This month’s site update contains a number of new features beyond the standard citation and reference updates. In particular, three new elements are now on the site. Two of these were discussed as upcoming in previous posts, but are now live. The third has its own description posted earlier today:
My previous discussions of range maps (see part 1 here) focused on areal (or linear) representations of where different species might be found. Another type of location data has been part of the site for awhile but was never explicitly compiled into a useful form…until now.
Individual records of species usages (such as those found on the citation pages) often list the particular location to which the name applies. This location can be fairly specific (e.g., a particular river mouth) or extremely broad (e.g., a country or ocean basin). These location names are stored as part of the database; a number of years ago I decided to standardize the names to allow for better analysis and consistency. As much as possible, the names found on the website reflect the current name of the location, rather than the historical name that might have appeared in the original publication, although the historical names are kept as synonyms (invisible until now).
Beyond just standardizing the names, every location was given a latitudinal and longitudinal representation. These coordinates lack precision, but represent a quick-and-dirty estimate of what the location name might represent. For a broad area, the coordinates usually match a major coastal city or the rough center of the coastal area; for smaller islands, the center of the island might have been used.
I’ve been using the coordinates for a few years to help identify misidentified records and/or errors in the database. By plotting the locations associated with different names, it is fairly easy to find major outliers; these can then be examined to determine if the error was a recording error on my part or a (potential) error made by the original authors.
In this new release, the point location data is now public and is used in a variety of different ways and places on the site.
The species pages (as well as the general geography page) now show a point location map in addition to the traditional range map. These point location maps represent all of the places where the database believes a particular species is actually found (as opposed to where an author said it was found). These should generally match the range maps, although there are certainly cases where they diverge; at some point these differences will be used to update/fix the range maps and/or the underlying point-species references. Additionally, the point maps are only up-to-date based on citation records added to the database. Aberrant locations where no fiddler crabs are found are indicated by a different color symbol.
The name pages (both compound and specific name) now show a point map indicating all of the places where a particular name was used, if any (some names are not associated with any specific location so maps are left off of those pages). This is different than the species pages, because this indicates author usage rather than the algorithmicaly and/or expertly adjusted species meanings found on the species pages.
Every location has its own page as well. Each location page includes the latitude and longitude used to indicate its position (as well as a map of the point), cross-references to locations that contain or are contained within the location based on the hierarchy mentioned below, lists of currently recognized species found within the location, and lists of names (both compound and specific) which have been used for species at the location.
The species and name lists automatically fill in missing names by including all names found at any sublocation of the hierarchy (names inferred as such are indicated by a symbol). This should allow one to more readily access complete lists of names or species for an area without having to worry about the vagaries of historical records.
Location pages can be accessed in two ways. First, from the general geography page, there are now a pair of indices of all locations. The first index represents a rough hierarchical sorting of locations, lumping sub-locations into larger locations (e.g., a city within a state within a country). The second index is a pure alphabetical listing of all locations, including the historical names, not just the modern names. Second, all location pages can also be accessed from any page/record which mentions that location; the locations in the tabular records automatically link to the associated location page.
Problems and Limitations
These location data are not perfect. Some known problems include:
Species may be missing from locations just due to holes in observations. For example, Uca pugilator is not recorded for Deleware, even though it falls right in the middle of the known range of the species and is found in neighboring states.
Less precise records may have a point location which seems to indicate the presence of a species well outside its actual range. Thus a species recorded from “Central America” might use a point location to indicate this area well outside the actual range of the species in question if it is limited to only one end of the region.
The hierarchy is imperfect in a number of ways. This limits how readily it can combine and infer records from subregions into large regions. Some of this can be fixed and updated over time, but other parts may be very difficult to fix, or impossible to put into a strict hierarchy. For example, take the country of Panama. We can combine all of the subrecords of Panama under the country entry to indicate all of the species found in Panama. However, once we do that, we cannot readily also combine the coasts of Panama into the individual Atlantic or Pacific regions which have separate species assemblages. Under the hierarchical structure, we either have to keep the two coasts of Panama separate or we have to abandon the Atlantic and Pacific basins as part of the natural hierarchy (for now I’ve done the latter).
