Towards a continent-wide ecological site condition database using calibrated expert evaluations.

A cost-effective way of undertaking comprehensive, continental-scale, assessments of ecological condition is needed to support large-scale conservation planning, monitoring, reporting, and decision-making. Currently, cross-jurisdictional inconsistency in assessment methods limits the capacity to scale-up monitoring. Here we present a novel way to build a coherent continent-wide site-level ecological condition dataset, using cross-calibration methods to integrate assessments from many observers. We focus on the use of condition assessments from individual expert observers, a currently untapped resource. Our approach has two components: (1) a simple online tool that captures expert assessments at specific locations; (2) a process of calibrating and rescaling disparate expert evaluations that can be applied to the data to provide a consistent dataset for use in conservation assessments. We describe a pilot study, involving 28 experts, who contributed 314 individual site condition assessments across a wide range of ecosystems and regions throughout continental Australia. A correction factor for each expert was used to rescale the contributed site condition assessment scores, based on a set of 77 photographic images, each scored for their condition by multiple experts, using a linear mixed model. Our approach shows strong promise for delivering the volumes of data required to develop continental-scale reference libraries of site condition assessments. Although developed from expert elicitation, the approach could also be used to harmonize the collation of existing condition datasets. The process we demonstrate can also facilitate online citizen scientists to make site condition assessments that can be cross-calibrated using contributed images.

Quantifying 25 years of disease-caused declines in Tasmanian devil populations: host density drives spatial pathogen spread.

Infectious diseases are strong drivers of wildlife population dynamics, however, empirical analyses from the early stages of pathogen emergence are rare. Tasmanian devil facial tumour disease (DFTD), discovered in 1996, provides the opportunity to study an epizootic from its inception. We use a pattern-oriented diffusion simulation to model the spatial spread of DFTD across the species' range and quantify population effects by jointly modelling multiple streams of data spanning 35 years. We estimate the wild devil population peaked at 53 000 in 1996, less than half of previous estimates. DFTD spread rapidly through high-density areas, with spread velocity slowing in areas of low host densities. By 2020, DFTD occupied >90% of the species' range, causing 82% declines in local densities and reducing the total population to 16 900. Encouragingly, our model forecasts the population decline should level-off within the next decade, supporting conservation management focused on facilitating evolution of resistance and tolerance.

Top carnivore decline has cascading effects on scavengers and carrion persistence.

Top carnivores have suffered widespread global declines, with well-documented effects on mesopredators and herbivores. We know less about how carnivores affect ecosystems through scavenging. Tasmania's top carnivore, the Tasmanian devil (Sarcophilus harrisii), has suffered severe disease-induced population declines, providing a natural experiment on the role of scavenging in structuring communities. Using remote cameras and experimentally placed carcasses, we show that mesopredators consume more carrion in areas where devils have declined. Carcass consumption by the two native mesopredators was best predicted by competition for carrion, whereas consumption by the invasive mesopredator, the feral cat (Felis catus), was better predicted by the landscape-level abundance of devils, suggesting a relaxed landscape of fear where devils are suppressed. Reduced discovery of carcasses by devils was balanced by the increased discovery by mesopredators. Nonetheless, carcasses persisted approximately 2.6-fold longer where devils have declined, highlighting their importance for rapid carrion removal. The major beneficiary of increased carrion availability was the forest raven (Corvus tasmanicus). Population trends of ravens increased 2.2-fold from 1998 to 2017, the period of devil decline, but this increase occurred Tasmania-wide, making the cause unclear. This case study provides a little-studied potential mechanism for mesopredator release, with broad relevance to the vast areas of the world that have suffered carnivore declines.

Does Size Matter to Models? Exploring the Effect of Herd Size on Outputs of a Herd-Level Disease Spread Simulator.

