Coding a Biophilic Core: Digital Design Tools for Toronto’s Avian Habitat Networks

Coding a Biophilic Core Digital Design Tools for Toronto’s Avian Habitat Networks by James Cameron Parkin

A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Architecture Waterloo, Ontario, Canada, 2018 Š James Cameron Parkin 2018

AUTHOR’S DECLARATION

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners.

I understand that my thesis may be made electronically available to the public.

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ABSTRACT

This research develops a methodology for computationally sensing, illustrating, and utilizing avian-focused patch networks to locate and inform ecological interventions in dense urban settings. These interventions are designed to extend the range of regional avian ecosystems, promoting beneficial urbanitefauna interaction, often referred to as biophilia. This research is in response to Toronto’s rapid densification, where in recent years, there has been a major increase of residential and mixed-use development in the downtown and central waterfront areas. Literature shows that as populations move to urban centers, there is a need for people to have access to thriving, biodiverse green space to foster mental health and environmental responsibility. At the same time, experts in landscape architecture and urbanism critique existing approaches to providing green space in cities, which often lead to sterile, ornamental lawns that limit urban biodiversity. To move beyond this approach, experts call for more dynamic and complex strategies in urban ecology.

As a response, this work explores computational methods of modeling networks and habitats that are borrowed from landscape ecology, graph theory, and parametric architecture, in the pursuit of a design methodology that thrives amidst the complexity and dynamic nature of urban and ecological systems.

The resulting body of work involves simulating two dimensional and threedimensional agent movement within patch networks, populating these networks with bird sighting data, and using this information to locate and inform a variety of intervention typologies. The work generated in this thesis is broken into three parts, with each part exploring a progressively smaller piece of urban fabric. The first part maps patch networks and suggests interventions in Toronto’s downtown and central waterfront, the second part explores how these interventions affect bird movement in the three-dimensional fabric of CityPlace and Fort York, and the final part composes an artificial habitat that attracts local bird species and acts as a biophilic amenity for urbanites in CityPlace’s Canoe Landing Park.

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ACKNOWLEDGMENTS

I would like to begin by thanking my supervisor Maya Przybylski. Over the years, your guidance, critical insight, and mentorship in the field of computational design has shaped my approach to architecture and imbued my relationship to design with a sense of fascination and vigour.

I would also like to thank my committee member Jane Hutton for your expertise and guidance in the field of landscape architecture, as well as your contagious enthusiasm towards the work.

Thank you to Philip Beesley and Matthew Spremulli, for supporting my early intuitions and helping me turn design interests into a research agenda.

To my mom Margaret and my partner Robyn, thank you for your unwavering support, and for always finding time to give perspective and feedback on my research.

Lastly, thank you to my peers for always creating an exciting and supportive environment and making this whole experience so positive.

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TABLE OF CONTENTS

iii Author’s Declaration v Abstract vii Acknowledgments ix Table of Contents xi List of Figures

INTRODUCTION 3 7 9 13

Biophilia and Urbanization Unpacking Toronto’s Greenspace Patch Netowrks Design Approach and Methods

PART I: REGIONAL NETWORK ANAYSIS + DATA INTEGRATION 19 21 27 31 37 43 51 59

Patch Composition and Qualities Network Study 01 Introducing Resistance Network Study 02 Network Study 03 Network Intervention Strategy Data Visualizaiton Data Integration

PART II: 3D NETWORK DEVELOPMENT + INTERVENTION MASSING 65 69 75 83

Fabric Selection Intervention Exploration Building a 3-D Network Massing Optimization

PART III: HABITAT COMPOSITION + ASSEMBLIES 89 Formal Approach 91 Habitat Structure 97 Habitat Composition 107 Assembly 115 Internvetion Illustrations

CONCLUSION + NEXT STEPS 131 Bibliography ix

LIST OF FIGURES

Figure 0.1.

Comparison of periphery and central green spaces in Toronto.

Images retrieved from http://www.discoverthedon.ca/page/6 https://www.tommythompsonpark.ca/about/ https://www.blogto.com/toronto/the_best_dog_parks_in_ toronto/ https://globalnews.ca/news/2184731/toronto-musicians-tostage-massive-group-photo-in- trinity-bellwoods-park/ Figure 0.2.

Movement diagrams: Patches, Edges, Corridors, Mosaics Landscape Ecology Principles.

Landscape Architecture and Land-Use Planning, 1996.

Wenche E. Dramstad, James D. Olson and Richard T.T. Forman.

Figure 0.3.

An appropriate planar graph presentation of the core habitat networks for different threshold distance scenarios (shown for 1, 7, 15, and 25 km).

Measuring Landscape Connectivity in a Urban Area for Biological Conservation, 2015.

Deyong Yu, Yupeng Liu, Bin Xun, Hongbo Shao.

Figure 0.4.

Multi-scalar computational design approach. By the author.

Figure 1.1.

Patch network elements. By the author.

Figure 1.2.

Indicators of patch success. By the author.

Figure 1.3.

Natural cover and green space in Toronto. By the author.

GIS data gathered from Toronto Open Data Toronto Region Conservation Authority, accessed thorough Scholars GeoPortal

Figure 1.4.

Regional mapping of connection densities. By the author.

Figure 1.5.

Connection densities - Detail. By the author.

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Figure 1.6.

Exploded resistance and accommodation layers. By the author.

GIS data gathered from Toronto Open Data Toronto Region Conservation Authority, accessed thorough Scholars GeoPortal Google Maps Aerial Imagery Figure 1.7.

Resistance map of West Downtown Toronto. By the author.

Figure 1.8.

Nearest neighbour network with paths adjusted for fabric resistance. By the author.

Figure 1.9.

Nearest neighbour network building process. By the author.

Figure 1.10.

Nearest neighbour network – Detail. By the author.

Figure 1.11.

Agent driven network. By the author.

Figure 1.12.

Agent network building process. By the author.

Figure 1.13.

Agent network - Detail. By the author.

Figure 1.14.

Patch network intervention strategies. By the author.

Figure 1.15. Interventions located using the network and resistance map.

By the author.

Figure 1.16.

Intervention placement process. By the author.

Figure 1.17.

Intervention locations - Detail. By the author.

Figure 1.18.

Snapshot of sighting data. By the author.

Figure 1.19.

Bird sightings in the past 5 years - species. By the author.

Data gathered from eBird.com.

Figure 1.20.

Bird sightings in the past 5 years - habitat. By the author.

Data gathered from eBird.com + Cornell Lab of Ornithology, All About Birds.

Figure 1.21.

Sightings by species - Detail. By the author.

Figure 1.22.

Sightings by habitat - Detail. By the author. xii

Figure 1.23.

Graph data structure. By the author.

Figure 1.24. Merging patch network and sighting data process. By the author.

Figure 2.1.

Core Circle.

Proposed Downtown Plan, 2017.

City of Toronto.

Figure 2.2.

3-D Fabric Model. By the author.

3-D context gathered from Toronto Open Data.

Figure 2.3.

Imported 2-D paths, patches, and interventions. By the author.

Figure 2.4.

Selected interventions for further investigation. By the author.

Figure 2.5.

Schematic intervention strategies. By the author.

Figure 2.6.

Exploded 3-D resistance and accommodation layers. By the author.

3-D context gathered from Toronto Open Data.

Figure 2.7.

3-D network development process. By the author.

Figure 2.8.

Existing 3-D bird movement network. By the author.

Figure 2.9.

3-D bird movement network with proposed interventions.

By the author.

Figure 2.10.

Patch enhance envelope with effect on local network and movement vectors – Axonometrics. By the author.

Figure 2.11.

Patch enhance envelope with effect on local network– Section. By the author.

Figure 3.1.

Reaction Diffusion simulation.

Images retrieved from https://www.youtube.com/ watch?v=vKX2FHrYLTc.

Figure 3.2.

Habitat photo compilations. By the author.

Images compiled from a Google image search.

Figure 3.3.

Habitat vertical structure analysis using elements as building blocks. By the author. xiii

Figure 3.4.

Habitat intervention understood as a collage of elements. By the author.

Figure 3.5.

Accessing species data sets based on intervention connectivity. By the author.

Figure 3.6.

Species data by degree of connection. By the author.

Figure 3.7.

Parametrically tuned geometries simulating habitat elements. By the author.

Figure 3.8.

Habitat breakdown and element arrangement in intervention envelope. By the author.

Figure 3.9.

Patch Enhance intervention - Section. By the author.

Figure 3.10.

Assembly compartment types. By the author.

Figure 3.11.

Plant selection by habitat and required sun. By the author.

Figure 3.12.

Assembly generation process. By the author.

Figure 3.13. Selection of plants and birds accommodated in assembly fragment. By the author.

Figure 3.14.

Shrub assembly fragment - Axonometric. By the author.

Figure 3.15.

Patch Enhance intervention – Axonometric. By the author.

Figure 3.16. Patch Enhance intervention – Perspective from street. By the author.

Figure 3.17. Patch Enhance intervention – Interior of grassland. By the author.

Figure 3.18. Patch Enhance intervention – View of grassland. By the author.

Figure 3.19. Patch Enhance intervention – Perspective from park. By the author.

Figure 3.20.

Patch Enhance intervention – Interior perspective from open woodland. By the author.

Figure 3.21.

Forest fragment - Axonometric. By the author. xiv

Figure 3.22.

Forest fragment – Detail. By the author.

Figure 3.23.

Grassland fragment - Axonometric. By the author.

Figure 3.24.

Grassland fragment – Detail. By the author.

Figure 3.25.

Open woodland fragment - Axonometric. By the author.

Figure 3.26.

Open woodland fragment - Detail. By the author.

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INTRODUCTION BIOPHILIA AND URBANIZATION UNPACKING TORONTO’S GREENSPACE PATCH NETWORKS DESIGN APPROACH AND METHODS

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BIOPHILIA AND URBANIZATION

The concept of biophilia is central to the motivation behind this work. This idea that interaction with other living beings is psychologically beneficial to humans was first dubbed biophilia by biologist and theorist E. O. Wilson in his 1984 book by the same title.1 Here, Wilson poetically describes biophilia as, “the innate tendency [in human beings] to focus on life and lifelike process. To an extent still undervalued in philosophy and religion, our existence depends on this propensity, our spirit is woven from it, hopes rise on its currents.”2 In the same year, this theory was strengthened by Roger S. Ulrich’s scientific study, where he found that patients who had a window that looked at a natural setting recovered from surgery more quickly.3 This idea of health and psychological benefits from exposure to nature was further explored in Environmental Psychologists Kaplan and Kaplan’s 1989 book, The experience of nature : a psychological perspective.4 This comprehensive book provided findings on beneficial relationships between humans and nature, as well as scientific methods for further research.

These foundational works buy Ulrich and Kaplan and Kaplan have since been expanded on by many studies, which have produced compelling findings, specifying the qualities and outcomes of these beneficial human-nature relationships. Key findings for this thesis include that a quantifiable “connectedness with nature” is linked to well being5, that biodiversity is perceived by the public and is positively linked to well-being,6 and that people exhibit better cooperation and make more environmentally sustainable decision after visual exposure to nature.7 Latent within these findings, is a critique of current perceptions regarding urban greenspace. In a study linking perceived biodiversity to psychological well-being, the authors noted, 1 Edward O. Wilson, Biophilia (Cambridge, Mass.; Cambridge, Mass. : Harvard University Press, 1984; Cambridge, Massachusetts: Harvard University Press, 1984).

2

Edward O. Wilson, Biophilia

3 Roger S. Ulrich, “View through a Window may Influence Recovery from Surgery,” Science 224, no. 4647 (1984), 420-421.

4 Rachel Kaplan, The Experience of Nature : A Psychological Perspective, ed. Stephen Kaplan (Cambridge; New York: Cambridge University Press, 1989).

5 Renate Cervinka, Kathrin Röderer and Elisabeth Hefler, “Are Nature Lovers Happy? on various Indicators of Well- being and Connectedness with Nature,” Journal of Health Psychology 17, no. 3 (2012), 379.

6 Richard Fuller et al., “Psychological Benefits of Greenspace Increase with Biodiversity,” Biology Letters 3, no. 4 (2007), 390-394.

7 John M. Zelenski, Raelyne L. Dopko and Colin A. Capaldi, “Cooperation is in our Nature: Nature Exposure may Promote Cooperative and Environmentally Sustainable Behavior,” Journal of Environmental Psychology 42 (2015), 24-31.

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Our results indicate that simply providing greenspace overlooks the fact that greenspaces can vary dramatically in their contribution to human health and biodiversity provision. Consideration of the quality of that space can ensure that it serves the multiple purposes of enhancing biodiversity, providing ecosystem services (Arnold & Gibbons 1996), creating opportunities for contact with nature (Miller 2005) and enhancing psychological well-being. 8 This call for a focus on biodiversity in the design of urban greenspace is also made by Ecologist James R. Miller, who states concerns about the majority of the worlds population living in urban centers becoming disconnected from nature.

He goes on to say that, If there is to be broad-based public support for biodiversity conservation, the places where people live and work should be designed so as to provide opportunities for meaningful interactions with the natural world. Doing so has the potential not only to engender support for protecting native species, but also to enhance human well-being.9 A key researcher bringing these ecological perspectives to the field of planning and architecture is Timothy Beatley. In his book, Biophilic Cities Integrating Nature into Urban Design and Planning, Beatley elaborates on the necessity of a biophilic city, saying, We need wonder and awe in our lives, and nature has the potential to amaze us, stimulate us, and propel us forward to want to learn more about our world. The qualities of wonder and fascination, the ability to nurture deep personal connection and involvement, visceral engagement in something larger than and outside ourselves, offer the potential for meaning in life few other things can provide. 10

8 Richard Fuller et al., “Psychological Benefits of Greenspace Increase with Biodiversity,” Biology Letters 3, no. 4 (2007), 390-394.

