Alternative conservation outcomes from aquatic fauna translocations: Losing and saving the Running River rainbowfish

1. The translocation of species outside their natural range is a threat to aquatic biodiversity globally, especially freshwater fishes, as most are not only susceptible to predation and competition but readily hybridize with congeners. 2. Running River rainbowfish (RRR, Melanotaenia sp.) is a narrow-ranged, small-bodied freshwater fish that recently became threatened and was subsequently listed as Critically Endangered, owing to introgressive hybridization and competition following the translocation of a congeneric species, the eastern rainbowfish (Melanotaenia splendida). 3. To conserve RRR, wild fish were taken into captivity, genetically confirmed as pure representatives, and successfully bred. As the threat of introgression with translocated eastern rainbowfish could not be mitigated, a plan was devised to translocate captive raised RRR into unoccupied habitats within their native catchment, upstream of natural barriers. The translocation plan involved careful site selection and habitat assessment, predator training (exposure to predators prior to release), soft release (with a gradual transition from captivity to nature), and post-release monitoring, and this approach was ultimately successful. 4. Two populations of RRR were established in two previously unoccupied streams above waterfalls with a combined stream length of 18 km. Post-release monitoring was affected by floods and low sample sizes, but suggested that predation and time of release are important factors to consider in similar conservation recovery programmes for small-bodied, short-lived fishes

Pygmy blue whale movement, distribution and important areas in the Eastern Indian Ocean

This study was conducted as part of AIMS’ North West Shoals to Shore Research Program (NWSSRP) and was supported by Santos as part of the company’s commitment to better understand Western Australia’s marine environment. Hydrophone pressure data from Ocean Bottom Seismometers (OBS) were provided by the CANPASS project, jointly funded by the National Natural Science Foundation of China (NSFC grants 91955210, 41625016), and the China Academy of Science (CAS program GJHZ1776). Instruments were provided by the Australian National instrument pool ANSIR (http://ansir.org.au/). ANSIR, OBS data was also made data available from the Geoscience Australia and Shell. Data was sourced from Australia’s Integrated Marine Observing System (IMOS).Pygmy blue whales in the South-east Indian Ocean migrate from the southern coast of Australia to Indonesia, with a significant part of their migration route passing through areas subject to oil and gas production. This study aimed at improving our understanding of the spatial extent of the distribution, migration and foraging areas, to better inform impact assessment of anthropogenic activities in these regions. Using a combination of passive acoustic monitoring of the NW Australian coast (46 instruments from 2006 to 2019) and satellite telemetry data (22 tag deployments from 2009 to 2021) we quantified the pygmy blue whale distribution and important areas during their northern and southern migration. We show extensive use of slope habitat off Western Australia and only minimal use of shelf habitat, compared to southern Australia where use of the continental shelf and shelf break predominates. In addition, movement behaviour estimated by a state-space model on satellite tag data showed that in general pygmy blue whales off Western Australia were mostly engaged in migration, interspersed with mostly relatively short periods (median = 28hours, range = 2 – 1080hours) of low move persistence (slow movement with high turning angles), which is indicative of foraging. Using the spatial overlap of time and number of whales in area analysis of the satellite tracking data (top 50% of grid cells) with foraging movement behaviour, we quantified the spatial extent of pygmy blue whale high use areas for foraging and migration. We compared these areas to the previously described areas of importance to foraging and migrating whales (Biologically Important Areas; BIAs). In some cases these had good agreement with the most important areas we calculated from our data, but others had only low (5%) to moderate (13%) overlap. Month was the most important variable predicting the number of pygmy blue whale units and number of singers (acting as indices of pygmy blue whale density). Whale density was highest in the southern part of the NW Australian coast and whales were present there between April-June, and November-December, a pattern also confirmed by the satellite tracking data. Available data indicated pygmy blue whales spent up to 124 days in Indonesian waters (34% of annual cycle). Since this area may also be the calving ground for this population, inter-jurisdictional management is necessary to ensure their full protection.Publisher PDFPeer reviewe

Data from: Genotyping-by-sequencing for estimating relatedness in non-model organisms: avoiding the trap of precise bias

There has been remarkably little attention to using the high resolution provided by genotyping-by-sequencing (i.e. RADseq and similar methods) datasets for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of dataset that could lead to downward-biased, yet precise, estimates of relatedness. Here we assess the applicability of genotyping-by-sequencing datasets for relatedness inferences given their relatively high genotyping error rates. Individuals of known relatedness were simulated under genotyping error, allelic dropout, and missing data scenarios based on an empirical ddRAD dataset, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (1996) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP dataset with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite dataset of tens of markers. The simulation-based approach used here can be easily implemented by others on their own genotyping-by-sequencing datasets to confirm the most appropriate and powerful estimator for their dataset

Data from: Ecological disturbance influences adaptive divergence despite high gene flow in golden perch (Macquaria ambigua): implications for management and resilience to climate change

