Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat

Erickson, Richard A.; Thogmartin, Wayne E.; Diffendorfer, James E.; Russell, Robin E.; Szymanski, Jennifer A.

doi:10.7717/peerj.2830

Cited by 30 publications

(19 citation statements)

References 61 publications

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“…For example, arboreal animals such as the Indiana bat live in colonies that vary greatly in size (Thogmartin & McKann, ). The smallest colonies would be most vulnerable to both demographic stochasticity and the Allee effect, something that has been indicated by previous modeling exercises (e.g., Erickson, Thogmartin, Russell, Diffendorfer, & Szymanski, ; Erickson, Thogmartin, Diffendorfer, Russell, & Szymanski, ).…”

Section: Discussionmentioning

confidence: 54%

Incorporating Allee effects into the potential biological removal level

Haider

Oldfield

et al. 2017

Natural Resource Modeling

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Potential biological removal (PBR) is an approach used to calculate sustainable harvest and “take” limits for populations. PBR was originally derived assuming logistic growth while ignoring the effects of small population size (i.e., an Allee effect). We derived a version of PBR that includes an Allee effect (i.e., small population size or densities limiting population growth rates). We found that PBR becomes less conservative when it fails to consider an Allee effect. Specifically, sustainable harvest and take levels based upon PBR with an Allee effect were between approximately 51% and 66% of levels based upon PBR without an Allee effect. Managers and biologists using PBR may need to consider the limitations if an Allee effect may be present in the species being modeled. Considerations for Management Based upon our finding, management considerations may include: acknowledging that populations under stress may also be subject to Allee effects; recognizing limitations of approaches such as PBR when applying them to small populations; and broadly considering the Allee effects when using population models for natural resource management.

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Section: Discussionmentioning

confidence: 54%

Incorporating Allee effects into the potential biological removal level

Haider

Oldfield

et al. 2017

Natural Resource Modeling

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“…More recently, models of the effects of bat fatalities from wind turbines also have considered continent‐ or country‐wide populations (Erickson et al. , Frick et al. ).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife

et al. 2020

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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.

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“…Photograph by Sheryl Markham, U.S. Geological Survey. Wayne Thogmartin,USGS Upper Midwest Environmental Sciences Center,wthogmartin@usgs.gov,(608) Thogmartin, W.E., Diffendorfer, J.E., Russell, R.E., and Szymanski, J.A., 2016, Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat: PeerJ, v. 4, e2830, https://doi.org/10.7717/peerj.2830.…”

Section: Wind Energy Effects On the Indiana Batmentioning

confidence: 99%

U.S. Geological Survey—Energy and wildlife research annual report for 2017

2017

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Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat

Cited by 30 publications

References 61 publications

Incorporating Allee effects into the potential biological removal level

Incorporating Allee effects into the potential biological removal level

Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife

U.S. Geological Survey—Energy and wildlife research annual report for 2017

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