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Designing Autonomy: Opportunities for New Wildness in the Anthropocene
Abstract
Maintaining wild places involves increasingly intensive human interventions. Several recent projects use semi-automated mediating technologies to enact conservation and restoration actions, including re-seeding and invasive species eradication. Could a deep-learning system sustain the autonomy of nonhuman ecological processes at designated sites without direct human interventions? We explore here the prospects for automated curation of wild places, as well as the technical and ethical questions that such co-creation poses for ecologists, conservationists, and designers. Our goal is to foster innovative approaches to creating and maintaining the autonomy of evolving ecological systems.
... In parallel, practitioners are progressively utilizing digital solutions for urban greening in efforts to optimize, and in some cases democra- tize, the delivery and implementation of UGI (Cantrell, Martin, & Ellis, 2017;DiSalvo & Jenkins, 2017). For example, automation is supporting UGI management in lawn care through autonomous lawn mowers ( Grossi et al., 2016), urban forest inventories feature digitally-tagged trees that transmit information to smart phone platforms (Luvisi & Lorenzini, 2014), biodiversity assessments are undertaken through gaming (Sandbrook, Adams, & Monteferri, 2015), citizen nature pre- ferences are monitored through Instagram images and hash tags (Guerrero, M?ller, Olafsson, & Snizek, 2016), and urban foraging is undertaken with community-developed semi-autonomous drones (DiSalvo & Jenkins, 2017). ...
... For example, automation is supporting UGI management in lawn care through autonomous lawn mowers ( Grossi et al., 2016), urban forest inventories feature digitally-tagged trees that transmit information to smart phone platforms (Luvisi & Lorenzini, 2014), biodiversity assessments are undertaken through gaming (Sandbrook, Adams, & Monteferri, 2015), citizen nature pre- ferences are monitored through Instagram images and hash tags (Guerrero, M?ller, Olafsson, & Snizek, 2016), and urban foraging is undertaken with community-developed semi-autonomous drones (DiSalvo & Jenkins, 2017). These technologies are driven by govern- ment and business aims at productivity (and profitability), but also creativity and innovation coupled with promises of 'smart' and 'real- time' solutions to environmental and societal demands and challenges (Cantrell et al., 2017;Taylor Buck & While, 2017;Gabrys, 2014). Taken together, these examples represent the kind of rapid technological de- velopment suggestive of potential disruption in the field of UGI plan- ning and management. ...
... The automation of UGI has social, ecological, and technological ramifications. At present, however, automation is discussed in the natural resource management literature in purely technical and ecolo- gical ( Cantrell et al., 2017;Luvisi & Lorenzini, 2014) or social and ecological terms ( Guerrero et al., 2016;Kahila-Tani, Broberg, Kytt?, & Tyger, 2016). Below, we develop an analytical framework to bridge the various interfaces of the automation of UGI, discussing the interactions amongst technical innovation, social systems, and ecosystem functions. ...
Contemporary society is increasingly impacted by automation; however, few studies have considered the potential consequences of automation on ecosystems and their management (hereafter the automation of urban green infrastructure or UGI). This Perspective Essay takes up this discussion by asking how a digital approach to UGI planning and management mediates the configuration and development of UGI and to whose benefit? This is done through a review of key issues and trends in digital approaches to UGI planning and management. We first conceptualize automation from a social, ecological, and technological interactions perspective and use this lens to present an overview of the risks and opportunities of UGI automation with respect to selected case studies. Results of this analysis are used to develop a conceptual framework for the assessment of the material and governance implications of automated UGIs. We find that, within any given perspective, the automation of UGI entails a complex dialectic between efficiency, human agency and empowerment. Further, risks and opportunities associated with UGI automation are not fixed but are dynamic properties of changing contextual tensions concerning power, actors, rules of the game and discourse at multiple scales. We conclude the paper by outlining a research agenda on how to consider different digital advances within a social-ecological-technological approach .
... These critical arguments keep developing and feed into contemporary environmental justice movements across fields, impelling designers, environmental engineers, conservationists, and preservationists to reflect their values and conceptions about nature and wilderness. For example, many environmental groups, such as Sierra Club and the Wilderness Society, have been addressing their ongoing inclusion and equity efforts in preservation and conservation practices; Also, recent land acknowledgment 北美各种机构开展的领土权属确认运动也肯定了原住居民长久以来的 土地管理者身份。 随着自然和荒野概念的拓展，不同领域的学者开始以多元化的 视角来理解和定义"野地"。在人类世的背景下，生态学家和生物学 家开始将研究重心转移到"新形式的野"(new wilds)和人类世生态 系统(novel ecosystems)上。 [22]~ [25] 近年来提出的环境策略也已不再强 调维持或恢复历史生态格局，而鼓励通过促进生态系统过程、非人类 物种和智能机器的自主性来实现"再野化"。 [26]~ [28] 人文和科学技术 论等领域的学者也不再批判自然和荒野，转而以"非人类能动性" (nonhuman agency)为概念框架来理解人类与非人类物种及机器之 间的合作生产和共同进化过程，探究三者间不同的关联方式。 [29]~ [34] 与此同时，景观设计师和规划师也不断吸纳新型生态系统、多物种相 互作用、机器智能和非人类能动性等新兴思想，以期畅想更具开放型 的设计项目。 [35]~ [39] 在承认荒野概念的核心在于自然的自主性(无或较少人工干预) With the thickening of the conceptions of nature and wilderness, scholars across fields have begun to embrace a plurality in interpreting and conceptualizing wild places. ...
... [22]~ [25] Recent environmental strategies have surpassed maintaining or recovering historical ecological patterns and instead promote the autonomy of ecosystem processes, nonhuman species, and intelligent machines as strategies for "rewilding." [26]~ [28] Scholars in Humanities and Science and Technology Studies (STS) have also turned their attention away from critiquing "nature" and "wilderness," and deployed "nonhuman agency" as a conceptual frame to understand the co-production and co-evolution between human and nonhuman species and machines, speculating different forms of associated relationships as well. [29]~ [34] Landscape architects and planners also incorporate emerging ideas about novel ecosystems, multispecies interactions, machine intelligence, and nonhuman agency to envision a greater openness in their projects. ...
This paper investigates the idea of cultivated wildness at the intersection of landscape design and artificial intelligence. The paper posits that contemporary landscape practices should overcome the potentially single understanding on wilderness, and instead explore landscape strategies to cultivate new forms of wild places via ideas and concerns in contemporary Environmental Humanities, Science and Technology Studies, Ecological Sciences, and Landscape Architecture. Drawing cases in environmental engineering, computer science, and landscape architecture research, this paper explores a framework to construct wild places with intelligent machines. In this framework, machines are not understood as a layer of “digital infrastructure” that is used to extend localized human intelligence and agency. Rather machines are conceptualized as active agents who can participate in the intelligence of co-production. Recent developments in cybernetic technologies such as sensing networks, artificial intelligence, and cyberphysical systems
can also contribute to establishing the framework. At the heart of this framework is “technodiversity,” in parallel with biodiversity, since a singular vision on technological
development driven by optimization and efficiency reinforces a monocultural approach that eliminates other possible relationships to construct with the environment. Thus, cultivated wildness is also about recognizing “wildness” in machines.
... The widespread use of RAS has been proposed as a mechanism to enhance urban sustainability 14 , but critics have questioned this technocentric vision 15,16 . Moreover, while RAS are likely to have far-reaching social, ecological and technological ramifications, these are often discussed only in terms of the extent to which their deployment will improve efficiency and data harvesting, and the associated social implications [17][18][19] . Such a narrow focus will probably overlook interactions across the social-ecological-technical systems that cities are increasingly thought to represent 20 . ...
... A greater proportion of non-environmental participants (76%; n = 22/29) also scored the challenge 'Pollution will increase if RAS are unable to identify or clean up accidents (for example, spillages) that occur during automated maintenance/ construction of infrastructure' (item 32) above zero compared with those with environmental expertise (45%; n = 22/29) (Fisher's exact test: odds ratio = 0.26; 95% CI = 0.08-0. 79 (4) GI management (7) Street vegetation irrigation (8) Wilder landscapes (9) Smart buildings (10) Vehicle-animal collision detection (16) Animal deterrence (17) Roadworks and transport system management (21) Traffic system noise pollution declines (22) Lighting systems (23) Pollutant mm (24) Waste production mm (25) Environmental law compliance monitoring (26) Traffic system pollutant run-off reductions (33) Water infrastructure mm (34) Water pollution monitoring (35) River intervention mm (36) Human nature interaction increases (41) Pollution decreases enhance recreation (42) Education and citizen science (43) Leisure time increases (44) New employment opportunities in GI mm (45) Transport system and car ownership decreases (54) Wheel-less transport infrastructure (55) Built structure declines (56) Self-repairing built infrastructure (57) Ecosystem service mimicry (58) Pest and invasive species mm (64) Food for urban exploiter species reduces (65) Urban agriculture increases (70) Food waste mm (71) similar pattern was observed for item 38 'RAS will alter the hydrological microclimate (for example, temperature and light), altering aquatic communities and encouraging algal growth' . A significantly greater proportion of non-environmental compared with environmental participants (60% (n = 12/20) and 26% (n = 11/42), respectively) allocated scores above zero (Fisher's exact test; odds ratio = 0.24; 95% CI = 0.07-0.84; ...
Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits.
... Dynamic and process-oriented approaches focus on the adaptive capacity of ecosystems (4) and the restoration of ecosystem processes promoting biodiversity, rather than aiming to maintain or restore particular ecosystem states characterized by predefined species compositions or particular bundles of ecosystem services. Such approaches recognize ecosystems as dynamic systems (20) whose future development cannot always be predicted (21,22). ...
... Passive rewilding actions include the creation of no-hunting areas, low-intervention forestry management, setting aside agricultural land, the removal of dispersal barriers, and the restoration of natural flood regimes (22,25,34). ...
Facilitating “wildness”
Humans have encroached upon a majority of Earth's lands. The current extinction crisis is a testament to human impacts on wilderness. If there is any hope of retaining a biodiverse planetary system, we must begin to learn how to coexist with, and leave space for, other species. The practice of “rewilding” has emerged as a method for returning wild lands, and wildness, to landscapes we have altered. Perino et al. review this concept and present a framework for implementing it broadly and in a way that considers ongoing human interaction.
Science , this issue p. eaav5570
... Dynamic and process-oriented approaches focus on the adaptive capacity of ecosystems (4) and the restoration of ecosystem processes promoting biodiversity, rather than aiming to maintain or restore particular ecosystem states characterized by predefined species compositions or particular bundles of ecosystem services. Such approaches recognize ecosystems as dynamic systems (20) whose future development cannot always be predicted (21,22). ...
... Passive rewilding actions include the creation of no-hunting areas, low-intervention forestry management, setting aside agricultural land, the removal of dispersal barriers, and the restoration of natural flood regimes (22,25,34). ...
... Até hoje não existe um conceito concreto amplamente difundido ou ferramenta envolvendo a modelagem da informação para a paisagem (LANGE, 2011;Cantrell et. al., 2017, Scheller, 2017. Tendo como base uma extensa revisão de práticas e literatura relacionadas ao tema, quatro diretrizes gerais relativas à modelagem da informação foram detectadas e apontadas, de (i) a (iv), para aplicá--las à paisagem e alcançar os resultados deste trabalho. Considerando a paisagem uma entidade complexa e de múltiplas es ...
Considerando os desafios impostos pelas mudanças climáticas e suas consequências, este trabalho de conclusão de curso apresenta um estudo de caso que explora o conceito de Landscape Information Modeling (em português, modelagem da informação voltada à paisagem) como uma ferramenta de análise e de tomada de decisão no projeto paisagístico, a fi m de avaliar estratégias de prevenção de inundações para uma Zona Especial de Interesse Social (ZEIS). A teoria LIM fornece uma nova abordagem para o projeto urbano e paisagístico e emprega sistemas da modelagem da informação de forma a analisar dados ambientais como inputs e propor simulações a partir das quais o cenário ideal pode ser alcançado. A ZEIS do Poço da Draga é uma comunidade com mais de 100 anos de história localizada no litoral da cidade de Fortaleza-CE, no Brasil. Como resultado da desigualdade social, esta comunidade há muito se assentou em uma área habitacional desfavorável e sofre com enchentes anuais. Embora importantes pesquisas e projetos já tenham avaliado o equilíbrio entre meio ambiente e sociedade no Brasil, este trabalho traz um novo olhar sobre estas questões.
... Such systems have devised unexpected behaviors in Go, chess, and some video game that expanded human players' understanding of these games and provided new insights (SCHRITTWIESER et al. 2020). Landscape architects and ecologists now also imagine how RL systems might manage the environment and construct wild landscapes (CANTRELL et al. 2017, ZHANG & CANTRELL 2021. One research team conducted RL experiments to prune a polyculture garden with a FarmBot to increase biodiversity (PRESTEN et al. 2022). ...
This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision. ELUA has two gantry robots, one indoors and the other outside on the rooftop of a 6-story campus building. Each robot can seed, water, weed, and prune in its garden. To support responsive landscape research, ELUA also includes sensor arrays, an AI-powered camera, and an extensive network infrastructure. This project demonstrates a way to integrate artificial intelligence into an evolving urban ecosystem, and encourages landscape architects to develop an adaptive design framework where design becomes a long-term engagement with the environment.
... Such systems have devised unexpected behaviors in Go, chess, and some video game that expanded human players' understanding of these games and provided new insights (SCHRITTWIESER et al. 2020). Landscape architects and ecologists now also imagine how RL systems might manage the environment and construct wild landscapes (CANTRELL et al. 2017, ZHANG & CANTRELL 2021. One research team conducted RL experiments to prune a polyculture garden with a FarmBot to increase biodiversity (PRESTEN et al. 2022). ...
This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision. ELUA has two gantry robots, one indoors and the other outside on the rooftop of a 6-story campus building. Each robot can seed, water, weed, and prune in its garden. To support responsive landscape research, ELUA also includes sensor arrays, an AI-powered camera, and an extensive network infrastructure. This project demonstrates a way to integrate artificial intelligence into an evolving urban ecosystem, and encourages landscape architects to develop an adaptive design framework where design becomes a long-term engagement with the environment.
... Practices remain speculative but merit further exploration. In a thought experiment, scholars imagined a DRL based machine "wildness creator", just like AlphaGo and AlphaStar, that can devise environmental management strategies and create places that are beyond human comprehension (CANTRELL et al. 2017). Rather than envisioning DRL based AI system as "superhumans" who can predict the environment in human discourse, "wildness creator" is conceptualized as a different type of intelligence that understands the environment differently than humans. ...
Taking on a historical lens, this paper traces the development of cybernetics and systems thinking back to the 1950s, when a group of interdisciplinary scholars converged to create a new theoretical model based on machines and systems for understanding matters of meaning, information, consciousness, and life. By presenting a genealogy of research in the landscape architecture discipline, the paper argues that landscape architects have been an important part of the development of cybernetics by materializing systems based on cybernetic principles in the environment through ecologically based landscape design. The landscape discipline has developed a design framework that provides transformative insights into understanding machine intelligence. The paper calls for a new paradigm of environmental engagement to understand matters of design and machine intelligence.
... There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. In a thought experiment, CANTRELL et al. (2017) have imagined a DRL (deep reinforcement learning) based AI called "wildness creator" that can devise strategies beyond human comprehension, in order to challenge environmental designers, conservationists, and environmental engineers to reflect on what it means to construct wild places. CANTRELL & ZHANG (2018) have formed machine intelligence as "a third intelligence" in landscape media that can interact with biological intelligence and material intelligence and co-evolve with other landscape agents to co-produce landscapes beyond designers' intention. ...
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment, in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.
... In addition to supplying the data necessary for sustainable long-term management, real-time information systems offer the possibility for near-immediate responses by decision-makers (Grasso et al., 2019;Sun & Scanlon, 2019). AI has even been suggested as a means to not only automate data collection and analysis but even decision-making and the subsequent actions taken (Cantrell et al., 2017). Consequently, we foresee an increasing role of intelligent surveillance in ecological research and management, aided by the rapid parallel developments of sensors, communication technologies, and AI. ...
Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5)that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame-by-frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons(2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational fieldstudies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high-resolution up-to-date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up-to-date support for conservation and ecosystem management.
... Such an approach may promise more efficient water use and lower plant mortality, while potentially altering the noticeable characteristics of plants (e.g., leaf color, species evenness). There are even discussions about completely removing human perception and control from ecological restoration, and instead using robotics and autonomous systems instead to rewild landscapes and support biodiversity (Cantrell, Martin, & Ellis, 2017;. ...
Cities worldwide are exploring nature-based solutions (NBS) for climate change adaptation and sustainable development. To innovatively use nature to tackle societal challenges, thinking around NBS increasingly focuses on practices that integrate engineering and technological components with natural processes. Such novel NBS are especially relevant in urban contexts where land is limited and environmental stressors such as disturbance and pollution are present. This dissertation calls attention to a rarely considered implication of novel NBS: they may introduce noticeable yet unfamiliar changes and affect how people perceive everyday urban landscapes. These perceptions can influence local community members' well-being and support for NBS adoption. A deeper understanding of community members' perceptions of novel NBS can inform their design, implementation, and assessment to realize more reliable and sustained community co-benefits. This dissertation presents three key chapters that are prepared as journal articles. Chapter 2 identifies everyday landscape experiences as an essential cultural ecosystem service and connects them with the social impacts of and local communities' support for NBS. Focusing on NBS managed by smart systems, it speculates their potential negative influences on everyday urban nature experiences and how to address this issue in NBS development. This chapter lays the conceptual basis for this dissertation. Chapter 3 investigates how microscale landscape elements may affect community members' perceptions of novel NBS through the example of retention ponds where smart systems manage stormwater storage. It examines both the effects of individual microscale elements on perceptions of smart ponds and the interacting effects of water level and other elements affected by design choices. Chapter 4 applies "risk as feelings" to understand how people perceive visible stormwater dynamics in everyday urban landscapes, considering both uncontrolled localized flooding and intentional stormwater detention in novel vs. traditional NBS measures. It examines how experiences of localized flooding and other contextual and socio-demographic factors may affect perceived urban flood risks and the perceived safety of different NBS practices for stormwater management. This dissertation connects different knowledge domains and employs quantitative social science methods to contribute to the understanding of public perception of novel NBS. It demonstrates that community members' perceptions can be affected by what is perceivable and manageable in the landscape, as well as their lived experiences and socio-demographic characteristics. This work has implications for the planning, design, and management of novel NBS to better address community members' experiences and gain broader societal support.
