High resolution predictions of potential and actual distribution of forest tree species for Europe (2000-2020) based on spatiotemporal Machine Learning
Bonannella, Carmelo
Carmelo Bonnanella, PhD candidate & research assistant at OpenGeoHub, presented the results of modeling species distribution maps for both potential and actual natural vegetation through spatiotemporal machine learning using a data-driven, robust, objective and fully reproducible workflow. The presentation focussed on the benefits of using ensemble machine learning for species distribution modeling to capture patterns of niche changes in both space and time: yearly (from 2000 to 2020) probability distribution maps for both potential and actual natural vegetation were shown for forest tree species that live in different climatic conditions across Europe. The high spatial (30 m) and temporal (1 year) resolution of the outputs should allow us to enhance and better understand the patterns of niche change.
 Yes, as Tom mentioned, today we will talk about vegetation mapping. For those who follow the training session on Monday, it will be talking about vegetation mapping again. So I hope I won't bore you a lot with more of the theoretical stuff that I already mentioned. So if for everyone that is not familiar with species distribution modeling, which is the broad term to which we identify vegetation mapping, I will just talk a bit about the theory. So yes, about the species distribution modeling, as I said, which is one of the techniques that we use to model the distribution either potential or actual of different species could be vegetation, as in this case, for example, forests, so it could be also mammals, all the kind of species. Then we will focus on which data and which covariates we use to actually try to understand how these species occur on a European scale. I will give some details about the modeling that we use, which is very similar to what already Martijn showed us, and also some results in the development server as the demo that already Martijn showed us. So we will talk about species distribution modeling on a correlative scale. So what we will do is just having a training dataset with the occurrence of the species that we want to predict, and then we associate some values from different predictor variables to the location where we actually observe the species or not. We fit a model to estimate similarity between these sites, and then we predict on either space or space time in the region that we are interested in, which in our case is Europe in terms of space, so the whole Europe, plus in terms of time on a time coverage that goes from 2000 to 2020. So let me also move this so you can see. So basically what I was talking about is the difference between the potential distribution of a species and the actual distribution of a species. Here you can see on the left the geographical space, so how we actually find the species in the environment. So for example here this is the area that considering all the conditions we see in the environment the species could live, and we can see that in gray is the area where the species is actually observed, while in white we can see that these are the areas where the species could potentially occur but is not observed. Therefore for example problems in terms of migration of moving of the species or because this niche is already occupied by the more competitive species. So what we do is actually observe the different predictor variables that theoretically speaking would describe the distribution of the species and analyze them in the feature space and see how the species basically occur in this feature space. So this is for example one of the two outputs that we can get from species distribution modeling. We can get either a map that shows on a 01 scale where the species could occur. For example here you can see this coming from the European Atlas of Forestry species for the European beach. The green area is where the species could occur, while instead the area that is in yellow is the North Aida, so there is zero, so all the areas where the species does not occur. Or we can get probability values. So areas where the species could potentially occur for isofabability is values that come from 90 to 100 percent where the species could actually live and prosper, while instead all the other areas are low probability values where the species does not occur. So what we did was during the last summer school, Ioannis Aisi created this harmonized dataset with all the tree distribution points that we have available for Europe coming from three different kinds of datasets. The National Forest Inventory dataset, the GBIF dataset and also some Lucas points. The code is available online, the dataset is available on Zenodo, so if you want to use the same dataset to replicate our procedure you can always do that. This is how the data looks like. So after we harmonize and put all together all these points we have more than three million of points scattered across all Europe divided by 70 different forestry species. And we can see that we have a higher number of points here in Spain compared to all the other countries, but actually if we analyze how these points are distributed we see that they are highly clusterized in some areas, while some areas for example have no survey or samples at all. So we faced this problem by using the land cover products that Martín was talking about. We created a forest mask for all 20 years that we are actually trying to study and then we overlay the points from this dataset with the forest mask. And all the points they show for all 20 years are higher value of high value of probability, so more than 19% in one of the classes that were put in the forest masks then we will use as points for the modeling. So for example you can see here that this is one of the points that we actually included in the dataset because it shows high value of probability of coniferous forest. This is for example a Norwitz-Bruz that comes from a European national forest inventory dataset that's located in Germany, while instead this point comes from the GBIF dataset and we didn't include it because we can see that the values of probabilities for the classes that we put in the forest mask are very low across all 20 years. This instead an overview of the covariates that we use. We use different climatic covariates both on