Biology subjects

Brook, R. K.

Publications and source records attributed to Brook, R. K..

9 recordsLinked to original sources

bioRxiv2025

Spatial and Temporal Patterns of Wolf Occurrences on the Summer Range of the Eastern Migratory Cape Churchill Caribou Population in the Hudson Bay Lowlands of Manitoba

Wolves (Canis lupus) function as a top predator across diverse ecosystems including the sub-arctic, and they have been managed in often controversial ways. Communities and scientists are increasingly supporting minimally invasive research and monitoring, including using trail cameras. We employed a network of 15 Reconyx trail cameras at three monitoring areas aimed at detecting the spatial and temporal aspects of wolf occurrences within the summer range of Eastern Migratory Cape Churchill caribou in Wapusk National Park in the Hudson Bay Lowlands of Manitoba, Canada from 2013-2021. In this first peer-reviewed quantitative study of wolves in the region, we found that wolves detection events were generally consistent across years. Wolf distribution was consistently positively skewed toward the southern part of the caribou summer range in all years. Wolves experienced extreme environmental conditions, with a 60{degrees}C range in temperature, from a low of -32{degrees}C in winter to a high of +28{degrees}C in summer and an annual change in day length of >11 hours between summer and winter. Wolves occurred most commonly in spring and summer and occurred at equal frequency during night and day overall but selected for nighttime in September, October, and November as day length shortened dramatically.

ecology↗

bioRxiv2024

The impact of wild-boar derived microbiota transplantation on piglet microbiota, metabolite profile and gut proinflammatory cytokine production differs from sow-derived microbiota

Colonization of co-evolved, species-specific microbes in early life plays a crucial role in gastrointestinal development and immune function. This study hypothesized modern pig production practices have resulted in the loss of co-evolved species and critical symbiotic host- microbe interactions. To test this, we reintroduced microbes from wild boars (WB) into conventional piglets to explore their colonization dynamics and effects on gut microbial communities, metabolite profiles, and immune responses. At postnatal day (PND) 21, 48 piglets were assigned to four treatment groups: 1) WB-derived mixed microbial community (MMC), 2) sow-derived MMC, 3) a combination of WB and sow MMC (Mix), or 4) Control (PBS). Post- transplantation analyses at PND 48 revealed distinct microbial communities in WB-inoculated piglets compared to Controls, with trends toward differentiation from Sow but not Mix groups. WB-derived microbes were more successful in colonizing piglets, particularly in the Mix group, where they competed with sow-derived microbes. WB group cecal digesta enriched with Lactobacillus helveticus, Lactobacillus mucosae, and Lactobacillus pontis. Cecal metabolite analysis showed that WB piglets were enriched in histamine, acetyl-ornithine, ornithine, citrulline, and other metabolites, with higher histamine levels linked to Lactobacillus abundance. WB piglets exhibited lower cecal IL-1{beta} and IL-6 levels compared to Controls and Sow groups, while the Mix group showed reduced IFN-{gamma}, IL-2, and IL-6 compared to the Sow group. No differences in weight gain, fecal scores, or plasma cytokines were observed, indicating no adverse effects. These findings support that missing WB microbes effectively colonize domestic piglets and may positively impact metabolite production and immune responses. ImportanceThis study addresses the growing concern over losing co-evolved, species-specific microbes in modern agricultural practices, particularly in pig production. The implementation of strict biosecurity measures and widespread antibiotic use in conventional farming systems may disrupt crucial host-microbe interactions that are essential for gastrointestinal development and immune function. Our research demonstrates that by reintroducing wild boar-derived microbes into domestic piglets, these co-evolved microbes can successfully colonize the gut, influence microbial community composition, and alter metabolite profiles and immune responses without causing adverse effects. These findings also suggest that these co-evolved microbes can fill an intestinal niche, positively impacting immune activation. This research lays the groundwork for future strategies to enhance livestock health and performance by restoring natural microbial populations that produce immune-modulating metabolites. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/623678v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@6ec5c2org.highwire.dtl.DTLVardef@79804corg.highwire.dtl.DTLVardef@d63016org.highwire.dtl.DTLVardef@1ef0010_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Spatial and temporal occurrences of prairie moose across an urban to rural gradient in Saskatoon, Canada

