 איקולוגי. והשכחה היא היאמפריקלות שאתה, כשאני קצת, בשביל את התמידים בין ביומקניקות ולכן לנבגה. אז מוגמנט, כשאנחנו מבחין את זה, מוגמנט איקולוגי הוא מוגמנט של את המדע, של את המדע, ולא של אורגן, או שלס, בין המדע, או שם, וזה קריטיקו לתחיל, לסביבה. זה עקב בגלל כל ספישי, תחילה הרבה ספישי, כמו סקויאר-טוויס וקורלוויףס. וזה קצת, בגלל עבולוסי, אז אנחנו רואים עבולוסי של דינוזר, ‫לרמנים הילדים וכי פיישן, ‫אמפיביאנים, ‫הלדים וכן, וכן. ‫מובימן הוא פרסיטה הכי, ‫הוא פרסיטה הכי פרסיטה ‫במקרו-אבולוציונגי פנומנה. ‫אז גם עם איקולוגיה, ‫לא יכולים להסתכל, ‫אני חושב, ‫האיקולוגיה של פרסיטה ‫שלא מובימן של אורגניזם, ‫הם פרסיטה קצת ‫באותו כך כמו שאתה חושבים ‫בסמינות איקולוגיה. ‫אז גם עם פרסיטות עוד פרסיטת, ‫באמנות על פרסיטות שאנחנו פרסיטים, ‫אז אין להם כך רבות, ‫חיפושים ופגמנות, ‫הגבירה היא תלתת מטעות, ‫אז איך הילדים ופרסיטות ‫הואים יכולים להסתכל ‫בסמינות או לא. On the other hand, we have species that have incredible movement capacity and this is the problem like diseases, personal agriculture, invasive species, so these are four of the main environmental concerns that we face today and in all of them movement is the key process. So it's important for basic science, it's important for problems that humanity face today and we do study movement a lot. These are just examples of several books on movements. In these ten years period there were at least 26,000 papers published and you can see the proportion is increasing, the absolute number is increasing and then we have another jump around ten years ago in the number of papers and the number of citations, so there's lots of work on movement and now it's time that we must consider in general the common reason for moving with any movement, whatever. This is the opening sentence of a book written by this person 2300 years ago, this is Aristotel and this is the multi-animalium, the original one, not the Borelli 2000 years later, but Aristotel as you know was trying to define basic principles. So he faced the complexity of nature and he was trying to find basic principles also in movement and I've tried to read this book several times, there are very interesting ideas, lots of emphasis on motivation and things like that but it was not clear what actually the general theory or the basic principles that Aristotel trying to promote. And in fact we can say that history tells us there's no general theory of movement. So this I think something that happened across all scientific disciplines, what we see is people get more and more specialized in a particular system and which is our students don't spread to think and be expert in your system and this is a very very successful method and lots of insights and lots of discoveries because of this method but it has some shortcomings as well and you need to really consider the importance of general theory and the prime example is the theory of natural selection. So Darwin wrote this theory that is now guiding research not only in biology but in many other fields of science and he was well aware of the importance of movement in two chapters of the origin were devoted to long distance dispersal. His first lecture was about movement of larvae, his last paper in nature was also about dispersal and one of his friends was this guy Robert Brown and in the autobiography of Darwin he described this person as kind of boring that he has a tendency to look too much into the details and not look at the big picture as Darwin wants to pursue a general theory but this very particular characteristic of Robert Brown this is why he is known until today because he looks at fine details and these fine details he saw in the Microsoft mean he said what is this you know Poland moving in a motionless fluid he described this observation in this paper that was more or less left unnoticed until this guy here used this kind of observation to prove that atoms and molecules exist and this was the paper that he published in the same year that he published the photoelectric effect and submitted his thesis and you know this miraculous year of Einstein and this is by the way the most cited paper of Einstein from this year so these are just examples and there were Galileo and Borelli and Newton and you know giants of science were working on these movements and still we have many challenges ahead of us this is a special feature published in science 2005 emphasizing the big challenges that still remain in science and one of them is the question of how do migrating organism find a way so this is the question of navigation and another big question is what determines movement performance for example flight in real life heterogeneous environment and another is what we always say again and again to explain why juveniles tend to die more than adults that they do not have enough experience so this is number one explanation for the high mortality of juveniles but very seldom we can pinpoint on what exactly they do, they perform less and how they functionally differ from adults is more unknown than known so it is a big opportunity now to do research on movement ecology because we have new technologies, we have new data analysis tools, computers and software and so on and new conceptual framework so if you look at these three components then I read this paper by Frima and Dyson a few years ago and got excited because for physics I am not in this field but for physics he was saying that we are standing now as he stood in the 50s between a cohenian dream of sudden illumination and the Galison reality of labor is exploding and then he said that Kuhn and Galison are running neck and neck in the race for glory, we are lucky to live in a time when both are going strong so Kuhn is Thomas Kuhn and in this book he coined the term paradigm shift meaning that science is progressing by great