ABSTRACT Floodplain wetlands are declining globally in extent and ecological condition. Changing climates, increasing agricultural activity and invasive species are among the key threats contributing to biodiversity loss, with native fish species increasingly in decline and listed as threatened or endangered. Floodplain wetlands present unique sampling challenges requiring bespoke methods to detect species across complex habitats. Environmental DNA (eDNA) metabarcoding has rarely been assessed against traditional fish sampling techniques in floodplain wetlands. We compared seine netting, fyke netting and eDNA metabarcoding across 34 floodplain wetlands in south‐eastern Australia. We assessed the effects of known constraints to eDNA detection: turbidity (10–450 NTU) and sample volume (2.3–7.6 L) on metabarcoding taxa richness. eDNA provided the most comprehensive detection of fish taxa overall. Common, abundant taxa were reliably detected by all methods, whereas rare species were predominantly detected using eDNA. Turbidity constrained filtration volume through filter clogging but did not compromise species detection; filtered water volume was not correlated with species richness. Sequence read depth showed a significant positive correlation with netting catch‐per‐unit‐effort at the species level, suggesting potential utility for ranking sites by relative fish abundance or biomass. eDNA provides complementary information to traditional netting methods, detecting species missed by physical capture while detecting all species captured by nets. This makes eDNA particularly valuable as a reconnaissance tool for initial biodiversity assessment and spatial prioritisation of sampling effort, complemented by physical capture to assess population demographics including age structure, size distributions, condition and sex ratios essential for understanding recovery and decline.

Understanding the genetic architecture of functional traits can provide key insights into the ecological dynamics and adaptive potential of species. We investigated whether genetic data can predict growth rate variation in a natural population of the widespread kelp, Ecklonia radiata. We tagged kelps and tracked their growth in situ over spring when growth is maximal. Individual kelps were then genotyped using reduced representation sequencing (ddRAD) and we employed multiple approaches to assess whether genetic variation corresponded with growth rate variation. Despite a limited sample size, we found evidence that growth rate can be strongly predicted from genetic variation, with approximately half of the variation in growth rate predicted by only 18 loci (R2 = 0.499). Leveraging published transcriptomic data, we confirm that most of these loci are expressed or are linked to expressed putative genes. However, many of these genes are of unknown function and do not match well-known gene families. These findings have important implications for understanding natural kelp forest dynamics and for applied approaches such as selective breeding and aquaculture. While our study offers an important first assessment of the possible genomic architecture underlying growth rate in E. radiata, future work is needed to confirm this apparent link between genetic and functional variation.

Thirty-six Merino wethers (10-mo-old) were fed ad libitum for 30 d 2 diets;1) low metabolizable energy diet (LME; 30% lucerne: 70% cereal chaff) and 2) high ME diet (HME; 40% rolled barley grain: 50% lucerne: 10% cereal chaff). Effects of diet on dry matter intake (DMI), ME intake (MEI), liveweight (LWT), average daily gain (ADG), carcass lean or fat gain, liver and empty rumen weight, and plasma metabolites were analyzed. A membrane-enriched protein fraction of rumen epithelium (RE) isolated enzymatically from whole depth rumen wall was quantified for each sheep using tandem mass tag mass spectrometry (TMT-MS). The presence or absence of acetylation of lysine residues on identified proteins was counted and the position of the lysine acetylation was recorded. In lambs fed the HME diet, DMI (P < 0.001), MEI (P < 0.001), ADG (P < 0.001), fat (P < 0.001), and lean gain (P < 0.001), as well as liver (P < 0.001) and empty rumen (P < 0.009) weight were greater than those fed the LME diet. Plasma glucose (P < 0.001) and βhydroxybutyrate (P < 0.001) at 3 and 5 h after feeding was greater in HME diet than in the LME-fed lambs. Changes in RE protein abundance in the LME versus HME-fed lambs were associated with metabolism in the peroxisome, protein processing in the endoplasmic reticulum (ER), valine, leucine and isoleucine degradation, and carbon metabolism. Acetylation of lysine was detected in enzymes involved in glycolysis, tricarboxylic acid (TCA) cycle, and fatty acid (FA) metabolism. Quantitative differences in the abundance of RE proteins that carry out intracellular processes of energy expenditure were associated with the concentration of ME (MJ/ kg DM) in the diet of growing lambs. The detection of lysine acetylation sites suggests a difference in the ME of the diet regulates enzymatic activity in central metabolic pathways in the RE cells.