9 James R. Miller, “Biodiversity Conservation and the Extinction of Experience,” Trends in Ecology & Evolution 20, no. 8 (2005), 430-434.

10 Timothy Beatley, Biophilic Cities Integrating Nature into Urban Design and Planning (Washington, DC: Island Press, 2011).

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Beatley’s words bring specific attention to the role of architecture and landscape architecture in accomplishing urban biophilia. Here it is evident that, while it is important to design urban habitats to support biodiversity, designers also play a key role in mediating the interaction between urbanites other species in a way that accentuates the quality of wonder and fascination.

While Miller and Beatley discuss the importance of incorporating ecologies into the city, other architects, landscape architects and urbanists engage in a strong critique of existing approaches to providing urban green space. An early contributor to this discourse, Toronto Landscape Architect Michael Hough, criticizes the values of existing urban form, saying it isolates humans by ignoring natural dynamics and processes. Hough exemplifies this point by stating that abandoned urban sites offer more ecological resilience and diversity than planned parks, which are weak, resource intensive, and only serve shallow aesthetic purposes.11 A more recent addition to Hough’s critique is Ecologists Cristina Ramalho and Richard Hobbs’ 2012 call for “dynamic urban ecology”. Here, they speak on the rapid, complex, and nonlinear growth of young modern cities (Toronto is a strong example of this), making a case for a methodology in urban ecology that is adaptable and dynamic, and thus, exhibits resiliency as urban context quickly and sporadically densifies.12 This approach to urban ecology is in contrast to the standard notion of an urban-rural gradient, where “nature” is most dominant outside the city, and as you move towards the city’s center, it fades and human’s built environment becomes more dominant.

Another voice seeking to break the traditional narratives in urban landscape is Architect Emma Flynn. Flynn calls for “flexible, resilient, and efficient urban models“ while focusing on how new technologies and socio-cultural shifts can create a scenario where urbanites and living ecologies are entwined as part of a larger system, rather than being viewed as separate entities.13 To leverage this new social and technological paradigm, it is important to understand how views on nature are shifting in the age of the Anthropocene. In the past, the natural world has often been seen either as a “silent and passive backdrop” 14 , or as a wild, untameable antagonist. 15As human’s effect on the worlds geography and climate becomes clearer, it is apparent that we are much more intertwined in natural systems than previously culturally understood.

11

Michael Hough, Cities and Natural Process (London ; New York: Routledge, 1995).

12 Cristina E. Ramalho and Richard J. Hobbs, “Time for a Change: Dynamic Urban Ecology,” Trends in Ecology & Evolution 27, no. 3 (2012), 179-188.

13 Emma Flynn, “( Experimenting with) Living Architecture: A Practice Perspective,” Architectural Research Quarterly 20, no. 1 (2016), 20-28.

14 222).

Chakrabarty, D The Climate of History: Four Theses. Critical Inquiry, 2009, (pg 197-

15 Margaret Atwood. Survival : A Thematic Guide to Canadian Literature, edited by House of Anansi Press, (Toronto: Anansi, 1972).

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This interconnectedness could be interpreted through Bruno Latour’s ActorNetwork Theory, where everything is equal and irreducible, and it is the connection between things that gives meaning.16 This philosophy puts us on the same ground as the natural systems we engage with and highlights our various interactions with the world around us as what is important. Object Oriented Ontologists, such as philosopher and Sci-Arc professor, Graham Harman take this leveling to the extreme by suggesting that humans, or plants or animals, by default are not any more important than any other object, such as a rock, or a smart-phone, or a pixel.

Timothy Morton uses this philosophy to address our understanding of the natural world in this book Ecology without Nature, where he attempts to separate the idea of “natural” from ecology, stating that if we want to understand ourselves as equal to the world around us and thus think more environmentally, we must remove the idea of natural vs artificial, and forget the romantic aesthetics of nature.17 By doing this we can better understand relationships between the elements in our environment, without prescribed notions of what is part of “nature” and what is not. In the scope of this thesis, Morton’s theory would reject re-creation of pristine nature, and instead require a more complex and involved approach to urban greenspace, evaluating a multitude of relationships.

By removing the importance of what is perceived as natural, Morton’s ideas also make room for technology and traditionally artificial constructs to help design biophilic green spaces. This thesis will aim to use this opportunity to digitally curate performative hybrid structures assembled from a variety of “natural” and “artificial” elements. These hybrid structures could be understood through the philosopher Donna Haraway’s cyborg myth, which she describes as being about “transgressed boundaries, potent fusions, and dangerous possibilities...”.18 Haraway’s cyborgs are products of the Anthropocene and do not respect traditional boundaries between human, animal, and machine, allowing for a novel urban ecology that intertwines all these elements.

In summary, It is important that designers acknowledge the importance of ecological agents, and provide for them as much as for humans. Rather than seeing ecology as it’s own system, it must be seen it an intertwined component of our urban systems. Moving forward it is also important to recognize technology’s role in synthesizing complex ecological relationships and creating potent hybrids.

16 Harman, Graham. The Importance of Bruno Latour for Philosophy. Cultural Studies Review, [S.l.], v. 13, n. 1, p. 31–49, may 2011.

17 Timothy Morton. Ecology without Nature : Rethinking Environmental Aesthetics.

(Cambridge, Mass. : Harvard University Press, 2007).

18 Donna Jeanne Haraway, Simians, Cyborgs, and Women : The Reinvention of Nature (New York: Routledge, 1991).

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UNPACKING TORONTO’S GREENSPACE

While biophilic design is an important area or research for any city, the work presented in this thesis focuses on the City of Toronto. Toronto is chosen based on it’s relative youth and rapid densification, which, as explained by Cristina Ramalho and Richard Hobbs, requires a new dynamic approach when it comes to designing greenspace.19 Toronto is also an interesting case study because of it’s underlying infrastructure of greenspace. While Toronto enjoys a large overall amount of greenspace, and considers itself a city within a park, preliminary research regarding the qualities of these spaces reveals that there are large differences in their ability to accommodate avian populations and provide biophilic experiences.

Within Toronto’s Ravines, Island/Spit, and older parks such as High Park, very robust habitats and ecologies can be found. These systems of greenspace carry a large amount of biodiversity, with Tommy Thompson park being of particular importance as a stop-over for migrating birds.

While these systems of green space are a strong resource for Toronto’s human and non-human species, as greenspace in more central areas is examined, it becomes clear that many are lacking in their ability to provide habitat outside of these ecosystems. Part of this comes form the fact that many downtown parks are very focused on human occupation, consisting primarily of manicured lawns and sparse trees. It is this approach to urban green space that was addressed by Hough, as being aesthetically focused and lacking engagement in natural systems.20 While these spaces may work for recreation, they do not provide habitat or a biophilic experience.

Toronto is in a position where its rapid development and sometimes questionable urban greenspace practices could risk isolating it’s habitat ecosystems. This being said, if Toronto can allow species to penetrate its high density residential developments and create spaces where interaction with these regional ecologies can occur, it is uniquely positioned to take advantage of its periphery ecosystems to provide downtown biophilia.

19 Cristina E. Ramalho and Richard J. Hobbs, “Time for a Change: Dynamic Urban Ecology,” Trends in Ecology & Evolution 27, no. 3 (2012), 179-188.

20

Michael Hough, Cities and Natural Process (London ; New York: Routledge, 1995).

7

Don Valley, Toronto

PERIPHERY GREENSPACES

Tommy Thompson Park, Toronto

DOWNTOWN GREENSPACES

Dog Park, Toronto

Trinity Bellwoods Park, Toronto

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Fig 0.1. Comparison of periphery and central green spaces in Toronto

PATCH NETWORKS

“Many things in cities take to the skies, and we should begin to understand the airspace above buildings, roads, and parks as life routes used by birds and bats and insects that spend at least some of their life in the air.”21 Timothy Beatley To achieve species movement from Toronto’s major habitats into it’s core, this research focuses on bird movement in habitat patch networks. This exploration draws on patch principles originally developed by Landscape Ecologists, Wenche Dramstad, James Olson, and Richard Forman in their book, Landscape Ecology Principles in Landscape Architecture and Land-use Planning.22 This book introduces the components and dynamics of patch networks, as described in Part I of this thesis.

While patch networks are quite complex, and require many important conditions to be effective, it has been shown that a network of small habitat patches can be effective in extending the range of large established habitats,23 and if the small patch networks are strong enough, they can support bird populations in urban areas without connection to a larger habitat.24 To better predict the success of a given path network, researchers in landscape ecology have developed a large body of work where multiple sophisticated methods of measuring landscape connectivity and species dispersal have been developed.25 This field contains a great depth of technical reports, however, through a review of studies, this thesis draws several fundamental methods and principles to develop a body of work that 21 Timothy Beatley, Biophilic Cities Integrating Nature into Urban Design and Planning (Washington, DC: Island Press, 2011).

22 Wenche E. Dramstad, Landscape Ecology Principles in Landscape Architecture and Landuse Planning, eds. James D. Olson and Richard T. T. Forman (Cambridge? Mass.] : Washington, DC : Washington, D.C.?]: Cambridge? Mass. : Harvard University Graduate School of Design ; Washington, DC : Island Press ; Washington, D.C.? : American Society of Landscape Architects, 1996).

23 Michael W. Strohbach, Susannah B. Lerman and Paige S. Warren, “Are Small Greening Areas Enhancing Bird Diversity? Insights from Community- Driven Greening Projects in Boston.

(Report),” Landscape and Urban Planning 114 (2013), 69.

24 Erik Andersson and Örjan Bodin, “Practical Tool for Landscape Planning? an Empirical Investigation of Network Based Models of Habitat Fragmentation,” Ecography 32, no. 1 (2009), 123-132.

25 Luc Pascual-Hortal and Santiago Saura, “Comparison and Development of New Graph- Based Landscape Connectivity Indices: Towards the Priorization of Habitat Patches and Corridors for Conservation,” Landscape Ecology 21, no. 7 (2006), 959-967.

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makes engaging Toronto’s habitat networks accessible for designers.

These principles are: - The use of graph structures to evaluate habitat connectivity 26 - The ability to sense habitat elements from aerial imagery including ecotones, barriers, and stepping stones 27 - The use of land cover data to measure resistance and calculate a “least cost path” between patches 28 - The importance of threshold distances in evaluating species ability to move between patches 29 By applying these network principles to Toronto’s avian habitats, traditional urban-rural gradients can be subverted and biophilic greenspace in Toronto can be designed using more dynamic, complex, and performance-based models.

26 Dean Urban and Timothy Keitt, “Landscape Connectivity: A Graph- Theoretic Perspective,” Ecology 82, no. 5 (2001), 1205-1218.

27 Wei Hou, Marco Neubert and Ulrich Walz, “A Simplified Econet Model for Mapping and Evaluating Structural Connectivity with Particular Attention of Ecotones, Small Habitats, and Barriers,” Landscape and Urban Planning 160 (2017), 28-37.

28 Deyong Yu et al., “Measuring Landscape Connectivity in a Urban Area for Biological Conservation,” CLEAN – Soil, Air, Water 43, no. 4 (2015), 605-613.

29 Deyong Yu et al., “Measuring Landscape Connectivity in a Urban Area for Biological Conservation.”

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Fig 0.2. Movement diagrams: Patches, Edges, Corridors, Mosaics Landscape Ecology Principles Landscape Architecture and Land-Use Planning, 1996 Wenche E. Dramstad, James D. Olson, and Richard T.T. Forman 11

Fig 0.3. An appropriate planar graph presentation of the core habitat networks for different threshold distance scenarios (shown for 1, 7, 15, and 25 km) Measuring Landscape Connectivity in a Urban Area for Biological Conservation, 2015 Deyong Yu, Yupeng Liu, Bin Xun, and Hongbo Shao

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DESIGN APPROACH AND METHODS

From the outset, this research has been heavily invested in computational workflows, with the goal of testing several general benefits of digital tools in the field of urban ecology at multiple scales. These benefits include: - The ability to focus on non-human design by using design parameters specific to other species - The ability to generate and analyse complex networks and quickly update them as the city changes - The ability to manage vast amounts of bird sighting data and utilize them in parametric habitat design - The ability to generate and manipulate geometry that mimics the complexity, variety, and structure found in avian habitats.

The computational tools developed in this thesis can be split into to categories.

The first category includes network tools, which involve two-dimensional and three-dimensional network studies as well intervention placement tools. The second category includes habitat composition and assembly tools.

The network tools are based on computational methods from the landscape ecology studies. The use of computation is key in sensing urban fabric, building and analysing complex networks, and suggesting network improvements. This not only allows networks to intricately respond to urban fabric, but also means they can be rapidly updated as the fabric inevitably changes. The use of computation in this suite of tools also allows the network to store large amounts of species data that can inform intervention design.

The habitat composition and assembly tools seek to utilize the networks of patches, interventions, and data, to generate man-made habitats with the ability to host large amounts of bird diversity. The form and composition of these scaffolds are digitally tuned using the tools developed to simulate a variety of well established habitats, while providing the correct plants, cavities, and perching opportunities to attract numerous targeted local species.

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The work presented in this thesis takes the form of several individual studies, each associated with a different computation tool. Each study is described using its inputs, process, and evaluation. The input section includes what data and portions of previous studies are used to inform the tool. The process section explains the logic and steps used by the computational tool. Lastly the evaluation section explains the results of the process and identifies how the outputs of the tool can inform further studies. While these studies occur at different scales and use different tools, the complete set of studies are informed by each other and work together to address the goals this thesis.

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15

Fig 0.4. Multi-scalar computational design approach

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PART I REGIONAL PATCH NETWORK ANALYSIS DATA INTEGRATION

Part I focuses on learning from landscape ecology to develop a language of patch networks. Here a series of network drawing studies are used to illuminate the networks of bird movement in the City of Toronto. Based on preliminary studies, West Downtown was chosen as a focus region, due to its potential connection to major ecosystems, existing park infrastructure, and heavy residential development.