... Here regenerative practitioners are both modernising an ecologically sensitive agricultural epistemology, whilst ecologising modern agricultural technology (Kearnes & Rickards, 2020). In doing so, they are shifting the position of their tools and gadgets in relation to the pastoral ideal from the degenerative 'machine in the garden' (Marx, 2000) towards a post-pastoral figure of the 'machine as gardener' (Cantrell et al., 2017); repurposing the very technologies that have enabled agricultural intensification as part of a holistic, remedial programme for regeneration. ...
Anxieties around the relationship between livestock agriculture and the environmental crisis are driving sustained discussions about the place of beef and dairy farming in a sustainable food system. Proposed solutions range from ‘clean‐cow’ sustainable intensification to ‘no‐cow’, animal free futures, both of which encourage a disruptive break with past practice. This paper reviews the alternative proposition of regenerative agriculture that naturalises beef and dairy production by invoking the past to justify future, nature‐based solutions. Drawing on fieldwork in the UK, it first introduces two of the most prominent strands to this green rebranding of cattle: the naturalisation of ruminant methane emissions and the optimisation of soil carbon sequestration via the use of ruminant grazing animals. Subsequent thematic analysis outlines the three political strategies of post‐pastoral storytelling, political ecological baselining, and a probiotic model of bovine biopolitics that perform this naturalisation. The conclusion assesses the potential and the risks of this approach to grounding the geographies and the temporalities of agricultural transition in the Anthropocene: an epoch in which time is out of joint and natures are multiple and non‐analogue, such that they provide slippery and contested grounds for political solutions.
... Such an approach may promise more efficient water use and lower plant mortality, while potentially altering the noticeable characteristics of plants (e.g., leaf color, species evenness). There are even discussions about completely removing human perception and control from ecological restoration, using robotics and autonomous systems instead to rewild landscapes and support biodiversity (Cantrell et al. 2017, Goddard et al. 2021. ...
Nature-based solutions that incorporate "smart" technologies to enhance ecosystem services delivery may change the way people experience urban nature in their everyday lives. We lay out a conceptual basis for considering such changes and their social impacts. Cities are increasingly recognized as complex social-ecological-technological systems in which sustainability and climate resilience require environmental function to be paired with innovative technology. Smart technologies for real-time monitoring and autonomous operation promise innovations in urban landscape management. However, this promise can be fully realized only with adequate consideration of social impacts. Drawing on literature in landscape studies, environmental psychology, behavioral economics, public health, and aesthetics, we initiate a discussion connecting everyday experiences of urban nature with the social impacts of smart nature-based solutions and with local communities' support for their implementation. We describe what makes pleasant everyday experiences of urban nature and their related well-being benefits and social and cultural values, and we elucidate how these experiences depend on perceivable landscape characteristics that are only sometimes directly linked to environmental functions. Then, based on this literature, we speculate about how adopting smart technologies to manage nature-based solutions may noticeably change the landscape in novel ways and have unintended negative impacts on everyday experiences of urban nature. We illustrate this with an example: smart stormwater management of retention ponds. We conclude that the risk of degraded everyday experiences of nature must be considered and addressed in the development of smart nature-based solutions. If pleasant everyday experiences are ensured through appropriate design, smart nature-based solutions may not only realize societal co-benefits, but also gain acceptance and continued support from the public for the whole set of ecosystem services they deliver.
... Data-driven environmental governance involves a turn to data as a neutral, objective resource for accountable and transparent decision-making around nature. Confronted with the limitations of existing "knowledge infrastructures" on climate change and conservation (Edwards, 2010), data technologies ranging from drones (Cantrell et al., 2017) to dashboards (Kitchin et al., 2015) are thought to give more direct access to "raw" information and make "invisible problems not only visible but solvable" because the results of management can be more precisely and rapidly measured (Bakker and Ritts, 2018;Krupp, 2018). Conservationists, corporations, and governments-the usual suspects in environmental governancenow find themselves integrating data in portals and platforms alongside newcomers to the scene such as tech giants such as Google, IBM, and Microsoft. ...
Conservationists, governments, and corporations see promise in digital technologies to provide holistic, rapid, and objective information to inform policy, shape investments, and monitor ecosystems. But it is increasingly clear that environmental data does more than simply offer a better view of the planet. This special issue makes a single overarching argument: that we cannot fully understand the current conjuncture in global environmental governance without understanding the platforms, devices, and institutions that comprise environmental data infrastructures. The papers draw together scholarship from political ecology and science and technology studies to demonstrate how data has become a significant site in which contemporary environmental politics are waged and socionatures are materialized. We address: (1) the contested practices of utilizing and maintaining data infrastructures; (2) the ways they are governed and the territorial statecraft they enable; (3) the socionatural materiality they arise within but also produce. The papers in this special issue show that, against its dominant representation, data is material, governed, practiced, and requires praxis. Political ecologists could adopt such an approach to make sense of the emerging ways in which data technologies shape environments and their politics.
... These processes of decentralized adaptive problem-solving have also been observed for astonishingly complex yet resilient indigenous farming systems in Bali [90], and could as proposed by some, be augmented and automatized through the extensive use of AI and associated technologies to support artificially intelligent curation of wild places and nature (e.g. Ref. [91]. DAI could also, at best, help interpret and respond to the complex systems properties and the continuous changes that characterize farming, forestry and marine systems under rapid change due to human activities and climate change. ...
Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors.
... The agency of digital technologies is nearly always shaped by humansthey are largely produced by and for human purposes. It is worth noting here that there are efforts underway to design autonomous processes which attempt to reduce human influences on more-than-humans, while also increasing levels of human management with digital interventions (Cantrell, Martin, & Ellis, 2017;Gulsrud et al., 2018). Nevertheless, these digital technologies that have a component of autonomy are initially conceptualised and supported by humans, so the source of agency still includes human activity. ...
These are uncertain times in the Anthropocene, where the health and resilience of all urban inhabitants should be key themes for cities striving for sustainability. To this end, local councils in Australia are applying digital technologies with increasing complexity as components of their urban forest management. This paper applies a more-than-human lens to analyse Australian local council urban forest policies, documents and project information for their inclusion and application of digital technologies. In this scoping review, digital geographies informed data collection to answer questions about the type, use and ownerships of tree data, and more-than-real and ‘lively data’ concepts were employed to extend their discussion.
Our analysis found that local government policies focused on general urban tree data and canopy percentages and utilised this data to justify and create policy and program parameters. There was a general lack of more-than-human considerations beyond the focus on trees in creating and designing smart urban forests, but it is unclear whether this was due to technical limitations, council desires or other factors. Challenges identified for successful outcomes included balancing priorities, access to resources and information, technological constraints, and community factors such as capacity to engage and cultural values. Digital technologies that facilitate smart urban forests tended to reinforce and re-solidify Western values. However, strengths of current applications are also evident, and we explore how they provide more-than-real possibilities for human-nature relationships to deepen and foster collaborations between disparate groups and entities in urban environments. Greater consideration and acknowledgment of the more-than-human and understanding of the more-than-real in co-creation and co-design of digital technologies and their applications may facilitate more positive outcomes for human and non-human urban inhabitants.
... The widespread use of 171 RAS has been proposed as a mechanism to enhance urban sustainability 14 , but critics have 172 questioned this techno-centric vision 15,16 . Moreover, while RAS are likely to have far-173 reaching social, ecological, and technological ramifications, these are often discussed only in 174 terms of the extent to which their deployment will improve efficiency and data harvesting, 175 and the associated social implications [17][18][19] . Such a narrow focus will likely overlook 176 interactions across the social-ecological-technical systems that cities are increasingly 177 thought to represent 20 . ...
Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems(RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits.
... Robotics, alone or in combination with other technologies, can increase the timeliness and costefficiency of various response measures (Cantrell et al. 2017). Robots provide additional labor for long hours in challenging conditions (e.g., underwater, during inclement weather, at night). ...
The 2016–2018National Invasive Species Council (NISC) Management Plan and Executive Order 13751 call for US federal agencies to foster technology development and application to address invasive species and their impacts. This paper complements and draws on an Innovation Summit, review of advanced biotechnologies applicable to invasive species management, and a survey of federal agencies that respond to these high-level directives. We provide an assessment of federal government capacities for the early detection of and rapid response to invasive species (EDRR) through advances in technology application; examples of emerging technologies for the detection, identification, reporting, and response to invasive species; and guidance for fostering further advancements in applicable technologies. Throughout the paper, we provide examples of how federal agencies are applying technologies to improve programmatic effectiveness and cost-efficiencies. We also highlight the outstanding technology-related needs identified by federal agencies to overcome barriers to enacting EDRR. Examples include improvements in research facility infrastructure, data mobilization across a wide range of invasive species parameters (from genetic to landscape scales), promotion of and support for filling key gaps in technological capacity (e.g., portable, field-ready devices with automated capacities), and greater investments in technology prizes and challenge competitions.
