The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

PARD’s Long Range Plan for Land, Facilities and Programs, Our Parks, Our Future (adopted November 2019) compared Austin’s park system to five peer cities: Atlanta, GA, Dallas, TX, Portland, OR, San Antonio, TX, and San Diego, CA. The peer cities were selected based on characteristics such as population, size, density, and governance type. Portland and San Diego were selected as aspirational cities known for their park systems.

Note that the table below presents each scoring area’s 1 to 100 index, where 100 is the highest possible score.

PARD’s Long Range Plan for Land, Facilities and Programs, Our Parks, Our Future (adopted November 2019) compared Austin’s park system to five peer cities: Atlanta, GA, Dallas, TX, Portland, OR, San Antonio, TX, and San Diego, CA. The peer cities were selected based on characteristics such as population, size, density, and governance type. Portland and San Diego were selected as aspirational cities known for their park systems. Note that the table below presents each scoring area’s 1 to 100 index, where 100 is the highest possible score.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

This dataset contains demographic data pertaining to Hesperocyparis forbesii on Otay Mountain in San Diego County, California, USA, over a 14-year study period from 2004 to 2017 following the 2003 Otay/Mine Fire. Site variables including elevation, incline, and slope were collected as well as pre-fire tree density and stand age for 16 study site locations. Tree density, height, and cone production was then monitored over the study period with data collection occuring in 2004, 2005, 2009, 2011, 2014, and 2017.

