These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

The DC Metropolitan Area Drug Study (DCMADS) was conducted in 1991, and included special analyses of homeless and transient populations and of women delivering live births in the DC hospitals. DCMADS was undertaken to assess the full extent of the drug problem in one metropolitan area. The study was comprised of 16 separate studies that focused on different sub-groups, many of which are typically not included or are underrepresented in household surveys. The Homeless and Transient Population study examines the prevalence of illicit drug, alcohol, and tobacco use among members of the homeless and transient population aged 12 and older in the Washington, DC, Metropolitan Statistical Area (DC MSA). The sample frame included respondents from shelters, soup kitchens and food banks, major cluster encampments, and literally homeless people. Data from the questionnaires include history of homelessness, living arrangements and population movement, tobacco, drug, and alcohol use, consequences of use, treatment history, illegal behavior and arrest, emergency room treatment and hospital stays, physical and mental health, pregnancy, insurance, employment and finances, and demographics. Drug specific data include age at first use, route of administration, needle use, withdrawal symptoms, polysubstance use, and perceived risk.This study has 1 Data Set.

This report displays the data communities reported to HUD about the nature of and amount of persons who are homeless as part of HUD's Point-in-Time (PIT) Count. This data is self-reported by communities to HUD as part of its competitive Continuum of Care application process. The website allows users to select PIT data from 2005 to present. Users can use filter by CoC, states, or the entire nation.

1.Natural and anthropogenic forest canopy disturbances significantly alter forest dynamics and lead to multi-dimensional shifts in the forest understorey. An understorey plant's ability to exploit alterations to the light environment caused by canopy disturbance leads to changes in population dynamics. The purpose of this work was to determine if population growth of a species adapted to low light increases in response to additional light inputs caused by canopy disturbance, or alternatively, declines due to long-term selection under low light conditions. 2.To address this question, we quantified the demographic response of an understorey herb to three contrasting forest canopy disturbances (ice storms, tent caterpillar defoliation and lightning strikes) that encompass a broad range of disturbance severity. We used a model shade-adapted understorey species, Panax quinquefolius, to parameterize stage-based matrix models. Asymptotic growth rates, stochastic growth rates and simulations of...

This dataset tracks the updates made on the dataset "Washington DC Metropolitan Area Drug Study Homeless and Transient Population (DC-MADST-1991)" as a repository for previous versions of the data and metadata.

The reintroduction of rare species in natural preserves is a commonly used restoration strategy to prevent species extinction. An essential first step in planning successful reintroductions is identifying which life stages (e.g., seeds or large adults) should be used to establish these new populations. Following this initial establishment phase, it is necessary to determine the level of survival, growth, and recruitment needed to maintain population persistence over time and identify management actions that will achieve these goals. In this 5-year study, we projected the short- and long-term population growth rates of a critically endangered long-lived shrub, Delissea waianaeensis. Using this model system, we show that reintroductions established with mature individuals have the lowest probability of quasi-population extinction (10 individuals) and the highest increase in population abundance. However, our results also demonstrate that short-term increases in population abundances are overly optimistic of long-term outcomes. Using long-term stochastic model simulations, we identified the level of natural seedling regeneration needed to maintain a positive population growth rate over time. These findings are relevant for planning future reintroduction efforts for long-lived species and illustrate the need to forecast short- and long-term population responses when evaluating restoration success.

1. Forest recovery after deforestation or logging is essential for restoring vegetation carbon stocks and biodiversity. Predicting the pace of forest recovery requires demographic models. However, our demographic knowledge of the short-term and time-varying (i.e., transient) dynamics in post-disturbance environments remains limited. This knowledge gap impedes the development of restoration practices that address both immediate and long-term recovery needs. 2. Using 11 years of demographic data from a degraded subtropical forest after selective logging, we constructed integral projection models (IPMs) to estimate both short-term transient (r) and long-term asymptotic (λs) population growth rates for Pinus massoniana, a pioneer and dominant tree widely used in reforestation projects in southeast China. We also conducted elasticity analysis to identify the key life stages and vital rates that had the largest effect on the asymptotic and transient population growth rates. 3. The transient growth rates revealed a rapid population growth in the first three years following logging (r > 1) that gradually declined over time (r

In the United States in 2023, **** percent of the homeless population living in El Dorado County, California were unsheltered.

Transient killers whales inhabit the West Coast of the United States. Their range and movement patterns are difficult to ascertain, but are vital to understanding killer whale population dynamics and abundance trends. Satellite tagging of West Coast transient killer whales to determine range and movement patterns will provide data to assist in understanding transient killer whale populations. Locational data.