... As a prototyping platform, the hydromorphology table facilitates many research and design projects over the years, shedding light on the possibility of constructing autonomous systems that can devise strategies beyond human comprehension to create "wild" places [23] . For instance, designer Leif Estrada tested the sensing-processing-actuating responsive framework in the project Towards Sentience [24] . ...
This paper maps out a new paradigm of prototyping that acts as an alternative to the model-making paradigm. By juxtaposing the cybernetics movement with landscape design, the authors have mapped out a development in landscape discourse that mirrors the movement of cybernetics in the 20th century and early 21st century. The early deterministic and linear understanding of systems dynamics is replaced by an emergent and open-ended view. Taking on a framework of emergence, the authors highlight a special type of model that does not fit within the conventional modelpredict-
control framework. Rather than models that represent another living system, these models are living systems in themselves with autonomy and lives. This special type of model can be understood as prototypes. Prototyping replaces model-making and exhibits three distinctive qualities: 1) A prototype has a life of its own, which
serves as the basis for design and creativity; 2) The real usefulness of a prototype lies in its undefined identity rather than its defined and direct application; And 3) the identified quality provides a wide range of possibilities, thus changing our relationship with the future from chance and prediction to anticipation and hope.
... Practices remain speculative but merit further exploration. In a thought experiment, scholars imagined a DRL based machine "wildness creator", just like AlphaGo and AlphaStar, that can devise environmental management strategies and create places that are beyond human comprehension (CANTRELL et al. 2017). Rather than envisioning DRL based AI system as "superhumans" who can predict the environment in human discourse, "wildness creator" is conceptualized as a different type of intelligence that understands the environment differently than humans. ...
Taking on a historical lens, this paper traces the development of cybernetics and systems thinking back to the 1950s, when a group of interdisciplinary scholars converged to create a new theoretical model based on machines and systems for understanding matters of meaning, information, consciousness , and life. By presenting a genealogy of research in the landscape architecture discipline, the paper argues that landscape architects have been an important part of the development of cybernetics by materializing systems based on cybernetic principles in the environment through ecologically based landscape design. Landscape discipline has developed a design framework that provides transformative insights into understanding machine intelligence. The paper calls for a new paradigm of environmental engagement to understand matters of design and machine intelligence.
... There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. In a thought experiment, CANTRELL et al. (2017) have imagined a DRL (deep reinforcement learning) based AI called "wildness creator" that can devise strategies beyond human comprehension, in order to challenge environmental designers, conservationists, and environmental engineers to reflect on what it means to construct wild places. CANTRELL & ZHANG (2018) have formed machine intelligence as "a third intelligence" in landscape media that can interact with biological intelligence and material intelligence and co-evolve with other landscape agents to co-produce landscapes beyond designers' intention. ...
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment , in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.
... Appropriate decisions for management can finally be made through combining data fusion, deep learning algorithms, and the expertise of ecological resource scientists to access and evaluate data collected by multi-platform and multi-source sensors. For example, deep learning can guide drones to replant seeds or robots to remove some invasive species, which indicates that automated management approaches will greatly reduce the interference of human activities on ecosystems (Cantrell et al., 2017;Christin et al., 2018). The field of ecological resources has entered the era of big data, but current applications through big data mining are relatively limited. ...
Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale, massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the
relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition, detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the
cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data, improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.
... Robotics, alone or in combination with other technologies, can increase the timeliness and costefficiency of various response measures (Cantrell et al. 2017). Robots provide additional labor for long hours in challenging conditions (e.g., underwater, during inclement weather, at night). ...
The 2016-2018 National Invasive Species Council (NISC) Management Plan and Executive Order 13751 call for US federal agencies to foster technology development and application to address invasive species and their impacts. This paper complements and draws on an Innovation Summit, review of advanced biotechnologies applicable to invasive species management, and a survey of federal agencies that respond to these high-level directives. We provide an assessment of federal government capacities for the early detection of and rapid response to invasive species (EDRR) through advances in technology application; examples of emerging technologies for the detection, identification, reporting, and response to invasive species; and guidance for fostering further advancements in applicable technologies. Throughout the paper, we provide examples of how federal agencies are applying technologies to improve pro-grammatic effectiveness and cost-efficiencies. We also highlight the outstanding technology-related needs identified by federal agencies to overcome barriers to enacting EDRR. Examples include improvements in research facility infrastructure, data mobilization across a wide range of invasive species parameters (from genetic to landscape scales), promotion of and support for filling key gaps in technological capacity (e.g., portable, field-ready devices with automated capacities), and greater investments in technology prizes and challenge competitions.
... Combined with GIS and mobile robots, aerial-aquatic-land drones included, the Salton Sea could become a renovated region in the USA's Southwest. Essentially, the Salton Sea could still be transformed into North America's first truly responsive and controlled landscape (Cantrell et al. 2017). ...
Macro-imagineered computer model of Salton Sea alternative futures under climate uncertainty and water transfer considerations. February 2019.
... It also ignores that they have previously, over evolutionary time, taken over the ecological roles of extinct species and are thus capable of doing so in the future (Kistler et al., 2015). This does not mean, in our view, that H. sapiens could or should aim to simulate and replace all or any nonhuman species' roles (Cantrell, Martin, & Ellis, 2017): Our argument is based on phenotype/niche similarities within the large omnivore guild, and the possibility of otherwise irreversible ecological extinctions. ...
Discussions of defaunation and taxon substitution have concentrated on megafaunal herbivores and carnivores, but mainly overlooked the particular ecological importance of megafaunal omnivores. In particular, the Homo spp. have been almost completely ignored in this context, despite the extinction of all but one hominin species present since the Plio‐Pleistocene. Large omnivores have a particular set of ecological functions reflecting their foraging flexibility and the varied disturbances they create, functions that may maintain ecosystem stability and resilience. Here, we put the ecology of Homo sapiens in the context of comparative interspecific ecological roles and impacts, focusing on the large omnivore guild, as well as comparative intraspecific variation, focusing on hunter‐gatherers.
We provide an overview of the functional traits of H. sapiens, which can be used to spontaneously provide the functions for currently ecologically extinct or endangered ecosystem processes. We consider the negative impacts of variations in H. sapiens phenotypic strategies, its possible status as an invasive species, and the potential to take advantage of its learning capacities to decouple negative and positive impacts.
We provide examples of how practices related to foraging, transhumance, and hunting could contribute to rewilding‐inspired programs either drawing on hunter‐gatherer baselines of H. sapiens, or as proxies for extinct or threatened large omnivores. We propose that a greater focus on intraspecific ecological variation and interspecific comparative ecology of H. sapiens can provide new avenues for conservation and ecological research.
... To go even further, deep learning has already been envisioned as a cornerstone in a fully automated system for managing ecosystems, using automated sensors, drones and robots. Such systems would allow continuous ecosystem management without requiring much human intervention (Cantrell, Martin, & Ellis, 2017). ...
A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well.
In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation.
We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning.
We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.
... Combined with GIS and mobile robots, aerial-aquatic-land drones included, the Salton Sea could become a renovated region in the USA's Southwest. Essentially, the Salton Sea could still be transformed into North America's first truly responsive and controlled landscape (Cantrell et al. 2017). ...
The aim of the present research was to simulate the Salton Sea elevation, volume, and total dissolved solids (TDS) to assess 34 different scenarios through the year 2024 in order to better evaluate the effects of potential water management scenarios. Parameterization of an existing Salton Sea simulation model, i.e., Salton Sea Stochastic Simulation Model (S⁴M), was performed to account for either an increase (+), decrease (−), or no change in precipitation (Pi), evapotranspiration (Eto), and river flow volume (Ri) in the Salton Sea Basin while simultaneously implementing two different water management policies: (1) water transfers to the Salton Sea end after 2017 (based on the Quantification Settlement Agreement (QSA)) or (2) water transfers to the Salton Sea at 2017 levels continue into the future. The S⁴M is formulated as a compartment model based on difference equations with a daily time step using STELLA® 8.0 software. One-way analysis of variance (ANOVA) and Bonferroni multiple post hoc statistical tests were performed using IBM® SPSS® Statistics v. 22.0 with α (Type I error) = 0.05. A significant difference existed between the Baseline scenario with water transfers ending in 2017, i.e., − 241 feet above sea level (fasl) and about 69,000 ppm TDS, and the scenario with continued water transfers at 2017 levels, i.e., year 2024 end simulation of − 236.95 fasl and 61,000 ppm TDS. The results indicate that in order to improve conditions for fish and keep salinity ≤ 50,000 ppm, continued QSA water transfers cannot achieve such a result alone, ceteris paribus.
