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20 results for “peregrine falcon”
Greenland Peregrine Falcon eggshell thickness monitoring data 1972 - 2019
<p>The Peregrine Falcon (Falco peregrinus tundrius) population in Greenland has been monitored in different survey areas in South and West Greenland since 1972. At visits to Peregrine Falcon nests, eggshell fragments from hatched eggs as well as addled (dead) eggs left behind have been collected with the aim of monitoring the thickness of the eggshells as well as analysing the whole eggs for contaminants. The shell thickness serves as a proxy for the falcons’ exposure to certain persistant organic pollutants, in particular DDT and its breakdown products (see summaries in Cade et al. 1988).</p> <p>This data set contains the raw data on 6665 eggshell thickness measurements of:<br> 1. Whole eggs from South Greenland 1986-2015<br> 2. Eggshell fragments from the study area in South Greenland 1981-2019<br> 3. Eggshell fragments from the study area around Kangerlussuaq in West Greenland 1972-1989</p> <p>The data set contains a mix of measurements of shell thickness including or excluding the eggshell membranes from the same clutch of eggs. Based on those measurements the average membrane thickness is 0.071 mm (SD=0.013) – a figure confirmed by other studies – and this ’membrane factor’ can be added or subtracted for comparisons with other data sets.</p> <p>Further details regarding the sampling areas, measurement methods and the results of trends analyses of changes in shell thickness are provided in Falk et al. (2006 and 2018).</p> <p>The file named <em>1_Data_Eggshell_Thickness_1972-2019.csv</em> contains the raw measurements data and the file <em>2_ReadMe_Eggshell_Thickness_1972-2019.txt</em> specifies the content. </p> <p>The file named <em>3_Rscript_Eggshell_Thickness_1972-2019.R</em> provides an R script for summarizing and plotting the data as shown in the file <em>4_Plot_Eggshell_Thickness_1972-2019.pdf</em></p>
Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
<p><span>The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons (<em>Falco peregrinus tundrius</em>) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data was collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average-sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand. </span></p>
Figure 2 in Histomorphometrical study of the tongue epithelium of the peregrine falcon (Falco peregrinus)
Figure 2. (A) Scanning electron micrograph of the dorsal surface of the lingual apex of the falcon showing that the lingual epithelium is in a carpet shape. (X,550); (B) Scanning electron micrograph of the dorsal surface of the lingual body of the falcon showing the opening of the lingual gland (arrows). (X,300); (C) Scanning electron micrograph of the dorsal surface of the lingual body of the falcon showing the small conical papillae (single arrow) and large conical papillae (double arrows). (X,27).
Figure 1. A in Histomorphometrical study of the tongue epithelium of the peregrine falcon (Falco peregrinus)
Figure 1. A. Photomicrograph of a transverse section of the lingual apex of the falcon showing the dorsal lingual epithelium (E) and lamina propria (LP). (X, 40). B. Photomicrograph of a transverse section of the lingual body of the falcon showing the dorsal lingual epithelium (E) lingual muscles (M) and paraglossum (P). (X, 40). C. Photomicrograph of a transverse section of the lingual body of the falcon showing the dorsal lingual epithelium (E), lingual muscles (M), paraglossum (P) and the lateral epithelium (arrow) (X,40). D. Photomicrograph of a transverse section of the lingual body of the falcon showing the dorsal lingual epithelium (E) lingual muscles (M), lingual glands (G) and opening of the lingual glands (arrow). (X, 40).
Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
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Data from: Food supplementing peregrine falcon (Falco peregrinus tundrius) nests increases reproductive success without changes in parental mean provisioning rate
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Peregrine Falcon Site Occupancy along Delmarva Peninsula, VA (2006-2009)
<p>We monitored the breeding activity of peregrine falcons along the seaside of the Delmarva Peninsula (2006-2009) to determine their influence on the distribution of foraging red knots along the Virginia barrier islands. Twelve breeding territories were used during the study period. Both activity status and productivity varied across sites. Activity status was used to examine the influence of peregrines on the distribution of foraging red knots relative to peregrine eyries. This database contains the results of survey efforts (2006-2009).</p>
Urban peregrine falcon (Falco peregrinus) breeding season diet in UK, 2020–2022
<p>Diets of urban peregrine falcons in UK were monitored via nest cameras during the breeding season (March-June) from 2020–2022. All prey items were then identified to species level where possible, by Ed Drewitt. This dataset contains the prey items recorded during each year of the study and location of the sites. </p>
Peregrine Falcon Site Occupancy along Delmarva Peninsula, VA (2006-2009)
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Urban peregrine falcon (Falco peregrinus) breeding season diet in UK, 2020–2022
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Microsatellite Dataset for: Weaving et al. Conservation genetics of regionally extinct peregrine falcons (Falco peregrinus) and unassisted recovery without genetic bottleneck in southern England
<p class="CxSpFirst">The peregrine falcon (<i>Falco peregrinus</i>) has been affected by persecution, pollution, trade, and habitat degradation, but it is considered a flagship conservation success story because of successful reintroductions and population recoveries across broad ranges. However, in the UK there were never formal reintroduction programmes for peregrine falcons, and it appears that UK populations—and specifically the Sussex peregrines of the English south coast—recently recovered from a population crash unassisted. To study this, we obtained samples from contemporary populations in southern England, Ireland, continental Europe, domestic-bred peregrine falcons, and from England pre-population crash. Using microsatellite and mtDNA control region data, the genetic diversity and structure, signatures of genetic bottlenecks, and potential origin of the Sussex peregrines was investigated. We found low levels of genetic diversity across all peregrine falcon groups, low but significant genetic differentiation, and a few private alleles, indicating some level of genetic structure among European peregrines. Although we could not pinpoint the origin of the Sussex peregrines, the data suggests that it is not likely to have originated from escaped domestic birds or from adjacent European populations. The results obtained here parallel other studies on peregrines elsewhere showing low genetic diversity but genetic structure. We conclude that not enough time elapsed for genetic erosion to occur due to the population bottleneck, and that at least for the Sussex peregrines there is no need for genetic conservation by wild-take and subsequent captive breeding programmes as long as current protection measures remain in place.</p>
South Greenland Peregrine Falcon population monitoring data (1981-2021)
<p>The Peregrine Falcon (<em>Falco peregrinus tundrius</em>) population in South Greenland has been monitored annually 1981-2021 (except 1993, 2004 and 2020). At visits to known breeding sites we recorded presence/absence of territorial falcons as well as their breeding outputs (number of eggs and/or young).</p> <p>The file named S_Greenland_Peregrine_monitoring_data-1981-2021.csv contains the raw data from 835 site checks (sometimes several per site per year).</p> <p>The file named S_Greenland_Summary_occupancy_and_productivity-1981-2021.csv contains a <em>summary</em> of the raw data, providing annual estimates of occupancy, productivity and average brood size (young/ successful nest).</p> <p>The respective ReadMe files specify the contents.</p>
Data from: Malar stripe size and prominence in peregrine falcons vary positively with solar radiation: Support for the solar glare hypothesis
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Microsatellite Dataset for: Weaving et al. Conservation genetics of regionally extinct peregrine falcons (Falco peregrinus) and unassisted recovery without genetic bottleneck in southern England
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Data from: Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon
Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.
Data from: Sexual size dimorphism, prey morphology, and catch success in relation to flight mechanics in the Peregrine Falcon: a simulation study
In common with many other raptors, female Peregrine Falcons Falco peregrinus are about 50% heavier than males. Their sexual dimorphism is thought to allow breeding pairs to exploit a wider range of prey through a division of labor: the male being able to catch more maneuverable prey species; the female capable of carrying larger ones. Given the difficulty of assessing the catch success and load carrying capacity of both sexes of falcon in the field, we here adopt a novel approach to test the division‐of‐labor theory by using a detailed physics‐based flight simulator of birds. We study attacks by male and female Peregrine Falcons on prey species ranging from small passerines to large ducks, testing how catch success relates to the flight performance of predator and prey. Males prove to be better than females at catching highly maneuverable prey in level flight, but the catch success of both sexes improves and becomes more similar when diving, because of the higher aerodynamic forces that are available to both sexes for maneuvering in high‐speed flight. The higher maximum roll acceleration of the male Peregrine Falcon explains its edge over the female in catching maneuverable prey in level flight. Overall, catch success is more strongly influenced by the differences in maneuverability that exist between different species of prey than between the different sexes of falcon. On the other hand, the female can carry up to 50% greater loads than the male. More generally, our detailed simulation approach highlights the importance of several previously overlooked features of attack and escape. In particular, we find that it is not the prey's instantaneous maximum centripetal acceleration but the prey's ability to sustain a high centripetal acceleration for an extended period of time that is the primary driver of the variation in catch success across species.
Data from: Terminal attack trajectories of peregrine falcons are described by the proportional navigation guidance law of missiles
The ability to intercept uncooperative targets is key to many diverse flight behaviors, from courtship to predation. Previous research has looked for simple geometric rules describing the attack trajectories of animals, but the underlying feedback laws have remained obscure. Here, we use GPS loggers and onboard video cameras to study peregrine falcons, Falco peregrinus, attacking stationary targets, maneuvering targets, and live prey. We show that the terminal attack trajectories of peregrines are not described by any simple geometric rule as previously claimed, and instead use system identification techniques to fit a phenomenological model of the dynamical system generating the observed trajectories. We find that these trajectories are best—and exceedingly well—modeled by the proportional navigation (PN) guidance law used by most guided missiles. Under this guidance law, turning is commanded at a rate proportional to the angular rate of the line-of-sight between the attacker and its target, with a constant of proportionality (i.e., feedback gain) called the navigation constant (N). Whereas most guided missiles use navigation constants falling on the interval 3 ≤ N ≤ 5, peregrine attack trajectories are best fitted by lower navigation constants (median N < 3). This lower feedback gain is appropriate at the lower flight speed of a biological system, given its presumably higher error and longer delay. This same guidance law could find use in small visually guided drones designed to remove other drones from protected airspace.
Data from: Sexual size dimorphism, prey morphology, and catch success in relation to flight mechanics in the Peregrine Falcon: a simulation study
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Data from: Terminal attack trajectories of peregrine falcons are described by the proportional navigation guidance law of missiles
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Data from: Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon
Open the record for dataset details and reuse information.
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