A growing body of evidence suggests that spatial population structure can develop in marine species despite large population sizes and high gene flow. Characterizing population structure is important for the effective management of exploited species, as it can be used to identify appropriate scales of management in fishery and aquaculture contexts. The California sea cucumber, Apostichopus californicus, is one such exploited species whose management could benefit from further characterization of population structure. Using restriction site-associated DNA (RAD) sequencing, we developed 2,075 single nucleotide polymorphisms (SNPs) to quantify genetic structure over a broad section of the species’ range along the North American west coast and within the Salish Sea, a region supporting the Washington State A. californicus fishery and developing aquaculture production of the species. We found evidence for population structure (global fixation index (FST) = 0.0068) with limited dispersal driving two patterns of differentiation: isolation-by-distance and a latitudinal gradient of differentiation. Notably, we found detectable population differences among collection sites within the Salish Sea (pairwise FST = 0.001–0.006). Using FST outlier detection and gene-environment association, we identified 10.2% of total SNPs as putatively adaptive. Environmental variables (e.g., temperature, salinity) from the sea surface were more correlated with genetic variation than those same variables measured near the benthos, suggesting that selection on pelagic larvae may drive adaptive differentiation to a greater degree than selection on adults. Our results were consistent with previous estimates of, and patterns in, population structure for this species in other extents of the range. Additionally, we found that patterns of neutral and adaptive differentiation co-varied, suggesting that adaptive barriers may limit dispersal. Our study provides guidance to decision-makers regarding the designation of management units for A. californicus and adds to the growing body of literature identifying genetic population differentiation in marine species despite large, nominally connected populations. Methods Approximately 50 adult A. californicus were collected by scuba divers from nine collection sites along the Pacific Coast of North America, ranging from Alaska to Oregon, including four collection sites within the southern Salish Sea. For each animal, a tissue sample was excised from a radial muscle band and stored in 100% ethanol. DNA was extracted from tissue samples using the EZNA Mollusc DNA Kit (OMEGA Bio-tek, Norcross, GA, USA) and the Qiagen DNeasy Kit (Qiagen, Germantown, MD, USA). DNA was quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) and DNA quality was checked by gel electrophoresis. DNA concentration was normalized to 500 ng in 20 μL of PCR-grade water. We selected samples with high DNA quality for restriction site associated DNA (RAD) sequencing and RAD libraries were prepared following standard protocols1. Briefly, DNA samples were barcoded with an individual six-base identifier sequence attached to an Illumina P1 adapter. Samples were then pooled into sub-libraries, containing approximately 12 individuals. Sub-libraries were sheared using a Bioruptor sonicator and size selected to 200-400 bp using a MinElute Gel Extraction Kit (Qiagen, Germantown, MD, USA). P2 adapters were ligated to DNA in sub-libraries and amplified with PCR using 12–18 cycles as in Etter et al. (2011). Finally, amplified sub-libraries were combined into pools of approximately 72 individuals. Paired-end 2 x 150-base pair sequencing was performed on an Illumina HiSeq4000 (San Diego, California, USA) at the Beijing Genomics Institute and the University of Oregon Genomics and Cell Characterization Core Facility. Only forward reads were used for analysis. To estimate genotyping error, 14 individuals were sequenced twice. Raw RAD sequencing data were demultiplexed using the process_radtags module in the pipeline STACKS v.1.442. A threshold of 800,000 reads was used to exclude poorly sequenced individuals. Because a genome was not available for A. californicus, we aligned individual sequences to the genome of a closely related species, A. parvimensis (GenBank accession number = GCA_000934455.1). The A. parvimensis genome was 760,654,621 bp, with 21,559 scaffolds and an N50 size of 9,587. We retained reads with a minimum mapping quality score of 20. Then, we used dDocent v.2.7.8 to perform a reference-guided locus assembly using the filtered reads and default parameters3. Additionally, a parallel de novo assembly was performed, which produced nearly identical results for population structure and 1.8–2.8% lower mean expected heterozygosity, 0.9–1.8% higher mean observed heterozygosity, and 1.2–3.3% higher proportions of polymorphic SNPs than in the with-reference assembly, although with similar patterns across collection sites. The reference-guided assembly was retained for further analyses due to decreased confidence in identifying genotyping errors in the de novo assembly4. We used vcftools v.0.1.165 to remove indels and to retain only single nucleotide polymorphisms (SNPs) with a minimum quality score of 20, minimum minor allele frequency of 0.05 and maximum missing data per locus of 30% across collection sites. Individuals with more than 30% missing data across SNPs were removed. In cases of multiple SNPs per RAD tag, we retained the SNP with the highest minor allele frequency6. SNPs that were not in Hardy Weinberg Equilibrium (HWE) were considered sequencing errors or poorly assembled loci and were removed from our data set, as selection and inbreeding are unlikely to cause significant deviations from HWE equilibrium at biallelic loci7. We tested SNPs for deviations from HWE using the R package genepop v.1.1.4 (Rousset, 2008). SNPs were identified as being out of HWE if they had a q-value below 0.05 in at least 2 of the collection sites after correcting for false discovery rate, following Waples (2015). We used a suite of R packages, stand-alone software, and custom scripts in the programming language R v.3.5.0 9 to quantify genetic diversity and population structure. Mean expected heterozygosity, observed heterozygosity, and the inbreeding coefficient (FIS) per SNP were calculated using the R package genepop. The proportion of polymorphic SNPs per collection site was calculated using a custom R script. To investigate population structure, we first calculated Weir-Cockerham fixation index (FST)10 to quantify population differentiation using the R packages genepop and hierfstat v.0.5.7. Exact G-tests11 were used to test for significant genic differentiation using the R package genepop. To investigate patterns of spatial differentiation among collection sites, the R package adegenet v.2.1.112 was used to conduct discriminant analyses of principal components (DAPC), a multivariate method that summarizes the between-group variation (i.e., population structure), while minimizing within-group variation13. The built-in optimization algorithm was used to retain the number of principal components that minimized over-fitting and under-fitting of the model. To determine the potential number of underlying populations, the program ADMIXTURE v.1.3.0 was used to conduct a clustering analysis14. Specifically, ADMIXTURE uses a maximum likelihood-based approach to estimate individual ancestries across different assumed numbers of populations, with the best fit selected using cross-validation. To examine the presence of hierarchical population structure, we conducted analyses of molecular variance (AMOVA) using the ade4 method of the R package poppr v.2.8.115. Significance of AMOVAs was determined using permutation tests with 1,000 iterations. Using AMOVA, we investigated whether the following oceanographic barriers limit dispersal: 1) the Victoria Sill (Victoria Sill grouping), 2) Admiralty Inlet (Admiralty Inlet grouping), and 3) the North Pacific Current (NPC grouping). Because AMOVAs for each oceanographic barrier include sites in an area with other potential oceanographic barriers, we added a fourth grouping of all three oceanographic barriers (All Barriers grouping) to investigate the relative role of oceanographic barriers compared to other factors. Additionally, we conducted an AMOVA by state or province (State grouping). Although not biologically meaningful, we included the State grouping to determine how much genetic variation is captured by regional management boundaries. Isolation-by-distance (IBD) was tested with Mantel tests16 in R as linear correlation between linearized FST17 using all SNPs and shortest Euclidean distance through water (in-water distance hereafter) approximated in Google Maps18 Following Xuereb et al (2018), we also tested IBD in the northern and southern population section separately. Following Buonaccorsi et al. (2005), we estimated mean dispersal distance from the slope of the regression of linearized FST and in-water distance. We used this 1-dimensional model because it is an appropriate approximation for coastal species with dispersal dimensions greater than one dimension of the habitat (e.g., dispersal distance likely greater than water depth for A. californicus). We estimated mean dispersal distance from a set of potential population density estimates as population density estimates were unavailable. We used two approaches to investigate putatively adaptive SNPs: FST outlier detection and gene-environment association. FST outlier detection is used to identify loci potentially under spatial selection20,21, although this method does not identify the potential cause of selection. Although gene-environment association does not explicitly test whether such associations are adaptive, this method is used to identify locus-environment associations as evidence for potential local