ideas that coming from time to time and shift the paradigm so there's people are doing research in a particular field there's a paradigm that is being developed and more or less incremental steps and then all of the sudden we have a paradigm shift big idea that is coming and Kuhn was arguing that this is the way the science is usually progressing whereas Peter Galison said that this is more or less continuous and what is driven is not driven, the science is not the big ideas but the tools that are being developed all the time from time to time we have, I don't know, microscope we have PCR, we have really incredible tools that advance science very much but the idea here is that it's more or less continuous and driven by tools and as opposed to from time to time and driven by ideas and what Freeman Dyson said is that they are running neck and neck in the race for glory, for physics and what they think for movement ecology happens in the last two decades or so is that the tools are running much faster and so now we have GPS and all these incredible tools that people shown in the last two days and we are not getting much closer to a general theory for example so where are the ideas, where are the big concept where are the generalization that we are trying to make and so these are the tools and these are the concept and I will talk a little bit about tracking animals so it's really a big time for us and mostly because of GPS devices that were developed for military purposes and then used by each one of us so there's a huge commercial market and all the development is not funded by scientists and we are enjoying these large efforts and for example vultures we can track with GPS we can put various tags on them and including for example acceleration from acceleration we can identify behaviors we can estimate energy expenditure and using machine learning algorithms we can get it quite right there's around 85-90% correct assignment so these tools are flourishing and there are maybe thousands of researchers they're using these tools nowadays a big center is Max Planck Institute for Onitology they have MoveBank, they have IKOROS so it's a lot of very successful work and new discoveries and so on I would argue that and I'm using that too for the majority of my research but I'm arguing that in regional scale actually it's big enough to cover the lifetime track of most species that we are interested on and at this scale moving beyond large charismatic species we need to track smaller species and so on and what we actually our desire is to get many individuals small enough, track them automatically and simultaneously a GPS accuracy and frequently enough for long times and pay much less we pay about $1,000, $2,000 per GPS tag we want to pay much less than that around $50 it's a good price for something like that and this sounds like impossible you greedy, you're too greedy you want too much and actually this is doable and this depends mostly on recruiting people like Sevan Toledo from computer science in Tel Aviv who developed the ATLAS system together with us in the Minerva Center for Movement Ecology and these are the people involved with this project from Ford University in Israel so the ATLAS system is a reverse GPS system it's the same equation as GPS but instead of your phone or the tag calculating and communicating with the satellites and calculating the position the system, like the satellites in our case it's base station reads the differences in the time of rivals and make the calculation on the system this allows the tag to be very small and then you can you see they can be as small as half a gram and you can still get frequent and high enough this was published in these papers so with ATLAS tag you can get about 80% of the bird species as opposed to GPS with remote download Yossi has a smaller GPS but it's not with remote download GPS with remote download is around 7 grams the smallest that you can really work on I'm not talking about those that give you 8 positions you cannot do anything with 8 positions but those that give you visible number of positions you can track about a fifth of the birds so we established this system in the Hula Valley that was the first prototype this valley is at the center of this migration flyway and it's flat and surrounded by mountains it's an ideal place for this kind of system that requires a line of sight and since then we also established ATLAS systems in UK, Netherlands and Germany and the unique feature of this system is that it's really producing lots of data so it's about 3 to 5 orders of magnitude more effective than GPS tracking in terms of localizations per dollar so for a dollar investment you get really much more data with ATLAS this is the initial investment that you need to do to establish the ATLAS system and therefore it's lower for the GPS it's already there and nobody can pay for what they did to establish it but now, so the more tags you get you pay the same you have the same localization per unit dollars but with ATLAS it's increasing and then getting a plateau of how cheap you can get it so you really can get lots of data this is the number of positions we get per day this is 1 million data points in a study of Barnalls this is 2.5 million data points per day that we got when we were tracking Barnalls and then we tracked again these passerines and this is just from one system that was still in the very beginning this is the number that in 2 years ago or 3 years ago were available in MoveBank from thousands of studies all over the world and one system of ATLAS has produced more data and thousands of projects around the world so it's really a shift from data pool to data rich science which can be problematic the downside of the ATLAS