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Feral

Feral describes the state or condition of organisms that have reverted to a wild or untamed state after having been domesticated.
This term is often used in the context of animals, such as cats, dogs, or livestock, that have escaped or been released from human care and adopted a life in the wild.
Feral organisms may exhibit behavioral and physiological adaptations that differ from their domesticated counterparts, and can have significant ecological impacts on native species and habitats.
Researchers may study feral populations to understand the process of domestication, the genetics and evolution of wild traits, and the management of invasive species.
The term can also be applied to human communities or individuals that have become detached from societal norms and structures, living in a more primal or unrestrained manner.
Underatnding the feral condition can provide insights into the complexities of the human-animal relationship and the resilience of life in the absence of direct human influence.

Most cited protocols related to «Feral»

1
Investigating Virus Transmission in Honey Bees
In Fall 2005, frames of bee bread (stored pollen) and honey were collected from colonies previously determined to have DWV (both from symptoms and with RT-PCR). Multiple cells of bee bread or honey were sampled at random from both sides of each frame and RNA was extracted from groups of 2–3 cells. RT-PCR was performed for DWV, SBV, and KBV. Only DWV was detected in the majority of the cells both for frames of honey or bee bread. These frames were stored at ambient temperature over the winter (fluctuating from below −6°C to 32°C), with protection from pests. Additional frames were power-washed to remove all deposits, leaving some wax; these were designated as “clean” frames. The wax did not have DWV as tested by RT-PCR.
Six months later in Spring 2006, new packages were placed into new hive equipment in an isolated apiary (Rock Springs Apiary) that had no known feral or managed colonies of honey bees located within 8 km. The surrounding area was forest, meadow and farmland. After one week when the colonies had established and the marked queens had began to lay eggs, egg samples (N = 4 samples of 5 eggs each, or 20 eggs per colony) and worker attendants (N = 15) were collected for each colony and analyzed for DWV, BQCV and SBV. A total of twelve packages or colonies were found to have workers free of DWV, KBV, and SBV; and the queens were laying virus-free eggs. These packages were randomly divided into three treatments with four colonies each: Controls (fed artificial bee pollen and sugar syrup, given “clean” frames), DWV-Honey (fed a frame of honey contaminated with DWV and artificial bee pollen), or DWV- Bee Bread (fed a frame of bee-bread contaminated with DWV and sugar syrup). Egg samples from each colony (N = 4 samples of 5 eggs each, or 20 eggs per colony) were collected every week for five weeks following introduction of the frames of food; and DWV and SBV infections and actin were determined by RT-PCR. Each marked queen was observed in its colony during the experiment, ensuring that the same individual queens were being monitored for viral infection.
Singh R., Levitt A.L., Rajotte E.G., Holmes E.C., Ostiguy N., vanEngelsdorp D., Lipkin W.I., dePamphilis C.W., Toth A.L, & Cox-Foster D.L. (2010). RNA Viruses in Hymenopteran Pollinators: Evidence of Inter-Taxa Virus Transmission via Pollen and Potential Impact on Non-Apis Hymenopteran Species. PLoS ONE, 5(12), e14357.
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Publication 2010
Actins Carbohydrates Cells Eggs feral Food Forests Honey Infection Natural Springs Plague Pollen Propolis Reading Frames Reverse Transcriptase Polymerase Chain Reaction Urticaria Virus Virus Diseases Workers
2
Exploring Genetic Diversity Across Sheep Breeds
In order to understand the relationship within and between breeds across each major geographic group, Principal Components Analysis (PCA) was performed using EigenStrat [55] (link). Initial PCA using all 2,819 animals revealed six breeds containing in excess of 100 animals skewed the clustering. This prompted a reduction in the number of animals used, where 1,612 animals were randomly selected to ensure 26 or fewer animals were included per breed (Figures 2 and S3). To ensure uncorrected LD did not distort the PCA [55] (link), SNP pruning was used to identify two SNP sets. First, all 49,034 markers were subjected to LD-based pruning (>0.05) using PLINK to identify 22,678 SNP. Secondly, 32,847 SNP that retain polymorphism within wild feral sheep were subjected to the same LD-based SNP pruning (>0.05) to identify 20,279 SNP. The PCA results obtained did not differ significantly dependent on the SNP set used. Model-based clustering was performed using the admixture model, correlated allele frequencies, and 15,000 burnin and 35,000 simulation cycles in STRUCTURE version 2.3 [18] (link). Convergence was checked using two runs for each value of K (number of subpopulations). For supervised clustering, prior population information was introduced from six meta-populations consisting of regional pool of breeds considered to represent ancestral populations. The same meta-populations were used for updating the allele frequencies during the simulations. NeighborNet graphs were constructed from a matrix of Reynolds' distances using Splitstree [56] (link).
Kijas J.W., Lenstra J.A., Hayes B., Boitard S., Porto Neto L.R., San Cristobal M., Servin B., McCulloch R., Whan V., Gietzen K., Paiva S., Barendse W., Ciani E., Raadsma H., McEwan J, & Dalrymple B. (2012). Genome-Wide Analysis of the World's Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent Selection. PLoS Biology, 10(2), e1001258.
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Publication 2012
Animals Breeding Domestic Sheep feral Genetic Polymorphism Population Group
3
Pathogen Challenge in Mouse Models
Petstore mice were purchased from a nation-wide retailer. Feral mice were trapped on a horse farm or rural outdoor petting zoo in Minnesota or Georgia, USA. Male or female petstore mice were introduced into the cages of six to eight week old C57BL/6 mice of the same sex purchased from the National Cancer Institute. Co-housing occurred within a BSL-3 facility. Age-matched C57BL/6 laboratory mice maintained in SPF facilities served as controls. Number of animals was determined based on previous experience in order to reach statistical significance. All animals surviving the experimental treatment were included in the final analysis. No method of randomization was used to allocate animals to experimental groups. Investigators were not blinded to the group allocation during experiments. Listeria monocytogenes (LM) was grown in tryptic soy broth containing streptomycin to log phase growth. The indicated groups of mice were infected i.v. with 8.5 × 104 CFU of wild type LM (provided by John Harty, University of Iowa). Bacterial load in the spleen and liver was determined 3 days post-challenge as previously described31 (link),32 . LM immune mice were generated by primary infection with LM-OVA, (provided by Hao Shen, University of Pennsylvania School of Medicine)33 5 months prior to secondary challenge. Plasmodium berghei ANKA (provided by Susan K. Pierce, NIH/NIAID) was propagated by passage in mice and blood collection. A million parasitized RBCs were injected i.p. into the indicated mice. Parasitemia was determined by flow cytometry on peripheral blood34 (link). All mice were used in accordance with the Institutional Animal Care and Use Committees guidelines at the University of Minnesota.
Beura L.K., Hamilton S.E., Bi K., Schenkel J.M., Odumade O.A., Casey K.A., Thompson E.A., Fraser K.A., Rosato P.C., Filali-Mouhim A., Sekaly R.P., Jenkins M.K., Vezys V., Haining W.N., Jameson S.C, & Masopust D. (2016). Recapitulating adult human immune traits in laboratory mice by normalizing environment. Nature, 532(7600), 512-516.
Publication 2016
Animals Animals, Laboratory BLOOD Equus caballus Erythrocytes Females feral Flow Cytometry Infection Institutional Animal Care and Use Committees Listeria monocytogenes Liver Males Mice, House Mice, Inbred C57BL Mice, Laboratory Parasitemia Plasmodium berghei Spleen Streptomycin Therapies, Investigational tryptic soy broth
4
Comparative Analysis of Chicken Genetic Diversity
Three groups of birds were included in the study (1) red jungle fowls (Gallus gallus gallus, RJFs), (2) broilers (BRs) and (3) layers (LRs) (Table 1). The RJFs were sampled from two geographical regions, Thailand (RJFt) and India (RJFi). The RJFt consisted of 25 DNA samples collected within a European collaborative research project AVIANDIV (https://aviandiv.fli.de/). RJFt was randomly down-sampled from ~150 RJFs caught in northern Thailand in 1997 and maintained since with random mating over four flocks; given the place and date, the RJFt samples likely have seen some contamination from domestic or feral populations prior to collection [34 (link)]. The DNA samples from RJFt were collected in 1999. For further information on the behaviour and morphology of these birds we refer to the AVIANDIV project webpage. The RJFi population involved 10 individuals of the Richardson line, originating from RJF caught in India in the 1960´s. This population has been extensively studied [35 –37 ], and appears to have been established from a wild population prior to major genetic contamination of red jungle fowl populations, such that it may represent a unique RJF line that is at least largely free of influence from domestic stocks. The second and third group of birds represent commercial chicken, comprising three broiler and three layer populations, respectively. The broilers (BRs) were represented by 20 DNA samples of each of two lines (BRA and BRB) established independently and previously collected as part of the AVIANDIV project. BRA was a sire line belonging to the company Indian River International (Texas) established in 1980 and closed since with a breeding population size of >10,000 birds. BRB was another sire line originally from France, developed in 1970 with a breeding population size varying between 10,000 to 70,000. The broiler group further involved a pooled sample of 25 birds from AVIANDIV’s broiler sire line D, hereafter denoted BRpD. This is a sire line originally from UK, established in 1974 and closed since with unknown population size. In the layer group (LRs), data from 25 birds each from purebred white (WL) and brown (BL) egg laying populations, sequenced in the frame of the SYNBREED project (http://www.synbreed.tum.de/index.php?id=2), were included. WL and BL birds represent parental lines of the LOHMANN Tierzucht GmbH that are originally established from White Leghorn and Rhode Island Red, respectively. Moreover, we used pooled sequence data of 48 birds from Rhode Island White (RWp), a crossbred layer population collected by the AVIANDIV project.
Qanbari S., Rubin C.J., Maqbool K., Weigend S., Weigend A., Geibel J., Kerje S., Wurmser C., Peterson A.T., Brisbin IL J.r., Preisinger R., Fries R., Simianer H, & Andersson L. (2019). Genetics of adaptation in modern chicken. PLoS Genetics, 15(4), e1007989.
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Publication 2019
Aves Chickens Europeans feral Parent Reading Frames Rivers Vision
5
Genetic Diversity and Gene Flow in Sheep
Within-breed genetic diversity was estimated with the following parameters that were obtained using PLINK [14 (link)]: proportion of polymorphic loci (Ppl); genetic diversity (He); inbreeding coefficient (F). The distribution of the number of SNPs over frequency bins for each population was obtained using PLINK and plotted on a graph using Microsoft Excel. Pair-wise IBS (identical by state) distances among the sheep samples were calculated using PLINK [14 (link)] and graphically represented by multi-dimensional scaling (MDS) analysis.
Genetic relationships among breeds and levels of gene flow and admixture were evaluated through the model-based clustering algorithm implemented in the software ADMIXTURE v. 1.22 [15 (link)] by applying the default settings. The default (5-fold) ADMIXTURE’s cross-validation procedure was carried out to estimate, for each K value (number of assumed clusters), prediction errors. The value of K that minimizes this estimated prediction error is assumed to be the most probable. Individual coefficients of membership to each K cluster produced by ADMIXTURE were visualized using the program DISTRUCT [16 (link)].
Relationships among breeds were also explored by neighbor-network analysis of the (1-IBS) distance [14 (link)] and the distances of Nei [17 (link)] and Reynolds et al. [18 (link)] calculated by PowerMarker [19 (link)]. Neighbor-networks were constructed using the Neighbor-Net algorithm [20 (link)] implemented in the SplitsTree4 package v. 4.13.1 [21 (link)].
In order to reconstruct historical relationships between the analyzed populations and to test for the presence of gene flow, we adopted the tree-based approach implemented in TREEMIX [22 (link)]. First, the TREEMIX program was run on the dataset described above, with animals classified in 37 breeds or populations. A variable number of migration events (M) ranging from 0 to 50 was tested, and the value of M that had the highest log-likelihood was selected as the most predictive model. Then, the 37 breeds or populations were grouped into six arbitrary groups (wild, feral, primitive, Merino and Merino-derived, Spanish non-Merino, Italian non-Merino) and the analyses were repeated as previously described. The tests f3 and f4 that are implemented in the TREEMIX computer package [22 (link)] were also performed on the dataset arranged into the six arbitrary groups. We used the f3-statistics (A, B, C) to determine if A was derived from the admixture of populations B and C, and the f4-statistics ((A, B,) (C, D)) to test the validity of a hierarchical topology in four-population trees. Significant deviations of the f4-statistics from 0 for the three possible topologies of four-population trees are evidence of gene flow in the tree, i.e., that the phylogeny of the four groups is not completely tree-like. A significantly positive Z-score indicates gene flow between populations related to either A and C or B and D and a significantly negative Z-score indicates gene flow between populations related to A and D or B and C.
