These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western …Show full descriptionThese aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. Version Description:

This statistic shows the results of a survey into the number of users of taxis and Uber in Australia in 2018, by state. During the period examined, **** percent of respondents said in New South Wales said they had travelled by taxi, compared to **** percent who said they had used Uber.

These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future.

According to the 2021 Census, 81.7% of the population of England and Wales was white, 9.3% Asian, 4.0% black, 2.9% mixed and 2.1% from other ethnic groups.

National and subnational mid-year population estimates for England and Wales by administrative area, age and sex (including components of population change, median age and population density).

1. Models of host-pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density.

2. Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogenous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host-pathogen interactions is not routinely evaluated.

3. We collected a 13-month dataset of tree-roosting Pteropus spp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree-level measures of abundance and density – the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost-level, subplot-level, tree-level) are predictive of these local density dynamics.

4. Roost-level features were not representative of tree-level abundance (bats per tree) or tree-level density (bats per m2 or m3), with roost-level models explaining minimal variation in tree-level measures. Total roost abundance itself was either not a significant predictor (tree-level 3-D density) or only weakly predictive (tree-level abundance).

5. This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.

6. Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host-pathogen models, rather than simply modifying the transmission function.

Methods This dataset presents data on three roosting structure of three species of flying-fox at eight sites in south-east Queensland and north-east New South Wales, Australia. Species included the grey-headed flying-fox (P. poliocephalus), black flying-fox (Pteropus alecto) and little red flying-fox (Pteropus scapulatus). All sites were previously documented as having a continuous population of grey-headed or black flying-foxes. Little red flying-foxes visited some roost sites intermittently, however no roost sites occurred within the distribution of spectacled flying-foxes.

RAW DATA

We mapped the spatial arrangement of all overstory, canopy and midstory trees in a grid network of 10 stratified random subplots (20 x 20 meters each) per roost site. Subplots were stratified throughout perceived “core” (five subplots) and “peripheral” (five subplots) roosting areas, classed as areas observed to be frequently occupied (core) or infrequently (peripheral) by bats (Welbergen 2005). Core and peripheral areas were evaluated from regular observations made prior to roost tree mapping, though note that these categories were revised subsequently with the quantitative data. Trees were mapped and tagged using tree survey methods described in the “Ausplots Forest Monitoring Network, Large Tree Survey Protocol” (Wood et al. 2015).

To evaluate spatio-temporal patterns in flying-fox roosting, we revisited all tagged trees and scored the extent of species occupancy using the following tree abundance index: 0= zero bats; 1= 1-5 bats; 2=6-10 bats; 3=11-20 bats; 4=21-50 bats; 5=51-100 bats, 6=101-200 bats, 7= >200 bats. For a subset of trees (N=60 per site, consistent through time) absolute counts and minimum/maximum roosting heights of each species were taken. Overall roost perimeter (perimeter of area occupied) was mapped with GPS (accurate to 10 meters) immediately after the tree survey to estimate perimeter length and roost area. Total abundance at each roost was also estimated with a census count of bats where feasible (i.e., where total abundance was predicted to be <5,000 individuals), or by counting bats as they emerged in the evening from their roosts (“fly-out”), as per recommendations in Westcott et al. (2011). If these counts could not be conducted, population counts from local councils (conducted within ~a week of the bat surveys) were used, as the total abundance of roosts is generally stable over short timeframes (Nelson 1965b). Because roost estimates become more unreliable with increasing total abundance, and because our estimation methods were intrinsically linked with total abundance, we converted the total estimated abundance into an index estimate (where bin ranges increase with total abundance) for use in analyses, as per values used by the National Flying-Fox Monitoring Program (2017). Index categories were as follows: 1: 1-499 bats; 2: 500-2,499 bats; 3: 2,500-4,999 bats; 4: 5,000-9,999 bats; 5: 10,000-15,999 bats; 6: 16,000-49,999 bats; and 7: 50,000+ bats.

Roosting surveys were repeated once a month for 13 months (August 2018 - August 2019).

