A spatial conservation prioritization approach for protecting marine birds given proposed offshore wind energy development

Winiarski, Kristopher J.; Miller, David L.; Paton, Peter W. C.; McWilliams, Scott R.

doi:10.1016/j.biocon.2013.11.004

Cited by 40 publications

(27 citation statements)

References 48 publications

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“…This suggests that occupancy models alone may be inadequate for assessing risk from anthropogenic disturbances and for describing the fine‐scale distribution of marine species. Previously, the statistically challenging features of count data have restricted their use in distribution models, meaning that most predictions have addressed only occupancy (Flanders et al, ; Winiarski, Miller, Paton, & McWilliams, ), and may thus have ignored important facets of sea duck distribution and risk exposure, particularly variation in abundance. For species such as sea ducks, which gather in dense social aggregations at preferred habitat locations, flock size is a key component of distributional patterns as it both reflects and enhances habitat suitability (Guillemette, Himmelman, & Barette, ) and may affect the distribution and intensity of risk factors on individuals (e.g., Schwemmer, Mendel, Sonntag, Dierschke, & Garthe, ).…”

Section: Discussionmentioning

confidence: 99%

“…The biophysical associations with sea duck occupancy derived from our models were relatively consistent among species, whereas their associations with sea duck conditional abundance were more species‐specific. Distance to land, which was associated with both occupancy and abundance, tends to be positively associated with bathymetry and often has a strong influence on sea duck occupancy estimates (Flanders et al, ; Guillemette et al, ; Lewis, Esler, & Boyd, ; Winiarski et al, ). Sediment grain size can also affect prey availability for foraging sea ducks (Goudie & Ankney, ; Loring, Paton, McWilliams, McKinney, & Oviatt, ; Lovvorn, Grebmeier, Cooper, Bump, & Richman, ) and was associated with occupancy and conditional abundance in this study.…”

Section: Discussionmentioning

confidence: 99%

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Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Smith

Hofner

Lamb

et al. 2019

Ecology and Evolution

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Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Smith

Hofner

Lamb

et al. 2019

Ecology and Evolution

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“…Proposed offshore energy development in the United States has brought increased public attention to potential impacts of anthropogenic activities on marine life, and has led, in part, to an increased emphasis on marine spatial planning in conservation (MSP; Winiarski et al 2014b). MSP involves managing socio-economic, political, financial, geophysical, and environmental tradeoffs (Fox et al 2006), the latter of which includes risk of adverse effects from offshore development on communities of marine birds, mammals, fish, and invertebrates (Caldow et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model

Goyert

Gardner

Sollmann

et al. 2016

Ecological Applications

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Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012-April 2014), along with associated remotely sensed habitat data, in the lower Mid-Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA. We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [SST], salinity, primary productivity). We expected that avian bottom-feeders would respond primarily to static covariates that characterize seafloor variability, and that surface-feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species-specific and community-level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom-feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface-feeders associated significantly with two dynamic covariates (SST, primary productivity). However, other cases revealed significant relationships between static covariates and surface-feeders (e.g., distance to shore) and between dynamic covariates and bottom-feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.

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“…Those who did simulate management scenarios generally used spatially explicit tools and approaches such as Ecospace (Fouzai et al, 2012), Zonation (Moilanen, 2013;Winiarski et al, 2014), or a combination of GIS and BN models to allow for a non-static assessment of cause-effect pathways.…”

Section: Discussionmentioning

confidence: 99%

Quantitative environmental risk assessments in the context of marine spatial management: current approaches and some perspectives

Stelzenmüller¹,

Fock²,

Gimpel³

et al. 2014

ICES Journal of Marine Science

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Marine spatial planning (MSP) requires spatially explicit environmental risk assessment (ERA) frameworks with quantitative or probabilistic measures of risk, enabling an evaluation of spatial management scenarios. ERAs comprise the steps of risk identification, risk analysis, and risk evaluation. A review of ERAs in in the context of spatial management revealed a synonymous use of the concepts of risk, vulnerability and impact, a need to account for uncertainty and a lack of a clear link between risk analysis and risk evaluation. In a case study, we addressed some of the identified gaps and predicted the risk of changing the current state of benthic disturbance by bottom trawling due to future MSP measures in the German EEZ of the North Sea. We used a quantitative, dynamic, and spatially explicit approach where we combined a Bayesian belief network with GIS to showcase the steps of risk characterization, risk analysis, and risk evaluation. We distinguished 10 benthic communities and 6 international fishing fleets. The risk analysis produced spatially explicit estimates of benthic disturbance, which was computed as a ratio between relative local mortality by benthic trawling and the recovery potential after a trawl event. Results showed great differences in spatial patterns of benthic disturbance when accounting for different environmental impacts of the respective fleets. To illustrate a risk evaluation process, we simulated a spatial shift of the international effort of two beam trawl fleets, which are affected the most by future offshore wind development. The Bayesian belief network (BN) model was able to predict the proportion of the area where benthic disturbance likely increases. In conclusion, MSP processes should embed ERA frameworks which allow for the integration of multiple risk assessments and the quantification of related risks as well as uncertainties at a common spatial scale.

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A spatial conservation prioritization approach for protecting marine birds given proposed offshore wind energy development

Cited by 40 publications

References 48 publications

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model

Quantitative environmental risk assessments in the context of marine spatial management: current approaches and some perspectives

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