To improve the networks illustrated, a strategy of four synergistic interventions types are proposed along with a digital tool to locate them. Data collected from Online bird sighting records is then located within the network to further inform the design of these interventions, as explored in Part III.

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PATCH COMPOSITION + QUALITIES

To begin understanding patch networks, it is important to identify the components of a patch network.

A patch is a significant area of fabric that can support species populations.

Based on a review of literature in landscape ecology, several qualities of patches have be identified as important factors in their success as part of an avian habitat network. The two most important qualities are the size and proximity of the patches. Beyond this, a constantly varying edge condition and large ecotone,30 as well as a high number of cavities and overall height of patch all contribute to its success. 31 The matrix is the fabric between patches that is not conducive to ecological habitation. Therefore, birds must move through the matrix to utilize a network of patches. Matrices can have varying levels of resistance caused by physical and environmental barriers which distort bird’s paths of travel and affect the distance they can travel from one patch to another.

An ecotone is the transition space between two habitat types, or in the case of this study, between a patch habitat and the matrix around it. Ecotones offer many benefits to a patch including increased biodiversity due to more variation in habitat and protection by acting as a buffer to the patch. An additional attribute, which greatly impacts this study, is the ability for an ecotone to promote bird movement from patch to patch through the matrix by softening what could be a harsh threshold.

30 Wenche E. Dramstad, James D. Olson and Richard T. T. Forman, Landscape Ecology Principles in Landscape Architecture and Land-use Planning, eds. James D. Olson and Richard T. T. Forman (Cambridge Mass.; Washington, DC: Harvard University Graduate School of Design ;Island Press; American Society of Landscape Architects, 1996).

31 Michael W. Strohbach, Susannah B. Lerman and Paige S. Warren, “Are Small Greening Areas Enhancing Bird Diversity? Insights from Community- Driven Greening Projects in Boston.

(Report),” Landscape and Urban Planning 114 (2013), 69.

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PATCH ECOTONE MATRIX

Fig 1.1. Patch network elements

SIZE

PROXIMITY

EDGE PERMEABILITY + RETICULATION + ECOTONE

CAVITIES

HEIGHT

Fig 1.2. Indicators of patch success 20

NETWORK STUDY 01

[input] To build the urban fabric in this study, information was collected from various geographic information system (GIS) databases. The purpose of this information is to identify and differentiate ecological territory in the target city. To do this, polygons delineating general “green space”, as well as significant vegetation cover were extracted from the city’s GIS database. In addition to this, boundaries of “natural cover” and “vegetation types” were gathered. All these boundaries were imported into Rhinoceros 3D, where the shapes containing vegetation and other natural cover were combined and defined as ecologically viable patches. Green space that did not contain significant vegetal cover was deemed a “potential patch”, assuming it could support species populations if altered.

21

Beach/Bluff Forest Meadow

Fig 1.3. Natural cover and green space in Toronto

Wetland

22

[process] To begin building a preliminary ecological network, all patch polygons are populated with a grid of points. The network is built by connecting every point to every other point, before removing any connection above a specified threshold. The thresholds were selected by referencing “Behavioral barriers to non-migratory movements of birds” by Rebecca J. Harris & J. Michael Reed.32 Based on the attributes of the species documented in this article, the network visualization procedure was completed at 100 m, 300 m, and 500 m thresholds. The paths generated at these thresholds are overlaid, with darker lines showing closer connections that are viable for more species.

To illustrate potential connections, 1000m threshold lines are shown in the lightest layer. [evaluation] This map is useful in revealing the density of ecological connections in different regions. Here overall conditions can be seen, making this map helpful in choosing areas of closer study. Based on the strength of connections in the island and waterfront parks, and how those connection quickly taper off towards the core, the west downtown and waterfront of Toronto are selected for closer examination.

In this study, the agents are only understood with respect to their travel distance thresholds for moving from one patch to another.

To perform more detailed studies, it is important to consider how qualities of patches, presence of ecotones, and resistance in the matrix effect these connections.

32 Rebecca J. Harris and J. M. Reed, “Behavioral Barriers to NonMigratory Movements of Birds,” Annales Zoologici Fennici 39, no. 4 (2002), 275-290.

23

1000m Connection

100m Connection

Fig 1.4. Regional mapping of connection densities

24

1000m Connection

100m Connection 25

Fig 1.5. Connection densities - Detail 26

INTRODUCING RESISTANCE

To generate more accurate habitat networks, a resistance map is created to inform the following studies. This maps illustrates the ability for birds to move through or occupy urban fabric. [input] The resistance map is made up of multiple GIS and aerial imagery layers that are assigned a gray value based on the resistance level of their contents. Features with high levels of resistance such as highways and tall buildings are lighter, and features that accommodate birds such as tree canopies and other natural ground covers are darker. [process] The layers are overlaid to create a bitmap that is the sum of all the resistance layers. The grey value of the layer, and its weight when it is overlaid are chosen by the designer, based on an understanding of how different urban elements effect accommodation or resistance of birds. The map is then blurred to reduce the effect of insignificant elements and allow for more consistent digital sampling of the image. [evaluation] While any representation of patches and resistance is an estimation, this multilayered approach is much more accurate than the previous use of greenspace polygons. In this method, the more layers compiled, the more sophisticated the representation of the fabric becomes. This resistance image can now inform what portions of fabric are considered a habitat patch, and how species moment will be affected by the matrix.

27

Low movement resistance

GIS TREES

AERIAL TREES

VEGETATION

NATURAL COVER

GIS GRASS

AERIAL GRASS

GREENSPACE

BUILDINGS

STREETS

WATER

High movement resistance

Fig 1.6. Exploded resistance and accommodation layers

28

Low movement resistance 29

Fig 1.7. Resistance map of West Downtown Toronto High movement resistance 30

NETWORK STUDY 02

[input] The resistance map is the only input for this study. [process] This study begins by sampling the resistance map image and pulling a grid of values based on the ability of each sample point to support or resist bird movement, with higher values relating to better habitat. Any points over a certain value are identified as patches and are subsequently connected to their nearest neighbours.

Any connection that is over the maximum bird movement threshold is then removed. To adjust the remaining connections to favour paths of lower resistance, the paths are assigned control points that sample the resistance strength of their surrounding fabric and move towards values of lower resistance. Now when the path is redrawn through the control points, it avoids areas of strong resistance.

For the final map, this process is run using multiple patch thresholds, with thicker lines connecting stronger patches. These paths can also be coloured according to the connection length and resistance met along the path. Here red shows the highest resistance and orange, the lowest. [evaluation] This network study begins to illustrate bird movement in urban fabric and can be useful in informing interventions to improve the network. However, there are some flaws with this type of network.

The first issue is that connecting any point within a specific distance, and then simply distorting the path if it met resistance leaves connections where they may not be possible. Another issue is that the more a path is distorted the less accurate the distortion becomes, leaving inaccurate paths in extreme areas. To combat this, the third network study develops an agent-based approach rather than using nearest neighbour connections.

31

Easy Path to Travel

Difficult Path to Travel

Fig 1.8. Nearest neighbour network with paths adjusted for fabric resistance

32

1

Patch Point

2

Connection 33

3

Connection > 1000 m

4

Fig 1.9. Nearest neighbour network building process Connection Distortion 34

Paths Between Stronger Patches

Paths Between Weaker Patches 35

Fig 1.10. Nearest neighbour network – Detail 36

NETWORK STUDY 03

[input] Again, the resistance map is the only input for this study. [process] The resistance map is sampled and this time habitat points are combined into a habitat patch polyline. “Agents� are then emitted outward from the patch into the matrix. As they move, these agents sense a portion of the resistance map in front of them, constantly moving forwards and towards areas that better support birds. If on their journey, one of these agents arrives at another patch, it stops moving and its path is solidified as a connection. Agents that do not reach a patch in 1000m of travel are disregarded. Once again, the simulation in carried out at multiple patch thresholds and coloured to illustrate connection strength. [evaluation] By allowing paths to emerge though agent movement, this map study offers a stronger illustration of the network. Here, key movement corridors and significant barriers in the matrix become much more prominent. However, while many corridors and paths are revealed, the number of red paths show how many of these vital connections are quite long and encounter a large amount of resistance.

37

Easy Path to Travel

Difficult Path to Travel

Fig 1.11. Agent driven network

38

1

Agent

Patch

2

Agent Path 39

3

Successful Connection

4

Fig 1.12. Agent network building process Unsuccessful Connection 40

Paths Between Stronger Patches

Paths Between Weaker Patches 41

Fig 1.13. Agent network - Detail 42

NETWORK INTERVENTION STRATEGY

To holistically strengthen this network, four intervention types have been developed and located in the urban fabric. The intervention types are as follows: Patch Add : Where travel distances between patches are too far, a habitat patch is added to increase stepping stone movement.

Patch Enhance: Where green space exists, but does not have the characteristics to accommodate bird populations, the qualities of the habitat are improved.

Ecotone Spread: Where strong ecological territory borders areas of high resistance, this transition is blurred to encourage species movement through the matrix and offer variation in habitat.

Matrix Smooth: Where the matrix offers high resistance along identified paths of travel and around patches, safety and accommodation of urban fabric is increased to boost willingness and ability of birds to move between patches.

43

PATCH ADD

PATCH ENHANCE

ECOTONE SPREAD

MATRIX SMOOTH

Fig 1.14. Patch network intervention strategies 44

[input] To locate these interventions in this complex network, a digital tool has been developed to evaluate the network and resistance map and place these interventions accordingly. [process] To place Patch Add interventions, network paths are evaluated based on length.

Where the length exceeds a specified threshold, a midpoint is placed to indicate the need for an additional patch. Closely spaced points are then conglomerated into patches that serve as intermediate stops on multiple paths of travel.

For the Patch Enhance interventions, to identify portions of the fabric that have potential to support bird populations but don’t currently have enough vegetal cover, the tool takes the values sampled from the resistance maps and highlights those within a certain range.

To place the Ecotone Spread intervention sites, the resistance map is sampled, and anytime an area of very low resistance borders and area of very high resistance, an ecotone is placed to encourage birds to cross this boundary.

For the Matrix Smooth interventions, areas of fabric with high resistance values that are within a certain radius of a patch or path are extracted from the resistance map and highlighted. [evaluation] The result it a collage of intervention suggestions that act as starting points for policy makers and designers. Because these network and intervention maps are generated computationally, they can easily be updated to test the effect of these interventions or other changes in the urban fabric on the network. Now that the network has been developed and interventions suggested, it becomes advantageous to make use of the vast amounts of bird data collected by avid birders watchers.

By locating this data within the network, the designers of these interventions can easily access this data to inform their design decisions.

45

Patch Add

Patch Enhance

Ecotone Spread

Matrix Smooth

Fig 1.15.

Interventions located using the network and resistance map

46

PATCH ADD

Added when path is longer than 400m

ECOTONE SPREAD

Added when very high resistance map values are in close proximity to very low values

47

PATCH ENHANCE

Added when resistance map values are between the patch and matrix thresholds

MATRIX SMOOTH

Added when high resistance values intersect paths and patches

48

Fig 1.16. Intervention placement process

Patch Add

Patch Enhance 49

Ecotone Spread

Matrix Smooth

Fig 1.17. Intervention locations - Detail 50

DATA VISUALIZATION

[input] The data visualized was acquired from the Online database eBird, where almost 30,000 bird sightings in this region in the past 5 years was used.33 Each sighting entry contains key data items including sighting coordinates, species, number of birds, and date. This data was supplemented by adding data reflecting design parameters for each species. For each entry, the species’ habitat, food, nesting, behaviour, conservation, and size, as gathered from the Cornell Lab of Ornithology’s Bird Guide, was added.34 [process] Species Map This first data visualization map shows all the species recorded at each sighting location. To achieve this, sightings recorded in the same location were grouped, and any sighting of the same species was combined, with the species numbers being added. The sightings were then sorted based on how recent the last species sighting occurred. Each group of sightings is graphically represented as a circle, with the radius of the circle representing the number of species seen and the species names and number of sightings recorded along the edge.

Habitat Map The second map records the habitat types of the birds seen in each location. After the data is grouped by sighting location, it is sorted by habitat type, and the number of species requiring each habitat is calculated. A circle is drawn based on the number of habitat types required by the birds at each location, with the colour of the circle representing the dominant habitat type. The habitats and species number per habitat are listed along the circle edge.

33 eBird Basic Dataset. Version: EBD_relNov-2017. Cornell Lab of Ornithology, Ithaca, New York. November 2017.

34 Cornell Lab of Ornithology, “All about Birds, Bird Guide,” Cornell University, (accessed Feb, 2017).

51

[evaluation] These visualizations of birds sightings in downtown Toronto allow conclusions to be drawn, such as the sheer number of species in the island, the importance of bird watching along the waterfront, and how preferred habitats types vary based on location in the fabric.

In addition to these visualizations, this tool is important in developing a workflow to geographically group data and combine or sort it based different species parameters. This is key in the next step of assigning this data to the previously developed patch networks, so that as interventions are placed and designed they can quickly pull species data based on the intervention’s connectivity to nearby patches.

