Biodiversity Theme Report
Australia State of the Environment Report 2001 (Theme Report)
Prepared by: Dr Jann Williams, RMIT University, Authors
Published by CSIRO on behalf of the Department of the Environment and Heritage, 2001
ISBN 0 643 06749 3
This section reports on the following environmental indicators, which are defined in Saunders et al. (1998).
|BD 9.1||Number of subspecific taxa|
|BD 9.2||Population size; numbers; and physical isolation|
|BD 9.3||Environmental amplitude of populations|
|BD 9.4||Genetic diversity at marker loci|
|BD 10.1||Number of species|
|BD 10.2||Estimated number of species|
|BD 10.3||Number of species formally described|
|BD 10.4||Percentage of number of species described|
|BD 10.5||Number of subspecies as a percentage of species|
|BD 10.6||Number of endemic species|
|BD 10.7 | a | b |||Conservation status of species|
|BD 10.8||Economic importance of species|
|BD 10.9||Percentage of species changing in distribution|
|BD 10.10||Number distribution and abundance of migratory species|
|BD 10.11||Demographic characteristics of target taxa|
|BD 11.1||Ecosystem diversity|
|BD 11.2||Number and extent of ecological communities of high conservation potential|
|BD 15.1||Number of recovery plans|
|BD 15.2||Amount of funding for recovery plans|
|BD 16.1||Number of ex situ research programs|
|BD 16.2||Number of releases to the wild from ex situ breeding|
Conserving genes and genetic diversity in populations may be achieved through the maintenance of genetic structure within and among subpopulations, or among sets of populations with common evolutionary histories. This strategy improves the ability of populations to adapt to novel environmental conditions and avoids the potentially negative effects of inbreeding (Frankham et al. 2001). Genetic information may also be used to guide the management of captive populations, to set conservation priorities by identifying unique populations (see the Conserving quantitative genetic variation box below), to provide information on population dynamics and mating systems that would otherwise be unobtainable, and to understand the consequences of historical events, such as range expansions, fragmentation and bottlenecks (Moritz 1995).
Monitoring the potential for adaptation to environmental change might be well served by an explicit focus on monitoring changes in quantitative environmental variation, particularly the component that is available for evolution, namely, heritable genetic variation.
However, studies that measure overall levels of genetic variation within and among populations do not provide any information on genetic variation of fitness traits that are under selection in the field. The focus on maintaining variability among such populations raises the real threat that the adaptive potential of larger but less distinct populations is being minimised. A rapidly changing environment where the maximisation of genetic variation for traits associated with stress and disease resistance will be critical for long-term persistence.
Alternate approaches for maintaining appropriate genetic variation in populations have been advocated. These include shifting the focus to phenotypic variation of traits associated with stress and disease resistance that will have the effect of selection of favourable genes at many loci (Woods et al. 1999). Another approach is to concentrate on populations that persist at ecological margins as these will have been selected for environmental stress resistance and are likely to have higher frequencies of stress resistance genes than population inhabiting more favourable environments (Hoffman & Parsons 1997). Marginal populations may have evolved generalised stress resistance mechanisms that will allow them greater resistance in stress environments other than those in which they have been selected.
Management strategies should aim to conserve species across a broad range of climatic regions and to conserve all races, variants and subspecies. This will ensure that any genotypes fixed because of local adaptation will be conserved and available to counter future climatic changes.
Number of subspecific taxa [BD Indicator 9.1]
Information to report on this indicator is unavailable.
Population size, numbers and physical isolation [BD Indicator 9.2]
There are insufficient data for this indicator to be a reliable reflection of any general or specific trends in the distribution of genetic variation as a response to changes in the environment.
Environmental amplitude of populations [BD Indicator 9.3]
The data for this indicator are unavailable.
Genetic diversity at marker loci [BD Indicator 9.4]
It is not possible to report in any detail on this indicator.
The entire genome of an individual cannot be measured. The closest science has come to achieving this goal is the Human Genome Project. This involved enormous human and financial resources, sequenced various parts of different humans, not of one individual alone, and was a one-off project. Even if it were possible to measure an entire genome, we would not know which parts of the genome had adaptive significance. The kinds of genetic data that may be acquired reflect the different regions of the genome from which they are sampled. Different regions have different characteristics and the data taken from them may have different applications.
Overall, the recommendations on genetic indicators by Brown et al. (1997) and Saunders et al. (1998) may generate some useful statistics for monitoring species but most current genetic studies of Australian species do not provide sufficient information for the relevant variables to be calculated.
Allelic richness and gene diversity measure the basic raw material available for evolution: allelic richness is more sensitive a measure, but is more susceptible to sampling artefacts. The observed heterozygosity per individual (H o) is a good indicator of processes such as mating systems, and may predict fitness. It is also possible to estimate levels of inbreeding from patterns of heterozygosity. The concepts of allelic richness, gene diversity and observed heterozygosity are explained in greater detail (see Measuring genetic variation).
Interspecific and intraspecific comparisons of allelic richness are compromised, however, because of biases in the selection of markers. For example, comparisons between taxa and between times will be affected by the choice of markers, because different marker systems produce different estimates of genetic diversity (Moran et al. 2000).
Brown et al. (1997) recommended identifying a set of species for detailed monitoring, providing baselines for assessing future changes in genetic diversity. Their recommendations depend on the identification of indicator taxa, and resampling of species genomes to ensure sufficient sample sizes to reliably detect trends in the distribution and amount of genetic variation. Table 45 reflects the current uneven availability of genetic data required for biodiversity monitoring. There is considerable effort expended on measuring and reporting genetic variation within and between populations using several markers. These data would be considerably more useful for biodiversity monitoring if collection and reporting reflected the procedures used by Coates and Hamley (1999) (see Monitoring Round-leaf Honeysuckle (Lambertia orbifolia)).
|Taxon||Total (expected) diversity (H e)||Observed diversity (H o)||Mean number of alleles per locus (A)||Proportion of variable loci (P)||Marker|
|All mammalsC||0.041 (0.035)||0.19 (0.14)||Isozymes|
|BirdsD||0.051 (0.029)||0.30 (0.14)||Isozymes|
|ReptilesE||0.083 (0.119)||0.26 (0.15)||Isozymes|
|AmphibiansF||0.067 (0.058)||0.25 (0.15)||Isozymes|
|FishG||0.051 (0.035)||0.21 (0.14)||Isozymes|
|InvertebratesH||0.100 (0.091)||0.38 (0.22)||Isozymes|
|Cherax quadricarinatus I||0.688||0.420||Microsatellite|
|Ctenophorus ornatus J||0.630||0.642||Microsatellite|
|All plantsL||0.075 (0.069)||0.30 (0.25)||Isozymes|
|Grevillea scapigera M||0.356||RAPDs|
|Eucalyptus lateritica N||0.318||Isozymes|
|Eucalyptus johnsonii N||0.139||Isozymes|
|Lambertia orbifolia O||0.11 (0.01)||0.06 (0.01)||1.5 (0.05)||Isozymes|
AAverage of two summaries (Sherwin & Murray 1990);BAverage of three studies including 18 species (Sherwin & Murray 1990);CAverage of 184 species for H and 181 species for P (Nevo et al. 1984);DAverage of 46 species for H and 56 species for P (Nevo et al. 1984); EAverage of 75 species for H and 84 species for P (Nevo et al. 1984);FAverage of 61 species for H and 73 species for P (Nevo et al. 1984);GAverage of 183 species for H and 200 species for P (Nevo et al. 1984); HAverage of 361 species for H and 371 species for P (Nevo et al. 1984);IBaker et al. (2000); JLebas & Spencer (2000);KSummarised by Hamrick and Godt (1989; 1996) from 400 studies;LAverage of 56 species for H and 75 species for P (Nevo et al. 1984);MRossetto et al. (1995);NMoran and Hopper (1987);OCoates and Hamley (1999).
Source: after Moran and Hopper (1987); Sherwin and Murray (1990); Sydes (1995).
Allelic richness: Allelic richness is defined as the number of alleles in a sample, standardised for sample size. Two statistics are relevant to allelic richness, the percentage of polymorphic loci (P), and the average number of alleles per locus (A). To compare allelic richness between species, or to compare samples from the same species at different times, it would be necessary to sample the same markers on each occasion, or to take a random sample of all possible markers. But methodological conventions are to survey available markers and to select informative ones for analysis and reporting. Selection is often haphazard but is rarely random (Glaubitz et al. 1999). In addition, loci are treated as polymorphic only if the frequency of the most frequent allele is less than 0.95, or sometimes less than 0.99 or 0.90. Typically, the number and identity of monomorphic markers are not published, sometimes by editorial policy, if not by convention.
It is also possible to estimate the number of alleles per polymorphic locus, which may be readily compared across studies. In addition, planned and coordinated monitoring strategies could overcome biases through appropriate marker choice.
Gene diversity: Gene diversity, He, is the expected level of heterozygosity in a randomly mating population, given observed allelic frequencies. In species with subdivisions (populations), the diversity may be partitioned into within-population (Hs) and between-population (GST) components. This information may be used to examine patterns of population structure and differentiation and non-random mating.
The level and type of information obtained from genetic studies depends on the markers chosen. Gene diversity is directly available in studies that use codominant markers (including restriction fragment length polymorphisms, RFLPs, isozymes and microsatellites). Studies using dominant markers (e.g. polymerase chain reaction-based markers including amplified fragment length polymorphisms and random amplified polymorphic DNA, RAPD) or haploid markers (e.g. mitochondrial DNA) usually report genetic distances. These studies do not provide any direct measure of gene frequencies. Mitochondrial DNA gives a measure of diversity equivalent to He (haplotype diversity) but no measure of inbreeding. This occurs because the various methods used to score genetic markers detect different proportions of the total variation in DNA base sequences. Moreover, the rates of mutation vary widely between different classes of marker. For example, isozymes and RFLPs have low mutation rates and microsatellites have very high mutation rates (Ennos 1996).
Observed heterozygosity: Observed heterozygosity, Ho, is the proportion of heterozygotes in a population, averaged over all loci, and is theoretically available in all studies that use codominant markers. However, this statistic is rarely reported in the literature. Most studies are concerned primarily with differentiation between populations in a single study, so that comparisons between times and between species are rarely considered. Microsatellite studies contrast with studies that use other codominant markers. Most microsatellite studies report both Ho and He, allowing computation of inbreeding coefficients (e.g. Baker et al. 2000; Lebas & Spencer 2000; Miwa et al. 2000).
Coates and Hamley (1999) measured the genetic variation within and between populations of Lambertia orbifolia, a large woody shrub restricted to seven populations in Western Australia. They found 12 out of 19 isozyme loci were polymorphic. Levels of genetic variation were roughly comparable with other long-lived woody shrub species endemic to the region. Genetic divergence between population groups was very high, with 44% of variation in genetic diversity being composed of between population differences. They estimated rates of gene flow between the populations to be very low and found consistently low levels of outcrossing. Coates and Hamley (1999) recommended that one population be recognised as a separate conservation unit, on the basis of the degree of genetic differentiation. They reported A, P, Heand Ho, together with standard errors for each statistic, for each of the populations, making it possible to resample these populations in future years, to measure trends in diversity.