Inferring fruit infestation prevalence from a combination of pre-harvest monitoring and consignment sampling data.

International trade in horticultural produce happens under phytosanitary inspection and production protocols. Fruit inspection typically involves the sampling and inspection of either 600-pieces or 2% of packed product within a single consignment destined for export, with the purpose of certification (typically with 95% confidence) that the true infestation level within the consignment in question doesn't exceed a pre-specified design prevalence. Sampling of multiple consignments from multiple production blocks in conjunction with pre-harvest monitoring for pests can be used to provide additional inference on the prevalence of infested fruit within an overall production system subject to similar protocols. Here we develop a hierarchical Bayesian model that combines in-field monitoring data with consignment sample inspection data to infer the prevalence of infested fruit in a production system. The results illustrate how infestation prevalence is influenced by the number of consignments inspected, the detection efficacy of consignment sampling, and in-field monitoring effort and sensitivity. Uncertainty in inspection performance, monitoring methods, and exposure of fruit to pests is accommodated using statistical priors within a Bayesian modelling framework. We demonstrate that pre-harvest surveillance with a sufficient density of traps and moderate detection sensitivity can provide 95% belief that the prevalence of infestation is below 1 × 10 - 6 . In the absence of pre-harvest monitoring, it is still possible to gain high confidence in a very low prevalence of infestation ( < 1 × 10 - 5 ) on the basis of multiple clean samples if the inspection sensitivity during consignment sampling is high and sufficient consignments are inspected. Our work illustrates the cumulative power of in-field surveillance and consignment sampling to update estimates of infestation prevalence.

Population trends in the vulnerable Grey-headed flying-fox, Pteropus poliocephalus; results from a long-term, range-wide study.

Monitoring is necessary for the management of any threatened species if its predicament and status are to improve. Monitoring establishes baseline data for tracking trends in distribution and abundance and is a key tool for informing threatened species management. Across much of the Old World, bats in the genus Pteropus (Pteropodidae, Chiroptera) face significant threats from habitat loss, conflict with humans, and hunting. Despite conflict with humans and their threatened status, few Pteropus are being monitored. Often, this is because of difficulties associated with their high mobility, large and easily disturbed aggregations, and their use of unknown or remote habitat. Here we describe 10 years of results from the National Flying-fox Monitoring Program (NFFMP) for the grey-headed flying-fox, (Pteropus poliocephalus) in Australia. Range-wide quarterly surveys were conducted over a three-day period since November 2012 using standardized methods appropriate to conditions encountered at each roost. For our analysis of the population and its trend, we used a state-space model to account for the ecology of the grey-headed flying-fox and the errors associated with the surveying process. Despite the general perception that the species is in decline, our raw data and the modelled population trend suggest the grey-headed flying-fox population has remained stable during the NFFMP period, with the range also stable. These results indicate that the species' extreme mobility and broad diet bestow it with a high level of resilience to various disturbance events. Long-term, range-wide studies such as this one, are crucial for understanding relatively long-lived and highly nomadic species such as the grey-headed flying-fox. The outcomes of this study highlight the need for such systematic population monitoring of all threatened Pteropus species.

Simulation to investigate site-based monitoring of pest insect species for trade.

Pest insect surveillance using lures is widely used to support market access requirements for traded articles that are hosts or carriers of quarantine pests. Modeling has been used extensively to guide the design of surveillance to support pest free area claims but is less commonly applied to provide confidence in pest freedom or low pest prevalence within sites registered for trade. Site-based surveillance typically needs to detect pests that are already present in the site or that may be entering the site from surrounding areas. We assessed the ability of site-based surveillance strategies to detect pests originating from within or outside the registered site using a probabilistic trapping network simulation model with random-walk insect movement and biologically realistic parameters. For a given release size, time-dependent detection probability was primarily determined by trap density and lure attractiveness, whereas mean step size (daily dispersal) had limited effect. Results were robust to site shape and size. For pests already within the site, detection was most sensitive using regularly spaced traps. Perimeter traps performed best for detecting pests moving into the site, although the importance of trap arrangement decreased with time from release, and random trap placement performed relatively well compared to regularly spaced traps. High detection probabilities were achievable within 7 days using realistic values for lure attractiveness and trap density. These findings, together with the modeling approach, can guide the development of internationally agreed principles for designing site-based surveillance of lure-attractant pests that is calibrated against the risk of non-detection.

Australian Bogong moths Agrotis infusa (Lepidoptera: Noctuidae), 1951-2020: decline and crash.

