A Decision-Support Framework for Conservation ... - Bad Request

A Decision-Support Framework for Conservation Planning in the Central Interior Ecoregion of British Columbia, Canada

Pierre Iachetti Nature Conservancy of Canada

Alcoa Foundation Conservation and Sustainability Fellowship and World Conservation Union (IUCN)

Photo: © Thomas Drasdauskis

Central Interior Conservation Planning Decision-Support Framework

Contact:

[email protected] 250 479-3191 x226

Cover photo:

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© Thomas Drasdauskis

Alcoa Foundation Conservation and Sustainability Fellowship


Acknowledgements This research was made possible through funding from the Alcoa Foundation Conservation and Sustainability Fellowship program. I owe a big thank you to Jeff McNeely, Nadine McCormick, Frederik Schutyser, Joshua Bishop, and Gonzalo Oviedo at the World Conservation Union (IUCN), and to Diana Simon and Illana Kurtzig at the Institute for International Education, for their support and advice throughout this process. Thank you to Jan Garnett, Dušan Markovic and Dave Nicolson at the Nature Conservancy of Canada for their support and assistance with data. Many of the concepts in this paper to do with conservation planning came from working with my colleagues at The Nature Conservancy (TNC) and the U.S. Washington Department of Fish and Wildlife (WDFW) on ecoregional conservation assessments in the Pacific Northwest. Thank you to John Floberg, Mark Goering, Dick vander Schaaf, Ken Popper and Michael Schindel at TNC, and to George Wilhere at WDFW. Thank you to Bob Pressey and Chuck Rumsey for their advice and support, and to Tracey Hooper and Lisa Leighton for their review and editing of this report.


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TABLE OF CONTENTS Acknowledgements .................................................................................3 Executive Summary.................................................................................6 1.0

Introduction ...................................................................................13 1.1

2.0

Overview ........................................................................................16 2.1

3.0

The Central Interior Ecoregion............................................16 2.1.1

Natural Features.................................................................. 16

2.1.2

Administrative Boundaries................................................... 18

2.1.3

Lodgepole Pine and Mountain Pine Beetle ......................... 21

Ecosystem-Based Management Approaches ............................27 3.1

3.2

3.3

4.0

Purpose .................................................................................15

Conservation Planning ........................................................29 3.1.1

A Comprehensive Approach ............................................... 29

3.1.2

The “Coarse- and Fine-Filter” Approach ............................. 31

3.1.3

Conservation-Planning Tools ............................................. 33

Spatial and Temporal Dynamics .........................................54 3.2.1

Ecological Connectivity ....................................................... 55

3.2.2

Natural Disturbance............................................................. 59

3.2.3

Climate Change................................................................... 62

Incorporating Socio-economic Values ...............................72 3.3.1

Land Prices.......................................................................... 73

3.3.2

Ecosystem Services ............................................................ 75

Decision-Support System Development.....................................82 4.1

Scenario Development.........................................................86

4.2

Building the decision-support systems .............................88

5.0

Conclusions ..................................................................................91

6.0

Recommendations ........................................................................97

7.0

References...................................................................................100

8.0

Glossary.......................................................................................111

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List of Tables Table 1. Effects of mountain pine beetle (Kimmins et al. 2005) ..........................22 Table 2. Fire regime condition class descriptions ................................................61 Table 3. Abiotic variables used to develop scenarios..........................................68 Table 4. Ecosystem services and functions used by Costanza et al. (1997) ......77

List of Figures Figure 1. Map of the Central Interior ecoregion, British Columbia, Canada ..........7 Figure 2. (L to R) BC Ecoregion Classification System ecodomain, ecodivision, and ecoprovince............................................................................................17 Figure 3. Regional districts in the Central Interior Ecoregion ..............................19 Figure 4. Protected areas in the Central Interior ecoregion.................................20 Figure 5. Cumulative extent of the BC mountain pine beetle outbreak in 2006 (Eng et al. 2005) ...........................................................................................24 Figure 6. Cumulative predicted extent of the BC mountain pine beetle outbreak by 2024 (Eng et al. 2005)..............................................................................25 Figure 7. Terrestrial irreplaceability index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b) ..................................................................46 Figure 8. Freshwater irreplaceability index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b) ..................................................................47 Figure 9. Conservation value index for the Canadian Rocky Mountains Ecoregional Assessment (Rumsey et al. 2004)............................................51 Figure 10. Terrestrial cost (or suitability/vulnerability) index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)..............................................52 Figure 11. Freshwater cost (or suitability/vulnerability) index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)..............................................53 Figure 12. Least-cost path analysis for Muskwa-Kechika Conservation Area design (Heinemeyer et al. 2004)...................................................................58


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Executive Summary Conserving biodiversity is the most critical sustainability issue we face on earth, because unlike other issues, in which recovery and regeneration may be possible, there is no recovery from extinction. In facing the critical challenge of protecting the earth’s biodiversity, we must also work to minimize conflicts with legitimate and unavoidable uses of natural resources. Progress has been slow but persistent, given limited financial resources. Unfortunately, efforts have not stopped the continued loss of biodiversity. The Need for Sustainable Decisions The current mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic in British Columbia, Canada, is the result of a combination of factors that include climate change and disruption of natural ecological processes. It puts forest values at risk and threatens the stability and long-term economic well-being of many communities. The epidemic presents a major challenge to planners and policy-makers. To be sustainable, their decisions about land use and resource management will need to integrate information about the ecology of ecosystem disturbance, the role of climate and climate change, the effects of forest harvesting, the values and environmental services that forest ecosystems provide to human society, and the relationships between human communities and forests. One way for researchers to support sustainable land use and resource management is to develop tools for exploring and making effective decisions using an ecosystem-based management approach. Computer-based decisionsupport tools provide information by means of forecasting models and access to geographical information systems (GIS) and databases. Previous conservation planning approaches tended to focus primarily on the scientific component, without much consideration of the socio-economic and political aspects. But these are key to implementing conservation strategies. We must therefore design approaches to conservation that are robust under a wide range of possible outcomes. To that end, this paper will review relevant planning approaches and decision-support tools, and propose a decisionsupport framework to aid conservation planning.

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The Region at Risk British Columbia’s Central Interior ecoregion covers approximately 24.6 million hectares (ha), or approximately 61 million acres. The ecoregion has a unique combination of topography and climate, and it helps form part of the largest intact forested ecosystem in the world. This immense landscape overlaps the administrative boundaries of a number of regional districts. The main economic driver is forestry, but cattle ranching, mining, agriculture and tourism also play important roles in the economy.

Figure 1. Map of the Central Interior ecoregion, British Columbia, Canada

Approximately 10 per cent, or 2,452,191 ha of the ecoregion is currently protected. Lodgepole pine forests make up 35 per cent of the forested land


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base in British Columbia and accounts for 25 per cent of the total timber volume harvested in the province. The Mountain Pine Beetle The mountain pine beetle is indigenous to western North American pine forests. Under normal conditions, the beetles occur at endemic levels and cause less than 2 per cent mortality in forest stands. Currently, however, the species is at epidemic levels and is the most damaging biotic disturbance agent in lodgepole pine stands in western Canada. The epidemic affects forest ecosystems, climate, disturbance, forest harvesting, values and environmental services, human communities and the economy, agriculture, tourism, conservation, wildlife habitat, and biological diversity objectives. More than 12 million ha of pine forest in BC are at risk of infestation. In 2006, the infestation covered more than 9.2 million ha. British Columbia’s Mountain Pine Beetle Action Plan 2006 - 2011 identifies an urgent need to ensure that conservation objectives are established and achieved in conjunction with mountain pine beetle management operations. Ecosystem-Based Management Planners are starting to incorporate ecosystem-based management (EBM) approaches as a basis for sustaining ecosystems and their underlying processes, and for supporting the long-term sustainability of ecological, economic and social systems. Ecosystem-based management approaches recognize that humans are an integral part of many ecosystems, and that decisions about conserving biodiversity and managing natural systems are societal choices. At the core of ecosystem-based management is the balance between protecting ecological integrity and maintaining high levels of human well-being. Successful conservation planning needs to ensure the persistence of biodiversity by accommodating ecological, evolutionary, and socio-political processes. The “coarse- and fine-filter” approach for identifying conservation-area networks has been in use for more than 30 years. Systematic conservation-

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area network design involves constructing a mathematical programming problem (algorithm). The “cost” of the conservation-area network is also taken into account. Cost can be defined by the total area or number of sites in the conservation-area network. Cost can also be calculated using morecomplex variables that take into account the human population living in the region, the price of land, the agricultural potential of the area, the probability of habitat loss, and the spatial aggregation of conservation areas to minimize transportation and other management costs. Tools and Concepts We can apply three well-known reserve-selection models, as well as some other useful concepts, to an ecosystem-based management decision-support framework for the Central Interior ecoregion:

Marxan (Marine Reserve Design using Spatially Explicit Annealing), a stand-alone optimization software that is used to select conservation areas based on their biological value and suitability for conservation,

C-Plan, which is designed to calculate and display information (e.g., tables, maps, or diagrams) that can be used to guide conservationplanning decisions,

Zonation, which is based on an algorithm that identifies areas that are important for retaining habitat quality and connectivity for multiple species.

irreplaceability,

conservation utility, and

vulnerability.

The Importance of Spatial and Temporal Dynamics A major challenge for conservation planners is to develop strategies that combine planning for biodiversity processes with implementation that is constrained by land-use dynamics. For conservation planning tools to incorporate spatial and temporal dynamics, they would need to explicitly model many stochastic events, such as site destruction and degradation, financial costs and opportunities, and changes in public interest in conservation relative to other land uses. Combining all these factors in a


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single model would be almost impossible, but incorporating some of these factors into the conservation planning process is essential. For a decision-support system to be useful, it must allow planners to explore conservation implications of varying landscape scenarios over time, considering dynamic influence such as ecological connectivity, natural disturbance, and climate change. Incorporating Socio-economic Values Biodiversity is of immense cultural and socio-economic value, so the conservation of biodiversity is the responsibility of all sectors of society. All conservation problems have scientific and socio-economic aspects, yet it is generally considered to be the socio-economic aspects that ultimately determine a conservation area’s success or failure, regardless of how sound it is scientifically. Understanding a planning region’s socio-ecological system is a prerequisite for effective conservation, because although conservation problems manifest ecologically, their root causes are typically social and economic. The development of systematic conservation planning techniques has improved the objectivity, cost-effectiveness, transparency and replicability of the planning process. However, they have typically focused on biological entities rather than on the broader socio-ecological systems in which conservation planning initiatives operate. We need to incorporate socioeconomic considerations (e.g., land prices and the value of ecosystem services) into conservation-area network design to create more realistic conservation scenarios. Ecosystems have to be understood in their economic context to prevent market distortions that undervalue natural resources, to provide incentives to promote biodiversity conservation, and to internalize the costs of biodiversity protection and conservation. Developing Decision-Support Systems Decision-makers are asked to integrate multiple information sources, show transparency in the decision-making process, and publicly evaluate trade-offs. Decision-makers need support from researchers to help them effectively evaluate the social, economic and environmental consequences of alternative management scenarios. Decision-support tools/systems can be used in further

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interactions between management and science when developing adaptive management strategies. Defining problems is critical to policy-making. Decision-support tools/systems, by definition, should aid in and strengthen the choice process. They are intended to aid research, convey knowledge, guide management, and publicly evaluate trade-offs. Scenario development, in which several conflicting or alternative scenarios are used to explore the uncertainty of the future consequences of a decision, is a potential framework for conservation planning and policy-making. It also offers us a platform for engaging stakeholders who have divergent viewpoints and competing


objectives.

Conclusions To help us evaluate conservation scenarios in the Central Interior ecoregion during conservation planning, we need a decision-support system that integrates both knowledge from across many disciplines, and data in different forms. Conventional approaches of integrating and applying knowledge are not adequate to examine the complex and highly variable ecological and socioeconomic issues that influence land-use and resource-management decisions and the effects they have on landscapes.


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Data layers need to be combined in a model with a user interface that allows the user to run scenarios and then understand the outcomes of his or her decisions. The timing of when to introduce the tool to the planning process is critical, as the people at the table need to be ready before it is introduced. Constructing a good system demands significant investments of time and money. A number of important technical considerations and challenges must be acknowledged in this conservation planning framework, including uncertainty, scale, data availability/data gaps, and reserve design. Recommendations We have an opportunity to incorporate many of the current spatial-assessment methodologies into a decision-support system to assist in conservation planning/ecosystem-based management in the Central Interior ecoregion of British Columbia. Considering the technical issues, data gaps, and other shortcomings of these tools, recommendations for developing such a tool for the Central Interior are as follows:

Use reserve-selection models to develop conservation plans.

Use more than one reserve-selection tool to compare results.

Incorporate socio-economic as well as ecological information.

Incorporate a cultural analysis.

Incorporate spatial and temporal dynamics into analyses and tools.

Involve as many relevant stakeholders as is feasible.

Conduct user-needs assessments to understand the types of decisions and information needs a decision-support system can support.

Incorporate different types of information from various sources.

The proposed framework and decision-support system is one piece of a land and resource management framework.

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1.0

Introduction

Conserving biodiversity is the most critical sustainability issue we face on earth, because unlike other issues, in which recovery and regeneration may be possible, there is no recovery from extinction (Tilman et al. 1994; Dale 2001; Sala et al. 2005). Biodiversity is increasingly being lost due to the cumulative

Biodiversity is the variability

impacts of invasive species, global warming, pollution, over-exploitation, the

among living organisms

disruption of natural ecological processes, and the conversion and

from all sources, including

fragmentation of natural habitats (Noss and Cooperrider 1994; Groves 2003;

inter alia, terrestrial, marine

Schaefer 2005). Habitat loss will most certainly lead to sharp declines in local

and other aquatic eco-

diversity and ecosystem services. It will also lead to global species

systems and the ecological

extinctions and the loss of associated ecosystem services over the next 50

complexes of which they

years (Sala et al. 2005). Habitat loss will also interact with climate change to

are part; this includes

make range shifts for species and ecosystems very difficult in many areas.

diversity within species, between species and of

In facing the critical challenge of protecting the earth’s biodiversity, we must

ecosystems (UNCBD

also work to minimize conflicts with legitimate and unavoidable uses of

2005).

natural resources. Because of lag times in species extinctions, there is still opportunity for decision-makers to change the fate of species that would otherwise become extinct within a few generations (Sala et al. 2005). Progress has been slow but persistent. With limited financial resources, some jurisdictions have managed to make well-targeted conservation efforts that set conservation priorities by identifying the most biologically important

Extinction is the complete

ecoregions and biodiversity hot spots around the world (Groves 2003). At the

disappearance of an entire

same time, some environmental organizations have made major efforts to

species (Gitay et al. 2002).

acquire key private land. But these efforts have not stopped the continued loss of biodiversity. The current mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic in British Columbia, Canada, is the result of a combination of factors that include climate change, conversion and fragmentation of natural habitats, and disruption of natural ecological processes. It is predicted that the epidemic will kill 80 per cent to 95 per cent of mature lodgepole pine (Pinus contorta var. latifolia Dougl.) in the province. The mountain pine beetle also has the potential to spread to jack pine (Pinus banksiana), which could


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dramatically affect the vast boreal forests of western and central Canada (Eng et al. 2005; Nigh et al. 2006). Currently, and into the future, this epidemic Habitat is the particular

will have profound effects on wildlife and wildlife habitats. It will put forest

environment or place where

values at risk and threaten the stability and long-term economic well-being of

an organism or species

many communities.

tends to live; a more locally circumscribed portion of the

The epidemic presents a major challenge to planners and policy-makers. To

total environment (Gitay et

be sustainable, their decisions about land use and resource management will

al. 2002).

need to integrate information about the ecology of ecosystem disturbance, the role of climate and climate change, the effects of forest harvesting, the values and environmental services that forest ecosystems provide to human society, and the relationships between human communities and forests (Kimmins et al. 2005). Chan-McLeod (2006, p.120) states that, “Beetle-killed stands must be logged before the dead trees deteriorate to an unmerchantable condition, but this need cannot preclude conservation strategies aimed at sustaining biodiversity in a massively defoliated landscape.” Sustainable land and resource management is made even more difficult because much of the ecological information is changing, which makes it nearly impossible to effectively apply this information to decision-making.

Decision support means

However, technological developments have opened up possibilities for

providing timely and useful

managing and analyzing information and assessing future scenarios. In turn,

information that addresses

improved data integration and modeling capabilities provide an opportunity to

specific questions (CCSP

support new and innovative governance models. These can improve

and SGCR 2003).

integration and collaboration in planning and in implementing effective landuse and resource-management decisions (A. Tautz, pers. comm.). One way for researchers to support sustainable land use and resource management is to develop tools for exploring and making effective decisions

Geographic information

using an ecosystem-based management approach. Computer-based decision-

systems (GIS) are software

support tools provide information by means of forecasting models and access

systems that store,

to geographical information systems (GIS) and databases. These can be used

manipulate and analyze

by decision-makers who are dealing with complex and un-/semi-structured

georeferenced data.

management issues. Decision-support systems are a collection of conceptual, methodological, and computer-based tools (Cain 2001), which can be used to structure a decision process by making data easily accessible and allowing

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“what-if” analyses of possible decisions to be made. Although it will not resolve uncertainty, developing potential future scenarios is a useful way of improving decision-making and stakeholder involvement in situations of high uncertainty (Lebel et al. 2005). A scenario is a coherent

1.1

description of a potential

Purpose

future situation that serves

Nearly all problems related to conserving biodiversity have a scientific, a

as input to more detailed

socio-economic, and a political aspect. Previous conservation planning

analyses or modeling.

approaches tended to focus primarily on the scientific component, without

Scenarios are tools to

much consideration of the socio-economic and political aspects. But these are

explore, “if …, then…”

key to implementing conservation strategies (Groves 2003). We will never

statements, and are not

have complete ecological information with which to make decisions, so we

predictions of or

must therefore design approaches to conservation that are robust under a wide

prescriptions for the future

range of possible outcomes (Bohensky et al. 2006). As well, we can better

(CCSP and SGCR 2003).

integrate existing models and tools to support decision-making and allow

“A scenario offers an

learning and experimentation (Maness 2005).

internally consistent and plausible explanation of

The purpose of this paper is to:

how events unfold over time. Sequences of events

review relevant planning approaches and decision-support tools, and

and interactions, rather

propose a decision-support framework to aid conservation planning in the

than specific time scales,

Central Interior ecoregion of British Columbia.

are usually emphasized” (Lebel et al. 2005, p. 232). A set of plausible narratives that depict alternative pathways to the future


(Bohensky et al. 2006).


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2.0

Overview

2.1

The Central Interior Ecoregion

Ecoregions are relatively large areas of land and water that contain geographically distinct assemblages of natural communities, with boundaries that are approximate. These natural communities share a large majority of their species, dynamics, and environmental conditions, and they function together effectively as a conservation unit at global and continental scales An ecosystem or ecological system is a community (i.e., an assemblage of

(Dinerstein et al. 1995; Groves 2003). Ecoregions are more effective at capturing the ecological and genetic variability of biodiversity than are political units.

populations of plants, animals, fungi, and microorganisms that live in an environment and interact with one another, forming together a distinctive living system with its own composition, structure, environmental relations, development, and function) and its environment treated together as a functional

As shown in the BC Ecoregion Classification System (Figure 2), the Central Interior ecoregion boundary encompasses the Sub-Boreal Interior and Central Interior ecoprovinces. The BC classification scheme stratifies terrestrial ecosystem complexity into discrete geographical units at five hierarchical levels. The two broadest levels – ecodomain and ecodivision – place BC’s ecosystems in a global context. The three lower levels – ecoprovince, ecoregion and ecosection – describe areas of similar climate, physiography, hydrology and vegetation. Increasingly more detailed, these levels relate ecosystems to one another on a provincial scale. Within the BC classification, the Central Interior falls within the Humid Temperate ecodomain and the Humid Continental Highlands ecodivision (Demarchi 1996).

system of complementary relationships and transfer and circulation of energy

2.1.1

Natural Features

and matter (CCSP and

The Central Interior ecoregion covers approximately 24.6 million hectares

SGCR 2003).

(ha), or approximately 61 million acres. It encompasses the flat-to-rolling Chilcotin, Cariboo, Nechako and McGregor plateaus; the Chilcotin, Bulkley, Thatsa and Hart ranges; and the Omineca and Skeena mountains. The ecoregion has a unique combination of topography and climate. It consists of a large interior plateau that grades into more hilly country in the north. In the south, the plateaus are underlain by the lava forms of coastal volcanoes.


The ecoregion is influenced by both coastal and interior climates. It lies in a rainshadow to the east of the Coast Mountains and has a typical continental climate – cold winters with outbreaks of Arctic air from boreal forests to the north.

Figure 2. (L to R) BC Ecoregion Classification System ecodomain, ecodivision, and ecoprovince (Central Interior study area outlined in orange)

The ecoregion helps form part of the largest intact forested ecosystem in the world (A. Harcombe, pers. comm.). Vegetation is dominated by sub-boreal spruce and Interior Douglas-fir ecosystems. Vegetation communities are A population is a group of

diverse, in response to variation in elevation, and other conditions.

individuals of the same

The ecoregion is nourished by the waters of the Skeena, Dean and Nass rivers,

species which occur in an

and it contains the headwaters of the Fraser River. This area supports both

arbitrarily defined

anadromous and freshwater fish, including chinook salmon (Oncorhynchus

space/time and are much

tshawytscha), sockeye salmon (Oncorhynchus nerka), steelhead trout

more likely to mate with

(Oncorhynchus mykiss), Pacific lamprey (Lampetra tridentata), both native and

one another than with

introduced populations of rainbow trout (Oncorhynchus mykiss), Dolly Varden

individuals from another

(Salvelinus malma), and the endangered white sturgeon (Acipenser

such group (Gitay et al.

transmontanus).

2002).

Numerous wetlands provide important waterfowl nesting habitats for migratory birds. The ecoregion supports 65% of all bird species known to occur in British Columbia, and 61% of all bird species known to breed in the province. The only breeding colony of the American white pelican (Pelecanus erythrorhynchos) in the province is found on the Chilcotin plateau, and the


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world centre of breeding abundance for the Barrow’s goldeneye (Bucephala islandica) is in this ecoregion. It is also the centre of breeding abundance in

BC for the Greater yellowlegs (Tringa melanoleuca) and Yellow-headed blackbird (Xanthocephalus xanthocephalus), and is one of two important breeding areas for the Long-billed curlew (Numenius americanus) and the Ring-billed gull (Larus delawarensis). Here, too, are found the highest breeding numbers in the province of the Herring gull (Larus argentatus) and the Black tern (Chlidonias niger). Moose (Alces americanus) are the most widespread ungulate, but there are also populations of mule deer (Odocoileus hemionus) and California bighorn sheep (Ovis canadensis californiana). The grizzly bear (Ursus arctos horribilis), black bear (Ursus americanus), gray wolf (Canis lupus), cougar (Felis concolor) and coyote (Canis latrans) are common in the ecoregion. We also find the western terrestrial garter snake (Thamnophis elegans), the common garter snake (Thamnophis sirtalis), the rubber boa (Charina bottae), the western toad (Bufo boreas) and the Columbia spotted frog (Rana luteiventris) in the Central Interior ecoregion (Demarchi 1996).

2.1.2

Administrative Boundaries

The immense landscape of the Central Interior ecoregion overlaps the administrative boundaries of a number of regional districts, including the Cariboo, Bulkley-Nechako, Peace River, Stikine, Kitimat-Stikine, and FraserFort George, and, to a lesser degree, Central Coast, Squamish-Lillooet, Mount Waddington, and Thompson-Nicola (Figure 3).

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Figure 3. Regional districts in the Central Interior Ecoregion

Major population centres in the Cariboo Regional District are Quesnel, Williams Lake, 100 Mile House, and Wells. For 2006, BC Stats estimated the district’s population at 70,849, with a population density of 0.9 persons per square kilometre (BCStats 2007b). The main economic driver for the area is

A landscape is groups of

forestry, but cattle ranching, mining and tourism also play important roles in

ecosystems (e.g., forests,

the economy. In 2006, the logging and forest products industry employed 23

rivers, lakes, etc.) that form

per cent of the workforce in the regional district, manufacturing employed 16

a visible entity to humans

per cent, and wood-products manufacturing employed 12.1 per cent.

(Gitay et al. 2002).

Major population centres in the Bulkley-Nechako Regional District are Smithers, Burns Lake, Houston, Fort St. James, Fraser Lake, Granisle, Telkwa and Vanderhoof. The population estimate for the district in 2006 was


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44,147, with a density of 0.6 persons per square kilometre (BCStats 2007a). The economy of this area is primarily driven by forestry, tourism, mining and agriculture. In 2006, logging and forest products employed 26.7 per cent of the workforce, manufacturing employed 17.6 per cent, and wood-products manufacturing employed 16 per cent. The Fraser-Fort George Regional District had a population estimate of 101,881 in 2006, with a density of 2.0 persons per square kilometre. The main population centre is the city of Prince George, which had a population of 77,148 in 2005. The principal economic driver in the regional district is forestry, with tourism playing a smaller but still important role (BCStats 2007c).

Figure 4. Protected areas in the Central Interior ecoregion

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Approximately 10 per cent, or 2,452,191 ha of the ecoregion is currently protected. As shown in Figure 4, the major provincial protected areas found

Through numerous

in the ecoregion are Ts’yl-os (approximately 233,000 ha), Itcha Ilgachuz

biological, chemical and

(112,000 ha), Entiako (122,529 ha), Big Creek (65,982 ha), and Tweedsmuir

physical processes,

(981,000 ha).

ecosystems provide both goods and services to humanity. Goods include

2.1.3

Lodgepole Pine and Mountain Pine Beetle

food, feed, fibre, fuel,

Lodgepole pine forests make up 35 per cent of the forested land base in

pharmaceutical products,

British Columbia and provide wildlife habitat, scenic and recreation areas,

and wildlife. Services

livestock range, and a wide variety of ecological goods and services.

include maintaining hydro-

Lodgepole pine also accounts for 25 per cent of the total timber volume

logic cycles, cleansing

harvested in the province (BCMoFR 2005). These forests are subject to

water and air, regulating

frequent natural disturbances, mainly from wildfires and insects, such as the

climate and weather, stor-

mountain pine beetle. Without these disturbances, lodgepole pine would be

ing and cycling nutrients,

replaced by late-successional species such as spruce and fir (Safranyik and

and providing beauty and

Wilson 2006).

inspiration. While many goods pass through markets, services rarely do

Photo: © Leslie Manning

(CCSP and SGCR 2003).

Diapause is a period during

The mountain pine beetle is indigenous to western North American pine

which growth or develop-

forests. Under normal conditions – which include low winter temperatures

ment is suspended and

that reduce populations by killing the larvae – the beetles occur at endemic

physiological activity is

levels and cause less than 2 per cent mortality in forest stands. Currently,

diminished, as in certain

however, the species is at epidemic levels and is the most damaging biotic

insects in response to

disturbance agent in mature lodgepole pine stands in western Canada (Hélie

adverse environmental

et al. 2005). The abundance of mature lodgepole pine, coupled with warmer,

conditions.

drier summers and infrequent cold winters has altered the balance between


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pest and host in these forest ecosystems. Other contributing factors include the beetle’s temperature-controlled development cycles and lack of diapause (Hamann and Wang 2006). As shown in Table 1, the mountain pine beetle epidemic affects forest ecosystems, climate, disturbance, forest harvesting, values and environmental services, and human communities and the economy (Kimmins et al. 2005).

Table 1. Effects of mountain pine beetle (Kimmins et al. 2005) Component affected by the mountain pine beetle

Forest ecosystems

Climate

Disturbance

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Sub-component

Atmosphere Vegetation Terrestrial and aquatic food chains Water Soil Temperature Rainfall Snow CO2 uptake and release Human-caused disturbances: Climate change Hunting Trapping Fishing Logging Fire Mining Recreation Manufacturing Non-human–caused disturbances: Climate change Wildfires Wind Floods Avalanches Landslides Insects Diseases Exotic species



Forest harvesting

Values and environmental services

Human communities and the economy

Plant products: Timber Berries Mushrooms Floriculture Terrestrial food chains: Hunting Trapping Aquatic food chains: Fishing Social aspects: Recreation Aesthetics Shelter Cultural values Spiritual values Existence values Employment Wealth creation Resources Environmental aspects: Wildlife Fish Biodiversity Watershed Carbon storage Avalanche protection Slope and soil stability Clean air Economy markets Manufacturing Transportation Parks and protected areas Employment Food

More than 12 million ha of pine forest in BC are at risk of infestation. In 2006, the infestation covered more than 9.2 million ha (BCMoFR 2007). Figures 5 and 6 show the predicted cumulative extent of the epidemic from 2006 to 2024.


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The epidemic is expected to create a short-term increase in economic activity, as the timber harvest levels are increased to make use of dead trees before they decay and lose their commercial value. This short-term surplus of timber will be followed by a significant reduction in allowable annual cut (AAC) levels as the epidemic runs its course and dead trees reach the end of their economic usefulness.

Figure 5. Cumulative extent of the BC mountain pine beetle outbreak in 2006 (Eng et al. 2005)

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Figure 6. Cumulative predicted extent of the BC mountain pine beetle outbreak by 2024 (Eng et al. 2005)

The mountain pine beetle epidemic also has consequences for agriculture, tourism, conservation, wildlife habitat, and biological diversity objectives (ProvBC 2005). British Columbia’s Mountain Pine Beetle Action Plan 2006 -

2011 calls for:

encouraging long-term economic sustainability for communities affected by the epidemic,

maintaining and protecting public health, safety and infrastructure,

recovering the greatest value from dead timber before it burns or decays, while respecting other forest values,


25


conserving the long-term forest values identified in land-use plans,

preventing or reducing damage to forests in areas that are susceptible but not yet experiencing epidemic infestations,

restoring the forest resources in areas that have been affected by the epidemic, and

In British Columbia, the

maintaining a project management structure that ensures coordinated and effective planning and implementation of mitigation measures.

allowable annual cut (AAC) is the amount of timber that

The Action Plan identifies an urgent need to ensure that conservation

the provincial government

objectives are established and achieved in conjunction with mountain pine

permits to be cut annually

beetle management operations. It calls for the following actions:

from a particular area. The AAC is used as the basis

Work with land-use plan monitoring committees and stakeholders to

for regulating harvest levels

ensure that beetle management and timber-salvage activities are carried

to ensure a sustainable

out in a way that respects the values identified in land-use plans.

Ensure that parks and protected areas management incorporates an assessment of the impacts of the epidemic on conservation values.

Incorporate conservation objectives into timber-salvage operations, leaving some areas unharvested as temporary conservation areas. (For a definition of “conservation area,” see section 3.1 in this report.)

Assess the impact of the epidemic on the full range of forest values, to provide information for future management decisions.


supply of timber.

26



3.0

Ecosystem-Based Management Approaches

In spite of a number of government-sponsored land-use planning exercises over the past decade in British Columbia, such as the Forest Practices Code, land and resource management planning, and the Protected Areas Strategy, many species are in decline, and more species are listed each year as

In British Columbia,

threatened or endangered.

approximately 95 per cent of the land base is public

Land-use and resource-management planning are undergoing a shift. Planners

(Crown) land that is

are starting to incorporate ecosystem-based management (EBM) approaches

managed primarily by the

as a basis for sustaining ecosystems and their underlying processes, and for

provincial government.

supporting the long-term sustainability of ecological, economic and social systems (for example, EBM is being implemented on the north and central BC coast: www.citbc.org/pubpcit.html.). And we have gained a better understanding of the importance of biodiversity for meeting human needs and maintaining ecological processes, and the threats that human activities pose to its maintenance. But we still face immense challenges in understanding the dynamic forces that organize natural systems, and in how we can use this knowledge to inform management and conservation actions across multiple scales (Johnson 1995; Green et al. 2005). Ecosystem-based management approaches recognize that humans are an integral part of many ecosystems. They also recognize that decisions about conserving biodiversity and managing natural systems are societal choices. These approaches recognize the following:

Human values play a significant role in ecosystem-management goals.

Humans are both agents of ecological change and participants in the effects of change.

We should seek an appropriate balance between conservation and use of biological diversity.

We must use appropriate spatial and temporal scales.

We should manage at ecologically appropriate scales that utilize ecosystem rather than political boundaries.

We need to change the structure of land-management agencies in order to work with ecosystem boundaries and ideas.


27


Management should be decentralized to the lowest appropriate level.

Management must recognize that change is inevitable.

In order to maintain ecosystem services, we should make conservation of ecosystem structure and functioning a priority.

We should also count, as priorities: maintaining viable populations of native species, ecosystem representation, and ecological processes; longterm management; and human use with constraints.

We should set objectives for ecosystem management for the long term, given that varying temporal scales and lag-effects characterize ecosystem processes.

Ecosystem managers should consider what effects their activities have on adjacent and other ecosystems.

There is usually a need to understand and manage the ecosystem in an economic context. Any such ecosystem management program should: {

reduce those market distortions that adversely affect biological diversity,

{

align incentives to promote biodiversity conservation and sustainable use,

{

internalize costs and benefits in the given ecosystem, to the extent feasible,

{

involve all relevant sectors of society and scientific disciplines, and

{

consider all forms of relevant information, including scientific, indigenous and local knowledge, innovations and practices (Grumbine 1997; Quinn 2002; Smith and Maltby 2003).

Ecosystem-based management is an interactive, ongoing cycle of four overlapping planning functions within and across scales. These planning functions are: assessment, design, integration and implementation. Assessment refers to a range of ecological, biophysical, cultural and socioeconomic inventory and analysis. From this, we develop the information we need to design, integrate and implement at various planning scales, and to monitor the outcomes of management activity (Cardinall et al. 2004).

28



3.1

Conservation Planning In a conservation area, the

At the core of ecosystem-based management is the balance between

primary management

protecting ecological integrity and maintaining high levels of human well-

concern is the conservation

being. To achieve this balance, we turn to conservation planning, to develop a

of specific biota or

system of conservation areas that will protect and help sustain important

environmental features

ecological, cultural and social values.

(Groves 2003). Following Sarkar et al. (2006), Groves

3.1.1

(2003), and others, the

A Comprehensive Approach

term “conservation area” is

A comprehensive conservation planning approach satisfies four criteria for

preferred to “reserve,” as

identifying conservation areas (Noss 1987; Groves 2003):

reserves are one extreme in a continuum of policy

Representativeness: Conservation areas should represent the biological

options for management ,

features and range of environmental conditions under which they occur.

which can range from

Resiliency: The elements of biodiversity selected in the conservation

reservation to restoration of

planning process (e.g., species and ecosystems) should be resilient to both

degraded areas.

natural and human-caused disturbances.

Redundancy: The elements of biodiversity selected in the conservation planning process should be represented multiple times within the system of conservation areas. This works as insurance against extinction or endangerment from natural and human-caused events.

Restoration: Planners should evaluate when, where and how restoration

Equilibrium island

can play a part in increasing the viability and integrity of the elements of

biogeography theory holds

biodiversity that are or have been selected in the conservation planning

that the number of species

process.

found on an island (the equilibrium number) is

Sarkar et al. (2006) note that successful conservation planning needs to

determined by two factors:

ensure the persistence of biodiversity by accommodating ecological,

the effect of distance from

evolutionary, and socio-political processes. Safeguarding the persistence of

the mainland, and the effect

biodiversity in a conservation-area network draws on the following concepts:

of island size. These factors would affect the rate

Biogeographical theory: A conservation-area network should consist of

of extinction on the islands

large circular conservation areas that are close together and linked by

and the level of

corridors. However, the authors caution against applying “equilibrium

immigration. (Wikipedia)

island biogeography theory” to terrestrial conservation areas, as there is


29


little evidence to support the analogy between oceanic islands and terrestrial reserves.

Metapopulation dynamics: Many species are distributed across

A metapopulation is a

landscapes as metapopulations. When planners are prioritizing areas for

“population of populations,”

conservation, they should include sites that provide connectivity between

or a system of local

local populations, to support migration and hedge against local

populations connected by

extinctions.

movements of individuals

Successional pathways: A conservation-area network should represent

among the population units

successional stages that correspond to the habitat requirements of

(Hilty et al. 2006).

conservation targets – in this case, species.

Spatial autoecological requirements: A conservation-area network should represent at least a minimum viable population for each species. That said, viability estimates are currently not available for the majority of species.

Source-sink population structures: When species have a source-sink population structure, in which a small percentage of habitat provides the most recruits for other habitat sites, planners must assign high conservation priority to the source (or core) habitats.

Effects of habitat modification: Conservation areas in fragmented landscapes require special management to safeguard conservation target persistence.

Species as evolutionary units: Planner should give higher priority to sites with physical properties that are thought to encourage speciation, and/or to sites containing taxonomically distinct species.

Davis et al. (2006) outline five objectives for assigning priorities for conserving biodiversity. These are:

Conserve hotspots of rare, endemic, threatened, and endangered species.

Conserve under-represented species and community types.

Conserve extensive wildlands for large carnivores and other areadependent species.

Conserve biophysical landscapes in order to maintain ecological and evolutionary processes.

30

Expand existing reserves.



3.1.2

The “Coarse- and Fine-Filter” Approach

In the 1970s, The Nature Conservancy, and subsequently Noss, and others, developed the “coarse- and fine-filter” approach for identifying conservationarea networks (Noss 1987; Jenkins 1996; Groves et al. 2000; Margules and Pressey 2000; Groves 2003; Floberg et al. 2004; Pryce et al. 2006a). This approach can generally be described as follows:

Identify conservation targets. Conservation targets are selected to represent the full range of biodiversity in a region, and to include any elements of special concern. The coarse-filter

Conservation targets are

approach hypothesizes that conservation of multiple, viable examples of all

those elements of

communities and ecological systems (i.e., coarse-filter targets) will also

biodiversity (e.g., plants,

conserve most species that occupy them. This is a way to compensate for the

animals, plant communities,

lack of detailed information on numerous poorly studied invertebrates and

ecological systems, etc.)

other organisms.

that are included in the conservation plan.

Fine-filter targets are species that we cannot assume will be captured by coarse-filter targets. Special efforts are required to ensure that fine-filter targets are represented in the conservation plan. These targets are typically rare or imperiled species, but they can also include wide-ranging species that require special consideration, or species that occur in other regions but which have genetically important disjunct populations within the region of concern.

Assemble information on the locations of targets.

Data on target occurrences (i.e., the location, and in some cases, spatial extent of a separate population or example of a species or community) are assembled from a variety of sources. Most data are gathered from existing agency databases. The assembled data for plant and animal targets are screened based on the date and spatial accuracy of the record. Decisions are then made about the best way to describe and map occurrences of each target. Targets are represented as specific location points, such as the location of rare plant populations, or polygons that show the spatial extent of fine- or coarsefilter targets. The data are stored in a Geographic Information System (GIS).


31


Assess existing conservation areas for their biodiversity values.

We must identify the biological features that are already under appropriate management within existing conservation lands and waters. This is an especially important step, given that limited conservation funds are available for establishing new conservation areas, and biological survey data for most of the world’s existing protected areas are lacking.

Set conservation goals. A conservation goal describes how target occurrences should be distributed across the region to best represent genetic diversity and environmental A conservation goal is the

variation. Goals also provide an estimate of how much effort will be needed

number, in a specific

to sustain those targets well into the future.

region, of populations, nest sites, or breeding sites for

Establishing conservation goals is difficult. Information on most extant

species targets, or the

targets is limited, so it’s hard to estimate the number and distribution of

amount of area for systems

occurrences that are needed to ensure the targets’ survival. For that reason,

targets that is needed to

the goals cannot be treated as conditions that ensure long-term survival of the

represent each target.

targets. However, goals are useful tools for assembling a network of conservation areas that capture multiple examples of the region’s biodiversity. The goals also give us a means of gauging the contribution of different portions of the region to the conservation of its biodiversity, and the progress of conservation in the region over time.

Rate the suitability of each area for conservation.

Each planning unit is compared to others using a set of factors that determine its “suitability” for conservation, or its likelihood of successful conservation. Planning units are the

Factors such as the extent of roads or developed areas, or the presence of

basic building blocks

dams in rivers are likely to affect the quality of habitat for native species.

around which GIS data and

Other factors, such as proximity to urban areas or the existence of established

other information are

conservation areas, may affect the cost of managing the area for conservation.

organized for use in reserve-selection models

and spatially explicit

Develop a network of conservation areas. Conservation assessments typically include hundreds of different targets at

conservation planning.

thousands of locations. The relative biodiversity value and conservation suitability of these potential conservation areas must be evaluated; experts cannot select the most efficient and complementary set of conservation areas

32



based on simple inspection. To address the complexity and large amount of data used in the analyses, we use reserve-selection algorithms, in which target occurrence and suitability data are attributed to each planning unit. Reserveselection algorithms are designed to meet target goals in the smallest area

A reserve-selection

possible, while maximizing suitability.

algorithm is a computer program that is applied to

the problem of finding the

Refine the network of conservation areas through expert review.

We need expert review and revision to compensate for data gaps and/or other

smallest possible set of

limitations (including errors of omission and inclusion) in the automated

areas that will achieve a

reserve-selection process.

nominated conservation goal (Pressey 1998).

3.1.3

Conservation-Planning Tools

In the BC Mountain Pine Beetle Action Plan, the provincial government has recognized that identifying conservation areas to protect the region’s biodiversity is a high priority. The tools for identifying specific areas for protecting and restoring biodiversity are well developed and are used worldwide to support major decisions about the use of land and water (Carwardine et al. 2006). Over the last decade, more-systematic methods for conservation planning have been developed, many of which address how best to maximize conservation gains while minimizing “costs” (Snelder et al. 2007). Spatially explicit approaches to identifying conservation-area networks that meet specified conservation goals (such as achieving a minimum number of separate populations of a group of species or communities), are now widely available. Most of these analytical tools run on personal computers, are freely available, and can be used to identify critical sets of conservation areas over any area for which data are available (Trombulak 2003).1

1

Sarkar et al. (2006) provide a review of the concepts and techniques on which conservation-planning tools are based, and identify some of the issues with these tools. The Ecosystem-Based Management Tools Network database (www.smartgrowthtools.org/ebmtools/index.php) also lists more than 90 decisionsupport tools that are used: to characterize the context for ecosystem-based management; to engage stakeholders; to set goals, analyze scenarios, select and implement actions; and in monitoring and adaptive management strategies. (Using the tools requires a range of technical and scientific expertise and training.)


33


To systematically design conservation-area networks, we need to:

identify problems and structure conservation goals, objectives, and constraints,

model and generate potential conservation-area network solutions using optimization methods or algorithms,

evaluate the potential conservation-area networks with a sensitivity analysis of the parameters we use, and develop action/implementation plans (Siitonen 2003).


Systematic conservation-area network design involves constructing a mathematical programming problem, which is referred to as a “reserveselection algorithm,” “site-selection algorithm,” or “area-prioritization method”2 (Moilanen et al. 2006).

2

Note that many of the terms used in describing reserve-selection models have different meanings, depending on the model. For example, “conservation target” and “conservation goal” are used to mean the same or different things, depending on the model. “Conservation feature” is used synonymously with “conservation target.” And “conservation-area network” is used interchangeably with “reserve network/system,” although the former is preferred, as discussed earlier.

34



Most of the problems solved by these algorithms are formalized as “constrained optimization problems” (Sarkar et al. 2006). These can be solved either by minimizing the area required to meet specified conservation goals for species or habitat types, or by maximizing the representation of habitats or species in a fixed area or number of sites (Kirkpatrick et al. 1983; Davis et al. 2006). These algorithms strive to achieve:

conservation “efficiency,” which is the ability to represent regional biodiversity in the least number of available sites, and

“complementarity,” which is the gain in “representativeness” of biodiversity – relative to conservation targets – when a site is added to the conservation-area network, so that there is minimum overlap and

Cost is a component of a

minimum redundancy of targets (Possingham et al. 2000).

reserve-selection algorithm (e.g., Marxan) that

The “cost” of the conservation-area network is also taken into account. Cost

encourages the algorithm

can be defined by the total area or number of sites in the conservation-area

to minimize the area of the

network. Cost can also be calculated using more-complex variables that take

conservation-area network.

into account the human population living in the region, the price of land, the

It does this by assigning a

agricultural potential of the area, the probability of habitat loss, and the

penalty to factors that

spatial aggregation of conservation areas to minimize transportation and other

negatively affect

management costs (Filho and Telles 2006).

biodiversity, such as proximity to roads and

The sections that follow describe three well-known reserve-selection models,

development. “Cost” is

as well as some other useful concepts, that we can apply to an ecosystem-

used synonymously with

based management decision-support framework for the Central Interior

“suitability.”

ecoregion.

3.1.1.1

Marxan

Marxan (Marine Reserve Design using Spatially Explicit Annealing) is standalone optimization software developed by Ian Ball and Hugh Possingham at the University of Queensland, Australia to help design a marine-reserve system for the Great Barrier Reef. Linked to a GIS, Marxan is used to select conservation areas based on their biological value and suitability for conservation (www.ecology.uq.edu.au/index.html?page=27710).


35


Now used in a variety of terrestrial and aquatic conservation-planning settings around the world, Marxan automates the planning process so that users can assess many different scenarios and resulting conservation-area networks (Ball and Possingham 2000). This helps planners explore alternative management options, and in multiple-stakeholder negotiations. Marxan is based on the premise that there are a large number of potential sites (or planning units) from which to select a conservation-area network. It uses GIS data and an optimization algorithm to achieve a reasonably efficient solution to the problem of selecting a network of spatially cohesive conservation areas that meet a suite of conservation goals simultaneously. The conservation-area network is made up of a selection of planning units that will satisfy a number of criteria. For instance:

certain species or ecosystem types are well-represented within the conservation-area network,

all defined habitat types are sufficiently represented by the conservationarea network, and

the conservation-area network does not unnecessarily affect resourceextraction activities within the region.

Marxan also considers that, while biodiversity conservation objectives may maximize the area within the conservation-area network, there are social, We use a Monte Carlo

economic, and management constraints that limit its size (Possingham et al.

procedure to obtain a

2000).

probabilistic approximation to the solution of a problem

Marxan uses “simulated annealing,” a Monte Carlo procedure, to minimize

by using statistical

the value of its objective function, which is to find the lowest-cost

sampling techniques.

conservation-area network. The application uses an algorithm that minimizes the cost and boundary length of the conservation-area network, while meeting all identified conservation goals. Marxan evaluates the effectiveness of its conservation-area network by measuring cost against goals and calculating whether a particular change to a conservation-area network would improve its effectiveness. Successful (i.e., effective) solutions have the lowest costs.

36



Cost is defined as a cost for each planning unit included in the conservationarea network, and a penalty for not achieving goals for each conservation target. Marxan treats goals as objectives rather than constraints; the goal for a conservation target can remain partly unmet if a large cost would be incurred to meet it completely. The emphasis on meeting goals can be adjusted with the target “penalty factor.” There is also an option for including a boundary-length modifier (BLM), which can be altered to control the relative importance of minimizing the overall boundary length of the conservation-area network, while still minimizing its area, thereby maximizing compactness (Ball and Possingham 2000). The conservation-area network cost consists of the following parts: • Combined planning unit cost Each planning unit is assigned a cost value based on its area, financial value, opportunity cost of being protected (e.g., lost income from farming), or any other relevant factor. Marxan calculates the combined cost of all of the


planning units in the conservation-area network.


37


• Boundary cost The boundary cost measures the amount of edge that the planning units in a conservation-area network share with unprotected units. This means that a conservation-area network containing one connected patch of units will have a lower boundary cost than a number of scattered, unconnected units. The cost is quantified as the length of edge in metres, kilometres, or any other measurement unit. Marxan then multiplies this value by the boundary length modifier constant (BLM), which is a user-defined number. Increasing this number increases the cost of having a fragmented conservation-area network. • Species penalty factor (or target penalty cost) Marxan calculates whether or not the conservation goal for each conservation target is met by a conservation-area network, and it includes a cost for any goal that has not been met. A separate penalty value can be set for each conservation target. These penalty values are referred to as “species penalty factors.” The total cost of a conservation-area network combines these three costs and is calculated as: Cost = combined planning unit cost + (boundary cost x BLM) + combined species penalty factors To achieve its objective function, Marxan incorporates three basic elements: iterative improvement; random cost increases; and repetitiveness (Smith 2004). Marxan initializes simulations with a set of planning units selected at random from the larger dataset. Marxan then randomly adds planning units to or removes them from the set in a series of iterations, with the value of each new set compared with that of the previous set. Changes that improve the set, as defined by the objective function, are always retained. Other changes are accepted, with some probability that diminishes over the course of the iterations. The rate of diminishing probability is set by an “annealing schedule.” By occasionally accepting bad changes, the algorithm is able to explore all possible sets, avoiding local optima in search of the global optimum that will eventually be found – given sufficient iterations (Cook and Auster 2005).