GLOBAL ULAST EDTAXO CATEGCOMMON NAME URN:Corne45:34.0 #### specie American Crow URN:Corne45:34.0 #### specie American Goldfinch URN:Corne45:34.0 #### specie American Kestrel URN:Corne45:34.0 #### specie American Redstart URN:Corne45:34.0 #### specie American Robin URN:Corne45:34.0 2881 specie Bald Eagle URN:Corne45:34.0 #### specie Barn Swallow URN:Corne45:34.0 #### specie Bay-breasted Warbler URN:Corne45:34.0 #### specie Blackburnian Warbler URN:Corne45:34.0 #### specie Black-capped Chickadee URN:Corne45:34.0 2233 specie Black-crowned Night-Heron URN:Corne45:34.0 #### specie Black-throated Blue Warbler URN:Corne45:34.0 #### specie Black-throated Green Warbler URN:Corne45:34.0 #### specie Blue-gray Gnatcatcher URN:Corne45:34.0 2966 specie Broad-winged Hawk URN:Corne45:34.0 277 specie Canada Goose URN:Corne45:34.0 #### specie Cape May Warbler URN:Corne45:34.0 4463 specie Caspian Tern URN:Corne45:34.0 #### specie Cedar Waxwing URN:Corne45:34.0 #### specie Chestnut-sided Warbler URN:Corne45:34.0 8096 specie Chimney Swift URN:Corne45:34.0 #### specie Chipping Sparrow URN:Corne45:34.0 #### specie Common Grackle URN:Corne45:34.0 1567 specie Common Loon URN:Corne45:34.0 #### specie Common Yellowthroat URN:Corne45:34.0 2821 specie Cooper's Hawk URN:Corne45:34.0 1988 specie Double-crested Cormorant URN:Corne45:34.0 #### specie Downy Woodpecker URN:Corne45:34.0 #### specie Eastern Kingbird URN:Corne45:34.0 #### specie Eastern Phoebe URN:Corne45:34.0 #### specie Eastern Wood-Pewee URN:Corne45:34.0 #### specie Gray Catbird URN:Corne45:34.0 2118 specie Great Blue Heron URN:Corne45:34.0 2144 specie Great Egret URN:Corne45:34.0 4328 specie Herring Gull URN:Corne45:34.0 553 specie Hooded Merganser URN:Corne45:34.0 #### specie House Finch URN:Corne48:11.0 #### specie House Sparrow URN:Corne45:34.0 #### specie House Sparrow URN:Corne45:34.0 #### specie House Wren URN:Corne45:34.0 #### specie Least Flycatcher URN:Corne48:11.0 #### specie Magnolia Warbler URN:Corne45:34.0 #### specie Magnolia Warbler URN:Corne45:34.0 396 specie Mallard URN:Corne45:34.0 5015 specie Mourning Dove URN:Corne45:34.0 296 specie Mute Swan URN:Corne45:34.0 #### specie Nashville Warbler URN:Corne45:34.0 #### specie Northern Cardinal URN:Corne48:11.0 #### specie Northern Cardinal URN:Corne45:34.0 2657 specie Northern Harrier URN:Corne45:34.0 #### specie Northern Waterthrush URN:Corne45:34.0 #### specie Olive-sided Flycatcher URN:Corne45:34.0 2376 specie Osprey URN:Corne45:34.0 4078 specie Pectoral Sandpiper URN:Corne48:11.0 #### specie Peregrine Falcon URN:Corne45:34.0 #### specie Philadelphia Vireo URN:Corne45:34.0 #### specie Pine Warbler URN:Corne45:34.0 #### specie Red-eyed Vireo URN:Corne45:34.0 2989 specie Red-tailed Hawk URN:Corne45:34.0 #### specie Red-winged Blackbird URN:Corne45:34.0 4319 specie Ring-billed Gull URN:Corne48:11.0 4687 domes Rock Pigeon URN:Corne45:34.0 8896 specie Ruby-throated Hummingbird URN:Corne45:34.0 4059 specie Sanderling URN:Corne45:34.0 2807 specie Sharp-shinned Hawk URN:Corne45:34.0 #### specie Song Sparrow URN:Corne45:34.0 #### specie Swainson's Thrush URN:Corne45:34.0 #### specie Tennessee Warbler URN:Corne45:34.0 #### specie Tree Swallow URN:Corne45:34.0 2362 specie Turkey Vulture URN:Corne45:34.0 #### specie Veery URN:Corne45:34.0 #### specie Warbling Vireo URN:Corne45:34.0 #### specie White-breasted Nuthatch URN:Corne45:34.0 #### specie Willow Flycatcher URN:Corne45:34.0 4103 specie Wilson's Snipe URN:Corne45:34.0 #### specie Wilson's Warbler URN:Corne45:34.0 #### specie Yellow Warbler URN:Corne50:57.0 #### specie American Redstart URN:Corne06:01.0 7775 specie Common Nighthawk URN:Corne50:57.0 #### specie House Sparrow URN:Corne50:57.0 #### specie Northern Cardinal URN:Corne00:38.0 #### specie Peregrine Falcon URN:Corne50:57.0 4319 specie Ring-billed Gull URN:Corne50:57.0 4687 domes Rock Pigeon URN:Corne18:52.0 #### slash Alder/Willow Flycatcher (Traill's Flycatcher) URN:Corne18:52.0 #### specie American Robin URN:Corne18:52.0 #### specie House Sparrow URN:Corne18:52.0 #### specie Peregrine Falcon URN:Corne18:52.0 4319 specie Ring-billed Gull URN:Corne18:52.0 4687 domes Rock Pigeon URN:Corne32:02.0 407 specie American Black Duck URN:Corne32:02.0 #### specie American Crow URN:Corne32:02.0 #### specie American Goldfinch URN:Corne32:02.0 #### specie American Redstart URN:Corne32:02.0 #### specie American Robin URN:Corne32:02.0 2881 specie Bald Eagle URN:Corne32:02.0 #### specie Baltimore Oriole

SCIENSUBSUB OBSBREAG CO CO STSUBCOSUIBABC US ATLLO LOLOLATLONOBSERVATI TIMOBSF LASSA PR PRODUREFFOEFF NUMALL GROHAS APRxcoord ycoord Corvus brachyr 5 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Spinus tristis 79 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Falco sparveriu 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga rutic16 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Turdus migrato 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Haliaeetus leuc 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Hirundo rustica 12 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga cast4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga fusc8 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Poecile atricapi 24 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Nycticorax nyct 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 1 1 314310.401 4830521.656 Setophaga cae 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga vire 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Polioptila caeru 9 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Buteo platypter 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 1 1 314310.401 4830521.656 Branta canaden55 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga tigri 14 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Hydroprogne ca9 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Bombycilla ced 63 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga pen 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Chaetura pelag 8 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Spizella passer 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Quiscalus quisc2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Gavia immer 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Geothlypis trich1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Accipiter coope 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Phalacrocorax a142 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Picoides pubes 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Tyrannus tyran 23 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Sayornis phoeb7 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Contopus viren 4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Dumetella caro 9 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Ardea herodias 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Ardea alba 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Larus argentatu6 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Lophodytes cuc1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Haemorhous m 7 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Passer domest 30 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-31 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Passer domest 9 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Troglodytes aed1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Empidonax min4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga mag1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-31 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Setophaga mag12 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Anas platyrhync7 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Zenaida macro 4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Cygnus olor 36 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Oreothlypis rufi 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Cardinalis card 6 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Cardinalis card 1 Mal CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-31 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Circus hudsoniu2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Parkesia noveb1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Contopus coop 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 1 1 314310.401 4830521.656 Pandion haliaet1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Calidris melano1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Falco peregrinu1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-31 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Vireo philadelph1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga pinu2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Vireo olivaceus 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Buteo jamaicen2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Agelaius phoen4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Larus delaware 52 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 ColumRoc Colu 20 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-31 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Archilochus col 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Calidris alba 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Accipiter striatu 5 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Melospiza melo4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Catharus ustula1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Oreothlypis per 3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Tachycineta bic3 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Cathartes aura 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Catharus fusce 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Vireo gilvus 4 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Sitta carolinens 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Empidonax trai 2 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Gallinago delica1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 1 1 314310.401 4830521.656 Cardellina pusil 5 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga pete1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-31 9:4 obs RDa S38eB EBI ## 6 1 1 0 1 314310.401 4830521.656 Setophaga rutic1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Chordeiles mino4 F CanCA OnCA- TorCA-ON13 Ca L6P 44 -79 2017-08-30 18: obs NMa S38eB EBIRD 1 0 0 1 311429.982 4832287.54 Passer domest 30 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Cardinalis card 1 Mal CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Falco peregrinu1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 16: obs GSegS38eB EBIRD 1 0 0 1 313990.817 4834386.258 Larus delaware 3 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 ColumRoc Colu 20 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-30 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Empidonax alno1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Turdus migrato 5 FemCanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Passer domest 30 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Falco peregrinu1 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Larus delaware 5 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 ColumRoc Colu 20 CanCA OnCA- TorCA-ON13 To L2H 44 -79 2017-08-29 12: obs E F S38eB EBI 30 0.5 1 1 0 1 313990.817 4834386.258 Anas rubripes 6 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656 Corvus brachyr 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656 Spinus tristis 30 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656 Setophaga rutic25 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656 Turdus migrato 8 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656 Haliaeetus leuc 1 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 1 1 314310.401 4830521.656 Icterus galbula 6 CanCA OnCA- TorCA-ON13 To L3H 44 -79 2017-08-26 9:1 obs RDa S38eB EBI ## 8 1 1 0 1 314310.401 4830521.656

Habitat Food Nesting Open WoodlandOmnivore Tree Open WoodlandSeeds Shrub Open WoodlandInsects Cavity Forest Insects Tree Open WoodlandInsects Tree Forest Fish Tree Grassland Insects Building Forest Insects Tree Forest Insects Tree Forest Insects Cavity Marsh Fish Tree Forest Insects Shrub Forest Insects Tree Forest Insects Tree Forest Mammals Tree Marsh Seeds Ground Forest Insects Tree Shoreline Fish Ground Open WoodlandFruit Tree Open WoodlandInsects Shrub Town Insects Building Open WoodlandSeeds Shrub Open WoodlandOmnivore Tree Lake/Pond Fish Ground Scrub Insects Shrub Forest Birds Tree Lake/Pond Fish Ground Forest Insects Cavity Grassland Insects Tree Open WoodlandInsects Building Forest Insects Tree Open WoodlandInsects Shrub Marsh Fish Tree Marsh Fish Tree Shoreline Omnivore Ground Lake/Pond Fish Cavity Town Seeds Tree Town Seeds Cavity Town Seeds Cavity Open WoodlandInsects Cavity Forest Insects Tree Forest Insects Tree Forest Insects Tree Lake/Pond Seeds Ground Open WoodlandSeeds Tree Lake/Pond Plants Ground Forest Insects Ground Open WoodlandSeeds Shrub Open WoodlandSeeds Shrub Grassland Mammals Ground Forest Insects Ground Open WoodlandInsects Tree Lake/Pond Fish Tree Grassland Insects Ground Mountains Birds Cliff Forest Insects Tree Forest Insects Tree Forest Insects Tree Open WoodlandSmall AnimTree Marsh Insects Shrub Lake/Pond Omnivore Ground Town Seeds Building Open WoodlandNectar Tree Shoreline Insects Ground Forest Birds Tree Open WoodlandInsects Shrub Forest Insects Shrub Forest Insects Ground Lake/Pond Insects Cavity Open WoodlandCarrion Cliff Forest Insects Ground Open WoodlandInsects Tree Forest Insects Cavity Marsh Insects Shrubs Marsh Insects Ground Scrub Insects Ground Open WoodlandInsects Shrub Forest Insects Tree Grassland Insects Ground Town Seeds Cavity Open WoodlandSeeds Shrub Mountains Birds Cliff Lake/Pond Omnivore Ground Town Seeds Building Marsh Insects Scrub Open WoodlandInsects Tree Town Seeds Cavity Mountains Birds Cliff Lake/Pond Omnivore Ground Town Seeds Building Lake/Pond Insects Ground Open WoodlandOmnivore Tree Open WoodlandSeeds Shrub Forest Insects Tree Open WoodlandInsects Tree Forest Fish Tree Open WoodlandInsects Tree

Behavior Concervation Length Wingspan Ground ForLeast Concern 45 90 Foliage GleLeast Concern 12 20 Soaring Least Concern 26 55 Foliage GleLeast Concern 12 17 Ground ForLeast Concern 25 35 Soaring Least Concern 85 204 Aerial ForagLeast Concern 17 31 Foliage GleLeast Concern 14 21 Foliage GleLeast Concern 11 20 Foliage GleLeast Concern 13 18 Stalking Least Concern 62 116 Foliage GleLeast Concern 12 18 Foliage GleLeast Concern 12 18 Foliage GleLeast Concern 10 16 Aerial Dive Least Concern 38 90 Ground ForLeast Concern 85 150 Foliage GleLeast Concern 12 20 Aerial Dive Least Concern 50 130 Foliage GleLeast Concern 15 26 Foliage GleLeast Concern 10 19 Aerial ForagNear Threatened 13 28 Ground ForLeast Concern 13 21 Ground ForLeast Concern 31 41 Surface DivLeast Concern 75 115 Foliage GleLeast Concern 12 17 Aerial ForagLeast Concern 38 75 Surface DivLeast Concern 80 120 Bark Forag Least Concern 16 27 Fly Catchin Least Concern 21 36 Fly Catchin Least Concern 15 27 Flycatching Least Concern 15 25 Ground ForLeast Concern 22 27 Stalking Least Concern 120 185 Stalking Least Concern 100 140 Ground ForLeast Concern 61 142 Surface DivLeast Concern 45 63 Ground ForLeast Concern 13 22 Ground ForLeast Concern 16 22 Ground ForLeast Concern 16 22 Foliage GleLeast Concern 12 15 Flycatching Least Concern 13 20 Foliage GleLeast Concern 12 18 Foliage GleLeast Concern 12 18 Dabbler Least Concern 57 92 Ground ForLeast Concern 28 45 Dabbler Least Concern 140 218 Foliage GleLeast Concern 11 18 Ground ForLeast Concern 22 28 Ground ForLeast Concern 22 28 Soaring Least Concern 48 110 Ground ForLeast Concern 13 23 Flycatching Near Threatened 19 35 Aerial Dive Least Concern 56 165 Probing Least Concern 22 43 Aerial Dive Least Concern 45 105 Foliage GleLeast Concern 12 20 Bark Forag Least Concern 13 21 Foliage GleLeast Concern 12 24 Soaring Least Concern 50 125 Ground ForLeast Concern 20 36 Ground ForLeast Concern 48 111 Ground ForLeast Concern 33 58 Hovering Least Concern 8 9 Probing Least Concern 19 35 Aerial Dive Least Concern 29 49 Ground ForLeast Concern 15 20 Foliage GleLeast Concern 17 30 Foliage GleLeast Concern 12 20 Aerial ForagLeast Concern 13 32 Soaring Least Concern 72 174 Ground ForLeast Concern 17 28 Foliage GleLeast Concern 12 22 Bark Forag Least Concern 13 23 Flycatching Least Concern 15 22 Probing Least Concern 30 42 Foliage GleLeast Concern 11 16 Foliage GleLeast Concern 12 18 Foliage GleLeast Concern 12 17 Aerial ForagLeast Concern 23 55 Ground ForLeast Concern 16 22 Ground ForLeast Concern 22 28 Aerial Dive Least Concern 45 105 Ground ForLeast Concern 48 111 Ground ForLeast Concern 33 58 Flycatching Least Concern 15 22 Ground ForLeast Concern 25 35 Ground ForLeast Concern 16 22 Aerial Dive Least Concern 45 105 Ground ForLeast Concern 48 111 Ground ForLeast Concern 33 58 Dabbler Least Concern 55 90 Ground ForLeast Concern 45 90 Foliage GleLeast Concern 12 20 Foliage GleLeast Concern 12 17 Ground ForLeast Concern 25 35 Soaring Least Concern 85 204 Foliage GleLeast Concern 18 26

