Information

Help identifying a fly by its remains


I recently hiked in a swampy forest in the Yaroslavl region(Eastern Europe) near lake Plesheevo and came across a clearing. Although the area was swampy and verdant, there was a 5 meter wide "bald spot" on the ground which looked like sand. Near that spot there was a lot of buzzing (which I initially mistook for the buzzing of wasps), and soon I found myself covered in these black flies.

The flies clung to my fabric hoodie, at least one tried crawling in my hair, and one tried to find its way into my eye. I tried shaking them off, but they seemed to be able to cling very firmly to my clothes. (later I discovered that even dead flies were really hard to remove from fabric) At that point I panicked, quickly retreated, and spent a good 15 minutes extracting every single one of them from myself and my garb.

When I came back, I took a picture of the carcasses of two flies that found their way into my pocket and silently passed away there from unknown causes. Unfortunately, I didn't get to take a picture of a living fly because I was too agitated at the time.

Their length was approximately 0.3 inches and when squashed, they produced yellow spots on my clothes.

I tried identifying them, but didn't have much success. They seem to look like a variety of horse-fly (or a dear-fly), but I think none of them actually bit me. Also, strangely enough, my clothes were sprayed with insect repellent which they seemed to either completely ignore or were actually attracted by.


They look very much like Hippoboscidae. https://en.wikipedia.org/wiki/Hippoboscidae This family are parasites of birds and mammals, but I am not aware that they are vectors of any diseases to humans. So don't worry. One member of the family is a wingless parasite on sheep (Melophagus ovinus)


Article: The Body Farm

  • Contributed by Shannan Muskopf
  • High School Biology Instructor at Granite City School District
  • Sourced from Biology Corner

The Remains of Doctor Bass

Written by Alan Bellows on 06 November 2008

Under normal circumstances, one would expect a wandering throng of students to demonstrate animated displeasure upon encountering a human corpse in the woods particularly a corpse as fragrant and festering as that which was found on an August afternoon in Knoxville, Tennessee. From a short distance the male figure almost appeared to be napping among the hummingbirds and squirrels, draped as he was over the pebbled ground. But something about his peculiar pose evoked a sense of grim finality&ndash the body language of the deceased.

The students knelt alongside the slumped form, seemingly untroubled by the acrid, syrupy tang of human decay which hung in the air. They remarked on the amount of decomposition that had become evident since their last visit, such as the sloughed skin and distended midsection. Their lack of alarm wasn&rsquot altogether surprising, for they were part of the organization responsible for dumping these corpses. They were forensic anthropology students from the University of Tennessee.

Affectionately referred to as the Body Farm, the facility was founded in 1981 by Dr. Bill Bass, a professor of anthropology at the university. Before the Body Farm was established, information on human decay was astonishingly inadequate, leaving criminal investigators poorly equipped for determining abandoned bodies&rsquo time of death. On one occasion, Dr. Bass was asked to estimate the post-mortem interval of some human remains, and conventional methods indicated approximately one year given the moist flesh still clinging to the man&rsquos bones. When other evidence later revealed that the body had been occupying its coffin since the Civil War, a flummoxed Dr. Bass took it upon himself to finally fill the forensic gap.

The professor convinced the university to set aside over an acre of woodland for his pioneering decay research. A chain-link fence with razor wire and a privacy fence were erected around the plot. To discourage those whose curiosity is aroused by pungent breezes and formidable fences, a series of signs were installed to warn away would-be interlopers, broadcasting their unsettling all-caps pronouncements across the countryside: RESEARCH FACILITY. BIOHAZARD. NO TRESPASSING.

As the lifeless subjects are interred into the grisly forest hideaway, each is assigned an anonymous identification number. At any given time, several dozen perished persons are scattered around the hillside within automobiles, cement vaults, suitcases, plastic bags, shallow graves, pools of water, or deposited directly upon the earth. Grad students and professors return periodically to check on the subjects&rsquo progress.

One of the facility&rsquos first non-living participants was Pig Doe, a hog who was anesthetized and shot on the facility grounds. Within eighty-seven seconds a vigilant blow fly made berth upon the unfortunate animal and installed a cluster of eggs. The predictable timing of infestation waves represents the main thrust of the research at the Body Farm: forensic entomology, the examination of insects for law-enforcement purposes.

Technically decomposition begins about four minutes after death, when cells are deprived of their usual supply of nourishment. Absent these food molecules, digestive enzymes begin gnawing upon the cells themselves, a process called autolysis. Within a few hours the chemicals that allow muscle fibers to slide freely are metabolized, causing a temporary profound stiffness known as rigor mortis. The body pales in color as its blood pools at the lowermost portions.

With the human immune system permanently off-line, the digestive bacteria in the gut gain the upper hand, causing an upset in the uneasy intestinal alliance. These bacteria begin nibbling on the body itself. As the host&rsquos cells steadily self-destruct from autolysis, their membranes rupture, spilling the nutrient-rich cell filling into the tissues. The bacteria thrive in this river of food, and they soon establish decomposition franchises at every extremity.