Larvae in the coastal ocean are transported toward shore by a variety of mechanisms. Crossing the surf zone is the last step in a shoreward migration and surf zones may act as semipermeable barriers altering delivery of larvae to the shore. We related variation in the structure of intertidal barnacle populations to surfzone width (surfzone hydrodynamics proxy), wave height, alongshore wind stress (upwelling proxy), solar radiation, and latitude at 40 rocky intertidal sites from San Diego, California to the Olympic Peninsula, Washington. We measured daily settlement and weekly recruitment of barnacles at selected sites and related these measures to surfzone width. Chthamalus density varied inversely with that of Balanus, and the density of Balanus and new recruits was negatively related to solar radiation. Across the region, long-term mean wave height and an indicator of upwelling intensity and frequency did not explain variation in Balanus or new-recruit densities. Balanus and new-recruit densities, daily settlement and weekly recruitment were up to three orders of magnitude higher at sites with wide (> 50 m), more dissipative surf zones with bathymetric rip currents than at sites with narrow (< 50 m) more reflective surf zones. Thirty to 50% of the variability in Balanus and new-recruit densities was explained by surfzone width. We sampled a subset of sites < 5 km apart where coastal hydrodynamics such as upwelling should be very similar. At paired sites with similar surfzone widths, Balanus densities were not different. If surfzone widths at paired sites were dissimilar, Balanus densities, daily settlement and weekly recruitment were significantly higher at sites with the wider more dissipative surf zone. The primary drivers of surfzone hydrodynamics are the wave climate and the slope of the shore and these persist over time, and therefore site-specific stability in surfzone hydrodynamics should result in stable barnacle population characteristics. Variations in surfzone hydrodynamics appear to play a fundamental role in regulating barnacle populations along the open coast, which in turn may have consequences for the entire intertidal community.