a long term scale and on a time series scale. Terrain covariates come from the DDMs or DDM derivatives, then reflectance data that comes from Lancet, that's the same dataset that we also use for land cover data and also maps that come from the European forest outlets to map the possibility of the same place being occupied by another species. So it's a way for us to include the interspecific competition between the species. This is how we face the clusterization of the points. So as we can see for example this is a cork oak which is mostly located in Spain and there are different clusters also in Sardinia or in Corsica, but we can see that among all the 300,000 points that we have been straining data for this species, one third of it was actually in one of the tiles of 30 kilometers size that we use for tiling the whole Europe. So of course this cluster can influence and possibly impair the models and we can actually get very big biases in the distribution of the species. So what we did was actually checking the distribution of the points across all the tiles for all the species in Europe and then we took a median that would be used as threshold value for how many points can be used in modeling in each tile. This is how the modeling looks like. So we first started testing different machine learning algorithms without parameter optimization or feature selection and we trained these different models on a subset of species, testing also how the species and the present data and the absent data would look like in terms of either the most occurring species which were more like than 500,000 points or very less occurring species which can be less than 1000 points across all Europe. And the three best learners that we got from this first part of the modeling were then used with a bigger sample, 5% of the training data set for the whole, for all the species that we are trying to model which at this point are 50 and we trained them on and we fine-tuned them and we trained them and I can show you the feature selection processing a bit. The results, so the parameters that we got in the feature space for all these three learners were then used to train an ensemble model where we use a logistic regression as a meta learner and we use a 70-30 split in the data set. So we have 70% for each species use a training and 30% as test and then we use 5-fold spatial cross-validation during the whole process and the parameters that we use for the base learners of the ensemble model come from the phase two. We then use everything, I didn't mention that everything here that you've seen is modeled using R and especially the MLR framework and for the feature selection we use Scikit-learn library in Python and we use log loss because we have different data set for different species so the proportion between presence and absence data is constantly changing and to avoid this uncertainty in how the probabilities can be predicted we use log loss instead of overall accuracy or other metrics to assess how the features can be selected. So the process goes by using a random forest classifier with a 5-fold spatial cross-validation and recursive feature elimination we checked the first deep in the values of log loss across all the covariates and that's the value of the covariates that is used in each model for each species. To have consistency in this we replicate this procedure five times for each species and we then took the average of the number that we get in the first deep for each species. And now you can see how this map looks like in the development server. They are not completely online, they will be online I think by tomorrow most of the species will be online so you can also check by yourselves and that's how this species looks like. Yeah this development server let me ask this. So we can for example check the probability distribution for the Norway spruce which is the same that was available in the slides. You can see the legend the colors go from probabilities that below 10% to 90% we can recover the whole Europe and our time frame as I mentioned it goes from 2000 to 2019. So you can check somewhere like in the length cover classification points and check for example the probability distribution for the forest species to be there. We also have points available so you can check and we also have a compare tool where we can actually compare the actual distribution which is modeled using reflectance so you can actually exclude CDs or also farming sites or other urban kind of length cover classification with instead probability distribution as I mentioned and they show in the one of the first slides in the geographical space usually the potential distribution is very much bigger than the actual distribution and that's something that you can check here with the slider. As you can see the condition for the species to be present in a specific area are already available for a lot of areas while instead the actual distribution of the species is very much smaller. Yeah because we do not include anything that does that's to do with urbanization or entropization in the environment so everything is basically excluded for the time frame that we are actually analysing. Yeah let's see for example Scandinavia. Here for example yes we have the conditions for the norwespers to be present but this is actually one of the species that is less occurring in the southernmost part of Sweden because it's mostly covered with broadleafs or for example here in Germany we can see that it's the same or any kind of patients or the Alps. This area for example. You can see the difference and it's kind of makes sense of course because it's from expert knowledge you can actually say that the top of the mountains or the glaciers is not an area where you would actually see the species or where the cities are. Yeah so this is to turn off the comparison map so as we said we focus on usability of the maps so what we actually provide is a probability of values for where one pixel for example can show the potential distribution of one species but we also provide for each layer an uncertainty map that shows okay should I use this value is it reliable or not. So we have a scale that here shows the variance based on the procedure that Tom explained in the first days of the training session so we use correction factor for the ensemble models to avoid for example the extrapolation problem that