Meanwhile, back on the surface, scores of flies are drawn to the fresh-corpse scent from up to a mile away. They lay their eggs at every exposed opening, and soon the newborn maggots are making a meal of the cadaver&rsquos subcutaneous fat. Forensic entomologists can measure the size of these developing fly larvae to determine &ldquotime since colonization.&rdquo Over several days the spongy brain will liquefy and leak from the ears and mouth, while blisters form on the skin which eventually evolve into large, peeling sheets. Often the skin from the hand will slough off in one piece, an effect known as gloving. Body Farm researchers have discovered that such skin can be soaked in warm water to restore its flexibility, and placed over a researcher&rsquos hand for the purposes of fingerprint identification.

By day four or so, the rigidity of rigor mortis has subsided, and the rapidly reproducing anaerobic bacteria have expelled enough gas that the skin takes on a green tinge. The sickly sweet smell of decay begins to saturate the air as bacterial byproducts such as putrescene and cadaverine cause swelling of the abdomen. Steadfast insects have thoroughly colonized the cadaver, with writhing mounds of maggots obscuring every orifice and a fog of flies swarming above. Maggot-hunting beetles and wasps may join the fray creating another measurable milestone for the entomologists.

As the tenth day of decay approaches, the bacteria-induced bloating becomes pronounced. Sometimes this pressure is relieved via post-mortem flatulence, but occasionally the abdomen will rupture with a wet pop. Ants, moths, and mites begin to capitalize on the corpse cornucopia along with the other insects, while the single-celled citizens dutifully dissolve the internal organs. Soon the soil beneath the corpse is sodden with liquids, while the skin&ndash unappetizing to most insects&ndash becomes mummified and draws in close to the bones. Natural soap buildup might also be present due to the interaction of bodily fats and acids, a process known as saponification.

When the decomposing donors have completed their stint at the Farm, their bones are steam-cleaned and added to the University of Tennessee skeletal archives.

Owing to the information harvested from the Body Farm, any forensic entomologist worth their salt can now determine time of death when presented with a reasonably fresh corpse. Using the results of numerous experiments, investigators have the data to properly adjust post-mortem interval estimates, taking into account environmental conditions. One example of such variation was Dr. Bass&rsquo underestimated civil War remains, which were found to be contaminated with lead from the cast-iron casket. This effectively embalmed the body, making the meat unpalatable to tiny foragers.

Dr. Bass has since retired from teaching, but he has continued as head of the Forensic Anthropology Center. While the prospect of having one&rsquos naked, lifeless husk flung into the woods lacks general appeal, there is nevertheless an ever-growing waiting list of enthusiastic, not-yet-deceased Body Farm volunteers. Dr. Bass himself has stated that his hatred of flies compels him to decline the opportunity to rot for the benefit of science.

Match the word with its definition/synonym (words are underlined in article)


Hawk Identification Clues by Geographic Range

The ranges of hawks vary both by location and by season. The rough-legged hawk, for example, winters in most of the continental U.S. except the southeast. The Cascades Raptor Center describes its summer breeding range as reaching the far northern parts of Canada and Alaska, separated from the winter range by a wide migration zone. You're not likely to see one in the southern range out of season.

If you're east of the Mississippi River, you might spot a red-shouldered hawk, or a broad-winged hawk with its high-pitched whistling call. In western states, you might spot the ferruginous hawk with its rusty-red back, or a sharp-shinned hawk. Red-tailed hawks can be spotted almost everywhere in the lower 48 states.

Use your geographic location to your advantage. For example, you may not spot hawks in Michigan that are only native to Northern Canada or Alaska. Understand your current location and the range of the hawk in order to rule out (or properly identify) the bird you've spotted.


Consider the Fruit Fly

Modern genetics would not be possible without the humble fruit fly.

In college, I worked briefly in a fruit-fly lab, where I spent most of my time just keeping different fly strains alive. It was not difficult—as anyone with a fruit-fly infestation can tell you—but the repetitive work imprinted itself on my brain. Even today, the way my slightly chubby white cat scrunches when he walks resembles nothing more to me than a third instar fly larva, swollen and ready to metamorphose.

This is to say that I came to First in Fly, a new book about fruit-fly research, with perhaps some special interest. In fact, a popular appreciation of fruit flies has seemed long overdue to me. No single animal has contributed as much to the field of genetics as the ordinary and ubiquitous Drosophila melanogaster.

These tiny, winged, exoskeleton-ed creatures—so different from us in appearance—have led to research illuminating a surprising amount about the human body: The genes that tell a fruit fly where to sprout its legs are quite similar to the ones that tell our bodies where to sprout limbs. As are the genes that form the pattern of fine hairs on a fly’s wing and the ones that orientate the tiny hairs in our ears. As are the genes that govern a fruit fly’s circadian rhythm and the ones that give us jet lag. And so on. Research into Drosophila has resulted in at least five Nobel Prizes.

