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Coyotes (Canis latrans) exist throughout North America and increasingly thrive in dense urban spaces; they also cause controversies when they eat small pets or seem to pose a threat. Based on fieldwork in Los Angeles, and an archive of over 400 conversations collected from the online application Nextdoor (2015–2019), we theorize the emergence of what we call the cloud coyote. Cloud coyotes are not representations but lively actors in coyote politics animated by discussion, debate, and a settler logic of property relations in places like Los Angeles. They do this by performing a threat and justifying a response that includes various attempts at extermination, containment, and assimilation, all of which—even supposedly humane alternatives—further sediment forms of settler colonialism in urban Los Angeles. We diagnose this process, show how it works, and argue that anticolonial practices—in both Los Angeles and its cloudy territories like Nextdoor—are needed to escape from perpetuating its violence.

Global change is increasing the frequency and severity of human-wildlife interactions by pushing people and wildlife into increasingly resource-limited shared spaces. To understand the dynamics of human-wildlife interactions and what may constitute human-wildlife coexistence in the Anthropocene, there is a critical need to explore the spatial, temporal, sociocultural and ecological variables that contribute to human-wildlife conflicts in urban areas. Due to their opportunistic foraging and behavioural flexibility, coyotes (Canis latrans) frequently interact with people in urban environments. San Francisco, California, USA hosts a very high density of coyotes, making it an excellent region for analysing urban human-coyote interactions and attitudes toward coyotes over time and space. We used a community-curated long-term data source from San Francisco Animal Care and Control to summarise a decade of coyote sightings and human-coyote interactions in San Francisco and to characterise spatiotemporal patterns of attitudes and interaction types in relation to housing density, socioeconomics, pollution and human vulnerability metrics, and green space availability. We found that human-coyote conflict reports have been significantly increasing over the past 5 years and that there were more conflicts during the coyote pup-rearing season (April–June), the dry season (June–September) and the COVID-19 pandemic. Conflict reports were also more likely to involve dogs and occur inside of parks, despite more overall sightings occurring outside of parks. Generalised linear mixed models revealed that conflicts were more likely to occur in places with higher vegetation greenness and median income. Meanwhile reported coyote boldness, hazing and human attitudes toward coyotes were also correlated with pollution burden and human population vulnerability indices. Synthesis and applications: Our results provide compelling evidence suggesting that human-coyote conflicts are intimately associated with social-ecological heterogeneities and time, emphasizing that the road to coexistence will require socially informed strategies. Additional long-term research articulating how the social-ecological drivers of conflict (e.g. human food subsidies, interactions with domestic species, climate-induced droughts, socioeconomic disparities, etc.) change over time will be essential in building adaptive management efforts that effectively mitigate future conflicts from occurring. Read the free Plain Language Summary for this article on the Journal blog.

Abstract The ecosystem system services framework has potential to help clarify wildlife management challenges at the single species scale, but existing methods struggle to capture the complex values and tradeoffs at play in human–wildlife interactions. We worked with community scientists to gather and use stories (n = 150) as a source of ecosystem services data about living alongside eastern coyotes (Canis latrans var) in rural Vermont, United States. Our a priori ecosystem service assessment showed that human–coyote interactions can have simultaneous positive and negative human well‐being impacts at both the sample scale and for individual interviewees. Our research identified emergent themes that lent insight into how interviewees justified different kinds of relationships with coyotes. We applied a tradeoffs lens to evaluate three policy options based on their potential to mitigate ecosystem disservices and maximize ecosystem services from human–coyote interactions. We found that ecosystem services assessment rooted in a dataset of stories revealed policy‐relevant understanding of value conflicts and alignments at the sample scale without overshadowing the nuances of individual interviewees' experiences.

Sign language (SL) recognition for individuals with hearing disabilities involves leveraging machine learning (ML) and computer vision (CV) approaches for interpreting and understanding SL gestures. By employing cameras and deep learning (DL) approaches, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), these models analyze facial expressions, hand movements, and body gestures connected with SL. The major challenges in SL recognition comprise the diversity of signs, differences in signing styles, and the need to recognize the context in which signs are utilized. Therefore, this manuscript develops an SL detection by Improved Coyote Optimization Algorithm with DL (SLR-ICOADL) technique for hearing disabled persons. The goal of the SLR-ICOADL technique is to accomplish an accurate detection model that enables communication for persons using SL as a primary case of expression. At the initial stage, the SLR-ICOADL technique applies a bilateral filtering (BF) approach for noise elimination. Following this, the SLR-ICOADL technique uses the Inception-ResNetv2 for feature extraction. Meanwhile, the ICOA is utilized to select the optimal hyperparameter values of the DL model. At last, the extreme learning machine (ELM) classification model can be utilized for the recognition of various kinds of signs. To exhibit the better performance of the SLR-ICOADL approach, a detailed set of experiments are performed. The experimental outcome emphasizes that the SLR-ICOADL technique gains promising performance in the SL detection process.