Most of the Google maps of individual point locations default to a higher zoom than one would prefer (essentially, the begin maximally zoomed). There’s an easier, imperfect solution to this and a tedious, better solution. I’m working on the latter, but it’ll be awhile before the default zoom will be fixed for all locations.
All of the point location data in the database are directly based on what has been reported in publications. There are other potential sources of location records that might be integrated at some point, including more formal museum specimen records such as those found in GBIF or more informal, citizen-science records such as those from iNaturalist (although I note that at least some of the latter records are included in the former). For now I’d prefer not to mix and match these with the data on this site, but in the long run it will be worthwhile to explore how to combine them.
I’ve been playing with these location data for a few years and the new pages offline for a few months. It seems time to make them generally available; if you find obvious problems or flaws, please let me know so we can endeavor to make them better.
I recently happened to stumble across the lab page of a faculty member who used a word cloud based on his research papers to display the key words that come up most often in his own research. I thought this was a rather interesting way to get an objective view of ones research so started playing with the idea for my own work. Unfortunately, this turned out to be more difficult than I expected, not because building a word cloud was hard (there are plenty of available tools for that), but because extracting the text from the PDFs of all of my publications led to a lot of weird biases and errors (this is a PDF issue) and it’ll take a lot more effort to dig up the original raw text documents than I’m willing to go through right now.
It occurred to me secondarily, however, that we could use the same approach on the fiddler name data to get a visualization of how often each name appears in the literature.
First we have the occurrences of binomial/compound names in the literature. The frequencies are based on the number of publications each name appears in if used as a valid name (thus, a paper which states that name A is a junior synonym of name B would only count the senior synonym B and not the junior synonym A). No matter how many times the name is used within the paper, it counts as only one occurrence with respect to this exercise.
The results are pretty much what one would expect, but it does provide a somewhat interesting (if not particularly statistical) rendering of the relative name uses.
As with other parts of the site, we can do the same thing with the specific names only (ignoring both genera and lumping alternate/misspellings). Again, the major names are what one would expect.
I may need to play with the visualization a bit (color schemes, shape, etc.), but these images will be added to the name summary part of the website on the next monthly release.
As highlighted in part two, even beyond general questions of their accuracy there are some potential problems with using the range maps as data, particularly if we are thinking about estimating the sizes of their ranges. Three parts of the solution are completely obvious. First, for any question involving coastline length (whether to measure available space or estimate a fiddler range), one must be very specific about the map scale and background map used to make such measures since changing these could change the results. Second, when comparing species and/or regions, one must use the same base maps (optimally) or maps constructed from identical scales (suboptimal, but tolerable if necessary) to calculated values for each species or region. Third, species ranges and coastline lengths have to be determined from the identical maps if they are to be at all compared.
This last part is where the most work suddenly looms. As detailed in the previous post, currently our species ranges are generated from one map set while our coastlines are generated from a different one. To make them match, we would likely need to recreate the range data…again…on our new coastlines. Doing so highlights the fragility of the current process…but also leads to the realization that there is likely a better way to store the base information about fiddler crab ranges.
I’ve mentioned a few times that fiddler crab ranges should likely be viewed as one-dimensional lines rather than two-dimensional areas. But we can take advantage of areas and polygons to define a fiddler crab range in an easy and flexible manner. The idea here (which one can view as theoretical since I have not implemented it yet) is that for a given species we only need to define the general boundaries of the range—define a loose polygon which includes all of the within-range coastline and none of the outside-range coastline. Whenever we need to draw the actual range or estimate a distance, we then just need an algorithm which compares that polygon to a coastline map and extracts just the coastline inside the polygon.
There are a number of clear advantages to this approach.
The range data is not fixed to a specific background coastline map, allowing any coastline map to be used to generate the actual mapped or measured range. Changing maps would not require redoing the range data.
Updating the range data for a species simply requires updating the enclosing polygon, likely a vaster simpler operation in most cases than the current system.
The polygon that describes the range data does not require tremendous precision over most of its boundaries. A simple rectangle might be adequate for many species (some will require more complicated shapes, unfortunately).
