Modeling the spatial effects of disturbance: a constructive critique to provide evidence of ecological thresholds

Abstract

Biologists and conservation planners are frequently asked to evaluate the spatial effects of anthropogenic disturbance on species of conservation concern. The linear response of a demographic parameter, such as survival or abundance, to the distance-from-disturbance is often used to inform spatial restrictions on development. The linear response, we argue, does not model the most common biological mechanisms that cause changes to demographic parameters, nor does it provide an estimate of a threshold that planners could use to protect species of concern. In the Great Plains of North America, biologists are increasingly concerned about the impact of energy development on populations of four species of grouse. To address this gap in our ability to properly assess distance thresholds, we developed a framework of four response patterns (null, linear, stair step, ramped) to describe the potential effects of a disturbance on biological processes relevant to nesting grouse located along a gradient from the disturbance. We simulated position and survival of grouse nests along a 25-km disturbance gradient to mimic the response to disturbances. We evaluated the relative support for a set of linear and nonlinear models in a known fate analysis of nest survival. Each of the underlying response patterns was detected with an appropriate model in a model selection framework (wAIC  0.61-0.75) when the sample size of nests was high (n  500), and thresholds were identified when present. In a low sample size scenario (n  50 nests) that may be typical of shortterm empirical sampling schemes, the stair step threshold was detected, but the more complex, ramped threshold was not detected. We provide recommendations regarding study design and inference for ecological and policy thresholds, and we encourage researchers to be cautious about the manner in which threshold responses are assessed and described.

Effects of power lines on habitat use and demography of greater sage‐grouse (Centrocercus urophasianus)

“…Similarly, we digitized locations of all known power lines using satellite imagery, and created a spatial surface of Euclidian distances from any power line for our study system, which we assigned to each individual in each analysis. We tested for a distance-from-power-line threshold by comparing models containing both linear and quadratic effects of distance from power lines to models allowing for threshold effects on behavior or demographic rates associated with distance to a power line (Powell et al 2017). We suspected a behavior or demographic rate would exhibit a more ramped response, in which a specific response would exhibit a linear pattern until an unknown distance threshold, and beyond this threshold we would not observe a response.…”

Bird Displacement by Wind Turbines: Assessing Current Knowledge and Recommendations for Future Studies

“…Each study was characterized according to the type of wind farm (offshore or onshore), country/geographic region, sampling design (BACI before-after control-impact; BAG before-after-gradient; BAG-C before-after-gradient, with a control; IG impact-gradient; IG-C impact-gradient, with a control; BA before-after and CI control-impact) [20], target groups following the IOC World Bird List [21], habitat, phenological season, methods or technology used for data collection and the number of years since the beginning of operation. Phenological season was classified as breeding, post-breeding, winter and migration, according to the categorization established by the original study.…”

“…Study design and systematic data collection are key elements in the monitoring of anthropogenic impacts [94]. Poorly designed experiments typically lead to inaccurate or inconclusive results, as evaluating displacement and other indirect impacts requires statistical power and control of potential confounding factors [20,95].…”

“…If the study considers the most external turbines or the arbitrary private property boundary as the limit of the wind farm area, it might also fail to detect effects in the most immediate area. Similarly, in gradient designs, if the distance bands are too wide (for small home-range species) or, conversely, are too narrow and the last band is located too close to the wind farm (e.g., at a 400-500 m distance, in a study targeting a species with large home-ranges), the study may fail to identify the displacement threshold, i.e., the maximum response distance [20,97].…”

A multispecies approach to manage effects of land cover and weather on upland game birds

“…The estimation of threshold responses for other similar species would allow managers to quantify change points at which populations will likely decrease or increase in response to habitat change. Moreover, traditional approaches to quantifying change points (e.g., generalized additive models or quadratic effects incorporated into linear models) involve detection of change points through visual estimation rather than explicit quantification with associated uncertainty (Powell et al., 2017), which can have limited practical applications (Toms & Villard, 2015). Implementing models with change points in a Bayesian hierarchical framework allows the estimation of change points and the ability to incorporate observation error (Wagner & Midway, 2014).…”