Predictive models in ecology: Comparison of performances and assessment of applicability

Date

2006-04-01

Author

Tan, Can Ozan
Ozesmi, Uygar
Beklioğlu, Meryem
Per, Esra
Kurt, Bahtiyar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Ecological systems are governed by complex interactions which are mainly nonlinear. In order to capture the inherent complexity and nonlinearity of ecological, and in general biological systems, empirical models recently gained popularity. However, although these models, particularly connectionist approaches such as multilayered backpropagation networks, are commonly applied as predictive models in ecology to a wide variety of ecosystems and questions, there are no studies to date aiming to assess the performance, both in terms of data fitting and generalizability, and applicability of empirical models in ecology. Our aim is hence to provide an overview for nature of the wide range of the data sets and predictive variables, from both aquatic and terrestrial ecosystems with different scales of time-dependent dynamics, and the applicability and robustness of predictive modeling methods on such data sets by comparing different empirical modeling approaches. The models used in this study range from predicting the occurrence of submerged plants in shallow lakes to predicting nest occurrence of bird species from environmental variables and satellite images. The methods considered include k-nearest neighbor (k-NN), linear and quadratic discriminant analysis (LDA and QDA), generalized linear models (GLM) feedforward multilayer backpropagation networks and pseudo-supervised network ARTMAP.

Subject Keywords

Ecological Modelling, Ecology, Modelling and Simulation, Computational Theory and Mathematics, Applied Mathematics, Ecology, Evolution, Behavior and Systematics, Computer Science Applications

URI

https://hdl.handle.net/11511/35574

Collections

Department of Biology, Article