we talked about in the training sessions so you can actually check for each pixel or each area that you're interested in if the values that we provide have a high tolerance in terms of reliability or not. And I think that's about it so if you have any question please. Let's see the chart. Anything on YouTube or mother most? Yup. I don't know if you hear that one. Obviously, thezać actively. So in general terms when thePlastic,lers out front. Yeah. On a long term scale we know that for example this is not possible we also provide uncertainty maps that we can say that that value for the specific here is probably due to noise, or also we can another thing that we can use on a long term scale is actually remove the noise by using trend analysis. So that's also some post processing that we could do. So we could provide potentially raw values for the maps and also post-processed maps. Oh, let's see. I see new messages in the chat. Yeah. Okay. Okay, that's a there's a question from Pablo, which says it is only possible to model part of the actual distribution if you consider a biotic biotic or movement limits. But we do we use this kind of predictor after the feature selection of the session and which variables are in there. Okay, so the maybe something that wasn't clear during my presentation is that we don't have one model that models all the species, each species as its own model, its feature selection, optimization and part. So we have to check basically in every in every model which ones were used or not. But yes, for example, the a biotic and a biotic is always there like climatic variables are always in there. And we also can see the differences between higher resolution layer that we use as predictors, for example, the DTM sort of the slopes that are highly available, or for example, long term a biotic covariates like the bio clean that about very course resolution. And other things that we that pops up in terms of covariates for almost all the species is the reflectance data, especially for the, for the, for the actual distribution because those are can actually help us exclude all those areas that instead are covered by cities. And in the potential distribution we also have some layers that come from the University of Maryland the show for example the barren areas of the Berlin, and that for some species actually comes up as one of the first covariates so we can actually exclude very quickly all the areas that do not show vegetation. But yes, we will publish the paper which is now in writing will provide like data for each model and you can see which ones are the covariates that actually pops up at the most important for every species. Oh, yeah, okay, yeah, this is another question is just an idea. Yeah, yeah. Yeah, someone here in the chat was suggesting for example to connect with JRC, we try to do that but they also have the same problem as us the amount of points that are available to model these kinds of things, the ones that we are using right now this is the highest amount it's publicly available. I mean, just for the audience. Yeah, it's possible. It's not a problem we have the computing infrastructure to do that. It would be also wiser to not use all of the million of points that you were mentioning like we could use most of them to validate our mix for example, which is looking for an independent data set in terms of trees and especially if you are modeling or forecasting is always a problem. So using all these points for example for as an independent data set in terms of sampling bias or everything could be also useful to make the maps more accurate. Yeah, that's fine. Yeah, basically that's a good question. You can first check the areas that you're interested in. And then in this area you can check which ones of the species that we model have a high value of probability in terms of actual distribution. So we, from that you can say okay these are the species that will actually do well in this area. And then based on your interest then on your timeframe interest so for example if you want to have like short rotation for a serial if you want to plan for like 100 years. And also what you're interested in which kind of forest do you want is that just production. Do you want do you have interest for example in protection of the area for example like forest that are planted in high in the mountains to protect against no fall and all these kind of problems. So that depends. But yeah you can, you can for sure use the maps as having a kind of an overview of the trees that you can plant there.
 Yes, as Tom mentioned, today we will talk about vegetation mapping.  For those who follow the training session on Monday, it will be talking about vegetation  mapping again.  So I hope I won't bore you a lot with more of the theoretical stuff that I already mentioned.  So if for everyone that is not familiar with species distribution modeling, which is the  broad term to which we identify vegetation mapping, I will just talk a bit about the  theory.  So yes, about the species distribution modeling, as I said, which is one of the techniques  that we use to model the distribution either potential or actual of different species could  be vegetation, as in this case, for example, forests, so it could be also mammals, all  the kind of species.  Then we will focus on which data and which covariates we use to actually try to understand  how these species occur on a European scale.  I will give some details about the modeling that we use, which is very similar to what  already Martijn showed us, and also some results in the development server as the demo  that already Martijn showed us.  So we will talk about species distribution modeling on a correlative scale.  So what we will do is just having a training dataset with the occurrence of the species  that we want to predict, and then we associate some values from different predictor variables  to the location where we actually observe the species or not.  We fit a model to estimate similarity between these sites, and then we predict on either  space or space time in the region that we are interested in, which in our case is Europe  in terms of space, so the whole Europe, plus in terms of time on a time coverage that goes  from 2000 to 2020.  So let me also move this so you can see.  So basically what I was talking about is the difference between the potential distribution  of a species and the actual distribution of a species.  