Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

Optimizing Vaccination Strategies against African Swine Fever Using Spatial Data from Wild Boars in Lithuania.

African swine fever (ASF) is one of the most severe suid diseases, impacting the pig industry and wild suid populations. Once an ASF vaccine is available, identifying a sufficient density of vaccination fields will be crucial to achieve eradication success. In 2020-2023, we live-trapped and monitored 27 wild boars in different areas of Lithuania, in which the wild boars were fed at artificial stations. We built a simulation study to estimate the probability of a successful ASF vaccination as a function of different eco-epidemiological factors. The average 32-day home range size across all individuals was 16.2 km2 (SD = 16.9). The wild boars made frequent visits of short durations to the feeding sites rather than long visits interposed by long periods of absence. A feeding site density of 0.5/km2 corresponded to an expected vaccination rate of only 20%. The vaccination probability increased to about 75% when the feeding site density was 1.0/km2. Our results suggest that at least one vaccination field/km2 should be used when planning an ASF vaccination campaign to ensure that everyone in the population has at least 5-10 vaccination sites available inside the home range. Similar studies should be conducted in the other ecological contexts in which ASF is present today or will be present in the future, with the objective being to estimate a context-specific relationship between wild boar movement patterns and an optimal vaccination strategy.

Estimating the Directional Spread of Epidemics in Their Early Stages Using a Simple Regression Approach: A Study on African Swine Fever in Northern Italy.

The early identification of the spreading patterns of an epidemic infectious disease is an important first step towards the adoption of effective interventions. We developed a simple regression-based method to estimate the directional speed of a disease's spread, which can be easily applied with a limited dataset. We tested the method using simulation tools, then applied it on a real case study of an African Swine Fever (ASF) outbreak identified in late 2021 in northwestern Italy. Simulations showed that, when carcass detection rates were <0.1, the model produced negatively biased estimates of the ASF-affected area, with the average bias being about -10%. When detection rates were >0.1, the model produced asymptotically unbiased and progressively more predictable estimates. The model produced rather different estimates of ASF's spreading speed in different directions of northern Italy, with the average speed ranging from 33 to 90 m/day. The resulting ASF-infected areas of the outbreak were estimated to be 2216 km2, about 80% bigger than the ones identified only thorough field-collected carcasses. Additionally, we estimated that the actual initial date of the ASF outbreak was 145 days earlier than the day of first notification. We recommend the use of this or similar inferential tools as a quick, initial way to assess an epidemic's patterns in its early stages and inform quick and timely management actions.

Estimation of R0 for the Spread of the First ASF Epidemic in Italy from Fresh Carcasses.

After fifty years of spread in the European continent, the African swine fever (ASF) virus was detected for the first time in the north of Italy (Piedmont) in a wild boar carcass in December, 2021. During the first six months of the epidemic, the central role of wild boars in disease transmission was confirmed by more than 200 outbreaks, which occurred in two different areas declared as infected. The virus entered a domestic pig farm in the second temporal cluster identified in the center of the country (Lazio). Understanding ASF dynamics in wild boars is a prerequisite for preventing the spread, and for designing and applying effective surveillance and control plans. The aim of this work was to describe and evaluate the data collected during the first six months of the ASF epidemic in Italy, and to estimate the basic reproduction number (R0) in order to quantify the extent of disease spread. The R0 estimates were significantly different for the two spatio-temporal clusters of ASF in Italy, and they identified the two infected areas based on the time necessary for the number of cases to double (td) and on an exponential decay model. These results (R0 = 1.41 in Piedmont and 1.66 in Lazio) provide quantitative knowledge on the epidemiology of ASF in Italy. These parameters could represent a fundamental tool for modeling country-specific ASF transmission and for monitoring both the spread and sampling effort needed to detect the disease early.

Estimating the risk of environmental contamination by forest users in African Swine Fever endemic areas.