system is that it's relative local it's the area that you can cover with the base station which is in our case around 25 by 35 km do you see aruption to how many animals this is? what's that? how many animals this is? how many animals is this? it's coming so there are about 2,500 animals because the tags are so cheap we can work with lots of animals so these are including very small species like the barn swallow that weights only 20 grams you cannot imagine to put GPS on that you cannot imagine to put GPS on even smaller bats so this is the ATLAS system this is all barn owls in the Hula Valley that are tracked for several months during the breeding season and as in many studies that we have really high quality data we have lots of discoveries for example barn owls fledgings tend to go to other nests and being fed by the parents and they all do it it was not known and not reported and for sure not in that amount all nest boxes of barn owls in the Hula Valley are visited by other fledgings and they are being fed and they all receive other fledgings and this could be for example an explanation for selection for early laying because in early laying your eggs hatch and your nestlings fledge and other parents are taking care of your fledging otherwise you will take care of other fledgings so this was not known until we got it so moving to talk about the concept movement ecology was born about 10 years ago we published a framework in PNAS and then several years ago we established a new journal and that Yossi and Anna are on the editorial board so it's a proposal for a unifying paradigm to study movement of organisms of all kinds and we want to stimulate the development of sharing of research tool to put on the understanding of causes, consequences patterns and mechanism of movement and to set the stage for the development of a theory and how we do it is that we are looking as a rest hotel for the basic principle so you can imagine that just hearing the talks during the last two days it's a huge diversity of phenomena and many many species and migration and dispersal and foraging and all those kind of movement types and ranging from microorganism even cells and moving on to plants and the diversity of animals so what we want to do is to look for the two or three or four basic colors that with different combination give rise to this high diversity and this is what we propose the four basic components one of them is the internal state the energetic state or the mental state from which the motivation to move is derived so the animal is now hungry the motivation is to find food so the animal moves to find food now there's a predator around so the animal is afraid of being preyed so it will run away there's a motivation to move and so on and so forth so the internal state of the individuals determines the motivation to move and this is why animals move or organism move and then they need some machineries and this is the big topic of this meeting biomechanics they need to produce the movement and then they navigate this is the second big topic of this meeting they need to navigate since they have a motivation and a machinery to move they need to know when and where to move and everything else this is the focal individual everything else which includes other individuals and other species and the physical environment and so on is including in this box that called external factors so to put the pieces of this puzzle together we draw this conceptual framework again animal is hungry so and so on this is the internal state it has wings and all these other locomotion modes and can produce the movement path it goes from the motivation to move goes from the say central nervous system and from here the decision making making process is done and kind of instructing the motion the machineries where and when to go and this produce the movement now the animal is moving is getting out of range of view and the internal state is changing of course the external factors affecting each one of these components and they have their own dynamics as well as the internal state we took this framework and choose at random 1000 studies from the literature and were able to say which one of these component what was it in each one of these studies and it was surprisingly easy to do it maybe because it's so simple and maybe this is the biggest merit of this framework that is so simple and you can think each one of you on your study system and I think you were able to map each one of these component very very easily but what we also understood when we realized this is a potentially general framework is that it actually map the existing paradigm of studying movement for example biomechanics is mostly focus on motion capacity not about the movement path and the cognitive process or the evolutionary trade off and so on and as opposed to cognitive science that focus on the navigation capacity and less so on biomechanics on the movement path and so on these two other paradigms is one is drawn from economy or from natural selection from evolutionary biology that are asking why what are the forces that drive evolutionary development traits and behaviors that promote movement and although this is optimal forging, optimal migration, optimal dispersal all genuine movement processes these are rarely look at for example biomechanics or navigation or the movement path and the last one is this random search drawn from random walk and so in this paradigm you look only on the track and try to understand everything Reiner is still here I don't know but you look at properties of the movement path whether it's levy walk or composite Gaussian and so on and and so you see that all of these components have been studied quite intensively and extensively and