38



3.1.1.2

C-Plan

C-Plan was developed by Bob Pressey and Mathew Watts of the New South Wales National Parks and Wildlife Service and the University of New England in Armidale, New South Wales in Australia (www.uq.edu.au/~uqmwatts/cplan.html). C-Plan is designed to calculate and display information (e.g., tables, maps, or diagrams) that can be used to guide conservation-planning decisions. This information includes:

the characteristics (in terms of the biodiversity targets they contain) of any of the sites involved in the assessment,

the collections of sites that have various combinations of characteristics,

the extent to which the conservation goal for any particular target (e.g., a

species or an ecosystem) has been reached by conservation decisions

Irreplaceability has been

made to that point, and

defined a number of

a log of the reasons for making all of the decisions.

different ways (Noss et al. 2002; Leslie et al. 2003;

C-Plan is used:

Stewart et al. 2004; Ferrier et al. 2000). The original

as an interactive tool during negotiations over land-use planning,

operational definition was

as an aid to planners in identifying alternative conservation-area networks

created by Pressey at al.

to meet conservation goals, and

(1994). They defined the

for simulating alternative futures for a study area.

irreplaceability of a site as

the percentage of

The C-Plan reserve-selection model is based on an algorithm that generates

alternative conservation

statistical estimates of “irreplaceability” – an indication of the likelihood a

area networks in which it

planning unit is required to achieve conservation goals for all conservation

occurs. (See also section

targets – without considering spatial objectives. The process involves the

3.1.1.4., following.)

initial calculation of the “combination size,” which is an estimate of the number of sites needed in the conservation-area network to meet the set of conservation goals. The irreplaceability of each site is then estimated, as the extent to which options for achieving the conservation goals, across all possible sets of sites of the combination size, are reduced if the site is made unavailable for conservation. Irreplaceability provides a measure of how essential the site is to the conservation-area network solution, based on how much of the conservation target is contained in other sites, and how many


39


times it is essential that a given site be part of a combination with other sites in order to meet the conservation goals. Irreplaceability values are calculated dynamically, as areas are added to or removed from the conservation-area network (Carwardine et al. 2006; Ferrier et al. 2000). The irreplaceability of a site can be used as a guide to the importance of that site for achieving a regional conservation goal. Highly irreplaceable sites are crucial for achieving the goal, and failure to select them usually means that the goal for one or more conservation targets cannot be achieved. Sites with a low irreplaceability are less crucial, because there are other sites that can contribute as much or more to achieving the conservation goal. However, some sites with low irreplaceability must be selected, if the regional goal is to be achieved (Pressey et al. 2005). The “Minset” feature allows the user to build an algorithm to select one or more sites in an iterative search. Each time a site is selected, using the algorithm, C-Plan recalculates all site indices and selects the next site using the same algorithm and the updated site indices. With this feature, it is possible to minimize any predefined costs (e.g., timber resource, land area selected, or acquisition cost) when selecting sites. It is also possible to enter a stopping condition that will stop site selection when the resource remaining drops to a specified level. C-Plan is interactive, in that it can recalculate and redisplay measures when one or more sites are selected for conservation. All recalculations take into account any changes. The result is mapped back into the GIS, to display a new pattern of options. All calculations in C-Plan are based on a matrix of sites and conservation targets, and are driven by the conservation goals. These may be an area or a number of localities of each species, an ecosystem, or another feature identified as requiring some form of conservation management. Again, the conservation goals are updated each time one or more sites are selected. Updating conservation goals changes the area or number of sites needed for conservation, according to their extent in the selected sites. In the GIS, sites are coloured according to whether they have been selected in C-Plan, or according to their current irreplaceability value. The patterns of

40



irreplaceability in a region change if the conservation goals for conservation targets are changed. Higher conservation goals for all targets will increase the irreplaceability of sites, effectively reducing the options for achieving all conservation goals. Lowering conservation goals will lower the irreplaceability values of most sites and produce more options for designing the conservation-area network. Raising the conservation goals for some conservation targets and lowering others can produce a similar range of irreplaceability values, but could alter the spatial pattern of those values. How conservation goals are set should reflect the broad aims of the project and


decisions made at a policy level (Pressey et al. 2005).

Carwardine et al. (2006) reviewed Marxan and C-Plan. They set Marxan to generate multiple conservation-area networks that met conservation goals with minimal area. The first scenario ignored spatial objectives, while the second selected compact groups of conservation areas. Marxan calculates the irreplaceability of each site as the proportion of solutions in which it occurs for each of the set scenarios. By contrast, C-Plan uses a statistical estimate of irreplaceability as the likelihood that each site is needed in all combinations of sites that satisfy the conservation goals. The authors found that Marxan and C-Plan gave similar outcomes when spatial objectives were ignored, and that conservation goals for all ecosystems were met using a similar amount of area. How important differences were, in the outcomes of the two methods,


41


depended on the question being addressed. The authors concluded that, in general, using two or more complementary tools for conservation planning was beneficial.

3.1.1.3

Zonation

Zonation was developed by Atte Moilanen at the University of Helsinki (www.helsinki.fi/bioscience/consplan/index.html). It is based on an algorithm that identifies areas that are important for retaining habitat quality and connectivity for multiple species. Zonation can be used to answer two common conservation planning questions: 1. Which parts of the landscape have the highest conservation priority? 2. Which part of the landscape includes at least x per cent of the distribution of each species? Zonation prioritizes the landscape in a hierarchical way, based on the conservation value of a planning unit. This is done by iteratively removing the least-valuable planning unit from the landscape, using minimization of marginal loss as the criterion to decide which planning unit is removed next, until no planning units remain. During planning-unit removal, Zonation calculates information about the decline in the levels of representation of conservation targets (e.g., species). Sites are ranked based on biological value, and the least-valuable planning units are removed, one or more at a time. This produces a sequence of landscape structures with increasingly important biodiversity features. Zonation produces a nested ranking of planning units. It also produces a set of curves that describe the performance of the solution at various levels of planning-unit removal. In this way, landscapes can be zoned according to their value for conservation. Zonation has three different planning-unit removal rules (Moilanen and Kujala 2004):

Core-area Zonation

Planning-unit removal minimizes biological loss by choosing the planning unit that has the smallest value for the most valuable occurrence over all

42



species in the planning unit. The removal is done by calculating a “removal index” for each planning unit. This rule is most appropriate to use when: {

there is a definite set of species, all of which are to be protected,

{

the user does want hierarchy of solutions and easy weighting of species, and

{

importance is given to core-areas – locations with the highest


occurrence levels.

Additive-benefit function

This takes into account the proportions of all species in a given planning unit, instead of the one species that has the highest value. Because the additivebenefit function sums values over all species, the number of species in a planning unit has a higher significance as compared with basic core-area Zonation. This rule is most appropriate when: {

the species are essentially surrogates or samples from a larger regional species pool, and trade-offs between species are fully allowed, and

{

the user does want hierarchy of solutions and easy weighting of species.


43


Target-based planning

This uses a benefit function to enable the Zonation process to converge on a solution that is close to the proportional coverage minimum set solution for the data. This rule is most appropriate when: {

the user knows, accurately, what proportion of the landscape can be had, and the hierarchy is not needed,

{

there is a definite set of species, all of which are to be protected,

{

occurrences are additive, and

{

the user does not need easy weighting of species.

Zonation also has three different options for aggregating planning units. Choosing the right one depends on conservation targets and computational issues (Moilanen and Kujala 2004):

Boundary-length penalty

This is the most commonly used option. It is a general, non-species–specific aggregation method that does not assess the actual effects of fragmentation on species. This method uses a penalty on a structural characteristic of the conservation-area network (i.e., boundary length) to produce more-compact conservation-area network solutions. This method is computationally quick and effective, but it might not be the most biologically realistic. It is qualitative, in the sense that the estimated conservation value of individual planning units – or, consequently, the conservation value of the entire conservation-area network – is not influenced by the degree of fragmentation. Rather, the user induces an aggregation via a penalty that he or she levies for the boundary length of the conservation area.

Distribution smoothing

This is a species-specific aggregation method. It retains areas that are wellconnected to others, so provides a more compact solution. Smoothing can be considered a two-dimensional habitat-density calculation that identifies areas of high habitat quality and density. This method assumes that fragmentation is generally bad for all species; it always favours uniform areas over patchy ones.

44



Boundary quality penalty

This is biologically the most realistic aggregation method in Zonation. It describes how the local value of a site for a species is influenced by the loss of surrounding habitat. The change in local value is based on species-specific responses to neighbourhood habitat loss, so local value may increase if the site includes species that benefit from fragmentation. The boundary quality penalty is a quantitative, species-specific way of inducing aggregation into Zonation solutions. It is also a way of approximating non-linear effects of connectivity that may be present in habitat models. Zonation’s outputs provide an analysis of conservation value. They should be fed into a broader planning framework, in which socio-economic and political values are balanced between different land uses.

3.1.1.4

Irreplaceability

Reserve-selection models generate numerous alternative sets of sites that can meet conservation goals, and many of them might be similarly efficient in terms of cost (such as total area required to meet conservation goals). Looking at a single conservation-area network solution gives no indication of the importance of each site, in terms of the potential to replace it with others in the region. Planners cannot determine if the site is unique in its contribution to conservation goals, or if the management of specific sites is open to negotiation with other stakeholders. The concept of irreplaceability was discussed earlier, under “C-Plan” in section 3.1.1.2. Irreplaceability provides a quantitative measure of the relative contribution different areas make to reaching conservation goals. This helps planners choose among alternative sites in a conservation-area network. As noted by Pressey (1998), irreplaceability can be defined in two ways: the likelihood that a particular area is needed to achieve an explicit conservation goal; or the extent to which the options for achieving an explicit conservation goal are narrowed if an area is not conserved.


45


Figure 7. Terrestrial irreplaceability index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)

Irreplaceability can be measured as a continuum of values between 0 and 1, in which sites with values of 1 are essential for achieving one or more conservation goals and are, therefore, irreplaceable. As values decrease from 1, a site has increasing numbers of potential replacements and becomes more replaceable. Measures of irreplaceability can be used to determine priorities for action, particularly since lack of resources and other competing land uses can prevent the achievement of all conservation goals. (See also section 3.1.1.6., following.)

46



Figure 8. Freshwater irreplaceability index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)

3.1.1.5

Conservation Utility

The concept of irreplaceability can be expanded upon by using “conservation utility”(Rumsey et al. 2004). This is calculated by running the particular reserve-selection algorithm with the planning-unit costs, incorporating a conservation “suitability index.” To generate irreplaceability values, planning-unit cost equals the planning-unit area. To generate conservationutility values, planning-unit cost reflects practical aspects of conservation


47


(e.g., current land uses, current management practices, habitat condition, etc.) In effect, conservation utility is a function of both biodiversity value and the likelihood (cost) of successful conservation.

Figure 9. Terrestrial Conservation Utility Index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)

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Figure 10. Freshwater Conservation Utility Index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b) 3.1.1.6

Conservation Value

Irreplaceability has also been developed into a broader measure of conservation value that is applied to each planning unit. Rumsey et al. (2004) calculated conservation value as a composite measure, scaled between 0 and 1, based on the following four criteria:

Rarity

This is the degree to which rare elements are represented within the planning unit. Rarity was calculated by assigning a rarity score to all global rankings


49


(G-ranks) for species. (For an explanation of G-ranks, see the Glossary at the end of this report.) A score of 1 was given to all G3 targets, 2 to all G2 targets, and 3 to all G1 targets. Targets that did not have G-ranks were assigned rarity scores of 1 for all Limited, Disjunct, and Peripheral targets, or 3 for Endemic targets. The rarity scores were then summed and scaled from 0 to 1.

Richness

This is a measure of the overall abundance of target elements and systems within the planning unit. Richness was quantified by first calculating the total amount of each target in the planning unit (i.e., number of occurrences, hectares, stream length, etc.) and expressing that as a proportion of the total amount found within the entire ecoregion. The richness score for the planning unit was then taken as the mean proportion of the total amount available in the ecoregion, for each target.

Diversity

Diversity is an assessment of the variety of elements and systems within a planning unit. Diversity was scored according to the number of different target types present within the planning unit.

Complementarity

This is a measure based on the principle of selecting conservation areas that complement or are “most different” from sites that are already conserved. The score for planning-unit complementarity was generated from the “sum runs” of the reserve-selection algorithm (SITES) analysis. “Sum runs” is the number of times each planning unit was selected by the algorithm in running it 20 times. Each run consisted of the algorithm going through 1 million iterations to arrive at a solution. Planning units were then assigned a conservation value by adding all four factors together and rescaling the result from 0 to 10.

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Figure 9. Conservation value index for the Canadian Rocky Mountains Ecoregional Assessment (Rumsey et al. 2004)

3.1.1.7

Vulnerability

Another key consideration in conservation planning is threat or vulnerability (Margules and Pressey 2000). The purpose of a conservation-area network is to mitigate at least some of the processes that threaten biodiversity. Incorporating information about threatening processes – and the relative


51


vulnerability of areas and features to these processes – into planning tools is therefore crucial for effective conservation planning (Sarkar et al. 2006). Margules and Pressey (2000) defined vulnerability as the risk of an area being transformed by extractive uses, but it could be defined more broadly as the risk of an area being transformed by degradative processes. This broader definition encompasses adverse impacts from stressors such as invasive species and fire suppression.

Figure 10. Terrestrial cost (or suitability/vulnerability) index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)

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Vulnerability could also be defined from the perspective of target species – the relative likelihood that target species will be lost from an area. Since target persistence depends on habitat, a vulnerability index would be a function of current and likely future habitat conditions. Future habitat conditions are generally determined by the management practices and policies associated with an area. The cost (or suitability) index can be used to incorporate factors that reflect both current habitat conditions and management. Therefore, the cost/suitability index could also be used as a vulnerability index (see Figures 12 and 13).

Figure 11. Freshwater cost (or suitability/vulnerability) index for the Okanagan Ecoregional Assessment (Pryce et al. 2006b)


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It can be argued that the more vulnerable or threatened an area is, the greater the urgency or need for conservation action. Based on available quantitative threat data (e.g., human population growth, development trends, and road density), a coarse vulnerability score for each planning unit can be created.

3.2

Spatial and Temporal Dynamics

Both ecosystems and human communities are dynamic and complex systems that are constantly changing, because of natural and anthropogenic factors. Something that is

Management approaches based on a static view of natural and social systems

anthropogenic results from

have led and will lead to further losses of biodiversity (Heckl et al. 2003).

or is produced by human

Static maps, analyses, and decision-support tools are limited in their ability to

beings.

predict conservation implications and opportunities over time. Conservation planning and subsequent implementation can take years to decades, and involve complex negotiations and land purchases – all the while, conservation objectives will compete with other land-use practices. Land that is available for conservation will be limited by political, economic, and social constraints. As well, degrading processes will continue to operate at various spatial scales (Possingham et al. 2000; Siitonen 2003). A major challenge for conservation planners is to develop strategies that combine planning for biodiversity processes with implementation that is

Stochastic events are

constrained by land-use dynamics (Sarkar et al. 2006). “Robust and feasible

determined by a random

approaches for incorporating temporal dynamics and various types of

distribution or pattern of

uncertainty represent one of the greatest challenges to theories and methods

probabilities. Their behav-

for reserve network design” (Possingham et al. 2000, p. 9-10). For

iour may be analyzed

conservation planning tools to incorporate spatial and temporal dynamics,

statistically, but not

they would need to explicitly model many stochastic events, such as site

precisely predicted.

destruction and degradation, financial costs and opportunities, and changes in public interest in conservation relative to other land uses. Combining all these factors in a single model would be almost impossible, but incorporating some of these factors into the conservation planning process is essential (Possingham et al. 2000).

