 And I work on marine biogeography and I look at the impact of climate change on the special distribution of marine biodiversity from global to regional. And today I'm going to talk about the shifts that we are seeing under climate change. And so I'm going to take you through the, how we calculate the species diversity and at different spatial scales and how we extract data and process that. And, and then look at the spatial distribution across latitude longitude and also depth. Sometimes, and if we have enough data, and, and then look at the, how they, this distribution has been shifting under climate change in the past present and also under future climatic scenarios. So, these are the key concepts. So, if we can see that there are is a site a which we can say is a local region and, and these different symbols represents different types of species. And if we just look at inside of the site a, and just calculate the simply the number of species, then it's, it's alpha, it's we designate as alpha diversity. And then, when we compare it, decide a with side B, and look how the composition of species is changing or the number is changing we, we call it as beta diversity. So, it depends on like if we find a completely new set of species, it's species, it's 100% turnover, or if it is just the difference is just because of the number of species that then we can say okay site a is nested within site B. And, and then when we look at the overall region, which includes both the sites. We designate that as karma diversity, which is basically the, you can say the species richness of overall bigger region. And, and so I work on global data sets. And so this is how we look at the whole earth we divide divide this whole into different grades, and that can be like a bigger grades and then smaller grades, and which could be actually regional bands like this. And then, and then at a smaller scale we look at the regions which could be divided into different hexagon size or square shaped grids. And they are of these, these sizes can vary between like 550,000 square kilometer hexagons or, or 800,000 or even smaller. Yeah, and then we look at the special gradient like how these alpha diversity gamma diversity which is at the bigger special skill and then within that is changing in using different diversity in dices. So these are the global databases that I use. It's basically ocean biodiversity information system and GP IF. That's where we get the data on the occurrence of the species records. And then after we have these data, we compare the name of the species with from the world register of marine species to see if the names are valid and if there are any errors. And if they, if they are accepted or not, and just to have this data quality control, and then for environmental data sets, we get the data from give go or hit least and bio record. In this example, we collected data from obis, and which was like 23.7 million distribution records and then cross to cross match the species name in worms, and, and just use the data for over almost like 50,000 element species. And then after quality control, we had only 12.6 million records that we looked at, and then classify these as pelagic and benthic based on the species. Information if they have one life stage as benthic we define them as benthic and if they are overall pelagic then they were pelagic and that's the number of species that we looked at. And then we use these occurrence records to plot against the latitude and the we use chow index to minimize sampling bias by in the number of samples. And in this graph, you can see that because you wanted to see how the distribution of species richness is changing across as we go from the north to the south. And if, if it is aligned with the hypothesis saying that we have more number of species at the tropics so that was very interesting to test when we have this much of data. And then in these many graphs you can see that the x axis is latitude and the y axis is the basically the estimated number of species using chow index. And we can see that on the, on the left hand side of the graph is the southern south. The southern hemisphere and in the middle is the equator the zero and on the right hand side is the is the northern hemisphere. So, well it didn't go by the hypothesis is what we would have expected that there will be more species at the equator, but there is a dip actually at the equator and this, this, this equatorial dip was quite common among in all, but it was there in all the species when we looked at all the species data. But when we define this biological and benefit, it was also very consistent. And then there was the peaks in the diversity was actually at the at the subtropics, not at the tropics, not at the equator. So, and then we also model this species such as against sea surface temperature, and, and found that on if we if the temperature goes above 20 degrees basically the species which is kind of declines there, except few of which are very tropical species like demersal and reef associated fishes, but they also kind of reaching to this asymptote. And that's, that's this temperature where we have the maxi that said the equator where we have the maximum temperature you know this red thing shows the maximum temperature and that's where all these species richness is elevating and that's where we are finding this equatorial tip. So, and then the next step was to see how the surface temperature has been changing in the different parts of the world. So we divided this into Arctic not separate tropics. And this is basically X axis shows the time time period from 1920 onwards until 2015. And then the Y axis is the SSD a sea surface temperature anomaly. And we can see that there has been consistent rising temperature in the in the northern hemisphere as compared to the southern hemisphere. So, to see how the latitude no distribution in species richness has changed over time. We could only do it for three different time periods of 20 years from 1955 to 1974 then 1975 to 1994 and 1995 to 2015. And because, because how much data we had, you know, so we had to make a balance between that we, we have enough data to derive significant conclusions. So, so from 19. So we can see in here in these graphs for all species that basically the green one shows the 1955 to 1994 the distribution is very much uni model and but then in the latest time period, it is going this, this we find this tip at the equator and then the diversity is kind of like has shifted in the subtropics, the peaks. And this is very much prominent in the pelagic species, but also in the benthic species to this kind of shift in the northern hemisphere as we can see here. When it comes to species turnover, we also did the same thing to model the species turnover against latitude. And interestingly, we found that there is the species turn also in the graph on the right hand side, you can see latitude and then species turnover, which is very much uni model. And species richness on the left side of the axis is species richness, which is actually by model. So, so it was very contrasting to see that we find high species turnover at the equator. And this is because we have more endemicity in the in the tropics, you know, and there are more geographic barriers, and you find more number of bi geographic realms there as well. But with the increasing temperature, this would indicate that there is more habitat in a fragmentation