The geographic range of North American moose (Alces alces) has expanded over recent decades as the population has recolonized historical habitats and dispersed into new areas, including a notable increase in moose sightings near developed, urban areas. The City of Saskatoon, Saskatchewan is situated on what would traditionally be considered atypical moose habitat, being in the semi-arid, open prairies and surrounded by high-intensity agriculture. However, this area has seen a persistent increase in the frequency of moose occurrences at the urban-rural interface over the last 30 years. We characterized spatial and temporal patterns in moose occurrences over the course of three years (2020-2023) using 29 trail cameras distributed along an urban to rural gradient within the city boundary of Saskatoon. We employed a generalized linear modelling method to assess the potential significance behind where and when moose were occurring along the gradient. Moose occurrence was negatively associated with urban sites containing higher proportions of development (>50% impervious surface cover). While we expected high occurrences during the rut, we found that moose occurrence was low through the fall months. This may be due to the high levels of human disturbance characteristic of the urban-rural interface acting as a deterrent for moose during the breeding season. Future research is warranted to better understand the underlying cause for this result. Moose also occurred most at night, coinciding with the period of lowest visibility and raising concerns for human safety. We provide suggestions and recommendations for future urban moose research and management.

ecology↗

bioRxiv2024

A comparison of wild boar and domestic pig microbiota does not reveal a loss of microbial species but an increase in alpha diversity and opportunistic genera in domestic pigs

The microbiome of wild animals is believed to be co-evolved with host species, which may play an important role in host physiology. It has been hypothesized that the rigorous hygienic practice in combination with antibiotics and diets with simplified formulas used in the modern swine industry may negatively affect the establishment and development of the gut microbiome. In this study, we evaluated the fecal microbiome of 90 domestic pigs sampled from 9 farms in Canada and 39 wild pigs sampled from three different locations on two continents (North America and Europe) using 16S rRNA gene amplicon sequencing. Surprisingly, the gut microbiome in domestic pigs exhibited higher alpha-diversity indices than wild pigs (P<0.0001). The wild pig microbiome showed a lower Firmicutes to Bacteroidetes ratio and a higher presence of bacterial phyla Elusimicrobiota, Verrucomicrobiota, Cyanobacteria, and Fibrobacterota compared to their domestic counterparts. At the genus level, wild pig microbiome had enriched genera that were known for fibre degradation and short-chained fatty acids production. Interestingly, the phylum Fusobacteriota was only observed in domestic pigs. We identified 31 ASVs that were commonly found in the pig gut microbiome regardless of host sources, which could be recognized as members of the core gut microbiome. Interestingly, we found a few ASVs missing in domestic pigs that were prevalent in wild ones, whereas domestic pigs harbored 59 ASVs that were completely absent in wild pigs. The present study sheds light on the impact of domestication on the pig gut microbiome, including the gain of new genera. ImportanceThe microbiome of pigs plays a crucial role in shaping host physiology and health. This study looked to identify if domestication and current rearing practices have resulted in a loss of co-evolved bacterial species by comparing the microbiome of wild boar and conventionally raised pigs. It represents a comparison of domestic and wild pigs with the largest sample sizes, and is the first to examine wild boars from multiple sites and continents. We were able to identify core microbiome members that were shared between wild and domestic populations, and counter to expectation, few microbes were identified to be lost from wild boar. Nevertheless, the microbiome of wild boars was distinct from domestic pigs, with notably lower abundance of important pathogenic genera. The differences in microbial composition may identify an opportunity to shift the microbial community of domestic pigs towards that of wild boar with the intent to reduce pathogen load.

microbiology↗

bioRxiv2022

Quantifying Spatio-Temporal Overlap of Invasive Wild Pigs and Domestic Pig Farms as a Proxy for Potential Disease Transmission Risk

Direct and indirect interactions between livestock and free-ranging wildlife creates important risks to animal health and agricultural productivity. The interface between newly established and rapidly spreading invasive wild pigs and the 2,549 domestic pig farms on the Prairie Provinces of western Canada has created important but poorly understood disease transmission risks. We mapped the spatial overlap of wild and domestic pigs to identify the areas of highest risk and associated distribution of diseases of concern using databases of wild pig occurrences and domestic pig farm locations. We also examined spatial and temporal overlap at the individual farm scale using GPS collared invasive wild pigs. Across the provinces of Alberta, Saskatchewan, and Manitoba, spatial overlap of invasive wild pigs with all combined, large-scale domestic pig farms, small-scale domestic pig farms, and domestic wild boar farms was 21%, 21%, 21%, and 53%. Invasive wild pig locations were significantly closer to domestic pig farms and domestic wild boar farms compared to random points on the landscape. The number of wild pig occurrences was greatest within 20 km of domestic pig farms and decreased linearly as distance increased. The Canadian distribution of wild pigs had considerable spatial overlap with recent areas detected with bovine tuberculosis (6,002 km2) in livestock and wildlife and Chronic Wasting Disease (156,159 km2) in wildlife, including mule deer, white-tailed deer, elk, and moose. The single best predictor of invasive wild pig occurrences across the landscape was close proximity to current or recently past existing domestic wild boar farms. The distance of GPS- collared wild pigs was significant for sex, farm type, month, and season and in southeastern Saskatchewan, average distance to domestic pig farms was 5.3 km. The weighted sum of cover type proportions, wild pig distance to domestic pig and wild boar farms, farm type, and farm density identified the relative risk of wild pig presence associated to each domestic pig farm occupied watershed. Risk was highest for small-scale domestic pig farms and lowest for large- scale domestic pig farms. Our findings highlight important potential routes for disease transmission at the invasive wild pig-domestic pig interface and identify areas where biosecurity improvements are urgently needed. While complete eradication of invasive wild pigs in Canada is no longer achievable, improved passive and active monitoring and removal of wild pigs is critical, especially where risks to domestic pig herds is highest.

