Calibrating an individual-based movement model to predict functional connectivity for little owls
Abstract
Dispersal is crucial for population viability and thus a popular target for conservation measures. However, the ability of individuals to move between habitat patches is notoriously 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 dispersal models using a hybrid of statistical parameter estimation and Approximate Bayesian Computation (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 solution 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.
What the paper shows and why it matters
(AI-generated)
Functional connectivity, the ability of individuals to move between habitat patches, matters for conservation but is hard to estimate. The usual shortcuts, least-cost paths and circuit theory, give a rough picture of corridors but ignore what is known about the species and assume that dispersing animals know the whole landscape. Movement models that follow each animal as an individual avoid this assumption, but they have no tractable formula for how likely the observed data are under given parameters, so likelihood-based statistics cannot fit them, particularly to irregularly timed tracking data. Approximate Bayesian Computation fits such simulation models by keeping parameter values whose simulations resemble the data, but its cost rises quickly with the number of parameters and it needs well-chosen summary statistics, for which there was little guidance. The paper’s aim is to calibrate such a model to real tracking data feasibly and efficiently.
The paper shows that a realistic individual-based model of little owl dispersal can be fitted to irregularly timed tracking data by splitting its parameters: habitat preference is estimated first with an ordinary statistical model, and only four movement parameters are calibrated with Approximate Bayesian Computation (ABC). A random forest (a machine-learning method) compresses the 49 summary statistics into four, one per parameter, instead of a linear least-squares regression. On simulated data with known parameters, the random forest summaries gave estimates close to the true values, with clearly narrower 80% credible intervals than least squares. As the worked example, the study uses 126 radio-tracked juvenile little owls in southwestern Germany. Females made longer and more directed flights than males. Simulations showed narrow dispersal corridors into northern Switzerland and only a slow build-up of simulated dispersers there.
The study offers this two-step procedure (statistical habitat model, then ABC with random-forest summaries) as a workflow for assessing dispersal connectivity across species, one that serves inference about behaviour and prediction of connectivity at once. The authors describe it modestly, as an example with a few smaller innovations. For the little owl, the results suggest that recolonisation of northern Switzerland is possible, but that without habitat improvement along the corridors it may not happen soon. The model covers only the movement phase and leaves out demography, so it cannot give recolonisation times.