Improving the Prediction Accuracy of Species Distribution Models using Artificial Neural Networks
Creators: name: Perrin, David R.
Publisher: name: The University of Texas at Austin
Year: 2020
Subjects: Artificial Neural Network
Fishes
Species Distribution Models

description: required statistics class paper, with notes on further development of the project subsequent to submission for a class grade
description: Modeling the distribution of aquatic species is a challenging task for scientists due to the inherent randomness of fish behavior as well as the complexity of aquatic ecosystems. While traditional modeling methods have been somewhat effective, there is much room for improvement. To improve the prediction accuracy, an artificial neural network (ANN) is proposed. ANNs excel at analyzing data which is complex, unbalanced, and nonlinear, such as ecological data, however they do suffer from issues such as overfitting the data and making overconfident predictions. This is especially common when analyzing smaller datasets. I show that these issues can be avoided to some extent, and that ANNs have strong potential for use in species distribution modeling. Additionally, the ANN is compared to another common method for species distribution modeling. The ANN performed as well as or better than the other model, however there were still some flaws. Finally, the network calculated the probability that a chosen fish species will be found within 2 kilometers of background points generated along the Brazos and Colorado Rivers. Update as of February 10th, 2020 Since the writing of the original paper, several changes have been made to the model. As I was limited on time and the total page count as per the rules set in place by the SDS department, I had to make some sacrifices. That said, the results have improved substantially as I’ve had more time to drill down on some of the issues. The population size has increased from 7,311 to 20,093, and the study area now includes areas of New Mexico, Oklahoma, Arkansas, and Louisiana. By the time the data processing is complete, I expect the total population size to be somewhere around 200,000. The prediction accuracy has increased substantially as well. With some species, such as H. placitus, the network achieves a 93% level of accuracy. When looking at the loss curves, the validation and training loss now converge for all species, which implies that the neural network is neither over nor under fitting the data. In other words, it’s in “the Goldilocks Zone”. Additionally, some of the species which were harder for the network to model, such as I. furcatus and M. hyostoma, can now be modeled and predicted with very promising results. However, with some species, the prediction accuracy has decreased. This is particularly prevalent in some of the predatory species, although I am fairly confident that adding a copredictor species (i.e. a common forage fish) will resolve the issue. There have also been some changes to the data preprocessing methods used which eliminate the network from “getting a peek” at the testing set. The standardization process (which means scaling the data so that it fits a standard normal distribution with a mean of 0 and a standard deviation of ± 1) is now carried out after splitting the data into the training, validation, and testing sets. Otherwise, the probability distribution of the data is already known, making it more difficult to generalize when given a completely fresh set of data. Additionally, the initial bias is now factored in, which increases efficiency during the training phase. The SHAP library (available for both Python and R) was used to analyze feature importance. Neural networks are considered a “black box” method for analysis, and by calculating the Shapley Values, one can gain further insight into what the model considers to be important. This method originates from coalitional game theory, and essentially treats the explanatory variables as a “team”. By turning variables on and off randomly, SHAP determines the combination of variables which contribute the most towards accurately predicting the species of interest. A model agnostic technique is used to analyze the model. As this is essentially a brute-force method, the k-means clustering algorithm is now used summarize the explanatory variables in order to increase computational efficiency and accuracy. These results were available when I submitted the paper, but once again, due to page constraints I had to leave them out despite their importance.