... On the contrary, the algorithmic revolution now permeates a number of government and private decision-making processes which in sum alter the biosphere. These can include network algorithms to sup-5 port landscape planning for Montreal's greenbelt in Canada 3 , the use of machinelearning methods that underpin species distribution models that feeds into conservation decisions (Cantrell et al. 2017), genetic learning algorithms to make fish stock assessments, image processing algorithms to classify the existence of gold ores, 3D object recognition algorithms to support deep sea mining of rare earth minerals, algorithms 10 used in agriculture to help analyze weather and soil data to maximize production, and many more. 4 Hence algorithms operate through actors and hardware at all spatial scales of planet Earth, with tangible influence on the ways we perceive global environmental change (e.g. ...
Anthropocene Encounters: New Directions in Green Political Thinking - edited by Frank Biermann February 2019
... Yet, "[r]estoration of wild places in the Anthropocene depends on valuing multiple forms of wildness, including novel anthropogenic forms that have yet to be imagined." (Cantrell et al., 2017) As Fry remarks (Fry, 2012, p. Part II.6.Passing Figures of Technology), " 'we' now exist in two kinds of intertwining 'natures': the biological and the technological. Both 'natures' are governed by specific but inherently internal processes (over which 'we' have very limited and diminishing control)." ...
What can the creation of artificial habitats to replace old-growth forests tell us about the process, value and future of design? This chapter takes a concrete and provocative example and uses it to rethink design as a gradual, ecological action. To illustrate this understanding, the chapter begins with a description of a proposal to provide artificial habitats for wild animals such as birds, bats and invertebrates. The controversial idea to replace rapidly disappearing old-growth trees with artificial structures puts in doubt habitual assumptions about the clients, procedures and goals of design. This example is of relevance to all design because the need to provide artificial habitats to nonhumans will be increasingly common under the influence of such phenomena as global warming or urbanisation. The proposal to provide artificial structures that can replace missing or degrated natural habitats is described in this chapter as an incitement to conduct further research into values, participants and methods of design. This discussion concludes with a proposal for an attitude of modesty in the face of increasingly overwhelming volumes of information as well as in the presence of ignorance about the futures of nondeterministic, volatile and incompletely controllable natural systems. The dilemma of design in these conditions is in the tension between its remit to act and the uncertainty that inescapably underlies any creative endeavour. © 2019 selection and editorial matter, Gretchen Coombs, Andrew McNamara, Gavin Sade; individual chapters, the contributors.
... [9] Landscape architects also start to discuss possibilities and challenges to apply these technologies to manage the environments. In a thought experiment, Bradley Cantrell et al. imagined a DRL-based AI "wildness creator" that could come up with environmental management strategies that are beyond human comprehension and challenge landscape architects, ecologists, and environmental activists to consider what that means to construct wilderness [10] . We believe that, in the near future, there will be more automated tools empowered by AI for designers to visualize, analyze, and predict landscape patterns in design processes. ...
... To go even further, deep learning has already been envisioned as a cornerstone in a fully automated system for managing ecosystems, using automated sensors, drones and robots. Such systems would allow continuous ecosystem management without requiring much human intervention (Cantrell, Martin, & Ellis, 2017). ...
A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.
Watershed ecological protection and restoration is the key link to restore the ecological service function of damaged watersheds and promote the harmonious coexistence between humans and nature. In practice, it is difficult to balance the interests of different parties in the process of watershed ecological protection and restoration in terms of water resource utilization, water environment protection, and biodiversity conservation, which makes it difficult to achieve the watershed ecological protection goals. Therefore, this study takes the Jianghuai watershed area as the research object and explores the core points of the ecological restoration of land space in the relevant experimental area in the form of algorithmic optimization frequency. The experimental data of the algorithm model show that the land types in the Jianghuai watershed are mainly woodlands and watersheds, with relatively uneven spatial distribution; a total of 85 ecological corridors were identified that showed a pattern of more in the middle and less around; and a total of 267 radiation channels were identified that showed irregular tree distribution. The empirical results show that the model has a fitting accuracy of 89%.
Deep learning algorithms are revolutionizing how hypothesis generation, pattern recognition, and prediction occur in the sciences. In the life sciences, particularly biology, and its subfields, the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications remains in the domain of experienced coders. Furthermore, many tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing. We built a biological species classifier that leverages existing open-source tools and libraries. We designed the corresponding tutorial for users with basic skills in python and a small, but well-curated image dataset. We included annotated code in form of a Jupyter Notebook that can be adapted to any image dataset, ranging from satellite images, animals to bacteria, or even data such as song or echolocation recordings transformed into images. The prototype developer is publicly available and can be adapted for citizen science as well as other applications not envisioned in this paper.
We illustrate our approach with a case study of 219 images of 3 three seastar species. We show that with minimal parameter tuning of the AI pipeline we can create a classifier with 87% accuracy. We include additional approaches to understand the misclassified images and to curate the dataset to increase accuracy. The power of AI approaches is becoming increasingly accessible. We can now readily build and prototype species classifiers that can have a great impact on research that requires species identification and other types of image analysis. Such tools have implications for citizen science, biodiversity monitoring, and a wide range of ecological applications.
Deep learning algorithms are revolutionizing how hypothesis generation, pattern recognition, and prediction occur in the sciences. In the life sciences, particularly biology, and its subfields, the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications remains in the domain of experienced coders. Furthermore, many tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing. We built a biological species classifier that leverages existing open-source tools and libraries. We designed the corresponding tutorial for users with basic skills in python and a small, but well-curated image dataset. We included annotated code in form of a Jupyter Notebook that can be adapted to any image dataset, ranging from satellite images, animals to bacteria, or even data such as song or echolocation recordings transformed into images. The prototype developer is publicly available and can be adapted for citizen science as well as other applications not envisioned in this paper.
We illustrate our approach with a case study of 219 images of 3 three seastar species. We show that with minimal parameter tuning of the AI pipeline we can create a classifier with 87% accuracy. We include additional approaches to understand the misclassified images and to curate the dataset to increase accuracy. The power of AI approaches is becoming increasingly accessible. We can now readily build and prototype species classifiers that can have a great impact on research that requires species identification and other types of image analysis. Such tools have implications for citizen science, biodiversity monitoring, and a wide range of ecological applications.
This article explores how new technologies – such as drones and satellites – are incorporated into disaster management and questions the implications for power relations between disaster authorities and subjects. This is a critical area of research, as the proliferation of aerial and networked technologies has made their use in disaster management and response more common. Although concerns have been raised about the potential use of aerial and networked technologies in the surveillance and spatial discipline of populations by commercial and government actors, few have considered the implications for disaster management. In response, this article turns to geographical literature on necropower, verticality, and planetary spatialities to analyse technological innovations in responses to desert locust upsurges in Kenya. Drawing from qualitative research carried out between February 2020 and January 2021, we explain how desert locust control operations have shifted from horizontal to vertical to networked and planetary in nature through experimentation with new technologies over the past century. We argue that aerial and networked technologies have led to a volumetric shift in desert locust management and response, giving remote and increasingly automated actors who operate ‘above’ greater power over the life and death of populations ‘below’. In making this argument, we adopt a more-than-human perspective to account for how nonhuman entities and lifeforms shape and are subjected to necropolitical disaster management and responses. We conclude by reflecting on what this analytical approach has to offer the study of vertical and volumetric geographies.
Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry.
The rapid global spread of the Anthropocene concept across disciplines, languages, cultures and religions has been extraordinary and is unique in scientific history for a basic concept.
La reciente propuesta de una nueva era geológica denominada Antropoceno a partir del año 1950, en donde el Homo sapiens es el principal actor le ha dado a las ciencias humanas y sociales la posibilidad de discutir y contribuir en su adecuada definición, más allá del golden spike que se pueda emplear. Nuestro interés es mostrar que estamos frente a un proceso que arranca, quizás, hace 10.000-8.000 años y que los humanos, a través de nuestra capacidad de generar rápidamente nuevos nichos en diferentes dimensiones espacio temporales, hemos ido incrementado su aceleración con el paso del tiempo.
Point 1 : Deep learning algorithms are revolutionizing how hypothesis generation, pattern recognition, and prediction occurs in the sciences. In the life sciences, particularly biology and its subfields, the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications remains in the domain of experienced coders. Furthermore, many tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing.
Point 2 : We built a biological species classifier that leverages existing open-source tools and libraries. We designed the corresponding tutorial for users with basic skills in python and a small, but well-curated image dataset. We included annotated code in form of a Jupyter Notebook that can be adapted to any image dataset, ranging from satellite images, animals to bacteria. The prototype developer is publicly available and can be adapted for citizen science as well as other applications not envisioned in this paper.
Point 3 : We illustrate our approach with a case study of 219 images of 3 three seastar species. We show that with minimal parameter tuning of the AI pipeline we can create a classifier with superior accuracy. We include additional approaches to understand the misclassified images and to curate the dataset to increase accuracy.
Point 4 : The power of AI approaches is becoming increasingly accessible. We can now readily build and prototype species classifiers that can have a great impact on research that requires species identification and other types of image analysis. Such tools have implications for citizen science, biodiversity monitoring, and a wide range of ecological applications.