what is movement ecology offering is an integration none of the components is you but the integration and the field devoted to study of movement that integrates all these components is what movement ecology trying to achieve so I'm moving to the second part of my talk and in this I'm going to give some examples for studies on biomechanics and navigation in my group we started on C dispersal and lots of biomechanics and modeling of C dispersal by wind and then I have the opportunity to move back to birds because I'm a bit further and and then we study lots of bird species as well as bats and some fish and some ants so there are quite diverse species that have been studied in my lab over the last two decades and I'm going to focus on these three questions that I mentioned earlier of navigation biomechanics and experience but there are some others a question that we that we ask like foraging collective movement individuality what affect fitness, microbiome and pathogens of related to migration and this will keep you for the future what are the most important things in life I would argue that if you understand well movement you can answer this this most basic question so these are the project in my lab that addressing this kind of question and I'm going to start with flight performance and this is a study of Nier Horowitz and Nier Sapir and we try to understand the gliding speed of migrating birds as you know loud sowing birds they use this this technique of terrestrial sowing they circle around raising air what we call thermal raising air column and then gaining enough light and then gliding between thermal and so on so the speed during the flight the glide between successive thermals is we call it V here and we ask what determines gliding speed in migrating birds so if you read Panikwick that's the bible of biomechanics or at least bird flight and so you will see that it scales with body mass wind rounding and other morphometrics and from this theory you can derive important speeds like best slide VBG which is the maximum distance you can get from a given height loss and this is if you consider just one thermal and but if you look at several thermals then you can climb between thermals and then this climb rate can produce an even faster flight if you can rely on thermals that will take you up and this is known as the Magrady theory or the Magrady speed or V op which is faster than VBG so we took data on this number lots of birds from 12 species that were obtained by Ryder observation this is excellent data set one hertz and identification of each species and we simulate the atmospheric conditions back then in this use by this atmospheric modeling platform and when we look at the data and it's quite easy to distinguish those thermal gliding cycles we actually found out there's no relationship with body mass and there's no relation with wing loading and other morphomotics that we check where none of them was significantly correlated with with gliding speed so what is wrong with the theory what is wrong with the theory is the first hint is coming here so these are the two flight speed that I mentioned before VBG and V op you can calculate each one of them for each species and what you see is that those species here they fly in the V op side on the right side of their capacity and as you move up with higher and higher wing loading you get the opposite so actually migrating birds birds converge to a narrow zone of gliding speed much narrower than the gray zone of the potential flight they can fly with so and this is why you do not get any significant correlation so they do not fly in a particular VBG or V opt but as you can see as we hint here there's a very nice pattern that we that encourage us to develop to propose a new theory that is not fixed plane like gliders and so on but birds behave and they behave in a risk sensitive manner which means that if you look at these two birds now when they finish climbing the thermal then there's the red bird who is more risk prone and it will glide faster in V opt but at the risk of reaching the ground and then it will either need to land or to switch to flopping which is energetically much more expensive as opposed to this risk averse bird the blue one that will glide slower and we lose less height and might get has better chance to get the next thermal without switching to flopping and so on so this can be quantifying in a very simple index that we call draft here risk aversion flight index that varies between zero for extreme risk upon bird that fly at V opt and one for extreme risk averse bird that flies at BG and when we put this index then all of the sudden we get a wonderful fantastic correlation with wind loading so you see the R square is 96% and it beautifully describe the variation among species so there is a correlation between blight speed and wind loading if you consider risk sensitive behavior so it's those with high wind loading they tend to avoid risk they have loads of mass on the wings so they are risk averse and those that have relatively high surface area of the wing compared to the body mass they tend to take risk and fly at V opt and also when there's turbulence in the air according to meteorological conditions then birds tend to take risk and when this is quantified by tubular kinetic energy and when there's no much turbulence then they avoid risk and also when they start the glide higher they tend to take risk it makes sense and when they start lower they tend to avoid risk so with that we can actually map the two kind of response so this is the wing loading that we call it evolutionary scale because it's the morphology of the bird that is probably evolved over evolutionary time scales whereas the conditions it's the ecological