Here you can see on the left the geographical space, so how we actually find the species  in the environment.  So for example here this is the area that considering all the conditions we see in the environment  the species could live, and we can see that in gray is the area where the species is actually  observed, while in white we can see that these are the areas where the species could potentially  occur but is not observed.  Therefore for example problems in terms of migration of moving of the species or because  this niche is already occupied by the more competitive species.  So what we do is actually observe the different predictor variables that theoretically speaking  would describe the distribution of the species and analyze them in the feature space and  see how the species basically occur in this feature space.  So this is for example one of the two outputs that we can get from species distribution  modeling.  We can get either a map that shows on a 01 scale where the species could occur.  For example here you can see this coming from the European Atlas of Forestry species for  the European beach.  The green area is where the species could occur, while instead the area that is in yellow  is the North Aida, so there is zero, so all the areas where the species does not occur.  Or we can get probability values.  So areas where the species could potentially occur for isofabability is values that come  from 90 to 100 percent where the species could actually live and prosper, while instead all  the other areas are low probability values where the species does not occur.  So what we did was during the last summer school, Ioannis Aisi created this harmonized  dataset with all the tree distribution points that we have available for Europe coming from  three different kinds of datasets.  The National Forest Inventory dataset, the GBIF dataset and also some Lucas points.  The code is available online, the dataset is available on Zenodo, so if you want to use  the same dataset to replicate our procedure you can always do that.  This is how the data looks like.  So after we harmonize and put all together all these points we have more than three million  of points scattered across all Europe divided by 70 different forestry species.  And we can see that we have a higher number of points here in Spain compared to all the  other countries, but actually if we analyze how these points are distributed we see that  they are highly clusterized in some areas, while some areas for example have no survey  or samples at all.  So we faced this problem by using the land cover products that Martín was talking about.  We created a forest mask for all 20 years that we are actually trying to study and then  we overlay the points from this dataset with the forest mask.  And all the points they show for all 20 years are higher value of high value of probability,  so more than 19% in one of the classes that were put in the forest masks then we will  use as points for the modeling.  So for example you can see here that this is one of the points that we actually included  in the dataset because it shows high value of probability of coniferous forest.  This is for example a Norwitz-Bruz that comes from a European national forest inventory  dataset that's located in Germany, while instead this point comes from the GBIF dataset  and we didn't include it because we can see that the values of probabilities for the classes  that we put in the forest mask are very low across all 20 years.  This instead an overview of the covariates that we use.  We use different climatic covariates both on a long term scale and on a time series scale.  Terrain covariates come from the DDMs or DDM derivatives, then reflectance data that comes  from Lancet, that's the same dataset that we also use for land cover data and also maps  that come from the European forest outlets to map the possibility of the same place being  occupied by another species.  So it's a way for us to include the interspecific competition between the species.  This is how we face the clusterization of the points.  So as we can see for example this is a cork oak which is mostly located in Spain and there  are different clusters also in Sardinia or in Corsica, but we can see that among all  the 300,000 points that we have been straining data for this species, one third of it was  actually in one of the tiles of 30 kilometers size that we use for tiling the whole Europe.  So of course this cluster can influence and possibly impair the models and we can actually  get very big biases in the distribution of the species.  So what we did was actually checking the distribution of the points across all the tiles for all  the species in Europe and then we took a median that would be used as threshold value for  how many points can be used in modeling in each tile.  This is how the modeling looks like.  So we first started testing different machine learning algorithms without parameter optimization  or feature selection and we trained these different models on a subset of species, testing also  how the species and the present data and the absent data would look like in terms of either  the most occurring species which were more like than 500,000 points or very less occurring  species which can be less than 1000 points across all Europe.  And the three best learners that we got from this first part of the modeling were then  used with a bigger sample, 5% of the training data set for the whole, for all the species  that we are trying to model which at this point are 50 and we trained them on and we  fine-tuned them and we trained them and I can show you the feature selection processing  a bit.  The results, so the parameters that we got in the feature space for all these three learners  were then used to train an ensemble model where we use a logistic regression as a meta  learner and we use a 70-30 split in the data set.  So we have 70% for each species use a training and 30% as test and then we use 5-fold spatial  cross-validation during the whole process and the parameters that we use for the base  learners of the ensemble model come from the phase two.  