http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00183.x/abstract#.Tvt1QWJ0ql0

One of the biggest problems studying seed dispersal is that seeds are hard to follow! With traditional seed tagging methods, many seeds simply cannot be found, especially when they travel long distances. One method researchers have used is to limit their search to a reasonable distance and track all the seeds within that radius. The problem with this method is that it tells one little/nothing about long distance dispersal. Given that long distance dispersal is believed to be particularly important, this lack of information is problematic. Patrick Jansen and colleagues developed a statistical method to reconstruct the shape of the tail of the seed dispersal distribution using the shape of the distribution inside the search radius. We thought this was a cool method and had the potential to be used more widely. Unfortunately, this method had never been tested with empirical data. Fortunately, we had a perfect dataset to test the method because we used radio-transmitters to track seeds in our project. We also decided to give it a name: the Censored Tail Reconstruction method (CTR). In general, we found that the CTR method worked exceptionally well at recreating the long-tail of the seed dispersal distribution. On the other hand, this method was highly sensitive to which mathematical function was used in the method, and what percentage of seeds were ‘overlooked’ by researchers searching for seeds. The upshot of our paper is that it is possible to calculate fairly accurate dispersal kernels using censored data collected with traditional low priced tagging methods. The caveat to this is that researchers need to be certain that they are able to find the vast majority of seeds within their search radius, and they must choose the most appropriate mathematical function for use in the CTR method (using AIC selection). We think this method should be widely adopted, especially by researchers who cannot afford tons of radio-transmitters.

To solve this question we needed to be able to identify individual agoutis, which otherwise all look alike. After substantial live-trapping effort we were able to catch >20 agoutis in one portion of the island (in and around the 25 ha plot). We individually marked each agouti we caught with a distinctive radio-collar, ear-tag and/or freeze brand. Then, we put a motion-sensitive camera next to a buried seed where we knew the “owner” and recorded every agouti (or other species) that walked by. This allowed us to determine whether the cached seeds were being dug up and moved by their owners, or by thieves. We are still crunching the numbers from these experiments but in the process discovered a cool video that shows just how crafty agouti thieves can be.

Many rodents bury seeds in times of plenty to save them for lean times, and these hidden food caches are critical to their survival. Rodents will go to great lengths to protect their seeds from potential thieves. For example, Michael Steele and colleagues found that if a squirrel wanted to bury a seed but was being watched, it would often behave “deceptively” by making fake caches and making caches behind trees so the observer could not see the cache being made. While I would have loved to do some similar experiments with the agoutis, it would have been hard to observe the agoutis without scaring them away. However, if you run enough camera traps for enough time you eventually record some surprising clips.

In this case, I didn’t notice what was going on the first time I saw this video, and only recently realized how it shows the subtle dynamics between different agoutis sharing an area. The seed that this camera was monitoring was previously cached by an agouti named Tracy, who can be identified by a small diagonal white freezebrand mark on her body. In the video you can see Tracy come to uncover her seed four months after she originally placed it there (we had a camera there the whole time). Given the high rates of cache movement, Tracy was lucky her seed lasted that long and her strategy of saving food underground for the low food season seems to be a good one. Unfortunately for her, as soon as she begins digging up her cached seed she gets chased away by another more dominant agouti who steals her seed. Even sadder is the fact that Tracy came back to the old cache location a few seconds later to see if it was still there (it wasn’t). While this isn’t fair to Tracy, the other dominant agouti got a free meal on the cheap. The degree to which our agoutis used this kleptoparastic strategy is unclear, but given the behaviors seen in this video, we think it might be a good idea if agoutis used the same sorts of deceptive behavior found in squirrels.

Photo: ARTS tower rises above the tropical rainforest canopy of BCI to help researchers track the movement and activity of animals and seeds, sending data back to the lab in real-time.

As far as I can tell, this question has never been studied – who wants to sit around and watch fruits slowly fall down all day, and night?  Yet this is a basic bit of natural history information that could be quite important when considering the survival strategies of agoutis, in particular, and daily rhythms in the forest, in general.