First in Fly by Stephanie Elizabeth Mohr is a thorough chronicle of the contributions of these creatures to science over the past century. Mohr herself is a fly scientist at Harvard Medical School, and she knows intimately the life of a “fly pusher.” (The name comes from the act of pushing flies around under a microscope.) She can at times drift too far into molecular biology for a lay reader, but her book is at its best when it conveys both the ingenuity and sheer labor necessary to coax biological secrets out of Drosophila. If you’ve ever looked at a fly and wondered what it could possibly tell you about the workings of the human body, well, it’s not easy for scientists either.

Consider the story of the gene memorably named Sonic hedgehog. In the 1970s, Christiane Nüsslein-Volhard and Eric Wieschaus in Heidelberg, Germany, were studying a topic that probably sounds hopelessly trivial: patterns in the cuticle, or the protective outer layer, of fruit-fly larvae. They performed what is called a “forward genetic screen”—in which tens of thousands of male fruit flies are fed a chemical that induces mutations and then individually mated with a female. Nüsslein-Volhard and Wieschaus then spent a year sitting side by side at the microscope, looking for individual mutants with unusual cuticles. “Performing a screen,” writes Mohr, “is often an endurance event.”

It paid off. Nüsslein-Volhard and Wieschaus found 15 mutations that resulted in odd-looking cuticles. One of them, which made the cuticle short and spiky, they named hedgehog.

Humans, it turns out, have versions of the hedgehog gene—three, in fact, derivatively named Indian hedgehog, desert hedgehog, and Sonic hedgehog. In fruit flies, the gene coordinates the body plan of the larva, which is manifested most clearly in the unusual shape of its cuticle when the gene is disrupted. In humans, it serves a similar function, telling the embryo which way is front and back, left and right. Babies with mutations in Sonic hedgehog are born with brains that lack distinct left and right hemispheres. So important is the Sonic hedgehog gene that its name has become controversial. What doctor wants to tell a new mother that her gravely disabled child has a mutation in the Sonic hedgehog gene?

Because so many genes have been discovered in Drosophila, many bizarre names originate with the behavior or appearance of the flies. You can imagine how in the drudgery of fly pushing, scientists might dream up fun names for new genes. Some of the most memorable ones in Mohr’s book include:

  • cheapdate: Discovered in fruit flies that are, well, especially sensitive to alcohol
  • hippo: Discovered in fruit flies with huge heads and wrinkles around their necks. In fruit flies and mammals, it controls the size of organs.
  • Van Gogh: Discovered in fruit flies with a “whirling pattern of orientations of the wing hairs, reminiscent of the whirling lines typical of the eponymous artist’s paintings,” writes Mohr. In mammals, a version of it is responsible for the development of hairs in the inner ear.
  • ether-à-go-go: Discovered in fruit flies whose legs twitch rhythmically when anesthetized with ether. In humans, a version of it codes for part of the potassium ion channel that coordinates the heartbeat.
  • spätzle: Discovered in fruit flies whose larvae are irregularly shaped like the German noodle. In fruit flies, spätzle makes a molecule that binds to Toll proteins, named after Christiane Nüsslein-Volhard’s expression “Das ist ja toll!” (“That’s amazing” in German.) Toll proteins are involved in immunity in both fruit flies and humans.

The imaginative gap between these gene names (based on the purpose they serve in fruit flies) and their function later discovered in humans makes obvious just how difficult it can be to anticipate the relevance of fruit-fly research beforehand. In total, Drosophila melanogaster has 14,000 genes, 8,000 of which have human analogues. To read First in Fly is to appreciate the full scope of fruit-fly research and to understand the intimate connections in the DNA of every human cell and Drosophila cell.


RESULTS

Pupariation, pigmentation and eclosion of selected and control stocks under LD 12:12

We assayed the timing of transitions between developmental stages such as pupariation and wing pigmentation that are not known to be under clock control in Drosophila to determine whether the selected stocks have evolved differences in developmental rate (Fig. 1A–F). We found no significant change in the pupariation profile in the selected stocks compared to control stocks (Fig. 1A). Whereas maximum pupariation was seen in control stocks at ∼106 h after egg-collection, pupariation peak was seen at ∼112 h for the selected stocks (Fig. 1A). However, the mean pupariation time of selected and control stocks and their standard deviation were not significantly different from each other (P>0.05 Fig. 1C,E Table 1). Since we found marginal differences in the pupariation profile between selected and control stocks, we looked at the profiles of replicate populations but found large variation across these populations which were not consistent within the stocks (Fig. S1). Moreover, we also did not find significant differences in pupariation time between the stocks when this experiment was repeated (Fig. S2). These results suggest that pupariation profiles of selected and control stocks are largely similar and the mean pupariation time and variation between individuals are not significantly different between selected and control stocks (Fig. 1A,C,E).