We then use everything, I didn't mention that everything here that you've seen is  modeled using R and especially the MLR framework and for the feature selection we use Scikit-learn  library in Python and we use log loss because we have different data set for different species  so the proportion between presence and absence data is constantly changing and to avoid this  uncertainty in how the probabilities can be predicted we use log loss instead of overall  accuracy or other metrics to assess how the features can be selected.  So the process goes by using a random forest classifier with a 5-fold spatial cross-validation  and recursive feature elimination we checked the first deep in the values of log loss across  all the covariates and that's the value of the covariates that is used in each model  for each species.  To have consistency in this we replicate this procedure five times for each species and  we then took the average of the number that we get in the first deep for each species.  And now you can see how this map looks like in the development server.  They are not completely online, they will be online I think by tomorrow most of the species  will be online so you can also check by yourselves and that's how this species looks like.  Yeah this development server let me ask this.  So we can for example check the probability distribution for the Norway spruce which is  the same that was available in the slides.  You can see the legend the colors go from probabilities that below 10% to 90% we can  recover the whole Europe and our time frame as I mentioned it goes from 2000 to 2019.  So you can check somewhere like in the length cover classification points and check for  example the probability distribution for the forest species to be there.  We also have points available so you can check and we also have a compare tool where we can  actually compare the actual distribution which is modeled using reflectance so you can actually  exclude CDs or also farming sites or other urban kind of length cover classification  with instead probability distribution as I mentioned and they show in the one of the  first slides in the geographical space usually the potential distribution is very much bigger  than the actual distribution and that's something that you can check here with the slider.  As you can see the condition for the species to be present in a specific area are already  available for a lot of areas while instead the actual distribution of the species is  very much smaller.  Yeah because we do not include anything that does that's to do with urbanization or entropization  in the environment so everything is basically excluded for the time frame that we are actually  analysing.  Yeah let's see for example Scandinavia.  Here for example yes we have the conditions for the norwespers to be present but this  is actually one of the species that is less occurring in the southernmost part of Sweden  because it's mostly covered with broadleafs or for example here in Germany we can see  that it's the same or any kind of patients or the Alps.  This area for example.  You can see the difference and it's kind of makes sense of course because it's from expert  knowledge you can actually say that the top of the mountains or the glaciers is not an  area where you would actually see the species or where the cities are.  Yeah so this is to turn off the comparison map so as we said we focus on usability of  the maps so what we actually provide is a probability of values for where one pixel  for example can show the potential distribution of one species but we also provide for each  layer an uncertainty map that shows okay should I use this value is it reliable or not.  So we have a scale that here shows the variance based on the procedure that Tom explained  in the first days of the training session so we use correction factor for the ensemble  models to avoid for example the extrapolation problem that we talked about in the training  sessions so you can actually check for each pixel or each area that you're interested  in if the values that we provide have a high tolerance in terms of reliability or not.  And I think that's about it so if you have any question please.  Let's see the chart.  Anything on YouTube or mother most?  Yup.  I don't know if you hear that one.  Obviously, thezać actively.  So in general terms when thePlastic,lers out front.  Yeah.  On a long term scale we know that for example this is not possible we also provide uncertainty maps that we can say that that value for the specific here is probably due to noise, or also we can  another thing that we can use on a long term scale is actually remove the noise by using trend analysis. So that's also some post processing that we could do. So we could provide potentially raw values for the maps and also post-processed maps.  Oh, let's see. I see new messages in the chat.  Yeah.  Okay.  Okay, that's a there's a question from Pablo, which says it is only possible to model part of the actual distribution if you consider a biotic biotic or movement limits.  But we do we use this kind of predictor after the feature selection of the session and which variables are in there.  Okay, so the maybe something that wasn't clear during my presentation is that we don't have one model that models all the species, each species as its own model, its feature selection, optimization and part.  So we have to check basically in every in every model which ones were used or not. But yes, for example, the a biotic and a biotic is always there like climatic variables are always in there.  And we also can see the differences between higher resolution layer that we use as predictors, for example, the DTM sort of the slopes that are highly available, or for example, long term a biotic covariates like the bio clean that about very course resolution.  And other things that we that pops up in terms of covariates for almost all the species is the reflectance data, especially for the, for the, for the actual distribution because those are can actually help us exclude all those areas that instead are covered by cities.  And in the potential distribution we also have some layers that come from the University of Maryland the show for example the barren areas of the Berlin, and that for some species actually comes up as one of the first covariates so we can actually exclude very quickly all the areas that do not show vegetation.  But yes, we will publish the paper which is now in writing will provide like data for each model and you can see which ones are the covariates that actually pops up at the most important for every species.  