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Predictive Modelling to Identify Near-Shore, Fine-Scale Seabird Distributions during the Breeding Season.

Warwick-Evans VC, Atkinson PW, Robinson LA, Green JA - PLoS ONE (2016)

Bottom Line: Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood.For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment.AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls.

View Article: PubMed Central - PubMed

Affiliation: School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom.

ABSTRACT
During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney's coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney's seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making.

No MeSH data available.


Related in: MedlinePlus

A confusion matrix.This describes how accuracy, sensitivity, specificity, negative predictive power and positive predictive power are calculated.
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pone.0150592.g003: A confusion matrix.This describes how accuracy, sensitivity, specificity, negative predictive power and positive predictive power are calculated.

Mentions: A Receiver Operating Characteristic (ROC) curve was created in R package pROC [40] in order to test the model for errors of omission (falsely predicted negative values) and commission (falsely predicted positive values) [41]. The ROC curve is a plot of true positive values (sensitivity) against 1- the false positive values (specificity), for all available thresholds of movement between classes (i.e the point at which absent becomes present). The “best” threshold is considered to be that where the difference between sensitivity and specificity is least [41]. The Area Under the Curve (AUC) was calculated to test the overall performance of the model [42]. AUC may range from 0.5 to 1, where a value of 0.5 is no better than random, and a value of 1 would be a perfect model [41]. Accepted thresholds for model performance are; low accuracy (0.5–0.7), useful applications (0.7–0.9) and high accuracy >0.9 [43]. In addition the positive predictive power (ppp), negative predictive power (npp), sensitivity and specificity were calculated (Fig 3).


Predictive Modelling to Identify Near-Shore, Fine-Scale Seabird Distributions during the Breeding Season.

Warwick-Evans VC, Atkinson PW, Robinson LA, Green JA - PLoS ONE (2016)

A confusion matrix.This describes how accuracy, sensitivity, specificity, negative predictive power and positive predictive power are calculated.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4816348&req=5

pone.0150592.g003: A confusion matrix.This describes how accuracy, sensitivity, specificity, negative predictive power and positive predictive power are calculated.
Mentions: A Receiver Operating Characteristic (ROC) curve was created in R package pROC [40] in order to test the model for errors of omission (falsely predicted negative values) and commission (falsely predicted positive values) [41]. The ROC curve is a plot of true positive values (sensitivity) against 1- the false positive values (specificity), for all available thresholds of movement between classes (i.e the point at which absent becomes present). The “best” threshold is considered to be that where the difference between sensitivity and specificity is least [41]. The Area Under the Curve (AUC) was calculated to test the overall performance of the model [42]. AUC may range from 0.5 to 1, where a value of 0.5 is no better than random, and a value of 1 would be a perfect model [41]. Accepted thresholds for model performance are; low accuracy (0.5–0.7), useful applications (0.7–0.9) and high accuracy >0.9 [43]. In addition the positive predictive power (ppp), negative predictive power (npp), sensitivity and specificity were calculated (Fig 3).

Bottom Line: Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood.For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment.AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls.

View Article: PubMed Central - PubMed

Affiliation: School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom.

ABSTRACT
During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney's coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney's seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making.

No MeSH data available.


Related in: MedlinePlus