozoa boards in this study was

therefore discontinued after a few weeks. As with pitfall traps

cryptozoa boards have been found useful for purposes other than

estimating the population. Jensen and Corbin (1966) showed that Isotoma

viridis had diurnal cycles of numbers caught under the boards. During

the period 0800-1500 hrs., low numbers were found in early afternoon

and the highest numbers in morning or late afternoon. They could rot

demonstrate any clear relationship between the presence of Isotoma viridis under the boards and the temperature or relative himidity

(or S. D.) beneath the boards. They also noted incidentally, that white boards caught more I. viridis than black boards, and also that the numbers appeared to be related to the perimeter size of the board rather than its area. Obviously the effect of cryptozoa boards on the collembolan fauna is rather complex.

Hand collection (by removing litter and debris from a quadrat, sieving

this onto a tray and picking up the Collembola by

pooter).

This method was found useful as a collectors method and produced less dimaged animals than suction sampling which tended to remove bristles and scales from the animal's body. Hand collection was therefore employed in obtaining animals for taxonomic or culture purposes. The method was generally much less efficient than sampling with the "D-vac" especially on areas with short grass and a sparce accumulation of litter.

Sweeping.

This method has been used by Gisin (1948) for collecting surface- dwelling Collembola but appears to be most effective under very humid conditions in the evening. Even under these conditions it proved to be comparatively inefficient, and of course did not give absolute estimates of the population. Turnbull (1966) found in sampling a pasture fauna that the Collembola formed 16. 5% of the total arthropod catch if a Dietrick suchtion sampler was used, but only 0. 3% of the catch if sweeping was employed.

Soil embedding and sectioning

This method has not been attempted here, but a gelatine- embedding technique like that described by Anderson and Healey (1970) 82

would give much useful information about the precise position of the animals in the soil/litter profile, as well as estimates of the population density. The method could thus throw some light on the precise niches occupied in different stages of the life-cycle or different seasons of the year and also give information on vertical migrations. The major disadvantage of .the method would seem to be its high cost if enough replicates are to be taken on each sampling occasion to make the study quantitatively accurate. The technique might also be rather more difficult in a habitat with a large amount of vegetation ground cover.

Capture-mark-recapture methods

Methods of marking animals and estimating the population by various capture-recapture procedures are reviewed in Southwood

(1966). To my best knowledge, these methods have not been used for collembolan populations. This is perhaps not surprising due to the practical difficulties. Firstly, there is the absence of suitable marking methods for these small and delicate animals. External marks are not practical due to the frequent moults undergone throughout the life of the animals. Marking by feeding with a dye (such as neutral red) is of limited use due to its tendency to be masked by food in the gut, and its short duration in the body (Reynolds unpublished). Radioisotopes in sufficient quantity to produce a durable mark -appear to have harmful effects (on Folsomia candida at least) (Reynolds unpublished). The mark must also be retained during the extra time required for extraction if funnel methods are to be used. The mark may also have to be durable in a collecting medium for Tullgren funnels, or alternatively in the various liquids used in flotation extraction. Secondly, due to the nature of the habitat and the locomotory powers of the animals the 83

assumptions of the marked animals becoming mixed with the population may not be fulfilled. Migration down to the deeper layers of the profile may mean the chance of recapture is not the same for each member of the population. Lastly, methods involving a series of recaptures do not seem very feasible for collernbola due to their delicate nature and the methods used in sampling and extracting them. 84

STAGE-GROUPING OF SAMPLES

Two sub-samples of Entomobrya nicoleti and Tomocerus longicornis were taken, one from the suction samples and one from the core samples, for each sampling occasion. The proportion of animals in each of the various stages in these sub-samples was determined.

For Entomobrya nicoleti this was accomplished by examination of the trochanteral organ (South, 1961). The procedure used was to pull the abdomen and the anterior of the thorax away with fine forceps, leaving a ring of metathoracic tissue with both hind legs attached. This was mounted on a slide in polyvinyl lactophenol, and examined under oil immersion. Nearly all the stages of development of the trochanteral organ were readily referable to South's illustrations, and if the trochanteral organ of one leg was aberrant or damaged the other leg could easily be examined. The first and second instars were not separated as this involved the use of the retinaculum which was not easily visible. As noted by South (1959) the trochanteral organ in the mature instars does not increase in complexity by easily predictable increments. Thus the mature instars were lumped into the following categories:

young adults, this approximates to instar 6.

mature adults (1), this category holds animals which are

trochanteral organ development up to Fig. 56 of South (1961).

mature adults (2), older instars with the trochanteral organ

like that of Fig. 56 of South or more complex, i. e.

approximately instar 14 and upwards. 85

As no method of distinguishing the instars of Tomocerus longicornis qualitatively was available, it was decided to stage group this species by size. Measurements were made on various parts of the body, but head-capsule diameter seemed the most reliable measure of general size. The heads of the animals were removed and mounted in lactic acid on a slide and measured with a micrometer eyepiece.

The resulting size categories were defined after a subjective examination of the size frequency curves for the total number of animals measured.

Thus the size groups are rather arbitrary, but they may have a resemblance to the instars, at least in the smallest size groups, as they correspond to the sizes of animals of known instar obtained from laboratory cultures, to a certain extent. 86

COLLECTION

The four species of Collembola studied here were collected for culturing by shaking litter over a tray and pooting the Collembola into tubes with a dampened plaster of paris/charcoal base to them. The method was most efficient when the litter was fairly damp, for example after a shower of rain, as then the Collembola tend to be more numerous in the surface layers. This method was preferable to vacuum methods for collecting Collembola for culture or taxonomic purposes as it resulted in less damage to the specimens than collection by the suction apparatus used in the main sampling programme. 87

CULTURING

The Collembola of all four species were cultured using vivaria with a mixture of plaster of paris and powdered charcoal in the bottom, similar to those of Goto (1960). The majority of culture vessels were of heat-resistant monax glass and of dimensions 4 cm dia. x 3.1 cm high, and are fitted with corks. For the rearing of egg batches and very young instars especially of Entomobrya and Lepidocyrtus smaller containers were more convenient. These were of dimensions 2.25 crn dia. and 2. 6 cm high with plastic push-on caps. A few cultures, for example, those with large numbers of Collembola, or containing a large sized species, e. g. Tomocerus longicornis were kept in jars with plastic, screw-on tops of dimensions 10. 5cm dia. and 8 cm high. Small holes were drilled in the lids of these jars to reduce condensation from the walls of the jar. Fungistatic agents were not employed, but contaminant fungi were removed daily and the vivaria changed frequently as contamination by the animals faeces soon built up in the cultures if this was not done. It was important for the health of the cultures to ensure that plaster of paris/charcoal base did not become too wet. If free water was present the Collembola generally avoided it. South (1959) also noted that free water in cultures tended to affect Entomobrya adversely. Joosse (1970) and Boyd (1967) note that Entomobrya prefers a lower humidity than the 100 per cent R. H. Excessive dampness in culture also promoted fungal growth and this tended to trap the young instars of Collembola. 88

All four species of Collembola appeared to thrive on

Cladosporium herbarum, cultured on potato-dextrose agar,(p.d.a.)

This fungi was used as food for Entomobrya by South (1959).

The fungus-can be stored on the p,d.a. in petri dishes at low temperature for a considerable time until required. This fungus was also suitable as it formed a thick mycelium which meant that it produced a large amount of material for the animals to eat. It was also suitable because it did not produce large numbers of conidiophores, or mycelial threads which might ramify over the culture vessel; both of these tend to entangle young Collembola and prevented the use of some other fungal species as food in cultures. Mills and Sinha (1971) found that out of forty-three species of fungi and actinomycetes tested, a

Cladosporium sp. was one of the four best for culturing the Collembolan.

Hypogostrua tullbergi.

Cladosporium herbarum is a common primary coloniser on the senescing tissues of a large range of plants (see Hudson 196 ), and thus would be a species commonly encountered by surface-living Collembola in the field. 89

SEASONAL ABUNDANCE OF THE FOUR SPECIES OF

SURFACE-DWELLING COLLEMBOLA

The mean densities per square foot sample throughout the sampling period for the four species studied are given on pp.92-93, and graphed on pages 97-101 . These figures were obtained by adding together the mean densities for the vacuum and core samples for each sampling occasion. Reference will be made below to the numbers of E. nicoleti in the vacuum samples alone, so these densities are given on pages 94-95.

The dynamics of the E. nicoleti population is the subject of detailed analysis in later sections, but some general remarks concerning the seasonal abundance of all of the species will be made here.

Firstly, concerning the accuracy of the sampling; it will be

shown in a later section that the negative binomial distribution is a good

empirical model of the dispersion of the species (with the exception of

some occasions with a very small number per sample-unit for

T. longicornis). The number of samples required to give an accuracy

of 10% and 20% were calculated using the sample mean and negative

binomial k parameter, and are shown on p.96.

Ten sample units were usually taken, and this gives an accuracy

of approximately 20% for E. nicoleti and L. 1 ignorum. I. viridis,

being more clumped in its distribution, would require about twenty

sample-units per occasion to achieve similar accuracy. Fifteen or

twenty sample-units were taken on some occasions, but it must be

admitted that sampling errors could be fairly large with I. viridis

and T. longicornis. 90

The actual population trends do not seem particularly erratic in I. viridis, however, but for T. longicornis in 1970 the estimates do appear to be erratic from one occasion to the next (p. 100 ).

This is probably due to the use ot the small sample-unit on some occasions in 1970, and this was not of sufficient size to obtain good estimates for this species. The large (square-foot) unit was used out throughi‘1971 and here the estimates are probably more reliable.

Another comment on the population estimates concerns the

December 1969 and January 1970 occasions for E. nicoleti. On these occasions numbers had apparently declined, but are seen to rise again on 11 February. As no recruitment was occurring during this period, the depression of numbers is probably due to the cold weather causing downward migration of the Collembola, thus making them less available to the vacuum sampler. It was not fully appreciated at this time in the study that the effect of weather could seriously distort the estimates of abundance due to alterations in the efficiency of sampling. This factor was taken into account during the rest of the study.

The population dynamics of the four species showed some similarity.

During mid-winter populations were low and either had remained fairly constant from the year before or had declined in numbers.

Population increases during April due to emergence of the hatchling occurred in all the species. The population increase in all of the species was hampered by a period of severe drought in June 1970, when the soil moisture level dropped to its lowest value during the study (9. 6%). The effect ot the drought was not surprisingly more obvious in the suction samplel than in the cores. For example, the 91 numbers of E. nicoleti in the suction samples declined in May and early June 1970, but the increase in numbers in the cores compensated for this. In the other three species, possibly because they are less resistant to desiccation than E. nicoleti, the overall population showed a decline. Numbers in all species rose dramatically again after the end of the drought on 10 June, and reached peak numbers in late June and early July.

Numbers declined generally in Autumn 1970, probably due to falling temperatures, although periods of dry conditions at the beginning of September and in late October may have hastened this decline. In the following year (1971) numbers appeared to start to increase in February1and in E. nicoleti 1st and 2nd instars showed an increase in this month.

This early emergence in February 1971 was probably associated with a rise in mean temperature, but the rate of recruitment was not maintained however as temperatures were very low in March.

Populations of L. lignorum and I. -viridis showed peaks in April in 1971, but these were lower than the corresponding peaks in the early months of 1971. The only reason for the difference in size of the emergence peak between 1970 and 1971,that occurs to rne )is that emergence in 1970 was synchronised by the preceding drought, but the matter is still open.

In spite of some decrease in rate of population build-up in

September 1971 (perhaps due to drought conditions) numbers in late

Autumn were high in E. nicoleti, I. viridis and T. longicornis. This was probably due to the mild conditions at this time allowing reproduction to continue for longer than normal.

Numbers of L. lignorum decreased in late Autumn unlike the other three species; the reason for this is not known. 92

Numbers er sauare-foot area of four species of surface-

dwelling Collembola (Combined numbers from vacuum and core samples) . . t • SAMPLING DATE E. nicoleti T. longicornis L. lignorum I. viridis 2nd Oct. 69 161.97 18.62 217.09 - 19th Nov. 69 154.07 28.65 • 276.16 - 3rd Dec. 69 268.81 18.61 173.43 - 18th Dec. 69 57.39 15.32 91.74 26.74

26th Jan. 70 100.82 11.20 145.25 25.40 11th Feb. 70 241.76 5.22 159.95 22.70 2nd March 70 174.89 7.64 261.83 17.92 2nd April 70 122.16 8.93 593.86 14.84 14th April 70 170.40 10.86 616.67 31.63 28th April 70 260.65 33.23 660.04 64.17 12th May 70 248.11 1.10 496.57 9.94 10th June 70 354.75 1.00 191.90 20.96 23rd June 70 950.53 6.04 858.67 74.00 8th July 70 1303.106 22.39 a- 1828.50 250.15 23rd July 70 694.02 12.22 528.26 78.69 20th Aug. 70 537.51 13.96 381.26 116.42 3rd Sept. 70 379.38 2.0 252.30 54.09 15th Sept. 70 601.51 6.94 559.06 • 64.69 29th Sept. 7o 263.25 15.83 323.04 75.32 16th Oct. 7o 132.05 3.76 90.10 20.28 28th Oct. 70 241.91 10.15 . 160.38 30.44 24th Nov. 70 181.05 15.21 79.02 25.57 , I 93

TABLE Continued.

• . SAMPLING DATE E. nicoleti T. lonr;icornis L. lignorum I. viridis 12th Jan. 71 198.82 6.74 196.05 18.55 9th Feb. 71 233.51 14.18 271.45 26.21 9th March 71 137.86 14.18 344.14 20.37 7th April 71 224.91 21.93. 627.55 37.43 24th April 71 201.81 16.56 613.01 164.81 16th May 71 245.36 14.07 441.96 146.97 15th June 71 305.62 24.31 456.19 113.70 24th July 71 420.44 4.35 386.95 25.81 14th Aug. 71 448.46 6.38 361.24 9.65 21st Sept. 71 461.94 10.47 326.13 22.23 15th Oct. 71 574.50 25.718 279.56 36.28 15th Nov. 71 619.13 27.318 245.32 70.78 13th Dec. 71 656.36 23.97 85.40 50.11 Feb. 72 390.85 11.90 284.31 21.66 94

Numbers of E. nicoleti per square-foot sample

Sampling Suction Soil - Core Occasion Samples Samples

2 Oct 69 100.75 61.22 19 Nov 69 138.66 15.42 3 Dec 69 259.40 9.41 18 Dec 69 54.43 2.96

26 Jan 70 85.40 15.42 11 Feb 70 228.10 13.66 2 March 70 163.33 11.56 2 April 70 93.25 28.91 14. April 70 89.00 169.51 28 April 70 118.03 14.2.63 12 May 70 79.50 168.61 10 June 70 69.50 285.25 23 June 70 428.00 522.53 8 July 70 552.12 750.97' / 23 July 70 436.18 257.83 20 Aug 70 481.25 56.25 3 Sept 70 290.00 89.38 15 Sept 70 498.38 103.13 29 Sept 70 232.31 30.194 16 Oct 70 108.50 23.55 28 Oct 70 223.57 18.33 24. Nov 70 170.74 10.31

12 Jan 71 162.15 36.67 9 Feb 71 202.00 31.51 9 March 71 111.50 26.36 7 April 71 136.67 88.24 24. April 71 107.50 94.31 16 May 71 141.13 104.23 15 June 71 181.87 123.76 24 July 71 289.30 131.14

/Cont'd.. 95

14. Aug 71 321.27 127.19 21 Sept 71 221.50 24.0.4.3 15 Oct 71 454.20 120.30 15 Nov 71 516.00 103.13 13 Dec 71 572.90 83.46 14. Feb 72 362.20 28.65

96

Calculation of the Number of Dietrick Vacuum Samples required to obtain a given accuracy, assuming a Negative Binomial distribution.

Southwood (1966) (quoting Rojas [1964]) gives the following relationship:

1 1 N = + - Where N = Number of samples x = Sample mean D2 k = Negative Binomial dispersion parameter D = Accuracy requiredias a decimal

1. Entomobrya nicoleti

Date of Goodness-of- 2 k N N sample fit to Negative when D=0.1 when D=0.2 Binomial 9.3.71 .70 - .50 111.50 2.2126 46.09 11.52 15.6.71 .80 - .70 181.86 3.9737 25.72 6.42 21.9.71 .5o - .3o 221.50 2.4080 41.98 10.49 14.2.72 .8o - .7o 244.30 1.6963 59.36 14.84,

2.Lepidocyrtus lignorum , 9.3.71 .90 - .80 32.45 2.464 43.66 10.92 15.6.71 .5o - .3o 220.13 2.717. 37.26 9.31 21.9.71 .80 - .70 32.60 4.725 24.33 6.06 14.2.72 74: .70 55.00 2.775 37.85 9.46

3.Isotoma viridis 9.3.71 .70 - .5o 11.20 1.234 89.96 22.49 15.6.71 .90 - .80 62.133 1.395 73.29 18.32 14.2.71 .90 - .80 14.40 0.711 147.57 36.89 NUMERS OF E. NICOLETI PER SQUARE — FOOT.

1 000

• z+■."/".'

't -4 • 4- 4- 4- -4- -+ - -4- - -t- ----+--r - -4-- -4------s-- +- -r - 4- - -+ - +- -I- +- -r- 4-- -+--4- -4- -+ -I■ 4.-- -44- 4■ z 1e 111 26 11 2 2 14 28.12 10 23 8 .23 20 3 15 29 16 28 24 12 9 9 724 16 15 2 4 ii4 21 15 . 15 13 . T. OCT NOV DEC JAN FEB .MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC JAN FEB 1971 . - , 1972 ' - 1569 . . 1970

NUMBERS OF E. NIC OLETI PER SQUARE. mar VACUUM • SAMPLE.

A/

- - - -I- - - -4- - -I- -+ - - +- - -+ -4- -/- -f-- -4- --f- 4-- 4- -+- -,.- -+ --- 2 10 19 3 18 26 11 2 15 2 14 28 12 10 23 8 23 20 3 15 29 16 28 24 12 9 9 7 24 16 15 24 14 21 15 15 15 14. OCT NOV DEC JAN FEB J1AR APR MAY JUN JUL AUG SEPT OCT NOV JAN FEB MAR APS MAY JUN JUL AUG SEPT OCT NOV DEC• JAN FEB

1969 1970 1971 1 972 2000 NM. NUMBERS OF L. LIGNORUM PER SQUARE—FOOT.

1000

0 --- —Z.* i -1.4 1§- 1.M" —lei —9- ---9 1- —16 A it Oct NOV DEC JAN FEB MAR APR IIAT JUN JUL AUG SEPT OCT NOV DEC JAN FEB MAR APR, MY JUN JUL AUG SEPT OCT NOV DLC JAN FEB 1 969 1 970 1 971 1972 NUMBERS OF T. LONGICORNIS PER SQUARE FOOT, .

t

30

20

10

0 • -4. - -1. ------• . _ -4.• 2 19 3 18 26 11 2 2 14 28 12 1023.8 .23 20 3 15.29 28-- _24 12- 9 9 7 24 16 15 24 14 21• 15 15 13 . 14- ocr NOW DEC JAN FEB MAR APR MAY JUN JUL AUG SEPT OC12 NOV ' JAN FUi MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC JAN FES 1969 1 970 1971 1972 NUMBERS OF I. VIRIDIS PER S QUAREFOOT.

200

150

100:

•••■• •• 4- - -+-- -4------I.- -.4. -4.- -4...- - - ../..”4.-....1•-•+••••-.-. 4.- .- +- -.I” -4.- ..- -4. - -4.• -.4 .,•• -*- -4.- --$- -F 4••••• 4- + .1- •■•• •4•••••• ••••■, .1... 26 11 2 2 14 28 12 10 23 8 23 20 3 15 29 16 a 24 12 9 9 7 24 16 15 24 14 21 15 15 13 14 JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC JAN FEB MAR .. APR MAY JUN JUL AUG SEPT OCT 'NOV DEC JAN FEB 1970 1971 1 972 102

ANALYSIS OF ACE-STRUCTURE: LIFE HISTORTTS IN THE FIELD

For E. hicoleti, a sub-sample was taken from the main sample on each-

occasion, and the individuals in the sub-sample stage-grouped as described

on p 84 . The numbers per square-foot in each of the stage-groups is

shown on p103 .

The post-embryonic E. nicoleti overwintered mainly as adults or sub- adults (4th and 5th instar), as with several other species of surface

Collembola (Joosse 1969 and South 1959). The numbers of 1st and 2nd

instars present in winter varied from year to year, as did the timing

of the increase in recruitment rate in the spring. For example, in the

winter of 1969/1970 no 1st or 2nd instars were present after November, and

none emerged until April in the following year. For the winter of

1970/1971, however, a few 1st and 2nd instars were present in January; and

further recruitment into this class took place in February coinciding with

a period of mean temperatures above 5°C; (this seems to suggest that

substantial numbers of eggs also overwinter). In winter 1971/1972,

probably due to mild conditions, there was an increase in the numbers

entering the 1st and 2nd instar in December, and it seems clear that

reproduction would continue throughout the winter if temperatures were

suitable.

For each occasion, a frequency distribution of the logarithm of the

number in each of the stage-groups was drawn up, (not shown here). The

changes in stage-structure of the population could be followed throughout

the study period. Peak recruitment occurred in July in 1970, but there

was no evidence of discrete generations in this year. Peak recruitment

in 1971 occurred in June, and there was a suggestion of a smaller peak the in August of that year which may indicate the presence of A second

generation. 10:5

Numbers per square-foot in each stage-group for E. nicoleti

I . . . MATURE MATURE YOUNG SAMPLING ADULTS ADULTS ADULTS 5th 4th 3rd 1st & 2nd DATE (2) (1 ) -4- 6th INSTAR INSTAR INSTAR INSTAR INSTARS 2/3rd Oct 69 8.77 32.84 30.98 29.98 31.54 13.02 14.83 19th Nov 69 25.21 50.42 43.54 17.19 2.29 7.70 7.70 3rd Dec 69 25.45 99.61 91.58 30.22 21.92 0 0 11th Feb 70 42.27 124.92 62.83 9.39 2.35 0 0 2nd March 70 33.85 86.04 41.67 9.99 3.33 0 0 2nd April 70 27.50 48.91 16.22 4.06 2.02 0 23.45 14th April 70 19,08 38.15 11.87 8.90 2.54 10.60 72.26 28th April 70 9.83 36.06 9.83 0 0 48.77 156.14 12th May 70116.47 17.44 8.71 21.32 26.18 40.70 117.26 10th June 70 20.21 48,25 28.02 55.99 24.25 54.24 123.75 23rd June 70 30.28 114.31 65.19 116.97 108.46 119.32 395.96 8th July 70 57.42 201.23 113.61 132.46 152.48 149.04 496.45 23rd "July 70 56.15 89.72 84.59 80.21 69.27 135.54 178.52 20th Aug '70 34.36 84.65 110.00 77.77 88.58 74.87 67.20 3rd Sept 70 46.02 51.96 42.09 30.23 76.93 53.90 78.23 15th Sept 70 89.04 100.93 91.02 90.89 105.03 54.33 70.19 29th Sept 70 11.40 63.24 52.15 64.95 42.16 6.55 22.80 16th Oct 70 5.90 17.02 27.77 22.94 24.06 13.98 20.37 28th Oct 70 9.86 44.42 65.75 50.99 39.46 11.53 19.90 24th Nov 70 17.31 57.24 51.37 31.56 11.77 8.37 3.41 12th Jan 71 28.06 56.96 52.07 29.64 24.75 4.89 2.i!4 9th Feb 71 47.35 61.77 51.42 25.96 14.89 10.40 21.71 1 0 4

. . . . MATURE SAMPLING MATURE YOUNG ADULTS ADULTS ADULTS 5th 4th 3rd 1st & 2nd DATE AR INSTAR INSTAR INSTAR INSTARS (2) (1) 6th INST 9th March 71 12.80 24.58 29.02 6.07 6.07 1.01 58.29 7th April 71 25.40 28.27 8.65 9.24 3.45 24.86 125.02 24th April 71 20.82 18.34 6.11 2.44 18.64 32.78 102.66 16th May 71 6.52 6.52 8.68 19.55 22.90 67.12 114.09 15th June 71 17.53 32.19 17.31 21.31 45.50 51.71 120.05 24th July 71 21.66 48.02 48.02 53.77 40.40 56.70 151.89 14th Aug 71 24.04 62.34 45.92 20.76 28.43 83.11 183.79 21st Sept 71 12.05 71.51 49.87 60.55 79.66 60.68 127.54 15th oct 71 31.25 98.18 74.73 80.86 89.77 68.6o 131.05 15th Nov 71 57.12 190.44 125.14 92.48 42.73 55.58 55.58 13th Dec 71 42.10 146.87 55.28 106.34 147.74 65.83 92.18 1st March 72 36.31 131.33 73.42 44.04 16.72 21.65 67.35 105

There seems no reason, from the evidence of development times in the field cultures, to assume that at least two generations do not occur in a year. For example, eggs laid in field cultures on April 14 hatched on 5 May and gave rise to 6th instars in early June. The last of the individuals in this culture died in the following May at the 38th moult.

Eggs laid in culture on 15 May hatched on 31 May and gave rise to 6th instars on 5 July. Again, laying on 20 October produced hatchlings on 4 November, these reached maturity (6th instar) on 10 February in the following year, and died-off in June. Although these development rates are probably slightly faster than those in nature, it seems likely that two generations can occur per year, and if temperatures are particularly suitable three may occur. The late hatching in December 1971 may possibly be a third generation. Joosse (1969) and South (1959) recorded two generations per year in E. nivalis and E. multifasciata respectively.

It also seems likely that some individuals lay in autumn and then survive the winter and continue laying in the next spring, although most of the very long-lived culture specimens were larger than any seen in the field.

The individuals forming the large peak of young instars which occurred in June 1970 could be followed through the subsequent months as a shift of the peak in the stage distribution with time. If these peaks in numbers in each of the stage classes are plotted on a logarithmic scale, and the peak joined by a line as shown on p 107 , an approximate survival curve can be obtained. The survival rate seems fairly constant during instars 2 to 5. It is perhaps to be expected that little age- specific mortality will occur in an animal with little change in form or habitats between different instars. The young adults appear to have a somewhat higher survival rate, than the younger instars. No information is available specifically for the 1st instar as this was grouped with 106

the 2nd in the stage groups; however the mortality rate would be expected to be equal to or greater than that of the other young instars.

Finally it is apparent that in spite of the protracted recruitment period and overlap of generations, there is no evidence of the age— structure of the population becoming stable, thus method3of analysis which depend on this (see introduction) are not applicable. . 1 0 7

1000

Shift of peak in age-class distribution. June-Nov.1970

4 5 YA MA1 AGE CLASS 108

Time did not permit detailed measurements of the age—structure of the T. longicornis, I. viridis or L. lignorum populations, but some statements can be made about the proportions of the different sizes of individual at various times of the year.

For I. viridis the overwintering individual are mainly of medium size (also noted by Joosse (1969) and Milne (1962)), and these seem to increase in size to produce rather large individuals in April and May.

(Joosse and Veltkamp (1970) also report some growth in body—size in overwintering populations). These large individuals have mostly died— off by June or July.

The very young instars of the new generation first appear in April, and the proportion of small individuals increases through May and June.

These appear to mature through July and August when most of the pro- portion of very small individuals increases again, presumably because of the hatching of a new generation, which increases to medium size during

November and December and overwinters mainly in this size category. No very young instars are produced in the months of December, January or

February. This scheme seems to agree with observations made on cultures in vivaria situated in the field, eg eggs laid on 26 February hatched on 5 April, and gave rise to 6th instars on 23 May and longest survivors died in the 21st instar about 12 September. Development is probably slightly faster here than under natural conditions because of the pure fungus food provided (Healey 1970).

Thus it appears that I. viridis has two overlapping generations annually, hatching out mainly in late April — May, and late August,

September and October respectively. This scheme seems to be in reasonable agreement with the observations of other authors. For example, Joosse (1969) working in the Dutch Wadden Islands, and Curry

(1971) working in Kildare in Ireland both believed I. viridis to have 109

two generations per year on their study areas. Joosse (1969) states that eggs laid in May give rise to hatchlings in June (apparently slightly later than in the present work), and mature animals in August. These animals are part of generation 1 which is produced during summer until

August by which time the overwintering animals have died. The animals of generation 1 produce a second generation in the autumn. Both generations are said to overwinter. In the present study it was not clear that the winter population was composed of two generations, one sexually mature and the other immature as required by Joosse•s scheme.

Milne (1962) reports that the small-sized individuals (< 1mm) were present from June to August in one of the years studied And April to

August in the other year. He quotes Weis-Vogh (1947) who found numerous small individuals in June.

Hale (1966) found only one generation per year under sub-arctic conditions in Westmorland. Here I. viridis lays in spring, the 1st instars are present from April to July and mature through the summer.

Overwintering is mainly in the adult stage.

Milne (1962), Weis-Fogh (1947) and the present author all found that the population at lower levels in the soil-profile contains higher proportion of small individuals.

The recruitment of larger numbers of juveniles into the population,

(combined with the mortality effect of the environmental agencies) cause the observed peaks in I. viridis numbers, (p101 ). The twin peaks caused by the main hatching periods of the two generations can be seen more clearly in 1971. In 1970 the dry period in May and early

June (soil moisture was 9.6% in early June) may have caused either the pause in recruitment or severe mortality in the early hatchlings which occurred in late April, and thus split the first population peak. Also 1 1 0

the second generation peak was not very distinct in this year, (soil moisture values were also low in late August (eg 12.48% on 18th August) which may have hampered the growth of the second population peak).

In Joosse's (1969) study the juvenile I. viridis first appeared in

May and caused peak population numbers in July. (A smaller peak presumably due to the second juveniles occurs in October). I. viridis populations in Curry's study (1971) peaked in June and November -

December, and Sherif (1971) noted a build-up in June.

Lepidocyrtus lignorum, under the sub-arctic conditions of the Moor

House Reserve in Westmorland, studied by Hale (1966), is an autumn layer, the adults mainly dying off in winter. Thus the species overwinters mainly in egg stage. The eggs hatch in spring (1st instars being found in May). The life-span is usually 7-8 months.

In the present study, the proportion of the L. lignorum population overwintering as eggs is not known, but the post-embryonic individuals which survive the winter are of medium or large size. These appear to die-off during April. The first hatchlings of the new generation appear in March and the numbers of this stage increase through April and May particularly in the core-samples. This generation matures throughout

June, July and August, by which time a sizable proportion of the population is medium-sized.

Cultures of ege.-;, laid and placed in the field on 12 March, hatched on 12 April, and the 6th instar was reached in early June; this timing appears to be in agreement with the inferences from the Sample data.

There appears to be a 2nd generation in late August-September when the proportion of very young instars increases again. 1 1 1

The two generations overlap more than in I. viridis. By November and December the individuals were mainly of medium or large again with an absence of young juveniles. (Some 1st instars were noted in the core samples in January but these had very probably hatched in the

Tullgren Funnels (see also Valpas 1969)). Joosse (1969) reported that another species ofaILLIELLtual caLieus, appeared to have two generations a year, with peaks of juveniles in July and October, in the Dutch Wadden

Islands.

The L. lignorum population figures for 1970 show the sudden decline of number in early June in the middle of the 1st generation recruitment period, followed by recovery in early July. The 2nd generation peak in

September is much smaller. The situation in thus very similar to that in I. viridis. In 1971 the 1st generation peak is smaller than that of

1970. This was the case with I. viridis and E. nicoleti as well, and it is difficult to see the reason for this.

The 2nd 1971 generation was more distinct in the core-samples than the total numbers, but there was no build-up of L. lignorum in late 1971 similar to that of E. nicoleti.

T. longicornis differed in its life-history from the other three species in that small individuals were present in mid-winter (January), in fact the bulk of the overwintering non-embryonic individuals were small to medium-sized rather than medium to large b,s in the other species. Some large individuals did overwinter as well however. The medium-sized individuals may predominate more if the winter is comparatively cold as they appear to be more cold-hardy than the large individuals (see section on cold-hardiness). The former may also be able to migrate downwards in the soil profile more easily than the large sized animals, thus escaping the worst effects of low temperatures. 1 1 2

The species is not particularly cold-hardy however (see p.275 ), and this may account for the observations of Gisin (1943, 1948) and Linnameni

(quoted in Gisin 1943), that this species survives the winter in

Switzerland and Finland only in the egg stage. Gisin (1943) does add, however, that there was a possibility that milder winters in Switzerland might be survivable by LicILL.cornk The effects of abnormally low temperature was demonstrated by the total loss of cultures in the field on the 29/30 and 30/31 January 1972 when air temperatures reached -5°C and -10°C respectively. The precise importance of this mortality factor depends on the proportion of the population overwintering as eggs, and also the reproductive potential of the overwintering, free-living individuals, (some of the large overwintering individuals have probably already laid). Other species of Tomocerus may be more resistant to cold as Gisin (1943) mentions that T. flavescens did pass the winter in Switzerland as adults, and this was also noted by Bellinger for

T. flavescens in Connecticut U.S.A. Sharma (1962) reported that ,

T. vulgaris overwinterd in the adult stage in Quebec, Canada. Joosse

(1970) states that T. minor overwintered mainly as sub-adults in the

Dutch Wadden Islands.

As recruitment of early instars of 2212nEicanis occurred throughout the year, it was difficult to ascertain the number of generations per year by a qualitative assessment of the age-structure.

Very large-sized iretividuals which did overwinter appeared to die mainly in March-April, probably after having laid.

The proportion of small instars appeared to increase in June and

July, and these are presumably the hatchlings from eggs laid in early spring, and form the first new generation. Large adults seemed common again from late August onwards, and these are probably 1st generation individuals that have instarred during the summer. 1 1. 3

They appear to produce a new generation in October as the percentage of

young instars in the samples shows a slight increase at this time. •

These new individuals appear to mature over the winter and early spring.

Besides these animals, many of the large adults of ;;he preceding

generation overwinter as well and lay again in early part of the next

year.

Observations on field cultures seemed to agree with the above

scheme. For example, a batch of eggs laid on 14 March, hatched on

30 April, and gave rise to 10th instars in early July. The remaining

individuals of the batch (all females) were killed by cold on

29/31 January when they were in the 35th instar. Again, a batch laid

on the 18 May, hatched on 12 June, the specimens reached the 10th

instar by approximately 11 August and died on 29/31 January after the

31st moult. A third example: eggs laid on 10 August, hatched on

26 August, reached instar 10 by 23 October and were in 22nd instar

when killed on 29/31 January. It appears then, that the animals

hatching early in the year could easily be in a condition to lay by

Autumn, and furthermore continue laying in the following year. The animals can be fairly long—lived. Specimens have lived for 16 months

in cultures at 2000, and have moulted approximately 50 times. Joosse and Veltkamp (1970) kept a T. minor individual for 18 months at 2000.