In theory, for some species with extreme simple ranges (a single contiguous coastline without any outlying islands), one could define the entire range by just noting the end points. In reality, we’ll likely define these by a simple shape that intersects the coast at those two points.
For example, below is a range map for Uca maracoani which I used in the first post of this series, with the addition of a blue rectangle to serve as the polygon denoting its “range.”
This rectangle adequately represents the range of the species, as long as we recognize that the range is the coastline within the rectangle and not the area of the rectangle itself. We need not concern ourselves with how far into the Atlantic the rectangle extends (as long as it isn’t so wide as to clip Africa), nor that it includes two landlocked countries without any shoreline. This rectangle would serve as the masking template for the species, to be applied to any coastline map. An algorithm would simply need to extract the coastline in the rectangle (marked in red) as needed for display or analytical purposes. If we need to change the range of the species we make the rectangle bigger or smaller or use a more complicated polygon as necessary (for example, we could not extend the rectangle west to encompass the Atlantic coast of Colombia and Panama without it intersecting the Pacific coast of Peru…in that case we’d have to use a slightly more complicated polygon that avoided intersecting the Pacific coast).
The biggest question mark about this approach is how complicated the computation will need to be to extract the correct coastline from a complex polygon of an arbitrary shape (simple polygons would be pretty easy), but my presumption is not too complicated. If nothing else, this problem is not unique and has been solved in many other applications (e.g., masking or clipping figures using complex shapes in vector drawing programs such as Illustrator or Inkscape) so likely a workable solution already exists.
While not solving every problem, using this simpler bounding concept with algorithmic coastline extraction seems like a much more flexible manner of storing the range data. Of course, maybe the way we are thinking about range maps is completely wrong to begin with. Stay tuned for final thoughts…
In the early days of the site I would collect any images of fiddler crabs I could find. One I stumbled across was this small photo of a stylized painting of a fiddler crab from Fiji (at this point I don’t recall if the painting, the crab, or both were from Fiji…the original site is long since gone).
A little over a decade ago, we had to have our swimming pool completely rebuilt. As part of the rebuild, my wife wanted to put some mosaics into the surface of the pool to add some color and style. These included some large swimming dolphins (complete with shadows to give them a 3D effect) and some small turtles on a bench. Obviously, I felt we should have a fiddler crab as well. Of course, you cannot find fiddler crab mosaics for pools (or anything else, for that matter). You can find other crabs (particularly blue crabs), but not fiddlers. It turned out, however, that one of the local tile suppliers could custom build mosaics for you, if you provided them with a design. The above painting seemed like a good starting model, so a little simplification and tile color matching, then…
We are likely the only people in the world with a fiddler crab mosaic in our swimming pool.
This is the second of four planned posts about how I constructed the range maps for fiddler crabs. The first part gave the history and background of how these maps were drawn in the first place. This part will discuss where the maps become problematic when we want to use them as input data for analysis. The third part will present a possible solution to the problem detailed in the second part. The fourth and final part will step back and ask if we’re actually thinking about range maps the wrong way entirely.
In the first post I discussed how the range maps were originally created and have evolved over time. As general tools for the display of information, they work perfectly fine (there are some limitations that will be raised in part 4).
But what if we want to go beyond the display itself and think about the ranges as input data for other analyses. What type of analysis? As an example, a few years ago, Jeff Levinton (my PhD advisor) published a study on the Latitudinal diversity relationships of fiddler crabs. In general, fiddler crab diversity declines as latitude increases (as it does for many other groups of species), and this paper explored potential factors that explain this pattern. In this paper, the species ranges themselves made up a key piece of the raw data.
For the purposes of that sort of study, there’s nothing wrong with the ranges as data. You mostly only need to know the upper and lower latitudes between which a species is found, and while there may be some uncertainty on the precise boundaries (I’ll come back to this in part four), small errors are not likely to make a huge difference in the results. Other aspects of the ranges may become more problematic if looked at too closely, however.
