Estimates of range-wide abundance, harvest, and harvest rate are fundamental for sound inferences about the role of exploitation in the dynamics of free-ranging wildlife populations, but reliability of existing survey methods for abundance estimation is rarely assessed using alternative approaches. North American mallard populations have been surveyed each spring since 1955 using internationally coordinated aerial surveys, but population size can also be estimated with Lincoln's method using banding and harvest data. We estimated late summer population size of adult and juvenile male and female mallards in western, midcontinent, and eastern North America using Lincoln's method of dividing (i) total estimated harvest, H, by estimated harvest rate, h, calculated as (ii) direct band recovery rate, f, divided by the (iii) band reporting rate, p. Our goal was to compare estimates based on Lincoln's method with traditional estimates based on aerial surveys. Lincoln estimates of adult males and females alive in the period June–September were 4.0 (range: 2.5–5.9), 1.8 (range: 0.6–3.0), and 1.8 (range: 1.3–2.7) times larger than respective aerial survey estimates for the western, midcontinent, and eastern mallard populations, and the two population estimates were only modestly correlated with each other (western: r = 0.70, 1993–2011; midcontinent: r = 0.54, 1961–2011; eastern: r = 0.50, 1993–2011). Higher Lincoln estimates are predictable given that the geographic scope of inference from Lincoln estimates is the entire population range, whereas sampling frames for aerial surveys are incomplete. Although each estimation method has a number of important potential biases, our review suggests that underestimation of total population size by aerial surveys is the most likely explanation. In addition to providing measures of total abundance, Lincoln's method provides estimates of fecundity and population sex ratio and could be used in integrated population models to provide greater insights about population dynamics and management of North American mallards and most other harvested species.

The dashboard was creating using Business Analyst Infographics. Read more about it here: https://www.esri.com/en-us/arcgis/products/data/overview?rmedium=www_esri_com_EtoF&rsource=/en-us/arcgis/products/esri-demographics/overview Data Source: U.S. Census Bureau, Census 2020 Summary File 1, 2021 American Community Survey(ACS), and ESRI 2022 Demographics and Tapestry Segmentation. For more information on Esri Demographics see HERE and for Tapestry see HERE.Geographies: The council district boundaries used in this dashboard are those that were effective as of May 6, 2023.Much of the science for determining the data for an irregular polygon is explained here:https://doc.arcgis.com/en/community-analyst/help/calculation-estimates-for-user-created-areas.htmCalculation estimates for user-created areasBusiness Analyst employs a GeoEnrichment service which uses the concept of a study area to define the location of the point or area that you want to enrich with additional information. If one or more points is input as a study area, the service will create a one-mile ring buffer around the points or points to collect and append enrichment data. You can optionally change the ring buffer size or create drive-time service areas around a point.The GeoEnrichment service uses a sophisticated geographic retrieval methodology to aggregate data for rings and other polygons. A geographic retrieval methodology determines how data is gathered and summarized or aggregated for input features. For standard geographic units, such as states, provinces, counties, or postal codes, the link between a designated area and its attribute data is a simple one-to-one relationship. For example, if an input study trade area contains a selection of ZIP Codes, the data retrieval is a simple process of gathering the data for those areas.Data Allocation MethodThe Data Allocation method allocates block group data to custom areas by examining where the population is located within the block group and determines how much of the population of a block group overlaps a custom area. This method is used in the United States, and similarly in Canada. The population data reported for census blocks, a more granular level of geography than block groups, is used to determine where the population is distributed within a block group. If the geographic center of a block falls within the custom area, the entire population for the block is used to weight the block group data. The geographic distribution of the population at the census block level determines the proportion of census block group data that is allocated to user specified areas as shown in the example.Note:Depending on the data, households, housing units or businesses at the block group level are used as weights. Employing block centriods is superior because it accounts for the possibility that the population may not be evenly distributed geographically throughout a block group.

pRF_Subject_1_Session_1This data set contains all data related to testing subject 1 in session 1. Specifically, the data set contains: - Anatomical MRI data (nifti format) - Functional MRI data (dicom format) for 6 functional runs including the stimulation protocol for each (.png image format) o Wedge stimulus presented orderly o Wedge stimulus presented randomly o Ring stimulus presented orderly o Ring stimulus presented randomly o Bar stimulus presented orderly o Bar stimulus presented randomlySubject_1_Session_1.zippRF_Subject_1_Session_2This data set contains all data related to testing subject 1 in session 2. Specifically, the data set contains: - Anatomical MRI data (nifti format) - Functional MRI data (dicom format) for 6 functional runs including the stimulation protocol for each (.png image format) o Wedge stimulus presented orderly o Wedge stimulus presented randomly o Ring stimulus presented orderly o Ring stimulus presented randomly o Bar stimulus presented orderly o Bar sti...

These data were generated to compare different methods of estimating population density from marked and unmarked animal populations. We compare conventional live trapping with two more modern, non-invasive field methods of population estimation: genetic fingerprinting from hair-tube sampling and camera trapping for the European pine marten (Martes martes). We used arrays of camera traps, live traps, and hair tubes to collect the relevant data in the Ring of Gullion in Northern Ireland. We apply marked spatial capture-recapture models to the genetic and live trapping data where individuals were identifiable, and unmarked spatial capture-recapture (uSCR), distance sampling (CT-DS), and random encounter models (REM) to the camera trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.41 – 0.99 animals per km2), despite differences in precision, cost, and effort being apparent. In addition to the data, we provide novel code for running unmarked spatial capture-recapture (uSCR) and random encounter models (REM) to the camera trap data where individual ID was not possible. Methods All fieldwork was carried out in the Ring of Gullion, Northern Ireland, UK. Cameras Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. At the end of the survey period, camera traps were checked and for each detection (the first image in a trigger sequence of an individual pine marten) distance to animal (m) and angle of detection (°) were measured in situ. Noninvasive genetic sampling Twenty hair tubes based on those developed by Mullins et al. (2010), were deployed across the study site between June and July 2019. Hair-tubes were checked weekly and sticky patches and bait were replaced on each visit. Hair samples were frozen at -20oC prior to DNA extraction. Microsatellite analysis to identify individual pine marten was carried out using up to 11 microsatellite markers. Each sample was analysed in duplicate and only samples giving identical results in the replicates were scored. Live traps Twelve Tomahawk 205 live cage traps were deployed along two perpendicular transects spaced approximately 400m apart. Trapping was conducted from August - October 2019 with daily trap checks. Trapped animals were anaesthetised with an intramuscular injection of ketamine (25mg per kg) and midazolam (0.2mg per kg) and scanned for a microchip. Statistical analyses Spatially explicit capture-recapture (SECR) models were used to estimate density for both live trapping and gNIS (Efford & Boulanger, 2019). Occasion lengths for live trapping were one day, whilst for gNIS were one week. For live trapping, we specified a single-use detector type, whilst for gNIS we specified a proximity-based detector type. Density was calculated from camera traps using REM (Rowcliffe et al. 2008), CT-DS (Howe et al. 2017) and uSCR (Chandler & Royle, 2013).

This dataset contains fitness-linked life history traits, environmental data, and protein and gene expression data for Daphnia magna. Life history data were measured in common garden experiments. For each clone of the same genotype across all experiments, size at maturity (distance between the head and the base of the tail spine), age at maturity (first time eggs were observed in the brood chamber), fecundity (total number of offspring released summing first and second brood), and mortality were measured. Critical thermal maximum (CTmax) was measured on the experimental animals. Environmental data consisted of Secchi disk depth (water transparency) ; total phosphorous; and total nitrogen for the years 1971–1999; a record of pesticides 1955–2010; temperature records over the past century 80 km from Lake Ring. Organic and carbonate contents of the sediment was estimated using the loss on ignition (LOI) method. Hb protein data consisted of constitutive Hb protein crude content animals reared in normoxic (saturated oxygen level) conditions at two experimental temperatures, 20 and 30°C. Heat shock protein expression was measured in four heat shock proteins (HSP20, HSP60, HSP70 and HSP90). Total RNA, qPCR, mean CT (cycle threshold) value per sample and per protein were collected.

