New conservation approaches that account for broad-scale ecological processes must underpin decisions about conservation planning in the world's remaining wilderness areas. Our goal is to make the relevant tools and methods that have been developed by conservation scientists accessible to conservation practitioners working towards wilderness preservation.
Wilderness areas, in particular the North American boreal region.
We describe prominent spatial tools from natural resource management, landscape ecology and conservation biology for incorporating natural disturbance dynamics into systematic conservation planning. Then, we identify emerging methods that combine and customize these types of tools to account for interacting ecological processes in wilderness conservation plans with a specific focus on conserving natural disturbances in the North American boreal region.
Two classes of tools are well suited to the task of conservation planning in dynamic landscapes: site-selection tools (e.g. Marxan and Zonation) and process-based modelling tools (e.g. CONSERV and LANDIS-II). Four methods for explicitly including natural disturbance dynamics into conservation plans emerge from the combination of these tools: spatial catalysts combined with site-selection tools, probability theory combined with site-selection tools, spatial simulation models and spatial simulation models combined with site-selection tools.
Globally, there are few wilderness areas remaining; therefore, there is increasing impetus to effectively protect the world's remaining intact areas. Careful combinations of probabilistic models, such as Markov chain models, or spatial simulation tools, such as CONSERV and Spatially Explicit Landscape Event Simulator, with site-selection tools, such as Benchmark Builder and Marxan, are promising approaches for accounting for natural disturbance dynamics when land use planning in wilderness areas such as the North American boreal region. The protection of natural disturbance dynamics will play an increasingly important role in the long-term persistence of biodiversity in earth's remaining wilderness areas as ongoing anthropogenic disturbances and climate change imperil broad-scale ecological processes.
Protected areas are cornerstones of global conservation plans but may not ensure long-term persistence of biodiversity (Possingham et al., 2006). A primary reason that protected areas may be ineffective in the long term is that conservation planners usually do not account for broad-scale ecological processes such as natural disturbance and secondary succession (Cabeza & Moilanen, 2001) or the uncertainty inherent in ecosystems (Halpern et al., 2006). Instead, most conservation plans assume that the spatial distributions of conservation features within ecosystems are well known and constant (Meir et al., 2004; Pyke et al., 2005). The consequences of not considering the dynamics of ecosystems during the planning process are predictably negative for biodiversity persistence (Margules et al., 1994; Leroux et al., 2007b; Rayfield et al., 2008; Lourival et al., 2011). For example, Leroux et al. (2007b) found that protected areas in the northern boreal region did not maintain their targets for woodland caribou (Rangifer tarandus caribou Gmelin) and vegetation communities under a natural forest fire disturbance regime. Similarly, Lourival et al. (2011) showed that planning for the temporal dynamics of flood disturbance and succession dynamics in South America improved the long-term adequacy of reserve networks. These studies are some of the few (see also Leroux et al., 2007a; Rayfield et al., 2008) that have explicitly compared the efficacy of protected areas designed with and without consideration for broad-scale ecological processes.
There is a burgeoning literature, expounding the need to incorporate natural ecosystem dynamics into conservation designs by planning for ecological processes (reviewed by Pressey et al., 2007). This literature identifies three main reasons to plan for processes: (1) most of our depictions of biodiversity are unrepresentative snapshots of constantly changing features (Margules et al., 1994; Nicholls, 1998; Rodrigues et al., 2000); (2) processes are elements of biodiversity that are to be valued in their own right (Norton, 2000); and (3) processes generate and maintain other elements of biodiversity (Balmford et al., 1998) therefore without planning for them explicitly, many processes may be lost (Pressey et al., 2007). Systematic conservation planning for both biodiversity patterns and processes will be particularly relevant in the world's remaining wilderness areas as these areas still support and are shaped by broad-scale processes.