Thus there seem to be two generations per year. However, this scheme is rather tentative and needs verification, especially as growth rates in culture may be much faster than under natural conditions.

Also it is rather abstract in that all stages of development are present nearly all of the time: and the generations are very much overlapped.

Thus reproduction is almost continuous with less of a pause in the winter months than in the other surface species. T. longicornis continued its moulting cycle albeit rather slowly, at temperatures of 1 1 4

1°C in cultures, and produced normal-sized egg-batches regularly at

5°C. Joosse (1969) and Joosse and Veltkamp (1970) noted that in

T. minor growth and reproduction continued at lower temperatures than in some other surface-dwelling genera.

It seems likely that in colder years or cold climates T. longicornis reproduction and growth would be inhibited during winter and this combined with greater mortality in the overwintering adults would produce more synchronized generations. Colder climates would also only permit a single generation per year. Cisin (1948) states that in

Switzerland T. longicornis adults did not appear before July and all were killed during winter. The life-histories of other Tomocerus spp. seem rather variable. Joosse (1969) reported three generations annually in T. minor. Bellinger (1954) states that T. flavescens had a single generation per year in Connecticut, U.S.A., immature specimens appearing in early summer and reaching full size in Autumn, but laying was thought to be delayed until spring. Sharma (1967) found a similar situation for T. vulgaris in Quebec, Canada, (he does say that it was conceivable that eggs might be laid in October or November of the first year, although he thought this improbable). Strebel (1938) reported laying in T. vulgaris from March to November. Agrell (1941) working in Sweden found T. vulgaris to have an annual cycle with the young appearing in spring, and not fully grown until late summer.

According to Uchida and Hongo (1962) on T. minutus, young individuals recruited in June-July overwintered as sub-adults, matured by the following June and died in July and August. Thus it appears that one generation annually is fairly normal in the genus, although most of the authors quoted above were working in fairly cold climates. 1 1 5

REGRESSION ANALYSIS

Multiple regression provides a technique of analysing the relationships between two or more predictor or independent variables, and a single criterion or dependent variable. It is a well known statistical technique, and is described in standard text books e. g.

Steele and Torrie (1960), Draper and Smith (1966), and Snedecor

(1956). The first-order linear model is:

e. (1) 13 0 F 131 li • • • • (3p x pi

Assuming: expectation (e. = 1) then population variance of = population variance (e.) = (2)

Where xii x . are fixed, concomitant variables assumed known before the start of the experiment, and not subject to chance.

The y's must be random values from populations with a common/ variance, and if normality is also postulated, maximum likelihood estimators will be obtained.

e. is an unobserveable random error variate; it is the increment 1 by which the observed value of y departs from the regression plane.

We have assumed that e, has the same variance for any set of x's.

The beta parameters (partial regression coefficients) are estimated by the least-squares procedure,

e. is a minimum 1 (3) i=1

Geometrically, the general equation locates a hyperplane of p-1 dimensions in the p-dimensional space from which projections are made to the criterion axis, Cooley and Lohnes (1971). 1 1 6

If there are differences in the scales of the measurements, as in this study, the variables can be standardised to zero mean and unit variance, so that the fitted line always passes through the origin of the axis system.

The use of multiple regression in the analysis of the inter- relationships between a natural population of animals and the various environmental variables acting upon it, has often been criticised, as the natural situation differs widely from that envisaged in the model

(e. g. see Discussion after Baltensweiller, Geise and Auer (1971)).

In these cases the data consists of unplanned observations, and multiple regression should strictly be used for predictive purposes only, and not for the deduction of the functional relationships of the variables. The independent variables are here not fixed but are random variables with a "true" part and an "error" part. All the error in the independent variables will be assigned to the dependent variable by the multiple regression technique. Errors in the measurement of an independent variable will disguise its apparent importance.

Another difficulty is due to the assumption in the model, that the predictor variables are independent or uncorrelated, which is

extremely unlikely in field data of this type. If an environmental

effect is measured to some degree by several variables which are thus correlated, the true weight of the factor will be dispersed among the variables. The higher the redundancy in the predictor system, the larger the joint contribution term in the determination of R2, and the more unreliable the beta coefficients become in assessing the relative importance of the variables. Thus a high degree of importance may 1 1 7

be attributed to an unimportant factor as the result of its correlation with another variable, e.g. see Mott (1966). In extreme cases where the correlations among the predictors approach unity, singular or near- singular matrices result and unique or stable solutions to the regression equations are not obtainable; in these cases no reliance can be put on the individual coefficients, (Cooley and Lohnes (1971), Kendall (1957)).

In this situation the variables selected by the tecnique are almost a matter of chance.

Needless to say, in many studies with field data, the number of cases is often small compared to the number of predictor variables, accentuating the above problem; this means the occurrance of large fluctuations by the regression weights will be the rule rather than the exception. This has led many workers to de-emphasise the beta weights as an interpretative device, (e. g. Cooley and Lohnes 1971).

Another difficulty is due to that fact that it is quite possible for a

variable to have a zero correlation with the criterion variable, and yet for it to contribute indirectly by suppressing that part•of another

variable which reduces the latter's correlation with the criterion,

(Hope 1968). It should be noted however, that additional information

on the relationships between the variables can be obtained by including

interactions between the prime independent variables, as new independent

variables in the analysis.

The small number of observations and lack of replication which

often occurs in field data gives rise to another problem: it means that

there is no rigorous method to test the goodness of fit of the model,

as the residual variance due to the regression on means may not

even be an estimate of the true error variance, since it is unlikely that the true model has been selected, (Kovner after Baltensweiller et al (1971)). The residuals should always be examined for normality and randomness in these cases.

In the present work, we are not interested in a predictive equation, and due to the paucity of data, little emphasis has been put on the precise magnitude of the beta weights in individual regression equations. Instead, attempts have been made to assess the causal importance of the variables by considering the first two or three variables selected by a step-wise regression technique.

Step-wise regression is discussed by Efroymson (1962) and

Draper and Smith (1966). In the present study Biomedical computer programme BMD 02 R was used, Dixon (1964). This step-wise procedure uses the correlation matrix, and the independent variable that is most highly correlated with the dependent variable is entered first. The independent variable added at each step after the first one is that which has the highest partial correlation with the dependent variable partialed on the variables which have already been included, i. e. it is the variable which makes the greatest reduction in the error sum of squares and which, if it were added, would have the highest F value. Independent variables are automatically removed if there F values become too low. Thus the method provides a judgement on the cont ribution of each variable as though it had been the most recent variable entered, irrespective of its actual point of entry. As the F values required for a variable to' enter the equation, are given for all the non-entered dependent variables at each step, the other candidates for entry at that step

can be appraised. 1 1 9

If the "best" solution to the problems of which variables to consider is defined as the one that maximises the multiple correlation between the selected variables and the dependent variable, the optimum sub-set of independent variables is obtained by the enumeration and examination of all possible regression equations.

Beale, Kendall and Mann (1967) discuss "cut-off" rules which prevent the consideration of poor. combinations. As we are here .-•,, interested more in the importance of variables rather than 2 maximising R , these refinements were not thought to be of great importance in the present study.

As all this fitting is done on one sample, and since only the criterion variable is treated as subject to errors, there is some danger of capitalisation on chance in generalising from sample to population (Cooley and Lohnes 1962, 1971). As with all multiple 2 regression methods R is a maximised rather than an estimated value. The shrinkage of the value of R 2and the validity of the selection made must therefore be demonstrated on replication samples.

There are also difficulties concerning the sequential employment of tests of significance of the difference between two values of the multiple regression coefficients (Hope 1968).

As the analysis of the natural situation by normal regression techniques is fraught with difficulties, at least in theory, the question arises as to whether the analysis of the natural system as opposed to an experimental, controlled system, is worthwhile. 120

However, as Mott (1966) has pointed out, we are concerned with discovering not what can happen in the experimentally controlled condition, but what does happen in the natural situation, for it is here that the secrets of natural regulation lie. In the classic experimental design for controlled experiments, problems such as the intercorrelations existing between variables are avoided by ensuring that each level of each variable occurs at all levels of the

'others. Thus, high levels of one variable do not occur only at the low levels of anothersor vice versa, as in the natural system.

Mott (1966) following Beer (1965) "believes that it is a more profitable to divide the not understood whole system into sub-systems which interact, and investigate the role of these not understood

sub-systems in the behaviour of the whole system. This progressive division of the sub-systems ultimately resolves the large system into the parts which would emerge from the classic investigative methods,

and, more importantly, the natural interactions among the parts".

Thus it seems likely that in seeking insights into the processes

occurring in natural populations, and lacking rigorous statistical

tools, the use of techniques like multiple regression analysis will

continue until statistical theory is developed such that biological

applications can meet the necessary assumptions.

The non-fulfilment of the assumptions of the general linear

model due to linear dependence in the columns of X is likely to

be the rule rather than the exception with ecological sample-data.

Tests for multicollinearity, and attempts to deal with it, are

discussed more fully in the econometric literature th-m in biological

texts (See e. g. Johnston (1972); Aigner (1971)). 1 2 1

The position seems to be that in the absence of additional data, the matter becomes a choice between either the removal of one of a pair of co-linear variables, thus tending to increase the precision of the partial regression coefficient of the other variable, or its continued inclusion to prevent bias in the model.

A more radical solution involves artifically orthogonalising the regression, and is discussed next.

Orthogonalised. Multiple Regression

Kendall (1957) suggested that orthogonalising a regression (with any set of independent variables, not just polynomials) might avoid the difficulties associated with the collinearities in the independent variables.

To date, the technique does not seem to have been widely employed in zoological studies. R. Anderson (1971) found it useful in an analysis of the interelations between the numbers of two species of parasite infecting a fish species, and various weather factors and physical features of the hosts.

Alcock, Lovett and Machin (1968) demonstrated the use of the technique for an agricultural problem, the effect of weather factors on the growth of a grass sward. They were able to progress in their analysis, using orthogonalised multiple regression, after the usual multiple regression technique had failed due to an unstable matrix arising from the high predictor intercorrelations, and lack of degrees of freedom. 122

Jeffers (1967) used orthogonalised regression to relate the strength of various sorts of timber to components extracted from a correlation matrix of some physical properties of the timber.

Jeffers also emphasises the need for extensive application of the various multivariate methods we have at our disposal so that their practical value can be tested.

Although orthogonalised regression is not mentioned,

Williamson (1971) discussed the ecological uses of a related approach; this involves the calculation of principle components of the data matrix, follwed by examination of their simple correlations with various relevant environmental variables. His examples are taken from work on plankton communities.

Austin (1972), discussing tools for the descriptions of interactions between environmental factors and vegetation, mentions the use of correlations between separate components of vegetation and environment. He favours multiple regression rather than

P. C.A. , however, as the former confers more flexibility than the linear P. C.A. model. He does not mention orthogonalised multiple regression as such, but advocates avoidance of multicollinearity in environmental variables by the use of indices which represent the interactions of the measured environmental variablesto produce the complex causal factor. However, the indices must be derived from theory, or previous knowledge, and in many cases this may not be available. These indices are, of course, similar in many cases to the components produced by P. C. A. , and when the former are 1 2 3 incorporated into a regression, a model is produced similar to that obtainable by the orthogonalised multiple regression technique.

The present study is thus an early attempt to evaluate the method's usefulness in the analysis of the interrelations of a natural insect population with various environmental parameters.

The procedure is to replace the original system of intercorrelated predictors by a new set of variables, which are derived so as to be uncorrelated with each other. These derived, orthogonal, standai-dised variates are then regressed against the original dependent variable.

The derivation of the new variables, Z is achieved by principal K' component analysis. This well-known multivariate statistical technique is discussed at some length by Kendall (1957), Seal (1964),

Morrison (1967), and Cooley and Lohnes (1971), among others. Hope

(1968) gives a very simple explanation with a minimum of mathematical derivation. The method reduces a complex system of relations to a normal form by determining the principal axes of the hyperellipsoicl of data points, thus achieving a parsimonious summarisation of the original observations. The first component or principal axis is positioned so that the sum of squared deviations between it and the data points is minimised. Each succeeding component is likewise determined, except that it is orthogonal to all the proceeding axes.

Thus we are making a general linear transformation of the type:

Z Q. X.. K Kj 124

and i = 1, 2 .... n where n = number of observations on a variable.

j = 1, 2 p where p = number of original variables.

k = 1, m (m p)

No assumptions need be made about the X's. This transformation is

achieved by extracting the latent roots (which specify the lengths of

the axes), and the latent vectors (which specify the directions of the

axes) of the correlation matrixpredictor variables by an iterative

process (see Lawley and Maxwell (1963) p. 47), so as to satisfy:

= o (1R rr where R is the correlation matrix, X = i-th latent root (i.= 1, 2

p), and Vi is its associated latent vector. The elements of

Vi will be normalised, i. e. the sums of their squared elements

will equal unity. This prevents the scales of the measurements from

differentially weighting the analysis. The normalised latent vectors

form a matrix of coefficients which are used to weight the

standardised data matrix to yield its principal components.

The magnitude of the normalised latent vectors for each

component gives the contributions of each of the original variables

to the component. Components may also be interpreted by plotting

out the elements of the components and the original observations against time (in this case) to see how each varies according to the

season.

The new components are then fitted in the equation: 0 Y = 0( . +E j=o JJ where the OC 's are linear functions of the (3 's of the usual multiple regression equation, and are given by: 125

. yZi

Z.2

The square of the correlation coefficient between the dependent

variable y and the component Z. gives the reduction in variance due

to the fitting of Z. and is given by:

.2 A . J J

where X , is the characteristic root corresponding to Z..

Kendall (1957) suggests that the significance of the component can 2 1 thus be tested by ,10‹.. , or alternatively by a student's t-test on the J 0( s. Orthogonalised standard partial regression coefficients b.,

(where i = 1, 2 .... p), are given by:

b. = V 01\ 1 j

where . is an element of the normalised latent vector. The 13.1 s

summed over the j components gives the standard regression

coefficients B identical to these of ordinary multiple regression (if no

components have been discarded). The orthogonalised standard

regression coefficients give the correlations of the primary variables

with that part of the variance of the original dependent variable

accounted for by the component under consideration.

As with any regression equation, coefficients of separate determination (Hope 1968) (d) may be calculated.

Here d. = r. b. J J where b. is the orthogonalised standard partial regression coefficient,

r. is the correlation coefficient between the dependent variable and the

j-th independent variable (Anderson, 1971): 126

The coefficients of separate determination measure the proportion of variance of the independent variable accounted for by a predictor variable, and to facilitate comparison of equations, the 2 sumum of the d.'s is set equal to R . The coefficients of separate determination impose a "workable" definition of the relative importance of the variables in an equation (Hope, 1968), though their theoretical justification seems uncertain. They are meaningless if they occur as negative values as no variable in a regression equation 2 can detract from the value of R .

As we were interested in the procedure as an exploratory technique in the present study, rather than in producing a precise

regression equation, the question of significance testing is not of paramount importance, and it will only be briefly discussed.

Kendall suggested discarding some of the components (before

regressing y on the retained principal components), and in his

example he achieves this by a subjective decision on the size of the latent roots. Another possibility here would be the use of Lawley's test (Lawley and Maxwell, 1963) to prevent the extraction of non-significant latent roots. However this test was of limited use in practice as the last root extracted was usually the only

non-significant one.

Furthermore, as we are only accounting for the variance within the predictor system at this stage, and as components

unimportant in describing the main trends among predictors could

conceivably be important factors in the determination of the

dependent variable, dis-.:arding components at this stage seems unwise. 127

Another procedure mentioned by Kendall is to delete a component from the regression equation if its 0( coefficient is not significantly different from zero (see above). McCallum (1970) advocates deletion of a component only if it reduces the mean square error of estimating the B coefficients, and that this does not necessarily mean those components with the smallest variance or the smallest t-values.

Incidentally, McCallum's (1971) paper is also interesting as it shows that artificial orthogonalisation of a regression can be useful even in cases where no physical interpretation of the derived variables has been possible. This is because the principal component estimators, although biased, will have smaller variances, and thus under certain conditions, smaller mean square errors than the unbiassed ordinary least squares estimators. 128

SELECTION OF VARIABLES FOR REGRESSION ANALYSIS

In many situations where regression analysis has been utilised, the selection of variables is straightforward, as their precise effects are known from theoretical consideration of the problem. In ecology, however, it is frequently the case that large numbers of environmental variables are involved, but their precise effects cannot be readily quantified from a priori considerations, and furthermore their effects are likely to be very complex.. Here then, we are not so much concerned with the testing of a specified model as in classical regression, but with examining a large mass of multi-dimensional data for useful hypotheses. So, on the one hand, we would like to include as many measured features of the environment as possible in order that the probability of detecting an important variable is increased, but in practice this leads to severe operational problems. A large number of the variables measured are likely to be (a) correlated with others and therefore partly redundant, (b) relatively invariant, or (c) irrelevant (Green, 1971).

The statistical consequences of using large numbers of highly correlated variables has been discussed above, and some provision against it is needed.

It is possible, of course, that the population size is determined by some very obscure factor, e. g. a rare mineral, which is exceedingly unlikely to have been measured in the absence of some a priori indication of its importance. In this case, the model would be unavoidably seriously biased. 129

In the present case, the variables utilised in the main series of analysis were all weather variables. Some other environmental variables, e. g. amounts of various chemical elements, were dealt with separately. The weather variables actually used were selected from a larger list of possible variables. The final list was chosen partly subjectively, and partly by eliminating variables which had either a high simple correlation with another variable (if the two could be reasonably supposed to be measuring the same environmental feature), or by an examination of the variable weightings in the eigenvectors produced by a principal component analysis of the larger group of variables.

The question then arises as to the period over which the variables should be measured. There is no simple answer to this question; the effects of temperature on, say, development are obviously continuous and long term in effect and should perhaps be measured over a period of several months. For example,

Glasgow (1939) found that partial correlations of his Collembolan populations with temperature increased, the greater the period of measurement (14 -3, 100 days preceding sampling date). Dhillon and Gibson (1963), on the contrary, found little or no increase in correlation when the mean temperature for the preceding three months was substituted for the mean temperature for the preceding month before the sampling date. On the other hand, the effect of some variables may be relatively instantaneous, and averaging out over a lengthy period might tend to mask their effect, e. g. lethal 130 effects of very low temperature. In the present study, two sets of analyses were undertaken; in the first variables were measured over a period of 4 weeks, and in the second 8 weeks, prior to the sampling date.

The following variables were used:

A. Mean temperature (°C)

B. Highest maximum temperature during the period

C. Lowest minimum temperature during the period

D. Range between the highest maximum and lowest minimum

temperature for the period.

E. The average of the daily maximum temperatures

F. The average of the daily minimum temperatures

G. Total rainfall (mm + . 01 mm)

H. The minimum relative humidity recorded during period (+ 5%)

I. Average of the minimum relative humidity figures for the

period.

J. Soil moisture and litter moisture (moisture as a percentage of

the dry weight, dried in oven at 105°C to constant weight).

K. Total number of days in which the ground was dry (state of

the surface of a plot of bare earth was scored either dry or

wet, at 0900 hrs. on recorded date).

L. Average of mean daily wind speed for period (knots). (This

was recorded at Heathrow Airport, not at Silwood Park).

M. Mean daily sun hours (+ .2 of an hour, from a Campbell-Stokes

chart).

Some brief remarks on the measurement of the weather variables are necessary. The temperature measurements were made 1 3 1 on the site with a 6-channel Grant recorder and thermisters.

The thermister probes were placed at three levels: (a) an upper level; this was under the dead or dying vegetation or between the bases of the grass stems, (b) a middle level, in the titter layer proper, and (c) a lower level, below the litter and at the boundary of the soil/humus layer. The daily mean temperatures were calculated from the chart readings at six 4-hourly intervals, starting at 0200 hours. The temperatures recorded at the middle layer and the bottom layer were actually almost interchangeable, the two sets of data measured over 1 year having a correlation of

. 995. The temperatures recorded from the upper layer were somewhat different in pattern, and these bore the greatest resemblance to the standard Stevenson screen temperatures.

The correlation between the temperatures recorded in the top layer and the standard screen temperatures was .942 for the year's data, while the screen temperatures and middle level site temperatures showed a lower correlation (. 885). The middle level temperatures were used in the regression analysis, as this level appeared to approximate to the part of the profile containing the bulk of the populations of the surface-dwelling Collembola.

Accurate, remote sensing, and continuous recording of the humidities in small spaces such as the cavities between litter or soil particles is a problem which has not been satisfactorily resolved to date. Thus the measure of the humidity adopted here

(daily minimum screen humidity) is a rather unsatisfactory comprOmise. The measurement of total water content of the soil 132 is the simplest approach to the measurement of the environmental variable actually affecting the collembolan. It should be noted, however, that Vannier (1970) believes that the quantity of accessible water expressed in terms of water retention or pF value is a more important ecological factor.

Allowance must also be made for the probable non-linear effects of the environmental variables in their association with the collembolan population size. Therefore, in the analyses, besides the raw variables (X's), the following transforms were included as 2 3 X additional variables: X , X , e , and log. X.

Finally, the effects of the interactions between some of the apparently important variables were examined by including the products of the variables and as further new variables.

As values of the dependent variable (i. e. numbers of

Collembola) for each occasion are the means of samples, one would expect them to be normally distributed (as is required for the regression analysis), and in fact the values of the dependent variable were not significantly different from those expected from a normal distribution. 133

The Use of Orthogonalized Multiple Regression

in the Analysis of the Inter-relationships between

the Numbers of Entomobrya nicoleti and Various

Meteorological Variables

As with the stepwise regression analysis the aim here was to produce a description of the trends in the dependent variables, to assess whether significant amounts of variance of the dependent variable were associated with the trends in the meteorological variables considered in conjunction with each other, and thus to attempt to generate hypotheses concerning the nature of the effects of weather on the abundance of the animals.

The 1971 suction data were analysed first, as these were thought to be the most accurately measured. Conclusions drawn when considering this year's data were then extended to the previous year's data. Firstly, the inter-relationships between the predictor variables themselves were considered. Various computer runs were made, differing in the number of variables considered, and in the length of the period over which the variables were measured.

The results obtained were similar for all the runs undertaken; typical examples are given on page 138-10.A summary of most of the inter-predictor variability, (91 - 95%) was achieved by the first four of the derived variables. Examples of the normalised latent vectors for the first four components are given on page 1/42 •

The first component, which accounted for 65 - 69% of the variance within the predictors, is interpreted as showing the main annual trend of weather variables. It has thus a rather unusual bipolar 134

appearance and contrasts those variables which rise during the summer, e. g. temperature variables, sun-hours, the total number of "dry days" and the average daylight, against those variables which tend to decrease during the summer, for example, variables related to the humidity, the soil moisture and the wind speed. The only variable which has a low weighting in this component is rainfall, which does not exhibit a sinusiodal annual variation.

Component 2 accounted for between 11 and 15% of the variance within the system of predictor variables. It removed part of the inter-predictor variance which could not be assigned to a simple, sinusiodal trend. It can be seen that rainfall produced the largest weighting in this latent vector, and to a certain extent rainfall is associated with grass-minimum temperature and contrasted with range of temperature. (A period of high rainfall being associated with mild conditions and the reduction in extremes of temperature).

Component 3 which typically accounted for 6 - 9% of the variance was rather difficult to interpret, but continued the removal of variance from the seasonally erratic variables, particularly temperature range.

Component 4 which removed 4 to 5% of the predictor variance, was an index contrasting dry substrate and low temperature with damp substrate and moderate temperatures. This component showed a high degree of association with the dependent variable and is discussed further below. Further axes were not considered, as they did not appear to be important in describing either the 135

predictor system, or variance in the dependent variable.

Considering now the importance of the above "derived variables" in describing variation in the dependent variable. (The results from

A typical computer run are shown on pages 138-4.0 ). As the density of Entomobrya nicoleti showed a general trend to increase throughout 1971, it would be expected that no close relationship would be shown between the dependent variable, and the main annual weather cycle. This is reflected in the very small amount of variance of the dependent variable accounted for by the first principal component

(less than 1%). As components 2 and 3 show an overall trend of decrease (in component 2), or increase (in component 3) during the year, some variance in the dependent variable is accounted for.

Thus as component 3 is dominated by range of temperature, and this variable takes low values at the beginning of the year and higher values later in the year, and thus some positive relationship must necessarily be shown with the dependent variable. However, in both components 2 and 3 the amount of criterion variance accounted for is somewhat variable between the different computer runs; (4. 6% to

12. 8% for component 2; 1. 1% to 12. 5% for component 3). Further, as the values of these derived variables vary erratically from one occasion to the next, and do not seem to provide a realistic biological explanation (as opposed to a mathematical one) for the fluctuations in animal numbers, they are not considered further.

In each of the runs made, component 4 consistently explained a considerable proportion of the variability in the dependent variable

(between 40. 9% and 53. 1%). Component 4, as can be seen from the 136

coefficient, had a negative relationship with animal numbers, high

values of the component occurring simultaneously with low animal

numbers, see Fig. P 143. If the value taken by the component on

a particular occasion is examined in connection with the values

taken by the weather variables which have high weightings in the

component, for the same occasion, the structure of the component

and its relevance to the animal population can be investigated. For

example, in Fig.piL:.3 it will be noticed that the component has low

values for the ninth, twelfth and thirteenth occasions (14th August,

15th November, 13th December). On these occasions, the

Collembolan population was relatively dense, the number of "dry" days was low and the litter/humus moisture high, i. e. damp

conditions prevailed. It will be noticed that soil moisture values were not particularly high on these occasions, and generally this

variable did not respond quickly enough to be a good indicator of the droughts and damp periods during the summer; i. e. this variable was "damped", and exhibited mainly the seasonal changes in

substrate moisture. This explains why it appears to have a different sign from the one anticipated in its weighting in the normalised latent vector, or in the orthagonalised regression

coefficients. Occasions 9, 12 and 13 were also associated with periods of relatively high temperature for the time of the year, which is reflected in the moderately high positive coefficients for the temperature variables. Note that the grass minimum temperature is much high-or in December than in the following

February. On an occasion where the component has a high value, 137

say the fifth occasion (24th April, 1971), a contrasting situation will be found. Here the population density is low, there were many "dry" days during the period, litter moisture was low, and temperatures relatively cool. To take another example, on occasion 10

(21st September), a sharp increase in the value of the component for this occasion is accompanied by a corresponding sudden decrease in animal numbers, and here again "dry" conditions prevail, and the average maximum temperature shows a considerable decrease from the previous occasion. Thus component 4 appears to be a general index of inclemency in Entomobrya nicoleti, periods of cold temperatures especially in early spring, and also periods of drought especially in mid-summer tending to reduce populations or prevent population build-up. Conversely, mild weather with adequate soil moisture especially later in the year tends to promote high densities. It should be noted that the latter conditions did, in fact, prevail in the last month of 1971, and population densities of this species reached the highest numbers found in the study period. ORTHOGONALISED MULTIPLE REGRESSION 1971 E. nicoleti Data 8 Independent Variables Variable 9 is the Dependent Variable, = E. nicoleti sample numbers Correlation Matrix of all Variables RAINFALL 1.0000 -.2122 .1291 .0448 -.2012 .2297 .0062 -.0674 -.0938 TEMP. RANGE -.2122 1.0000 .2567 -.8036 .4856 -.5989 -.4942 .6272 .2629 GRASS MIN. TEMP. .1291 .2567 1.0000 -.6807 .6976 -.6916 -.7554 .8564 -.0415 MIN R.H. .0448 -.8036 -.6807 1.0000 -.7146 .7456 .7771 -.8440 -.1692 TOTAL "DRY" DAYS -.2012 .4856 .6976 -.7146 1.0000 -.6094 -.8842 .7552 -.3469 SOIL MOISTURE .2297 -.5989 -.6916 .7456 -.6094 1.0000 .7745 -.8140 -.3409 LITTER MOISTURE .0062 -.4942 -.7554 .7771 -.8842 .7745 1.0000 -.7879 .0797 AVERAGE MAX. -.0674 .6272 .8564 -.8440 .7552 -.8140 -.7879 1.0000 .0940 TEMP.

E. nicoleti -.0938 .2629 -.0415 -.1692 -.5469 • -.3409 .0797 .0940 1.0000

1 3 9

Latent Roots 5.239 1.182 .743 .421

Normalised Latent Vectors -.054 .828 .464 .089 .305 -.369 .667 .113 .359 .373 -.302 -.302 -.401 .033 -.341 -.063 .375 -.012 -.317 .637 -.381 .113 .052 .555 -.398 -.139 .171 -.326 .412 .070 .001 -.252

Percentage Variance of Principal Components 65.493 14.778 9.292 5.266

Trace = 8.0

Principal Components -2.929 -.999 -.209 -.442 -2.907 1.521: -.344 .002 -3.138 -.620 -.621 .141 -.766 .680 -1.682 .741 .983 -.379 -.097 1.056 1.968 -.144 1.027 .863 1.110 2.686 .589 .098 2.728 -.721 -.615 -.003 1.741 1.065 .170 -1.104 3.221 -.887 -.977 -.127 2.302 -.696 .703 -.248 -.579 -.648 .511 -.797 -1.113 -.204 -.112 -.759 -2.620 -.658 1.657 .58o

Alpha Coefficients .028 -.199 .415 -.986

Percentage Variance of Dependent Variable

.400 4.659 12.776 40.947 140

Orthogonalised Regression Coefficients -.ow -.164 .192 -.088 .008 .073 .277 -.111 .010 -.074 -.125 .298 -.011 -.006 -.141 .063 .010 .002 -.131 -.628 -.011 -.022 .021 -.547 -.011 .028 .071 .321 .011 -.014 .000 .248

Cumulative Percentage Variance of Dependent Variable

.400 5.060 17.836 58.783

Cumulative Orthogonalised Regression Coefficients -.ow -.166 .026 -.061 .008 .082 .358 .247 .010 -.064 -.189 .109 -.011 -.018 -.159 -.096 .010 .013 -.119 -.747 -.011 -.033 -.012 -.559 -.011 .017 .088 .409 .011 -.002 -.002 .246 ORTHOGONALISED MULTIPLE REGRESSION ANALYSIS

OF RELATIONSHIP BETWEEN E. NICOLETI NUMBERS AND WEATHER:

THE NORMALISED LATENT VECTORS ASSOCIATED WITH FIRST FOUR COMPONENTS

KEY TO ORIGINAL VARIABLES CONTRIBUTING TO

PRINCIPAL COMPONENTS

1) RAINFALL 5) TOTAL WJMBER OF "DRY" DAYS

2) TEMPERATURE RANGE 6) SOIL MOISTURE

3) GRASS MINIMUM TEMPERATURES 7) LITTER MOISTURE

14) MINIMUM RELATIVE HUMIDITY 8) AVERAGE MAXIMUM TEMPERATURE 142

9 Component A B 8

7 •

6 •

6

4

3

2 2 3 5 8 6 1 • 8

0

1 4 6 7 5 -2

-3

-4

9 D

7 8 1 2

-3 • ORTHOGONALISED MULTIPLE _REGRESSION ANALYSIS OF E.NICOLETI 1971

SUCTION-LAMpLE DATA AND WEATHER (8 VARIABLES).

'WEIGHTS' ON COMPOTENT 4 FOR EACH OCCASION'

0 0 0 0.6 0 0.4 ER

0,2 0 EATH

0 W .0 CY IN

0 EN ••0.2 CLEM •0.14 - ASING -0.6 DECRE 0 -0.8 0 -1.0 0

.•1.2 ' 0 2 9 9 7' 24 16 15 24 14 21 15 15 13 14 JAN FEB MAR APR APR MAY JUN JUL AUG SEP OCT NOV DEC FEB 1971. 1972 1 4 4

REGRESSION ANALYSIS OF RELATIONSHIPS BETWEEN WINTER POPULATIONS

OF E. NICOLETI AND WEATHER

It was noticed when examining the 1971 suction sample data of

E. nicoleti in first few months of the year (before the population build—up occurred in late April), that it bore a close relationship with temperature,

and in particular minimum temperatures. This relationship was examined

further by stepwise regression analysis of winter E. nicoleti

population numbers and weather. The total E. nicoleti data for occasions

in December to early April 1969-1970 and 1970-1971, (ie 9 occasions), was made the dependent variable in the analysis. The erroneous

population estimates for 18th December, 1969 and 26th January, 1970

were omitted; (if included they would have increased rather than decreased the correlations obtained).

It can be seen (p.145 ) that the logarithm of the lowest minimum temperature for each period was the variable selected in step 1 of the

analysis. This variable showed a significant correlation (P = .05)

with the E. nicoleti numbers, and accounted for 44.6% of their variance. The second variable selected was the average of the maximum temperatures for the period, and both variables together removed 82.6% of the variance in the dependent variable. No further selection was made. Another

computer run was made using data for some extra occasions (viz in

November and late April) making twelve occasions in all. Log. low

minimum temperature was still selected and again showed a significant correlation (P = .05) with E. nicoleti numbers.

The following effects of low temperature are possible in this connection: a) The overwintering animals (plus amir emerging hatchlings) are subject to lethal effects of sub—zero temperatures. It has been shown (p. 287) SUMMARY TABLE FOR STEPWISE REGRESSION OF WINTER POPULATIONS OF E. NICOLETI AND WEATHER

A : 9 cases - 4 week data

Step Multiple Increase F value to REGFESSION Variable Entered 2 Number r R R in R remove ANOVAR

1 Log. low minimum temp. . 667 . 6674 . 4455 . 4455 5. 6233 P = 5%

2 Average maximum temp. .159 . 9086 . 8256 . 3801 13. 0792 P < 1%

B 12 cases - 4 week data

1 Log. low minimum temp. . 573 . 5728 . 3281 . 3281 4. 8842 P = 5% 146

that a temperature of -8.4°C say for 6 h will kill 50% of the

E. nicoleti population. (These figures were measured on the population in mid winter, and a proportion of the less resistant individuals may have already been removed from the population. The experiment did not include very small instars which are probably less resistant). Grass - minimum temperatures of -10°C or more occur nearly every winter at

Silwood Park (Luff 1966), and occurred in both the winters involved here. It is, of course, debatable whether or not the animals can avoid extremes of temperature by downward migration in the soil profile, but even in the litter temperatures of -6.5°C occurred on a few occasions during the study and it seems quite likely that some mortality occurs with the sudden onset of particularly low temperatures.

