Towards standardised seabirds at sea census techniques in connection with environmental impact assessments for offshore wind farms in the U.K.

Camphuysen, Cornelis Jan

doi:10.13140/rg.2.1.2230.0244

Cited by 3 publications

(3 citation statements)

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“…Species difficult to observe may warrant crewed low-level flights (Sasse, 2003). For example, long-standing survey protocols for large ungulates (Conroy et al, 2014(Conroy et al, , 2018, Galliformes (Butler et al, 2007;McRoberts et al, 2011), cranes (Butler et al, 2019), waterfowl (Prenzlow and Lovvorn, 1996) and seabirds (Camphuysen et al, 2004) require flight altitudes of approximately 30-80 m above ground level.…”

Section: Traditional Data Collection Methodsmentioning

confidence: 99%

Remote sensing and machine learning to improve aerial wildlife population surveys

Converse,

Lippitt,

Koneff

et al. 2024

Front. Conserv. Sci.

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Technological and methodological advances in remote sensing and machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments to explore improvements to the efficiency and effectiveness of aerial wildlife monitoring from a management perspective. The core objective of the CoP is to organize the development and testing of remote sensing and machine learning methods to improve aerial wildlife population surveys that support management decisions. Beginning in 2020, the CoP collaboratively identified the natural resource management decisions that are informed by wildlife survey data with a focus on waterbirds and marine wildlife. We surveyed our membership to establish 1) what management decisions they were using wildlife count data to inform; 2) how these count data were collected prior to the advent of remote sensing/machine learning methods; 3) the impetus for transitioning to a remote sensing/machine learning methodological framework; and 4) the challenges practitioners face in transitioning to this framework. This paper documents these findings and identifies research priorities for moving toward operational remote sensing-based wildlife surveys in service of wildlife management.

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Section: Traditional Data Collection Methodsmentioning

confidence: 99%

Remote sensing and machine learning to improve aerial wildlife population surveys

Converse,

Lippitt,

Koneff

et al. 2024

Front. Conserv. Sci.

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“…Many historical seabird datasets come in this form (Jespersen, 1924;Tasker et al, 1984) and strip transects are still used for shipboard observations of birds in flight and aerial digital image capture (Oedekoven et al, 2012). The alternative, continuous or punctuated records from line transect surveys (Camphuysen et al, 2004;Oedekoven et al, 2012) are analysed by distance sampling methods (Buckland et al, 2001;Buckland et al, 2008), to effectively bring them into line with the assumptions of strip transect surveys.…”

Section: Pro Ce Ss and Obs Ervati On Model Smentioning

confidence: 99%

“…Mark-recapture data have rarely been used to map seabird distributions and fit habitat models (Camphuysen et al, 2004); however, they are a potentially valuable repository of spatial data that are also individually referenced (e.g. colour-ringing).…”

Section: Combination With Mark-recapture Datamentioning

confidence: 99%

Integrated modelling of seabird‐habitat associations from multi‐platform data: A review

Matthiopoulos

Wakefield

Jeglinski

et al. 2022

Journal of Applied Ecology

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1. Quantifying current and future overlap between human activities and wildlife is a core and growing aim of ecological study, spurring ever more spatial data collection and diversification of observation techniques (surveys, telemetry, citizen science, etc.). 2. To meet this aim, data collected via multiple platforms, across different geographical and temporal regions, may need to be integrated, yet many ecologists remain unclear about the relationships between data types and therefore how they can be combined.3. In seabird research, these applied questions can be particularly pressing because many human activities (e.g. tidal and wind renewables, fishing, shipping, etc.) are concentrated in coastal waters, where many seabirds also aggregate, especially while breeding. In addition, seabird coloniality and density dependence present unique analytical challenges. 4. We review the relevant literature on data integration and illustrate it with example models and data (in an accompanying R-library and supplementary vignette ), to derive methodological and quantitative guidelines for best practice in conducting joint inference for multi-platform data. We use systematic survey data to motivate the key arguments, but also overview developments in integration with other data (e.g. telemetry tracking, citizen science, mark-recapture).5. We make recommendations on (a) the use of response and explanatory data, (b) the treatment of survey design and observation errors, (c) exploiting dependencies across space and time, (d) accounting for biological phenomena, such as commuting costs from the colony (i.e. accessibility) and density dependence, and (e) the choice of statistical framework.6. Synthesis and application. Integrated analysis of multi-platform data turns many of the seabird-specific challenges into opportunities for inferring habitat

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Temporal patterns in offshore bird abundance during the breeding season at the Dutch North Sea coast

et al. 2021

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The expanding development of offshore wind farms brings a growing concern about the human impact on seabirds. To assess this impact a better understanding of offshore bird abundance is needed. The aim of this study was to investigate offshore bird abundance in the breeding season and model the effect of temporally predictable environmental variables. We used a bird radar, situated at the edge of a wind farm (52.427827° N, 4.185345° E), to record hourly aerial bird abundance at the North Sea near the Dutch coast between May 1st and July 15th in 2019 and 2020, of which 1879 h (51.5%) were analysed. The effect of sun azimuth, week in the breeding season, and astronomic tide was evaluated using generalized additive modelling. Sun azimuth and week in the breeding season had a modest and statistically significant (p < 0.001) effect on bird abundance, while astronomic tide did not. Hourly predicted abundance peaked after sunrise and before sunset, and abundance increased throughout the breeding season until the end of June, after which it decreased slightly. Though these effects were significant, a large portion of variance in hourly abundance remained unexplained. The high variability in bird abundance at scales ranging from hours up to weeks emphasizes the need for long-term and continuous data which radar technology can provide.

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Towards standardised seabirds at sea census techniques in connection with environmental impact assessments for offshore wind farms in the U.K.

Cited by 3 publications

References 0 publications

Remote sensing and machine learning to improve aerial wildlife population surveys

Remote sensing and machine learning to improve aerial wildlife population surveys

Integrated modelling of seabird‐habitat associations from multi‐platform data: A review

Temporal patterns in offshore bird abundance during the breeding season at the Dutch North Sea coast

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