Pupariation and pigmentation in selected and control populations under LD 12:12. (A) Percentage of larvae that pupated in every 2 h interval measured from the time of egg collection in selected (PP, total n=1085 pupae across four populations) and control (CP, total n=1134) stocks under LD 12:12. (B) Percentage of pupae from selected (n=1084 pupae) and control (n=1119) stocks that showed wing pigmentation in every 2 h interval under LD 12:12. (C) Mean pupariation time of selected and control stocks under LD 12:12. (D) Mean pigmentation time of selected and control stocks under LD 12:12. (E) Variation in pupariation time estimated by the standard deviation in pupariation time across all individuals of selected and control stocks. (F) Variation in pigmentation time across all individuals of selected stocks is greater than that of control stocks (P<0.05). All values are estimated from single vials containing 30 flies and subsequently averaged across 10 vials for each replicate population. Bar graphs and line plots represent mean values and error bars are s.e.m. across four replicate populations for each stock (n=4). All statistically significant differences reported are based on ANOVA followed by post-hoc comparisons using Tukey's HSD test. *P<0.05.

Pupariation and pigmentation in selected and control populations under LD 12:12. (A) Percentage of larvae that pupated in every 2 h interval measured from the time of egg collection in selected (PP, total n=1085 pupae across four populations) and control (CP, total n=1134) stocks under LD 12:12. (B) Percentage of pupae from selected (n=1084 pupae) and control (n=1119) stocks that showed wing pigmentation in every 2 h interval under LD 12:12. (C) Mean pupariation time of selected and control stocks under LD 12:12. (D) Mean pigmentation time of selected and control stocks under LD 12:12. (E) Variation in pupariation time estimated by the standard deviation in pupariation time across all individuals of selected and control stocks. (F) Variation in pigmentation time across all individuals of selected stocks is greater than that of control stocks (P<0.05). All values are estimated from single vials containing 30 flies and subsequently averaged across 10 vials for each replicate population. Bar graphs and line plots represent mean values and error bars are s.e.m. across four replicate populations for each stock (n=4). All statistically significant differences reported are based on ANOVA followed by post-hoc comparisons using Tukey's HSD test. *P<0.05.

Compiled summary results of one-way or two-way ANOVA for each experiment (figure numbers shown in brackets) with effect of each factor and interactions (if any), corresponding degrees of freedom along with degrees of freedom of the error term, F-values and P-values

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We also recorded the time that these pupae started showing wing pigmentation and found that the timing of wing pigmentation was largely similar in the selected and control stocks. The peak of wing pigmentation was at ∼190 h after egg-collection in the selected stocks while the peak was at ∼192 h in the controls (Fig. 1B). These results suggest that wing pigmentation time is marginally advanced in the selected stocks with respect to the controls though not statistically significant (P>0.05 Fig. 1D Table 1). However, the selected stocks showed significantly higher standard deviation in pigmentation time compared to control stocks (P<0.05 Fig. 1D Table 1). Hence, it appears that the selected stocks are not different in their mean pigmentation time but show greater inter-individual variation in pigmentation time compared to control stocks (Fig. 1D,F). Thus, these differences do not explain the accuracy of eclosion rhythms seen in the selected stocks.

We also assayed the timing of eclosion from the time of egg collection of both selected and control flies following pupariation and pigmentation described above to compare the differences in early development to the final stage of eclosion of the same set of flies. Since only 30 eggs were collected in each vial, eclosion occurred mainly on the 9th day with negligible eclosion (<5%) occurring on the 8th day and no eclosion after the 9th day. Hence, the figure depicts only eclosion on the 9th day (Fig. 2A). The eclosion of flies in the selected stocks peaked at ZT2 (which includes the selection window of ZT1–ZT2) as expected and the peak was sharp and narrow with very little eclosion before and after this peak (Fig. 2A). While control stocks also showed a peak of eclosion at ZT2, this peak was lower and their eclosion was more spread out compared to selected stocks with greater eclosion both prior to lights-on and after the peak at ZT2 (Fig. 2A). ANOVA followed by post-hoc comparisons revealed that eclosion at ZT2 was higher, and at ZT0 and ZT4 was lower, in the selected stocks compared to controls (Fig. 2A P<0.05 Table 1). Thus, selected stocks do not show delay in eclosion time despite starting eclosion later since they also terminate eclosion earlier. This is also indicated by the lack of significant difference in mean eclosion time between selected and control stocks (P>0.05 Fig. 2B Table 1) while standard deviation in eclosion time was significantly lower in the selected stocks compared to controls (P<0.05 Fig. 2C Table 1). Hence, selected stocks show reduced variation in eclosion time even at low densities (30 eggs/vial) similar to the accuracy of eclosion previously reported at higher densities (Kannan et al., 2012a) compared to control stocks, despite no reduction in variation observed in pupariation or pigmentation time. Moreover, despite delay in pupariation time in selected stocks, they do not show overall delay in mean eclosion time compared to controls.


155 Best Forensic Science Research Topics For Your Paper

Forensic science or criminalistics applies scientific methodology and principles to solving crime and aid criminal justice procedures and laws. This area of study covers many fields ranging from computer forensics to doctoral research and forensic psychology.

