Search USGS⌕ Search

USGS · 5211150

Modelling postfledging survival and age-specific breeding probabilities in species with delayed maturity: A case study of Roseate Terns at Falkner Island, Connecticut

Also available from

Abstract

We modeled postfledging survival and age-specific breeding probabilities in endangered Roseate Terns (Sterna dougallii) at Falkner Island, Connecticut, USA using capture-recapture data from 1988-1998 of birds ringed as chicks and as adults. While no individuals bred as 2-yr-olds during this period, about three-quarters of the young that survived and returned as 3-yr-olds nested, and virtually all surviving birds had begun breeding by the time they reached 5 years of age. We found no evidence of temporal variation in age of first breeding of birds from different cohorts. There was significant temporal variation in the annual survival of adults and the survival over the typical 3-yr maturation period of prebreeding birds, with extremely low values for both groups from the 1991 breeding season. The estimated overwinter survival rate (0.62) for adults from 1991-1992 was about three-quarters the usual rate of about 0.83, but the low survival of fledglings from 1991 resulted in less than 25% of the otherwise expected number of young from that cohort returning as breeding birds; this suggests that fledglings suffered a greater proportional decrease in survival than did adults. The survival estimates of young from 1989 and 1990 show that these cohorts were not negatively influenced by the events that decimated the young from 1991, and the young from 1992 and 1993 had above-average survival estimates. The apparent decrease since 1996 in development of fidelity of new recruits to this site is suspected due mainly to nocturnal disturbance and predation of chicks causing low productivity.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 41.21052697828058° to 41.21402980164528° latitude; -72.65531301498413° to -72.6528239250183° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jeffrey A. Spendelow, James D. Nichols, J.E. Hines, Jean-Dominique Lebreton, Roger Pradel. 2010-05-14. Modelling postfledging survival and age-specific breeding probabilities in species with delayed maturity: A case study of Roseate Terns at Falkner Island, Connecticut. https://doi.org/10.1080/02664760120108764

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

KEEP EXPLORING

Related USGS reports

Review of Bayesian dynamic occupancy models and the implications of incomplete sampling

One popular tool for modeling long-term wildlife monitoring data is the dynamic occupancy model. Dynamic occupancy models require detection/non-detection observations recorded during repeated visits to the same site across many seasons. However, sampling all sites repeatedly for multi-year studies is not always possible due to limited funding, sites being difficult to access, the addition of sites after monitoring has begun, or even global pandemics. We explore the ramifications of incomplete sampling of sites across seasons on the modeling of dynamic occupancy data. We review four popular choices for modeling dynamic occupancy data, then, with simulation, we explore the implications of incomplete sampling on the inferences gleaned from the different models. These models are also fit to a data set monitoring American Bullfrog breeding populations along the Yellowstone River floodplain in Montana, USA. Results from the bullfrog data analyses are consistent with the results from the simulation, and help support our modeling conclusions. Ideally, if there are no limitations on data collection, we suggest repeatedly visiting all sites in all seasons, however this is often difficult, or impossible for a variety of reasons. For data already collected, regardless of incomplete sampling, we suggest using the dynamic or auto-logistic models.

Montana↗

Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART

We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (i.e. directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error rule was more likely to choose the correct model than were the other tree-selection rules. The minimum-risk-complexity rule was more likely to choose the correct model than were the other tree-selection rules (1) with weaker relationships and equally important explanatory variables; and (2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.

Journal of Applied Statistics↗

Occam's shadow: levels of analysis in evolutionary ecology - where to next?

Evolutionary ecology is the study of evolutionary processes, and the ecological conditions that influence them. A fundamental paradigm underlying the study of evolution is natural selection. Although there are a variety of operational definitions for natural selection in the literature, perhaps the most general one is that which characterizes selection as the process whereby heritable variation in fitness associated with variation in one or more phenotypic traits leads to intergenerational change in the frequency distribution of those traits. The past 20 years have witnessed a marked increase in the precision and reliability of our ability to estimate one or more components of fitness and characterize natural selection in wild populations, owing particularly to significant advances in methods for analysis of data from marked individuals. In this paper, we focus on several issues that we believe are important considerations for the application and development of these methods in the context of addressing questions in evolutionary ecology. First, our traditional approach to estimation often rests upon analysis of aggregates of individuals, which in the wild may reflect increasingly non-random (selected) samples with respect to the trait(s) of interest. In some cases, analysis at the aggregate level, rather than the individual level, may obscure important patterns. While there are a growing number of analytical tools available to estimate parameters at the individual level, and which can cope (to varying degrees) with progressive selection of the sample, the advent of new methods does not reduce the need to consider carefully the appropriate level of analysis in the first place. Estimation should be motivated a priori by strong theoretical analysis. Doing so provides clear guidance, in terms of both (i) assisting in the identification of realistic and meaningful models to include in the candidate model set, and (ii) providing the appropriate context under which the results are interpreted. Second, while it is true that selection (as defined) operates at the level of the individual, the selection gradient is often (if not generally) conditional on the abundance of the population. As such, it may be important to consider estimating transition rates conditional on both the parameter values of the other individuals in the population (or at least their distribution), and population abundance. This will undoubtedly pose a considerable challenge, for both single- and multi-strata applications. It will also require renewed consideration of the estimation of abundance, especially for open populations. Thirdly, selection typically operates on dynamic, individually varying traits. Such estimation may require characterizing fitness in terms of individual plasticity in one or more state variables, constituting analysis of the norms of reaction of individuals to variable environments. This can be quite complex, especially for traits that are under facultative control. Recent work has indicated that the pattern of selection on such traits is conditional on the relative rates of movement among and frequency of spatially heterogeneous habitats, suggesting analyses of evolution of life histories in open populations can be misleading in some cases.

Journal of Applied Statistics↗