Fig 1.18. Snapshot of sighting data 52

Hole Size

13.32 2.96 8.14 2.516 5.18 30.192 4.588 3.108 2.96 2.664 17.168 2.664 2.664 2.368 13.32 22.2 2.96 19.24 3.848 2.812 4.144 3.108 6.068 17.02 2.516 11.1 17.76 3.996 5.328 3.996 3.7 3.996 27.38 20.72 21.016 9.324 3.256 3.256 3.256 2.22 2.96 2.664 2.664 13.616 6.66 32.264 2.664 4.144 4.144 16.28 3.404 5.18 24.42 6.364 15.54 2.96 3.108 3.552 18.5 5.328 16.428 8.584 1.332 5.18 7.252 2.96 4.44 2.96 4.736 25.752 4.144 3.256 3.404 3.256 6.216 2.368 2.664 2.516 8.14 3.256 4.144 15.54 16.428 8.584 3.256 5.18 3.256 15.54 16.428 8.584 13.32 13.32 2.96 2.516 5.18 30.192 3.848

Height

78.48 17.44 47.96 14.824 30.52 177.888 27.032 18.312 17.44 15.696 101.152 15.696 15.696 13.952 78.48 130.8 17.44 113.36 22.672 16.568 24.416 18.312 35.752 100.28 14.824 65.4 104.64 23.544 31.392 23.544 21.8 23.544 161.32 122.08 123.824 54.936 19.184 19.184 19.184 13.08 17.44 15.696 15.696 80.224 39.24 190.096 15.696 24.416 24.416 95.92 20.056 30.52 143.88 37.496 91.56 17.44 18.312 20.928 109 31.392 96.792 50.576 7.848 30.52 42.728 17.44 26.16 17.44 27.904 151.728 24.416 19.184 20.056 19.184 36.624 13.952 15.696 14.824 47.96 19.184 24.416 91.56 96.792 50.576 19.184 30.52 19.184 91.56 96.792 50.576 78.48 78.48 17.44 14.824 30.52 177.888 22.672

Width

45 10 27.5 8.5 17.5 102 15.5 10.5 10 9 58 9 9 8 45 75 10 65 13 9.5 14 10.5 20.5 57.5 8.5 37.5 60 13.5 18 13.5 12.5 13.5 92.5 70 71 31.5 11 11 11 7.5 10 9 9 46 22.5 109 9 14 14 55 11.5 17.5 82.5 21.5 52.5 10 10.5 12 62.5 18 55.5 29 4.5 17.5 24.5 10 15 10 16 87 14 11 11.5 11 21 8 9 8.5 27.5 11 14 52.5 55.5 29 11 17.5 11 52.5 55.5 29 45 45 10 8.5 17.5 102 13

Length

46.8 10.4 28.6 8.84 18.2 106.08 16.12 10.92 10.4 9.36 60.32 9.36 9.36 8.32 46.8 78 10.4 67.6 13.52 9.88 14.56 10.92 21.32 59.8 8.84 39 62.4 14.04 18.72 14.04 13 14.04 96.2 72.8 73.84 32.76 11.44 11.44 11.44 7.8 10.4 9.36 9.36 47.84 23.4 113.36 9.36 14.56 14.56 57.2 11.96 18.2 85.8 22.36 54.6 10.4 10.92 12.48 65 18.72 57.72 30.16 4.68 18.2 25.48 10.4 15.6 10.4 16.64 90.48 14.56 11.44 11.96 11.44 21.84 8.32 9.36 8.84 28.6 11.44 14.56 54.6 57.72 30.16 11.44 18.2 11.44 54.6 57.72 30.16 46.8 46.8 10.4 8.84 18.2 106.08 13.52

2017 2016 2015 2014 2013

Fig 1.19. Bird sightings in the past 5 years - By species

53

Town Mountain Forest Open Woodland Scrub Grassland Marsh Shoreline River/Stream Lake/Pond Ocean

Fig 1.20. Bird sightings in the past 5 years - By habitat

54

55

Fig 1.21. Sightings by species - Detail 56

57

Fig 1.22. Sightings by habitat - Detail 58

DATA INTEGRATION

By locating bird sighting data within the network, the designers of these interventions can easily access information in local bird species and their habitat requirements to inform their design decisions. [input] The dataset from the previous visualization study is used. This data is located within the agent network, and the resistance map is used to inform new connections. [process] To begin integrating data and the patch network, any geographic group of sightings that is inside or within a certain distance of a patch is grouped and stored in reference to the patch. The remaining sighting groups are tested for their distance to each other and then combined if they are close enough. Any sighting location that has five or more unique species and is not in or near a patch is considered an important sighting location. While the birds seen here are not occupying a defined habitat patch, it is important to note their existence in the fabric. To determine the sighting location’s connectivity in the habitat network, agents are expelled, and connections are made to any habitat patch they reach, as per Network Study 03.

Now that all significant bird sightings are assigned to either a patch, or a sighting location, and these patches and locations have agent lines connecting them where possible, this network can be simplified and evaluated based on connectivity.

Here, any patch connected by a least one agent line is considered connected, and a simple edge is drawn, representing this connectivity. Now, all the data is stored in the form of a graph, where there is a list of bird species and their attributes at each node, and each edge signifies that those patches are connected. [evaluation] While less rich and complex than the previously illustrated networks, this graph structure offers a different set of advantages. These advantages include the ability to mathematically test the effect of an intervention on the connectivity of the network, as well as quickly access large amounts of species data based on this connectivity.

To create more accurate network connectivity analysis, a weighted graph could be developed as a next step. Here, the edges would be assigned values based on the strength of the connections, rather than all the connections being treated equally.

59

Bird species data list

Connection

60

Fig 1.23. Graph data structure

Number of species seen in habitat patches

Locations with many sightings connected to nearby habitats 61

Data network based on patch and sighting connectivity

Fig 1.24. Merging patch network and sighting data process Bird species data list

Connection 62

PART II 3D NETWORK DEVELOPMENT INTERVENTION MASSING

Part II sees the fabric sensing and network building strategies developed in Part I adapted for three-dimensional fabric. In addition, examples of the intervention typologies outlined in Part I are explored using example sites. Specific attention is given to a Patch Enhance intervention case study located in CityPlace’s Canoe Landing Park. At this phase, an evolutionary solver is used to optimize a massing envelope for this intervention that generates the most connectivity in the region, before more detailed habitat composition is explored in Part III.

64

FABRIC SELECTION

The portion of fabric selected to test intervention strategies and three-dimensional networks stretches from the waterfront to King Street and from Spadina Ave to Strachan Ave. This area includes several major parks and heavy residential development, and is key in linking the island and waterfront ecosystems to the Garrison Creek series of green spaces, which are tracings of where a creek system used to run through Toronto. This represents a key opportunity to expose Toronto’s downtown residents, who use Garrison Creek parks heavily, to regional bird species.

This Toronto Island to Garrison Creek connection is illustrated in the City of Toronto’s Proposed Downtown Plan where they locate a ring of connected greenspace called a “core circle” in the city, saying “Connecting these large natural features creates a continuous and connected circular network around Downtown, builds on Toronto’s strong identity as a “city within a park” and provides opportunities to acknowledge our history and natural setting.”35 While in this report the City of Toronto focuses on connecting these greenspaces with circulation infrastructure for urbanites, if these green spaces are to provide urban biophilia, they need to be connected for regional species as well. To ensure this green loop is connected for birds as well as people, the location where the circle crosses heavy development is a key area of study, and network studies can offer a much more robust examination than this green ring diagram.

35

City of Toronto, Proposed Downtown Plan, 2017.

65

Fig 2.1.

Core circle, City of Toronto - Proposed Downtown Plan, 2017

66

Tree canopy 67

Fig 2.2. 3-D Fabric Model

Grass cover 68

INTERVENTION EXPLORATION

To begin analysing this piece of 3D fabric, the patch intervention locations and paths from the 2D studies in Part 1 are directly imported and overlaid on the 3-D fabric model. This allows insight on which intervention suggestions should be explored further. To illustrate the intervention typologies introduced in Part I, four locations were chosen for closer examination. While these interventions would not necessarily be initially carried out in such close proximity, locating them beside each other illustrates their importance as a cohesive system.

The first study location is on top of a low building where a patch add can maximize connectivity and bird penetration in the surrounding residential development.

The second location, which will be explored in more detail, is a patch enhance to allow Canoe Landing park to connect existing green spaces and act as an amenity to the surrounding residential neighborhood and proposed community center and school.

The third location is the edge of Fort York, where the park is sunken below street level, and borders heavy development. Here the script located an ecotone spread and matrix smooth to encourage movement.

The final location is between Fort York and Coronation Park, where busy streets and large glassy building mass calls for a matrix smooth These four interventions are schematically explored, then, to further inform these interventions, networks are calculated in three dimensions.

69

Patch Add

Patch Enhance

Ecotone Spread

Matrix Smooth

Fig 2.3. Imported 2-D paths, patches, and interventions

70

3

4

Patch Add

Patch Enhance 71

Ecotone Spread

2

1

Matrix Smooth

Fig 2.4. Selected interventions for further investigation 72

1

PATCH ADD - Add green roof in accordance with City of Toronto’s Guidelines for Biodiversity Green Roofs - If possible add height and cavities - Choose plants that attract birds and pollinators

3 ECOTONE SPREAD - Add street trees and shrubs - Add height to increase sight lines - Replace paving with permeable paving and natural cover where possible

73

2

PATCH ENHANCE - Increase variety of vertical structure - Increase height and surface area - Add nesting and perching opportunity - Choose plants that attract birds and pollinators

4

MATRIX SMOOTH - Retrofit or include bird frit as outlined in Toronto’s Bird friendly guidelines - Include more frit when located near patches and paths - Green roof in accordance with City of Toronto’s Guidelines for Biodiverse Green Roofs - Add street trees and shrubbery

Fig 2.5.

Schematic intervention strategies 74

BUILDING A 3D NETWORK

To create 3-D networks in urban fabric, the resistance layers, fabric sensing, and nearest neighbour network building is adapted from Part I for this context. [input] The digital fabric model is built using 2-D and 3-D information from the City of Toronto’s open data. The 2-D information is extruded and located in space.

Using the same principles of the resistance layers in Part I, each layer of this digital model is evaluated based on its ability to accommodate or resist bird movement.

These model layers are the input for the network building process.

75

BUILDINGS

TREES

GRASS

STREETS

WATER

High movement resistance

Low movement resistance

Fig 2.6. Exploded 3-D resistance and accommodation layers

76

[process] To begin, a 3-D grid of sample points are placed in the fabric. Each point measures its distance to the nearest element on each resistance layer, before performing a series of calculations to weight the effect of these elements and arrive at an overall accommodation value for each point. As seen in Part I, the higher the value, the more the location in the fabric accommodates bird’s habitation, and the lower, the more it resists it. To locate the nodes of the 3-D network, points that have high accommodation values are isolated. These points are then interconnected based on their nearest neighbours, and any connections that are above the distance threshold, or are interrupted by the fabric are removed. The remaining connections represent the 3-D avian movement network. [evaluation] When carried out at the scale of this piece of fabric, it can be seen that connections are currently lacking between the waterfront parks and Garrison Creek parks.

However, when the previously explored interventions are added with optimized height and massing, these patches become much more connected, and birds are able to penetrate residential developments. This optimization is explored in the following study.

While these 3-D networks can illustrate bird movement in a vertical city, and help tune interventions to maximize connectivity, there are many advancements that could be made in this process. The first advancement would be adding more layers that affect bird movement in the 3D input model to make fabric sensing more robust. In addition, to avoid generically extruding tree canopy, recent advancement in Waveform Airborne Lidar to generate 3-D vegetation structure could be employed.36 Finally, computing limitations are encountered when working in 3-D space. Because of this, the resolution of sample points is limited, and agent networks weren’t simulated. In continuing studies of 3D networks, these limitations would need to be overcome.

36 Stefano Casalegno et al., “Ecological Connectivity in the Three-Dimensional Urban Green Volume using Waveform Airborne Lidar,” Scientific Reports 7 (April 2017, 2017).

