scholarly journals A Unified Approach to Analyzing Nest Success

The Auk ◽  
2004 ◽  
Vol 121 (2) ◽  
pp. 526-540 ◽  
Author(s):  
Terry L. Shaffer
Keyword(s):  
Logistic Regression ◽  
Linear Models ◽  
Nest Success ◽  
Nest Survival ◽  
Exposure Model ◽  
Survival Models ◽  
Suitable Alternative ◽  
Explanatory Variables ◽  
Exposure Method ◽  
Time Specific

Abstract Logistic regression has become increasingly popular for modeling nest success in terms of nest-specific explanatory variables. However, logistic regression models for nest fate are inappropriate when applied to data from nests found at various ages, for the same reason that the apparent estimator of nest success is biased (i.e. older clutches are more likely to be successful than younger clutches). A generalized linear model is presented and illustrated that gives ornithologists access to a flexible, suitable alternative to logistic regression that is appropriate when exposure periods vary, as they usually do. Unlike the Mayfield method (1961, 1975) and the logistic regression method of Aebischer (1999), the logistic-exposure model requires no assumptions about when nest losses occur. Nest survival models involving continuous and categorical explanatory variables, multiway classifications, and time-specific (e.g. nest age) and random effects are easily implemented with the logistic-exposure model. Application of the model to a sample of Yellow-breasted Chat (Icteria virens) nests shows that logistic-exposure estimates for individual levels of categorical explanatory variables agree closely with estimates obtained with Johnson (1979) constant-survival estimator. Use of the logistic-exposure method to model time-specific effects of nest age and date on survival of Blue-winged Teal (Anas discors) and Mallard (A. platyrhynchos) nests gives results comparable to those reported by Klett and Johnson (1982). However, the logistic-exposure approach is less subjective and much easier to implement than Klett and Johnson's method. In addition, logistic-exposure survival rate estimates are constrained to the (0,1) interval, whereas Klett and Johnson estimates are not. When applied to a sample of Mountain Plover (Charadrius montanus) nests, the logistic-exposure method gives results either identical to, or similar to, those obtained with the nest survival model in program MARK (White and Burnham 1999). I illustrate how the combination of generalized linear models and information-theoretic techniques for model selection, along with commonly available statistical software, provides ornithologists with a powerful, easily used approach to analyzing nest success.

scholarly journals Striped-tailed Yellow-finch nesting success in abandoned mining pits from central Brazilian cerrado

2015 ◽  
Vol 75 (1) ◽  
pp. 191-197 ◽  
Author(s):  
DT. Gressler ◽  
MÂ. Marini
Keyword(s):  
Breeding Season ◽  
Nest Success ◽  
Nest Survival ◽  
Survival Rates ◽  
Survival Models ◽  
Nest Size ◽  
Suitable Alternative ◽  
Daily Survival ◽  
Breeding Habitats ◽  
Nest Height

Suitability of degraded areas as breeding habitats can be tested through assessment of nest predation rates. In this study we estimated nest success in relation to several potential predictors of nest survival in the Stripe-tailed Yellow-finch (Sicalis citrina) breeding in abandoned mining pits at Brasília National Park. We monitored 73 nests during the 2007-breeding season. Predation was the main cause of nest failure (n = 48, 66%); while six nests were abandoned (8%) and 19 nests produced young (26%). Mayfield’s daily survival rates and nest success were 0.94 and 23%, respectively. Our results from nest survival models on program MARK indicated that daily survival rates increase linearly towards the end of the breeding season and decrease as nests aged. None of the nest individual covariates we tested - nest height, nest size, nest substrate, and edge effect - were important predictors of nest survival; however, nests placed on the most common plant tended to have higher survival probabilities. Also, there was no observer effect on daily survival rates. Our study suggests that abandoned mining pits may be suitable alternative breeding habitats for Striped-tailed Yellow-finches since nest survival rates were similar to other studies in the central cerrado region.

scholarly journals Time-Specific Variation in Passerine Nest Survival: New Insights into Old Questions