that is happening. So we are losing not just we are not just losing species, but also this kind of peak indicates the just the fragmentation of species diversity basically. So the next step was to look more closely into this subset of species, which indicates represents some ecosystem and we chose coral reef ecosystem to look for it. And this is basically why because coral reefs are very important as we all know, and they are also very sensitive indicators of climate change. And now the next step was to because until now we looked at the observed data and how this has changed over time and but how this will change in the future. We wanted to do some more future modeling. And this is basically interesting the model, like when you do predictions, you can we can match. We match the species occurrence with the environmental data, where they occur, and then predict where the species are going to be occurring in the in the new environmental set. So these kind of habitat modeling is important because it completes the spatial gap in point data distribution as because this was the problem that we were finding in the in the previous research, but it was more important to do that because we were looking at the observed data as how it was, and if it shows something important related to climate change impacts. And, and now we are moving to this predicted habitat sociability models under different scenarios. And for the coral reef, this research, we use the 57 species of coral corals and their inhabitants, which included fish mollusks, orthoports and polykeets. And these are basically occurring in the warm water, which is represented by the yellow color data points and also in the cold water. So these are different sets of species representing cold and warm water coral reef ecosystem. And we use max and modeling, sorry, we use these environmental data from by a record and this in this temperature, sorry, the environmental layers were see bottom temperature salinity current velocity and we predicted the species distribution in the present situation and in the in the future scenarios under RCP 4.5 and 8.5 and also used geographically adepts to see how it varies with depth. So, so we use max and modeling to to to predict the species distribution. So that's what I previously mentioned that the species occurrence data was matched with the environmental layers and then the models were calibrated using different parameters in there. And then we had this final models and then global predictions. And then these global predictions were then compared the future ones were compared with the present ones and then we got the estimate of like how the difference between the present and the future suggested if the species has lost it, it's occurrence in the future or has gained a new occurrence in somewhere else in in in the future scenarios. So these were the results and basically we modeled for each species these different under different scenarios and then overlaid all the models. All the differences that we can we found in the distribution of species, and then we overlaid all these models together to see if there are regions where we find a collective loss of the species or gain of the species. And here in this slide. You see the collective loss of the species in different parts of the world. So we did the global projections but it shows these data are actually five arc minute. This is the resolution five arc minute so they're very precise like much more smaller grades than what I showed you in the previous time. So, we can see that almost all the warm water species have lost their distribution, and then this RCP 4.5 scenario in 2050. And this, this color graph actually shows the number of species that have have been losing their distribution. And, and in our in 8.5 scenario, this, this is even more intense, especially in Southeast Asia. And here, this is the in 2100. And you can see that almost more than 99% of the coral reefs and their associated species they have lost their distribution. And it was really heartbreaking to see these results for me. And interestingly, the, the species and the cold water they actually gained a lot of their distribution in the, in the Arctic Ocean. But there is no real gain for the tropical ones here, as we can see, there is no gain in these parts. So we calculated the, the area that was lost or gained in each of these different groups. And we can see of course, on, on, on the graphs you can see on the left, y axis is the area, and on the x axis is the groups. And, and this orange dark orange color represents the worst case scenario in RCP 8.5 in 2100 where we have the maximum loss. And yeah, and then if you compare to the gain, and this is for warm water species. The gain is much, much less than, than the loss basically, except for fish. Well, it's also less but fish, we can say they are more mobile and they could cover more distance. So, so it's kind of they still can gain a little bit more than others other groups. And this is the result for cold water species. So the loss, and it is a lot but also there is, we can see there is gain. And when we go more into the details of which species is gaining or losing more than loss or gaining more than, you know, so we can see for the warm water species of course all of them are losing more than they gain in the future. Whereas the cold water species, this is quite mixed. So, and when we look closely into which species are actually losing more, these are actually those species which are occurring much higher up in the north, especially in the, they are more Arctic specific species. But the ones which are gaining more like the polycates, they are actually the species which has a wider distribution, and they are more generalist. So we can derive a conclusion that all the species which has less or restricted geographical range and they occur in a very specific environmental conditions. They are at high risk here, as compared to the ones which has wider geographic range and much wider environmental range as well. So, we might see a more generalist species appearing as, as we can see from these results. So, we don't know how these this kind of turnover in the species, especially in the north is going to affect whether it's if it is a positive change that we're going to see in the ecosystem or a negative change, but definitely there will be some kind of change that we can expect in the ecosystem functioning in both the warm and the cold waters. So, these results basically are well, these, these data are mostly coastal and shallow water. So, definitely we need more data. Well, from the from the study from the PNAS one was mostly shallow water data so definitely we need more data from the deep sea to see how it is working. Yeah, the results. We have found are quite striking and these models can be improved more by including more species physiological information their life cycle information and integrating that into these these kind of prediction modelings. Nevertheless, these models are, I believe are quite good to give us the overview of how the future may look like. And, and I believe the a lot can be done to by integrating these models in the strategic implementation of the resource management. And yeah. So, that's it from my side and I would like to thank all my porters my friends and people who have been so supportive in in these research. Yeah. Thank you. And I'm open for the questions. Thank you so much for for.