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Stygofauna are aquatic fauna that have evolved to live underground. The impacts of anthropogenic climate change, extraction and pollution on groundwater pose major threats to groundwater health, prompting the need for efficient and reliable means to detect and monitor stygofaunal communities. Conventional survey techniques for these species rely on morphological identification and can be biased, labour‐intensive and often indeterminate to lower taxonomic levels. By contrast, environmental DNA (eDNA)‐based methods have the potential to dramatically improve on existing stygofaunal survey methods in a large range of habitats and for all life stages, reducing the need for the destructive manual collection of often critically endangered species or for specialized taxonomic expertise. We compared eDNA and haul‐net samples collected in 2020 and 2021 from 19 groundwater bores and a cave on Barrow Island, northwest Western Australia, and assessed how sampling factors influenced the quality of eDNA detection of stygofauna. The two detection methods were complementary; eDNA metabarcoding was able to detect soft‐bodied taxa and fish often missed by nets, but only detected seven of the nine stygofaunal crustacean orders identified from haul‐net specimens. Our results also indicated that eDNA metabarcoding could detect 54%–100% of stygofauna from shallow‐water samples and 82%–90% from sediment samples. However, there was significant variation in stygofaunal diversity between sample years and sampling types. The findings of this study demonstrate that haul‐net sampling has a tendency to underestimate stygofaunal diversity and that eDNA metabarcoding of groundwater can substantially improve the efficiency of stygofaunal surveys. [ABSTRACT FROM AUTHOR]

A new species of deep‐water epinephelid fish is described from the west coast of Australia based on 14 specimens, 99–595 mm standard length. Hyporthodus griseofasciatus sp. nov. is endemic to Western Australia from Barrow Island to Two Peoples Bay in depths of 76–470 m. It has a series of eight grey bands alternating with eight brown bands along the body and the soft dorsal, soft anal and caudal fin margins are pale cream to white. It is distinguished from its nearest congener, H. ergastularius, by the presence of a star‐like pattern of radiating lines on the head versus an overall brownish colour in the latter as well as significant differences in the quantitative analyses of 25 morphological characters. The two species have allopatric distributions on either side of the Australian continent. H. griseofasciatus is distinguished from H. octofasciatus by several grey bands being distinctly narrower than other grey bands (vs. all grey bands subequal in the latter) and the presence of broad white margins on the dorsal, caudal and anal fins (vs. narrow or absent in the latter). Some scale counts appear to also differ. Analyses of mitochondrial cytochrome oxidase subunit 1 sequences revealed reciprocally monophyletic clades with fixed differences and genetic distances typical of recently diverged species of fishes. [ABSTRACT FROM AUTHOR]

Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables. [ABSTRACT FROM AUTHOR]