Idealized ring species, with approximately continuous gene flow around a geographic barrier but singular reproductive isolation at a ring terminus, are rare in nature. A broken ring species model preserves the geographic setting and fundamental features of an idealized model but accommodates varying degrees of gene flow restriction over complex landscapes through evolutionary time. Here we examine broken ring species dynamics in Calisoga spiders, which like classic Ensatina salamanders, are distributed around the Central Valley of California. Using nuclear and mitogenomic data we test key predictions of common ancestry, ring-like biogeography, biogeographic timing, population connectivity and terminal overlap. We show that a ring complex of populations shares a single common ancestor, and from an ancestral area in the Sierra Nevada mountains, two distributional and phylogenomic arms encircle the Central Valley. Isolation by distance occurs along these distributional arms, although gene flow restriction is also evident. Where divergent lineages meet in the South Coast Ranges we find rare lineage sympatry, without evidence for nuclear gene flow, and with clear evidence for morphological and ecological divergence. We discuss general insights provided by broken ring species, and how such a model could be explored and extended in other systems and future studies.

Age, sex and length data provide population dynamics information that can indicate how populations trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore particularly important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes age, sex and length data compiled from annual sampling of commercial and subsistence salmon harvests and research projects in the Upper Cook Inlet. It includes data on five salmon species: chinook, chum, coho, pink and sockeye. Age estimates were made by examining scales or bony structures (e.g. otoliths - ear bones). Scales were removed from the side of the fish; usually the left side above the lateral line. Scales or bony structures were then mounted on gummed cards and pressed on acetate to make an impression. The number of freshwater and saltwater annuli (i.e. rings) was counted to estimate age in years. Age is recorded in European Notation, which is a method of recording both fresh and saltwater annuli. For example, for a fish that spent one year in freshwater and 3 years in saltwater, its age is recorded as 1.3. The total fish age is the sum of the first and second numbers, plus one to account for the time between deposition and emergence. Therefore the fish in this example is 5 years old. Fish sex was determined by either examining external morphology (eg. head and belly shape) or internal sex organ. Length was measured in millimeters, generally from mid-eye to the fork of the tail. This data package includes a .csv of ASL data (ASL_formatted_LowerCookInlet.csv) derived from an original data file (ASL_ALL.csv), a file containing location information (HEADER_ALL.csv), a file containing gear information (LUT_GEAR), and a file containing sex information (LUT_GENDER_DETERMINATION_CODE.csv). The reformatting script (ASL_Formatting_LowerCookInlet.R) draws pertinent columns from the data file and combines it with information from the metadata and locations files.

The parameter index matrices of the cause-specific mortality rates model used in the MARK program.

The global diamond ring market, estimated at USD XXX million in 2023, is projected to grow at a CAGR of XX% from 2023 to 2033, reaching USD XXX million by 2033. The surging demand for diamond rings from various applications, such as weddings, festivals, and fashion, is driving market growth. Additionally, the increasing popularity of online jewelry shopping and the rising disposable income of consumers worldwide further fuel the market. The market for diamond rings is segmented into various categories based on application, type, and region. In terms of application, wedding rings dominate the market due to the growing trend of lavish weddings and the cultural significance of diamond rings in marriage ceremonies. The festival segment also holds a significant share, driven by the use of diamond rings as special occasion jewelry during festive seasons and religious celebrations. The fashion segment is gaining traction as diamond rings become increasingly popular as fashion accessories. Regionally, North America, Europe, and Asia Pacific are the key markets for diamond rings, with North America holding the largest market share due to the high disposable income of consumers and the presence of established jewelry brands. The Asia Pacific region is projected to witness the highest growth rate during the forecast period, driven by the rising middle-class population and increasing urbanization in countries like China and India.

we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI). American Community Survey (ACS):

Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States. The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions. It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households. The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.

CDC/ATSDR Social Vulnerability Index (SVI):

Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events. SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability. SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.

In our utilization of these sources, we likely integrated data from both the ACS and the SVI to analyze and understand various socio-economic and demographic indicators at the state, county, and possibly tract levels. This integrated data would have been valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States

Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variablesCSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2014-2018 ACSEP_PCIEP_PCIPer capita income estimate, 2014-2018 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2014-2018 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2014-2018 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2014-2018 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2014-2018 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

