Estimating age-dependent survival from age-aggregated ringing data-extending the use of historical records.

Bird ring-recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid-20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring-recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age-dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651-795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring-recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651-795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring-recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.

Modelling the progression of bird migration with conditional autoregressive models applied to ringing data.

Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908-2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area ('progression of migration') and to investigate the change in timing of migration over the same areas between three time periods (1908-1969, 1970-1990, 1991-2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals.

Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time.

The growing need for baseline data against which efforts to reduce the rate of biodiversity loss can be judged highlights the importance of long-term datasets, some of which are as old as ecology itself. We review methods of evaluating change in biodiversity at the community level using these datasets, and contrast whole-community approaches with those that combine information from different species and habitats. As all communities experience temporal turnover, one of the biggest challenges is distinguishing change that can be attributed to external factors, such as anthropogenic activities, from underlying natural change. We also discuss methodological issues, such as false alerts and modifications in design, of which users of these data sets need to be aware.

Factors influencing detection of density dependence in British birds : I. Population trends.

We question why density dependence has remained elusive in series of annual abundances of British birds. In particular, an earlier study reported that significant temporal trends in abundances occur in up to 74% of time series from the Common Birds Census. Several studies showed that such trends can hinder detection of density dependence. Temporal trends do not preclude the presence of density dependence and two published tests for density dependence include temporal trends in the null hypothesis model. We explore the extent to which detection of density dependence was hindered by temporal trends in bird abundance data. We used a conservative method to test for trends, which found significant (P<0.05) linear population trends in only 7 of 60 time series of abundances (of 17-31 years) compiled from the Common Birds Census data. However, both of the tests for density dependence that allow for trends and a third method gave P-values that were strongly influenced by the strength of trends, including trends that were not significant (P>0.05). This shows that density dependence may be falsely rejected or detected when trends are present, even when these trends are weak and not statistically significant. To circumvent this problem we detrended the time-series prior to testing for the presence of density dependence. To minimize subjectivity we used simulated time series to check that this procedure did not increase the level of type I error (false rejection of density independence). Additionally, we confirmed that the method gave acceptable levels of type II error, where the test fails to reject density independence in series generated using a density dependent model. This showed that the detrending method was acceptable and represents a major improvement in our ability to detect density dependence in time series that contain temporal trends. Detrending the bird time series increased the number of series in which significant (P<0.05) density dependence was found from 10 (17%), when trends are ignored, to 27 (45%) when series are detrended. However, this rate of 45% is still surprisingly low by comparison to other taxa, and we believe that other factors may contribute to this, which we explore in the second of this pair of papers.

Factors influencing detection of density dependence in British birds : II. Longevity and population variability.

If censuses are taken at less than generation intervals, the number of successive censuses in which a given individual is recorded will depend on longevity. Repeatedly recording the same individuals could produce under-estimates of population variability and influence detection of density dependence. We investigated this possibility in 60 time series of abundances of British birds compiled from the Common Birds Census data and then used simple population models to illustrate the proposed mechanism. Species had average lifespans of 2-10 years and were censused annually. Density dependence was detected (at P<0.05) much more frequently in bird species with long lifespans than in those with short lifespans; 75% of the 12 longest-lived species showed density dependence compared to 46% of all species. Population variability measured in annual censuses (termed "annual variability") was lower in bird species with longer lifespans. We used discrete time models based on difference equations to demonstrate how longevity influences population variability and detection of density dependence in series of annual censuses. A model in which only first-year birds experienced density dependence was rejected because annual variability was greater and detection of density dependence was less likely when longevity was greater, the opposite of the observed effects of longevity in birds. A model in which all age classes experienced density dependence gave time series with lower annual variability and in which density dependence was detected more frequently when longevity was greater, which is the pattern observed in British birds. Analysis of data from this model showed that the amount of density dependence actually present caused only small changes in annual variability, whereas detection of density dependence from simulated series was strongly influenced by annual variability. The high annual variability of series from short-lived bird species could mask any density dependence that was present. Correcting for trends lead us to detect density dependence in 75% of the 12 longest lived bird species. There is no reason to believe that this rate is not also representative of short-lived species.