BACKGROUND: African Swine Fever (ASF) is a highly lethal viral disease caused by the African Swine Fever Virus (ASFV), the only virus of the Asfaviridae family, which affects different species of wild and domestic suids, and for which no vaccination or effective medical treatment is currently available. The virus can survive for long periods in the environment, and humans can unintentionally act as vectors through infected fomites, a risk that is linked to the ASF introduction into pig farms. We ran a simulation study, in which we reconstructed the probability process leading to the different forms of human-mediated ASF contamination in ASF endemic areas. We compared the infection risks related to different types of human forest activities and produced estimates of the minimum expected number of human-induced contamination events occurring annually at the scale of some European countries. RESULTS: When analysed on a short temporal scale and in a relatively small spatial context, ASF environmental contamination appeared as a rather unlikely event for most of the simulated forest uses, with contamination probabilities often lower than 0.1%. When scaling up the contamination process to a whole year and to large geographic areas, though, the accumulation of the same forest activities, repeated several times per month within the same patch of forest, produced the expectation that thousands of contamination events would occur each year, with potentially relevant epidemiological consequences. Wild boar supplemental feeding and forest logging emerged as the riskiest activities in terms of contamination probabilities, but risk was highly influenced by the frequency and intensity of the different types of forest use. CONCLUSIONS: The risk of human-mediated ASF environmental contamination should not be disregarded when planning management actions to reduce ASF circulation and prevent its breach into the pig farming system. Supplemental feeding should be strongly reduced or avoided in ASF affected areas. Wild boar hunting, which is often employed as an active management tool in ASF affected areas, should be seen as both a tool for controlling wild boar density and as a potential risk for further contamination. It is essential to implement and enforce strict biosecurity measures for all forest-based human activities in ASF endemic areas.

Combining hunting and intensive carcass removal to eradicate African swine fever from wild boar populations.

African Swine Fever (ASF) is a highly lethal viral disease, which affects different species of wild and domestic suids. After its human-caused introduction in Georgia in 2007, the ASF virus has found a new ecological reservoir in the large and continuous wild boar (Sus scrofa) populations of Eurasia, spreading both eastward and westward. ASF has also breached into the intensive pork meat production system. Although the disease has no zoonotic potential, its consequences on wild boar populations and the economic losses for the pig industry have been dramatic. As no vaccine or effective medical treatment is available to reliably protect wild boar or domestic pigs against ASF, eradication efforts are mainly based on intensive wild boar hunting and on removing a significant portion of the infected wild boar carcasses, which are the main environmental virus reservoir. Both strategies have produced poor results, so far, and ASF is becoming endemic. We compared wild boar hunting and carcass removal as alternative and combined strategies for the eradication of ASF in its endemic state, using a spatially explicit individual-based model, which incorporated the demography and spatial dynamics of a wild boar population, the spatial epidemiology of ASF in its endemic phase, and a management system acting for the eradication of the disease from the population. When no eradication effort was simulated, ASF exhibited a clear and strong tendency to persist and remain endemic in the wild boar population. Both hunting and carcass removal, when used alone, provided either a low power to remove the virus from the population, or required unrealistic field effort. The best performing scenario corresponded to the combined use of a 30% annual hunting rate and of an intensive carcass removal, during a 2-month period in late winter (February-March). Eradicating ASF from wild boar populations remains a hard task. Managers should promote a drastic increase in the effort dedicated to systematically identify and remove as many infected wild boar carcasses as possible from the affected areas, with at least 5-15 carcasses removed for each 100 hunted wild boar.

African swine fever endemic persistence in wild boar populations: Key mechanisms explored through modelling.

African swine fever (ASF) is a serious global concern from an ecological and economic point of view. While it is well established that its main transmission routes comprise contact between infected and susceptible animals and transmission through contaminated carcasses, the specific mechanism leading to its long-term persistence is still not clear. Among others, a proposed mechanism involves the potential role of convalescent individuals, which would be able to shed the virus after the end of the acute infection. Using a spatially explicit, stochastic, individual-based model, we tested: (1) if ASF can persist when transmission occurs only through infected wild boars and infected carcasses; (2) if the animals that survive ASF can play a relevant role in increasing ASF persistence chances; (3) how hunting pressure can affect the ASF probability to persist. The scenario in which only direct and carcass-mediated transmission were contemplated had 52% probability of virus persistence 10 years after the initial outbreak. The inclusion of survivor-mediated transmission corresponded to slightly higher persistence probabilities (57%). ASF prevalence during the endemic phase was generally low, ranging 0.1-0.2%. The proportion of seropositive individuals gradually decreased with time and ranged 4.5-6.6%. Our results indicate that direct and carcass-mediated infection routes are sufficient to explain and justify the long-term persistence of ASF at low wild boar density and the ongoing geographic expansion of the disease front in the European continent. During the initial years of an ASF outbreak, hunting should be carefully evaluated as a management tool, in terms of potential benefits and negative side-effects, and combined with an intensive effort for the detection and removal of wild boar carcasses. During the endemic phase, further increasing hunting effort should not be considered as an effective strategy. Additional effort should be dedicated to finding and removing as many wild boar carcasses as possible.