The idea of the Anthropocene presents a paradox for conservation: to restore and protect wild species and ecosystems, greater human intervention is required through efforts such as artificial propagation. This paradox is evident in efforts to conserve Pacific salmon. Salmon hatcheries produce millions of salmon to augment wild populations and sustain fishing industries, but emerging knowledge about salmon genomics has called into question the “wildness” of hatchery salmon. This article examines how the scientific uncertainties regarding wild species are contested by a range of stakeholders and how particular frames become concretized in policy frameworks. Despite the significance of laws and policies to the governance of such hybrid species, they have received limited attention. Drawing on archival documents, legislation, policies, government reports, and media sources, we conduct a cross-national comparative analysis of how wildness is framed in policy debates in Canada and the United States. We find that hatchery-born salmon occupy a position at the threshold of scientific and cultural definitions of wildness and this ambiguity facilitates political contests among groups with divergent interests in conservation and views of the human–nature relationship. As a result, hatchery salmon have been regulated differently across time and geographic and jurisdictional space. These findings contribute to an expanding literature on conservation in the Anthropocene and managing wild species.
Cambridge Core - Natural Resource Management, Agriculture, Horticulture and forestry - Shepherding Nature - by J. Michael Scott
Smart cities are increasingly part of urban sustainability discourses. There is a growing interest in understanding how citizen engagement, connected technology, and data analytics can support sustainable development. Evidence has also repeatedly shown that green infrastructure such as urban forests address diverse urban challenges and are critical components of urban sustainability and resilience. Nevertheless, it is unclear whether green space and urban forest management are gaining significant traction in smart city planning. It is thus timely to consider whether and to what extent urban forests and other green spaces can be effectively integrated into smart city planning, to maximize green benefits for all city dwellers.
We address this gap by exploring current and emerging smart city trends and technologies, and highlight practical applications for urban forest and green space management. Current “smart urban forest” projects reveal a focus on novel monitoring techniques using sensors and Internet of Things (IoT) technologies, as well as open data and citizen engagement, particularly through the use of mobile devices, applications (“apps”), and open-source mapping platforms. We propose a definition and promising approach to “smart urban forest management”, emphasizing both the potential of digital infrastructure to enhance forest benefits and the facilitation of citizen stewardship and empowerment in green space planning. Cities are getting faster and smarter – can (and should) the trees, and their managers, do the same?
Cambridge Core - Natural Resource Management, Agriculture, Horticulture and forestry - Rewilding - edited by Nathalie Pettorelli
The scale, rate, and intensity of humans' environmental impact has engendered broad discussion about how to find plausible pathways of development that hold the most promise for fostering a better future in the Anthropocene. However, the dominance of dystopian visions of irreversible environmental degradation and societal collapse, along with overly optimistic utopias and business-as-usual scenarios that lack insight and innovation, frustrate progress. Here, we present a novel approach to thinking about the future that builds on experiences drawn from a diversity of practices, worldviews, values, and regions that could accelerate the adoption of pathways to transformative change (change that goes beyond incremental improvements). Using an analysis of 100 initiatives, or " seeds of a good Anthropocene " , we find that emphasizing hopeful elements of existing practice offers the opportunity to: (1) understand the values and features that constitute a good Anthropocene, (2) determine the processes that lead to the emergence and growth of initiatives that fundamentally change human–environmental relationships, and (3) generate creative, bottom-up scenarios that feature well-articulated pathways toward a more positive future.
The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.
The exhibition of increasingly intensive and complex niche construction behaviors through time is a key
feature of human evolution, culminating in the advanced capacity for ecosystem engineering exhibited by
Homo sapiens. A crucial outcome of such behaviors has been the dramatic reshaping of the global biosphere,
a transformation whose early origins are increasingly apparent from cumulative archaeological and
paleoecological datasets. Such data suggest that, by the Late Pleistocene, humans had begun to engage in
activities that have led to alterations in the distributions of a vast array of species across most, if not all,
taxonomic groups. Changes to biodiversity have included extinctions, extirpations, and shifts in species
composition, diversity, and community structure. We outline key examples of these changes, highlighting
findings from the study of new datasets, like ancient DNA (aDNA), stable isotopes, and microfossils, as
well as the application of new statistical and computational methods to datasets that have accumulated
significantly in recent decades. We focus on four major phases that witnessed broad anthropogenic
alterations to biodiversity—the Late Pleistocene global human expansion, the Neolithic spread of agriculture,
the era of island colonization, and the emergence of early urbanized societies and commercial networks.
Archaeological evidence documents millennia of anthropogenic transformations that have created
novel ecosystems around the world. This record has implications for ecological and evolutionary research,
conservation strategies, and the maintenance of ecosystem services, pointing to a significant need for
broader cross-disciplinary engagement between archaeology and the biological and environmental sciences.
The wildland-urban interface (WUI) is the area where structures and other human development meet or intermingle with undeveloped wildland, and it is where wildfires have their greatest impacts on people. Hence the WUI is important for wildfire management. This document and associated maps summarize the extent of the WUI in the conterminous United States in 2010. The maps and summary statistics are designed to inform both national policy and local land management concerning the WUI. The data presented here summarize the 2010 WUI at a national scale and for each of the 48 conterminous States. All products of this assessment—including maps, statistics, and the WUI GIS dataset—are available at http://www.nrs.fs.fed.us/data/WUI.
Most current conservation strategies focus on the immediate social, cultural, and economic values of ecological diversity, functions, and services (1). For example, the Intergovernmental Platform on Biodiversity and Ecosystem Services (2) mostly addresses the utilitarian management of biodiversity from local to global scales. However, besides urgent diagnosis and actions (3, 4), processes that occur over evolutionary time scales are equally important for biodiversity conservation. Strategizing for conservation of nature at such long time scales will help to preserve the function—and associated services—of the natural world, as well as providing opportunities for it to evolve. This approach will foster a long-term, sustainable interaction that promotes both the persistence of nature and the wellbeing of humans.
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Trophic rewilding is an ecological restoration strategy that uses species introductions to restore top-down trophic interactions and associated trophic cascades to promote self-regulating biodiverse ecosystems. Given the importance of large animals in trophic cascades and their widespread losses and resulting trophic downgrading, it often focuses on restoring functional megafaunas. Trophic rewilding is increasingly being implemented for conservation, but remains controversial. Here, we provide a synthesis of its current scientific basis, highlighting trophic cascades as the key conceptual framework, discussing the main lessons learned from ongoing rewilding projects, systematically reviewing the current literature, and highlighting unintentional rewilding and spontaneous wildlife comebacks as underused sources of information. Together, these lines of evidence show that trophic cascades may be restored via species reintroductions and ecological replacements. It is clear, however, that megafauna effects may be affected by poorly understood trophic complexity effects and interactions with landscape settings, human activities, and other factors. Unfortunately, empirical research on trophic rewilding is still rare, fragmented, and geographically biased, with the literature dominated by essays and opinion pieces. We highlight the need for applied programs to include hypothesis testing and science-based monitoring, and outline priorities for future research, notably assessing the role of trophic complexity, interplay with landscape settings, land use, and climate change, as well as developing the global scope for rewilding and tools to optimize benefits and reduce human–wildlife conflicts. Finally, we recommend developing a decision framework for species selection, building on functional and phylogenetic information and with attention to the potential contribution from synthetic biology.
Healthy ecosystems with intact biodiversity provide human societies with valuable services such as clean air and water, storm protection, tourism, medicine, food, and cultural resources. Protecting this natural capital is one of the great challenges of our era. Species extinction and ecological degradation steadily continues despite conservation funding of roughly U.S. $20 billion per year worldwide. Measurements of conservation outcomes are often uninformative, hindering iterative improvements and innovation in the field. There is cause for optimism, however, as recent technological advances in sensor networks, big data processing, and machine intelligence can provide affordable and effective measures of conservation outcomes. We present several working case studies using our system, which employs deep learning to empower biologists to analyze petabytes of sensor data from a network of remote microphones and cameras. This system, which is being used to monitor endangered species and ecosystems around the globe, has enabled an order of magnitude improvement in the cost effectiveness of such projects. This approach can be expanded to encompass a greater variety of sensor sources, such as drones, to monitor animal populations, habitat quality, and to actively deter wildlife from hazardous structures. We present a strategic vision for how data-driven approaches to conservation can drive iterative improvements through better information and outcomes-based funding mechanisms, ultimately enabling increasing returns on biodiversity investments.
Automated sensors are ubiquitous in ecological “research networks” and academic-led studies. We conducted a survey of academic ecologists to assess the extent of sensor use and how sensor-captured data are managed. There were 135 survey respondents, each representing a unique research group and collectively representing more than 1800 researchers from more than 90 universities in the US. This pool of represented academic researchers collectively matched the financial expenditure, sensor use, and data volumes of several large national research networks. Few respondents reported the use of metadata and workflows while almost 70% stored data locally instead of within institutional archives, though most recognized the importance of doing the latter. Most respondents also indicated that their research could be enhanced by taking advantage of cyberexpertise and software tools. Access to sensor-generated datasets may be improved by several means, including the promotion of targeted training, high-profile studies showcasing researchers’ participation in large-scale syntheses, and incentives for industry to develop, adopt, or adapt technologies that facilitate data documentation, discovery, and sharing.