Methodological details are described in detail in the published paper 'Conventional wisdom on roosting behaviour of Australian flying foxes – a critical review, and evaluation using new data' (DOI https://doi.org/10.1002/ece3.8079). Raw data are available from this Dryad Dataset (https://doi.org/10.5061/dryad.g4f4qrfqv)

PROCESSED DATA

Information collected during the bat roosting surveys were used to calculate measures of bat density and abundance at three scales: roost-level, subplot-level and tree-level. For a visual summary of metrics see Figure 2 in the published paper ('Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations'). Note that where index abundance scores were used in calculations, the middle value of the index range was taken.

Roost-level density was calculated by dividing the total roost index abundance score by the total roost area (Figure 2A). Measures of subplot-level density were estimated with two methods: either as the tally of tree-level index abundance scores per subplot divided by subplot area (“subplot-level density”, Figure 2B), or as the average of fixed-bandwidth weighted kernel estimates, estimated using the spatstat package in R (Diggle 1985) (“subplot-level kernel density”, Figure 2C). Kernel estimates are spatially explicit and give the density of a spatial pattern, estimated per pixel over a smoothed area (Baddeley 2010). Kernels were estimated from the spatial location of trees weighted by tree-level index abundance scores, with Gaussian kernel smoothing and a smoothing bandwidth of 0.6. Bandwidth was selected by comparing projected kernel density values to expected density values based on tree abundance and canopy area. Kernel averages were then calculated per subplot. To prevent dilution of the density estimates with unoccupied space, we included only occupied pixels in the subplot average (pixel size = 0.156 x 0.156 meters). This latter approach has the advantage of explicitly incorporating the spatial distribution of bats into the density estimate, and therefore gives a better representation of aggregations in occupied space. Note that neither roost nor subplot-based density measures consider the vertical distribution of bats.

Measures of tree-level density were estimated in either two-dimension (2-D; for comparison with other two-dimensional estimates) or three-dimension (3-D). Tree-level 2-D density was estimated from tree-level index abundance scores and canopy area (Figure 2D). Tree-level 3-D density was estimated for the tree subset, as the absolute count of bats divided by the volume of tree space occupied (i.e. per cubic metre rather than square metre, Figure 2E). Volume of tree space was calculated from the height range occupied (maximum height minus minimum height) and the approximate crown area of trees. To estimate crown area of tagged trees for both measures, we computed the area of Dirichlet-Voronoi tessellations from tree distribution maps of canopy trees per subplot, with the spatstat package in R (Baddeley 2010). To control for edge effects, and to prevent overestimation of crown area for overstory trees and trees outside of the canopy, we imposed a maximum crown area of 199 m2 (radius ~8 m). This value was selected based on mean values reported across species of eucalypts in New South Wales (Verma et al. 2014), eucalypts being broadly representative of trees in these roost sites (Brooks 2020). In total, 218 of the 2,522 tagged trees (8%) were imposed with the maximum crown area value. Crown area of midstory trees was assigned as the first quartile of canopy tree crown area (5.8 m2), to reflect observations that trees beneath the canopy were typically smaller than trees within the canopy. Mean calculated crown area was 30.4 m2 (crown radius ~ 3.1 m). To investigate whether the choice of maximum crown area impacted results, we also repeated analyses for additional values of maximum crown area (140 m2, 170 m2 and 230 m2) chosen to cover the range in smallest to largest mean values reported for individual eucalypt species in Verma et al. (2014).

The number of deaths in Australia decreased significantly in 2023 compared to previous years. The number of deaths recorded in 2023 was approximately 46 thousand, compared to about 171 thousand in 2023. Number of deaths due to road accidents In Australia, there were 1,234 road-related fatalities during the course of the year. Drivers had the highest number of road fatalities, followed by motorcyclists and passengers. In the same year, New South Wales recorded the highest number of road deaths with a total of 334 road deaths. This does represent an increase from 2022 and the second-highest number of road fatalities in the last five years. New South Wales and South Australia exhibit comparable tendencies. Number of deaths due to COVID-19 On March 1, 2020, Australia recorded its first COVID-related death. The country recorded fewer than one thousand COVID-19-related deaths within the first year of the pandemic in 2020. By 2022, Australia recorded a total of 16,284 confirmed deaths from COVID-19. Australia has recorded the deaths of 4,258 women between the ages of 80 and 89 due to COVID-19. Moreover, more men between the ages of 80 and 89 have died of COVID-19 in 2022. At the time, the number of deaths among those under the age of 50 was significantly lower than that of those in older age groups.

