Window Area and Development Drive Spatial Variation in Bird-Window Collisions in an Urban Landscape

Abstract

Collisions with windows are an important human-related threat to birds in urban landscapes. However, the proximate drivers of collisions are not well understood, and no study has examined spatial variation in mortality in an urban setting. We hypothesized that the number of fatalities at buildings varies with window area and habitat features that influence avian community structure. In 2010 we documented bird-window collisions (BWCs) and characterized avian community structure at 20 buildings in an urban landscape in northwestern Illinois, USA. For each building and season, we conducted 21 daily surveys for carcasses and nine point count surveys to estimate relative abundance, richness, and diversity. Our sampling design was informed by experimentally estimated carcass persistence times and detection probabilities. We used linear and generalized linear mixed models to evaluate how habitat features influenced community structure and how mortality was affected by window area and factors that correlated with community structure. The most-supported model was consistent for all community indices and included effects of season, development, and distance to vegetated lots. BWCs were related positively to window area and negatively to development. We documented mortalities for 16/72 (22%) species (34 total carcasses) recorded at buildings, and BWCs were greater for juveniles than adults. Based on the most-supported model of BWCs, the median number of annual predicted fatalities at study buildings was 3 (range = 0–52). These results suggest that patchily distributed environmental resources and levels of window area in buildings create spatial variation in BWCs within and among urban areas. Current mortality estimates place little emphasis on spatial variation, which precludes a fundamental understanding of the issue. To focus conservation efforts, we illustrate how knowledge of the structural and environmental factors that influence bird-window collisions can be used to predict fatalities in the broader landscape.

Evaluating the Effectiveness of Select Visual Signals to Prevent Bird-window Collisions

“…This awareness has led to calls to determine which types of houses and windows are most problematic, because it is poorly understood why one house has more collisions than another , Loss et al 2014). Having such information would aid in the design of effective mitigation strategies for reducing bird-window collisions (Klem 1989, Dunn 1993, Klem et al 2004, Bayne et al 2012, Hager et al 2013, Klem and Saenger 2013, Kummer et al 2016.…”

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“…If this option is pursued, there are two important pieces of information to consider. First, not all birds are equally vulnerable to hitting windows (Hager et al, 2013;Loss et al, 2014), and variation in bird community structure among regions likely leads to regional variation in the daily timing of collision mortality. Unfortunately, assessing when collisions happen during the day is logistically difficult, and time and labor intensive.…”

“…Note that multiple passes by a single observer may not be statistically independent. See Supplemental Information Text S1, Figure S1, and Table S1 in Hager et al (2013) for methods on how to estimate detection probability for each worker.…”

“…Collisions with building windows are thought to be a significant source of mortality to birds in urban landscapes (Klem, 1989;Bayne et al, 2012;Hager et al, 2013;Machtans et al, 2013;Loss et al, 2014). Much of our understanding of bird-window collisions has been generated from studies that range from incidental mortality observations to those employing systematic sampling and that also accounted for the biases (e.g., scavenging of bird carcasses) that lead to imperfect detection of carcasses (Loss et al, 2014).…”

“…Moreover, supplies and equipment are inexpensive. Applicability of the protocol stems from recent research assessing bird carcass detection next to buildings in urban landscapes (Hager et al, 2012(Hager et al, , 2013. Thus, it reduces bias associated with imperfect detection of carcasses due to the effects of scavengers and field worker abilities.…”