A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

Phylogeography of the pharaoh cuttle Sepia pharaonis based on partial mitochondrial 16S sequence data

The pharaoh cuttle Sepia pharaonis Ehrenberg, 1831 (Mollusca: Cephalopoda: Sepiida) is a broadly distributed species of substantial fisheries importance found from east Africa to southern Japan. Little is known about S. pharaonis phylogeography, but evidence from morphology and reproductive biology suggests that Sepia pharaonis is actually a complex of at least three species. To evaluate this possibility, we collected tissue samples from Sepia pharaonis from throughout its range. Phylogenetic analyses of partial mitochondrial 16S sequences from these samples reveal five distinct clades: a Gulf of Aden/Red Sea clade, a northern Australia clade, a Persian Gulf/Arabian Sea clade, a western Pacific clade (Gulf of Thailand and Taiwan) and an India/Andaman Sea clade. Phylogenetic analyses including several Sepia species show that S. pharaonis sensu lato may not be monophyletic. We suggest that "S. pharaonis" may consist of up to five species, but additional data will be required to fully clarify relationships within the S. pharaonis complex.

Evaluation of aerial photography for predicting trends in structural attributes of Australian woodland including comparison with ground-based monitoring data

The accurate assessment of trends in the woody structure of savannas has important implications for greenhouse accounting and land-use industries such as pastoralism. Two recent assessments of live woody biomass change from north-east Australian eucalypt woodland between the 1980s and 1990s present divergent results. The first estimate is derived from a network of permanent monitoring plots and the second from woody cover assessments from aerial photography. The differences between the studies are reviewed and include sample density, spatial scale and design. Further analyses targeting potential biases in the indirect aerial photography technique are conducted including a comparison of basal area estimates derived from 28 permanent monitoring sites with basal area estimates derived by the aerial photography technique. It is concluded that the effect of photo-scale; or the failure to include appropriate back-transformation of biomass estimates in the aerial photography study are not likely to have contributed significantly to the discrepancy. However, temporal changes in the structure of woodlands, for example, woodlands maturing from many smaller trees to fewer larger trees or seasonal changes, which affect the relationship between cover and basal area could impact on the detection of trends using the aerial photography technique. It is also possible that issues concerning photo-quality may bias assessments through time, and that the limited sample of the permanent monitoring network may inadequately represent change at regional scales

Investigation of movement and factors influencing post-release survival of line-caught coral reef fish using recreational tag-recapture data

Six species of line-caught coral reef fish (Plectropomus spp., Lethrinus miniatus, Lethrinus laticaudis, Lutjanus sebae, Lutjanus malabaricus and Lutjanus erythropterus) were tagged by members of the Australian National Sportsfishing Association (ANSA) in Queensland between 1986 and 2003. Of the 14,757 fish tagged, 1607 were recaptured and we analysed these data to describe movement and determine factors likely to impact release survival. All species were classified as residents since over 80% of recaptures for each species occurred within 1 km of the release site. Few individuals (range 0.8-5%) were recaptured more than 20 km from their release point. L. sebae had a higher recapture rate (19.9%) than the other species studied (range 2.1-11.7%). Venting swimbladder gases, regardless of whether or not fish appeared to be suffering from barotrauma, significantly enhanced (P < 0.05) the survival of L. sebae and L. malabaricus but had no significant effect (P > 0.05) on L. erythropterus. The condition of fish on release, subjectively assessed by anglers, was only a significant effect on recapture rate for L. sebae where fish in "fair" condition had less than half the recapture rate of those assessed as in "excellent" or "good" condition. The recapture rate of L. sebae and L. laticaudis was significantly (P < 0.05) affected by depth with recapture rate declining in depths exceeding 30 m. Overall, the results showed that depth of capture, release condition and treatment for barotrauma influenced recapture rate for some species but these effects were not consistent across all species studied. Recommendations were made to the ANSA tagging clubs to record additional information such as injury, hooking location and hook type to enable a more comprehensive future assessment of the factors influencing release survival.

Phylogenetic relationships of graminicolous downy mildews based on cox2 sequence data

Graminicolous downy mildews (GDM) are an understudied, yet economically important, group of plant pathogens, which are one of the major constraints to poaceous crops in the tropics and subtropics. Here we present a first molecular phylogeny based on cox2 sequences comprising all genera of the GDM currently accepted, with both lasting (Graminivora, Poakatesthia, and Viennotia) and evanescent (Peronosclerospora, Sclerophthora, and Sclerospora) sporangiophores. In addition, all other downy mildew genera currently accepted, as well as a representative sample of other oomycete taxa, have been included. It was shown that all genera of the GDM have had a long, independent evolutionary history, and that the delineation between Peronosclerospora and Sclerospora is correct. Sclerophthora was found to be a particularly divergent taxon nested within a paraphyletic Phytophthora, but without support. The results confirm that the placement of Peronosclerospora and Sclerospora in the Saprolegniomycetidae is incorrect. Sclerophthora is not closely related to Pachymetra of the family Verrucalvaceae, and also does not belong to the Saprolegniomycetidae, but shows close affinities to the Peronosporaceae. In addition, all GDM are interspersed throughout the Peronosporaceae s lat., suggesting that a separate family for the Sclerosporaceae might not be justified.

Concordant molecular and phenotypic data delineate new taxonomy and conservation priorities for the endangered mountain yellow-legged frog

The mountain yellow-legged frog Rana muscosa sensu lato, once abundant in the Sierra Nevada of California and Nevada, and the disjunct Transverse Ranges of southern California, has declined precipitously throughout its range, even though most of its habitat is protected. The species is now extinct in Nevada and reduced to tiny remnants in southern California, where as a distinct population segment, it is classified as Endangered. Introduced predators (trout), air pollution and an infectious disease (chytridiomycosis) threaten remaining populations. A Bayesian analysis of 1901 base pairs of mitochondrial DNA confirms the presence of two deeply divergent clades that come into near contact in the Sierra Nevada. Morphological studies of museum specimens and analysis of acoustic data show that the two major mtDNA clades are readily differentiated phenotypically. Accordingly, we recognize two species, Rana sierrae, in the northern and central Sierra Nevada, and R. muscosa, in the southern Sierra Nevada and southern California. Existing data indicate no range overlap. These results have important implications for the conservation of these two species as they illuminate a profound mismatch between the current delineation of the distinct population segments (southern California vs. Sierra Nevada) and actual species boundaries. For example, our study finds that remnant populations of R. muscosa exist in both the southern Sierra Nevada and the mountains of southern California, which may broaden options for management. In addition, despite the fact that only the southern California populations are listed as Endangered, surveys conducted since 1995 at 225 historic (1899-1994) localities from museum collections show that 93.3% (n=146) of R. sierrae populations and 95.2% (n=79) of R. muscosa populations are extinct. Evidence presented here underscores the need for revision of protected population status to include both species throughout their ranges.

A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

Background: Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies. Results: An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion: Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Application of phytotoxicity data to a new Australian soil quality guideline framework for biosolids

To protect terrestrial ecosystems and humans from contaminants many countries and jurisdictions have developed soil quality guidelines (SQGs). This study proposes a new framework to derive SQGs and guidelines for amended soils and uses a case study based on phytotoxicity data of copper (Cu) and zinc (Zn) from field studies to illustrate how the framework could be applied. The proposed framework uses normalisation relationships to account for the effects of soil properties on toxicity data followed by a species sensitivity distribution (SSD) method to calculate a soil added contaminant limit (soil ACL) for a standard soil. The normalisation equations are then used to calculate soil ACLs for other soils. A soil amendment availability factor (SAAF) is then calculated as the toxicity and bioavailability of pure contaminants and contaminants in amendments can be different. The SAAF is used to modify soil ACLs to ACLs for amended soils. The framework was then used to calculate soil ACLs for copper (Cu) and zinc (Zn). For soils with pH of 4-8 and OC content of 1-6%, the ACLs range from 8 mg/kg to 970 mg/kg added Cu. The SAAF for Cu was pH dependant and varied from 1.44 at pH 4 to 2.15 at pH 8. For soils with pH of 4-8 and OC content of 1-6%, the ACLs for amended soils range from 11 mg/kg to 2080 mg/kg added Cu. For soils with pH of 4-8 and a CEC from 5-60, the ACLs for Zn ranged from 21 to 1470 mg/kg added Zn. A SAAF of one was used for Zn as it concentrations in plant tissue and soil to water partitioning showed no difference between biosolids and soluble Zn salt treatments, indicating that Zn from biosolids and Zn salts are equally bioavailable to plants.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.