Dataset Information

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection.

ABSTRACT: The demographic Allee effect, or depensation, implies positive association between per capita population growth rate and population size at low abundances, thereby lowering growth ability of sparse populations. This can have far-reaching consequences on population recovery ability and colonization success. In the context of marine fishes, there is a widespread perception that Allee effects are rare or non-existent. However, studies that have failed to detect Allee effects in marine fishes have suffered from several fundamental methodological and data limitations. In the present study, we challenge the prevailing perception about the rarity of Allee effects by analysing nine populations of Atlantic herring (Clupea harengus), using Bayesian statistical methods. We find that populations of the same species can show either strong evidence for Allee effects or compensation. We explicitly demonstrate how the evidence for Allee effects is strongly provisional on observations made at low population abundances. We contrast our statistical approach with previous attempts to detect Allee effects and illustrate methodological issues that can lead to erroneous conclusions about the nature of population dynamics at low abundance. The present study demonstrates that there is no substantive scientific basis to support the perception that Allee effects are rare or non-existent in marine fishes.

SUBMITTER: Perala T

PROVIDER: S-EPMC5577492 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection.

Perälä Tommi T Kuparinen Anna A

Proceedings. Biological sciences 20170801 1861

The demographic Allee effect, or depensation, implies positive association between <i>per capita</i> population growth rate and population size at low abundances, thereby lowering growth ability of sparse populations. This can have far-reaching consequences on population recovery ability and colonization success. In the context of marine fishes, there is a widespread perception that Allee effects are rare or non-existent. However, studies that have failed to detect Allee effects in marine fishes ...[more]

PMID: 28855368

Similar Datasets

Project description:Using imperfect tests may lead to biased estimates of disease frequency and measures of association. Many studies have looked into the effect of misclassification on statistical inferences. These evaluations were either within a cross-sectional study framework, assessing biased prevalence, or for cohort study designs, evaluating biased incidence rate or risk ratio estimates based on misclassification at one of the two time-points (initial assessment or follow-up). However, both observations at risk and incident cases can be wrongly identified in longitudinal studies, leading to selection and misclassification biases, respectively. The objective of this paper was to evaluate the relative impact of selection and misclassification biases resulting from misclassification, together, on measures of incidence and risk ratio. To investigate impact on measure of disease frequency, data sets from a hypothetical cohort study with two samples collected one month apart were simulated and analyzed based on specific test and disease characteristics, with no elimination of disease during the sampling interval or clustering of observations. Direction and magnitude of bias due to selection, misclassification, and total bias was assessed for diagnostic test sensitivity and specificity ranging from 0.7 to 1.0 and 0.8 to 1.0, respectively, and for specific disease contexts, i.e., disease prevalences of 5 and 20%, and disease incidences of 0.01, 0.05, and 0.1 cases/animal-month. A hypothetical exposure with known strength of association was also generated. A total of 1,000 cohort studies of 1,000 observations each were simulated for these six disease contexts where the same diagnostic test was used to identify observations at risk at beginning of the cohort and incident cases at its end. Our results indicated that the departure of the estimates of disease incidence and risk ratio from their true value were mainly a function of test specificity, and disease prevalence and incidence. The combination of the two biases, at baseline and follow-up, revealed the importance of a good to excellent specificity relative to sensitivity for the diagnostic test. Small divergence from perfect specificity extended quickly to disease incidence over-estimation as true prevalence increased and true incidence decreased. A highly sensitive test to exclude diseased subjects at baseline was of less importance to minimize bias than using a highly specific one at baseline. Near perfect diagnostic test attributes were even more important to obtain a measure of association close to the true risk ratio, according to specific disease characteristics, especially its prevalence. Low prevalent and high incident disease lead to minimal bias if disease is diagnosed with high sensitivity and close to perfect specificity at baseline and follow-up. For more prevalent diseases we observed large risk ratio biases towards the null value, even with near perfect diagnosis.

Dataset Information

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection.

Publications

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets