About
12
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Introduction
I am a postdoc with the EcoGenetics lab led by Adam Miller and Craig Sherman of Deakin University. My current research is primarily focused on investigating patterns of stock connectivity in Southern Ocean crab fisheries using genomic and modelling techniques. I also work with an extended network of collaborators on a suite of other genomic research projects, investigating how both terrestrial and aquatic species are likely to evolve and adapt to climate change and other environmental changes.
Current institution
Publications
Publications (12)
Aim
The white shark (Carcharodon carcharias) is one of the world's largest and most recognisable marine predators but has suffered significant declines since the mid‐twentieth century. Conservation efforts remain complicated by persistent knowledge gaps associated with white shark biology and ecology, including the biological connectedness of white...
Ocean warming and extreme heatwaves threaten marine species supporting commercial fisheries and aquaculture. Predicting the responses of these industries to chronic and acute warming depends on understanding which life stages are most vulnerable, the potential for stocks to adapt to changing thermal environments, and the availability of thermally a...
Wildlife surveys are central to the conservation and restoration management of wetland habitats; however, the often laborious and costly nature of traditional survey methods can constrain the spatial and temporal extent and replication of survey efforts.
Environmental DNA (eDNA) technologies now provide the opportunity to reduce some of these limit...
The white shark ( Carcharodon carcharias ) is one of the world's largest apex predators found throughout the world's temperate and subtropical marine environments. However, the species has suffered significant declines in recent decades and effective conservation programs require a sound knowledge of white shark biology and ecology. In particular,...
The role of macroalgae (seaweed) as a global contributor to carbon drawdown within marine sediments - termed 'blue carbon' - remains uncertain and controversial. While studies are needed to validate the potential for macroalgal‑carbon sequestration in marine and coastal sediments, fundamental questions regarding the fate of dislodged macroalgal bio...
Infectious diseases are recognized as one of the greatest global threats to biodiversity and ecosystem functioning. Consequently, there is a growing urgency to understand the speed at which adaptive phenotypes can evolve and spread in natural populations to inform future management. Here we provide evidence of rapid genomic changes in wild Australi...
• Dispersal is a critically important process that dictates population persistence, gene flow, and evolutionary potential, and is an essential element for identifying species conservation risks. This study aims to investigate the contributions of dispersal syndromes and hydrographic barriers on patterns of population connectivity and genetic struct...
Worldwide, rising ocean temperatures are causing declines and range shifts in marine species. The direct effects of climate change on the biology of marine organisms are often well documented; yet, knowledge on the indirect effects, particularly through trophic interactions, is largely lacking. We provide evidence of ocean warming decoupling critic...
Habitat fragmentation imperils the persistence of many functionally important species, with climate change a new threat to local persistence due to climate‐niche mismatching. Predicting the evolutionary trajectory of species essential to ecosystem function under future climates is challenging but necessary for prioritizing conservation investments....
Questions
Questions (5)
I'm trying to streamline my metagenomics protocols for my lab. I have previously followed the 16S protocol (https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf) with minimal modification.
Can anyone explain what the purpose of the first cleanup step is? I understand that this removes primer dimers, adapters and other nasties, but is there any reason you can't just skip the first clean up, index your amplicon PCR products immediately after PCR, and then clean up post indexing, still removing undesirable products? In our case we would then proceed with library normalisation via Qubit, size selection via Blue Pippin or Pippin Prep, and sequencing.
Thanks!
I have a metagenomic dataset originating from the gut contents of a marine mollusk. I am trying to identify the appropriate statistical workflow for analysing these data. While I do have abundance data, it is based on read count, which is a notoriously inaccurate measure of abundance due to numerous factors. Due to this, we are opting to use presence/absence data to focus more on the frequency of occurrence of the different taxa across our metadata.
I've produced a Jaccard distance matrix, specifying binary data, using `vegan` in R. Using that matrix I've performed a permutational multivariate analysis of variance (adonis), though its slightly dubious, as I am receiving significant outputs on some metadata that don't appear to have any visual differences on NMDS plots produced with the same Jaccard matrix. Now I fear I might be completely barking up the wrong tree, as stats are not my strong point.
Would anyone with experience analysing these kinds of data be able to point me in the right direction so I know I'm conducting the correct analysis?
Thank you very much!
I am conducting a population genetic study on several species of fishes using microsatellite alleles. I have used BayesAss (http://www.rannala.org/software/) to estimate migration percentages to and from each population in my datasets for each species. Superficially, the outputs provided seem to be apparently correct and adhere to pre-conceived hypotheses. However, the output data do not provide me with p-values to indicate if the estimates are statistically significant. For each estimate (mean) the SD is provided in the output file, and after reading the generated Trace file (https://www.beast2.org/tracer-2/ ) several other outputs are calculated (SE, SD, variance, upper and lower 95% CIs) but no p-value. Could anyone help me either a) identify the p-value in the output that I am somehow missing? or b) calculate the p-value from the outputs provided? Unfortunately statistics aren't my strong suit so I feel like the answer might be obvious with the results I've been given but I can't figure it out from what I've looked up so far.
Thanks!
I've been running some preliminary PCR assays for a stomach content metabarcoding project targeting plant DNA, and on my gels I've been getting bands in my negative control lane. I've taken several measures to improve sterility and try to identify my contamination source, including using some donated DNA-free water and found that the MilliQ water I was using to prepare the mastermixes for my reactions was likely the source (very small lab, so the filter may have been neglected). I wanted to confirm that this was the source before buying any water so I ran a PCR using DNA-free water and MilliQ water in place of DNA and reused the buffer from the previous run for the gel and buffer, which I was told is an acceptable practice. I got contamination again in the DNA-free and MilliQ lanes, and so I'm questioning whether just reusing TAE buffer can cause false positives at all?
I'm trying out some methods of plant (algal) DNA extraction in abalone and plan on depositing the entire contents of the digestive system in a tube for frequent trials. What is the best method to preserve the genetic material in the content? I would assume storing at -20˚C in 70% ethanol would be best, but the gut content is rather aqueous and I fear that this may not be best for such a liquid sample.
