Consequences

What are Consequences?  

What we know about where animals are, their exposure to stressors, and how they respond can be integrated to inform simulation models (e.g., individual-based models, population viability analysis) to predict how populations might be affected under different scenarios. These models help guide decisions about when and where activities should take place and explore mitigation options if potential impacts are too high. In contrast, retrospective analyses can be used to understand the consequences of past events on wildlife populations. 

What has CREEM contributed? 

CREEM researchers have developed a unified risk assessment framework across receptor groups and more specifically, developed methods for mapping relative risk to seabird species from planned offshore wind developments, including cumulative development scenarios. 

The types of simulation models described above have been used to understand the population-level effects of disturbance or multiple stressors. In CREEM, we have been involved in the development of conceptual frameworks — known as Population Consequences of Disturbance (PCoD) and Population Consequences of Multiple Stressors (PCoMS) — that provide a guiding approach to predict the long-term impacts of human activities on wildlife populations and thus support better management and conservation planning. CREEM has also been at the forefront of investigating how to translate these frameworks into operational models.  

What species are these methods used for? 

The majority of CREEM research on consequences relates to the marine environment, particularly marine mammals and seabirds.  For example: 

  • We have applied the PCoD and PCoMS frameworks to a number of marine mammal case studies, e.g., to simulate the effects of different regimes of navy sonar on beaked and blue whale populations, and to predict the effects of proposed offshore windfarm developments on critically endangered North Atlantic right whales. 
  • We have also applied PCoMS models retrospectively to understand the consequences of past events, such as assessing how environmental disasters like the Deepwater Horizon oil spill affected animal populations. In particular, with data-rich scenarios (e.g., with North Atlantic right whales and gray whales), we have developed hierarchical models that can estimate the effects of stressors on health, survival, growth and reproduction directly. 
  • We have undertaken several projects looking at risk assessment frameworks in the context of offshore wind development.  As part of the Wildlife and Offshore Wind project we have developed a unified risk assessment framework across receptor groups. We have also developed methods for mapping relative risk to seabird species from planned offshore wind developments in Danish waters, including cumulative development scenarios (see here). 

Who in CREEM works on these methods? 

PhD Students: Grace Edmondson & David Sweeney 

A few relevant and recent publications by CREEM staff 

Hin, V., de Roos, A.M., Benoit-Bird, K.J., Claridge, D.E., DiMarzio, N., Durban, J.W., Falcone, E.A., Jacobsen, E.K., Joes-Todd, C.M., Pirotta, E., Schorr, G.S., Thomas, L. & Watwood, S. (2023) Using individual-based bioenergetic models to predict the aggregate effects of disturbance on populations: A case study with beaked whales and Navy sonar. PLoS ONE vol. 18(8): e0290819.

Pirotta, E., R.S. Schick, P.K. Hamilton, C.M. Harris, J. Hewitt, A.R. Knowlton, S.D. Kraus, E. Meyer-Gutbrod, M.J. Moore, H.M. Pettis, T. Photopoulou, R.M. Rolland, P.L. Tyack & L. Thomas. (2023) Estimating the effects of stressors on the health, survival, and reproduction of a critically endangered, long-lived species. Oikos, e09801. 

Pirotta, E., Booth, C.G., Calambokidis, J., Costa, D.P., Fahlbusch, J.A., Friedlaender, A.S., Goldbogen, J.A., Harwood, J., Hazen, E.L., New, L., Santora, J.A., Watwood, S.L., Wertman, C. & Southall, B.L. (2022), From individual responses to population effects: Integrating a decade of multidisciplinary research on blue whales and sonar. Animal Conservation. Vol. 25: 796-810.

Schwacke, L.H., Marques, T.A., Thomas, L., Booth, C., Balmer, B.C., Barratclough, A., Colegrove, K., De Guise, S., Garrison, L.P., Gomez, F.M., Morey, J.S., Mullin, K.D., Quigley, B.M., Rosel, P., Rowles, T.K., Takeshita, R., Townsend, F.I., Speakman, T.R., Wells, R.S., Zolman, E.S. & Smith, C.R. (2022). Modeling population effects of the Deepwater Horizon oil spill on a long-lived species.  Conservation Biology, vol. 36, no. 4, e13878.

Tyack, P.L., Thomas, L., Costa, D., Hall, A.J., Harris, C.M., Harwood, J., Krauss, S., Miller, P.J., Moore, M., Photopoulou, T., Pirotta, E., Rolland, R., Schwacke, L., Simmons, S. & Southall, B. (2022). Managing the effects of multiple stressors on wildlife populations in their ecosystems: developing a cumulative risk approach. Proceedings of the Royal Society of London Series B: Biological Sciences, vol. 289, no. 1987, 20222058.

Pirotta, E., Thomas, L., Costa, D., Hall, A.J., Harris, C.M., Harwood, J., Kraus, S., Miller, P.J., Moore, M., Photopoulou, T., Rolland, R., Schwacke, L., Simmons, S., Southall, B. & Tyack, P.L. (2022). Understanding the combined effects of multiple stressors: a new perspective on a longstanding challenge. Science of the Total Environment, vol. 821, 153322.

Joy, R., Schick, R.S., Dowd, M., Margolina, T., Joseph, J.E. & Thomas, L. (2022). A fine-scale marine mammal movement model for assessing long-term aggregate noise exposure. Ecological Modelling, vol. 464, 109798.

Donovan, C.R., Harris, C.M., Milazzo, L., Harwood, J., Marshall, L. & Williams, R. (2017). A simulation approach to assessing environmental risk of sound exposure to marine mammals.  Ecology and Evolution, vol. 7, no. 7, pp. 2101-2111.

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