Integrating ecology, psychology and neurobiology within a food-hoarding paradigm.

Many animals regularly hoard food for future use, which appears to be an important adaptation to a seasonally and/or unpredictably changing environment. This food-hoarding paradigm is an excellent example of a natural system that has broadly influenced both theoretical and empirical work in the field of biology. The food-hoarding paradigm has played a major role in the conceptual framework of numerous fields from ecology (e.g. plant-animal interactions) and evolution (e.g. the coevolution of caching, spatial memory and the hippocampus) to psychology (e.g. memory and cognition) and neurobiology (e.g. neurogenesis and the neurobiology of learning and memory). Many food-hoarding animals retrieve caches by using spatial memory. This memory-based behavioural system has the inherent advantage of being tractable for study in both the field and laboratory and has been shaped by natural selection, which produces variation with strong fitness consequences in a variety of taxa. Thus, food hoarding is an excellent model for a highly integrative approach to understanding numerous questions across a variety of disciplines. Recently, there has been a surge of interest in the complexity of animal cognition such as future planning and episodic-like-memory as well as in the relationship between memory, the environment and the brain. In addition, new breakthroughs in neurobiology have enhanced our ability to address the mechanisms underlying these behaviours. Consequently, the field is necessarily becoming more integrative by assessing behavioural questions in the context of natural ecological systems and by addressing mechanisms through neurobiology and psychology, but, importantly, within an evolutionary and ecological framework. In this issue, we aim to bring together a series of papers providing a modern synthesis of ecology, psychology, physiology and neurobiology and identifying new directions and developments in the use of food-hoarding animals as a model system.