Summary Hodgson et al. [Journal of Applied Ecology 46 (2009) 964] argue that connectivity is complex and uncertain, that it can be improved incidentally by increasing habitat extent, and that connectivity conservation is unlikely to be effective under climate change. 2. We believe that they have overlooked recent research on dispersal behaviour and structural connectivity, which has improved our understanding of functional connectivity and revealed that it will not necessarily increase with habitat extent. 3. New modelling techniques including least-cost path models incorporate this more detailed understanding of connectivity into conservation planning, facilitating the true aim of connectivity conservation -to ensure appropriate interactions between habitat extent, quality and connectivity. 4. Synthesis and applications. Advances in behavioural research and modelling techniques allow us to manage structural connectivity with as much certainty as we manage extent and quality of habitat. Successful landscape conservation to address both current threats and future climate change must manage these three elements in concert.

Most ecological and evolutionary processes are thought to critically depend on dispersal and individual movement but there is little empirical information on the movement strategies used by animals to find resources. In particular, it is unclear whether behavioural variation exists at all scales, or whether behavioural decisions are primarily made at small spatial scales and thus broad-scale patterns of movement simply reflect underlying resource distributions. We evaluated animal movement responses to variable resource distributions using the grey teal (Anas gracilis) in agricultural and desert landscapes in Australia as a model system. Birds in the two landscapes differed in the fractal dimension of their movement paths, with teal in the desert landscape moving less tortuously overall than their counterparts in the agricultural landscape. However, the most striking result was the high levels of individual variability in movement strategies, with different animals exhibiting different responses to the same resources. Teal in the agricultural basin moved with both high and low tortuosity, while teal in the desert basin primarily moved using low levels of tortuosity. These results call into question the idea that broad-scale movement patterns simply reflect underlying resource distributions, and suggest that movement responses in some animals may be behaviourally complex regardless of the spatial scale over which movement occurs.

A complete understanding of animal dispersal requires knowledge not only of its consequences at population and community levels, but also of the behavioural decisions made by dispersing individuals. Recent theoretical work has emphasised the importance of this dispersal process, particularly the phase in which individuals search the landscape for breeding opportunities. However, empirical advances are currently hampered by a lack of tools for quantifying these dispersal search tactics. Here, we review existing methods for quantifying movement that are appropriate for the dispersal search process, describe several new techniques that we developed for characterising movement and behaviour through an individual's dispersal range, and illustrate their use with data from Australasian treecreepers (Climacteridae). We also describe how the quantitative parameters we discuss are calculated in a freely available computer software package that we designed. Specifically, we present methods for calculating the area searched during dispersal, search rate, thoroughness, intensity, philopatry of search, timing of exploration, and surreptitiousness. When we applied this approach to the study of dispersal in treecreepers, we found that search area, philopatry and timing of exploration showed the greatest individual variation. Furthermore, search area, search rate, thoroughness and philopatry of search were all correlated, suggesting they may be useful parameters for further research on the causes and consequences of different dispersal search tactics. Finally, we make recommendations for modifying radiotracking protocols to facilitate more accurate assessment of individual variation in the dispersal process, and suggest future directions for this type of empirical work at the interface of population and behavioural ecology.

The use of fractal analysis to study animal movement paths has been criticized because the inherent assumptions of the technique are rarely discussed, and most movement paths violate the assumption of scale invariance. While this violation may prohibit the use of the technique for population‐level prediction, it need not restrict the analysis of individual variation in movement patterns, an application of fractal theory that has received relatively little research attention. Therefore, we review fractal analysis and its assumptions, highlighting three ways in which it can yield useful information about individual movement paths regardless of whether or not the assumption of scale invariance has been met. We used these techniques to analyze patterns of individual variation and potential causes of variation in the dispersal searching paths of two species of Australian treecreeper (Passeriformes: Climacteridae). By comparing relative fractal D, or the relative tortuosity and thus thoroughness of search paths, we found that individuals faced a trade‐off between thoroughness and the extent of searching. Thoroughness also differed between the sexes and the species, possibly as a direct consequence of mating and social systems. For almost all individuals, thoroughness varied depending on the spatial scale at which it was examined, revealing three distinct domains of scale in which movement tactics vary because movement is used for very different purposes. Variability in movement tactics was greatest in the largest spatial‐scale domain, the one used exclusively for dispersal movements, suggesting that dispersal tactics show more intraspecific variation than other types of movement because dispersal decisions are influenced by a greater variety of factors. Our results reveal that fractal analysis can provide useful information on the causes of and constraints on individual movement strategies, creating empirically based models of animal movement and thus a firm foundation for modeling movement processes from individual to landscape scales.

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