The Bogong moth Agrotis infusa is well known for its remarkable long-distance migration - a return journey from the plains of southeast Australia to the Australian Alps - as well as for its cultural significance for Indigenous Australians. Each spring, as many as four billion moths are estimated to arrive in the Australian Alps to aestivate in cool mountain caves and in boulder fields, bringing with them a massive annual influx of energy and nutrients critical for the health of the alpine ecosystem. However, a massive decline in moths present at their aestivation sites has occurred over the past 3 years, with only a few individuals present where hundreds of thousands could earlier be found. In order to understand the possible sources of decline, we analysed historical records of Bogong moth numbers at aestivation sites in the Australian Alps, including observations on Mt. Gingera (NSW) in the early 1950s, observations from 1980 onwards in the Snowy Mountains (NSW) and an almost-unbroken series of observations each summer over the past 53 years in three caves at different elevations on Mt. Buffalo (Victoria). This analysis shows that moth numbers were probably steady from 1951 until about 1980, fluctuated and slowly fell from then until 2016 and dramatically crashed in 2017. In the Murray-Darling Basin, the main winter breeding ground of Bogong moths, changes in farming practices, such as increasing land clearing for crops (which has removed around a quarter of a billion moths annually from the mountains compared to pre-European levels), has probably driven some of the decline in Bogong moth numbers observed from 1980 to 2016. The impact of insecticide remains unclear and is in urgent need of further study. Even though we found little evidence that increasing global temperatures per se are responsible for the Bogong moth decline, the Australian climate has nonetheless become drier and warmer over past decades, possibly hampering the survival of immature stages in the breeding areas and confining adult aestivation to gradually higher elevations. The crash in moth numbers from 2017 is most likely due to the recent severe drought in the moth's breeding grounds.

A state-space modelling approach to wildlife monitoring with application to flying-fox abundance.

Monitoring flying-foxes is challenging as their extreme mobility produces highly dynamic population processes, considerable logistic difficulty, and variability in estimated population size. We report on methods for inferring population trend for the population of the spectacled flying-fox (Pteropus conspicillatus) in Australia. Monthly monitoring is conducted at all known roost sites across the species' range in the Wet Tropics Region. The proportion of animals in camps varies seasonally and stochastic environmental events appear to be influential. We develop a state-space model that incorporates these processes and enables inference on total population trends and uses early warning analysis to identify the causes of population dynamics. The model suggests that population growth rate is stable in the absence of cyclones, however, cyclones appear to impact on both survival and reproduction. The population recovered after two cyclones but declined after a third. The modelling estimates a population decline over 15 years of c. 75% (mean r = - 0.12yr-1 and belief of negative trend is c. 83%) suggesting that conservation action is warranted. Our work shows that a state-space modelling approach is a significant improvement on inference from raw counts from surveys and demonstrates that this approach is a workable alternative to other methods.

Making inference from wildlife collision data: inferring predator absence from prey strikes.

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Limits of use of social media for monitoring biosecurity events.

Compared to applications that trigger massive information streams, like earthquakes and human disease epidemics, the data input for agricultural and environmental biosecurity events (ie. the introduction of unwanted exotic pests and pathogens), is expected to be sparse and less frequent. To investigate if Twitter data can be useful for the detection and monitoring of biosecurity events, we adopted a three-step process. First, we confirmed that sightings of two migratory species, the Bogong moth (Agrotis infusa) and the Common Koel (Eudynamys scolopaceus) are reported on Twitter. Second, we developed search queries to extract the relevant tweets for these species. The queries were based on either the taxonomic name, common name or keywords that are frequently used to describe the species (symptomatic or syndromic). Third, we validated the results using ground truth data. Our results indicate that the common name queries provided a reasonable number of tweets that were related to the ground truth data. The taxonomic query resulted in too small datasets, while the symptomatic queries resulted in large datasets, but with highly variable signal-to-noise ratios. No clear relationship was observed between the tweets from the symptomatic queries and the ground truth data. Comparing the results for the two species showed that the level of familiarity with the species plays a major role. The more familiar the species, the more stable and reliable the Twitter data. This clearly presents a problem for using social media to detect the arrival of an exotic organism of biosecurity concern for which public is unfamiliar.

Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework.