The Auk ◽  
2005 ◽  
Vol 122 (2) ◽  
pp. 661-672 ◽  
Author(s):  
Todd A. Grant ◽  
Terry L. Shaffer ◽  
Elizabeth M. Madden ◽  
Pamela J. Pietz
Keyword(s):  
Life Histories ◽  
Nest Survival ◽  
Survival Rates ◽  
Egg Laying ◽  
Species Variation ◽  
Specific Information ◽  
Bird Conservation ◽  
Explanatory Variables ◽  
Brood Rearing ◽  
Time Specific

Abstract Understanding nest survival is critical to bird conservation and to studies of avian life history. Nest survival likely varies with nest age and date, but until recently researchers had only limited tools to efficiently address those sources of variability. Beginning with Mayfield (1961), many researchers have averaged survival rates within time-specific categories (e.g. egg and nestling stages; early and late nesting dates). However, Mayfield’s estimator assumes constant survival within categories, and violations of that assumption can lead to biased estimates. We used the logistic-exposure method to examine nest survival as a function of nest age and date in Clay-colored Sparrows (Spizella pallida) and Vesper Sparrows (Pooecetes gramineus) breeding in north-central North Dakota. Daily survival rates increased during egg laying, decreased during incubation to a low shortly after hatch, and then increased during brood rearing in both species. Variation in survival with nest age suggests that traditional categorical averaging using Mayfield’s or similar methods would have been inappropriate for this study; similar variation may bias results of other studies. Nest survival also varied with date. For both species, survival was high during the peak of nest initiations in late May and early June and declined throughout the remainder of the nesting season. On the basis of our results, we encourage researchers to consider models of nest survival that involve continuous time-specific explanatory variables (e.g. nest age or date). We also encourage researchers to document nest age as precisely as possible (e.g. by candling eggs) to facilitate age-specific analyses. Models of nest survival that incorporate time-specific information may provide insights that are unavailable from averaged data. Determining time-specific patterns in nest survival may improve our understanding of predator-prey interactions, evolution of avian life histories, and aspects of population dynamics that are critical to bird conservation.

scholarly journals Mayfield Logistic Regression: A Practical Approach for Analysis of Nest Survival

The Auk ◽  
2004 ◽  
Vol 121 (3) ◽  
pp. 707-716
Author(s):  
Kirsten R. Hazler
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Survival Data ◽  
Habitat Structure ◽  
Nest Survival ◽  
Seasonal Effects ◽  
Analysis Framework ◽  
Explanatory Variables ◽  
Mayfield Method ◽  
Experimental Treatments

Abstract Mayfield logistic regression is a method for analyzing nest-survival data that extends the traditional Mayfield estimator by incorporating explanatory variables (e.g. habitat structure, seasonal effects, or experimental treatments) in a logistic-regression analysis framework. Although Aebischer (1999) previously showed that logistic regression can be used to fit Mayfield models, few ornithologists have put that finding into practice. My purpose here is to reintroduce this underused method of nest-survival analysis, to compare its performance to that of a dedicated survival-analysis program (MARK), and to provide a practical guide for its use. Like the traditional Mayfield method, Mayfield logistic regression accounts for the num ber of “exposure days” for each nest and allows for uncertain fates (censoring), thus avoiding the bias introduced by typical applications of logistic regression. Mayfield logistic regression should be widely applicable when nests are found at various stages in the nesting cycle and multiple explanatory variables influencing nest survival are of interest.

Nest cameras do not affect nest survival in a meadow-nesting shorebird

2020 ◽  
pp. 1-10
Author(s):  
VOLKER SALEWSKI ◽  
LUIS SCHMIDT
Keyword(s):  
Nest Survival ◽  
Bird Species ◽  
Conservation Strategies ◽  
Survival Models ◽  
Northern Germany ◽  
Term Study ◽  
Long Term Study ◽  
Limosa Limosa ◽  
Study Sites