AbstractDivergence is often ephemeral, and populations that diverge in response to regional topographic and climatic factors may not remain reproductively isolated when they come into secondary contact. We investigated the geographic structure and evolutionary history of population divergence within Sceloporus occidentalis (Western Fence Lizards), a habitat generalist with a broad distribution that spans the major biogeographic regions of Western North America. We used double digest RAD sequencing to infer population structure, phylogeny, and demography. Population genetic structure is hierarchical and geographically structured with evidence for gene flow between biogeographic regions. Consistent with the isolation-expansion model of divergence during Quaternary glacial-interglacial cycles, gene flow and secondary contact are supported as important processes explaining the demographic histories of populations. Although populations may have diverged as they spread northward in a ring-like manner around the Sierra Nevada and southern Cascade Ranges, there is strong evidence for gene flow among populations at the northern terminus of the ring. We propose the concept of an “ephemeral ring species” and contrast S. occidentalis with the classic North American ring species, Ensatina eschscholtzii. Contrary to expectations of lower genetic diversity at northern latitudes following post-Quaternary-glaciation expansion, the ephemeral nature of divergence in S. occidentalis has produced centers of high genetic diversity for different reasons in the south (long-term stability) versus the north (secondary contact). MethodsSampling and RAD sequencing A total of 108 Sceloporus occidentalis were sampled from 87 sites throughout their range in western North America (Fig. 1; Table S1). Double digest RAD sequencing data (ddRADseq) were collected using standard protocols (Peterson et al. 2012). Genomic DNA (500 ng per sample) was double-digested with 20 units each of a rare cutter SbfI (restriction site 5'-CCTGCAGG-3') and a common cutter MspI (restriction site 5'-CCGG-3') in a single reaction with the manufacturer recommended buffer (New England Biolabs) for 8 hours at 37°C. Fragments were purified with SeraPure SpeedBeads before ligation of barcoded Illumina adaptors onto the fragments. The libraries were size-selected (between 415 and 515 bp after accounting for adapter length) on a Blue Pippin Prep size fractionator (Sage Science). The final library amplification used proofreading Taq and Illumina's indexed primers. The fragment size distribution and concentration of each pool was determined on an Agilent 2200 TapeStation, and qPCR was performed to determine library concentrations before multiplexing equimolar amounts of each pool for sequencing on a single Illumina HiSeq 2500 lane (50-bp, single-end reads) at the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley. Bioinformatics The raw Illumina reads were filtered and demultiplexed using PyRAD v.3.0.3 (Eaton 2014) and assembled using Stacks v.2.54 (Catchen et al. 2013; Rochette et al. 2019) and STACKS_PIPELINE v.2.4 (Portik et al. 2017). Reads potentially arising from PCR duplicates during sequencing were not explicitly accounted for because they occur at low frequency and presumably do not significantly impact most population genetic parameter estimates (Schweyen et al. 2014). During filtering, sites with < 99% base call accuracy (Phred score = 20) were converted to missing data and reads with ≥ 10% missing sites were discarded. No barcode mismatches were allowed during demultiplexing. Reads were aligned into stacks with a minimum depth of coverage of 5 and a maximum of 2 nucleotide differences between stacks. The minor allele count was set to 2 to eliminate singletons, which reduces errors in model-based clustering methods (Linck & Battey 2019). Loci that were invariant, non-biallelic, or absent from > 20% of samples were removed. Samples with > 70% missing data were also removed. One random variable site per locus was sampled to minimize the chance of retaining physically linked SNPs. The resulting unlinked SNP dataset was used to generate input files for downstream analyses. Population structure Two methods were used to estimate population structure. The maximum likelihood method ADMIXTURE v1.3.0 (Alexander et al. 2009) was used to estimate (1) the number of populations (K) and (2) admixture proportions of samples to identify putative "hybrids" of mixed population ancestry. Samples with admixture proportions < 0.95 were considered admixed. The cross-validation errors for analyses from K = 1 to K = 10 were compared to determine which K minimized group assignment error; e.g., the K with the lowest cross-validation (CV) error is the model best supported by the data. However, parametric methods for estimating population structure are sensitive to violations of model assumptions prevalent in natural populations (Lawson et al. 2018). Therefore,...

Although black spruce is the dominant treeline species in the eastern boreal forest, its distribution stops several kilometers short of treeline in the Brooks Range in Alaska, and white spruce is the dominant treeline species. The explanation for this distribution is not known, but two hypotheses are plausible. First, black spruce may be less tolerant of climatic conditions near treeline than white spruce. Second, black spruce may be unable to regenerate successfully near treeline due to long intervals between fires. We are establishing permanently marked study plots along a transect from the Yukon River basin, where black spruce is the dominant species, to the foothills of the Brooks Range, where it reaches its distributional limit. We are reconstructing recruitment history of both black and white spruce at our study sites, and are reconstructing recent fire history from analysis of fire scars and stand age structures. These data are being used to parameterize matrix population models, with which we are describing patterns of population stability.