Citizen science and crowdsourcing have been emerging as methods to collect data for surveillance and/or monitoring activities. They could be gathered under the overarching term citizen surveillance. The discipline, however, still struggles to be widely accepted in the scientific community, mainly because these activities are not embedded in a quantitative framework. This results in an ongoing discussion on how to analyze and make useful inference from these data. When considering the data collection process, we illustrate how citizen surveillance can be classified according to the nature of the underlying observation process measured in two dimensions-the degree of observer reporting intention and the control in observer detection effort. By classifying the observation process in these dimensions we distinguish between crowdsourcing, unstructured citizen science and structured citizen science. This classification helps the determine data processing and statistical treatment of these data for making inference. Using our framework, it is apparent that published studies are overwhelmingly associated with structured citizen science, and there are well developed statistical methods for the resulting data. In contrast, methods for making useful inference from purely crowd-sourced data remain under development, with the challenges of accounting for the unknown observation process considerable. Our quantitative framework for citizen surveillance calls for an integration of citizen science and crowdsourcing and provides a way forward to solve the statistical challenges inherent to citizen-sourced data.

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port-specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed-effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one-tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports.

Inferring the distribution and demography of an invasive species from sighting data: the red fox incursion into Tasmania.

A recent study has inferred that the red fox (Vulpes vulpes) is now widespread in Tasmania as of 2010, based on the extraction of fox DNA from predator scats. Heuristically, this inference appears at first glance to be at odds with the lack of recent confirmed discoveries of either road-killed foxes--the last of which occurred in 2006, or hunter killed foxes--the most recent in 2001. This paper demonstrates a method to codify this heuristic analysis and produce inferences consistent with assumptions and data. It does this by formalising the analysis in a transparent and repeatable manner to make inference on the past, present and future distribution of an invasive species. It utilizes Approximate Bayesian Computation to make inferences. Importantly, the method is able to inform management of invasive species within realistic time frames, and can be applied widely. We illustrate the technique using the Tasmanian fox data. Based on the pattern of carcass discoveries of foxes in Tasmania, we infer that the population of foxes in Tasmania is most likely extinct, or restricted in distribution and demographically weak as of 2013. It is possible, though unlikely, that that population is widespread and/or demographically robust. This inference is largely at odds with the inference from the predator scat survey data. Our results suggest the chances of successfully eradicating the introduced red fox population in Tasmania may be significantly higher than previously thought.

Quantifying extinction probabilities from sighting records: inference and uncertainties.

Methods are needed to estimate the probability that a population is extinct, whether to underpin decisions regarding the continuation of a invasive species eradication program, or to decide whether further searches for a rare and endangered species could be warranted. Current models for inferring extinction probability based on sighting data typically assume a constant or declining sighting rate. We develop methods to analyse these models in a Bayesian framework to estimate detection and survival probabilities of a population conditional on sighting data. We note, however, that the assumption of a constant or declining sighting rate may be hard to justify, especially for incursions of invasive species with potentially positive population growth rates. We therefore explored introducing additional process complexity via density-dependent survival and detection probabilities, with population density no longer constrained to be constant or decreasing. These models were applied to sparse carcass discoveries associated with the recent incursion of the European red fox (Vulpes vulpes) into Tasmania, Australia. While a simple model provided apparently precise estimates of parameters and extinction probability, estimates arising from the more complex model were much more uncertain, with the sparse data unable to clearly resolve the underlying population processes. The outcome of this analysis was a much higher possibility of population persistence. We conclude that if it is safe to assume detection and survival parameters are constant, then existing models can be readily applied to sighting data to estimate extinction probability. If not, methods reliant on these simple assumptions are likely overstating their accuracy, and their use to underpin decision-making potentially fraught. Instead, researchers will need to more carefully specify priors about possible population processes.

Combining a climatic niche model of an invasive fungus with its host species distributions to identify risks to natural assets: Puccinia psidii Sensu Lato in Australia.

Puccinia psidii sensu lato (s.l.) is an invasive rust fungus threatening a wide range of plant species in the family Myrtaceae. Originating from Central and South America, it has invaded mainland USA and Hawai'i, parts of Asia and Australia. We used CLIMEX to develop a semi-mechanistic global climatic niche model based on new data on the distribution and biology of P. psidii s.l. The model was validated using independent distribution data from recently invaded areas in Australia, China and Japan. We combined this model with distribution data of its potential Myrtaceae host plant species present in Australia to identify areas and ecosystems most at risk. Myrtaceaeous species richness, threatened Myrtaceae and eucalypt plantations within the climatically suitable envelope for P. psidii s.l in Australia were mapped. Globally the model identifies climatically suitable areas for P. psidii s.l. throughout the wet tropics and sub-tropics where moist conditions with moderate temperatures prevail, and also into some cool regions with a mild Mediterranean climate. In Australia, the map of species richness of Myrtaceae within the P. psidii s.l. climatic envelope shows areas where epidemics are hypothetically more likely to be frequent and severe. These hotspots for epidemics are along the eastern coast of New South Wales, including the Sydney Basin, in the Brisbane and Cairns areas in Queensland, and in the coastal region from the south of Bunbury to Esperance in Western Australia. This new climatic niche model for P. psidii s.l. indicates a higher degree of cold tolerance; and hence a potential range that extends into higher altitudes and latitudes than has been indicated previously. The methods demonstrated here provide some insight into the impacts an invasive species might have within its climatically suited range, and can help inform biosecurity policies regarding the management of its spread and protection of valued threatened assets.