For students specializing in forensic science studies, it is common to have to write an essay, research paper, or dissertation on the subject’s topics. The tricky part here is to select the perfect topic from a wide array of forensic science topics for a research paper. You could work on something that focuses on a neglected area of study in the field or go in for a controversial topic. You can also pick a common topic and throw new light on it, or simply choose a topic highlighting societal trends.

Whatever you choose to work on, it is essential to clearly state your research question/topic, offer defensible logic, have a well-elaborated body and a concise conclusion to score well.

Here is a list of some of the most interesting research topics in forensic science, which will allow you to write a good essay and score well. Take a look:


Why Some People Want to Believe They’re Transgender

The characteristics that define one’s personal identity are the nuclear elements of personality. People with a chronically unstable self-image, poor self-esteem, and an ill-defined sense of self are poorly equipped to deal with the stresses of ordinary life. This group constitutes the vast majority of the self-identified transgendered who undertake the full sex-change regimen of hormone treatment and “sex-reassignment” surgery.

However, most people suffering from such common personality disorders do not focus on gender dissatisfaction as the cause of their global dysfunction and do not regard sex change as the remedy. Why would a few people with a complex and multidimensional disorder of personal identity decide that their problem consists in having been “assigned” the wrong sex? The answer lies both in the nature of the personality disorder itself, and in powerful social, cultural, and political influences.

The LBGT movement has achieved enormous success in exploiting the psychological vulnerabilities of people who lack a coherent sense of self, providing both activist leaders and a “noble” cause with which to identify. Flush with success following the Supreme Court’s same-sex marriage decision, the movement has taken on an aura of invincibility.

The success of the transgender rights crusade, based as it is on the cultural delusion of denying biologic difference between the sexes, would suggest there are no limits to the movement’s goal of reshaping American culture and its institutions. Attaching oneself to such a powerful force can be a heady experience for someone whose self-identity is largely defined by the people and causes with which he or she identifies.


Additional data files

The following additional data are available with the online version of this paper. Additional data file 1 is a table listing previously identified X-box motifs in C. elegans. These motifs were used as input to generate an HMM profile for finding novel X-box motifs. Additional data file 2 is a table listing known and newly identified X-box-regulated genes in C. elegans. Additional data file 3 is a table listing Affymetrix microarray analysis results. Additional data file 4 is a list of sequencing primers for identifying dyf-5.


3 Free-flight paradigms revisited

3.1 Conceptual overview

However sophisticated tethered-flight paradigms may become, it goes without saying that the natural state of flight is free flight. It does not follow,however, that free flight is necessarily natural flight – in most experimental situations, the subject will be trailing leadwires, carrying a load, flying in a wind tunnel, or simply flying in a confined space. Nevertheless, it is only possible to have the chance of identifying true closed-loop dynamics in free flight, and for this reason free-flight paradigms are likely to play an increasingly important part in our developing understanding of animal flight control. The key difficulty from a flight dynamics perspective is that the forces and moments cannot be directly measured – only the animal's consequent motion. This is problematic because although Newton's Second Law tells us that knowledge of mass and acceleration is equivalent to knowledge of force for a moving particle, things are more complicated for a solid body. For example, a measured roll acceleration might reflect the direct application of a roll torque, but it might also reflect a non-zero product of the angular velocity components about the pitch and yaw axes if their moments of inertia are unequal. The issues of coupling alluded to in section 2.1.2 therefore mean that it will not in general be possible to treat different degrees of freedom separately.

Given that it is generally incorrect to infer the forces and moments in one axis from the accelerations and angular accelerations in only that axis, there is no substitute for using a physically complete set of equations of motion to analyse acceleration data obtained in free flight. Hence, in contrast to a designed tethered-flight experiment – in which the animal's response to a prescribed stimulus can be measured and the parameters of the model fitted separately – free-flight paradigms will generally require all of the parameters of the model to be fitted simultaneously. This brings us into the domain of system identification (Ljung,1998). System identification has been used successfully for over half a century to determine experimentally the dynamics of what in control engineering is termed the `plant' of a control system – typically the physical system being controlled, and in our case the animal's flight dynamics. System identification is becoming increasingly common as a means of modelling aircraft flight dynamics and control, as witnessed by the recent publication of several texts on the subject(Klein and Morelli, 2006 Jategaonkar, 2006 Tischler and Remple,2006).

There are three general approaches to system identification: (a)`white-box', where we know the physical model of the plant through sound application of fundamental laws of physics and seek to estimate the physical parameters of that model from measured data (b) `grey-box', where we postulate a model structure at the level of assuming, say, a first order system with dead time, and seek to estimate its physical parameters (c)`black-box', where we seek to estimate both the model structure and its parameters from data (Jategaonkar,2006). In each case, the model structure and/or parameters are identified using maximum likelihood methods, minimization of prediction error,or other similar optimization procedures. Since a white-box approach is based on a physical model of the system, there is little risk of over-fitting the model with unnecessary parameters with a grey-box or black-box approach, this can be avoided by model reduction techniques and statistical control of the overall type I error. It is common in the identification of aircraft flight dynamics to use a white-box approach, as the basic underlying dynamic model of a fixed-wing aircraft can be readily derived. By the same token, it should be possible to use a white-box approach in the identification of the flight dynamics of gliding animals. A white-box approach may not be feasible for flapping flight, however, which is much more difficult to model theoretically,and it is likely that a grey-box or black-box approach will be required in such cases.

