Protecting Endangered Species: Do the Main Legislative Tools Work?

It is critical to assess the effectiveness of the tools used to protect endangered species. The main tools enabled under the U.S. Endangered Species Act (ESA) to promote species recovery are funding, recovery plan development and critical habitat designation. Earlier studies sometimes found that statistically significant effects of these tools could be detected, but they have not answered the question of whether the effects were large enough to be biologically meaningful. Here, we ask: how much does the recovery status of ESA-listed species improve with the application of these tools? We used species' staus reports to Congress from 1988 to 2006 to quantify two measures of recovery for 1179 species. We related these to the amount of federal funding, years with a recovery plan, years with critical habitat designation, the amount of peer-reviewed scientific information, and time listed. We found that change in recovery status of listed species was, at best, only very weakly related to any of these tools. Recovery was positively related to the number of years listed, years with a recovery plan, and funding, however, these tools combined explain <13% of the variation in recovery status among species. Earlier studies that reported significant effects of these tools did not focus on effect sizes; however, they are in fact similarly small. One must conclude either that these tools are not very effective in promoting species' recovery, or (as we suspect) that species recovery data are so poor that it is impossible to tell whether the tools are effective or not. It is critically important to assess the effectiveness of tools used to promote species recovery; it is therefore also critically important to obtain population status data that are adequate to that task

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646

Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team

Aim The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Methods Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. Results 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). Conclusions Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement

Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis.

Statistical heterogeneity and small-study effects are 2 major issues affecting the validity of meta-analysis. In this article, we introduce the concept of a limit meta-analysis, which leads to shrunken, empirical Bayes estimates of study effects after allowing for small-study effects. This in turn leads to 3 model-based adjusted pooled treatment-effect estimators and associated confidence intervals. We show how visualizing our estimators using the radial plot indicates how they can be calculated using existing software. The concept of limit meta-analysis also gives rise to a new measure of heterogeneity, termed G(2), for heterogeneity that remains after small-study effects are accounted for. In a simulation study with binary data and small-study effects, we compared our proposed estimators with those currently used together with a recent proposal by Moreno and others. Our criteria were bias, mean squared error (MSE), variance, and coverage of 95% confidence intervals. Only the estimators arising from the limit meta-analysis produced approximately unbiased treatment-effect estimates in the presence of small-study effects, while the MSE was acceptably small, provided that the number of studies in the meta-analysis was not less than 10. These limit meta-analysis estimators were also relatively robust against heterogeneity and one of them had a relatively small coverage error