Calibrating an individual-based movement model to predict functional connectivity for little owls SEVERIN HAUENSTEIN , 1,8 JULIEN FATTEBERT , 2,3 MARTIN U. GR € UEBLER , 2 BEAT NAEF-DAENZER , 2 GUY PE’ER , 4,5,6 AND FLORIAN HARTIG 1,7 1 Department of Biometry and Environmental System Analysis, University of Freiburg, 79106 Freiburg, Germany 2 Swiss Ornithological Institute, CH-6204 Sempach, Switzerland 3 School of Life Sciences, University of KwaZulu-Natal, 4000 Durban, South Africa 4 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany 5 Department of Conservation Biology, UFZ–Helmholtz Centre for Environmental Research, Department of Economics and Department Ecosystem Services, 04318 Leipzig, Germany 6 University of Leipzig, 04109 Leipzig, Germany 7 Theoretical Ecology, University of Regensburg, 93053 Regensburg, Germany Citation: Hauenstein, S., J. Fattebert, M. U. Gr€ uebler, B. Naef-Daenzer, G. Pe’er, and F. Har- tig. 2019. Calibrating an individual-based movement model to predict functional connectivity for little owls. Ecological Applications 00(00):e01873. 10.1002/eap.1873 Abstract. Dispersal is crucial for population viability and thus a popular target for conser- vation measures. However, the ability of individuals to move between habitat patches is notori- ously difficult to estimate. One solution is to quantify functional connectivity via realistic individual-based movement models. Such simulation models, however, are difficult to build and even more difficult to parameterize. Here, we use the example of natal little owl (Athene noctua) dispersal to develop a new analysis chain for the calibration of individual-based disper- sal models using a hybrid of statistical parameter estimation and Approximate Bayesian Com- putation (ABC). Specifically, we use locations of 126 radio-tracked juveniles to first estimate habitat utilization by generalized additive models (GAMs) and the biased random bridges (BRB) method. We then include the estimated parameters in a spatially explicit individual- based model (IBM) of little owl dispersal and calibrate further movement parameters using ABC. To derive efficient summary statistics, we use a new dimension reduction method based on random forest (RF) regression. Finally, we use the calibrated IBM to predict the dispersal potential of little owls from local populations in southwestern Germany to suitable habitat patches in northern Switzerland. We show that pre-calibrating habitat preference parameters while inferring movement behavioral parameters via ABC is a computationally efficient solu- tion to obtain a plausible IBM parameterization. We also find that dimension reduction via RF regression outperforms the widely used least squares regression, which we applied as a benchmark approach. Estimated movement parameters for the individuals reveal plausible inter-individual and inter-sexual differences in movement behavior during natal dispersal. In agreement with a sex-biased dispersal distance in little owls, females show longer individual flights and higher directional persistence. Simulations from the fitted model indicate that a (re)colonization of northern Switzerland is generally possible, albeit restricted. We conclude that the presented analysis chain is a sensible work-flow to assess dispersal connectivity across species and ecosystems. It embraces species- and individual-specific behavioral responses to the landscape and allows likelihood-based calibration, despite an irregular sampling design. Our study highlights existing, yet narrow dispersal corridors, which may require enhancements to facilitate a recolonization of little owl habitat patches in northern Switzerland. Key words: Approximate Bayesian Computation; Athene noctua; choosing optimal summary statistics; IBM parametrization; landscape connectivity; movement ecology. INTRODUCTION Understanding and predicting animal dispersal is central for ecology and conservation. One important aspect of dispersal is connectivity: the flow of individuals or genes within or among populations (e.g., Jangjoo et al. 2016), which is crucial for their long-term persistence (Hanski 1998). Estimating the degree of connectedness for particular species, functional groups, or individuals has led to the concept of functional con- nectivity, which recognizes that effective ecological con- nectivity emerges from the interplay of landscape features with species traits (Taylor et al. 1993, Baguette and Van Dyck 2007). Manuscript received 31 August 2018; revised 19 December 2018; accepted 1 February 2019. Corresponding Editor: Marissa L. Baskett. 8 E-mail: severin.hauenstein@biom.uni-freiburg.de Article e01873; page 1 Ecological Applications, 0(0), 2019, e01873 © 2019 by the Ecological Society of America