Assessing the population size of the European Storm Petrel (Hydrobates pelagicus) using spatial autocorrelation between counts from segments of crisscross ship transects (original) (raw)
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The Wilson Journal of Ornithology, 2012
The St Pierre and Miquelon Archipelago hosts the only French Leach's Storm-Petrel (Oceanodroma leucorhoa) colony. We conducted a survey during the 2008 breeding season to estimate the breeding population size on Grand Colombier Island. This survey included an estimation of burrow detection probability using a double-observer approach. We estimated that 3% of Leach's Storm-Petrels nests had failed before we started the survey. Nest occupancy probability was neither affected by slope nor vegetation type and was 0.546 6 0.029. Burrow density was positively affected by slope and, consequently, was much lower on the plateau than on island slopes. Burrow detection probability was neither affected by observer nor by habitat and was 0.89 6 0.01. We estimated the population to be 363,787 [95% CI 5 295,502-432,072] breeding pairs, which is among the largest Leach's Storm-Petrel colonies in the northwestern Atlantic Ocean.
Factors affecting breeding distribution of Storm-petrelsHydrobates pelagicusin Orkney and Shetland
Bird Study, 2006
Capsule The main factors are past and present human activities, especially the introduction of rats to islands. Aims To assess factors that influence breeding distribution and abundance of Storm-petrel. Methods We used a database for 142 islands in Shetland and Orkney. Breeding status of Storm-petrel was related to data for each island on introduced and indigenous predators, other human-related features, and aspects of island geography. Results Although 92% of the total land area of the archipelagos comprised islands with rats present, Storm-petrel colonies were almost totally restricted to rat-free islands. They also occurred more frequently on islands with cliffs, far from neighbouring islands with humans, and on islands with a low rate of human visits. Colony size was smaller on the smallest occupied islands. Breeding numbers of Great Skuas Stercorarius skua, Great Black-backed Gulls Larus marinus, and Storm-petrels all correlated, as each increased with island size. Conclusions The presence or absence of rats is the single most important influence on Storm-petrel breeding distribution in Orkney and Shetland. However, geographical and human-related effects, such as the presence of cliffs or the occurrence of human visits, also appear to influence the distribution of Storm-petrels, whereas avian predators appear to have had little effect until now.
2012
Storm-petrels have been shown to use dimethyl sulfide (DMS) as a foraging cue, suggesting that this compound may be used to predict their distribution. We describe a new distribution model that employs machine learning software and geographic information systems to model storm-petrel distribution. We used environmental predictor variables that included newly available climatologies of sea surface DMS concentrations to construct distribution maps of fork-tailed storm-petrel (Oceanodroma furcata) and Leach's storm-petrel (O. leucorhoa) in the North Pacific and Bering Sea. Model accuracy was assessed by using the area under the receiver operating characteristic curve (AUC) values and (2) comparing predicted distributions to presence and non-detection data from two opportunistic pelagic surveys performed in summer 2008. Models using all predictor variables gave AUC values of 0.89 and 0.75, sensitivity values of 0.73 and 0.61, and specificity values of 0.83 and 0.73 for fork-tailed and Leach's storm-petrel, respectively. Models using all predictor variables except DMS gave AUC values of 0.87 and 0.74, sensitivity values of 0.81 and 0.60, and specificity values of 0.77 for fork-tailed and Leach's storm-petrel,
Bird Conservation International, 2007
The White-chinned Petrel Procellaria aequinoctialis is the second most commonly captured species by Argentinean longliners. The severe declines that this species has experienced in some of its populations (e.g. South Georgia Islands) have been principally attributed to incidental mortality associated with longliners. In this study we analyse the spatio-temporal variability in the mortality rates of White-chinned Petrels on the Patagonian Shelf and the effects that environmental and operational variability have on such mortality. The average capture rate (¡ 1 SD) for the period 1999-2003 was 0.014 ¡ 0.090 White-chinned Petrels for every 1,000 hooks deployed. Higher capture rates were observed when short longlines were deployed. Capture rates were not affected by the wind speed or by the time to the full moon. The distribution of the captures differed throughout the year. During autumn-winter most captures took place in the north of the Patagonian Shelf, whereas during spring-summer incidental captures occurred principally to the south between 45uS and 50uS.
Ages of Storm Petrels Hydrobates pelagicus prospecting potential breeding colonies
Ringing & Migration, 2005
Each year, ringers using sound lures mark and recapture immature Storm Petrels Hydrobates pelagicus prospecting potential breeding colonies. Attempts have been made to estimate the size of this population, but a full demographic model has not been possible since the age structure of this population has not been determined. To address this, between 1990 and 2004, 799 Storm Petrel chicks were ringed at the breeding colony on the Island of Mousa, Shetland, to establish a sample of marked known-age birds. There were 39 subsequent recaptures of these individuals, mainly by using sound lures at sites away from breeding colonies. Only one recapture related to a first-year bird: the largest cohort was of second-year birds and the relative frequencies of third-year and fourth-year cohorts progressively diminished as birds entered the breeding population and ceased responding to sound lures. These data provide demographic information to facilitate the construction of population models. Given the difficulty in determining the size of breeding populations of Storm Petrels and in long-term annual monitoring of breeding productivity, there may be considerable conservation benefit from ringing and recapture of Storm Petrels each year to monitor the size of the pre-breeding population.
