scholarly journals Anthropogenic factors influence the occupancy of an invasive carnivore in a suburban preserve system

2020 ◽  
Author(s):  
John P. Vanek ◽  
Andrew U. Rutter ◽  
Timothy S. Preuss ◽  
Holly P. Jones ◽  
Gary A. Glowacki
Keyword(s):  
Native Species ◽  
The United States ◽  
Camera Traps ◽  
Anthropogenic Factors ◽  
List Type ◽  
Occupancy Models ◽  
Domestic Cats ◽  
Building Density ◽  
Nature Preserves ◽  
Free Ranging

AbstractDomestic cats (Felis catus) are one of the world’s most damaging invasive species. Free-ranging cats kill billions of wild animals every year, spread parasites and diseases to both wildlife and humans, and are responsible for the extinction or extirpation of at least 63 species. While the ecology and conservation implications of free-ranging cats have well studied in some locations, relatively little is known about cats inhabiting urban nature preserves in the United States. To address this knowledge gap, we used camera traps to study the occupancy and activity patterns of free-ranging cats in 55 suburban nature preserves in the Chicago, IL metropolitan area. From 2010–2018 (4,440 trap days), we recorded 355 photos of free-ranging cats across 26 preserves (ψnaïve = 0.45) and 41 randomly distributed monitoring points (ψnaïve = 0.18). Cats were detected every year, but rarely at the same point or preserve, and cats were largely crepuscular/diurnal. Using single-season occupancy models and a “stacked” design, we found that cat occupancy increased with building density and detectability was highest near the urban/preserve boundary. Based on our top-ranked model, predicted occupancy within individual preserves ranged from 0.09 to 0.28 (ψmean = 0.11) and was poorly correlated with preserve size or shape. Overall, our results suggest that free-ranging cats are rare within suburban preserves in our study area, and that these cats are most likely owned or heavily subsidized by people (which pose different risks and management challenges than truly feral cats). We discuss the conservation and management implications for urban natural areas.HighlightsWe surveyed for domestic cats across 55 suburban preserves from 2010-2018.We modeled occupancy and detectability as a function of urban covariates.Cat occupancy was low overall and best predicted by building density.The risk to native species is highest near preserve boundaries bordered by built environments.

scholarly journals Domestic Cat Abundance and Activity Across a Residential Land Use Gradient

2021 ◽  
Vol 9 ◽  
Author(s):  
Kevin F. P. Bennett ◽  
Brian S. Evans ◽  
J. Alan Clark ◽  
Peter P. Marra
Keyword(s):  
Land Use ◽  
Native Species ◽  
Management Strategies ◽  
Urban Environments ◽  
Camera Traps ◽  
Domestic Cat ◽  
Land Use Patterns ◽  
Residential Land ◽  
Residential Land Use ◽  
Free Ranging

Free-ranging domestic cats are a detriment to wildlife and humans by preying on native species and transmitting disease. As a result, removing free-ranging cats from the landscape has become a conservation and public health priority. Estimating cat population size with an unbiased sampling design, however, especially in human-dominated areas, is logistically challenging and rarely done. The lack of robust cat population sampling limits our understanding of where cats pose risks, which is important for evaluating management strategies, such as trap-remove or trap-neuter-return. We hypothesized that cat abundance and activity both depend on human land use and demographics. Using a network of sites participating in a community science program, we conducted transect and camera trap surveys to test predictions of cat population abundance and activity across a gradient of residential land use intensity. Both sampling methods determined that cat abundance was greatest in areas with intermediate human population density and lower educational attainment. Transect data also provided evidence that cat abundance was greatest at intermediate levels of impervious surface cover (e.g., road and buildings), while data from camera traps also showed that cat abundance was positively associated with household income. Using counts of cats observed on cameras, we found that the timing of cat activity varied depending on the degree of urban intensity. Cats were more strictly nocturnal in medium and high intensity residential land-use areas, possibly because a greater proportion of these cats are unowned or because they avoid human activity. These results suggest that transect surveys conducted during the day may undercount cats in urban environments where unowned free-ranging cats predominate. Taken together, our results highlight the importance of incorporating human demographics, land use patterns, and urban context in estimating the abundance of free-ranging cats to better inform management decisions and improve conservation outcomes.

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

2020 ◽  
Author(s):  
Michael A. Tabak ◽  
Mohammad S. Norouzzadeh ◽  
David W. Wolfson ◽  
Erica J. Newton ◽  
Raoul K. Boughton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Animal Model ◽  
Learning Algorithms ◽  
The United States ◽  
Camera Trap ◽  
Camera Traps ◽  
Machine Learning Algorithms ◽  
List Type ◽  
Vast Number ◽  
Out Of Sample

AbstractMotion-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.We 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.”Our 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.Our 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.

scholarly journals Exposure of Domestic Cats to Three Zoonotic Bartonella Species in the United States