77

Fabric sampling point

Avian accommodation value

High avian accommodation point

Connection Connection above distance threshold

Connection Interrupted connection

Fig 2.7. 3-D network development process 78

Connection network 79

Fig 2.8. Existing 3-D bird movement network 80

Optimized Patch Add

Optimized Patch Enhance 81

Optimized Ecotone Spread

Connection network

Fig 2.9. 3-D bird movement network with proposed interventions 82

MASSING OPTIMIZATION

[input] To begin testing an intervention envelope, or massing, a bounding box of possible volume is located on the site. The 3D network points and connections are used to test how interventions affect the network’s connectivity. [process] This optimization of the intervention envelope comes from an evolutionary solver which rapidly generates massings within the given region. With each massing, the amount of fragmented network the intervention connects is measured, before moving on to the next. Through this process of testing hundreds of massings, the solver learns which are most effective in generating connectivity.

To design what could be referred to as the zoning envelope for the Patch Enhance intervention, this optimization was considered as well as the intervention’s context.

Based on the optimization, this envelope features height at the north and south sides to increase connections to Fort York and the Spadina Quay Wetlands. The envelope is also raised to accommodate existing trees and pathways, and slopes down in locations to respect neighbouring buildings and park sight-lines. [evaluation] By combining the rapid network testing tool with a designer’s hand, an intervention envelope can be developed that is well adapted to complex ecological and urban dynamics in a dense vertical city.

83

Optimized Patch Enhance envelope

Connection network

Optimized Patch Enhance envelope

Avian movement vector

Fig 2.10. Patch enhance envelope with effect on local network and movement vectors – Axonometrics 84

Optimized Patch Enhance envelope

85

Connection network

Fig 2.11. Patch enhance envelope with effect on local network– Section 86

PART III HABITAT COMPOSITION ASSEMBLIES

Part III continues the Patch Enhance intervention introduced in Part II. This exploration speculates on how the data gathered from the network in Part I can be synthesized to generate a novel avian habitat that accommodates all species in the surrounding network. To design an artificial habitat scaffold, the composition of natural habitats is analyzed, deconstructed, and replicated using an assemblage of parametrically-generated assemblies.

88

FORMAL APPROACH

The design of this artificial habitat takes formal cues from Reaction Diffusion models, which represent two entangled systems that occupy the same space and react with each other to create an intricate interface. While this is an apt metaphor for an intervention that curates human and avian interaction within a dense urban environment, this system was selected because its constant variation provides a multiplicity of micro-environments and protected areas for plants and bird species, while creating a sense of wonder and exploration for human occupants. The correlation between variation in habitat structure and biodiversity is well documented in landscape ecology, and by housing diverse bird species in a captivating form, this intervention seeks to invoke the biophilic sense of wonder Timothy Beatley discusses. To ensure this formal approach specifically attracts target species, data gathered from the network will be used to digitally curate the habitat structure, plant selection, and nesting opportunities.

89

Fig 3.1. Reaction Diffusion simulation

90

HABITAT STRUCTURE

To guide the composition of this artificial habitat, it is important to examine vertical structure. The vertical structure of a habitat is essentially the contents and arrangement of its layers. In this study, different types of avian habitats are broken into elements. This was achieved by compiling imagery to analyse habitats, before re-drawing them using hatches for each element type. Each habitat type can be composed using a mixture of these nine elements: - Ground Litter - Grass - Shrub - Understory - Canopy - Overstory - Cliff - Gravel - Water Once this method of representing habitat structure is established, it can be schematically applied to the intervention massing to begin testing habitat compositions for this Patch Enhance intervention.

91

Forest

Grassland

Open Woodland

Scrub

Lake/Pond

Marsh

Shore

Town

Mountain

River/Stream

Fig 3.2. Habitat photo compilations 92

HABITAT 50m

50m

0m

0m Forest

Grassland

Lake/

50m

50m

0m

0m

Open Woodland

Scrub

Shore

ELEM

Floor Litter

93

Grasses

Shrub

Understory

Canopy

T TYPES 50m

50m

0m

0m

/Pond

50m

0m Mountain

Marsh 50m

50m

50m

0m

0m

0m

eline

Town

River/Stream

MENTS

Overstory

Water

Rock/Cliff

Gravel

Aquatic Vegetation

94

Fig 3.3. Habitat vertical structure analysis using elements as building blocks

Floor Litter

95

Grasses

Shrub

Understory

Canopy

Overstory

Water

Rock/Cliff

Gravel

Aquatic Vegetation

96

Fig 3.4. Habitat intervention understood as a collage of elements

HABITAT COMPOSITION

[input] To begin composing this habitat, the intervention location is placed in the network graph to retrieve the bird species recorded at the intervention location, as well as in neighbouring patches. The species lists from first and second-degree connections are analysed to create a habitat breakdown outlining the amount of each habitat required at the intervention. [process] To begin, the zoning envelope is broken into habitats based on the breakdown, the envelope height, and logical adjacencies. The habitats are then creating by arranging the elements according to the vertical structures outlined in the previous study. To create these elements with tangible geometries, the reaction diffusion algorithm is tuned using different values for verticality, amount of branching, density, and thickness. The resulting geometries have specific attributes that mimic the core habitat elements. The tuned geometries then fill these element regions, and human circulation in woven through the spaces created. [evaluation] When applied at scale, this generates a habitat with the ability to support large and diverse populations of birds and act as a key component in an avian habitat network.

97

Intervention location

1st degree neighbour

2nd degree neighbour

Fig 3.5. Accessing species data sets based on intervention connectivity

98

HABITAT BREAKDOWN Forest - 28% Grassland - 6 % Lake/Pond - 23 % Marsh - 7 % Mountains - 1% Ocean - 1% Open Woodland - 20% River/Stream - 1 % Scrub - 6% Shoreline - 6% Town - 4% NESTING BREAKDOWN Building - 3% Burrow - 3% Cavity - 25% Cliff - 2% Floating - 4% Ground - 35% Shrub - 10% Tree - 27%

99

LOCATED AT INTERVENTION White-throated Sparrow Bufflehead Greater Scaup Canada Goose Swamp Sparrow House Sparrow

1 6 2 1 6 17

TWO CONNECTIONS AWAY

Forest Lake/Pond Lake/Pond Marsh Marsh Town

Seeds Insects Insects Seeds Insects Seeds

Ground Cavity Ground Ground Shrub Cavity

Ground ForLeast Conce Surface DivLeast Conce Surface DivLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce

17 37 57 85 13 16

21 55 75 150 18 22

Barn Swallow Eastern Phoebe Rock Pigeon Bank Swallow Belted Kingfisher Northern Rough-winged Swallow B e Wlahcitke-c-tahprpoeadteCdhSicpkaardroew DuofwflneyheW B adoodpecker H GareirayteWr oSocaduppecker R Caenda-bdraeaGsotoesdeNuthatch SWwhaitmep-bSrepasrrteodwNuthatch WoinutserSW H parrernow Yellow-bellied Sapsucker Bu fleShweaaldlow arfn ECaosm temrnonPhGooeldbeneye C Rom ckmPoigneoMnerganser H d aMlleorwganser BaonokdeSw P Minagrtfin Buerltpelde K sher TNroerethSewrn allRoowugh-winged Swallow Alm npKeedstCrheilckadee B acekr-icap C Daorwonliny aWW oroednpecker EuariroypW eaonoSdtpaerlcinkegr H Heodu-sbereSapsatrerd owNuthatch R Pehreitger-ibnreeaFastlceodnNuthatch W TWuirnkteeyr V Wurletunre A m e r i Yellowc-abnelClioeodtSapsucker Caunffvleahsbeacdk B Hom rnm edonGrGeobledeneye C Ro ed eaodn Merganser C mhm Reodo-dneedckM ederG H garenbser GurerpalteBM laackrt-ibnacked Gull P TBrlaecekS-awnadll-owwhite Warbler O raenrgicea-cnroKw Am esnteredl Warbler TeanronleinsaseW e rW C enarbler W Euhroitpee-tahnroSataterldinSgparrow K HiolludseeerSparrow Saevreagnrnin ahe SFp P alacrornow wllture TSnuo rkwey O Vu American B laoctk Duck Co Co anmvm asobnacLkoon Dou H rnbeled-cGrreesbted Cormorant LReesdsheer aSdcaup LRoendg-n-teacilkeeddDGurcekbe M GraelalatrBdlack-backed Gull Mallard Black-anxd-American white WaBlack rbler Duck (h MruatnegSew O -caronwned Warbler R Tendn-bersesaeseteWdaM rbelergranser R Wehdi-tteh-rtoharoteadteLdoSopnarrow Rilnldge-beirlled Gull K TSaruvm anpneatherSSpw ararnow W tey-w ngl ed Scoter Snhoiw Oiw Brman A ertican Black Duck anmam daonGoLoosen Co G aduw Do blael-lcrested Cormorant LVeirsgsienriaScRaauilp A Lomnegr-itcaailnedTrDeuecSkparrow B Mraelw lasrtder's Warbler (hybrid) H e r m it TxhAmerican rush Mallard Black Duck (h P Maulm te W Swaarbnler Eaesdt-ebrrneaTsotw eerganser R edheM Fieedld-tShproaarrtoew R d Loon Liinncgo-blnil'lseSdpGau rrlol w R itep-ectreorwSnweadnSparrow TWruhm ilsitoen-w 's iW Wh ngaerdblSecroter Carasnptian Tern B Haenrraidnag Go uo ll se C Spaodtw teadll Sandpiper G Wirilgsionnia'sRPaliol ver V Blm acekr-itcharnoTarteed SBpluaerrW A owarbler Rreedw -wstin B erg'esdWBalarbcklebrir(dhybrid) Sw mipt TShpraursrhow H eram Palm Warbler Eastern Towhee Field Sparrow Lincoln's Sparrow White-crowned Sparrow Wilson's Warbler Caspian Tern Herring Gull Spotted Sandpiper Wilson's Plover Black-throated Blue Warbler Red-winged Blackbird Swamp Sparrow

59 36 23 37 5 38 4 1 2 6 1 2 1 1 6 9 17 1 19 2 5 5 36 83 6 2 4 37 13 5 28 5 3 1 4 1 2 30 1 30 1 2 1 3 9 1 1 12 1 5 15 85 6 5 4 5 13 4 25 3 1 6 1 80 5 3 6 30 3 2 1 3 4 1 1 729 1 2 155 1375 147 5 1 4 22 3 29 6 1 85 95 6 5 3 31 3 4 46 1 3 729 1 2 17 2 13 1 147 2 1 22 4 29 3 1 7 95 10 5 2 31 3 3 46 14 3 1 7 12 220 1 2 2 4 3 7 10 2 3 3 14 1 7 220 2

Grassland Open Woodland Town Lake/Pond Lake/Pond River/Stream Forest LFaokres/tPond LFaokres/tPond Foarressht M Foarressht M TForwenst Forest Larkaess/P G laonndd LOapkeen/PW onododland TLaokwen/Pond Lake/Pond Lake/Pond Laivkeer//PSotrnedam R FOopreenstWoodland O FopreenstWoodland TForwenst TForwenst FMooreusnttains O FopreenstWoodland FLaokres/tPond Lake/Pond Lake/Pond Lake/Pond Lake/Pond LSahkoer/ePlionned LFaokres/tPond FopreenstWoodland O FopreenstWoodland O TForwenst TGorawsnsland G Mroaussnltaanid ns GrpaesnslW anododland O Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond LSahkoer/ePlionned LFaokres/tPond LFaokres/tPond LFaokres/tPond LFaokres/tPond LGarkaes/sP laonndd Larkaess/P G laonndd LGarkaess/P laonndd LMaakres/hPond LMaakres/hPond LMaakres/hPond M Laakres/hPond LOapkeen/PW onododland LOapkeen/PW onododland O Lapkeen/PW onododland LOapkeen/PW onododland SLacrkueb/Pond LSacrkueb/Pond LSacrkueb/Pond LSacrkueb/Pond LSacrkueb/Pond SMhaorrsehline Shaorrsehline M SMhaorrsehline Shaorrsehline M FopreenstWoodland O M OpaersnhWoodland MpaersnhWoodland O Open Woodland Scrub Scrub Scrub Scrub Scrub Shoreline Shoreline Shoreline Shoreline Forest Marsh Marsh

Insects Building Insects Building Seeds Building Insects Burrow Fish Burrow Insects Burrow ISneseedcsts C Garoviutynd Insects Cavity Insects C Garoviutynd Caroviutynd SIneseedcsts G Insects SCharvuitby SIneseedcsts Cavity Insects Cavity Cauvilidtying Insects B Cauvilidtying Insects B C SFeisehds Bauvilidtying C IFnishects Bauvrritoyw IFnishects Cauvrritoyw B Insects C Bauvrritoyw Insects Cavity Insects Cavity Insects Cavity SIneseedcsts Cavity B Claifvfity Inirsdescts C Claifvfity Inasrerciotsn Flaovaitying IPnlasenctsts C Flaovaitying IPnlasenctsts C Flaovaitying Insects C P Flaovaitying Filsahnts C Flaovaitying Fish C O Inm sencitvsore G Caovuin tyd Garoviutynd Insects C Garoviutynd Insects C Garo Insects C viutynd Garoviutynd ISneseedcsts C Garoviutynd SIneseedcsts C IBnirsdescts Glrio C ffund Maarm Glrio C riomnals C ffund Inlasenctsts P FGlrooautinndg FPilsahnts G Flrooautinndg FInishects G Flrooautinndg IPnlasenctsts FGlrooautinndg IFni shects G Flrooautinndg SOem ed nsivore Groouunndd SIneseedcsts Ground P Ground Inlasenctsts FInishects Ground FSiesehds Ground O Inm sencitvsore Ground IPnlasenctsts Ground IM nsaemctm s als Ground IPnlasenctsts Ground FSiesehds Ground P Filsahnts Ground Insects Ground ISneseedcsts Ground ISneseedcsts Ground ISneseedcsts Ground IPnlasenctsts Ground O Fim shnivore Ground IFnishects Ground Inm sencitvsore Ground O IPnlasenctsts Ground Insects Ground FPilsahnts Ground O nsivore Ground Sem ed SPm lanaltlsAnim Ground Insects Ground Shro ruubnd SIneseedcsts G Shro ruubnd Insects G Shro ruubnd Insects G Insects Ground Omnivore Ground Insects Ground Insects Ground Insects Ground Insects Ground Fish Ground Omnivore Ground Small Anim Ground Insects Ground Insects Shrub Insects Shrub Insects Shrub