Evaluation of the Efficiency of Active and Passive Surveillance in the Detection of African Swine Fever in Wild Boar.

African swine fever (ASF) is one of the most severe diseases of pigs and has a drastic impact on pig industry. Wild boar populations play the role of ASF genotype II virus epidemiological reservoir. Disease surveillance in wild boar is carried out either by testing all the wild boar found sick or dead for virus detection (passive surveillance) or by testing for virus (and antibodies) all hunted wild boar (active surveillance). When virus prevalence and wild boar density are low as it happens close to eradication, the question on which kind of surveillance is more efficient in detecting the virus is still open. We built a simulation model to mimic the evolution of the host-parasite interaction in the European wild boar and to assess the efficiency of different surveillance strategies. We constructed a deterministic SIR model, which estimated the probability to detect the virus during the 8 years following its introduction, using both passive and active surveillance. Overall, passive surveillance provided a much larger number of ASF detections than active surveillance during the first year. During subsequent years, both active and passive surveillance exhibited a decrease in their probability to detect ASF. Such decrease, though, was more pronounced for passive surveillance. Under the assumption of 50% of carcasses detection, active surveillance became the best detection method when the endemic disease prevalence was lower than 1.5%, when hunting rate was >60% and when population density was lower than 0.1 individuals/km2. In such a situation, though, the absolute probability to detect the disease was very low with both methods, and finding almost every carcass is the only way to ensure virus detection. The sensitivity analysis shows that carcass search effort is the sole parameter that increases proportionally the chance of ASF virus detection. Therefore, an effort should be made to promote active search of dead wild boar also in endemic areas, since reporting wild boar carcasses is crucial to understand the epidemiological situation in any of the different phases of ASF infection at any wild boar density.

R0 Estimation for the African Swine Fever Epidemics in Wild Boar of Czech Republic and Belgium.

African swine fever (ASF) is a contagious haemorrhagic fever that affects both domesticated and wild pigs. Since ASF reached Europe wild boar populations have been a reservoir for the virus. Collecting reliable data on infected individuals in wild populations is challenging, and this makes it difficult to deploy an effective eradication strategy. However, for diseases with high lethality rate, infected carcasses can be used as a proxy for the number of infected individuals at a certain time. Then R0 parameter can be used to estimate the time distribution of the number of newly infected individuals for the outbreak. We estimated R0 for two ASF outbreaks in wild boar, in Czech Republic and Belgium, using the exponential growth method. This allowed us to estimate both R0 and the doubling time (Td) for those infections. The results are R0 = 1.95, Td = 4.39 for Czech Republic and R0 = 1.65, Td = 6.43 for Belgium. We suggest that, if estimated as early as possible, R0 and Td can provide an expected course for the infection against which to compare the actual data collected in the field. This would help to assess if passive surveillance is properly implemented and hence to verify the efficacy of the applied control measures.

The risks of learning: confounding detection and demographic trend when using count-based indices for population monitoring.

Theory recognizes that a treatment of the detection process is required to avoid producing biased estimates of population rate of change. Still, one of three monitoring programmes on animal or plant populations is focused on simply counting individuals or other fixed visible structures, such as natal dens, nests, tree cavities. This type of monitoring design poses concerns about the possibility to respect the assumption of constant detection, as the information acquired in a given year about the spatial distribution of reproductive sites can provide a higher chance to detect the species in subsequent years. We developed an individual-based simulation model, which evaluates how the accumulation of knowledge about the spatial distribution of a population process can affect the accuracy of population growth rate estimates, when using simple count-based indices. Then, we assessed the relative importance of each parameter in affecting monitoring performance. We also present the case of wolverines (Gulo gulo) in southern Scandinavia as an example of a monitoring system with an intrinsic tendency to accumulate knowledge and increase detectability. When the occupation of a nest or den is temporally autocorrelated, the monitoring system is prone to increase its knowledge with time. This happens also when there is no intensification in monitoring effort and no change in the monitoring conditions. Such accumulated knowledge is likely to increase detection probability with time and can produce severe bias in the estimation of the rate and direction of population change over time. We recommend that a systematic sampling of the population process under study and an explicit treatment of the underlying detection process should be implemented whenever economic and logistical constraints permit, as failure to include detection probability in the estimation of population growth rate can lead to serious bias and severe consequences for management and conservation.

