Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management

As a consequence of global climate-driven changes, marine ecosystems are experiencing polewards redistributions of species – or range shifts – across taxa and throughout latitudes worldwide. Research on these range shifts largely focuses on understanding and predicting changes in the distribution of individual species. The ecological effects of marine range shifts on ecosystem structure and functioning, as well as human coastal communities, can be large, yet remain difficult to anticipate and manage. Here, we use qualitative modelling of system feedback to understand the cumulative impacts of multiple species shifts in south-eastern Australia, a global hotspot for ocean warming. We identify range-shifting species that can induce trophic cascades and affect ecosystem dynamics and productivity, and evaluate the potential effectiveness of alternative management interventions to mitigate these impacts. Our results suggest that the negative ecological impacts of multiple simultaneous range shifts generally add up. Thus, implementing whole-of-ecosystem management strategies and regular monitoring of range-shifting species of ecological concern are necessary to effectively intervene against undesirable consequences of marine range shifts at the regional scale. Our study illustrates how modelling system feedback with only limited qualitative information about ecosystem structure and range-shifting species can predict ecological consequences of multiple co-occurring range shifts, guide ecosystem-based adaptation to climate change and help prioritise future research and monitoring

Corrigendum to: Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management

This article was published in GCB (2016/22:2462–2474). The authors of the paper ‘Modelling marine community responses to climate-driven species redistribution to guide monitoring and adaptive ecosystem-based management’ would like to inform readers that the R code supplied as online Supporting Information (SI2), includes an error that affects the colour scaling of the response signs of model variables to long-term perturbations, as shown in Figures 4 and 5. Specifically, in both the figures, a greater proportion of predicted responses ought to be interpreted as ‘ambiguous’ (shown in grey). The overall message of the paper and the general interpretation of the results remain unchanged, as only the visualisation of predicting ambiguity is affected by this error and not the direction and overall patterns of predicted model responses. The authors would like to apologize for the erroneous R code, and for any confusion it may have caused.</p

Predicting missing biomarker data in a longitudinal study of Alzheimer disease

Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD