vol. 171, no. 5 the american naturalist may 2008  An Empirical Test of a Diffusion Model: Predicting Clouded Apollo Movements in a Novel Environment Otso Ovaskainen, 1,* Miska Luoto, 2,3,† Iiro Ikonen, 4‡ Hanna Rekola, 5,§ Evgeniy Meyke, 1,k and Mikko Kuussaari 3,# 1. Department of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, FI-00014 Helsinki, Finland; 2. Department of Geography, University of Oulu, P.O. Box 3000, FI-90014 Oulu, Finland; 3. Finnish Environment Institute, Research Program for Biodiversity, P.O. Box 140, FI-00251 Helsinki, Finland; 4. Southwest Finland Regional Environment Centre, P.O. Box 47, FI-20801 Turku, Finland; 5. Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland Submitted April 13, 2007; Accepted September 28, 2007; Electronically published March 17, 2008 Online enhancements: appendixes. abstract: Functional connectivity is a fundamental concept in con- servation biology because it sets the level of migration and gene ﬂow among local populations. However, functional connectivity is difﬁcult to measure, largely because it is hard to acquire and analyze move- ment data from heterogeneous landscapes. Here we apply a Bayesian state-space framework to parameterize a diffusion-based movement model using capture-recapture data on the endangered clouded apollo butterﬂy. We test whether the model is able to disentangle the inherent movement behavior of the species from landscape structure and sampling artifacts, which is a necessity if the model is to be used to examine how movements depend on landscape structure. We show that this is the case by demonstrating that the model, parameterized with data from a reference landscape, correctly predicts movements in a structurally different landscape. In particular, the model helps to explain why a movement corridor that was constructed as a man- agement measure failed to increase movement among local popu- lations. We illustrate how the parameterized model can be used to * Corresponding author; e-mail: otso.ovaskainen@helsinki.ﬁ. † E-mail: miska.luoto@oulu.ﬁ. ‡ E-mail: iiro.ikonen@ymparisto.ﬁ. § E-mail: hanna.rekola@helsinki.ﬁ. k E-mail: evgeniy.meyke@helsinki.ﬁ. # E-mail: mikko.kuussaari@ymparisto.ﬁ. Am. Nat. 2008. Vol. 171, pp. 610–619.  2008 by The University of Chicago. 0003-0147/2008/17105-42541$15.00. All rights reserved. DOI: 10.1086/587070 derive biologically relevant measures of functional connectivity, thus linking movement data with models of spatial population dynamics. Keywords: animal movement, capture-recapture, random walk, dif- fusion, corridor, connectivity. All species disperse, and the dispersal strategy adopted by a given species has fundamental consequences for its eco- logical, genetic, and evolutionary dynamics (Turchin 1998; Clobert et al. 2001; Hanski and Gaggiotti 2004; Sugden and Pennisi 2006). Dispersal combined with other popu- lation dynamic processes can generate complex dynamics and spatial patterns, even without any environmental het- erogeneity (Sole ´ and Bascompte 2006) or in the context of simple descriptions of landscape structure, such as the meta- population concept (Hanski 1998). In heterogeneous en- vironments, the redistribution of individuals also depends on the structure of the underlying landscape, leading to a wide variety of possible outcomes (Goodwin and Fahrig 2002). Landscape ecological research focuses on the interplay between environmental heterogeneity and ecological pro- cesses, especially animal movement (e.g., Taylor et al. 1993; Tischendorf and Fahrig 2000; With 2004; Tischendorf et al. 2005). The rate of movement is associated with the concept of connectivity (Taylor et al. 1993), the exact meaning of which has been heavily debated, partly because connectivity has been deﬁned differently in different ﬁelds (Moilanen and Hanski 2001; Tischendorf and Fahrig 2001). In landscape ecology, the central concept is land- scape connectivity, which is a property of an entire land- scape (Tischendorf and Fahrig 2000). In metapopulation biology, the focus is on patch connectivity, which is a property of a particular patch within the landscape (Moi- lanen and Nieminen 2002). Measures of landscape con- nectivity can sometimes be obtained by averaging the cor- responding measures of patch connectivity over the entire landscape. Connectivity measures can also be classiﬁed by structural and functional measures (Goodwin 2003). Structural measures are based solely on the physical land- scape structure, whereas functional measures are derived