Use of multiple data sources and analytical approaches to derive a marine protected area for a breeding seabird

Perrow, Martin R.; Harwood, Andrew; Skeate, Eleanor R.; Praca, Emilie; Eglington, Sarah M.

doi:10.1016/j.biocon.2015.08.031

Cited by 14 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While MCBs have no ecological basis (Perrow et al. ), it has been suggested that they balance the proportion of a population protected against the extent of the protected area and have been used by statutory bodies to define boundaries for delimiting avian marine protected areas in the UK (O'Brien et al. ).…”

Section: Methodsmentioning

confidence: 99%

“…, Perrow et al. ). However, both marine protected area identification and wider spatial planning at sea are being hampered by a lack of colony‐specific distribution estimates (Perrow et al.…”

Section: Introductionmentioning

confidence: 99%

“…Two measures adopted by governments in UK and elsewhere in the European Union that contribute to seabird conservation are the extension of existing colony-based Special Protection Areas (SPAs) for seabirds to adjacent waters that are used for "maintenance activities" (e.g., foraging, courtship, etc.) and secondly, the establishment of marine SPAs around important foraging areas , Perrow et al 2015. However, both marine protected area identification and wider spatial planning at sea are being hampered by a lack of colony-specific distribution estimates (Perrow et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…and secondly, the establishment of marine SPAs around important foraging areas , Perrow et al 2015. However, both marine protected area identification and wider spatial planning at sea are being hampered by a lack of colony-specific distribution estimates (Perrow et al 2015). In the absence of such information, policy-makers frequently make the unrealistic assumption that seabirds are uniformly distributed out to some threshold distance from their colonies, such as their putative maximum foraging range (Thaxter et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Breeding density, fine‐scale tracking, and large‐scale modeling reveal the regional distribution of four seabird species

Wakefield

Owen

Baer³

et al. 2017

Ecological Applications

103

126

View full text Add to dashboard Cite

Abstract. Population-level estimates of species' distributions can reveal fundamental ecological processes and facilitate conservation. However, these may be difficult to obtain for mobile species, especially colonial central-place foragers (CCPFs; e.g., bats, corvids, social insects), because it is often impractical to determine the provenance of individuals observed beyond breeding sites. Moreover, some CCPFs, especially in the marine realm (e.g., pinnipeds, turtles, and seabirds) are difficult to observe because they range tens to ten thousands of kilometers from their colonies. It is hypothesized that the distribution of CCPFs depends largely on habitat availability and intraspecific competition. Modeling these effects may therefore allow distributions to be estimated from samples of individual spatial usage. Such data can be obtained for an increasing number of species using tracking technology. However, techniques for estimating population-level distributions using the telemetry data are poorly developed. This is of concern because many marine CCPFs, such as seabirds, are threatened by anthropogenic activities. Here, we aim to estimate the distribution at sea of four seabird species, foraging from approximately 5,500 breeding sites in Britain and Ireland. To do so, we GPS-tracked a sample of 230 European Shags Phalacrocorax aristotelis, 464 Black-legged Kittiwakes Rissa tridactyla, 178 Common Murres Uria aalge, and 281 Razorbills Alca torda from 13, 20, 12, and 14 colonies, respectively. Using Poisson point process habitat use models, we show that distribution at sea is dependent on (1) density-dependent competition among sympatric conspecifics (all species) and parapatric conspecifics (Kittiwakes and Murres); (2) habitat accessibility and coastal geometry, such that birds travel further from colonies with limited access to the sea; and (3) regional habitat availability. Using these models, we predict space use by birds from unobserved colonies and thereby map the distribution at sea of each species at both the colony and regional level. Space use by all four species' British breeding populations is concentrated in the coastal waters of Scotland, highlighting the need for robust conservation measures in this area. The techniques we present are applicable to any CCPF.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Breeding density, fine‐scale tracking, and large‐scale modeling reveal the regional distribution of four seabird species