Decomposing risk: landscape structure and wolf behavior generate different predation patterns in two sympatric ungulates.

Recolonizing carnivores can have a large impact on the status of wild ungulates, which have often modified their behavior in the absence of predation. Therefore, understanding the dynamics of reestablished predator-prey systems is crucial to predict their potential ecosystem effects. We decomposed the spatial structure of predation by recolonizing wolves (Canis lupus) on two sympatric ungulates, moose (Alces alces) and roe deer (Capreolus capreolus), in Scandinavia during a 10-year study. We monitored 18 wolves with GPS collars, distributed over 12 territories, and collected records from predation events. By using conditional logistic regression, we assessed the contributions of three main factors, the utilization patterns of each wolf territory, the spatial distribution of both prey species, and fine-scale landscape structure, in determining the spatial structure of moose and roe deer predation risk. The reestablished predator-prey system showed a remarkable spatial variation in kill occurrence at the intra-territorial level, with kill probabilities varying by several orders of magnitude inside the same territory. Variation in predation risk was evident also when a spatially homogeneous probability for a wolf to encounter a prey was simulated. Even inside the same territory, with the same landscape structure, and when exposed to predation by the same wolves, the two prey species experienced an opposite spatial distribution of predation risk. In particular, increased predation risk for moose was associated with open areas, especially clearcuts and young forest stands, whereas risk was lowered for roe deer in the same habitat types. Thus, fine-scale landscape structure can generate contrasting predation risk patterns in sympatric ungulates, so that they can experience large differences in the spatial distribution of risk and refuge areas when exposed to predation by a recolonizing predator. Territories with an earlier recolonization were not associated with a lower hunting success for wolves. Such constant efficiency in wolf predation during the recolonization process is in line with previous findings about the naive nature of Scandinavian moose to wolf predation. This, together with the human-dominated nature of the Scandinavian ecosystem, seems to limit the possibility for wolves to have large ecosystem effects and to establish a behaviorally mediated trophic cascade in Scandinavia.

Home range size variation in a recovering wolf population: evaluating the effect of environmental, demographic, and social factors.

Home range size in mammals is a key ecological trait and an important parameter in conservation planning, and has been shown to be influenced by ecological, demographic and social factors in animal populations. Information on space requirements is especially important for carnivore species which range over very large areas and often come into direct conflict with human interest. We used long-term telemetry-location data from a recovering wolf population in Scandinavia to investigate variation in home range size in relation to environmental and social characteristics of the different packs. Wolves showed considerable variation in home range size, which ranged from 259 to 1,676 km(2). Although wolf density increased fourfold during the study period, we found no evidence that intraspecific competition influenced range size. Local variation in moose density, which was the main prey for most packs, did not influence wolf home range size. Home ranges increased with latitude and elevation and decreased with increased roe deer density. Although prey biomass alone did not influence range size, our data suggest that there is a correlation between habitat characteristics, choice of prey species and possible hunting success, which currently combine to shape home range size in Scandinavian wolves.

Predicting the potential demographic impact of predators on their prey: a comparative analysis of two carnivore-ungulate systems in Scandinavia.

1. Understanding the role of predation in shaping the dynamics of animal communities is a fundamental issue in ecological research. Nevertheless, the complex nature of predator-prey interactions often prevents researchers from modelling them explicitly. 2. By using periodic Leslie-Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator-prey demographic interactions and compared the dynamics of the roe deer-red fox-Eurasian lynx-human harvest system with those of the moose-brown bear-gray wolf-human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula. 3. The functional relationship of both roe deer and moose λ to changes in predation rates from the four predators was remarkably different. Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population λ. Elasticity values of lynx, wolf, fox and bear predation rates were -0·157, -0·056, -0·031 and -0·006, respectively, but varied with both predator and prey densities. 4. Differences in predation impact were only partially related to differences in kill or predation rates, but were rather a result of different distribution of predation events among prey age classes. Therefore, the age composition of killed individuals emerged as the main underlying factor determining the overall per capita impact of predation. 5. Our results confirm the complex nature of predator-prey interactions in large terrestrial mammals, by showing that different carnivores preying on the same prey species can exert a dramatically different demographic impact, even in the same ecological context, as a direct consequence of their predation patterns. Similar applications of this analytical framework in other geographical and ecological contexts are needed, but a more general evaluation of the subject is also required, aimed to assess, on a broader systematic and ecological range, what specific traits of a carnivore are most related to its potential impact on prey species.