For example, one of the results that can be found in the above paper is a slight northern bias to fiddler crab species diversity: globally, peak diversity is not found at the equator, but rather about 10° north (regionally, the northern bias is strongest in the Americas, but largely absent from the Indo-West Pacific). There are any number of reasons why there may be a slight northern bias, but one hypothesis that could be suggested is that there is more land in the northern hemisphere than the southern and diversity is partially tracking habitat availability. Since land area as a whole is fairly meaningless to fiddler crabs, this hypothesis can only make sense if the increased land mass in the northern hemisphere corresponds to an increased coastline. There are a number of reasons I suspect this hypothesis about fiddler diversity is likely incorrect (the simplest of which is that the greatest species diversity is often found in very small areas), but what if we wanted to test it? We would need a way of measuring the amount of of coastline available. Because fiddlers only live on coastlines, this also leads to the idea of measuring the “size” of a fiddler crab range by the total length of coastline it inhabits. Unlike most other species, fiddler crab ranges can be thought of as one-dimensional lengths measured in km, rather than two-dimensional areas measured in km2 (this 1D argument might fall apart for some of the species which range over large parts of the western Pacific islands, but that is a discussion for another time).
So, whether we are interested in the range of a species of the potential habitat it inhabits, we are looking at measuring the length of the coastline. How do we do this? The coastlines and species ranges on our maps are recorded as a series of connected coordinates, so it’s simple to imagine simply calculating the distances between connected pairs and adding them together. Voilà, species range and/or coastline length! Except, now we need to go back and look at our map data more closely.
The coastlines and countries boundaries in our new cartoon maps (see previous post) came from the Natural Earth data sets. These maps come in three different scales: 1:10 m, 1:50 m, and 1:110 m. Essentially, fine scale to rough scale. For simple display purposes, most of the fiddler crab ranges could use the medium or roughest scale; the finer scale maps are only really needed if we need to zoom in to fairly small regions. For example, Uca osa is a recently described species of fiddler crab known only from the Gulf of Dulce, in Costa Rica. Below is a zoomed-in look at the Gulf drawn from two of these data sets.
At this level, the two maps are strikingly different. Because the gulf is so small, it does not even show up in the 1:110 m map data (not shown)! But it is more than just a visual difference. The measurement of coastline will be different in each of these. The finer the scale of the map, the longer the coastline.
This issue has been known for a long time; in fact, coastline length is considered to be a fractal mathematics problem called the coastline paradox. Theoretically (if not practically), if you could keep measuring a coastline at greater and greater accuracy, it’s length would continue to increase…all the way to infinity. One of my favorite oddities of fractal mathematics is the proof that a finite area can contain an infinitely long line (e.g., see the Koch Snowflake). If we want to measure the length of the coastline, we need to be concerned with the scale at which we measure it.
Since our species ranges are also based on coastlines, they have the same issue. But here, a secondary problem arises. The ranges are currently based on yet a different map set (this one extracted from Google Earth). If we draw the coastline data for Uca osa on top of one of our Gulf maps…
we immediately find that it is at yet a different scale than any of our background maps.
This all just highlights how we need to be careful about thinking of these species ranges as data. They’re perfectly good for generally asking about where species are and questions of overlap, but if we want to translate these ranges into measures of distance or area, more thought is needed. Some of those more thoughts in part three…
I’m always on the lookout for good fiddler crab arts or crafts…there pretty much isn’t any: most of what you find is reproductions of old figures and/or poor quality, while a lot of the rest that is labeled as fiddler crabs are actually other types of crabs entirely. Recently, however, I heard that there was a fiddler crab LEGO set; I found that rather surprising and went looking for it. Turns out the information was partly correct: nanoblock, a LEGO competitor from Japan, does make a fiddler crab mini-set. Naturally, I had to get it.
If you’re not familiar with them, nanoblocks are very similar to LEGOs, except much smaller. Way way smaller. I honestly wasn’t prepared for how small the pieces were. This makes building the set quite a bit more challenging, if for no other reason than my thick fingers had trouble dealing with tiny pieces at times.
The set comes in a small, resealable envelope. More than 150 pieces seems a bit non-specific, but that may be in part because it comes with a decent number of extras beyond those necessary to build the model. Ages 8+ (whew…I cleared that bar), mostly because younger would likely eat the small pieces. Actually, an 8 year old can probably handle the tiny pieces way better than I can. Interestingly, it rates 4 out 5 on the difficulty scale.
