Improving the ability to manage and forecast ecological processes would enhance conservation practices and further our understanding of the natural world. Ecology is transitioning from a discipline that was once data poor to a discipline that is increasingly data rich. With the aggregation of data into large repositories, significant investments in long-term ecological monitoring networks, and the development of richly detailed process-based simulators, ecologists need new tools to support the analysis of extensive data sets. Recently, scientists outside of ecology have used neural network models to solve formerly intractable problems characterized by large data sets. Ecologists have started to use neural networks to make progress on challenging questions, but the majority of this work has been limited to automated monitoring. In this dissertation, I explore applications of neural network models for conservation decision-making and ecological forecasting. Chapter 2 presents how concepts and methods taken from the field of reinforcement learning can be used to solve decision-making problems in conservation. Chapter 3 investigates the ability of neural network models to forecast critical transitions observable in ecological systems. And, lastly, in Chapter 4, I compare the forecasting performance of neural network models on water quality data taken from the National Ecological Observatory Network. Together, these chapters demonstrate that neural networks have the capacity to provide novel insights on ecological processes.