Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

For decades, biogeographers have sought a better understanding of how organisms are distributed among islands. However, the island biogeography of humans remains largely unknown. Here, we investigate how human population size varies among 486 islands at two spatial scales. At a global scale, we tested whether population size increases with island area and declines with island elevation and nearest mainland, as is common in non-human species, or whether humans escape such biogeographic constraints. At a regional scale, we tested whether population sizes vary among islands within archipelagos according to the positioning of different cultural source pools. Results illustrate that on a global scale, human populations increased in size with island area, similar to non-human species, yet they did not decline in size with elevation and distance to nearest mainland. At a regional scale, human population size often varied among islands within archipelagos relative to the location of different cultural source pools. Despite broad-scale similarities in the geographical distribution of human and non-human species among islands, results from this study indicate that the island biogeography of humans may also be influenced by archipelago-specific social, political and historical circumstances.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97.

COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f

Vaccination Status Definitions:

·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine.

·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received.

·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains.

Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows.

Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated.

Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates.

Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup.

Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

Compiled data utilized to run model parameters for Requena-Mullor et al. 2023. These data lead to the following conclusions: • Human population growth contributes to the decline of sagebrush-steppe rangelands. • More accessible rangelands from population centers have higher quality. • Open space preservation provides opportunities for rangeland conservation in cities. • Coordinated conservation strategies are necessary to protect rangeland ecosystems.

Demographic data for introduced crab from multiple bays along the Central California coast, shallow subtidal (<3 m depth), in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trappings of invasive European green crabs to gather demographic data from several bays in northern California: Bodega Harbor, Tomales Bay, Bolinas Lagoon, San Francisco Bay, and Elkhorn Slough. All sites were accessed by foot via shore entry. At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. Traps arrays were set with fish and minnow traps alternating and with each 20 m apart. Traps were retrieved 24 hours later and traps were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, and injuries were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, and frozen overnight prior to disposal.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Demographic data for introduced crab from multiple bays in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701751.1 Easternmost_Easting=-121.738422 geospatial_lat_max=38.316968 geospatial_lat_min=36.823953 geospatial_lat_units=degrees_north geospatial_lon_max=-121.738422 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701751 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. instruments_0_dataset_instrument_nid=701774 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=folding Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701751 Northernmost_Northing=38.316968 param_mapping={'701751': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701751/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=36.823953 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Submit to the LFDP PIs a short (1 page) description of how you intend to use the data; · Invite LFDP PIs to be co-authors on any publication that uses the data in a substantial way (some PIs may decline and other LFDP scientists may need to be included); If the LFDP PIs are not co-authors, send the PIs a draft of any paper using LFDP data, so that the PIs may comment upon it; In the methods section of any publication using LFDP data, describe that data as coming from the "Luquillo Forest Dynamics Plot, part of the Luquillo Experimental Forest Long-Term Ecological Research Program"; Acknowledge in any publication using LFDP data the "The Luquillo Experimental Forest Long-Term Ecological Research Program, supported by the U.S. National Science Foundation, the University of Puerto Rico, and the International Institute of Tropical Forestry"; · Supply the LFDP PIs with 10 reprints of any publication using LFDP data. · Accept that the LFDP PIs can not guarantee that the LFDP data you intend to use, has not already been submitted for publication or published.

Many species show range expansions or contractions due to climate-change-induced changes in habitat suitability. In cold climates, many species that are limited by snow are showing range expansions due to reduced winter severity. The European polecat (Mustela putorius) occurs over large parts of Europe with its northern range limit in southern Fennoscandia. However, it is to date unknown what factors limit polecat distribution. We thus investigated whether climate or land-use variables are more important in determining the habitat suitability for polecats in Sweden. We hypothesized that 1) climatic factors, especially the yearly number of snow days, drive habitat suitability for polecats, and that, 2) as the number of snow days is predicted to decline in the near future, habitat suitability in northern Sweden will increase. We used a combination of sightings data and a selection of national maps of environmental factors to test these hypotheses using MaxEnt models. We also used maps of future climate predictions (2021–2050 and 2063–2098) to predict future habitat suitability. The number of snow days was the most important factor, negatively determining habitat suitability for polecats, as expected. Consequently, the predictions showed an increase in suitable habitat both in the current distribution range and in northern Sweden, especially along the coast of the Baltic Sea. Our results suggest that the polecat distribution is limited by snow and that reduced snow cover will likely result in a northward range expansion. However, the exact mechanisms for how snow limits polecats are still poorly understood. Consequently, we expect the Scandinavian polecat population to increase in numbers, in contrast to many populations elsewhere in Europe, where numbers are declining. Due to polecat predation, the expansion of the species might have cascading effects on other wildlife populations. Methods Polecat sightings data To determine the distribution and habitat suitability of polecats in Sweden, we used sightings data of polecats gathered by volunteers and documented to the Swedish Species Information Centre between 1960 and 2020 (Figure 1; Swedish Species Information Centre 2020). We validated data at the edge of the distribution by contacting the person that reported the sighting, and removed data points if there was uncertainty about the sighting, we also removed data that was categorized as roadkill. As a result, we discarded 44 of the 425 sightings before analysis. Due to an increase of popularity of the sightings platform, the majority of sightings (78%) used in the analyses was from the period 2010–2020. Description of covariates We used nine covariates distributed over four different categories to test our hypotheses (Table 1). We included these covariates based on habitat and diet preferences of the polecat found in previous studies All parameters were rasterized and aggregated to 1-km2 grid cells over the whole of Sweden in ArcGis Pro version 2.6.0

Land cover and soil moisture

We selected several land-cover types and an index of soil moisture from the ungeneralised version of the National Land Cover Database (NMD) Sweden. This project provides a land cover map for the whole of Sweden divided into 24 different land-cover types, as well as a measure of soil moisture as a spectrum from dry to wet soil. We reclassified 17 of the 24 land-cover types into three different groups: coniferous forest, deciduous forest and open landscapes (Table S1.1). We selected these three groups for ease of analysis and due to previous studies showing that polecats selected or avoided these land-cover types at small spatial scales. Polecats were found to avoid coniferous forest (Baghli and Verhagen 2005, Zabala et al. 2005), select for deciduous forest (Jedrzejewski et al. 1993, Baghli et al. 2005), and use landscapes that were characterised by a variety of open habitat and forest (Blandford 1987, Lodé 1993, 2000a, Baghli et al. 2005). Furthermore, we used soil moisture as a variable that could further distinguish areas that would be wet during part of the year, which could result in increased amphibian populations, which are an important part of the polecat’s diet (Lodé 1993, 1997, 2000b, Hammershøj et al. 2004, Malecha and Antczak 2013). After reclassification, we determined the proportion of surface covered by each land-cover group, as well as the average soil moisture index, for each 1-km2 grid cell. Table S1.1: The landcover type clusters and which variables are merged from the original landcover data.

Cluster of land-cover types

Land-cover type number as presented in the NMD

Coniferous forest

111 (Pine forest not on wetland), 112 (Spruce forest not on wetland), 113 (Mixed coniferous not on wetland), 121 (Pine forest on wetland), 122 (Spruce forest on wetland), 123 (Mixed coniferous on wetland)

Deciduous forest

115 (Deciduous forest not on wetland), 116 (Deciduous hardwood forest not on wetland), 117 (Deciduous forest with deciduous hardwood forest not on wetland), 125 (Deciduous forest on wetland), 126 (Deciduous hardwood forest on wetland), 127 (Deciduous with deciduous hardwood forest on wetland)

Open landscapes

2 (Open wetland), 3 (Arable land), 41 (Non-vegetated other open land), 42 (Vegetated other open land), 118 (Temporarily non-forest not on wetland), 128 (Temporarily non-forest on wetland)

Snow cover and Minimum winter temperature The Swedish Meteorological and Hydrological Institute provides snow cover data for Sweden as the average number of days with snow with a depth above 20mm. The data is provided in 4 time periods, including two future projections (P1=1961–1990, P2 = 1991–2013, P3 = 2021–2050, P4= 2069–2098; Swedish Meteorological and Hydrological Institute 2021). The future projections we included are based on the 4.5 RCP scenario(Thomson et al. 2011). We included these data as a previous study showed that polecats had more difficulty catching prey when there is snow on the ground (Weber 1989). The data consists of interpolated data from 200 weather stations with an average given per municipality. We have rasterized the data giving average values for cells crossing municipality boundaries. We used data averages per cell of P2 for model building, while we used averages of P3 and P4 per cell for model projections of future scenarios.