I’ll skip describing the build process beyond a few general comments.
The instructions are not as clear as they could be and there were a few places I had to backtrack to discover that I’d missed something.
Beyond the difficulties generated by their size, nanoblocks don’t stick together as tightly as I remember LEGOs doing so. This seems to primarily be due to the lack of tubes in the underside of the brick providing extra support. There are obvious pluses and minuses to this: the plus is it allows you to combine bricks off center in a way you cannot with most LEGOs; the minus is a lack of stability and tendency for pieces to come apart when you don’t want them to.
The end result is pretty nice. It definitely looks like a fiddler crab, other than the eyes which strike me as a bit more ghost-crabby. It’s life-size for some of the larger fiddler crab species (the carapace of the model is slightly more than 3 cm wide, which puts it in my large category of fiddler crabs). Because the company is from Japan (although the toy itself is made in China), the color-scheme makes me think it might be Uca arcuata, but I’m sure I’m reading a bit too much into that. The large and small claws (and to a very tiny extent, the legs) can actually be rotated and adjusted in or out, so we can pose him with the claws in as if he were feeding or out as if he were a horizontal waver (to move the claw vertically would require redesigning the build and the pieces are too fragile and small for me to bother).
The built model is sitting in a carefully chosen spot on my desk at home where cats and/or cleaning implements are unlikely to bump into it and cause it to fall apart.
The simple answer to the question posed in the title of this post is: small.
Fiddler crabs are very small compared to most of the crabs people are familiar with. The largest species is relatively small (barely reaching 5 cm wide in the largest individuals) and the smallest species is quite tiny (under 1 cm wide). However, within fiddler crabs there is quite a lot of range and when tasked with describing a particular species (e.g., in my Fiddler Crab of the Week series), one of the first things I generally want to comment on is its size (compared to other fiddlers).
For species toward the ends of the size range this is fairly simple, but for a lot of those in the middle we don’t really have good guidelines to what a small or medium or large species is. For example, the following figure from my dissertation was used to illustrate the relative sizes of 20 species of fiddlers observed on the Pacific coast of Panama.
The largest of these is among the largest in the genus and the smallest among the smallest in the genus, so it more-or-less encompasses the entire size range, but as it only represents about 20% of the genus it may not be representative of the overall distribution of sizes within that range. Based on this figure, it would be difficult to set distinct barriers barriers between size classes (not that I would believe there necessarily should be any), making the designation of large, medium, or small more strikingly arbitrary.
The question of species size distribution may be more than an esoteric one; many fiddler crab species live in the same places (sympatry), but ecological interactions among them tend to be restricted to those that are roughly the same size. Males will defend territory and burrows against males of other species if they are of similar size (even directly fighting on occasion) and sometimes even attempt to court females of similar-sized species. Generally, they mostly seem to ignore individuals of species very dissimilar in size.
For the purposes of this discussion, I will restrict my measurement of size to carapace width (sometimes called carapace breadth). Sometimes this is measured as the widest part of the carapace, other times as the the distance between the tips (antero-lateral angles) of the front of the carapace (in many species, the distance between these tips is the widest part of the carapace, but as in the figure below, not always). The difference between these two measures is very small relative to the total width of the carapace.
There are a few practical reasons for choosing carapace width/breadth as our measure of crab size. (1) This width is easier to make and subject to less measurement error than the length or depth of the carapace. (2) The mass of the crab is dependent on hydration and mass measurements of dead specimens may be very different than live specimens, let alone live specimens in different hydration states. Carapace width is a stable measurement for both living and dead specimens. (3) The measure applies to both males and females, unlike, for example, claw length. (4) This measure has been used as a standard in many studies and allometrically scales with most other measures one might use instead.
