Legally Blind Birding

Testing the Patience of Birding Guides Around the World

Today in Petchaburi Province of Thailand, we unwittingly waded into the taxonomic nightmare of Lophura nycthemera crawfurdi vs. Lophura leucomelanos crawfurdi.

Let’s start with a photo, taken at a blind referred to by our guide as Bo Lung Sin:

KK pheasant1

Kalij or Silver?

Our guide called this a Kalij Pheasant. This surprised us, as we had been studying ahead of time and expected the Kalij to be much darker. We consulted out 2002 edition of Robson, which indicated that this should be the crawfurdi ssp. of the Silver Pheasant.

“No Silver Pheasant in Kaeng Krachan,” said our guide. He pointed out the plates in the Birds of Thailand (Lekhakhun):

Birds of Thailand

The book our guide was using, published in 2013. Nice plates, but limited english.


Note that crawfurdi is denoted as a ssp of L. nycthemra on the plate. Yet it is shown as number 19, which refers to Kalij on the opposite page.

We looked at his book, and we figured that the it simply contained an error, as it refers Kalij on the plate (#19) but lists the scientific name of the Silver (L.n.). So we thought we had this pegged as a Silver, until I attempted to enter this into eBird, which gives only Kalij as a possibility in this region! Now what?

Further searching revealed that there is still some debate about the where the crawfurdi ssp. belongs, with some authors putting them under Silver and others under Kalij. This site for Thai National Parks is fun; it states that Kalij is found in Kaeng Krachan, but Silver is not – ok, good; yet it then states that Kalij has grey legs (!) That doesn’t jive with what we saw; and then, as if to prove my point, they include a photo of an individual from Kaeng Krachan with red legs!

Since I use eBird, I’m going with Kalij for now, but I plan to query them on this in order to be certain that they agree that this red-legged bird is being categorized correctly. It seems that the majority opinion I can find so far would indicate Kalij. But I’m open to being corrected!

The previous post showed a simple cartogram of the US using the eBird species counts by state in order reshape the map. The relative sizes of the states in the cartogram are proportional to the species diversity.

A more detailed version can be obtained by using the species counts at the county level. Data as of early December 2017 was collected from eBird to generate the cartogram shown in Figure 1. The color scheme is based on the number of lists submitted by county, with blue representing more and red less.


Figure 1. Cartogram of eBird species reported by county. Colors represent the number of lists submitted, with blue being highest and red being lowest.

It is an interesting image because it would seem to indicate that greater species diversity in the central US as opposed to the Pacific and Rocky Mountain states. But that really isn’t the case. The counties in the west are typically much larger than those in the central and eastern US. And those large counties do not have, on average, proportionately more bird species. So we end up with a map for which it is difficult to see much of any difference in the new sizes of the counties.

The color scheme shows a broad swath of reddish tones from Montana to the southwest and into Louisiana, then up into Appalachia. Judging from relative county size, this means there are a lot of under-birded but potentially very rewarding counties out there to be explored.

What does not jump out here is the fact that the nine counties with the highest species totals are in California. You’d think the opposite, given how the state has shrunken so much. Again, it is because those counties are so big to start with. And so, we see an example of how a cartogtram can seem to undercut its own purpose.

In this specific case, the problem is exacerbated by the fact that the overall distribution of birds by county does not span much range. The histograms for the species counts (left) and total number of lists submitted (right) are shown in Figure 2.


Figure 2. Left: Distribution of species reported per county. Right: Distribution of lists by county.

The distribution of species counts is not very wide; 805 of the data lies between 157 and 318, and the maximum value is scarcely more than the mean. The number of lists submitted is quite different; the 90th percentile is 15550. Only a very few counties get significantly larger numbers.

Maybe we should make our cartogram the other way around? Let’s let the total number of species reported by county be reflected by the color, and warp the map based on the total number of lists submitted. Here it is, in Figure 3:

county lists

Figure 3. Cartogram of eBird lists submitted by county. Color represented number of species.

Not surprisingly, highly populated areas and coastline counties are swelled up. Does it help to illustrate the differences in eBird data? Hard to say. Multivariate data is always a challenge to present.

The previous post presented a cartogram for global eBird data by county. Here, we do the same for the States (and Washington D.C.). It is worth noting what the starting conditions are; that is, what base map will be used to generate the cartogram. I am using the WGS84 geographic coordinate system/projection, shown here without any morphing:


Base map image from Natural Earth

A cartogram of the same map based on eBird species counts by states was produced using the QGIS cartogram plugin and is shown in the following figure. The colors of the states correspond to the number of lists submitted, with darker colors representing more lists.


Cartogram of total eBird species counts. Colors indicate number of lists submitted.