Human pressure

We used the human footprint index as published by NASA in 2018 as a measure of human pressure. We did this as previous studies have shown that polecats tend to select for areas with extensive human use (Sidorovich et al. 1996, Rondinini et al. 2006) while avoiding urban centres (Zabala et al. 2005). The dataset is based on the global human footprint between 1995 and 2004. The human footprint is an index based on population density, land-use, infrastructure (buildings, lights, land use/cover) and human access (roads, railways; Venter et al. 2018). This raster dataset is publicly available and has a resolution of 1 km2. We only clipped the dataset to the borders of Sweden. Water availability We used the Water & Wetness geo data from Copernicus (CLMS 2018) as a measure of water availability. We did this as previous studies showed that polecats select for riparian habitat (Baghli et al. 2005) as amphibians are an important part of their diet (Lodé 1993, 1997, 2000b, Hammershøj et al. 2004, Malecha and Antczak 2013). This dataset includes all waterways and waterbodies with a resolution of 10 m. We have outlined all waterbodies and then made a buffer of 30 meters around all waterlines to represent near-water (riparian) habitat. We then calculated the proportion of near-water habitat in each 1-km2 grid cell. Elevation We used elevation data from the Copernicus Land Monitoring Service - EU-DEM project. We did this as previous studies showed that polecats avoid high-elevation areas. The dataset is provided as a raster with a spatial resolution of 25 meters. We calculated the average elevation for each 1-km2 grid cell. Bias correction for sampling intensity Due to the nature of citizen science data, it is prone to come with a bias. This bias manifests itself mostly in a discrepancy in spatial sampling effort. To account for this bias, we created a density kernel (as recommended by Kramer-Schadt et al. 2013 and Morelle and Lejeune 2015 and in line with Rutten et al. 2019) based on all mustelid sightings (n = 25686) reported to the Swedish Species Information Centre between 1972 and 2021 (Swedish Species Information Centre 2020), except for the Eurasian badger (Meles meles), the wolverine (Gulo gulo) and the polecat. We excluded the badger and wolverine as we expect this species to be much easier to identify and see compared to the polecat and other mustelids. Furthermore, badger and wolverine have a limited distribution in Sweden, while all other species – Eurasian otter (Lutra lutra), pine marten (Martes martes), American mink (Neovison vison), stoat (Mustela erminea), and weasel (Mustela nivalis) – have a distribution that covers the whole of Sweden (Swedish Species Information Centre 2020). We created the kernel with the ‘Kernel Density’ function in ArcGIS Pro (Esri 2021) with the mustelid sighting coordinates and the 1 km2 raster grid used for the covariates. The use of this density kernel is based on the assumption that people reporting other mustelids would also report a polecat if they saw one, and thus that the distribution of mustelid sightings is representative of the distribution of potential polecat reporters.

Demographic data from introduced crab in Seadrift Lagoon (Central California coast, shallow subtidal (<3 m depth)) in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trapping of invasive European green crabs to gather demographic data in Seadrift Lagoon, Stinson Beach, CA (lat 37.907440 long -122.666169).\u00a0All sites were accessed by either kayak or by foot via shore entry.\u00a0At each of six sites, we placed 10 baited traps (folding Fukui fish traps) in shallow (<2 m) subtidal areas. Traps were retrieved 24 hours later and were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, injuries, and presence of marks were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, frozen overnight disposed of in commercial agricultural compost. \u00a0

