Bunny Tails 5: Hare-raising Finale
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The user can also edit genes select whether the mutation for fur color, tail length and teeth length is dominant or recessive.
The simulation output includes a chart with the population of bunnies number of bunnies on the y-axis, plotted against time on the x-axis. There is an option to see a pedigree when the user clicks on an individual bunny moving within the environment equator or arctic. They can run the simulation, controlling and changing variables, and analyze the data generated by the simulation output graph. The simulation itself does not come with instructions or a lesson plan. However, there are several lesson plans posted on the website. These lesson plans are from authors independent of the authors of the simulation.
This review is limited to reviewing the PHET natural selection simulation, and not any of the lesson plans from outside authors. HS-LS Construct an explanation based on evidence that the process of evolution primarily results from four factors: 1 the potential for a species to increase in number, 2 the heritable genetic variation of individuals in a species due to mutation and sexual reproduction, 3 competition for limited resources, and 4 the proliferation of those organisms that are better able to survive and reproduce in the environment. Clarification Statement: Emphasis is on using evidence to explain the influence each of the four factors has on number of organisms, behaviors, morphology, or physiology in terms of ability to compete for limited resources and subsequent survival of individuals and adaptation of species.
Examples of evidence could include mathematical models such as simple distribution graphs and proportional reasoning. Assessment Boundary: Assessment does not include other mechanisms of evolution, such as genetic drift, gene flow through migration, and co-evolution. This resource appears to be designed to build towards this performance expectation, though the resource developer has not explicitly stated so.
Teachers should embed this simulation into their instructional sequence where they feel it best fits. Teacher questioning and support of students is dependent on student background knowledge of the concept and their familiarity with simulations. Students should be working on this simulation with a partner, so they have the opportunity to discuss what they are observing, what questions they could be investigating with this simulation, how to display and analyze data, and how to write an evidence-based explanation answering their questions.
Teachers should monitor for student understanding throughout the activity, and use some form of large-group share-out to make sure that all students are making progress toward the performance expectation. Of the 4, image sets viewed by experts, Variance in raw classifications strongly predicted whether image sets were classified correctly.
We provide guidelines in the Usage Notes for using measures of disagreement to measure certainty that a consensus classification is correct and to target image sets for review or exclusion in any given analysis.
We envision broad applications for these datasets in ecology, informatics, computer vision, and education. Here we provide additional details and guidelines. The consensus classifications are equivalent to raw data produced by standard camera trapping surveys and include all metadata necessary for applying robust analytical procedures that explicitly consider variations in detection probability.
We provide dates of activity for every camera trap, as well as dates, times, and locations for every image. All images are downloadable and identified to species, so capture histories of individually recognized animals can be constructed for species with distinct pelage patterns e. In such cases, sophisticated mark-recapture analyses can permit spatially-explicit inference 45 , Crowdsourcing and citizen science are being used increasingly often to produce science datasets 22 — 24 , but they require robust methods to measure and validate data quality.
While our consensus dataset derives from a simple plurality algorithm, more complex algorithms can improve upon these results. For example, Hines et al. Our raw classification dataset could be used to develop and test algorithms that employ user-weighting or even apply a Bayesian framework to incorporate information about species likelihood based on previous or subsequent images.
Object search-and-recognition research requires large data sets of labelled imagery. Reliable data sets of wild animals are rare, due to the enormous task of hand-annotating large numbers of images. By using the raw images together with the consensus dataset, machine-learning algorithms could be developed to automatically detect and identify species, using part of the dataset for training the image-recognition algorithm and the rest for testing the algorithm.
Raw images could be used separately, or in conjunction with the consensus data set, to research automatic detection of textures, patterns, and other characteristics of outdoor scenes. Higher education teachers can use the consensus dataset and metadata to develop curricula around the scientific method, using charismatic fauna to engage students.
As examples, students can ask questions about species abundances and distributions, daily activity patterns and seasonal movements. The dataset can be tailored by the instructor for use by undergraduates for authentic research experiences. This dataset is also suitable for graduate level coursework in ecology, informatics, and computer vision. We made no effort to distinguish among species of hare, jackal, and mongoose.
Users typically selected as many behaviours as applicable for a given species in each image but sometimes classified two individuals as displaying two different behaviours by listing the same species twice. We standardized classifications by combining multiple classifications of the same species within a single ClassificationID before applying the consensus algorithm.
Note that the raw classification data set contains separate assessments made by each volunteer and thus does not combine multiple records within ClassificationID for any single image. However, the accuracy varies by species and difficulty and certain analyses may require greater accuracy than obtained from the plurality algorithm. High values indicate high certainty. High values for NumBlanks and Evenness for single species image sets tend to reflect more difficult image sets for which the consensus vote is more likely to be incorrect.
As a result, difficult images are more likely to have more blank classifications. As described in the Technical Validation, higher evenness score reflects lower agreement among classifications and were more likely to be incorrect for single species captures. To increase certainty of datasets for analyses, analyses can be restricted to data that meet any threshold for percent support, Evenness , or NumBlanks. How to cite this article: Swanson, A. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna.
Data doi: Springer, Surridge, A. Striped rabbits in Southeast Asia. Nature Holden, J.
Oryx 37 , 34—40 Karanth, K. Estimating tiger Panthera tigris populations from camera-trap data using capture—recapture models. Rowcliffe, J. Estimating animal density using camera traps without the need for individual recognition. Density estimation of sympatric carnivores using spatially explicit capture-recapture methods and standard trapping grid.
Royle, J. Academic, MacKenzie, D. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84 , — Academic Press, Fegraus, E. Krishnappa, Y.
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Software for minimalistic data management in large camera trap studies. Swinnen, K. A novel method to reduce time investment when processing videos from camera trap studies. Holdo, R. Opposing rainfall and plant nutritional gradients best explain the wildebeest migration in the Serengeti. Packer, C. Fear of darkness, the full moon and the nocturnal ecology of African lions. Sinclair, A. Serengeti: Dynamics of an Ecosystem. University of Chicago Press, Aerial Census in the Serengeti Ecosystem.
Tanzania Wildlife Research Institute, Strauss, M. Using claw marks to study lion predation on giraffes of the Serengeti. Tulloch, A. Realising the full potential of citizen science monitoring programs. Bonney, R. Citizen Science: A developing tool for expanding science knowledge and scientific literacy. BioScience 59 , — Dickinson, J. Citizen science as an ecological research tool: challenges and benefits. Sauermann, H. Crowd science user contribution patterns and their implications. Natl Acad. Sci , — Hines, G. Russell, B. LabelMe: a database and web-based tool for image annotation.
Vis 77 , — Tobler, M. An evaluation of camera traps for inventorying large- and medium-sized terrestrial rainforest mammals. Rovero, F. Camera trapping photographic rate as an index of density in forest ungulates. On the use of automated cameras to estimate species richness for large- and medium-sized rainforest mammals.
Kosmala, M. Lintott, C.
British bunny hops into Guinness Book of Records for having the longest tail in the world
Snapshot Serengeti. Carbone, C. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Methods and Analyses. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83 , — Elsevier, Mackenzie, D. Designing occupancy studies: general advice and allocating survey effort. Bailey, L. Sampling design trade-offs in occupancy studies with imperfect detection: examples and software. Nichols, J. Occupancy estimation and modeling with multiple states and state uncertainty.
Usually, kids are just stumbling upon the bird when its mother is away doing something else. Getty "I just want some goddamned time to my goddamned self. It's a big enough problem that it's actually against the law to try and raise a native species of baby bird yourself; it's punishable by up to six months in jail and a fine up to 15 grand.
Damn, having to admit that to your fellow inmates would actually be worse than the sentence itself. Getty "They caught me selling a dimebag of baby crows to an undercover. First off, birds hardly use their noses ; instead they rely on their eyes and ears. So no, they can't immediately sense when a baby has been tainted by human stench, whether you pick it up with your hands or try to rub your balls on it. Second, birds don't really care if humans touch their chicks or not. While touching a bird when it is learning to fly can be detrimental to its ability to fly, it doesn't stop mother birds from feeding or caring for their chicks.
They're still bonded to them through good and bad, just like your parents are with you. Getty Except for the times they left you in the woods, only for the wolves to return you in disgust. We're going to go with Snopes' theory that it was the only way to get people to stop trying to freaking pick up baby birds. Animal welfare organizations have long maintained even now that you shouldn't help newly born animals with anything because they're still learning. But, if you've ever met a 7-year-old, you know they're not easily deterred. The baby needs my help getting back to the nest! You see for mommies, it's a thin line between nurturing, and gladly ridding ourselves of a foul smelling accident that ruined her life.
Getty "I'm not sure how we got on to this. For some of you, this was your first attempt at hands-on animal biology. You're in the back yard and you find an earthworm. You cut it in half, because you're a sadist, and look at that! Both halves are still alive! Getty "One day, this will be Father.
Then some helpful grownup comes along and says, "You know, eventually both ends will heal and grow back, and you'll have two worms! To a kid, the logic is sound: If lizards and such can regenerate new tails they've lost, and worms are nothing but tail-shaped things, they can probably regenerate their whole bodies.
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And they're such basic creatures, really just tubes of ooze capable of nothing but wriggling and creeping out year-old girls. The whole thing is a lie. Try this. Buy yourself a cow. Now sever the cow straight down the middle. How many cows do you now have? Did each half grow a new half cow? Didn't think so.
If you think we're being silly by picking such a completely different animal, fine. Try it with a fly, or a cockroach. Because earthworms are as biologically complex as any insect. They, too, have heads and tails and more importantly entire systems for eating and metabolizing food. They have brains, and hearts, the whole bit. Wikipedia We can't find the bit labeled "burger," and we can usually find it pretty quickly. So the idea that if you cut the head off, the ass end of the worm will just grow a new head is just as insane as thinking a cow or a dog can do it.
It would be pretty freaking amazing if they could. And if it were true, we would be living on the planet equivalent of the movie Slither , because every worm that ever got chopped up would magically become a whole family of worms. Getty This is about to get a little chest-burstery. The myth probably comes from a simple misunderstanding. Earthworms, like most insects, do have regenerative abilities, just not nearly as advanced as we think. So if you cut part of an earthworm's tail off, it might be able to regrow a stunted replacement.
And they do keep moving after you cut them in half, but that's just because both sides are wriggling in pain as the final nerve signals shoot through.
The same has been observed to happen with decapitated chickens and humans. Both with chickens and earth worms, the non-head side eventually dies off. So to recap, the first experiment that taught many a child that nature is a mysterious, wondrous thing was actually just chopping an animal in half, and watching it writhe around in the throes of death.
But at least your parents warned you before you molested those birds into the orphanage. Do a Google Image Search for "cartoon rabbit" and within the first five results you'll find one eating a carrot, even if it's not Bugs Bunny. Carrots are to rabbits what bananas are to monkeys. The only thing more certain than a rabbit's carrot addiction is this: Mice love cheese. If a mousetrap doesn't have a big triangular wedge of cheese in it, the mouse is going to walk right by that sucker. That fact has been well established everywhere from commercials to song lyrics to kids' movies.
Getty We can just tell this is about to be solved in a funny and heartwarming way.