A new approach to automated incubation recess detection using temperature loggers

Rebecca Croston; C. Alex Hartman; Mark P. Herzog; Michael L. Casazza; Joshua T. Ackerman

doi:10.1650/condor-18-6.1

Home/Archive/USGS

USGS · 70199046

A new approach to automated incubation recess detection using temperature loggers

Rebecca Croston·C. Alex Hartman·Mark P. Herzog·Michael L. Casazza·Joshua T. Ackerman

View original source ↗Export metadata ↓

Abstract

Nest attendance during incubation is an important facet of avian nesting behavior, and understanding the number, timing, and duration of incubation recesses can improve our understanding of the factors determining avian reproductive success. Temperature loggers are a low-cost, noninvasive method for studying nest attendance, but processing and interpreting the data present logistical challenges for investigators. We developed an accurate automated method for processing data from temperature loggers to identify incubation recesses. This automated method combines absolute changes in nest temperature over time and changes relative to daily nest-specific variation in temperature to identify incubation recesses. We validated this method through comparison with recesses observed during continuous infrared video monitoring of 3 Mallard ( Anas platyrhynchos ) and 7 Gadwall ( Mareca strepera ) nests in northern California, USA. Of 116 recesses observed on camera, we detected 102 (88%) with automated recess detection. After excluding 7 recesses in which nest temperature did not decrease during the recess, and which would therefore have been undetectable without ancillary data, we detected 102 of 109 (94%) recesses with automated recess detection. The time lag in detecting a hen's departure from her nest (i.e. when the recess had begun) was influenced by ambient temperature, although detection of the recess itself was not. The lag in detecting the start of a recess was (mean ± SD) 6.9 ± 2.7 min when ambient temperatures were below 30°C, and 13.7 ± 3.2 min at temperatures above 30°C. The lag in detecting the end of a recess was 1.7 ± 3.2 min and was not affected by ambient temperature. Recesses observed on camera were slightly longer (178.3 ± 122.2 min) than those estimated with automated recess detection (158.7 ± 93.1 min), with the time lag in detecting the start of a recess under warm ambient temperatures contributing the most to the difference. These results demonstrate the accuracy of the automated method that we have developed for identifying the timing and duration of incubation recesses using nest temperature data. This method was developed using data from dabbling ducks but is readily adaptable to other avian taxa with appropriate changes in user-defined criteria for identifying incubation recesses.

Explore related subjects

The Condor Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Rebecca Croston, C. Alex Hartman, Mark P. Herzog, Michael L. Casazza, Joshua T. Ackerman. 2018. A new approach to automated incubation recess detection using temperature loggers. https://doi.org/10.1650/condor-18-6.1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2020

Migratory status determines resource selection by American Woodcock at an important fall stopover, Cape May, New Jersey

Migration is a period of high activity and exposure during which risks and energetic demand on individuals may be greater than during nonmigratory periods. Stopover locations can help mitigate these threats by providing supplemental energy en route to the animal’s end destination. Effective conservation of migratory species therefore requires an understanding of use of space that provides resources to migratory animals at stopover sites. We conducted a radio-telemetry study of a short-distance migrant, the American Woodcock ( Scolopax minor ), at an important stopover site, the Cape May Peninsula, New Jersey. Our objectives were to describe land-cover types used by American Woodcock and evaluate home range habitat selection for individuals that stopover during fall migration and those that choose to overwinter. We radio-marked 271 individuals and collected 1,949 locations from these birds (0–21 points individual –1 ) over 4 yr (2010 to 2013) to inform resource selection functions of land-cover types and other landscape characteristics by this species. We evaluated these relationships at multiple spatial extents for (1) birds known to have ultimately left the peninsula (presumed migrants), and (2) birds known to have remained on the peninsula into the winter (presumed winter residents). We found that migrants selected deciduous wetland forest, agriculture, mixed shrub, coniferous wetland forest, and coniferous shrub, while wintering residents selected deciduous wetland forest, coniferous shrub, and deciduous shrub. We used these results to develop predictive models of potential habitat: 7.80% of the peninsula was predicted to be potential stopover habitat for American Woodcock (95% classification accuracy) and 4.96% of the peninsula was predicted to be potential wintering habitat (85% classification accuracy). Our study is the first to report habitat relationships for migratory American Woodcock in the coastal U.S. and provides important spatial tools for local and regional managers to support migratory and winter resident woodcock populations into the future.

New Jersey↗

USGS2020

Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain

Conservation status and management priorities are often informed by population trends. Trend estimates can be derived from population surveys or models, but both methods are associated with sources of uncertainty. Many Arctic-breeding shorebirds are thought to be declining based on migration and/or overwintering population surveys, but data are lacking to estimate the trends of some shorebird species. In addition, for most species, little is known about the stage(s) at which population bottlenecks occur, such as breeding vs. nonbreeding periods. We used previously published and unpublished estimates of vital rates to develop the first large-scale population models for 6 species of Arctic-breeding shorebirds in North America, including separate estimates for 3 subspecies of Dunlin. We used the models to estimate population trends and identify life stages at which population growth may be limited. Our model for the arcticola subspecies of Dunlin agreed with previously published information that the subspecies is severely declining. Our results also linked the decline to the subspecies’ low annual survival rate, thus potentially implicating factors during the nonbreeding period in the East Asian-Australasian Flyway. However, our trend estimates for all species showed high uncertainty, highlighting the need for more accurate and precise estimates of vital rates. Of the vital rates, annual survival had the strongest influence on population trend in all taxa. Improving the accuracy, precision, and spatial and temporal coverage of estimates of vital rates, especially annual survival, would improve demographic model-based estimates of population trends and help direct management to regions or seasons where birds are subject to higher mortality.

The Condor↗

USGS2020

At the end of the road: Lessons learned from comparing model- and design-based approaches to estimate population sizes of boreal birds in Alberta, Canada

Estimating population abundance is a challenging task complicated by the amount, type, and quality of available data. Conservationists have relied on design-based estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS) to estimate populations sizes. However, the BBS was not designed to estimate population sizes. We developed models incorporating land cover and climate variables based on roadside and off-road point-count surveys. We calculated spatially explicit, model-based population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, and compared these to PIF estimates. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the two estimators. We showed that the key assumptions of the PIF population size estimator were commonly violated in this region, and the two approaches provided very different population size estimates for most species. The average differences between estimators were explained by differences in the detection distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population size ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population size estimates for these regions need to be treated with caution for certain species. Model-based integration of available data sources and additional sampling can contribute towards more accurate population size estimates for conservation in remote areas of North America.

Alberta↗