scale they can change within hour within show time scales so these maps where birds tend to be risk averse or risk prone in terms of the wing loading and thermal conditions and so I'm moving from this to a related issue but the main question here was the role of experience and vultures fly and want to cover lots of distances to do so with such large body you need to so efficiently so you need to climb thermals very efficiently you need to glide in a right speed and these could require some experience to perform to achieve this kind of performance so we took lots of data that we have from GPS on vultures and asked this question of what exactly vultures learn in this very basic task of their life they fly almost all day in good days and they look for carcasses and they really need to find carcasses after we know on average four days after four days they start to starve and they lose motivation and they forage less and they can get into troubles so they need to find a carcass in a large landscape and for that they need to climb very efficiently the thermals so what given that we had this high resolution data of juveniles and here when I say juveniles it's the first two months of the flight after the fledge we put GPS very very early and we get in one hertz their foraging movements in in one hertz and then we use this the same model rams and what I will show you now is the kind of oops don't touch this is the kind of data that we have so this is one bird blue is sinking and red is climbing you see that is circling now a thermal somewhere in the desert of Israel and getting how now two birds will come from here flying next to each other and start to climb the same thermal and then so you can see you can have an essence of the wealth of data that we have and this is an example of a vulture that is climbing a drifted thermal when רועי sent me this this data I was in sabbatical in Australia and I was learning to fly a glider myself so I was in the club and there were really excellent excellent glider pilots over there and then I showed them in a TV that was there and then all of a sudden there was silence in there so they were quite excited when this picture came up there was silence and then one of them came is one of the champions of Australia and shook my hand and say young man I'm not that young but young man congratulations said why so he was looking at that and said this is exactly how we try to climb a drifted thermal and I'm going to explain you but it will take some time I'm going to explain what is so unique about what you see here first of all in order to understand what is going on we need to know the winds so we used the amount of drifts of the thermal you can calculate actually the wind speed so and this is what you see here in green when you took for the same locations and the same times the ECMWF data as no really it's useless there's no correlation any point here can be in correlation with any other point but when you use drums you see that the two distribution are very very similar and you have a significant correlation so we use drums in order to estimate the winds and when you see when you compare the juveniles and adults you see that adults climb significantly faster than the juveniles so if I look at this guiding framework then I will first look at the motivation so both are using a thermal to find food and the most important external factor is the strength of the thermal and there's no significant difference between juveniles and others they fly in thermals of similar strength and another option is you look at the motion capacity the wing loading and there's no significant difference between juveniles and adults when you look at the navigation capacity then one of the first measures of navigation capacity was the decision they make during circling which was which radius to circle and here we found a significant difference but the meaning of this difference is so adults circle in a larger radius but as you can see here I'll explain what you see here this is the core of the thermal here and as you get further from the core of the thermal the terminal velocity of the air not of the bird is declining say linearly okay and this is the sync rate of the bird and when it's changing its banking error in order to make the circle more narrow okay, shorter circling radius so the bird is losing lift because it's not getting enough enough lift from the air motion and therefore if it's using too high banking angle then it will sync and then if it will use a less dramatic banking angle it will gain more and more height and this is the optimal banking angle that the bird should use but what is this optimal compared to what we observe it can be calculated but it's difficult to say that what is the meaning so you learn to fly in I don't know 45 meters radius this is what vultures learn so the hint comes from again the observation of drifter thermal what you see is that the circle radius is higher for adults in certain you know so with no winds thermal is not different there's no difference between juvenile and adults and as you increase the wind you get some difference this is 94-5% confidence intervals and then in much stronger wheels there's still again no significant difference so I'm getting into the explanation that we eventually found by exploring the data what you see here is that the adults is using the same direction of circling throughout the climbing is using the same direction this is what glider pallets are trained to do to avoid accidents okay but birds can do, can change and what you see here is that this juvenile is changing quite a lot so he is not say patient enough or does not aware of the right system how to climb the thermal and therefore this could be a reason