No abstract

A hallmark of ecological research is dealing with complexity in the systems under investigation. One strategy is to diminish this complexity by constructing models and theories that are general.Alternatively, ecologists can constrain the scope of their generalisations to particular phenomena or types of systems. However, research employing the second strategy is often met with scathing criticism. I offer a theoretical argument in support of moderate generalisations in ecological research, based on the notions of interdependence and causal heterogeneity and their effect on the tradeoff between generality and realism.Acknowledgements: I would like to thank Roberta Millstein, Steve Peck, Chris Eliot, Adrian Currie, an anonymous reviewer and the audience at the 2016 PSA for helpful and constructive comments.

This article reviews the recent literature on idealization, specifically idealization in the course of scientific modeling. We argue that idealization is not a unified concept and that there are three different types of idealization: Galilean, minimalist, and multiple models, each with its own justification. We explore the extent to which idealization is a permanent feature of scientific representation and discuss its implications for debates about scientific realism.

Individuals differ. This seemingly trivial statement has nevertheless led to paradigm shifts, as three different fields of organismal biology have seen a marked change in key concepts over the past few decades. In animal behaviour, it has increasingly been realised that behavioural differences among individuals can be stable over time and across contexts, giving rise to the concept of animal personalities. In ecology, an increasing focus is likewise on the considerable variation in the ecological niche realised by species, populations, and individuals, giving rise to the concept of niche specialisation or individual niche variation. In evolutionary biology, where individual variation has always been central, there is an increasing awareness of the complexity with which individuals interact with the environment in producing unique phenotypes. Recent theoretical and empirical work has highlighted that the fitness landscape is rather complex, with multiple fitness peaks. It depends on the individual with its genotype, in interaction with its specific environment, which local or global fitness peak is attainable. Over the past 15 years, the need for more integrated conceptual frameworks transcending disciplines has been voiced ever more strongly. Whereas initially the ecological time scale was deemed to be fundamentally different from the evolutionary one, this notion has recently been replaced by a more integrative one, where evolution can indeed happen over ecological time scales. While in each of the three fields behaviour, ecology, and evolution, the concept of individualisation has contributed to major scientific progress, sufficient cross-fertilisation is lacking. Here, we propose a new level of conceptual unification: the individualised niche. By merging the niche concept with the fitness and animal personality concepts, new explanatory power for both ecological and evolutionary processes emerges.

Many phenomena in the natural world are complex, so scientists study them through simplified and idealised models. Philosophers of science have sought to explain how these models relate to the world. On most accounts, models do not represent the world directly, but through target systems. However, our knowledge of target systems is incomplete. First, what is the process by which target systems come about? Second, what types of entity are they? I argue that the basic conception of target systems, on which other conceptions depend, is as parts of the world. I outline the process of target system specification and show that it is a crucial step in modelling. I also develop an account of target system evaluation, based on aptness. Paying close attention to target system specification and evaluation can help scientists minimise the frequency and extent of mistakes, when they are using models to investigate phenomena in complex real-world systems.