ecology↗

bioRxiv2022

A free classified advertising website is a high-risk unregulated source of domestic wild boar, pot-bellied pigs, and their hybrids with domestic swine in Canada: Implications for African Swine Fever Risk Preparedness

Invasive wild pigs are a global problem and are established in large areas across Canada. Domestic European wild boar were first introduced to Canadian farms in the 1980s, and their escape and intentional release have since led to free-ranging populations, which have expanded rapidly in numbers and range. Wild pigs are associated with widespread damage to crops and natural environments, as well as risks of disease transmission. African Swine Fever (ASF) is a highly contagious viral disease of global concern, that can spread between domestic and free-ranging swine and cause catastrophic economic impacts. As ASF preparedness requires a detailed understanding of the distribution and movements of all pig types, this paper aimed to characterize an unregulated grey swine market in Canada to understand risks to domestic swine production and potential contributions to free-ranging wild pig populations. Kijiji.ca is a free Canadian internet classified advertising service, almost exclusively cash-based with no receipts and few, if any, records kept of transactions. We monitored Kijiji for sales of domestic wild boar, pot-bellied pigs, and their hybrids across Canada over two months from April 28 - June 30, 2021. Data was collected from all advertisements, including how the seller labelled pigs breed, age, sex, number for sale, sexual intactness, presence of tattoos and/or ear tags, as well as the date and location of the posting. Locations were mapped and compared to the spatial distribution of existing free-ranging wild pigs in Canada to identify areas they were likely to facilitate new populations or potentially supplement and genetically diversify existing populations. We identified 151 advertisements on Kijiji, the most coming from Ontario (34.4%; n = 52) and Alberta (29.1%; n = 44), followed by Saskatchewan (11.3%; n = 17), British Columbia (10.6%; n = 16), Nova Scotia (7.3%; n = 11), New Brunswick (4.0%; n = 6), Manitoba (2.0%; n = 3), Quebec (0.7%; n = 1), and Prince Edward Island (0.7%; n = 1). No listings were observed in Newfoundland and Labrador, or Yukon, Nunavut and Northwest Territories. Overall, 62 (41%) of advertisements were within watersheds with existing wild pigs that may contribute to supplementing additional swine, and 89 (59%) were within watersheds where wild pigs have not yet been identified. African swine fever preparedness should include policies and action to address this unmonitored and unregulated sale of swine in Canada, including ear tags, tattoos, genetic analysis, and mandatory reporting.

scientific communication and education↗

bioRxiv2022

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

Motion-activated wildlife cameras, or camera traps, are widely used in biological monitoring of wildlife. Studies using camera traps amass large numbers of images and analyzing these images can be a large burden that inhibits research progress. We trained deep learning computer vision models using data for 168 species that automatically detect, count, and classify common North American domestic and wild species in camera trap images. We provide our trained models in an R package, CameraTrapDetectoR. Three types of models are available: a taxonomic class model classifies objects as mammal (human and non-human) or avian; a taxonomic family model that recognizes 31 mammal, avian, and reptile families; a species model that recognizes 75 domestic and wild species including all North American wild cat species, bear species, and Canid species. Each model also includes a category for vehicles and empty images. The models performed well on both validation datasets and out-of-distribution testing datasets as mean average precision values ranged from 0.80 to 0.96. CameraTrapDetectoR provides predictions as an R object (a data frame) and flat file and provides the option to create plots of the original camera trap image with the predicted bounding box and label. There is also the option to apply models using a Shiny Application, with a point-and-click graphical user interface. This R package has the potential to facilitate application of deep learning models by biologists using camera traps.

ecology↗

bioRxiv2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

O_LIMotion-activated wildlife cameras (or "camera traps") are frequently used to remotely and non-invasively observe animals. The vast number of images collected from camera trap projects have prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. C_LIO_LIWe used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the "species model," and one that determines if an image is empty or if it contains an animal, the "empty-animal model." C_LIO_LIOur species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36-91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91-94% on out-of-sample datasets from different continents. C_LIO_LIOur software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths. C_LI

ecology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