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Joint species distribution models (JSDMs) simultaneously model the distributions of multiple species, while accounting for residual co‐occurrence patterns. Despite increasing adoption of JSDMs in the literature, the question of how to define and evaluate JSDM predictions has only begun to be explored.We define four different JSDM prediction types that correspond to different aspects of species distribution and community assemblage processes. Marginal predictions are environment‐only predictions akin to predictions from single‐species models; joint predictions simultaneously predict entire community assemblages; and conditional marginal and conditional joint predictions are made at the species or assemblage level, conditional on the known occurrence state of one or more species at a site. We define five different classes of metrics that can be used to evaluate these types of predictions: threshold‐dependent, threshold‐independent, community dissimilarity, species richness and likelihood metrics.We illustrate different prediction types and evaluation metrics using a case study in which we fit a JSDM to a frog occurrence dataset collected in Melbourne, Australia.Joint species distribution models present opportunities to investigate the facets of species distribution and community assemblage processes that are not possible to explore with single‐species models. We show that there are a variety of different metrics available to evaluate JSDM predictions, and that choice of prediction type and evaluation metric should closely match the questions being investigated. [ABSTRACT FROM AUTHOR]

We examine the seasonality of asthma-related hospital admissions in Melbourne, Australia, in particular the contribution and predictability of episodic thunderstorm asthma. Using a time-series ecological approach based on asthma admissions to Melbourne metropolitan hospitals, we identified seasonal peaks in asthma admissions that were centred in late February, June and mid-November. These peaks were most likely due to the return to school, winter viral infections and seasonal allergies, respectively. We performed non-linear statistical regression to predict daily admission rates as functions of the seasonal cycle, weather conditions, reported thunderstorms, pollen counts and air quality. Important predictor variables were the seasonal cycle and mean relative humidity in the preceding two weeks, with higher humidity associated with higher asthma admissions. Although various attempts were made to model asthma admissions, none of the models explained substantially more variation above that associated with the annual cycle. We also identified a list of high asthma admissions days (HAADs). Most HAADs fell in the late-February return-to-school peak and the November allergy peak, with the latter containing the greatest number of daily admissions. Many HAADs in the spring allergy peak may represent episodes of thunderstorm asthma, as they were associated with rainfall, thunderstorms, high ambient grass pollen levels and high humidity, a finding that suggests thunderstorm asthma is a recurrent phenomenon in Melbourne that occurs roughly once per five years. The rarity of thunderstorm asthma events makes prediction challenging, underscoring the importance of maintaining high standards of asthma management, both for patients and health professionals, especially during late spring and early summer. [ABSTRACT FROM AUTHOR]

Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large. [ABSTRACT FROM AUTHOR]

Active adaptive management balances the requirements of management with the need to learn about the system being managed, which leads to better decisions. It is difficult to judge the benefit of management actions that accelerate information gain, relative to the benefit of making the best management decision given what is known at the time. We present a first step in developing methods to optimize management decisions that incorporate both uncertainty and learning via adaptive management. We assumed a manager can allocate effort to discrete units (e.g., areas for revegetation or animals for reintroduction), the outcome can be measured as success or failure (e.g., the revegetation in an area is successful or the animal survives and breeds), and the manager has two possible management options from which to choose. We further assumed that there is an annual budget that may be allocated to one or both of the two options and that the manager must decide on the allocation. We used Bayesian updating of the probability of success of the two options and stochastic dynamic programming to determine the optimal strategy over a specified number of years. The costs, level of certainty about the success of the two options, and the timeframe of management all influenced the optimal allocation of the annual budget. In addition, the choice of management objective had a large influence on the optimal decision. In a case study of Merri Creek, Melbourne, Australia, we applied the approach to determining revegetation strategies. Our approach can be used to determine how best to manage ecological systems in the face of uncertainty. [ABSTRACT FROM AUTHOR]

We examine the logic of designing nature reserves to understand better how to integrate the concepts of representativeness and persistence. Simple models of viability are used to evaluate how the expected number of species in the reserve system changes with variation in the risk of extinction among species, their rate of occurrence, and the distribution of species. The optimal size of individual reserves increased with the mean and variance of the probability of extinction among species and with the rate at which the risk of extinction declines with the cost of each reserve. In contrast, the rate of occurrence of species within reserves and their rate of accumulation with increasing reserve area had a relatively minor influence on the optimal size of reserves. Patterns of endemism were most important for the location of reserves. Including differences among species in the analysis reduced the optimal number of individual reserves (and increased the size of each) when operating under a fixed budget compared with reserve designs based on single species. A case study in the city of Melbourne, Australia, demonstrates the conservation value of small (∼1 ha) grassland reserves and the underrepresentation of Melbourne's volcanic plains in the region's conservation network. [ABSTRACT FROM AUTHOR]

Over the previous three decades, the rainbow lorikeet ( Trichoglossus haematodus Family Psittacidae) has increased in urbanized areas of Australia. To help understand the nature of this increase, we investigated the influence of road density, tree cover and season on the occurrence of the rainbow lorikeet in the Melbourne region. Bayesian logistic regression was used to construct models to predict the occurrence of rainbow lorikeets, using Birds Australia atlas data at 207 2-ha sites. The results demonstrate a strong relationship between tree cover and urbanization and the distribution of the species. The best model incorporated quadratic terms for road density and tree cover, and interaction terms, as well as season as a categorical variable. Probability of occurrence of rainbow lorikeets was highest at medium tree cover (40% to 70% of the site covered) and medium road density (9% to 12% of the surrounding area covered by roads). There was a close correspondence between the predictions of the model and new observations from bird surveys conducted at randomly selected field sites. The increased abundance of the species in urban areas has occurred despite a paucity of hollows that would act as suitable nesting sites, suggesting that only a small proportion of the population is breeding in these areas. [ABSTRACT FROM AUTHOR]