Joosse (1969) believed that the winter mortality observed by her was due to weather factors rather than predation as the activity of predators was very low in winter. b) The second possible effect of low temperatures could be due to losses from mortality not being balanced by natality because of i) low mean temperatures hindering both the development of eggs and maturation of sub-adults, and ii) low mean temperatures inhibiting or reducing oviposition. South (1959) believed that with E. multifasciata tho factor limiting recruitment of 1st instars into the population in winter was the slow egg-development rate below 4-5°C. He also maintained that below about 5°C oviposition is inhibited in this species. In E. nicoleti

I found that some eggs were laid at 5°C, but it seems likely that at temperatures a degree or so below this oviposition would be negligible.

More evidence for the limiting effects of winter temperature on reproduction in collembolan populations can be found in Hale's (1965,

1966) work. It was shown that peak populations of several species of

Collembola were due to the hatching of eggs in spring, and that the timing of this event could be predicted from egg-development speeds in the laboratory and mean field temperatures. The timing of population build-up in spring in various surface-dwelling collembolan species 147

shows variation from year to year (eg see Joosse 1969 & Milne 1962)

which was thought to be due to the effects of mean winter temperatures

on development. There is no evidence in the literature of egg diapause

or adult diapause, occurring in arthropleonan collembolan populations

overwintering in cold climates, (a "diapause"in aestivating S. viridis

eggs in Australia has been described by Wallace (1968) however).

How do the results from regression analysis in the present study tie

in with the two postulated effects of temperature? The relation of

numbers of E. nicoleti in winter to minimum temperature point to effect

(a) above, ie the lethal action of very cold night temperatures. The

logarithmic form of the relationship may be a fortuity, but an

increasingly severe effect as temperature falls seems plausible. It is

perhaps worth noting that the lowest minimum temperature shows a good

correlation with collembolan numbers only when the variable is assessed

over a four week period; with the eight week data the relationship is

not apparent. This points to a short term rather than a cumulative

effect of temperature. Also, particular decreases in numbers of

E. nicoleti were noted in March 1970 and March 1971, and these

corresponded with periods of severe cold.

The lethal effect of sub—zero temperature alone cannot provide a total explanation for the observed trends in winter of E. nicoleti

populations however, as in 1971 (though apparently not in 1970 and 1972),

some recruitment in to the population was observed giving rise to an

overall increase in numbers during February. This increase coincided

with a temporary rise in mean temperature above 5°C (February 1971 mean temperature was 5.48°C, as against 2.35°C in January and 3.6°C in March

of that year). This seems to be due to increased egg development rate

producing early hatching. South (1959) noted a similar phenomenon in 148

E. multifasciata; in January 1958 he observed the appearance of 1st and 2nd instars, coinciding with an increase in mean temperature, to

5.2°C, for 14 days.

As minimum temperatures are obviously correlated with mean temperatures, some of the effect of the latter may well be 'bound—in' with apparent effect of the former in a multiple regression analysis) and an experiment would have to be performed to conclusively separate the two processes. However, the second variable entered in the regression equation, ie the average maximum temperature seems likely to be expressing the action of temperature in determining natality rates. This temperature is similar to the mean temperature but is

"weighted" by spells of warmer weather, during which any recruitment would occur. 149

REGRESSION ANALYSIS OF THE RELATIONSHIP BETWEEN THE

E. NICOLETI POPULATION AND WEATHER :

MAIN ANALYSIS

In the preceding section relationships between E. nicoleti winter populations and weather were analysed. In this section the analysis is

extended to the rest of the season. The 1971 suction sample data was

examined first, as some effect s of weather, eg drought, might be more obvious in the surface layers of the soil profile.

As the numbers of E. nicoleti showed a roughly linear increase from late April onwards, a variable taking account of this rate of increase seems necessary. Here a simple time variable (days elapsed since the beginning of the year) was used, but a variable relating the numbers present at a certain time to those that were present one generation-time

earlier would perhaps have been more generally realistic. The results

of this analysis are shown on p. 150.

'Days from the beginning of the year' was entered as a squared term in this analysis, as the general trend of increase in number was

curvilinear due to the delay in population growth in the early part of the

year when numbers are kept low by the effects of temperature. Log lowest minimum temperature and average maximum temperature are

also entered, as in the analysis reported in the preceding section, where the effects of these variables have been discussed. These three variables

accounted for 97. 57% of the dependent variable variance.

The winter occasions were then removed from the analysis to see

whether any of the effects of weather on the collembolan summer pop-

ulation had been obscured by the main variables already in the equation. 1 5 0

SUMMARY TABLE OF STEPWISE REGRESSION ANALYSIS OF E. NICOLETI

1971 SUCTION DATA FOR 14 OCCASIONS.

STEP VARIABLE MULTIPLE 2 INCREASE F VALUE TO NUMBER ENTERED R R IN R2 REMOVE

2 1 Days .9305 .8659 .8659 77.4699 2 Log. Min. .9560 .9140 .0481 6.1523 Temperature

3 Average Max. .9878 .9757 .0617 25.4062 Temperature i 51

If the winter months were removed, eleven occasions remained, and

on these occasions the E. nicoleti population increased linearly, if a

normal numerical scale was used. This is this analysis (see p. 152)

86. 55% of the variance in the E. nicoleti numbers was accounted for

by the 'days' variable. Deviations from this linear trend were accounted

for by the variable 'total number of "dry" days'. If 'total "dry" days' was

excluded from the analysis, the variables 'number of sun hours' and

'minimum relative humidity' were found to produce a similar result.

This was interpreted as showing the effect of drought conditions in

limiting the build-up of E. nicoleti populations in summer. The decline

in numbers in the upper strata recorded on 21 September 1971 did in

fact coincide with a low soil moisture value (9. 36%).

Vannier (1970), in a careful study of the effect of moisture on the

collembolan population in a woodland habitat, concluded that soil

moisture deficit was not a limiting factor, and population fluctuations

were independent of variations in this variable. However, in parts of

the present habitat, viz. those without. a deep litter layer or thick

vegetation cover, the soil could become extremely dry after a prolonged

drought. (Summers at Silwood Park tend to be somewhat dry as the

climate of the area tends towards the "continental" type compared to

most of Britain). In some areas during very dry periods, large numbers

of dead collembola were actually found. Death due to dessication can

probably be avoided by horizontal and vertical migration, unlegs the drought is prolonged however.

Drought may also inhibit population increase other than by causing mortality due to dessication. For example if part of the habitat is rendered 1 5 2

SUMMARY TABLE OF STEPWISE REGRESSION ANALYSIS OF E. NICOLETI

1971 SUCTION DATA FOR 11 OCCASIONS (ie winter occasions removed)

F VALUE TO VARIABLE MULTIPLE 2 INCREASE ENTERED R R IN R2 REMOVE

Days .9303 .8655 .8655 57.8902 Total nos. of .9652 .9317 .0662 7.7506 dry days 153

unsuitable the population may be crowded into a smaller favourable area,

and competition for food, oviposition sites etc may increase. In some

collembolan species, low hiimidity can apparently have rather striking

effects eg. construction of protective cells, ecomorphosis, neutralization

of the sexes and retardation of development in juveniles (see Butcher et

al (1971). These effects do not seem to occur in Entomobryids as far as

I know.

Thus a variable expressing the effect of drought (total number of dry

days) was included in the general model.

Some Entomobrya species may also be deleteriously effected by

excess moisture, eg E. multifasciata in South's (1959) study. Although mortality due to excess moisture was not observed in the field in the present study, the fact that E. nicoleti was often associated with the dryer -

areas of the plot (see section on dispersion) perhaps indicated that a high moisture content of the litter was harmful. In view of this 'litter moisture' was included in the general model.

The five variables which had been selected viz, mean temperature x maximum temperature, minimum teinperature, days, litter moisture and total number of "dry" days, were then used in a multiple regression

equation with the data from all sampling occasions (except the two in winter 1969-1970 which were believed erroneous). The results are shown on p. 155. The model accounted for most of the variance (85%) in the dependent variable. The exponential form of the temperature variables is due to the fact that a small rise in temperature at a critical level is associated with a considerable rise in population numbers. The 154

mean temperature weighted by maximum temperature shows the greateL•t association with the abundance of E. nicoleti, and we may tentatively

,suggest that sub-optimal mean temperature is one of the main factors limiting the population numbers of this species in the field. The loga- rithmic form of the moisture variables may indicate that they have little effect until their value becomes very low. \Their effects appear to be relatively minor, compared to those of temperature, however.

The favourable combinations of temperature and moisture required for population growth indicated in the stepwise regressions, are paralleled by the results of the orthogonalised regression analysis reported in a preceding section.

The hypotheses generated in the above analysis should now be tested by experiments. These could either take the form of simple lab- oratory experiments on the effects of the variables (eg the critical soil moisture values at which mortality in the collembolan will occur due to dessication), or experimental manipulations of the natural environment could be undertaken (eg. artificial drying out of litter by fans, say, as in Vannier 1970)

The more detailed information on the various biotic effects of the weather variables obtained from the experiments can then be used to formulate sub-models of the effects of weather. These may then be combined in a predictive stimulation model, and values for the abundance of the species generated by this model tested against the values observed in the field. SUMMARY TABLE OF MULTIPLE REGRESSION ANALYSIS OF TOTAL E. NICOLETI DATE (34 OCCASIONS) AGAINST FIVE SELECTED VARIABLES

VARIABLE MULTIPLE 2 INCR4SE F. VALUE REGRESSION ENTERED R R IN R TO REMOVE ANOVAR SIGNIFICANCE

1) eMean Temp. x eMax. Temp. .7872 .6198 .6198 52.1566 (P < .001) P 4 .001

2) Lowest Minimum Temp. .8418 -.7087 .0889 9.4641 (P4.001) P 4 .001

3) e Days .8819 .7777 .0690 9.3192 (P 4..001) P 4 .001

4) Log. Litter Moisture .9068 .8224 .0446 7.2814 (P4.001) P < .001

5) Log. Total Dry Days .9217 .8495 .0272 5.0545 (P 4 .01) P 4 .001 156

EGG MORTALITY IN THE FIELD

Due to the practical difficulties involved, estimates of the egg stage in the field were not made. Some attempt to measure the intensity of the factors causing egg loss in the field throughout the year was made however.

Methods

Eggs of Entomobrya nicoleti and Tomocerus longicornis were removed from laboratory cultures and placed in grooves and pits in small plaster of paris/charcoal blocks. The blocks were moistened with water and then positioned in the litter, or under layers of dead vegetation, on the study area. On few occasions the blocks were provided with lids of the same material. Due to the rough surfaces of • the block and lid small arthropods were still able to move between a block and its lid. The blocks were recovered after periods of approximately one week or two weeks, and the number of eggs lost or killed was noted. The percentage egg mortality per day for each species, for each of the periods, was calculated. The relationship between the egg mortality and various environmental variables such as the average day light for the period, the average temperature, and the moisture content of the litter, were investigated.

Discussion

The majority of the lost eggs had been removed altogether, a few were punctured and shrivelled. The fact that the eggs were on a 157

moist substrate, and the siting of the eggs in the field, meant that loss due to dehydration did not occur in these experiments. As the precise location of the natural oviposition sites was unknown, it was not possible to ascertain whether egg mortality due to dehydration was important under natural conditions. It was noticed that if pieces of natural material were placed in the culture containers, some eggs of Tomocerus and Entomobrya were laid either singly or in twos and threes in small cavities such as cracks in moist pieces of wood, under pieces of debris, and in the axils of moss or grass stems.

Presumably these locations are similar to the natural oviposition sites utilized in the field. Some eggs on the other hand were laid in pits in the plaster of paris base, contrary to the observations of South (1959) on Entomobrya multifasciata. This tendency for surface-dwelling

Entoloy,bryids to lay eggs in small numbers and in concealed locations, has been noted by other workers, e. g. Waldorf (1971). Mayer (1957) suggested that this habit was an adaptation providing protection for the eggs from predation or desiccation.

Isotoma viridis unlike the other two species mentioned above, layed its eggs in large groups sometimes consisting of eggs from more than one female, and nearly always on the base of the culture jar. The eggs of Isotoma viridis also seemed much more susceptible to dehydration in an unsaturated atmosphere, then did the eggs of Entomobrya or

Tomocerus. Presumably in the field the eggs of Isotoma viridis are not subject to severe mortalities from predation, and are also laid in the 158

deeper layers where the possibility of dehydration is less likely.

In some species of Entomobrya, excess moisture as well as a lack of moisture may have deleterous effects. South (1959) working with Entomobrya multifasciata , believed that the uneven distribution of the animals on his study area, was due to a differential egg mortality caused by uneven distribution of favourable oviposition sites in the f. layer. Not only were sites unfavourable if they were subject to desiccation in dry weather, if they had a film of free water covering them in wet conditions this also appeared to be detrimental. In the present study it was noticed that the numbers of Entomobrya nicoleti seemed to be correlated with the Agrostis tenuis areas, which may reflect a tendency to avoid the wetter places on the study area, at least at certain times of the year.

However possible egg mortality due to the effect of various moisture regimes are not considered in this experiment and have been mentioned in passing. The present experiment is measuring the intensity of egg predation throughout the season, and as these losses were not diminished appreciably by the presence of the lids covering the blocks, it is reasonable to conclude that predation is mainly due to small arthropods, which can enter the crevice between the lid and block. The agents of this egg predation have not been identified. Large mesostigmatid mites, such as Pergamasus crassipes readily consume Collembolan eggs, 159

in both the adult and nymphal stages (Maddison 1969).

Other small carnivorous arthropods like Staphalinid and Carabid beetles, and small centipedes might also be important egg predators.

Some Collembola such as Friesia miriablis are also known to be specialised egg predators, although this species is eudaphic and thus rather unlikely to be the cause of the present egg losses, as these eggs were placed in the surface layers. Egg cannibalism has been noted in some Entomobryids, e. g. Sinella curviseta (Waldorf, 1972), but has not been observed in the species studied here.

Considering the relationships between the intensity of the egg predation and various environmental variables, it can be seen

(page 160-i) that for both Entomobrya and Tomocerus the egg loss is most highly correlated with a mean temperature and in both cases the correlation is highly significant. As the egg losses are more highly correlated with the mean temperature than the average hours of daylight presumably the relationship does not merely reflect seasonal variations in the abundance of the egg predators, but also an increased activity of the predators with higher temperatures. For Entomobrya, 94. 6% of the variance in the egg mortality data was accounted for by one variable, namely mean temperature,in a regression equation. Thus it did not seem necessary to include a specific term for egg mortality in Entomobrya in the later regression analysis, as this process was so closely related to the temperature alone. In the case of Tomocerus mean temperature accounted for 79. 7% of the variance in the egg mortality data, and if TOMOCERUS LON GI CORNIS

0

0

> - •

0 0 2 Y= -5.65445 + 1.07117X 0 0. w ZERO MORTALITY AT 5.20 • 0 0 0

0

4 6 8 1i 12 14 16 MEAN TEMPERATURE 00 EN TO MOBRYA NICD LETI

18 Y = -9.381 + .82686 X

14. >- ZERO MORTALITY AT 1.1* 0 12 cc 0 >- 10 0 cc 0

CD 0 cD 0

4 8 10 12 14 1G 18 MEAN TEMPERATURE °C 1 6 1,

litter moisture squared were included in the equation a further 7. 6% of the variance was removed. The inclusion of the litter moisture term seems reasonable as in dry conditions the mobility of many egg predators may have been severely limited due to the dangers of desiccation, and a combination of high temperature and humid conditions would seem to be more optimal for free movement in the surface layers of the litter. 163

THE ANALYSIS OF DISPERSION

The measurement and analysis of the dispersion of a population

(1. e. the pattern of the spatial distribution of the animals), and the changes in dispersion with time, are complementary with analysis of the size and changes in size in a complete description of the population.

A knowledge of the dispersion pattern, besides being essential for the design of the sampling programme and subsequent analysis of the data, is also a useful aid in interpreting the population dynamics, and may elucidate ecological processes which are important in their own right

(Southwood, 1966).

Brief summaries of studies on the dispersion of natural

Collembolan populations can be found in Hale (1967) and in Butcher,

Snider and Snider (1971).

Most of the early workers simply tested the form of the dispersion by using the variance/mean ratio (or Fisher coefficient of dispersion), e. g. Haarlov (1960), Poole (1961), Hale (1966). Other authors have taken blocks of contiguous samples (Poole, 1964; Usher,

1969), or transects (Poole, 1962). Usher (1969) analysed his blocks

(a) by defining aggregations subjectively, with the suggestion that in a large sample, assuming that aggregated distributions can be approximated by a Poisson distribution skewed by a few large counts, aggregations could be analysed by the removing of the largest count and testing the residual distribution for randomness by the Chi-squared criteria, then removing the next largest count and so on. Secondly,

(b) if the aggregations were large compared to the size of the block, 164

gradients of numbers across the block were analysed by the analysis of variance.

Glasgow (1939) and Hughes (1962) have calculated the spatial dimensions of aggregations, the first-named author using the analysis of variance of paired sample data, the the latter author using his

"Tie-line" method.

Joosse (1970) seems to have been the only author who has analysed aggregation in Collembola using Taylor's power law, although

Takeda (1973) has used the related approach of Iwao (1968), viz.

regression of mean crowding on mean density.

The description of Collembolan dispersion by the fitting of statistical distributions to sample numbers has been undertaken on a few occasions only. Hartenstein (1961) and Ibara, Wallwork and

Rodriguez (1965) fitted the Poisson, Neyman A and Negative Binomial distributions to sets of samples of Collembola. The Poisson distribution was not found to be applicable. The Negative Binomial performed better than the Neyman A, but gave a poor fit to some groups of samples, with excessive variation between expected and observed values. These authors did not distinguish different species of

Collembola so their work seems of limited usefulness. Sherif (1971) did distinguish separate species, and obtained good fits to the Negative

Binomial distribution for all the species he considered except two

(Folsomia quadrioculata and Isotomodes productus) for which the

Neyman A distribution performed better. Hughes (1954) found a good agreement between the observed counts and those expected from the

Negative Binomial for I. viridis, Folsomia quadrioculata, and

L. cyaneus. 1 6 5

Among the explanations for the aggregated dispersion patterns found in Collembolan populations, Poole (1961) considered the following: poor dispersal from the egg-cluster, active gregariousness, and the effects of patchiness in the environmental factors.

Most of the studies concerned with the first factor are on soil- dwelling Collembola. Poole (1961) attempted to show that aggregations in the populations studied were not related to egg clusters; he believed that a combination of environmental factors were responsible (e. g. the rhizo-sphere around the root system of trees may be influential, Poole,

1964). Hale (1966) maintained there was no evidence to suggest that aggregations did not result from egg batches. Neither author separated the different age groups in their samples, but this would seem to be a pre-requisite in investigations into aggregation related to egg clusters, especially in view of Usher's (1964) observation that juveniles of Isotoma sensibilis remain at the site of a cluster for the first three instars, provided there is sufficient food. Usher (1969) found that the juveniles of all the Collembolan species in his analysis had an aggregated pattern characterised by having a positive significant correlation (rn ) between the population density and the number of aggregates, whilst the correlation

(rs ) between the population density and the mean number of animals per aggregate was non-significant. This type of aggregation could result from some fixed attribute of the species such as egg-cluster size.

Healey (1967) found in Onychiurus procampatus that maximum 'aggregation coincided with a period with large numbers of juveniles. Thus there seems to be some evidence for the proposition that aggregation may be partially due to slow dispersal from egg clusters, at 1,,,tast in soil dwellers. 166

Another fixed attribute of the species which may influence aggregation is the development of the locomotary organs. Haarlov (1960) suggested that the better developed the locomotary organs, the less aggregated the dispersion pattern of the species in question. Hale (1966), however, disputed this contention. Dispersal in epigeic niches may be more easy than it is deeper in the soil profile as the former habitat is more "open". It is likely that surface-dwelling Collembola are able to be dispersed to a greater extent by external factors such as wind and water. One would therefore expect more redistribution of individuals in surface niches unless there are behavioural factors counteracting this.

No convincing evidence has been presented as yet for active gregarisation in Collembola, although this can certainly not be ruled out.

It has often been noticed that in culture surface-dwelling Collembola tend to congregate in a certain part of the culture vessel while moulting, and that moults are synchronchized, and South (1959) notes the occurrence of large numbers of moulted Entomobrya cuticles in one location under bark in the wild. It is possible that aggregation occurs at the time of moulting, so as to maximise the chances of sperm transfer during the next reproductive phase (see also Joosse, 1970).

Christiansen (1964) believed that some behavioural phenomena may be involved in the mass swarming outbreaks known in some species

(e. g. Goto, 1957).

It is, however, difficult in the absence of careful experimentation to separate active gregariousness and the effects of behaviour in response to environmental patchiness. Under Usher's (1969) system, aggregations resulting from movemen' of insects into areas with particularly suitable 167

environmental conditions, would produce non-significant values of rn and positive and significant rs values. In his populations, this type of aggregation could not be demonstrated. This is, perhaps,

surprising as Joosse and Groen (1970) have shown in laboratory conditions that, in surface-dwelling species at least, undirected movements in response to saturation deficit occurred, and this would presumably lead the animals to aggregate in sites with optimum

conditions of humidity and temperature. Joosse (1970) has shown that distinct humidity preferences occur, and these are related to the resistance to desication and degree of a,l-gregation of the species. Thus species with a low resistance to desiccation and- a preference for high humidity (e. g.

Isotoma viridis and Tomocerus minor), show a higher degree of aggregation than species with more resistance to desiccation, and a preference for slightly lower humidity (e. g. Entomobrya nivalis) or a broad range of humidity preference indicating a euryhygric nature

(e. g. Orchesella cincta). One of Joossets (1970) results is rather

surprising however; this is the higher aggregation found in the winter months (December to April). If, as she suggests, drought is the

stimulus leading to aggregation, one might expect a lower aggregation in the winter. Thus it remains a possibility that the higher aggregations found in winter are in response to some other factor,

e. g. excessive moisture or low temperatures.

METHODS: -

Models relating to the dispersion of a species to its population dynamics in a realistic manner are difficult to construct as they require a large amount of detailed biological knowledge about the species and its interactions with the environment, and also are rather mathematically complex due to the stochastic elements which would have to be incorporated. 168

In a preliminary analysis of dispersion, like the one undertaken in the present study, a much more simple approach must be used.

Five main methods were utilised, they were:-

a) The fitting of theoretical distributions.

b) The calculation of Indices of dispersion.

c) Calculation of the parameters of Taylor's power law.

d) The measurement of the association of the Collembola with

various environmental features in each sub-sample.

e) Cluster analysis.

This last method summarises and compares all the ecological attributes of each species as expressed in the samples; population size as well as distribution is involved. Thus these analyses are described in a later section.

Fitting of theoretical distribution models

One of the most basic and the most general approaches to dispersion is the fitting of various well-known theoretical distributions to the counts of individuals per quadrat. This provides a summarisation of the sampling frequency table in terms of a few parameters. It thus gives an objective description of the dispersion using readily obtainable information, and is easily comparable with other data of the same type.

There is, of course, no way of deciding from the frequency table data alone which of the underlying models, if any, are involved.

However, comparison of data for different species and from different occasions, combined with information from other sources, and bearing in mind the various postulac.ions which could generate the theoretical distribution, may suggest hypotheses about the biological causes of the 169

dispersion pattern.

A computer programme written by Reyna Robles (1969) was used to fit theoretical distributions to the frequency distribution of observed counts per sample unit for each of the Collembolan species, for each separate sampling occasion. Depending on the variance/mean ratio, the following distributions were fitted: -

1) The binomial distribution (Hoel, 1971).

2) The Poisson distribution (Hoel, 1971).

3) The normal distribution (Hoel, 1971, computed as in Golden, 1965).

4) The double Poisson distribution (Thomas, 1949).

5) The Neyman A distribution (Neyman, 1939, Douglas 1955).

6) The negative binomial distribution (Anscombe, 1950,

Bliss and Fisher, 1953).

7) The truncated Poisson distribution (David and Johnson, 1952)„,'

8) The truncated Neyman A distribution (Neyman, 1939; Douglas, 1955).

9) The truncated negative binomial distribution (Sampford, 1955).

10) The logarithmic distribution (Fisher, Corbet and Williams, 1943;

Williams, 1964).,

Moment estimators were calculated for the double Poisson distribution (Thomas, 1949), and moment estimators were also used as first approximations in the calculation of Maximum Likelihood estimators in the case of the truncated Poisson (Plackett, 1953), and the truncated negative binomial (Brass, 1958). Moment estimators were also used in other cases where acceptable Maximum Likelihood estimators were not found,

Two procedures were used to test the goodness-of-fit of the theoretical distribution models to the observed data. The standard 170

procedure was based on the computation of calculated frequencies for each class interval and the comparison of these with the corresponding observed frequencies by the chi-squared statistic:

L= (o - CVal X where o = observed frequencies c = calculated frequencies

This test may be affected when the comparisons involve very small calculated frequencies, and the usual procedure is to group contiguous frequency classes so that no calculated frequency is less than an arbitrary value, usually 5. However, combining categories unnecessarily reduces the degrees of freedom for the test, and sometimes obscures differences between the distributions which lie in their tails,

(Cochran, 1954). In the present case therefore all expected frequencies of less than 2 were grouped. The above difficulties can be avoided by the calculation of an alternative chi-squared test (Fisher, 1950) and this test can be used when there are insufficient degrees of freedom for the standard test as was sometimes the case in the present data..

It is given by: N

Z 0 teg. e 0 = observed frequencies C = calculated frequencies

Indices of aggregation

For each case where a Maximum Likelihood estimate of a parameter (or parameters) of the distribution was obtained the Index of

Dispersion (Fisher, 1963), and Lefkovitch's4(1966) were calculated. The

Index of Dispersion is used for testing the departure of observed counts from the conditions of a given hypothesis. For example, to test the 1 7 1

departure from the hypothesis of a Poisson distribution, we have: 2 (N-1) S ID = x 2 where N = the number of observation in the sample, S is the variance

and x is the mean. The index is distributed approximately as a

chi-squared distribution with N-1 degrees of freedom. Lefkovitch's

(1966) index is given by:

2 4 -1 S z Tr tan (71--2 ) in radians 2 where S = theoretical variance and 6 2 observed variance.

Differences between two values of A can be tested using value of 4 tan-1 Tr ( ( p, n/N)) - 1 given by Lefkovitch's(1966) e_

where p is given probability

n is degrees of freedom

If estimation and comparison of the degree of aggregation is

required, rather than the significance testing, several indices based on

the variance/mean ratio are useful summarisations of the frequency table

data, although they waste some information. Green (1966), Cancela de

Fonseca (1966), Lefkovitch (1966) and Reyna Robles (1969) have discussed

the merits of various indices of use in animal ecology. Reyna Robles

(1969) recommends the Disturbance Index of lexis ( X ), Beall (1935),

Cancela de Fonseca (1966), as the most satisfactory for non-aggregated

distributions, and the is of Morisita (1959,1962, 1971), as reliable for

most of the aggregated range, thus these two indices were calculated for

each sampling frequency table. We have:

2 S = sample variance = sample mean 172- .

It= N2x1-2x Ex (fix-I) where x is a quadrant count

Mean Crowding and Patchiness

Lloyd (1967) proposed a parameter of "crowding" determined by the mean number per individual of other individuals in the same quadrat.

For the complete count the mean crowding is:

= ( Z x2 / x) - m

For the universe: = 1u + {6i 4 )- 1

where A-A = theoretical mean

6u = theoretical variance

It is the amount by which the ratio of the variance to the mean density

exceeds unity added to the mean density itself, it estimated from the

sample would not be reliable, thus a relatively unbiased sample estimate•

of mean crowding was obtained using the maximum likelihood estimates

of the parameters of the negative binomial, where this distribution

described the data well. Thus the sample estimate of mean crowding is:

+ sample mean estimate of negative parameter k

The ratio mean crowding to mean density iln/m = 1 k , and is termed the

patchiness (Lloyd, 1967). It purports to measure how many times as

crowded an individual is, on average, as it would have to be if the same

population had a random distribution. It is virtually the same as

Morisita's IS 173

Taylor's Power Law

Taylor (1961, 1965) showed that in samples of many animal species, the variance was a function of the mean over a large range of densities and theoretical distribution characteristics. Thus a,ub 6.2 = iu where a is the variance of the distribution when the mean = unity.„ and b is supposed to reflect the behavioural interaction with the environment and thus is characteristic of the species. The value of b is usually found from the linear regression: 2 log S = log a + b log)? 2 where S is the sample variance and the sample mean.

However as both the mean and the variance are subject to error, and we are interested in the true functional relationship of X and S2, rather than predicting one given the other, Bartlett' (1949) technique seems more appropriate. The parameters were calculated in the present study by a computer programme slightly modified from Davies (1971, page 208). This programme also computes the t tests for determining the significance of the regression, determining whethee b is significantly greater than 1, and whether there is any significant deviation from linearity, (Simpson, Roe

Lewontin, 1960). A subroutine was added to obtain the upper and lower confidence limits of b by finding the roots of the quadratic expression given on p. 234 of Simpson, Roe & Lewontin (1960).

Incidentally, the power law was also used to find a variance- stabilising transformation.

The transformed value Z = XP

where X is the original number and p = 1-fb 174

Another regression method for analyzing the distributional pattern of individuals, which will be mentioned in passing, is that of Iwao (1968) and Iwao and Kuno (1971). It related the mean crowding (m) of Lloyd (1967) to the mean density, for a series of having common properties, thus:

m =0C + /3jL

0( is said to indicate whether a single individual or a positive or negative association of individuals is the basic component of the distribution, while /3 suggests how such basic components distribute themselves over the space (Iwao 72).

Iwao (1972) has recently extended the method by allowing

for analysis by successive changes in quadrat size in a single

population, as well as analysing the relation for a series of

populations at a particular quadrat size.

Interpretations of the parameters of both these methods

must be made with care, as it is possible that various situations

may give rise to the same type of line. It is also possible that a

curvi- linear relationship could exist. 175

Spatial distribution of the Collembola as related to some environmental features On several occasions during the year various features of

the habitat were assessed for each square-foot sample unit at the

same time as the Collembolan sample was taken. The features were

chosen mainly because they were measured or assessed with little

extra labour. They were:

The position of the sample on the plot

Dominant grass species.

Soil moisture and litter moisture.

Thickness of the vegetation.

The presence or absence of vole runs.

(6) The amount of fallen leaves.

In addition to the above features the number of other Collembola

in each sample unit was available for every sampling occasion. In

the statistical analysis of the relationship of the Collembolan

species to these environmental features non-parametric tests were

used. For correlations between species Kendall's tau correlation

coefficient was used (Kendall, 1970). If the measured variable

could be subjectively assignedto 2 or 3 categories (e. g. in the case

of the grass type), the medians of the number of collembola under

each category were tested for significant differences by the Mann-

Whitney U-test or the Kruskall-Wallis test, (Campbell, 1967).

As the labour of calculating Kendall's tau for all combinations

of the four species for each sampling occasion was rather great, the

analysis were carried out by computer. A main routine was ow, 1 7 6

written for use with the KRANK subroutine (I. B. M. system/ 360

Scientific Subroutine Package Version III (1969) page 69), however, this SSP routine to calculate tau has apparently been programmed from an example in Siegal, (1956), and in this example ties are only present in one variable. Thus the programme gives an incorrect value for tau when ties are present. This fault can be

corrected by the inclusion of the statement:

IF ( R ( I ). EQ. R ( L ) ) GO TO 60

after statement K = N + L which is statement KRAN 930 of the

KRANK subroutine. Even with this alteration this programme was

not completely satisfactory if large numbers of ties were present

(as is the case with the Tomocerus longicornis samples), as it

does not correct the standard deviation for ties and makes no

correction for continuity, (Kendall, 1970; page 55). Both of these

faults have been overcome in the new programme written by Dr.

Brian Baughan of the University of London Institute of Education

(see Institute of Education Computing Bulletin 5A March 1972).

This this programme was used for calculation of Kendall's tau

correlation in the present study. 177

RESULTS OF FITTING OF THEORETICAL FREQ._UENCX DISTRIBUTIONS TO THE COUNTS OF COLLEMBOLA FROM SAMPLES

It was noted that the chi-squared values for the goodness-of- fit test differed somewhat depending on which of the tests was used.

The alternative chi-squared procedure appeared to be more decisive in rejecting unsatisfactory distributions, and is the one reported here. Fits to the Double Poisson distribution were deleted by the programme on many occasions as the parameter was too large for exponentiation, and this also occurred with the Poisson on some occasions with high densities of E. nicoleti or L. lignorum.

The programme was unable to find M. L. estimators on some occasions with the truncated distributions; this was particularly the case with the truncated Neyman A.

Considering the vacuum sample data:- The binomial distribution was not applicable as a description of the dispersion of E. nicoleti, I. viridis or L. lignorum; the probability of obtaining by chance a deviation larger than the observed one was usually less than 0. 1% i. e. the fit was very poor. The fits to the Poisson were also generally poor but slightly better than with the binomial.

It will be noticed, however, that on a few occasions the binomial and more particularly the Poisson gave reasonable fits to the data. (e. g. on 2. 3.70 and 9. 6.70 for E. nicoleti, on 10. 10. 69 and 20.8.70 for L. lignorum and on 9.6.70 and 28.8.70 for

I. viridis). However, with the exception of 9. 6.70 I. viridis sample, the binomial and Poisson probably did not provide a good 178

model for the dispersion on these occasions as a) the variance was greater than the mean and the probability values corresponding to the Index of Dispersion values show that the observed counts always departed greatly from the conditions of this Binomial hypothesis

(p. < . 001), and the conditions required by the Poisson were not met with either (P = .1 - . 001). Secondly b) on these occasions the data were readily fitted by all or nearly all of the theoretical distributions, (this was also reported by Reyna Robles (1969) for some of his samples); but the aggregated distributions gave better fits than the binomial or Poisson. For the 9.6. 70 sample of I. viridis the variance equalled the mean.

Apart from this anomalous result it can safely be concluded that E. nicoleti, L. lignorum and I. viridis were "not random" in dispersion.

Turning to the "aggregated" distributions: the Negative Binomial • distribution formed a good empirical model for E. nicoleti, I. viridis and L. lignorum on most occasions. The E. nicoleti data gave the best, or equal best fit to this distribution on all but four sampling occasions, the I. viridis data on all but five occasions, and the

L. lignorum data on all but six occasions. For I. viridis the probability values for the Goodness-of-fit tests were always fairly high (none were less than P = .2 - . 1).

