Behavioral strategies and the spatial pattern formation of nesting

This dataset contains data from a combined field and simulation study regarding spatial pattern formation of nesting, described in the paper: "Batsleer, F., Maes, D., Bonte, D. (2021) Behavioral strategies and the spatial pattern formation of nesting. The American Naturalist". The study investigates the relative importance of environmental and behavioral mechanisms in nest aggregations of the ground-nesting digger wasp Bembix rostrata. A field study was combined with an individual-based model that simulated the possible behaviors of spatial organisation of nesting. In the first analysis, a microhabitat model was built based on the location of the nests from a capture-mark-recapture study (CMR) and environmental variables NDVI (vegetation) and insolation (sun irradiance), derived from detailed remote sensing data from a drone flight. In the second analysis, an IBM was built that combined three possible mechanisms of nest choice to simulate the emerging spatial and network patterns found in the field. This was done by combining an environmental cue (based on the microhabitat model) and two relevant behavioral mechanisms related to local site fidelity and conspecific attraction. Strengths and combinations of the mechanisms could vary. Simulations were compared to the field data to find which combinations and strengths of mechanisms can best explain the emerging spatial and network patterns. The main results are that 1) the observed pattern in nature is best predicted by the simultaneous effect of a weak environmental cue and strong behavioral mechanisms. 2) individuals differ in their combination of mechanisms used and will either use local site fidelity (personal information) or conspecific attraction (inadvertent social information), but not both simultaneously. 3) We demonstrate that the nest pattern formation of a central place foraging insect cannot be considered as the sum of environmental and behavioral mechanisms.The readme file contains a description of each of the data files, a list of variables and their definitions in each data file, missing data codes for each data file and how the data files are related to each other. Information regarding the separate analyses and details about field study and individual-based model (IBM) can be found in the associated manuscript referenced above, including its supplementary materials (including ODD-protocol for IBM). Code can be found at http://doi.org/10.5281/zenodo.5212680 Funding provided by: Fonds Wetenschappelijk OnderzoekCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100003130Award Number:The field study was collected during a capture-mark-recapture (CMR) study at the Belgian coast (De Panne, Westhoek nature reserve) and the IBM was built at the Terrestrial Ecology Unit (TEREC) of Ghent University, with simulations ran on computational resources (Stevin Supercomputer Infrastructure) and services provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO and the Flemish Government. The microhabitat model was build using a GLMM with INLA, to adjust for spatial autocorrelation, that explained where nests are present according to NDVI (vegetation) and insolation (sun irradiance). An IBM was built in Python and simulations were compared with the field data using Approximate Bayesian Computation (ABC) analysis. This produced a MS accepted for publication in The American Naturalist