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For a decision-support system to be useful, it must allow planners to explore conservation implications of varying landscape scenarios over time. For example, possible changes to landscapes because of a variety of anthropogenic and natural factors can be predicted. So the decision-support system would allow users to assess how predicted future landscapes could

A reserve network is a

influence the ecological values they represent. One criticism of Marxan and

system of conservation

many of the reserve-selection tools is that they are unable to explicitly

areas.

consider spatial population or landscape dynamics, or the dynamics of human economic systems (Munro 2006). Efforts to incorporate spatial and temporal dynamics into the conservationplanning process are described in the following sections.

3.2.1

Ecological Connectivity

Maintaining ecological linkages is critical to the long-term viability of all species, as well as to key ecological processes. Landscape patterns that promote connectivity for species, communities, and ecological processes are a key element of conserving biodiversity in landscapes modified by human

Connectivity refers to the

activities (Bennett 2003). To address species persistence, the conservation-

degree to which a

area network should be large enough to maintain entire populations of species

landscape facilitates or

in the long term, or they should be located close enough to each other so that

impedes movement of

species could efficiently re-colonize other habitat patches (Siitonen 2003).

organisms among resource

This is complicated by the fact that a landscape is perceived differently by

patches (Sutherland et al.

different species, and so the level of connectivity varies between species and

2007).

between communities. There are two main components that influence potential connectivity for a particular species, community, or ecological process – a structural component and a behavioural component. The structural component is determined by the spatial arrangement of different types of habitats in the landscape, and is influenced by factors such as the continuity of suitable habitat, the extent and length of gaps, the distance to be traversed, and the presence of alternative pathways or network properties. The behavioural component of connectivity relates to the behavioural response of individuals and species to the physical structures of the landscape. It is influenced by factors such as the scale at


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which a species perceives and moves with the environment, its habitat requirements and degree of habitat specialization, its tolerance of disturbed habitats, the life stage and timing of dispersal movements, and the species’ response to predators and competitors (Bennett 2003).

Least-Cost Path Analysis

When assessing the functional role of habitat for species, and whether it can be accessed or can accommodate movements, we need to assess the functional spatial relationship between habitat locations in the landscape according to the movement capability of the species. This concept is implemented as a least-cost movement surface that represents the impedance – or “cost” representing the apparent degree of attractiveness of a given cell (i.e., site or planning unit) – faced by individuals as they move through the landscape. This surface can be thought of as representing the likelihood that an observer would find a moving animal in each location relative to other locations if sampled at an equivalent frequency. Therefore, higher-cost locations on the least-cost movement surface are locations at which animals are less likely to be found, relative to lower-cost locations. Once cells (or sites or planning units) are classed, the rate of movement into adjacent cells is implemented by a selection process using the least-cost surface. The higher the cost of a cell, the less likely it will be selected by the model for use for dispersal or movement, if lower-cost pathways are available. Selection of adjacent cells for movement into a given cell is random without replacement (Sutherland et al. 2007). In their conservation-area design, Heinemeyer et al. (2004) used a least-cost path modeling approach to address connectivity and generate predictions about animal movement potential across the Muskwa-Kechika Management Area. This approach models potential movement paths or corridors as the most cost-effective route connecting two points. The “cost” of movement is modeled as a combination of total distance (i.e., horizontal movement distance), topographic considerations, and habitat values (based on generalized habitat values and on the avoidance of human development features). For example, under this approach, shorter distances are preferred, but this is moderated by the cost of traversing across steep topography, a

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preference for higher quality habitats, and an aversion (cost) to moving through landscapes that contain human development features. The actual movement routes were determined based on a grid, and the costs of selecting a cell to move into were based on a cost score. Four factors were used to determine the cost score of movement from one cell to another:

Distance cost modified by surface distance

On a flat surface, the distance cost is set at 1 for movement between the four adjacent cells, and at 1.41 to move to diagonal cells. Additional realized surface distance is also added, if moving up or down a slope. This was calculated as the length of the hypotenuse of a right-angle triangle, calculated based on the opposite angle being set equal to degrees of slope, as calculated between the centre points of the cell. For movement to diagonal cells, the adjacent leg of the hypotenuse was lengthened to 1.41, as compared to 1, for the distance to adjacent cells, and the total hypotenuse length was calculated as above.

Vertical cost

Impact cost Impact costs reflect the friction of moving through cells that contain human development features.

Habitat cost

In addition to the influence of human use or infrastructure, vegetative characteristics can have a potentially significant influence on the paths animals choose to take across landscapes. The cost of moving to a surrounding cell was determined by these costs in the following formula: Cost = (distance cost modified by surface distance) x vertical factor x (impact cost x habitat cost) To identify paths and associated corridors, the researchers established start/end points (nodes) across the landscape, locating the nodes by the goals


57


of the analysis (i.e., permeability, core connectivity, or sheep connectivity). For each analysis, they created path-cost grids for each point or node. Pathcost grids calculate the costs of moving to the source node, starting from the cells adjacent to the source and calculating grid-cell–specific costs by sequentially moving outward. Each grid cell stores its cost value, accounting for distance from the source node, as well as characteristics that define additional costs (e.g., vertical factor, habitat costs, etc.) specific to that cell. These grids store costs encountered in movements toward the specified source node, and can be used to determine the least-cost path origination anywhere on the cost grid, and ending at the source point. To identify corridors associated with least-cost paths, the researchers defined a pathspecific threshold cost value using the highest cost accepted by the least-cost path connecting two points. The potential corridors between the two points were defined by selecting grid cells with cost values that were less than or equal to this threshold value; these areas identified linkage habitats of relatively low movement costs between two points.

Figure 12. Least-cost path analysis for Muskwa-Kechika Conservation Area design (Heinemeyer et al. 2004)

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Land-Use/Land-Cover Change

Given the interactions between human development patterns and the natural systems that support ecological processes and ecological goods and services, it is important for us to understand the rates and patterns of land-use change that result from human development. For sustainable land and resource management planning, it is important for us to address the process and pattern of past land-use change, to forecast future land-use change, and to determine the risk of those changes to the environment. Land-use change models attempt to project future changes in land use based on past trends, and on the drivers behind the conversions of land between different categories. Central to understanding the human and ecological aspects of land-use and land-cover change is an integration of social, ecological, and information sciences. Conservation-area network selection projects have generally avoided using formal scenarios of land-use change, and have rarely specified the fiscal cost of conservation action (Davis et al. 2006). Land-use change models explain past and present use, as well as projections of future land use. A number of spatially explicit, dynamic landchange models have been developed (Kareiva et al. 2005). The types of land-use change that are responsible for converting most natural areas around the world include agriculture, logging and, to some extent, mining, and urban and residential development. Agriculture is expanding in approximately 70 per cent of the countries of the world, and in two-thirds of those countries, forest area is decreasing. Extensive agricultural use has resulted in landscape-level changes in many parts of the world (Hilty et al. 2006).

3.2.2

Natural Disturbance

All forested ecosystems are subject to natural disturbance regimes of varying types, severity, frequency, and size. Disturbances can range from low severity, which only modify the understorey and leave the forest canopy intact (standmaintaining), to high severity, in which most of the forest canopy is destroyed and regeneration of the forest is required (stand-replacing) (Sutherland et al.


59


2007). The two main natural disturbances in the Central Interior ecoregion are pest outbreaks, such as the mountain pine beetle, and fire.

Fire

The dry Interior forests were characterized by frequent low-intensity, standmaintaining surface fires that cleared out brush and small trees in the forests and grasslands. Such fires left a mosaic of seral stands and openings in the forests, and prevented trees from encroaching on the grasslands. Occasionally, fire torched the tree crowns, killing large trees and creating small openings where trees, shrubs, forbs and grasses could germinate. Lethal or standreplacing fires did occur, but they were infrequent and played a minor role in shaping the landscape. Fire intensity and frequency were inherently variable, producing a natural mosaic of uneven-aged forests interspersed with grassy


and shrubby openings.

In the past 150 years, humans have significantly altered the fire regimes in the ecoregion. In the recent past, fire suppression was seen as the standard method of dealing with forest fires, but this practice has created major threats to biodiversity and natural ecological processes. Fire suppression activities, carried out primarily to protect fibre production or property, have also dramatically increased the average fire interval. The historical fire frequency

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in the dry Interior forests varied between seven and 20 years; in more recent times, most sites have not had fires for between 30 and nearly 90 years (Daigle 1996).

In their conservation plan for the Okanagan ecoregion, Pryce et al. (2006a) used spatial data on the current fire-regime condition as part of their analysis of threats to biodiversity. Fire-regime condition class is a classification of the amount of departure from the natural regime. Divergence from the historic fire regime, particularly in the dry Interior forests, negatively affects biodiversity through: excessive tree ingrowth within forest stands; tree encroachment into areas that were historically grasslands; excessive buildup of fuel, resulting in higher-severity fires; and increased incidence of pests and disease. Condition class was mapped as irregular polygons, based on historic natural fire regime, forest-cover mapping, and burn history. They considered a higher class as posing a greater threat to biodiversity.

Table 2. Fire regime condition class descriptions Condition class

Departure from historic range of variation

Class 1

Low

Attributes

Fire regimes are within or near an historical range. The risk of losing key ecosystem components is low. Fire frequencies have departed from historical frequencies by no more than one return interval. Vegetation attributes (i.e., species composition and structure) are intact and functioning within an historical range. Disturbance agents, native species habitats, and hydrologic functions are within the historical range of variability.


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Class 2

Moderate

Fire regimes have been moderately altered from their historical range. The risk of losing key ecosystem components has increased to moderate. Fire frequencies have departed (increased or decreased) from historical frequencies by more than one return interval. This results in moderate changes to one or more of the following: fire size, frequency, intensity, severity, or landscape patterns. Disturbance agents, native species habitats, and hydrologic functions are outside the historical range of variability.

Class 3

High

Fire regimes have been significantly altered from their historical range. Fire frequencies have departed from historical frequencies by multiple return intervals. This results in dramatic changes to one or more of the following: fire size, frequency, intensity, severity, or landscape patterns. Vegetation attributes have been significantly altered from their historical range. Disturbance agents, native species habitats, and hydrologic functions are substantially outside the historical range of variability.

3.2.3

Climate Change

Climate change presents a global challenge with potentially catastrophic consequences for our social and economic infrastructure, as well as for the natural environment. Climate change affects individuals, populations, species, and ecosystem composition and function. Its effects are seen and felt directly, through increases in temperature and changes in precipitation, water temperature, sea level, etc., and indirectly through changes in the intensity and frequency of disturbances such as wildfires (IPCC 2000). While the magnitude of predicted changes is uncertain, there is a high degree of confidence in the direction of changes, and that the effects will persist for many centuries. According to Stern 2007 (p. 55): “Average global temperature increases of only 1–2° C [Celsius] (above pre-industrial levels) could commit 15–40 % of species to extinction. As temperatures rise above 2–3° C, as will very probably happen in the latter part of this century, so the risk of abrupt and large-scale damage increases, and the costs associated with

62



climate change—across the three dimensions of mortality, ecosystems and income—are likely to rise more steeply.” In northern regions, such as Canada,

Climate change is a

temperature increases of 2° or 3° C could bring net benefits, through higher

statistically significant

agricultural yields, lower heating requirements, and increased tourism

variation in either the mean

potential. But these regions will also experience the most rapid rates of

state of the climate or in its

warming, which will have serious consequences for biodiversity and local

variability, persisting for an

livelihoods (Stern 2007).

extended period (typically decades or longer). Climate

Predictions about the ways in which species respond to climate change can be

change may be caused by

broadly summarized into the following categories:

natural internal processes, by external forcing

(including changes in solar

Effects on physiology

Changes in atmospheric CO2 concentration, temperature or precipitation will

radiation and volcanic

directly affect rates of metabolism and development in many animals. They

eruptions), or by persistent

will also affect processes such as photosynthesis, respiration, and growth in

human-induced changes in

plants.

atmospheric composition or in land use (CCSP and

SGCR 2003).

Effects on distributions

A 3° C change in mean annual temperature corresponds to a shift in isotherms of approximately 300 kilometres (km) to 400 km in latitude (in the temperate zone), or 500 metres (m) in altitude. Species that are capable of changing their geographic ranges relatively rapidly are expected to move upward in altitude or toward the poles in response to shifting climate zones.

Effects on phenology

Life-cycle events triggered by environmental cues, such as degree days, may be altered. The result may break the coupling of life-cycle interactions

An isotherm is a line drawn

between species.

on a weather map or chart which links all points of

Changes in the physiology, phenology, and distributions of individual species

equal or constant

will alter their competitive relationships and other interactions with other

temperature.

species. This will lead to changes in the local abundance of species, and to changes in the composition of communities. Inevitably, some species will become extinct, either as a direct result of physiological stress, or via interactions with other species. The most vulnerable species will be those with long generation times, low mobility, highly specific host relationships,


63


small or isolated ranges, and low genetic variation. Species that are unable to tolerate changed conditions within their current range, or that cannot migrate Phenology is the study of

fast enough to keep up with moving climate zones, face eventual extinction

natural phenomena that

(Lovejoy and Hannah 2005).

recur periodically (e.g., blooming and migrating),

The global warming experienced so far has had measurable impacts on the

and their relation to climate

distributions, physiology, and life cycles of a range of plants and animals.

and seasonal changes

Current evidence indicates that the distributions of some species of birds,

(Gitay et al. 2002).

mammals and insects have moved toward the poles or upward in altitude in response to shifting climatic zones. There is also increasing evidence of earlier flowering and fruiting in plants, and earlier reproduction in amphibians and birds in response to warmer temperatures (Munro et al. 2003). Conservation planning has generally assumed that climate was a constant

A degree day is the

feature of the environment, and that species distributions were effectively

measure of the speed of

constant in space and time (Cowling 1999). Or it has been ignored, because it

insect development.

was assumed that it was too difficult to predict the possible effects of climatic

Growing degree days are a

change (Siitonen 2003). Modeling the changes in biodiversity in response to

trial-and-error tool in

climate change requires projections of climate change at high spatial and

phenology. They are used

temporal resolution, and often depends on the balance between variables that

by horticulturists and

are poorly projected by climate models (e.g., local precipitation and

gardeners to predict the

evaporative demand). It also requires an understanding of how species

date that a flower will bloom

interact with each other, and how these interactions affect the communities

or a crop reach maturity.

and ecosystems of which they are a part (Gitay et al. 2002). The ability of conservation areas to represent and maintain biodiversity in the long-term may drastically decline because of the effects of climatic change on species ranges. This can have important implications for the design of activities aimed at mitigating and adapting to climate change. Conserving

An analogue is a thing that

genotypes, species and functional types – along with reducing habitat loss,

is similar to or has the

fragmentation and degradation – may promote both the long-term persistence

same function as another

of ecosystems and the provision of ecological goods and services (Lovejoy

thing. It is therefore said to

and Hannah 2005). “Systems, to conserve biodiversity in the face of such

be analogous to something

dynamics, must engage entire landscapes” (Lovejoy and Hannah 2005 p.389).

else.

Conservation planning based on protecting representative samples of natural areas will need to hit a moving target of ecological representativeness, as

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climate change raises the fundamental question of what is to be considered a “representative natural area” in an era characterized by transitory ecosystems. Also, steady-state representative conservation that is based on current-species assemblages would exclude future non-analogue assemblages. Because

A climatic envelope is

possible non-analogue assemblages are unknown, comprehensive

the range of climatic

representation in a system of protected areas will become an increasingly

variation in which a species

impractical objective (Munro et al. 2003).

can currently persist in the face of competitors,

One of the most common ways to study the impact of climate change on the

predators and disease. At

distribution of ecosystems is to describe their climatic envelope and compare

its simplest, it involves

those envelopes against climate-change scenarios provided by global-

“linking” species’ current

circulation models. At its simplest, this involves “linking” species’ current

distributions with combin-

distributions with combinations of current climate data, then plotting spatial

ations of current climate

shifts of these “climate envelopes” using climate-change scenario data. In

data, then plotting spatial

many cases, biomes will shift geographically along with changes in climate.

shifts of these “climate

In general, the edges of current ecological zones are affected the most. Each

envelopes” using climate-

species would respond independently, because they have different

change scenario data.

environmental requirements and a different capacity to adapt (Sala et al. 2005). There are several current approaches to assessing the magnitude of risk posed by climate change to biodiversity. These include:

niche models that project future distribution patterns from current or historical relationships between climate and biota (Peterson et al. 2002),

deterministic regression tree analysis models that incorporate climatic and physical factors, such as soils, that constrain species distributions but will be invariant under climate change (Iverson et al. 1999), and

Dynamic Global Vegetation Models that project the distribution of plant functional types based on environmental parameters (Sitch et al. 2003).

