Dataset Information

Conclusions beyond support: overconfident estimates in mixed models.

ABSTRACT: Mixed-effect models are frequently used to control for the nonindependence of data points, for example, when repeated measures from the same individuals are available. The aim of these models is often to estimate fixed effects and to test their significance. This is usually done by including random intercepts, that is, intercepts that are allowed to vary between individuals. The widespread belief is that this controls for all types of pseudoreplication within individuals. Here we show that this is not the case, if the aim is to estimate effects that vary within individuals and individuals differ in their response to these effects. In these cases, random intercept models give overconfident estimates leading to conclusions that are not supported by the data. By allowing individuals to differ in the slopes of their responses, it is possible to account for the nonindependence of data points that pseudoreplicate slope information. Such random slope models give appropriate standard errors and are easily implemented in standard statistical software. Because random slope models are not always used where they are essential, we suspect that many published findings have too narrow confidence intervals and a substantially inflated type I error rate. Besides reducing type I errors, random slope models have the potential to reduce residual variance by accounting for between-individual variation in slopes, which makes it easier to detect treatment effects that are applied between individuals, hence reducing type II errors as well.

SUBMITTER: Schielzeth H

PROVIDER: S-EPMC2657178 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Conclusions beyond support: overconfident estimates in mixed models.

Schielzeth Holger H Forstmeier Wolfgang W

Behavioral ecology : official journal of the International Society for Behavioral Ecology 20081127 2

Mixed-effect models are frequently used to control for the nonindependence of data points, for example, when repeated measures from the same individuals are available. The aim of these models is often to estimate fixed effects and to test their significance. This is usually done by including random intercepts, that is, intercepts that are allowed to vary between individuals. The widespread belief is that this controls for all types of pseudoreplication within individuals. Here we show that this ...[more]

PMID: 19461866

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Project description:As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is the general region including the species' range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data-poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data-poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species' congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low-data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species' true occupancy within the post-processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post-processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

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Conclusions beyond support: overconfident estimates in mixed models.

Publications

Conclusions beyond support: overconfident estimates in mixed models.

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