Quantifying social distancing arising from pandemic influenza.

Local epidemic curves during the 1918-1919 influenza pandemic were often characterized by multiple epidemic waves. Identifying the underlying cause(s) of such waves may help manage future pandemics. We investigate the hypothesis that these waves were caused by people avoiding potentially infectious contacts-a behaviour termed 'social distancing'. We estimate the effective disease reproduction number and from it infer the maximum degree of social distancing that occurred during the course of the multiple-wave epidemic in Sydney, Australia. We estimate that, on average across the city, people reduced their infectious contact rate by as much as 38%, and that this was sufficient to explain the multiple waves of this epidemic. The basic reproduction number, R0, was estimated to be in the range of 1.6-2.0 with a preferred estimate of 1.8, in line with other recent estimates for the 1918-1919 influenza pandemic. The data are also consistent with a high proportion (more than 90%) of the population being initially susceptible to clinical infection, and the proportion of infections that were asymptomatic (if this occurs) being no higher than approximately 9%. The observed clinical attack rate of 36.6% was substantially lower than the 59% expected based on the estimated value of R0, implying that approximately 22% of the population were spared from clinical infection. This reduction in the clinical attack rate translates to an estimated 260 per 100000 lives having been saved, and suggests that social distancing interventions could play a major role in mitigating the public health impact of future influenza pandemics.

The waiting time for inter-country spread of pandemic influenza.

BACKGROUND: The time delay between the start of an influenza pandemic and its subsequent initiation in other countries is highly relevant to preparedness planning. We quantify the distribution of this random time in terms of the separate components of this delay, and assess how the delay may be extended by non-pharmaceutical interventions. METHODS AND FINDINGS: The model constructed for this time delay accounts for: (i) epidemic growth in the source region, (ii) the delay until an infected individual from the source region seeks to travel to an at-risk country, (iii) the chance that infected travelers are detected by screening at exit and entry borders, (iv) the possibility of in-flight transmission, (v) the chance that an infected arrival might not initiate an epidemic, and (vi) the delay until infection in the at-risk country gathers momentum. Efforts that reduce the disease reproduction number in the source region below two and severe travel restrictions are most effective for delaying a local epidemic, and under favourable circumstances, could add several months to the delay. On the other hand, the model predicts that border screening for symptomatic infection, wearing a protective mask during travel, promoting early presentation of cases arising among arriving passengers and moderate reduction in travel volumes increase the delay only by a matter of days or weeks. Elevated in-flight transmission reduces the delay only minimally. CONCLUSIONS: The delay until an epidemic of pandemic strain influenza is imported into an at-risk country is largely determined by the course of the epidemic in the source region and the number of travelers attempting to enter the at-risk country, and is little affected by non-pharmaceutical interventions targeting these travelers. Short of preventing international travel altogether, eradicating a nascent pandemic in the source region appears to be the only reliable method of preventing country-to-country spread of a pandemic strain of influenza.

Testing the irruptive paradigm of large-herbivore dynamics.

A dominant paradigm in understanding and managing large herbivores is that, after introduction to new range or release from harvesting, the herbivore population increases to peak abundance, crashes to a lower abundance, and then increases to a carrying capacity lower than peak abundance. However, support for the paradigm has been largely anecdotal. We first developed two mathematical models to better describe irruptive dynamics. The models differed in the form of the postcrash growth toward carrying capacity: the "Caughley model" included a time lag that generated dampening oscillations, and the "Leopold model" did not. We then evaluated which of four models (theta-logistic, delayed-logistic, Leopold, and Caughley) best described the dynamics of seven ungulate populations either introduced to new range (n = 5 populations) or released from harvesting (n = 2). The dynamics of six of the populations were best described by irruptive models (two by the Leopold, one by the Caughley, and three by the delayed-logistic), and one of the populations did not display irruptive dynamics (theta-logistic model). The limited data thus support the widespread existence of irruptive dynamics, and we encourage the consideration of irruptive models in studies of large-herbivore dynamics.