Disease spread modeling is widely used by veterinary authorities to predict the impact of emergency animal disease outbreaks in livestock and to evaluate the cost-effectiveness of different management interventions. Such models require knowledge of basic disease epidemiology as well as information about the population of animals at risk. Essential demographic information includes the production system, animal numbers, and their spatial locations yet many countries with significant livestock industries do not have publically available and accurate animal population information at the farm level that can be used in these models. The impact of inaccuracies in data on model outputs and the decisions based on these outputs is seldom discussed. In this analysis, we used the Australian Animal Disease model to simulate the spread of foot-and-mouth disease seeded into high-risk herds in six different farming regions in New Zealand. We used three different susceptible animal population datasets: (1) a gold standard dataset comprising known herd sizes, (2) a dataset where herd size was simulated from a beta-pert distribution for each herd production type, and (3) a dataset where herd size was simplified to the median herd size for each herd production type. We analyzed the model outputs to compare (i) the extent of disease spread, (ii) the length of the outbreaks, and (iii) the possible impacts on decisions made for simulated outbreaks in different regions. Model outputs using the different datasets showed statistically significant differences, which could have serious implications for decision making by a competent authority. Outbreak duration, number of infected properties, and vaccine doses used during the outbreak were all significantly smaller for the gold standard dataset when compared with the median herd size dataset. Initial outbreak location and disease control strategy also significantly influenced the duration of the outbreak and number of infected premises. The study findings demonstrate the importance of having accurate national-level population datasets to ensure effective decisions are made before and during disease outbreaks, reducing the damage and cost.

Risk factors for fouling biomass: evidence from small vessels in Australia.

Invasive non-indigenous species (NIS) are a threat to marine biodiversity and marine reliant industries. Recreational vessels are recognised as an important vector of NIS translocation, particularly domestically. This paper reports on a novel application of multilevel modelling and multiple imputation in order to quantify the relationship between biofouling biomass (wet weight) and the vessel-level characteristics of recreational and fishing vessels. It was found that the number of days since the vessel was last cleaned strongly related to the biofouling biomass, yet differed dependent on vessel type. Similarly, the median number of trips undertaken was related to the biofouling biomass, and varied according to the type of antifouling paint (AF) used. No relationship was found between vessel size and biofouling biomass per sample unit. To reduce the spread of NIS, vessel owners should use an AF paint suitable to their vessel's operational profile, and follow a maintenance schedule according to the paint manufacturer's specifications.

Predicting farm-level animal populations using environmental and socioeconomic variables.

Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics.

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.

Disease-induced decline of an apex predator drives invasive dominated states and threatens biodiversity.

Apex predators are important in protecting biodiversity through top-down influence on food webs. Their loss is linked with competitive release of invasive mesopredators and species extinctions. The Tasmanian devil (Sarcophilus harrisii) has experienced severe declines over a 15-yr period as a novel transmissible cancer has spread across its current geographic range. We surveyed the mammalian community, using hair traps, across the spatial extent of the devil's progressive population decline. We found increased activity of alien invasive species (feral cats, black rats), and reduced small and medium-sized native prey species in response to the timing of the decline. In areas of long-term devil decline, invasive species comprised a significantly larger proportion of the community. The results provide evidence that the devil plays a keystone role in Tasmania's ecosystem with their decline linked to a shift toward an invasive state and biodiversity loss in one of Australia's most intact faunal communities.

Relaxation of risk-sensitive behaviour of prey following disease-induced decline of an apex predator, the Tasmanian devil.

Apex predators structure ecosystems through lethal and non-lethal interactions with prey, and their global decline is causing loss of ecological function. Behavioural changes of prey are some of the most rapid responses to predator decline and may act as an early indicator of cascading effects. The Tasmanian devil (Sarcophilus harrisii), an apex predator, is undergoing progressive and extensive population decline, of more than 90% in long-diseased areas, caused by a novel disease. Time since local disease outbreak correlates with devil population declines and thus predation risk. We used hair traps and giving-up densities (GUDs) in food patches to test whether a major prey species of devils, the arboreal common brushtail possum (Trichosurus vulpecula), is responsive to the changing risk of predation when they forage on the ground. Possums spend more time on the ground, discover food patches faster and forage more to a lower GUD with increasing years since disease outbreak and greater devil population decline. Loss of top-down effects of devils with respect to predation risk was evident at 90% devil population decline, with possum behaviour indistinguishable from a devil-free island. Alternative predators may help to maintain risk-sensitive anti-predator behaviours in possums while devil populations remain low.