The world's remaining wilderness areas represent unique opportunities for proactive conservation planning prior to significant human encroachment (Mittermeier et al., 2003). These large natural areas with minimal habitat loss are critical: they retain their biodiversity autonomously (i.e. without need for management interventions); they provide many ecosystem services such as nitrogen fixation and carbon sequestration (Daily, 1997); they can buffer against climate change (Heller & Zavaleta, 2009); and they can serve as reference points to assess the health of ecosystems (Arcese & Sinclair, 1997; Lindenmayer & Franklin, 2002). The main trait that sets natural areas apart from human-modified areas is that natural areas have an intact suite of broad-scale and long-term ecological and evolutionary processes. For example, these areas maintain ongoing biotic and abiotic flows, such as seasonal or episodic species or water movements (Soulé et al., 2004), and active natural disturbance regimes, such as forest fire (Whelan, 1995). Protecting large natural areas and their natural processes requires a different set of approaches than conventional conservation planning (Pressey et al., 2007; Klein et al., 2008). These approaches make use of planning tools and methods such as spatially explicit simulation models (e.g. Leroux et al., 2007b; Rayfield et al., 2008), probabilistic theory (e.g. Drechsler et al., 2009) and spatial optimization algorithms (e.g. Lourival et al., 2011). Advanced approaches such as these will play increasingly important roles if we wish to ensure the long-term persistence of biodiversity in earth's remaining wilderness areas despite ongoing anthropogenic disturbances and climate change.
Here, we review existing tools and emerging methods from natural resource management, landscape ecology and conservation biology for incorporating broad-scale ecological processes into systematic conservation planning in wilderness areas. To narrow the scope of this review, we focus on ecological processes involved in natural disturbance dynamics in wilderness areas (see Pressey et al., 2007 for a review of dynamic costs and threats to planning units; Van Teeffelen et al., 2012 for a review of metapopulation dynamics). The focal processes in this review include natural disturbances and secondary succession, which are fundamental parts of biodiversity and which interact to maintain other elements of biodiversity and essential ecological functions (Franklin, 1993; Gauthier et al., 1996; Hunter, 1999). We illustrate potential applications of these tools and methods for conservation planning in the boreal biome – the location of the world's largest remaining forest and wetland ecosystems (Mittermeier et al., 2003), the majority of which have no legal protection (Lee & Cheng, 2010). In particular, we focus on the North American boreal region because of the current investment in broad-scale land use planning initiatives across this region prior to significant development such as mining, forestry and hydrodams (Leroux & Kerr, 2013). Our goal is to provide guidance for conservation practitioners working across the North American boreal region, but these tools and methods also may be applicable to planning in other wilderness areas.
Tools for conservation planning for natural disturbance dynamics
Conservation planners are well equipped with tools for the selection of sites for conservation (e.g. site-selection software) and modelling broad-scale ecological processes (e.g. process-based models). Site-selection software packages share the goal of supporting decisions about which areas to protect based on quantitative biodiversity targets. These software differ in their application of optimization algorithms (Rodrigues & Gaston, 2002) and output types (e.g. solution sets vs. individually ranked units). Process-based modelling tools include direct applications of basic probability theory (Drechsler et al., 2009), ‘off-the-shelf’ models that can be applied or adapted to represent the dynamics of a particular system of interest (Mladenoff, 2004) and highly customized spatially explicit models designed for a system (Sturtevant et al., 2007). The choice of an appropriate tool will depend on the conservation planning objectives and the available data resources (Table 1) as well as an understanding of what relevant tools are available and how each works. An exhaustive review of all specific site-selection and process-based modelling tools would neither be practical nor instructive. Consequently, we chose to review three site-selection tools and four process-based modelling tools that are the most commonly used tools in boreal ecosystems based on Web of Science searches and our own experiences conducting conservation planning in boreal Canada over the past decade. These tools can be customized and combined into conservation planning methods that incorporate natural disturbance dynamics, detailed in the next section.
