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Record W1573443299 · doi:10.32800/abc.2004.27.0297

Dispersal and migration

2004· article· en· W1573443299 on OpenAlexaff
Carl J. Schwarz, F. Bairlein

Bibliographic record

VenueAnimal Biodiversity and Conservation · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiological dispersalGeolocationBird migrationRingingBayesian probabilityGeographyEcologyComputer scienceBiologyPopulationArtificial intelligence

Abstract

fetched live from OpenAlex

Ringing of birds unveiled many aspects of avian migration and dispersal movements. However, there is even much more to be explored by the use of ringing and other marks. Dispersal is crucial in understanding the initial phase of migration in migrating birds as it is to understand patterns and processes of distribution and gene flow. So far, the analysis of migration was largely based on analysing spatial and temporal patters of recoveries of ringed birds. However, there are considerable biases and pitfalls in using recoveries due to spatial and temporal variation in reporting probabilities. Novel methods are required for future studies separating the confounding effects of spatial and temporal heterogeneity of recovery data and heterogeneity of the landscape as well. These novel approaches should aim a more intensive and novel use of the existing recovery data by taking advantage of, for instance, dynamic and multistate modeling, should elaborate schemes for future studies, and should also include other marks that allow a more rapid data collection, like telemetry, geolocation and global positioning systems, and chemical and molecular markers. The latter appear to be very useful in the delineating origin of birds and connectivity between breeding and non–breeding grounds. Many studies of migration are purely descriptive. However, King and Brooks (King & Brooks, 2004) examine if movement patterns of dolphins change after the introduction of a gillnet ban. Bayesian methods are an interesting approach to this problem as they provide a meaningful measure of the probability that such a change occurred rather than simple yes/no response that is often the result of classical statistical methods. However, the key difficulty of a general implementation of Bayesian methods is the complexity of the modelling —there is no general userfriendly package that is easily accessible to most scientists. Drake and Alisauskas (Drake & Alisauskas, 2004) examine the philopatric movement of geese using a classic multi–state design. Previous studies of philopaty often rely upon simple return rates —however, good mark–recapture studies do not need to assume equal detection probabilities in space and time. This is likely the most important contribution of multi–state modelling to the study of movement. As with many of these studies, the most pressing problem in the analysis is the explosion in the number of parameters and the need to choose parsimonious modelss to get good precision. Drake and Alisauska demonstrate that model choice still remains an art with a great deal of biological insight being very helpful in the task. There is still plenty of scope for novel methods to study migration. Traditionally, there has been a clear cut distinction between birds being labelled as “migrant” or “resident” on the basis of field observations and qualitative interpretations of patterns of ring–recoveries. However, there are intermediate species where only part of the population migrates (partial migrants) or where different components of the population migrate to different extents (differential migrants). Siriwardena, Wernham and Baillie (Siriwardena et al., 2004) develop a novel method that produces a quantitative index of migratory tendency. The method uses distributions of ringing–to–recovery distances to classify individual species’ patterns of movement relative to those of other species. The areas between species’ cumulative distance distributions are used with multi–dimensional scaling to produce a similarity map among species. This map can be used to investigate the factors that affect the migratory strategies that species adopt, such as body size, territoriality and distribution, and in studies of their consequences for demographic parameters such as annual survival and the timing of breeding. The key assumption of the method is the similar recovery effort of species over space and time. It would be interesting to overlay maps of effort to try and remove any induced artefacts in the data. Differences in timing or the route of migration has often been studies separately. Lokki and Saurola (Lokki & Saurola, 2004) develop an omnibus procedure to test if the migration timing and/or route differ among two populations of birds (e.g. males vs females). It uses a randomization test to calibrate the test statistic. However, it makes the key assumptions about equal recovery effort in time and space so that the method may be most applicable to comparison among species with similar migration timing and movement to keep differential sighting/recovery rates from affecting the result. Of course, it is in these cases where it is most difficulty to separate the groups which will require substantial samples to have good performance. Thorup and Rahbek (Thorup & Rahbek, 2004) provide a framework for accounting for unequal spatial recovery probability investigating the geometric influence of ocean and sea on observed migratory patterns. Taking the data set of Pied Flycatchers (Ficedula hypoleuca) ringed as nestlings in Scandinavia and recovered en route on their initial migration and using a model based on the clock–and–compass innate navigation hypothesis they are showing that geometric constraints explain quite a bit of the variation in ring–recoveries. The model also shows that ring recovery patterns do reflect the migratory patterns, and that they are suitable for an analysis of the concentration of the migratory route which is important for the general use of ringing data in studies of migration. This is important for the general use of ringing data in studies of migration and dispersal. The new approach has also implications for understanding the migratory orientation program. The compiled papers highlight some novel ideas of how to analyse band recoveries to investigate migration routes and migration behaviour as well as dispersal patterns among birds and dolphins. Multistate modeling appears as a valuable tool as it enables to include various covariates and to analyse patterns of movement that change in time, are influenced by weather, or are different between age classes or sex.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.191
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2004
Admission routes1
Has abstractyes

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