Wakefield

Owen

Baer³

et al. 2017

Ecological Applications

103

126

View full text Add to dashboard Cite

show abstract

“…Development of separate habitat models using data from each sampling perspective, and then comparing and integrating results across models, presents a powerful tool to quantify factors influencing marine mammal and seabird distributions and habitat use (Watanuki et al, ). This integrative approach has facilitated ongoing efforts to identify and delineate marine protected areas for multiple mobile marine predators (Ballard, Jongsomjit, Veloz, & Ainley, ; Camphuysen, Shamoun‐Baranes, Bouten, & Garthe, ; Perrow et al, ), as well as dynamic ocean management approaches (Hazen et al, ,; Maxwell et al, ). This type of habitat modeling could be a useful next step for the data presented here, especially in combination with Eulerian survey data from areas used by tagged murres in California and British Columbia to provide a comprehensive analysis of common murre spatial distributions along the west coast.…”

Section: Discussionmentioning

confidence: 99%

Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions

Phillips

Horne

Zamon

et al. 2019

Ecology and Evolution

View full text Add to dashboard Cite

Studies estimating species' distributions require information about animal locations in space and time. Location data can be collected using surveys within a predetermined frame of reference (i.e., Eulerian sampling) or from animal‐borne tracking devices (i.e., Lagrangian sampling). Integration of observations obtained from Eulerian and Lagrangian perspectives can provide insights into animal movement and habitat use. However, contemporaneous data from both perspectives are rarely available, making examination of biases associated with each sampling approach difficult. We compared distributions of a mobile seabird observed concurrently from ship, aerial, and satellite tag surveys during May, June, and July 2012 in the northern California Current. We calculated utilization distributions to quantify and compare variability in common murre ( Uria aalge ) space use and examine how sampling perspective and platform influence observed patterns. Spatial distributions of murres were similar in May, regardless of sampling perspective. Greatest densities occurred in coastal waters off southern Washington and northern Oregon, near large murre colonies and the mouth of the Columbia River. Density distributions of murres estimated from ship and aerial surveys in June and July were similar to those observed in May, whereas distributions of satellite‐tagged murres in June and July indicated northward movement into British Columbia, Canada, resulting in different patterns observed from Eulerian and Lagrangian perspectives. These results suggest that the population of murres observed in the northern California Current during spring and summer includes relatively stationary individuals attending breeding colonies and nonstationary, vagile adults and subadults. Given the expected growth of telemetry studies and advances in survey technology (e.g., unmanned aerial systems), these results highlight the importance of considering methodological approaches, spatial extent, and synopticity of distribution data sets prior to integrating data from different sampling perspectives.

show abstract

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding

Garcia‐Quintas,

Roy,

Barbraud

et al. 2023

Ecology and Evolution

View full text Add to dashboard Cite

The way animals select their breeding habitat may have great impacts on individual fitness. This complex process depends on the integration of information on various environmental factors, over a wide range of spatiotemporal scales. For seabirds, breeding habitat selection integrates both land and sea features over several spatial scales. Seabirds explore these features prior to breeding, assessing habitats' quality. However, the information‐gathering and decision‐making process by seabirds when choosing a breeding habitat remains poorly understood. We compiled 49 historical records of larids colonies in Cuba from 1980 to 2020. Then, we predicted potentially suitable breeding sites for larids and assessed their breeding macrohabitat selection, using deep and machine learning algorithms respectively. Using a convolutional neural network and Landsat satellite images we predicted the suitability for nesting of non‐monitored sites of this archipelago. Furthermore, we assessed the relative contribution of 18 land‐ and marine‐based environmental covariates describing macrohabitats at three spatial scales (i.e. 10, 50 and 100 km) using random forests. Convolutional neural network exhibited good performance at training, validation and test (F1‐scores >85%). Sites with higher habitat suitability (p > .75) covered 20.3% of the predicting area. Larids breeding macrohabitats were sites relatively close to main islands, featuring sparse vegetation cover and high chlorophyll‐a concentration at sea in 50 and 100 km around colonies. Lower sea surface temperature at larger spatial scales was determinant to distinguish the breeding from non‐breeding sites. A more comprehensive understanding of the seabird breeding macrohabitats selection can be reached from the complementary use of convolutional neural networks and random forest models. Our analysis provides crucial knowledge in tropical regions that lack complete and regular monitoring of seabirds' breeding sites.

show abstract

Use of multiple data sources and analytical approaches to derive a marine protected area for a breeding seabird

Cited by 14 publications

References 37 publications

Breeding density, fine‐scale tracking, and large‐scale modeling reveal the regional distribution of four seabird species

Breeding density, fine‐scale tracking, and large‐scale modeling reveal the regional distribution of four seabird species

Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding

Contact Info

Product

Resources

About