Table 1. Description, data requirements, availability of tools and corresponding methods for incorporating natural disturbance dynamics into conservation planning. Additional details on methods are found in Table 2
A raster-based model of natural disturbance and vegetation succession with the ability to simulate over large landscapes and long time-scalesb
Spatial data on initial state of landscape features
State transition rules
Process parameters (e.g. fire size distribution)
Raster layers at subsequent timesteps
Indicators/metrics at cell, patch, class and landscape scales
Spatial simulation models
Spatial simulation models + site selection
Three of the most commonly used site-selection software in wilderness areas are Benchmark Builder, Marxan and Zonation (Table 1). However, the site-selection software toolbox also includes C-plan (Pressey et al., 2009) and ConsNet (Ciarleglio et al., 2009).
The Benchmark Builder is a user-friendly software application designed to construct ecological benchmarks using a deterministic construction algorithm that aggregates hydrological catchments along stream networks to a user-defined size and intactness (Anderson, 2009). Ecological benchmarks are large and intact areas that act as anchors of a conservation network and serve as reference sites or controls for understanding the natural dynamics of ecosystems and their response to human activities. Benchmark Builder was designed by the Canadian BEACONs project for specific applications in the boreal region. It has been used to identify candidate benchmark areas in the south-west Yukon (Anderson, 2009) and northern Quebec (Saucier, 2011) and is being developed for broader applicability across the boreal region.
Marxan is a decision support tool for a variety of conservation planning problems (Ball & Possingham, 2000; Ball et al., 2009). The software can be used to design new protected areas (Lourival et al., 2011), evaluate existing protected areas (Klein et al., 2008), and it has been modified (i.e. Marxan with Zones) for applications to multi-use zoning plans for land use planning (Watts et al., 2009). Marxan uses the simulated annealing optimization algorithm to solve the minimum set problem (Cabeza & Moilanen, 2001), which is to select a reserve network that meets a set of biodiversity targets while minimizing the cost of the reserve network and the boundary length of the network (Ball et al., 2009). Marxan has been applied in a range of wilderness areas including the Canadian boreal region (Leroux et al., 2007b; Rayfield et al., 2008).
Zonation is a spatial conservation planning software (Moilanen et al., 2011) that ranks planning units (typically grid cells in a raster map) to identify spatial priorities for conservation (Moilanen et al., 2005) and restoration (Thomson et al., 2009). The ranking of planning units is a function of user-defined factors such as richness, rarity, complementarity (Moilanen et al., 2005; Moilanen & Wintle, 2007), data uncertainty (Moilanen et al., 2006), species conservation priorities (weights; Moilanen et al., 2005) and connectivity requirements (Moilanen et al., 2005; Moilanen & Wintle, 2006). Zonation can also be used for site selection to meet biodiversity targets, similar to Marxan (Moilanen, 2007). Zonation has been applied to identify near-optimal connected reserve networks that include a given fraction of cells that contribute the most towards the conservation value of the landscape in southern boreal forest zones in Canada (Rayfield et al., 2009) and Finland (Lehtomäki et al., 2009).
Process-based modelling tools
The suite of process-based modelling tools includes mathematical models that incorporate system dynamics using probability theory and spatial simulation tools such as CONSERV, Spatially Explicit Landscape Event Simulator (SELES) and LANDIS-II, which are formulated and parameterized to simulate the dynamics of large natural disturbances (e.g. forest fire and succession) through time and space (Table 1).
Probabilistic models such as Markov chain models increasingly are being used to incorporate dynamic features into conservation plans (Game et al., 2008, 2009; Drechsler et al., 2009; Lourival et al., 2011). Many natural disturbances (e.g. insect outbreaks and corresponding vegetation dynamics) undergo transition between different successional stages, and these transition probabilities can be derived from probabilistic models. The resulting probability matrix identifies the likelihood of each planning unit being at each seral stage for a given time based on disturbance history (Drechsler et al., 2009; Lourival et al., 2011). Mathematical analysis and rigorous statistical parameter estimation may be possible for some classes of probability-based models such as stochastic state-space models (Otto & Day, 2007). In most cases, however, these models are analysed through numerical simulations. Many landscape simulation models (see below) incorporate simple probabilistic vegetation succession models to describe landscape dynamics after an explicit disturbance event.