These approaches are robust where relationships between the environment and taxa or growth form are well-known and strong. They need to be complemented by generic tools to guide decision-making for the majority of the biota whose historic, current, or potential environmental ranges are unknown, and for identifying future sets of environmental conditions that


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have no current analogue, and hence, no readily predictable community structure or composition (Saxon et al. 2005).

3.2.3.1

Incorporating Climate Change Scenarios in Conservation Planning

Alaska-Yukon Ecoregion Researchers incorporated climate-change scenarios into the Alaska-Yukon ecoregional assessment to explore the magnitude of environmental change and geographic shifts in the distribution of “environmental domains” under different scenarios for greenhouse-gas emissions. By comparing current and future conditions, they were able to locate habitats that were expected to change environmentally and those that were likely to remain stable. Saxon et al. (2005) applied the Had3CM General Circulation Model to represent future climate conditions under two scenarios for greenhouse gas emissions between now – with average climate conditions for 1961 to 1990 – and 2100. Three scenarios were mapped to depict associations among soil attributes, topographic attributes, and: 1. the current climate (i.e., average climate conditions for the period 1961 to 1990), 2. the climate expected in 2100 if greenhouse gases increase rapidly (A2 scenario), and 3. the climate expected in 2100 if greenhouse gases increase more moderately (B2 scenario). The scenarios were based on assumptions about future population growth, energy use by developed economies, and economic disparities between developed and developing countries. Scenario A2 represented a reduction from the current rate of growth in greenhouse gas emissions, but a continued rapid increase in emissions. Scenario B2 reflected a significant reduction in the rate, but still a moderate increase in emissions. The A2 scenario predicted reaching concentrations of 735 to 1080 parts per million (ppm) CO2 in 2100. The B2 scenario predicted reaching concentrations of 545 to 770 ppm CO2 in 2100. These predictions span a range from approximately twice to four times the pre-industrial levels of 275 ppm CO2 (Nakicenovic and Swart 2000).

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© The Nature Conservancy


To develop the three scenarios, researchers analyzed the co-occurrence in space and time of 14 abiotic environmental variables. They selected seven climate variables that are important for plant and animal communities, and seven environmental variables (three topographic and four edaphic) (Table 3). To give equal weight to stable and dynamic variables, and to minimize their

Edaphic: Of or relating to

cross-correlation, they used a Principal Components Analysis to select the

soil, especially as it affects

seven most distinct climate variables. These included both process-limiting

living organisms.

variables, such as moisture stress (i.e., annual precipitation/potential evapotranspiration), and distribution-limiting variables, such as seasonal extremes of moisture and temperature. All 14 variables received equal weighting in the analyses, and were normalized. Temperature, edaphic variables, and precipitation load together explained 77.4 per cent of the total variation. The same metrics were used when comparing the current landscape with a future landscape at the same location, and when comparing two concurrent landscapes at different locations.


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Table 3. Abiotic variables used to develop scenarios Climate variables (fast-changing)

Topographic variables (invariant)

Potential evapotranspiration

Elevation

Precipitation/potential evapotranspiration

Compound topographic index

Precipitation coldest quarter

Potential solar radiation

Precipitation warmest quarter

Edaphic variables (slowchanging)

Mean temperature coldest quarter

Available water capacity

Mean temperature warmest quarter

Soil bulk density

Bio-temperature

Soil carbon density Total soil nitrogen

To apply the scenarios, researchers divided the ecoregion into 5-km–square grid cells, and attributed each grid cell with the 14 environmental variables for each of the three scenarios. A parallel-processing supercomputer then assessed the degree of similarity among all cells, with the goal of minimizing variance among parameters, and assigned every cell to one of 500 unique clusters. Each cell’s cluster assignment was then transferred back to the current and future scenario grid maps to show change among clusters across space and time. Clustering allowed the co-occurrence of the model’s variables to be spatially located across the landscape. Having this geographical reference, the following questions could be answered: 1. Where will certain environmental clusters reside within the ecoregion in 100 years? 2. How different will future climatic conditions be from current conditions at any given location in the ecoregion? Results showed that, under a future scenario, current landscapes often shrank or disappeared altogether from the study area. They also showed that new, non-analogue landscapes commonly appeared. In addition, future nonanalogue landscapes had one or more variables entirely outside the range of otherwise similar current landscapes. Even under the B2 scenario, combinations of abiotic environmental factors that currently characterize 13.3 per cent of the study area disappear from it, while 53.6 per cent of the study area will have non-analogue landscapes by 2100. In the scenarios, the

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climate-associated variables fluctuated quite rapidly over time and varied with each scenario. The edaphic or soil-related variables fluctuated much more slowly, and the topographic variables did not change perceptibly in the model’s 100-year time frame. The method used by Saxon et al. (2005) translates emissions scenarios into environmental domains and quantifies the uneven spatial distribution of environmental change among them. This can guide interventions to help ecosystems adapt, without the loss of their component species or ecosystem services. Climate-dynamic landscapes provide an objective basis for designing conservation-area networks in conjunction with models of other biodiversity surrogates, models of land-use change, and other threats to biodiversity and species-specific assessments, where available (Saxon 2003). All else being equal, ecosystems will be least at risk from climate change in landscapes that maintain their location and extent. These are also potential refugia for species. Landscapes that shrink and/or move will require deliberate conservation strategies to enhance the adaptive capacity of their ecosystems and thereby reduce the loss of biodiversity. Biodiversity will be at greatest risk where landscapes disappear and non-analogue ones take their place (Saxon et al. 2005). Cape Floristic region, South Africa Midgley et al. (2003) developed regional and species-level assessments of climate change impacts on biodiversity in the Cape Floristic region of South Africa. They set out to address a number of questions related to the impacts of climate change on the region: 1. What biome-level patterns may be expected under future climate change, and what are the comparative potential range shifts in representatives of a dominant taxon, the Proteaceae, in areas of potential biome contraction? 2. How much has land transformation constrained the potential migration of species, in response to climate change? 3. What proportion of species are under severe threat of extinction under the projected climate-change scenarios? 4. What are the implications of these patterns for conservation planning?


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www.capeaction.org.za/template.php?intDynamicPageID=23

Future climate scenarios were derived from ~ 3° x 3° coarse-scale projections from the HadCM2 General Circulation Model for the South Africa region interpolated to a 1 x 1 minute scale. To derive finer-scale data, the researchers used an interpolation technique that established relationships between current climate and altitude, topography and continentality. Five climatically derived parameters that were considered critical to plant physiological function and survival were used to construct climate envelopes for the Fynbos Biome as a whole, and to build Generalized Additive Models of climate constraints for individual species. These variables were derived for South Africa under present and projected (2050) climatic conditions (Schulze 1997). The researchers used the following parameters:

mean minimum temperature of the coldest month (Tmin) is likely to discriminate between species based on their ability to assimilate soil water and nutrients, and continue cell division, differentiation, and tissue growth at low temperatures (lower limit), as well as on their chilling requirement for processes such as bud break and seed germination (upper limit),

heat units (HU18, annual sum of daily temperatures [° C] exceeding 18° C) discriminates between species based both on their requirement for a minimum temperature to complete growing cycles (lower limit) and on their ability to tolerate excess tissue temperature (upper limit),

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annual potential evaporation (PE) discriminates through processes related to transpiration-driven water flow through the plant and xylem vulnerability, to cavitation and efficiency of water transport,

winter soil moisture days (SMDwin) is the number of days in winter for which soil moisture and air temperature were favourable for plant growth, and

summer soil moisture days (SMDsum) is the number of days in summer for which soil moisture and air temperature were favourable for plant growth.

As well, three soil variables – fertility and sand and clay content – were included in the species models. These are the minimum set of soil factors that are likely to influence plant performance, through the availability of nutrients and the effects of soil texture on soil-water availability. Researchers assessed the impact of future land-use changes on species’ ranges by screening out areas currently identified as having already been transformed; it was assumed that these were unsuitable habitat for the species. Using presence/absence data for 28 Proteaceae species, the researchers applied Generalized Additive Models to derive bioclimatic relationships for the selected species. Results showed that climate change could represent a significant threat to the persistence of biota in the Cape Floristic region. The 28 species that were modelled also faced three major risks in a changing climate:

range elimination: under the future climate-change scenario, five of the 28 species were projected to have no suitable geographic range,

range reductions: 12 of the 28 species were projected to have reduced ranges, and

range shifts: 23 of the 28 species showed potential range shifts; of those 23, 13 species had no geographic overlap between current and projected ranges.

The authors concluded that climate change had a greater impact on projected ranges than did current land transformation. The results showed the importance of species-level modeling and indicated that climate change may


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be critical to the medium- and long-term success of conservation efforts in the region (Midgley et al. 2003).

3.3

Incorporating Socio-economic Values

Biodiversity is of immense cultural and socio-economic value, so the conservation of biodiversity is the responsibility of all sectors of society. Conservation actions are ultimately societal choices, and social factors (e.g., markets, cultural beliefs and values, laws and policies, and demographic change) shape human interactions with the environment and our choices to exploit or conserve biodiversity (Fox et al. 2006). All conservation problems have scientific and socio-economic aspects, yet it is generally considered to be the socio-economic aspects that ultimately determine a conservation area’s success or failure, regardless of how sound it is scientifically. Understanding a planning region’s socio-ecological system is a prerequisite for effective conservation, because although conservation problems manifest ecologically, their root causes are typically social and economic (Knight et al. 2006). The development of systematic conservation planning techniques has improved the objectivity, cost-effectiveness, transparency and replicability of the planning process (Margules and Pressey 2000; Leslie et al. 2003). However, they have typically had a focus on biological entities rather than on the broader socio-ecological systems in which conservation planning initiatives operate. To date, assessments of socio-economic factors have mostly been conducted as a post hoc filter of areas that were selected only with regard to biological data and conservation-area network design considerations (Stewart and Possingham 2005). We need to incorporate socio-economic considerations into conservation-area network design to create more realistic conservation scenarios. Ecosystems have to be understood in their economic context to prevent market distortions that undervalue natural resources, to provide incentives to promote biodiversity conservation, and to internalize the costs of biodiversity protection and conservation (Heckl et al. 2003). To incorporate socio-political criteria into conservation planning, we must view the process as solving a multi-criteria

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decision problem involving criteria other than the representation and

Photo: © Pierre Iachetti

persistence of biodiversity (Sarkar et al. 2006).

3.3.1

Land Prices

Richardson et al. (2006) explored the use of different resolutions of economic data in reserve-selection tools, and found that the use of low-resolution economic data could result in an underestimate of the profitability of some sites. They also found that it could pose a risk of inadvertently including these in the conservation-area network. The authors concluded that the use of detailed socio-economic data is essential for designing cost-effective conservation-area networks. The economic costs of establishing conservation-area networks are also spatially heterogeneous at a fine scale, but are not routinely incorporated in conservation planning in such detail. Accounting for the spatial heterogeneity of economic costs in systematic conservation planning under budgetary constraints results in far more cost-effective conservation than if cost differences are ignored.


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Following on the methods of Dobson et al. (1997) by using county-level data on the distribution of endangered species within the United States, Ando et al. (1998) showed that a large number of endangered species were contained within a relatively small number of counties. The authors concluded that efficiency would be achieved by concentrating conservation efforts in those areas. They took it one step further and addressed the shortcoming of Dobson et al.’s work (1997) by studying the effect of heterogeneous land prices on the efficiency of the reserve-selection process. The goal of the analysis was to compare optimal site selection when the loss was measured by the number of sites versus by the cost of the sites. They ran two versions of the reserveselection algorithm. In the first version, called the “set coverage problem,” the objective was to minimize the number or cost of conservation areas subject to the constraint that all species were captured in the conservationarea network. In the second version, called the “maximal coverage problem,” the objective was to maximize coverage of conservation targets subject to the constraint that the loss not exceed a specified amount. They used county-level data on the estimated distribution of endangered species compiled by the U.S. Environmental Protection Agency Office of Pesticide Programs. The corresponding county-level data on 1992 agricultural land values, in dollars


per acre, were compiled by the U.S. Department of Agriculture.

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3.3.2

Ecosystem Services

Ecosystem services – essentially, the supply of benefits from ecosystems to society – are poorly integrated into conservation planning mainly because of the difficulty in quantifying them. While some goods and services are easy to value because they have a market, others have traditionally been free goods that are difficult to value (Dale 2001; Balmford et al. 2002; Maness 2005), mainly because they are not captured by conventional, market-based economic activity and analysis (Balmford et al. 2002).

Hedonic pricing is a model that identifies price factors

The health, well-being, and ultimate survival of humanity is linked to, and

per the premise that price is

dependent upon, the health and sustainability of ecological systems. These

determined both by internal

systems provide the basic elements essential to life: fixation of solar energy;

characteristics of the good,

protection from harmful cosmic influences; regulation of the chemical

and external factors affect-

composition of the atmosphere; operation of the hydrologic cycle; water

ing it. The most common

catchment and groundwater recharge; regulation of local and global climate

example of the hedonic

and energy balance; formation of topsoil and maintenance of soil fertility;

pricing method is the

prevention of soil erosion and sediment control; food production by food

housing market. The

webs; biomass production; storage and recycling of nutrients and organic

hedonic pricing model

matter; assimilation, storage, and recycling of waste; maintenance of habitats

would be used to estimate

for migration and breeding; maintenance of landscape scenery and

the extent to which each

recreational sites; and provision of historic, spiritual, religious, aesthetic,

factor affects the price.

educational, and scientific information and cultural and artistic inspiration. Ecosystem services can basically be divided into:

provisioning services or goods (e.g., food, water, timber, fibre),

regulating services (e.g., affecting climate, floods, disease, wastes)

supporting services (e.g., soil formation, photosynthesis, nutrient-cycling), and

cultural services (e.g., providing recreational, aesthetic and spiritual opportunities).

Biodiversity conservation involves complex issues of valuation rarely addressed in management decisions. For example, what value does a particular species have for Canadians? Will its decline or disappearance affect


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Canadian society? Do we consider that we would be worse off, or are we indifferent to whether or not it becomes extinct? (Dale 2001) Most decisions about ecosystem services involve trade-offs (Rodriguez et al. 2006). Trade-offs occurs between different services, as well as between the Contingent valuation is a

present and future supply of a service. In trade-off decisions, people often

method of estimating the

prefer (in sequence), provisioning services, followed by cultural and

non-market value of

regulating services. We tend to undervalue regulating services and the

environmental attributes or

ecosystem processes (or supporting ecosystem services) that create them

amenities, such as the

(Carpenter et al. 2006).

values of a canyon, an endangered species, or

The principal challenge in managing ecosystem services is twofold: they are

recreational or scenic

not independent of each other, and the relationships between them may be

resources, etc. These

highly non-linear. Attempts to optimize a single service often lead to

values are generally

reductions in or losses of other services. For example, forested areas provide

measured based on our

a variety of extractive and non-extractive goods and services. If a region is

willingness to pay for an

managed for mining, this may decrease the forest’s value for carbon

improved environment, or

sequestration, flood control, or wilderness and biodiversity protection. We

our willingness to accept

need to understand and be aware of the interactions between ecosystem

compensation for a

services in order to make sound decisions about appropriately managing

damaged environment (or

natural systems (Rodriguez et al. 2006).

to accept being deprived of the improved environment).

Costanza et al. (1997) published a synthesis of methods to value ecosystem goods and services, using a range of techniques including hedonic pricing, contingent valuation, and replacement-cost methods. Many of the valuation techniques either directly or indirectly attempted to estimate individuals’ “willingness to pay” for ecosystem services. The authors used case studies to derive average values per hectare for each of 17 services across 16 biomes. They then extrapolated to the globe by multiplying by each biome’s area. They estimated the average aggregated annual value of nature’s services (in 1994 U.S. dollars ha-1 yr-1) at $38 trillion (range = $18 to $61 trillion). The ecosystem services and functions used in their study are outlined in Table 4.

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Table 4. Ecosystem services and functions used by Costanza et al. (1997) Ecosystem service

Ecosystem functions

Examples

1. Gas regulation

CO2/O2 balance, O3 for UVB protection Greenhouse-gas regulation

4. Water regulation

Regulation of atmospheric chemical composition Regulation of global temperature, precipitation, and other biologically mediated climatic processes at global or local levels Capacity, damping, and integrity of ecosystem response to environmental fluctuations Regulation of hydrological flows

5. Water supply

Storage and retention of water

6. Erosion control and sediment retention 7. Soil formation

Retention of soil within an ecosystem

Provisioning of water by watersheds, reservoirs, and aquifers Prevention of soil loss by wind, runoff, or other processes

2. Climate regulation

3. Disturbance regulation

8. Nutrient cycling

9. Waste treatment

10. Pollination

Soil formation processes

Storage, internal cycling, processing, and acquisition of nutrients Recovery of mobile nutrients and removal or breakdown of excess nutrients and compounds Movement of floral gametes

Storm protection, flood control, drought recovery Provisioning of water for agricultural or industrial processes or transportation

Weathering of rock and accumulation of organic material Nitrogen fixation

Waste treatment, pollution control, detoxification Provisioning of pollinators for the reproduction of plant populations

11. Biological control

Trophic-dynamic regulations of populations

Keystone predator control of prey species

12. Refugia

Habitat for resident and transient populations Portion of gross primary production extractable as food

Nurseries, habitat for migratory species Production of fish, game, crops, nuts, fruits by hunting, gathering, subsistence farming or fishing Production of lumber, fuel or fodder

13. Food production

14. Raw materials

Portion of gross primary production extractable as raw materials

15. Genetic resources

Sources of unique biological materials and products


Medicine, products for materials science, genes for resistance to plant pathogens and crop pests

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16. Recreation

Opportunities for recreational activities

17. Culture

Opportunities for non-commercial uses

Eco-tourism, sport fishing, and other outdoor recreational activities Aesthetic, artistic, educational, spiritual, and/or scientific values of ecosystems

Most ecosystem services, such as the provisioning of food or clean water, depend on the presence of sufficient numbers of individuals of each species. In the Millennium Ecosystem Assessment, Sala et al. (2005) showed that these services will decline locally with the local reduction or extirpation of populations, long before global extinctions take place. For other ecosystem services, such as those that rely on genetic diversity, the central issue is species richness. In these instances, the provision of services ceases after global extinction (Sala et al. 2005). Assessing these trade-offs in the decisionmaking process requires scientifically based analysis to quantify responses to different management alternatives. Scientific advances over the past few decades, particularly in computer modeling, remote sensing, and environmental economics, now make it possible to assess the linkages


between ecosystem services (Defries et al. 2005).

Deriving conclusions about the important trends in ecosystem condition and trade-offs among ecosystem services requires the following basic information: 1. What are the current spatial extent and condition of ecosystems?

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2. What are the quality, quantity, and spatial distributions of services provided by the systems? 3. Who lives in the ecosystem, and what ecosystem services do they use? 4. What are the trends in ecosystem condition and their services in the recent (decades) and more distant past (centuries)? 5. How does ecosystem condition, and, in turn, ecosystem services, respond to the drivers of change for each system? 3.3.2.1

Incorporating ecosystem services in the Central Coast, California Ecoregional Assessment

Chan et al. (2006) developed ecosystem service conservation plans and compared them to the existing biodiversity conservation plan for the Central Coast ecoregion in California to gain an understanding of trade-offs and benefits between biodiversity conservation and ecosystem services. They defined and mapped the following ecosystem services for the assessment:

Carbon storage This is the carbon sequestered in above- and below-ground biomass of primary producers. The authors used data that provided an estimate of the total (realized) carbon stored in above- and below-ground vegetation (expressed as tons/ha) for each planning unit. They set a conservation goal of 50 per cent of the carbon in above- and below-ground vegetation in the Marxan model.

Crop pollination

This is the pollination of crops by natural pollinators. Through literature review and expert input, the authors generated a list of crops requiring or benefiting significantly from animal pollinators. The total economic value of these crops was summed to the county level. To obtain a planning unit’s pollination value, a scaling factor was multiplied by a function of the land cover in the planning unit (i.e., the percentage of agricultural land + 0.25 x % in natural land cover) for units with agricultural land cover.

Flood control

This is the mitigation of flood risk by land cover. The authors created a model that attempted to capture the flood-prevention and -mitigation capacity of


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upland terrestrial habitats. A planning unit’s flood-control score was an additive function of four factors: the percentage of natural cover in a planning unit, the percentage of cover within the riparian zone of streams and rivers, the distance of drainage routes from a planning unit to the 100-year floodplain, and the percentage of land in agricultural cover.

Forage production

This is the production of forage for grazing domestic livestock. The value of forage production in an area was modelled as a function of climate, primary production of forage species, nutritional content of the forage, ability to withstand grazing, and the ability of ranchers to capture forage value through sales of livestock and livestock products.

Outdoor recreation This is the provision of recreation opportunities by natural and semi-natural landscapes. The authors estimated the value of recreation in an area as a function of the amount of natural and semi-natural habitat. The measured the accessibility of the area by its proximity to population centres and major roads, and by the rights to access.

Water provision

This is the supply of fresh water to meet the demands of agricultural, industrial and residential sectors. The authors developed a model that attempted to quantify the amount of surplus clean water that was potentially available for human consumption. Water provision was represented as precipitation minus evapotranspiration. The authors addressed the following questions: 1. How much of each service is being generated by each land parcel? 2. What are the spatial associations between the lands required for protecting biodiversity and supplying different ecosystem services? 3. How much of each ecosystem service – plus biodiversity protection – is provided by a network of lands prioritized for biodiversity, compared to networks designed for multiple services?

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Chan et al. (2006) used the reserve-selection model Marxan to develop conservation-area networks for each ecosystem service. This allowed them to compare conservation-area networks developed by The Nature Conservancy

In the Pearson correlation,

that had been optimized for biodiversity conservation. To evaluate the spatial

the correlation coefficient

correspondence of biodiversity and the provision of services, two tests were

determines the extent to

performed: service correlation (Pearson’s r), and network overlap. Results

which values of two

showed that the seven benefit functions (i.e., biodiversity, carbon storage,

variables are “proportional’”

flood control, forage production, pollination, recreation, and water provision)

to each other.

had distinctly different spatial distributions. The average correlation between biodiversity conservation priorities and ecosystem services was low. Despite the generally low correlations, there were hotspots where high values of


multiple benefits coincided.


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4.0

Decision-Support System Development

Decision-makers are asked to integrate multiple information sources, show transparency in the decision-making process, and publicly evaluate trade-offs. The more complicated the process and the greater the volume of information to be considered, the more difficult it is to assimilate and interpret essential information, which increases the likelihood that poor decisions will be made. Sustainable land-use and resource-management decisions need to integrate complex ecological processes; social, cultural, and political values; and economic feasibility (Cain 2001). Decision-makers need support from researchers to help them effectively evaluate the social, economic and environmental consequences of alternative management scenarios (Quinn 2002; Tremblay et al. 2004). An integrated approach to land-use and resource-management planning recognizes that a successful strategy must meet multiple objectives through a variety of means. Integrated planning and new governance models are increasingly seen as being critical to addressing complex land-use and resource-management issues. How different groups and jurisdictions work together (e.g., how they share information, conduct planning, understand and identify interests and responsibilities, etc.), is often as important as the scientific information that they use to support decisionmaking (A. Tautz, pers. comm.). Decision-support tools/systems can be used in further interactions between management and science when developing adaptive management strategies (Tremblay et al. 2004). Defining problems is critical to the process of making policy. Stakeholders often do not explicitly recognize the ways in which their knowledge and understanding frame their perspectives on land-use and resource-management policy. Although conflict is a feature of many land-use and resourcemanagement processes, it is often assumed to reflect differences in material interests between stakeholders. In such circumstances, conflict may be managed by trading off different management objectives or by attempting to reconcile multiple interests in land use and resource management. Stakeholders draw on their current knowledge and understanding to frame a specific land-use and resource-management problem. As a consequence, differences in knowledge, understanding, preconceptions, and priorities are

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often obscured in conventional policy dialogue. This may provide a deeper explanation of conflict. When different stakeholders reveal different interpretations of key issues, the policy debate can be most productive. The knowledge that allows stakeholders to define the problems of land and resource use falls into three realms: knowledge of the empirical context; knowledge of laws and institutions; and beliefs, myths and ideas. If each stakeholder is able to define problems and test the set of possible response options only in the context of his or her particular knowledge and understanding, then agreement is less likely, both in terms of perceptions and problem definition, as well as of desired response to the problem. So policy conflict arises because differences in knowledge and understanding between stakeholders frame their perceptions of land and resource use problems, as well as possible solutions to these problems. Policy dialogue needs to be structured so that differences in knowledge, understanding, ideas and beliefs in the public arena are


recognized (Adams et al. 2003).

To make sustainable land-use and resource-management decisions, we need to consider a number of complex and competing interests and values that interact in complex and non-linear ways. Decision-makers are constantly under pressure to maximize the socio-economic benefits from land and resource use decisions. Understanding the complex and dynamic forces that


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drive natural systems is incredibly daunting if not nearly impossible; it is no small feat to gain some semblance of understanding of these systems within a time frame that is useful for decision-making. Scientists, in particular, are under pressure to provide meaningful research and recommendations that are scientifically defensible, statistically rigorous, and in a format that is understandable and useful for managers and policy-makers. Decision-support tools/systems, by definition, should aid in and strengthen the choice process. They encompass a range of techniques, from statistics (e.g., Bayesian statistics, classification, and regression-tree analysis), to optimization (e.g., linear programming, and simulated annealing), and a variety of simulation techniques and tools. They are all models because they simplify and abstract reality. Although diverse, all decision-support tools/systems share two broad features: simplification and translation. Faced with complexity or the desire to consider relations over large areas or long periods, decision-makers simplify and attempt to abstract the most important relations. Decision-support tools/systems are intended to serve four broad purposes (Bunnell and Boyland 2003):

Aid research

Convey knowledge

Given the expanded set of values requested by the public, the success of both applied researchers and managers depends on their abilities to incorporate, into management plans and actions, relations that govern new values.

Guide management The success of managers depends on their ability to accurately predict the consequences of the actions they take. The appropriate criterion to evaluate models to guide management is the ability to make empirically correct predictions in most instances. One way decision-support tools/systems can assist decision-making is by focusing attention on the most influential issues affecting management decisions. The largest benefit gained from using the tools/systems is in thinking through the questions, variables, and choices

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carefully. That is, the benefits accrue primarily when constructing the tool/system rather than when using it.

Publicly evaluate trade-offs

The public is increasingly acting as an arbiter of management actions by requesting to be shown consequences of these actions. More than ever, practitioners must seek social license to enact management practices. For example, most forest certification procedures require evidence of public participation in the selection of management actions. Any decision-support tool/system intended to aid management has to have the ability to evaluate trade-offs. Trade-off analysis allows decision-makers to consider trade-offs between different criteria when evaluating alternative management options. Trade-offs are quantified with respect to environmental economics, social analysis, and ecological modeling (Brown et al. 2000). Land managers experience technical, emotional and political barriers to using scientific information. Technical and emotional barriers include a lack of adequate synthesis; lack of access to timely information; lack of time or skills to find, understand, and implement research; lack of trust in science; and cultural barriers between researchers and managers. In addition, some barriers are driven by politics and human values. Scientific and technical information is considered and used in landuse decision-making, but economics, politics and personal and subjective factors tend to weigh more heavily in terms of affecting decision outcomes. Involve end-users in the process of creating knowledge All too often, researchers will develop a product and pass on the final report, publication or design to managers, with the expectation that it will be embraced with enthusiasm and implemented immediately. Implementers are presented with a product for which they feel little ownership and which may not, in fact, suit their particular needs, capabilities or resource realities. Early and ongoing interaction with end-users is the surest way to increase compatibility between knowledge innovations and resource management needs. Such interaction helps scientists more fully grasp and respond to implementation realities, such as the management agency’s capability and


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resource constraints. At the same time, managers can personally experience the new knowledge and technology in action, help shape the ultimate product, and develop ownership. In this way, they develop “user readiness.” When this is lacking, it commonly constrains both knowledge and the adoption of


technology (Roux et al. 2006).

Information packaging for managers Managers often experience information overload. They may also perceive scientific messages as promoting a particular viewpoint that is driven by undue self-interest; they know they cannot trust all information sources equally, and contradictory information makes it harder for managers to assess the risk of embracing or ignoring a particular message. To managers, scientific information can be useful, but only if it is packaged to be unambiguous, is not excessively complex, and is compatible with existing planning models (Roux et al. 2006).

4.1

Scenario Development

Scenario development is a potential framework for conservation planning and policy-making in an uncertain and changing world. We principally develop scenarios to consider a variety of possible futures that include many of the

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important uncertainties in a system, rather than focusing on a single outcome. Several conflicting or alternative scenarios are used to explore the uncertainty of the future consequences of a decision. Scenarios are useful for encouraging systematic planning in situations of uncertainty or for revealing a range of dynamic processes and causal chains that lead to alternative outcomes. Developing scenarios is a fundamental prerequisite of strategic planning. And although the virtues of scenario planning have long been appreciated in business and other fields, it has not been used widely in ecology or conservation. Scenarios with an environmental dimension exist, but they generally have several limitations. Existing environmental scenarios have usually ignored cross-scale processes – for example, interactions among global climate, national policies, and local population dynamics (Bohensky et al. 2006). Scenario development also offers us a platform for engaging stakeholders who have divergent viewpoints and competing objectives. Having interest groups participate in the process can build a shared understanding and generate conservation decisions that may be more readily adopted by the stakeholders (Siitonen 2003). In scenario development, we use a holistic, integrated and participatory approach to help us understand the intrinsic heterogeneity and uncertainty of ecosystem management. We begin by determining a set of key questions or issues, in consultation with stakeholders. We then assess the current state of the system and identify alternative pathways that the system might take. The key questions often revolve around uncertainties or unknowns in the system.

Whereas forecasting is the

The next step is to build projections of these questions into the future, which

process of predicting the

can be done qualitatively or quantitatively (Lebel et al. 2005).

future based on current trend analysis, backcasting approaches the challenge

The scenario development process involves (Lebel et al. 2005):

of discussing the future

deciding on the purpose of scenario and stakeholder involvement,

getting people to think about the long-term future,

introducing the concept of scenarios,

backcasting,


from the opposite direction.

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identifying the main areas of uncertainty,

developing focal questions to be addressed by scenarios,

identifying the main drivers of change,

developing a first set of scenarios,

deciding on modeling capacity,

modeling runs,

critically assessing scenarios and incorporating model results,

identifying important surprises,

identifying the implications of scenarios for the main stakeholders,

deciding on final scenarios,

evaluating the implications of each scenario for addressing identified uncertainties,

incorporating wider stakeholder feedback and scenario iterations, and

reporting on the scenarios and their implications.

4.2

Building the decision-support systems

There is no consistent means of evaluating success in conveying knowledge. However, two features of a decision-support system/tool can facilitate this: transparency and the ability to “game.” We facilitate transparency when we can readily display and modify the workings of the tool/system. Those using or exploring the tool can easily call up graphic displays of relationships and the data used to form them. The ability to “game” or evaluate alternative management scenarios and underlying relationships benefits from transparency, but also from model efficiency (speed) and convenient displays of outputs, including the ability to easily contrast scenarios. These features are not difficult to create, but they require time and effort, and therefore, funds (Bunnell and Boyland 2003). Level 1: A Regional Atlas There are numerous examples of web-based, interactive Geographic Information System (GIS) applications that allow users to view and manipulate data. These applications are able to display model results and other spatial data layers on their own, or overlaid on one another. Most

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applications also allow users to download the datasets for use in their own applications. This would be the foundation for the decision-support system.

Examples include:

iMapBC (pictured at left):

http://webmaps.gov.bc.ca/imfx/imf.jsp?site=imapbc

Cowichan Valley Regional District [on Vancouver, Island, BC] Habitat Atlas: www.shim.bc.ca/atlases/CVRD/Main.htm

Georgia Basin Habitat Atlas: www.shim.bc.ca/atlases/bb/main.htm

The Community Mapping Network: www.shim.bc.ca/

The Boreal Information Centre: www.borealinfo.org/en/default.htm

Level 2: A Decision-Support System The next level of a decision-support system – beyond the ability to view, overlay, and download data – is the ability for the models to interact with and affect each other (i.e., a change in one model causes changes in another model). One of the biggest challenges in creating a decision-support system is coupling models of different processes together and attempting to incorporate feedbacks among processes (Kareiva et al. 2005). We are able to run scenarios by:

rasterizing the datasets so that each raster cell has values for all of the datasets and models, and

using geodatabase/lookup tables: models interact by calculations that show an X change in a variable/model results in a Y change in another variable/model.


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The decision-support system could also display data as indicators. An indicator is a scientific construct that uses quantitative data to measure ecosystem condition and services, drivers of change, and human well-being. Properly constituted, an indicator can convey relevant information to policymakers. Indicators are designed to communicate information quickly and easily (Defries et al. 2005). The decision-support system could use a Bayesian model as a basis for how the different models interact with each other. Bayesian networks are useful for land-use planning because they are transparent; variables, classes and relationships are clearly presented. Bayesian networks have been described as Bayesian networks are

holistic and easy to use, and they encourage stakeholder involvement.

multivariate models that attempt to explain the

Bayesian networks were originally developed to allow the impact of

interactions between

uncertainty about management systems to be accounted for in the decision-

multiple factors. For

making process. This means that decision-makers can balance the desirability

example, what is the

of an outcome against the chance that the management option they select may

relationship between

fail to achieve it. This is particularly important for environmental

recreation value, cultural

management, in which the complexity of the natural world makes it difficult

value, timber value, and

to predict the exact impact of any management intervention (Cain 2001).

forest management strategy?

The basis of a Bayesian network is a diagram that conceptualizes the environmental system to be managed. To construct this diagram, we need to think carefully about how the system works as an integrated whole. This is not easy, but it improves our understanding of how management options may affect the system. As a result, it is more likely that users will make a decision based not only on the outputs of a decision-support system, but on a full understanding of how those outputs have been produced.

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5.0

Conclusions

To help us evaluate conservation scenarios in the Central Interior ecoregion during conservation planning, we need a decision-support system that integrates both knowledge from across many disciplines, and data in different forms. The latter includes spatial and tabular databases, the results of mathematical models, spatial analyses, and expert opinions. Conventional approaches of integrating and applying knowledge are not adequate to examine the complex and highly variable ecological and socioeconomic issues that influence land-use and resource-management decisions and the effects they have on landscapes (Berry et al. 1996). The decisionsupport system outlined here is data-driven and requires spatially referenced biological and socio-economic information, as well as scenarios of future environmental change. In reality, conservation planners and decision-makers need to go beyond available spatial data and consider expert opinion, as well as many intangibles related to socio-political feasibility, indirect benefits, and conservation opportunities, such as willing sellers. Ultimately, good conservation decisions rest on the decision-makers’ experience and sound judgment. However, that judgment still depends on scientific data and information on the known geography of resource quality, threat, and conservation costs. The decision-support system should organize and render information in the way that is most useful to the decision-making process (Davis et al. 2006). Data layers need to be combined in a model with a user interface that allows the user to run scenarios and then understand the outcomes of his or her decisions. This is no small order, because the logic behind the results needs to be easily and quickly understood. Systematic assessment involves the scientific evaluation of valued elements of nature. This generates information to assist decision-making on where conservation should be enacted – but not on how these initiatives should be undertaken. Planning takes the next step toward action, by linking systematic assessments to processes for developing an implementation strategy, in collaboration with stakeholders. It is essential that we define the scope of each of these activities, because some activities conserve nature, while others


91


do not (Knight et al. 2006). Calls are increasingly being made for the science and practice of conservation biology to help define and achieve sustainable visions of the future. Although scenarios offer a promising mechanism, the tools for the task need to be honed. Through scenarios, scientists and decision-makers can collectively embrace uncertainty, prepare for a range of potential futures, and turn would-be crises into opportunities for positive


change (Bohensky et al. 2006).

It cannot be stressed enough that this is a decision-support tool, not the decision itself. Decision-makers can become over-reliant on a decisionsupport system, to the extent that they allow it to make decisions for them. The timing of when to introduce the tool to the planning process is critical, as the people at the table need to be ready before it is introduced. Consideration also needs to be given to the fact that the group will have different abilities to interpret the results that the tool provides. Decision-makers assume that all

92



relevant factors are included in the decision-support system, but other important issues may have been ignored, or may have arisen since the system was first constructed. A poorly designed decision-support system can transfer power from the users to the system designers. Constructing a good system demands significant investments of time and money (Cain 2001). The questions and purpose determine the kinds of abstraction and simplification that occur during construction of the decision-support system. Effective use of a system depends on how errors and limits created by abstraction and simplification are dealt with (Bunnell and Boyland 2003). Building scenarios and anticipating changes are modeling exercises, and the reliability of models depends on their data inputs and the models themselves. Including stakeholders in the scenario development and validation process helps make explicit the circumstances under which winners and losers emerge (Lebel et al. 2005). Technical considerations A number of important technical considerations and challenges must be acknowledged in this conservation planning framework:

Uncertainty

A common problem with conservation planning is the uncertainty of planning inputs. Uncertainty is acceptable in the model-development and -planning process, as long as it is acknowledged up front. Uncertainties come mainly from a lack of data, but they can also arise from outdated or false observation, the use of predicted data (e.g., distribution models), or factors such as the potential for anthropogenic land-use changes or climate change. Taking into account both biological value and uncertainty creates four possible scenarios: {

Areas with high conservation value and high certainty of information would be important for conservation.

{

Areas with low conservation value and high certainty would normally rank low as conservation priorities.

{

Areas with high estimated conservation but low certainty have potential to produce negative results for conservation.


93


{

Areas with low conservation value and also low certainty could potentially produce positive results for conservation (Moilanen and Kujala 2004.

Scale Ecosystems respond to management changes on a range of time and space scales, so it is critical that we carefully define the scales that are included in analyses. Ideally, researchers would analyze at varying scales in order to properly assess trade-offs. In particular, in analyzing trade-offs, it is essential to consider non-linear responses of ecosystems to perturbations, such as loss of resilience to climate variability below a threshold number of plant species


(Defries et al. 2005).

Data availability/data gaps

The availability and accuracy of data sources and methods for conservation planning are variable for different ecosystem services and geographic regions. Data on regulating, supporting, and cultural services such as nutrient cycling, climate regulation, or aesthetic value are seldom available, making it necessary to use indicators, model results, or extrapolations from case studies as proxies. Methods to quantify relationships between ecosystem services and human well-being are at an early stage of development. Ultimately, decisions about trade-offs in ecosystem services call for balancing utilitarian and non-

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utilitarian, short- and long-term, and local- and global-scale societal objectives (Defries et al. 2005). Decision-support tools are most effective when there is available data on the terrestrial and freshwater ecosystems and species that occupy the study area. In British Columbia, there is a relative richness of data for land-use planning and decision-support tools. How useful would these tools be in areas that lack basic data (e.g., mapping of terrestrial and freshwater ecosystems and speciesoccurrence records)? Data also need to be converted to digital files in order to be used in reserve-selection models. These tools have the advantage of being spatially explicit and providing graphic depiction of the conservation-area network design. If the displays can use data, such as Digital Elevation Models, and satellite imagery, such as that provided by Google™ Earth, we can generate a more realistic picture of conservation scenarios. This would also allow those who know the landscape to visualize potential conservation areas and planning outcomes. The issues associated with land-use and resource-management planning are incredibly complex and becoming more so as a result of a changing climate and the cumulative effects of human impacts on species and ecosystems. Existing decision-support tools and systems and models can be used to support the decision-making process. But there is a danger of trying to make the system be everything for everyone, by adding too much complexity and data to account for all of the sustainability issues associated with land use and future implications. Several factors/variables and models were not considered in this framework, as they would require further research on methods and applications. These include the incorporating and spatially depicting cultural considerations, such as Aboriginal traditional knowledge/traditional ecological knowledge. The local-level knowledge held by Aboriginal peoples can be a crucial tool and source of knowledge for long-term sustainability and immediate resource conservation. The value of this knowledge lies in the fact that it is associated with a long and intimate history of resource use in a particular area and is, therefore, the cumulative and dynamic product of many generations of experience and practice (Menzies 2006).


95


Reserve design

There is no guarantee that the conservation-area network developed from the conservation-area design process is adequate for conserving biodiversity. This can only be assessed using explicit population models (Possingham et al. 2000). Conservation-area network design consists of several interacting dynamic systems, and its management requires understanding of the systems and their uncertainties. These uncertainties are related to every phase of reserveselection, which include: {

defining and specifying the overall goals by the objectives and costfunctions,

{

the sensitivity of algorithms to deficient data and biased objectives,

{

the ability of the algorithms to solve the planning problems, and

{

interpretation of the results (Siitonen 2003).

When researchers have tested shortcuts (e.g., indicator species, taxa and environmental variables), they have seen varying results on the shortcuts’ ability to indicate overall biodiversity, particularly its persistence (Siitonen


2003).

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6.0

Recommendations

We have an opportunity to incorporate many of the current spatial-assessment methodologies into a decision-support system to assist in conservation planning/ecosystem-based management in the Central Interior ecoregion of British Columbia. A number of analyses and methodologies that have been developed in conservation planning around the world can be applied to our geography and land-use and resource-management issues. Those include the effects of the mountain pine beetle epidemic, and the issues stated earlier about spatial and temporal dynamics of those effects, the fact that ecological information is changing, and the need for decision-makers to incorporate vast amounts of information to inform their decisions. The conservation-planning tools such as Marxan, Zonation and C-Plan are relatively robust and have been successfully applied to several planning processes around the world. They are no longer cutting-edge technologies, but through trial and error, have become state of the art (J. Ardron, pers. comm.). Users around the world are collaborating on their experiences – what worked and didn’t work for them. Researchers are making inroads into spatially quantifying many of the landscape dynamics and ecosystem goods and services that have not previously been incorporated into the decision-making process, either because they were not considered in the thought process, or our data and computing abilities were not previously available. Other advances – in data development, modeling, computing power, and use of the Internet to host data and decisionsupport tools and allow greater access and use of information – now give us an opportunity to address many of the impediments that have existed in the past to making effective decisions for sustainable land and resource management. The advantage of using these conservation-planning tools is that the analyses that need to be put into them can be done in a relatively short period of time (i.e., in one to three years). As explained earlier in this report, the BC Mountain Pine Beetle Action Plan recognizes conservation of biodiversity as a high priority in a dramatically affected landscape, and it emphasizes that conservation needs to happen sooner rather than later, because the damage


97


and its consequences are occurring at an accelerated pace. Right now, we still


have an opportunity to influence conservation decisions on the landscape.

These tools also allow decision-makers to look at “what if” scenarios to explore the potential implications of their decisions. The hope is that providing this type of ability to users will result in better, more-effective decisions that have explored the many facets of these land and resource use issues. To attempt to manually review all of this information and develop scenarios of possible outcomes would be nearly impossible, especially given need for immediate information. That said, and as numerous authors have noted, these tools are just one aid to support decision-making; their outputs are a starting point for discussion, not the decision itself. Considering the technical issues, data gaps, and other shortcomings of these tools, recommendations for developing such a tool for the Central Interior are as follows:

Use reserve-selection models to develop conservation plans.

Use more than one reserve-selection tool to compare results.

Incorporate socio-economic as well as ecological information.

Incorporate a cultural analysis:

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{

Recognize and accommodate Aboriginal rights and title, and interests by respecting First Nations governance and authority, and by working with First Nations to achieve mutually acceptable resource planning and stewardship, and fair distribution of economic benefits (Cardinall et al. 2004).

Incorporate spatial and temporal dynamics into analyses and tools.

Involve as many relevant stakeholders as is feasible.

Conduct user-needs assessments to understand the types of decisions and information needs a decision-support system can support.

Incorporate different types of information from various sources.

The proposed framework and decision-support system is one piece of a land and resource management framework. The decision-support system is designed to provide information in a format that allows users to review data and analyses and undertake a “what if” type of scenario-building to help make effective and sound decisions. “A full EBM approach consists of a hierarchical set of goals, objectives, requirements, targets and indicators. The purpose is to provide clear guidance as to where people want to go, how they plan to get there, and how they can tell if they’re on the right road, and how to tell when they


arrive.” (Cardinall et al. 2004, p.8)


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8.0

Glossary

Bioinformatics The collection, organization and analysis of large amounts of biological data, using networks of computers and databases. Climate impacts The consequences of climate change on natural and human systems (CCSP and SGCR 2003). Climate scenario A plausible and often-simplified representation of the future climate, based on an internally consistent set of climatological relationships, which has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change. This often serves as input to impact models. Climate projections often serve as the raw material for constructing climate scenarios, but climate scenarios usually require additional information, such as information about the observed current climate. A “climate-change scenario” is the difference between a climate scenario and the current climate (CCSP and SGCR 2003). Global ranking (G-rank) An assessment of a biological element’s (i.e., a species’ or a plant association’s) relative imperilment and conservation status across its geographic distribution. The ranks range from GX (presumed extinct) to G5 (secure). These ranks are assigned by the Natural Heritage Network and are determined by the number of occurrences or total area of coverage (plant associations only), modified by other factors such as condition, historic trend in distribution or condition, vulnerability, and impacts. The definitions of these ranks, which are not to be interpreted as legal designations, are as follows:


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GX GH

Presumed Extinct: Not located, despite intensive searches, and virtually no likelihood of rediscovery. Possibly Extinct: Missing; known only from historical occurrences, but still some hope of rediscovery.

G1

Critically Imperiled: At high risk of extinction due to extreme rarity (i.e., often five or fewer occurrences), very steep declines, or other factors.

G2

Imperiled: At high risk of extinction due to very restricted range, very few populations (i.e., often 20 or fewer), steep declines, or other factors.

G3

Vulnerable: At moderate risk of extinction due to a restricted range, relatively few populations (i.e., often 80 or fewer), recent and widespread declines, or other factors. Apparently Secure: Uncommon but not rare; some cause for longterm concern due to declines or other factors. Secure: Common; widespread and abundant.

G4 G5

G(#)T(#): Trinomial (T) rank applies to subspecies or varieties; these taxa are T-ranked using the same definitions as the G-ranks above. Variant Global Ranks G#G#

Range Rank: A numeric range rank (e.g., G2G3) is used to indicate uncertainty about the exact status of a species or community. Ranges cannot skip more than one rank (e.g., GU should be used, rather than G1G4).

GU

Unrankable: Currently unrankable due to lack of information or substantially conflicting information about status or trends. NOTE: Whenever possible, the most likely rank is assigned, and the questionmark qualifier is added (e.g., G2?) to express uncertainty, or a range rank (e.g., G2G3) is used to delineate the limits (range) of uncertainty. Not ranked: Global rank not assessed.

GNR

Rank Qualifiers ?

Inexact Numeric Rank: Denotes inexact numeric rank.

Q

Questionable taxonomy that may reduce conservation priority: Distinctiveness of this entity as a taxon at the current level is questionable; resolution of this uncertainty may result in change from a species to a subspecies or hybrid, or inclusion of this taxon in another taxon, with the resulting taxon having a lower-priority (numerically higher) conservation status rank.

Phylogenetics This is the field of biology that studies the evolutionary relationships between organisms. It includes the discovery of these relationships, and the study of the causes behind this pattern.

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Uncertainty An expression of the degree to which a value is unknown. Uncertainty can result from a lack of information or from disagreement about what is known or even knowable. It may have many types of sources, from quantifiable errors in the data, to ambiguously defined concepts or terminology, or uncertain projections of human behaviour. Uncertainty can therefore be represented by quantitative measures (e.g., a range of values calculated by various models) or by qualitative statements (e.g., reflecting the judgment of


a team of experts) (Gitay et al. 2002).


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A Decision-Support Framework for Conservation ... - Bad Request

A Decision-Support Framework for Conservation ... - Bad Request

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