Camera traps are a perfect tool for this question, and our student Vivian Mass set some aimed at the fruits of Astrocaryum palm trees.  For this to work she had to find another tree at just the right distance away from the targeted palm-fruits and then get a camera trap up there, which she did with the help of ladders and some canopy access climbing by Daniel and Alejandro.  This was a bit of a side project from Vivian’s main thesis, and the data remained unanalyzed until this spring when high school student Tessa (Taz) Holliday joined our team as an intern from the Emma Willard School.

Taz looked through each picture and noted if any fruits had fallen off since the last picture.  In some cases it was obvious – a big howler monkey was on the tree picking fruits, eating the fleshy part, and then dropping the nut down to the ground, where agoutis and other terrestrial critters could find them.  In other cases fruits would fall off without any animal intervention, just because they were ripe.  In total Taz noted 450 seed fall events from 8 different Astrocaryum trees, with slightly less than half being dropped by animals.

Looking at the fruit fall over the entire fruiting period (figure 1) it was obvious that arboreal animals had a huge effect on when a fruit would fall.  A few trees that had no arboreal animals visit would drop a few fruits a day for 2-3 weeks.  Once an animal visited they would drop all, or nearly all, of the fruits in one sitting.

Fig 1. Timing of fruit-fall over four weeks for 6 Asrocaryum trees.

These arboreal animals also had an impact on what time of day the fruits hit the ground. The monkey- and parrot-fed fruits fell in the day while the kinkajou-fed fruits fell at night (figure 2).  Overall, this led to more fruits falling during the afternoon than you might otherwise expect (figure 3b), although with only 4 trees in this analysis (the time lapse photos didn’t work for 4 trees), we should be careful in what we conclude from it.

Fig 2. Time of day that fruits fell out of 4 Astrocaryum trees

Even more surprising was considering what time the fruits fell when no animals knocked them off. This is tree-behavior, when do they ‘want’ their fruits to fall to the ground.  Surprisingly, this showed a strong trend to more fruits in the late night (midnight-5am, figure 3a).  We know from our agouti tracking that this is the most dangerous time for agoutis to be out foraging, we have found quite a few agoutis killed by ocelots in this time period.  Are the trees trying to temp the agoutis out for an early breakfast?

Fig 3. Expected and observed times of day that fruits fell out of trees, with and without arboreal animals (significantly different than random p<0.0001)

Lets call this the “Machiavellian plant behavior hypothesis”.  Long-lived agoutis might be bad for trees because they remember where they buried all the seeds and come back and eat them year after year.  However, if dropping seeds more at night led to a higher turnover of agoutis nearby due to ocelot predation, it might also lead to a better chance that their cached seeds survived, since the “new agouti on the block” wouldn’t know where the “recently deceased” buried the seeds. This Machiavellian plant behavior seems a long shot, but this preliminary result that Taz teased out of data collected earlier by Vivian suggests it might be worth following up on.

One of the ways that a cached seed might avoid being eaten by an agouti is if the agouti which buried the seed dies. This leads to the question: do predators like ocelots help palm trees by killing agoutis before they have a chance to go back and dig up palm seeds?   Related to this, is the question: are agoutis living where food is scarce more likely to get killed because they must work harder for their food, thus take more risks?

These are the questions asked by Willem-Jan Emsens, a Masters student at Wageningen University. He studied activity patterns, refuge use, and space use of agoutis in relation to predation risk. He compared rich and poor agoutis: animals that differed strongly in the amount of Astrocaryum fruits they had in their home range. As any loyal reader of the Agouti Enterprise knows, Astrocaryum fruits are the most important food resource to agoutis.

Willem-Jan used the Automated Radio Tracking System (ARTS) to track the animals 24/7 and determine the home range size of the radio-collared agoutis. This showed that poor agoutis had larger home ranges than rich agoutis. Not surprisingly, poor agoutis seem to need a larger area to find enough food. Therefore, they may take more risks of getting caught by an ocelot.

Willem-Jan also took his radio-tracking receiver and tromped around BCI at night, when agoutis are sleeping, to find out exactly where agoutis had their refuges. He found agoutis used three types of sleeping sites: dense vegetation, burrows, and hollow logs. Each agouti had a 2-8 different places they could sleep on different nights.