In financial year 2023, it was estimated that 64.32 thousand more Chinese migrated to Australia than emigrated. This marked a significant increase in Chinese net migration compared to 14.72 thousand people in the previous financial year.

The Chinese community in Australia

Chinese migration to Australia dates back to the Australian gold rush of the 1850s and 60s, however, exclusionary migration policies up until the 1970’s restricted migration from China for some time. Since then, immigration from China has increased steadily and Chinese migrants now represent Australia’s third largest migrant group after the UK and India. The 2016 Australian census showed that Mandarin was the second most common language spoken at home in Australia, and Cantonese came in fourth. The Australian Chinese community also includes a significant proportion of the international students from China choosing to study in Australia.

Chinese investment in Australia

Although foreign investment in Australia still comes primarily from its traditional trade partners, the United States and the United Kingdom, Chinese investment has been increasing in recent years. The bulk of Chinese investment in Australia goes toward commercial real estate and agribusiness. In New South Wales alone, real estate investment from China totaled almost 1.25 billion Australian dollars, which accounted for around a half of all Chinese real estate investment in the country. By comparison, in 2019 the import value of Australian food products to China displayed yet another year on year increase, totaling more than two billion U.S. dollars.

The Australian Marine Microbial Biodiversity Initiative (AMMBI) provides methodologically standardized, continental scale, temporal phylogenetic amplicon sequencing data describing Bacteria, Archaea and microbial Eukarya assemblages. Sequence data is linked to extensive physical, biological and chemical oceanographic contextual information. Samples are collected monthly to seasonally from multiple depths at seven National Reference Stations (NRS) sites: Darwin Harbour (Northern Territory), Yongala (Queensland), North Stradbroke Island (Queensland), Port Hacking (New South Wales), Maria Island (Tasmania), Kangaroo Island (South Australia), Rottnest Island (Western Australia). The Integrated Marine Observing System (IMOS) NRS network is described at http://imos.org.au/facilities/nationalmooringnetwork/nrs/ North Stradbroke Island NRS is located 6.6 nm north east of North Stradbroke Island at a depth of 60 m over sandy substrate. It is 30 km southeast of the major city of Brisbane, Queensland (population 2.099 million), at the opening to large, shallow, Moreton Bay. The site is impacted by the southerly flowing EAC and its eddies, which may cause periodic nutrient enrichment through upwelling. This latitude is the biogeographic boundary for many tropical and subtropical species. The water column is well mixed between May-August and stratified for the remainder of the year and salinity may at times be affected by floodwaters from the nearby Brisbane River outflow.

Site details from Brown, M. V. et al. Continental scale monitoring of marine microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci. Data 5:180130 doi: 10.1038/sdata.2018.130 (2018). Site location: North Stradbroke Island National Reference Station (NRS), Queensland, Australia Note on data download/processing: Data downloaded from Australian Microbiome Initiative via Bioplatforms Australia Data Portal on 17 June 2022. The search filter applied to download data from Bioplatforms Australia Data portal are stored in the Darwin Core property (identificationRemarks). Taxonomy is assigned according to the taxonomic database (SILVA 138) and method (Sklearn) which is stored in the Darwin Core Extension DNA derived data property (otu_db). Prefix were removed from the taxonomic names as shown in the example (e.g. d_Bacteria to Bacteria). Scientific name is assigned to the valid name available from the highest taxonomic rank. This collection is published as Darwin Core Occurrence, so the event level measurements need to be replicated for every occurrence. Instead of data replication, the event level eMoF data are made available separately at https://www.marine.csiro.au/data/services/obisau/emof_export.cfm?ipt_resource=bioplatforms_mm_nrs_nsi Please see https://www.australianmicrobiome.com/protocols/acknowledgements/ for citation examples and links to the data policy.

