Combining community science and MaxEnt modeling to estimate Wild Turkey ( Meleagris gallopavo ) winter abundance and distribution

Abstract

Understanding the distribution and abundance of species is a fundamental aspect of conservation biology. Species distribution models aim to predict distributions based on species observations and ecologically relevant information. To understand the contemporary distribution of Wild Turkeys (<em>Meleagris gallopavo</em>) in Ontario, we curated and collated Wild Turkey flock observations from eBird and iNaturalist submitted during winter 2018. We combined these with environmental predictors to build distribution models using MaxEnt and evaluated model fit using 10-fold cross validation. We also estimated total population size for this species under different modeling scenarios. The potential presence of unknown spatial bias in community science datasets is a complex problem often requiring context-specific statistical solutions. Data cleaning, sometimes referred to as thinning, filtering, or culling, is often proposed to manage this bias. As such, we tested the effect of data cleaning on model outputs and on subsequent analyses. We evaluated all models using area under the curve (AUC). We found building density to be the most important environmental variable followed by winter severity. We validated our habitat suitability estimates using fine-scale GPS data and found that data cleaning had no effect on habitat suitability estimates inside available Wild Turkey habitat or inside core-use areas, except at one site in 2012 (t = -2.2, P = 0.04, df = 14). Use of community collected data offers a cost-efficient and collaborative method to obtain data for species distribution modeling and management. We discuss implications for Wild Turkey management and present potential contemporary distribution maps for this species.