Humans have fundamentally altered global patterns of biodiversity and ecosystem processes. Surprisingly, existing systems for representing these global patterns, including biome classifications, either ignore humans altogether or simplify human influence into, at most, four categories. Here, we present the first characterization of terrestrial biomes based on global patterns of sustained, direct human interaction with ecosystems. Eighteen "anthropogenic biomes" were identified through empirical analysis of global population, land use, and land cover. More than 75% of Earth's ice-free land showed evidence of alteration as a result of human residence and land use, with less than a quarter remaining as wildlands, supporting just 11% of terrestrial net primary production. Anthropogenic biomes offer a new way forward by acknowledging human influence on global ecosystems and moving us toward models and investigations of the terrestrial biosphere that integrate human and ecological systems.
Humans, unlike any other multicellular species in Earth’s history, have emerged as a global
force that is transforming the ecology of an entire planet. It is no longer possible to understand, predict, or successfully manage ecological pattern, process or change without understanding why and how humans reshape these over the long-term. Here, a general causal theory is presented to explain why human societies gained the capacity to globally alter the patterns, processes and dynamics of ecology and how these anthropogenic alterations unfold over time and space as societies themselves change over human generational time. Building on existing theories of ecosystem engineering, niche construction, inclusive inheritance, cultural evolution, ultrasociality, and social change, this theory of anthroecological change holds that sociocultural evolution of subsistence regimes based on ecosystem engineering, social specialization and nonkin exchange, or “sociocultural niche construction”, is the main cause of both the long-term upscaling of human societies and their unprecedented transformation of the biosphere. Human sociocultural niche construction can explain, where classic ecological theory cannot, the sustained transformative effects of human societies on biogeography, ecological succession, ecosystem processes, and the ecological patterns and processes of landscapes, biomes and the biosphere. Anthroecology theory generates empirically testable hypotheses on the forms and trajectories of long-term anthropogenic ecological change that have significant theoretical and practical implications across the subdisciplines of ecology and conservation. Though still at an early stage of development, anthroecology theory aligns with
and integrates established theoretical frameworks including social-ecological systems, social metabolism, countryside biogeography, novel ecosystems and anthromes. The "fluxes of nature" are fast becoming "cultures of nature". To investigate, understand, and address the ultimate causes of anthropogenic ecological change, not just the consequences, human sociocultural processes must become as much a part of ecological theory and practice as biological and geophysical processes are now. Strategies for achieving this goal and for advancing ecological science and conservation in an increasingly anthropogenic biosphere are presented.
AimBiologists increasingly recognize the roles of humans in ecosystems. Subsequently, many have argued that biodiversity conservation must be extended to environments that humans have shaped directly. Yet popular biogeographical frameworks such as biomes do not incorporate human land use, limiting their relevance to future conservation planning. ‘Anthromes’ map global ecological patterns created by sustained direct human interactions with ecosystems. In this paper, we set to understand how current conservation efforts are distributed across anthromes.LocationGlobal.Methods
We analysed the global distribution of IUCN protected areas and biodiversity hotspots by anthrome. We related this information to density of native plant species and density of previous ecological studies. Potential conservation opportunities in anthromes were then identified through global analysis and two case studies.ResultsProtected areas and biodiversity hotspots are not distributed equally across anthromes. Less populated anthromes contain a greater proportion of protected areas. The fewest hotspots are found within densely settled anthromes and wildlands, which occur at the two extremes of human population density. Opportunities for representative protection, prioritization, study and inclusion of native species were not congruent.Main conclusionsResearchers and practitioners can use the anthromes framework to analyse the distribution of conservation practices at the global and regional scale. Like biomes, anthromes could also be used to set future conservation priorities. Conservation goals in areas directly shaped by humans need not be less ambitious than those in ‘natural areas’.
This paper draws together recent literatures on the geography of experiments and the potential of experimental modes of conducting science and politics. It examines their implications for environmentalism in the Anthropocene. We differentiate between two different conceptions of an experiment, contrasting the singular, modern scientific understanding of an experiment with recent appeals for deliberative public experiments. Developing the concept of wild experiments we identify three axes for critical enquiry. These relate to the status of the nonhuman world as found or made, the importance afforded order and surprise in the conduct of any experiment and the degree and means by which publics are included in decisionmaking. We then illustrate the potential of this framework through a case study investigation of nature conservation, critically examining efforts to rewild and de-domesticate a polder landscape and its nonhuman inhabitants at the Oostvaardersplassen in the Netherlands. This is a flagship example of the wider enthusiasm for rewilding in nature conservation. In conclusion we reflect on wider significance and potential of these wild experiments for rethinking environmentalism in the Anthropocene.
This manuscript uses the landscape of the Sierra and Stanislaus National Forests to illustrate concepts of climate change adaptation that can be applied to national forest planning. The paper begins by making the case that climate change requires a fundamental shift in the purpose of forest planning from the scheduling of outputs to the management of risk. The national forests must be managed to reduce the probability of loss of ecosystem elements and thereby sustain the potential for future ecosystems to provide the many values of the national forests, collectively known as ecosystem services. The main body of the paper breaks risk down to its component parts of vulnerability, exposure, and uncertainty and suggests that risk can be managed by identifying and reducing any or all of these components. Last and most important, the paper argues that the uncertainty that attends climate change demands that risk be spread among a “portfolio” of management approaches that include accepting change, resisting change, and guiding change. We recommend allocating national forest lands to three zones dedicated to observation, restoration, and innovation corresponding to the three strategies. These zones are intended as a loose overlay of the national forests that help guide how forest-wide objectives are to be met. For example, old-growth forest conservation may be accomplished merely by leaving the forest to develop on its own in the observation zone, by actively managing to sustain historical pattern and process in the restoration zone, and by anticipating climate change and managing for resilient, if novel, conditions in the innovation zone.
Land can be described in a space defined by two fundamental qualities: naturalness and freedom. The axis of naturalness describes the wholeness of the ecosystem relative to a historical norm, while the axis of freedom describes the degree to which land remains outside of human control. Some land can be natural but not free, and vice versa, but the most natural and free are the most wild -- and are the lands we recognize as wilderness. These concepts are illustrated through the mapping of indicators of wildness, derived from readily available data in a Geographic Information System.
Summary 1. Increasingly, river managers are turning from hard engineering solutions to ecologi- cally based restoration activities in order to improve degraded waterways. River resto- ration projects aim to maintain or increase ecosystem goods and services while protecting downstream and coastal ecosystems. There is growing interest in applying river restoration techniques to solve environmental problems, yet little agreement exists on what constitutes a successful river restoration effort. 2. We propose five criteria for measuring success, with emphasis on an ecological perspective. First, the design of an ecological river restoration project should be based on a specified guiding image of a more dynamic, healthy river that could exist at the site. Secondly, the river's ecological condition must be measurably improved. Thirdly, the river system must be more self-sustaining and resilient to external perturbations so that only minimal follow-up maintenance is needed. Fourthly, during the construction phase, no lasting harm should be inflicted on the ecosystem. Fifthly, both pre- and post- assessment must be completed and data made publicly available. 3. Determining if these five criteria have been met for a particular project requires development of an assessment protocol. We suggest standards of evaluation for each of the five criteria and provide examples of suitable indicators. 4. Synthesis and applications. Billions of dollars are currently spent restoring streams and rivers, yet to date there are no agreed upon standards for what constitutes ecolog- ically beneficial stream and river restoration. We propose five criteria that must be met for a river restoration project to be considered ecologically successful. It is critical that the broad restoration community, including funding agencies, practitioners and citizen restoration groups, adopt criteria for defining and assessing ecological success in restoration. Standards are needed because progress in the science and practice of river restoration has been hampered by the lack of agreed upon criteria for judging ecological success. Without well-accepted criteria that are ultimately supported by funding and implementing agencies, there is little incentive for practitioners to assess and report restoration outcomes. Improving methods and weighing the ecological benefits of various restoration approaches require organized national-level reporting systems.
Geoengineering is the intentional large-scale manipulation of the environment, particularly manipulation that is intended to reduce undesired anthro-pogenic climate change. The post-war rise of climate and weather modification and the history of U.S. assessments of the CO 2 -climate problem is reviewed. Proposals to engineer the climate are shown to be an integral element of this history. Climate en-gineering is reviewed with an emphasis on recent developments, including low-mass space-based scattering systems for altering the planetary albedo, simulation of the climate's response to albedo modification, and new findings on iron fertilization in oceanic ecosystems. There is a continuum of human responses to the climate problem that vary in resemblance to hard geoengineering schemes such as space-based mirrors. The distinction between geoengineering and mitigation is therefore fuzzy. A definition is advanced that clarifies the distinction between geoengineering and industrial car-bon management. Assessment of geoengineering is reviewed under various framings including economics, risk, politics, and environmental ethics. Finally, arguments are presented for the importance of explicit debate about the implications of countervailing measures such as geoengineering.
Species threatened with extinction are the focus of mounting conservation concerns throughout the world. Thirty-seven years after passage of the U.S. Endangered Species Act in 1973, we conclude that the Act's underlying assumption—that once the recovery goals for a species are met it will no longer require continuing management—is false. Even when management actions succeed in achieving biological recovery goals, maintenance of viable populations of many species will require continuing, species-specific intervention. Such species are “conservation reliant.” To assess the scope of this problem, we reviewed all recovery plans for species listed as endangered or threatened under the Act. Our analysis indicates that 84% of the species listed under the Act are conservation reliant. These species will require continuing, long-term management investments. If these listed species are representative of the larger number of species thought to be imperiled in the United States and elsewhere, the challenge facing conservation managers will be logistically, economically, and politically overwhelming. Conservation policies will need to be adapted to include ways of prioritizing actions, implementing innovative management approaches, and involving a broader spectrum of society.