Gene flow can have rapid effects on adaptation and is an important evolutionary tool available when undertaking biological conservation and restoration. This tool is underused partly because of the perceived risk of outbreeding depression and loss of mean fitness when different populations are crossed. In this article we briefly review some theory and empirical findings on how genetic variation is distributed across species ranges, describe known patterns of gene flow in nature with respect to environmental gradients, and highlight the effects of gene flow on adaptation in small or stressed populations in challenging environments (e.g., at species range limits). We then present a case study involving crosses at varying spatial scales among mountain populations of a trigger plant (Stylidium armeria: Stylidiaceae) in the Australian Alps to highlight how some issues around gene flow effects can be evaluated. We found evidence of outbreeding depression in seed production at greater geographic distances. Nevertheless, we found no evidence of maladaptive gene flow effects in likelihood of germination, plant performance (size), and performance variance, suggesting that gene flow at all spatial scales produces many offspring with high adaptive potential. This case study demonstrates a path to evaluating how increasing sources of gene flow in managed wild and restored populations could identify some offspring with high fitness that could bolster the ability of populations to adapt to future environmental changes. We suggest further ways in which managers and researchers can act to understand and consider adaptive gene flow in natural and conservation contexts under rapidly changing conditions. Methods Study system We examined F1 hybrid performance of the thrift-leaved trigger plant, Stylidium armeria, a species common throughout the montane and high elevation woodland areas of southeastern Australia (with the current focus on the Australian Alps) (Figure 3). The alpine areas in Australia form a rare ecosystem, with treeless alpine vegetation covering ~0.15% of the continent, and like other alpine environments around the world, they are highly vulnerable to the effects of climate change (Hughes, 2003). Cuttings from wild plants were harvested from various sites throughout the Victorian and New South Wales high country. Outcrossing of the different populations was performed and the F1 progeny of these outcrossings were germinated under controlled nursery conditions. Stylidium armeria is morphologically variable throughout its distribution, with differences thought to be related to surrounding vegetation, soil type and climatic factors (Raulings & Ladiges, 2001). The pollination unit is a zygomorphic flower, which is characterized by the fusion of staminate and pistillate tissues into a motile, protandrous column, which is “triggered” when pollinators, usually native bees, land on the corolla (Armbruster & Muchhala, 2009). Twelve populations were sourced, 4 from each of 3 mountain regions within the Australian Alps (Bogong High Plains, Victoria; Mount Buller region, Victoria; and Kosciuszko region, New South Wales) to include in the experimental crosses (Table 1, Figure 4). The 3 regions vary in distance from each other between ca. 50-200 km. Stylidium armeria is genetically highly differentiated between Victorian alpine regions based on pooled SNP data, comparable to differentiation seen for alpine herbaceous plants, which tend to be more differentiated than alpine shrubs (Bell et al., 2018). Within each region, the four populations differ in elevation by 174-227 m and distance by 3-15 km from each other. Within each population, at least 30 plants were collected as cuttings from rhizomes, each including a basal rosette of leaves. Each collection spanned at least a 100 m2 area. Plants were first transferred in Fall 2011 after collection into planting tubes with a “native mix” soil medium used at Burnley Campus (University of Melbourne) glasshouses, which consists of pine bark, peat and sand. After winter, plants were transferred individually into 7-inch pots. After pollinations were initiated some plants died due to lack of soil drainage, which may have contributed to some failed pollinations.