Identifying the dynamics of an animal's flight control system requires knowledge not only of the animal's motion but also of the control input which produces that motion. Furthermore, the control inputs we measure must be sufficient to excite all of the animal's modes of motion and the components of its motion that we measure sufficient for us to observe all of those modes. Once fitted, it is usual to validate the model by comparing its predictions of flight behaviour against validation data not used in the fitting of the model. Naturally, the accuracy of any such analysis rests on the accuracy of the kinematic data that are used to infer the dynamics. These may be collected either using instrumentation forming part of the measured system, such as onboard inertial measurement units and cameras, or using instrumentation external to the measured system, such as ground-based cameras.

3.1.1 Inertial measurement systems

The approach of getting the animal to carry sensors to measure its kinematics is at present restricted to larger animals, for which the required load forms a small enough proportion of body mass not to interfere unduly with the flight dynamics. As a rough rule of thumb, we might aim for a system constituting <10% of body mass. Birds were first made to carry inertial sensors as early as 1982, in a wind tunnel study mounting accelerometers on pigeons (Bilo et al., 1982). However, it has only become possible to mount accelerometers on birds flying freely without the constraint of trailing wires with the recent miniaturization of data loggers(Weimerskirh et al., 2005). Accelerometer data have been used to answer a variety of behavioural and biomechanical questions (Bilo et al.,1982 Hedrick et al.,2004 Weimerskirh et al.,2005), but for flight dynamics purposes this needs to be combined with information on rotation from angular sensors such as magnetometers and rate gyros. As the smallest commercially available inertial measurement units and data loggers providing these facilities have a combined mass of the order of 0.05 kg, this presently limits us to animals with a body mass of the order of 0.5 kg for studies of wide-ranging free flight. With the use of trailing wires, smaller species of bird may also be considered, although this will obviously constrain their flight dynamics. While it is impractical for insects to carry inertial measurement systems at present, tiny induction coils transducing position and orientation have been carried by blowflies flying freely in a small flight arena with an applied magnetic field(Schilstra and Van Hateren,1999). Unfortunately, this technique is not well suited to flight dynamics measurements because the insect is constrained by trailing wires, and the instantaneous position and orientation data collected in this manner still need to be differenced in order to extract velocity and acceleration.

Frame from a video sequence at 50 frames s –1 of a steppe eagle's tail taken by an onboard wireless video camera. The graphs plot the measured tail bank angle and angle of attack as functions of time with no filtering applied. Tail bank angle (ϕ) is extracted from the angle of the trailing edge, as shown by the construction lines on the image. Tail pitch angle (θ) is extracted by measuring the deviation of the trailing edge from its average position perpendicular to the line AB at the point A, and making use of the known distance of the camera to the base of the tail. Tail spread angle is not shown, but can also be determined from these data, giving 3 measurable kinematic degrees of freedom for the tail.

Frame from a video sequence at 50 frames s –1 of a steppe eagle's tail taken by an onboard wireless video camera. The graphs plot the measured tail bank angle and angle of attack as functions of time with no filtering applied. Tail bank angle (ϕ) is extracted from the angle of the trailing edge, as shown by the construction lines on the image. Tail pitch angle (θ) is extracted by measuring the deviation of the trailing edge from its average position perpendicular to the line AB at the point A, and making use of the known distance of the camera to the base of the tail. Tail spread angle is not shown, but can also be determined from these data, giving 3 measurable kinematic degrees of freedom for the tail.

Inertial measurements from a steppe eagle in soaring flight. The graph plots total measured acceleration against time: all three components of acceleration, angular velocity and orientation are logged by the inertial measurement unit, but are not shown. The thumbnails show synchronized frames from a hand-held camcorder (upper row) to provide context, and from a rearward-facing onboard camera (lower row) to confirm that the instrumentation remains steady throughout. Dashed lines denote the correspondence of the graph with the numbered frames. Note how the circled tan-coloured rump contour feathers remain steady (position of circle identical between images),indicating that the instrumentation is static with respect to the body. The visible transients therefore denote real accelerations of the bird, and are presumably excited by gusts, etc., as the bird is not actively manoeuvring in this sequence. The downy white feathers that are visible on either side of the circled contour feather are blowing freely in the wind, so provide no information on the position of the instrumentation with respect to the body.