This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK and crown dependencies postcode districts (e.g. 'AB') for the years 2016-2019, inclusive.

The paper describing this dataset is available here: https://www.nature.com/articles/s41597-022-01135-6

The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.

The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at this Zenodo archive. The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.

The code used to make the estimations is available at this Zenodo archive.

The postcode data in postcode_district_data.csv are collated from several sources:

https://www.doogal.co.uk/UKPostcodes.php (population figures for the UK (UK Census 2011))

https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm (postcode boundary polygons for UK and crown dependancies)

https://www.gov.gg/population (Guernsey (GY) population data for end June 2020)

https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx (Jersey (JE) population data for end 2019)

https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf (Isle of Man (IM) population data for April 2016)

The data-set is presented in CSV format, as six files:

postcode_district_data.csv: location metadata (region_id, geometry, description, population, country)

regional_site_counts.csv: a table showing the number of sites for each measurement (columns), for each region_id (rows). region_id's match those in the postcode_district_data.csv file.

turing_regional_estimates_aq_daily_met_pollen_pollution_imputed_data.csv: uses imputed site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)

turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)

turing_regional_estimates_aq_loc_type_daily_imputed_data.csv: uses imputed site data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)

turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)

• Air quality site types:

Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)

'Rural background' (14 sites)

'Urban background' (48 sites)

'Urban traffic' (47 sites)

These data were used in the paper:Avanzi C, Heer K, Buentgen U, Labriola M, Leonardi S, Opgenoorth L, Piermattei A, Urbinati C, Vendramin GG, Piotti A (-) Individual reproductive success in Norway spruce natural populations depends on growth rate, age and sensitivity to temperature but not on drought responses. Heredity, under review----Sheet1: Nuclear SSRsSheet2: Chloroplast SSRsSheet3: Individual spatial positions (UTM)Sheet4: Dendrophenotypes

Methods for estimating the variance of measurement error.

Insects Two different populations of Prostephanus truncatus were used in the bioassays, one originated from the invaded range in Ghana, and the other from the native range in Mexico. Both populations were maintained in the Laboratory of Entomology and Agricultural Zoology (LEAZ), at the Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, Greece, on whole maize kernels, at 26°C and 55% relative humidity (RH) and continuous darkness. European Maize Hybrids Three different maize hybrids (“PICO”, “HAMILTON”, and “AGN 672”) were obtained from American Genetics SA, Sindos, Greece. All hybrids were cultivated at Serres, in northern Greece according to the local farming practices. The hybrid “PICO” has a great production potential and it is adapted to multiple soil types and can produce high-weight grain. The hybrid “HAMILTON” is a dual-purpose hybrid, that has excellent early vigor and it is tolerant to fungi, and the hybrid “AGN 672” has excellent early vigor and it is also tolerant to fungi. Population Growth on Different Maize Hybrids Three different maize hybrids (PICO, Hamilton, and AGN 672) were used for experimentation. These hybrids were untreated and uninfested, and kept at ambient conditions until the beginning of the experiments. Before proceeding with the bioassays, grain moisture content (M.C.) was assessed, using a moisture meter (mini-GAC plus, Dickey-John Europe S.A.S., Colombes, France). Standardized plastic vials as in prior work (Quellhorst et al. 2023; Lampiri et al. 2022) were used here (3 cm in diameter, 8 cm in height). Vials were then filled with 20 g of one of the three maize hybrids with lids added after. The commodity was weighed with a Precisa XB3200D compact balance (Alpha Analytical Instruments, Gerakas, Greece). The upper rings of the vials were treated with Fluon (Northern Products Inc., Woonsocket, USA) to prevent insects from moving away from the grain and/or escaping. The top of each vial also had small holes punched to allow ventilation. Each vial then received 10 P. truncatus adults of mixed sex and age from one of two different strains. Two different populations of P. truncatus were used as mentioned above. The vials were placed inside incubators set at 30°C and 65% R.H. in continuous darkness. The vials were removed from the incubators after 45 d and adult progeny production was recorded. We also recorded the weight of frass, the number of insect-damaged kernels (IDK), and the total weight of the kernels within each vial. For each combination, i.e. hybrid × strain, there were n = 9 replicates.