Experimental crosses Experimental pollinations between all populations, including within population crosses, were conducted to examine outcrossing effects of three broad spatial categories of gene flow: within site/population (WP), the site of collection; within range (WR), between sites within a mountain range; and between mountain ranges (BR). Crosses were completed during summer and fall of 2012 (443 total crosses). Within each population, sire plants were chosen randomly during flowering and were mated with up to 2 dams from each population, including their originating population. Dams were chosen randomly within each population, with replacement. Plants within each population were used only once as pollen donors (sires) to individuals in their population and to all other populations, but could also serve as pollen receivers (dams) to other sires in the study. Two replicate flower pollinations were made for each cross. Crosses were completed with equal directionality between populations; that is to say, each population served as both pollen donor and pollen receiver to each other population. This design produced more possible crossing combinations in increasing order of geographic scale: WP = 12 possible types; WR = 18 possible types; BR = 48 possible types. We used this design to increase sampling size and variety in anticipation of outbreeding depression from longer-distance gene flow crosses, and we considered WP crosses to serve as a natural control against which to compare WR and BR crosses. To test for background or contamination pollination, 69 individual flowers, randomly chosen, were marked to test for unintended seed set rate. Thirty flowers were randomly chosen and self-pollinated to test for self-incompatibility. Of the total pollinations observed (including crosses, self-pollinations, and tests of inadvertent pollination), 262 yielded seeds (59.1% of 443). Some crosses may have been unsuccessful due to plant stress that occurred from lack of soil drainage, but this effect was independent of population of origin. Capsules were harvested when mature and dried, and the resulting seeds were later counted and the seed lots weighed to produce an average seed weight (number of seeds divided by total weight). F1 seeds were sowed into 7 x 8-cell seedling trays during autumn of 2014. The cells were partially filled with the native mix and covered with a layer of “seed raising mix,” which consisted of 5 parts medium-grade pine bark, 5 parts fine pine bark, 1 part coarse sand, and 1 part sieved peat, including the additives Saturaid (1500 g/m2) and dolomite (750 g/m2). Each replicate included three seeds sown per cell. Depending on seed availability, 1-15 replicates were sown per seed family and all were completely randomized across 50 trays included in the study, for a total of 2,800 replicates sowed. Following the completion of sowing, trays were treated with Regen Smokemaster 2000, a smoke water solution applied at a rate of 100 mL per 1L of water, sprayed per square meter in order to trigger earlier germination. Trays were randomly rotated once weekly, during which trays were monitored for evidence of germination and survival. Seedlings (520 total) were transferred into larger pots with new potting material after several months’ growth. Plants were grown for 235 days between the first germinant and the end of the growth trial. Plant sizes were estimated by multiplying the longest aboveground height and width measurements of surviving plants (493 in total). Plant size was also standardized by dividing the final plant size by the number of days since germination. Percent germinated plants, seed number, average seed weight, final plant size, and the variance in final plant size were all compared to assess the outcomes of outcrossing among the three general spatial categories, WP, WR, and BR.

Statistics Our main question was, what is the effect of crossing type among the three spatial categories? We used means comparison methods (ANOVA–and non-parametric Kruskal-Wallis tests by ranks) to test for differences among crossing types. We also used generalized linear models (GLM) to model additional effects, including the variation of specific crosses within WP, WR, and BR categories. Models incorporating all cross types simultaneously could not run due to too many missing rows of data (e.g., a row with a WP cross type cannot have a BR cross type, and vice versa). However, we did test these effects in reduced models, separating WP and WR effects from BR effects (see Supplemental Information). Plant size data were square root-transformed to meet parametric assumptions and analysis of variance (ANOVA) was used to detect significant differences among crossing categories. Because transformations of seed number and average seed weight data still failed to meet parametric assumptions, we instead used Kruskal-Wallis tests to test for differences among crossing types for these two variables. Chi-squared analysis was used to test for differences in germination. Levene’s test of equality of variances was used to test for significant variance differences among crossing types in plant size. Finally, we tested for the effect of spatial distance of plant crosses using the estimated Haversine distance between populations on the above seed and plant size variables. Crosses within populations were assigned a distance of 0 km. Because crossing distance data failed to meet assumptions of parametric analyses, we used Spearman’s rho (ρ) rank correlations to test the effect of crossing distance on seed and plant size traits. All analyses were carried out in the JMP statistical package (version 16.0.0, SAS Institute, Inc.,

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The predicted current distribution of Darling River Hardyhead (Craterocephalus amniculus) in the Hunter River catchment, NSW. All available records of the species were collated and assessed for accuracy. For current distribution, only records after 1 January 1994 were used. Within the framework of the Australian Hydrological Geospatial Fabric V2 surface hydrology network, the records were associated with attributes from the National Environmental Stream Attributes Database. Modelling the current geographic distribution of each listed threatened freshwater aquatic species or population was undertaken using MaxEnt 3.3.3; a widely used species distribution modelling program that utilises presence records to generate probabilities of occurrence based on a suite of environmental variables quantified across the area of interest.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Darling River Hardyhead (Craterocephalus amniculus)is a small-bodied native fish that occurs in the upper tributaries of the Darling River near the Queensland-New South Wales border. A small population also occurs in the Hunter River catchment.TheHunterRiver catchment population is endangered- http://www.dpi.nsw.gov.au/fisheries/species-protection/conservation/what-current/endangered-populations/darling-river-hardyhead.The methods used to create the predicted current distribution of Darling River Hardyhead (Craterocephalus amniculus) are described fully in: NSW Department of Primary Industries (2015), NSW Fish Community Status 2015 - Final Report.