In her monograph, Jocelyn Crane lists sizes for most of the species under their descriptions, but usually only a measurement for 2-3 males and 2-3 females (often categorized as large, medium, or small) rather than a more thorough population sampling. She usually tried to include the largest individual she could find, and then one or two other representative sizes. For females, she usually tried to include a small ovigerous female, in addition to the largest female. In total, she provides carapace breadth for 213 individuals from 88 different taxa. Here is a summary of all of her measurements:
A few things immediately stand out. First, across all species, the largest male is almost always larger than the largest female. Otherwise, male and female sizes do not appear strikingly different from this figure. Second, there is more than a five-fold difference in size among species; the largest species top out just over 50 mm, the smallest under 8 mm. Third, there are relatively fewer large species overall (as illustrated by the change in slope of the largest male after the first dozen or so species), as opposed to medium and small species. Finally, for the most part, there are no distinct divisions that would allow one to set clear size classes; for most of the size range the transition to smaller and smaller crabs is fairly smooth. The possible exception is seen at the very top of the distribution, where a few slight gaps representing an 8–10% difference in size between adjacently-ranked species stand out. However, it is worth noting that a 5–10% difference can be found in many of the smaller contrasts as well, they just do not stand out in the figure when plotted on this scale. More importantly, we don’t know how representative these measurements are.
To get at this, let’s compare this to a different data set. As part of my dissertation, I measured the width of over 850 adult male crabs from about the same number of species. Samples per species ranged from as low as a single individual to as many as 50. There were a couple of species from Crane’s data set which I did not have comparison data for (although I left her measurements on the figure below); six additional species for which I do have data, but which were not part of her data set are not shown (they generally fall in the middle of the overall distribution).
Again, a few things stand out. First, there were a small number of species for which I measured a larger (sometimes quite a bit so) individual than the largest recorded by Crane. The size of one species in particular (Uca pugnax) was clearly underestimated by Crane, as I recorded many individuals larger than what she reported and, in fact, her largest measure was only a little above the average of my sample (this is the stack of red points above the blue top line directly under the “a” in “Crane”).
For most of the species, however, her largest measures either correspond with or are larger than what I recorded. Overall, her ranges likely overestimate slightly the average size of most of the species, as there are frequently large numbers of individuals in my sample below her lowest measure. For most species, the average carapace breadth in my data set (not shown) tends to be below or close to the smallest breadth reported by Crane.
The literature certainly contains additional data on individual species which we could use to enhance these figures further, but the main message is clear. Fiddlers as a whole more-or-less uniformly occupy the size range between the smallest and largest species, and any boundary between small, medium, and large is functionally arbitrary. Based on the above figures, I’d likely just divide the size space into 10 mm intervals. Species that grow above 40 mm are “very large,” between 30–40 mm are “large,” 20–30 mm are “medium,” 10–20 mm are “small,” and below 10 mm are “very small.” Using Crane’s measures of largest size, we would find the following distribution:
The modal maximum size is 18–20 mm, right at the boundary of small and medium, with more than 2/3 of the genus within 10 mm of that size and classified in one of those two categories. That seems quite reasonable and puts slightly more than 10% of the genus in the large category and less than 10% in each of the very small and very large categories.
Obviously the divisions are arbitrary and a number of species will move up a category as we find larger individuals (e.g., three of the species would move up a size class just based on my own data from the previous figure) but this is a workable definition of fiddler crab size and we can always hedge species near the boundaries as small–medium or medium–large.
If this were the grade distribution for a large class, the students near the mode would all be complaining about +/- and begging me to change the curve. Thankfully, fiddler crabs don’t care.
So I guess I have my own answer now as to what I’ll mean if I say a species is small, medium, etc.
One final point: It’s useful to remember that even the measures for the species with better sample sizes likely do not entirely capture the species adult size range. Others have observed that there can be differences in size among populations within a species (likely driven by environmental factors, although genetic differences cannot be ruled out). It is also quite feasible that there may be seasonal differences in population size, particularly for temperate species which are dormant over the winter but may go through a number of growth molts over the summer.
To truly characterize the size of a species and the contributing factors would be quite a bit of work, let alone to compare across the genus as a whole.
This is the first of four planned posts about how I constructed the range maps for fiddler crabs. This first part (likely the longest) will give the history and background of how these maps were drawn in the first place. The second part will discuss where the maps become problematic when we want to use them as input data for analysis. The third part will present a possible solution to the problem detailed in the second part. The fourth and final part will step back and ask if we’re actually thinking about range maps the wrong way entirely.