To detect pairs of populations that share common ancestry and/or have experienced gene flow, we investigated the extent of pair-wise haplotype sharing with two approaches. The first approach is based on the calculation of correlation coefficients r for the same pair of SNPs in two different breeds as measures of linkage disequilibrium (LD) [23 (link)]. First, pairs of SNPs were binned in intervals of 0 to 1000, 1000 to 2000 bp and so on. Then, following Kijas et al., [12 (link)], r coefficients were calculated for pairs of SNPs that were separated by 0 to 10 kb, 10 to 25 kb and 100 to 250 kb, respectively. Breeds for which r coefficients increase for SNPs at longer distances are expected to share a more recent common ancestor. To avoid data flattening, out-groups (O. orientalis, O. argali and O. ammon) were removed from the dataset for this analysis. The second method is based on the direct inference of population haplotypes using fastPHASE [24 (link)], a software program that has been shown to perform well even with moderately low LD [25 (link)]. Then, for all possible population pairs, haplotype diversity was measured at haplotype blocks that comprised three SNPs. Haplotype blocks for which the inter-population haplotype diversity is less than 0.3 and that are more than 0.5 cM long are considered to be shared by populations. Finally, the length of all shared segments was summed for each pair of populations.
Ciani E., Lasagna E., D’Andrea M., Alloggio I., Marroni F., Ceccobelli S., Delgado Bermejo J.V., Sarti F.M., Kijas J., Lenstra J.A, & Pilla F. (2015). Merino and Merino-derived sheep breeds: a genome-wide intercontinental study. Genetics, Selection, Evolution : GSE, 47(1), 64.
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Publication 2015

Most recents protocols related to «Feral»

1
Humane Feral Swine Eradication Operations
Not available on PMC !
Removal of feral swine as part of eradication operations was conducted by government experts in an agreement with USDA Wildlife Services (WS), the Federal agency responsible for managing conflicts with wildlife Only approved and humane methods to euthanise animals conforming to guidelines in the 2013 Report of the American Veterinary Medical Association Panel on Euthanasia (American Veterinary Medical Association 2013) and set forth as agency policy in USDA/APHIS/WS Directive 2.505 were used.
Feral swine were primarily removed by capture in pen traps and sharpshooting. After identification of the most favourable locations to carry out trapping activities, pen traps were constructed and baited with soured corn to condition the feral swine to feeding at trap sites. After feral swine were consistently entering the pen trap to feed, the trap would be set and triggered remotely. During the times when control experts were on the Island to conduct trapping activities, they also were opportunistically removing feral swine by sharpshooting, including a small number of animals removed by sharpshooting from a helicopter. All feral swine were lethally removed by USDA/APHIS/Wildlife Services personnel during the regular course of their official duties. Control personnel were not permanently stationed on the Island, but carried out control activities there according to budget cycles, when their efforts would have maximal impact and demand for their services elsewhere in Florida. The methods for lethal removal in addition to ethical considerations regarding lethal take were fully considered in accordance with the National Environmental Policy Act (USDA 2015). All other components of the study received agency approval by way of the USDA/APHIS/Wildlife Services/ National Wildlife Research Center Quality Assurance (QA) procedure: QA1394.
Engeman R.M., Tillman E.A., Evans B.A., Griffin J.C., Grobaski G., Smith B.S., Stark J., & Kluever B.M. (2024). Eradication of feral swine from a barrier island in Florida, USA: an examination of effort and multi-method, multi-species population indexing. NeoBiota, 93, 91-116.
Publication 2024
2
Smart Feral Cat Shelter Automation
Not available on PMC !
The assembly of the smart system for the feral cat shelter is shown below in Fig. 1. Considering the Arduino Mega as the core central microcontroller, the other sensors were connected to it. The LCD was used for monitoring the working condition and was connected to I2C, SDL and SDA pins. The Fans and heating pad were also connected through the relay modules which connected to the 5V and GND pins. The DHT22, HC-SR04, ESP8266, and Gikfun Liquid sensors were all connected to different analog and digital pins. The Wyze Camera was connected outside of the Arduino through a power outlet.
HONG J. (2024). Building an Automatic Controlled Smart Feral Cat Shelter Using a Microcontroller Arduino Mega. Deleted Journal, 30(4), 1-12.
Publication 2024
3
Mapping Feral Pig Density Near Swine Sites
Using QGIS 3.22.16, all 368 swine site coordinates were plotted on a World Geodetic System (WGS84) projection. Using a publicly available landcover data set from the National Land Cover Data Base (NLCD 2021) obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium and The U.S. Geological Survey (USGS) collaboration (https://www.mrlc.gov/), landcover data from a 10-km (6.2-mile) buffer radius surrounding each site was extracted. The chosen buffer radius was based on the largest surveillance zone width recommended by the USDA African Swine Fever Response Plan “The Red Book” (18 ). The spatial resolution of the NLCD was 30 m2 and contained 16 land cover types [Open water, Perennial ice and snow, Developed (open space), Developed (low intensity), Developed (medium intensity), Developed (high intensity), Barren land, Deciduous forest, Evergreen forest, Mixed forest, Shrub/Scrub, Grassland/herbaceous, Pasture/hay, Cultivated crops, Woody wetlands and Emergent herbaceous wetlands]. The developed land cover types represent urban environments.
An expected feral pig density data set was obtained from the U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Veterinary Services, Fort Collins, Colorado. The expected feral pig density data used in this study was derived from the predictive models and approximations developed and described in Lewis et al. (10 (link), 19 (link)). In brief, biotic and abiotic factors such as, but not limited to, landcover, land use, enhanced vegetation index (EVI), forest canopy cover, predation pressure, precipitation, humidity and temperature, were used in multiple linear regression models to predict feral pig distribution across both native and non-native ranges with the assumption made that the feral pig population had reached biological equilibrium. The outcome of the models provided expected feral pig densities based on local landscape factors and likelihood of the predictive distribution of feral pig populations. Additional details are described in Lewis et al. (10 (link)). All swine site coordinates were plotted in QGIS and a buffer area with a 10-km (6.2-mile) radius surrounding each swine site was created, as previously described, from which the average expected feral pig density estimation was extracted. The spatial resolution for the expected feral pig density data set was 1.0 km2, or the number of estimated feral pigs/km2. The expected feral pig population density ranges used in our study were based on previously set thresholds by (19 (link)), low = 0–2 pigs/km2; moderate= 3–5 pigs/km2, and high = >5 pigs/km2) for the contiguous U.S.
Campler M.R., Hall M., Mills K., Galvis J.A., Machado G, & Arruda A.G. (2024). Description of swine producer biosecurity planning for foreign animal disease preparedness using the Secure Pork Supply framework. Frontiers in Veterinary Science, 11, 1380623.
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Publication 2024
4
Isolation and Characterization of Feral Chicken Gut Microbiome
Protocols used in this study for sample collection and chicken experiments were reviewed and approved by the Institutional Animal Care and Use Committee (18-058A, 18-063A) at the South Dakota State University, Brookings, South Dakota. For the isolation of bacteria from the feral chicken gut, six intestinal samples were pooled together. The pooled intestinal sample was serially diluted and was plated on modified brain heart infusion agar with 12 different selective conditions (Table S1). All cultures were performed inside an anaerobic chamber (Coy Laboratories) containing 85% CO2, 10% H2, and 5% N2 maintained at 37°C. Total of 1,300 colonies was picked from all conditions and dilutions based on colony morphologies. Selected colonies were streaked on base BHI-M agar, and a single colony was selected for preparing stocks and species identification. Species identity of the isolates was determined using matrix-assisted laser desorption/ionization-time of flight or 16S rRNA gene sequencing. For MALDI-TOF identification, a single colony was smeared on the MALDI-TOF target plate and lysed by 70% formic acid. MALDI-TOF targets were covered with 1 µL of a matrix solution. MALDI-TOF was performed through Microflex LT system (Bruker Daltonics). A MALDI-TOF score >1.9 was considered as positive species identification. Isolates that could not be identified at this cutoff were identified using 16S rRNA gene sequencing. To identify these isolates, genomic DNA of overnight culture from a single colony was extracted using a DNeasy Blood & Tissue kit (Qiagen), according to the manufacturer’s instructions. Then 16S rRNA gene sequences were amplified using universal primer set 27F [5′- AGAGTTTGATCMTGGCTCAG-3′ (53 )]; and 1492R [5′- ACCTTGTTACGACTT- 3′ (53 , 54 , 54 )], and sequenced using a Sanger DNA sequencer (ABI 3730XL; Applied Biosystems) using 27F primer. The 16S rRNA gene sequence was used to verify species using the GenBank (www.ncbi.nlm.nih.gov/genbank/) and EZBioCloud (www.ezbiocloud.net/eztaxon) databases (27 (link)). All identified isolates were maintained in BHI-M medium with 10% (vol/vol) dimethyl sulfoxide (DMSO) at −80°C. Aerotolerance of the bacterial species was tested by culturing in aerobic, anaerobic, and microaerophilic conditions. To this end, individual bacteria were first cultured overnight in BHI-M broth at 37°C under anaerobic condition. The optical density at 600 nm (OD600) of the cultures was adjusted to 0.5. Then, 1% of OD600 adjusted cultures were inoculated in fresh BHI-M media in triplicates. Each replicate of cultures was then incubated under anaerobic, microaerophilic, and aerobic conditions. For microaerophilic condition, a hypoxic box was used to incubate the culture. After 24 hours of incubation, the growth of individual bacteria was determined by measuring OD600.
Wongkuna S., Ambat A., Ghimire S., Mattiello S.P., Maji A., Kumar R., Antony L., Chankhamhaengdecha S., Janvilisri T., Nelson E., Doerner K.C., More S., Behr M, & Scaria J. (2024). Identification of a microbial sub-community from the feral chicken gut that reduces Salmonella colonization and improves gut health in a gnotobiotic chicken model. Microbiology Spectrum, 12(3), e01621-23.
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Publication 2024
5
Point-Intercept Transects for Ecological Data
Within each of the 24 plots, 1 m point-intercept transects were used to collect ecological data. Ten parallel transects of 10 m each yielded 100 points per plot. Data collection was performed with a purpose-designed CyberTracker® electronic data collection application (Liebenberg et al. 2017 (link)). CyberTracker enabled predetermined ‘single choice’ selections similar to Ens et al. (2012 (link) and 2016 (link)) for the ground surface feature, ground cover feature and overhead foliage projected cover. Possible choices for ground surface features were: pig damage (tracks or digging), buffalo damage (track or wallow (large circular depression)), flat ground or disturbed ground. Ground cover feature choices were: bare ground, leaf litter, dead wood, water, grass or tree.
Sloane D.R., Ens E., Wunungmurra Y., Mununggurr L., Falk A., Wunungmurra R., Gumana G., Towler G, & Preece D. (2024). Can Exclusion of Feral Ecosystem Engineers Improve Coastal Floodplain Resilience to Climate Change? Insight from a Case Study in North East Arnhem Land, Australia. Environmental Management, 73(6), 1150-1166.
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Publication 2024

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More about "Feral"

The term 'feral' describes the condition of organisms that have reverted to a wild or untamed state after being domesticated.
This is often observed in animals like cats, dogs, or livestock that have escaped or been released from human care and adapted to life in the wild.
Feral organisms can exhibit behavioral and physiological traits that differ from their domesticated counterparts, and they can have significant ecological impacts on native species and habitats.
Researchers may study feral populations to understand the process of domestication, the genetics and evolution of wild traits, and the management of invasive species.
The term 'feral' can also be applied to human communities or individuals that have become detached from societal norms and structures, living in a more primal or unrestrained manner.
Understanding the feral condition can provide insights into the complexities of the human-animal relationship and the resilience of life in the absence of direct human influence.
Techniques like the DNeasy Blood & Tissue Kit, QIAamp DNA Mini Kit, and the Illumina Equine GGP65Plus array can be used to study the genetic and molecular characteristics of feral organisms.
Additionally, the use of FBS, Incomplete Freund's adjuvant, and the NanoDrop ND-1000 spectrophotometer can aid in the analysis and characterization of feral samples.
Researchers may also employ tools like Dynabeads and the LSM Exciter confocal microscope to investigate the cellular and physiological adaptations of feral organisms.
The MiSeq platform can be utilized for high-throughput sequencing to explore the genomic and transcriptomic profiles of feral populations.
By leveraging these advanced techniques and technologies, scientists can gain a deeper understanding of the feral condition and its implications for ecology, evolution, and the human-animal interface.