The predicted current distribution ofDarling River Hardyhead (Craterocephalus amniculus) in the HunterRiver catchment,NSW. All available records of the species were collated and assessed for accuracy. For current distribution, only records after 1 January 1994 were used. Within the framework of the Australian Hydrological Geospatial Fabric V2 surface hydrology network, the records were associated with attributes from the National Environmental Stream Attributes Database.Modelling the current geographic distribution of each listed threatened freshwater aquatic species or population was undertaken using MaxEnt 3.3.3; a widely used species distribution modelling program that utilises presence records to generate probabilities of occurrence based on a suite of environmental variables quantified across the area of interest.

The Australian Hydrological Geospatial Fabric V2 surface hydrology network (Geofabric) is a fully connected and directed stream network based a 9 second DEM. It allocates a unique stream segment number to each river reach in Australia. The Environmental Attributes Database is a set of lookup tables supplying attributes describing the natural and anthropogenic characteristics of the stream and catchment environment that was developed by the Australian National University (ANU) in 2011 and updated in 2012. The data is supplied as part of the supplementary Geofabric products which is associated with the 9 second DEM derived streams and the National Catchment Boundaries based on 250k scale stream network. 30 Stream variables were assessed for the modelling.

MaxEnt 3.3.3 is a widely used species distribution modelling program that utilises presence records to generate probabilities of occurrence based on a suite of environmental variables quantified across the area of interest. It was used to model the current geographic distribution of each listed threatened freshwater aquatic species or population. We utilised logistic output to plot the predicted distribution of each species. This output equates to a probability that the species will be observed in each river reach, given the environmental conditions that exist there relative to the environmental conditions where the species is known to occur. For this mapping, above 33% probability was considered predicted presence. In addition, predicted separate populations were connected by manual interpretation. The predicted values for each river reach were converted from the Geofabric framework to the higher resolution 2013 NSW Strahler Stream Order Hydroline.

Dataset Citation

NSW Department of Primary Industries (2015) Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015. Bioregional Assessment Source Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/643c6228-6435-4f5a-9f09-5f53efe234d2.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

In 2018, marriages performed by civil celebrants accounted for 79.7 percent of marriages in Australia. Civil celebrants are performing an increasing share of marriages in Australia as opposed to marriages performed by a minister of religion.

 Marriage and religion

Marriages in Australia and most English-speaking countries have traditionally been performed in a church by a Christian minister or priest. However, preferences are changing to suit Australia’s diverse population. Islamic, Jewish, and Hindu weddings ceremonies are becoming more common but the biggest change is in the number of people having secular weddings. This separation from traditional church weddings may have something to do with the increasing numbers of Australians who do not identify with a religion.

 Same-sex marriage

At the end of 2017, the Marriage Act 1961 was amended to allow same-sex couples to legally marry in Australia. This came after a nation-wide postal survey asking Australians to give their opinion on whether the law should be changed to allow same-sex couples to marry. Australians voted in favor of the legislation change and in the following year, over 2,000 same-sex marriages were officiated by civil celebrants in the state of New South Wales alone.