For each date and site, crabs from all traps (e.g. 10 traps per site) are pooled for counting and measuring.
Traps Used for each date (some with macroalgae "Ulva"):
02/19/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
02/20/2015\u00a0\u00a0 \u00a010 baited traps + 5 with ulva
03/05/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva per site
03/06/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
03/24/2015\u00a0\u00a0 \u00a010 traps/site
04/08/2015\u00a0\u00a0 \u00a010 traps/site
04/15/2015\u00a0\u00a0 \u00a010 baited traps + 4 traps with ulva
04/24/2015\u00a0\u00a0 \u00a010 traps/site
05/27/2015\u00a0\u00a0 \u00a0site 1 & 5 had 10 traps, site 3 had 9 traps
06/23/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
06/24/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
07/21/2015\u00a0\u00a0 \u00a0traps per site: site 1=20, site 2=20, site 3=17, site 4=15, site 5=10, site 6=10, site 7=20
08/25/2017\u00a0\u00a0 \u00a010 traps/site
08/26/2015\u00a0\u00a0 \u00a010 traps/site
08/27/2015\u00a0\u00a0 \u00a010 traps/site
09/01/2015\u00a0\u00a0 \u00a010 traps/site
09/02/2015\u00a0\u00a0 \u00a010 traps/site
09/30/2015\u00a0\u00a0 \u00a010 traps/site
10/01/2015\u00a0\u00a0 \u00a010 traps/site
10/02/2015\u00a0\u00a0 \u00a010 traps/site
12/01/2015\u00a0\u00a0 \u00a010 traps/site
12/02/2015\u00a0\u00a0 \u00a010 traps/site
12/03/2015\u00a0\u00a0 \u00a010 traps/site

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Monthly trapping in Seadrift Lagoon in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 02 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701863.1 Easternmost_Easting=-122.6661694 geospatial_lat_max=37.90744 geospatial_lat_min=37.90744 geospatial_lat_units=degrees_north geospatial_lon_max=-122.6661694 geospatial_lon_min=-122.6661694 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701863 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow ( instruments_0_dataset_instrument_nid=701870 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701863 Northernmost_Northing=37.90744 param_mapping={'701863': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701863/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.90744 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=lagoon,latitude,longitude version=1 Westernmost_Easting=-122.6661694 xml_source=osprey2erddap.update_xml() v1.3

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-ZIP-Code/2ani-ic5x. NOTE, 3/30/2023: We have added columns for bivalent (updated) doses to this dataset. We have also added age group columns for 0-17 and 18-64 and stopped updating the 5+ and 12+ columns, although previously published values remain for those columns. COVID-19 vaccinations administered to Chicago residents based on the home ZIP Code of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). The ZIP Code where a person lives is not necessarily the same ZIP Code where the vaccine was administered. Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. ·Total doses administered: Number of all COVID-19 vaccine doses administered. Data Notes: Daily counts are shown for the total number of doses administered, number of people with at least one vaccine dose, number of people who have a completed vaccine series, and number of people who have received a bivalent dose. Cumulative totals for each measure as of that date are also provided. Vaccinations are counted based on the day the vaccine was administered. Coverage percentages are calculated based on cumulative number of people who have received at least one vaccine dose, cumulative number of people who have a completed vaccine series, and cumulative number of people who have received a bivalent dose in each ZIP Code. Population counts are from the U.S. Census Bureau American Community Survey 2015-2019 5-year estimates and can be seen in the ZIP Code, 2019 rows of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa). Actual counts may exceed population estimates and lead to >100% coverage, especially in areas with small population sizes. Additionally, the medical provider may report a work address or incorrect home address for the person receiving the vaccination which may lead to over or under estimates of vaccination coverage by geography.  All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19. Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau American Community Survey