Oh, yeah, okay, yeah, this is another question is just an idea. Yeah, yeah.  Yeah, someone here in the chat was suggesting for example to connect with JRC, we try to do that but they also have the same problem as us the amount of points that are available to model these kinds of things, the ones that we are using right now this is the highest amount it's publicly available.  I mean, just for the audience. Yeah, it's possible. It's not a problem we have the computing infrastructure to do that.  It would be also wiser to not use all of the million of points that you were mentioning like we could use most of them to validate our mix for example, which is looking for an independent data set in terms of trees and especially if you are modeling or forecasting is always a problem.  So using all these points for example for as an independent data set in terms of sampling bias or everything could be also useful to make the maps more accurate.  Yeah, that's fine.  Yeah, basically that's a good question. You can first check the areas that you're interested in. And then in this area you can check which ones of the species that we model have a high value of probability in terms of actual distribution.  So we, from that you can say okay these are the species that will actually do well in this area.  And then based on your interest then on your timeframe interest so for example if you want to have like short rotation for a serial if you want to plan for like 100 years. And also what you're interested in which kind of forest do you want is that just production.  Do you want do you have interest for example in protection of the area for example like forest that are planted in high in the mountains to protect against no fall and all these kind of problems.  So that depends. But yeah you can, you can for sure use the maps as having a kind of an overview of the trees that you can plant there.
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Yes, as Tom mentioned, today we will talk about vegetation mapping. For those who follow the training session on Monday, it will be talking about vegetation mapping again. So I hope I won't bore you a lot with more of the theoretical stuff that I already mentioned. So if for everyone that is not familiar with species distribution modeling, which is the broad term to which we identify vegetation mapping, I will just talk a bit about the theory. So yes, about the species distribution modeling, as I said, which is one of the techniques that we use to model the distribution either potential or actual of different species could be vegetation, as in this case, for example, forests, so it could be also mammals, all the kind of species. Then we will focus on which data and which covariates we use to actually try to understand how these species occur on a European scale. I will give some details about the modeling that we use, which is very similar to what already Martijn showed us, and also some results in the development server as the demo that already Martijn showed us. So we will talk about species distribution modeling on a correlative scale. So what we will do is just having a training dataset with the occurrence of the species that we want to predict, and then we associate some values from different predictor variables to the location where we actually observe the species or not. We fit a model to estimate similarity between these sites, and then we predict on either space or space time in the region that we are interested in, which in our case is Europe in terms of space, so the whole Europe, plus in terms of time on a time coverage that goes from 2000 to 2020. So let me also move this so you can see. So basically what I was talking about is the difference between the potential distribution of a species and the actual distribution of a species. Here you can see on the left the geographical space, so how we actually find the species in the environment. So for example here this is the area that considering all the conditions we see in the environment the species could live, and we can see that in gray is the area where the species is actually observed, while in white we can see that these are the areas where the species could potentially occur but is not observed. Therefore for example problems in terms of migration of moving of the species or because this niche is already occupied by the more competitive species. So what we do is actually observe the different predictor variables that theoretically speaking would describe the distribution of the species and analyze them in the feature space and see how the species basically occur in this feature space. So this is for example one of the two outputs that we can get from species distribution modeling. We can get either a map that shows on a 01 scale where the species could occur. For example here you can see this coming from the European Atlas of Forestry species for the European beach. The green area is where the species could occur, while instead the area that is in yellow is the North Aida, so there is zero, so all the areas where the species does not occur. Or we can get probability values. So areas where the species could potentially occur for isofabability is values that come from 90 to 100 percent where the species could actually live and prosper, while instead all the other areas are low probability values where the species does not occur. So what we did was during the last summer school, Ioannis Aisi created this harmonized dataset with all the tree distribution points that we have available for Europe coming from three different kinds of datasets. The National Forest Inventory dataset, the GBIF dataset and also some Lucas points. The code is available online, the dataset is available on Zenodo, so if you want to use the same dataset to replicate our procedure you can always do that. This is how the data looks like. So after we harmonize and put all together all these points we have more than three million of points scattered across all Europe divided by 70 different forestry species. And we can see that we have a higher number of points here in Spain compared to all the other countries, but actually if we analyze how these points are distributed we see that they are highly clusterized in some areas, while some areas for example have no survey or samples at all. So we faced this problem by using the land cover