The North American Bird Banding Program is directed in the United States by the U.S. Geological Survey (USGS) Bird Banding Laboratory (BBL), Eastern Ecological Science Center at the Patuxent Research Refuge (EESC) and in Canada by the Bird Banding Office (BBO), Environment and Climate Change Canada (ECCC). The respective banding offices have similar functions and policies and use the same bands, reporting forms and data formats. Data contributors are US and Canadian bird banding permit holders: federal, state, tribal, local government, non-government agencies, business, university and avocational biologists. Bird banders capture wild birds and mark them with a metal leg band with a unique 9-digit number. Extra markers may be added. Attributes of a bird such as age, sex, condition, molt and morphometrics may be taken before the bird is released. This long-term dataset is made up of over 76 million bird banding records with over 1,000 species, and 5 million encounter records with nearly 800 species. Federal bands are used on species included in the Migratory Bird Treaty Act (MBTA). Banding, encounter and recapture records are available for years 1960 to present. The data is curated at BBL on a daily basis, therefore each yearly version may differ from previous releases. The BBL produces one data release annually. Each yearly release is available for request. Data quality is established by contributors submitting their data. Incoming data must pass automatic validation rules to meet quality standards, and in some cases additional validation is conducted by staff at BBL and BBO. It is imperative to understand the codes used by the BBL and BBO. In early days of storage space restrictions for electronic data, an efficient system of codes was developed. Some examples include: bird status code, coordinate precision, inexact date, minimum age at encounter. BBL terminology is important as well: an encounter refers to a sighting or direct encounter with a banded or auxiliary-marked bird by any person; recapture denotes a banded bird recaptured during banding operations; recovery refers a harvested gamebird. Please cite as: Celis-Murillo A, M Malorodova, E Nakash. 2022. North American Bird Banding Dataset 1960-2022 retrieved 2022-07-14. U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge.

This dataset includes Appledore Island bird banding totals, weather surveillance radar measures from two nearby NEXRAD stations, and NARR measures of abiotic conditions centered on Appledore Island.

Field Methods: Birds will be captured via passive mist netting as well as by using target taxa song playbacks. The use of species-specific song playbacks will reduce the potential for incidental captures. From each target taxa, we will take two tail feathers and one drop (~50ul) of blood from the subbrachial wing vein. From incidental captures we will sample 2 tail feathers. All captured birds will be affixed with a metal, government issued bird band. Any birds showing signs of stress will be released without processing or banding. Nets will be placed so they do not catch the direct sun and will be checked three times per hour. Netting will not be conducted in excessive hot, cold, wet or windy conditions.

Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species —mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada goose (Branta canadensis) — in North America using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterize the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway identification in Canada geese exhibiting higher complexity. For mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals.

Band-tailed Pigeons (Patagioenas fasciata) are Californias only native wild pigeon. The birds in California are part of the Pacific Coast population (P. f. monilis), and inhabit montane coniferous forests and oak woodlands in the Coast Ranges and western Sierra Nevada (Keppie and Braun 2000). Though some populations appear to be resident, the species is generally considered migratory (Smith 1968). Breeding occurs in the coniferous forests of California, Oregon and Washington (Blackmon 1976; Keppie and Braun 2000; Smith 1968). These birds are characterized by slate gray to purple plumage with males having a deeper purple coloration. There is a bright white crescent on the hindneck of adults with a patch of greenish bronze iridescent patch below (Keppie and Braun 2000). A k-selected species, Band-tailed Pigeons have the lowest reproductive potential of any game bird in California (Grinnell 1913). Breeding peaks in May/ June and extends through the summer, with usually not more than 1 nesting attempt per season and a typical clutch size of 1 egg, though larger numbers of nesting attempts and eggs per clutch have been reported (Keppie and Braun 2000; Leonard 1998). Feeding is variable and dependent on fruit/mast availability. During the non-breeding season, birds are nomadic and localize to acorn masting events. Birds have been documented feeding on fresh buds in spring then moving into elderberries, cascara, madrone and other fruits. In fall and into winter, acorns are taken whole (Keppie and Braun 2000). Pacific Coast Band-tailed pigeons continue to experience long term population declines. Harvest independent population monitoring comes from the Breeding Bird Survey and the Mineral Site Survey. The Breeding Bird Survey provides evidence of a significant downward population trend of 2.0% per year over a term from 1968-2012. Mineral Site Survey results show downward trends but 9 and 5 year trend confidence intervals include zero and are therefore considered inconclusive (Sanders 2012). The Mineral Site Survey is coordinated by the USFWS and is a cooperative effort amongst California, Oregon, Washington and British Columbia (via the Canadian Wildlife Service). The survey takes advantage of the birds habit of congregating at springs and seeps that have high levels of mineral content. Fifty sites are surveyed annually; 10 in California, 22 in Oregon, 14 in Washington and 4 in British Columbia (Sanders 2012). The results of this survey are used to index the band-tailed pigeon population at the population level. These data describe the location of the 10 sites required for the flyway survey as well as additional sites that are surveyed if possible to improve precision. Coordinates of locations are available and may be distributed upon request and written justification of need.

This data release presents the data, JAGS models, and R code used to manipulate data and to produce results and figures presented in the USGS Open File Report, "Decision-Support Framework for Linking Regional-Scale Management Actions to Continental-Scale Conservation of Wide-Ranging Species, (https://doi.org/10.5066/P93YTR3X). The zip folder is provided so that other can reproduce results from the integrated population model, inspect model structure and posterior simulations, conduct analyses not present in the report, and use and modify the code. Raw source data can be sourced from the USGS Bird Banding Laboratory, USFWS Surveys and Monitoring Branch, National Oceanic and Atmospheric administration, and Ducks Unlimited Canada. The zip file contains the following objects when extracted: Readme.txt: A plain text file describing each file in this directory. Figures-Pintail-IPM.r: R code that generates report figures in png, pdf, and eps format. Generates Figures 2-11 and calls source code for figures 12 and 13 found in other files. * get pintail IPM data.r: R source code that must be run to format data for the IPM code file. * getbandrecovs.r: R code that takes Bird Banding Lab data for pintail band releases and recoveries and formats for analysis. This file is called by 'get pintail IPM data.r'. File was originally written by Scott Boomer (USFWS) and modified by Erik Osnas for use for the IPM. * Model_1_post.txt: Text representation of the posterior simulations from Model 1. This file can be read by the R function dget() to produce an R list object that contain posterior draws from Model 1. The list is the BUGSoutput$sims.list object from a call to rjags::jags. * Model_2_post.txt: As above but for Model 2. * Model_S1_post.txt: As above but for Model S1. * Pintail IPM.r: This is the main file that defines the IPM models in JAGS, structures the data for JAGS, defines initial values, and calls runs the models. Outputs are text files that contains JAGS model files, R work spaces that contains all data models, and results, include the output from the jags() function. From this the BUGSoutput$sims.list object was written to text for each model. * MSY_metrics.txt: Summary of results produced from running code in source_figure_12.R. This table is a text representation of a summary of the maximum sustained yield analysis at various mean rainfall levels, used for Table 1 of report and can be reproduced by running the code in source_figure_12.R. To understand the structure of this file, you must consult the code file and understand the structure of the R objects created from that code. Otherwise, consult Figure 12 and Table 1 in report. * source_figure_12.R: R code to produce Figure 12. Code is written to work with Rworkspace output from Model 1, but can be modified to use the Model_1_post.txt file without re-running the model. This would allow use of the same posterior realizations as used in the report. * source_figure_13.R: This is the code used to product the results for Figure 13. Required here is the posterior from Model 1 and data for the Prairie Parkland Model based on Jim Devries/Ducks Unlimited data. These are described in the report text. * Data: A directory that contains the raw data used for this report. * Data/2015_LCC_Networks_shapefile: A directory that contain ESRI shapefiles for used in Figure 1 and to define the boundaries of the Landscape Conservation Cooperatives. Found at (https://www.sciencebase.gov/catalog/item/55b943ade4b09a3b01b65d78) * Data/bndg_1430_yr1960up_DBISC_03042014.csv: A comma delimited file for banded pintail from 1960 to 2014. Obtained from the USGS Bird Banding Lab. This file is used by 'getbandrecovs.r' to produce and 'm-array' used in the Integrated Population Model (IPM). A data dictionary describing the codes for each field can be found here, https://www.pwrc.usgs.gov/BBL/manual/summary.cfm * Data/cponds.csv: A comma delimited file of estimated Canadian ponds based on counts from the North American Breeding Waterfowl and Habitat Survey, 1955-2014. Given is the year, point estimate, and estimated standard error. * Data/enc_1430_yr1960up_DBISC_03042014.csv: A comma delimited file for encounters of banded pintail. Obtained from the USGS Bird Banding Lab. This file is use by 'getbandrecovs.r' to produce and 'm-array' used in the Integrated Population Model (IPM). A data dictionary describing the codes for each field can be found here, (https://www.pwrc.usgs.gov/BBL/manual/enc.cfm) * Data/nopiBPOP19552014.csv: A comma delimited file of estimated northern pintail based on counts from the North American Breeding Waterfowl and Habitat Survey, 1955-2014. Given is the year, pintail point estimate (bpop), and pintail estimated standard error (bpopSE), mean latitude of the pintail population (lat), latitude variance of the pintail population (latVAR), mean longitude of the pintail population (lon), and the variance in longitude of the pintail population (lonVAR). * Data/Summary Climate Data California CV 2.csv: Rainfall data for the California central valley downloaded from National Climate Data Center (www.ncdc.noaa.gov/cdo-web/) as described in report text (https://doi.org/10.5066/P93YTR3X) and publication found at https://doi.org/10.1002/jwmg.21124 . Used in 'get pintail IPM data.r' for IPM. * Data/Summary data MAV.csv: Rainfall data for the Mississippi Aluvial valley downloaded from National Climate Data Center (www.ncdc.noaa.gov/cdo-web/) as described in report text (https://doi.org/10.5066/P93YTR3X) and publication found at https://doi.org/10.1002/jwmg.21124 . Used in 'get pintail IPM data.r' for IPM. * Data/Wing data 1961 2011 NOPI.txt: Comma delimited text file for pintail wing age data for 1961 to 2011 from the Parts Collection Survey. Each row is an individual wing with sex cohorts 4 = male, 5 = female and age cohorts 1 = After Hatch Year and 2 = Hatch Year. Wt is a weighting factor that determines how many harvested pintails this wing represent. See USFWS documentation for the Part Collection survey for descriptions. Summing Wt for each age, sex, and year gives an estimate of the number of pintail harvested. Used in 'get pintail IPM data.r' for IPM. * Data/Wing data 2012 2013 NOPI.csv: Same as 'Wing data 1961 2011 NOPI.txt' but for years 2012 and 2013.

Predation is a common cause of death in numerous organisms, and a host of anti-predator defenses have evolved. Such defenses often have a genetic background as shown by significant heritability and micro-evolutionary responses towards weaker defenses in the absence of predators. Flight initiation distance (FID) is the distance at which an individual animal takes flight when approached by a human, and hence it reflects the life history compromise between risk of predation and the benefits of foraging. Here we analyzed FID in 128 species of birds in relation to three measures of genetic variation, band sharing coefficient for minisatellites, observed heterozygosity and inbreeding coefficient for microsatellites in order to test whether FID was positively correlated with genetic variation. We found consistently shorter FID for a given body size in the presence of high band sharing coefficients, low heterozygosity and high inbreeding coefficients in phylogenetic analyses after controlling statistically for potentially confounding variables. These findings imply that anti-predator behavior is related to genetic variance. We predict that many threatened species with low genetic variability will show reduced anti-predator behavior, and that subsequent predator-induced reductions in abundance may contribute to unfavorable population trends for such species.

No loss of marks is a critical assumption of mark-recapture models so they provide unbiased estimates of population parameters. We double-banded male and female wild turkeys with aluminum rivet bands and estimated the probability that a bird would be recovered with both bands <1-225 weeks since banding ( = 51.2 weeks, SD = 44.0). We found that 100% of females (n = 37) were recovered with both bands. For males, we recovered 6 of 188 turkeys missing a rivet band for a retention probability of 0.984 (95% CI = 0.96-0.99). If male turkeys are double-banded with rivet bands the probability of recovering a turkey without any marks is <0.001. We failed to detect a change in band retention over time or differences between adults and juveniles. Given the low cost and high retention rates of rivet aluminum bands, we believe they are an effective marking technique for wild turkeys and, for most studies, will minimize any concern about the assumption that marks are not lost.

Genetic variation is a fundamental component of biodiversity, and studying population structure, gene flow, and demographic history can help guide conservation strategies for many species. Like other aerial insectivores, the purple martin (Progne subis) is in decline, and yet their genetic background remains largely unknown. To address this knowledge gap, we assessed population structure in the nominate eastern subspecies (P. s. subis) with relation to natal dispersal and examined historical genetic patterns in all three subspecies (P. s. subis, P. s. arboricola, P. s. hesperia) across their North American breeding range by estimating effective population sizes over time. We used next-generation sequencing strategies for genomic analyses, integrating whole-genome resequencing data with continent-wide band encounter records to examine natal dispersal. We documented population structure across P. s. subis, with the highest differentiation between the northern (Alberta) and more southern colonies and following patterns of isolation-by-distance. Consistent with spatial patterns of genetic differentiation, we also found greater longitudinal than latitudinal natal dispersal distances, signifying potential latitudinal constraints on gene flow. Earlier contractions in effective population sizes in the western P. s. arboricola and P. s. hesperia compared to the eastern P. s. subis subspecies suggest these subspecies originated from two different glacial refugia. Together, these findings support latitudinal distinction in P. s. subis, and elucidate the origin of subspecies differentiation, highlighting the importance to conserve populations across the range to maximize genetic diversity and adaptive potential in the purple martin. Methods This dataset contains the unfiltered and filtered single-nucleotide-polymorphisms (SNPs) for 71 purple martins and corresponding code for the filtering process and population analyses. DNA was sequenced through skimSeq low-coverage whole-genome sequencing. The processed dataset has been filtered for quality (QUAL > 20, MQ > 20, MAF > 0.05, missingness < 20%), HWE, bi-allelic, autosomes, and LD-pruned with r2=0.2. Raw sequence reads are available on the Sequence Read Archive (SRA). Additionally included is code for analyzing bird band encounter records (data was obtained from North American Bird Banding Program in May 2019).

We captured birds in urban parks and peri-urban forests in the cities of Lugano and Zurich. The urban sites were located in an urban forest (Lugano) and in a cemetery (Zurich), and the bird assemblages were comparable with those of the surrounding forests. We captured individual birds during sessions lasting multiple days using mist-nets, concentrating the activities within 3-4 hours after sunrise and before sunset. We extracted birds from nets using single-use nitrile gloves and gently wiped their body parts with a dry foam swab MW940 (Medical Wire & Equipment), which was then placed in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions.Then, we marked each bird with an alphanumeric leg band, identified the species, determined the sex and age whenever possible, measured for relevant biometrics (e.g. wing chord), the amount of body fat and pectoral muscle (i.e. body condition), and weighed. Afterwards, we placed each bird in a disposable paper bag within a cotton bag that ensured respiration and left it for few minutes in a safe and calm place to allow for defecation and the collection of faecal samples. The faecal sample was collected from insectivorous and omnivorous birds, and few granivorous birds that are known to occasionally eat invertebrates, such as the house sparrow Passer domesticus. After releasing the bird, we stored the faecal sample in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions. We wore single-use nitrile gloves and used sterile toothpicks to collect solid samples. The tubes were initially stored in the refrigerator during the field campaign and no later than seven days after collection at -20°C until prepared for DNA extraction. DNA extraction and library preparation was performed at WSL Phytopathology facilites, Sequencing was performed at Lausanne Genomic Technologies Facility (University of Lausanne; https://wp.unil.ch/gtf/) on an AVITI benchtop sequencing system (Element Biosciences). Bioinformatics was performed at the Genetic Diversity Center (ETH Zurich) using a custom pipeline.

Description: Survey of fluctuating asymmetry and ptilochronology in understory birds across a gradient of forest degradation, with sampling transects in the interior, edge, and matrix of 10ha fragments at SAFE Project: This dataset was collected as part of the following SAFE research project: Alterations in avian physiology and behaviour across a gradient of fragmentation XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: Peel_data_2018.xlsx Peel_data_2018.xlsx This file contains dataset metadata and 2 data tables:

Fluctuating asymmetry in bird species across a gradient of fragmentation (described in worksheet fluctuating_asymmetry) Description: This is a dataset of the lengths of tarsi of birds captured in mist nets at SAFE. Captured birds had each tarsus measured three times using digital calipers precise to .02mm. Tarsus length was measured from the notch at the back of the tibia-tarsus joint to the plane formed by bending the foot perpendicular to the tarsus. An average value was calculated for each tarsus and the absolute difference between the right and left tarsus taken as a measure of fluctuating asymmetry in an individual bird. Number of fields: 15 Number of data rows: 711 Fields:

band: Band number of captured bird (Field type: ID) species: Species of captured bird (Field type: Taxa) site: Full name of site where bird was caught (Field type: Location) location: type of site at which bird was caught (Field type: Categorical) fragment: fragment in which bird was caught (Field type: Categorical) date: Date bird was captured (Field type: Datetime) left1: Length of left tarsus (Field type: Numeric Trait) right1: Length of right tarsus (Field type: Numeric Trait) left2: Length of left tarsus (Field type: Numeric Trait) right2: Length of right tarsus (Field type: Numeric Trait) left3: Length of left tarsus (Field type: Numeric Trait) right3: Length of right tarsus (Field type: Numeric Trait) left.average: Average of left tarsus measurements (Field type: Numeric Trait) right.average: Average of right tarsus measurements (Field type: Numeric Trait) abs.diff: Absolute difference between left and right average (Field type: Numeric Trait)

Ptilochronological measurements of bird species across a gradient of fragmentation (described in worksheet growth_bands) Description: This is a dataset of growth bands measured on rectrice feathers of captured birds. The third rectrice feather of birds captured using mist-nets was plucked in the field and the distal end of all visible dark growth bands marked with a pin. Distance between pin marks was measured as single growth bands. This dataset contains each individual growth band, as well as averages across the whole feather, and averages for the ten growth bands proximal to the 3/4 point on the feather. Number of fields: 40 Number of data rows: 746 Fields:

date: Date bird was captured (Field type: Date) site: Full name of site where bird was caught (Field type: Location) location: type of site at which bird was caught (Field type: Categorical) fragment: fragment in which bird was caught (Field type: Categorical) band: Band number of captured bird (Field type: ID) species: Species of captured bird (Field type: Taxa) age: Age of bird (Field type: Categorical Trait) length: Length of rectrice (Field type: Numeric Trait) band1: Width of growth band (Field type: Numeric Trait) band2: Width of growth band (Field type: Numeric Trait) band3: Width of growth band (Field type: Numeric Trait) band4: Width of growth band (Field type: Numeric Trait) band5: Width of growth band (Field type: Numeric Trait) ban6: Width of growth band (Field type: Numeric Trait) band7: Width of growth band (Field type: Numeric Trait) band8: Width of growth band (Field type: Numeric Trait) band9: Width of growth band (Field type: Numeric Trait) band10: Width of growth band (Field type: Numeric Trait) band11: Width of growth band (Field type: Numeric Trait) band12: Width of growth band (Field type: Numeric Trait) band13: Width of growth band (Field type: Numeric Trait) band14: Width of growth band (Field type: Numeric Trait) band15: Width of growth band (Field type: Numeric Trait) band16: Width of growth band (Field type: Numeric Trait) band17: Width of growth band (Field type: Numeric Trait) band18: Width of growth band (Field type: Numeric Trait) band19: Width of growth band (Field type: Numeric Trait) band20: Width of growth band (Field type: Numeric Trait) band21: Width of growth band (Field type: Numeric Trait) band22: Width of growth band (Field type: Numeric Trait) band23: Width of growth band (Field type: Numeric Trait) band24: Width of growth band (Field type: Numeric Trait) total.sum: sum of all growth bands measured (Field type: Numeric Trait) number: number of growth bands measurable in a single feather (Field type: Numeric Trait) total.avg: average of all measured growth bands in a feather (Field type: Numeric Trait) three.quarters: the three quarters point in a feather: a point three quarters along the distance from the tip of the calamus to the distal end of the feather vane (Field type: Numeric Trait) ten.sum: sum of the ten (or fewer) growth bands proximal to the three quarters point (Field type: Numeric Trait) ten.number: number of bands proximal to the three quarters point (ten maximum) (Field type: Numeric Trait) ten.avg: average of the ten (or fewer) bands proximal to the three quarters point (Field type: Numeric Trait) sd: standard deviation of all the bands present in a feather (Field type: Numeric Trait) Date range: 1900-01-22 to 2018-05-30 Latitudinal extent: 4.6921 to 4.7293 Longitudinal extent: 117.4703 to 117.6201 Taxonomic coverage: All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets. Animalia - Chordata -  - Aves -  -  - Columbiformes -  -  -  - Columbidae -  -  -  -  - Chalcophaps -  -  -  -  -  - Chalcophaps indica -  -  - Coraciiformes -  -  -  - Alcedinidae -  -  -  -  - Ceyx -  -  -  -  -  - Ceyx erithaca -  -  -  -  -  -  - Ceyx erithaca erithaca -  -  - Cuculiformes -  -  -  - Cuculidae -  -  -  -  - Cacomantis -  -  -  -  -  - Cacomantis merulinus -  -  - Passeriformes -  -  -  - Cisticolidae -  -  -  -  - Orthotomus -  -  -  -  -  - Orthotomus atrogularis -  -  -  -  -  - Orthotomus sericeus -  -  -  -  - Prinia -  -  -  -  -  - Prinia flaviventris -  -  -  - Dicaeidae -  -  -  -  - Dicaeum -  -  -  -  -  - Dicaeum trigonostigma -  -  -  -  - Prionochilus -  -  -  -  -  - Prionochilus maculatus -  -  -  -  -  - Prionochilus xanthopygius -  -  -  - Estrildidae -  -  -  -  - Lonchura -  -  -  -  -  - Lonchura atricapilla -  -  -  -  -  - Lonchura fuscans -  -  -  - Laniidae -  -  -  -  - Lanius -  -  -  -  -  - Lanius tigrinus -  -  -  - Monarchidae -  -  -  -  - Hypothymis -  -  -  -  -  - Hypothymis azurea -  -  -  -  - Rhipidura -  -  -  -  -  - Rhipidura javanica -  -  -  - Muscicapidae -  -  -  -  - Copsychus -  -  -  -  -  - Copsychus stricklandii -  -  -  -  - Cyornis -  -  -  -  -  - Cyornis caerulatus -  -  -  -  - Enicurus -  -  -  -  -  - Enicurus leschenaulti -  -  -  -  - Luscinia -  -  -  -  -  - Luscinia cyane -  -  -  -  - Rhinomyias -  -  -  -  -  - Rhinomyias umbratilis -  -  -  - Nectariniidae -  -  -  -  - Aethopyga -  -  -  -  -  - Aethopyga siparaja -  -  -  -  - Arachnothera -  -  -  -  -  - Arachnothera everetti -  -  -  -  -  - Arachnothera hypogrammicum -  -  -  -  -  - Arachnothera longirostra -  -  -  - Pellorneidae -  -  -  -  - Alcippe -  -  -  -  -  - Alcippe brunneicauda -  -  -  -  - Malacocincla -  -  -  -  -  - Malacocincla malaccensis -  -  -  -  -  - Malacocincla sepiaria -  -  -  -  - Malacopteron -  -  -  -  -  - Malacopteron affine -  -  -  -  -  - Malacopteron cinereum -  -  -  -  -  - Malacopteron magnirostre -  -  -  -  -  - Malacopteron magnum -  -  -  -  - Pellorneum -  -  -  -  -  - Pellorneum bicolor -  -  -  -  -  - Pellorneum capistratum -  -  -  - Pycnonotidae -  -  -  -  - Alophoixus -  -  -  -  -  - Alophoixus bres -  -  -  -  -  - Alophoixus

This dataset contains radar track data of avian targets from DeTect's 7360 s-band radar during the large barge deployment from June 2024 though September 2024.

Scottish Marine and Freshwater Science Vol 6 No 14 The report describes the data required, and the methods used, to estimate collision risk. It is accompanied by a worked example and R code (available at http://dx.doi.org/10.7489/1657-1), which enables the collision risk calculations to be performed in a standardised and reproducible way. In the UK, the most frequently used avian collision risk model is commonly known as 'the Band model' (Band, Madders & Whitfield 2007) and was originally conceived in 1995. Since then it has undergone several iterations with the most recent associated with the Strategic Ornithological Support Services (SOSS) (Band 2012a; b). The Band model (Band 2012b) provides four different options for calculating collision risk. * Option 1 - Basic model, i.e. assuming that a uniform distribution of flight heights between the lowest and the highest levels of the rotors and using the proportion of birds at risk height as derived from site survey. * Option 2 - Basic model, but using the proportion of birds at risk height as derived from a generic flight height distribution provided. * Option 3 - Extended model and using a generic flight height distribution. * Option 4 - Extended model and using a flight height distribution generated from site survey.

Analysis of ‘Band-tailed Pigeon Surveys Generalized - CDFW [ds889]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/afdd3967-b5a1-45d1-8ea5-47c3200a0417 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Band-tailed Pigeons (Patagioenas fasciata) are Californias only native wild pigeon. The birds in California are part of the Pacific Coast population (P. f. monilis), and inhabit montane coniferous forests and oak woodlands in the Coast Ranges and western Sierra Nevada (Keppie and Braun 2000). Though some populations appear to be resident, the species is generally considered migratory (Smith 1968). Breeding occurs in the coniferous forests of California, Oregon and Washington (Blackmon 1976; Keppie and Braun 2000; Smith 1968). These birds are characterized by slate gray to purple plumage with males having a deeper purple coloration. There is a bright white crescent on the hindneck of adults with a patch of greenish bronze iridescent patch below (Keppie and Braun 2000). A k-selected species, Band-tailed Pigeons have the lowest reproductive potential of any game bird in California (Grinnell 1913). Breeding peaks in May/ June and extends through the summer, with usually not more than 1 nesting attempt per season and a typical clutch size of 1 egg, though larger numbers of nesting attempts and eggs per clutch have been reported (Keppie and Braun 2000; Leonard 1998). Feeding is variable and dependent on fruit/mast availability. During the non-breeding season, birds are nomadic and localize to acorn masting events. Birds have been documented feeding on fresh buds in spring then moving into elderberries, cascara, madrone and other fruits. In fall and into winter, acorns are taken whole (Keppie and Braun 2000). Pacific Coast Band-tailed pigeons continue to experience long term population declines. Harvest independent population monitoring comes from the Breeding Bird Survey and the Mineral Site Survey. The Breeding Bird Survey provides evidence of a significant downward population trend of 2.0% per year over a term from 1968-2012. Mineral Site Survey results show downward trends but 9 and 5 year trend confidence intervals include zero and are therefore considered inconclusive (Sanders 2012). The Mineral Site Survey is coordinated by the USFWS and is a cooperative effort amongst California, Oregon, Washington and British Columbia (via the Canadian Wildlife Service). The survey takes advantage of the birds habit of congregating at springs and seeps that have high levels of mineral content. Fifty sites are surveyed annually; 10 in California, 22 in Oregon, 14 in Washington and 4 in British Columbia (Sanders 2012). The results of this survey are used to index the band-tailed pigeon population at the population level. These data describe the location of the 10 sites required for the flyway survey as well as additional sites that are surveyed if possible to improve precision. Coordinates of locations are available and may be distributed upon request and written justification of need.

--- Original source retains full ownership of the source dataset ---

This data release contains eight datasets that represent the entirety of the data collected for a study that examined breeding-bird densities in native mixed-grass prairie from 2003 to 2012 at and adjacent to wind facilities in North Dakota and South Dakota, USA. Data were collected to determine breeding-bird density per 100 hectares (ha) by distance bands from turbines and by excluding habitat that may not be considered suitable as breeding habitat for particular bird species. A subset of the data that included only one year prior to turbine construction and several years post-construction and that lent itself to a before-after-control-impact (BACI) assessment was published as its own data release and paper in 2016 in Conservation Biology by authors J. Shaffer and D. Buhl. The all-inclusive data release described hereafter is of the same basic format but includes all years and all study sites (also referred to as study plots), even those that did not lend themselves to a BACI assessment. The data release contains eight datasets with discrete topics of information, namely on bird occurrence; years of study organized by study site and treatment (that is, impact—indicating post-turbine construction) or control status and pre- or post-treatment status; overall study plot area; plot area by habitat-exclusion areas and by distance bands from turbines; turbine locations; vegetation structural data; locations of survey grid points; and bird codes and associated English common names and scientific names for the list of bird species observed during the study. The ‘bird occurrence’ dataset includes bird species identification, sex, mating-pair status, and geographic locations of individual birds, which were obtained to determine locations of individual birds from nearest turbine location and to ultimately determine bird density per 100 ha. The ‘pre post years’ dataset indicates the years that individual study sites (also known as study plots) within study areas were surveyed, whether the study site was a control or treatment site (thus indicating whether the site was never expected to experience turbine construction or whether the site was expected to experience turbine construction), and whether the study site was considered a pre-treatment or post-treatment year (thus indicating for treatment sites whether the site did not have or did have turbines that particular year). The ‘overall plot area’ dataset provides the overall areal extent of each study plot within which bird and vegetation data were obtained and to aid in determining bird density per 100 ha. The ‘plot area by distance band habitat’ dataset represents a refinement of plot area by categorizing area within up to four habitat-exclusion variables and by four distance bands in concurrent rings from turbines (that is, 0–100 meters [m], 100–200 m, 200–300 m, and greater than 300 m from turbines); these data allow one to calculate bird density per 100 ha by distance band and with exclusion of habitat in which the bird species would not be expected to be occupying. The ‘turbine location’ dataset indicates geographic location of individual turbines, which was obtained in order to determine distance from individual bird locations to nearest turbine location. The ‘vegetation’ dataset contains measurements that characterize average vegetation structural measurements and life form and was collected to determine if there were differences in vegetation structure between control and treatment sites and pre-treatment and post-treatment years. The ‘study survey grid point’ contains geographic location of individual grid points by study site, which indicates the exact location of each study site. The ‘bird codes and names’ dataset indicates the four-letter bird codes and the English common and scientific names that they represent and also provides a list of bird species observed during the study.

This indicator is a continuous index of highly productive areas for birds that feed exclusively or mainly at sea. It uses seasonal predictions of relative abundance for seventeen species of marine birds (Audubon’s shearwater, white-winged scoter, black scoter, horned grebe, band-rumped storm-petrel, Bermuda petrel, Manx shearwater, black-capped petrel, Northern gannet, Bonaparte's gull. common loon, red-throated loon, Cory's shearwater, royal tern, great shearwater, sooty shearwater, common tern). This indicator originates from Marine-life Data and Analysis Team marine bird models. Reason for Selection Marine birds help identify key areas of ocean productivity and overall ocean health. Seabirds are often considered useful ecological indicators for the marine environment because they “essentially represent the top of the food chain” and therefore are likely to signal changes in lower trophic levels and the surrounding environment. Seabird populations also respond to anthropogenic pressures such as “such as overexploitation of their food resources and pollution from industrial discharge.” Long-term monitoring has “generated high-quality data on population counts and demographic parameters” and seabirds “are considered to be of international importance and have high resonance with the public and policy-makers” (Parsons et al. 2008). This indicator complements the marine mammal index by providing finer spatial resolution and stronger connections to forage fish productivity. Input Data

South Atlantic Blueprint 2021 extent The following marine bird data: Potential species to include in this indicator began with Tier 1 and Tier 2 priority species in Bird Conservation Region 27 (the Southeastern Coastal Plain) in the Southeast United States Regional Waterbird Conservation Plan. This plan did not include waterfowl species, so additional waterfowl were added from priority species identified by the Northwest Atlantic Birds at Sea Conservation Cooperative (now the Atlantic Marine Bird Cooperative). We narrowed down this larger combined list in two ways. First, we removed species from the list that didn’t have spatial models available. Then, we removed species that had models with poor predictive performance and/or large spatial errors. The Marine-Life Data Analysis Team provided input data for this indicator. These relative abundance models cover the entire U.S. Atlantic. They use aggregated survey information and oceanographic variables to predict the relative abundance of marine birds throughout the region. Species with seasonal models used in this indicator are: Audubon’s shearwater (fall, spring, summer), white-winged scoter (fall, spring, winter), black scoter (fall, spring, winter), horned grebe (winter), band-rumped storm-petrel (fall), Bermuda petrel (fall), Manx shearwater (fall, spring, summer), black-capped petrel (spring, summer, winter), Northern gannet (spring, summer, winter), Bonaparte's gull (spring, winter), common loon (spring, summer, winter), red-throated loon (spring, winter), Cory's shearwater (fall, spring, winter), royal tern (fall, spring, summer), great shearwater (fall, spring, summer), sooty shearwater (fall, spring, summer), and common tern (fall, spring, summer).

Mapping Steps

To identify high quality areas for each species during each season, use the core-area algorithm in Zonation (edge removal = 0, warp = 1). Include the seasonal relative abundance layer for each marine bird species as a separate input and weight all but one equally. For Bonaparte’s gull in spring, the sole exception, reduce the weight from 1 to 0.1 to reduce the impact of a spatial artifact related to out-of-sample-area prediction. To account for boundary effects, run all the models across the entire U.S. Atlantic. Reproject the data to Albers Equal area. Resample the data to 30 m cell size using the nearest neighbor method (the source data is 2 km by 2 km cells). Convert from a floating point raster with a range of 0-1 to an integer raster ranging from 0-100. As a final step, clip to the spatial extent of the South Atlantic Blueprint.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2022 Data Download under BlueprintInputs > BaseBlueprint2022 > 6_Code. Final Indicator Values The final indicator is continuous, with values ranging from:

High: 100 (most important for seasonal abundance of marine bird index species) Low: 4 (least important for seasonal abundance of marine bird index species)

Known Issues

Models are likely underpredicting the importance of areas in the eastern part of the South Atlantic marine ecosystem. Survey effort was very low in that area and many input models did not even extend their predictions into the eastern area. This indicator does not capture fine resolution patterns nearshore. Model predictions are fairly coarse and do not capture finer variations in relative abundance nearshore and near estuaries. This indicator doesn’t cover the estuarine ecosystem. Many of these marine bird species could also be excellent indicators in the open water portion of the estuarine ecosystem; however, spatial models covering the full area of the estuarine ecosystem are not available at this time. While this layer has a 30 m resolution, the source data was coarser than that. We downsampled 2 km pixels to 30 m.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Curtice, C., Cleary J., Shumchenia E., Halpin P.N. 24 June 2019. Marine-life Data and Analysis Team (MDAT) technical report on the methods and development of marine-life data to support regional ocean planning and management. Prepared on behalf of the Marine-life Data and Analysis Team (MDAT). [https://seamap.env.duke.edu/models/mdat/MDAT-Technical-Report.pdf]. Hunter, W.C., Golder, W., Melvin, S., Wheeler, J., 2006. Southeast United States Regional Waterbird Conservation Plan. U.S. Fish and Wildlife Service, Atlanta, GA. [https://static1.squarespace.com/static/5bb3865d2727be6f94acf2fc/t/5c79a2efe5e5f0214c34c48c/1551475442564/SE_US_Waterb_Plan_2006.pdf]. Moilanen, A., L. Meller, J. Leppänen, F.M. Pouzols, H. Kujala, A. Arponen. 2014. Zonation Spatial Conservation Planning Framework and Software V4.0, User Manual. [https://github.com/cbig/zonation-core/releases/download/4.0.0/zonation_manual_v4_0.pdf]. Parsons, Matt, Ian Mitchell, Adam Butler, Norman Ratcliffe, Morten Frederiksen, Simon Foster, James B. Reid, Seabirds as indicators of the marine environment, ICES Journal of Marine Science, Volume 65, Issue 8, November 2008, Pages 1520–1526, [https://doi.org/10.1093/icesjms/fsn155].

Albatross and petrel populations have declined globally due to interactions with fishing operations. The survival of four albatross and two giant petrel species breeding on Macquarie Island is threatened and ongoing monitoring is essential to assess their conservation status and mitigate negative influences. Long-term studies are required to obtain reliable information on population size and productivity and age- and sex- related survival parameters. The birds' oceanic movements is also being investigated so that questions regarding temporal and spatial overlap with fisheries can be addressed.

Demographic and population data collected for the 2012-13 breeding season on Macquarie Island for 4 species of albatross and 2 species of giant petrel are summarised in the annual report (pdf) and all data contained in tables therein or attached xlxs spreadsheets and access database. Data collected includes breeding census, breeding success, nest location, banding and resight data for the 2012-13 season. The Access database contains data from 1950-2012.

2013-2014 information are held in the 2013-2014 folder, which includes several excel spreadsheets, an updated access database, and a copy of the final report.

2014-2015 information are held in the 2014-2015 folder, which includes several excel spreadsheets, a copy of the report, and updated database tables.

2015-2016 information are held in the 2015-2016 folder, which includes several excel spreadsheets, a copy of the report, and updated database tables.

2016-2017 information are held in the 2016-2017 folder, which includes several excel spreadsheets.

2017-2018 information are held in the 2017-2018 folder, which includes several excel spreadsheets and a pdf document showing the location of nesting sites (waypoints provided in the excel files).

2018-2019 information are held in the 2018-2019 folder, which includes several excel spreadsheets and a pdf document showing the location of nesting sites (waypoints provided in the excel files).

This project has replaced project 2569 (which in turn replaced project 751).

Efficient and unbiased sampling of ecological interactions is essential to our understanding of the functions they mediate. Seed dispersal by frugivorous birds is a key mutualism for plant regeneration and community dynamics. Mist-netting is one of the most widely used methods to sample avian seed dispersal through the identification of seeds in droppings of captured birds kept inside cloth bags. However, birds may drop seeds on the ground before being extracted from the net, leading to a fraction of missing information due to ineffective sampling. Worryingly, this fraction could be unevenly distributed across bird and plant species, leading to sampling biases. Here, we assess the effectiveness of using a 1- m wide mesh below mist nets to sample seeds dropped by entangled birds. We used data from birds mist-netted during one-year-round. We sampled nearly 50% of interaction events and 75% of dispersed seeds on the mesh band below the mist nets (i.e. lost information without this optimization). The proportion of seeds sampled on the mesh bands was not evenly distributed among bird species but strongly related to bird size, ranging from 57-63% in warblers to 84-94% in thrushes. Moreover, the proportion of seeds sampled on the mesh was negatively related to seed size, although this relationship was weaker. We also evaluated accumulation curves of species and pairwise interactions with increasing sampling effort, both with and without using the mesh bands. The number of seed species sampled increased by 21% when using the mesh bands and the number of pairwise interactions by 36%. Our findings provide strong evidence on how inefficient and biased traditional mist-netting can be for sampling community-wide seed-dispersal interactions. We thus urge the use of mesh bands in future studies to increase sampling effectiveness and avoid biases, which will ultimately improve our understanding of the seed dispersal function.

Animal ecology is shaped by energy costs, yet it is difficult to measure fine-scale energy expenditure in the wild. Because metabolism is often closely correlated with mechanical work, accelerometers have the potential to provide detailed information on energy expenditure of wild animals over fine temporal scales. Nonetheless, accelerometry needs to be validated on wild animals, especially across different locomotory modes. We merged data collected on 20 thick-billed murres (Uria lomvia) from miniature accelerometers with measurements of daily energy expenditure over 24 h using doubly labelled water. Across three different locomotory modes (swimming, flying and movement on land), dynamic body acceleration was a good predictor of daily energy expenditure as measured independently by doubly labelled water (R2 = 0.73). The most parsimonious model suggested that different equations were needed to predict energy expenditure from accelerometry for flying than for surface swimming or activity ...

Bands put on Adélie penguin chicks and adults, Ross Island, Antarctica, starting in 1996. Bands were attached at Cape Royds, Cape Bird, Cape Crozier, and Beaufort Island.