Pathogens ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 354
Author(s):  
Lynn M. Osikowicz ◽  
Kalanthe Horiuchi ◽  
Irina Goodrich ◽  
Edward B. Breitschwerdt ◽  
Bruno Chomel ◽  
...  
Keyword(s):  
New England ◽  
The United States ◽  
Severe Illness ◽  
Domestic Cats ◽  
Bartonella Species ◽  
Serum Samples ◽  
North Central ◽  
Middle Atlantic ◽  
South Central ◽  
Central Regions

Cat-associated Bartonella species, which include B. henselae, B. koehlerae, and B. clarridgeiae, can cause mild to severe illness in humans. In the present study, we evaluated 1362 serum samples obtained from domestic cats across the U.S. for seroreactivity against three species and two strain types of Bartonella associated with cats (B. henselae type 1, B. henselae type 2, B. koehlerae, and B. clarridgeiae) using an indirect immunofluorescent assay (IFA). Overall, the seroprevalence at the cutoff titer level of ≥1:64 was 23.1%. Seroreactivity was 11.1% and 3.7% at the titer level cutoff of ≥1:128 and at the cutoff of ≥1:256, respectively. The highest observation of seroreactivity occurred in the East South-Central, South Atlantic, West North-Central, and West South-Central regions. The lowest seroreactivity was detected in the East North-Central, Middle Atlantic, Mountain, New England, and Pacific regions. We observed reactivity against all four Bartonella spp. antigens in samples from eight out of the nine U.S. geographic regions.

scholarly journals Trissolcus japonicus (Hymenoptera: Scelionidae) Causes Low Levels of Parasitism in Three North American Pentatomids Under Field Conditions

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Joshua M Milnes ◽  
Elizabeth H Beers
Keyword(s):  
North American ◽  
Native Species ◽  
Classical Biological Control ◽  
The United States ◽  
Host Finding ◽  
Pest Species ◽  
Case Scenario ◽  
Worst Case ◽  
Egg Masses ◽  
Trissolcus Japonicus

Abstract Trissolcus japonicus (Ashmead), an Asian parasitoid of Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), was first detected in North America in 2014. Although testing in quarantine facilities as a candidate for classical biological control is ongoing, adventive populations have appeared in multiple sites in the United States, Canada, and Europe. Extensive laboratory testing of T. japonicus against other North American pentatomids and H. halys has revealed a higher rate of parasitism of H. halys, but not complete host specificity. However, laboratory tests are necessarily artificial, in which many host finding and acceptance cues may be circumvented. We offered sentinel egg masses of three native pentatomid (Hemiptera: Pentatomidae) pest species (Chinavia hilaris (Say), Euschistus conspersus Uhler, and Chlorochroa ligata (Say)) in a field paired-host assay in an area with a well-established adventive population of T. japonicus near Vancouver, WA. Overall, 67% of the H. halys egg masses were parasitized by T. japonicus during the 2-yr study. Despite the ‘worst case’ scenario for a field test (close proximity of the paired egg masses), the rate of parasitism (% eggs producing adult wasps) on all three native species was significantly less (0.4–8%) than that on H. halys eggs (77%). The levels of successful parasitism of T. japonicus of the three species are C. hilaris > E. conspersus > C. ligata. The potential impact of T. japonicus on these pentatomids is probably minimal.

scholarly journals Susceptibility of Sixteen Citrus Genotypes to ‘Candidatus Liberibacter asiaticus’

Plant Disease ◽  
2016 ◽  
Vol 100 (6) ◽  
pp. 1080-1086 ◽  
Author(s):  
Greg McCollum ◽  
Mark Hilf ◽  
Mike Irey ◽  
Weiqi Luo ◽  
Tim Gottwald
Keyword(s):  
The United States ◽  
Insect Vector ◽  
Candidatus Liberibacter Asiaticus ◽  
Citrus Germplasm ◽  
Citrus Production ◽  
Candidatus Liberibacter ◽  
Liberibacter Asiaticus ◽  
Florida Citrus ◽  
Citrus Macrophylla ◽  
Free Ranging

Huanglongbing (HLB) disease is the most serious threat to citrus production worldwide and, in the last decade, has devastated the Florida citrus industry. In the United States, HLB is associated with the phloem-limited α-proteobacterium ‘Candidatus Liberibacter asiaticus’ and its insect vector, the Asian citrus psyllid (ACP; Diaphorina citri). Significant effort is being put forth to develop novel citrus germplasm that has a lower propensity to succumb to HLB than do currently available varieties. Effective methods of screening citrus germplasm for susceptibility to HLB are essential. In this study, we exposed small, grafted trees of 16 citrus types to free-ranging ACP vectors and ‘Ca. L. asiaticus’ inoculum in the greenhouse. During 45 weeks of exposure to ACP, the cumulative incidence of ‘Ca. L. asiaticus’ infection was 70%. Trees of Citrus macrophylla and C. medica were most susceptible to ‘Ca. L. asiaticus’, with 100% infection by the end of the test period in three trials, while the complex genetic hybrids ‘US 1-4-59’ and ‘Fallglo’ consistently were least susceptible, with approximately 30% infection. Results obtained in this greenhouse experiment showed good agreement with trends observed in the orchard, supporting the validity of our approach for screening citrus germplasm for susceptibility to HLB.

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

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