Trophic cascades following the disease-induced decline of an apex predator, the Tasmanian devil.

As apex predators disappear worldwide, there is escalating evidence of their importance in maintaining the integrity and diversity of the ecosystems they inhabit. The largest extant marsupial carnivore, the Tasmanian devil (Sarcophilus harrisii) is threatened with extinction from a transmissible cancer, devil facial tumor disease (DFTD). The disease, first observed in 1996, has led to apparent population declines in excess of 95% in some areas and has spread to more than 80% of their range. We analyzed a long-term Tasmania-wide data set derived from wildlife spotlighting surveys to assess the effects of DFTD-induced devil decline on populations of other mammals and to examine the relative strength of top-down and bottom-up control of mesopredators between 2 regions with different environmental conditions. Collection of the data began >10 years before DFTD was first observed. A decrease in devil populations was immediate across diseased regions following DFTD arrival, and there has been no indication of population recovery. Feral cats (Felis catus) increased in areas where the disease was present the longest, and feral cat occurrence was significantly and negatively associated with devils. The smallest mesopredator, the eastern quoll (Dasyurus viverrinus), declined rapidly following DFTD arrival. This result suggests the species was indirectly protected by devils through the suppression of larger predators. Rainfall deficiency was also a significant predictor of their decline. Environmental variables determined the relative importance of top-down control in the population regulation of mesopredators. In landscapes of low rainfall and relatively higher proportions of agriculture and human settlement, top-down forces were dampened and bottom-up forces had the most effect on mesopredators. For herbivore prey species, there was evidence of population differences after DFTD arrival, but undetected environmental factors had greater effects. The unique opportunity to assess population changes over extensive temporal and spatial scales following apex predator loss further demonstrated their role in structuring ecosystems and of productivity in determining the strength of top-down control.

Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis.

Changing ecosystem dynamics are increasing the threat of disease epidemics arising in wildlife populations. Several recent disease outbreaks have highlighted the critical need for understanding pathogen dynamics, including the role host densities play in disease transmission. In Australia, introduced feral cats are of immense concern because of the risk they pose to native wildlife through predation and competition. They are also the only known definitive host of the coccidian parasite, Toxoplasma gondii, the population-level impacts of which are unknown in any species. Australia's native wildlife have not evolved in the presence of cats or their parasites, and feral cats may be linked with several native mammal declines and extinctions. In Tasmania there is emerging evidence that feral cat populations are increasing following wide-ranging and extensive declines in the apex predator, the Tasmanian devil, from a consistently fatal transmissible cancer. We assess whether feral cat density is associated with the seroprevalence of T. gondii in native wildlife to determine whether an increasing population of feral cats may correspond to an increased level of risk to naive native intermediate hosts. We found evidence that seroprevalence of T. gondii in Tasmanian pademelons was lower in the north-west of Tasmania than in the north-east and central regions where cat density was higher. Also, samples obtained from road-killed animals had significantly higher seroprevalence of T. gondii than those from culled individuals, suggesting there may be behavioural differences associated with infection. In addition, seroprevalence in different trophic levels was assessed to determine whether position in the food-web influences exposure risk. Higher order carnivores had significantly higher seroprevalence than medium-sized browser species. The highest seroprevalence observed in an intermediate host was 71% in spotted-tailed quolls (Dasyurus maculatus), the largest mammalian mesopredator, in areas of low cat density. Mesopredator release of cats may be a significant issue for native species conservation, potentially affecting the population viability of many endangered species.