Incorporating Human Beliefs and Behaviors into Wildlife Ecology

Like much of the global biosphere, wildlife species have experienced rapid declines during the Anthropocene. Wildlife ecologists have responded to these crises by developing a range of technologies, techniques, and large datasets, which together have revolutionized the field, provided novel insights into the movements and behaviors of animals, and identified new risks and impacts to wildlife in a human-dominated world. While these advances have been vitally important, wildlife ecology has been slower to recognize and incorporate humans themselves into its new research domains. The chapters of this dissertation explore methods for better incorporating human behaviors, beliefs, actions, and infrastructure into the theories and approaches in wildlife ecology that have flourished in the last two decades. The research presented here demonstrates the importance of linking human beliefs and behaviors to wildlife ecology both by presenting novel findings and by showing the opportunities missed when narrow approaches are applied to complex socio-ecological problems.In Chapter 1, I provide a general introduction on the theories underlying this research, contextualize the research questions in light of the loss and recovery of large predators, and describe the research site where I collected much of the data for this dissertation. In Chapter 2, I apply the methods of movement ecology to some of the first fine-scale telemetry data collected on rifle hunters. I draw conclusions about their individual, site-level, and regional-level hunting behaviors and discuss the broad implications of these findings for hunting management. In Chapter 3, I examine livestock-predator conflict using approaches from both ecology and the social sciences. I describe a form of selection bias that is likely widespread but unreported due to the omission of social data from ecological models of conflict, and I offer guidelines for combining and translating ecological and social research on conflict. In Chapter 4, I explore the ecological impacts of one of the most globally widespread human constructions, the fence. I show for the first time the potential extent of fencing at large scales and discuss the wide variety of ecological effects of fences for both humans and ecosystems. I further highlight biases and gaps in fence research that have thus far limited a complete understanding of the environmental effects of these features. In Chapter 5, I conclude by making recommendations regarding how research might better incorporate human perceptions, decisions, and actions into ecology

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple SummaryMonitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals' movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and Challenges of Confned and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confned operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative fve-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This fve-step process acts as a guide to realize anticipated benefts from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confned and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confned operations will beneft from required advances in precision technology and PSMs, ultimately strengthening the benefts from precision technology to achieve short- and long-term goals

nocaps: novel object captioning at scale

Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes. Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin

A Primer for Monitoring Water Funds

This document is intended to assist people working on Water Funds to understand their information needs and become familiar with the strengths and weaknesses of various monitoring approaches. This primer is not intended to make people monitoring experts, but rather to help them become familiar with and conversant in the major issues so they can communicate effectively with experts to design a scientifically defensible monitoring program.The document highlights the critical information needs common to Water Fund projects and summarizes issues and steps to address in developing a Water Fund monitoring program. It explains key concepts and challenges; suggests monitoring parameters and an array of sampling designs to consider as a starting-point; and provides suggestions for further reading, links to helpful resources,and an annotated bibliography of studies on the impacts that result from activities commonly implemented in Water Fund projects

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

IENE 2020 International Conference “LIFE LINES – Linear Infrastructure Networks with Ecological Solutions””. Programme Book,

In the past 12 to 14 of January 2021, the University of Évora, in the framework of the LIFE LINES project, and the Infrastructure and Ecology Network Europe held the online event IENE International Conference, under the theme “LIFE LINES – Linear Infrastructure Networks with Ecological Solutions”. The local organising committee of the IENE 2020 had contributors from several institutions including the Mediterranean Institute for Agriculture, Environment and Development; REN; Infrastructures of Portugal; and the Municipalities of Montemor-o-Novo and Évora. This was the first IENE International Conference entirely online and participants could attend it from home and working place, regardless of their location in the world. We had 293 confirmed attendees (from 354 registered) from 40 different countries representing the five continents. During three days, participants were able to assist to 115 full oral presentations, 36 lightning talks, 13 workshops and chat with 40 posters authors, representing studies and projects worldwide. The event counted with 50 thematic sessions, running in five parallel sessions mixing live and pre-recorded interventions