Tracking seabirds to identify ecologically important and high risk marine areas in the western North Atlantic

Montevecchi, William A.; Hedd, April; Tranquilla, Laura McFarlane; Fifield, David; Burke, Chantelle M.; Regular, Paul M.; Davoren, Gail K.; Garthe, Stefan; Robertson, Gregory J.; Phillips, Richard A.

doi:10.1016/j.biocon.2011.12.001

Cited by 87 publications

(61 citation statements)

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“…Several studies have used satellite telemetry to describe the horizontal movement data of large marine vertebrates Block et al, 2011;Hawkes et al, 2011;Hazen et al, 2012;Yurkowski et al, 2016;Citta et al, 2017;Vaudo et al, 2017) and to determine overlap with anthropogenic threats such as fisheries (seabirds: Suryan et al, 2007;Bugoni et al, 2009;Žydelis et al, 2011;sea turtles: da Silva et al, 2011;Witt et al, 2011;Revuelta et al, 2015; marine mammals: Geschke and Chilvers, 2010;Rosenbaum et al, 2014;sharks: Holmes et al, 2014), shipping (marine mammals: Mate et al, 1997;Schorr et al, 2009), and in-water habitat degradation (seabirds: Montevecchi et al, 2012;marine mammals: Johnson and Tyack, 2003;Rosenbaum et al, 2014). Satellite telemetry has been critical in evaluating threat exposure for marine species (Witt et al, 2008;Maxwell et al, 2013;Lascelles et al, 2014) and assessing how efficient conservation boundaries, such as Marine Protected Areas (MPA), are at encompassing the wide ranging habitat distribution of migratory species (Hart et al, 2010;Scott et al, 2012;Young et al, 2015;Maxwell et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Informing Marine Protected Area Designation and Management for Nesting Olive Ridley Sea Turtles Using Satellite Tracking

Dawson

Formia²,

Agamboué³

et al. 2017

Front. Mar. Sci.

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Understanding the horizontal and vertical habitat of olive ridley sea turtles (Lepidochelys olivacea), a threatened species, is critical for determining regions for protection and relevant gear modifications that may effectively reduce bycatch, the largest threat to this species. Satellite transmitters were used to determine the movement and dive behavior of 21 female olive ridley turtles tagged in Pongara National Park, Gabon during the 2012, 2013, and 2015 nesting seasons. A switching state-space model was used to filter the tracking data and categorize the internesting and post-nesting movements. Gridded utilization distribution (UD) home range analysis of tracking data revealed that the entire core habitat occurred in the Komo Estuary during the internesting period. Within the Komo Estuary, 58% of this core UD occurred in shipping lanes. Dive data from the 2015 tagging season revealed that during the internesting period, turtles spent the majority of their time resting on the estuary seabed. Approximately 20% of all dive time was spent on the bottom and all maximum dive depths corresponded to the depth of the seabed, indicating that bottom set gear during the internesting period may pose the greatest potential for fisheries interactions. National parks currently protect many of the nesting sites and the Gabon Bleu initiative has formally designated 10 new marine parks and a network of community and industrial fishing zones; this data was a layer used in determining the park and zone boundaries. Shared use of the estuary by fisheries, shipping, and olive ridley turtles creates a need for management measures to reduce interactions. Thus, the results from this study can further provide detailed information that can be used to support the development of evidence-based management plans.

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Section: Introductionmentioning

confidence: 99%

Informing Marine Protected Area Designation and Management for Nesting Olive Ridley Sea Turtles Using Satellite Tracking

Dawson

Formia²,

Agamboué³

et al. 2017

Front. Mar. Sci.

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“…GPS, pop-up archival), the predators' movement, and initial tagging location, but offer exceptional insight into top predator behavior, distribution, and their use of multiple marine ecosystems (e.g. hotspot connectivity, residency: Block et al 2011, Bailey et al 2012; tagging through the stages: Montevecchi et al 2012). (3) Shipboard surveys employ a variety of discrete and continuous sampling devices (e.g.…”

Section: Scales Of Hotspotsmentioning

confidence: 99%

Scales and mechanisms of marine hotspot formation

Hazen¹,

Suryan²,

Santora³

et al. 2013

Mar. Ecol. Prog. Ser.