Abstract Disentangling the impact of Late Quaternary climate change from human activities can have crucial implications on the conservation of endangered species. We investigated the population genetics and demography of the Mediterranean monk seal (Monachus monachus), one of the world's most endangered marine mammals, through an unprecedented dataset encompassing historical (extinct) and extant populations from the eastern North Atlantic to the entire Mediterranean Basin. We show that Western-Sahara/Mauritania (Cabo Blanco), Madeira, Western Mediterranean (historical range) and Eastern Mediterranean regions segregate in four populations. This structure is likely the consequence of recent drift, combined with long-term isolation by distance (R2 = 0.7), resulting from prevailing short-distance (< 500 km) and infrequent long-distance dispersal (< 1,500 km). All populations (Madeira especially), show high levels of inbreeding and low levels of genetic diversity, seemingly declining since historical time, but surprisingly not being impacted by the 1997 massive die-off in Cabo Blanco. Approximate Bayesian Computation analyses support scenarios combining local extinctions and a major effective population size decline in all populations during Antiquity. Our results suggest that the early densification of human populations around the Mediterranean Basin coupled to the development of seafaring techniques were the main drivers of the decline of Mediterranean monk seals. This repository contains all the scripts and most of the intermediary files necessary to replicate the analyses of the preprint "The antique genetic plight of the Mediterranean monk seal (Monachus monachus)" available at: https://biorxiv.org/cgi/content/short/2021.12.23.473149v3 This version of the data has been revised according to two rounds of review by two reviewers' comments and suggestions during a submission process at Proceedings of the Royal Society B. Within each of the different zipped folders a readme.txt file briefly explains how the analyses are organized. We thank all the collectors and museums listed in Table S1 for providing access to genetic samples. We are grateful to Sophie Courjal and the staff of the “Plateau technique - Biologie moléculaire et microbiologie” at EDB for their assistance during lab work, to P. Kyritsis [Archipelagos], for his logistic help, to I. Carvalho for early comments on the manuscript and two anonymous reviewers who significantly helped to improve the manuscript. This work was funded by the Fondation Prince Albert II de Monaco (project “Génétique de la conservation du phoque moine de Méditerranée”). The Genotoul bioinformatics (Bioinfo Genotoul) platforms provided computing resources. JS was supported by PANGO-GO (ANR-17-CE02-0001), and LABEX TULIP (ANR-10-LABX-0041).

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Coastlines data are derived from the land area grids to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 that fall along waterbodies. The coastlines are designed for cartographic use with the GRUMPv1 population raster data sets. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Urbanization and associated environmental changes are causing global declines in vertebrate populations. In general, population declines of the magnitudes now detected should lead to reduced effective population sizes for animals living in proximity to humans and disturbed lands. This is cause for concern because effective population sizes set the rate of genetic diversity loss due to genetic drift, the rate of increase in inbreeding, and the efficiency with which selection can act on beneficial alleles. We predicted that the effects of urbanization should decrease effective population size and genetic diversity, and increase population-level genetic differentiation. To test for such patterns, we repurposed and reanalyzed publicly archived genetic data sets for North American birds and mammals. After filtering, we had usable raw genotype data from 85 studies and 41,023 individuals, sampled from 1,008 locations spanning 41 mammal and 25 bird species. We used census-based urban-rural designations, human population density, and the Human Footprint Index as measures of urbanization and habitat disturbance. As predicted, mammals sampled in more disturbed environments had lower effective population sizes and genetic diversity, and were more genetically differentiated from those in more natural environments. There were no consistent relationships detectable for birds. This suggests that, in general, mammal populations living near humans may have less capacity to respond adaptively to further environmental changes, and be more likely to suffer from effects of inbreeding.

Dizygotic twinning, the simultaneous birth of siblings when multiple ova are released, is an evolutionary paradox. Twin bearing mothers often have elevated fitness1-5; but despite twinning being heritable6, twin births only occur at low frequencies in human populations7. We resolve this paradox by showing that twinning and non-twinning are not competing strategies, instead dizygotic twinning is the outcome of an adaptive conditional ovulatory strategy of switching from single to double ovulation with increasing age. This conditional strategy when coupled with the well-known decline in fertility as women age, maximizes reproductive success and explains the increase and subsequent decrease in twinning rate with maternal age that is observed across human populations8-10. We show that the most successful ovulatory strategy would be to always double ovulate as an insurance against early fetal loss, but to never bear twins. This finding supports the hypothesis that twinning is a byproduct of selection for double ovulation rather than twinning.

Methods These data are used to generate the figures in the manuscript.

Figure 1 is a composite data set where twinning rates are taken from different populations and fitted with the model as described in the ms.

Fig 2 are data generated from the simulation model.

Fig 3 are generated from the analytical model

The R code is supplied by the authors with the request that future users of the code are aware that this is code represents an ongoing research enterprise of ours and that they contact the authors in a spirit of collaboration (joseph.tomkins@uwa.edu.au). We think that there are lots of interesting things to do with the simulation and we are happy to engage in collaborations and test new ideas. We are currently working on variation within and across populations.

The code runs the simulation, numerous replicates of which, with appropriate adjustment to the input variables, are used to generate the data in Figure 2.

The manual is a help file for the code.