Ongoing and predicted range loss of kelp forests in response to climatic stressors is pressing marine managers to look into the adaptive capacity of populations to inform conservation strategies. Characterising how adaptive genetic diversity and structure relate to present and future environmental variation represents an emerging approach to quantifying kelp vulnerability to environmental change and identifying populations with genotypes that potentially confer an adaptive advantage in future ocean conditions. The dominant Australian kelp, Ecklonia radiata, was genotyped from 10 locations spanning 2000 km of coastline and a 9.5°C average temperature gradient along the east coast of Australia, a global warming hotspot. ddRAD sequencing generated 10,700 high-quality single nucleotide polymorphisms (SNPs) and characterized levels of neutral and adaptive genomic diversity and structure. The adaptive dataset, reflecting portions of the genome putatively under selection, was used to infer genomic vulnerability by 2050 under the RCP 8.5 scenario. There was strong neutral genetic differentiation between Australia mainland and Tasmanian populations, but only weak genetic structure among mainland populations within the main path of the East Australian Current. Genetic diversity was highest in the center of the range and lowest in the warm-edge population. The adaptive SNP candidates revealed similar genetic structure patterns, with a spread of adaptive alleles across most warm (northern) populations. The lowest, but most unique, adaptive genetic diversity values were found in both warm and cool population edges, suggesting local adaptation but low evolutionary potential. Critically, genomic vulnerability modeling identified high levels of vulnerability to future environmental conditions in Tasmanian populations. Populations of kelp at range edges are unlikely to adapt and keep pace with predicted climate change. Ensuring the persistence of these kelp forests, by boosting resilience to climate change, may require active management strategies with assisted adaptation in warm-edge (northern) populations and assisted gene flow in cool-edge (Tasmania) populations.

The analysis of environmental DNA (eDNA) is a powerful and non-invasive method for monitoring the presence of species in ecosystems. However, ecologists and laboratory staff can find it challenging to use eDNA analysis software effectively due to the unfamiliar command-line interfaces used by many of these packages. Therefore, we developed the eDNA-container app, a free and open-source software package that provides a simple user-friendly interface for eDNA analysis. The application is based on the popular QIIME2 library and is distributed as a Docker image. The use of Docker makes it compatible with a wide range of operating systems and facilitates the reproducible analysis of data across different laboratories. The application includes a point-and-click user interface for selecting sequencing files, configuring parameters, and accessing the results. Key pipeline outputs, such as sequence quality plots, denoising, and ASV generation statistics, are automatically included in a PDF report. This open-source and freely available analysis package should be a valuable tool for scientists using eDNA in biodiversity and biosecurity applications.

The value of crops such as perennial wheat (PW) for grain and grazing compared to conventional wheat (W), or the addition of lucerne to PW (PWL) is still being determined. This research sought to determine if these diets were associated with changes in the membranebound proteins that transport nutrients in the rumen epithelium (RE). Crossbred ewes (Poll Dorset × Merino) were fed W, PW, or PWL (50:50) fresh-cut forage ad libitum for 4 weeks. Average daily gain (ADG; p < 0.001) was highest in the W-fed lambs compared to the PW and PWL. Metabolisable energy intake (MEI) was higher in lambs fed W (p < 0.001) compared to PW and PWL. In pairwise comparisons of the PW and PWL diet group we found protein abundance was significantly (p < 0.05, FDR < 0.05, Benjamini p < 0.05) different in fatty acid metabolism, oxidative phosphorylation, and biosynthesis of cofactors pathways. There were not any differences in protein abundance related to nutrient transport or energy metabolism in the RE between W- vs. PW- and W- vs. PWL-fed lambs. However, in the PW- vs. PWL-fed lambs, there was a difference in the level of proteins regulating the metabolism of fatty acids and energy production in the mitochondria of the rumen epithelium.

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Table S17. FT variant used for revealing diversity in BnaA07g33120D among 21 accessions resequenced. (XLSX 10 kb)

Table S13. Genetic correlation between different traits measured in the doubled haploid population from Skipton/Ag-Spectrum//Skipton across environments. (XLSX 15 kb)