abmibirds: Raw Dataset of Bird Point Counts
In mefa4: Multivariate Data Handling with S4 Classes and Sparse Matrices
Description
Usage
Format
Details
Source
References
Examples
Description
A data set of bird point counts collected by the Alberta
Biodiversity Monitoring Institute (ABMI, http://www.abmi.ca).
Usage
1
Format
A data frame with 59341 observations on the following 21 variables.
Rotationa factor. Reference describing when data was
collected at a broad level.
Code definition: Prototype = 2003–2006, Rotation 1 = 2007–2012
ABMI.Sitea numeric vector. Reference number given to
each ABMI data collection site (1–1656).
Yeara numeric vector. Collection year.
Field.Datea factor. Day, month, and year data was
collected.
Field.Crew.Membersa factor. Initials for the field
technicians collecting the field data.
Identification.Datea factor. Day, month, and year
data was analyzed by specialist.
Identification.Analysta factor. Initials for the
technicians/specialists identifying the specimens.
Point.Count.Stationa numeric vector. Point count
station where recording was made: 9 stations were
located around each ABMI site (1–9).
Wind.Conditionsa factor. Estimate of wind conditions
on a scale of 0–5.
0 = no wind, 1 = calm, 2 = leaves rustling, 3 small branches
moving, 4 = large branches moving, 5 = large branches moving and
the tree is swaying
Precipitationa factor. Classification for precipitation
conditions in 5 categories.
Input value: Drizzle, Fog, Rain, Sleet, Snow, None
Start.of.Point.Counta factor. Time of day recording was
started.
Input value: 24 hour clock (hh:mm).
End.of.Point.Counta factor. Time of day recording was
finished. Input value: 24 hour clock (hh:mm).
Common.Namea factor. Common name of bird species detected
during point counts.
Scientific.Namea factor. Scientific name of bird species
detected during point count.
Unique.Taxonomic.Identification.Numbera factor. Globally
unique identifier of bird species detected during point
count. Unique taxonomic identifiers are generally taken from the
International Taxonomic Information System
(ITIS; http://www.itis.gov/).
Taxonomic.Resolutiona factor. Resolution to which bird
species was identified (e.g. Family, Genus, Species etc.).
Time.First.Detecteda factor. Approximate time the bird
analyst first detects a bird species from
the recording; listed in 10-second intervals.
Interval.1a factor. First time interval of the 10-minute
point count (0–200 seconds)
when bird species are detected and identified.
Interval.2a factor. Middle time interval of the 10-minute
point count (201–400 seconds) when bird species are detected or
re-detected.
Interval.3a factor. Last time interval of the 10-minute
point count (401–600 seconds) when bird species are detected or
re-detected.
Behavioura factor. Classification given to each species
detection (if possible).
Details
Breeding birds were measured at nine point count stations. Point count stations
were in a grid pattern with point count station no. 1 located at site-centre and the
remaining stations located 300 m apart surrounding site centre.
We conducted breeding bird surveys from one half hour before sunrise to 10:00 hrs.
We recorded vocalizations of birds for 10 minutes at each point count station using
an omni-directional microphone (CZM microphone; River Forks Research Corp.)
mounted at ear level on a professional tripod and connected to a mini hard drive
recorder. We recorded birds on a Marantz PM D670 or PM D660 Solid State
recorder at 320 kbps in .mp3 format. We calibrated the recorder volume to be in
the mid ranges.
While conducting the 10 minute bird
recordings, we scanned the areas surrounding the point count station for all birds (even those
vocalizing), noting species, number of individuals (including flock sizes of birds flying overhead), and
distance from the point count station, for all bird observations. We also noted factors that potentially
bias bird recordings, such as wind speed and precipitation. Bird recordings were later analyzed by bird
identification specialists in a laboratory setting.
If a bird point fell in open water, we established a new point if we were able to get within 100 m of the
original point, recording distance and direction from that original point. If it was not possible to get
within 100 m of the original point (i.e., <200 m from the last point), we conducted a 10 minute visual
point count of the waterbody recording observations into the microphone. We may not have sampled
certain points because they were inaccessible (e.g., a stream made access hazardous or impossible).
We analyzed bird recordings in a laboratory setting. We identified the species, time of first detection
(within 10 second intervals), behaviour (e.g., singing, calling, or alarm-calling), and the time interval that
individual birds were detected. We recognized 3 time intervals: Interval 1 (0–200 seconds), Interval 2
(201–400 seconds), and Interval 3 (401–600 seconds). Individual birds were detected in 1, 2, or 3 of
the time intervals. We identified red squirrel (Tamiasciurus hudsonicus) vocalizations in addition to bird
vocalizations. Bird recordings are randomly sampled and verified by other experts in bird identification
to ensure accuracy.
Throughout ABMI raw data files, the following codes and definitions are applied.
None or 0:
None or 0 is applied to any variable that was examined by field crews and found to be absent.
None is used for text entries and 0 is used for numerical entries. For example, when field crews
examine the canopy and find no Veteran trees in the canopy, this is recorded as None. When
there is no slope at the survey site, slope is recorded as 0. The numeral 0 can also be used as a
nominal code, for example, wind conditions can be recorded as 0.
VNA Variable Not Applicable:
Some ABMI data is collected in a nested manner. For example Tree Species is a parent variable. This
variable has a number of child variables that are used to describe the parent variable in more detail
(e.g., condition, DBH, decay stage). When the parent variable is recorded as None, child variables
are no longer applied and are recorded as VNA. VNA is also used when the protocol calls for a
modified sampling procedure based on site conditions (e.g., surface substrate protocol variant for
hydric site conditions). The use of VNA implies that users of the data should not expect that any
data could be present.
DNC, Did Not Collect:
DNC is used to describe variables that should have been collected but were not. There are a
number of reasons that data might not have been collected (e.g., staff oversight, equipment failure,
safety concerns, environmental conditions, or time constraints). Regardless of the reason data was not
collected, if under ideal conditions it should have been, the record in the data entry file reads DNC.
The use of DNC implies that users should expect the data to be present, though it is not.
PNA, Protocol Not Available:
The ABMI's protocols were, and continue to be, implemented in a staged manner. As a result, the
collection of many variables began in years subsequent to the start of the prototype or operational
phases or where discontinued after a few years of trial. When a variable was not collected because the
protocol had yet to be implemented by the ABMI (or was discontinued by the ABMI), the data entry
record reads PNA. This is a global constraint to the data (i.e., a protocol was not implemented until
2006, therefore, previous years cannot have this variable). PNA is to be used to describe the lack of
data collection for entire years.
SNI, Species Not Identified:
In various fields related to species identification, SNI is used to indicate that the organism was not
identified. Some possible reasons that identification was not possible include insufficient or deficient
sample collected and lack of field time.
Source
RAW_T26BreedingBirds28621.csv, http://www.abmi.ca
References
Raw breeding bird data (2004–2006 inclusive) from the Alberta Biodiversity Monitoring
Institute was used, in whole or part, to create this product. More information on the Institute
can be found at: http://www.abmi.ca
Examples
1
2
Example output
Loading required package: Matrix
Loading required package: pbapply
mefa4 0.3-5 2018-03-24
'data.frame': 59341 obs. of 21 variables:
$ Rotation : Factor w/ 2 levels "Prototype","Rotation 1": 1 1 1 1 1 1 1 1 1 1 ...
$ ABMI.Site : int 630 630 630 630 630 630 630 630 630 630 ...
$ Year : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ...
$ Field.Date : Factor w/ 132 levels "01-Jun-03","01-Jun-04",..: 46 46 46 46 46 46 46 46 46 46 ...
$ Field.Crew.Members : Factor w/ 67 levels "ABL","ABL/RSW",..: 15 15 15 15 15 15 15 15 15 15 ...
$ Identification.Date : Factor w/ 135 levels "01-Feb-10","01-Jun-08",..: 135 135 135 135 135 135 135 135 135 135 ...
$ Identification.Analyst : Factor w/ 13 levels "CF/CS/TH","CLS/MBI",..: 11 11 11 11 11 11 11 11 11 11 ...
$ Point.Count.Station : int 1 1 1 1 1 1 1 1 2 2 ...
$ Wind.Conditions : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Precipitation : Factor w/ 5 levels "DNC","Drizzle",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Start.of.Point.Count : Factor w/ 434 levels "10:00","10:01",..: 152 152 152 152 152 152 152 152 178 178 ...
$ End.of.Point.Count : Factor w/ 42 levels "10:07","10:21",..: 42 42 42 42 42 42 42 42 42 42 ...
$ Common.Name : Factor w/ 217 levels "Accipiters","Alder Flycatcher",..: 184 184 142 142 142 150 208 161 208 181 ...
$ Scientific.Name : Factor w/ 218 levels "Accipiter","Accipiter gentilis",..: 207 207 173 173 173 209 201 149 201 57 ...
$ Taxonomic.Resolution : Factor w/ 5 levels "Family","Genus",..: 3 3 3 3 3 3 3 3 3 3 ...
$ Unique.Taxonomic.Identification.Number: Factor w/ 215 levels "174469","174479",..: 130 130 214 214 214 149 122 161 122 195 ...
$ Time.First.Detected : Factor w/ 129 levels ".1",".2",".3",..: 87 97 112 87 31 40 13 68 31 13 ...
$ Interval.1 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Interval.2 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Interval.3 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Behaviour : Factor w/ 21 levels "Alarm Calling",..: 19 19 19 19 19 19 19 19 19 19 ...
mefa4 documentation built on May 2, 2019, 6:15 p.m.
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Description Usage Format Details Source References Examples
Description
A data set of bird point counts collected by the Alberta Biodiversity Monitoring Institute (ABMI, http://www.abmi.ca).
Usage
1 |
Format
A data frame with 59341 observations on the following 21 variables.
Rotationa factor. Reference describing when data was collected at a broad level. Code definition: Prototype = 2003–2006, Rotation 1 = 2007–2012
ABMI.Sitea numeric vector. Reference number given to each ABMI data collection site (1–1656).
Yeara numeric vector. Collection year.
Field.Datea factor. Day, month, and year data was collected.
Field.Crew.Membersa factor. Initials for the field technicians collecting the field data.
Identification.Datea factor. Day, month, and year data was analyzed by specialist.
Identification.Analysta factor. Initials for the technicians/specialists identifying the specimens.
Point.Count.Stationa numeric vector. Point count station where recording was made: 9 stations were located around each ABMI site (1–9).
Wind.Conditionsa factor. Estimate of wind conditions on a scale of 0–5. 0 = no wind, 1 = calm, 2 = leaves rustling, 3 small branches moving, 4 = large branches moving, 5 = large branches moving and the tree is swaying
Precipitationa factor. Classification for precipitation conditions in 5 categories. Input value: Drizzle, Fog, Rain, Sleet, Snow, None
Start.of.Point.Counta factor. Time of day recording was started. Input value: 24 hour clock (hh:mm).
End.of.Point.Counta factor. Time of day recording was finished. Input value: 24 hour clock (hh:mm).
Common.Namea factor. Common name of bird species detected during point counts.
Scientific.Namea factor. Scientific name of bird species detected during point count.
Unique.Taxonomic.Identification.Numbera factor. Globally unique identifier of bird species detected during point count. Unique taxonomic identifiers are generally taken from the International Taxonomic Information System (ITIS; http://www.itis.gov/).
Taxonomic.Resolutiona factor. Resolution to which bird species was identified (e.g. Family, Genus, Species etc.).
Time.First.Detecteda factor. Approximate time the bird analyst first detects a bird species from the recording; listed in 10-second intervals.
Interval.1a factor. First time interval of the 10-minute point count (0–200 seconds) when bird species are detected and identified.
Interval.2a factor. Middle time interval of the 10-minute point count (201–400 seconds) when bird species are detected or re-detected.
Interval.3a factor. Last time interval of the 10-minute point count (401–600 seconds) when bird species are detected or re-detected.
Behavioura factor. Classification given to each species detection (if possible).
Details
Breeding birds were measured at nine point count stations. Point count stations were in a grid pattern with point count station no. 1 located at site-centre and the remaining stations located 300 m apart surrounding site centre. We conducted breeding bird surveys from one half hour before sunrise to 10:00 hrs.
We recorded vocalizations of birds for 10 minutes at each point count station using an omni-directional microphone (CZM microphone; River Forks Research Corp.) mounted at ear level on a professional tripod and connected to a mini hard drive recorder. We recorded birds on a Marantz PM D670 or PM D660 Solid State recorder at 320 kbps in .mp3 format. We calibrated the recorder volume to be in the mid ranges.
While conducting the 10 minute bird recordings, we scanned the areas surrounding the point count station for all birds (even those vocalizing), noting species, number of individuals (including flock sizes of birds flying overhead), and distance from the point count station, for all bird observations. We also noted factors that potentially bias bird recordings, such as wind speed and precipitation. Bird recordings were later analyzed by bird identification specialists in a laboratory setting.
If a bird point fell in open water, we established a new point if we were able to get within 100 m of the original point, recording distance and direction from that original point. If it was not possible to get within 100 m of the original point (i.e., <200 m from the last point), we conducted a 10 minute visual point count of the waterbody recording observations into the microphone. We may not have sampled certain points because they were inaccessible (e.g., a stream made access hazardous or impossible).
We analyzed bird recordings in a laboratory setting. We identified the species, time of first detection (within 10 second intervals), behaviour (e.g., singing, calling, or alarm-calling), and the time interval that individual birds were detected. We recognized 3 time intervals: Interval 1 (0–200 seconds), Interval 2 (201–400 seconds), and Interval 3 (401–600 seconds). Individual birds were detected in 1, 2, or 3 of the time intervals. We identified red squirrel (Tamiasciurus hudsonicus) vocalizations in addition to bird vocalizations. Bird recordings are randomly sampled and verified by other experts in bird identification to ensure accuracy.
Throughout ABMI raw data files, the following codes and definitions are applied.
None or 0: None or 0 is applied to any variable that was examined by field crews and found to be absent. None is used for text entries and 0 is used for numerical entries. For example, when field crews examine the canopy and find no Veteran trees in the canopy, this is recorded as None. When there is no slope at the survey site, slope is recorded as 0. The numeral 0 can also be used as a nominal code, for example, wind conditions can be recorded as 0.
VNA Variable Not Applicable: Some ABMI data is collected in a nested manner. For example Tree Species is a parent variable. This variable has a number of child variables that are used to describe the parent variable in more detail (e.g., condition, DBH, decay stage). When the parent variable is recorded as None, child variables are no longer applied and are recorded as VNA. VNA is also used when the protocol calls for a modified sampling procedure based on site conditions (e.g., surface substrate protocol variant for hydric site conditions). The use of VNA implies that users of the data should not expect that any data could be present.
DNC, Did Not Collect: DNC is used to describe variables that should have been collected but were not. There are a number of reasons that data might not have been collected (e.g., staff oversight, equipment failure, safety concerns, environmental conditions, or time constraints). Regardless of the reason data was not collected, if under ideal conditions it should have been, the record in the data entry file reads DNC. The use of DNC implies that users should expect the data to be present, though it is not.
PNA, Protocol Not Available: The ABMI's protocols were, and continue to be, implemented in a staged manner. As a result, the collection of many variables began in years subsequent to the start of the prototype or operational phases or where discontinued after a few years of trial. When a variable was not collected because the protocol had yet to be implemented by the ABMI (or was discontinued by the ABMI), the data entry record reads PNA. This is a global constraint to the data (i.e., a protocol was not implemented until 2006, therefore, previous years cannot have this variable). PNA is to be used to describe the lack of data collection for entire years.
SNI, Species Not Identified: In various fields related to species identification, SNI is used to indicate that the organism was not identified. Some possible reasons that identification was not possible include insufficient or deficient sample collected and lack of field time.
Source
RAW_T26BreedingBirds28621.csv, http://www.abmi.ca
References
Raw breeding bird data (2004–2006 inclusive) from the Alberta Biodiversity Monitoring Institute was used, in whole or part, to create this product. More information on the Institute can be found at: http://www.abmi.ca
Examples
1 2 |
Example output
Loading required package: Matrix
Loading required package: pbapply
mefa4 0.3-5 2018-03-24
'data.frame': 59341 obs. of 21 variables:
$ Rotation : Factor w/ 2 levels "Prototype","Rotation 1": 1 1 1 1 1 1 1 1 1 1 ...
$ ABMI.Site : int 630 630 630 630 630 630 630 630 630 630 ...
$ Year : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ...
$ Field.Date : Factor w/ 132 levels "01-Jun-03","01-Jun-04",..: 46 46 46 46 46 46 46 46 46 46 ...
$ Field.Crew.Members : Factor w/ 67 levels "ABL","ABL/RSW",..: 15 15 15 15 15 15 15 15 15 15 ...
$ Identification.Date : Factor w/ 135 levels "01-Feb-10","01-Jun-08",..: 135 135 135 135 135 135 135 135 135 135 ...
$ Identification.Analyst : Factor w/ 13 levels "CF/CS/TH","CLS/MBI",..: 11 11 11 11 11 11 11 11 11 11 ...
$ Point.Count.Station : int 1 1 1 1 1 1 1 1 2 2 ...
$ Wind.Conditions : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Precipitation : Factor w/ 5 levels "DNC","Drizzle",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Start.of.Point.Count : Factor w/ 434 levels "10:00","10:01",..: 152 152 152 152 152 152 152 152 178 178 ...
$ End.of.Point.Count : Factor w/ 42 levels "10:07","10:21",..: 42 42 42 42 42 42 42 42 42 42 ...
$ Common.Name : Factor w/ 217 levels "Accipiters","Alder Flycatcher",..: 184 184 142 142 142 150 208 161 208 181 ...
$ Scientific.Name : Factor w/ 218 levels "Accipiter","Accipiter gentilis",..: 207 207 173 173 173 209 201 149 201 57 ...
$ Taxonomic.Resolution : Factor w/ 5 levels "Family","Genus",..: 3 3 3 3 3 3 3 3 3 3 ...
$ Unique.Taxonomic.Identification.Number: Factor w/ 215 levels "174469","174479",..: 130 130 214 214 214 149 122 161 122 195 ...
$ Time.First.Detected : Factor w/ 129 levels ".1",".2",".3",..: 87 97 112 87 31 40 13 68 31 13 ...
$ Interval.1 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Interval.2 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Interval.3 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Behaviour : Factor w/ 21 levels "Alarm Calling",..: 19 19 19 19 19 19 19 19 19 19 ...
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