Hughes (1954) and Sherif (1971) also found I. viridis samples to be in good agreement with those expected from the negative binomial distribution. Sherif (1971) found a small value for k (k = 0.13 for "ic = 3. 2), and concluded that the animals were highly aggregated, 179

but Hughes (1954) concluded that the species was not certainly

"not random" in his samples.

For E. nicoleti and L. lignorum there were a few samples which gave rather poor fits to the negative binomial. There did not appear to be any particular sample-mean or k-value associated with this, (except just possibly a tendency to higher values of k for

E. nicoleti). It is well-known that the comparison of actual and 2 expected frequencies by the X test can be distorted by chance irregularities, and it is likely that that is what is occurring here.

There was no evidence from the Index of Dispersion of a significant departure in the observed counts from the conditions required by the negative binomial.

It is often useful to know whether a common k-parameter can be applied to all of the samples (Bliss and Owen 1958).

The relationship between 1/k and log. mean was studied for

E. nicoleti (see P.180 ). It can be seen that there is a significant trend between these quantities (correlation coefficient = -. 5677, n = 27, and thus the applicability of a common k is questionable.

It is interesting to note that this trend arises from the contribution to the correlation of the large-sized sample-units, and there is little trend in the small-sized sample-unit counts alone

(correlation coefficient = .2048, P greater than 10% level).

The Neyman A distribution produced a number of good fits, and gave an equal or slightly better performance than the negative binomial on 5 out of 27 occasions with the E. nicoleti data, and 9 out of 27 for L. lignorum. The programme did succeed in finding suitable estimators at high sample densities however.

THE RELATIONSHIP OF THE k-PARAMETER OF THE NEGATIVE BINOMIAL DISTRIBUTION 1 AND THE LOGARITHM OF THE SAMPLE MEAN FOR THE E. NICOLETI SUCTION SAMPLES. k

LOG. 5.--< 1 8 1

The Neyman A is strictly applicable only when rather discrete, large clusters occur at random and the numbers of individuals within the cluster are distributed independently in a Poisson distribution. If the edge of the cluster becomes less distinct and the density of individuals in the centre of the cluster increases, the negative binomial distribution becomes more appropriate. In practice however small clusters formed by compounding a Poisson distribution by another Poisson would give good fits to the Neyman A (Skellam 1958).

The similar Double Poisson distribution gave some good fits but it was very erratic from occasion to occasion, and was thus an inferior model to the Neyman A or negative binomial.

The data for the three species especially Lepidocyrtus lignorum was often approximately normal possibly because the high means tend to promote normality (eg. a slight improvement in fit seemed to occur with increasing density with L.lignorum).

It is unlikely that this distribution forms a realistic fundamental model.

Considering the truncated distributions: as remarked on above, M. L. estimators were not found for the Neyman A on many occasions, and this distribution will not be considered further.

The truncated Poisson had the same deficiencies as the complete

Poisson. The truncated negative binomial was apparently the most useful of the truncated distributions on the occasions when it was applicable, but fits were generally inferior to the full distribution. 182

The Logarithmic model has uses when the k of the negative binomial tends to zero. Here the goodness-of-fit varied rather erratically from occasion to occasion, and suitable M. L. estimates were not found with high sample means.

The Tomocerus longicornis suction samples appeared at first sight to indicate a different dispersion pattern to that of the other three species, in that the T. longicornis samples appeared to best fit the Poisson on several occasions, (and here the sample mean • was very close to the variance in size or even exceeded it slightly).

On the other hand, the Negative Binomial appeared to form a very good empirical model on many other occasions. The good fits with the Poisson were nearly always obtained with the small size sample-unit (4" diameter), and occasions where this small unit was used and the counts did not fit the Poisson were occasions of high density (eg. on 19/11/69, 28/4/70, 29/9/70). After

24/11/70 all sample-units were of 1 square-foot size and the

Poisson was not applicable to these (P < . 02).

Thus this was almost certainly an example of a very low density in the sample-unit producing an apparent dispersion pattern in which non-randomness cannot be distinguished, i. e. the sensitivity of tests for departure from a Poisson distribution are being reduced and affected by sampling procedure (also noted by

Cole 1946, Cassie 1962, Elliott 1971). Also the underlying conditions which could give rise to randomness (eg. homogeneous 1 8 3

environment, passive dispersal, or very mobile organisms) are most unlikely with the animal in question.

The core samples for E. nicoleti were also fitted to the theoretical distributions. The negative binomial was again the most useful distribution, (although the very low densities during the winter produced a tendency to the apparent "randomness" phenomenon remarked on above).

The next question to be answered is; if it can be assume d that the negative binomial is a useful fundamental as well as empirical model, which set of processes are responsible for generating the observed pattern in the field?

The negative binomial can be derived from several mathematical or biological models (see eg. Reyna Robles 1969).

True contagion may well occur, and account for the negative binomial form of the observed distribution. I. viridis lays its eggs in batches and the hatchlings from these would be contagious in distribution.

The other surface-dwellers studied do not do this, but certain areas may be particularly suitable for oviposition, or for survival in the early instars, as high E. nicoleti aggregation appeared to coincide with maximum numbers of the early instars. (see section on

Indices of aggregation). There is also the suggestion of aggregation of the adults preceding synchronous moulting, possibly to facilitate sperm-transfer (Joosse 1970). 184

Among other models,the symetric dispersion of

individuals that have multiplied around each of a set of random

centres can give rise to the negative binomial. The compounding

or generalizing of other distributions could possibly apply to this

situation, eg. if clumps of individuals are distributed at random,

and the numbers in each clump are distributed independently in a logarithmic distribution, then the negative binomial again arises.

Without additional information one cannot, of course, proceed further in deciding which of the possible hypotheses is

closest to the real situation. This raises some doubt as to the

usefulness of theoretical distribution fitting as an analytical technique (as opposed to a mathematical description of spatial

dispersion required for insertion into a larger model, say). If a

simple summary of a series of counts is required in a form in which/ it can be compared to other series of counts (eg. example to follow

changes of aggregation during the season), the Indices of

Aggregation seem at least as useful as the results of the

distribution fitting, and much easier to calculate. If one requires

an overall statement of the dispersion of the species in a habitat to

compare with the same species in another habitat, or with another

species or one wishes to find transformations for the data, an

approach such as Taylor's power law seems more valuable. If a

detailed knowledge of the dispersion pattern at a particular time and

place is required, then 'maps' produced by extensive systematic

sampling will produce a better picture than attempts to deduce 185

patterns from counts from random samples. All in all the

technique of fitting theoretical distributions, by itself, seems

rather limited as an analytical tool.

Comparison of the Dispersion Pattern of all Species or Groups found in Core-Samples

For four sets of core samples, counts were made of all

the Collembola in the sample units (not just the surface-dwellers),

and a comparison of the dispersion pattern between the different

species and groups was made. Table p 197-8. shows the

performances of various theoretical distributions fitted to these

samples. The 1$ and Patchiness values are also given.

It can be seen that the negative binomial distribution once

again provides a reasonable fit to most of the species and groups,

thus this distribution would be the most applicable statistical model

for a general census of the collembolan fauna of an area. Only in

the case of the Sminthuridae and possibly Tomocerus does the

negative binomial model appear inappropriate, the dispersion tending

towards apparent randomness, or in the case of the Sminthuridae

towards regularity. However, as has been discussed above, the

tendency to randomness in Tomocerus is probably an artefact of

the low numbers found in the quadrat; it is not reflected in the

Indices of aggregation. With the Sminthuridae there appears to be

a real decrease in the level of aggregation, perhaps as an

adaptation to surface life. Conversely the soil-dwelling groups 1 8 6

i. e. Folsomia and Onychiurus, tend to have high indices of aggregation. In these species, on occasions where aggregation is moderate, the negative binomial gives very good fits, but on occasions of hi gher aggregation sample-numbers approach the logarithmic limit of the negative binomial distribution. For

Hypogastrura sp. the data is rather poor but aggregation can be extreme, and if an aggregate happens to be included in sample the logarithmic distribution gives good fits, eg. in sample (b).

The surface-dwelling species eg. Entomobrya nicoleti and

Lepidocyrtus lignorum have generally lower indices of aggregation than the soil-dwellers. (Some Entomobrya spp. which are associated with a patchy microhabitat, eg. corticolous species which prefer a certai n species of tree, may show patchiness on some scale of sample due to their microhabitat). I. viridis although a surface-dweller shows a high aggregation; it can be regarded as being between the soil-dwellers and the more highly-evolved surface-living species like Entomobrya. (see also section on

Taylor's power law).

As some evidence has been found in the present study for the hypothesis that aggregation depends on the juvenile animals one might speculate that the higher levels of aggregation found in the soil-dwellers may be due to the fact that their eggs are laid in large batches, and further, that dispersal from these egg masses is slowed by the enclosed nature of the deeper soil habitat and/or the lesser powers of locomotion in the soil-dwellers. The effects 187

of dispersal agents such as wind and water would also be less in the deeper layers of the profile.

The high aggregation of the surface-dwelling I. viridis may also be due to its habit of laying eggs in large batches (cf. the other surface-dwellers). This species may possibly also be restricted by its intolerance of low humidity, at least during some periods of the summer; the same applies to T. longicornis. The matter is discussed further in a later section. TABLE A Probability values for the Goodness-of-Fit Tests on the Entomobrya nicoleti sample data for various theoretical distributions.

SAMPLING DOUBLE NEYMAN NEGATIVE TRUNCATED LOGARITHMIC DATE BINOMIAL POISSON NORMAL POISSON A BINOMIAL NEG. BINOM. 2/10/69 .001 D, MTL .30 - .20 D, MTL .30 - .20 .50 - .30 .20 D 3/10/69 .001 .001 .50 D, MTL .90 - .8o .80 .30 - .20 10/10/69 .001 .001 .50 - .3o .70 - .50 .5o - .30 .70 - .5o .70 - .50 19/11/69 .001 .001 .30 - .20 .001 .05 - .20 .70 - .50 .50 - .30 .50 - .30 18/12/69 .001 .001 .20 .20 - .10 .20 - .10 .10 - .05 .10 .01 - .001 26/ 1/70 .001 .001 .50 - .30 D, MTL .50 - .30 .70 - .50 D 2/ 3/70 .001 • D, MTL .70 - .50 D, MTL .70 - .50 .70 - .50 .70 - .50 D 2/ 3/70 .50 - .30 .80 - .70 .90 - .80 .90 - .80 .90 - .80 .90 - .80 .70 - .50 .30 - .20 2/ 4/70 .001 D, MTL .50 - .30 D, MTL .30 - .20 .50 - .30 GIN D 14/ 4/70 .001 D, MTL .10 - .05 D, MTL D .50 - .30 D 28/ 4/70 .001 .001 .5o - .30 .001 .70 - .50 .90 - .80 .8o - .70 .30 9/ 6/70 .001 .001 .50 - .30 D, MTL .10 - .05 .70 - .50 .50 9/ 6/70 .05 - .02 :30 - .20 .50 - .30 .8o - .70 .80 - .7o .8o - .70 .50 - .30 .50 - .30 21/ 7/70 .001 .001 .10 - .05 D, MTL .10 - ,05 .70 /NW .70 - .50 20/ 8/70 .001 .01 - .001 .20 - .10 .001 .50 - .30 .70 - .50 .50 - .30 3/ 9/70 .001 .001 .05 - .02 .001 .10 - .05 .50 - .30 •on .50 - .30 29/ 9/70 .001 .001 .20 .30 - .20 .30 - .20 .20 - .10 .01 - .001 28/10/70. .02 .20 - .10 .50 - .30 .30 - .20 .30 - .20 .30 - .20 .01 - .001 24/11/70 .001 D, MTL .70 - .50 D, MTL .98 - .95 D

00

TABLE A Continued

r SAMPLING DOUBLE NEYMAN NEGATIVE TRUNCATED BINOMIAL POISSON NORMAL A BINOMIAL NEG. BINOM. LOGARITHMIC DATE. ..- POISSON 9/ 2/71 .001 D, MTL .01 - .001 D, MTL D .02 - .01 . - D 9/ 3/71 .001 D, MTL .10 - .05 D, MTL D .70 - .50 - D 7/ 4/71 .001 D, MTL .02 - .01 D, MTL D .70 - .50 - D 15,/ 6/71 .001 D, MTL .70 - .50 D, MTL D .80 - .70 - D 14/ 8/71 .001 D, MTL .05 - .02 D, MTL .20 - .10 .05 - .02 - D 21/ 9/71 .001 D, MTL • .20 - .10 D, MTL D .50 - .30 - D 15/11/71 .001 D, MTL .50 - .30 D, MTL D .50 - .30 - D 14/ 2/72 .001 D, MTL .20 - .10 D, MTL D - .80 - .70 - D

CD k0 TABLE B Probability values for the Goodness-of-Fit Tests on the Lepidocyrtus lignorum sample data for various theoretical distributions.

SAMPLING DOUBLE NEYMAN NEGATIVE LOGARITHMIC DATE BINOMIAL POISSON NORMAL POISSON A BINOMIAL 2/10/69 .001 D, MTL .20 D, MTL .20 - .10 .30 - .20 D 3/10/69 .001 .001 .70 - .50 .01 - .001 .50 - .30 .95 - .90 .90 - .80 10/10/69 .20 - .10 .50 - .30 .30 - .20 .30 .50 - .30 .50 - .30 .20 - .10 19/11/69 .001 .001 .30 D, MTL D .90 - .80 .70 - .50 18/12/69 .001. .30 - .20 .80 - .70 .80 - .70 .90 - .80 .90 - .80 .20 - .10 26/ 1/70 .001 .02 - .01 .20 - .10 .01 - .001 .30 - .20 .50 - .30 26/ 1/70 .001 D, MTL .30 - .20 D, MTL .30 - .20 .30 D 2/ 3/70 .001 .001 .30 - .20 D, MTL .30 - .20 .20 - .10 .05 - .02 2/ 3/70 .01 - .001 .10 .70 - .50 .50 - .30 .80 .90 - .80 .70 - .50 2/ 4/7o .001 .001 .30 D, MTL .20 - .10 .30 - .20 ..10 14/ 4/70 .001 D, MTL .80 - .70 D, MTL .10 - .05 .70 - .50 D 28/ 4/70 .001 .001 .20 - .10 .20 - .10 .10 - .05 .20 .02 - .01 9/ 6/70 .001 .02 - .01 .50 .70 - .50 .70 - .50 .50 - .30 .10 - .05 21/ 7/70 .001 .001 .20 - .10 D, MTL .20 - .10 .80 - .70 .50 - .30 2o/ 8/70 .10 - .05 .70 - .50 .90 - .80 .95 - .90 .95 - .90 .95 .30 3/ 9/70 .02 - .01 .20 - .10 .50 - .30 .01 - .001 .95 - .90 .95 - .90 .50 - .30 29/ 9/70 .001 .001 .20 - .10 .05 - .02 .10 - .05 .20 - .10 ' .02 28/10/70 .01 .20 - .10 .80 - .70 .90 - .80 .90 - .80 .80 - .70 .10 - .05

O

TABLE B Continued

SAMPLING DOUBLE NEYMAN NEGATIVE DATE BINOMIAL POISSON NORMAL POISSON A BINOMIAL LOGARITHMIC 24/11/70 .001 .001 .10 .20 - .10 .20 - .10 .30 - .20 .001 9/ 2/71 .001 .001 .50 - .30 .50 - .30 .50 - .30 .50 - .30 .001 9/ 3/71 .001 .001 .50 - .30 D, MTL .70 - .50 .90 - .80 .01 - .001 7/ 4/71 .001 D, MTL .20 - .10 D, MTL D .20 D 15/6/71 .001 D, MTL .05 - .02 D, MTL D .10 - .05 D 14/ 8/71 - ' .001 D, MTL .95 - .90 D, MTL .90 - .80 .90 - .80 D 21/ 9/71 .001 .001 .30 - .20 .30 - .20 .80 - .70 .30 - .20 .001 15/11/71 .001 D, MTL .05 D, MTL .05 .20 - .10 D 14/ 2/72 .001 .001 .70 - .50 D, MTL .70 - .50 .70 D

TABLE C Probability values for the Goodness-of-Fit Tests on the Tomocerus longicornis sample data for various

theoretical distributions.

SAMPLING DOUBLE NEYMAN NEGATIVE TRUNCATED BINOMIAL POISSON NORMAL LOGARITHMIC DATE POISSON A BINOMIAL NEG. BINOM.

2/10/69 .30 - .20 .90 - .8o ...ft"' .70 .80 .70 '.80 - .70 .8o - .7o .01 - .001 3/10/69 .20 - .10 .5o - .30 D D D D D D 1c/10/69 .30 - .20 mm,f1= .50 D D D D D D 19/11/69 .001 7=n7 .01 ,n= .30 .n= .30 .50 - .30 .90 - .80 .50 - .30 .50 - .30 18/12/69 .50 - .30 =8= .80 .30 X X X x X 26/ 1/70 .01 - .001 .10 - .05 .20 - .10 .30 - .20 .30 - .20 .50 - .30

2/ 3/70 d .02 .10 - .05 - .30 .50 - .30 .11.= .50 .50 - .30 .50 - .30 .10 - .05 2/ 3/70 tot .20 .50 - .30 D D D D D D

2/ 4/70 .10 - .05 .50 - .30 D X X X X X

14/ 4/7o .00l -=z.00l .10 - .05 .001 .10 - .05 .30 - .20 D .20 - .10

28/ 4/70 .001 =a= .ol =I=I .30 .20 - .10 .70 - .50 .70 - .50 D .50 - .30

21/ 7/70 .30 - .20 .5o - .3o .10 - .05 .001 .30 - .20 .30 D D 29/ 9/70 .01 - .001 .10 - .05 .20 - .10 .70 - .50 .80 - .70 .95 - .90 -^- .8o -A- .7o

28/10/70 .90 - .8o .7o - .5o D X X X

24/11/70 =8= .02 .90 - .80 .80 - .70 .go - .8o .90 - .80 .8o - .70 .20 - .10 9/ 2/71 -=z .001 .001 .70 - .50 .001 72.1= .95 .99 .99 .50 - .30 9/ 3/71 .001 -=z .001 .30 - .20 C .001 .70 - .50 .98 - .95 to= .98 .70 - .50

7/ 4/71 .001 .01 - .001 .10 - .05 .001 =1= .30 .70 - .50 -ti- .5o .50 - .30 15/ 6/71 .001 .50 - .30 - .001 .50 - .30 .90 - .80 ' .8o - .7o .30 - .20 '0

TABLE C Continued

DOUBLE NEYMAN NEGATIVE TRUNCATED SAMPLING BINOMIAL POISSON NORMAL LOGARITHMIC DATE POISSON A, BINOMIAL NEG. BINOM.

14/ 8/71 -c-7= .001 .01 - .001 .20 - .10 .20 - .10 .30 - .20 .50 - .30 .20 - .10 .20 - .10 21/ 9/71 .001 =nr .01 .10 - .05 .70 - .50 ____ .70 .50 - .30 .50 - .30 330 - .20 15/11/71 .--.. .001 -.-- .001 .10 - .05 -== .001 .10 - .05 ...,--....- .50 .50 - .30 .50 - .30 14/ 2/72 .. .001 .001 .80 - .70 .10 - .05 .70 - .50 .98 - .95 .90 - .80 =a= .70 Table D Probability values for the Goodness-of-Fit Tests on the Isotoma viridis sample data for various theoretical distributions. • . . SAMPLING DOUBLE NEIMAN NEGATIVE TRUNCATED LOGARITHMIC DATE BINOMIAL POISSON NORMAL POISSON A BINOMIAL NEG. BINOM. 18/12/69 .01 - .001 .10 - .05 .70 - .50 .70 - .50 =-111. .70 .70 - .50 .70 - .50 .50 - .30 26/1/70 .001 .001 .20 - .10 .30 - .20 D .20 - .10 - .10 - .05 2/ 3/70 (1) .001 .001 .50 - .30 .50 - .30 712-- .30 .50 - .30 - .30 - .20 2/ 3/70 (2) .001 .01 - .001 .50 - .30 .01 - .001 .50 - .30 .99 - .98 .70 - .50 .70 - .50 2/ 4/70 .01 - .001 .05 - .02 D D D D D D 14/ 4/70 .001 .001 .80 - .70 D, MTL D ' .50 - .30 :=1.1- .70 .30 - .20 L8/ 4/70 .001 .01 - .001 .001 .50 - .30 .80 - .70 .70 - .50 .70 - .50 .20 - .10 9/ 6/70 .10 - .05 .50 - .30 D X X X X X 21/ 7/70 .001 .001 .70 - .50 .10 - .05 .70 - .50 .98 - .95 .95 - .90 .80 - .70 20/ 8/70 .20 - .10 .50 - .30 La- .90 .80 - .70 D .80 - .7o .8 - .70 .10 - .05 29/ 9/70 .001 .001 .5o - .3o , .001 .50 7. .3o .99 - .98 .98 - .95 .98 - .95 28/10/70 .001 .01 - .001 .10 - .05 .001 .30 - .20 .70 - .50 .50 - .30 .50 - .30 24/11/70 .001 .001 .05 - .02 D, MTL .02 - .01 .80 - .70 - .50 - .30 9/ 2/71 .001 .001 .7o .8o .8o .95 - .90 - .05 - .02 9/ 3/71 .001 .001 .05 - .02 .001 .30 - .20 .70 - .50 .20 - .10 7/ 4/71 .001 .001 .05 - .02 D, MTL .20 - .10 .50 - .73 .50 - .30 .05 - .02 15/6/71 .001 .001 .95 - .90 D, MTL .50 - .30 .90 - .80 D 14/ 8/71 .001 .001 .02 - .01 .001 .02 - .01 .50 - .30 .50 - .50 .50 - .30 15/11/71 .001 .001 .20 - .10 ,D, MTL .:11=. .01 .30 - .20 - -_ .05 14/ 2/72 .001 .001 .50 - .30 - -.001 .20 - .10 .90 - .80 .80 - .70 .70 - .50 I • _ , TABLE E Probability values for the Goodness-of-Fit Tests on the Entomobrya nicoleti core samples, for various theoretical distributions

SAMPLING DOUBT NEYMAN NEGATIVE TRUNCATED TRUNCATED LOGARITHMIC DATE BINOMIAL POISSON NORMAL POISSON A BINOMIAL POISSON NEG. BINOM. 2/3/10/69 .70 - .50 .70 - .50 .70 X X x X X X 19/11/69 .30 - .20 .50 D D D D D D 26/ 1/70 .30 - .20 .50 D D D D D D 2/ 4/70 .01 - .001 .02 - .01 .01 .30 - .20 D .20 - .10 .70 - .50 D D 28/ 4/70 .001 .001 .10 .001 .30 .90 - .80 .01 - .001 .8o .80 25/ 5/70 .001 .001 .30 .10 .50 - .30 .50 - .30 .01 - .001 .30 .20 - .10 9/ 6/7o .001 . .001 .01 - .001 .001 .01 - .001 .10 - .05 IMP .10 - .05 24/ 6/70 .001 .001 .30 - .20 .02 D .50 - .30 .10 24/ 6/7o .001 .001 .20 - .10 D, MTL .05 .70 .30 - .20 8/ 7/70 .001 .001 .10 .05 D, MTL D .20 - .10 ■■■ 21/ 7/70 .001 -"\- .001 .90 - .80 .80 - .70 .8o - .70 .8o - .7o - .001 2o/ 8/70 .001 .02 .50 .30 .50 - .30 .50 - .3o .70 - .5o .11•11, .70 - .50 3/ 5/70 .001 .001 ,20 - .10 .70 - .50 .30 - .20 .5o .001 .50 - .30 .20 15/ 9/7o .01 - .001 .02 - .01 .01 - .001 .001 .05 .05 - .02 .01 - .001 28/10/70 .30 - .20 .50 - .30 .50 - .30 .50 - .30 .50 - .30 .50 - .30 .50 - .30 .30 - .20 .20 24/11/70 .30. - .20 .30 - .20 D D D D D D 19/ 1/71 .001 .001 .10 - .05 .01 - .001 .20 - .10 .50 - .30 .001 .50 - .30 .50 9/ 3/71 .20 - .10 .30 .50 - .30 .70 - .50 .70 - .50 .80 - .70 .50 - .30 .70 .70 • • •

Ui

TABLE E Continued

. , , . . SAMPLING BINOMIAL POISSON DOUBLE NEYMAN NEGATIVE TRUNCATED TRUNCATED DATE NORMAL POISSON A BINOMIAL POISSON NEG. BINOM. LOGARITHMIC 7/ 4/71 .001 .01 - .001 .30 .10 - .05 .70 - .50 .80 - .70 - - .70 - .50 - 9/ 5/71 .001 .01 - .001 .80 - .70 .30 - .20 .95 - .90 > .99 - - .80 15/ 6/71 .001 .001 .50 .10 .50 - .30 .50 - .30 .001 .50 - .30 .10 - .05 24/ 7/71 :1-.001 , .10 - .05 .30 .50 - .30 .70 - .50 .70 - .50 - .02 13/ 8/71 .001 .001 .30 .001 .50 .90 - .80 .001 .80 - .70 .50 - .30 15/ 9/71 .001 .001 .20.- .10 .20 - .10 .50 - .30 .70 - .50 .001 .70 - .50 .10 - .05 15/ 9/71 .001 .001 .05 D, MTL .20 - .10 .30 - .20 .001 .30 - .20 .05 15/10/71 .001 .02 .70 - .50 .70 - .50 .70 - .50 .70 - .50 .02 .70 - .50 .10 - .05 15/11/71 .001 .001 .70 - .50 .10 - .05 .70 - .50 ' .80 - .70 .001 .70 - .50 .30 - .20 1/ 3/72 - .50 - .30 .70 - .50 .80 - .70 .70 - .50 .70 - .50 .70 - .50 .90 - .80 .70 - .50 .05 - .20

%0 INDICES OF AGGREGATION AND PROBABILITY VALUES FOR THE GOODNESS-OF-FIT TESTS FOR VARIOUS THEORETICAL DISTRIBUTIONS : COMPARISONS BETWEEN ALL SPECIES OR GROUPS TAKEN IN SOIL CORES (DATA CONSISTS OF FOUR CORE SAMPLES)

BINOMIAL POISSON NORMAL DOUBLE NEYMAN NEGATIVE TRUNCATED TRUNCATED TRUNCATED LOGARITH I S LLOYD'S POISSON A BINOMIAL POISSON NEYMAN A NEG. BINON. -MIC PATCHINESS SAMPLE A .. E. nicoleti . <.001 < .001 .20-.10 -.'= .02 D . .50-.30 NA ' NA NA -11-- .10 1.58 1.79 T. longicornis .20-.10 .30-.20 D ------3.0 2.74 L. lignorum < .001 < .001 .05-.02 .01-.001 .10-.05 .20-.10 NA NA NA .02-.01 1.64 1.65 Isotomidae < .001 < .001 -=..70 .20-.10 .30-.20 .70-.50 NA NA NA .30-.20 1.46 1.75 (Not I.viridis) Folsomia sp. < .001 < .001 .02-.01 D,MTL D .50-.30 <.00l .30-.20 n .50-.,30 5.40 5.88 Onychiurus sp. < .001 i < .001 .01-.001 D == .001 .50-.30 < .001. <:.001 .70-.50 .70-.50 3.75 3.32 Eypogastrura sp. L L L L L -I L L L L - - Sminthuridae == .30 .70-.50 D L L L L L L L 1.50 -

SANPLE B E.. nicoleti < .001 <.001 .20-.10 D,MTL == .05 ==.7O NA NA NA .30-.20 2.31 2.17 T. longicornis - - . ------. - - L. liGnorum < .001 < .001 .05-.02 D,MTL D .20-.10 NA NA NA D 1.67 1.62 Isotomidae < .80-.70 e=.70 D,MTL == .70 1.24 1.28 (Not I.viridis) .001 D,MTL .50-.30 D,MTL .80-.70 D Folsomia sp. < ,001 <. .001 .50-.30 D,MTL D .98-.95 4..001 == .01 .95-.90 .70-.50 1.91 2.44 Onychiurus sp. < .001 4.001 ..20 -.10 D,MTL D .95-.90 NA NA NA .70-.50 2.00 2.02 Eypogastrura sp. <.001 <.001 < .001 D,MTL D .50-.30 4.001 D D .70-.50 6.93 6.27 Sminthuridae .80-.70. .70-.50, .02-.01 x X X x X x X 0.76 -

SAMPLE C E. nicoleti -4.001 .4.001 .20-.10 .20-.10 .50-.30 .70-.50 NA NA NA .10-.05 1.58 1.53 T. longicornis .05-.02 ==.10 D D D D D D D 2.00 2.23 L. lignorum < .001 4..001 .10-.051 • .10-.05 .10-.05 .20-.10 NA NA NA < .001 1.38 1.47 I. viridis < .001 4.001 .10-.05 < .001 .05-.02 .70-.50 4.001 D .50-.30 ==.50 2.68 3.80 Isotomidae < .001 4.001 .30-.20 D,MTL .05-.02 .70-.50 NA NA NA .20-.10 ' 1.61 1.78 Folsomia sp. < .001 <.001 .70-.50 .20-.10 '.=, .80 .80-.70 4.001 D .80-.70 .30-.20 1.e.5. 2.26 Onychiurus sp. 4..001 <.001 ==.02 D,MTL < .001 .50-.30 <401 <-001 .50-.30 .20-.10 2.68 2.78 %()

• BINOMIAL POISSON NORMAL DOUBLE HEYMAN NEGATIVE TRUNCATED TRUNCATED 'TRUNCATED LOGARITH Is LLOYD'S POISSON A BINOMIAL POISSON NEIMAN A NEG. BINOM. -MIC PATCHINESS

Hypogastrura sp. 4.001 <.001 .05-.02 D,MTL D .70,..50 4.001 D .70-.50 ==.50 2.14 2.84 Sminthuridae L L L X X X X X X X - - . . SAMPLE D E. nicoleti 4.001 4.001 ==.05 D,MTL .20-.10 .30-.20 NA NA NA .05-.02 1.84 1.80 T. longicornis L L L• ' L L L L L L L - - L. lignorum 4.001 4..001 .20-.10 ==.50 .70-.50 ==.50 NA NA NA .01-.001 1.28 1.37 I. viridis 4.001 .4.001 .05-.20 ==..20 .30-.20 =:.80 41:. .05 D L L 3.26 5.12 Isotociidae 4.001 4.001 .02-.01 D,MTL .30-.20 ==.30 4 .001• .05-.02 .50-.30 .05-.02 2.57 2.10 Folsomia sp. .4.001 <.001 .01-.001 D,MTL .02-.01 .20-.10 4.001 • D .20-.10 .20-.10 3.44 3.19 Cnychiurus sp. .4.001 <.001 .50-.30 D,MTL .30-.20 .70-.50 4..001 D .70-.50 .20-.10 1.80 2.15 Hypogastrura sp. .4.001 <.001 .05-.02 .50-.30 D .05-.02 .05-.02 D .10-.05 .01-.001 1.91 - Sminthuridae L L L L L L L L L L - -

Fit Deleted. (Usually suitable estimators not found). D,MTL = Deleted, Parameter too large for exponentiation. Sample numbers very low/Insufficient degrees of freedom. X = VARIANCE less than MEAN - AGGREGATED DISTRIBUTION NOT APPLICABLE. N/A = Trurtated distributions not applicable. 199

RESULTS and DISCUSSION FOR INDICES OF AGGREGATION

The indices of aggregation were tested on some Entomobrya nicoleti sample data. Lexis's is showed a significant correlation

(P . 001) with the sample mean (especially at higher density), and was thus not satisfactory for the comparison of aggregation in different species and using different quadrat sizes. High correlations with sample number were shown by both the Index of

Lexis (P 6 . 0 01) , and Lefkovitch's A (P = . 01 - . 001).

Morasita's I s was only slightly related to sample mean

(correlation not significant), and virtually independent of the number of sub-samples at the densities tested. In view of this, and its good performance for random and aggregated populations in Reyna

Robles' (1969) study, 15 was utilised in the present study.

Lloyd's "Patchiness" using k parameter of the negative

binomial distribution gave values very similar to those to the I values when tested on the Entomobrya nicoleti samples (correlation

coefficient = .880 with 22 d. f. ). I 6 was generally preferred however, because of ease of calculation, and the fact that some

samples of some species did not follow the negative binomial

distribution very closely.

For Entomobrya nicoleti, the variation in degree of

aggregation (as measured by I 8 ), was examined in connection

with population size and age-structure, and various weather factors.

Some of the results can be seen on pages 201-14..

The degree of aggregation was not determined purely by

population size, as there was a low correlation between I 5 and the 200

numbers of E. nicoleti in the suction samples. Rather, the degree of aggregation appears to be related to the numbers of

early instars present in the samples, the aggregation increasing in late March or April with the appearance of the young instars.

