The layer was based on the geoprocessing buffer analysis tool. The buffer analysis was applied to libraries in Broward County. The purpose of the data is for 2020 Census planning purposes.

The "Create Buffers" analysis tool in ArcGIS Online can be used to identify areas within a given distance of existing features, be those points, lines or polygons.The distance used for the buffers can either be a fixed distance from all features, or could be taken from a numerical value within each features attributes.

This analysis of park deserts uses LA County's parks & gardens layer to create a buffer of areas within 0.5 miles of a park. A park desert is identified as an area outside of that boundary.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This data set represents a series of 502 mixed-species bird flock compositions, and derived taxonomic, functional, and phylogenetic diversity indices, that were gathered along a gradient of forest fragment sizes (range = 10-173 ha) in the Colombian Western Andes. We sampled mixed-species flocks using transect surveys along 14 transects in 8 fragments and a continuous forest reference site in the same landscape and at the same elevation (~1900-2200 m.a.s.l.). We also used buffer analysis to quantify the proportion of forest cover and forest edge within 1 km of each transect, and calculated local vegetation density and complexity, as well as distance from edge, for each 100-meter transect segment (n = 70 segments). Flock composition data observed on a transect were used to calculate overall species richness and flock size as well as two indices of functional and phylogenetic diversity; we calculated the stadardized effect size (SES) of each measure to account for the correlation between these measures and species richness. We also provide the raw counts of each species for each flock composition. These data were used for the analyses in Jones and Robinson (2020).

Methods Study System and Sites

We conducted all fieldwork in subtropical humid forests located within the municipality of El Cairo, Valle del Cauca department in Colombia. The study region is part of the Serrania de los Paraguas in the Western Andes mountain range, a center of avian threatened species diversity and endemism within Colombia. The study landscape in this municipality consists of a patchwork of forest fragments embedded in a matrix of cattle pasture, regenerating scrub, and coffee farms. Within this landscape, we selected eight fragments representing a gradient in patch sizes (range 10 to 170 ha). Sites are in the same altitudinal belt (1900-2200 m.a.s.l.) and matrix type (cattle pasture) to control for effects of altitude and matrix type on flock size and composition. Within-patch disturbance is common in fragmented Andean forests in Colombia, particularly illegal selective logging, which in our landscape typically occurred as removal of select old-growth trees for lumber by landowners; logging histories varied considerably from historical to ongoing, and extensive to limited, both within and between patches. We established 500-meter transects through forest interior (n = 14 total transects) which were opportunistically placed on existing trails, at variable distances from the edge of the fragments. We further divided each transect into 100-meter segments to account for heterogeneity in vegetation structure within transects. We accounted for edge effects by measuring the distance to forest edge of each transect segment.

We stratified forest fragments into large (≥ 100 ha), medium (~30-50 ha), and small (≤ 20 ha) size categories and selected a minimum of two replicates of each; these represent the range of fragment sizes available in our study landscape. We also included a non-fragmented reference site (Reserva Natural Comunitária Cerro El Inglés, ~750 ha) connected to over 10,000 ha of continuous forest to the north and west along the spine of the Serranía de los Paraguas. We only selected fragments with primary or late-successional secondary forest; vegetation structure and canopy height varied substantially between patches based on intensities of selective logging and land-use histories (see above). Fragments were all separated by ≥ 100 meters to minimize among-patch movement of birds, and all transects in different fragments were at least 250 meters apart.

Transect Surveys for Mixed-species Flocks

We performed transect surveys for mixed-species flocks, adapted from Goodale et al. (2014), in forest fragments from June-August 2017 (boreal migrants absent) and January-March 2018 (boreal migrants present). Both sampling periods corresponded to a dry season in the Western Andes, which has a bimodal two-dry, two-wet seasonality pattern. For each transect, we spent two and a half sequential field days performing continuous transect surveys; we conducted surveys in small fragments, large fragments, and continuous forest sites in random order to avoid a temporal bias in sampling. Surveys were distributed across the morning (7:30-11:30) and evening (15:00-17:30) hours. Transects were walked slowly and continuously by 2-3 observers, including local birdwatchers familiar with all species (Harrison Jones present for all surveys); flocking birds were identified by both sight and sound. When we encountered a flock, we noted the time of day and transect segment in which it was observed and spent up to a maximum of 45 minutes characterizing it with 10x binoculars. At least 5 minutes were spent with each flock, following it if possible. Because detection of species in flocks was imperfect, we only included a flock observation in the analysis if we felt that at least 80% of the individuals were observed (e.g. after spending several minutes of continuous observation at the end of the survey period without observing a new species or individual); incomplete flock observations were not included in analyses. We feel that our survey methodology accurately described flock composition because birds moved and called frequently in flocks, leading to high detectability. We noted the start and end time of each survey, and the presence of incomplete flocks to calculate flock encounter rate. We also supplemented the transect surveys with data from flocks opportunistically observed on a transect while performing other fieldwork. Some flocks in the data set represent flock compositions recorded near but not on a transect; these compositions have no associated transect segment.

Calculation of Landscape-level Variables

    We obtained landscape-level variables for analyses using geographic information software (GIS) analysis in ArcGIS (ArcMap 10.3.1; Esri; Redlands, CA). To quantify landscape composition and configuration, we buffered each transect (n = 14) by 1 km; buffers extended from the entire length of the transect. We then calculated measures of landscape composition and configuration using a recent land-cover/use categorization made by the Corporación Autónoma Regional del Valle del Cauca, converted to a 25-m cell-size raster. To quantify landscape composition, we calculated percentages of the forest-cover type within each buffer using the ‘isectpolyrst’ tool in Geospatial Modelling Environment (version 0.7.4.0). We measured landscape configuration for each transect as edge density, or length of all forest edges (in meters) divided by total buffer area (in hectares). The distance to edge was calculated in meters for each 100-meter transect segment (n = 70) as the shortest straight-line distance between the center point of the segment and the nearest edge of the fragment. 

Vegetation Measurements and Principal Component Analysis

    We measured vegetation structure in each 100-m transect segment used for flock sampling. Vegetation measurements were made from June-August 2017; based on our observations of vegetation, we assumed variation between the two sampling periods was minimal. We used the sampling methodology of James and Shugart (1970), following the modifications made by Stratford and Stouffer (2013), and further modified to be used with belt transects. Broadly, the methodology comprises two components for every 100-meter transect segment: (1) the quantification of canopy cover, ground cover, canopy height, and foliage height diversity of vegetation using point sampling every 10 meters and (2) the quantification of shrub, vine, fern, palm, and tree fern and tree density using 3 meter-wide belt sampling.

For the point sampling, we measured eight variables at ten-meter intervals, for 10 points per 100-meter segment. As a measure of foliage height diversity along the transect, we noted the presence or absence of live vegetation at five heights: <0.5 m, >0.5–3 m, >3–10 m, >10–20 m, and >20 m. Above 3 meters, we used a rangefinder to determine heights while sighting through a tube with crosshairs. Canopy and ground cover were calculated to the nearest 1/8th of the field of view by sighting through a vertical canopy densiometer (GRS Densiometer, Geographic Resource Solutions, Arcata, CA). For each segment, we averaged values for canopy cover, and ground cover, and calculated the proportion of points at which vegetation was present for each height category. For the belt transect sampling, we surveyed vegetation along the same transects and calculated densities for each 100-m transect interval. We counted all shrubs, vines, ferns, tree ferns, and palms encountered on 1.5 meters to either side. Secondly, we counted all trees (woody vegetation > 2 m in height) within 1.5 meters of the transect and measured their diameter at breast height (DBH). Trees were later categorized into six DBH size classes for analysis: 1-7 cm, 8-15 cm, 16-23 cm, 24-30 cm, 31-50 cm, and > 50 cm. We additionally recorded the largest tree’s DBH.

    To quantify foliage height diversity, we calculated the Shannon Diversity Index of the proportion of points with vegetation present in each of the five height bands for each segment (n = 70 segments). To reduce redundancy and minimize correlation between variables, we (separately) ordinated our tree DBH and understory plant density data using principal component analysis (PCA: McGarigal et al. 2000) for each 100-meter transect segment. We column (Z score) standardized data prior to ordination to account for differences in the units of measurement and used the covariance matrix to run the PCA. The principal components were interpreted using the significance of the principal component loadings. The PCA was run in R (version 3.5.1) using the princomp function in the stats package. The Shannon Index was calculated using the diversity function of the vegan package

Survey summary map for the August 20, 2025, Buffer Lake, SK tornado. Ground survey conducted August 23, 2025. Map includes ground photos, drone photos, survey route, drone flight paths, tornado centreline, and worst damage point. Note that additional straight-line wind damage was also found in the surrounding area. View Survey Summary Map