for the slower climb rate but again it's not that what is actually happening was revealed when Roy analyzed the different parts of the circle this is the wind going from left to right trying with tail wind and then in the lee side so this is an example and what you see is that with almost no winds there's no difference between adults and juveniles and here comes the important answer so when the thermal is drifted then the adults perform much better on the lee side of the thermal which means that as you can see here they wait patiently probably juveniles are not patient enough to wait look at this track here for example they wait about 7 seconds in this case with tail wind okay so they fly with tail winds 1, 2, 3, 4, 5, 6, 7 seconds without any elevation gain for those of you who flew a glider 7 seconds it's a lot and the thermal is so narrow so you're sure the thermal is there and there and there so you get lost and you start to do mistakes because you don't know you don't even have experience and you're not patient enough but adults are patient enough and then when they feel the bump when they feel the core of the acceleration vertical acceleration then they change immediately they start to circle like that and you see that all the elevation gain is with this phase with these 7 seconds again it's 7 seconds in this case they gain all the elevation gain is done here and then the thermal is keep on drifting so they need to patiently glide and they do it in the same in the same speed the gliding with the tail wind they do it in the same speed but here again when they do this turn the adults are doing that in higher airspeed higher airspeed higher performance patient enough to not wait for the core of the thermal and only then turn so this is what young vultures should learn in order to perform as good as adults they are flying in the same thermals and the consequences of this is that this is a very nice measure of competition between glider pilots you want to be less time in thermals and more between thermals because you want to win you want to get farther away not to climb up to get first to be the first to I don't know 300 kilometers so you want to minimize the time in thermals and get as much as possible in terms of displacement so the ratio of adults is much higher meaning that they cover more area and they flap less which is energy expenditure and this is a measure of energy expenditure and again they expend less energy than juveniles so they cover more area with less energy and our explanation is this is maybe we don't know to what extend the tactic of climbing a thermal and if the winds is explaining this but probably it is significant so I'm moving to the last part of navigation and this is a study of foot baths that Yossi also mentioned this is a soft soar who did the PG in my lab it's with Nahum Ulanowski as well from the Weizmann Institute who was mentioned in the previous talk several times these are foot baths just to give you what you what you need to notice here is how big the eyes are you tend to think of blind baths but you see this species has very big eyes and we put a very small GPS and they can carry GPS the females carry their pops at that time it was with no remote download so we needed to retrieve the tags and here Yossi is finding, you see a tag somewhere in the cave and when we look at the data we see trucks like this so faster than your sear boat you are welcome to flight number 79 departing from Sgafink a cage you can see that the bath is now flying in about 35 kilometers per hour about 100 meters above the ground very straight flight and ignoring lots of fruit trees and orchards these are all orchards here and we have lots of fruit trees and these are just some of the fruit trees around and it keeps on moving for a long distance flying straight straight straight straight now it will reach this settlement here so we will visit for a few minutes this small snigger here and another one here and another one here for a few minutes and then move to low level flight and reach this tree here about 25 kilometers from the cave and it happens that this particular tree is very special for this bath because it will come back night after night after night several nights as Yossi already mentioned they do this quite often this was a discovery that we had at this stage and this was a reason to ask how do they navigate you see that it's interpretation of course but it seems that this bath knows exactly where he wants to move the second that he got out of the cave so these are all these favorite trees that were used by baths from this from this colony so we took them to the desert and this is the foraging area and part in this experiment part of the baths were released early at night with no feeding with the expectation they will go to the favorite tree and this is exactly what this bath did this was the first one that was released and this is the second night that he flew from the cave to the to the same tree and another part of the experiment was bath that released late at night after feeding expecting them to fly to the cave this is what this bath did and actually this is all 5 or 5 that were supposed to go to the tree went to the tree and 4 of the 5 that were supposed to fly to the cave went to the cave the exception is a very interesting truck that you see here I'm going to show you a movie of this truck first of all notice that they they I don't know if they never been here but it's very unlikely but they do within second fly on the right on the right direction you see it's not that straight on the right direction this is the cave this bath is approaching the cave but it was released a bit too early and here's a decision making point interpretation of course and it is now flying about 10 km to its favorite tree spending about 6 minutes over there and then flying back to the cave again about 10-12 km so what we understood is that they can return from quite large distances beyond the normal foraging range of this population how they do it by affection by magnetic field in this area we have one of the largest magnetic anomalies in the Middle East and so it it was important but one of our friends who's a pilot himself he told us that when you fly just 50 meters above this place in the Negev then you see all the lights of the area they forage so although they might unlikely to be familiar with this particular site they can see familiar visual landmarks and Asaf did a line of site analysis and found all these points that they can see from both the release site and the foraging area so we had a challenge to find a way to block the vision so you can imagine of turning off the lights in half of Israel which some people maybe can do but the idea was to take them further south to the Negev where we have erosion craters and in the bottom of the erosion craters you can see nothing nothing not the lights of the junction you see walls and dark so part of them were released in the bottom of the crater in this mountain here and what you see is those that release it's 84 kilometers from the cave were again showing these very straight flights back home but those within the crater got lost for about 40 kilometers and eventually all of them flew out of the crater in the right direction to the north after spending lots of time meaning Anna can tell you that maybe they have created a secondary magnetic mechanism during these flights back and forth none of them went high enough to see the lights of the study area so this was a consider evidence for navigational map probably by vision and the last thing that I want to tell you is very similar to what Yossi has done with his bats in the colony in Tel Aviv so we again motivated by the wonderful discoveries in neuroscience and the potential of this field to really understand this long lasting question we are trying to get field evidence for a cognitive map or map like navigation and so is Yossi and what we are the problem of the study that I show you is this GPS start work for a few hours or one night or very few nights and so it was not possible really to know what these animals are doing during the routine behavior so we use ATLAS the system that I mentioned earlier and with ATLAS you can really track lots of animals for long time so you have 156 bats track every second or every 8 seconds for up to 36 nights and nights each one of them and all together we have this number of full nights and 14 million localization and the data is of high quality this is David who is running this project now attaching ATLAS to the bat and the bat will be released with this tag and we get these GPS level tracks that you have seen and when you look at this on these numbers of many many tracks that we have for many many bats what is coming up is the large number of straight tracks so I will show you that in a formal analysis what we also did is to map all trees all fruit trees in this landscape of about 25 to 35 kilometers so it includes 4 million trees in orchard this is quite easy you just get the density of the orchard and the size and you calculate it so you don't really map each one of them but this to map 14,000 individual trees in quite large area it took us more than a year and I'll show you why is that important so what you see here is what the bats are doing so when they fly from the cave to the tree or from the tree to the tree or from the cave to the cave or out of range they always fly straight this is a straight index of more than 0.9 they always fly straight so and you see this is the turning angle and the goal direction and you see that they fly in one direction this is what fruit bats are doing in the hula valley they fly straight and they fly straight from the very beginning this is the proportion of the truck and you see that the turning angle or the heading are the same from the very beginning and it's for short trucks and for long trucks it doesn't matter so bats in the hula valley fly straight but in the city they fly a little bit lower and they are building so probably this is why the straight index in your study is lower but in the hula valley they really fly straight all the time so they can fly straight not because they had they know the map of the landscape not necessarily only because of this they know the landscape they want to get from the tree to the cave and so on so they fly straight from each point to each point they can fly also straight because this is a very efficient forging search strategy as you know the submarines of the Germans were located by those trucking system they were trained to move in straight lines because when you move in straight lines you do not repeat the same area and you have the higher chances to fire a rare target so it could be that they just take a direction at random when they get out of the cave and then they fly 20, 10, 20 kilometers and then they found the tree and they land there and this is why we need to map all the trees when you calculate the probability of returning to the same tree by random chance again and again after 5 nights 10 nights, 20 nights and so on then this is the expected probability and this is what they do so they do not at all select a direction at random select particular directions and they do it again and again and again so they do not exhibit random search by straight flights and as you also mentioned this short cutting is the hallmark evidence for cognitive map here is a bat that is doing after 9 day and nights doing a short cut very straight line again to 