Small, fragmented or isolated populations are at risk of population decline due to fitness costs associated with inbreeding and genetic drift. The King Island scrubtit Acanthornis magna greeniana is a critically endangered subspecies of the nominate Tasmanian scrubtit A. m. magna, with an estimated population of < 100 individuals persisting in three patches of swamp forest. The Tasmanian scrubtit is widespread in wet forests on mainland Tasmania. We sequenced the scrubtit genome using PacBio HiFi and undertook a population genomic study of the King Island and Tasmanian scrubtits using a double-digest restriction site-associated DNA (ddRAD) dataset of 5,239 SNP loci. The genome was 1.48 Gb long, comprising 1,518 contigs with an N50 of 7.715 Mb. King Island scrubtits formed one of four overall genetic clusters, but separated into three distinct subpopulations when analysed independently of the Tasmanian scrubtit. Pairwise FST values were greater among the King Island scrubtit subpopulations than among most Tasmanian scrubtit subpopulations. Genetic diversity was lower and inbreeding coefficients were higher in the King Island scrubtit than all except one of the Tasmanian scrubtit subpopulations. We observed crown baldness in 8/15 King Island scrubtits, but 0/55 Tasmanian scrubtits. Six loci were significantly associated with baldness, including one within the DOCK11 gene which is linked to early feather development. Contemporary gene flow between King Island scrubtit subpopulations is unlikely, with further field monitoring required to quantify the fitness consequences of its small population size, low genetic diversity and high inbreeding. Evidence-based conservation actions can then be implemented before the taxon goes extinct. Methods 2.1 Sample collection To obtain indicative genetic diversity metrics across mainland Tasmania, we sampled between five and eleven scrubtits from seven a-priori subpopulations on mainland Tasmania (including Bruny Island) during the non-breeding season (January – March 2021). Due to small population sizes and licensing restrictions on King Island, we sampled five individuals from each of the three locations during the same non-breeding season (Table 1, Figure 1). We trapped scrubtits using a single 6m mist net and one minute of scrubtit song broadcast using portable speakers (ANU animal ethics permit # A2021/33). We sampled blood (< 20 μl per individual) using the standard brachial venepuncture technique with a 0.7mm needle into 70% ethanol. For two individuals from whom we were unable to safely obtain blood, we collected feathers shed during handling. One male Tasmanian scrubtit was collected under licence (see acknowledgements) for genome sequencing, from which organ tissue samples (heart, spleen, kidney, gonads, brain, liver) were taken (Table S1). For each individual we took standard morphometric measurements and scanned for any unusual physical features such as feather abnormalities or skin lesions that may be indicators of poor health. A single observer (CY) sampled and measured all birds, and the maximum capture time was 35 minutes. No birds showed adverse reactions to sampling and all flew off strongly upon release. The fifteen individuals sampled on King Island was the maximum permissible sample size under licence conditions. 2.2 DNA extraction, sexing and sequencing High molecular weight DNA was extracted from flash frozen heart and kidney using the Nanobind Tissue Big DNA Kit v1.0 11/19 (Circulomics). A Qubit fluorometer (Thermo Fisher Scientific) was used to quantify DNA concentrations with the Qubit dsDNA BR assay kit (Thermo Fisher Scientific). RNA was extracted from heart, spleen, kidney, gonads, brain, and liver stored in RNA later using the RNeasy Plus mini Kit (Qiagen) with RNAse-free DNAse (Qiagen) digestion. RNA quality was assessed via Nanodrop (Thermo Fisher Scientific). We extracted DNA for population genomics from blood and feather samples using the Monarch® Genomic DNA Purification Kit (New England BioLabs, Victoria, Australia). We quantified DNA concentrations using a Qubit 3.0 fluorometer (yield range 10.3 – 209 ng μl-1, Table S1) and standardised the concentration of each sample to 10-30 ng µl-1 DNA for 20 – 25 μl and determined the sex of individuals using a polymerase chain reaction (PCR) protocol adapted from Fridolfsson and Ellegren (1999, Supplementary file S1). We arranged the samples on a single 96 well plate, containing five technical replicates of the samples with the highest DNA concentrations, an additional 21 non-technical replicates including all of the King Island samples, five extra samples from mainland Tasmania and one negative control. Double-digest restriction associated DNA (ddRAD) sequencing following Peterson et al. (2012) was undertaken at the Australian Genome Research Facility, Melbourne on an Illumina NovaSeq 6000 platform using 150bp paired-end reads. Samples were first quantified using Quantifluor and visualised on 1 % agarose e-gel to ensure all samples exceeded the minimum input DNA quantity of 50 ng. Three establishment samples with at least 250 ng DNA that were representative of the distribution of the samples (2 Tasmanian scrubtits, 1 King Island scrubtit) were used to determine the optimal combination of restriction enzymes, which were EcoRI and HpyCH4IV. Further details on the library preparation protocol are provided in Supplementary file S1. 