Best Birds 2015 Edition

Truth be told, I am actually surprised I birded and generated a list at least once every month! I did cut it rather close with February and November. Perhaps  I should change my  handle to badbirder….  Anyway 27 minutes until the clock strikes 12….

So how many lists did I submit? It was an  up and down year, but over all lower than  in years past.  This was also the first year that I was in grad school for the entirety of the year. Grad school and birding do not go as well together as one would think!

Lists Submitted to Ebird by Month

month	2013	2014	2015
January	50	2	26
February	34	4	1
March	25	4	4
April	22	26	12
May	24	36	13
June	16	10	15
July	5	3	19
August	22	6	6
September	4	9	2
October	8	17	4
November	5	5	1
December	8	9	5
Year	223	131	109

So still respectable?  In regards to species, it became even more extreme with ups and downs.  I had a drop of 98 species between September 2014 and September 2015, but an increase of 101 from January 2014 to January  2015.  Travel makes a difference!

Species By Month

month	2013	2014	2015
January	70	30	131  (65) *
February	52	39	17 (0)
March	60	46	40
April	48	87	52
May	114	162	101
June	64	85	54 (0) **
July	37	32	48 (0) **
August	65	40	45 (13) **
September	72	102	10
October	57	114	34
November	63	86	6
December	79	90	65
Year	200	222	287 (169)***

* Includes Florida birding efforts.NY/NJ totals in ()
** Includes Honduran birding efforts. NY/NJ totals in ().
*** Global total. NY/NJ totals in ().

In Florida, Tara and I picked up 100 species, and while I don’t have all the records updated yet, I believe I also observed 100 birds in Honduras (ebird currently has 81 listed).

As I compile this review, what surprises me the most is now many life birds I picked up.  Traveling for nearly  3 months really helps!   So  91 new lifers added to the list.  I won’t bore you by listing them all.

However, I will close with some of my favorite photos already  shared this year:

Eastern Whip-poor-will from another angle. Rutgers University – Newark. Photo taken on May 2, 2015.

RBA Yellow-headed Blackbird in Florida. Makes up for missing it in the Meadowlands. Photo taken on January 5, 2015 with a Nikon 3200 Sigma 500mm.

Male and female Buffleheads recorded during Lower Hudson Christmas Bird Count. Liberty State Park, NJ. Photo taken on December 20, 2015.

Glorious detail of Double-crested Cormorant feathers. Double-crested Cormorant feathers are cool. Photo taken on January 8, 2015 with a Nikon 3200 Sigma 500mm.

Sanderling struggles with a snack. Sandy Hook, NJ. Photo taken on January 1, 2015 with a Nikon 3200, Sigma 500mm.

Tricolored Heron, hunting. Photo taken on January 6, 2015 with a Nikon 3200 Sigma 500mm.

The detail on the photo is rubbish. But I love the clouds and the alignment of the birds. Authentic photo, no photoshopping here! Photo taken on January 8, 2015 with a Nikon 3200 Sigma 500mm.

Wood Storks are the most alien looking birds, ever. Photo taken on January 6, 2015 with a Nikon 3200 Sigma 500mm.

Anhinga preening. Photo taken on January 5, 2015 with a Nikon 3200 Sigma 500mm.

White-faced Quail-Dove in the hand. Cusuco National Park, Honduras. Photo taken July 4, 2015.

Glossy Ibis balances between preening sessions. Photo taken on January 5, 2015 with a Nikon 3200 Sigma 500mm.

Great Egret and Common Gallinule experience the downpour. Photo taken on January 6, 2015 with a Nikon 3200 Sigma 500mm.

Vibrantly colored Tricolored Heron. Photo taken on January 5, 2015 with a Nikon 3200 Sigma 500mm.

And my  clock  warns me that I have less than  5 minutes remaining…

Perils of Data-based Birding

As a birder-scientist, not only do I have the joys of exploring farmlands and forests for novel encounters, but I also have the “joys” of exploring fields…. as in data fields.

As part of my PhD program, to become more knowledgeable about birds, I have to spend more time away from real birds (much of the current knowledge transfer system is based on similar logic…e.g. to improve learning we should spend less time learning and more time testing).  For the spring semester, I opted to do a rotation in the lab of Dr. Gareth Russell.

The Russell Lab does fascinating work looking at animal choice and response to their environment by modeling behavior and movement.  Current and recent work has followed Grizzly Bears in Alaska, Big Horn Sheep in California, Baleen Whales social networks in the Atlantic Ocean, Elephants in Africa.  The modeling element comes in when scientist try to interpret why the animals made the choices they did.  What elements of the environment most resonated with the animal causing its behaviour?

Turns out since you can’t actually ask the animals, you can use a computer to figure it out.

For my project, I can share the details later, I obtained  USGS banding records for songbirds along the east coast to determine movements over time.  My goal was to use this data to answer questions about their migratory movements in the fall.

As it goes with a venture into any new area, there’s a steep learning curve as you get to know the landscape.  You have weird experiences such as when I reloaded the data set and every bird name involving a color (say yellow warbler and white-throated sparrow) became {#*code#) warbler and (#&code#(-throated sparrow.

Twitter Screen Capture.

So after months of learning to program in Mathematica (mixed success), I managed to create a map! This beautiful rendered map shows 50+ years worth of records where in the birds presented:

• were banded and recaptured after July 1,
• were banded and recaptured in the same year,
• were recaptured in a new location.

Mathematica output: Apparently one bird didn’t get the memo and went west, not south.  H1: That bird can’t read a compass. H2: That bird is a rebellious juvenile.

However, as beautifully colored as it is and as important as it looks, it’s only ~800 pieces of data across 44 species and 50+ years, not enough for a solid analysis. Back to the drawing board.

When after significant hours of effort, you’re still coming up with nothing, you doubt yourself, even when other people have faith. “There aren’t any birds here!”

Twitter Screen Capture.

Then you do something really basic, like identifying an American Robin, or writing a function which figures out how many days into a year it is.

Twitter Screen Capture.

Now you know there are birds there, and you’re just not finding them.  You feel conflicted about that.

Three weeks to go and we’re still “exploring the data”.  The sun is nearly setting.

Mathematica output: Tree swallow presence (blue) and absence (white) data with years as the x axis and days into the year as the y axis.

So, finally we made a graph.  This graph, for Tree Swallows, shows presence (blue) and absence (white) data with years as the x axis and days into the year as the y axis.  Think January at the bottom and December at the top.

Now that I’d found one bird surely I could find them all, but could I do it by family?

Yes… Probably… How hard could it be?… Maybe… If I had more time… Impossible… Wait, what?… No… Maybe… Nope… Did I get it?

After a very dark night:

Mathematica output: 10 families of songbirds tracked over the years for arrivals and departures. years are represented on the x axis and days into the year as the y axis.

 

I actually succeeded as the sun rose.  Talk about symbolism!  I’ll have a chance to share this in my next meeting on Friday. Hopefully we can call this success.

Can only imagine what’s next?  3D versions?

Stay tuned for Rotation 2 news coming Summer 2015 when I return to real fields (or forests anyway)!