Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies.

Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach.

Public investment does not crowd out private supply of environmental goods on private land.

In landscapes where private land tenure is prevalent, public funds for ecological landscape restoration are sometimes spent subsidising the revegetation of cleared land, and the protection of remnant vegetation from livestock. However, the total area treated may be unclear because such projects are not always recorded, and landholders may undertake similar activities without subsidisation. In the absence of empirical data, in the state of Victoria, Australia, a reporting assumption has been employed that suggests that wholly privately funded sites match publicly subsidised sites on a hectare for hectare basis (a so-called "x2" assumption). Conversely, the "crowding out" theory of investment in public goods such as environmental benefits suggests that public investment may supplant private motivation. Using aerial photography we mapped the extent of revegetation, native vegetation fencing and restoration on 71 representative landholdings in rural south-eastern Australia. We interviewed each landholder and recorded the age and funding model of each site. Contrary to the local "x2" reporting assumption, about 75% of the total area of the 412 sites was from subsidised sites, and that proportion was far higher for the period after 1997. However, rather than displacing unsubsidised activity, our modelling showed that landholders who had recently been subsidised for a project were more likely to have subsequently completed unsubsidised work. This indicates that, at least in terms of medium-term economic impact, the large increase in public subsidies did not diminish privately funded activity, as might be expected according to the theory of crowding out.

Exploring how functional traits modulate species distributions along topographic gradients in Baxian Mountain, North China.

The associations between functional traits and species distributions across environments have attracted increasing interest from ecologists and can enhance knowledge about how plants respond to the environments. Here, we applied a hierarchical generalized linear model to quantifying the role of functional traits in plant occurrence across topographic gradients. Functional trait data, including specific leaf area, maximum height, seed mass and stem wood density, together with elevation, aspect and slope, were used in the model. In our results, species responses to elevation and aspect were modulated by maximum height and seed mass. Generally, shorter tree species showed positive responses to incremental elevation, while this trend became negative as the maximum height exceeded 22 m. Most trees with heavy seeds (> 1 mg) preferred more southerly aspects where the soil was drier, and those light-seed trees were opposite. In this study, the roles of maximum height and seed mass in determining species distribution along elevation and aspect gradients were highlighted where plants are confronted with low-temperature and soil moisture deficit conditions. This work contributes to the understanding of how traits may be associated with species occurrence along mesoscale environmental gradients.

When less is more: a comparison of models to predict fluoride accumulation in free-ranging kangaroos.

Vegetation contaminated by industrial fluoride emissions can cause disease in herbivorous mammals. Spatially explicit exposure models offer a quantitative approach for evaluating and managing the potentially toxic effects of chronic fluoride consumption on wildlife. We monitored eastern grey kangaroos (Macropus giganteus) inhabiting a high-fluoride environment in the buffer zone of an aluminium smelter in southeastern Australia between 2010 and 2013. We measured fluoride levels at 19 pasture sites and determined the foraging range of 37 individual kangaroos. A series of generalised linear models were developed to estimate bone fluoride accumulation as a function of pasture exposure. Model outputs were compared to identify the most appropriate predictive tool for kangaroo bone fluoride accumulation relative to exposure. Accounting for age there was a negative association between bone fluoride concentration and distance of the central emission point from both the mean centre of foraging range and the point of death. The mean foraging range centre was the best predictor, with point of death just as suitable (and simpler), whereas more complex parameters such as monthly and cumulative fluoride exposure were poor predictors of bone fluoride concentration. The more complex dietary fluoride exposure estimates did not improve predictive capability compared with the simple, spatial models. We conclude that in actively managed wildlife populations, simple, locally validated models can provide estimates of bone fluoride accumulation sufficient to support decision-making.

Identifying species at coextinction risk when detection is imperfect: Model evaluation and case study.

Losing a species from a community can cause further extinctions, a process also known as coextinction. The risk of being extirpated with an interaction partner is commonly inferred from a species' host-breadth, derived from observing interactions between species. But observational data suffers from imperfect detection, making coextinction estimates highly unreliable. To address this issue and to account for data uncertainty, we fit a hierarchical N-mixture model to individual-level interaction data from a mutualistic network. We predict (1) with how many interaction partners each species interacts (to indicate their coextinction risk) and (2) how completely the community was sampled. We fit the model to simulated interactions to investigate how variation in sampling effort, interaction probability, and animal abundances influence model accuracy and apply it to an empirical dataset of flowering plants and their insect visitors. The model performed well in predicting the number of interaction partners for scenarios with high abundances, but indicated high parameter uncertainty for networks with many rare species. Yet, model predictions were generally closer to the true value than the observations. Our mutualistic plant-insect community most closely resembled the scenario of high interaction rates with low abundances. Median estimates of interaction partners were frequently much higher than the empirical data indicate, but uncertainty was high. Our analysis suggested that we only detected 14-59% of the flower-visiting insect species, indicating that our study design, which is common for pollinator studies, was inadequate to detect many species. Imperfect detection strongly affects the inferences from observed interaction networks and hence, host specificity, specialisation estimates and network metrics from observational data may be highly misleading for assessing a species' coextinction risks. Our study shows how models can help to estimate coextinction risk, but also indicates the need for better data (i.e., intensified sampling and individual-level observations) to reduce uncertainty.

Dental fluorosis and skeletal fluoride content as biomarkers of excess fluoride exposure in marsupials.

Particulate and gaseous fluoride emissions contaminate vegetation near fluoride-emitting industries, potentially impacting herbivorous wildlife in neighboring areas. Dental fluorosis has been associated with consumption of fluoride-contaminated foliage by juvenile livestock and wildlife in Europe and North America. For the first time, we explored the epidemiology and comparative pathology of dental fluorosis in Australian marsupials residing near an aluminium smelter. Six species (Macropus giganteus, Macropus rufogriseus, Wallabia bicolor, Phascolarctos cinereus, Trichosurus vulpecula, Pseudocheirus peregrinus) demonstrated significantly higher bone fluoride levels in the high (n=161 individuals), compared to the low (n=67 individuals), fluoride areas (p<0.001). Necropsy examinations of all six species from the high-fluoride area near the smelter revealed dental lesions considered characteristic of dental fluorosis in eutherian mammals. Within the high-fluoride area, 67% of individuals across the six species showed dental enamel lesions, compared to 3% in the low-fluoride areas. Molars that erupted before weaning were significantly less likely to display pathological lesions than those developing later, and molars in the posterior portion of the dental arcade were more severely fluorotic than anterior molars in all six species. The severity of dental lesions was positively associated with increasing bone fluoride levels in all species, revealing a potential biomarker of excess fluoride exposure.

The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy.

Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data-derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.