Populations that are adaptively divergent but maintain high gene flow may have greater resilience to environmental change as gene flow allows the spread of alleles that have already been tested elsewhere. In addition, populations naturally subjected to ecological disturbance may already hold resilience to future environmental change. Confirming this necessitates ecological genomic studies of high dispersal, generalist species. Here we perform one such study on golden perch (Macquaria ambigua) in the Murray-Darling Basin (MDB), Australia using a genome-wide SNP dataset. The MDB spans across arid to wet and temperate to sub-tropical environments, with low to high ecological disturbance in the form of low to high hydrological variability. We found high gene flow across the basin and three populations with low neutral differentiation. Genotype-environment association analyses detected adaptive divergence predominantly linked to an arid region with highly variable riverine flow, and candidate loci included functions related to fat storage, stress and molecular or tissue repair. The high connectivity of golden perch in the MDB will likely allow locally adaptive traits in its most arid and hydrologically variable environment to spread and be selected in localities that are predicted to become arid and hydrologically variable in future climates. High connectivity in golden perch is likely due to their generalist life history and efforts of fisheries management. Our study adds to growing evidence of adaptation in the face of gene flow, and highlights the importance of considering ecological disturbance and adaptive divergence in biodiversity management

<b>GLOBAL CONSERVATION GENOMICS OF BLUE WHALES CALLS INTO QUESTION SUBSPECIES TAXONOMY AND REFINES KNOWLEDGE OF POPULATION STRUCTURE</b>.

Bmus_16661SNP_276.vcfVCF with 276 blue whale samples genotyped for 16,661 high-quality SNPs.</p

Data from: Phylogenomic history of enigmatic pygmy perches: implications for biogeography, taxonomy and conservation

Pygmy perches (Percichthyidae) are a group of poorly dispersing freshwater fishes that have a puzzling biogeographical disjunction across southern Australia. Current understanding of pygmy perch phylogenetic relationships suggests past east-west migrations across a vast expanse of now arid habitat in central southern Australia, a region lacking contemporary rivers. Pygmy perches also represent a threatened group with confusing taxonomy and potentially cryptic species diversity. Here, we present the first study of the evolutionary history of pygmy perches based on genome-wide information. Data from 13,991 ddRAD loci and a concatenated sequence of 1,075,734 bp were generated for all currently described and potentially cryptic species. Phylogenetic relationships, biogeographic history and cryptic diversification were inferred using a framework that combines phylogenomics, species delimitation and estimation of divergence times. The genome-wide phylogeny clarified the biogeographic history of pygmy perches, demonstrating multiple east-west events of divergence within the group across the Australian continent. These results also resolved discordance between nuclear and mitochondrial data from a previous study. In addition, we propose three cryptic species within a southwestern species complex. The finding of potentially new species demonstrates that pygmy perches may be even more susceptible to ecological and demographic threats than previously thought. Our results have substantial implications for improving conservation legislation of pygmy perch lineages, especially in southwestern Western Australia

Pygmy blue whale movement, distribution and important areas in the Eastern Indian Ocean

Pygmy blue whales in the South-east Indian Ocean migrate from the southern coast of Australia to Indonesia, with a significant part of their migration route passing through areas subject to oil and gas production. This study aimed at improving our understanding of the spatial extent of the distribution, migration and foraging areas, to better inform impact assessment of anthropogenic activities in these regions. Using a combination of passive acoustic monitoring of the NW Australian coast (46 instruments from 2006 to 2019) and satellite telemetry data (22 tag deployments from 2009 to 2021) we quantified the pygmy blue whale distribution and important areas during their northern and southern migration. We show extensive use of slope habitat off Western Australia and only minimal use of shelf habitat, compared to southern Australia where use of the continental shelf and shelf break predominates. In addition, movement behaviour estimated by a state-space model on satellite tag data showed that in general pygmy blue whales off Western Australia were mostly engaged in migration, interspersed with mostly relatively short periods (median = 28hours, range = 2 – 1080hours) of low move persistence (slow movement with high turning angles), which is indicative of foraging. Using the spatial overlap of time and number of whales in area analysis of the satellite tracking data (top 50% of grid cells) with foraging movement behaviour, we quantified the spatial extent of pygmy blue whale high use areas for foraging and migration. We compared these areas to the previously described areas of importance to foraging and migrating whales (Biologically Important Areas; BIAs). In some cases these had good agreement with the most important areas we calculated from our data, but others had only low (5%) to moderate (13%) overlap. Month was the most important variable predicting the number of pygmy blue whale units and number of singers (acting as indices of pygmy blue whale density). Whale density was highest in the southern part of the NW Australian coast and whales were present there between April-June, and November-December, a pattern also confirmed by the satellite tracking data. Available data indicated pygmy blue whales spent up to 124 days in Indonesian waters (34% of annual cycle). Since this area may also be the calving ground for this population, inter-jurisdictional management is necessary to ensure their full protection

Phylogenomics and species delimitation of pygmy perches (Teleostei: Percichthyidae): implications for biogeography, taxonomy and conservation from Phylogenomic history of enigmatic pygmy perches: implications for biogeography, taxonomy and conservation

Supplementary material for manuscript entitled "Phylogenomic history of enigmatic pygmy perches: implications for biogeography, taxonomy and conservation