Summary Identifying the fate of birds’ nests and the causes of breeding failure is often crucial for the development of conservation strategies for threatened species. However, collecting these data by repeatedly visiting nests might itself contribute to nest failure or bias. To solve this dilemma, automatic cameras have increasingly been used as a time-efficient means for nest monitoring. Here, we consider whether the use of cameras itself may influence hatching success of nests of the Black-tailed Godwit Limosa limosa at two long-term study sites in northern Germany. Annually between 2013 and 2019, cameras were used to monitor godwit nests. In 2014 and 2019, nests were randomly equipped with cameras or not, and nest survival checked independently of the cameras. Nest-survival models indicated that survival probabilities varied between years, sites and with time of the season, but were unaffected by the presence of cameras. Even though predation is the main cause of hatching failure in our study system, we conclude that predators did not learn to associate cameras with food either when the cameras were initially installed or after they had been used for several years. Cameras were thus an effective and non-deleterious tool to collect data for conservation in this case. As other bird species may react differently to cameras at their nests, and as other sets of predators may differ in their ability to associate cameras with food, the effect of cameras on breeding success should be carefully monitored when they are used in a new study system.

Life Course Socioeconomic Position, Intergenerational Social Mobility, and Hypertension Incidence in ELSA-Brasil

2021 ◽  
Author(s):  
José Aparecido Soares Lopes ◽  
Luana Giatti ◽  
Rosane Harter Griep ◽  
Antonio Alberto da Silva Lopes ◽  
Sheila Maria Alvim Matos ◽  
...  
Keyword(s):  
Life Course ◽  
Social Mobility ◽  
Socioeconomic Position ◽  
Linear Models ◽  
Sociodemographic Factors ◽  
Incidence Rates ◽  
Explanatory Variables ◽  
Rate Ratios

Abstract Background Life course epidemiology is a powerful framework to unravel the role of socioeconomic position (SEP) disparities in hypertension (HTN). This study investigated whether life course SEP is associated with HTN incidence. Specifically, to test whether cumulative low SEP throughout life and unfavorable intergenerational social mobility increased HTN incidence. METHODS Longitudinal analysis of 8,754 ELSA-Brasil participants without HTN or cardiovascular in visit 1 (2008–2010). The response variable was the incidence of HTN between visits 1 and 2 (2012–2014). The explanatory variables were childhood, youth, and adulthood SEP, cumulative low SEP, and intergenerational social mobility. Associations were estimated by incidence rate ratios (IRRs) obtained by generalized linear models, with Poisson distribution and logarithmic link function, after adjustment for sociodemographic, behavioral, and health factors. RESULTS The incidence of HTN was 43.2/1,000 person-years, being higher in males, elderly (70–74 years), self-declared black, and low SEP individuals. After considering sociodemographic factors, low SEP in childhood, youth, and adulthood remained statistically associated with increased HTN incidence. Individuals in the third (IRR: 1.26; 95% confidence interval (CI): 1.11–1.44) and fourth top quartiles (IRR: 1.29; 95% CI: 1.11–1.49) of cumulative low SEP, vs. first, as well as those with low stable intergenerational trajectory (IRR: 1.29; 95% CI: 1.16–1.43), vs. high stable, also had increased HTN incidence rates. Conclusions Socioeconomic disparities at all phases of the life cycle appear to raise HTN incidence rates, being the individuals with greater accumulation of exposure to low SEP and with more unfavorable intergenerational mobility at greatest risk, even in a short follow-up time.

TIME-SPECIFIC VARIATION IN PASSERINE NEST SURVIVAL: NEW INSIGHTS INTO OLD QUESTIONS

The Auk ◽  
2005 ◽  
Vol 122 (2) ◽  
pp. 661 ◽  
Author(s):  
Todd A. Grant ◽  
Terry L. Shaffer ◽  
Elizabeth M. Madden ◽  
Pamela J. Pietz
Keyword(s):  
Nest Survival ◽  
Specific Variation ◽  
Time Specific

Car Ownership and the Built Environment: A Spatial Modeling Approach

2021 ◽  
pp. 036119812110494
Author(s):  
Jerome Laviolette ◽  
Catherine Morency ◽  
Owen D. Waygood ◽  
Konstadinos G. Goulias
Keyword(s):  
Built Environment ◽  
Spatial Dependence ◽  
Linear Models ◽  
Census Data ◽  
Model Specification ◽  
Car Ownership ◽  
Grocery Stores ◽  
Modeling Framework ◽  
Explanatory Variables ◽  
Environment Variables

Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

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