Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence"

This thesis bridges the gap between field observations and the empirical understanding of ecological systems by applying AI models at different spatial scales and hierarchical levels to study ecological complexity. The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.

Firstly, a Python package HSC3D, was developed to quantify habitat structural complexity (HSC) at the community level. Built with machine learning algorithms and novel, scale-invariant metrics, this package provides more precise, scale-invariant representations of HSC than traditional approaches, which can be applied to a variety of habitats in different ecosystems.

Secondly, at the population level, deep learning (DL) models were benchmarked to inform the best practices in quantifying distribution patterns of intertidal mussels over large spatial scales. By classifying and segmenting mussels at the pixel level, these DL models reduced both false positive samples and user workload, providing an efficient and accurate transferable approach for large scale population surveys. DL models, in combination with novel species annotation strategies, were applied to address a perennial challenge in the identification of populations of invasive mussels from natural populations. This methodology was also adapted for use on mobile phones, allowing users to conduct in-situ surveys and post-hoc analysis, and can be modified for broad accessibility, facilitating immediate application in ecological fieldwork.

Lastly, extending to the individual level, explainable AI techniques were employed in the detection of subtidal sea cucumbers. By visualising morphological features important for model predictions, XAI verified that AI decisions align with biologically meaningful traits, thus empowering transparent, individual-based assessments that can also be generalised to other ecosystems.