Inertial measurements from a steppe eagle in soaring flight. The graph plots total measured acceleration against time: all three components of acceleration, angular velocity and orientation are logged by the inertial measurement unit, but are not shown. The thumbnails show synchronized frames from a hand-held camcorder (upper row) to provide context, and from a rearward-facing onboard camera (lower row) to confirm that the instrumentation remains steady throughout. Dashed lines denote the correspondence of the graph with the numbered frames. Note how the circled tan-coloured rump contour feathers remain steady (position of circle identical between images),indicating that the instrumentation is static with respect to the body. The visible transients therefore denote real accelerations of the bird, and are presumably excited by gusts, etc., as the bird is not actively manoeuvring in this sequence. The downy white feathers that are visible on either side of the circled contour feather are blowing freely in the wind, so provide no information on the position of the instrumentation with respect to the body.

External photogrammetric measurement of the wing kinematics of a free-flying steppe eagle Aquila nipalensis coming in to perch on its handler's arm. Left panel shows one of a stereo pair of images taken at 500 frames s –1 . Right panel shows a calibrated reconstruction of the lower surface of the left wing based on stereo-matching of natural features of the plumage. Black points on the wing denote measurements,connected by straight lines to assist in visualizing the wing topography the colour map denotes the local geometric angle of attack of the interpolated wing surface with respect to the horizontal. The isolated black points denote reference measurements on the head and tail, indicating the longitudinal axis of the bird. Note that whereas the angle of attack and camber of the proximal section of the wing is relatively consistent in a spanwise direction, the distal portion of the wing is set at a much greater angle of attack. This reflects the angle of attack of the interpolated surface and does not take account of the local twist of the primary feathers, which will be measured in future work. An animation of this perching sequence is available (Movie 1 in the supplementary material).

External photogrammetric measurement of the wing kinematics of a free-flying steppe eagle Aquila nipalensis coming in to perch on its handler's arm. Left panel shows one of a stereo pair of images taken at 500 frames s –1 . Right panel shows a calibrated reconstruction of the lower surface of the left wing based on stereo-matching of natural features of the plumage. Black points on the wing denote measurements,connected by straight lines to assist in visualizing the wing topography the colour map denotes the local geometric angle of attack of the interpolated wing surface with respect to the horizontal. The isolated black points denote reference measurements on the head and tail, indicating the longitudinal axis of the bird. Note that whereas the angle of attack and camber of the proximal section of the wing is relatively consistent in a spanwise direction, the distal portion of the wing is set at a much greater angle of attack. This reflects the angle of attack of the interpolated surface and does not take account of the local twist of the primary feathers, which will be measured in future work. An animation of this perching sequence is available (Movie 1 in the supplementary material).

3.1.2 External measurement systems

The alternative to onboard instrumentation is an external measurement system, which poses special problems of its own. Usually an external measurement system will be fixed in an inertial frame, but there is no particular reason why it should not be fixed to a moving frame of reference,provided that the motion of that frame of reference is known. The commonest examples of fixed measurement systems are high-speed cameras, which may be used to monitor target position and orientation (e.g. Fry et al., 2003 Hedrick and Biewener, 2007 Hedrick et al., 2007). Naturally, the fixedness of the measurement system constrains the flight volume that can be covered, and for this reason all previous work has been done indoors. Examples of moving measurements systems include pan-tilt mounted cameras used to track insects flying around a large room(Fry et al., 2000 Müller and Robert, 2001). A far greater flight volume can be covered if the external measurement system follows the free-flying animal (Fry et al.,2000), but the need to know the motion of the measurement system introduces an additional source of measurement error that may not be tolerable in flight dynamics studies. In any case, the most fundamental limitation of using any kind of external measurement system for flight dynamics measurements is that velocity, angular velocity and acceleration cannot be measured directly. Instead, the kinematics must be estimated from the measured target position and orientation using, for example, a numerical differencing procedure or Kalman filtering. This amplifies the measurement error and concomitantly reduces the useful bandwidth of the system, so that a very high degree of spatial and temporal resolution is required in order to make measurements suitable for flight dynamics modelling.

3.2 New techniques for free-flight analysis

In order to overcome some of the issues discussed in section 3.1, we have developed complementary external and onboard measurement systems for analysing the flight dynamics of free-flying birds of prey. The methods are described in detail by Carruthers et al. (Carruthers et al., 2007) and Taylor et al.(Taylor et al., 2007),respectively: here we provide a brief summary of the techniques used, together with preliminary data to demonstrate the kinds of measurements that can be made. These data are offered by way of illustration only, and it is not intended that any detailed conclusions be drawn from them: the system identification approach that we have outlined above requires large datasets and a great deal of mathematically involved analysis, which falls outside the scope of this review.