Partial migration in the Mediterranean Storm Petrel Hydrobates pelagicus melitensis
Marine ornithology, 2019
Studying the migration routes and wintering areas of seabirds is crucial to understanding their ecology and to inform conservation efforts. Here we present results of a tracking study carried out on the little-known Mediterranean Storm Petrel Hydrobates pelagicus melitensis. During the 2016 breeding season, Global Location Sensor (GLS) tags were deployed on birds at the largest Mediterranean colony: the islet of Filfla in the Maltese Archipelago. The devices were retrieved the following season, revealing hitherto unknown movements and wintering areas of this species. Most individuals remained in the Mediterranean throughout the year, with birds shifting westwards or remaining in the central Mediterranean during winter. However, one bird left the Mediterranean through the Strait of Gibraltar and wintered in the North Atlantic. Our results from GLS tracking, which are supported by data from ringed and recovered birds, point toward a system of partial migration with high inter-individual variation. This highlights the importance of trans-boundary marine protection for the conservation of vulnerable seabirds.
Journal of Applied Ecology, 2002
1Data on the spatial distribution of seabirds at sea is commonly used in risk assessments of the possible impact of oil spills. The validity of such assessments depends on the stability of the observed spatial pattern through time. In this study we explored the year-to-year predictability in the spatial distribution of guillemots (Uria spp.) from a 9-year data set covering an area of approximately 1000 × 600 km2 in the Barents Sea from late January to early March.2Spatial correlograms were used to elucidate the strength and scale of the spatial patterns within years, and the concordance of these patterns between years. Broad-scale oceanographic features were used in linear regressions to model the spatial pattern in guillemot distribution for each year. The ability of these models to predict their spatial distribution in other years was then evaluated.3The analyses revealed two nested levels of patchiness. The large-scale pattern, with a characteristic scale of 300 km, had a weak (R2 = 0·06) but significant spatial predictability between years. The predictability increased marginally when the data set was divided into two time periods (R2 equal to 0·07 and 0·17, respectively). Nested within the large-scale pattern, the analyses revealed a small-scale level of patchiness with no significant spatial year-to-year predictability.4The broad-scale oceanographic variables could explain from 14% to 42% of the variance in the spatial distribution of guillemots within each year. The models were on average significantly better than using a random model when predicting other years. We found no relationships between the fit of the models and the ability to predict guillemot distribution in other years. There was a large variation in the parameter estimates between years, resulting in a large range in predicted values.5This study illustrates some of the difficulties associated with predicting the spatial distribution of mobile and patchy organisms. Our results indicate that the use of restricted survey data in extrapolating and predicting the distribution of seabirds may be misleading. Restricted survey data should mainly be used to identify vulnerable populations on a regional scale. Only when larger data series are available is it possible to evaluate, quantify and model the predictable spatial pattern in more detailed assessment analyses. There is a need to develop methods that quantify how unpredictable patchiness may affect the vulnerability of populations to surface pollutants and other environmental disturbances.Data on the spatial distribution of seabirds at sea is commonly used in risk assessments of the possible impact of oil spills. The validity of such assessments depends on the stability of the observed spatial pattern through time. In this study we explored the year-to-year predictability in the spatial distribution of guillemots (Uria spp.) from a 9-year data set covering an area of approximately 1000 × 600 km2 in the Barents Sea from late January to early March.Spatial correlograms were used to elucidate the strength and scale of the spatial patterns within years, and the concordance of these patterns between years. Broad-scale oceanographic features were used in linear regressions to model the spatial pattern in guillemot distribution for each year. The ability of these models to predict their spatial distribution in other years was then evaluated.The analyses revealed two nested levels of patchiness. The large-scale pattern, with a characteristic scale of 300 km, had a weak (R2 = 0·06) but significant spatial predictability between years. The predictability increased marginally when the data set was divided into two time periods (R2 equal to 0·07 and 0·17, respectively). Nested within the large-scale pattern, the analyses revealed a small-scale level of patchiness with no significant spatial year-to-year predictability.The broad-scale oceanographic variables could explain from 14% to 42% of the variance in the spatial distribution of guillemots within each year. The models were on average significantly better than using a random model when predicting other years. We found no relationships between the fit of the models and the ability to predict guillemot distribution in other years. There was a large variation in the parameter estimates between years, resulting in a large range in predicted values.This study illustrates some of the difficulties associated with predicting the spatial distribution of mobile and patchy organisms. Our results indicate that the use of restricted survey data in extrapolating and predicting the distribution of seabirds may be misleading. Restricted survey data should mainly be used to identify vulnerable populations on a regional scale. Only when larger data series are available is it possible to evaluate, quantify and model the predictable spatial pattern in more detailed assessment analyses. There is a need to develop methods that quantify how unpredictable patchiness may affect the vulnerability of populations to surface pollutants and other environmental disturbances.