Aerial ForagLeast Conce Fly Catchin Least Conce Ground ForLeast Conce Aerial ForagLeast Conce Aerial Dive Least Conce Aerial ForagLeast Conce Foro liaugned GFloerLeast Least C once G Conce eLeast Conce SBuarfkaFco erDagivLeast eLeast Conce SBuarfkaFcoerDagivLeast Barorkun FodrFaogrLeast eLeast Conce G Barorkun FodrFaogrLeast eLeast Conce G Ground ForLeast Conce Bark ForageLeast Conce Suerfiacl eFoDriavLeast gLeast Conce A Conce FSluyrfCaactechDiinvLeast Least C once Suro rfuanced D G FoivLeast rLeast Conce SAuerfiacl eFoDriavLeast gLeast Conce FoivreagLLeast Conce Aerial D east C once Aerial FForag oragLeast Conce FSolaiarigneg Gle Least Conce G Barorkun FodrFaogrLeast eLeast Conce G Barorkun FodrFaogrLeast eLeast Conce G Barorkun FodrFaogrLeast eLeast Conce A Least C once BaerkiaFl oDriavgeeLeast Conce SGoraoruind g ForLeast Least C once Conce SBuarfkaFcoerDagivLeast eLeast Conce Surface DivLeast Conce vVulnerable Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce G Aeroriuanl dFoFroarLeast gLeast Conce B AaerkiaFl oFroargaeLeast gLeast Conce SFoaliarigneg Gle Least Conce Foro liaugned GFloerLeast Least C once G Conce Ground ForLeast Conce Ground ForLeast Conce Geroriuanl dDiFvoerLeast Conce A Least C once SAoearirainl gDive Least Conce Least C once SDuarbfb acleer DivLeast Conce Surface DivLeast Conce Surface DivVulnerable vLeast Conce Surface DivLeast Conce Surface DivVulnerable vLeast Conce D Least C once Garobubnledr ForLeast Conce D Least C once BarbkbFleorrageLeast Conce D FoalbiabglerGle Least Conce SFu Conce orlifaagcee GDlievLeast Least C once SGuro rfuanced D FoivLeast rLeast Conce Ground ForLeast Conce Darobubnledr ForLeast Least C once G Conce Suerfiacl eDiDvievLeast Conce A Least C once Garobubnledr ForLeast Conce D Least C once FoivLeast rLeast Conce SGuro rfuanced D Least C once SDuarbfb acleer DivLeast Conce P Least C once Surorfbaicneg DivLeast Conce FoivVulnerable rLeast Conce SGuro rfuanced D FoalbiabglerGle LLeast Conce D east C once G Conce Darobubnledr ForLeast Least C once G Conce Darobubnledr ForLeast Least C once FoivLeast rLeast Conce SGuro rfuanced D FoivLeast rLeast Conce SGuro rfuanced D Ground ForLeast Conce G Conce Darobubnledr ForLeast Least C once FSo Least C once urlifaagcee GDlievLeast Conce A Least C once Geroriuanl dDiFvoerLeast Conce Ground ForLeast Conce Praobbbilnegr Least Conce D Probing Least Conce Foro liaugned GFloerLeast Least Conce G orLeast FGoro liaugned GFle Least Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Aerial Dive Least Conce Ground ForLeast Conce Probing Least Conce Probing Least Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce

17 15 33 13 31 13 13 17 16 37 22 57 11 85 13 10 16 20 37 17 45 15 2 33 45 13 19 31 13 26 13 13 16 21 22 16 11 45 13 72 10 40 20 52 37 35 45 48 2 50 45 75 19 12 13 12 26 12 13 17 21 24 16 12 45 61 72 55 40 75 52 80 35 35 48 48 50 57 75 57 12 140 12 58 12 60 17 48 24 148 12 53 61 62 55 85 75 52 80 24 35 14 48 13 57 16 57 13 140 19 58 13 60 14 48 15 148 11 53 50 62 61 85 19 52 18 24 12 14 20 13 13 16 13 19 13 14 15 11 50 61 19 18 12 20 13

31 27 58 27 54 28 18 21 27 55 37 75 19 150 23 18 14 22 37 55 31 80 27 86 58 63 27 40 54 32 28 55 18 29 27 35 37 22 19 105 23 174 14 60 37 85 55 60 80 77 86 75 63 155 40 20 32 19 55 20 29 21 35 47 22 21 105 134 174 90 60 115 85 120 60 72 77 72 75 92 155 92 20 218 19 70 20 110 21 111 47 203 21 80 134 1200 90 150 115 84 120 35 72 24 72 26 92 27 92 20 218 24 70 20 110 18 111 22 203 16 80 130 1200 142 150 29 84 38 35 18 24 36 26 18 27 20 24 20 18 22 16 130 142 29 38 18 36 18

American Goldfinch Chipping Sparrow Gray Catbird Northern Cardinal Song Sparrow Yellow Warbler Clay-colored Sparrow Common Yellowthroat American Redstart Bay-breasted Warbler Blackburnian Warbler Blue Jay Blue-gray Gnatcatcher Blue-headed Vireo Broad-winged Hawk Brown Creeper Cape May Warbler Cooper's Hawk Eastern Wood-Pewee Golden-crowned Kinglet Magnolia Warbler Red-eyed Vireo Ruby-crowned Kinglet Scarlet Tanager Sharp-shinned Hawk Wood Thrush Yellow-rumped Warbler Brown-headed Cowbird Osprey American Crow American Robin Baltimore Oriole Common Grackle Mourning Dove Olive-sided Flycatcher Red-tailed Hawk Warbling Vireo House Finch Barn Swallow swallow sp.

Eastern Phoebe Chimney Swift Rock Pigeon Bank Swallow Belted Kingfisher Northern Rough-winged Swallow Black-capped Chickadee Downy Woodpecker Hairy Woodpecker Red-bellied Woodpecker Red-breasted Nuthatch White-breasted Nuthatch Winter Wren Yellow-bellied Sapsucker Eastern Bluebird Bufflehead Common Goldeneye Common Merganser Hooded Merganser merganser sp.

Purple Martin Tree Swallow Wood Duck American Kestrel Carolina Wren Great Crested Flycatcher House Wren Northern Flicker Red-headed Woodpecker European Starling House Sparrow Cliff Swallow Common Raven falcon sp.

Peregrine Falcon Turkey Vulture Iceland Gull American Coot

132 7 8 4 11 24 1 1 1 79 6 28 8 3 1 2 5 1 4 2 3 29 10 1 2 2 52 2 1 8 197 2 7 2 1 1 32 3 268 15 77 50 1111 56 9 115 313 49 9 13 7 6 22 14 7 609 108 268 65 5 58 173 18 2 7 4 14 108 1 197 697 56 2 1 19 92 6 8

Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Scrub Scrub Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Grassland Lake/Pond Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Town Grassland Grassland Open Woodland Town Town Lake/Pond Lake/Pond River/Stream Forest Forest Forest Forest Forest Forest Forest Forest Grassland Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Town Town Lake/Pond Mountains Mountains Mountains Open Woodland Shoreline Lake/Pond

Seeds Shrub Seeds Shrub Insects Shrub Seeds Shrub Insects Shrub Insects Shrub Seeds Shrub Insects Shrub Insects Tree Insects Tree Insects Tree Omnivore Tree Insects Tree Insects Tree Mammals Tree Insects Tree Insects Tree Birds Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Birds Tree Insects Tree Insects Tree Seeds Tree Fish Tree Omnivore Tree Insects Tree Insects Tree Omnivore Tree Seeds Tree Insects Tree Small Anim Tree Insects Tree Seeds Tree Insects Building Insects Building Insects Building Insects Building Seeds Building Insects Burrow Fish Burrow Insects Burrow Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Fish Cavity Fish Cavity Fish Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Insects Cavity Omnivore Cavity Insects Cavity Seeds Cavity Insects Cliff Omnivore Cliff Birds Cliff Birds Cliff Carrion Cliff Omnivore Cliff Plants Floating

Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Aerial Dive Least Conce Bark ForageLeast Conce Foliage Gle Least Conce Aerial ForagLeast Conce Flycatching Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Aerial Dive Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Aerial Dive Least Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Flycatching Near Threa Soaring Least Conce Foliage Gle Least Conce Ground ForLeast Conce Aerial ForagLeast Conce Aerial ForagLeast Conce Fly Catchin Least Conce Aerial ForagNear Threa Ground ForLeast Conce Aerial ForagLeast Conce Aerial Dive Least Conce Aerial ForagLeast Conce Foliage Gle Least Conce Bark ForageLeast Conce Bark ForageLeast Conce Bark ForageLeast Conce Bark ForageLeast Conce Bark ForageLeast Conce Ground ForLeast Conce Bark ForageLeast Conce Ground ForLeast Conce Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce Aerial ForagLeast Conce Aerial ForagLeast Conce Dabbler Least Conce Soaring Least Conce Ground ForLeast Conce Flycatching Least Conce Foliage Gle Least Conce Ground ForLeast Conce Flycatching Near Threa Ground ForLeast Conce Ground ForLeast Conce Aerial ForagLeast Conce Ground ForLeast Conce Aerial Dive Least Conce Aerial Dive Least Conce Soaring Least Conce Aerial Dive Least Conce Surface DivLeast Conce

12 13 22 22 15 12 13 12 12 14 11 27 10 14 38 13 12 38 15 10 12 12 10 16 29 20 13 20 56 45 25 18 31 28 19 50 12 13 17 17 15 13 33 13 31 13 13 16 22 24 11 13 10 20 18 37 45 2 45 62 19 13 50 26 13 19 12 30 21 21 16 13 62 42 45 72 55 40

20 21 27 28 20 18 21 17 17 21 20 38 16 22 90 19 20 75 25 16 18 24 17 27 49 32 21 36 165 90 35 26 41 45 35 125 22 22 31 31 27 28 58 27 54 28 18 27 37 38 19 23 14 37 28 55 80 86 63 86 40 32 70 55 29 34 15 48 42 35 22 29 117 105 105 174 120 60

ONE CONNECTION AWAY

Fig 3.6. Species data by degree of connection

Semipalmated Sandpiper Spotted Sandpiper tern sp.

Wilson's Plover Alder/Willow Flycatcher (Traill's F Alder Flycatcher Black-throated Blue Warbler Gray-cheeked Thrush Swainson's Thrush Red-winged Blackbird Swamp Sparrow American Goldfinch Chestnut-sided Warbler Chipping Sparrow Gray Catbird Indigo Bunting Northern Cardinal Prairie Warbler Song Sparrow Yellow Warbler Brown Thrasher Clay-colored Sparrow Common Yellowthroat Northern Mockingbird Willow Flycatcher American Redstart Bald Eagle Bay-breasted Warbler Blackburnian Warbler Blackpoll Warbler Black-throated Green Warbler Blue Jay Blue-gray Gnatcatcher Blue-headed Vireo Broad-winged Hawk Brown Creeper Cape May Warbler Cerulean Warbler Cooper's Hawk Eastern Wood-Pewee Empidonax sp.

Golden-crowned Kinglet Least Flycatcher Magnolia Warbler Northern Goshawk Northern Parula Philadelphia Vireo Pine Warbler Red-eyed Vireo Rose-breasted Grosbeak Ruby-crowned Kinglet Rusty Blackbird Scarlet Tanager Sharp-shinned Hawk Wood Thrush Yellow-rumped Warbler Brown-headed Cowbird Eastern Kingbird Bonaparte's Gull Osprey Black-crowned Night-Heron Great Blue Heron Great Egret Green Heron Solitary Sandpiper American Crow American Robin Baltimore Oriole Cedar Waxwing Common Grackle Merlin Mourning Dove Northern Shrike Olive-sided Flycatcher Orchard Oriole Pine Siskin Red-tailed Hawk Ruby-throated Hummingbird

1 29 3 3 1 5 51 7 13 1223 23 308 24 70 58 2 148 1 77 196 12 4 27 3 8 31 4 87 19 6 27 1419 79 15 2 16 8 2 10 8 2 188 19 31 1 35 9 7 48 26 94 130 3 86 10 232 88 92 39 9 11 11 17 1 3 86 526 107 183 79 4 75 2 2 7 21 9 6

Shoreline Shoreline Shoreline Shoreline Marsh Scrub Forest Forest Forest Marsh Marsh Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Scrub Scrub Scrub Town Marsh Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Grassland Grassland Lake/Pond Lake/Pond Marsh Marsh Marsh Marsh Marsh Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland Open Woodland

Insects Ground Small Anim Ground Fish Ground Insects Ground Insects Scrub Insects Scrub Insects Shrub Insects Shrub Insects Shrub Insects Shrub Insects Shrub Seeds Shrub Insects Shrub Seeds Shrub Insects Shrub Insects Shrub Seeds Shrub Insects Shrub Insects Shrub Insects Shrub Omnivore Shrub Seeds Shrub Insects Shrub Omnivore Shrub Insects Shrubs Insects Tree Fish Tree Insects Tree Insects Tree Insects Tree Insects Tree Omnivore Tree Insects Tree Insects Tree Mammals Tree Insects Tree Insects Tree Insects Tree Birds Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Birds Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Insects Tree Birds Tree Insects Tree Insects Tree Seeds Tree Insects Tree Insects Tree Fish Tree Fish Tree Fish Tree Fish Tree Fish Tree Insects Tree Omnivore Tree Insects Tree Insects Tree Fruit Tree Omnivore Tree Birds Tree Seeds Tree Birds Tree Insects Tree Insects Tree Seeds Tree Small Anim Tree Nectar Tree

