Metrics of progress in the understanding and management of threats to Australian birds.

Although evidence-based approaches have become commonplace for determining the success of conservation measures for the management of threatened taxa, there are no standard metrics for assessing progress in research or management. We developed 5 metrics to meet this need for threatened taxa and to quantify the need for further action and effective alleviation of threats. These metrics (research need, research achievement, management need, management achievement, and percent threat reduction) can be aggregated to examine trends for an individual taxon or for threats across multiple taxa. We tested the utility of these metrics by applying them to Australian threatened birds, which appears to be the first time that progress in research and management of threats has been assessed for all threatened taxa in a faunal group at a continental scale. Some research has been conducted on nearly three-quarters of known threats to taxa, and there is a clear understanding of how to alleviate nearly half of the threats with the highest impact. Some management has been attempted on nearly half the threats. Management outcomes ranged from successful trials to complete mitigation of the threat, including for one-third of high-impact threats. Progress in both research and management tended to be greater for taxa that were monitored or occurred on oceanic islands. Predation by cats had the highest potential threat score. However, there has been some success reducing the impact of cat predation, so climate change (particularly drought), now poses the greatest threat to Australian threatened birds. Our results demonstrate the potential for the proposed metrics to encapsulate the major trends in research and management of both threats and threatened taxa and provide a basis for international comparisons of evidence-based conservation science.

Medidas de Progreso en el Entendimiento y el Manejo de las Amenazas que Enfrentan las Aves Australianas Resumen Aunque los métodos basados en evidencias se han vuelto muy comunes para la determinación del éxito de las medidas de conservación del manejo de los taxones amenazados, hoy en día no existen medidas estandarizadas para la evaluación del progreso de la investigación o el manejo. Desarrollamos cinco medidas para cumplir con esta necesidad que tienen los taxones amenazados y para cuantificar la necesidad de una mayor acción y un alivio efectivo de las amenazas. Estas medidas (falta de investigación, éxito de la investigación, falta de manejo, éxito del manejo y porcentaje de reducción de amenazas) pueden agregarse para examinar las tendencias de un taxón individual o las tendencias de las amenazas para múltiples taxones. Probamos la utilidad de estas medidas por medio de su aplicación en aves australianas amenazadas, que parece ser la primera vez que se evalúa el progreso en la investigación y en el manejo de amenazas para el caso de varios taxones amenazados dentro de un grupo faunístico a escala continental. Se ha realizado algún tipo de investigación sobre casi tres cuartas partes de las amenazas conocidas para los taxones, y hay un claro entendimiento de cómo aliviar casi la mitad de las amenazas con el impacto más alto. Se ha intentado algún tipo de manejo con casi la mitad de las amenazas. Los resultados del manejo variaron desde ensayos exitosos hasta la mitigación completa de la amenaza, incluso para un tercio de las amenazas de alto impacto. Tanto el progreso en la investigación como en el manejo tendió a ser mayor para los taxones que estaban siendo monitoreados, o que ocurrían en islas oceánicas. La depredación por gatos tuvo el puntaje más como amenaza potencial. Sin embargo, ha habido poco de éxito en la reducción del impacto de la depredación por gatos, así que ahora el cambio climático (particularmente la sequía) es la mayor amenaza para las aves amenazadas en Australia. Nuestros resultados demuestran el potencial que tienen las medidas propuestas de encapsular las tendencias más importantes en la investigación y en el manejo tanto de las amenazas como de los taxones amenazados y de proporcionar una base para comparaciones internacionales de la ciencia de la conservación basada en evidencias.

Mathematically aggregating experts' predictions of possible futures.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

Reconciling uncertain costs and benefits in Bayes nets for invasive species management.

Bayes nets are used increasingly to characterize environmental systems and formalize probabilistic reasoning to support decision making. These networks treat probabilities as exact quantities. Sensitivity analysis can be used to evaluate the importance of assumptions and parameter estimates. Here, we outline an application of info-gap theory to Bayes nets that evaluates the sensitivity of decisions to possibly large errors in the underlying probability estimates and utilities. We apply it to an example of management and eradication of Red Imported Fire Ants in Southern Queensland, Australia and show how changes in management decisions can be justified when uncertainty is considered.

Modeling species-habitat relationships with spatially autocorrelated observation data.

Spatial autocorrelation in wildlife observation data arises when extrinsic environmental processes and patterns that influence the spatial distribution of wildlife are themselves spatially structured, or when species are subject to intrinsic population processes, causing contagion or dispersion effects. Territoriality, Allee effects, dispersal limitations, and social clustering are examples of intrinsic processes. Both forms of autocorrelation can violate the assumptions of generalized linear regression models, resulting in biased estimation of model coefficients and diminished predictive performance. Such consequences may be avoided for extrinsic autocorrelation when autocorrelated environmental variables are available for use as model covariates, whereas intrinsic spatial autocorrelation requires an alternative modeling approach. The autologistic model provides an approach suited to the binary observations often obtained in wildlife surveys, but its performance has not been tested across widely varying sampling intensities or strengths of intrinsic spatial structure. Here we use simulated data to test the autologistic model under a range of sampling conditions. The autologistic model obtains better fits and substantially better predictive performance than the standard logistic regression model over the full range of sampling designs and intensities tested. We provide a simple Bayesian implementation of the autologistic model, which until now has not been achieved with standard statistical software alone. A step-by-step procedure is given for characterizing and modeling spatial autocorrelation in binary observation data, along with computer code for fitting autologistic models in WinBUGS, a freeware Bayesian analysis package. This approach avoids normal approximations to the pseudo-likelihood, in contrast to previous Bayesian applications of the autologistic model. We provide a sample application of the autologistic model, fitted to survey data for a gliding marsupial in southeastern Australia.