1. The Original Maps
In mid-2002 I decided to add maps depicting the distributions of each fiddler crab species to the website. I don’t recall why; possibly I just wanted updated information, possibly there was another motivation. This might be around when I expanded the site beyond its original collection of photos, videos, and references to be a bit more species-information-centric. The maps that ended up on the site were constructed in the roughest way possible.
Using this as the starting range, I pored through the literature post-1975 (more or less) to look for publications that might have information altering the ranges presented by Crane. Once I’d established an updated range for a species, a background map of the coastline was drawn for the appropriate area using the first version of my spatial software PASSaGE. This map was imported into a graphics program (Photoshop? MS Paint?) and the range was hand drawn with a transparent brush and saved as a raster image (GIF of all things) for display on the site. Not precisely high-tech, but it got the job done.
One obvious problem with this approach was updating a map often required redrawing it from scratch, particularly if the range needed to be reduced rather than expanded. Not the most efficient approach and not the best quality.
2. The Interactive Maps
At some point between 2011 and 2013, the entire website was rebuilt from the ground up; this is when the site was transitioned from hand-coded to dynamically created from a data back-end. How to handle the range maps was an interesting question, and after some exploration Google Maps seemed to be a good solution. By creating a custom KML layer for each species, I could use the Google Maps API to insert an interactive map into the webpages.
The challenge was how best to create the KML layers with the species ranges. The solution I came up with had a couple of different parts. One key element was to take advantage of the fact that (adult) fiddler crabs are essentially restricted to marine coastlines (they go a little bit inland into river mouths and estuaries and the like, but on the global scale this is still within the margin of error of the “coast”). Juveniles presumably get a bit more into the ocean, but even there the limited data suggests most of them seem to stick close to the shore.
Thus, a set of coastline data can provide the entire framework on which to build a fiddler crab range: basically, any given piece of coastline can either be in the range or out of the range, and you don’t have to particularly worry about any space in between coastlines (whether land or open ocean). To create these, I imported a full set of world country borders from within Google Earth (Google Earth made it easy to quickly display, check, and update distributions without fighting with the vagaries of the online Google Maps API). For each country whose coastline had fiddler crabs, I exported the individual country to a KML file, then manually removed the parts of the boundaries that represented interior land borders, leaving only coastlines. Countries with borders that included multiple major oceanic regions were split into their constituent parts (e.g., an Atlantic Panama coast and a Pacific Panama coast). All of these coast outlines were re-imported into Google Earth. For each species it was then just a matter of copying the appropriate country borders into a new folder to represent that species; when only part of a country was in the species range, a copy of that country’s coastline was again exported, manually trimmed to the correct range, then re-imported. While seemingly a lot of up front work, for most species it became a fairly quick and easy method to produce the ranges. Each species had its own folder within Google Earth and the whole set was exported as a single KML file. I then wrote a program to extract all of the individual maps from this file, standardizing certain style elements, and exporting each one to its own individual file for integrating with the Google Maps API on the website. This also allowed the creation of the map which overlaps all of the species ranges with high transparency to get a worldwide view of species density.
Updating or editing the maps, while still a bit of work, was substantially easier than with the original ones since it only required changing a subset of the coastline data, rather than redrawing an entire map by hand.
Interestingly, a few people complained about the new maps. As rough as they were, the old ones displayed the ranges in a fairly simple cartoon form which is lost with the more complicated Google Map backdrop; also, the new images are not easily exportable for use in another format (beyond doing a screen grab, as I had to do to display the above figure).
3. The New Static Maps
Recently, as part of some potential and planned updates to the site, I came to the realization that the inability to automatically export the maps to an image for use outside a webpage had become somewhat problematic. After a lot of time spent trying (and failing) to come up with ways to automatically export the layered Google Map, it finally occurred to me that the obvious solution was to back away from the Google Map approach entirely for non-web-based use. In fact, part of the solution was to go back to the beginning. As mentioned above, I’ve written code in the past that can draw a background map given a set of lines or polygons describing coastal outlines (data readily available from a number of online sources). And although it was never part of the original plan, the KML file that is being used to add the Google Map layers has all of the boundary data for the ranges in an already parsable format. Combining these into the site creation code with a drawing module that could export directly to a file suddenly allows us to automatically draw higher quality cartoon maps, much like the site originally had, but automated from the code rather than hand painted. (1) Use the world map data to draw a nice representation of key coastlines. (2) Draw the range data from the KML file on top of this in a different color and with a slightly thicker line to help make it stand out. (3) Export as a vector file format (SVG) rather than raster file to allow scalability for high quality figures. I still use the Google Maps and KML layers on the website for interactivity, but these new maps can be seen and downloaded by clicking on the link directly below the corresponding Google map (the link can be seen in the previous figure).
