These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

Aim: Invasive plants may evolve a suite of distinctive traits during spread in the new range. Among these traits, dispersal ability is an important trait determining the invasion speed of exotic plants. There is evidence that higher dispersal ability is favored at the invasion front, where population density may be low. However, no study has explicitly tested how planting density in a common garden affects the dispersal ability of invasive plants. Location: Hainan island of China. Methods: In this study, using 27 populations of an invasive plant, Mikania micrantha, which is expanding its range on Hainan island of China, we examine how three dispersal-related traits (i.e., dispersal ability, fruit mass, and pappus radius) change with distance from invasion centre and field population density, and how planting density in a common garden affects dispersal traits. Results: Dispersal traits did not change with distance from the invasion centre and field population cover either in the natural..., , , # This is the raw data showing the population information, treatments, and dispersal traits of the 27 Mikania micrantha populations.

Description of the data and file structure

In the first page, data of the common garden experiment are shown. These include block, planting density treatments, pappus radius, fruit mass, and area-mass ratio (AMR). A 'Novalue' cell indicates that the corresponding plants did not set any fruits. In the second page, data in the natural environment are shown. These include pappus radius, fruit mass, and area-mass ratio (AMR). In each page, the population origin (western or eastern), distance from invasion center, field cover, and frequency of field occurance of each of the 27 populations are also shown.

p = significance level (α = 0.05); a-Population; b-Settlements; c-Urban centers; d-Unpaved road; e-Paved road; f-Total roads.

The raster dataset represents top location score areas suitable for vegetable storage, filtered by exclusive criteria: access to finance, distance to major roads, access to IT (mobile broadband connection).

Access to finance and roads are defined using a linear distance threshold:

• Banks - 10km buffer radius.

• Major roads - 5km buffer radius.

• Access to IT.

• Electrification.

Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map.

The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids.

This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Data publication: 2024-02-23

Contact points:

Resource Contact: FAO-Data

Resource Contact: Justeen De Ocampo

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets:

1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. https://data.apps.fao.org/catalog/iso/304c21fb-0f5a-44ad-9948-2af6a7144fb5
2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution
   within disaggregated units. https://data.apps.fao.org/map/catalog/srv/eng/catalog.search;jsessionid=4AA4D377D0F90E3328ACBDB5C21BFFC6?node=srv#/metadata/0c6be5d1-3a73-4516-953b-dbe2b511d6b3
3. OpenStreetMap.
4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. https://data.apps.fao.org/catalog/dataset/mobile-broadband-coverage-global-1km.
5. Asset Wealth Index - Atlas AI 2020 and Electrification 2021. https://data.apps.fao.org/catalog/iso/7b3be5a0-945e-4cb0-8e94-fc6cddad3c60

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Crop Storage Final Location: Vegetables (Djibouti - ~ 500 m)

Characteristics of included infants from the BILD cohort.

The scene shows the affected area and population by earthquake. The red shades in the scene shows different level of amount of population get affected by earthquake. The circles show the affected population by radius. We can tell neighbour countries such as China and India also get affected. One of slide I captured is Mt Everest. According to the scene, Mt Everest was affected by the earthquake as well which leads to multiple deaths of mountaineer.

Risk factors for respiratory symptoms.

Results of the principal components analysis (PCA) of the anthropogenic drivers.

A Certificate of Necessity (“CON”) is required to operate a ground ambulance and transport patients in Arizona. The Arizona Department of Health Services (“ADHS”) regulates the operating and response times of ambulance services to meet the needs of the public and ensure adequate service, pursuant to Arizona Revised Statute (“A.R.S.”) § 36-2232. Under A.R.S. § 36-2232(A)(3), response times shall follow uniform standard definitions for urban, suburban, rural, and wilderness geographic areas within a CON. Under Arizona Administrative Code (“A.A.C.”) R9-25-901, “Scene locality” is defined as an urban, suburban, rural, or wilderness area. Scene locality is sometimes also referred to as “urbanicity”. The current scene locality / urbanicity maps were developed based on the 2020 Census urban areas and block groups, to geographically represent areas within a CON defined under A.A.C. R9-25-901 as the following:“Urban area” means a geographic region delineated as an urbanized area by the United States Department of Commerce, Bureau of the Census. “Suburban area” means a geographic region within a 10-mile radius of an urban area that has a population density equal to or greater than 1,000 residents per square mile.“Rural area” means a geographic region with a population of less than 40,000 residents that is not a suburban area. “Wilderness area” means a geographic region that has a population density of less than one resident per square mile.Additional Information:The 2010 definition for urbanized areas is applied, as the 2020 Census doesn't delineate urban into two categories.Updates occur as needed based on the most recent decennial census, adhering to Administrative Statute and Code.Regulatory authority and definitions for scene localities can be found in the Statute and Rule Book, under A.R.S. § 36-2232 and A.A.C. R9-25-901.For more information about the Certificates of Necessity program, please visit the ADHS Ground Ambulance Program website or call (602) 364-3150.Last Updated: Update Frequency: As Needed; requires Administrative Code change

The Gunnison sage-grouse (Centrocercus minimus) habitat suitability surface for Dove Creek satellite population represented here reflects breeding season at a patch scale context (30 m x 30 m pixel and radius window extents [radius] of 45 m, 120 m, 180 m, 270 m, 390 m, and 570 m). Habitat suitability estimated for areas constrained within the thresholded landscape model (containing 95% of use locations) developed for Colorado Parks and Wildlife critical habitat extent (southwestern Colorado). We developed habitat selection models for Gunnison sage-grouse (Centrocercus minimus), a threatened species under the U.S. Endangered Species Act. We followed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses. Models were developed for six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) from use locations collected between 1991 and 2016 (see larger citation for map of population boundaries). For each population, models were developed at two life stages (breeding and summer) and at two hierarchical scales (landscape and patch). We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. These resource selection function models relied on spatial data describing habitat conditions at different spatial scales, where environmental conditions differ, and habitat selection occur at different spatial scales for different available resources.

Density map of seafloor temperature developed with the “point density” algorithm of ArcGIS®. Pixel value, number of data per 100,000 km2. Parameters: population field, none; cell size, 5000; radius, 178,415 metres; areal units, square kilometres; method, geodesic. Knowledge gap, raster value < 1 Reference: https://doi.org/10.3390/app11062865

The Gunnison sage-grouse (Centrocercus minimus) habitat suitability surface for Crawford satellite population represented here reflects summer season at a patch scale context (30 m x 30 m pixel and radius window extents [radius] of 45 m, 120 m, 180 m, 270 m, 390 m, and 570 m). Habitat suitability estimated for areas constrained within the thresholded landscape model (containing 95% of use locations) developed for Colorado Parks and Wildlife critical habitat extent (southwestern Colorado). We developed habitat selection models for Gunnison sage-grouse (Centrocercus minimus), a threatened species under the U.S. Endangered Species Act. We followed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses. Models were developed for six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) from use locations collected between 1991 and 2016 (see larger citation for map of population boundaries). For each population, models were developed at two life stages (breeding and summer) and at two hierarchical scales (landscape and patch). We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. These resource selection function models relied on spatial data describing habitat conditions at different spatial scales, where environmental conditions differ, and habitat selection occur at different spatial scales for different available resources.

The polygon provided in this data layer represents surface designated critical habitat areas based on our best assessment at the time of areas that are within the geographical range of the Austin blind salamander and are considered to contain features essential to the conservation of this species. When determining surface critical habitat boundaries, we were not able to delineate specific stream segments on the map due to the small size of the streams. Therefore, we drew a circle with a 262-ft (80-m) radius representing the extent the surface population of the site is estimated to exist upstream and downstream. The surface critical habitat includes the spring outlets and outflow up to the ordinary high water line (the average amount of water present in nonflood conditions, as defined in 33 CFR 328.3(e)) and 262 ft (80 m) of upstream and downstream habitat (to the extent that this habitat is ever present), including the dry stream channel during periods of no surface flow. The surface habitat does not include manmade structures (such as buildings, aqueducts, runways, roads, and other paved areas) within this circle. For more information on Austin blind salamanders, see https://www.fws.gov/southwest/es/AustinTexas/ESA_Sp_Salamanders.html.