Stochastic dynamic programming can be used to find exact solutions to probabilistic models when the goal is to identify an optimal sequence of sites to protect over the course of multiple iterations of the stochastic process (Possingham et al., 1993, 2009; Meir et al., 2004). While stochastic dynamic programming is a powerful optimization method, it can only be applied on models with discrete state space. There has been some progress in applying these techniques for dynamic budgets (e.g. Costello & Polasky, 2004; Meir et al., 2004; Drechsler, 2005), but progress towards the incorporation of natural disturbance dynamics has been slower.
CONSERV is a landscape model that simulates broad-scale ecological processes in the boreal forest to inform conservation planning (e.g. Leroux et al., 2007a,b). More specifically, CONSERV was designed for the following purposes: (1) simulate forest fire, patch dynamics and species suitable habitat; (2) evaluate the efficacy of multiple, competing reserve network designs; and (3) identify and evaluate candidate minimum dynamic reserves (sensu Leroux et al., 2007b). CONSERV recognizes the complexity and inherent variability in large natural disturbances and uses statistical models to capture the natural range of variability in disturbances paired with stochastic percolation models to simulate the natural spread of disturbance. CONSERV is based on previous models derived to explore boreal forest and species dynamics under different management scenarios (e.g. Cumming & Armstrong, 2004). The original version of CONSERV was parameterized for northern Northwest and Yukon Territories, but it has since been parameterized for use in the south-west Yukon Territory (Anderson, 2009) and is being developed for broader applicability across the boreal region.
Spatially Explicit Landscape Event Simulator is a modelling framework for developing and simulating spatio-temporal landscape models (Fall & Fall, 2001). It consists of a high-level, declarative modelling language and a simulation engine to execute the models. The SELES language is flexible to facilitate the construction of models that integrate both anthropogenic (e.g. various logging models; Côté et al., 2010) and natural processes (e.g. fire, succession, insect outbreaks; Fall et al., 2004; James et al., 2011). The Vermillion Landscape Model (VLM) is an example of a spatially explicit, stochastic model of boreal forest dynamics implemented in SELES (James et al., 2011). The VLM is capable of simulating multiple ecological processes (including fire, spruce budworm, logging, forest growth and forest succession) for the purposes of habitat modelling, disturbance modelling, land use scenario planning and ecosystem management (Fall et al., 2004; Didion et al., 2007; James et al., 2007, 2011; Rayfield et al., 2008; Côté et al., 2010; Tittler et al., 2012).
LANDIS-II is a flexible forest landscape simulation model with a large user community (Mladenoff, 2004). It is an individual-based species model that simulates succession (changing species composition) and multiple natural (e.g. fire, wind and insects) and anthropogenic (e.g. harvesting) disturbances. LANDIS-II focuses on spatially dynamic processes that depend on neighbouring or surrounding landscape features. Most applications are for natural resource management (e.g. forest restoration; Ravenscroft et al., 2010), but some groups have begun to use LANDIS-II for broader conservation planning (e.g. Larson et al., 2004). LANDIS-II has been used for natural resource management in the Canadian boreal forest (e.g. Shinneman et al., 2010), and it has been integrated with the metapopulation model RAMAS to evaluate the effects of timber harvest (Akçakaya et al., 2004) and fire regimes (Akçakaya et al., 2005) on the viability of avian populations.
Emerging methods for incorporating natural disturbance dynamics into conservation planning
The expanding suite of methods for incorporating ecological processes into conservation planning encompasses both implicit and explicit treatments of large-scale ecosystem dynamics. These methods make use of the tools described above by customizing and combining them in innovative ways. Based on our experience in the field and review of recent research, we have identified four emerging methods that hold the most promise for addressing natural disturbance dynamics for conservation planning in wilderness areas: (1) combining spatial catalysts and site-selection tools; (2) combining probability theory and site-selection tools; (3) spatial simulation models; and (4) combining spatial simulation models and site-selection tools (Fig. 1, Table 2).
Table 2. Overview of the advantages and disadvantages of using the four different methods we review for incorporating natural disturbance dynamics into conservation planning in wilderness areas. Further illustration of the methods is found in Fig. 1
Spatial catalysts + site-selection tools
Minimal data requirements relative to other methods
Designed for use with user-friendly site-selection tools
Useful for processes with clear spatial footprints (e.g. hydrological flow)
Maps surrogates of processes as opposed to actual process dynamics
Assumes processes have clear spatial patterns and simple temporal dynamics
Probabilistic models + site-selection tools
Modest data requirements relative to other methods
Provides an analytical solution to simple conservation planning problems
Designed for use with user-friendly site-selection tools
Enables a priori consideration of ecological processes
Simple probabilistic models may not be able to capture complex process dynamics
Requires familiarity with probabilistic models such as Markov chain models
Not fully integrated with all site-selection tools
Spatial simulation models
Explicit modelling of stochasticity
Potential for detailed estimates of process size and frequency
Potential to explicitly model interacting processes
Potential to analyse transient dynamics
High data requirements relative to other methods
Computationally demanding for large extents
Not well integrated with other conservation planning tools
Software is not user-friendly and may require some basic programming knowledge
Spatial simulation models + site-selection tools
Same advantages as spatial simulation models
Allows for adaptive site-selection through time
Same disadvantages as spatial simulation models
Not fully integrated with site-selection tools
Spatial catalysts combined with site-selection tools
Spatial catalysts are spatially fixed surrogates for biodiversity processes (sensu Pressey et al., 2007). They are defined by structural attributes such as topography, geology and vegetation. Spatial catalysts map the footprint of processes, not the processes themselves, but they can have inherent conservation value on top of being valuable surrogates for processes. For example, Cowling et al. (2003) use river gorges to represent movement corridors for plants and animals between refuge regions. Klein et al. (2009) use drought refugia (i.e. areas of highest gross primary productivity during the least productive years) as key areas for the long-term persistence of species in Australia. Anderson & Ferree (2010) map enduring geophysical features as the footprint of long-term processes (i.e. glaciation, rock weathering) and use these to identify regions where biodiversity will persist in the light of climate change. Maps of spatial catalysts are often included as biodiversity features in site-selection analyses along with other biodiversity features such as species and habitat types. The use of spatial catalysts is convenient for setting process targets for the formulation of static minimum or maximum set conservation problems within site-selection tools. Similar to the application of focal species as surrogates for biodiversity (see Nicholson et al., 2013), the success of spatial catalysts will depend on the validity of the assumption that the chosen spatial catalysts are good surrogates for ecological processes of interest and on the ability of conservation planners to accurately map the footprints for those processes (see Table 2). We are aware of no studies that formally test these key elements of success; therefore, conservation planners should use caution when applying spatial catalysts.
Probability theory combined with site-selection tools
A method for incorporating ecosystem dynamics a priori in the planning process is to combine probability theory and site-selection tools (Game et al., 2008; Drechsler et al., 2009; Lourival et al., 2011). Under this method, probabilistic models such as Markov chain models can be used to determine successional probabilities for dynamic features over a given time frame. Subsequently, site-selection tools such as Marxan can be used to select planning units that maximize the probability of representing each seral stage over a given time frame based on the current and projected distributions and thereby achieving a given target for the succession process. This methodology has been applied in the Great Barrier Reef, Australia (Game et al., 2008) and in the Pantanal wetland region of South America (Drechsler et al., 2009; Lourival et al., 2011). Zonation also has been applied to spatial conservation prioritization problems with dynamic features based on both current and projected future species distributions in south-eastern Australia (Thomson et al., 2009), the Pacific Northwest USA (Carroll et al., 2010) and Europe (Kujala et al., 2013).