products that Martín was talking about. We created a forest mask for all 20 years that we are actually trying to study and then we overlay the points from this dataset with the forest mask. And all the points they show for all 20 years are higher value of high value of probability, so more than 19% in one of the classes that were put in the forest masks then we will use as points for the modeling. So for example you can see here that this is one of the points that we actually included in the dataset because it shows high value of probability of coniferous forest. This is for example a Norwitz-Bruz that comes from a European national forest inventory dataset that's located in Germany, while instead this point comes from the GBIF dataset and we didn't include it because we can see that the values of probabilities for the classes that we put in the forest mask are very low across all 20 years. This instead an overview of the covariates that we use. We use different climatic covariates both on a long term scale and on a time series scale. Terrain covariates come from the DDMs or DDM derivatives, then reflectance data that comes from Lancet, that's the same dataset that we also use for land cover data and also maps that come from the European forest outlets to map the possibility of the same place being occupied by another species. So it's a way for us to include the interspecific competition between the species. This is how we face the clusterization of the points. So as we can see for example this is a cork oak which is mostly located in Spain and there are different clusters also in Sardinia or in Corsica, but we can see that among all the 300,000 points that we have been straining data for this species, one third of it was actually in one of the tiles of 30 kilometers size that we use for tiling the whole Europe. So of course this cluster can influence and possibly impair the models and we can actually get very big biases in the distribution of the species. So what we did was actually checking the distribution of the points across all the tiles for all the species in Europe and then we took a median that would be used as threshold value for how many points can be used in modeling in each tile. This is how the modeling looks like. So we first started testing different machine learning algorithms without parameter optimization or feature selection and we trained these different models on a subset of species, testing also how the species and the present data and the absent data would look like in terms of either the most occurring species which were more like than 500,000 points or very less occurring species which can be less than 1000 points across all Europe. And the three best learners that we got from this first part of the modeling were then used with a bigger sample, 5% of the training data set for the whole, for all the species that we are trying to model which at this point are 50 and we trained them on and we fine-tuned them and we trained them and I can show you the feature selection processing a bit. The results, so the parameters that we got in the feature space for all these three learners were then used to train an ensemble model where we use a logistic regression as a meta learner and we use a 70-30 split in the data set. So we have 70% for each species use a training and 30% as test and then we use 5-fold spatial cross-validation during the whole process and the parameters that we use for the base learners of the ensemble model come from the phase two. We then use everything, I didn't mention that everything here that you've seen is modeled using R and especially the MLR framework and for the feature selection we use Scikit-learn library in Python and we use log loss because we have different data set for different species so the proportion between presence and absence data is constantly changing and to avoid this uncertainty in how the probabilities can be predicted we use log loss instead of overall accuracy or other metrics to assess how the features can be selected. So the process goes by using a random forest classifier with a 5-fold spatial cross-validation and recursive feature elimination we checked the first deep in the values of log loss across all the covariates and that's the value of the covariates that is used in each model for each species. To have consistency in this we replicate this procedure five times for each species and we then took the average of the number that we get in the first deep for each species. And now you can see how this map looks like in the development server. They are not completely online, they will be online I think by tomorrow most of the species will be online so you can also check by yourselves and that's how this species looks like. Yeah this development server let me ask this. So we can for example check the probability distribution for the Norway spruce which is the same that was available in the slides. You can see the legend the colors go from probabilities that below 10% to 90% we can recover the whole Europe and our time frame as I mentioned it goes from 2000 to 2019. So you can check somewhere like in the length cover classification points and check for example the probability distribution for the forest species to be there. We also have points available so you can check and we also have a compare tool where we can actually compare the actual distribution which is modeled using reflectance so you can actually exclude CDs or also farming sites or other urban kind of length cover classification with instead probability distribution as I mentioned and they show in the one of the first slides in the geographical space usually the potential distribution is very much bigger than the actual distribution and that's something that you can check here with the slider. As you can see the condition for the species to be present in a specific area are already available for a lot of areas while instead the actual distribution of the species is very much smaller. Yeah because we do not include anything that does that's to do with urbanization or entropization in the environment so everything is basically excluded for the time frame that we are actually analysing. Yeah let's see for example Scandinavia. Here for example yes we have the conditions for the norwespers to be present but this is actually one of the species that is less occurring in the southernmost part of