The role of old-growth forests in frequent-fire landscapes

Classic ecological concepts and forestry language regarding old growth are not well suited to frequent-fire landscapes. In frequent-fire, old-growth landscapes, there is a symbiotic relationship between the trees, the understory graminoids, and fire that results in a healthy ecosystem. Patches of old growth interspersed with younger growth and open, grassy areas provide a wide variety of habitats for animals, and have a higher level of biodiversity. Fire suppression is detrimental to these forests, and eventually destroys all old growth. The reintroduction of fire into degraded frequent-fire, old-growth forests, accompanied by appropriate thinning, can restore a balance to these ecosystems. Several areas require further research and study: 1) the ability of the understory to respond to restoration treatments, 2) the rate of ecosystem recovery following wildfires whose level of severity is beyond the historic or natural range of variation, 3) the effects of climate change, and 4) the role of the microbial community. In addition, it is important to recognize that much of our knowledge about these old-growth systems comes from a few frequent-fire forest types

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

OBJECTIVE: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work. MATERIALS AND METHODS: A systematic literature search was conducted by 2 independent reviewers using prespecified keywords. RESULTS: Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles). DISCUSSION: This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods. CONCLUSIONS: A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

OBJECTIVE: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work. MATERIALS AND METHODS: A systematic literature search was conducted by 2 independent reviewers using prespecified keywords. RESULTS: Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles). DISCUSSION: This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods. CONCLUSIONS: A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work

Left, Right, and Center: Strategic Information Acquisition and Diversity in Judicial Panels

In the last fifteen years, a number of empirical studies of multi-member judicial panels have documented a phenomenon popularly known as "panel effects. " Two principal findings of this literature are: (1) the inclusion (non-pivotal) members from outside the dominant ideology on the panel predicts higher reversal rates of administrative agencies that are “like minded ” with the panel’s median voter; and (2) when mixed panels do not reverse, they frequently issue unanimous decisions. These apparently moderating effects of mixed panel composition pose a challenge to conven-tional median voter theory. In the face of this challenge, many scholars have offered their own explanation for panel effects (including collegial-ity; deliberation effects, whistle-blowing, and others). In this paper, we propose a general model that (among other things) predicts panel effects as a byproduct of strategic information acquisition. The kernel of our argument is that (non-pivotal) minority members of mixed panels have incentives to engage in costly searches for information in cases where the majority members would rationally choose not to do so. As a result, the inclusion of ideologically diverse members may induce more information production in a way that increases the likelihood that a mixed panel will overturn ideologically allied agency actors. Our informational account — if true — has normative implications for the composition of judicial panels in particular, and for deliberative groups more generally