Agouti RefugeTypes

Once he identified the sleeping sites, Willem-Jan put camera traps at burrow entrances to see exactly when they came and went. Agoutis never left their refuge before sunrise, but were more variable in the time they went to bed. Most agoutis retired to bed around sunset, but 13% came back at night. The cameras also caught some amazing footage of ocelots coming to these sleeping sights and looking for agoutis. You can actually see the ocelots looking into logs, presumably scaring the crap out of an agouti, but then leaving empty handed. Ocelots were the only predators that visited the refuges, and they visited refuges more often than random locations. It seems that ocelots knew where the agoutis were sleeping dropped by hoping to catch one near the entrance to their burrow.

Some of the agoutis that Willem-Jan was studying were killed by ocelots. By analyzing the ARTS data, he was able to determine the exact time the agouti was killed. Willem-Jan found that agoutis are most susceptible to predation around dusk and dawn, which is also when they overlap most in activity with the nocturnal ocelots. This may also be tied to their entrances and exits from dens. If ocelots know these locations, they might wait nearby to catch a commuting agouti.

One of the surprising results came from comparing the location of the refuges with the overall space use of agoutis. We presumed that agoutis would act like many other burrowing animals, using the refuge as a ‘central place’ and conducting feeding bouts more near the hiding hole than far from it. But this was not the case. Maybe the fear of a lurking ocelot is enough to make an agouti keep its kitchen and bedroom in different parts of its home range.

Finally, combining his work with data collected by an earlier student on the project, Lennart Suselbeek, we were able to compare the risks taken by the rich and poor agoutis. The ghetto agoutis had to be more active to find food in their enlarged home ranges, and accomplished this by getting up earlier and going to bed later. This is risky behavior when your neighborhood is patrolled by nocturnal ocelots. All of this makes the agoutis nervous, but is quite fine with the Astrocaryum trees, who root for the agoutis to burry their seeds and then the ocelot to make them disappear.

Images from Vivan Maas’s thesis on what eats palm fruit, and Helen Esser’s on what animals live on canal islands, were included in the site.  We would have included more from our research on BCI, we have a lot more, but didn’t want to overload folks with agoutis.  Even with these two sub-projects, we contributed over 88,000 agouti pictures to the collection.

This project was a collaboration between different scientists working at the Smithsonian, including our Agouti experts.  In fact, they built the site of the database started on BCI by Bart Kranstauber and used by our team ever since.

The site just launches last week, and the visitation has been off the charts.  We were even featured in an NBC news story last night.

People love the site, the crowd is going wild.  They are going to want more pictures.  Good thing Helen is coming back for her PhD research!

Untamed Science Video on Remote Sensing

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Animals do help.  Many trees have co-evolved relationships with animals and offer them some reward for moving their seeds around – a bit of nutritious fruit-bribe surrounding the seeds.  But some animals are not helpful, eating the fruit without moving the seed away from the mother tree, or even consuming the seed along with the fruit.  Trees don’t appreciate these cheaters, but evolution’s rule book allows it.  The trees can fight back by co-evolving with certain species of animals that are the most beneficial to them, and putting up defenses against the others.  When evaluating these relationships biologists consider the ‘disperser effectiveness’ as the value of an animal species as a disperser for a given tree species.

Palm trees have been bribing animals and fighting cheaters for millennia, evolving a variety of sweet fruit lures surrounding hard nuts.  The trees “want” a cooperative animal to eat the fruit and move the seed away from the mother tree.  Our earlier work using cameras to monitor animals feeding under fruiting Astrocaryum palms trees found Agoutis, Spiny Rats, Red-tailed Squirrels, and Collard Peccaries to make up 99% of the visitors. The next question was to evaluate the disperser effectiveness for this group.  Lieneke Bakker took this challenge on as her MS thesis project at Wageningen University.

Lieneke used small radio-transmitters to follow the movement and fate of Astrocaryum seeds, and used camera traps to determine which species of animal was doing the moving.  She considered a number of factors to evaluate the effectiveness of each species as a disperser including: how often they moved seeds, how far they moved them, rather or not they buried them and, if so, how long the seeds survived.

