Insect pollinators are responsible for at least a part of the pollination of more than 90 % of wild flowering plants and 75% of food crops worldwide (IPBES, 2016). Among those, wild bees are known to participate in the pollination of a wide variety of flowering plants and are declining at alarming rates (Cameron et al., 2011; Potts et al., 2010). To predict the pollination potential in a landscape, a comprehensive knowledge of their foraging movement behavior is essential. Thus, in the majority of the models aiming to estimate pollination at a landscape scale, the probability of discovering a resource mainly depends on the distance to the nest without considering landscape heterogeneity.

Animal movement has been modeled by a large variety of algorithms. Among them, the stochastic movement simulator (SMS) presents better performances in estimating relative connectivity (Palmer et al., 2011). This method is based on a series of sequential movement decisions incorporating path memory, a directional parameter, a perceptual range and the movement cost, allowing to take into account the spatial arrangement of the landscape.

This study aims to evaluate the contribution of landscape heterogeneity (composition and configuration) on wild bee's pollination services estimates using a “central place forager” (CPF) model and SMS. First, the role of the landscape heterogeneity on the pollination estimates was investigated by performing simulations on a set of virtual landscapes to cover a gradient of fragmentation, patch isolation, and composition. The results obtained by the CPF-SMS model were compared to those obtained by a distance-weighted kernel model (InVEST Lonsdorf et al., 2009). In the second part, the model sensitivity to path memory, directional and perceptual range parameters was explored using a real agricultural landscape located in the long-term Socio-Ecological Research site PYGAR (Occitanie, France) and two species differing by their size and movement ability (Lasioglossum marginatum and Andrena flavipes). The preliminary results show large differences in pollination estimates for intermediate fragmented landscapes. CPF-SMS model is sensitive to the memory path by increasing the total number of passage whereas the perceptual parameter augmentation slightly decreases the number of passage. Finally, the model's estimates where compared to an abundance dataset containing wild bee's captured from three trapping sessions in April 2016 (30 traps), 2017 (10 traps) and 2019 (16 traps) in order to select the best parameters values for each model species. The results showed that a different set of parameters gives the best capture rates estimates for each study species.