Advances in understanding avian nesting ecology are hindered by a prevalent lack of agreement between nest-site characteristics and fitness metrics such as nest success. We posit this is a result of inconsistent and improper timing of nest-site vegetation measurements. Therefore, we evaluated how the timing of nest vegetation measurement influences the estimated effects of vegetation structure on nest survival. We simulated phenological changes in nest-site vegetation growth over a typical nesting season and modeled how the timing of measuring that vegetation, relative to nest fate, creates bias in conclusions regarding its influence on nest survival. We modeled the bias associated with four methods of measuring nest-site vegetation: Method 1-measuring at nest initiation, Method 2-measuring at nest termination regardless of fate, Method 3-measuring at nest termination for successful nests and at estimated completion for unsuccessful nests, and Method 4-measuring at nest termination regardless of fate while also accounting for initiation date. We quantified and compared bias for each method for varying simulated effects, ranked models for each method using AIC, and calculated the proportion of simulations in which each model (measurement method) was selected as the best model. Our results indicate that the risk of drawing an erroneous or spurious conclusion was present in all methods but greater with Method 2 which is the most common method reported in the literature. Methods 1 and 3 were similarly less biased. Method 4 provided no additional value as bias was similar to Method 2 for all scenarios. While Method 1 is seldom practical to collect in the field, Method 3 is logistically practical and minimizes inherent bias. Implementation of Method 3 will facilitate estimating the effect of nest-site vegetation on survival, in the least biased way, and allow reliable conclusions to be drawn.
Keywords: grasslands; logistic exposure; measurement bias; nesting ecology; nest‐site selection; simulation; vegetation structure.