Spatial simulation models
Spatial simulation models can be used to define the size of area required to maintain the dynamics of key natural disturbances (e.g. Leroux et al., 2007b; Anderson, 2009). Under this method, models are used to simulate the natural range of variability in an ecological process of interest, for example forest fire. Simulation experiments can be used to determine the characteristic frequency, size and spatial heterogeneity of natural disturbance in a landscape of interest. This information then can be used to determine the size and location of reserves. For example, Leroux et al. (2007a) use CONSERV to identify the size and location of reserves that have a high probability of maintaining internal recolonization sources through time in light forest fire and vegetation succession. The results of these simulations then can be used as a size target for site-selection tools such as Benchmark Builder. Anderson (2009) and Saucier (2011) provide two applications of this method in the Canadian boreal region.
Spatial simulation models combined with site-selection tools
Spatial simulation models can be integrated with site-selection tools to evaluate the efficacy of conservation networks and/or spatial catalysts through time under natural disturbance dynamics (Leroux et al., 2007a; Rayfield et al., 2008; Ban et al., 2012). In this approach, site-selection tools are used to design reserves based on traditional conservation targets or process-based targets such as spatial catalysts. These reserves then can be input into spatially explicit dynamic simulation models such as CONSERV, VLM in SELES or LANDIS-II (see Table 1 and previous section) where planners can use simulation experiments to evaluate the effectiveness of the reserves designed with spatial catalysts. The simulation models allow landscape-level indicators to be tracked at fixed or variable temporal intervals over long time frames and broad spatial extents (e.g. habitat quality and connectivity; Rayfield et al., 2008), which can be used to evaluate the overall conservation value of the landscape and can feed back into the site-selection software to re-evaluate priority areas. Here, the main question is how well the reserves represent and maintain the conservation features and ecological processes of interest over time. Leroux et al. (2007b) found that spatially fixed reserves designed with static conservation features did not maintain their targets very well through time under a natural disturbance regime. Rayfield et al. (2008) also found that moveable reserves were unable to effectively maintain high-quality habitat on the landscape in the presence of logging and natural disturbances. An iterative approach may be particularly desirable when planning within an adaptive management framework where learning through time is a desired outcome.
Incorporating disturbance dynamics into conservation planning in the boreal biome
Conservation context in the North American boreal region
Conservation planners in the North American boreal region must concern themselves not only with the natural dynamics that sustain these ecosystems but also the dynamic policy environment that sets conservation priorities and budgets. The North American boreal region's policy environment is currently in a critical state of willingness for comprehensive conservation planning, which presents a valuable opportunity to incorporate ecological processes into conservation area design. We anticipate growing interest in conservation planning in the North American boreal region because of the new Aichi targets (CoP10, 2010), the potential role of northern regions as ecological reservoirs (Bradshaw et al., 2009) and the growing appreciation of the threats facing both ecological and cultural values of boreal regions (Chapin et al., 2004; Cardillo et al., 2006).
In the past decade, a number of cooperative agreements among industry, environmental leaders and First Nations have been reached; these synergies along with increasing political will offer tangible opportunities for proactive conservation planning across the North American boreal region (Leroux & Kerr, 2013). In essence, there is an increasing need for tools and methods designed specifically for incorporating the inherent dynamics of boreal ecosystems to ensure comprehensive planning in this region.
Guidelines for conservation planning in the North American boreal region
Conservation planning in wilderness areas must consider dynamic landscape features a priori if they wish to develop effective long-term conservation plans. Above, we reviewed the key tools and methods that are currently available for planning for natural disturbance dynamics. But with the number of tools and methods available, how do conservation practitioners decide what to use? Applying the principles of systematic conservation planning (Margules & Pressey, 2000), the first step for incorporating natural disturbance dynamics into conservation planning is to review the literature and consult regional experts to identify natural disturbances on the landscape of interest. In the northern boreal region, aboriginal traditional knowledge will play an important role in defining key processes of interest. Consultations with regional experts and stakeholders should be an iterative communication process throughout (Sturtevant et al., 2007). These consultations serve to identify processes of conservation concern, set conservation targets and improve modelling of the relevant dynamics.