Sweden because it's mostly covered with broadleafs or for example here in Germany we can see that it's the same or any kind of patients or the Alps. This area for example. You can see the difference and it's kind of makes sense of course because it's from expert knowledge you can actually say that the top of the mountains or the glaciers is not an area where you would actually see the species or where the cities are. Yeah so this is to turn off the comparison map so as we said we focus on usability of the maps so what we actually provide is a probability of values for where one pixel for example can show the potential distribution of one species but we also provide for each layer an uncertainty map that shows okay should I use this value is it reliable or not. So we have a scale that here shows the variance based on the procedure that Tom explained in the first days of the training session so we use correction factor for the ensemble models to avoid for example the extrapolation problem that we talked about in the training sessions so you can actually check for each pixel or each area that you're interested in if the values that we provide have a high tolerance in terms of reliability or not. And I think that's about it so if you have any question please. Let's see the chart. Anything on YouTube or mother most? Yup. I don't know if you hear that one. Obviously, thezać actively. So in general terms when thePlastic,lers out front. Yeah. On a long term scale we know that for example this is not possible we also provide uncertainty maps that we can say that that value for the specific here is probably due to noise, or also we can another thing that we can use on a long term scale is actually remove the noise by using trend analysis. So that's also some post processing that we could do. So we could provide potentially raw values for the maps and also post-processed maps. Oh, let's see. I see new messages in the chat. Yeah. Okay. Okay, that's a there's a question from Pablo, which says it is only possible to model part of the actual distribution if you consider a biotic biotic or movement limits. But we do we use this kind of predictor after the feature selection of the session and which variables are in there. Okay, so the maybe something that wasn't clear during my presentation is that we don't have one model that models all the species, each species as its own model, its feature selection, optimization and part. So we have to check basically in every in every model which ones were used or not. But yes, for example, the a biotic and a biotic is always there like climatic variables are always in there. And we also can see the differences between higher resolution layer that we use as predictors, for example, the DTM sort of the slopes that are highly available, or for example, long term a biotic covariates like the bio clean that about very course resolution. And other things that we that pops up in terms of covariates for almost all the species is the reflectance data, especially for the, for the, for the actual distribution because those are can actually help us exclude all those areas that instead are covered by cities. And in the potential distribution we also have some layers that come from the University of Maryland the show for example the barren areas of the Berlin, and that for some species actually comes up as one of the first covariates so we can actually exclude very quickly all the areas that do not show vegetation. But yes, we will publish the paper which is now in writing will provide like data for each model and you can see which ones are the covariates that actually pops up at the most important for every species. Oh, yeah, okay, yeah, this is another question is just an idea. Yeah, yeah. Yeah, someone here in the chat was suggesting for example to connect with JRC, we try to do that but they also have the same problem as us the amount of points that are available to model these kinds of things, the ones that we are using right now this is the highest amount it's publicly available. I mean, just for the audience. Yeah, it's possible. It's not a problem we have the computing infrastructure to do that. It would be also wiser to not use all of the million of points that you were mentioning like we could use most of them to validate our mix for example, which is looking for an independent data set in terms of trees and especially if you are modeling or forecasting is always a problem. So using all these points for example for as an independent data set in terms of sampling bias or everything could be also useful to make the maps more accurate. Yeah, that's fine. Yeah, basically that's a good question. You can first check the areas that you're interested in. And then in this area you can check which ones of the species that we model have a high value of probability in terms of actual distribution. So we, from that you can say okay these are the species that will actually do well in this area. And then based on your interest then on your timeframe interest so for example if you want to have like short rotation for a serial if you want to plan for like 100 years. And also what you're interested in which kind of forest do you want is that just production. Do you want do you have interest for example in protection of the area for example like forest that are planted in high in the mountains to protect against no fall and all these kind of problems. So that depends. But yeah you can, you can for sure use the maps as having a kind of an overview of the trees that you can plant there.
distribution either potential or actual of will talk about species distribution modeling on Europe, plus in terms of time on a time coverage that goes from 2000 to 2020. So let me also move considering all the conditions we see in the environment the species could live, and we can outputs that we can get from species distribution climatic covariates both on a long term scale and a way for us to include the interspecific across all the tiles for all the species in different machine learning algorithms without the species that we are trying to model which at The results, so the parameters that we got in the feature space for all these three learners were we use for the base learners of the ensemble process goes by using a random forest classifier probability distribution for the forest species have a high value of probability in terms of