The majority of terrestrial primary production is performed by plants, the ontogenetic growth trends of which greatly influence biomass and carbon dynamics. Here, we study ontogenetic trends of primary (apical) and secondary (stem thickening) growth in Arctic (Svalbard, Norway) and alpine (Krkonoše, Czechia) populations of black crowberry (Empetrum nigrum), the dominant plant species of certain tundra communities. The environmental conditions in alpine areas are more favourable for plant growth than those in the High Arctic, where temperatures are lower, there is less precipitation and soils are shallower, among other differences. These differences were reflected in significant differences in absolute growth rates and shrub age between the populations under study. However, we found almost no differences in ontogenetic growth trends between the populations. In both populations, primary growth and secondary (stem base) growth decrease throughout ontogeny whereas secondary (stem top) growth and basal area increment increase. No significant differences in the slope of the trends were found in either primary or secondary (stem base) growth. Trends in the ratio between basal area increment and primary growth revealed neither absolute nor relative differences between the populations. Ontogenetic trends in the shrubs analysed were surprisingly stable despite the prominently different environmental conditions. Empetrum plants have adapted to the different environments by altering their absolute growth rate only. This adaptation has probably also resulted in the different longevity of plants constituting the study populations, confirming the theory that slower-growing plants live longer. Primary growth and secondary (diameter) growth at the stem base seem to be more basic characteristics of plant growth compared to basal area increment and secondary (diameter) growth at the apex because the latter two seem to be dependent on the absolute growth rate. Methods Study sites and sampling Arctic and alpine populations of the black crowberry (Empetrum nigrum L.), be it the nominal subspecies or E. nigrum subsp. hermaphroditum (Hagerup) Böcher, is exposed to different climatic, soil, and vegetation conditions. We sampled forty individuals from an Arctic population at the end of August 2018 in the Colesdalen valley (78°6′ N; 15°5′ E), Svalbard, Norway. The average annual precipitation in the valley is 190 mm, mostly in the form of snow. From 1969 to 1990, the average annual temperature was −6.7°C, the coldest month was February, with an average temperature of −16.2°C, and the warmest month was July, with an average temperature of 5.9°C (Longyearbyen Airport station, 18 km north of the site; Norwegian Meteorological Institute, 2010). The onset of the growing season spans between days number 165 and 174 (12–23 June) in the Svalbard Islands. Samples were taken at elevations between 40 to 80 m a.s.l. on a south-facing slope with an inclination of 10–20%. Shallow and poor soils were present on the site. There were no other shrubs or herbs growing in the vicinity of the individuals sampled. The temperate alpine population was represented by 41 individuals collected in July and August 2019 in the Krkonoše Mountains, Czechia. Both co-occurring subspecies of E. nigrum (subsp. nigrum and subsp. hermaphroditum) were sampled because they are impossible to identify in the field. Individuals were sampled at three localities: Zadní Planina (50°42′49″ N; 15°40′35″ E), Pančavská louka (50°45′56″ N; 15°32′38″ E), and Labská louka (50°46′12″ N; 15° 32′20″ E). All samples were taken at elevations between 1,300 and 1,400 m a.s.l. The locality at Zadní Planina was on a 20% steep south-eastern slope and the other two localities were flat. At the elevation of 1,603 m a.s.l., the average annual temperature is 0.2°C, the warmest month being July (8.3°C) and the coldest January (−7.0°C). The average annual precipitation reaches 1,400 mm. The onset of cellular division (i.e. the growing season) in co-occurring Pinus mugo is between days 138 and 145 (18–25 May) in the Krkonoše Mts. Region is botanically relatively well described and Krkonoše Mts show high plant endemism from the central European perspective. Sampled shrubs occurred in closed vegetation with Calluna vulgaris, Vaccinium myrtillus, Vaccinium uliginosum, Pinus mugo, and several grass species (Poaceae). The sampling consisted of the extraction of complete individuals (in clonal alpine individuals only the relevant part), including lignified roots (to allow the laboratory assessment of radial growth and plant age from the root collar) and whole aboveground organs. Root samples were cleaned, placed in a marked plastic bag, and treated with 40% alcohol to prevent mold. Growth measurements The method of serial sectioning was used in order to determine the rate of primary growth. It consisted of tree-ring measurements of cross-sections at different heights and cross-dating the obtained series at the intra-individual level. The first cross-section was always made at the root collar or the bottom part of the stem and was followed by one to ten cross-sections evenly spaced across the shrub body so that the distances between the cross-sections were approximately 10 cm. All branches longer than 10 cm were included to account for the species’ greater growth complexity, as it branches heavily. The exact distances between all the cross-sections were measured for subsequent primary growth analysis. Slices no thicker than 20 µm were extracted from all cross-sections using a sledge microtome. The slices were stained using a 1:1 Safranin/Astra blue solution and embedded in Canada balsam. The entire area of each slice (i.e. whole cross-section) was photographed at 100× magnification in order to measure annual rings along multiple axes as well as to detect partially missing annual rings and other growth defects. Measurements were obtained from the images using NIS-Elements software for two radii per cross-section. The first measurement was always performed for the longest radius whereas the second measurement, aimed to reveal partially missing rings, was made at an angle of at least 90° to the first radius. Cross-dating was done mostly visually using PAST5 software, first at the cross-sectional level by comparing radii measurements and, secondly, at the level of the whole individual (including branches) by comparing mean cross-sectional series. In total, we were able to use a series from 30 individuals from Svalbard (92 missing and 232 partially missing annual rings detected) and 33 individuals from the Krkonoše Mts (23 missing and 126 partially missing annual rings detected). The remaining samples were not cross-dated successfully and were excluded from further analyses. We calculated the primary growth rate between cross-sections using the difference in the number of tree rings and stem length between two consecutive cross-sections for each considered branch. For the corresponding set of tree rings, we also calculated the rate of secondary growth at the shrub base (using measurements from the basal cross-section) and at the shrub top (using the measurements at the lower cross-section of each pair). We also calculated secondary growth expressed as basal area increment (measured in mm2) and the ratio between basal area increment and primary growth (measured in mm). Basal area increment, in contrast to simple ring width, includes the circumference of the stem on which the ring is produced and is, therefore, assumed to be more representative of stem conducting capacity and stem biomass produced. Statistics To test for relationships between the growth variables (primary growth, secondary growth at the base and top, basal area increment, and growth ratio), shrub age, and population age structure, we used multiple linear and polynomial regression models. These models were based on average growth rates within shrub sections. We also used a subset of the data for shrubs younger than ten years to avoid bias caused by large differences in shrub age between the two study regions. Similarly, to remove the age effect from the relationship, the residuals of growth vs age models were used to test for a relationship between primary and secondary growth. Finally, we calculated the age distribution of each sample population and tested for differences in these distributions between the populations. All the analyses were performed in R (R Core Team 2020).