In the North American boreal region, the dominant natural disturbances include forest fire (Payette, 1992), insect outbreaks (Holling, 1992) and windthrow (Bouchard et al., 2009). However, the dominant processes in one area may not be so important in other areas (Bonan & Shugart, 1989); therefore, it is important to consult local experts and data sources. It has been argued that systematic conservation planning is best suited to promote the persistence of processes at intermediate scales rather than fine-scale processes that persist without the need for specific planning or broad-scale processes that are beyond the scope of most conservation plans (Pressey et al., 2007). Accordingly, planning in the boreal region has typically focused on the persistence and consequences of a subset of biodiversity-generating processes: forest fires, species movements, succession, windthrow and hydrological flows (e.g. Leroux et al., 2007b; Rayfield et al., 2009; Côté et al., 2010; see Table 1). These diverse processes have different characteristic intensities and spatio-temporal scales (Fig. 2) that complicate conservation planning for multiple processes concurrently (Margules & Pressey, 2000; Pressey et al., 2007). However, as described above (Fig. 1, Table 2), spatial simulation modelling allows interactions between multiple, natural processes to inform conservation targets (Leroux et al., 2007a), reserve design strategies (Leroux et al., 2007b; Rayfield et al., 2008; Tittler et al., 2012) and sustainable logging practices (Fall et al., 2004; James et al., 2007, 2011; Côté et al., 2010).
The spatial and temporal extent of the study area and duration ought to be scaled to the processes of interest (Wiens et al., 1993) while accounting for the conservation context. Studies in boreal contexts combining spatial simulation modelling with site-selection algorithms have varied in their spatial extents from 4300 to 115,000 km2, in their spatial grains from 0.0025 to 0.25 km2 and in their temporal extents from 150 to 490 years (temporal grain was 1 year in all studies, Fig. 2, see Table S1 in Supporting Information). This range highlights the spatial and temporal variability in boreal processes and suggests that planners have some level of flexibility when it comes to selecting the study scale. For example, finer-scale dimensions of processes can be summarized at coarser resolutions to be tractable in a broad-scale study such as Markov succession models that capture fine-scale competitive and seed-bank dynamics with coarser-scale transition probabilities. Whenever possible, the degree of matching between spatio-temporal scales of processes and studies should be chosen in function of the goals of the study. Leroux et al. (2007a) used a large spatial extent in their study to capture the full range of fire dynamics to identify the minimum reserve size required to maintain disturbance dynamics, whereas Rayfield et al. (2008) used a fine spatial resolution to be able to track the home range quality for American marten (Martes americana Turton) within a dynamic landscape through time.
Of course, it may be impossible to gather data on all relevant processes in a study region; therefore, among intermediate-scaled processes (sensu Pressey et al., 2007), those with the broadest spatial and temporal scales should be considered first as they have the most wide-ranging impacts on other elements of biodiversity (Fig. 2). By adequately planning for processes with broad characteristic spatial and temporal scales, we also may be planning for smaller processes, although this assumption should be tested where possible. After considering the process with the broadest spatial and temporal scales, planners should seek to determine the next process with complementary dynamics and spatio-temporal scale and so on. For example, in the boreal region, protecting forest fires also maintains important vegetation successional processes by activating dormant soil seed banks and reducing competition for light-demanding species (Fig. 2). By considering processes with complementary spatio-temporal scales and dynamics, planners will ensure a more comprehensive coverage.