The correlation coefficient increases progressively from the

slightly negative coefficient shown by the mature adults (stage 1) to the positive coefficient significant at the 5% level for the 1st and

2nd instars from the core samples. As was discussed in the

review of aggregation several authors have postulated that aggregations in soil Collembolan species may be due-to poor dispersal from the site of the egg clusters (e. g. Murphy, 1955;

Usher, 1964, 1969; Healey, 1967). However, Entomobrya nicoleti does not lay eggs in batches but singly or in two's and three's, therefor`e it is slightly surprising that the juveniles seem to be the main cause of the aggregations. Although the eggs of surface- dwelling Collembola are not laid in batches, there may be favourable and unfavourable egg laying sites, thus producing

aggregation in the hatching young. Alternatively the very young instars may well be less tolerant of environmental conditions, e. g. low humidity, and thus tend to congregate in optinumal areas, from which they gradually disperse as they get older. It will be

noticed that the mature adults (stage 2) have quite a high correlation with I S . This may be due to the coincidence of the peak of these

older adults with the peak of the young juveniles of the next generation, and thus the correlation is fortuitous and does not

reflect a change in behaviour during the later adult instars. 201

Indices of Aggregation for Vacuum Samples of E. nicoleti

SAMPLING . MORASITA'S LLOYD'S Date IS Patchiness 2/10/69 1.25 1.34 10/10/69 1.37 1.48 19/11/69 2.06 2.87 18/12/69 1.63 _ 26/1/70 1.31 1.45 2/ 3/70 1.14 1.25 2/ 3/70 1.25 - 2/ 4/70 1.01 - . 14/ 4/70 1.95 1.86 28/ 4/70 1.99 2.12 9/ 6/70 1.61 1.81 9/ 6/70 1.71 1.83 21/ 7/70 2.26 2.18 20/ 8/70 2.05 2.12 P 3/ 9/70 2.76 2.93 29/ 9/70 1.29 1.38 28/10/70 1.18 1.31 24/11/70 1.69 1.65 9/ 2/71 1.65 1.48 9/ 3/71 1.54 1.45 7/ 4/71 2.31 1.73 , 15/ 6/71 1.26 1.25 14/ 8/71 1.12 1.16 21/ 9/71 1.41 1.41 15/11/71 1.33 • 1.37 14/ 2/72 1.51 1.58 202

Indices of Aggregation for-Core Samples of E. nicoleti

SAMPLING 0 MORASITA'S LLOYD'S Date I S Patchiness 2/10/69 0.87 . -. 19/11/69 1.67 1.37 26/1/70 1.67 1.37 2/ 4/70 2.33 - 28/ 4/70 2.51 2.76 25/ 5/70 1.78 2.10 9/ 6/70 1.71 1.75 24/6/70 2.31 2.17 . 8/ 7/70 1.54 1.64 21/ 7/70 1.14 0 1.19 20/ 8/70 1.47 1.5o 3/ 9/70 2.14 .2.13 15/ 9/70 2.36 1.91 28/10/70 1.08 1.02 24/11/70 2.78 2.79 12/1/71 1.98 - 2.06 9/ 3/71 2.57 2.55 17/ 4/71 1.71 1.69 9/5/71 1.70 1.72 15/ 6/71 1.47 - 24/ 7/71 1.34 1.38 14/ 8/71 - 2.03 2.09 15/ 9/71 (1) 1.58 1.53 15/ 9/71 (2) 1.84 1.80 15/10/71 1.24 . 1.31 15/11/71 ' 1.62 1.88 1/ 3/72 1.27 - 203

THE RELATIONSHIP OF THE Is INDEX OF AGGREGATION AND THE st PUYIERS GF AID 2nd INSTARS IN THE CORE-SiMPLES FOR E. NICOLETI.

• ...... **"

. • • . • • . • • 2 .• • .• • . • •

. • • •• • • . • • . • • • •

Correlation coefficient = .422

111. 50 10L0 150 2001 250 1 st nd NUMBERS PER SQUARE-FOOT OF 1 .AND 2 INSTAR,S IN THE CORE-SAMPLES 204

There does not appear to be evidence particularly in favour of the hypothesis that aggregation is due to the coming-

together of animals at moulting periods.

The action of the weather factors on the degree of

aggregation might be expected to be complex, being mediated by various other physical and biotic factors (e. g. those considered in

the next section). Thus it is not surprising that correlations between measures of aggregation and various weather factors were not significant.

E. nicoleti: Correlations between the 13. Index of Aggregation

and the numbers in the stage-groups. r. p. All stage-groups .179 NS

Stage group 1 & 2 .431 P = 5%

II Il 3 .347 NS

11 to 4 .239 NS

II fl 5 .040 NS

II II 6 .020 NS

11 ti MA - . 136 NS 1 MA .357 NS 2

Results of Fitting of Taylor's Power Law

The b parameter of Taylor's Power Law was calculated for the ringed Soil-core samples. For the Isotomidae (excluding

I. viridis), Onychiuridae and Folsomia, the confidence limits of b were rather large, and the b values were not significantly different TAYIOR'S POWER LL FOR SOIL-CORE SAMPLES:

RESULTS OF TESTS FOR SIGNIFICANCE OF A DIFFERENCE BETWEEN

THE SLOPES OF TWO REGRESSION LINES (PROBABILITY VALUE)

Sminthuridae E nicoleti L lignorum T longicornis I viridis Hypogastruridae

Sminthuridae - E nicoleti NS - L lignorum N3 NS - T longicornis NS NS NS - I viridis 7.0z. 0.05 NS NS NS -. Hypogastruridae 71L- 0.02 mil 0.05 0.02-0.01 721: 0.001 NS -

NS = No significant difference Results of fitting of core-sample data to Taylorts power "loan

Power law parameters Sminthuridae E. nicoleti L. lignorum T. longicornis I. viridis Hypogastrurida

a 0.64 1.42 1.15 3.45 2.86 1.59

b 0.87 1.77 1.79 1.01 1.9J 2.23

p 0.56 0.12 0.11 0.09 0.01 -0.11 Confidence limits of b 0.62 1.42 1.13 1.49 1.69 2.04 0.89 2.10 2.40 2.16 2.32 2.29 Approx.confidence limits of a +0.37 +0.30 +0.36 +0.20 +0.34 +0.13

Significance Tests (probability values) ..n= .02 4.001 4:.001 4.001 .4.001 .02-.01 T for b 0 T for b = 1 .8-.7 4.001 =)==.01r 4.001 4 .001 .02-.01 T for deviations from .6 .7-.6 > .9 .01-.001 -21.- .1 -LI:. .05 linearity

1

O 207

from zero. This was probably due to the small total number of cores and the high densities per core. These groups were not analysed further. The results for the remaining species and groups are shown on P.206 . These regressions are significant, and in all cases except the Sminthurids the slope is significantly different from unity. Thus the dispersions tend to aggregation except in the case of the Sminthuridae which might be randomly distributed. The T. longicornis core samples show highly significant deviations from linearity, i. e. do not obey the power law well.

(This may be an artefact, due to the inefficiency of the core-sampling method on some stages of population, rather than a true deviation from the power law in dispersion pattern. Kuno (1972) notes that small means may produce discrepancies from power law however.)

The b values form a sequence demonstrating increasing aggregation; from the Sminthuridae with a rather 'random' dispersion, through E. nicoleti, L. lignorum and T. longicornis with similar aggregation level (b -e= 1. 8), then I. viridis with an apparently slightly greater aggregation, and lastly the Hypogastruridae with a highly aggregated dispersion pattern. (Van der Kraan (1971) also found this last group to be highly aggregated).

The results of the approximate test for the significance of a difference between the slopes of any two regression lines are shown on

P.205.

As can be seen the Hypogastruridae show a significantly greater b value (P . 05) than all the species or groups except I. viridis.

The Sminthuridae samples, on the other hand, differ significantly in 208

regression slope only from I. viridis and the Hypogastruridae.

Taylor's power law was also applied to the suction samples

(see F:209 ).

To be a useful measure of aggregation b values should not

change under various sampling regimes. It can be seen that for

E. nicoleti and L. lignorum the b values are virtually identical for

core or suction methods. I. viridis shows a slight drop in the value

of b in the vacuum samples compared to the soil samples, and

T. longicornis a more considerable drop, although in neither case

are the slopes produced by the different sampling techniques.

significantly different at 5% probability level. If a real difference

is present for T. longicornis, for example, it is possible that it

could be due to different proportions of the two age-groups taken by

the two sampling methods if the age-groups had differences in

dispersion pattern. (Mature specimens with lower aggregative

tendency would be rarer in the core samples and these would have

a higher b value).

To ascertain whether a. change in size (area) of the sample unit

would produce an apparent change in the degree of aggregation, the

regression slopes from the square-foot vacuum - samples and the

4-inch vacuum samples were compared.

For E. nicoleti, L. lignorum and T. longicornis, differences

were non-significant, but for I. viridis the difference was significant

at the 5% level, with 4-inch diameter sample unit having the highest

b value. If real, this result could be obtained if the I. viridis

aggregations are of approximately the same area as the small sample unit and much smaller than the large sample unit. 209

RESULTS OF FITTING OF DATA FROM SUCTION SAMPLES TO TAYLOR'S POdER "LAW"

Power law parameters:- E. nicoleti Laignorum T.longicornis I.viridis

a 1.39 1.04 1.91 1.21

b 1.77 1.77 1.57 1.86

p 0.12 0.11 0.22 0.70 1.60 , 1.61 1.41 1.62 Confidence limits of b 1.94 1.94 . 1.74 2.07 Approx confidence limits +0.27 +0.21 +0.19 +0.20 of a

Significance Tests:- (probability values) T for b = 0 <:.001 <.001 <..001 4..001 T for b = 1 ‹.001 ‹.001 4.001 4.001 T for deviations from linearity .5-.4 .5-.4 .5-.4 *9 2 1 0

According to some authors (e. g. Southwood 1966), the b parameter

is a measure of aggregation constant for the species. However, it is

difficult to see theoretically why the value of b must necessarily

remain constant for a species in different habitats, often of differing

suitability for the species. Here the b parameter for I. viridis is

1.98 for the Tullgren samples, while Joosse (1970) obtained a value of

b of 1. 51. Joosse's values for Collembola of the genera Entomobryat

Lepidocyrtus and Tomocerus, (though not the same species as here),

are all 0.3 - 0.4 lower than those in the present study. The genera ordtred arexby b value similarly in both studies however. (E. g. the

Entomobrya spp. having a low value and I. viridis having the highest

value of the four genera.) Joosse (1970) interprets her sequence of

degree of aggregation in terms of the humidity reactions of the species,

and thus relates decreasing aggregation to progressive emancipation

from the soil niche of the various groups of Collembola and the

progressive development of reproductive behaviour apparently

accompanying it.

Joosse (1970) does not report whether the small differences in

b value in her data are significantly different from each other.

However, the present data (on a wider variety of groups than Joossets)

gives some support to the contention that evolution from humus layer

dwellers (Hypogastruridae) through 'hygrophilic' surface-dwellers

(e. g. some Isotomidae) and 'xenophilic' surface-dwellers

(e. g. Entomobryidae) to vegetation-dwellers (Sminthuridae) is often

accompanied by a decrease in aggregation. Takeda (1973), using a

similar approach for the analysis of dispersion (Iwao's (1968) regression

method), concluded "that the degree of aggregation is closely related 2 1 1 with the property of vertical distribution", although the relationship is not unambiguously demonstrated in his data.

It seems best not to be too dogmatic about the depth-aggregation relationship, and allow for the possibility that some Families or species show more or, less aggregative dispersion than the "life-form" categorisation would predict.

Experience gained in the present study using several methods of analysis indicates that the approach embodied in methods like Taylor's power law and Iwao's regression method represents the most generally useful procedure for producing a summary of the dispersion pattern of a species in a particular habitat. Furthermore, these approaches seem the most promising for future methodological development

(Iwao 1972). 2 1 2

RESULTS OF THE ANALYSIS OF DISTRIBUTION PATTERN

AS RELATED TO VARIOUS ENVIRONMENTAL FACTORS

Although the results below indicate that the distribution of the common species of surface-dwelling Collembola on the experimental area can be related to some obvious features of their environment, it may well be that the Collembola are reacting to more fundamental variables (or combinations of these) which are, in turn, correlated with the factor measured. Thus interactions between the factors ought to be considered, but the simple methods of assessment of the factors precludes this. However, even in this exploratory analysis some of the differences in the niche dimensions of the species can be discerned.

Anderson and Healey (1972) gave as one explanation of the large diversity of micro-arthropods found within a small area, and apparently existing on the same resources, the presence of undetected microhabitat differences between the species.

It is often the case that closer examination of the system demonstrates differences in spatial distribution of the species'due to tolerance of microclimatic factors or some other aspect of their ecology.

If the experimental area is divided into sub-areas, and these scored for the position of the highest-density sample unit, on each occasion, it can be seen (p. 214.-5) that each species has a separate c?.ntre of distr nution. The observed distribution can be explained to a certain extent in terms of preference for, or tolerance of different microclimates. Some of the correlations with other 2 1 3

features of the habitat discussed below can be similarly explained.

Several authors, e. g. Davies (1928), Joosse (1970), Joosse and

Groen (1970) and Mais (1970), have investigated the resistance to dessication of surface-dwelling Collembola and found that the sequence of decreasing resistance is:-

ENTOMOBRYA LEPIDOCYRTUS ISOTOMA VIRIDIS TOMOCERUS

(Some species of Tomocerus apparently seem more resistant to dessication than I. viridis, others less. But if the covering of scales of Tomocerus is lost or damaged, transpiration is greatly increased

(Mais (1970), and this could be an explanation of the variable results).

At least some species of Entomobrya also have a relatively high rate of cuticlar absorption of water, Mais (1970), which partially accounts for this resistance to dessication. Boyd (1967) and Joosse (1970) found in E. purpurascens and E. nivalis respectively a preference for humidities below saturation (85% R.H. in the former).

The above sequence of tolerance and/or preferences was reflected in the field distribution of Collembola in the present study.

E. nicoleti, for example, probably prefers, and is adapted to, somewhat dryer conditions than the other three species, and therefore tends to be most numerous at (a) the north-west side of the experimental plot (which is well-drained as it is. at the top of the slope), and also (b) the north-east corner of the plot (where the soil is particularly light and sandy). These dryer areas tend to have more Agrostis sp. grass.

Tomocerus longicornis tends to be most numerous in the middle of the southern side of the plot which has a heavier soil and relatively Jn O 1-3O 0 O H Pzt 9 ■-4 Cn

:::.:::::::.:::::::::i::':.:::.::.::::':`1 •0 0 0 0 0 0"Z :::::::::::::::::::***:::::::::::::::: .: Q C301313 D 13130°0 an E ...... :**:::::: ...... ‘ .: n1:2 0° a Cin D 0(3 00 12 13 r :::::::::::::: .::: ...... i. :::•:•:.. .. . El 0000❑ et Cl Cl 3(jtIOCV00 0 r,i3 0 ...... '...... Y•:-.•:-:..... ' '-'-'••••••:.conepoo.-:-:-•-:-:-:---"• n ti n 0 0 ;.-2- LI c::-.•-:-.•-:-:-::•. • • .•• •.0 0 0 0 0 0 I" •:•:•:::•:•:•:::::. ••• .. • •.• , . t.... nen Oa Lln Oa 00 Clr .:::.:.::.:::::::::•:•: •• . ... .P.000E3000 •:•:•:•":":*:. : : ::: .:ci in a a a o a r.:-•••-•:•:':'•••• • ••••.• •-.• a n II n ... r.... .-:-:•:-:•:•:•:•:-:•:•:•:•:•:•;-:••••,: --• * ••• ' -• : :-• . -.• - : :- -. ••• :. :...... - .

- . .• '1 0

1:1 CI) VO :::::-.• ' - O OII -:::::::::::::::-. *•,. ••• •••- ACE

• :::::::•:•:•:•:-... . •• ,: SM ...... • -DWEL ..::::::::::::::•:- , i 0 0 1.1 LI 0 0 L IC10000 0 C•••••• ...... • ••••• . tai

icioraucoc0 10 0 0 0 0 0 •• •••-•••••••■•:•:•-•-••••••••• • • • • • • • • .....:•:•-.• LI . 1000000L...... octooarla - - - NG C OLL H icoaancE:::::::::::::-:•:-:::::::•••••••••'-:•:•:•• - -x-:::: • orloopoo ,,„„,...,•.-...-.-.•.-... • la 13 •. • • • • • • • •...... :. :•:•:•:-. 00000001'...••••••••••:•:•••••:•:•:•••••013000 ••

11200001:"...... 1:•:•:•%:•:•:•::•:•:•:•.•.. ga i

, ----- EMB Sl O Lid L A INCREASING DENSITY v

0 vaa s a 83 • . c a •• •• . a a •••• vs 3 .• • a g) • • • • a • • a . 215

COMPAR [SON OF THE LOCATION OF HIGH DENSITY 1 IISAS ( Continued) •:•:•:•:•:•:•:•:•:•:::;::::::::::::::::::::: :::•:::::::.;:•:•:•:. • • :•• ••••••:::::::::: ::::::::::::::::::::::•:•:•:•::::::::::::::: :::.::::;.;:;:;:;:;:;:i.; ''.• ..•:':•••••••.....• . '.....;:.;:.;•:;.:.?....•.:.;.::.:.;-..;:.;:.:,:.;:.;•:::::::::::::.:: ...:::.::::•::.::::::';:•:•:•:.. ' • • • :•• ::::.:::::::.;.:::::::::::.::::::::::::::•:: :•:-:::•:•::::::::::::"?::.:::•:.'.'.. '' ••• ...::::::;:.::::::.::::::::::::::::::::::;:::::: :::::::::::::::::::::;:•:•:•• .. '• • •:::::-:•:::::-:::::::::::-:::•:.:::::::::::: :::::::::::::::::::::. ••• •• '• ' :::::::::::.::.;:.::::::-:•:•:•:•:•:•:•:•:•:•:: :•:•:•:•:•:•:•:•:•:•:•. •• •• : :• •:::::::::::-:•::::•::::::-::::.::::•:•:::•:. ::::::::::::;:;:::::•••:•:•:•.•.• : • :::::::::::::::•:•:•:. • •::::•:•:•:.

ISOTOMA iii:.?..;::::::•:•:•:•:•:•: . • . • • ...... -::::::::::::::: • •• .' ...... • • • • • • • • • • VIRIDIS ::::::::::::::::::::::::: • • •• • .• .. • • •:•::::::::::;::::::::::::. :::::::::::::.:•••• • • . • • .• .. '.:. •• .. '••••:-.:::::::::• ::::::::::::::••.• ' • • • • .' ' .' • .' • ' • • '

::::::::::::.;.::::::::::::::::•:•:•:•:•:•:::•:•:•:•:•:•:•:...... '• :::::::•:-:•:•:•:•:•:-:•:•:•:•:::::::::::::: ::::::::::::::: • • • '.'

•:..-.i...?.....:::.::...... •• . •• :•:•:•:::•:•:-: • ..' ••• ••

::::::::::-:•:•:::•. . • . : •• . . •• ''' '. :::::::::::::::•:•:•:-:•:•:-. • :••,, • • .• .' ••• ••• :•:•:•:•:•:•:•::::::::::::::::.... •' ••• :' .. ... : .• '.' :'• ••• . 2 1 6

more Holcus spp. grass. This provides the most humid environment with good vegetation cover required by this species. Isotoma viridis was also more numerous on the southern half of the plot but was less concentrated than T. longicornis.

L. lignorum also showed some "preference" for the southern

(Holcus-dominated) area, but only in the better-drained western side of the plot at the top of the slope. L. lignorum was thus intermediate in between E. nicoleti on one hand and I. viridis and T. longicornis on the other. (Thus it is interesting to note that Joosse (1970) found that another Lepidocyrtus species (cyaneus) showed both a significant positive partial regression coefficient to depth of litter/humus layer, and a significant negative partial regression coefficient to moisture).

Jensen, Jacobsen and Willard (1973) found a similar situation for surface-dwelling Collembola (of the same four genera) on their experimental plot. This was believed to be due to differing adaptation to microclimatic conditions, the "xeric" to "mesic" sequence being

Entomobrya bicolor - Lepidocyrtus cyaneus I. viridis -

Tomocerus flavescens. Joosse (1970) found that humidity relations were important in determining the distribution of these four genera of surface-dwelling Collembola in a woodland habitat, and reported the same generic sequence of tolerance of low humidity.

Species Associations

The significance levels of the rank correlations between each pair of the four species were examined for each sampling occasion. E. nicoleti and L. lignorum were correlated at the P. le 5% level on eleven occasions (out of 28); L. lignorum was significantly correlated with

I. viridis on six occasions, and with T. longicornis on four occasions. 2 1 7

On the other hand E. nicoleti showed significant correlations with

T. longicornis on only two occasions, and with I. viridis on only one occasion and this was a negative correlation. As there seemed to be little discernable seasonal pattern in the strength of the correlations, they are not reported in full. However, if the range of density of

each species can be assumed to be reasonably comparable on each

occasion the results can be summarised by the standard scores

calculated from the rank correlations from the square-foot

samples bulked for the whole period.

These are given below, and are to be treated as standardised

normal deviates.

E. nicoleti, L. lignorum, T. longicornis, I. viridis

E. nicoleti

L. lignorum 6.286

T. longicornis .793 2.202

I. viridis .142 4.383 4.649

These associations of the species agree with the more

subjective spatial relation ships mentioned in the proceeding section.

They reflect the postulated sequence of resistance to low humidity

shown by the species in connection with their distribution on the

experimental plot.

Relationship With Plant Species

According to Christiansen (1964) the effects of the macroflora

upon the collembolan fauna are probably indirect. The main influence

is likely to be one moderation of microclimatic extremes of

temperature and humidity. 2 1 8

COMPARISON OF NUMBERS OF COLLEMBOLA FOUND ON HOLCUS AND AGROSTIS AREAS OF EXPERIMENTAL PLOT

Collembolan Occasion Holcus Agrostis U-test of significance of species difference between means

Entomobrya 07.04.71 90.00 323.33 5% nicoleti 14.08.71 322.00 289.00 NS 21.09.71 204.75 288.50 NS 15.11.71 414.91 794.00 5% 14.02.72 184.75 482.50 5%

Tomocerus 07.04.71 34.16 4.33 NS longicornis 14.08.71 2.71 0 NS 21.09.71 1.62 o 15.11.71 5.90 0.25 5% 14.02.72 7.87 7.00 NS

Luidocyrtus 07.04.71 89.90 251.33 NS lignorum 14.08.71 175.13 43.25 54 21.09.71 34.38 25.50 NS 15.11.71 103.25 150.09 NS 14.02.72 58.75 40.00 Ns

Isotoma 07.04.71 16.16 19.33 NS viridis 14.08.71 8.29 0.25 5% 21.09.71 15.11.71 37.90 32.25 NS 14.02.72 17.75 1 Almost % 2 1 9

In the present study E. nicoleti usually tended to be more

numerous in Agorstis -dominated areas than the Holcus areas (see P.218 ).

The grasses probably indicate different moisture regimes on the

experimental plot, Holcus occupying the damper areas.

Thus the distribution of E. nicoleti was interpreted as showing

that this species does not favour very moist conditions. It is not known

whether migration or differential survival produce the observed

distribution. South (1959) found a similar situation in E. multifasciata;

that the animals did not thrive in very moist conditions. It will be

noticed however that on two occasions (14.8.71 and 21. 9. 71) in the summer

the distribution between Holcus and Agrostis areas had equalised.

This is interpreted as being due to drought conditions making the

Agrostis areas too dry and the Holcus areas more favourable.

Certainly on 29. 9. 71 the soil moisture value was the lowest recorded '

(9. 36%), and conditions were also very dry in late July preceding the

14 August sampling occasion. Periods of excessive drought were

accompanied by fair numbers of dead E. nicoleti especially in the

Agrostis areas.

Tomocerus longicornis is more numerous in the samples containing

more Holcus than Agrostis (see P.218.), except on 14 February 1972.

As T. longicornis had a low density compared to the other species,

many of the sample units had none of this species in them. This

produced many tied samples and thus the differences are non-significant

when tested by the u-test.

Thus the difference between means from the two areas (if real) could be more convincingly demonstrated using sample-units of greater area. 220

T. longicornis is a large-sized species and not very resistant to dessication; thus it may well require the more humid conditions present in, and to a certain provided by, the Holcus stands.

During February 1972 the soil moisture figure was the highest recorded during the study (24. 85%), thus humidity was likely to be adequate in the habitat generally and this may explain the absence of any association with grass species on this occasion.

Lepidocyrtus lignorum and Isotoma viridis showed little consistent difference in distribution between the areas dominated by the two grass species; (there is a suggestion of a "preference" for Holcus rather than Agrostis on some occasions). These species again occupy an intermediate position between Entomobrya and Tomocerus.

Soil Moisture

As the distribution of Collembola appears to be related to the humidity relati ons of the animal, it might be thought that the distribution could be related to soil moisture. Hale (1963), for example, has shown that the distribution of moorland Collembola is governed by physical factors which bring about changes in the water content of the habitat. However, significant correlations between the soil or litter moisture of a sample and the number of Collembola in it, were not obtained. This may be due to the fact that a range of soil moisture values will give rise to a suitable humidity. Also soil moisture may not be related closely enough to the variable acting on the animal.

(Vannier (1970) recommends the p. F. as a more relevant ecological measurement.

However, the trends seem in the right direction. For example it was found that in March or April (with adequate moisture available) 221

Entomobrya had a negative correlation with soil moisture (almost significant on 7th April), while Tomocerus and Isotoma showed no trend.

In August Entomobrya had a virtually zero correlation with soil moisture and the other species had positive relationship with this variable.

Vegetation thickness (see P.222.)

This variable will be correlated with grass species to a certain extent, Holcus stands tending to be thicker (i. e. the grass is higher) than Agrostis stands.

In view of what has been said above one would expect T. longicornis to be more numerous in the thicker vegetation. This appears to be the case, the species being abundant in category 3 (i. e. thickest vegetation) than in categories 1 and 2. Again, however, the differences are not significant perhaps due to the large number of zero counts. E. nicoleti showed no consistent relationship with the thickness of the vegetation!

L. lignorum and I. viridis were significantly associated with the thicker vegetation on one occasion only, so this factor does not seem important.

It will be mentioned in passing that subjectively L. lignorum appeared more numerous in sites with more decaying grass, than say E. nicoleti, and Joosse (1970) found a partial correlation of another species of

Lepidocyrtus with depth of litter, as mentioned above.

Runs

The subjective observation that T. longicornis was more numerous in the vole runs in the grass-mat than on the plot as a whole, was supported to a certain extent by the result (P.223 .) that areas with runs produced more specimens than areas without them. The runs could form refuges of favourable humidity, (particularly in drought conditions), of sufficient size for this large species. The distribution of the other 222

COMPARISON OF THE NUMBERS OF TOMOCERUS

LONGICORNIS FOUND IN SAMPLES TAKEN IN THREE

CATEGORIES OF VEGETATION THICKNESS (Category

3 is the thickest)

Vegetation thickness Significance Sampling 1 2 3 of difference Occassion by Kuskal- Wallis Test (P. value)

7 April 71 2.20 2.33 5.14 NS

14 April 71 0 1 2.75 NS

21 Sept. 71 0 0.25 2.40 NS

15 Nov. 71 3.00 1.70 8.00 NS ,

14 Feb. 72 2.33 7.25 13.66 P* 01 WM/ 223

COMPARISON OF NUMBERS OF TOMOCERUS LCNGICORNIS AND ISOTONA VIRIDIS IN AREAS WITH AND WITHOUT VOLE RUNS

Occasion Runs Runs not Significance of present present difference between means

Tomocerus 07.04.71 5.80 2.50 NS longicornis 14.08.71 3.00 0.66 NS 15.11.71 7.00 2.13 14.02.72 10.75 5.66 NS

Isotoma 07.04.71 13.40 18.50 NS viridis 14.08.71 6.8o 4.10 NS

15.11.71 44.71 29.13 NS

14.02.72 28.5o 5.00 .9% 224

three species does not seem to be related to this environmental feature.

Dead leaves

Another obvious environmental feature was the accumulations of

dead leaves on parts of the experimental plot.

No correlation between the number of the various collembolan

species and the dry weight of leaves could be demonstrated on any of

the occasions. The state of decay of the leaves was not considered

however.

We have here only considered one dimension of the niche, and

others may be of equal importance. (As shown in a later section there

are differences in resistance to cold between the species and some may

well require more shelter from the vegetation than others because of

this mortality factor). However, the results of this section combined/

with the observations of other workers provide some evidence for the hypothesis that differences in humidity and moisture requirements are

one of the factors giving rise to the distribution of surface-dwelling

Collembola, and may reduce the competition between spe'cies co-existing in the same habitat. 225

CLUSTER ANALYSIS

The main objectives in undertaking the following analysis were:-

(1) To obtain an overall picture of the relationships of the collembolan species of the habitat with regard to the sum of their ecological attributes as expressed by the numbers of each species in each sub-sample in the total sampling data; to condense a large mass of field observations into a simple grouping of species reflecting their ecological relationships with one another.

(2) To study the methodology of some "clustering" techniques as applied to ecological data; to investigate whether the different methods produce essentially the same results, or if they do not, to investigate which characteristics of the data they emphasize; to evaluate the usefulness of the various technique in the analysis of this type of data.

Two main approaches were followed. They were:-

(a) A classification produced by a hierarchical, clustering technique resulting in a dendrogram.

(b) An ordination, produced by some multivariate technique and resulting in a scatter diagram along several axes.

A general review; from the statistical viewpoint is given by

Cormack (1971), Blackith and Reyment (1971) and Pielou (1969) who discuss the uses of classification and ordination techniques in ecology and phytosociology. The latter field in particular is being very actively developed at the moment and a large number of techniques and variations in technique exist. Williams, Lambert and Lance (1966), Gower (1967),

Orloci (1967), Pritchard and Anderson (1971) and Williams (1971) among others, have discussed and compared various methods of cluster analysis. 226

The uses of ordinations in plant ecology have been demonstrated by

Orloci (1966), Austin and Orloci (1966), Gittins (1969), Anderson (1971) and Gauch & Whittaker (1972). Although in plant ecology, the ordination technique and the classification technique arise from different concepts of the nature of the communities being studied (Pielou 1969), in practice the two are not really competing approaches and the use of both on the same multivariate sample may overcome some of the inherent deficiencies of either technique. Ordinations are more likely to be satisfactory at low levels of variation where the points are diffusely scattered, and classification would tend to impose structure arbitrarily. •

Due to the large number of new methods proposed over the last few years, there is little general agreement upon which technique is best for a particular purpose. The selection of suitable techniques is therefore rather difficult. Another problem is the lack of a precise, a priori definition of what really determines a cluster (Blackith and Reyment 1971,

Cormack 1971), and thus clustering must be regarded as descriptive rather than profundly analytical.

Although some interesting methods of analysis of species/quadrat data have recently been proposed, eg. the application of information analysis,Orloci (1968), Williams, Lambert and Lance (1966), Dale and

Anderson(1972), it seemed better in a preliminary, exploratory study such as the present one, to employ the most obvious and straightforward techniques, at least in the first instance. Remaining within the general

multivariate framework for the ordinations, and employing simple pro- cedures for cluster analysis has the advantage that the mathematical background of the technique is well known, and its behaviour under various conditions is more easily predicted. 227

Data utilised:-

Three sets of data were used in the following analyses. They were

(a) The total square foot, suction sample data for the main four

collembolan species throughout the total sampling period.

(b) The total soil-core sampling data for the four main collembolan

species, again throughout the total sampling period.

(c) A rather less extensive series of soil cores considering eight

collembolan species or groups.

Classification Methods

The overall clustering strategy to be adopted is determined by two

choices. They are firstly, what similarity or dis-similarity coefficient

is to be employed, and secondly what sorting method is to be employed.

Considering first the coefficients. If each species is represented by a

point in an n-dimensional co-ordinate frame, the co-ordinates of each

point being given by the number of the species in the sample unit, then

the squared Euclidean distance between the two points is given by

Dij = (xik - xjk)2 k = 1

where i = 1, 2, 3.... m j = 1, 2, 3.... m m . number of species

n . number of q"adrats

Here an average distance is computed; it is

dij

The Euclidean distance has been used in taxonomy as the taxonomic

distance,Sokal and Sneath (1963, page 147), and also in many phyto-

sociological papers eg. Austin and Orloci (1966), Anderson (1971) among 228

others. Some authors eg. Gauch and Whittaker (1972) do not favour Eclidean distance measurement. (it was said to exaggerate major species' weights and sampling errors), but it is advocated by Kendall (after Cormack

1971) for reasons of invariance under orthogonal rotation of axes (so that one can proceed straight to principal components) and also because of lesser number of computations required. We are here considering the number of the species in the quadrat as an attribute of the species ie. a Q-type analysis. The phytosociolo- gists generally consider the quantity of the species in the quadrat as an attribute of the quadrat ie. the R-type analysis. Two otherilissimilarity coefficients used were merely variants of the above, ie. the distance . coefficient standardised (to zero mean and unit standard devistion) by sample-unit, and secondly, the distance coefficient standardised by species. The third coefficient employed was merely the produce-moment correlation coefficient used as a similarity coefficient.

The choice of a sorting method is a controversial matter, various authors recommending different strategies. Here we have employed the simplest of the fusion strategies, ie. single-link sorting (nearest- neighbour sorting). If Dk.ij is the distance from cluster K to the union of clusters I and J, and clusters I, J and K contain nil nj and nk quadrats respectively, then the distance between I and J is taken as the least of the ni, nj distances between elements of I and the elements of J.

Therefore Dk.ij = A(Dik + Djk - tDik - Djk) ) for single-linkage clustering (Pritchard and Anderson, 1971). Jardine and Sibson (1968) give some theoretical justification for the use of single

linkage clustering. They suggest certain conditions that a cluster method

should satisfy, and r,how that of the cluster methods in use the single 229

link method is the only one that satisfies these conditions. They suggest that the defects of the single-linkage method should be regarded as the defects of hierachic classification itself, and should be overcome by the use of non-hierachic, overlapping classificatory systems. It should be noted that Jardine and Sibson have been critized on the grounds that their criteria, although meeting certain orthodox mathematical requirements, were not appropriate for the normal biological uses to which the class- ification procedures are put (Williams 1971). The problem comes down to the fact that hierochical clustering will not allow a system of homogenous clusters, and, at the same time a non-arbitary representation of the data

(Jardine, discussion after Cormack 1971).

One advantage of the single-linkage method is that successive fusions always occur at lower levels of inter-cluster similarity (Cormack 1971).

The classification used here is hierochical, agglomerative, and

polythetic (Pielou, 1969; Blackith and Reyment„ 1971; Williams 1971).

The single-link cluster analyses were run on a computer utilising a

programme written by Mr R. G. Davies of Imperial College.

Ordination Methods

Ordination involves "re-plotting" the points (species) in a space of

less than the original n-dimensions without seriously distorting the main

features of the original pattern. There is no need to assume a hierachical

structure using these methods. The procedure is to form a given association

matrix, and to subject this to some multivariate technique such as

principal component analysis or principal co-ordinate analysis. All the

analysis in the present study were Q-mode analysis ie. based on the

between-species association matrix. Ordinations were carried out on the

following matrices: 230

(a) the unstandardised covariance matrix. Here no allowance is made for

between-species and between-sub-sample density differences.

(b) the unstandardised correlation matrix. This is based on a covariance

matrix standardised by species, so that it might be expected to minimise

effects of differences in species densities.

(c) covariance matrix from logarithmically transformed data. This is

rather similat to (b), but represents a form of transformation applying

equally to between-species and between-sub-sample differences.

(d) unstandardised distance matrix. This is based on the Euclidean

distance coefficient defined above. It might be expected to give results;•

somewhat similar to (a).

(e) distance matrix standardised by species;this variant might resemble

(b) above.

(f) distance matrix standardised by sub-samples. This should tend to'

reduce between occasions differences while still being influenced by

between species density differences.