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Identifying areas of high species diversity and abundance is important for understanding ecological processes and conservation planning. These areas serve as foraging habitat or important breeding or settlement areas for multiple species, and are often termed 'hotspots'. Marine hotspots have distinct biophysical features that lead to their formation, persistence, and recurrence, and that make them important oases in oceanic seascapes. Building upon a session at the North Pacific Marine Science Organization (PICES), this Theme Section explores the scales and mechanisms underlying hotspot formation. Fundamentally, understanding the mechanisms of hotspot formation is important for determining how hotspots may shift relative to ocean features and climate change, which is a prerequisite for determining management priorities.

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“…The modelled inter-annual variation in breeding Despite the limitations discussed above, we believe that this model is a substantial improvement on previous models predicting the effects of environmental change on seabirds. This model could easily be adapted to predict the impacts from other examples of spatial change, such as oil spills (Montevecchi et al, 2012), fisheries depletion (Gremillet, Peron, Provost, & Lescroel, 2015), changes to fisheries bycatch policies (Votier et al, 2010) or comparing the likely success of proposed MPAs (Pichegru et al, 2012). Furthermore, it could be modified for other seabird species for which tracking data are available if the behaviour and physiology of the species is reasonably well understood, and the tracking data sufficiently represents the home range of the population (Soanes, Arnould, Dodd, Sumner, & Green, 2013).…”

Section: Discussionmentioning

confidence: 99%

Predicting the impacts of wind farms on seabirds: An individual‐based model

Warwick-Evans

Atkinson

Walkington

et al. 2017

Journal of Applied Ecology

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Abstract1. Individual-based models (IBMs) are a powerful tool in predicting the consequences of environmental change on animal populations and supporting evidence-based decision making for conservation planning.2. There are increasing proposals for wind farms in UK waters and seabirds are a vulnerable group, which may be at risk from these developments.3. We developed a spatially explicit IBM to investigate the potential impacts of the installation of wind farms in the English Channel and North Sea on body mass, productivity and mortality of a breeding population of Northern gannets for which we have tracking data. A baseline model with no wind farms accurately represented the status of a sampleof tracked gannets at the end of the 90-day chick-rearing period, and the behaviourtime budget was similar to that of tracked gannets. Model simulations in the presence of wind farms indicated that installations shouldhave little impact on the gannet population, when either avoidance behaviour or collision risk scenarios were simulated. Furthermore, wind farms would need to be ten times larger or in more highly used areas in order to have population-level impacts on Alderney's gannets.6. Synthesis and applications. Our spatially explicit individual-based models (IBM) highlight that it is vital to know the colony-specific foraging grounds of seabirds that may be impacted, when identifying potential wind farm sites, in order to account for cumulative impacts from multiple sites. Avoiding areas highly used for foraging and commuting, and avoiding large-scale developments should be effective in limiting gannet mortality as a result of collision, competition and energy expenditure. Our IBM provides a robust approach which can be adapted for other seabird populations or to predict the impacts from other types of spatial change in the marine environment. K E Y W O R D Scollision risk, evidence-based decision making, individual based models, Morus bassanus, Northern gannets, offshore development, predictive modelling, seabirds, wind farmsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | Journal of Applied EcologyWARWICK-EVANS Et Al. | INTRODUCTIONThe marine environment is under increasing pressure from anthropogenic activities including overfishing, climate change and offshore developments such as wind farms (Halpern et al., 2012). These installations may enhance the environment by creating de facto no fishing zones (Inger et al., 2009), but there is concern about the negative impacts they may have on Europe's breeding seabirds (Garthe & Hupop, 2004). These impacts may include direct mortality from (2016) conclude that the current methods to predict the impacts of offshore wind farms on seabirds are inadequate, and this demonstrates a demand to establish a robust methodology which can be used by planners to mitigate the impacts on seabirds, when identifying wind farm sites.Evid...

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Tracking seabirds to identify ecologically important and high risk marine areas in the western North Atlantic

Cited by 87 publications

References 50 publications

Informing Marine Protected Area Designation and Management for Nesting Olive Ridley Sea Turtles Using Satellite Tracking

Informing Marine Protected Area Designation and Management for Nesting Olive Ridley Sea Turtles Using Satellite Tracking

Scales and mechanisms of marine hotspot formation

Predicting the impacts of wind farms on seabirds: An individual‐based model

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