What jumps out is the large size of the New England states, an increase in the area of Florida, and a reduction to Texas. The loss in area for Texas leaves California slightly larger, reflecting its status as our leading state for species diversity.

Speaking of California, it is also the home to the best counties in the US for bird variety. There are nine counties in the state with higher species totals than any other county in the rest of the US:

  1. San Diego, 543
  2. Los Angeles, 540*
  3. Santa Barbara, 500
  4. Marin, 499
  5. Monterey, 495
  6. Orange, 483
  7. Humboldt, 474
  8. Ventura, 473
  9. San Francisco, 472
  10. Cochise (Arizona), 464

This seems counter-intuitive, at least to someone living far enough to the east; you would think that none of the many eastern warblers, south Texas or south Florida specialties would contribute to these high numbers. (A reader noted in the comments section that a number of eastern birds have been seen in these counties. A more detailed analysis is in order…)

Future posts will look into the county data more closely.

*I never would have thought that one could live in our second largest city and have access to more avian diversity than what is found in 46 other states, without leaving the county! And the 5th best county alone, Monterey, has more bird species than 40 entire states: Colorado, the 10th higher state, has 496, with number 11 New York at 484.

Cartograms provide a pleasing way to present geographical data. The idea is to redraw a map so that the relative sizes of its units (e.g. countries, states, counties, etc.) correspond to some measured quantity. A common example is a cartogram of population by country, in which the relative sizes of China and India become exaggerated, while large countries such as Russia and Canada shrink due to their small populations.


World population cartogram © Copyright Benjamin D. Hennig (Worldmapper Project) cc by [CC BY-SA 4.0 (, via Wikimedia Commons

Various repositories of cartograms can be found online; a good place to start is worldmapper. Details about the nuts and bolts follows at the end of this post.

Instead of human population, let’s consider global bird species distribution by country. The raw data needed to construct this image was pulled from eBird. The figure below shows the result, where several regions, such as the UK and Belgium, were broken into component states. While the size of the country represents the number of species reported, the color scheme is based on the number of eBird lists submitted, with lighter colors corresponding to fewer lists.


Cartogram of eBird species counts, by country. Darker colors correspond to higher numbers of lists submitted. Data is from early December, 2017.

The results are not surprising, but the map is striking nonetheless: Russia and Canada are barely perceptible, their landmasses reduced by a relative paucity of species; Central America bulges out and dwarfs the continental US; Australia shrinks in relation to the Indonesian island region; Northern Africa becomes compressed while the sub-Saharan countries bulk up; and northern South America shows its true dominance: little Ecuador is now practically as large as Brazil.

The color scheme illustrates how the majority of our worldwide birding efforts remain mostly in regions with less species diversity. It can also be viewed as an indicator of the infrastructure for birding that a given nation has, in some cases. Ghana, for example, has an adjusted size comparable to other West African countries, but its new size and color together show that it has developed into the most attractive destination in the region for global birders.

Making Cartograms

Constructing a cartogram requires an algorithm that will morph an existing map in such a way that the relative areas reflect the data. But a good cartogram must also produce an image where borders remain intact, and the component regions must retain enough of their original shape so that they are recognizable.

The process of making cartograms is not too difficult, but it isn’t simple either. There does not exist a single program that allows one to merely enter data, push a button, and have an image. Instead, several steps using different packages and data sources are required. The work here started with a query of eBird by-country data as of early December 2017, and the initial maps were downloaded from the Natural Earth site. The QGIS package was used to join the eBird data in with the map shapefile. The plots for global data were produced using ScapeToad to perform the morphing, using the highest quality (longest-running) setting. Finally QGIS was used to edit the ScapeToad output and produce the shading in the final cartogram.

Additional novel cartograms related to birding are in the works.

The eBird site administered by the Cornell Lab of Ornithology was launched in 2002 and has arguably advanced birding more than any technology since binoculars. Beyond acting as a repository for one’s records, it affords many tools to explore the data submitted by tens of thousands of birders, while giving ornithologists an ever-growing data set that has no peer in other biological sciences.

Analyses of eBird data has so far tended to focus on distributions and migrational movements. But it can also give insights into the engagement of birders. Of interest here is the data from the “Top 100” page, which provides a snapshot of the efforts of the most active participants, and which can be tailored for specific geographical areas and/or time frames. For example, one can look at the highest total number of species reported, or lists submitted, for any year, and for any geographic region. With a goal of recording at least 2,500 species in 2018 while working full-time, a study of the Top 100 records seemed instructive. Specifically, records for the past 30 years were analyzed, with a focus on the maximum number of species (as opposed to maximum number of lists) for worldwide birding.

Consider the total number of species reported vs. year, as shown in Figure 1. The bright green points at 2002 indicate the start of eBird; the bluish data to the left consists of historical records that had to be entered by users at a later time, while the reddish data to the right features data added more in ‘real-time.’ Note that the 2015 record Big Year (6,042) of Noah Stryker is not shown here – it was removed as it is such a huge outlier – both statistically and in terms of the typical effort given by even the most prolific eBirders. Also, a word about the data for 2017: because the Top 100 data was queried on November 24, 2017, it represented an incomplete year – the specific values here for 2017 are an extrapolation of the counts as of late November, out through the rest of the year.


Figure 1. Last 30 years of species count data, for the Top 100 worldwide. Note that the 2015 record of 6,042 by N.Stryker is not shown in order to not compress the y-axis scale.

We see a clear, increasing trend in the number of species seen by the Top 100. In 1986, someone recording 1,000 species would have been the third most prolific eBirder, but in 2017, they would not even get into the Top 100. Also, after about 2005, there is a divergence in the average slopes of the top and bottom of the envelope of data – even as those in the lower portions of the Top 100 list are recording more birds every year, the handful of individuals within the top five or so are pushing the species totals up at an even faster rate. This almost certainly reflects a wider range of birding locales being explored.

A natural question at this point involves the makeup of the eBirding community reflected in this plot. The data was analyzed in terms of the number of years in which each individual appeared in these 30 Top 100 lists. There were a total of 22 eBirders that have been in the Top 100 sixteen or more times. Results for these 22 individuals alone are shown in Figure 2. Colors correspond to different eBirders.


Figure 2. Species vs. year for the 22 eBirders that were in the Top 100 at least sixteen of the 30 years spanned by the x-axis.

Figure 2 illustrates that the growing high species counts are not due to different, more prolific birders showing up more recently. Rather, it shows that the most continuously active eBirders are, on average, reporting more species every year.

Two other data sets were examined to see if trends were different. One set was based on highest species counts, but restricted to the United States only. The other set involved looking at the Top 100 worldwide, but using the total number of lists submitted instead of the species count as the determining factor for inclusion in the Top 100.

The median results for each case vs. year were determined and are shown in Figure 3. (The median captures an average sense of the Top 100 performance without being strongly influenced by any outliers.) The green line is for the original data set, where maximum species count is expected. The blue line is the same approach, but limited to the United States. The red line is for the case of highest numbers of lists, worldwide. Note that the blue curve does not include data for 2017 – it did not seem appropriate to extrapolate and project forward the numbers, given the lower ceiling on the total number of species possible in the USA as opposed to worldwide.


Figure 3. Median species vs. year for Top 100 data for three cases: The green line is worldwide lists maximizing species count. The blue line is for USA only. The red line is for worldwide lists but for maximum number of lists submitted.

In both the worldwide and US-only data, the number of species is increasing. Obviously the US-only curve will eventually ‘hit a wall’ before the worldwide data will. Meanwhile, the slope for the worldwide data has been increasing since about 2010.

One might expect that the number of lists needed to reach these increasingly larger species counts would show a proportional increase. This is not the case. Figure 4 shows the median number of species vs. the median number of lists, with a spline fit applied to show the general trend. Below 100 lists, and above, there is a dramatic change in slope.


Figure 4. Median number of species vs. median number of lists for a year, for worldwide Top 100 (by species)

Figure 5 shows the same plot, but for the US-only Top 100. The results are qualitatively identical to what is seen in the global data.


Figure 5. Median number of species vs. median number of lists for a year, for US-only Top 100 (by species)

While in both cases, 100 lists roughly marks the inflection point, one should note the colors, which reflect the year: the change in slope occurs around 2005 in both cases. This suggests we look at the data for the median number of lists submitted vs. year, which is in Figure 6 below.


Figure 6. Median number of lists submitted. The red line is for worldwide lists but for maximum number of lists submitted. The blue line is for USA only maximizing species count. The green line is worldwide lists maximizing species count.

Clearly the changes in slope in Figures 4 and 5 are simply due to a very large increase in the median number of lists submitted by year, starting around 2005. This growth dramatically outpaces the increase in number of species reported. The establishment of eBird appears to have caused more engagement among serious birders, with significantly more lists, and lists-per-species, on average, being submitted on a yearly basis since 2005. And this is true worldwide as well as in the US.

These trends should please the creators of eBird; not only is the army of observers growing, the efforts made by the most dedicated members are steadily increasing on a yearly basis, accelerating the size of the data set.

Why didn’t the sudden shift in median number of lists submitted happen in 2002, when eBird began?  It seems reasonable that it took several years for ‘word to get out’ and for birders to realize the utility of this powerful resource. There is no reason to expect the trends to change anytime soon, although they will reach some asymptotic limits eventually. When, and at what values? It will be interesting to watch and see.