2.3 Genome sequencing and assembly Full methodological details of the genome and transcriptome sequencing and assembly are provided in Supplementary file S2. In summary, high molecular weight DNA was sent for PacBio HiFi library preparation with Pippin Prep and sequencing on one single molecule real-time (SMRT) cell of the PacBio Sequel II (Australian Genome Research Facility, Brisbane, Australia). Total RNA was sequenced as 100 bp paired-end reads using Illumina NovaSeq 6000 with Illumina Stranded mRNA library preparation at the Ramaciotti Centre for Genomics (University of New South Wales, Sydney, Australia). Genome assembly was conducted on Galaxy Australia (The Galaxy Community, 2022) following the genome assembly guide (Price & Farquharson, 2022) using HiFiasm v0.16.1 with default parameters (Cheng et al., 2021; Cheng et al., 2022). Transcriptome assembly was conducted on the University of Sydney High Performance Computer, Artemis. Genome annotation was performed using FGENESH++ v7.2.2 (Softberry; (Solovyev et al., 2006)) on a Pawsey Supercomputing Centre Nimbus cloud machine (256 GB RAM, 64 vCPU, 3 TB storage) using the longest open reading frame predicted from the global transcriptome, non-mammalian settings, and optimised parameters supplied with the Corvus brachyrhynchos (American crow) gene-finding matrix. The mitochondrial genome was assembled using MitoHifi v3 (Uliano-Silva et al., 2023). Benchmarking universal single copy orthologs (BUSCO) was used to assess genome, transcriptome and annotation completeness (Manni et al., 2021). 2.4 Bioinformatics pipeline and SNP filtering Raw sequence data were processed using Stacks v2.62 (Catchen et al., 2013) and aligned to the genome with BWA v0.7.17-r1188 (Li & Durbin, 2009). Full details of the bioinformatics pipeline, which produced a variant call format (VCF) file containing 45,488 variants for SNP filtering in R v4.0.3 (R Core team 2020) are provided in Supplementary file S1. We filtered genotyped variants using the “SNPfiltR” v1.0.0 package (DeRaad, 2022) based on (i) minimum read depth (≥ 5), (ii) genotype quality (≥ 20), (iii) maximum read depth (≤ 137), and (iv) allele balance ratio (0.2 – 0.8). Then, using a custom R script, we filtered SNPs based on (i) the level of missing data (< 5%); (ii) minor allele count (MAC ≥ 3), (iii) observed heterozygosity (< 0.6), and (iv) linkage disequilibrium (correlation < 0.5 among loci within 500,000 bp). To ensure that relationships between individuals could be accurately inferred from the data, we used these SNPs and samples to construct a hierarchical clustering dendrogram based on genetic distance, with visual examination of the dendrogram confirming that all 24 replicates paired closely together on long branches (Figure S1). The percentage difference between called genotypes of technical replicates was also used to confirm that genotyping error rates were low after filtering (mean 99.91% ± 0.005% SE similarity between replicates). We therefore removed one of each replicate pair from all further analyses. We also made a higher-level bootstrapped dendrogram by using genetic distances among sampling localities instead of individuals (Figure S2). We used “tess3r” (Caye et al. 2016, 2018) to perform a genome scan for loci under selection, using the Bejamini-Hochberg algorithm (Benjamini & Hochberg, 1995), with a false discovery rate of 1 in 10,000 to correct for multiple testing. Because this method identified zero candidate loci under selection, we also used the gl.outflank function in “dartR” v2.0.4 to implement the OutFLANK method (Whitlock & Letterhos 2015) to infer the distribution of FST for loci unlikely to be strongly affected by spatially diversifying selection. This method also identified zero putatively adaptive loci, leaving a final dataset for formal population genetic analysis containing all 70 originally sampled individuals, 5,239 biallelic SNPs, and an overall missing data level of 0.98 %. The number of SNPs and samples removed from the dataset at each filtering step is provided in Table S2. See accompanying Supplementary File for further information on library preparation, molecular sexing, library preparation, bioinformatics, genome sequencing, assembly and annotation. References cited above are provided in the main document.