Once the key processes and scales are identified for a study region, planners should assemble data that may be relevant for planning for these processes. For example, if forest fire is identified as an important process in a boreal study region, the total area burned per year, number of fires per year, mean and maximum size of fires, and successional dynamics of affected vegetation communities are data that will be useful for planning for forest fire. For example, Leroux et al. (2007a,b) gathered historical forest fire data from territorial governments and the Canadian large-fire database (Stocks et al., 2002) to parameterize the forest fire and vegetation succession processes in CONSERV. Given the processes of interest and type of data available, planners should then consult the set of tools and methods that are available to support the planning exercise to determine which approaches are well suited for their spatial conservation prioritization problem (Tables 1 and 2). It is likely that different tools and methods may be most appropriate for a particular study area and/or available data set. Because of the uncertainty inherent in dynamic environments, practitioners should use multiple suitable approaches when possible and compare the output of these approaches. By doing so, practitioners may capitalize on the strengths of multiple approaches. Similar to the ensemble forecasting approach in species distribution modelling (see BIOMOD Thuillier, 2003), priority areas of congruence as identified by multiple approaches will identify regions that are most likely to provide long-term protection of biodiversity pattern and process (Carwardine et al., 2007). The solutions for a single method may be highly uncertain, but areas identified as priorities by multiple suitable methods will help to reduce this uncertainty (Langford et al., 2011).
Globally, there are few wilderness areas remaining (Sanderson et al., 2002; Mittermeier et al., 2003). This fact places an impetus for countries that are stewards of the world's remaining intact areas to take action to effectively protect their natural resources. In particular, urgent and effective protection of boreal regions is needed because they are under increasing threat from human activities such as forestry, oil and gas exploration, and mining (Bradshaw et al., 2009). In other biomes, human activity has driven ecological processes beyond the range of natural variability to such a level and scale that the system is unlikely to recover its form and function within a time-scale that is relevant to that system (Millennium Ecosystem Assessment, 2005). A number of studies have already demonstrated that conservation plans that do not account for natural disturbance dynamics do not effectively protect biodiversity in the long term (Leroux et al., 2007a,b; Rayfield et al., 2008; Lourival et al., 2011). Consequently, proactive and effective conservation planning in wilderness areas must start by understanding and accounting for large ecological processes. In essence, these dynamic features are the theatre in which remaining biodiversity plays out.
We provide an overview of the main tools and emerging methods to account for dynamic features in conservation planning. Probabilistic models such as Markov chain models and spatially explicit dynamic simulation tools such as CONSERV and SELES in combination with decision support tools such as Benchmark Builder and Marxan offer promising approaches for accounting for natural disturbance dynamics when land use planning in wilderness areas such as the North American boreal region. Further developments and applications of these tools and methods will benefit from easier access and training for dynamic models and an integration of spatial optimization techniques into dynamic simulation models. Climate change represents a dynamic threat to boreal regions (Soja et al., 2006), and planning for both dynamic threats and processes simultaneously must be the way forward for conserving North American boreal ecosystems (Pressey et al., 2007). We hope that this review provides a critical link between conservation scientists and conservation practitioners because this interaction is necessary if we wish to provide comprehensive protection of the world's remaining wilderness areas.
SJL and BR were supported by postdoctoral fellowships from the Natural Sciences and Engineering Research Council of Canada (NSERC). SJL also was supported by a Discovery Grant from NSERC. We thank P. Vernier for help reviewing tools and A. Moilanen, F. Schmiegelow, Y. Wiersma, P. Vernier, K. Lisgo and an anonymous referee for constructive feedback on a draft of the manuscript.
Shawn J. Leroux is an assistant professor in the Department of Biology at Memorial University of Newfoundland, Canada. He is an ecosystem ecologist with a strong interest in conservation biogeography. For the past decade, Shawn has been working with the Canadian Boreal Ecosystem Analysis for Conservation Networks (BEACONs) project (see beaconsproject.ca) on developing a science-based framework for conservation planning in the Canadian boreal region.
Bronwyn Rayfield is a postdoctoral fellow in the Biology Department at McGill University, Montreal, Canada. She is a spatial ecologist focusing on the conservation of forest biodiversity in human-modified landscapes. She studies the role of connectivity among forest patches in maintaining biodiversity and ecosystem function. Her work combines spatial modelling, laboratory experiments and field studies to produce actionable science relevant for conservation planning.
Author contributions: Both authors contributed equally to the ideas, data collection, analysis and writing of this manuscript.