The idea of a Global Big Working Year is to see as many species as possible between Jan 1 and Dec 31 without geographical constraint, but rather with a more pedestrian limitation: you have to work normal hours at your full-time day job, just like during any other year. (And no, if your full-time day job is being a professional birding guide, that doesn’t count.) In 2018 I will try to reach 2,500 species in this manner.

Is that a lot? A little? Is it a record, or not even close? For all I know this has been done before. (An official keeper of such records would certainly be welcome.) One can look at the eBird Top 100 for the world in order to get a feel for the numbers, but there are no details as to the time constraints at play. As of Nov 24th, 2017, there were seven individuals with more then 2,000 species recorded during the year. Based on their profiles, a few of these individuals appear to be active birding guides, so that wouldn’t fit with the GBWY criteria. In any case, 2,500 does not seem out of the question given sufficient travel on the weekends and vacations.

Without an official ‘record’ to try to break, I hope to make my 2018 GBWY mean more than just growing my life list at a healthy clip. I will pledge a fixed donation amount of at least $5 per species, for every species, over the year. That makes $12,500 or more my goal. Where is that money going to go? I plan to split it two ways, with half going for bird conservation efforts* and the other part going to research on blindness.

Maybe this is where I’d ask for others to contribute based on my result, or to do some kind of matching. That would be nice, but I wouldn’t ask for that. I can think of few things less interesting than birding or listing or doing anything, for that matter, vicariously. Instead, I’d rather challenge other birders to make a similar goal and pledge. Commit to some amount, any amount, per bird. Then pick a cause of your choice. Tell them what you are going to do, and do it. Tell other birders too.

This could even be done for just a day – such as the Cornell Lab of Ornithology global Big Day. They have a fundraiser based on the tally that their “Team Sapsucker” reaches during 24 hours – this is a great idea and it raises money for our common cause. Yet I can’t help but think that this could be augmented even more by having a Big Day Pledge fundraiser based on one’s own tally. You set the pledge level, you get the birds, and the accomplishment and the donation is a more personal event. Most importantly, it gets more involvement and more funds.

*Update Jan 6 2018: I’ve decided which birding cause I will support: Friends of Sax-Zim Bog. The timing on this is absolutely perfect, as they have just started a fundraising campaign for a “Big Half Year”

We recently took a custom birding tour in Ghana and had a fantastic experience. We worked with Ahanti African Tours and they delivered an outstanding trip.

Claire and I have birding together for 24 years, mostly in the US, but abroad starting in 2002. Up to now, South America has been our most productive destination, but we have a new champion. Our nine days in West Africa netted us 282 species and 212 lifers. Ghana is a spectacular birding destination, and an overnight flight from NYC to Accra gets you here fairly painlessly from North America.

Our trip included the Shai Hills in the southeast, Kakum National Park and surrounding areas in the south, the Picathartes sites, and finally Mole National Park in the north. This gives an excellent sampling of rainforest and savanna habitats. Every new location that we visited brought new species; there was no duplication of effort.

Our guides, William, Ebenezer, and Kojo, went out of their way to make this a great tour. William in particular was one of those guides that has an uncanny ability to determine exactly where a bird has moved to after it has moved out of immediate visual range. I’ll never understand how this is done. They know their calls and songs intimately, and I tested them by recording as many vocalizations as I could and later checking them against xeno-canto. They didn’t misidentify anything.

We were especially impressed with their commitment to helping the local communities, such as the village of Bonkro, in addition to guiding. This is critical outreach, not only for improving the lives of their neighbors, but also in educating them about the importance of conservation and the protection of the priceless wildlife resources around them.


In Bonkro after seeing the Picathartes and making new friends. With driver/guide Kojo.

Our guides were able to accommodate for my vision issues, and because of their perseverance, I didn’t miss any birds. They also dealt with an unexpected challenge: on our final day, needing to get to the airport in Tamale for a flight to Accra, our vehicle broke down. This could have been a disaster but they managed to get us to the airport in time for our flight, and we made it back home as planned.

We are also happy to report that we didn’t have any digestive complaints during or after this tour. I wish I could say that about every tropical birding trip we have done. We were taken to good restaurants and hotels that always served very hot meals.

I cannot recommend this outfit highly enough, and I cannot think of a better introduction to West Africa birding. They offer trips in other locations as well, and we plan on using their services in Uganda at some point.

One final note; US citizens need a visa for Ghana, and it is highly recommended to pursue this through the Ghana consulate in Houston– it was fast and easy to get done. Reading about other traveler’s experiences indicated that other offices in the US were not always as efficient or consistent as they are in Houston.