In tropical grazer assemblies with abundant large predators, smaller herbivores have been shown to be limited by predation and food quality, while the larger species are regulated by food abundance. Much less is known on herbivore resource partitioning in temperate grazing ecosystems, where humans are typically the regulators. In the Oostvaardersplassen ecosystem in The Netherlands, a unique multispecies assemblage of cattle, horses, red deer and geese developed after initial introduction of a few individuals in 1983. During the first 35 years, this herbivore assemblage without predation or human regulation gradually changed into increasing dominance of the smaller herbivore species. Carrying capacity was reached around 2008, after which numbers started fluctuating depending on winter conditions. A population crash, especially of red deer, in winter 2018 led to heavy societal debate around animal welfare, after which active population regulation was introduced. This suggests strong niche overlap and competition between these very different-sized herbivores, possibly due to their homogenising effect on vegetation composition and structure at high densities. We used eDNA metabarcoding of dung to quantify the diet composition of cattle, horse, red deer and geese, annually in early winter from 2018-2021 and calculated their niche overlap. Overall, we found strong diet overlap between species. The diet of horse and cattle remained mostly unaltered and it was the one of red deer that changed the most across the years. Niche overlap decreased with increasing red deer population size, the most abundant species. When calculated as total energy expenditure, we found niche overlap was more linked to the shifts in red deer than to the total herbivore energy fluctuation. We suggest red deer changed their diet mainly in response to their own population size, reducing their niche overlap with increasing red deer population. In this case, resource competition translated into shorter vegetation height, reducing resource availability and forcing herbivores to consume different plant taxa. We conclude that in this temperate ecosystem, inter- and intraspecific resource competition are key factors structuring community composition and dynamics from small to large herbivores, with a competitive advantage of the smaller species, but with also opportunities for resource partitioning. Methods Dung samples were collected across the grassland part of the OVP, with collections divided in three sub-areas (see Suppl. Figure 1A) during November 2018, 2019, 2020 and 2021 for the four main herbivore species, i.e., cattle, horse, red deer and geese (Barnacle geese and Greylag geese combined as the species were not identified from the dung shape). Per species and year, 15 scat samples were collected (5 per sub-area, Suppl. Figure 1A), leading to a total of 60 scat per year. Samples were spaced by at least 10 m to reduce the chance of re-sampling the same individual, and GPS coordinates were taken for each sample. Only freshly deposited dung samples were collected, and samples were then stored in dried silica beads at room temperature, in order to dry and preserve them, without need for freezing, until DNA extraction could be done at the University of Lausanne, Switzerland. DNA extraction We used between 0.5 and 1 g of dry dung as the starting point for the extraction. Extractions were performed using the NucleoSpin Soil Kit (Macherey-Nagel, Düren, Germany) following the manufacturer protocol. A subset of the extractions was tested for inhibitors with quantitative real-time PCR (qPCR) applying different dilutions (2x, 10x and 50x) in triplicates. qPCR reagents and conditions were the same as in DNA metabarcoding PCR reactions (see below), with the addition of 10,000-fold diluted SybrGreen (Thermo Fisher Scientific, USA). Following these analyses, all samples were diluted 5-fold before PCR amplification. All extractions were performed in a laboratory restricted to low DNA-content analyses. DNA metabarcoding DNA extracts were amplified using a generalist plant primer pair (Sper01, (Taberlet et al., 2018)), targeting all vascular plant taxon (Spermatophyta). Sper01 targets a 10-220 bp gene fragment of the P6 loop of trnL intron, chloroplast DNA. To assign the DNA sequences to each sample, primers were tagged with eight variable nucleotides added to their 5’-end with at least five differences between tags. The PCR reactions were performed in a final volume of 20 µL. The mixture contained 1 U AmpliTaq® Gold 360 mix (Thermo Fisher Scientific, USA), 0.04 µg of bovine serum albumin (Roche Diagnostics, Basel, Switzerland), 0.2 µM of tagged forward and reverse primers and 2 µL of 5-fold diluted template DNA. PCR cycling conditions were denaturation for 10 minutes at 95 °C, followed by 40 cycles of 30 s at 95 °C, 30 s at 52 °C and 1 min at 72 °C, with a final elongation step of 7 min at 72 °C. Amplifications were performed separately for each species and in replicates (4 per sample) in PCR plates each containing 60 DNA extracts, 12 blanks as well as 8 extraction, 8 negative and 8 positive PCR controls (DNA assembly of 10 plant species with increasing relative concentrations). The use of blanks allows estimating the proportion of tag switches (i.e., false combination of tags, generating chimeric sequences) during library preparation (Schnell et al., 2015). Amplification success and fragment sizes were confirmed on agarose gel. PCR products were subsequently pooled per PCR plate. Amplicons were purified using a MinElute PCR Purification Kit (Qiagen, Hilden, Germany). Purified pools were quantified using a Qubit® 2.0 Fluorometer (Life Technology Corporation, USA). Library preparation was done following the TagSteady Protocol (Carøe & Bohmann, 2020). After adapter ligation, libraries were validated on a fragment analyzer (Advanced Analytical Technologies, USA). Final libraries were quantified, normalised and pooled before 150 paired-end sequencing on an Illumina MiniSeq sequencing system with a Mid Output Kit (Illumina, San Diego, CA, USA). Bioinformatic data analyses The bioinformatic processing of the raw sequence output was performed using the OBITools package (Boyer et al., 2016). Initially, forward and reverse reads were assembled with a minimum quality score of 40. The joined sequences were assigned to samples based on unique tags combinations. Assigned sequences were then de-replicated, retaining only unique sequences. All sequences with less than 100 reads per library were discarded as well as those not fitting the range of metabarcode lengths. This was followed by two different clustering methods. First, pairwise dissimilarities between reads were computed and lesser abundant sequences with single nucleotide dissimilarity were clustered into the most abundant ones. Second, we used the Sumaclust algorithm (Mercier C, 2013) to further refine the resulting clusters based on a sequence similarity of 97 %. It uses the same clustering algorithm as UCLUST (Prasad, D.V., 2015) and it is mainly used to identify erroneous sequences produced during amplification and sequencing, derived from its main (centroid) sequence. Remaining sequences were assigned to taxa using a reference database. We built a database for Sper01 by running an in silico PCR based on all the plant sequences available in the EMBL database (European Molecular Biology Laboratory). We kept a single sequence per taxonomic id that was annotated at least to genus level. Further data cleaning and filtering was done in R (version 4.0.2) using the metabaR package (Zinger et al., 2021). Sequences that were more abundant in extraction and PCR controls than in samples were considered as contamination and removed. Operational taxonomic units (OTUs) with similarity to the reference sequence lower than 97 % were also eliminated from the dataset. Removal of tag-leaked sequences was done independently for each library. This approach allowed us to discard single OTUs instead of whole PCR replicates. However, PCR replicates with too small reads count were also discarded. Remaining PCR replicates were merged by individual, keeping the mean relative read abundance (RRA), frequency of occurrence (FOO) and presence-absence.

The PED ans MAP files contain SNP data used in Urnikyte A et al. (Patterns of genetic structure and adaptive positive selection in the Lithuanian population from high-density SNP data).

The data set consists of 424 samples and 532,836 SNPs after filtering. Genotyping was performed at the Department of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Lithuania with the Illumina HumanOmniExpress-12v1.1 (296 samples) and the Infinium OmniExpress-24 (129 samples) arrays (Illumina, San Diego, CA, USA).