The latent vectors extracted from the above matrices, are principal

co-ordinates when based on a distance matrix, otherwise they are principal

components. Principal components projects the original space on to a

space a fewer dimensions in such a way that the arrangement of the points

suffers the least possible distortion. This is done by a rigid rotation

of the original axes, or equivalently linear combinations of the original

variate values that will yield derived variates (Z's) with the following

properties, Pielou (1969): the first new axis Z1 has a variance which is

as great as possible. Then the variance of the Z2 axis is arranged so as to be as great as possible, subject to the restriction that the Z2 axis 231

must be orthogonal to the ZI axis. Thus the variates Z1 and Z2 are

uncorrelated. Thus a sequence of coordinate axis is produced such that

each is perpendicular to all proceeding axes, and is parallel to the line

of greatest remaining variation in the points. Proof of the principal

components solution can be found in Anderson (1958), Kendall (1957) and

Morrison (1967) among others (see also P 125 ).

Most recent authors have favoured the use of standard multivariate

statistical methods for ordinations rather than specially invented

techniques, but the dissenting view of Gauch and Whittaker (1972) and

Beals (1973) should be noted. They believe that principle component

analysis will not choose meaningful and effective axes when used on

typical ecological data.

Principal coordinate analysis is similar to principal components,

but the points (representing the species) can be re—plotted so that the

distance between them is equal to a measure of the differences between

the species as expressed in the sample. The method was propounded by

Gower (1966), and is further explained by Gower (1967) and Blackith and

Reyment (1971).

Results and Discussion of the Single—Linkage Classifications

One of the main uses of clustering procedures is to present the

important features of an association matrix in an easily assimilable form.

This is especially important where the matrix is large.

With the small numbers of individuals (ie. species in this case) used in the present study, the gross relationships are, perhaps, rather obvious and thus we are interested in the "finer detail" of the matrix structure, which one hopes may reveal some ecological relationship not so obvious from preliminary inspection. The nature of the classification procedure, 232

and the fact that it results in a one-dimensional representation means that certain gross features tend to dominate the analysis, unless specific measures are taken to prevent this. If certain obvious attributes seem to obscure other interesting features employment of certain transformations, standardisations or other modifications of procedure seem justifiable.

There is of course, some danger in employing such techniques a posteri, to produce results more in accordance with a subjective assessment of the relationships although paradoxically the data are often used as a test of the method.

The dendrograms resulting from the classification clustering are shown on page243 As is usual with these representations, the relation- ships are reflected well by the tips of the branches, but may not be so reliably represented by the lower levels of the dendrogram. Nothing can be said concerning the relationships between the individuals on different main branches of the dendrograms. The clusters on long "stalks" are more likely to be stable than those on short stalks.

Examination of the dendrograms la and for the core samples using the eight collembolan groups, demonstrates this affect of the clusters being mainly a reflection of some rather obvious features of the data. One might expect the unstandardised distance coefficient to reflect between- sample unit density differences for each group, and also overall group abundance, and this appears to be so, producing a classification based on the magnitude of the standard deviation of the mean of each collembolan group (la). The usual between-rows standardisation reduces the effect of between sample unit variation, and thus this procedure has clustered the groups on overall abundance (1b).

Standardisation by columns ie. between groups is necessary if the effects of overall group abundance on the distance matrix are to be eliminated. This results in classification lc, which closely resembles ld, the clusters resulting from the untransformed correlation matrix. 233

This similarity is not surprising as the correlation matrix can be regarded as based on a covariance matrix standardised by groups. 1c and

ld show a clustering of Entomobrya nicoleti, Leallaystla lignorum and gypogastrura sp. on one hand, and the Onychiurids, Folsomia sp. and the

Isotomids on the other. Tomocerus longicornis and to a lesser extent the Sminthurids occupy a rather isolated position.

Thus the groups of Collembola occupying the lower part of the soil

profile ie. the Onychiurids, Folsomia species and the Isotomids have been

separated from the surface-dwellers (Entomobrya, Lepidocyrtus). Note

here that the Isotomids consist of small hemiedaphic species (mainly

Isotoma notabilis), and Isotoma viridis has not been included. Examining the actual correlation matrix itself we find that high correlations are

shown between Entomobrya nicoleti and Lepidocyrtus lignorum and especially

between Lepidocyrtus lignorum and Hypogastrura species in one group, and the Isotomids and Folsomia, and the Isotomids and the Onychiurids in the

other group.

However, as the animals are aggregated, especially Hypogastrura, the

data columns are rather heterogenous, and so some equalizing of variances

and promotion of normality in the data by logarithmic transformation would

seem to be desirable to avoid excessive weighting of sub-samples which

happen to include an aggregation. Thus the effects of random "noise" in

the system would be minimised. The clustering methods themselves do not

of course, depend on any special distributions in the data.

The clusters based on the correlation matrix of the logarithmically

transformed data (1e) is perhaps the best of the variants attampted here.

It can be seen that the very high correlation between Lepidocyrtus lignorum

and the gypogastrura species shown in the correlation matrix based on the

untransformed data, has been reduced and would seem to have been exaggerated 234

before by the extreme aggregation shown by Hypogastrura. This species

now occupies a more isolated position. Likewise the links between the

Sminthurids and the Isotomids Onychiurids Folsomia cluster, which did

not seem realistic, are now broken. The association between the lower

profile dwellers (Isotomids, Folsomia, Onychiurids) has been strengthened.

Incidentally, significant correlations at the 1% level are shown by

Folsomia and the Onychiurids, the Isotomids and Folsomia, and the Isotomids

and the Onychiurids.

A significant correlation at the 1% level is also shown between the

surface-dwelling Entomobrya nicoleti and Lepidocyrtus lignorum which

seems realistic.

Thus it appears that a small area which is a favoured environment

for the surface-dwelling Collembola, tends not to be optimal for the deeper

living species, and vice versa. Thus there seems to be some spatial

separation of Collembola according to their life-form category.

There is also a high correlation between Isotomids and Lepidocyrtus

lignorum; these two groups presumably forming the link between the

extremes of surface and deep-dwelling Collembola. The sequence is:

Onychiurus Folsomia - Isotomids - Lepidocyrtus lignorum -

Entomobrya nicoleti

which agrees with subjective morphological classifications (life forms).

Tomocerus longicornis and the Sminthurids are not associated with the

other surface-dwelling species as might be expected, and take up an

isolated position in all of the analyses carried out. This is perhaps

because their occurrence at low density in the sample units furnishes

little information about how they should be united with the other clusters.

Turning now to the cluster analysis of the four surface-dwelling

species, we find for both the core samples and the square-foot suction 235

samples that the variations in procedure produce a sequence of dendrograms similar to those which resulted from the eight groupfanalysis discussed above. However, when the appropriate standardisations were performed on the association matrices to remove the effects of overall species abundancelt6

RE vertical stalks between the clusters short in the extreme. This means that the data, although large in number of sub-samples, was not divided into stable clusters by the method. This may mean that these four surface— dwelling species are equally similar to each other in their ecological relationships, and that no structure can be recognised in their association matrix. It is likely, however, that the result is due in part at least to the single—linkage method which tends to produce rather rapid coalescence of groups, and subsequent Condensation of detail.

Logarithmic transformation of the correlation matrix tends to result in slightly more stable clusters (Pigs. 2e and 3e). In both the core samples and the square foot suction samples Entomobrya nicoleti and

LeDidocyrtus lignorum are the most associated, and reference to the original correlation matrix shows that these two species are highly correlated in both sets of samples. Tomocerus longicornis shows no association with Entomobrya nicoleti in either set of samples. The position of Isotoma viridis varies somewhat; it shows a moderately' high correlation with Entomobrya nicoleti in the core samples, and thus is placed closer to the Entomobrya, Lepidocyrtus cluster in that dendrogram. In the suction samples however, Entomobrya and Isotoma show no association but there is a moderately high correlation with Tomocerus which causes Isotoma and

Tomocerus to be grouped together in this dendrogram. One possible explanation of the change in the association of Isotoma viridis with depth in the profile, could be the following: on the surface where low relative 236 humidity is more likely to occur, Isotoma viridis tends to congregate in very humid places where- Tomocerus longicornis also tends to be found, both being highly susceptible to desiccation (Joosse, 1970), and thus show no association with artm2bal nicoleti which prefers slightly less moist places. Lower down in the soil litter profile this effect is unimportant, as the relative humidity is nearly always high.

Results and Discussion of Ordinations

The results of the ordinations are shown on pages 247-258.

In these presentations, the eigenvectors have been scaled by multiplying the Normalised eigenvector by /latent root. The object of scaling is to reproduce approximately the distances between the species when the latter are considered in terms of the original untransformed variables.

No attempt to use significance tests on the principal components analyses has been made, as the available tests (Lawley and Maxwell, 1972) assume multivariate normality in the data, and are therefore inapplicable in the present work. All the analyses are Q-mode (between-species matrix).

The 4-species samples will be considered first; with so few points interpretations will perhaps, be rather unconvincing, but the behaviour of the data under the various procedures can be followed. There is obviously a possibility (especially with few points and where several planes are being considered) of a configuration suggesting some fallacial biological explanation. This can be avoided to a certain extent, by following through the splitting of the major variation, under a variety of analyses, to see whether it is likely that the evidence of the biological phenomena could be transmitted from data matrix through association matrix to final configuration of points. As pointed out by Williamson (1971) in multivariate descriptions of data of this type, the between-species variance has a clear meaning and we are mainly interested in interpreting a combination of the between-cores variance and within species and cores variance. 237

If we examine the principal component analysis on the covariance matrix of species (p254. Fig.8.) (basic P.C.A.1

it can be seen that relationships between the species can be legitimately represented in two dimensions as

98.5% of the variance is taken up by axes I and II. (axes III,

IV will be ignored).

On the first axis as expected the species are arranged according to variance of species around their means (ie. in this case arranged according to species density), and this feature probably accounted for most of the 81.3% of the variation removed, by this axis.

The only biological interpretation of the second axis (which accounts for 11.24% of the variation), which occurs to me, is that it separates the species with similar fluctuations in time and space. If the number for each species, in a sub-sample is scored +1 - or 0 according to whether it shows an increase or decrease or has remained the same as in the previous sub-sample, the resultant scores for each species show relationships like those of Fig.8.

Leoidocyrtus is somewhat asychronous in its fluctuations compared to the other three species; this could indicate some avoidance of competition between the more numerous species, Entomobrya and Lepidocyrtus.

However, part at least of the displacement of Lepidocyrtus along axis II may be a mathematical figment rather than a representation of a biological phenomenon.

The principle-coordinates analysis on the euclidean-distance matrix

(p250, Fig.5) graphs out this matrix in virtually two dimensions

(94.38% of variance taken up by axes I and II), producing a similar configuration to the principal components analysis as expected.

Again species density differences probably give rise to the arrange- ment of species on axis I (88.14% of variance removed by this axis). 238

Principle-coordinate analysis on the Euclidean-distance matrix standardised by samples (Fig.6) should tend to reduce between-occasions differences, but the representation obtained is very similar to the two previous analyses, (percentage variation accounted for is 81.6% - axis

14.4% - axis II).

The position of Lenidocyrtus is brought only slightly lower down axis II. This seems to indicate that only a small part of the variation along this axis could be due to the degree of synchrony between the species numbers, which rather militates against the explanation, mentioned above, of this axis largely in these terms.

The P.C.A. of the correlation matrix is shown in Fig.10. It is similar to the principal-coordinate analysis based on Euclidean distance standardised by species (Fig.7), (except that here the configuration of species points lies at 90° from that of Fig.10).

With the effect of differences in species-density eliminated, more subtle aspects of the data matrix are displayed. Three axes are now required to take up most of the variance. The analysis based on the correlation matrix (Fig.10) now removes only 41.0 of variance on axis I with axis II accounting for 27.6% and the third axis 1 7.66%.

The result of tile analysis of Euclidean distance matrix with standard- isation by species (Fig.7) is similar, (axis I - 46.8%, axis II - 29.8%, axis III -

The positions of points in these figures are now determined by the habitat, viz. Entomobrya nicoleti being least associated with Isotoma viridis and Tomocerus longicornis, and more associated with Lepidocyrtus lignorum. Axis II in Fig.10A (or axis I in Fig.7A) might therefore represent a division of the niches due to a factor such as humidity reactions of the species, with Entomobrya which prefers slightly lower humidity, at one end of axis I and Tomocerus and Isotoma requiring high 239

humidity at the other. Tomocerus and Isotoma are then distinguished on axis III Fig.10B which may be relation to depth distribution of the species.

Axis III in Fig.7B apparently offered no information useful in biological terms.

If loss of information on the variance of each species around its mean, is thought undesirable a logarithmic scale can be used, and then the variances are comparable (Williamson 1971). The covariance matrix from the logarithmically-transformed data is graphed out in Fig.9A. Axis I of this figure (accounting for 51.8% of the variance) is related to the variance/ mean ratio and the species arranged according to degree of aggregation,

(arrangement agrees with an index of aggregation calculated on the data such as I6 ). Axis III of Fig.9B is probably analogous with the same axis of Fig.10B, ie. it separated species Tomocerus and Isotoma possibly on depth distribution in profile.

Considering the 8 species (or groups) core-samples:-

Here the arrangement or ordering of the eight points is less likely to suggest some erroneous explanation by chance than with the four-species analysis so these analyses are a fairer test of the biological utility of methods.

With the principal co-ordinates based on Euclidean distance (Fig.4A 1)247), the first axis takes out 50.68% of the total variance and, as usual, orders 'the species according to density.

The second axis (27.55% of variation) appeared to arrange species' according to their association with each other in the habitat and this appeared to be primarily due to the level in the profile at which the species occurs. With the removal of variation due to occasions (Fig.4B), and with the correlation matrix (4c) the rather anomalous position of the

Sminthuridae is improved. The species can be seen to be divided into the 240

surface-dwellers (Entomobrya and Leoidocyrtus, Hypogastruridae, Tomocerus

and Sminthuridae) on one hand, and the more subterranean species (small

Isotomids (not including I. viridis) Folsomia and Onychiurus) on the other.

This division parallels that found by the single-linkage classifica-

tions (p 245).

The third axis in 4b appears to distinguish the Hypogastruridae with

their extreme aggregation from the other species, but degree of aggregation

is better displayed on axis III of the correlation analysis (4c) where

the species are ordered by degree of aggregation, from the Hypogastruridae

with very high aggregation to the Sminthuridae with the lowest aggregation.

This sequence,:matches that calculated from Taylor's power law.

This method produces similar results to that of the single-linkage

classifications, but the latter produce one-dimensional representations

which, while convenient for simple classification of data, are less

suitable for analytical investigations.

Thus employment of ordination techniques may be more appropriate in

this type of study, in that obvious attributes of the data matrix are

displayed on the first axes leaving the more subtle relationships (if

present) displayed on later axes of the same analysis. Ordination also

avoids the imposition of a hierarchy which may not be particularly

appropriate in small scale ecological investigations.

In principal-coordinate analysis all the sets of coordinate axes

are required for a complete description of the data, but usually a fairly

high proportion of the variation is reflected in the first few axes. An advantage of principal coordinate analysis, in this respect, is that the amount of distortion which one can expect of taking only say, the first three sets of coordinat.s, is given immediately. There is no simple

equivalent to this measure of distortion in single-linkage analysis. On

the other hand, ordinations do present a problem of reification of the

axes, which is often difficult in ecology cf. taxonomy where 241

reification is not required. For instance, one problem that arises in deciding whether variation between the samples was "random noise" and little structure was present in the data initially, or whether the situation is genuinely hypermultivariate, and each species differs from the other by axes peculiar to it (Blackith & Reyment 1971). The problem of reification of axes is more acute in the present study as the few individuals involved do not permit extensive clusters or gradients to occur. The use of some environmental data to produce ecological variates in the analysis, would perhaps promote easier biological identification of axes although unfortunately this was not done in the present study.

Another difficulty in the present study is the lack of the equivalent

R-type analyses, which had to be omitted due to computer storage problems.

The inverse analysis is often useful as an aid to interpretation, and furthermore the R-mode analysis would contain vector elements ("weights") which were not available in the Q-mode programmes.

Williamson (1971) draws attention to two limitations which should-be borne in mind when using ordinations in population ecology. Firstly, as has often been mentioned, there is the simple maximum varianceArthogonality criterion for finding axes, which may not be particularly appropriate.

Secondly, it is assumed that population numbers are in equilibria with the variables. Thus the method only suggests hypotheses about variations in these equilibria, not about their existence.

However, as long as the limitations of the techniques are borne in mind, the methods seem useful tools, especially as the amount of time required to run the analyses and examine the results is a very small fraction of the time required to obtain the raw data, and once the latter have obtained it seems a pity not to utilise them to the full.

It must be admitted that the present study has revealed rather little of biological importance, but does demonstrate which facets of the data are displayed under the various computational procedures. It also draws T• 242

attention to the need for further experience in interpretation of

axes produced by multivariate studies in population ecology.

/ 1A 1B

T S F O L H E Id T S H E Id 0— 0 —

10-

20- 1.0—

30- 1-5—

20—

50— 1C 1D 0-- L H E Id F 0 S T L H E Id F 0 S T Cr 1.0— \

0•5- .75--

1 0— .5 —

1.5— .25— 1E

F Old L E H S T 10—

•75—

.25—

2E 3E

'0 E L T E 1.0- 1.0-- N

.75- .75-

•50- 50-

•25- •25- • - 4 -.1-..i.^.....zzcprrma,-...;:ae...... orvel•-zwpww-wearasiCiramillerrpsa.,”..,,-rrrawtosm•zkeztr.:-4s. ,-,7 ' • ," •

247 4a

E

• H 1■■ T AXIS1

IS • S

.0

AXIS 2 4a

•H

.0 •L AXIS 1 IS ' T F

E

AXIS 3

AXIS 3 I 248

4b

AXIS 1

IS ,

4b

AXIS 1

4b .E

s-S ,,, ° H ,,,

• F

•IS

.0 C • p 249

.1

•I S

AXIS -1 •S

•T •H

•L AXIS 2 •E

4c

4c

. v...,;.$4,241irguct 5

AXIS 1

AXIS 2

AXIS 1 I r k V r r yr I II t I III1 ►

AXIS 2 252 7A

r AXIS 1 1

AXIS 2

7B

AXIS 1

AXIS 2

253

•1 7C

• L

AXIS 3 1 AXIS 1 4

E

AXIS 2 0 T

I AXIS 1 T I 1 I I l I L k L I II till lir

AXIS 2 255

9A E 0 L 0

AXIS 1 I I I I

0

0

AXIS 2

E 9B 0 L0

AXIS 3

0

0 I AXIS 2 256

9C .0\

AXIS 1 t •I I T I I 1 I I- I

0 1 AXIS 3 257

10 A T

AXIS 1 I I I I- 1

aL

AXIS 2

10 B

a

AXIS 1

T

AXIS 3 258

10 C

AXIS 3 f I I 1 1

E AXIS 2

259

The Relationship of Egg Development time and temperature

As some data on the time taken for egg development under several constant temperature regimes had been obtained (p260), an empirical relationship between these two variables was sought.

The effect of temperature on development rates is reviewed by

Wigglesworth (1965) and Watt (1968) among others.

The data on p260for E.nicoleti was used, plus the figure of 9. 3 days

o at 17 C given for this species by South (1959)

Due to the limited nature of the data, use of a sophisticated model such as advocated by Watt (1968) was not thought to be worthwhile. If one ignores the fact that egg development is likely to be seriously retarded at very high temperatures, (which would not be experienced in the field), a form of the logistic curve has been suggested as providing a reasonable model, Davidson (1944).

As described by Davidson (1944) the data has a curve fitted to it given by - K 100 = a+bx 1 + e where 100 is the average percentage development per unit time.

Xis the given temperature, k,a and b are constants k represents the distance between upper and lower asymptote. b represents the degree of acceleration of development of the eggs in relation to temperature. a indicates the position of the origin of the curve on the abscissa ..

1 260

TIM TIME REQUIRED FOR EGG•DEVELOPMENT IN U. NICOLETI

AT SEVERAL CONSTANT TEMPERATURES.

TEMPERATURE C) EGG DEVELOPMENT TIME (DAYS)

25 5,2

20 7.0

15 • 1100 10.5 22.0 6 60.25 261

k was calculated from three values of 104 at three equally spaced temperatures, (in this case 25, 20 and 15°C, or 20 15 and 10°C.)

Then loge k - p is regressed on temperature to obtain a and b.

(p = ) See p. 263-264.

Values of 100/j for each value of temperature can then be calculated giving the temperature - velocity curve.

The results of fitting the logistic were compared to a normal regression of velocity (reciprocal of development time) on temperature 0

(P265 )•

The logistic, of course, has the advantage of producing a deve- lopmental rate which tends to zero as the temperature does the same, but neither of the two logistic curves fitted as well as the linear

regression,to the actual data themselves. Thus it appears that the Not logistic does form a very good model for egg development rates in

E.nicoleti, and the developmental velocity shows a linear increase with temperatures in the range 10°C to 25°C.

Hale (1965) has given the velocity tem-

perature relation for a number of moorland collembolen species. It

is interesting to note that the "developmental zeros" are close to 0°C

(or even apparently below it), instead of about 5°C as in South's (1959)

work and the present study. This presumably reflects an adaptation

to the sub-Arctic moorland climate of the collembola in Hale's study

which is not found in the collembola of the southern, lowland area

represented at Silwood. 262

, However it should perhaps be mentioned that constant temperatures may in themselves retard development,as Hale noticed that the rate of egg development in his species was greater under fluctuating temperatures. FITTING THE LOGISTIC CURVE TO THE RATE OF EGG DEVELOPMENT WITH TEMPERATURE FOR E. NICOLETI THE RELATIONMiTP OF L . If. -P / P AID Tr7TERATURE ( K CALCULATED FROM VALbES OF THE AVERAGE % DEVELOPMENT PER DAY FOR TEMPERATURES OF 25 , 20 & 15 ° C ) 3

2

3.

-ta -ts -f k .268292602919ex 02 y■ .349997906650 + -.190706395413 -1 t -.201033301285ex 02 r-.995087702294

-2

-3 ▪ ▪

FITTING THE LOGISTIC CURVE TO THE RATE OF EGG. DEVELOPMENT WITH TEMPERATURE FOR E. NICOLETI THE RELATIONSHIP OF LOG. (K-P) P AND TEMPERATURE. (K CALCULATED FROM VALUES OF THE AVERAGE % 3 DEVELOPMENT PER DAY FOR TEMPERATURES OF 20, 15& 10 ° C . )

n.

2

• 1

.1. .t. 1 —20 —1 5 —11) 5, 10* 15 20 25• .. TEMPERICURE ° C k .203166226912ex 02 4,17301819084ex 01 + —.267014767075 X -1 —.141840853467ex. 02 r— .990204882295 -

—2

—3

—4

265

10.2 THE LINEAR REGRESSION OF THE RECIPROCAL OF EGO DEVELOPMENT TIM (DAYS) AND TEMPERATURE FOR E.NICOLETI.

f f If

0.15

• a . • • • ." + •• •113 ;i •• •• .4 ••

• ••

p.o5

+ •

5 10 15 20 25

TEMPERATURE C

7' -.0485265+ .0094655(+ .0004355)x

x=. 104.7593370+ 5.2144859(+ 4.8204190)7 Correlation coefficient= .9957920 Sy.x= .0065796

Sx " .6921915 .

t(b=0)- 21.73242 for 4 degrees of freedom P. < 401

15.58333 , 7'. .09397 DEVELDPIENTAL t ZERO I a* 5.12 266

COLD-HARDINESS

Introduction

As all four of the species dealt with here have overwintering, free-living stages (mostly adults and sub-adults) it was interesting to know whether the lower densities of animals which usually occurred in mid winter and in early spring were due merely to cessation, or slowing of the reproductive rate, combined with senility, or whether the low temperatures themselves caused appreciable mortality. Joosse (1969) believed that the approximately 50% reduction in the overwintering populations of surface-dwelling Collembola studied by her, was due to weather factors rather than senility or predation.

There is however little precise information on the cold-hardiness of

Collembola except that of Agrell (1941), and Thibaud (1968).

Cold-hardiness or cold-resistance in insects generally has been reviewed by Salt (1961, 1966 a), and Asahina (1969), and these authors give many references.

Several effects of the low temperatures must be distinguished,

(Salt 1961). "Chilling" generally refers to cooling without freezing, and usually in insects that do not encounter cold temperatures normally.

They may or may not show acclimation, i. e. a physiological change induced by a single factor (temperature)which results in the adjustment of various metabolic processes , resulting in greater cold tolerance, 267

e. g. Nuttal (1970). Animals overwintering in temperate or cold

climates can usually withstand fairly low temperatures however,

although the species may become inactive (at the lower motility limit)

and physiological deterioration may occur if temperatures

are low for a long period of time. Here however we are only

concerned with the "cold-death point", and furthermore only with

"freezing-susceptible" animals, where the freezing of the body tissues

is fatal. Freezing is avoided here to a greater or lesser extent by

super-cooling, Salt (1961). (In contrast, some animals are "freezing-

resistant", i. e. do not need to avoid freezing, as it is not injurous to

them (Asahina, 1969)). When freezing does occur it can be due to

either innoculation or nucleation. Innoculation of the body fluids by

external ice can occur if the cuticle of the insect is in contact with ice

or freezing water, at temperatures above the usual super-cooling

temperature. The phenomenon is discussed by Salt (1963). He found

the rate of innoculative freezing was proportional to the area of cuticle

in contact with the ice, and inversely proportional to the temperature

down to -10° C. It also depended on the properties of the cuticle and

the cell membranes, and factors affecting the rate of ice growth through

the pore canals, glands and other openings in the cuticle. If the

insect surface is dry however the insect super-cools until the

formation of ice crystal nuclei form within the body and initiate freezing.

The general temperature range in which freezing occurs is determined

by intrinsic factors like body size, body water-content, and particularly by the quality and quantity of the nucleating agents, while the specific 268

freezing temperature depends on the time-temperature cooling pattern and on chance (Salt, 1966 a, b, c and 1968). Nucleation usually begins in the gut contents, and the freezing temperature is more accurately defined by the quantity of the nucleators than by the mass of the body water (Salt 1968).

Laboratory investigation of the super-cooling properties of an insect can proceed in two ways, (Salt 1966 a).

1. The cooling rate is held constant (or reproduceable), and the temperature at which freezing takes place (super-cooling point) is recorded. This gives a near-minimum surviveable temperature and is a convenient figure for the comparison of cold hardiness in

"freezing-susceptible" insects.

2. The temperature is held constant at a sub freezing level,

and the time to freezing noted. This situation may approximate to the field situation where cooling is often very slow. Salt, (1966 c) found the logarithm of the mean freezing time linearly related to temperature.

Methods

Two methods of investigating the cold-hardiness of the four

species of Collembola were employed. Firstly, the super-cooling properties of the animals were investigated by determining the 269

super-cooling point using method 1 above. To the best of the author's knowledge no determinations of super-cooling points have been made for a Collembolan species. Animals were collected from the field at various dates throughout the winter, and tested the same day unless otherwise stated. They were stored in a fridge at 2°C until required, so that they would not loose any cold hardiness they had acquired. Individuals were then taken from the fridge and anaesthetised with carbon dioxide. The end of a thermocouple was smeared with a small amount of vas eline and touched gently against the collembolan. causing it to adhere to the thermocouple tip. The collembolan was then allowed to recover from the anesthetic. The thermocouple wire with the Collembolan at the tip was then inserted a single ended, glass tube, and this in turn was inserted into the cooling device. Cooling was accomplished thermoelectrically by a frigistor, using an apparatus similar to that described and figured by Luff (1966). When cooling had commenced, the falling temperature of the animal was recorded on a Rnstrac Paper Chart Recorder. The temperature at which the rebound started due to the liberation of latent heat from the frozen animal, was noted. The cooling rate produced by the apparatus was unfortunately not constant but was reproduceable. Only the linear portion of the transient cooling curve was used. Salt (1966 a) advocated the fastest cooling rate that allowed near uniform cooling of the animal so that steep gradients would not set up in the body thus making temperature . o measurements inaccurate. A rate of 2-5 per minute was thought to be appropriate for small animals, and in the present work the cooling rate 270

o was 5 per minute approximately. As the cooling rate decreases

the mean super-cooling point rises, but Salt (1966 a) believed that the

change was negligible until the rate slowed to a degree or so per hour.

However in a later paper (1966•b) he discovered that the mean super-

cooling point altered with the rate of cooling over a large range of

cooling rate values, and thus he proposed the adoption of a standard

rate of cooling of 1° per minute. This source of variation has not been

quantified in the present work but Salt (1966 b) found the actual change

to be fairly small and Sullivan (1965) found cooling rates in the range

feasible for lab testing to show no major differences in super-cooling

point, and in any event this problem does not affect comparisons

between the species.

A separate glass holder was used for each Collembolan used, to

avoid innoculation by water which condensed on the used tubes as they

were withdrawn from the apparatus. After freezing the animals were

recovered from the holder and stored in alcohol. Later, determinations

of the size as measured by the head diameter, and the proportion of the

gut filled with food (scored from 0 to 10) were made for Tomocerus

longicornis and Isotoma viridis. Determination of the sex of the specimens

was difficult as the animals nearly always died with the furca extended

backwards, thus the sex of the animals was not recorded.

The mean super cooling point for each species, for each occasion were tested for significant differences to investigate whether the cold 271

hardiness varied with the season. Correlations were also sought between the super cooling point and the size of the animal, the amount of food taken into the gut, and the temperature over the preceding period.

An experiment was performed to examine whether cold hardiness would be lost or would diminish if the animals were taken in from the field and kept at a higher temperature for a period before the super-cooling point was determined. Collembola were thus brought in from the field and divided into two groups, one was tested for super cooling point immediately, and the others were kept at 15°C for four weeks under the usual culture conditions before testing. On the day that these animals were tested, a new group were brought in from

the field to compare them with.

The second method of investigation of the cold-hardiness of these

Collembola involved a determination of the survival at less extreme

temperatures experienced for longer periods of time, with the

additional possibility of innoculative freezing due to being in contact

with a frozen substrate. Batches of the four Collembolan species

were collected from the field at various times during the winter, and

placed in special experimental containers. These consisted of petri dishes with a layer of plaster of paris/charcoal moistened with water lining 272

the base and sides of the dish and a piece of moist filter paper covering the lid, so that the animal was obliged to stand on a moist or frozen substrate at all times. The lid of the petri dish had small holes pierced in it to allow rapid equilibriation of the temperatures inside the dish with those outside. The Collembola were kept in the experimental containers at field temperatures for a period of 1 hour to check for any individuals damaged during the collection. In some dishes were transferred to refridgerators at various temperatures between -3°C and -12°C, while others were kept at +5°C as a control.

The animals were examined at frequent intervals especially at first, and later were examined at daily intervals. Water was added to the dish daily to make up for evaporation losses. Collembola were counted as dead if they did not exhibit coordinated locomotion when touched after being allowed to warm to a few degrees above zero.

For each replicate, at each temperature the cummulative mortality after each period of time at that temperature, was calculated for each of the four species.

The resulting data was subjected to a probit plane analysis (Finney, 1962), with log10 temperature and log10 time of exposure, as variables X 1 and X2. 273

Firstly, estimates of parameters in the model;

E (p) = (a_ ÷ bX1 cX2 -5)

were calculated, by an iterative procedure.

E (p) is the expected proportion of subjects responding to the joint action of temperature and time.

t 2

2 where (t) = 1 e • du 2 1T - oo

The iteration with the log liklihood a maximum was chosen.

The natural response rate, i. e. the proportion (e) of subjects responding to zero temperature and time, was also estimated by maximum liklihood.

The variance - covariance matrix of b, c and e was computed, and then the sum of weights, weighted means, sums of squares and products.

Then using the estimates of a, b, c a table of expected and observed frequencies was calculated.

Secondly, the existence of an interaction between the temperature and the exposure time in the experiments was examined by fitting a probit Hyperbolic Paraboloid model, i. e. probit plane with interaction; 274

E (P) = (a' + b' x1 + c'x2 + d'x1x2 - 5)

and similar set of output computed as in the first model.

Then the contribution of the interaction and heterogeneity about

the regression to the total heterogeneity was displayed in a chi-square table.

The heterogeneity and interaction are each tested for significance

at 5% level, and correction factors introduced to correct the variance -

covariance matrices of b and c, (or b', c' and d', if the heterogeneity

was significant.)

Finally equations giving the Effective Dose combinations 50%

(EDC 50), and the hyperbolae delimiting their 95% confidence regions

were computed.

The analyses were run using program S102 of the statistical

research service of the Canada Department of Agriculture written

by P. Morse, A. Petrasavits and A. Bickle, July, 1967. 275

RESULTS AND DISCUSSION OF COLD-HARDINESS EXPERIMENTS

The mean under-cooling points (U.C.P.) ( . supercooling points) for the four species of Collembola are shown on p276. The U.C.P. is a useful measure for a comparison of cold-hardiness between species.

The sequence of mean cold-hardiness increases thus: T. longicornis - I. viridis - L. lignorum - E. nicoleti There was no significant difference in U.C.P. between the two occasions

shown in the table on p276 .

However, for T. longicornis measurements were made over a longer

period, and as can be seen from the results on p277 there was a

seasonal change of U.C.P., (the December mean value being significantly

lower (P 7. .05) than the May value). The cold-hardiness did not appear to be significantly related to the mean or minimum field

temperatures recorded for seven days immediately preceding the test

date, but seemed to be a seasonal increase which is not lost when the

temperatures rise temporarily. A similar pattern of cold resistance

can be found in many insects which overwinter in fairly cold climates,

(see eg Luff (1966)). In some insects the U.C.P. has been shown to be

inversely related to the glycerol content of the body, and this substance

is,probably synthesised in response to the decreasing night temperatures

in autumn eg Krunic and Salt (1971). Agrell (1941) found that

resistance to cold increased with the decreasing autumn temperatures

in all species of Collembola tested by him, but the mechanism of cold

resistance is not known for this group of animals.

Freezing may occur in super-cooled animals by nucleation around

the gut-contents (Salt 1966a), but here the U.C.P. was not related to

the amount of food in the gut in either T. longicornis or I. viridis. 276

Comparison of the Under Cooling Points of Four species of

surface-dwelling Collembola

1 . Occassion E. nicoleti T. longicornis L. lignorum I. viridis

20th Oct 71 13.41 + 0.24 6.26 + 0.092 10.04 + 0.24 7.43 + 0.56 n = 12 n = 12 n = 10 n. =10 25th Jan 72 12.49 + 0.28 - 8.31 + 0.29 6.69 + 0.30 n = 21 n = 11 n = 11 277

Table Seasonal Variation in the mean Under-Cooling Point of TOMOCERUS

LONGICORNIS specimens taken from the field.