In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.
Scenarios of changes in biodiversity for the year 2100 can now be developed based on scenarios of changes in atmospheric carbon
dioxide, climate, vegetation, and land use and the known sensitivity of biodiversity to these changes. This study identified
a ranking of the importance of drivers of change, a ranking of the biomes with respect to expected changes, and the major
sources of uncertainties. For terrestrial ecosystems, land-use change probably will have the largest effect, followed by climate
change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration. For freshwater ecosystems, biotic
exchange is much more important. Mediterranean climate and grassland ecosystems likely will experience the greatest proportional
change in biodiversity because of the substantial influence of all drivers of biodiversity change. Northern temperate ecosystems
are estimated to experience the least biodiversity change because major land-use change has already occurred. Plausible changes
in biodiversity in other biomes depend on interactions among the causes of biodiversity change. These interactions represent
one of the largest uncertainties in projections of future biodiversity change.
Technical developments in computer hardware and software now make it possible to introduce automation into virtually all aspects of human-machine systems. Given these technical capabilities, which system functions should be automated and to what extent? We outline a model for types and levels of automation that provides a framework and an objective basis for making such choices. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.
The sensing, processing, and visualizing that are currently in development within the environment boldly change the ways design and maintenance of landscapes are perceived and conceptualised. This is the first book to rationalize interactive architecture and responsive technologies through the lens of contemporary landscape architectural theory. Responsive Landscapes frames a comprehensive view of design projects using responsive technologies and their relationship to landscape and environmental space. Divided into six insightful sections, the book frames the projects through the terms; elucidate, compress, displace, connect, ambient, and modify to present and construct a pragmatic framework in which to approach the integration of responsive technologies into landscape architecture. Complete with international case studies, the book explores the various approaches taken to utilise responsive technologies in current professional practice. This will serve as a reference for professionals, and academics looking to push the boundaries of landscape projects and seek inspiration for their design proposals.
The increasing abandonment of marginal land creates new opportunities for restoration, reintroduction, and rewilding, but what do these terms mean in a rapidly and irreversibly changing world? The ‘re’ prefix means ‘back’, but it is becoming clear that the traditional use of past ecosystems as targets and criteria for success must be replaced by an orientation towards an uncertain future. Current opinions in restoration and reintroduction biology range from a defense of traditional definitions, with some modifications, to acceptance of more radical responses, including assisted migration, taxon substitution, de-extinction, and genetic modification. Rewilding attempts to minimize sustained intervention, but this hands-off approach is also threatened by rapid environmental change.
The view of nature and technology inhabiting totally different, even opposite, spheres persists across time and cultures. Most people would consider an English countryside or a Louisiana bayou to be "natural," though each is to an extent the product of technology. Pollution, widely thought to be a purely man-made phenomenon, results partly from natural processes. All around us, things from the natural world are brought into the human world. At what point do we consider them part of culture rather than nature? And does such a distinction illuminate our world or obscure its workings? This compelling new book challenges the view that a clear and unwavering boundary exists between nature and technology. Rejecting this dichotomy, the contributors show how the history of each can be united in a constantly shifting panorama where definitions of "nature" and "technology" alter and overlap. In addition to recognizing the artificial divide between these two concepts, the essays in this book demonstrate how such thinking may affect societies' ability to survive and prosper. The answers and ideas are as numerous as the landscapes they consider, for there is no single path toward a more harmonious vision of technology and nature. Technologies that work in one place may not in another. Nature that is preserved in one community might become the raw material of technological progress somewhere else. Add to this the fact that the natural world and technology are not passive players, but are profoundly involved in cultural construction. Understanding such dynamics not only reveals a new historical complexity; it prepares us for coping with many of the most difficult and pressing social issues facing us today.ContributorsPeter Coates. Craig E. Colten. Stephen H. Cutcliffe. Hugh S. Gorman. Betsy Mendelsohn. Joy Parr. Peter C. Perdue. Sara B. Pritchard. Martin Reuss. William D. Rowley. Edmund Russell. Joel A. Tarr. Ann Vileisis. James C. Williams. Thomas Zeller. © 2010 by the Rector and Visitors of the University of Virginia. All rights reserved.
IntroductionEcological History and HRVBeyond Baselines: The Extended HRV ConceptGeorge Webber's DilemmaAcknowledgmentsReferences
To satisfy a growing population, much of Earth's surface has been designed to suit humanity's needs. Although these ecosystem designs have improved human welfare, they have also produced significant negative environmental impacts, which applied ecology as a field has attempted to address and solve. Many of the failures in applied ecology to achieve this goal of reducing negative environmental impacts are design failures, not failures in the science. Here, we review (a) how humans have designed much of Earth's surface, (b) the history of design ideas in ecology and the philosophical and practical critiques of these ideas, (c) design as a conceptual process, (d) how changing approaches and goals in subfields of applied ecology reflect changes and failures in design, and (e) why it is important not only for ecologists to encourage design fields to incorporate ecology into their practice but also for design to be more thoroughly incorporated into ours.
After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence
(AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence
that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several
decades that address challenges of perception and action under uncertainty through the lens of computation. Advances include
the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling
reflection and decisions about tradeoffs in effort, precision, and timeliness of computations. These tools are deployed toward
the goal of computational rationality: identifying decisions with highest expected utility, while taking into consideration
the costs of computation in complex real-world problems in which most relevant calculations can only be approximated. We highlight
key concepts with examples that show the potential for interchange between computer science, cognitive science, and neuroscience.
Few biologists have studied the evolutionary processes at work in indoor environments. Yet indoor environments comprise approximately 0.5% of ice-free land area - an area as large as the subtropical coniferous forest biome. Here we review the emerging subfield of 'indoor biome' studies. After defining the indoor biome and tracing its deep history, we discuss some of its evolutionary dimensions. We restrict our examples to the species found in human houses - a subset of the environments constituting the indoor biome - and offer preliminary hypotheses to advance the study of indoor evolution. Studies of the indoor biome are situated at the intersection of evolutionary ecology, anthropology, architecture, and human ecology and are well suited for citizen science projects, public outreach, and large-scale international collaborations.
Copyright © 2015 Elsevier Ltd. All rights reserved.
There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.
Fences can both enhance and detract from the conservation of wildlife, and many detrimental impacts are associated with creating physical barriers. By contrast, virtual fences can restrict, control or minimize animal movement without the creation of physical barriers, and present key benefits over traditional fences, including: (1) no need for construction, maintenance or removal of traditional fences; (2) rapid modification of boundaries both temporally and spatially based on specific conservation concerns; (3) application of novel conservation approaches for wildlife that integrate monitoring, research and management; and (4) social-psychological benefits that may increase support for conservation. We review the various types of sensory, biological and mechanical virtual fences, and the potential benefits and costs associated with fully integrating virtual fences into protected area management and wildlife conservation. The recent development of real-time virtual fences represents the potential for a new ‘virtual management’ era in wildlife conservation, where it is possible to initiate management actions promptly in response to real-time data. Wide-scale application of virtual fences faces considerable technological and logistical constraints; however, virtual fences are increasingly popular and soon will offer realistic management strategies for both terrestrial and avian wildlife conservation.
The major challenge to stewardship of protected areas is to decide where, when, and how to intervene in physical and biological processes, to conserve what we value in these places. To make such decisions, planners and managers must articulate more clearly the purposes of parks, what is valued, and what needs to be sustained. A key aim for conservation today is the maintenance and restoration of biodiversity, but a broader range of values are also likely to be considered important, including ecological integrity, resilience, historical fidelity (ie the ecosystem appears and functions much as it did in the past), and autonomy of nature. Until recently, the concept of “naturalness” was the guiding principle when making conservation-related decisions in park and wilderness ecosystems. However, this concept is multifaceted and often means different things to different people, including notions of historical fidelity and autonomy from human influence. Achieving the goal of nature conservation intended for such areas requires a clear articulation of management objectives, which must be geared to the realities of the rapid environmental changes currently underway. We advocate a pluralistic approach that incorporates a suite of guiding principles, including historical fidelity, autonomy of nature, ecological integrity, and resilience, as well as managing with humility. The relative importance of these guiding principles will vary, depending on management goals and ecological conditions.
There is an increasing consensus that global climate change occurs and that potential changes in climate are likely to have important regional consequences for biota and ecosystems. Ecological restoration, including (re)-afforestation and rehabilitation of degraded land, is included in the array of potential human responses to cli-mate change. However, the implications of climate change for the broader practice of ecological restoration must be considered. In particular, the usefulness of historical eco-system conditions as targets and references must be set against the likelihood that restoring these historic eco-systems is unlikely to be easy, or even possible, in the changed biophysical conditions of the future. We suggest that more consideration and debate needs to be directed at the implications of climate change for restoration practice.