1 tree here and then from this tree this is going to a tree that he visited before and they do lots of short cuts and here is another one from this tree to back to the cave and so we analyzed these short cuts and as you can see all of them were also very, very straight we also did the translocation experiment so you have seen that this is the area where most bats visit so we took them just beyond the area bats seldom reach these points that are not that far but they are rarely visited all these sites and also from these sites they return in straight flight this is all the black the black tracks that you see here so they return to the goal either a tree or a cave from the release location also in straight flight and this is what they did after the experiment again moving in the landscape in straight flight this is what food bats in the hula valley knows how to do and this is what the straightness as a function of nights since tagging so unlike a project of Yossi that he truck in bats from the very beginning early stages of life we truck adult bats so we don't really know if they've been there before or not but this, but you can see that they fly straight up to a month and when we gather more and more data I think we will see that what is the memory capacity of these bats just one month according to this data and according to Yossi it's probably more so this is an evidence that they do not do a random search a forage in straight flights and with more analysis that we did we rule out pilot team beckoning and path integration and what this is considered and evidence for a cognitive map and why have developed such a wonderful capacity so Yossi also mentioned that there are four give-alls the foods remain on the tree for long enough period and it's beneficial to learn where the food when and where the food are available it's quite predictable they are very social at least during roosting they do not fly together this is also an observation that we made but they forage they congregate in the trees and they also of course congregate in their cages and they live long so these are probably good condition to develop special memory and special cognitions and the very very last very short thing I want to say is about stone koulous this is another navigation project that we have but these are nocturnal waders and what Yotam did is to again transocate them as you have seen also in the talk of Anna and unlike food bots and pigeons stone koulous do not return home immediately almost all of them return home but look at this one it was released about 100 km from the nesting site it's during nesting they nest on runaways of air force that they want to get rid of them so we take them away and they have a high motivation to return to the nest and this is what they do after the return so this is the distance from home you see this stone koulous is moving away from home staying about the same distance from home and then getting closer and then moving away and then getting closer and after 10 days within a moment as if you realize we solve the map in our terminology the map and compass model and then return straight home within hours and you can see this is again the case for this example and also the case for this example so they cover in the wandering phase large area and you can see here and then all of a sudden it has no relationship with distance to the home they kind of resolve them up and fly straight home really straight flight so we hypothesize that they learn something during this wandering phase and what do they learn is maybe an opportunity to understand to link neurobiology and movement ecology because we have maybe something changing the brain during this 10 days or so so what we wanted to do is to chop them and put them in MRI before the release and then right after the release and to do so we tried to habituate them to humans and Naama imprinted them to humans and Naama was the student running this so you see she was walking in the campus with little stone koulous but 1 year of very successful imprinting all of them flew away and none of them come back none of them so what Naama did is to compare younger individuals of 6 months old that were raised in captivity for this imprinting they were always in a cave some of them were always in a cave and she dropped the same age individuals in the wild that were foraging normally and she scanned them with MRI and what you see are 2 different methods and you can see the brain regions that are significantly different between the 2 groups between stone koulous that were confined to the cage environment, boring no much movement and so on and those that that were foraged freely so again the hippocampus was mentioned and the optic tectrum and the factory cortex so that's not again resolving them up it's another special problem but maybe if we manage to drop them exactly when they return then maybe we will be able to pinpoint which brain area is changing and maybe then get further into the mechanism as well okay so keep pushing technology limits and happy that I'm quite happy that theory is lagging behind because we've bettered other, we have now an opportunity to build a better theory that is more truthful with reality and I think that young people should be encouraged to think big and try to push us forward and so these are just some thoughts about mostly about implementing behavior and walking on animals in the wild and also do manipulation and try to couple this with normal biology so I would like to thank my dream team, my wonderful students and postdocs the institute for advanced studies is where we work to formulate the movement ecology framework and my collaborators the funding agency thank you very much for your patience and and attention, thank you