Effects of pulsed resources on the dynamics of seed consumer populations: a comparative demographic study in wild boar

Classical molecular analyses of Mycobacterium bovis based on spoligotyping and Variable Number Tandem Repeat (MIRU-VNTR) brought the first insights into the epidemiology of animal tuberculosis (TB) in Portugal, showing high genotypic diversity of circulating strains that mostly cluster within the European 2 clonal complex. Previous surveillance provided valuable information on the prevalence and spatial occurrence of TB and highlighted prevalent genotypes in areas where livestock and wild ungulates are sympatric. However, links at the wildlife–livestock interfaces were established mainly via classical genotype associations. Here, we apply whole genome sequencing (WGS) to cattle, red deer and wild boar isolates to reconstruct the M. bovis population structure in a multi-host, multi-region disease system and to explore links at a fine genomic scale between M. bovis from wildlife hosts and cattle. Whole genome sequences of 44 representative M. bovis isolates, obtained between 2003 and 2015 from three TB hotspots, were compared through single nucleotide polymorphism (SNP) variant calling analyses. Consistent with previous results combining classical genotyping with Bayesian population admixture modelling, SNP-based phylogenies support the branching of this M. bovis population into five genetic clades, three with apparent geographic specificities, as well as the establishment of an SNP catalogue specific to each clade, which may be explored in the future as phylogenetic markers. The core genome alignment of SNPs was integrated within a spatiotemporal metadata framework to further structure this M. bovis population by host species and TB hotspots, providing a baseline for network analyses in different epidemiological and disease control contexts. WGS of M. bovis isolates from Portugal is reported for the first time in this pilot study, refining the spatiotemporal context of TB at the wildlife–livestock interface and providing further support to the key role of red deer and wild boar on disease maintenance. The SNP diversity observed within this dataset supports the natural circulation of M. bovis for a long time period, as well as multiple introduction events of the pathogen in this Iberian multi-host system.

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Priority areas identified through spatial habitat suitability index and network analysis: Wild boar populations as proxies for tigers in and around the Hupingshan and Houhe National Nature Reserves

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.145067 ◽

2021 ◽

Vol 774 ◽

pp. 145067

Author(s):

Tao Tang ◽

Jiping Li ◽

Hua Sun ◽

Chao Deng

Keyword(s):

Network Analysis ◽

Wild Boar ◽

Habitat Suitability ◽

Nature Reserves ◽

Habitat Suitability Index ◽

Priority Areas ◽

Suitability Index ◽

National Nature Reserves ◽

Spatial Habitat

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Zero-inflated-censored Weibull and gamma regression models to estimate wild boar population dispersal distance

Japanese Journal of Statistics and Data Science ◽

10.1007/s42081-021-00124-0 ◽

2021 ◽

Author(s):

Eduardo de Freitas Costa ◽

Silvana Schneider ◽

Giulia Bagatini Carlotto ◽

Tainá Cabalheiro ◽

Mauro Ribeiro de Oliveira Júnior

Keyword(s):

Wild Boar ◽

Regression Models ◽

Likelihood Function ◽

Complex Structure ◽

Dispersal Distance ◽

Wild Boar Population ◽

Gamma Distributions ◽

Excess Of Zeros ◽

Censored Observations ◽

Positive Effect

AbstractThe dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrating that both regression models behave well. The simulation results point to the consistency and asymptotic unbiasedness of the developed methods. Afterwards, we adjusted both models to a simulated dataset of wild boar dispersal, including excess of zeros, right-censored observations, and two covariates: age and sex. We showed that the models were useful to extract inferences about the wild boar dispersal, correctly describing the data mimicking a situation where males disperse more than females, and age has a positive effect on the dispersal of the wild boars. These results are useful to overcome some limitations regarding inferences in zero-inflated-right-censored datasets, especially concerning the wild boar’s population. Users will be provided with an R function to run the proposed models.

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