3.2.1 Onboard measurement techniques

Miniature inertial measurement units (IMUs) providing 3D information on orientation, angular velocity and acceleration have only recently become commercially available. We used an MTx/MTi unit (XSens Technologies B.V.,Enschede, The Netherlands) together with a custom-built logger (M. Bacic,Department of Engineering Science, Oxford University) to record at 100 Hz the instantaneous 3D orientation, angular velocity and acceleration of a trained male steppe eagle Aquila nipalensis weighing 2.5 kg. A pair of miniature PAL wireless video cameras were fixed rigidly to the IMU and used simultaneously to record the eagle's head and tail movements, using a ground-based video receiver recording to MiniDV. The video data were later deinterlaced to provide sequences at 50 frames s –1 . The instrumentation was carried on the eagle's back and was worn on a removable harness made of webbing material and Velcro straps: the total load carried in the experiments we describe here was <0.25 kg, or 10% of body mass, but we have since managed to reduced the combined weight of the instrumentation to<0.1 kg.

Fig. 3 shows the kind of information that is available on tail kinematics during flight, while Fig. 4 shows a typical set of inertial data recorded during coastal soaring. The inertial data are shown alongside synchronized video footage of the eagle from a handheld camcorder and from a rearward-facing onboard camera. The view of the body recorded by the onboard camera is stationary throughout, confirming that the instrumentation remained steady with respect to the bird during the manoeuvre. Unfortunately, the IMU heaves with the scapular region on which it is seated during flapping, so that at present we are only able to apply the technique successfully to gliding flight, during which the IMU remains steady on the bird. Together, these data demonstrate that it is possible to use an inertial measurement system to record the body kinematics of a large bird in wide-ranging free flight, while simultaneously recording parameters of its wing or tail kinematics using the onboard video. Given a sampling frequency of 50 Hz for the input measurements (from the onboard cameras) and 100 Hz for the output measurements (from the IMU), the bandwidth over which we can identify the response of the bird ranges up to a theoretical maximum of 25 Hz. This is much broader than the bandwidth we have observed the eagle to make control inputs over, and the technique therefore permits identification of its frequency response over the full range of input frequencies that it employs.

3.2.2 External measurement techniques

Independent validation of the response properties identified using onboard instrumentation is possible by making use of a ground-based external measurement system. This has only recently become feasible with the development of ruggedized high-speed digital video cameras, which allow stereo-photogrammetric measurements to be made under field conditions with sufficient spatiotemporal resolution to extract usable flight dynamics parameters. The disadvantage of this approach is that the bird must be close to the cameras during the measurement, which limits the duration of the flight record that can be obtained. Since the lowest frequency that can be identified is inversely related to the length of the flight record, this means that high-speed video data can only be used to identify the response of a bird at higher frequencies. As such, the method is complementary to the onboard instrumentation techniques that we have developed, which can be used to obtain flight records lasting many tens of minutes and therefore offer better resolution at lower frequencies.

The photogrammetric method uses a pair of synchronized Motionscope M3 cameras (Redlake Imaging Inc., Tucson, AZ, USA) giving 1280 pixel × 1024 pixel resolution at 500 frames s –1 . Using manual tracking of approximately 70 recognizable natural features of the plumage of the wings,head and tail, and using self-calibrating bundle adjustment calibration techniques we have been able to reconstruct the 3D position of each of these points on the bird as it comes in to perch on its handler's arm. Self-calibrating bundle adjustment is the state of the art in photogrammetric reconstruction techniques (e.g. Atkinson,1996). Our implementation uses non-linear least squares optimization to solve for jointly optimal estimates of the camera parameters and target coordinates. Fig. 5plots a reconstruction of the lower surface of the wing and of reference points on the head and tail. The camber and spanwise twist of the wing are clearly visible in the surface colour, which represents the local geometric angle of attack. The photogrammetric data therefore provide a rich source of information for identifying multiple-input multiple-output models of the flight dynamics, complementary to the simpler kinematic data derived from the onboard instrumentation. An animation of a short section of the perching sequence shown in Fig. 5 is provided in the supplementary material, in order to demonstrate that it is possible to extract detailed wing and body kinematic measurements from free-flying birds using natural features of the plumage alone.


8.8 MEASURING THE SIZE OF GLOSSINA

Two methods have been used for measuring the relative size of Glossina. One uses the size of the 'cutting edge' of the hatchet-shaped cell on the wing the other measures the size of the thorax.

8.8.1 Wing vein size The wings have to be cut off the fly and mounted for microscopical examination. The length of the 'cutting edge' of the hatchet-shaped cell (Figure 8.11 A, length X), is measured using a micro meter eyepiece. Both wings are measured and the average taken. Estimates of wing fray may be done at the same time (see 8.5.1).

8.8.2 Thorax size A measurement that is known to match more exactly the real size of the fly can be made by multiplying together two lengths on the thorax:

the distance between the points of insertion of the largest of the humeral bristles on either side of the thorax (Figure 8.11 B, length Y)

The distance between the base of the median scutellar bristles and the mesonotal suture (Figure 8.11 B, length Z).

These distances are measured using a binocular (dissecting) microscope fitted with a micrometer eyepiece.

Fig. 8.11 Dimensions useful in measuring the size of Glossina A, the "cutting edge" (X) of the "hatchet cell" B, the distance (Y) between the points of insertion of the largest humeral bristles on the thorax and the distance (Z) between the point of insertion of the scutellar bristles and the mesonotal suture.