Differences among major taxa in the extent of ecological knowledge across four major ecosystems

Seq' and ARMS shall find: DNA (meta)barcoding of Autonomous Reef Monitoring Structures across the tree of life uncovers hidden cryptobiome of tropical urban coral reefs

Coral reefs are among the richest marine ecosystems on Earth, but there remains much diversity hidden within cavities of complex reef structures awaiting discovery. While the abundance of corals and other macroinvertebrates are known to influence the diversity of other reef-associated organisms, much remains unknown on the drivers of cryptobenthic diversity. A combination of standardized sampling with 12 units of the Autonomous Reef Monitoring Structure (ARMS) and high-throughput sequencing was utilized to uncover reef cryptobiome diversity across the equatorial reefs in Singapore. DNA barcoding and metabarcoding of mitochondrial cytochrome c oxidase subunit I, nuclear 18S and bacterial 16S rRNA genes revealed the taxonomic composition of the reef cryptobiome, comprising 15,356 microbial ASVs from over 50 bacterial phyla, and 971 MOTUs across 15 metazoan and 19 non-metazoan eukaryote phyla. Environmental factors across different sites were tested for relationships with ARMS diversity. Differences among reefs in diversity patterns of metazoans and other eukaryotes, but not microbial communities, were associated with biotic (coral cover) and abiotic (distance, temperature and sediment) environmental variables. In particular, ARMS deployed at reefs with higher coral cover had greater metazoan diversity and encrusting plate cover, with larger-sized non-coral invertebrates influencing spatial patterns among sites. Our study showed that DNA barcoding and metabarcoding of ARMS constitute a valuable tool for quantifying cryptobenthic diversity patterns and can provide critical information for the effective management of coral reef ecosystems.

Utilizing field collected insects for next generation sequencing: Effects of sampling, storage, and DNA extraction methods

DNA sequencing technologies continue to advance the biological sciences, expanding opportunities for genomic studies of non-model organisms for basic and applied questions. Despite these opportunities, many next generation sequencing protocols have been developed assuming a substantial quantity of high molecular weight DNA (>100 ng), which can be difficult to obtain for many study systems. In particular, the ability to sequence field-collected specimens that exhibit varying levels of DNA degradation remains largely unexplored. In this study we investigate the influence of five traditional insect capture and curation methods on Double-Digest Restriction Enzyme Associated DNA (ddRAD) sequencing success for three wild bee species. We sequenced a total of 105 specimens (between 7-13 specimens per species and treatment). We additionally investigated how different DNA quality metrics (including pre-sequence concentration and contamination) predicted downstream sequencing success, and also compared two DNA extraction methods. We report successful library preparation for all specimens, with all treatments and extraction methods producing enough highly reliable loci for population genetic analyses. Although results varied between species, we found that specimens collected by net sampling directly into 100% EtOH, or by passive trapping followed by 100% EtOH storage before pinning tended to produce higher quality ddRAD assemblies, likely as a result of rapid specimen desiccation. Surprisingly, we found that specimens preserved in propylene glycol during field sampling exhibited lower-quality assemblies. We provide recommendations for each treatment, extraction method, and DNA quality assessment, and further encourage researchers to consider utilizing a wider variety of specimens for genomic analyses.

Cryptic lineages in the Wolf Cardinalfish living in sympatry on remote coral atolls

Coral reef health and biodiversity is under threat worldwide due to rapid climate change. However, much of the inter- and intra-specific diversity of coral reefs are undescribed even in well studied taxa such as fish. Delimiting previously unrecognised diversity is important for understanding the processes that generate and sustain biodiversity in coral reef ecosystems and informing strategies for their conservation and management. Many taxa that inhabit geographically isolated coral reefs rely on self-recruitment for population persistence, providing the opportunity for the evolution of unique genetic lineages through divergent selection and reproductive isolation. Many such lineages in corals and fish are morphologically similar or indistinguishable. Here, we report the discovery and characterisation of cryptic lineages of the Wolf Cardinalfish, Cheilodipterus artus, from the coral atolls of northwest Australia using multiple molecular markers from mitochondrial (CO1 and D-loop) and nuclear (microsatellites) DNA. Concordant results from all markers identified two highly divergent lineages that are morphologically cryptic and reproductively isolated. These lineages co-occurred at daytime resting sites, but the relative abundance of each lineage was strongly correlated with wave exposure. It appears, therefore, that fish from each lineage are better adapted to different microhabitats. Such cryptic and ecologically based diversity appears to be common in these atolls and may well aid resilience of these systems. Our results also highlight that underwater surveys based on visual identification clearly underestimate biodiversity, and that a taxonomic revision of the Cheilodipterus genus is necessary.

Challenges of transferring models of fish abundance between coral reefs

Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% <R² < 50.6% for Acanthuridae) compared to total fish abundance (9% <R² < 18.6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R² < 6%, p > 0.05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Understanding uncertainties in non-linear population trajectories: a Bayesian semi-parametric hierarchical approach to large-scale surveys of coral cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.

Global coordination and standardisation in marine biodiversity through the World Register of Marine Species (WoRMS) and related databases

The World Register of Marine Species is an over 90% complete open-access inventory of all marine species names. Here we illustrate the scale of the problems with species names, synonyms, and their classification, and describe how WoRMS publishes online quality assured information on marine species. Within WoRMS, over 100 global, 12 regional and 4 thematic species databases are integrated with a common taxonomy. Over 240 editors from 133 institutions and 31 countries manage the content. To avoid duplication of effort, content is exchanged with 10 external databases. At present WoRMS contains 460,000 taxonomic names (from Kingdom to subspecies), 368,000 species level combinations of which 215,000 are currently accepted marine species names, and 26,000 related but non-marine species. Associated information includes 150,000 literature sources, 20,000 images, and locations of 44,000 specimens. Usage has grown linearly since its launch in 2007, with about 600,000 unique visitors to the website in 2011, and at least 90 organisations from 12 countries using WoRMS for their data management. By providing easy access to expert-validated content, WoRMS improves quality control in the use of species names, with consequent benefits to taxonomy, ecology, conservation and marine biodiversity research and management. The service manages information on species names that would otherwise be overly costly for individuals, and thus minimises errors in the application of nomenclature standards. WoRMS' content is expanding to include host-parasite relationships, additional literature sources, locations of specimens, images, distribution range, ecological, and biological data. Species are being categorised as introduced (alien, invasive), of conservation importance, and on other attributes. These developments have a multiplier effect on its potential as a resource for biodiversity research and management. As a consequence of WoRMS, we are witnessing improved communication within the scientific community, and anticipate increased taxonomic efficiency and quality control in marine biodiversity research and management.