On these new maps I decided to use a background map with country boundaries to help make the ranges more obvious, and filled in the land areas to make the maps clearer for species with more limited ranges, but unlike with the original maps, these decisions can be quickly changed and the maps redrawn with minimal effort since they are created programmatically. The range data itself is identical to that from the Google Maps.
For display purposes outside of webpages, these newer maps are quite nice. But all is not perfect if we actually start thinking about using these ranges as data rather than just visual guides. To be continued in part two…
One of the things to realize about the site is that it is now as much a research project as it is an information portal (it didn’t start this way…maybe I’ll write up the history of the site at some point). The research project end is a bit poorly defined, but could roughly be viewed as “what types of things can I say, infer, or visualize about fiddler crabs if I collect a whole lot of data on the back end.” Yeah…not very specific.
One idea that had been floating in the back of my mind for quite awhile was how to to visualize changes in taxonomic name usage over time. If one name replace another, how do we show that visually? Not surprisingly, others have thought about this and come up with some solutions. In late December I was sitting in an airport preparing to leave the country for the holidays when I stumbled across a blog post by Rod Page, Taxonomic name timelines for BHL, about just such a question. He mentioned an earlier project called Synonyms that would produce nice looking name chronology charts by mining data from the Biodiversity Heritage Library; unfortunately, Synonyms had gone defunct and Rod had hacked together a rough replacement (as is his wont to do). The Synonyms graphs were exactly what I had been trying to come up with so immediately made note to try to do this with my own data when I got back.
It only took me a few hours in early January to extract the data I needed and get some basic-Synonymy style charts plotted. It took quite a bit longer to fiddle with their formatting to get them “just right.” Let’s look at an example.
For a simple case, let’s compare the names of the genera. For most of their history, fiddler crabs have been placed in one of two genera: for most of the 19th century they were considered to be Gelasimus; since the start of the 20th century they have mostly been placed in Uca. Most of the other genera which have included fiddlers (23 in total) make up a tiny proportion of references relative to these two. Here are their chronology plots (I’m still struggling to come up with a good name for these):
These charts represent the number of publications per year that used each genus as an accepted name (whether a publication named one species or ten, it only counted as one valid use of the name).
Caveat: the database is still largely incomplete after about 1977, so the parts of the figure from the last 40 years are not representative of the full pattern.
A few things stand out from looking at these charts (some of which I knew already):
Generally, references to fiddler crabs skyrocketed starting in the 1950’s (this is when fiddlers really started being used as model organisms to study certain aspects of behavior and physiology).
It took about 15 years for the transition to Gelasimus to Uca to really take hold; from the late 1890’s through the late 1910’s, both genera were used in roughly equal numbers.
Although Gelasimus was technically abandoned by taxonomists at the start of the 20th century, the genus continued to be used at a small, but steady pace at least through the 1970’s. It’s hard to see on the graph, but there has been at least one use of the genus as valid as recently as 2006.
Something not obvious from this chart: most of the uses of Uca prior to 1897 are actually references to a different type of crab. For most of the 19th century, Uca was used for crabs in the genus we now call Ucides (the somewhat tortured history of these names has been discussed by others and is summarized in the Systematics section of the main website).
To some extent, these figures aren’t quite as exciting as I’d hoped they’d be, but they still visually illustrate the name usage in a direct and attractive way. These charts are not live on the website yet, but the capability to add them is now built-in to the code and graphs depicting name usage for binomials and specific names, as well as directly comparing synonyms will all be made available at some point in the future, once a few more kinks are worked out.