Under Cooling Point Date of collection and of determination CC) of U. C. P. Mean S. E. N.

3rd November 1970 -6.03 0.247 8

3rd December 1970 -6.10 0.099 20

11th January 1971 -6.8 5 0.164 23

. 18th February 1971 -5.23 0.114 16

12th May 1971 -4.23 0.143 14

20th October 1972 -6.26 0.092 12 0 0 1112

CU

2 3 11 18 12 OCT N OV DEC .1 N MAR A PR MAY 279

Mortality due to freezing may be affected by acclimation, and to test this the mean U.C.P. of T. longicornis brought in from the field was compared with that of specimens which had been kept at 20°C for one month. The result was surprising. The mean U.C.P. of animals straight from the field (on 18 February) was-5.23°C, while the individuals acclimatized at 20°C had a much lower mean U.C.P. of

-13.4°C (medians significantly different by a U-test). The experiment was repeated with similar results. The only explanation that I can think of is that the great increase in cold-hardiness was brought about by the diet in culture, which reduced the quantity of nucleating agents in the gut, or increased the ease of synthesis of substances promoting cold-hardiness. The matter requires further study.

Another observation made on T. longicornis was that the U.C.P. was significantly, negatively correlated (P .c-.001) with the size of the individual, as measured by the length of the head-diagonal (see

P280 ). The seasonal change in the correlation between U.C.P. and .body-Size was examined, and the results are shown below:

20 October - Not significant 3 November - Not significant 3 December - Negative & Significant, .01 > P> .001 11 January - 131*-1:.02 12 May P .02 Thus the smaller individuals appear to acquire relatively more cold- hardiness and retain it for longer, than the larger individuals. A similar significant and negative correlation of U.C.P. and size was shown by I. viridis on the only occasion that both were measured,

(25 January). Agrell (1941) found that in I. viridis and Lepidocyrtus lanuginosus the cold-hardiness was greater in the adult than in the young individuals. This result is not at variance with that from the present shady as the smaller individuals in the latter are sub-adults rather than very young individuals. In all four of the surface species TOMOCERUS LONGICORNIS

-8 •

0 • •

• 0 • • • • • • 0 4- • • • • a- • • • • •• •••1 ° • 11 • • • • •• • 2 • • • 0000 010 00 • • •• D • • o• • • I 000 • • • 0 •• -2 • • • • • • • • - y= 8.5 583 + -.04503X • • • • 0 LENGTH OF HEAD DIAGONAL 281

studied here, hatchlings and young juveniles were not very resistant to low temperature, and for this reason populations overwinter mainly as sub-adults, adults or eggs. A possible explanation of the greater told-hardiness in the medium-sized rather than large individuals of

T. longicornis is that cold-hardiness is not as strongly selected for in the large individuals, as these have already laid and are relatively

"spent".

To test the field survival of T. longicornis, some individuals were placed in special petri-dishes (described above), and these placed in the field overnight on several occasions in December and January

1971. The temperatures inside the dishes were continuously monitored by a thermistor probe placed through an aperture in the side of the dish. The specimens were examined the following morning, and the head- capsule diagonal measured. Animals with a head-capsule diameter of

50 units or more were designated "large". The results confirmed that smaller individuals were more resistant to cold. For example, on

21/22 December temperatures fell to -2°C for about 4.5 hours, killing 8 out of 14 specimens of T. longicornis, (this mortality seems to agree with that predicted from the laboratory experiments, (see p.11.10). Six of the individuals killed were "large" however and only two were "small", while all six survivors were "small". Again on 25/26 December a temperature of -2.5°C was recorded for 3.5 hours in the dishes. The following morning, two "large" individuals were found to be dead while another two were moribund, the eight healthy individuals were all

"small". The above experimental dishes had been placed in exposed sites, ie in short grass. On one night the temperature in these dishes fell to -8°C and all the individuals were killed. As some smaller T. longicornis had U.C.P. as low as -10°C and -12°C it seems

likely that some inoculative freezing occurs in the dishes in the field. • 282

In dishes placed in some sheltered sites on the other hand, eg dense

Holcus stands, temperatures did not drop below -1.0°C during the period of the experiment and no mortality occurred. Thus it seems that in normal winters T. longicornis would suffer very high mortality unless aggregated in sheltered sites. The species tends to occur in the higher vegetation on the experimental plot anyway (see section on

Aggregation), but it appeared subjectively to be particularly aggregated in sheltered sites during cold periods in winter (also noticed by J. Anderson pers. corn.).

The results of the probit-plane analyses of the experiments.on survival at sub-zero temperatures are shown on pages 283-4 & 287-.290.

The equations for the estimated 50% response level and its 95% confidence bounds is given for the E.D.C. 50% in terms of log. temperature and log. time of survival.

The E.D.C. 50% lines for the four species can be compared on p 284_ Some actual values of the estimated combinations of time and temperature required for 50% kill are also given.

The E.D.C. 50% line for T. longicornis shows that this species is the least cold-hardy. However the line is based on rather few values as it was difficult to collect large numbers of this species for use

in the experiments. This may explain the slight slope of the

E.D.C. 50% line which would give erroneous values for survival time

if extrapolated to low temperatures. The E.D.C. 50% values within a

range for this species given on page 290 seem reasonable.

The E.D.C. 50% lines for the other three species indicate much

more cold-hardiness than in T. longicornis. I. viridis appears to

be far closer to E. nicoleti in cold-hardiness than one would have 283

PROBIT -PLANE (WITHOUT INTERACTION) ANALYSIS OF SURVIVAL OF FOUR SPECIES OF COLLEMBOLA AT SUB-ZERO TEMPERATURES.

Analytical Description of the 95% confidence region for Effective Dose

Combination 50% in terms of Log. Doses (i.e. Log. Time (X2) and Log. temperature (X2)). The Estimates 50% RESPONSE LEVEL is given by:

* 1.5412 + 3.3207 X2 + 3.3741 X2 = 5.00 for T. longicornis -3.1293 + 1.7190 X1 + 7.7220 X2 = 5.00 for I. viridis -7.7348 + 2.0715 X2 + 12.4211 X2 = 5.00 for L. lignorum -2.9350 + 1.5369 X2 + 7.2492 X2 = 5.00 for E. nicoleti

The confidence bounds are given by a hyperbola whose equation is:

2 2 -2.8947 X - 26.0533 X1 X2 - 57.9903 X2 + 33.0658 X1 + 147.9195 12 - 94..0671 = 0 for I. viridis 2 -3.8255 X1 - 45.9873 X1 X2 - 136.9343 X2 + 47.5582 X1 283.9151 12, -14.7.0306 = 0 for L. linorum 2 -2.3019 XI. - 21.7109 X X - 50.9135 X + 23.744/1 X 1 2 2 1 + 111.5416 12 - 61.0517 = 0 for E. nicoleti

* Probably not accurate at low temperatures see Text. 284

SURVIVAL AT SUB -ZERO _TEMILERATURI

EFFECTIVE DOSE COMBINATIONS 50 0/0 . LOG. DOSES

•■•■11■1. IE L

tn •••••■■ 0 ■1•■•••

. •••••■•11 0

I - .2 - —6 -•8 -1-0 LOG. TEMPERATURE ° C ) 285

expected from the U.C.P. values. This may be due to the use of larger-sized individuals in the U.C.P. determinations, which are less cold-hardy as has been mentioned above.

There does not appear to be any evidence of inoculative freezing in these laboratory experiments, like that which occurred, for

T. longicornis at least, in the field. This is probably due to wetter conditions in the field causing the animals to be more in contact with moisture than in the laboratory experiments.

The results for the E.D.C. 50% determinations for E. nicoleti,

L. lignorum and I. viridis are somewhat puzzling apart from this, as they show short survival times (less than an hour) at temperatures similar to or slightly below the U.C.P. temperatures, whereas one would expect the latter to be the lower. This discrepancy also appears in the paper of Luff (1966), his fig.2. showing a 50% survival of his beetles at -13°C after one day, while the mean U.C.P. on dry: leaves of his animals was virtually the same (-13.4°C). In the present case the discrepancy might be due to the fact that the positioning of the probit plane does not take into account the inter- action between the temperature variable and the time variable. The interaction is, however, significant (P rC .001), as would be expected for these three species, as they survived for considerable periods at many of the temperature regimes used.

References on the lower temperature limit for survival of

Collembola are given by Bellinger (1954), and Christiansen (1964).

As these data are in the form of spot temperatures beloW which there is no survival, they are not readily comparable with the data given here.

The cold-hardiness of the three more resistant species seems greater than that of most of the species quoted in the llterature,(if one ignores the extreme adaptations found in some Antarctic Collembola,

M ‘1 286

temperatures of —500C were survivable). It is interesting that

Collembola as a group seem rather insensitive to cold compared to mites, Remmert and Wisniewski (1970), and this is presumably why they form a large proportion of the fauna at high latitudes.

PROBIT PLANE ANALYSIS (WITHOUT INTERACTION) OF SURVIVAL OF E. NICOLETI AT SUB-ZERO TEMPERATURES

NUMERICAL DESCRIPTION. OF THE 95 % CONFIDENCE REGION FOR EFFECTIVE DOSE COMBINATION 50 % IN TERMS OF ORIGINAL DOSES (Z1, Z2)

Contours of the Hyperbola which Describes Effective Dose Combination 50% The 95% Confidence Region For Effective Dose Combination 50% in Log. Transformed to Original Doses Doses Transformed to Original Doses

Z1 (Time Z2 (Negative Z1 Z2 Z1 Z2 in Hrs) temp. )

3880.2524 2.1563 3808.7425 1.9761 3940.4966 2.3179 778.5973 3.0311 767.9846 2.8421 787.4320 3.1959 156.2305 4.2608 154.7378 4.0732 157.4715 4.4219 31.3486 5.9894 31.1129 5.7811 31.5566 6.1781 6.2903 8.4192 6.2349 8.0769 6.3450 8.7685 1.2621 11.8348 1.2468 11.1755 1.2784 12.5666 . 2532 16.6361 . 2491 15.4084 . 2577 18.0736 . 0508 23.3851 . 0497 21.2154 . 0519 26.0293 . 0102 32.8722 . 0099 29.1924 . 0104 37.5107

PROBIT PLANE ANALYSIS (WITHOUT INTERACTION) OF SURVIVAL OF L. LIGNORUM AT SUB-ZERO TEMPERATURES

NUMERICAL DESCRIPTION OF THE 95 % CONFIDENCE REGION FOR EFFECTIVE DOSE COMBINATION 50 % IN TERMS OF ORIGINAL DOSES (Z1, Z2)

Contours of the Hyperbola which Describes Effective Dose Combination 50% The 95% Confidence Region For Effective Dose Combination 50% in Log. Transformed to Original Doses Doses Transformed to Original Doses -

Z1 (Time Z2 (Negative Z1 Z2 Z1 Z2 in Hrs) temp. )

4030.0261 2.6545 3986.8267 2.4894 4096.3762 2.9257 886.0578 3.4174 879.7308 3.2746 897.8892 3.6984 194.8122 4.3995 193.9936 4.2905 196.9384 4.6934 42.8322 5.6639 42.7125 5.5703 43.2621 6.0111 9.4172 7.2917 9.3828 7.1342 9. 5252 7.8041 2.0705 9. 3872 2.0579 9.0536 2.1004 10.2253 . 4552 12.0850 . 4510 11.4446 .4634 13.4502 . 1000 15.5582 . 0988 14.4432 . 1023 17.7214 . 0220 20.0295 . 0216 18.2129 . 0225 23.3677

N.) o) Co

PROBIT PLANE ANALYSIS (WITHOUT INTERACTION) OF SURVIVAL OF I. VIRIDIS AT SUB-ZERO TEMPERATURES

NUMERICAL DESCRIPTION OF THE 95 % CONFIDENCE REGION FOR EFFECTIVE DOSE COMBINATION 50 % IN TERMS OF ORIGINAL DOSES (Z1, Z2

Contours of the Hyperbola which Describes Effective Dose Combination 50% The 95% Confidence Region for Effective Dose Combination 50% in Log. Transformed to Original Doses Doses Transformed to Original Doses

ZI (Time Z2 (Negative Z1 Z2 Z1 Z2 in Hrs) temp)

4909.9886 1.7025 4786.5941 1.5200 5015.0827 1.8709 1108.7766 2.3710 1089.0741 2.1890 1125.3881 2.5334 250.3846 3.3022 247.6137 3.1425 252.7202 3.4416 56.5420 4.5989 56.1290 4.4512 56.9223 4.7383 12.7683 6.4049 12.6424 6.1283 12.9031 6.7116 2.8833 8.9202 2.8390 8.3252 2.9336 9. 6348 . 6511 12.4231 . 6370 11.2733 . 6674 13.8756 . 1470 17.3017 . 1429 15.2495 . 1519 20.0039 . 0332 24.0961 . 0320 20.6188 . 0345 28.8519

PROBIT PLANE ANALYSIS (WITHOUT INTERACTION) OF SURVIVAL OF T. LONGICORNIS AT SUB-ZERO TEMPERATURES

NUMERICAL DESCRIPTION OF THE 95 % CONFIDENCE REGION FOR EFFECTIVE DOSE COMBINATION 50 % IN TERMS OF ORIGINAL DOSES (Z1, Z2)

Contours of the Hyperbola which Describes Effective Dose Combination 50% The 95% Confidence Region For Effective Dose Combination 50% in Log. Transformed to Original Doses Doses Transformed to Original Doses

Z1 (Time Z2 (Negative Z1 Z2 Z1 Z2 in Firs) temp)

2.2469 4.7761 1.6925 3.8858 3.1797 6.1501 3.0864 3.4945 2.4942 2.9924 3.6196 3.9244 4.2396 2.5567 2.8393 1.9094 5.3342 3.0221 5.8238 1.8707 2.9319 1.1349 8.6658 2.4984 7.9997 1.3687 2.9757 . 6661 14.3230 2.0916 10.9888 1.0014 3.0047 . 3895 23.7952 1.7576 15.0946 . 7327 3.0276 . 2274 39.6165 1.4792 291

GENERAL DISCUSSION

The present work has been largely concerned with the analysis of determination of an animal population by weather factors. As a large data set was not available, it cannot be claimed that the results obtained have an unqualified validity. It may have been better to take an extensive body of published data from the literature, but none was available for an arthropleonan Collembolan species which is where the author's interest lies. The limitations of data have, however, led to a consideration of the problems of regression analysis of ecological field data, and some of the difficulties and methods of avoiding them have been discussed. Experience with the present data has shown that standard multiple regression incorporating a fair number of variables was Completely unhelpful. Large percentages of the criterion variance could be accounted for in several different ways.

Anderson (1971), by comparing the standard multiple regression method with orthogonalised multiple regression, gives examples of misleading conclusionsarising from the intercorrelations in the predictors, when all the variables are used in the standard method.

The standard technique only seems useful when a very accurate specification of the model is available from other considerations, and the problems of co-linearities in the data are not severe.

Stepwise regression, in spite of the theoretical difficulties, seemed to offer the possibility of reasonable solutions, although whether these are finally attained depends to a large extent on the skill of the research worker in avoiding the inevitable pitfalls. The use of multiple regression for the interpretation of biological systems has often been objected to, but the objectors often appear to assume a 292

completely uncritical use of the method. Some new approaches are

certainly required, but it seems that one cannot reject a method out

of hand just because it does not do everything that one would like.

In the present analysis it was found that progress could be

made by (a) considering only the first two or three variables entering

the equation, (b) setting significance levels for entry such that only vlriance variables making a large reduction in the percentage accounted for in the criterion variable were entered, and (c) assessing the

reasonableness of the choice of variables by a knowledge of the biology

of the animals concerned. Once again, one of the main contributions

of the method was to stimulate a more searching examination of the data.

In this connection orthogonalised multiple regression seems to be

a very useful tool due to the extra information on the structure of the data

and the sources of variation within it. Whether one prefers the stepwise method to orthogonalised regression for the interpretation of the effects

of variables would seem to depend on whether the possibility of mistaken

conclusions due to multicollinearities in the dependent variables is serious

enough to warrant the loss of the original variables in the analysis.

In practice, there seems no reason why a reasonable stepwise regression model should not first be obtained, and then the relative importance of the effects of the variables "checked" by the orthogonalised regression.

In the end, one's results are of course hypotheses which would normally be confirmed by experiment. The approach does, however,

allow some progress to be made in situations where experimental, manipulative investigations would be impracticable or very costly.

For example, Williamson (1971) gives a demonstration of a multivariate analysis of the variation in plankton counts from year to year which was 293 found to be related to the stability of the thermocline. In this case, experimental investigation would be virtually impossible (one cannot imagine the oceans being manipulated).

Considering implications of the results of this study for the factors determining abundance of surface-dwelling collembola, particularly E. nicoleti, it has been shown that certain effects of weather are probably "disturbing" factors acting on survival to produce population changes. For instance, the number of overwintering animals can be reduced by a spell of very cold temperatures, or the severe drought in summer can cause severe mortality in some parts of the study area. Heavy rainfall apparently caused severe decline with a delay of fourteen days in South's (1959) study. This may have been due to excessive moisture causing increased egg-mortality, or perhaps an increase in the incidence of infection by a microsporidian disease. No correlations with rainfall as such were found in the present study.

However, the main factor determining the mean density of the

E. nicoleti population in the present study appeared to be the amount of time during the year that temperatures were high enough to permit a development rate such that recruitment will exceed mortality. The number of animals is thus limited by shortage of time that 7' , the rate of increase, is positive (Andrewartha and Birch, 1954). Block

(1966) suggested that length of the reproductive season was the main influence determining the numbers of moorland Crypto stigmata.

Increase in temperature does not cause all factors to promote an increase in r , however, as increases in egg-predation (and maybe predation in general) also accompanied rises in temperature. 294

It seems however that a preliminary study of this sort is very likely to highlight the density independant factors, such as weather, as these are often the most obvious and easiest to measure. Thus, it seems likely that biotic factors not studied here may also be very important in determining population numbers in Collembolan populations. Wallace (1967) has shown that density dependent mortality factors can be important in determining the numbers of

Sminthurus viridis. This species is not very typical of most collembolan species, however. As little information is available on the population effects of sub-optimal diet on litter-dwelling, omnivorous

C ollembola, it might be worth noting the situation in other groups where more is known. For example, in Isopod populations, it has been shown

(Merriam, 1971) that food quality can change the growth rate, thus affecting the age at first reproduction - a very important population parameter (Cole, 1954). Food could also regulate natality, as the size of brood produced was related to female body size and therefore growth rate. Food could also gravidity directly.

Some of the interactions between Collembola and their food are very complex (as mentioned in the review at the beginning of the thesis) and there does not seem anv reason why similar processes to those operating in Isopods population should occur in collembolan populations.

It has been found by the present author, and also by Healey (1970), that collembola can grow faster in the laboratory than they do in the wild.

Food quality might in extreme cases increase the number of generations possible within the limited "warm" period of the year. Unless one postulated the existence of an excess of food in the environment, due 295 to the population being limited by other factors such as climate, some degree of competition for food would exist. This competition would

introduce some density-dependant mortality into the situation.

This does not imply regulation of the population, however, as this depends on rather dubious ideas about the "balance" of nature. If

regulation is not important in the population it follows that extinctions by chance will occur sooner or later. There seems no reason to suppose

that this small scale extinction does not occur. Drought, for instance, may easily cause part of the habitat to become untenable; but even

within a small area there is a great deal of environmental heterogeneity

in the flavourability of the habitat as shown in the section on dispersion.

The action of density-independent factors which make part of the habitat too harsh for survival may well improve another part of the environment,

e. g. parts of Holcus areas may become more favourable for E. nicoleti

as they dry out, and part of the population may continue to survive in

their new refuges until the other area becomes habitable again.

Environmental heterogeneity could thus moderate the effect of

density-independent factors.

We can do little more than speculate about the factors determining

Collembolan populations until the relevant data has been obtained. The

present study has hopefully taken some steps towards a deterministic

model showing the effects of weather factors on the population.

Regarding the next and more difficult step, I can only echo the words of

Healey (1971) when he states that the information most urgently required

for an understanding of the ecology of collembolan populations is,

firstly, information on the age-structure, cohort identity and absolute growth rates of the population, and secondly, information on the nature of the diet in these animals. 296

SUMMARY

1 The food and feeding habits of surface-dwelling collembolan have been

reviewed in detail. It is suggested the greater diversity and probable

higher quality of food materials available on the surface has led to a

greater selectivity for food type quality than is found in many soil

dwellers.

2 Predation has also been reviewed. Predators of collembolan tended to

be unspecialised'in their preferences, and regulation of the collembolan

population by predation was thought to be unlikely. Spiders appeared to

be particularly important as predators in the present study. Some

predators, eg Neobisium muscoruni a pseudoscorpion, were very

efficient but of too low a density to materially effect the populations

studied. Increase in size, eg in Tomocerus longicornis, or agility, eg

Lepi -docyrtus seem to be the main adaptations of surface-dwelling

collembolansagainst predation.

3 A new method of sampling surface Collembola. in grasslands was devised,

consisting of vacuum sampling combined with Tullgren extraction of soil

cores. The advantages of the new method over other available sampling

methods were pointed out. The efficiency of the vacuum sampling was

shown to be affected by the behaviour of the collembolan (particularly

in response to microclimatic changes), and also various mechanical

reasons (such as design of the vacuum machine, size of sample-unit,

height and dampness of the vegetation.

4 The life-histories of four species of surface-dwelling collembolan are

reported. E. nicoleti, I. vixidis, L. 1 ignorum, possibly T. 1 ongicornis

normally have two generations a year. All four species overwintered

297

mainly as young adults and sub-adults. Peak populations usually occured

in spring or early summer due to the recruitment of hatchlings into the

population. Reproduction is retarded during the winter by low mean

temperatures. There does not seem to be any true. dimpo.use.

5 Some development times from laying to sexual maturity for the four

species under field temperatures are given.

6 An approximate survivorship curve was produced for E. nicoleti. There

was no difference in the death rate between the 2nd and 5th instar. Young

adults appeared to have a higher survival rate than juveniles.

7 Egg losses due to predation showed a close linear relationship with

temperature.

8 The problems associated with the use of regression methods for the

analysis of ecological field data have been discussed. Orthogonalising

a regression by the use of the principal components technique was

advocated as way of avoiding difficulties due to multicollinearities in

the independent variables.

9 An example of the use of orthogonised regression was given. One com-

ponent accounted for 40-53% of the variance in the E. nicoleti suction-

sample data. This component was an index of inclemency in the weather

(ie low temperatures, dry sub-strate) as regards suitable conditions for

population increase in E. nicoleti.

10 Stepwise regression was also utilised to investigate the relationships of

E. nicoleti and weather. Most of the variance in the E. nicoleti numbers

could be accounted for by the exponent of mean temperature weighted by

the exponent of maximum temperature. It was postulated that the effect

of low temperatures in limiting recruitment of juveniles into the population 298

by slowing gi:owth and fecumdity rates was one of the main factors

determining abundance in E. nicoleti.

11 Severe drought and spells of very low temperature seemed important

in determining survival. The latter effect seemed particularly

important in T. longicornis as this species was shown to be less cold-

hardy than the other three species.

12 The negative binominal distribution, and to a lesser extent the Neyman

A distribution, provided the best empirical models of the dispersion

patterns of the collembola except the Sminthiuridae whose distribution

appeared to be not distinguisable from random.Aggregation in E. nicoleti

appeared to be related to the proportion of 1st and 2nd in stores in the

population. Aggregation (as measured by Taylor's power low) generally

decreased with increasing adaptation to surface-life. Some groups, eg

Hypogastrura showed a greater aggregation than would be expected on

a life-form basis however.

13• The four species showed different centres of distribution on the experi-

mental plot. This was probably due to varying humidity requirements.

The presence of a definite litter layer or thick vegetation cover was an

essential requirement for the presence of Tomocerus in a habitat. The

cover probably moderates microclimatic extremes, but may also give

protection from predators (eg birds).

14 An application of single-linkage clustering, and ordination techniques to

ecological data were givenl emphasising the different facets of the data

exposed by the different procedures. Axes representing density, vertical

distribution, species associations and aggregation were obtained from

the ordination techniques. 299

REFERENCES

ADAMS, E.C.G. & SALMON, J.T., 1972. The mouthparts and feeding methods of Brachysl-omella parvula (Schaeffer) Collembola: Brachyslomellidae. Trans. R. ent. Soc. Lond., 121 (3): 269-286. AGRELL, I., 1941. Zur Okologie der Collembolen. Untersuchungen in Schwedisch Lappland. ODUSC. ent. (suppl.), 1-236. AIGNER, D.J., 1971. Basic econometrics. Prentice - Hall. ALCOCK, M.B., LOVETT, J.V. & MACHIN, D., 1968. Techniques used in the study of the influence of environment on primary pasture production in hill and lowland habitats. amp. Br. ecol. Soc., 8: 191-203. ANDERSON, A.J.B., 1971. Ordination methods in ecology. J. Ecol., 52(3): 713-726. ANDERSON, J.M., 1972. Food and feeding of NotioDhilus bi ttatus F. (Coleoptera: Carabidae). Rev. Ecol. Biol. Sol., 2 i): 177-184. ANDERSON, J.M. & HEALEY, I.N., 1972. Seasonal and inter-specific variation in major components of the gut contents of some woodland Collembola. J. Anim. Ecol., 4.1_ (2): 359-368. ANDERSON, R.M., 1971. A quantitative ecological study of helminth parasites of the bream Abramis brama (L.). Ph.D. Thesis, University of London. ANDERSON, T.W., 1958. An introduction to multivariate statistical analysis. New York, John Wiley & Sons. 374 pp. ANDREWARTHA, H.G. & BIRCH, L.C., 1954. The distribution and abundance of animals. Chicago, Univ. of Chicago Press. 782 pp. ANSCOMBE, F.J., 1950. Sampling theory of the negative binomial and logarithmic series distributions. Biometrika, 2/: 358-382. ASAHINA, E., 1969. f=rost resistance in insects. Adv. Insect Physiol., 6: 1-49. AUSTIN, M.P., 1972. Models and analysis of descriptive vegetation data. pp. 61-86. In: Jeffers, J.N.R. (Ed.). Mathematic1 models in ecology. Oxford, Blackwell Scientific Publications. AUSTIN, M.P. & ORLOCI, L., 1966. Geometric models in ecology. II. An evaluation of some ordination techniques. J. Ecol., : 217-227. BACKLUND, H.O., 1945. Wrack fauna of Sweden and Finland. Opusc. ent. (suppl.), ,: 1-236. 300

BALTENSWEILER, W., GIESE, R.L. & AUER, C., 1971. The grey larch bud moth: its population fluctuation in optimum and suboptimum areas.v pp. 401-420. In: Patil, G.P., Pieloy, E.G. & Waters, W.E. (Eds). Statisical ecology Vol.2. Sampling and modelling biological populations and population dynamics. Penn. State. Univ. Press. BARTLETT, M.S., 1949. Fitting of a straight line when both variables are subject to error. Biometrics, ,: 207-212. BEALE, E.M.L., KENDALL, M.G. & MANN, D.W., 1967. The discarding of variables in multivariate analysis. Biometrika, .21: (3-4): 341-365. BEALS,E.W,..., 1973. Ordination: mathematical elegance and ecological naivete. 3. Ecol., 61 (1): 23-35. BEALL, G., 1935. Study of arthropod populations by the method of sweeping. Ecology, 16: 216-225. BEM, S., 1965. The world, the flesh and the metal: the prerogatives of systems. Nature, Lond., mi.: 223-231. BERA, A., 1966. Studies on the flight activit of various Miridae (Heteroptera) using traps. D.I.C. Thesis, Imperial College, London. BLACKITH, R., 1962. The handling of multiple measurements. pp. 37-42. In: Murphy, P.W. (Ed.). Progress in soil zoolcey. London, Butterworths. BLACKITH, R.E. & REYMENT, R.A., 1971. Multivariate morohometrics. London & New York, Academic Press. 412 pp. BLAKE, I.H., 1931. Further studies on deciduous forest animal communities. Ecology, 12: 508-527. BLISS, C.I. & FISHER, R.A., 1953. Fitting the negative binomial distribution to biological data, and a note on the efficient fitting of the negative binomial. Biometrics, 2: 176-200. BLISS, C.I. & OWEN, A.R.G., 1958. Negative binomial distributions with a common k. Biometrikal .12: 37-58. B6DUARSSON, H., 1970. Alimentary studies of seven common soil inhabiting Collembola of Southern Sweden. 'Ent. Scand., 1: 74-80. BONNET, L., CASSAGNAU, P. & IZARRA, D.C., 1970. Etude ecologique des Collemboles musciocles du Sidobre (Tarn). II Modeie mathgmatique de la distribution des especes sur un rocher. Bull. Soc. Hist. nat. Toulouse, 106: 127-145. BOYD, E.A.1 1967. An investigation into the behaviour of Entomobrya purpurascens on potatoes. B.A. Thesis, Trinity College, Dublin. BRASS, W., 1958. Simplified methods of fitting the truncated negative binomial distribution. Biometrika, Et: 59-68. BULAN, C.A. & BARRETT, G.W., 1971. The effects of two acute stresses on the arthropod component of an experimental grassland ecosystem. .122.2-21Z, 2(4): 597-605. 301

BUTCHER, J.W., SNIDER,,R. & SNIDER, R.J., 1971. Bioecology of edaphic Collembola and Acarina. Ann. Rev. Ent., 16: 249-288. CAMPBELL, R.C., 1967. Statistics for biologists. London, Cambridge University Press. CANCELA DA FONSECA, J.P. 1966. L'outil statistique en biologie du sol. III. Indices d'interet ecologique. Revue. Rev. Ecol. Biol. Sol., 3 (3): 381-407. CASSAGNAU, P., 1961. Ecologie du sol dans les pyrenges centrales. Les bioc6noses de Collemboles. Actual. scient. ind., no. 1283: 235 pp. CASSAGNAU, P. & ROUWET, 0., 1963. Les Collemboles gdaphiques du Jardin Botanique de Toulouse (France). Essai de biocenotique dynamique. Pedobiologia, 2: 15-40. CASSIE, R.M., 1962. Frequency distribution models in the ecology of plankton and other organisms. J. Anim. Ecol., 31: 65-92. CHALLET, G.L. & BOHNSACK, K.K., 1968. The distribution and abundance of Collembola at Point Barrow, Alaska. Pedobiologia, 8: 214-222. CHERNOV, Yu.I., 1968. [The distribution and abundance of Collembola in the Tundra Zone.] Ent. Obozr., AD 80-85. `In Russian.] (Engl. transl: Ent. Rev. Wash., .41777-749. CHIBA, S., UCHIDA, H. & FUJITA, K., 1966. On a gregarine parasitic in Tomocerus minutus Tullberg (Insecta: Collembola). Zool. Mag., Tokyo, .7.1 7771-50. CHRISTIANSEN, K., 1964. Bionomics of Collembola. Ann. Rev. Ent., 147-178. CHRISTIANSEN, K., LYMON, S. & JOHNSON, D., 1972. Contact behaviour in Collembola and the effect of food deprivation, density and culture origins. Pedobiologia, 12 (3): 222-228. CLARK, L.R.1 GEIER, P.W. et al., 1967. ecology of in theory and practice. London, Methuen. 232 pp. CLARK, R.D. & GRANT, P.R., 1968. An experimental study of the role of spiders as predators in a forest litter community. Part I. Ecology, 42 (6): 1152-1154. COCHRAN, W.G., 1954. Some methods of strengthening the common chi-square tests. Biometrics, 10: 417. COLE, L.C., 1946. :costluodgyy,lo.:Ither::::::zoa of an Illinois woodland. Ecol. Monoa., 16: 49-86. COLE, L.C., 1946. A theory for analysing contagiously distributed populations. COLE, L.C.! 1954. The population consequences of life-history phenomena. Q. Rev. Biol., 22: 103-137. 302

COLEMAN, D.C. & McGINNIS, 'LT., 1970. Quantification of fungus - small arthropod food chins in the soil. Oikos, 21: 134-137. COOLEY, W.W. & LOHNES, P.R., 1971. Multivariate data analysis. New York, John Wiley & Sons. CORMACK, R.M., 1971. Review of Classification. Jl. R. statist. Soc. (A), !:321-367. DAHL, F., 1912. Ueber die Fauna des Plogefennigebietes, Part A: Analytische Ubersicht der unter A. genannten Tiere. Beitr. Nat. Denkm. Pflege, 3: 341-638. DALE, M.B. & ANDERSON, D.J., 1972. Qualitative and quantitive information analysis. J. Ecol. 60 (3): 639-652. DAVID, F.N. & JOHNSON, N.L., 1952. The truncated poisson. Biometrics, 8: 275-285. DAVIES, M.J., 1953. The contents of crops of some British carabids. Ent. mon. Mag., 82: 18-23. DAVIES, M.J., 1959. A contribution to the ecology of species of Notiophilus and allied genera (Col. Carabidae). Ent. mon. Mag., 22: 25-28. DAVIES, R.G., 1971. Computer programming in quantitative biology. London & New York, Academic Press. 492 pp. DAVIES, W.M., 1928. The effect of variations in relAtive humidity on certain species of Collembola. Brit. J. exp. Biol., 6: 79-86. DELANY, M.J., 1956. The animal communities of three areas of pioneer heath in south-west England. J. Anim. Ecol., 22: 112-126. DELANY, M.J., 1960. Food of some heath dwelling invertibrates. Proc. zool. Soc. Lond., 303-311. DEMPSTER, J.P., 1961. The analysis of data obtained by regular sampling of an insect population. J. Anim. Ecol., 22: 1-5. DHILLON, B.S. & GIBSON, N.H.E., 1962. A study of the Acarina and Collembola of agricultural soils. I. Numbers and distribution in undisturbed grassland. Pedobiologia, 1: 189-209. DIEM, K., 1903. Untersuchungen ilber die Bodenfauna in den Alpen. Inaug. Dissert., Jahrb. St.Gall. naturw. Ges. pp 234-414. DIETRICK, E.J., SCHLINGER, E.I. & VAN DEN BOSCH, R., 1959. A new method for sampling arthropods using a suction collecting machine and modified Berlese funnel separator. J. econ. Ent., 5.2: 1085-1091. DIXON, N.J., 1967. BMD. Biomedical computer programs. Berkely & Los Angeles, Univ. of California Press. DOUGLAS, J.B., 1955. Fitting the Neyman Type A (two parameter) contagious distribution. Biometrics, 11 (2): 149-173. 303

DUNGER, W., 1963. Tiere im Boden.