Ground ForNear Threa Probing Least Conce Aerial Dive Least Conce Probing Least Conce Flycatching Least Conce Flycatching Least Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Flycatching Least Conce Foliage Gle Least Conce Soaring Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Aerial Dive Least Conce Bark ForageLeast Conce Foliage Gle Least Conce Foliage Gle Vulnerable Aerial ForagLeast Conce Flycatching Least Conce Flycatching Least Conce Foliage Gle Least Conce Flycatching Least Conce Foliage Gle Least Conce Aerial Dive Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Bark ForageLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForVulnerable Foliage Gle Least Conce Aerial Dive Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Fly Catchin Least Conce Dabbler Least Conce Aerial Dive Least Conce Stalking Least Conce Stalking Least Conce Stalking Least Conce Stalking Least Conce Probing Least Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Aerial ForagLeast Conce Ground ForLeast Conce Aerial Dive Least Conce Flycatching Near Threa Foliage Gle Least Conce Foliage Gle Least Conce Soaring Least Conce Hovering Least Conce

14 19 34 18 15 15 12 16 17 20 13 12 10 13 22 12 22 11 15 12 26 13 12 24 15 12 85 14 11 14 12 27 10 14 38 13 12 11 38 15 15 10 13 12 58 11 12 13 12 19 10 23 16 29 20 13 20 21 32 56 62 120 100 43 21 45 25 18 15 31 27 28 23 19 16 12 50 8

29 29 77 38 22 22 18 28 30 36 18 20 19 21 27 20 28 20 20 18 30 21 17 33 22 17 204 21 20 22 18 38 16 22 90 19 20 20 75 25 23 16 20 18 109 17 20 21 24 31 17 37 27 49 32 21 36 36 78 165 116 185 140 66 42 90 35 26 26 41 58 45 32 35 25 20 125 9

Canvasback Horned Grebe Pied-billed Grebe Redhead Red-necked Grebe Great Black-backed Gull Black-and-white Warbler Canada Warbler Dark-eyed Junco Fox Sparrow Mourning Warbler Nashville Warbler Northern Waterthrush Orange-crowned Warbler Ovenbird Tennessee Warbler Veery warbler sp. (Parulidae sp.)

White-throated Sparrow Yellow-bellied Flycatcher American Pipit Common Nighthawk Horned Lark Killdeer Lapland Longspur Northern Harrier Savannah Sparrow Snowy Owl American Black Duck American Wigeon Blue-winged Teal Cackling Goose Common Loon Double-crested Cormorant goose sp.

Greater Scaup Greater/Lesser Scaup Lesser Scaup Long-tailed Duck Mallard Mallard (Domestic type) Mallard x American Black Duck (h Mute Swan Northern Pintail Red-breasted Merganser Red-throated Loon Ring-billed Gull Ring-necked Duck Trumpeter Swan Tundra Swan White-winged Scoter Brant Canada Goose Gadwall Greater Yellowlegs Least Sandpiper Nelson's Sparrow Virginia Rail Black Scoter Surf Scoter American Tree Sparrow Brewster's Warbler (hybrid) Hermit Thrush Palm Warbler Eastern Towhee Field Sparrow Lincoln's Sparrow White-crowned Sparrow Wilson's Warbler Caspian Tern Common Tern Dunlin Herring Gull Larus sp.

Lesser Black-backed Gull Piping Plover Sanderling Semipalmated Plover Warbling Vireo Yellow-throated Vireo House Finch Graylag Goose (Domestic type)

10 4 3 399 244 12 74 4 90 7 1 14 5 12 16 9 11 172 1176 1 125 2 1 63 19 7 36 6 29 2 2 1 19 10979 2 1229 13 48 1447 1165 1 2 180 1 889 2 2773 9 17 8 34 3 558 29 2 8 1 2 33 5 18 1 67 145 12 21 24 198 12 75 81 4 244 7 1 19 3 1 41 1 70 1

Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Shoreline Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Grassland Grassland Grassland Grassland Grassland Grassland Grassland Grassland Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Lake/Pond Marsh Marsh Marsh Marsh Marsh Marsh Marsh Ocean Ocean Open Woodland Open Woodland Open Woodland Open Woodland Scrub Scrub Scrub Scrub Scrub Shoreline Shoreline Shoreline Shoreline Shoreline Shoreline Shoreline Shoreline Shoreline Open Woodland Open Woodland Town x

Plants Insects Insects Plants Fish Omnivore Insects Insects Seeds Insects Insects Insects Insects Insects Insects Insects Insects Insects Seeds Insects Insects Insects Seeds Insects Insects Mammals Insects Mammals Insects Plants Seeds Plants Fish Fish Plants Insects Insects Insects Insects Seeds Seeds Seeds Plants Seeds Fish Fish Omnivore Plants Plants Plants Insects Plants Seeds Plants Insects Insects Insects Insects Insects Insects Seeds Insects Insects Insects Omnivore Insects Insects Insects Insects Fish Fish Insects Omnivore Omnivore Omnivore Insects Insects Insects Insects Insects Seeds x

Surface DivLeast Conce Surface DivVulnerable Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce Ground ForLeast Conce Bark ForageLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Flycatching Least Conce Ground ForLeast Conce Aerial ForagLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Soaring Least Conce Ground ForLeast Conce Aerial Dive Least Conce Dabbler Least Conce Dabbler Least Conce Dabbler Least Conce Ground ForLeast Conce Surface DivLeast Conce Surface DivLeast Conce Ground ForLeast Conce Surface DivLeast Conce Surface DivLeast Conce Surface DivLeast Conce Surface DivVulnerable Dabbler Least Conce Dabbler Least Conce Dabbler Least Conce Dabbler Least Conce Dabbler Least Conce Surface DivLeast Conce Surface DivLeast Conce Ground ForLeast Conce Surface DivLeast Conce Dabbler Least Conce Dabbler Least Conce Surface DivLeast Conce Ground ForLeast Conce Ground ForLeast Conce Dabbler Least Conce Probing Least Conce Probing Least Conce Ground ForLeast Conce Probing Least Conce Surface DivNear Threa Surface DivLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Foliage Gle Least Conce Aerial Dive Least Conce Aerial Dive Least Conce Probing Least Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForLeast Conce Ground ForNear Threa Probing Least Conce Ground ForLeast Conce Foliage Gle Least Conce Foliage Gle Least Conce Ground ForLeast Conce x x x

52 35 34 48 50 75 12 13 15 16 12 11 13 12 12 12 17 13 17 14 15 23 18 24 15 48 12 61 55 50 38 63 75 80 63 57 57 35 48 57 57 57 140 62 58 60 48 42 148 135 53 62 85 52 31 14 12 24 48 55 14 13 16 13 19 13 14 15 11 50 34 19 61 51 58 17 19 18 12 14 13

85 60 48 77 75 155 20 19 21 22 18 18 23 19 23 20 28 19 21 19 27 55 32 47 25 110 21 134 90 84 60 90 115 120 90 75 75 72 72 92 92 92 218 90 70 110 111 62 203 168 80 1200 150 84 60 27 20 35 84 77 24 26 27 20 24 20 18 22 16 130 77 37 142 142 142 25 35 28 22 23 22

100

Floating Floating Floating Floating Floating Gound Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Ground Tree Tree Tree x

x

FLOOR LITTER

GRASSES

Horizontal - Vertical Branching - Surface Thickness Density

101

SHRUBS

UNDER STORY

CANOPY

OVERSTORY

CLIFF

GRAVEL

Fig 3.7. Parametrically tuned geometries simulating habitat elements

102

MOUNTAIN

FOREST

103

FOREST

GRASSLAND

RIVER/LAKE EDGE

MARSH

OPENWOODLAND

Fig 3.8. Habitat breakdown and element arrangement in intervention envelope

104

50

Avian accommodation value

Potential flight path

105

Fig 3.9. Patch Enhance intervention - Section

106

ASSEMBLY

[input] To generate the assembly for each element in the habitat, the element geometry from the habitat compose exercise is used as a base. To inform the assembly, nesting box dimensions from the target species list, and a list of plants for each habitat type are used. [process] The base of the assembly is a system of laminated timber ribs that divide the volume into compartments. These ribs run through the habitat, creating structural continuity between elements. Once each element is divided into compartments, each compartment is digitally assigned as either as a perching mesh, a nesting box/ ledge, or a planter, based on its location in the habitat and its orientation.

The perching meshes are located in the upper portions of understory, canopy, and overstory elements. The mesh’s density changes based on it’s location in the habitat.

The nesting boxes or ledges are located where there are outward facing compartments in the lower portions of understory, canopy, and overstory elements, as well as floor litter, shrub, and cliff elements. To size the nesting boxes, the tool measures the compartments, compares this to the requirements of the target species, and subdivides the compartments accordingly to create the desired mix of box dimensions. Where species require a ledge, the compartment is left open, and where they require a box, one is inserted with the proper hole size.

Planters are located in grassland, shrub, gravel, and floor litter elements where compartments face upwards. The plants used in these planters are local species that are commonly known to attract birds. These plants are categorized based on which elements they are to be in and sorted based on sun requirements. To locate the plants, the tool performs a sun study on the relevant compartments, before placing the plants according to where they can thrive. [evaluation] By using these parametric tools, the assemblies benefit from the vast amount of data made accessible in Part I to accommodate specific local birds

107

Perching mesh infill

Nesting cavity infill

Fig 3.10. Assembly compartment types

Planter infill 108

109

Fig 3.11. Plant selection by habitat and required sun

110

Base mesh

Structural ribs, pixel division & allocation, sun study

Fig 3.12. Assembly generation process Assigned plants + Nesting boxes 111

Plant species locations

Sample of bird species accommodated

112

Fig 3.13. Selection of plants and birds accommodated in assembly fragment

113

Fig 3.14. Shrub assembly fragment - Axonometric

114

INTERVENTION ILLUSTRATIONS

The following set of images illustrate the results of the habitat composition and assembly procedures in generating a Patch Enhance intervention located at Canoe Landing Park. As a flagship in a network of interventions, this habitat brings attention to non-human species that we share the city with. In addition to it performative roles, the language of the intervention subverts traditional forms of built environment and landscape and presents a new typology. This new typology represents a novel habitat for a novel urban ecosystem.

115

50

Avian accommodation value Potential flight path

Fig 3.15. Patch Enhance intervention – Axonometric 116

50

Avian accommodation value Potential flight path

117

Fig 3.16. Patch Enhance intervention – Perspective from street

Fig 3.17. Patch Enhance intervention – Interior of grassland 118

50

Avian accommodation value Potential flight path

Fig 3.18. Patch Enhance intervention – View of grassland 119

50

Avian accommodation value Potential flight path

120

Fig 3.19. Patch Enhance intervention – Perspective from park

Fig 3.20. Patch Enhance intervention – Interior perspective from open woodland 121

ASSEMBLY (CONTINUED)

Fig 3.21. Forest fragment - Axonometric 123

Fig 3.22. Forest fragment – Detail 124

Fig 3.23. Grassland fragment - Axonometric 125

Fig 3.24. Grassland fragment – Detail 126

Fig 3.25. Open woodland fragment - Axonometric 127

Fig 3.26. Open woodland fragment - Detail 128

CONCLUSION

While the majority of the concepts and methods used in these studies already exist in the field of landscape ecology, the application of these processes in the field of urban design, as well as the addition of agent-based path networks, 3D networks, and data populated networks, has potential to be very effective in helping architects, landscape architects, and planners work amidst the interactions between urban fabric and regional ecologies.

While the upfront investment in acquisition of data and development of digital tools is substantial , the focus on computation has been successful as both an illustrative and analytical tool and has made it possible to reveal and utilize complex and dynamic patch networks.

As discussed earlier, these computational tools are divided into two categories: the network tools and the habitat composition/assembly tools. The habitat and assembly tools developed in Part III make the most direct use of the data accessed through the network, while pushing boundaries related to the perception on urban green space and nature. While the case study design is very resourceintensive, the work-flows and tools developed could be applied more at different scales and intensities at other locations throughout the network. In addition, the network tools developed can help inform any designer or policy maker operating in the realm of urban ecology to make decisions that strengthen this urban habitat network.

In conclusion, this work, through its network studies and biophilic habitat design, acts as both a tool and a catalyst for pushing our ability to design for the species that bring life to our cities.

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NEXT STEPS

Testing Testing the results of these design activities would offer direction for next steps and improvements. At the network scale, different interventions could be rapidly tested using the network simulation tools. Each intervention’s effect on the connectivity of the network can be measured, giving a hierarchy to the intervention suggestions. At the habitat scale, the assemblies proposed could be paired down to something that could be easily fabricated and tested in the field to see how well it accommodates birds.

Interdisciplinary collaboration While this work was heavily informed by studies in biophilia and landscape ecology, collaboration would be key in bringing these tools to real world use.

It would be advantageous to have the network illustrations evaluated by experts in landscape ecology to ensure their accuracy, and keep them up to date with current research. In addition, collaboration with city planners could help identify additional key areas of research and bring to light urban forces not yet addressed in this work.

Within the design realm, sharing this framework with designers in architecture and landscape architecture has potential to produce a wide variety of interventions.

Documenting how and whether their designs benefit from the network and data tools could further inform this work.

Additional Investigation Now that these tools are developed, they could to deployed to test habitat networks of different cities, allowing many comparative evaluations. In addition to evaluating different locations, these network these tools have potential to focus on specific avian and other vagile species. A series of maps could be generated that illustrate how differently specific species are able to move through urban fabric, which would add sophistication to the designer’s understanding of urban habitat networks.

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