Corey Chivers

Economic Impacts of Non-Native Forest Insects in the Continental United States

Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly

Rising complexity and falling explanatory power in ecology

1.7 billion in local government expenditures and approximately

Validation and calibration of probabilistic predictions in ecology

830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors.

In response to “development, implementation and impact of an automated early warning and response system for sepsis”

Analyses of published research can provide a realistic perspective on the progress of science. By analyzing more than 18 000 articles published by the preeminent ecological societies, we found that (1) ecological research is becoming increasingly statistically complex, reporting a growing number of P values per article and (2) the value of reported coefficient of determination (R2) has been falling steadily, suggesting a decrease in the marginal explanatory power of ecology. These trends may be due to changes in the way ecology is studied or in the way the findings of investigations are reported. Determining the reason for increasing complexity and declining marginal explanatory power would require a critical review of the scientific process in ecology, from research design to dissemination, and could influence the public interpretation and policy implications of ecological findings.

Data and plot scripts for "Rising complexity and falling explanatory power in ecology"

Summary Predictive models in ecology are important for guiding policy and management. However, they are necessarily abstractions of natural systems, making predictive validation imperative. Models, which make predictions about binary outcomes (e.g. Species distribution models, population viability analysis, disease/invasion models), are widespread in the ecological literature. When supporting probability-based management decisions, these predictions need to be assessed with respect to the degree to which predicted probabilities agree with future outcomes. Many predictive models are not validated using external data and are often only assessed in terms of their ability to discriminate between outcomes rather than the degree to which they predicted the correct probabilities. We develop a novel Validation Metric Applied to Probabilistic Predictions (VMAPP), which provides a goodness-of-fit test of calibration for probabilistic prediction models using binary data (e.g. presences and absences in models of species distributions). We analyse the theoretic properties of this test and compare its performance against existing methods, and apply it to a published model in invasion biology, which forecasts the establishment probability of the zooplanktivorous spiny water flea (Bythotrephes longimanus). We selected 102 additional sites to sample four years after the training data were collected and use this independently collected data to assess predictive reliability using VMAPP. Theoretic simulation analysis shows that VMAPP outperforms existing metrics (Coxs regression technique and Hosmer & Lemeshows χ2 test) in terms of statistical power to identify model miscalibration. Further, we find that under realistic conditions where model parameters are estimated (and have associated uncertainty) that VMAPP is more robust, retaining the appropriate type-I error rates (5%) where previous metrics fail (≤17%). Application of VMAPP to a published invasion model using empirical validation data shows that in addition to having high discriminative power, the models probabilistic predictions agree with the observed outcomes as measured by VMAPP. We argue that quantifying ecological predictions as probabilities with associated uncertainty provides the most useful information to support management decisions. Ecological predictions, while uncertain, should still be rigorously validated. Identifying the circumstances in which our predictions deviate from observation can further inform the next generation of the model, bringing prediction and reality ever closer.

Importing risk: quantifying the propagule pressure–establishment relationship at the pathway level

We greatly appreciate the letter (1) regarding our publication describing the development, implementation and impact of an automated Early Warning and Response System (EWRS) for Sepsis at our institution (2). We used established criteria for severe sepsis as predictors in our algorithm to expedite the derivation, validation and implementation of the EWRS. This resulted in implementation across a multi-hospital healthcare system within one year from the date that vital signs first became available in our electronic health record (EHR). Although the ability of our model as a predictive classifier was fair, its clinical utility was robust. With a positive likelihood ratio >5 and screen positive rate <5%, the model enabled the timely identification and care of patients at risk for sepsis, improved sepsis documentation, and potentially reduced mortality, thereby supporting the notion that simple classifiers can compete with more complex algorithms (3). Although the original model implemented could have been improved using other variables and approaches, we were concerned at the time about the diminishing returns of additional complexity, particularly when deploying the model in a production environment with high demands. We thus favored simplicity at all stages, with the knowledge that complexity could be added once the clinical feasibility of the system was established. More recently, we have applied machine learning algorithms to leverage “big data” available from our EHR, using random forest models to predict hospital readmissions (4) and improve sepsis predictions (in an initiative labelled EWRS 2.0). As Drs. Bhattacharjee and Edelson suggest, these methods can have advantages over using more manual regression approaches to select predictors, a particularly cumbersome process when an acutely ill inpatient can generate hundreds of variables and >1,000 data points daily (5). Yet, although machine learning algorithms can theoretically be constructed using all variables in real-time, from an implementation perspective, it can be difficult to continuously collect and maintain such a complete information set for each patient. Therefore, we strive to develop models using subsets of predictors without sacrificing performance. What we’ve learned is that the development of highly accurate predictive algorithms using these approaches is often less complex than the technical and administrative aspects of implementing these algorithms into practice. To address these challenges, we are currently developing an open platform to allow researchers and data scientists to tap into the wealth of data available in the EHR and other connected devices in order to build, test, and deploy novel intelligent predictive solutions. Our goal in developing this platform is to accelerate the development and deployment of innovative solutions. Once in production, algorithm performance can then be improved iteratively as more data (in terms of volume and velocity) become available through technological advances such as streaming health data from wearables and other connected devices. With the implementation of EWRS 1.0, we thus set the stage for the development of high performance predictive models, as well as the implementation of these models into practice, with the ultimate goal of improving the quality and value of care we provide.

A reinforcement learning approach to weaning of mechanical ventilation in intensive care units

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