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Whole-range assessment and inhibition index: a method for analysing allelopathic dose-response data

Min An, Jim Pratley, Terry Haig and De Li Liu

EH Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
Email man@csu.edu.au

Abstract

Based on the typical biological responses of an organism to allelochemicals, concepts of whole-range assessment and inhibition index were developed for better analysing allelopathic data. Application examples from the literature are presented. The method is concise and comprehensive, and makes data grouping and multiple comparisons simple, logical, and possible. It improves data interpretation, enhances research outcomes, and is a statistically efficient summary of the plant response profiles.

Media summary

Whole-range assessment is a new data analysing method. It could provide a better outcome for allelopathic data and enhance understanding of phenomena observed.

Key Words

Biological response, hormesis, allelopathy, data analyses, inhibition index, allelochemical(s)

Introduction

When investigating the allelopathic potential of a plant or an allelochemical, a test species is often employed and its responses are measured and data are collected. Then the data are analysed by a conventional statistical method, eg a regression analysis. Subsequently a conclusion is made based on such analysis. However, due to the biologically variable nature of a plant test species and its non-linear responses to a set of treatments, ie. hormesis, the conventional statistical analyses often fail to fully utilize the information contained in such a set of data collected, and can not deliver a satisfactory comprehensive outcome, particularly in the cases of multiple comparisons under a set of concentration treatments or equivalents.

This article aims to provide a better method for allelopathic data analysis and to help ease data assessment and interpretation.

Methods

Dose-response relationship (hormesis), i.e. stimulation at low concentrations of allelochemicals and inhibition as the concentration increases, is well recognised in allelopathy (Lovett et al. 1989). The extent of stimulation and inhibition is not balanced. Generally speaking, over a normal realistic range of concentration levels, inhibition dominates biological responses and increases with increasing concentration levels. Stimulation occurs at the very low levels of the range and only represents a small portion of the overall biological responses. Therefore, in the whole-range assessment only inhibition is considered.

Whole-range assessment. Instead of assessing the effect of individual allelochemical concentrations on test species, the overall effect across the whole range of allelochemical rates was considered. The approach was first to normalize biological responses by taking the control as a reference, and then to calculate the inhibition area between the control response (ie. 100 %) over the whole range of treatments (ie. concentrations or equivalents on X axis) and the dose-response curve (ie. test species responses), as generated by allelochemical concentrations (Figure 1).

where C is the allelochemical concentration or equivalent, CT is the threshold concentration for causing inhibition in the test species. f(C) can be any mathematical functions describing the nonlinear dose-response relationships and only has theoretical meaning here. Overall biological activity across the whole range of concentrations or equivalents is then summarised, calculated, and presented by a single value, “inhibition index”, which is defined as the percentage of the inhibition area to the total area.

The actual computation of the inhibition area and the inhibition index can be easily done with any mathematical functions attached softwares, particularly with integration function, such as MicroCal Origin. The inhibition index is a summary of the overall biological response of an organism to a tested allelochemical or equivalent and provides a relative strength indication of biological responses. Large values indicate species to be sensitive or allelochemical possessing strong allelopathic potential / biological activity, whilst small values indicate tolerance or weak potential/biological activity. It can also be subject to a conventional statistical method for further analysis, such as analysis of variance, for easing grouping or significant testing of multiple comparisons (Figure 1).

Figure 1. Diagrammatic representation of biological response to allelochemical concentrations or equivalents. The shaded section represents the inhibition area. CT -- the threshold concentration for causing inhibition.

Two cases from the literature

(i) Biological activity of identified allelochemicals from Vulpia

Vulpia is a significant allelopathic weed in Australia. Twenty allelochemicals identified in Vulpia residues were individually and collectively tested using wheat as a test species for their biological activity (An et al. 2001). Each exhibited characteristic allelochemical behaviour toward the test plant, ie., inhibition at high concentrations, and stimulation or no effect at low concentrations. Assessments of those data by the inhibition index revealed that individual activities of these allelochemicals varied. Allelochemicals present in large quantities, such as syringic, vanillic, and succinic acids, possessed low activity, while those present in small quantities, like catechol and hydrocinnamic acid, possessed strong inhibitory activity. They could be grouped according to their biological activities (Table 1). Such assessments further identified the individual contribution of each allelochemical to the overall Vulpia allelopathy, and determined the factors affecting such contributions. It was revealed that the majority of compounds possessed low or medium biological activity and contributed most of the Vulpia allelopathy, while compounds with high biological activity were in the minority and only present at low concentration (Table 1).

In this case the data assessments enhanced the result outcomes and provided valuable insights, which are important in understanding of allelopathy fundamentals, allelochemical modes of actions, and in employment of allelopathy for developing natural herbicides.

Table 1 Biological activities of Vulpia allelochemicals as assessed by inhibition index and their relative contribution to overall Vulpia allelopathy (data from An et al. 2000 and 2001)

Chemical name

Quantity in Vulpia residue (mg/g)

Inhibition index

Allelopathic potential

Relative* contribution to Vupia allelopathy

Coniferyl alcohol

0.0044

2.60

 

0.57

Protocatechuic acid

0.0163

2.95

 

2.40

3, 4-Dimethoxyphenol

0.0021

3.85

 

0.40

Hydrocaffeic acid

0.0038

4.25

Weak

0.81

Syringic acid

0.0565

4.25

 

12.01

Succinic acid

0.0722

4.48

 

16.19

Hydroquinone

0.0016

4.52

 

0.37

p-Hydroxybenzoic acid

0.0158

4.81

 

3.81

3-(4-Hydroxyphenyl) propanoic acid

0.0393

5.29

 

10.39

Gentisic acid

0.0096

5.61

 

2.70

Vanillic acid

0.0731

5.98

 

21.87

p-Coumaric acid

0.0047

6.02

 

1.40

Ferulic acid

0.0072

6.24

Medium

2.24

Pyrogallol

0.0064

6.55

 

2.10

p-Hydroxy-phenylacetic acid

0.0071

6.65

 

2.36

2-Hydroxy-3-phenyl propanoic acid

0.0150

6.75

 

5.06

Catechol

0.0003

6.87

 

0.12

Benzoic acid

0.0114

7.61

 

4.34

Salicylic acid

0.0268

7.90

Strong

10.60

Hydrocinnamic acid

0.0007

8.43

 

0.28

where i = Coniferyl ... Hydrocinnamic acid ,
C = inhibition index of each individual compound multiplied by their mass found in Vulpia residue.

(ii) Susceptibility of plants to vulpia allelopathy

In an attempt to widening strategies for managing detrimental effects of Vulpia residues, An et al. (1997) tested genotypic variation of 12 plant species and 12 cultivars from 2 plant species under the series of aqueous extracts of Vulpia residues and analysed the data by the whole-range assessment and the inhibition index. It showed that all test plants exhibited the characteristic responses to the Vulpia extracts. Marked differences in tolerance toward the Vulpia phytotoxicity existed among species and cultivars. Such differences were widespread among plant species, with generally cocksfoot, Vulpia spp., canola, and phalaris being relatively tolerant, while lupins and barley were the most susceptible. Wheat and subterranean clover were relatively susceptible with a few cultivars being exceptional. By employment of the inhibition index it not only enabled the susceptibility of each plant to the Vulpia allelopathy being presented in a concise, comprehensive, and meaningful format, but also grouped the plant species and cultivars according to their susceptibility (Table 2), which provides the basis for widening management options and for choosing the right species and cultivar for minimising the negative effects of Vulpia residues.

Evaluation by the principal component analysis

The above species data were used to evaluate the inhibition index itself by the principal component analysis. The original variables for principal component analysis were germination rate, and root and coleoptile length of each species measured at seven concentrations of Vulpia extract. The first principal components in all parameters measured accounted for more than 60% of the variation in the original data. Significant correlation was found between inhibition indices and their corresponding first principal components. The correlation for germination was 0.96, for root 0.87, for coleoptile 0.94, and 0.88 for the combination of germination and seedlings (Figure 2).

Conclusion

From above-presented examples it is clear that the whole-range assessment and the inhibition index can be used in a wide range of data analyses. It analyses data comprehensively, and yet presents analysing outcome in a concise and meaningful format, and makes data grouping and multiple comparisons simple, logical, and possible. It has proved to be a statistically efficient summary of the plant response profiles. It enhances data outcomes and provides directions for further investigations.

Table 2. Species and cultivars susceptibility to Vulpia allelopathy as assessed by inhibition index (after An et al. 1997)

Species & cultivars

Overall

Susceptibility to

 

inhibition index

Vulpia allelopathy

Cocksfoot

8.1

 

Subclover (cv. Trikkala)

9.3

 

V. myuros

9.4

Tolerant

Phalaris

9.7

 

V. bromoides

10.8

 

Canola

13.4

 

Lucerne (cv. Trifecta)

17.1

 

Oats

20.0

Less tolerant

Lucerne (cv. Aurora)

21.7

 

Wheat (cv. Ford)

27.7

 

Subclover (cv. Seaton Park)

28.9

 

Subclover (cv. Karridale)

31.0

Medium

Field Peas

32.0

 

Wheat (cv. Darter)

32.2

 

Subclover (cv. Clare)

35.7

 

Subclover (cv. Woogenellup)

35.9

 

Wheat (cv. Dollarbird)

35.9

Less sensitive

Subclover (cv. Junee)

37.6

 

Wheat (cv. Rosella)

37.9

 

Lupins

41.3

 

Wheat (cv. Janz)

41.7

Sensitive

Wheat (cv. Vulcan)

47.3

 

Barley

56.7

 

Figure 2. Comparison between inhibition index analysis and principal component analysis for a combination of germination and seedling length (An et al. 1997).

References

Lovett JV, Ryuntyu MY and Liu DL (1989). Allelopathy, chemical communication, and plant defense. Journal of Chemical Ecology 15, 1193-1201

An M, Pratley JE, Haig T and Jellett P (1997). Genotypic variation of plant species to the allelopathic effects of vulpia residues. Australian Journal of Experimental Agriculture 37, 647-660.

An M, Pratley JE and Haig T (2000). Phytotoxicity of vulpia residues: II. Separation, identification and quantitation of allelochemicals from the residue. Journal of Chemical Ecology 26, 1465-1471

An M, Pratley JE and Haig T (2001). Phytotoxicity of vulpia residues: III. Biological activity of identified allelochemicals from vulpia residues. Journal of Chemical Ecology 27, 381-392.

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