"Ratio of Homeless Population to General Population in major US Cities in 2010. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. A 2010 result is only available for Seattle, WA. Other cities either did not conduct a count in 2010, or their 2010 results are not yet available. 2009 unsheltered census figures were used for Los Angeles, San Francisco, Miami, and Washington, DC, and Boston; the 2007 estimate is used for Chicago. General population figures are the latest estimates from the U.S. Census Bureau."

Gompertz, logistic and Weibull population growth models fitted to each of the eight experimental population and values of Akaike Information Criterion (AIC), Root-Mean-Square Error (RMSE) and carrying capacity K.

Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, but little is known about the degree of variation among spatially segregated populations of the same species in relation to environmental conditions. We assessed the relative importance of two cross-season correlations in survival and productivity, for three Atlantic puffin (Fratercula arctica) populations with contrasting population trajectories and non-overlapping year-round distributions. The two correlations reflected either a relationship between adult survival prior to breeding on productivity or a relationship between productivity and adult survival in the subsequent year. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a tra...

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

Stage structures of populations can have a profound influence on their dynamics. However, not much is known about the transient dynamics that follow a disturbance in such systems. Here we combined chemostat experiments with dynamical modeling to study the response of the phytoplankton species Chlorella vulgaris to press perturbations. From an initially stable steady state, we altered either the concentration or dilution rate of a growth-limiting resource. This disturbance induced a complex transient response—characterized by the possible onset of oscillations—before population numbers relaxed to a new steady state. Thus, cell numbers could initially change in the opposite direction of the long-term change. We present quantitative indexes to characterize the transients and to show that the dynamic response is dependent on the degree of synchronization among life stages, which itself depends on the state of the population before perturbation. That is, we show how identical future steady states can be approached via different transients depending on the initial population structure. Our experimental results are supported by a size-structured model that accounts for interplay between cell-cycle and population-level processes and that includes resource-dependent variability in cell size. Our results should be relevant to other populations with a stage structure including organisms of higher order.

Data Prepared by Los Angeles Homeless Services AuthorityJune 26, 2019Homeless Count 2019 Dashboard MethodologyTotal number of people experiencing homelessness is the sum of (1) the sheltered population (the total number of people staying in emergency shelter, transitional housing, or safe haven programs on the night of the point-in-time count) and (2) the unsheltered population (the total number of people counted by volunteers and the estimated number of people sleeping in the dwellings counted by volunteers).

(1) The total number of people experiencing homelessness who slept in an emergency shelter, transitional housing, or safe haven program was reported to LAHSA by each provider and assigned to a census tract. For shelter programs with multiple scattered sites in the LA CoC, an administrative address is used for locating the sheltered population in this dashboard. Shelters that serve persons fleeing domestic or intimate partner violence are excluded due to confidentiality concerns. Persons receiving motel vouchers are excluded in this dashboard because the location of the motel is unknown.

(2) The total number of people experiencing homelessness who slept on the street or in a dwelling not meant for human habitation were counted by volunteers on January 22nd, 23rd, or 24th. 3,873 demographic survey interviews were conducted with persons experiencing unsheltered homelessness from December 2018 to March 2019 to describe the population’s demographics and approximate the number of people in each dwelling. The total persons in uninhabitable dwellings was estimated for each type (car, van, camper/RV, tent, or makeshift shelter) and was estimated at the SPA-level for individual and for family households and can be found on our website. Estimates of the people inside these dwellings was rounded to whole numbers for the purposes of this dashboard.Density ScoringThere are 4 columns seen in the data that represent the density of homeless Individuals per square mile. The 4 column labeled RFP-Scoring is based on the data range between the min and max of homeless calculated of LA County's Homeless Individual numbers. For break down the data is given a specific score based on the density. Below are the ranges:0=01= 1-32= 4-73= 8-114= 12-185= 19-276= 28-427= 43-638= 64-999= 100-17910= 180-5341The breakdown of the data used was quantitative statistical range for 11 categories, 0 being one of the ranges.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

This dataset contains estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth provided by The U.S. Department of Housing and Urban Development. The estimates cover the period of years 2007-2017 and are at national, state and Continuums of Care (CoC) Point-In-Time (PIT) level.

Homelessness is a social crisis in the United States of America. According to McKinney–Vento Homeless Assistance Act, homeless people are those who lack a fixed, regular and adequate nighttime residence. "Ratio of Homeless Population to General Population in major US Cities in 2012.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

Details on populations and species used with indices and results from transient and stochastic analyses.