How high is the brand awareness of Ring in Germany?When it comes to smart home users, brand awareness of Ring is at 47% in Germany. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Ring in Germany?In total, 14% of German smart home users say they like Ring. However, in actuality, among the 47% of German respondents who know Ring, 30% of people like the brand.What is the usage share of Ring in Germany?All in all, 8% of smart home users in Germany use Ring. That means, of the 47% who know the brand, 17% use them.How loyal are the users of Ring?Around 6% of smart home users in Germany say they are likely to use Ring again. Set in relation to the 8% usage share of the brand, this means that 75% of their users show loyalty to the brand.What's the buzz around Ring in Germany?In August 2022, about 11% of German smart home users had heard about Ring in the media, on social media, or in advertising over the past three months. Of the 47% who know the brand, that's 23%, meaning at the time of the survey there's little buzz around Ring in Germany.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Ring galaxies are unique laboratories to study the effects of galaxyinteractions: they are characterized by high SFR,enhanced Xray emission, andlarge number of ULXs. However only 4 source are published. We selected allcollisional rings at z<0.02 from the Arp&Madore sample of southern ring galaxiesfor a statistical sample of 12. As a first step we ask for XMMNewton snapshotsof 8 galaxies that suit the XMMNewton characteristics. We expect to detect thering as a bright Xray source, to which both gas and point sources (mostly ULXs)contribute. The nondetection of Xray sources would represent an unexpected andimportant discovery, indicating that known the sources are quite different fromthe rest of the population. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O-ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O-ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection.