Hawk Images

[Emigdio Binet]

Medium-sized hawk with slim body and fairly long tail. Wings are broad and somewhat square at the tips when soaring. Adults have extensively reddish brown underparts. Tail and flight feathers are banded black and white.

Immatures from Florida have heavier brown streaking on the breast than on the belly. The flight feathers and the tail have narrow bands of brown and white, more subtle than the black-and-white patterning of adults.

Note fairly long tail, slim body, and squared-off wings with translucent crescents at the tips. Immatures of the California subspecies have dark patterning on the wings and strongly banded tails, in addition to brown streaks on the breast.

Adults are colorful hawks with dark-and-white checkered wings and warm reddish barring on the breast. The tail is black with narrow white bands. Immatures are brown above and white below streaked with brown. All ages show narrow, pale crescents near the wingtips in flight.

Red-shouldered Hawks soar over forests or perch on tree branches or utility wires. Its rising, whistled kee-rah is a distinctive sound of the forest. They hunt small mammals, amphibians, and reptiles either from perches or while flying.

Look for Red-shouldered Hawks in deciduous woodlands, often near rivers and swamps. They build stick nests in a main crotch of a large tree. During migration, Red-shouldered Hawks often move high overhead along ridges or along the coast.

She was intently devouring a freshly killed bird and if she had not been very hungry, I doubt she would have allowed me to move in so close. At one point, after having nearly eviscerated the entire bird, she tried to lift and carry away the carcass with her claw-shaped talons (one of the last photos in the batch). She did not succeed and finding more body parts, continued to eat.

After a bit, some boisterous folks came up from behind, startling both the hawk and myself, and off she flew to the far side of the pond. I found a stick and turned the dead carcass over onto its back. The head was missing, but by looking at the black webbed feet as well as the chest and belly feathers, it quickly became apparent that the victim was a Great Cormorant. I am sad to say that I think it was the very same juvenile Great Cormorant that had been living at Niles Pond for the past month as I have not seen another since.

There were birders in the neighborhood earlier that morning, the morning of the winter solstice, December 21st. I wonder if they saw the Hawk kill the Cormorant, or if the Hawk came upon the freshly killed bird and it had been taken down by another predator. If you were one of the birders watching the Hawk out on Eastern Point near Niles Pond, on December 21st, please write. Thank you so much!

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I like this image very much. It is far more dynamic than the usual Red Tail on pole shot and it shows off the hawk nicely. The foreground is a bit busy, with all of those branches and twigs, but in this instance I think it works, showing the Red Tail in a natural setting.

When I took this frame I realized the hawk was much closer than what I would have expected it to be and it struck me that the young raptor might land closer to me than it had been before. I kept my toes crossed because my fingers were busy. Good thing I was wearing my hiking sandals.

The juvenile Red-tailed Hawk landed on top of these richly toned, dark rocks that was much closer than where I had originally seen it. By then the morning was getting warm (okay, it was hot) and the bird kept its wings slightly away from its body in an effort to keep cool.

This series was sure worth the bite, I would think! It is amazing what damage the deer flies can do. That kind of attack would make most people run for cover. On a canoe trip, my friend got bit on the lip at Mono Lake and his whole mouth swelled up. I still have scars the bites I got at Corkscrew Swamp from my Florida trip back in March.

This is a wonderful series of photos. I never get tired of seeing the various feather patterns in red-tails. You can almost identify birds by the various patterns. Around here they are extremely distinct. This one you show here is beautifully marked.

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Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.

The importance of assessing the quality of SMLM images is widely recognised but is a challenging problem to solve. In general, images must be assessed without access to the ground truth structure, meaning that any image assessment method must make some type of comparison with alternative data or analysis. For example, Fourier ring correlation (FRC)8 splits the dataset into two images, both of which will be subject to the same algorithmic bias. Therefore, when artificial sharpening is present, FRC will simply report the reduced scatter of localisations typical of biased reconstructions as higher resolution. Most localisation algorithms suffer from similar artificial sharpening effects, so comparisons made between them have limited effectiveness5. Here, we use the term resolution to indicate the length scale at which resolvable structures are authentically reproduced in the reconstruction. In other words, the local resolution is the scale at which the measured biases are smaller than the scale of the structures themselves.

This problem of common biases in each image is averted in the super-resolution quantitative image rating and reporting of error locations method (SQUIRREL)9. This method downscales the SMLM image and compares it to a linear transformation of the widefield image. However, this has two major disadvantages. Firstly, the downscaling eliminates the fine structure in the image, meaning that only differences on or above the scale of the PSF can be quantified (see Supplementary Note 1 and Supplementary Figs. 1, 2). Secondly, sharpened images will score more highly than accurate reconstructions if their reconstruction intensity has a more linear relation to their labelling density (which is likely for a substantial number of algorithms, see Supplementary Fig. 2).

There are also approaches to quantify how the algorithm used for SMLM can limit accuracy/resolution and introduce bias5,10. While these can demonstrate bias and artificial sharpening when the ground truth structure (or some defining property e.g. as with a spatially random structure) is known5,10, and can be used to assess the relative performance of algorithms, they cannot assess the quality of reconstructed experimental images. Additionally, the relative performance of different algorithms on simulated test data cannot be guaranteed to reproduce the effects observed on real samples containing varied types of structure.

We exploit the accuracy and reliability of HAWK to identify potential artefacts in a localisation microscopy image produced without HAWK, and to indicate where HAWK preprocessing has reduced localisation precision sufficiently that underlying fine structure could have been made unresolvable. This is achieved by quantifying structural differences between the original image and the HAWK-processed reconstruction as produced using the same algorithm. This measure can be used to map out areas which have artefacts, such as artificial sharpening, and to ascribe a confidence level to local regions of the image at the sub-diffraction level. A measure of the local resolution (in the sense defined above of the length scale at which structure is correctly reproduced), can be ascertained by progressively blurring the input images with a Gaussian kernel for longer length scales and repeating the comparison. The length scale at which reasonable agreement (quantified by the degree of local correlation) between the HAWK-preprocessed and non-HAWK-processed output images in the local region is achieved, indicates the resolution obtained. From this, a map of the maximum scale of artefacts in the image can be produced.

HAWKMAN takes as input data a test (super-resolved reconstructed) image and reference (HAWK-preprocessed) image, and a maximum length scale over which the performance of the algorithm will be evaluated (Fig. 1). The input images are intensity-flattened to suppress the influence of isolated outlying high intensity points (due to repeated sampling), which frequently occur in SMLM. The flattened images are then blurred with a Gaussian kernel of width equivalent to the current length scale of interest (ranging in integer multiples of a single pixel up to a user-specified maximum). These blurred images are then binarised according to a length scale-specific adaptive threshold. This produces two images for each input: one (the sharpening map) is produced from a threshold of roughly 50% of the local maximum, the other (the structure map) is produced from a higher threshold (85%) and subsequently skeletonised and then re-blurred with the same Gaussian kernel. It should be noted that the optimum thresholds will vary slightly depending on the local dimensionality of the sample, but these variations are not critically important. A higher threshold may be required if there is only a small difference in the labelling density of adjoining structure, such as may result from nonspecific labelling. The optimum value is only required to resolve these adjacent structures, default parameters will still identify strong sharpening artefacts. Supplementary Fig. 3 shows how to choose the optimum parameters. Default parameters are used for all data presented here except for the clathrin-coated pit data below.

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