ENM GARP 1

This is the GARP XXXXX that's for genetic XXXX and rules of prodution although I have changed the name to roots of prediction and this was developed in the eighties or nineties by XXXXXXX from Australia and this is a XXXXXXX procedure that wasn't XXXXX for different XXXXXXXX and it was implemented for niche modellinmg by XXXXXXXX but the general idea of genetic algorithms is that there is XXXXXX methods or disXXXXX and error methods for solutions of very complex problems for which deterministic methods like statistical methods are inefficient in the sense that could be large large months of data for analysis they are called genetic algorithms because they inspire in the process of natural selection in the sense that they evaluate a model XXX puts XXXX on the original models and then select the most fit one and discard the ones that are not performing well. we'll see, all the steps that I XXXXX producing a niche model in regard XXXXXXXX one of the XXXXXX that I have seen in the development of this field is that more and more soft work are available which is good but because XXXX become very easy to use by general users then comes a black box.. do you know what's inside the XXXXXX? this is the case for GARP. It was very popular like five ten years ago and many people started to use it, but most of the people were not knowledgeable about how it works so they weren't very efficient users of this software the steps of XXXXXXX. let's sayyou have a set of hundred data for one animal the first step is that usually for XXXXXXX into 2 sets One for XXXXX the role model, the joining set and the other set is for external XXXXXX of the model so you are not going to use that set of points in any part of the process. we'll have varient or conjugative XXXXXX from the training data, that's the second step, XXXXX the system with samples of these data let's say you kept XXXXX 50 of the 100 points if you sample these fifty points to 1250 presence data and it takes from the rest of the study area where you don't have XXXXX, another 1250 pixels known as pseudo absences it's assuming that in these areas you don't have to XXXXXX on species it's an absence of. if you have a full set of 2500 points of presences and absences it doesn't matter how many points you have in your initial set your own points, you end up with 2500 points of presence and absence then split this whole set of 2500 in half and use one half for training model and the other half for internal testing of the model so the first step for creating the niche model is that these internal drawing points are used to characterize the conditions in which you can find these presences and absences in the form of rules that is called in GARP which are these if then statements and it produces these rules on the four different methods three forms of XXXXX XXXX such as lightning and XXXXXXX of a logistic regression so in a way GARP is a meta algorithm because it uses all the algorithms like these ones to produce niche model and then this is a genetic algorithm to modify these original rules so it produces a set of tens, I don't know, it could be one of these rules with the joining points then it uses the internal XXXXXnts X points XXXXXX data sets to see how well these rules predict XXXXXXX points here the confusion matrix, (we'll see what is a confusion matrix) In a sense what it does is to see how many parts of presence and absence XXXXXX predicted by this XXXXXX Basic process course on percentage points will predict presences and absences it keeps like a qualification for ps great for each on of the groups and builds a set of XXXX or XXXXX of these rules sorted in terms of the performance, the rules are performed best at the top and so forth. then the entrance to the genetic algorithms. the genetic algorithm XXXXXX is to input variation to these rules see for example if we have here the rule that says "XXXXXX between 10 and 20 XXXX will be this" and precipitation is between 250 and 200, and the elevation is between 1000 and 2000 then, this species is present that's a rule. That's climatic XXXXXX the genetical XXXX is changed in XXXXXX bodies XXXXXXXXXXXXXXX there are different rules for changing the XXXXX XXXXX certainly by deletions, deletions by plant location etc but the idea is to input some XXXXX into these rules and then what it does is that it XXXXXXXXX the performance of this new rule, this XXXXXX if the XXXXXXX rule performs well than the modern then it substitutes this rule in the rules and XXXXXXX if it doesn't you just scrap that this isn't a rule so if you do this process of rule generation or evaluation of substitutional relation for many number of interactions as you said as elusive or until each converges, which means that it doesn't improve the whole performance of the XXXXXXXX if you study the process you'll see that intiation of the process you will improve and improve your prediction capacity well, there is a point in which you don't improve in this prediction capacity it stops at that point and it ends up with a set of these "if" "then" rules that characterizes the conditions for the species presence and absence then with this set of rules goes to the geographic space and asksto each one of the peices if it needs conditions XXXXX by the rules and gives the communication of presence or absence to each one of the XXXXXXX depending on the conditions of the biomes or XXXXXXXXX so the final call of GARP more than in process is in binary math in math that says species is present or species is absent in contrast to what XXXXXXXXXX produces this is only a binary well, there are 2 implementations of GARP this is the best of GARP, this is a stand alone software that we used through the last few years and there is an implementation as you saw XXXXXXXXXXXXX actually nothing more than there are 4 implementations of GARP it's well known to use the version implemented in novel model XXXXXXX well, there is something else that's important for you to know XXXXXXXXXX run processes in their own generation of niche model since the very beginning of the XXXXXXXXXXX then the generation of the rules also is running process every time you run a model in GARP with subtle XXXX of them you get a somehow different math so that could become a problem because you save which of this bunch of maps or which one of the 100 maps is the good one which one is not good so XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX selecting the models that are more informative based on the predictive capacity the way this implementation works is based on the evaluation of the errors of the model. when you produce a model there are 2 ways that the model can run when it fails to predict the known presence which is XXXXXXXXX falls outside the prediction that's called a XXXXXXXXXXXXX and the other way you can face is when it over-predicts or it fails to predict the absences when the model says that this species should be present and XXXXXXX absent it does form the commissioner and that's evaluating these confusion makers we use this information to evaluate the quality of the models