Determining hatch dates for skuas: an egg density calibration curve
* Correspondence author. Email: firstname.lastname@example.org
1Centre for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Universitetparken 15, DK-2100 Copenhagen, Denmark;
2Faroe Islands National Museum, Kúrdalsvegur 15, FO-188 Hoyvík, Faroe Islands;
3Institute of Biodiversity, Animal Health & Comparative Medicine, Graham Kerr Building, University of Glasgow, G12 8QQ, Glasgow, UK;
4Environment Agency, Traðagøta 38, FO-165 Argir, Faroe Islands.
Key life-history events, such as breeding phenology, underlie much ecological research and inform conservation efforts. Simple methods that improve efficiency during breeding studies are valuable, particularly in remote locations and extreme climates. Building on an earlier study, we investigated the relationship between egg density and incubation progression in two Arctic- and subarctic-breeding seabird species, Arctic Skua Stercorarius parasiticus and Great Skua S. skua, to statistically test its application as a calibration method. Corresponding with the preceding study we found that the decrease in calculated egg density during incubation can be described by a quadratic relationship with egg development for our populations. In addition, we demonstrate that this relationship was not confounded by multiple egg clutches nor differences in measurement intervals. From this relationship, a calibration curve was constructed to predict hatching dates within an error of c. three days for Arctic Skua and c. four days for Great Skua, using a single measure of the length, breadth and mass of an egg. Furthermore, when combining the data generated in this study, we found model support for a calibration curve independent of species, suggesting that this calibration may have the potential to be extended to other species with similar ecology. This technique can be used to inform the timing of colony visits and thereby maximise research and monitoring efforts for these species with minimal researcher disturbance.
Long-term monitoring of seabirds is an important component of nature management and conservation research, partly due to the historical role of seabirds as bioindicators for marine ecosystems (Piatt et al. 2007), and partly due to conservation concerns based on large seabird population declines in recent decades (Paleczny et al. 2015). Declines in populations have been attributed to changes in the environment, resource availability and anthropogenic disturbance (Halpern et al. 2008). As we begin to understand how these trends relate to climate change (Crick 2004), seabird research may aid the understanding of the ecological implications of this phenomenon (Grémillet & Boulinier 2009). Many seabirds are long-lived K-selected species with low annual productivity and delayed sexual maturity. This makes them particularly vulnerable to rapid environmental change (Irons et al. 2008; Ainley & Hyrenbach 2010) and poses challenges for research, as population dynamics incur an inherent lag.
A potential environmental factor influencing seabird productivity, driven by warming springs, is a phenological mismatch between breeding initiation and optimal environmental conditions such as weather and food availability for breeding adults and chicks (Lameris et al. 2018). Egg laying and hatching dates are measurable parameters for understanding this process, creating a timeframe for the breeding season. Combined with existing estimates for the duration of chick morphological development from hatching until fledging, the knowledge of laying and hatching dates also allows a prediction of fledging dates for chicks, which in turn assists planning of fledging rate surveys and chick-ringing projects.
Obtaining estimates for time-sensitive events such as laying and hatching often demands intensive fieldwork effort, and human disturbance may negatively affect productivity (Anderson & Keith 1980). Minimising researcher presence and interaction is therefore recommended at all times. Considerations for reducing disturbance impose constraints on fieldwork and necessitate the development and refinement of time-efficient and sensitive practical techniques.
There are three main quantitative or qualitative field techniques for estimating hatch dates based on the physical properties of egg development: candling (Lokemoen & Koford 1996), flotation (Rizzolo & Schmutz 2007), and density (Furness & Furness 1981). All methods require some degree of calibration and verification. These techniques are more economical and efficient for general field use than the technology-based monitoring generally utilised for other primary purposes (Grémillet et al. 2004; Renfrew & Ribic 2012; Mougeot et al. 2014; Islam et al. 2015; Eichhorn et al. 2017).
