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Butterfly Monitoring Schemes (BMSs) engage the public in conservation and provide data sets that cover broad geographical areas over long timescales. Most existing BMSs are in temperate climates; however, the Israeli Butterfly Monitoring Scheme (BMS-IL), established in 2009, is a notable exception as it encompasses a large climatic gradient from Euro-Siberian through Mediterranean to hyper-arid regions. Israel's climate poses challenges in analyzing data from year-round butterfly activity, as in other tropical or arid countries. The Regional Generalized Additive Model (Regional GAM) is a butterfly phenology and abundance model based on repeat visits throughout species' flight season. We tested the applicability of Regional GAM for species with complex flight seasonality (e.g., multivoltine) by comparing estimated abundance and seasonal indices for the full data set and rarefied subsets. We assessed the reliability of modeled flight seasons and compared abundance estimates per site resulting from biologically plausible and unreliable seasonal models. The reliability of Regional GAM rises with the number of observations, and the model tends to produce more biologically plausible models for species with simple phenologies (e.g., univoltine with a single peak in activity). Abundance estimates based on unreliable models produce values with inter-quartile ranges of 90%-153% compared with biologically plausible models, while peak time changes with an interquartile range of 0-22.5 d when comparing all rarefied models with the full data set. Regional GAM should be applied with great caution for rare species and those with a complex flight season, and the date of year start needs to be carefully chosen for species that are active year-round. We identified the key sources of error and propose an operational workflow to address them. With few adaptations, Regional GAM can support new BMSs in analyzing data where butterflies are active year-round, including tropical climates. We propose guidelines for analyzing BMS data for species or regions with long activity periods and complex phenologies.

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.