DRAPER, N. & SMITH, H., 1966. Applied regression analysis. New York, John Wiley & Sons. 407 pp. EDWARDS, C.A. & FLETCHER, K.E.1 1971. A comparison of extraction methods for terrestrial arthropods. pp. 150-185. In: Phillipson, J. (E(1.) Methods of study in quantitative soil ecology: population production and energy flow. Oxford, Blackwell Scientific Publications. EDWARDS, C.A., REICHLE, D.E. & CROSSLEY, D.A. Jr, 1970. The role of soil invertebrates in turnover of organic matter and nutrients. pp. 147- 172. In: Reichle, D.E. (Ed.). Analysis of temperate forest ecosystems. London, Chapman & Hall. EDWARDS, C.A., THOMPSON, A.R. & LOFTY, S.R., 1967. Changes in soil invertebrate populations caused by some organophosphorus insecticides. Proc. 4th Br. Insecticides Fungicides Conf., 1: 48. EFROYMSON, M.A., 1962. Multiple regression analysis. In:'Ralston, A. & Waif, H.S. (Eds.). Mathematical methods for digital computers. New York, John Wiley & Sons. ELLIOTT, J.M., 1971. Some methods for the statistical analysis of samples of benthic invertebrates. Scient. Pubis Freshwat. biol. Ass., no.25: 144 pp. FARAHAT, A.Z., 1966. Studies on the influence of some fungi on Collembola and Acari. Pedobiologica, 6: 258-268. FOLSOM, J.W., 1933. Economic importance of Collembola. J. econ. Ent., 26.: 934-939. FINNEY, D.J., 1962. Probit analysis. 2nd Ed. Cambridge University Press. FINNEY, D.J. & VARLEY, G.C., 1955. An example of the truncated poisson distribution. Biometrics, 11 (3): 387-394. FISHER, R.A., 1950. The significance of deviations from expectation in Poisson Series. Biometrics, 6: 17-24. FISHER, R.A., 1963. Statistical methods for research workers. Edinburgh, Oliver & Boyd. FISHER, R.A., CORBET, A.S. & WILLIAMS, C.B., 1943. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol., 12: 42-58. GAUCH, H.G. & WHITTAKER, R.H., 1972. Comparison of ordination techniques. Ecology (5): 868-875. GILL, R.W., 1969. Soil microarthropod abundance following old-field litter manipulation. Ecology, 5.2 (5): 805-816. GILMORE, S.K., 1970. Collembola predation on nematodes. Search- Agriculture, 1 (3): 1-12. 304

GILMORE, S.K. & RAFFENSPERGER, E.M., 1970. Foods ingested by Tomocerus spp. (Collembola, Entomobryidae), in relation to habitat. Pedobiologial 10: 135-140.

GISIN, H., 1943. Okologie and Lebensgemeinschaften der Collembolen in Schweizerischen Exkursionsgebiet Bosels. Revue suisse Zool., 131-224. GISIN, H., 1948. Etudes ecologiques sur les Collemboles ‘piggs. Mitt. schweiz. ent. Ges., 21: 489. GISIN, H., 1951. La biocenotique. Colloq. int. Cent. nat. Rech. scient. Paris, no. 27: 1-8. GISIN, H., 1960. Collembolen- Fauna Europas. Geneva, Museum d'Histoire Naturelle. GISIN, H., 1964. Collemboles d'Europe, VI. Revue suisse Zool., a (2): 383-400. GITTINS, R., 1969. The application of ordination techniques. Symp. Br. ecol. Soc., 2: 37-66. GLASGOW, 'J.P., 1939. A population study of subterranean soil Collembola. J. Anim. Ecol., 8: 323-353. GODDARD, D.G., 1972. Collembola collected by pitfall traps in a birch wood habitat in Arctic Norway. Ent. mon. Mag., 108: 91. GOLDEN, J.T., 1965. Fortran IV programming and computing. New Jersey, Prentice - Hall. GOTO, H.E., 1956. Folsomia cavicola Delamare Deboutteville, 1954 (Collembola, Isotomidae) new to Britain and a record of fungi parasitic on collembolan eggs. Ent. mon. Mag., 22: 12-13. GOTO, H.E., 1956a. Architomocerura Denis, 1931 (Collembola, Isotomidae) an immature stage of Tomocerus Nicolet, 1841 (Collembola, Tomoceridae). Ent. mon. Mag., 22: 49-51. GOTO, H.E., 1957. A new species of Collembola, Proisotoma stachi n.sp. from Nigeria, West Africa. Acta zool. cracov., 2: 1-15. GOTO, H.E., 1960. Simple techniques for the rearing of Collembola and a note on the use of a fungistatic substance in +he cultures. Ent. mon. Mag., 316: 138-140. GOWER, J.C., 1966. Some distance properties of latent root and vector method used in multivariate analysis. Biometrika, 2: 325-338. GOWER, J.C., 1967. Multivariate analysis and multidimensional geometry. Statistician, 11 (1): 13-28. GOWER, J.C., 1967. A comparison of some methods of cluster analysis. Biometrics, 23: 623-637. 305

GREEN,-R.H., 1966. Measurement of non—randomness in spatial distributions. Researches Popul. Ecol. Kyoto Univ., 8 (1): 1-7. GREEN, R.H., 1971. A multivariate statistical approach to the Hutchinsonion niche: bivalve molluscs of central Canada. Espluz, (4): 543-556. GREENSLADE, P.J.M., 1961. Studies in the ecology of Carabidae (Colecotera). Ph.D. Thesis, University of London. HM2LOV, N., 1960. Microarthropods from Danish soils. Ecology,phenology. -Oikos Copenhlgen, Suppl. HAIRSTON, N.G., SMITH, F.E. & SLOBODKIN, L.B., 1960. Community structure, population control, and competition. Am. Nat., j: 421-425. HALE, W.G., 1963. The Collembola of eroding blanket bog. pp. 406-413 In: Doekson, J. & Van der Drift, J. (Eds.). Soil organisms. Amsterdam, North Holland Publishing Co. HALE, W.G.1 1965a. Observations on the breeding biology of Collembola. pedobiologia,1: 146-152; 161-177. HALE, W.G., 1966a. A population study of moorland Collembola. pedobiologia, 6: 65-99. HALE, W.G., 1966b. The Collembola of the Moor House National Nature Reserve, Westmorland: a moorland habitat. Rev. Ecol. Biol. Sol., 3: 97-122. HALE, W.G., 1967. Collembola. Ch.12, pp. 397-411. In: Burges, A. & Raw, F. (Eds.). Soil biology. London & New York, Academic Press. HANDSCHINJ, E., 1926. Collembola. Biologie Tiere Dtl., 25: 7-56. HARTENSTEIN, R., 1961. On the distribution of forest soil micro— arthropods and their fit to "contagious" distribution function. Ecology, AD 190-194. HEALEY, I.N., 1970. The study of production and energy flow in populations of soft—bodied microarthropods. pp. 175-182. In: Phillipson, J. (Ed.). Methods Paris, UNESCO, HEALEY, I.N., 1971. Apterygotes pauropods and symphylans. pp. 203-232. In: Phillipsorl J. (Ed.). Methods of studyinsantitative soil ecology:,pooulations, oroduction and energy flour. Oxford, Blackwell Scientific Publications. NOEL, J.G., 1971, Introduction to mathematical statistics. 4th edn, New York, Wiley. HOPE, K., 1968. Methods of multivariate analysis. University of London Press. 287 pp. 306

HUDSON, Ha., 1968. The ecology of fungi on plant remains above the soil. New Phytol., oa: 837-854. HUGHES, R.D., 1954. The problem of sampling of a grassland insect population. D.I.C. Thesis, Imperial College, London. 152 pp. HUGHES, R.D., 1962. A method for estimating the effects of mortality on aphid populations. J. Anim. Ecol., 11: 389-396. HURLBUTT, H.W., 1965. Systematics and biology of the genus Veigaia (Acarina: Mesostigmota) from Maryland. Acarologia, J (4): 598-623. IBARRA, E.L., WALLWORK, J.A. & RODRIGUEZ, J.G., 1965. Ecological studies of mites found in sheep. I. Distribution patterns of Oribatid mites. Ann. ent. Soc. Am., LiSi: 153-159. IWAO, S., 1968. A new regression method for analyzing the aggregation pattern of animal population. Researches Popul. Ecol. Kyoto Univ., 10: 1-20. IWAO, S., 1972. Application of the m m method to the analysis of spatial patterns by changing the quadrat size. Researches Popul. Ecol. Kyoto Univ., IA (1): 97-128. IWAO, S. & KUNO, E., 1971. An approach to the analysis of aggregation pattern in biological populations. pp. 461-512. In: Statistical ecology Vol.I. Penn. State Univ. Press. JANETSCHEK, H., 1949. Tierische Successionen auf hochalpinen Neuland, Nach Untersuchungen am Hintercis-Niederfochund Gepatschferner in den otztaler Alpen. Ber. naturw.-med. Ver. Innsbruck, ,42-.42: 1-215. JARDINE, N. & SIBSON, R., 1968. The construction of hierarchic and non-hierarchic classifications. Computer J., 11: 177-184. JEFFbRS, J.N.R., 1967. Two case studies in the application of principal component analysis. Appl. Statist., 16: 225-236. JENSEN, P. & CORBIN, K.W., 1966. Some factors affecting aggregation of Isotoma viridis Bourlet, and Arion fasciatus Nilsson. Ecology, AD 332-334. JOHNSTON, J., 1972. Econometric methods 2nd Edn. New York, McGraw-Hill. 437 pp. JOOSSE, E.N.G., 1965. Pitfall trapping as a method for studying surface dwelling Collembola. Z. Morph. dkol. Tier e, .5,2: 587-596. JOOSSE, E.N.G., 1969. Population structure of some surface dwelling Collembola in a coniferous forest soil. Neth. J. Zool., 1.91.: 621-634. JOOSSE, E.N.G., 1970. The formation and biological significance of aggregations in the distribution of Collembola. Neth. J. Zool., 20: 299-314. 307

JOOSSE, & GROEN, J.B., 1970. Relationship between saturation deficit and the survival and locomotory activity of surface dwelling Collembola. Ent. exp. appl., 13: 229-235. JOOSSE, E.N.G. & KAPTEIJN, J.M., 1968. Activity-stimulating phenomena caused by field-disturbance in the use of pitfall-traps. Oecologia, (Berl.), 1: 385-392. JOOSSE, E.N.G. & WELTKAMP, E., 1970. Some aspects of growth, moulting and reproduction in five species of surface dwelling Collembola. Neth. J. Zool., 20: 315-328. KARG, W., 1961. Oekologische Untersuchungen von edaphischen Gamasiden (Acarina, Parasitiformes). Pedobiologica, 1: 53-98; 77-98. KARG, W., 1964. Untersuchung iiber die Wirkung von Dinitroorthokresol (DNOC) auf die Mikroarthropoden des Bodens unter BerUcksichtigung der Beziehungen Zwischen Microflora and Mesofauna. Pedobiologia, 138-157. KENDALL, M.G., 1965. A course in multivariate analysis. London, Charles Griffin. 185 pp. KEVAN, P.G. & KEVAN, D.K. McE., 1970. Collembola as pollen-feeders and flower-visitors, with observations from the high Arctic. Quaest. Ent., 6: 311-326. KNIGHT, C.B., 1961. The Tomercerinae in old field stands of North Carolina. Ecology, 221. 140-149. KNIGHT, C.B. & ANGEL, R.A., 1967. A preliminary study of the dietary requirements of Tomocerus (Collembola). Am. Midl. Nat., 22:, 510-517. KNIGHT, C.B. & READ, V., 1969. Microstratification of Tomocerus (Collembola) in a pine-open field continuum. Rev. Ecol. Biol. Sol., 61: 221-234. KRUNIC, M.D. & SALT, R.W., 1971. Seasonal changes in glycerol content and supercoding points of Megachile rotundata (F.) and M. relativa Cress. Can. J. Zool., AI: 663-666. KUHNELT, W., 1961. Soil biology. London, Faber & Faber. OHNELT, W., 1963. Ueber den Einfluss des Nycels von Clitocybe, infundibuliformes auf die Streufauna. pp. 281-288. In: Doekson, J. & Van der Drift, J. (R,Os.). Soil organisms. Amsterdam, North Holland Publishing Co. LAURENCE, B.R., 1951. The prey of some tree trunk frequenting Empididae and Dolicopodidae. Ent. mon,zae., SD 166-169. LAWLEY, D.N. & MAXWELL, A.E., 1963. Factor analysis as a statistical method. London, Butterworths. 117 pp. LEATHERDALE, D., 1958. Host catalogue of British entomogeneous fungi. Ent. mon. Nag., 2.AL: 103-105. 308

LEFKOVITCH, L.P., 1965. The study of population growth in organisms grouped by stages. Biometrics, 21: 1-18.

LEFKOVITCH, L.P., 1966. An index of spatial distribution. Researches PoDul. Ecol. Kyoto Univ., 8: 89-92. LLOYD, M., 1967. Mean crowding. J. Anim. Ecol., 26: 1-30. LUFF, M.L., 1966. Cold hardiness of some beetles living in grass tussocks. Ent. exp. appl., 2: 191-199. McCALLUM, B.T., 1970. Artificial orthogonalisation in regression analysis. Rev. Economics Statistics, 2: 110-113. MACFADYEN, A., 1961. Improved funnel-type extractors for soil arthropods. J. Anim. Ecol., 30: 171-184.

MACFADYEN, A., 1962. Soil arthropod sampling. Adv. ecol. Res., .1: MACFADYEN, A., 1968. Notes on methods for the extraction of small soil arthropods by high gradient apparatus. Pedobiologia, 401-406. MACFADYEN, A., 1971. The soil and its total metabolism. pp. 1-13. In: Phillipson, J. (Ed.). Methods of study in quantitative soil ecology: population production and energy flow. Oxford, Blackwell Scientific Publications. MACLAGEN, D.S., 1932. An ecological study of the "Lucerne Flea" (Smynthurus viridis L.). Bull. ent Res., 23: 101-145; 151-190. MACLEOD, D.M., 1963. Entomophthorales infections. pp. 189-231. In Steinhaus, E.A. (Ed.) Insect pathology: an advanced treatise. Vol.2. New York & London Academic Press.

McMILLAN, J.H. & HEALEY, I.N., 1971. A quantitative technique for the analysis of the gut contents of Collembola. Rev. Ecol. Biol. Sol., 8: 295-300.

MACNAMERA, C., 1924. The food of Collembola. 0,11. Ent., 99-105.

McNEILL, S., 1969. A study of the energy ecology of some Steno-demini (Miridae). Ph.D. Thesis, University of London.

McNEILL, S., 1971. The energetics of a population of Leptoterna dolabrata (Heteroptera: Miridae). J. Anim. Ecol., go: 127-140.

MADDISON, P., 1969. Some effects of soil pesticides on soil faunal interactions. Ph.D. Thesis, University of London.

MADELIN, M.P., 1963. Diseases caused by hyphomycetous fungi. pp. 233-271. In: Steinhaus, E.A. (Ed.) Insect pathology: an advanced treatise. Vol. 2. New York & London, Academic Press. MA IS, K., 1970. Beitrag zum Wasserhaushalt oberfrochenbewohnender, hemiedaphischer and euedaphischer Collembolen. Pedobiologia, 10: 81-103. 309

MARLIER, G.J., 1942. Notes sur les Collemboles I. La faune des Collemboles de la Belgique. Medea. natuurh. Mus. Beige, 18 (8): 1-11. MAYER, H., 1957. Zur Biologie and Ethologie einheimischer Collembolen. Zool. Jb. (Syst.), 501-570. MELLANBY, K., 1962. Sticky traps for the study of animals inhabiting the soil surface. pp. 226-227. In: Murphy, P.W. (Ed.) Progress in soil zoology. London, Butterworths. MERRIAM, H.G., 1971. The sensitivity of terrestrial isopod populations (Armadillidium) to food quality. Can. J. Zool., AD 667-674. MILES, P.M., 1970. Nematodes associated with Orchesella villosa (Geoff.) (Collembola: Entomobryidae). Ent. mon. Mag., 106: 193-194. • MILNE, S., 1962. Phenology of a natural population of soil Collembola. Pedobiologia, 2: 41-52. MILNE, S., 1960. Studies on the life history of various species of Arthropleone Collembola. Proc. R. ent. Soc. Lond. (A), 22: 133-140.

MORISITA, M., 1962. 4 index, a measure of dispersion of individuals. Researches Popul. Ecol. Kyoto Univ., A: 1-7. MORISITA, LOIS/. Measuring of the dispersion of individuals and analysis of the distributional patterns. Mem. Pao. Sci. Kyushu Univ., Series E, (Biol.), 2: 215-235. MORRISON, D.F., 1967. Multivariate statistical methods. New York, McGraw-Hill. MOTT, D.G., 1966. The analysis of determination in population systems. pp. 179-194. In: Watt, K.E.F. (Ed.) Systems analysis in ecology. New York & London, Academic Press. MOULDER, B.C., REICHLE, D.E. & AUERBACH, S.I., 1970. The significance of snider predation in the energy dynamics of forest floor anthropod communities. Oak Ridge Nat. Lab. U.S.A., ORNL-4452. MURPHY, P.W., 1955. Ecology of the fauna of forest soils. pp. 99-124. In: Kevan, D.K.McE. (Ed.) Soil zoology. London, Butterworths. MURPHY, P.W. (Ed.), 1962. Progress in soil zoology. (Papers from a colloquium on Research methods organised by the Soil Zoology Committee of the International Society of Soil Science held at Rothamsted Experimental Station, Hertfordshire 10-14th July, 1958). London, Butterworths. 398 pp. NEF, L., 1971. Influence de l'humidite sur llgeotactisme des Oribates (Acarina) dans l'extracteur de Berlese-Tullgren. Pedobiologia, 11 (6): 433-445. NEYMAN, J., 1939. On a new class of 'contagious' distributions, applicable in entomology and bacteriology. Ann. math. Statist., 10: 35-57.

31 0

NORRIS, K., 1938. A population study of the red-legged earth mite with notes on associated mites and Collembola. Aust. Coun. sci. ind. Res., 21: 1-25.

NOSEK, J., 1967. The investigation on the Apterygotan fauna of the Lower Tatras. Acta. Univ. Carol. Biologica l 1967: 349-528.

FUTTALL, R.M., 1970. The effect of acclimation upon the survival of Ptinus tectus and Tenebrio molitor when exposed to low temperatures. Ent. exp. appl., 13: 217-228 -----

OKELY, E., 1965. An investigation into the locomotory activities of some Collembola. B.A. Moderatorship Thesis, Dept. of Zoology, Trinity College, Dublin.

ORLOCI, L., 1966. Geometric models in ecology. I. The theory and application of some ordination methods. J. Ecol., LA: 193-205. ORLOCI, L., 1967. An agglomerative method for classification of plant communities. J. Ecol., 51: 193-206.

ORLOCI, L., 1968. Information analysis in phytosociology. J. theoret. Biol., 22: 271-284.

OWEN, B.L. & OWEN, W.L., 1958. Collembola (Entomobrya unostrigata) damage to seedling cotton. J. econ. Ent., a: 252-253.

PACLT, J., 1956. Bionomie and 6kologie, Biologie der prirnar flu5ellosen Insekten. pp, 91-117.

Jena, Gustav Fisher Verlag.

PANIC, J., 1963. Das Verhalten Ameisen gegenUber bodenbewohnenden Kleinarthropoden. Pedobiologia, 2: 223-234.

PEDIGO, L.P., 1970pL. Activity and local distribution of surface active Collembola. I. Woodland populations. Am. mid. Nat., 842: 107-118.

PEDIGO, L.P., 1970b. Activity and local distribution of surface-active Collembola. II. Pond-shore populations. Ann. ent. Soc. Am., 63: 753-760.

PENNY, M.M., 1966. Studies on certain aspects of the ecology of Nebria brevicollis (F.) (Coleoptera: Carabidae). J. Anim. Ecol., 505-512.

PHILLIPS, S.M., 1946. Occurrence of nematodes in a Collembolan. Ent. mon. Map., 82: 218-219.

PHILLIPSON, J., 1960. The food consumption of different instars of Moms morio (phalangida) under natural conditions. J. Anim. Ecol., 22: 299-307.

PHILLIPSON, J. (Ed.), 1971. Methods of study in quantitive soil Fla/1u ooulation production and energy flow. I.B.P. Handbook No.1 . Oxford, Blackwell Scientific Publications. 297 pp. 3 1 1

PIELOU, E.C.1 1969. An introduction to mathematical ecology. New York, John Wiley & Sons. 286 pp. PLACKETT, R.I., 1953. The truncated Poisson distribution. Biometrics, 2.: 485-488. POOLE, T.B., 1959. Studies of food of Collembola in a Douglas fir forest. Proc. zool. Soc. Lond., 132: 78-82. POOLE, T.B.1 1961. An ecological study of Collembola in a coniferous forest soil. Pedobiologia, 1: 113-137. POOLE, T.B., 1962. The effect of some environmental factors on the pattern of distribution of soil Collembola in a coniferous woodland. Pedobiologia, 2: 169-182. POOLE, T.B., 1964. A study of distribution of soil Collembola in 3 small areas in a coniferous woodland. Pedobiologia, A: 35-42. PRITCHARD, N.M. & ANDERSON, A.J.B., 1971. Observations on the use of cluster analysis in botany with an ecological example. J. Ecol., 51(3): 727-747. RAW, F., 1955. A flotation extraction process for soil micro-arthropods. PP. 341-346. In: Kevan, D.K.McE. (Ed.) Soil Zoology. London, Butterworths. REVWERT, H. & WISNIEWSKI, W., 1970. Low resistance to cold of polar animals in summer. Oecologia, A: 111-112. REYNA ROBLES, R., 1969. Studies on the dispersion of insect populations. Ph.D. Thesis, University of London. RICHARDS, O.W. & WALOFF, N., 1954. Studies on the biology and population dynamics of British grasshoppers. Anti-Locust Bull., no.17: 184 pp. RUSEK, J., 1968. Die Apterygotengemeinschaft der Acareto-fraxinetum Wald assoziation des MUhrischen Karstes. Acta. Soc. Zool. Bohemoslov. 32 (3): 237-361. SALMON, M., 1964. An index to the Collembola. Bull. R. Soc. N.Z., (2 vols.): 1-644. SALT, G. & HOLLICK, F., 1944. Studies of wireworm populations. I. A census of wireworms in pasture. Ann. appl. Biol., 31: 53-64. SALT, R.W., 1961. Principles of insect cold-hardiness. Ann. Rev. Ent., 6: 55-74. SALT, R.W., 1963. Delayed inoculative freezing of insects. Can. Ent., 1190-1202, • SALT, R.W., 1966a. Factors affecting nucleation in supercooled insects. Can, J. Zool., 14: SALT, R.W., 1966b. Effect of cooling rate on the freezing temperatures of supercooled insects. Can. J. Zool., AA: 655-659. 31 2

SALT, R.W., 1966c. Relation between time of freezing and temperature in super cooled larvae of Cephus cinctus Nort. Can. J. Zool., AA: 947-952. SALT, R.W., 1968. Location and quantitative aspects of ice nucleators in insects. Can. J. Zool., 329-333. SAMPFORD, M.R., 1955. The truncated negative binomial distribution. Biometrikal gat, 58-69. SCHALLER, F., 1949. Notiophilus biguttatus F. (Coleopt) and Japyx solifugus Holiday (Diplura), als spezielle CollemboleniOuber. Zool. Jb. (Syst.), 12: 244-296.

SEAL, H., 1964. Multivariate statistical analysis for biologists. London, Methuen. 207 pp. SHARMA, G.D., 1967. Bionomics of Tomocerus vul ris Tullberg (Collembola: Entomobryidae). Proc. R. ent. Soc. Lond. A Az (1-3): 30-34. SHARMA, G.D. & KEVAN, D.K.McE., 1963. Observations on Isotoma notabilis in eastern Canada. Pedobiologia, 2: 34-47. SHERIF', A.A., 1971. Studies on the persistence and effects of soil- applied carbomate and phosphorus insecticides on the soil fauna, with special reference to the Collembola. Ph.D. Thesis, University of London. SIEGEL, S., 1956. Non-parametric statistics for the behavioural sciences.. New York, McGraw-Hill. 312 pp. SIMON, H.R., 1964. Zur ErhArungsbiologie collembolenfangender Arthropoden. Biol. Zentralbl., 83: 273-296. SIMPSON, G.C., ROE, A., & LEWONTIN, R.C., 1960. Quantitative Zoology 2nd Edn. New York, Harcourt Brace Janovitch. SINGH, S.B., 1964. Periodicity in the feeding habit of Tomocerus longicornis (Muller). Indian J. Ent., 26: 123-124. SINGH, S.B., 1969. Preliminary observations on the food preference of certain Collembola (Insecta). Rev. Ecol. Bioi. Sol., 6: 461-467. SKELLAM, J.G., 1958. On the derivation and applicability of Neyman's type A distribution. Bxometrika, : 32-36, SNEDECOR, G.W., 1956. Statistical methods. Ames, Iowa, Iowa State College Press. SOKAL, R. & SNEATH, P.H., 1963. Principles of numerical taxonomy. San Francisco, W.H. Freeman & Co. 359 pp. SOUTH, A., 1959. The taxonomy and biology of British species of Entomobrya (Co1771177). Ph.D. Thesis: University of London. SOUTH, A., 1961. The taxonomy of the British species of Entomobra (Collembola). Trans. R. ent. Soc. Lond., 113: 387-416. 3 1 3

SOUTHWOOD, T.R.E., 1966. Ecological methods with particular reference to the study of insect populations. London, Methuen. 391 pp. SOUTHWOOD, T.R.E.,& VAN.EMDEN, H.F., 1967. A comparison of the fauna of cut and uncut grasslands. Z. angew. Ent., 60: 188-198. STACH, J., 1947-60. BLLEIlmeat2114MAS1222-2111jalX2221i91112:Elaft fauna of this group of insects. vols. Krakow. STEBAYEVA, S.K., 1963. Ecological distribution of springtails in forest and steppes of South Tuva. Pedobiologia, 2 (1): 75-85. STKUE, R.G.D. & TORRIE, J.H., 1960. Principles and procedures in statistics. New York, McGraw-Hill. 481 pp. STREBEL, 0., 1938. Biologische Studien an einheimischen Collembolen. III. Zur Biologie des Tomocerus vulgaris Tullb. Konowia, 272-291. STRENZKE, K., 1949. Okologieche Studien iiber die Collembolen - geselbschaften Feuchten Boden Ostholsteins. Arch. Hydrobiol., Ag: 201-203. SZEPTYCKI, A., 1967. Fauna of springtails (Collembola) of the Ofcow National Park in Poland. Acta zool. cracov., 12 (10): 219-280. TAKEDA, H., 1973. A preliminary study on collembolan populations in a pine forest. Researches Popul. Ecol. Kyoto Univ., 12 (1): 76-89. TAMURA, H., 1967. Some ecological observations on Collembola in Sappora, Northern Japan. J. Fac. Sol. Hokkoido Univ. (Series 6), 16 (2):, 238-252. TAYLOR, L.R., 1961. Aggregation, variance and the mean. Nature, Lond., la: 732-735. TAYLOR, L.R., 1965. A natural law for the spatial disposition of insects. Proc. 12th int. garyr. Ent. London 1964: 396-397. THEOBALD, F.V., 1908. A new hop pest (Entomobrya invalis Linn.). In: Report on Economic Zoology for year ending April 1st 1908. J. S-Eastern Agric. Coll. Wye fl: 65-183. THEOBALD, F.V., 1910. Springtails (Collembola) their economic importance with notes on some unrecorded instances of damage. Report on Economic Zoology for the year ending 30th Sept. 1910. J. S-Eastern Agric. Coll., Wye, 73-211. Colleen (183-199) THOMAS, M., 1949. A generalisation of Poisson's binomial limit for use in ecology. Biometrika, 36: 18-25. THOMPSON, M., 1924. The soil population: an investigation of the biology of the soil in certain districts of Aberystwyth. Ann. appl. Biol., 11: 349-394. TORNE, E. VON, 1967. Beispiele fir unikvolsiogeneEinflUsse auf den Massenwechsel von Bodentieren. Pedobiologia, 296-305. 3 1 4

T6RNE, E. VON, 1968. Beispiele far mikrobiogene Einfliisse auf den Massenwechsel von Bodentieren. II. Pedobiologia, 8: 526-535. TURNBULL, A.L., 1966. A population of spiders and their potential prey in an overgrazed pasture in eastern Ontario. Can. 3. Zool., gAt 557-583. UCHIDA, H. & HONGO, 1962. Studies on the development of T. minutus Tullberg (Tnsecta: Collembola). III. Statistical analysis on the developmental stages. Zool. Mag. Tolgro, 21: 91-97. USHER, M.B., 1964. Collembolan population structure in a soots pine forest soil. B.Sc. Thesis, University of Edinburgh. USHER, M.B., 1969. The efficiency of counting samples of soil microarthropods. Proc. 2nd int. Congr. Acarology, 1967: 57-60. USHER, M.B., LONGSTAFF, B.C. & SOUTHALL, D.R., 1971. Studies on populations of Folsomia candida (Insecta: Collembola). The productivity of populations in relation to food and exploitation. Oecologia (Berl.), 68-79. VAIL, P.V., 1965. Colonisation of Hypogastrura manubrialis (Collembola: Poduridae), with notes on its biology. Ann. ent. Soc. Am., 2.: 555-561. VALPAS, A., 1969. Hot rod technique, a modification of the dry funnel technique for extracting Collembola, especially from frozen soil. Ann. Zool. Fennici 6 (3): 269-274. VAN TER DRIFT, J., 1951. Analysis of the animal community in a beech forest floor. 2: 1-168. Also Tydschr. Ent., 21: 1-16 ,

VAN DER KRAAN, C., 1971. Some aspects of field-dependent distribution in a population of Hypogastrura viatica Tullberg 1872 (Collem. Hypo- gastruridae). Rev. Ecol. Biol. Sol., 8: 99-102.

VANNIER, G., 1969. Aperqu theorique sur l'extraction des microarthropodes du sol par la methode selective...... :021CliAPr LIC...orlat .1291.0a7 1967, 541-552. Eng. summary. VANNIER, G., 1970. Reactions de microarthropodes aux variations de l'gtat hydrique du sol. Techniques relative a7 l'extra:tion des Arthropodes du sol. (Programme Biologique International Recherche Cooperative sur Programme de C.N.R.S. No.40). Editions de Centre National de la Recherche Scientifique, Paris, 319. Summary in Rev. Ecol. Biol. Sol. Z (2),

VANNIER, G., 1971., Signification de la persistence de la pgdofaune apre's le point de fle trissement permanent dans les sole. Rev. Ecol. Biol. Sol., 8: 343-365. VANNIER, G., 1971a. Les fourmis, prgdateurs permanents de certains types de Collemboles. Rev. Ecol. Biol. Sol., 8: 119-132. 31 5

VARLEY, G.C. & GRADWELL, G.R., 1963. The interpretation of insect population change. Proc. Ceylon Ass. Adv. Sci. (D) 18:(1962): 142-156. VOLTZ, P., 1934. Untersuchungen aer die Mikroschichtung der Fauna von Waldboden. Zool. Jb. (Syst.), 66: 153-210. WALDORF, E., 1971. Selective egg cannibalism in Sinella curviseta (Collembola: Entomobryidae). Ecology, a: 673-675. WALLACE, M.M.H., 1967. The ecology of Sminthurus viridis (L.) (Collembola). I.Processes influencing numbers in pastures in Western Australia. Aust. J. Zool., 15: 1173-1206. II. Diapause in the aestivating egg. ibid, 16 (196B):-871-8d3. WALLACE, M.M.H. & MAHON, J.A., 1971. The ecology of the Sminthurus, viridis (Collembola). III. The influence of climate and land use on its distribution and that of an important predator Bdellodes Lapidaria (Acari: Bdellidae). Aust. J. Zool., a: 177-188. WALLACE, M.M.H., 1972. A portable power-operated apparatus for collecting Epigaeic Collembola and Acari. J. Aust. ent. Soc., 11 (3): 261-263. WALLWORK, J.A., 1967. Acari. pp. 363-395. In: Burges A. & Raw, F. (Eds.). Soil biology. London, Academic Press. WALLWORK, J.A., 1970. The ecology of soil animals. Maidenhead, McGraw-Hill. 283 pp. WALTERS, M.C., 1968. A study of Sminthurus viridis (L.) (Collembola), in the Western Cape province. Ent. Mem. Pretoria, 1967: 1-98. WATT, K.E.F., 1968. Ecology and resource . management. New York, McGraw- Hill. 450 pp. WEIS-FOGH, T., 1948. Ecological investigations on mites and collemboles in the soil. Natura Jutlandicae, 1: 139-277. WHEELER, A.G., 1972. Studies on the arthropod fauna of alfalfa (Medigo sativa). II. Arthropod-Fungus associations. Can. Ent., Ili: 1405-1412. WIGGLESWORTH, V.B., 1965. The principles of insect physiology. London, Methuen. 741 pp. WILLIAMS, C.B., 1964. Patterns in the balance of nature and related problems in quantitative ecology. London & New York, Academic Press. 324 pp. WILLIAMS, W.T., 1971. Principles of clustering. Ann. Rev. Ecology Systematics, 2: 303-326. WILLIAMS, W.T., LAMBERT, J.M. & LANCE, G.N., 1966. Multivariate methods in plant ecology. V. Similarity analyses and information-analysis. J.Ecol., LA: 427-445. 31 6

WILLIAMSON, M.H., 1972. The analysis of biological populations. London, Edward Arnold. 180 pp. WILSON, E.0., 1950. Bull. Brook. ent. Soc., 4: 85-86. WITKAMP, M., 1971. Soils as components of ecosystems. Ann. Rev. Ecol. Systematics, 2: 85-105. WOOD, T.G., 1965. Comparison of a funnel and a flotation method for Acari and Collembola from a moorland soil. Pedobiologia, 2: 131-139.

BELLINGER, P.F. 1954. Studies of the soil fauna with special reference to Collembola. Bull. Connecticut agric. exp. sta. : 1-67. BHATTACHARYYA, S.K. 1962. Laboratory studios on the feeding habits and life cycles of soil inhabiting mites. Pedobiologia 1 : 291-298. CRISTIANSEN, K. 1971. Factors affecting predation on .Collembola by various arthropods. Ann. Spelsol. 26 : 97-106. MILLS, J.T. & SINHA, R.N.' 1971. Interactions between a springtail Hypogastrura tullbergi , and soil-borne fungi. J. °con. Ent. : 398-401.