The Costa Rica Bird Observatories is a nationwide monitoring initiative created and managed through partnerships among the National Institute of Biodiversity (INBio), US Forest Service, Klamath Bird Observatory, and many other collaborators, both private and public. The Observatories’ primary objective includes the promotion of bird conservation and education in Costa Rica through scientific monitoring.

Humans and birds depend on intact ecosystems for food resources, shelter and other broad environmental processes such as carbon sequestration and atmospheric regulation. Human enterprise routinely degrades ecosystems causing the global decline of many bird populations. To manage and conserve bird species in peril we must identify factors preventing population-level recovery, thereby moving beyond estimates of mere population size to demographics and to the underlying causes of population changes.

It should be noted that this data is now somwhat dated!

Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes.

Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment.

The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys.

The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis.

Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

The 2003 Santarem dataset consists of 8 interconnected datasets and 1 linking file. The primary unit of analysis is the rural property or lot. Each lot in the sample contains a minimum of 1 household with a mean of 1.33 households per lot in the final sample. Within households, data were collected on subsets of individuals as well as additional properties used by the households in the study. These 2003 Santarem data come from interviews with farm families in an agricultural zone south of the city of Santarem in the Brazilian state of Para. Santarem is a relatively old settlement within the Brazilian Amazon that has experienced waves of regional settlement in the 1930s, mid-century, and the 1970s. The study region is adjacent to the confluence of the Amazon and Tapajos Rivers and the northern terminus of the BR-163 (the Cuiaba-Santarem Highway). BR-163 links intensive agropastoral production (particularly mechanized soybean farming) in the state of Mato Grosso to Santarem, where the multinational corporation Cargill runs a deepwater port (opened in 2003) for loading soybeans onto oceangoing ships. The opening of this port has accelerated the process of urbanization and led to a transformation from a landscape of small family farming to a landscape of mechanized agriculture (description adapted from VanWey, Leah K., and Kara B. Cebulko, 2007, Journal of Marriage and the Family 69: 1257-1270). The discourse on deforestation has focused on the alarming rates of deforestation in the Amazon Basin to the neglect of the dynamic and reciprocal influences between the human population and the environment. Deforestation is a process mediated by human intervention, from the act of clearing to how such a clearing is used and managed over time. It would be helpful to know whether observable rates of forest removal represent a stage in the developmental cycle of households or represents the simple and direct impact of increasing population in these environments. From the point of view of theory and method, it is necessary to develop new approaches that effectively link demographic process to the interactive relationship of population to specific aspects of an environmental matrix. This project addressed multiple scales, from household dynamics to landscape dynamics and has developed methods by which to scale between them. We hypothesize that as households occupy frontier areas past the first generation, they move from a strategy of managing their land under the constraints of available household labor to a strategy that gives greater recognition of the constraints posed by land quality and of the risks to their farm operation coming from external socioeconomic forces and biophysical constraints. In the first generation, the labor available to a household is determined by the size of the household making the initial trip to the frontier (primarily young couples is common in frontier regions) and later by the fertility of these initial migrants. As these initial migrants age and their children enter adulthood (thereby becoming the second generation), labor supply is determined by the reproductive and land use choices of these children. Given the precipitous decline in female fertility, other factors gain salience in the second generation: the suitability of the land for various uses, the availability of off-farm employment and educational opportunities (both locally and those requiring migration), and macroeconomic factors affecting the economic viability of farming. These decisions then directly determine the entries into and exits from the household. This study investigated five basic questions: (1) Does the changing availability of household labor over the household life cycle affect the trajectory of deforestation and land use change in the same way for later generations of Amazonian farmers as for first generation in-migrants? (2) What are the determinants of changing household labor supply? Specifically, what are the biophysical and socioeconomic determinants of entries into and exits from the household through fertility, migration, and marriage? (3) How are the decisions of households regarding land use and labor allocation constrained by soil quality, access to water supplies, interannual drought events (e.g. El Nino type events), and other resource scarcities? (4) Are there notable differences in land use choices made by la

