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To make this situation clear, I'll use a somewhat silly, but conceptually simple example. Imagine I record teams of movers carrying furniture down the block. I measure the furniture's position/speed over time (say every 15 seconds), the size of the furniture (small, medium, or large), and the numbers of people carrying the object at each 15 second time point. People on the teams join in and help transport or let go and step back freely; maybe they join when they feel they could be useful or something, who knows, it just changes.
I record these trips multiple times across different furniture sizes, etc. I now want to determine HOW the number of movers carrying the furniture and the size of furniture affect the speed of transport. If there were only one trip, I think I would use some sort of ARIMAX model and regress the number of movers against their instantaneous speed over the single time-series run (with ARIMA errors). However, I want to make a generalization ACROSS all these recordings and I want to see if the categorical variable of furniture size has an effect as well (or how it interacts with mover number). How in the world do I incorporate all of these recorded trips (many different time-series iterations) in one analysis?
Additionally, imagine I think both the speed and the number of helpers are non-stationary, but in different ways. I think it's likely that the speed increases throughout the trip, and the number of workers increases initially (as they try to get it going, say) and then decreases (once it's moving along, maybe extra movers just get in the way). What would I do in that situation?
Lastly, I'm trying to do this in R. Crazy bonus points if you can explain it using R code!
Ah, I think there was a misunderstanding! I meant, generate all the possible start-to-ending paths possible that will include the animal walking the full path. If there's one "loop de loop," calculate the tortuosity of the path as if the animal goes around the loop CCW and calculate it again as if it went CW, then average these two FULL paths together? I mean, it'll depend on your data. Otherwise, I think your method of calculating chunks weighted by length might be good.
Yeah, what you're referring to is called "rediscretization" and some measures of tortuosity DEPEND on it. If you're using R, check out Jim McLean's "trajr" package. He has a whole package dedicated to this sort of thing:
Oooof, that is gonna be a difficult one. It probably depends on the amount of overlap / loops you have. Does the animal ever cross over its own tracks? If not and you have only one possible trajectory through space (irrespective of time), then it should be easy. Many measures don't need time components.
However, if it DOES cross over its own path, then multiple possible trajectories are possible. I might try computing tortuosity for each path and then averaging/finding the median?
So, I have several directed (multi-edged) networks, and within them each node has been assigned to one of seven categories (based on some *a priori* circumstances). Each category *should* have a higher within-category interaction rate, but I want to test the statistical significance of this.
Since "a set of nodes, densely connected internally" is pretty much the definition of a community, I want to manually impose my community assignments on the nodes in the network and then test whether this assignment is statistically more "community-like" than a random assignment. **In essence, my question is: "Is this given community structure statistically significant?"**
I found [this paper] which seems to have a way of measuring the statistical significance of a single community group in the network, but it doesn't seem to apply to a given, entire community structure. I also found [this baby], but it seems to only be focused on much smaller, local structures.
There's gotta be a way to do this for directed, multi-edged networks, I just can't seem to find any! (Additionally, I'll have to do this analysis in R, so mega-triple-extra bonus points if you know of an R package that already does this.)
Heritable variation is essential for evolution by natural selection. In Neotropical army ants, the ecological role of a given species is linked intimately to the morphological variation within the sterile worker caste. Furthermore, the army ant Eciton burchellii is highly polyandrous, presenting a unique opportunity to explore heritability of morph...
I will be doing a video analysis of crabs swarming behavior in the field, via aerial drones. However, I have no previous experience buying drones and the video cameras that go on these drones. What models of both drones and Camera would be acceptable for this type of work? Could I just use a system with a GoPro? Is anything relatively good quality, cheap, and durable? Thanks!
The most important question you should ask yourself is WHY are you doing this? Are you expanding the patch model? The predator-prey OFT model? If you're adding new theory, cool! Are you just trying to show that OFT works? This has been done countless times already, in different situations. Although I'm not exactly sure what you're trying to test (nutritional density, volume, etc?) I would imagine most low-hanging fruit (a adequate metaphor with OFT) has already been picked here.
Don't show that this oft-proven theory works just to show it works. Remember, models are useful tools for exploring biology; by themselves, they are not much, but as a null model or a way to shape your expectations, they can be quite revealing. If your organisms DON'T conform to the OFT, that would be interesting. What about their biology is causing this apparent "sub-optimal" strategy? What does the fact that they conform/don't conform reveal?
Colony size is an incredibly important factor in social insect ecology: it affects everything from foraging strategies to colony defense to mating systems to the degree of polymorphism. However, colony sizes vary dramatically among ant species (Formicidae): sizes range from several workers living together to super-colonies that stretch for hundreds...