Agoutis moved by far the most seeds in Lieneke’s experiments, followed by squirrels and then rats.  Peccaries did not move any seeds, probably because Lieneke cleaned the fruit off the seeds for her experiments, and peccaries only eat the fruit part, being unable to crack the hard seed.    All three rodents moved seeds about the same distance (average 9-15m), but they treated them much different, with agoutis and rats burying most of their seeds in underground caches, and squirrels taking about half of them up into the trees to eat or store them.  Finally, the rat-buried seeds were typically dug back up after just 1-2 days, while the squirrel or agouti buried seeds remained underground for three weeks, or more.

Lieneke defended her Masters Thesis this month, concluding that agoutis are by far the most effective initial disperser of Astrocaryum seeds, moving more of them, burying more of them, and leaving them in their underground caches longer than the other rodent species.  Add to this Veronica’s earlier results showing that once a seed is dug up from an agouti burrow it might get re-buried into another underground cache (i.e. secondary dispersal), and Agoutis come out as the main mobility assistant for Astrocaryum trees.

Congratulations to Lieneke on an outstanding Masters thesis.

Sumana in the field

Animal trackers move on from towers to satellites and cameras

The Automated Radio Telemetry System (ARTS) on STRI’s Barro Colorado Island (BCI) is taking down its towers used to track animals with radio-transmitters and switching to GPS and camera trap systems that produce more data with less infrastructure.

Experiences with ARTS over the last eight years on BCI have led to the development of new technologies, including the miniaturization of GPS tracking devices, revolutionary camera trap monitoring techniques, a Smithsonian repository of camera trap images, and a global archive of animal tracking data www.Movebank.org

BCI is famous as a training ground for pioneer ecological research systems that allow scientists to ask new questions. In 2003, researchers Roland Kays (New York State Museum) and Martin Wikelski (Princeton University, now at the Max Plank Institute) founded an experimental Automated Radio Telemetry System (ARTS) to track the activity and movement of animals wearing small radio-transmitters. According to Kays, “at that time tracking options were limited because GPS devices were so large they were carried by surveyors in backpacks and camera traps were limited to rolls of 36-exposure film.”

With support from the National Geographic Society and the Levinson and National Science Foundations, Kays and Wikelski brought a team of specialists to BCI to erect a network of seven towers that streamed live data on the location and activity of animals that had been fitted with small, inexpensive radio transmitters. Since then, the ARTS has been used to track 374 individuals from 38 species, including 17 mammal species, 12 birds, seven reptiles or amphibians, and two species of plant seeds. The unique data gathered by ARTS have allowed researchers to tackle previously intractable questions about the ecology and behavior of species ranging from palms and bees to monkeys, by providing a means to “see” cryptic events and track animal movements and activities over large distances and long time periods.

However, ARTS-based tracking is limited to BCI because of its extensive infrastructure requirements, and thus researchers have also been looking past radio transmitters to new, more flexible technologies. GPS devices have been improved and miniaturized over the past three years, spurred on in part by former ARTS engineer Franz Kuemmeth (founder of E-obs GPS tracking company) and ARTS biologists, who rapidly adopted the new technology to track animals on BCI…and off. New sensors are also being developed to work in concert with GPS tags to provide detailed information about animal behavior and physiology.

ARTS researchers also developed new methods for monitoring animal movement with camera traps. This approach was initially developed to monitor animals moving palm seeds that were being tracked by the ARTS, but is now being implemented at SIGEO sites around the globe. The BCI camera trap data are also being shared with the public through a new ‘SI Wild’ website that combines the images from ten Smithsonian camera trap studies around the world and will launch in 2011.

Many of the most important moments in an animal’s life are hard to study because they are rare or difficult to observe. Due to the shy nature of most species, tracking animals is necessarily a high-tech enterprise. The development ARTS and related technologies on BCI over the last eight years offers another example of how STRI-supported science can help develop new fields; in this case, one where detailed data on animal movement, physiology, and behavior can be integrated to address the next generation of scientific and conservation questions.