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opencc-by-4.0Mar 2020View details →

Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update

These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid >>> file  "January_2020_HY_nut01_10x10.tif" - Downscaling to 2x2 km grid   >>> file "January_2020_HY_nut00_2x2.tif" Model settings and predictors:     - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps: - To update wild boar hunting yield data for some specific regions - To increase hunting yield data resolution - To explore model independent parametrization for each bioregion For further details and methodological approach see the paper: ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787. Permission for reuse hunting yield outputs is granted under the terms indicated  by EFSA.

opencc-by-4.0Mar 2020View details →

Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update

These maps  are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid - Downscaling to 2x2 km grid Model settings and predictors:     - Model from ENETWILD report August 2019 - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps - To update wild boar hunting yield data for some specific regions; - To increase hunting yield data resolution; - To explore model independent parametrization for each bioregion. Files: August_2019_HY_nut00_10x10        >> Model outputs based on hunting yield GLM analyses August_2019_occurrences_bioclim   >> Model outputs based on Bioclim analyses August_2019_occurrences_glm         >> Model outputs based on Generalised linear model August_2019_occurrences_ksvm      >> Model outputs based on Support vector Machine analyses August_2019_occurrences_maxent   >> Model outputs based on Maxent analyses August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses --------------------------------------------------------------------------------------------------------------------------------------------------------- These maps are models obtained in intermediate phases of the ENETWILD project based on available information.  There are frequent updates in order to improve the results. For methodological approach and details check the paper:  ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a> . Permission for reuse occurrence  outputs records is granted under the terms of a CC-BY-NC license. Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.

opencc-by-4.0May 2020View details →

Model outputs for update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect, May 2020 update

These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results. Objectives: - Incorporate additional data to provide new maps of wild boar suitability with a resolution of 2x2 km >>> file 3_June_2020_suitability_2x2.tif - New model based on hunting yield with different approaches to handle the biorregion effect >>> files 1_June_2020_HY_nut01_10x10_twostep.tif  &  2_June_2020_HY_nut01_10x10_pca.tif Model settings and predictors:     - Hunting yield modeling including biorregion effect as bioclimatic PCA scores - Hunting yield addressing biorregion effect in a two-step procedure with independent parametrization for each bioregion Conclusions guiding future methodological steps: - For wild boar suitability maps at 2x2 km, additional data on survey effort is critical in the southern bioregion - Hunting yield model predictions at 10x10 km grids overestimated the hunting bag numbers obtained from the external datasets - HY model with independent parametrization for each bioregion performed better that previous and new strategies For further details and methodological approach see the paper: ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, Soriguer, J. Vicente (2020) update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect. EFSA supporting publication 2020 TO BE COMPLETED Permission for reuse hunting yield outputs is granted under the terms indicated  by EFSA.

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Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)

The rapid expansion of wild pigs (Sus scrofa) throughout the United States (US) has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs – which overwhelmingly represent domestic pig-wild boar hybrids – pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pigs found within the US. We organized reference high-density single nucleotide polymorphism genotypes (1,039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed-commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well-established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (sensu genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis – that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6,566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.

opencc-zeroDec 2023View details →

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An inbreeding perspective on the effectiveness of wildlife population defragmentation measures: A case study on wild boar (Sus scrofa) of Veluwe, The Netherlands

Pervasive inbreeding is a major genetic threat of population fragmentation and can undermine the efficacy of population connectivity measures. Nevertheless, few studies have evaluated whether wildlife crossings can alleviate the frequency and length of genomic autozygous segments. Here, we provided a genomic inbreeding perspective on the potential effectiveness of mammal population defragmentation measures. We applied a SNP-genotyping case study on the ~2500 wild boar Sus scrofa population of Veluwe, The Netherlands, a 1000-km2 Natura 2000 protected area with many fences and roads but also, increasingly, fence openings and wildlife crossings. We combined a 20K genotyping assessment of genetic status and migration rate with a simulation that examined the potential for alleviation of isolation and inbreeding. We found that Veluwe wild boar subpopulations are significantly differentiated (FST-values of 0.02-0.07) and have low levels of gene flow. One noteworthy exception was the Central and Southeastern subpopulation, which were nearly panmictic and appeared to be effectively connected through a highway wildlife overpass. Estimated effective population sizes were at least 85 for the meta-population and ranging from 31 to 52 for the subpopulations. All subpopulations, including the two connected subpopulations, experienced substantial inbreeding, as evidenced through the occurrence of many long homozygous segments. Simulation output indicated that whereas one or few migrants per generation could undo genetic differentiation and boost effective population sizes rapidly, genomic inbreeding was only marginally reduced. The implication is that ostensibly successful connectivity restoration projects may fail to alleviate genomic breeding of fragmented mammal populations. We put forward that defragmentation projects should allow for (i) monitoring of levels of differentiation, migration and genomic inbreeding, (ii) anticipation of the inbreeding status of the meta-population, and, if inbreeding levels are high and/or haplotypes have become fixed, (iii) consideration of enhancing migration and gene flow among meta-populations, possibly through translocation.

opencc-zeroDec 2023View details →

T a b l e 4 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)

T a b l e 4. Dietary overlaps (the Morisita's index) between vertebrate predators in the cold season in coniferous-small-leaved forests of Belarussian Paazerje, Northern Belarus, upper right corner — before a depopulation of the Wild Boar (1982–2011), bottom left corner — aft er a large-scale depopulation of the Wild Boar (2013–2019)

Fig. 2 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)

Fig. 2. Th e Golden and White-tailed Eagles feed regularly on carrion and physical interference takes place quite often.

Fig. 1 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)

Fig. 1. Dietary similarity of 17 vertebrate predators in the cold season in Belarussian Paazerje, 1972–2012.

Fig. 4 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)

Fig. 4. Dietary similarity of 10 vertebrate predators in the cold season in Belarussian Paazerje, 2013–2019.

opencc-zeroApr 2022View details →

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Negative temporal autocorrelation in mast seeding dynamics positively influences both the long and short-term dynamics of a wild boar population

Temporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale, as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics with cycles of length 2 years. Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (ca. 2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to generation time of the focal population is required, especially under global warming where the cyclicality in resource dynamics is likely to change.

opencc-by-4.0Apr 2024View details →

Fig. 1 in Short communication First documented observation of differential dorsoventral coat colouration in wild boar Sus scrofa (Artyodactyla: Suidae) in Italy

Fig. 1 - The juvenile wild boar object of this note showing the differential dorsoventral colouration pattern (right) next another wild-type individual (left). Additional footage available at: https://youtu.be/gTc0BFSE9kA. / Il giovane esemplare di cinghiale oggetto di questa nota in cui è visibile il pattern cromatico a demarcazione dorsoventrale (a destra) accanto a un altro individuo con la tipica colorazione marrone uniforme (a sinistra). È anche disponibile un filmato aggiuntivo: https://youtu.be/ gTc0BFSE9kA. (Photo and video: / Foto e video: Francesco Gallozzi).

opencc-by-4.0Dec 2013View details →

Fig. 1 in Trichinella species circulating in wild boar (Sus scrofa) populations in Poland

Fig. 1. Example of electrophoretic patterns obtained from multiplex PCR on Trichinella larvae collected from wild boar. Lane 1 and 8 molecular weight marker (Fermentas 100 bp DNA Ladder); lanes 2 and 4, T. spiralis; lanes 3 and 5, T. britovi; lane 6, T. spiralis and T. britovi mixed infection; lane 7, negative control.

opencc-by-4.0Apr 2024View details →

Fig. 2 in Seroprevalence of Toxoplasma gondii in wild boars (Sus scrofa) hunted in Ukraine

Fig. 2. Box plot of Toxoplasma gondii serology results from wild boars from Ukraine, obtained using a locally available enzyme-linked immunosorbent assay and majority criteria based on results of three tests (locally available enzyme-linked immunosorbent assay (ELISA), commercial ELISA (ID Screen Toxoplasmosis Indirect Multi-Species), and an indirect immunofluorescence test (IFAT)). ELISA proportion (OD sample/mean OD of positive controls *100) using the locally available ELISA is on the Y-axis and majority criteria is on the X-axis.

opencc-by-4.0Apr 2024View details →

Fig. 1 in Seroprevalence of Toxoplasma gondii in wild boars (Sus scrofa) hunted in Ukraine

Fig. 1. Seroprevalence of Toxoplasma gondii infection among wild boars by region in Ukraine, based on results from a locally available enzyme-linked immunosorbent assay (ELISA). For regions with at least one seropositive wild boar, the number of seropositive wild boards out of number of tested wild boars is shown.

Fig. 2 in Hepatozoon apri n. sp. (Adeleorina: Hepatozoidae) from the Japanese wild boar Sus scrofa leucomystax (Mammalia: Cetartiodactyla)

Fig. 2. Inflammatory lesion with released merozoites or gamonts in the femoral muscle of Japanese wild boar.

Fig. 1. a–b in Hepatozoon apri n. sp. (Adeleorina: Hepatozoidae) from the Japanese wild boar Sus scrofa leucomystax (Mammalia: Cetartiodactyla)

Fig. 1. a–b) Gamonts of Hepatozoon apri n. sp. in the cytoplasm of neutrophils detected in the blood smear of a boar (ID: 28-11), showing acentric and rounded nuclei (arrows) and a small protrusion containing eosinophilic granules (arrowheads).

Fig. 4 in Hepatozoon apri n. sp. (Adeleorina: Hepatozoidae) from the Japanese wild boar Sus scrofa leucomystax (Mammalia: Cetartiodactyla)

Fig. 4. Phylogenetic analysis of Hepatozoon apri n. sp. based on 18S rDNA sequences (522-bp). Adelina dimidiata (accession no. DQ096835) was chosen as the outgroup to root the phylogeny. Neighbor-joining (NJ) and maximum likelihood (ML) analysis showing the phylogenetic relationships among boar isolates and two Hepatozoon spp. (KF318170, KF318171) detected in Dermacentor ticks collected from wild boar in Thailand. Sequences included in the comparison were downloaded from the DDBJ/EMBL/GenBank databases. Filled circles indicate Hepatozoon species reported from Japan. Nodal support values based on 1000 bootstrap replicates (NJ/ML) are represented on the ML tree. Scale bar represents 0.01 nucleotide substitutions per site.

Fig. 3. a–d in Hepatozoon apri n. sp. (Adeleorina: Hepatozoidae) from the Japanese wild boar Sus scrofa leucomystax (Mammalia: Cetartiodactyla)

Fig. 3. a–d) Various developmental stages of Hepatozoon apri n. sp. detected in the muscles. a) A trophozoite (arrow) in the femoral muscles. The outer layer contains a fibroblast-like nucleus (arrowhead). b–c) Immature meronts found in the heart. d) Mature meront in the femoral muscles. H&E stain. Bar = 20 μm.