Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife

Abstract

Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population-level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count-based manner (e.g., number of dead animals) that is difficult to translate into rate-based estimates important to infer population-level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population-level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count-based field data on individuals to rate-based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field-based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor-induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.

Incidental eagle carcass detection can contribute to fatality estimation at operating wind facilities

“…Another advantage of incidental detection is that O&M staff are on site for the life of the facility, potentially offering long-term information on eagle and other large raptor mortality at all operating wind facilities. An improved understanding of eagle mortality would assist wildlife agencies and researchers aiming to evaluate how installed and future wind energy build-out could affect population trends of raptors [37][38][39].…”

Vulnerability of avian populations to renewable energy production

“…We assessed the significance of change from the original λ under current conditions to new λs using a counterfactual ratio of impacted subpopulations to the original population (λs/λoriginal, hereafter ‘CIU’; electronic supplementary material, table S3) [18,52–54]. We classified species as ‘moderately vulnerable’ if vulnerability (1 – CIU) ≥ 0.2 (i.e.…”

“…Steps 1–4 of this process are defined in Katzner et al . [ 18 ], implemented here with context-specific alterations and improvements (see electronic supplementary material for full details on the methods).…”

“…9: 211558 then estimated vulnerability for each using a five-step framework: (1) determining the geographical origin of fatalities at renewable energy facilities, (2) defining the geographical 'catchment area' encompassing the fatalities and estimating the size of subpopulations within those catchment areas, (3) building species-specific demographic models to estimate rates of survival, fecundity and population growth, (4) assessing species-and subpopulation-specific vulnerability with sensitivity analyses to estimate the demographic effects of additional fatalities, and (5) evaluating taxonomic and ecological correlates of vulnerability (figure 2). Steps 1-4 of this process are defined in Katzner et al [18], implemented here with context-specific alterations and improvements (see electronic supplementary material for full details on the methods).…”

Counterfactuals to Assess Effects to Species and Systems from Renewable Energy Development

“…Because their counterfactual analysis clearly explained alternative scenarios, it also was useful as the foundation for robust debate (Diesendorf, 2016;Hendrickson, 2016;Henle et al, 2016). Recently, counterfactuals have been used to assess population-level impacts to wildlife from renewable energy development (Katzner et al, 2020;Conkling et al, 2022), and to understand effects on bird populations from climate change (Saether et al, 2019). Thus, combining these two types of models should be eminently feasible with modern technical tools.…”