The impacts of human activities and climate change on animal populations often take considerable time before they are reflected in typical measures of population health such as population size, demography, and landscape use. Earlier detection of such impacts could enhance the effectiveness of conservation strategies, particularly for species with slow population growth. Passive acoustic monitoring is increasingly used to estimate occupancy and population size, but this tool can also monitor subtle shifts in behavior that might be early indicators of changing impacts. Here we use data from an acoustic grid, monitoring 1250 km2 of forest in the northern Republic of Congo, to study how forest elephants (Loxodonta cyclotis) assess the risk of poaching across a landscape that includes a national park as well as active and inactive logging concessions. By quantifying emerging patterns of behavior at the population level, arising from individual-based decisions, we gain an understanding of how..., Core data are 24-hour continuous sound files acquired on 50 digital sound recorders deployed in the northern Republic of Congo. Sound files are processed with a detection algorithm running in MatLab, which tags putative elephant rumble vocalizations. Output files from the detection process are reviewed to remove false positive detections. Data tables were compiled from the edited detector output files, which include the location, date, and time of each verified vocalization. Ecological metadata (season, habitat, study stratum, etc.) were then added to these files to construct analysis dataset tables., , # Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants

This README file was generated on 2024-07-09 by Peter H. Wrege

GENERAL INFORMATION

Author Information

• Principal Investigator Contact Information
• name: Peter H. Wrege
• Institution: Cornell University
• Email: p.wrege@cornell.edu

Date of data collection: 15Jan2017-27April2021

Geographic location of data collection: Nouabale-Ndoki National Park, Republic of Congo

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain
2. Publications that cite or use the data: Peter H. Wrege, Frelcia Bien-Dorvillon Bambi, Phial Jackel Ferdy Malonga, Onesi Jared Samba, Terry Brncic (2024). Early detection of human impacts using acoustic monitoring: an example with forest elephants. PLOS ONE
3. Data is also available at: The Registry of Open Data on Amazon Web Services ...

Humans are regularly cited as the main driver of current biodiversity extinction, but the impact of historic volcanic activity is often overlooked. Pre-human evidence of wildlife abundance and diversity are essential for disentangling anthropogenic impacts from natural events. Réunion Island, with its intense and well-documented volcanic activity, endemic biodiversity, long history of isolation and recent human colonization, provides an opportunity to disentangle these processes. We track past demographic changes of a critically endangered seabird, the Mascarene petrel Pseudobulweria aterrima, using genome-wide SNPs. Coalescent modeling suggested that a large ancestral population underwent a substantial population decline in two distinct phases, ca. 125,000 and 37,000 years ago, coinciding with periods of major eruptions of Piton des Neiges. Subsequently, the ancestral population was fragmented into the two known colonies, ca. 1,500 years ago, following eruptions of Piton de la Fournaise. In the last century, both colonies declined significantly due to anthropogenic activities, and although the species was initially considered extinct, it was rediscovered in the 1970s. Our findings suggest that the current conservation status of wildlife on volcanic islands should be firstly assessed as a legacy of historic volcanic activity, and thereafter by the increasing anthropogenic impacts, which may ultimately drive species towards extinction. Methods Single Nucleotide Polymorphism (SNP) genotyping was carried out by Diversity Arrays Technology (DarT Pty Ltd, Canberra) using the DArTseq protocol. DArT library was prepared using DNA from 93 birds and the restriction enzymes PstI and SphI. Loci were aligned to the genome assembly of Calonectris borealis (family Procellariidae; GCA_013401115.1). The raw DArTseq data (67,095 SNPs) was filtered by the authors using the dartR v 2.1.4 R package (see manuscript for details). Four genomic datasets (dataset 1 – dataset 4) were generated and used for the downstream analyses (See Text S1 and Fig. S8 for details). Specimen metadata was collated during fieldwork.

Data from 1) The inter-calibrated stable night lights version 4, 2) The Gridded Population of the World (GPW) version 3, and 3) The History Database of the Global Environment (HYDE) 3.1 were spatially aggregated to a resolution of 5.0 arc minutes (approximately 10km2 at Equator), the original resolution of the HYDE 3.1 cropland data. This aggregation caused some loss of resolution for the other 2 data sets (approximately 2.8 km2 for stable nightlights and 5 km2 for human population density). For each terrestrial pixel, we calculated the difference between values in the first and last year. This was done separately for the 3 layers. We transformed human population density to the square root. Data transformation of variables is a standard procedure for spatial pressure mapping, and it allows comparison between different data types and distributions . We chose square-root transformation because it accounted for the expected declining impacts per person in densely populated areas...