Data from: Biodiversity and local features of 103 public urban squares in Munich, Germany
Data files
Aug 16, 2024 version files 25.32 KB
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100_square_data.csv
18.53 KB
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README.md
6.80 KB
Abstract
This dataset contains biodiversity and square features of 103 public urban squares (Platz in German) in Munich, Germany. Data were collected to investigate how designed features of urban squares influences the biodiversity occurring there. Data were collected between April 2017 and July 2020. Square features were characterised using Colour-infrared Aerial images (CIR, Bavarian Agency for Digitisation, High-Speed Internet and Surveying, Munich) and land register maps and measurements using a telemeter and a tape measure. Biodiversity of arthropods, bats, birds, bryophytes, pollinators, small mammals, and spontaneous vegetation was determined using taxa-specific sampling methods and deriving richness and abundance (or activity) for each taxon.
README: Data from: Biodiversity and local features of 103 public urban squares in Munich, Germany
https://doi.org/10.5061/dryad.bcc2fqznq
Description of the data and file structure
Biodiversity and local features of 103 public urban squares in Munich, Germany.
Data were collected to investigate how designed features of urban squares influence the biodiversity occurring there. Data were collected between April 2017 and July 2020. Complete methods can be found in the associated manuscript and supplementary information.
Files and variables
100_squares_data.csv contains the square features, abundance (or activity) and richness of arthropods, bats, birds, bryophytes, pollinators, small mammals, spontaneous herbaceous vegetation, and multidiversity. Missing values are indicated as "NA".
The first five rows of the .csv file contain the metadata.
Row 1: Column label (repeated in row 6)
Row 2: A short description of the variable.
Row 3: A short description of the method used to calculate the variable.
Row 4: The datatype (string, binary, numeric).
Row 5: The unit of the variable.
squareId
unique short id of the square
Four letter code based on the full name of the square.
string
name
full name of the square
Extracted from Google Maps
string
district
governmental unit of Munich to which the square belongs
Extracted from the table of Sebastian Dengler's thesis
string
waterPresence
source of water (e.g. fountains) on the square
Zero or one based on presence of fountains on the square
binary
numberStreets
number of streets that are touching the square boundaries
Data extracted from Google Maps
numeric
size
total area of each square
Area in sqm /10000
numeric
distance
distance to the city center (Marienplatz)\
Calculated as eucledian distance between the centroids of the squares and the centroid of Marienplatz
numeric
km
greenBufferProp
proportion of the vegetated area in a 1km-buffer around squares to characterize the surrounding of squares
Using color-infrared aerial images (20 cm x 20 cm resolution per pixel) we calculated the NDVI. We defined a threshold of NDVI values larger than 0.2 to characterize vegetated pixel and calculated the proportion of vegetated pixels for 1km buffers around each square.
numeric
proportion
shrubVolume
average shrub volume per squaremeter ground
Areas of shrubby vegetation were recorded separatly for four different height classes (<1m, 1-2m, 2-3m, >3m). The total shrub volume on a square was calculated by multiplying the absolute areas with the respective heights, and the total volume was devided by the area of the square resulting in an average shrub volume per squaremeter square.
numeric
m3/m2
flowersArea
absolute area of planted flower beds and flower tubs on a square
Sum of areas of flower beds and flower tubs. Aerial images (google earth and land register maps were used for estimating the ground area covered by unsealed surfaces. When areas were covered with trees, or patches were not clearly identifiable on the areal images, they were measured on the squares with a measuring tape and telemeter.
numeric
m2
grassProp
proportion of grass covered area on a square
Area of a square coverd by grass devided by total area of the square.
numeric
proportion
treeRichness
species richness of trees on the square
All trees on a square with dbh>7cm have been identified to species and the number of tree species occuring on a square has been calculated.
numeric
n
treeDensity
density of trees on a square
Absolute number of trees multiplied with 100 divided by area of the square: tabund*100/area (m2) to standardize tree abundance for the differing size of squares.
numeric
1/100m2
treeMedianDbh
average size of trees on a square
Median of the the diameter at breast height (dbh) calculated for all trees on a square with dbh>7cm.
numeric
cm
treeDbhVar
variability of size of trees on a square
Coefficient of variation of the the diameter at breast height (dbh) calculated for all trees on a square with dbh>7cm.
numeric
unitless
oldTreeAbundance
absolute number of old trees on a square
All trees with a dbh>60cm have been defined as old trees and their number counted per square
numeric
n
oldTreeProp
proportion of old trees on a square
Old tree abundance devided by total tree abundance: oldtabund/tabund
numeric
proportion
numberHumans
average number of people on a square
At three times (morning, noon, afternoon; on different days) a photo was taken from the center of squares in all four cardinal directions. The number of people was counted on each picture. Numbers were summed for the four pictures per sampling day and averaged over the three sampling days.
numeric
n
alan
artificial light at night
Calculated as average grey value per square using Luojia-1 satellite images fromNovember 16, 2018.
numeric
mean
multidiversity
multidiversity
Calculated by scaling the species richness to the highest observed richness across all squares and averaging the proportional richness across all taxonomic groups.
numeric
mean
arthropodAbundance
abundance of arthropod orders
numeric
n
arthropodRichness
richness of arthropod orders
numeric
n
batRichness
richness of bat species and species groups
numeric
n
batActivity
artivity of bat species or species groups
Determined by calculating the total number of minutes per sampling period in which a call of that species was recorded.
numeric
n
pigeonAbundance
abundance of Columba livia domestica
numeric
n
birdAbundance
abundance of birds
numeric
n
birdRichness
richness of birds
numeric
n
meanDomestic
average number of domestic animals (cats and dogs)
Determined as the average number of cats or dogs observed during the bird surveys.
numeric
mean
bryophyteRichness
richness of bryophytes
numeric
n
pollinatorAbundance
abundance of pollinator orders
numeric
n
pollinatorRichness
richness of pollinator orders
numeric
n
flowerAbundance
abundance of racemes, spikes and umbels
Calculated as the number of racemes, spikes and umbels in a three-meter radius around phytometers used for pollinator monitoring.
numeric
n
flowerRichness
richness of flowers
Calculated as the richness of flowers in a three-meter radius around phytometers used for pollinator monitoring.
numeric
n
wildMammalActivity
activity of small wild mammals, predominately hedgehods
The proportion of valid track sheets per square for which small mammals were detected.
numeric
proportion
pestMammalActivity
activity of pest small mammals, predominately rats and mice
The proportion of valid track sheets per square for which small pest mammals were detected.
numeric
proportion
vegetationRichness
richess of spontaneous vegetation
numeric
n
Code/software
No code or software is required to view our data.
Methods
Arthropods were sampled using suction sampling for sealed surfaces, grass, and flowerbeds. Trees and shrubs were sampled using knockdown sampling. Sampled arthropods were collected in jars and sorted to order level in the laboratory.
Bats were sampled using an ecoObs Batcorder 3.1 ultrasonic recorder (quality = 20, threshold = -36 dB, post-trigger = 800 ms, critical frequency = 14kHz). Recording took place from one hour before sunset to one hour post sunrise four times on single nights. Bat calls were identified to species level where possible and where not, the lowest level species group was attributed based on the acoustic properties.
Birds (separately considering the feral pigeon, Columba livia domestica) were determined visually and aurally by observing all birds within a 25m radius along transects for a total of 20 minutes on each square in spring, fall, and winter.
Bryophytes (including mosses and liverworts) was determined by collecting bryophytes on three different types (wood, soil, and stone). Each substrate type was sampled for a maximum of 20 minutes each and sampling stopped if no new species were detected for several minutes. Collected samples were dried overnight at 50°C and were identified to species level where possible, otherwise identified as species groups or morphospecies using relevant guidebooks and comparison with herbarium specimens.
Pollinators on each square was determined by counting visits of pollinator functional groups (Coleoptera, Bombus, Lepidoptera, Apis mellifera, Syrphidae, Diptera, Apoidae, and Vespidae) to each of five different phytometers representing different pollination syndromes. Phytometers were: Nicotiana sanderae, Salvia farinacea, Lobelia erinus, Sanvitalia procumbens, Lobularia maritima. Due to disease, the Lobularia maritima were replaced by Euphorbia hypericifolia, part way through the sampling. Phytometers were observed for 30 minutes at three different times of day: morning, midday, and afternoon.
Small and pest mammal species (mice and rats) were determined using footprint tunnels baited with cat food containing ink pads and blank paper for recording tracks. Tunnels were in place for five consecutive nights on each square with ten tunnels per square covering different combinations of surfaces (sealed or gravel and grass) and linear structures (hedge and wall). The tunnels were checked daily and the track sheets replaced if necessary. Tracks were later identified to species level or species group based on characteristics of the footprint.
Spontaneous herbaceous vegetation was determined by observing eight surface types frequently mown grass (<15cm), infrequently mown grass (>15cm), tree plates, woody structures other than trees, planting beds, planters, pavement cracks, unsealed surfaces and additionally furnishings, e.g., benches) on each square for 15-minutes each. All observed plants were identified to species level.
Multidiversity (Allan et al. 2014) was determined by scaling the species richness to the highest observed richness across all squares and averaging the proportional richness across all taxonomic groups.
References
Allan E, Bossdorf O, Dormann CF, et al (2014) Interannual variation in land-use intensity enhances grassland multidiversity. Proc Natl Acad Sci 111:308–313. https://doi.org/10.1073/pnas.1312213111