The candling technique involves backlighting the egg to visualise its contents through the shell and assess embryonic stage and quality. It is useful in controlled environments such as incubators, but has been used in the field (Deeming 1995; Lokemoen & Koford 1996), and tested comparatively with egg flotation (Reiter & Andersen 2008).
The flotation method assesses the water suspension gradient of the egg throughout development, caused by the increasing ratio of atmospheric gas to wet material. It has been utilised for waterfowl (Reiter & Andersen 2008), waders (Liebezeit et al. 2007; Ackerman & Eagles-Smith 2010; Hansen et al. 2011), and gamebirds (McNew et al. 2007).
The egg density technique is more rarely employed and capitalises on the same principle of water loss underlying egg flotation, but uses egg biometrics to determine density (Westerskov 1950; Barth 1953). Volume and density can be determined from three egg biometrics: length, breadth and mass (Hoyt 1979). Although this technique has been previously described (Furness & Furness 1981; Yalden & Yalden 1989; Jarrett et al. 2003) and is potentially the most time-efficient and least invasive, it has not yet been subjected to rigorous statistical analysis.
Here, we investigate the relationship between egg density and incubation progression of the colonial and ground-nesting Arctic Skua Stercorarius parasiticus and Great Skua S. skua. Following the methodology of Furness and Furness (1981) for the Faroese population, we test if the relationship, described by a quadratic curve, is reproducible for different populations, and we expand the statistical analysis to account for potential confounding effects of repeated measures of mass and the simultaneous incubation of multiple eggs in the same nest. We assess if a speciesspecific egg density calibration curve delivers low-error prediction of hatching dates from a single nest visit, and furthermore, we investigate the potential for a global calibration which can be applicable more broadly across related species.
Danish National Research Foundation supported Center for Macroecology, Evolution and Climate (DNRF96); and Research Council Faroe Islands, Statoil Faroe Islands, and Selskab for Arktisk Forskning og Teknologi (Society for Arctic Research and Technology) supported this study. Field assistance was provided by Kees H. T. Schreven, Høgni Hammer, Levi H. Hammer, Eyð H. Hammer, Jógvan Hammer and Jens-Kjeld Jensen. Land use permits granted by the board of land owners for Southern and Northern Kirkjuhagi, Fugloy. Harry Jensen granted the permit for land use in Skúvoy. Animal work was approved by the Danish Nature Agency by permission to the Copenhagen Bird Ringing Centre (J.nr. SN 302-009). We thank Bo Markussen at the Data Science Lab, University of Copenhagen, for statistics consultation and Robert W. Furness and the two reviewers for valuable comments on the manuscript.
Light increases the rate of embryonic development: implications for latitudinal trends in incubation period. Functional Ecology 25: 769–776.
The hatching sequence of Ostrich (Struthio camelus) embryos with notes on development as observed by candling. British Poultry Science 36: 67–78.
Arctic Skua. In: Bird Monitoring Methods: A Manual of Techniques for Key UK Species: 244–249. Royal Society for the Protection of Birds, Bedfordshire.
The use of eggs and diet of Great Skuas as biomonitors in the Faroe Islands. PhD thesis, University of Glasgow.
Handbook of the Birds of the World. Lynx Edicions, Barcelona. Hoyt, D. F. 1979. Practical methods of estimating volume and fresh weight of bird eggs. The Auk 96: 73–77.
Using egg density and egg mass techniques for incubation stage assessment to predict hatch dates of Greater Flamingo Phoenicopterus ruber roseus eggs. Wildfowl 54: 131–142.
Using candlers to determine the incubation stage of passerine eggs. Journal of Field Ornithology 67: 660–668.
Estimating the stage of incubation for nests of Greater Prairie-chickens using egg flotation: a float curve for grousers. Grouse News 38: 12–14.
SAS for Windows. SAS Institute, Cary, NC.
Accuracy of egg flotation in determining age of Canada Goose nests. Wildlife Society Bulletin 25: 854–857.
Estimating the date of hatching of eggs of Golden Plover Pluvialis apricaria. Wader Study Group Bull. 55: 19–20.