Systemic Lupus Erythematosus (SLE) is a complicated autoimmune disease that can present with a variety of clinical symptoms, making precise prognosis difficult. Because SLE has a wide range of symptoms and may overlap with other autoimmune and inflammatory disorders, making a diagnosis can be challenging. This study creates a precise and accurate model for the prediction of SLE using the GEO dataset. For cost-effective data collection and analysis, feature selection might be essential in some applications, particularly in healthcare and scientific research. The strength of Artificial Neural Networks (ANN) for Systemic Lupus Erythematosus prediction and the Coyote Optimization Algorithm (COA) for feature selection are combined in this study. The COA is an optimization method influenced by nature and coyote hunting behavior. This study attempts to improve the effectiveness of subsequent predictive modeling by using COA to identify a subset of significant features from high-dimensional datasets linked to SLE. A Multi-layer Feed-forward Neural Network, a potent machine learning architecture renowned for its capacity to discover complex patterns and correlations within data, is then given the chosen features. Because the neural network is built to capture SLE's intricate and non-linear structure, it offers a reliable foundation for precise classification and prediction. The accuracy of the COA-ANN model was 99.6%.

Abstract Global change is increasing the frequency and severity of human‐wildlife interactions by pushing people and wildlife into increasingly resource‐limited shared spaces. To understand the dynamics of human‐wildlife interactions and what may constitute human‐wildlife coexistence in the Anthropocene, there is a critical need to explore the spatial, temporal, sociocultural and ecological variables that contribute to human‐wildlife conflicts in urban areas. Due to their opportunistic foraging and behavioural flexibility, coyotes (Canis latrans) frequently interact with people in urban environments. San Francisco, California, USA hosts a very high density of coyotes, making it an excellent region for analysing urban human‐coyote interactions and attitudes toward coyotes over time and space. We used a community‐curated long‐term data source from San Francisco Animal Care and Control to summarise a decade of coyote sightings and human‐coyote interactions in San Francisco and to characterise spatiotemporal patterns of attitudes and interaction types in relation to housing density, socioeconomics, pollution and human vulnerability metrics, and green space availability. We found that human‐coyote conflict reports have been significantly increasing over the past 5 years and that there were more conflicts during the coyote pup‐rearing season (April–June), the dry season (June–September) and the COVID‐19 pandemic. Conflict reports were also more likely to involve dogs and occur inside of parks, despite more overall sightings occurring outside of parks. Generalised linear mixed models revealed that conflicts were more likely to occur in places with higher vegetation greenness and median income. Meanwhile reported coyote boldness, hazing and human attitudes toward coyotes were also correlated with pollution burden and human population vulnerability indices. Synthesis and applications: Our results provide compelling evidence suggesting that human‐coyote conflicts are intimately associated with social‐ecological heterogeneities and time, emphasizing that the road to coexistence will require socially informed strategies. Additional long‐term research articulating how the social‐ecological drivers of conflict (e.g. human food subsidies, interactions with domestic species, climate‐induced droughts, socioeconomic disparities, etc.) change over time will be essential in building adaptive management efforts that effectively mitigate future conflicts from occurring. Read the free Plain Language Summary for this article on the Journal blog.

The ecosystem system services framework has potential to help clarify wildlife management challenges at the single species scale, but existing methods struggle to capture the complex values and tradeoffs at play in human–wildlife interactions. We worked with community scientists to gather and use stories (n = 150) as a source of ecosystem services data about living alongside eastern coyotes (Canis latrans var) in rural Vermont, United States. Our a priori ecosystem service assessment showed that human–coyote interactions can have simultaneous positive and negative human well‐being impacts at both the sample scale and for individual interviewees. Our research identified emergent themes that lent insight into how interviewees justified different kinds of relationships with coyotes. We applied a tradeoffs lens to evaluate three policy options based on their potential to mitigate ecosystem disservices and maximize ecosystem services from human–coyote interactions. We found that ecosystem services assessment rooted in a dataset of stories revealed policy‐relevant understanding of value conflicts and alignments at the sample scale without overshadowing the nuances of individual interviewees' experiences. [ABSTRACT FROM AUTHOR]

Healthcare is an area of concern where the application of human-centred design practices and principles can enormously affect well-being and patient care. The provision of high-quality healthcare services requires a deep understanding of patients’ needs, experiences, and preferences. Human activity recognition (HAR) is paramount in healthcare monitoring by using machine learning (ML), sensor data, and artificial intelligence (AI) to track and discern individuals’ behaviours and physical movements. This technology allows healthcare professionals to remotely monitor patients, thereby ensuring they adhere to prescribed rehabilitation or exercise routines, and identify falls or anomalies, improving overall care and safety of the patient. HAR for healthcare monitoring, driven by deep learning (DL) algorithms, leverages neural networks and large quantities of sensor information to autonomously and accurately detect and track patients’ behaviors and physical activities. DL-based HAR provides a cutting-edge solution for healthcare professionals to provide precise and more proactive interventions, reducing the burden on healthcare systems and improving patient well-being while increasing the overall quality of care. Therefore, the study presents an improved coyote optimization algorithm with a deep learning-assisted HAR (ICOADL-HAR) approach for healthcare monitoring. The purpose of the ICOADL-HAR technique is to analyze the sensor information of the patients to determine the different kinds of activities. In the primary stage, the ICOADL-HAR model allows a data normalization process using the Z-score approach. For activity recognition, the ICOADL-HAR technique employs an attention-based long short-term memory (ALSTM) model. Finally, the hyperparameter tuning of the ALSTM model can be performed by using ICOA. The stimulation validation of the ICOADL-HAR model takes place using benchmark HAR datasets. The wide-ranging comparison analysis highlighted the improved recognition rate of the ICOADL-HAR method compared to other existing HAR approaches in terms of various measures.

This study introduces a novel tactic to efficiently size the selection of gas engines for Combined Cooling, Heating, and Power (CCHP) systems in rural areas. This method is based on an adapted bio-inspired method, called Improved Coyote Optimizer (ICO). The proposed ICO technique uses a combination of metaheuristic optimization techniques and the system model to determine an optimal overall engine size while satisfying given operating constraints and minimizing the total cost. The analysis revealed that the fuel consumption of the gas engine and boiler in the proposed system is dependent on the power level. At 40 kW, the engine consumed 1558 units of fuel, while the boiler consumed 1430 units, with this difference increasing to 411 units at 110 kW. These findings emphasize the importance of improving the fuel efficiency of the engine, particularly at higher power levels. Furthermore, the study assessed the system’s CO2 emissions, finding that the minimum yearly emissions of 220,600 kg occurred at an engine power of 1 kW, while the maximum CO2 reduction rate of 40% was achieved at 120 kW. This suggests that larger engines are more efficient, resulting in lower CO2 emissions due to reduced fuel consumption. In terms of financial considerations, the engine power dimension of 118.9 kW yielded the highest cash flow value of 49,840 $. However, variations in engine power between 60 and 100 kW indicate a wider range of production costs and output quantities. To accurately evaluate the return on investment, more detailed information on production costs and output within this range is necessary. Lastly, the study compared different optimization methods and found that the Improved Coyote Optimizer (ICO) demonstrated the fastest convergence, reaching its ideal values in 3000 iterations. Both the ICO-based method and the ABC-based technique successfully minimized the cost function, with the ICO-based method achieving the best value of 0.20. Furthermore, the proposed algorithm is practical to a wide variety of CCHP system configurations, making it a valuable tool for practitioners.

Abstract Extreme ecosystem modification by humans has caused drastic reductions in populations and ranges of top mammalian predators, while simultaneously allowing synanthropic mesopredator species to expand. These conditions often result in inflated local densities of highly adaptable mesopredators that disrupt trophic dynamics and place unsustainable predation pressure on native prey populations. Colonization of a dominant predator may lead to top-down control of mesopredators and restore trophic balance. Coyotes are a novel colonizer of some coastal barrier islands of eastern North America, offering an opportunity to test how the addition of an apex predator impacts an established guild of mesopredators. To assess their trophic impact, we conducted 75,576 camera trapping hours over an 18-month study period, capturing > 1.5 million images across 108 coastal camera sites. Using two-species occupancy and habitat use models, we found sizeable effects of coyote habitat use on that of red foxes and free-ranging domestic cats, suggesting that coyotes function as apex predators in barrier island ecosystems. In fact, the only factor that determined the spatial pattern of highly ubiquitous red foxes was the sympatric habitat use of the largest carnivore in the food web—coyotes. That ‘novel’ apex predators can become established in coastal food webs illustrates the highly dynamic nature of conservation challenges for habitats and species at the edge of the sea.

The modern building sector faces the challenge of meeting energy demands while minimizing environmental impacts and promoting energy efficiency. This research paper presents a comprehensive approach to optimizing building energy systems using a modified metaheuristic, the Developed Coyote Algorithm (DCO). The increasing importance of storage equipment in energy organizations, driven by changes in peak-load demand and the growing adoption of renewable energy sources, necessitates efficient storage solutions. Battery Energy Storage (BES) and Thermal Energy Storage (TES) are commonly used to store excess energy generated from renewable sources and supply it during peak demand. By applying the DCO algorithm, the operational plans of energy systems comprising BES, air-source heat pumps, and TES can be efficiently optimized with minimal computational requirements. The proposed method aims to enhance the productivity and sustainability of energy systems while providing valuable insights for policymakers and stakeholders involved in renewable energy development. The discharge of batteries and thermal energy storage occurs during low electricity prices, with charging power fluctuating between 100 and 150 kW. GWO and IWWO results show that charging is done at night with 150 kW, with a reduced amount used during other times. Thermal energy storage can store energy from 500 to 800 kW during night time, while DCO generates 700–900 kW. The results of this research contribute to the effective management of batteries, heat sources, and thermal storage in building energy systems, further advancing the utilization of renewable energy resources.

IntroductionZoonoses associated with urban wildlife are increasingly concerning for human health and include the recent emergence of alveolar echinococcosis (AE) in North America. AE develops following infection with the tapeworm Echinococcus multilocularis. In Alberta, up to 65% of urban coyotes (Canis latrans) are infected with E. multilocularis, and infected scats contain eggs that can be accidentally ingested by people. Our goal was to determine the predictors of infection prevalence and intensity in coyote scats in Edmonton, Canada, and to identify the predictors of coyote scat deposition and content, especially as related to anthropogenic food sources and infrastructure.MethodsTo study infection prevalence and intensity, volunteers collected 269 scats, which were tested for E. multilocularis using polymerase chain reaction. We compared infection prevalence and shedding intensity by habitat and scat content. To determine predictors of scat presence and content, we used snow tracking to identify 1263 scats. We compared landscape characteristics at scats and available points, and among scats with different contents. We used negative binomial regression to predict scat abundance in city-delineated green spaces.Results26.0% of tested scats were positive for E. multilocularis (n = 70), and infection was twice as common as expected near compost and 1.3x more common than expected when scats contained anthropogenic food. Scats were more common than expected near other scats (80% within 1 m of scats, 27% at 11.5 m), buildings (19% at buildings, 16% at 80 m), and the camps of people experiencing homelessness (24% at camps, 20% at 60 m). Scats frequently contained fruit (52.9%), anthropogenic material (36.7%), and birdseed (16.0%), and scats containing anthropogenic material often occurred near human infrastructure, supporting a relationship between anthropogenic attractants and scat accumulation.DiscussionThese results suggest that abundant food sources and anthropogenic food increase coyote aggregation, increasing both scat abundance and infection rates, which in turn increases risk of exposure to zoonotic parasites for humans. Risk to humans might be reduced by preventing coyote access to anthropogenic and aggregated food sources and educating people who are likely to encounter infected soil or vegetation, including gardeners, park users, and people experiencing homelessness.

El presente escrito remite al análisis de dos relatos orales de la cultura úza' y forma parte de un trabajo de investigación sobre tradición oral realizado en Ranzo úza' (Misión de Chichimecas), comunidad ubicada en el municipio de San Luis de la Paz, al noreste del estado de Guanajuato, México. El trabajo propone un estudio basado en la propuesta de Teun A. van Dijk, donde la organización de macroestructuras y superestructuras nos guían para abordar el análisis textual. Del mismo modo, profundizamos en las acciones de los personajes para establecer su importancia y función cultural. En los relatos seleccionados, los animales representan una parte de la cosmovisión de la comunidad úza,'; “La sierpe” y “El conejo y el coyote” son muestras de relatos que luchan por permanecer en un espacio y época donde la cultura está en situación crítica de desaparición.

The optimal power flow (OPF) of power system is to optimize the objective function such as generation cost by adjusting control variables on the premise of satisfying operation constraints and supply–demand balance. A mathematical distribution coyote optimization algorithm (MDCOA) is proposed. Crossover operation is added to the algorithm initialization process to improve the randomness and diversity of offspring. In order to improve its search efficiency and balance the process of exploration and exploitation, the step size is generated by random numbers with seven mathematical distributions, including Gaussian distribution, Uniform distribution, Exponential distribution, Beta distribution, Gamma distribution, Weibull distribution and Rayleigh distribution. Firstly, the original COA and seven improved COA were simulated under the CEC2017 functions, wCOA ranks first among these comparison algorithms and was selected to be compared with PSO, CMA-ES, MFO, HHO, HBA and WOA to verify its effectiveness. Aiming at the OPF problem, the simulation experiments are carried out on IEEE 30-bus system and IEEE 57-bus system. The generation cost, active power loss, voltage stability and bus voltage offset are selected as objective functions respectively. The proposed method can find a better solution than the standard COA and eCOA has the best comprehensive performance in all cases. Simulation are compared with ARCBBO, ECHT-DE, I-NSGA-III, MSA and other algorithms and the results show that the MDCOA can effectively solve the OPF problem.

For many species, the relationship between space use and diet composition is complex, with individuals adopting varying space use strategies such as territoriality to facilitate resource acquisition. Coyotes (Canis latrans) exhibit two disparate types of space use; defending mutually exclusive territories (residents) or moving nomadically across landscapes (transients). Resident coyotes have increased access to familiar food resources, thus improved foraging opportunities to compensate for the energetic costs of defending territories. Conversely, transients do not defend territories and are able to redirect energetic costs of territorial defense towards extensive movements in search of mates and breeding opportunities. These differences in space use attributed to different behavioral strategies likely influence foraging and ultimately diet composition, but these relationships have not been well studied. We investigated diet composition of resident and transient coyotes in the southeastern United States by pairing individual space use patterns with analysis of stable carbon (δ13C) and nitrogen (δ15N) isotope values to assess diet. During 2016–2017, we monitored 41 coyotes (26 residents, 15 transients) with GPS radio-collars along the Savannah River area in the southeastern United States. We observed a canopy effect on δ13C values and little anthropogenic food in coyote diets, suggesting 13C enrichment is likely more influenced by reduced canopy cover than consumption of human foods. We also observed other land cover effects, such as agricultural cover and road density, on δ15N values as well as reduced space used by coyotes, suggesting that cover types and localized, resident-like space use can influence the degree of carnivory in coyotes. Finally, diets and niche space did not differ between resident and transient coyotes despite differences observed in the proportional contribution of potential food sources to their diets. Although our stable isotope mixing models detected differences between the diets of resident and transient coyotes, both relied mostly on mammalian prey (52.8%, SD = 15.9 for residents, 42.0%, SD = 15.6 for transients). Resident coyotes consumed more game birds (21.3%, SD = 11.6 vs 13.7%, SD = 8.8) and less fruit (10.5%, SD = 6.9 vs 21.3%, SD = 10.7) and insects (7.2%, SD = 4.7 vs 14.3%, SD = 8.5) than did transients. Our findings indicate that coyote populations fall on a feeding continuum of omnivory to carnivory in which variability in feeding strategies is influenced by land cover characteristics and space use behaviors.

It is within the framework of situated knowledges in the field of biology and technology studies, in its relation to feminism that the coyote figuration is conceptualized by Donna Haraway. In what respect are they conceived by Donna Haraway to be figures of emancipation? What kind of affinity does it establishes with the figure of cyborg, as figure of posthumanism? Certainly, Donna Haraway hypothesizes the privilege of a ‘partial perspective’ having to play a role in expanding the epistemic horizon of feminist thought. This paper probes into the potential role of coyote figures as metaphors illuminating feminist readings of the relation between philosophy and technology. However, coyote figures have a generic character of transgression, and hence, borders on epistemological reduction of its ontico-ontological phenomenality which Catherine Malabou problematizes. Malabou’s eschewal of flexibility counters this binary by developing the role played by the “fantastic” in the espousal of plasticity of being. Being conceived in this originary mutability eliminates the interior-exterior division of beings. What are the comparable features of coyote figurations and the fantastic? And how does it add to the feminist understanding of philosophy of technology?

A lo largo de este artículo analizaremos las múltiples facetas del famoso coyote en la tradición oral de los nativos americanos siguiendo la clasificación de Mac Linscott Rickets. Relacionado con un héroe cultural, con el ladrón que roba el fuego, el vagabundo, el imitador y el Creador y Destructor de vida, el espectador se siente identificado con su niño interior y busca encontrar una respuesta a las dicotomías de la vida. Este personaje polifacético hablará a través de Leslie Marmon Silko en Storyteller luchando por preservar las tradiciones antiguas y al mismo tiempo adaptarlas a un mundo moderno, tan distantes entre sí. En la misma línea, Harry Fonseca también lo representará como un cowboy, un seductor y un santo.

A common adage is “you can’t manage what you can’t measure.” How applicable this saying may be to wildlife conservation and management is debatable; however, understanding the “where,” “who,” and “why” of human-wildlife conflict can help managers evaluate and prioritize incident response and conflict mitigation efforts. It is critical to note that no tracking or reporting system is capable of effectively capturing all human-wildlife incidents. The format and functionality of the tracking system, how the system is advertised to the public, and who manages the system are all important factors in the accessibility, utility, and success of each tracking system. Here, we examine three different systems for tracking reported human-coyote incidents and encounters in California: 1) the Wildlife Incident Reporting system, operated by the California Department of Fish and Wildlife; 2) Coyote Cacher, operated by the University of California - Agricultural and Natural Resources Cooperative Extension; and 3) iNaturalist, a citizen science initiative, operated by a non-governmental organization. We find that because each system offers different incentives to the public (and poses different potential challenges or barriers to reporting), each receives a significantly different volume of coyote reports. Each system provides a unique perspective of reported human-coyote conflicts in California. Understanding these differences and being cognizant of the inherent or potential limitations of a reporting system are crucial for integrated, scientifically defensible, and robust wildlife management, effective policy development, and informed decision-making.

For many species, the relationship between space use and diet composition is complex, with individuals adopting varying space use strategies such as territoriality to facilitate resource acquisition. Coyotes (Canis latrans) exhibit two disparate types of space use; defending mutually exclusive territories (residents) or moving nomadically across landscapes (transients). Resident coyotes have increased access to familiar food resources, thus improved foraging opportunities to compensate for the energetic costs of defending territories. Conversely, transients do not defend territories and are able to redirect energetic costs of territorial defense towards extensive movements in search of mates and breeding opportunities. These differences in space use attributed to different behavioral strategies likely influence foraging and ultimately diet composition, but these relationships have not been well studied. We investigated diet composition of resident and transient coyotes in the southeastern United States by pairing individual space use patterns with analysis of stable carbon (δ13C) and nitrogen (δ15N) isotope values to assess diet. During 2016–2017, we monitored 41 coyotes (26 residents, 15 transients) with GPS radio-collars along the Savannah River area in the southeastern United States. We observed a canopy effect on δ13C values and little anthropogenic food in coyote diets, suggesting 13C enrichment is likely more influenced by reduced canopy cover than consumption of human foods. We also observed other land cover effects, such as agricultural cover and road density, on δ15N values as well as reduced space used by coyotes, suggesting that cover types and localized, resident-like space use can influence the degree of carnivory in coyotes. Finally, diets and niche space did not differ between resident and transient coyotes despite differences observed in the proportional contribution of potential food sources to their diets. Although our stable isotope mixing models detected differences between the diets of resident and transient coyotes, both relied mostly on mammalian prey (52.8%, SD = 15.9 for residents, 42.0%, SD = 15.6 for transients). Resident coyotes consumed more game birds (21.3%, SD = 11.6 vs 13.7%, SD = 8.8) and less fruit (10.5%, SD = 6.9 vs 21.3%, SD = 10.7) and insects (7.2%, SD = 4.7 vs 14.3%, SD = 8.5) than did transients. Our findings indicate that coyote populations fall on a feeding continuum of omnivory to carnivory in which variability in feeding strategies is influenced by land cover characteristics and space use behaviors. [ABSTRACT FROM AUTHOR]